diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000000000000000000000000000000000000..22859b92af00c81451c473e4eb43d93ebc86c070
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,89 @@
+# Exclude heavy folders from Docker image
+**/__pycache__
+**/.venv
+**/.git
+**/.ipynb_checkpoints
+**/hf_cache
+**/node_modules
+
+# Exclude local database data
+**/postgres-data
+**/mysql-data
+
+# Exclude large model weights and research artifacts
+Agent/Gemma3_1B_weights
+Agent/merged_gemma1b_text2Sql
+Agent/merged_gemma3_1b_text2Sql
+Agent/Qwen2.5_3b_weights
+Agent/Qwen2.5-3B-Instruct
+merged_qwen2_5_3b_text2sql
+merged_gemma3_1b_text2sql
+.venv-merge/
+
+# Exclude all Jupyter Notebooks EXCEPT the proof-of-work notebook
+**/*.ipynb
+!Fine_Tuning_Qwen_2_5_3B.ipynb
+**/.ipynb_checkpoints
+
+# Exclude redundant project clutter
+backups/
+debug_files/
+test_files/
+test_results/
+dump-mysql.bat
+dump-postgres.bat
+agentic_improvements.md
+invariants_analysis.md
+progress_summary.txt
+mcp_server.log
+merge.log
+response.json
+run-dev.ps1
+setup_ollama.bat
+
+
+# Exclude experimental/redundant Python scripts
+Agent/trial*.py
+Agent/test*.py
+Agent/old_*.py
+Agent/OLLAMA_GPU_NOTES.md
+**/debug_*.py
+
+# Exclude local uploads and generated files (keep empty folder)
+chat_store/*
+!chat_store/.gitkeep
+!uploads/.gitkeep
+
+# Exclude local configuration (Hugging Face uses Secrets instead)
+.env
+.env.local
+
+# Exclude local logs and IDE settings
+*.log
+.vscode/
+.idea/
+.DS_Store
+
+# Exclude Python build artifacts
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+.Python
+env/
+venv/
+.venv/
+pip-log.txt
+pip-delete-this-directory.txt
+.toast
+.mypy_cache/
+.pytest_cache/
+.hypothesis/
+
+# Exclude large local data
+uploads/*
+!uploads/.gitkeep
+chat_store/*
+!chat_store/docs/.gitkeep
+!chat_store/.gitkeep
+.idea/
diff --git a/.env.example b/.env.example
new file mode 100644
index 0000000000000000000000000000000000000000..9626123fb12be50afa6c6bb26582ff71ea7eecab
--- /dev/null
+++ b/.env.example
@@ -0,0 +1,29 @@
+# Core DB
+# DB_CONNECTION_URL=postgresql://user:pass@localhost:5432/dbname
+
+# ── Text-to-SQL LLM ──────────────────────────────────────────────
+# Recommended: HF Inference API (same as the Hugging Face Spaces deployment).
+# Set USE_HF_CLOUD=1 to force cloud-only. With USE_HF_CLOUD=0 the local Ollama
+# model is still preferred, but if it is not installed and HF_API_TOKEN is set,
+# the worker auto-falls back to HF Inference automatically.
+USE_HF_CLOUD=1
+HF_API_TOKEN=
+HF_LLM_MODEL=Qwen/Qwen2.5-Coder-7B-Instruct
+
+# Cloud VQA via Groq (free tier)
+GROQ_API_KEY=
+GROQ_VISION_MODEL=qwen/qwen3.8-27b
+
+# Local Ollama (optional — only when models are installed locally)
+# USE_HF_CLOUD=0
+# OLLAMA_BASE_URL=http://localhost:11434
+# OLLAMA_MODEL_NAME=qwen2.5-coder:1.5b
+# GRANITE_MODEL_PATH=qwen3.5:2b
+# OLLAMA_OCR_QA_MODEL=gemma3:1b-it-qat
+# GRANITE_MODEL=qwen3.5:2b
+
+# Workers/timeouts
+WORKER_PORT=8700
+MCP_PORT=8701
+OCR_QA_TIMEOUT=45
+INVARIANTS_BASE_TIMEOUT=180
diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..50d97698f7c7222127f0dc3748fc1cb6b92eb718 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+employee-sample-database/mysql/schema.png filter=lfs diff=lfs merge=lfs -text
+employee-sample-database/sqlite/dataset_small/employee.db filter=lfs diff=lfs merge=lfs -text
diff --git a/.pytest_cache/.gitignore b/.pytest_cache/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..08a7f458f1f002823bc794c47ca1996a57e72c86
--- /dev/null
+++ b/.pytest_cache/.gitignore
@@ -0,0 +1,2 @@
+# Created by pytest automatically.
+*
diff --git a/.pytest_cache/CACHEDIR.TAG b/.pytest_cache/CACHEDIR.TAG
new file mode 100644
index 0000000000000000000000000000000000000000..fce15ad7eaa74e5682b644c84efb75334c112f95
--- /dev/null
+++ b/.pytest_cache/CACHEDIR.TAG
@@ -0,0 +1,4 @@
+Signature: 8a477f597d28d172789f06886806bc55
+# This file is a cache directory tag created by pytest.
+# For information about cache directory tags, see:
+#	https://bford.info/cachedir/spec.html
diff --git a/.pytest_cache/README.md b/.pytest_cache/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..c7526af2448672de4537dfed042ed74daadb17bf
--- /dev/null
+++ b/.pytest_cache/README.md
@@ -0,0 +1,8 @@
+# pytest cache directory #
+
+This directory contains data from the pytest's cache plugin,
+which provides the `--lf` and `--ff` options, as well as the `cache` fixture.
+
+**Do not** commit this to version control.
+
+See [the docs](https://docs.pytest.org/en/stable/how-to/cache.html) for more information.
diff --git a/.pytest_cache/v/cache/lastfailed b/.pytest_cache/v/cache/lastfailed
new file mode 100644
index 0000000000000000000000000000000000000000..980914741f27fb0c4bbc8d9ddc730a833aab75eb
--- /dev/null
+++ b/.pytest_cache/v/cache/lastfailed
@@ -0,0 +1,5 @@
+{
+  "Agent/test_adapter_weights.py": true,
+  "Agent/test_query_runner.py": true,
+  "Agent/test_merged_model.py": true
+}
\ No newline at end of file
diff --git a/.pytest_cache/v/cache/nodeids b/.pytest_cache/v/cache/nodeids
new file mode 100644
index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc
--- /dev/null
+++ b/.pytest_cache/v/cache/nodeids
@@ -0,0 +1 @@
+[]
\ No newline at end of file
diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000000000000000000000000000000000000..81628fcb0171d9fd7c06d6b2de3c630ee422525b
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,4 @@
+{
+    "python-envs.defaultEnvManager": "ms-python.python:conda",
+    "python-envs.defaultPackageManager": "ms-python.python:conda"
+}
\ No newline at end of file
diff --git a/Agent/agentic_workflow.py b/Agent/agentic_workflow.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ec4183039d136a4e4450cb659ef1c43c1fdd140
--- /dev/null
+++ b/Agent/agentic_workflow.py
@@ -0,0 +1,6712 @@
+"""
+Agentic Workflow Main Module
+---------------------------
+This file implements the core agentic workflow for text-to-SQL, including:
+- LLM orchestration (Ollama, MCP, fallback)
+- Memory management and safety checks
+- SQL cleaning, validation, and artifact stripping
+- Agentic orchestration (plan/micro/LLM phases)
+- Robust fallback and error handling
+
+All agent logic is preserved. Comments clarify key logic and utility functions.
+"""
+
+import os
+import sys
+
+# Windows console safety: module-level prints (emoji etc.) crash the whole
+# import when the console codec is cp1252/latin-1. Reconfigure to UTF-8 with
+# replacement so logging can never take the app down.
+try:
+    _enc = (getattr(sys.stdout, "encoding", "") or "").lower()
+    if _enc and _enc not in ("utf-8", "utf8") and hasattr(sys.stdout, "reconfigure"):
+        sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+        if hasattr(sys.stderr, "reconfigure"):
+            sys.stderr.reconfigure(encoding="utf-8", errors="replace")
+except Exception:
+    pass
+
+import asyncio
+import re
+import gc  # Explicit garbage collection import
+import signal  # For timeouts
+import threading  # For thread management
+import weakref  # For weak references to avoid memory leaks
+import time  # For timing operations
+from typing import Dict, Any, List, Optional
+import json
+import uuid
+from datetime import datetime
+from pathlib import Path
+import psutil  # Import for memory monitoring
+import requests
+
+# Database-related imports
+from sqlalchemy import create_engine, text, Table, MetaData, inspect
+from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
+from sqlalchemy.orm import sessionmaker
+from sqlalchemy.exc import NoSuchTableError
+
+try:
+    from .db_uri import normalize_db_uris
+except ImportError:  # module imported as top-level (not part of the Agent package)
+    from db_uri import normalize_db_uris
+
+# LLM imports
+from llama_index.core import Settings
+from llama_index.llms.ollama import Ollama
+from llama_index.core.agent import ReActAgent
+from llama_index.core.tools import FunctionTool
+from llama_index.core.llms import ChatMessage, MessageRole, LLMMetadata, CompletionResponse
+try:
+    from llama_index.core.llms.custom import CustomLLM
+except ImportError:
+    from llama_index.core.llms import CustomLLM
+from llama_index.core.storage.chat_store import SimpleChatStore
+try:
+    from huggingface_hub import InferenceClient
+except ImportError:
+    InferenceClient = None
+
+try:
+    from .invariants import enforce_invariants, classify_question
+except (ImportError, ValueError):
+    try:
+        from Agent.invariants import enforce_invariants, classify_question
+    except ImportError:
+        def enforce_invariants(*args, **kwargs): return args[0] if args else None
+        def classify_question(*args, **kwargs): return "unknown"
+
+from dotenv import load_dotenv
+import os as _os
+_dotenv_candidates = [
+    _os.path.join(_os.path.dirname(__file__), '..', '.env'),    # project root .env
+    _os.path.join(_os.path.dirname(__file__), '.env'),          # Agent/.env  (most specific)
+]
+for _dotenv_path in _dotenv_candidates:
+    if _os.path.isfile(_dotenv_path):
+        print(f"[DEBUG] Loading .env from: {_dotenv_path}")
+        load_dotenv(dotenv_path=_dotenv_path, override=False)     # Environment variables (Docker) now win over .env files
+del _os, _dotenv_candidates, _dotenv_path
+print(f"[DEBUG] Before CWD load_dotenv, DB_CONNECTION_URL={os.environ.get('DB_CONNECTION_URL')}")
+load_dotenv()  # also load CWD .env for any remaining vars
+print(f"[DEBUG] After CWD load_dotenv, DB_CONNECTION_URL={os.environ.get('DB_CONNECTION_URL')}")
+
+# Simple initialization
+DB_CONNECTION_URL = os.environ.get("DB_CONNECTION_URL", None)
+print(f"[DEBUG] Final DB_CONNECTION_URL={DB_CONNECTION_URL}")
+if not DB_CONNECTION_URL:
+    print(
+        "\n" + "=" * 68 +
+        "\n[WARN] DB_CONNECTION_URL is not set — starting WITHOUT a database.\n"
+        "  The app still works: connect a database at runtime from the web UI's\n"
+        "  'Database' panel (connection URL, or upload a .sqlite/.csv file).\n"
+        "  PRE-SET STARTING DATABASE WHERE OTHERS COME FROM:\n"
+        "  To autoload the built-in employee demo instead, set DB_CONNECTION_URL\n"
+        "  in Agent/.env, e.g.\n"
+        "    DB_CONNECTION_URL=postgresql://postgres:postgres@localhost:5432/employee\n" +
+        "=" * 68
+    )
+
+# Import our memory management utilities (robust import with fallbacks)
+import importlib
+import sys
+
+memory_manager = None
+safe_check_memory = None
+TimeoutManager = None
+_last_import_error = None
+
+# Try several import strategies: package-relative, package-absolute, and top-level
+try_names = []
+if __package__:
+    # e.g. when imported as Agent.agentic_workflow, __package__ == 'Agent'
+    try_names.append(f"{__package__}.memory_manager")
+# Also try the absolute package path
+try_names.append("Agent.memory_manager")
+# Finally try top-level fallback
+try_names.append("memory_manager")
+
+for modname in try_names:
+    try:
+        mod = importlib.import_module(modname)
+        # Extract expected symbols if present
+        memory_manager = getattr(mod, 'memory_manager', None)
+        safe_check_memory = getattr(mod, 'safe_check_memory', None)
+        TimeoutManager = getattr(mod, 'TimeoutManager', None)
+        if memory_manager is not None and safe_check_memory is not None and TimeoutManager is not None:
+            break
+    except Exception as e:
+        _last_import_error = e
+
+if memory_manager is None or safe_check_memory is None or TimeoutManager is None:
+    # Provide a clear diagnostic so startup logs show why import failed
+    print("Failed to import Agent.memory_manager via tried paths:", try_names)
+    print("cwd=", __import__('os').getcwd())
+    print("sys.path[0]=", sys.path[0] if len(sys.path) > 0 else None)
+    if _last_import_error:
+        # Re-raise the last error to preserve traceback
+        raise _last_import_error             
+    else:
+        raise ImportError("Could not locate memory_manager module; ensure Agent/memory_manager.py exists and package imports are correct")
+
+# Ollama configuration
+OLLAMA_BASE_URL = os.environ.get("OLLAMA_BASE_URL", "http://localhost:11434")
+
+
+# --- Employee Database Schema for Prompt Injection ---
+EMPLOYEE_DB_SCHEMA = (
+    "Database schema:\n"
+    "- employee(emp_no, birth_date, first_name, last_name, gender, hire_date)\n"
+    "- department(dept_no, dept_name)\n"
+    "- dept_emp(emp_no, dept_no, from_date, to_date)\n"
+    "- dept_manager(emp_no, dept_no, from_date, to_date)\n"
+    "- salary(emp_no, salary, from_date, to_date)  # column name is 'salary' in this dataset\n"
+    "- title(emp_no, title, from_date, to_date)\n"
+    "- views: current_dept_emp (current department per employee), dept_emp_latest_date\n"
+    "\n"
+    "CRITICAL Rules:\n"
+    "- The 'department' table does NOT have a 'to_date' column. Never filter department by to_date.\n"
+    "- To get CURRENT records (managers, salaries, department assignments), ALWAYS filter by `to_date = '9999-01-01'` in the 'salary', 'dept_emp', 'dept_manager', or 'title' tables.\n"
+    "- To get MANAGER NAMES, you MUST join `dept_manager` (dm) with `employee` (e) on `emp_no`.\n"
+    "- To get DEPARTMENT NAMES for employees, join `dept_emp` (de) with `department` (d) on `dept_no`.\n"
+    "- The 'employee' table does NOT have a 'dept_no' column. To find an employee's department name, you MUST join 'dept_emp' (de) with 'department' (d) on 'dept_no'.\n"
+    "- Salaries are in table `salary` (column `salary`). Always use `s.to_date = '9999-01-01'` for current pay.\n"
+)
+
+
+# Allow a dedicated finetuned model name (merged LoRA) via FINETUNED_MODEL_NAME env.
+# Resolution order:
+#  1. FINETUNED_MODEL_NAME (e.g., gemma1b-text2sql)
+#  2. OLLAMA_MODEL_NAME (defaults to base gemma3:1b)
+#  3. Hard fallback 'gemma3:1b'
+FINETUNED_MODEL_NAME = os.environ.get("FINETUNED_MODEL_NAME")
+BASE_MODEL_FALLBACK = "qwen2.5-coder:1.5b" #"gemma3:1b-it-qat"
+OLLAMA_MODEL_NAME = FINETUNED_MODEL_NAME or os.environ.get("OLLAMA_MODEL_NAME", BASE_MODEL_FALLBACK)
+FALLBACK_MODEL_NAME = BASE_MODEL_FALLBACK  # Keep fallback stable
+if FINETUNED_MODEL_NAME:
+    print(f"🦙 Using finetuned Ollama model: {OLLAMA_MODEL_NAME} (base fallback: {FALLBACK_MODEL_NAME})")
+else:
+    print(f"🦙 Using base Ollama model: {OLLAMA_MODEL_NAME} (fallback: {FALLBACK_MODEL_NAME})")
+
+# Schema profile global (populated at DB init)
+try:
+    from . import schema_profile as _schema_profile_mod
+except Exception:
+    import Agent.schema_profile as _schema_profile_mod
+
+ACTIVE_SCHEMA_PROFILE = _schema_profile_mod.SchemaProfile(
+    name="uninitialized",
+    db_url=None,
+    is_demo=False,
+)
+
+
+def _schema_notes_block() -> str:
+    """Owner-supplied domain hints for the active database, if any.
+
+    Structure comes from live introspection, but semantics (what 'sold_date
+    IS NULL' means, price distinctions, aliases, revenue rules) only the
+    database owner knows. These notes are labeled authoritative so the LLM
+    treats them above its own guesses.
+    """
+    try:
+        notes = (getattr(ACTIVE_SCHEMA_PROFILE, "notes", "") or "").strip()
+    except Exception:
+        notes = ""
+    if not notes:
+        return ""
+    return (
+        "SCHEMA NOTES from the database owner (authoritative for column meanings "
+        "and business rules):\n" + notes
+    )
+
+
+def _dynamic_schema_block(schema_text: str) -> str:
+    """Schema section for LLM prompts.
+
+    Employee demo profile  -> curated EMPLOYEE_DB_SCHEMA block (battle-tested).
+    Any other live profile -> aggregates the live introspected schema instead,
+    so no hardcoded table assumptions leak into user databases.
+    Falls back to the per-relevant-table schema snippets when the profile is
+    not yet populated. Owner schema notes are appended in every branch.
+    """
+    profile = ACTIVE_SCHEMA_PROFILE
+    _notes = _schema_notes_block()
+    try:
+        if profile is not None and profile.is_employee_db and profile.tables:
+            return EMPLOYEE_DB_SCHEMA + (("\n\n" + _notes) if _notes else "")
+        if profile is not None and profile.tables:
+            if schema_text and schema_text != "(schema unavailable)":
+                return (
+                    f"Database schema (live):\n{profile.build_digest(max_tables=15)}\n"
+                    f"Focus tables (columns):\n{schema_text}"
+                ) + (("\n\n" + _notes) if _notes else "")
+            return (
+                f"Database schema (live):\n{profile.build_digest(max_tables=15)}"
+            ) + (("\n\n" + _notes) if _notes else "")
+    except Exception as _e:
+        print(f"[DYNAMIC-SCHEMA] profile digest unavailable: {_e}")
+    base = f"Database schema:\n{schema_text}" if schema_text else EMPLOYEE_DB_SCHEMA
+    return base + (("\n\n" + _notes) if _notes else "")
+
+
+def _current_row_rules(default_rule: str) -> str:
+    """Rules text for current-row filtering. The to_date='9999-01-01' pattern is
+    employee-schema specific; other schemas get a generic recency hint."""
+    if ACTIVE_SCHEMA_PROFILE.is_employee_db:
+        return default_rule
+    return (
+        "If a table has a date column indicating record validity/ends, filter to the "
+        "current/active rows when the question implies 'current' or 'now'. "
+        "Otherwise do NOT invent date filters."
+    )
+
+# OCR Q&A model configuration
+# PRIMARY: Granite Vision VLM (GRANITE_MODEL_PATH env) — used when image file is available
+# FALLBACK: lightweight Ollama text model — used for old docs without a saved image
+GRANITE_MODEL_PATH = (
+    os.environ.get("GRANITE_MODEL_PATH") or
+    os.environ.get("GRANITE_MODEL") or
+    "qwen2-vl:2b"  # local Ollama fallback (dev only)
+)
+if os.environ.get("USE_HF_CLOUD", "0") in ("1", "true", "True"):
+    print(f"[GRANITE_VQA] Target Model: {os.environ.get('GROQ_VISION_MODEL', 'qwen/qwen3.8-27b')} (via Groq Vision)")
+else:
+    print(f"[GRANITE_VQA] Target Model: {GRANITE_MODEL_PATH} (via Ollama)")
+# Text-only fallback: Ollama model used when no image file exists for the doc.
+# NOT the same as GRANITE_MODEL_PATH — this runs via Ollama REST API, not HuggingFace.
+OLLAMA_OCR_QA_MODEL = os.environ.get("OLLAMA_OCR_QA_MODEL", "gemma3:1b-it-qat")
+print(f"[OCR_QA] Text fallback model (Ollama): {OLLAMA_OCR_QA_MODEL}")
+OCR_QA_TIMEOUT = float(os.environ.get("OCR_QA_TIMEOUT", "45"))
+print(f"[OCR_QA] Text fallback timeout={OCR_QA_TIMEOUT}s")
+
+def check_memory_safety():
+    """Check if there's enough memory to safely process a request"""
+    # Use our improved memory checking system
+    return safe_check_memory()
+
+# Global variables for database connection
+available_tables = []
+inspector = None
+sql_database = None
+async_engine = None
+async_session = None
+# True once initialize_database() finished successfully at least once.
+# Databases without an async SQLAlchemy dialect (mssql/oracle) keep
+# async_session None permanently, so "connected" must not be inferred from it.
+_DB_CONNECTED = False
+
+# Initialize chat store directory and files
+chat_store_dir = Path('chat_store')
+chat_store_dir.mkdir(parents=True, exist_ok=True)
+
+# Define chat store file paths
+public_store_path = chat_store_dir/'chat_store_public.json'
+private_store_path = chat_store_dir/'chat_store_private.json'
+# Initialize empty chat store structure with conversation key
+initial_store = {"conversation": []}
+
+# Create JSON files if they don't exist
+if not public_store_path.exists():   
+    with open(public_store_path, 'w') as f:
+        json.dump(initial_store, f, indent=2)
+
+if not private_store_path.exists():    
+    with open(private_store_path, 'w') as f:
+        json.dump(initial_store, f, indent=2)
+
+# Initialize chat stores — self-healing loader.
+# Abrupt process exits (OOM kills, Space runtime restarts) can truncate a
+# store file mid-write; a 0-byte or invalid JSON file must not brick the
+# worker at import time. Repair to the known empty structure and retry.
+def _load_or_repair_chat_store(path: Path) -> SimpleChatStore:
+    try:
+        return SimpleChatStore().from_persist_path(str(path))
+    except (json.JSONDecodeError, ValueError) as store_err:
+        print(
+            f"[CHAT-STORE] '{path.name}' unreadable "
+            f"({store_err.__class__.__name__}) — resetting it"
+        )
+        try:
+            with open(path, "w", encoding="utf-8") as f:
+                json.dump(initial_store, f, indent=2)
+        except Exception as repair_err:
+            print(f"[CHAT-STORE] reset failed: {repair_err}")
+            return SimpleChatStore()
+        try:
+            return SimpleChatStore().from_persist_path(str(path))
+        except Exception as reload_err:
+            print(f"[CHAT-STORE] reload after reset failed: {reload_err}")
+            return SimpleChatStore()
+
+print("Debug: Initializing chat stores")
+chat_store = SimpleChatStore()
+chat_store_public = _load_or_repair_chat_store(public_store_path)
+chat_store_private = _load_or_repair_chat_store(private_store_path)
+print("Debug: Chat stores initialized")
+
+# Utility: robustly extract clean SQL from LLM/artifact wrappers
+def extract_clean_sql(raw: str) -> str:
+    """Remove <<<SQL_START>>>, <<<SQL_END>>>, code fences, and similar wrappers from SQL text."""
+    import re
+    if not raw or not isinstance(raw, str):
+        return raw
+    s = raw.strip()
+    # Remove <<<SQL_START>>> ... <<<SQL_END>>> blocks
+    s = re.sub(r"<<<SQL_START>>>[\s\S]*?<<<SQL_END>>>", lambda m: m.group(0).replace('<<<SQL_START>>>','').replace('<<<SQL_END>>>','').strip(), s, flags=re.IGNORECASE)
+    # Remove <<<SQL_REVISED_START>>> ... <<<SQL_REVISED_END>>> blocks
+    s = re.sub(r"<<<SQL_REVISED_START>>>[\s\S]*?<<<SQL_REVISED_END>>>", lambda m: m.group(0).replace('<<<SQL_REVISED_START>>>','').replace('<<<SQL_REVISED_END>>>','').strip(), s, flags=re.IGNORECASE)
+    # Remove code fences (```sql ... ``` or ```)
+    s = re.sub(r"```(?:sql)?([\s\S]*?)```", lambda m: m.group(1).strip(), s, flags=re.IGNORECASE)
+    # Remove any remaining <<<...>>> wrappers
+    s = re.sub(r"<<<[A-Z_]+>>>", "", s)
+    
+    # Extract only the SQL portion before any explanations.
+    # Handles both plain "Explanation:" and markdown "### Explanation:" headers.
+    _explanation_pattern = r';\s*\n[\s\S]*?(?:#{1,3}\s*[Ee]xplanation|[Ee]xplanation)\s*:'
+    if re.search(_explanation_pattern, s):
+        s = re.split(_explanation_pattern, s)[0].rstrip() + ';'
+    elif re.search(r';\s*\n\s*[Ee]xplanation:', s):
+        s = re.split(r';\s*\n\s*[Ee]xplanation:', s)[0] + ';'
+
+
+    # Remove leading/trailing whitespace and repeated newlines
+    s = s.strip()
+    # Remove any leading/trailing code fence lines
+    s = re.sub(r"^```[a-zA-Z]*\s*|\s*```$", "", s, flags=re.MULTILINE)
+    
+    # Remove any trailing explanation text that might still be there
+    lines = s.split('\n')
+    sql_lines = []
+    for line in lines:
+        line = line.strip()
+        # Stop at explanation markers — including markdown ### headers
+        if re.match(r'^#{1,3}\s*(explanation|note|this query)', line, re.IGNORECASE):
+            break
+        if re.match(r'^(explanation|note|this query):', line, re.IGNORECASE):
+            break
+        if line.lower().startswith('explanation:'):
+            break
+        if re.match(r'^\d+\.\s+', line):  # Numbered explanation steps
+            break
+        sql_lines.append(line)
+    
+    s = '\n'.join(sql_lines).strip()
+    
+    # NEW: Safety check for multiple commands. 
+    # If the LLM outputted multiple queries (separated by semicolons), 
+    # we only want the LAST valid one (which is usually the most refined).
+    if s.count(';') > 1:
+        # Split by semicolon, keep only those that look like queries
+        statements = [stmt.strip() for stmt in s.split(';') if 'select' in stmt.lower()]
+        if statements:
+            # Use the last one as it's typically the final refinement
+            s = statements[-1] + ';'
+            print(f"[CLEAN_SQL] Multiple queries detected. Selected last valid statement.")
+            
+    return s.strip()
+
+# -----------------------------
+# OCR Q&A dedicated helpers
+# -----------------------------
+def _get_ocr_text_by_doc_id(doc_id: str) -> str:
+    try:
+        p = Path('chat_store') / 'docs' / f"{doc_id}.txt"
+        if not p.exists():
+            return ""
+        return p.read_text(encoding='utf-8')
+    except Exception:
+        return ""
+
+def _get_image_path_by_doc_id(doc_id: str) -> str:
+    """Return the saved image file path for doc_id, or empty string if not found."""
+    try:
+        p = Path('chat_store') / 'docs' / f"{doc_id}.img_path"
+        if not p.exists():
+            return ""
+        return p.read_text(encoding='utf-8').strip()
+    except Exception:
+        return ""
+
+def _granite_vision_qa(image_path: str, question: str) -> str:
+    """Answer a question about an image using IBM Granite Vision 3.1-2b (direct VLM Q&A).
+
+    Loads the model from local HF cache with aggressive quantization to stay within
+    the RAM budget (4-bit → 8-bit → fp16 fallback chain). The model is explicitly
+    deleted after generation to free memory for the SQL model to reload.
+    """
+    import gc
+    try:
+        import torch
+        from transformers import AutoProcessor, AutoModelForVision2Seq
+        from PIL import Image as PILImage
+    except ImportError as e:
+        return f"[ERROR] Granite Vision dependencies not installed: {e}"
+
+    model_path = GRANITE_MODEL_PATH
+    cache_dir  = os.environ.get("HF_HOME") or r"D:\hf_cache"
+    offload_dir = os.path.join(cache_dir, "offload")
+    os.makedirs(offload_dir, exist_ok=True)
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"[GRANITE_VQA] Loading {model_path} on {device} from {cache_dir}")
+
+    processor = vlm_model = None
+    try:
+        processor = AutoProcessor.from_pretrained(model_path, local_files_only=True, cache_dir=cache_dir)
+
+        # Try 4-bit → 8-bit → plain fp16 to fit within RAM budget
+        load_kwargs = dict(local_files_only=True, cache_dir=cache_dir, low_cpu_mem_usage=True)
+        loaded = False
+        for attempt, extra in enumerate([
+            {"load_in_4bit": True},
+            {"load_in_8bit": True},
+            {"device_map": "auto", "offload_folder": offload_dir},
+        ]):
+            try:
+                vlm_model = AutoModelForVision2Seq.from_pretrained(model_path, **load_kwargs, **extra)
+                print(f"[GRANITE_VQA] Loaded (attempt {attempt+1}: {list(extra.keys())[0]})")
+                loaded = True
+                break
+            except Exception as le:
+                print(f"[GRANITE_VQA] Load attempt {attempt+1} failed: {le}")
+        if not loaded:
+            return "[ERROR] Granite Vision could not be loaded — all quantisation attempts failed."
+
+        img = PILImage.open(image_path).convert("RGB")
+        conversation = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "url": image_path},
+                    {"type": "text",  "text": (
+                        f"{question}\n\n"
+                        "Answer concisely using only information visible in the image. "
+                        "If you see a table, read the correct row and column carefully."
+                    )},
+                ],
+            }
+        ]
+        inputs = processor.apply_chat_template(
+            conversation,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+        )
+        # Move inputs to model device
+        try:
+            model_device = next(vlm_model.parameters()).device
+            inputs = {k: v.to(model_device) if hasattr(v, 'to') else v for k, v in inputs.items()}
+        except Exception:
+            pass
+
+        with torch.no_grad():
+            output_ids = vlm_model.generate(**inputs, max_new_tokens=256, do_sample=False)
+        answer = processor.decode(output_ids[0], skip_special_tokens=True)
+
+        # Strip echoed prompt (Granite echoes conversation before the answer)
+        if "ASSISTANT" in answer.upper():
+            answer = answer.split("ASSISTANT")[-1].strip(" :").strip()
+        elif question.lower()[:20] in answer.lower():
+            idx = answer.lower().rfind(question.lower()[:20])
+            answer = answer[idx + len(question):].strip(" :").strip() if idx != -1 else answer
+
+        print(f"[GRANITE_VQA] Answer (chars={len(answer)}): {answer[:200]}")
+        return answer.strip() or "[ERROR] Granite Vision returned an empty response."
+
+    except Exception as e:
+        print(f"[GRANITE_VQA] Generation failed: {e}")
+        return f"[ERROR] Granite Vision Q&A failed: {e}"
+    finally:
+        # Aggressively free VLM memory so SQL model can reload
+        try:
+            del vlm_model, processor
+        except Exception:
+            pass
+        gc.collect()
+        try:
+            import torch
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+        except Exception:
+            pass
+        print("[GRANITE_VQA] Model unloaded; RAM released.")
+
+
+def _structure_invoice_text(raw: str) -> str:
+    """Convert flat Tesseract OCR output into a structured KEY: VALUE format.
+
+    Tesseract merges field labels and values on the same line and squashes
+    table rows together, causing a 1B LLM to confuse GST numbers with phone
+    numbers, amounts with dates, etc.  This function:
+    - Recognises common invoice field patterns via regex
+    - Emits explicit 'FIELD_NAME: value' lines for the LLM
+    - Preserves the rest of the text verbatim
+    So the model receives unambiguous context like:
+        GSTIN (Seller): 24HDE7487RE5RT4
+        Customer Phone: 9372346666
+        Customer GSTIN: 07AOLCC1206D126
+    """
+    import re
+    lines = raw.replace('\r\n', '\n').replace('\r', '\n').split('\n')
+    structured = []
+    seen_keys = set()
+
+    def emit(key: str, val: str):
+        tag = f"{key}: {val.strip()}"
+        if tag not in seen_keys:
+            seen_keys.add(tag)
+            structured.append(tag)
+
+    # Regex patterns for common Indian invoice fields
+    patterns = [
+        # GSTIN – 15-char alphanumeric starting with 2 digits
+        (r'GSTIN\s*(?:No\.?|:)?\s*([0-9]{2}[A-Z0-9]{13})', 'GSTIN'),
+        # Phone numbers (10-digit Indian mobile or landline with STD)
+        (r'(?:PHONE|TEL|MOB(?:ILE)?)\s*[:\.]?\s*([0-9\-\+\s]{8,15})', 'Phone'),
+        # Invoice number
+        (r'Invoice\s*No\.?\s*[:\.]?\s*([A-Z0-9/\-]+)', 'Invoice Number'),
+        # Challan number
+        (r'Challan\s*No\.?\s*[:\.]?\s*([0-9]+)', 'Challan Number'),
+        # Invoice / Challan dates
+        (r'Invoice\s*Date\s*[:\.]?\s*(\d{1,2}[-/]\w{3,9}[-/]\d{2,4})', 'Invoice Date'),
+        (r'Challan\s*Date\s*[:\.]?\s*(\d{1,2}[-/]\w{3,9}[-/]\d{2,4})', 'Challan Date'),
+        (r'Due\s*Date\s*[:\.]?\s*(\d{1,2}[-/]\w{3,9}[-/]\d{2,4})', 'Due Date'),
+        (r'Delivery\s*Date\s*[:\.]?\s*(\d{1,2}[-/]\w{3,9}[-/]\d{2,4})', 'Delivery Date'),
+        # E-Way number
+        (r'E-?Way\s*No\.?\s*[:\.]?\s*([A-Z0-9]+)', 'E-Way Number'),
+        # LR / PO numbers
+        (r'L\.?R\.?\s*No\.?\s*[:\.]?\s*([0-9]+)', 'LR Number'),
+        (r'P\.?O\.?\s*No\.?\s*[:\.]?\s*([0-9]+)', 'PO Number'),
+        # PAN
+        (r'PAN\s*[:\.]?\s*([A-Z]{5}[0-9]{4}[A-Z])', 'PAN'),
+        # Bank details
+        (r'Bank\s*(?:Account|A/?C)\s*(?:No\.?|Number)?\s*[:\.]?\s*([0-9]+)', 'Bank Account Number'),
+        (r'IFSC\s*[:\.]?\s*([A-Z]{4}0[A-Z0-9]{6})', 'Bank IFSC'),
+        # Totals
+        (r'Total\s+(?:Amount)?\s*After\s*Tax\s*[:\.]?\s*[\u20B9Rs\.]*\s*([\d,\.]+)', 'Total Amount After Tax'),
+        (r'Taxable\s+(?:Amount|Value)\s*[:\.]?\s*([\d,\.]+)', 'Taxable Amount'),
+        (r'Add\s*[:\.]?\s*IGST\s*[:\.]?\s*([\d,\.]+)', 'IGST Amount'),
+    ]
+
+    # Scan all lines for known field patterns
+    full_text = '\n'.join(lines)
+    # Track which GSTINs we've seen so we can label seller vs customer
+    gstin_count = 0
+    for line in lines:
+        for pat, label in patterns:
+            m = re.search(pat, line, re.IGNORECASE)
+            if m:
+                val = m.group(1).strip()
+                if label == 'GSTIN':
+                    gstin_count += 1
+                    lbl = 'Seller GSTIN' if gstin_count == 1 else 'Customer GSTIN'
+                    emit(lbl, val)
+                elif label == 'Phone':
+                    emit('Customer Phone', val)
+                else:
+                    emit(label, val)
+
+    # Extract company name (typically first non-empty ALL-CAPS-ish line)
+    for line in lines:
+        stripped = line.strip()
+        if len(stripped) > 4 and stripped.isupper() and not re.search(r'\d{5,}', stripped):
+            emit('Company Name', stripped)
+            break
+
+    # Extract customer name after 'M/S' or 'Bill to'
+    for i, line in enumerate(lines):
+        if re.search(r'^(M/?S|Bill\s*to)\b', line.strip(), re.IGNORECASE):
+            # Customer name is likely the next non-empty line or on same line after M/S
+            rest = re.sub(r'^(M/?S|Bill\s*to)\s*', '', line.strip(), flags=re.IGNORECASE).strip()
+            if rest:
+                emit('Customer Name', rest)
+            elif i + 1 < len(lines) and lines[i+1].strip():
+                emit('Customer Name', lines[i+1].strip())
+            break
+
+    # --- Product line items ---
+    # Scan for rows like: "2 | Stanley Hammer 8295 1.00 PCS 568.00 568.00 9.00 51.12 619.12"
+    # OCR often squashes all rows onto one long line; use the item-number+pipe as separator.
+    item_pat = re.compile(
+        r'\b(\d{1,3})\s*[|Ill1]\s*'          # item number + pipe  (I/l/1 are common OCR artefacts for |)
+        r'([A-Za-z][A-Za-z\s\-]{2,40}?)\s+'  # product name
+        r'(\d{4,8})\s+'                        # HSN / SAC code
+        r'(\d+(?:\.\d+)?)\s+'                  # quantity
+        r'(?:PCS|NOS|KG|LT|MT|SET|BOX|PC|NO|EA)\.?\s+'  # unit (consumed but not captured)
+        r'([\d,]+(?:\.\d+)?)'                  # rate (first numeric after unit)
+        r'((?:\s+[\d,]+(?:\.\d+)?){1,4})',     # remaining numbers (taxable value, GST, total etc.)
+        re.IGNORECASE
+    )
+    for m in item_pat.finditer(full_text):
+        item_no    = m.group(1)
+        name       = m.group(2).strip()
+        hsn        = m.group(3)
+        qty        = m.group(4)
+        rate       = m.group(5)
+        rest_nums  = re.findall(r'[\d,]+(?:\.\d+)?', m.group(6) or '')
+        # rest_nums order depends on invoice: [taxable_value, gst%, gst_amount, total]
+        taxable    = rest_nums[0] if len(rest_nums) > 0 else rate
+        total      = rest_nums[-1] if len(rest_nums) > 1 else taxable
+        label = f"Product {item_no} - {name}"
+        emit(label, f"HSN={hsn}, Qty={qty}, Rate={rate}, Taxable Value={taxable}, Total={total}")
+
+    # Build the final structured block
+    header = "=== STRUCTURED DOCUMENT FIELDS ==="
+    body = "=== RAW OCR TEXT ===\n" + raw.strip()
+    if structured:
+        return header + '\n' + '\n'.join(structured) + '\n\n' + body
+    return body
+
+
+def _ollama_unload_model(model: str) -> bool:
+    """Ask Ollama to immediately unload a model from RAM (keep_alive=0).
+
+    Uses /api/generate with a minimal prompt and keep_alive=0 to signal Ollama
+    to release the model. Works for models loaded by any client (LlamaIndex etc).
+    Returns True if the request succeeded, False otherwise (non-fatal).
+    """
+    base = OLLAMA_BASE_URL.rstrip('/')
+    try:
+        # A single-space prompt ensures Ollama actually processes the request
+        # (empty string may be skipped); keep_alive=0 causes immediate unload.
+        resp = requests.post(
+            f"{base}/api/generate",
+            json={"model": model, "prompt": " ", "keep_alive": 0, "stream": False},
+            timeout=90.0,
+        )
+        print(f"[OCR_QA] Unloaded model '{model}' from Ollama RAM (status={resp.status_code})")
+        return resp.status_code < 300
+    except Exception as e:
+        print(f"[OCR_QA][WARN] Failed to unload '{model}': {e}")
+        return False
+
+
+def _ollama_generate_text(model: str, prompt: str, timeout: float) -> str:
+    """Minimal REST call to Ollama generate API to avoid llama_index routing.
+
+    Uses OLLAMA_BASE_URL env and returns the concatenated response text.
+    num_ctx is chosen adaptively based on available RAM so we don't cause
+    excessive paging on memory-constrained machines.
+    """
+
+    url = f"{OLLAMA_BASE_URL.rstrip('/')}/api/generate"
+
+    # Pick num_ctx based on current available memory
+    # ≥800 MB free  -> 2048  (full invoice fits comfortably)
+    # 400–800 MB     -> 1024  (still fits most invoices)
+    # <400 MB        -> 512   (minimal; avoid paging)
+    try:
+        stats = memory_manager.get_memory_stats()
+        avail_mb = stats.get("available_mb", 999.0)
+    except Exception:
+        avail_mb = 999.0
+
+    if avail_mb >= 800:
+        num_ctx = 1024
+        num_predict = 200
+    elif avail_mb >= 400:
+        num_ctx = 512
+        num_predict = 150
+    else:
+        num_ctx = 256
+        num_predict = 100
+
+    payload = {
+    "model": model,
+    "prompt": prompt,
+    "stream": False,
+    "keep_alive": 0,   # unload immediately after response — prevents gemma3 squatting in RAM
+    "options": {
+        "temperature": 0.2,
+        "num_ctx": num_ctx,
+        "num_predict": num_predict,
+        "num_gpu": -1,      # keep on CPU same as Qwen; prevents GPU/CPU swap latency
+        "num_thread": 2,   # 2 threads is sufficient for 1B model on CPU
+        }
+    }
+    try:
+        # Debug request meta (do not print full prompt to avoid huge logs here)
+        try:
+            print(f"[DEBUG][OLLAMA_REQUEST] url={url} model={model} prompt_chars={len(prompt)} timeout={timeout} num_ctx={num_ctx} avail_mb={avail_mb:.0f}")
+        except Exception:
+            pass
+        r = requests.post(url, json=payload, timeout=timeout)
+        r.raise_for_status()
+        data = r.json()
+        # Log only response length, not full body (can be very large)
+        resp_text = data.get('response') or data.get('text') or str(data)
+        print(f"[DEBUG][OLLAMA_RESPONSE] model={model} response_chars={len(resp_text)}")
+        # Non-stream returns {response: "..."}
+        txt = resp_text
+        return txt
+    except Exception as e:
+        print(f"[ERROR][OLLAMA_API] Ollama generate failed: {e}")
+        return f"Error: Ollama generate failed: {e}"
+
+def ocr_agent_qa(question: str, doc_id: str, *, model: str | None = None, timeout: float | None = None) -> str:
+    """Answer a question about a previously uploaded document.
+
+    PRIMARY:  If the original image is available (.img_path), routes to Granite Vision
+              via MCP (granite_vision.qa tool) for direct visual Q&A.
+    FALLBACK: If only OCR text is available (.txt), builds a grounded prompt and
+              calls OCR_TEXT_FALLBACK_MODEL (gemma3) via Ollama.
+    """
+    text = _get_ocr_text_by_doc_id(doc_id)
+
+    # PRIMARY PATH: Granite Vision direct visual Q&A
+    image_path = _get_image_path_by_doc_id(doc_id)
+
+    # ── DIAGNOSTIC (temporary) ──────────────────────────────────────────
+    _img_path_file = Path('chat_store') / 'docs' / f"{doc_id}.img_path"
+    print(f"[OCR_QA_DIAG] doc_id          = {doc_id}")
+    print(f"[OCR_QA_DIAG] .img_path file  = {_img_path_file.resolve()} | exists={_img_path_file.exists()}")
+    print(f"[OCR_QA_DIAG] image_path      = {image_path!r}")
+    print(f"[OCR_QA_DIAG] isfile(image)   = {os.path.isfile(image_path) if image_path else 'N/A (empty)'}")
+    # ────────────────────────────────────────────────────────────────────
+
+    if image_path and os.path.isfile(image_path):
+        print(f"[OCR_QA] Using Granite Vision for doc_id={doc_id}, image={image_path}")
+        # Only perform RAM swapping/unloading if we are NOT in cloud mode
+        if not (os.environ.get("USE_HF_CLOUD", "0") in ("1", "true", "True")):
+            print(f"[OCR_QA] Swapping: unloading SQL model '{OLLAMA_MODEL_NAME}' to free RAM for Granite Vision")
+            _ollama_unload_model(OLLAMA_MODEL_NAME)
+            time.sleep(1.0)  # let OS reclaim pages
+        else:
+            print(f"[OCR_QA] 🌩️ CLOUD MODE: Skipping local RAM swap.")
+        # Route through MCP so Granite Vision runs as a proper registered agent tool
+        try:
+            from .mcp_client import mcp_call_tool
+        except ImportError:
+            from Agent.mcp_client import mcp_call_tool
+        try:
+            ctx = {
+                "tool_call": {
+                    "name": "granite_vision.qa",
+                    "args": {
+                        "image_path": image_path,
+                        "question": question,
+                        "model_path": GRANITE_MODEL_PATH,
+                    },
+                }
+            }
+            resp = mcp_call_tool(prompt="run granite vision qa", timeout=300.0, context=ctx)
+            tool_result = resp.get("tool_result") if isinstance(resp, dict) else None
+            if isinstance(tool_result, dict):
+                return tool_result.get("answer", "[ERROR] No answer returned from granite_vision.qa")
+            return str(tool_result) if tool_result else "[ERROR] granite_vision.qa returned no result"
+        except Exception as e:
+            print(f"[OCR_QA] MCP granite_vision.qa failed: {e}")
+            return f"[ERROR] Vision Q&A timed out or failed. Please ensure Docker has enough RAM (6GB+) and try again. ({e})"
+
+    # No image available — this doc was uploaded before the Granite Vision upgrade.
+    # Returning a clear message instead of a wrong answer from the old gemma3 pipeline.
+    print(f"[OCR_QA] No image found for doc_id={doc_id} — document predates Granite Vision upgrade")
+    return (
+        "⚠️ This document was uploaded before the Granite Vision upgrade. "
+        "Please click 'Upload Image' to re-upload the image — "
+        "Granite Vision will then answer your question directly from the image."
+    )
+
+# ---------------------------------------------------------------------------
+# Conversation Memory Index (improves recall vs naive substring search)
+# ---------------------------------------------------------------------------
+question_memory_index = {}  # normalized_question -> last answer
+
+def normalize_question(q: str) -> str:
+    """Create a stable normalization of a user question for indexing.
+    Steps: lowercase, remove punctuation except underscores, collapse whitespace.
+    """
+    import re
+    q = q.lower()
+    # remove any document snippet brackets if present (they make later repeats unmatchable)
+    q = re.sub(r"\[document .*?excerpt end\]", "", q, flags=re.IGNORECASE|re.DOTALL)
+    # strip bracketed metadata sections
+    q = re.sub(r"\[[^\]]+\]", "", q)
+    # punctuation -> space (keep underscores)
+    q = re.sub(r"[^a-z0-9_]+", " ", q)
+    q = re.sub(r"\s+", " ", q).strip()
+    return q
+
+def rebuild_memory_index():
+    """Rebuild in-memory index from persisted chat history."""
+    question_memory_index.clear()
+    msgs = chat_store_private.get_messages(key="conversation")
+    last_user = None
+    for m in msgs:
+        if m.role == MessageRole.USER:
+            last_user = m.content
+        elif m.role == MessageRole.ASSISTANT and last_user:
+            key = normalize_question(last_user)
+            question_memory_index[key] = m.content
+            last_user = None
+    print(f"[MEM-INDEX] Rebuilt memory index with {len(question_memory_index)} entries")
+
+# Build initial index
+try:
+    rebuild_memory_index()
+except Exception as e:
+    print(f"[MEM-INDEX][WARN] Failed to build initial index: {e}")
+
+# ---------------------------------------------------------------------------
+# Feature flags / global controls
+# ---------------------------------------------------------------------------
+# Disable ALL template-based fallback logic to enforce pure LLM agentic path
+DISABLE_TEMPLATE_FALLBACK = True
+# Feature toggles
+ENABLE_INVARIANTS = True  # can be turned off for debugging
+USE_SIMPLE_LLM_CALL = True  # bypass TimeoutManager for main generation
+# Configurable generation timeouts (seconds); can override via env vars
+FIRST_ATTEMPT_TIMEOUT = float(os.environ.get("FIRST_ATTEMPT_TIMEOUT", 180))  # Increased to 180s for initial attempt
+SECOND_ATTEMPT_TIMEOUT = float(os.environ.get("SECOND_ATTEMPT_TIMEOUT", 150))  # Increased to 150s for retry
+THIRD_ATTEMPT_TIMEOUT = float(os.environ.get("THIRD_ATTEMPT_TIMEOUT", 120))   # Increased to 120s for final attempt
+DEFAULT_LLM_REQUEST_TIMEOUT = float(os.environ.get("LLM_REQUEST_TIMEOUT", 150))
+
+# Micro-verification timeouts
+MICRO_VERIFY_MCP_TIMEOUT = float(os.environ.get("MICRO_VERIFY_MCP_TIMEOUT", 30))
+MICRO_VERIFY_LLM_TIMEOUT = float(os.environ.get("MICRO_VERIFY_LLM_TIMEOUT", 60))
+
+# Debug: Print timeout values to verify they're correct
+print(f"[TIMEOUT_DEBUG] FIRST_ATTEMPT_TIMEOUT={FIRST_ATTEMPT_TIMEOUT}s")
+print(f"[TIMEOUT_DEBUG] SECOND_ATTEMPT_TIMEOUT={SECOND_ATTEMPT_TIMEOUT}s")
+print(f"[TIMEOUT_DEBUG] THIRD_ATTEMPT_TIMEOUT={THIRD_ATTEMPT_TIMEOUT}s")
+STABLE_REUSE_LLM = True  # don't re-init on pure timeout, only on MemoryError
+# Absolute safety thresholds to avoid system instability / BSOD risk
+ABS_MIN_FREE_MB = float(os.environ.get("LLM_ABSOLUTE_MIN_FREE_MB", "200"))  # Lowered for GPU mode
+SAFE_POST_QUERY_TARGET_MB = float(os.environ.get("LLM_SAFE_POST_QUERY_TARGET_MB", "300"))  # Lowered for GPU mode
+# Placeholder tokens that should never appear in final executable SQL
+PLACEHOLDER_TOKENS = {
+    "your title", "some title", "sample title", "your table", "table_name",
+    "column_name", "some_column", "placeholder", "value_here"
+}
+PLACEHOLDER_REGEXES = [
+    r"your_[a-z0-9_]*",  # generic your_ placeholder tokens
+]
+
+# (Duplicate EMPLOYEE_DB_SCHEMA removed to prevent overwriting)
+
+# Global readiness flag so we can re-attempt handshake before first real generation
+LLM_HANDSHAKE_COMPLETE = False
+LAST_GENERATED_SQL = None  # updated each time a final SQL statement is produced
+LAST_MCP_HINT_SQL = None   # last validated fast-SQL hint (deterministic fallback path)
+# Cached real department names from DB — populated during initialize_database
+# Fallback: known employee-DB departments (used if DB query fails)
+_cached_dept_names: set = {
+    'Customer Service', 'Development', 'Finance', 'Human Resources',
+    'Marketing', 'Production', 'Quality Management', 'Research', 'Sales'
+}
+
+# Function to initialize LLM with better memory management
+_HF_QUOTA_DOWN = False  # sticky flag: set once HF Inference is confirmed out of credits
+def build_cloud_llm():
+    """Construct the HF Inference API LLM (default: Qwen/Qwen2.5-Coder-7B-Instruct).
+
+    This is the same path used on Hugging Face Spaces. It is also used as an
+    automatic fallback inside initialize_llm() when the configured local
+    Ollama model is not installed.
+
+    Credits-resilience: on HF Inference 402/401/429 (credits exhausted, expired
+    token, rate limit) the same prompt is re-issued to Groq's OpenAI-compatible
+    endpoint (GROQ_API_KEY is already a Space secret). The Groq model is
+    auto-discovered from Groq's live /models list; override with GROQ_LLM_MODEL."""
+    hf_token = os.environ.get("HF_API_TOKEN")
+    hf_model = os.environ.get("HF_LLM_MODEL", "Qwen/Qwen2.5-Coder-7B-Instruct")
+    if not hf_token:
+        raise RuntimeError("HF_API_TOKEN is not set; cannot use the HF Inference API")
+    if InferenceClient is None:
+        raise RuntimeError("huggingface_hub is not installed; cannot use the HF Inference API")
+
+    print(f"[INFO] CLOUD MODE: Initializing Official HF InferenceClient ({hf_model})")
+
+    class HFClientWrapper(CustomLLM):
+        # Use a dummy model name that LlamaIndex accepts
+        model_name: str = hf_model
+
+        @property
+        def metadata(self) -> LLMMetadata:
+            return LLMMetadata(
+                context_window=4096,
+                num_output=1024,
+                model_name=self.model_name,
+            )
+
+        def complete(self, prompt: str, **kwargs) -> CompletionResponse:
+            # Provider routing: Qwen via HF Inference is primary. Once HF has
+            # been confirmed out of credits/rate-limited this process, go
+            # straight to Groq (saves one dead 402 round-trip per level).
+            global _HF_QUOTA_DOWN
+            provider = "hf"
+            if _HF_QUOTA_DOWN:
+                provider = "groq"
+            if provider == "hf":
+                hf_client = InferenceClient(model=self.model_name, token=hf_token)
+                try:
+                    resp = hf_client.chat_completion(
+                        messages=[{"role": "user", "content": prompt}],
+                        max_tokens=1024
+                    )
+                    return CompletionResponse(text=resp.choices[0].message.content)
+                except Exception as hf_exc:
+                    if _is_provider_quota_error(hf_exc):
+                        _HF_QUOTA_DOWN = True
+                        print(f"[LLM-FALLBACK] HF Inference unavailable "
+                              f"({hf_exc.__class__.__name__}); falling back to Groq "
+                              f"(sticky for this process)", flush=True)
+                    else:
+                        raise hf_exc
+            try:
+                groq_text = _groq_text_completion(prompt)
+                return CompletionResponse(text=groq_text)
+            except Exception as groq_exc:
+                print(f"[LLM-FALLBACK] Groq fallback failed: {groq_exc}", flush=True)
+                raise
+
+        def stream_complete(self, prompt: str, **kwargs):
+            # Not needed for our agent, but required by base class
+            raise NotImplementedError()
+
+        def chat(self, messages, **kwargs):
+            # Convert ChatMessage list to string prompt for stability
+            prompt = ""
+            for m in messages:
+                role = getattr(m, 'role', 'user')
+                content = getattr(m, 'content', str(m))
+                prompt += f"{role}: {content}\n"
+            return self.complete(prompt)
+
+    llm = HFClientWrapper()
+    # Register it in Settings so downstream tools can use it
+    global LLM_HANDSHAKE_COMPLETE
+    Settings.llm = llm
+    LLM_HANDSHAKE_COMPLETE = True
+    return llm
+
+
+def _is_provider_quota_error(exc: Exception) -> bool:
+    """Detect HF Inference failures that a Groq fallback can plausibly fix:
+    out of credits (402), expired/missing token (401) and rate limits (429)."""
+    status = getattr(exc, "status_code", None)
+    if status is None:
+        response = getattr(exc, "response", None)
+        status = getattr(response, "status_code", None)
+    if status in (401, 402, 429):
+        return True
+    msg = str(exc)
+    return any(sig in msg for sig in (
+        "402", "Payment Required", "no remaining credits",
+        "401", "Unauthorized", "429", "rate limit",
+    ))
+
+
+GROQ_TEXT_MODEL = os.environ.get("GROQ_LLM_MODEL", "")  # optional override; empty = auto-discover
+
+_GROQ_MODEL_CACHE = None  # cached list of chat-capable Groq model ids
+
+# Static candidates used when /models discovery is unavailable (e.g. key issue).
+# Ordered: cheapest/longest-lived first; anything retired is skipped automatically.
+_GROQ_CANDIDATE_FALLBACKS = [
+    "llama-3.1-8b-instant",
+    "llama-3.3-70b-versatile",
+    "openai/gpt-oss-20b",
+    "qwen/qwen3-32b",
+]
+
+
+def _groq_chat_models() -> list:
+    """Live model list from Groq, filtered to chat-capable ids (no ASR/TTS/guard)."""
+    global _GROQ_MODEL_CACHE
+    if _GROQ_MODEL_CACHE is not None:
+        return _GROQ_MODEL_CACHE
+    groq_key = os.environ.get("GROQ_API_KEY")
+    text_models: list = []
+    if groq_key:
+        try:
+            r = requests.get(
+                "https://api.groq.com/openai/v1/models",
+                headers={"Authorization": f"Bearer {groq_key}"},
+                timeout=15,
+            )
+            r.raise_for_status()
+            ids = [m.get("id") for m in r.json().get("data", []) if m.get("id")]
+            exclude_tokens = ("whisper", "tts", "playai", "guard")
+            text_models = [
+                i for i in ids
+                if not any(tok in i.lower() for tok in exclude_tokens)
+            ]
+        except Exception as models_err:
+            print(f"[LLM-FALLBACK] Groq /models listing failed: {models_err}", flush=True)
+    _GROQ_MODEL_CACHE = text_models
+    return text_models
+
+
+def _groq_text_completion(prompt: str, max_tokens: int = 1024, timeout: float = 90.0) -> str:
+    """Chat completion against Groq's OpenAI-compatible endpoint.
+
+    Model resolution: GROQ_LLM_MODEL override -> live /models discovery ->
+    static candidates. Retired model ids (404/400) are skipped automatically,
+    so model deprecations on Groq's side never break the fallback."""
+    groq_key = os.environ.get("GROQ_API_KEY")
+    if not groq_key:
+        raise RuntimeError("GROQ_API_KEY is not set; Groq text-LLM fallback unavailable")
+
+    candidates: list = []
+    if GROQ_TEXT_MODEL:
+        candidates.append(GROQ_TEXT_MODEL)
+    candidates.extend(_groq_chat_models())
+    if not candidates:
+        candidates.extend(_GROQ_CANDIDATE_FALLBACKS)
+    # Prefer Qwen-family ids (closest match to the dormant HF primary,
+    # Qwen/Qwen2.5-Coder) while keeping the explicit override untouched.
+    ordered = candidates[:1]  # explicit override, if set, stays first
+    ordered += sorted(
+        candidates[1:],
+        key=lambda m: 0 if "qwen" in m.lower() else 1,  # stable sort: same bucket keeps live-list order
+    )
+    seen: set = set()
+    candidates = [c for c in ordered if not (c in seen or seen.add(c))][:8]
+    if not candidates:
+        raise RuntimeError("No Groq text models available (check GROQ_API_KEY)")
+
+    last_err: Exception | None = None
+    for model in candidates:
+        try:
+            r = requests.post(
+                "https://api.groq.com/openai/v1/chat/completions",
+                headers={"Authorization": f"Bearer {groq_key}"},
+                json={
+                    "model": model,
+                    "messages": [{"role": "user", "content": prompt}],
+                    "max_tokens": max_tokens,
+                },
+                timeout=timeout,
+            )
+            r.raise_for_status()
+            return r.json()["choices"][0]["message"]["content"]
+        except requests.HTTPError as http_err:
+            status = getattr(getattr(http_err, "response", None), "status_code", None)
+            if status in (404, 400):
+                # Model id retired/invalid on Groq's side -> try the next one
+                print(f"[LLM-FALLBACK] Groq model '{model}' unavailable ({status}); trying next candidate", flush=True)
+                _GROQ_MODEL_CACHE = None  # invalidate cache; refresh on next call
+                last_err = http_err
+                continue
+            last_err = http_err
+            break  # auth/quota/network errors: retrying other models won't help
+    raise RuntimeError(f"All Groq candidates failed: {last_err}")
+
+
+def _ollama_model_available(model_name: str, base_url: str) -> bool:
+    """Return True if the given model tag exists on the Ollama server.
+
+    A failed probe (server down, network error) conservatively returns True so
+    legacy behavior (handshake + retry warnings) is preserved.
+    """
+    try:
+        r = requests.get(f"{(base_url or '').rstrip('/')}/api/tags", timeout=3)
+        r.raise_for_status()
+        installed = set()
+        for m in r.json().get("models", []):
+            nm = (m.get("name") or "").strip()
+            if nm:
+                installed.add(nm)
+                installed.add(nm.split(":")[0])
+        return model_name in installed or model_name.split(":")[0] in installed
+    except Exception:
+        return True
+
+
+def initialize_llm(progressive_reduction=0):
+    """
+    Initialize the LLM model with Ollama with proper memory management.
+    
+    Args:
+        progressive_reduction: Level of resource reduction (0=normal, 1=reduced, 2=minimal)
+    
+    Returns:
+        An initialized Ollama LLM instance
+    """
+    # Make sure we have a clean slate
+    gc.collect()
+    
+    # Get current memory stats
+    global LLM_HANDSHAKE_COMPLETE
+    stats = memory_manager.get_memory_stats()
+    available_mb = stats["available_mb"]
+    
+    # CLOUD MODE: If USE_HF_CLOUD is enabled, skip local GPU/memory checks
+    if os.environ.get("USE_HF_CLOUD", "0") in ("1", "true", "True"):
+        if not os.environ.get("HF_API_TOKEN"):
+            print("[WARN] HF_API_TOKEN not found! Falling back to local Ollama even though USE_HF_CLOUD=1")
+        elif InferenceClient is None:
+            print("[WARN] huggingface_hub not installed! Falling back to local Ollama.")
+        else:
+            return build_cloud_llm()
+
+    # Hard safety abort first
+    if available_mb < ABS_MIN_FREE_MB:
+        raise MemoryError(f"Refusing to initialize LLM: free {available_mb:.1f}MB < ABS_MIN_FREE_MB {ABS_MIN_FREE_MB}MB (system safety)")
+
+    # Automatically determine if we need to increase resource reduction based on memory
+    if available_mb < memory_manager.critical_mb * 1.5:
+        # Force at least level 2 (minimal) if memory is near critical
+        progressive_reduction = max(progressive_reduction, 2)
+        print(f"Memory critically low ({available_mb:.2f}MB): Forcing minimal resource mode")
+    elif available_mb < memory_manager.threshold_mb:
+        # Force at least level 1 (reduced) if memory is below threshold
+        progressive_reduction = max(progressive_reduction, 1)
+        print(f"Memory low ({available_mb:.2f}MB): Using reduced resource mode")
+    
+    # Determine resource usage level based on available memory and progressive reduction
+    # Allow environment overrides for mode thresholds
+    try:
+        env_ultra = float(os.environ.get("LLM_ULTRA_MB", "800"))
+        env_min = float(os.environ.get("LLM_MIN_MB", "600"))
+    except Exception:
+        env_ultra, env_min = 400.0, 200.0
+    ultra_low_memory_mode = available_mb < env_ultra or progressive_reduction >= 1
+    minimal_mode = available_mb < env_min or progressive_reduction >= 2
+    
+    # Adjust parameters based on available memory
+    # Allow explicit overrides so user can test larger context to reduce repeated token delays
+    ctx_override = os.environ.get("LLM_CTX" )
+    thread_override = os.environ.get("LLM_THREADS")
+    try:
+        # Set absolute floor at 512 to prevent "forgetting" the schema hints
+        ctx_size = int(ctx_override) if ctx_override else (512 if minimal_mode else 768 if ultra_low_memory_mode else 1024)
+    except Exception:
+        ctx_size = 512
+    
+    try:
+        num_thread = int(thread_override) if thread_override else 1
+    except Exception:
+        num_thread = 1
+    timeout = 12.0 if minimal_mode else 18.0 if ultra_low_memory_mode else 25.0
+    
+    # Log mode based on memory constraints
+    mode_desc = "minimal" if minimal_mode else "ultra-low" if ultra_low_memory_mode else "low"
+    print(f"Initializing LLM with ctx_size={ctx_size}, num_thread={num_thread}, timeout={timeout:.1f}s in {mode_desc} memory mode")
+    
+    # Use smaller model if in minimal mode and memory is critically low
+    # Permit explicit model override via env for experimentation (e.g. smaller model to prevent timeouts)
+    model_name = os.environ.get("LLM_MODEL_OVERRIDE") or (
+        FALLBACK_MODEL_NAME if minimal_mode and available_mb < memory_manager.critical_mb * 1.2 else OLLAMA_MODEL_NAME
+    )
+
+    # Local Ollama model missing? Auto-fallback to the HF Inference API instead
+    # of burning handshake budget against a 404. This mirrors the HF Spaces
+    # configuration (Qwen/Qwen2.5-Coder-7B-Instruct) on a local machine.
+    if not _ollama_model_available(model_name, OLLAMA_BASE_URL):
+        print(f"[LLM_INIT] Ollama model '{model_name}' not found on {OLLAMA_BASE_URL}")
+        if os.environ.get("HF_API_TOKEN") and InferenceClient is not None:
+            print(f"[LLM_INIT] Auto-falling back to HF Inference API "
+                  f"({os.environ.get('HF_LLM_MODEL', 'Qwen/Qwen2.5-Coder-7B-Instruct')})")
+            try:
+                return build_cloud_llm()
+            except Exception as cloud_err:
+                print(f"[LLM_INIT] Cloud fallback failed: {cloud_err} — continuing with local Ollama anyway")
+        else:
+            print("[LLM_INIT] No HF_API_TOKEN set; cannot auto-fallback to cloud. "
+                  "Set USE_HF_CLOUD=1 + HF_API_TOKEN in Agent/.env, or pull the model with 'ollama pull'.")
+    
+    # Create the LLM instance with optimized settings but do NOT register yet.
+    # We perform a lightweight handshake first and then register only if memory allows.
+    llm = None
+    try:
+        llm = Ollama(
+            model=model_name,
+            base_url=OLLAMA_BASE_URL,
+            temperature=0.05,  # Restored from 7.7
+            request_timeout=DEFAULT_LLM_REQUEST_TIMEOUT,
+            additional_kwargs={
+                # Optimized parameters for memory efficiency
+                "num_ctx": ctx_size,
+                "num_batch": 1,        # Restored from 7.7
+                "num_gpu": -1,         # Keep GPU but with stable batching
+                "f16_kv": True,       # Half-precision for key/value cache
+                "mirostat": 0,        # Disable mirostat sampling
+                "num_thread": num_thread,
+                "seed": 42            # Consistent seed
+            }
+        )
+
+        # Handshake strategy: if strict mode enabled, we block until success (within max timeout budget);
+        # otherwise we warn and continue, with a later retry on first real query.
+        strict_mode = os.environ.get("LLM_HANDSHAKE_STRICT", "0") not in ("0", "false", "False")
+        base_timeout = float(os.environ.get("LLM_HANDSHAKE_TIMEOUT", "30"))  # single-attempt timeout
+        max_total = float(os.environ.get("LLM_HANDSHAKE_MAX_TOTAL", "120"))  # total budget across attempts
+        handshake_prompts = ["SELECT 1;", "-- warmup\nSELECT 1;"]
+        attempt = 0
+        start_all = time.time()
+        handshake_ok = False
+        while not handshake_ok and (time.time() - start_all) < max_total and attempt < len(handshake_prompts):
+            hp = handshake_prompts[attempt]
+            attempt += 1
+            remaining_budget = max_total - (time.time() - start_all)
+            this_timeout = min(base_timeout, remaining_budget)
+            print(f"[HS] Handshake attempt {attempt} timeout={this_timeout:.1f}s remaining_budget={remaining_budget:.1f}s")
+            try:
+                # Use direct call bounded by TimeoutManager to avoid internal indefinite waits.
+                resp = TimeoutManager.run_with_timeout(lambda: llm.complete(hp), timeout=this_timeout)
+                # Some wrappers return object with .text, others raw string
+                _txt = getattr(resp, 'text', None)
+                if resp is not None and (_txt is None or 'error' not in str(_txt).lower()):
+                    elapsed_all = time.time() - start_all
+                    print(f"[HS] LLM handshake succeeded in {elapsed_all:.2f}s (model={model_name})")
+                    handshake_ok = True
+                    break
+            except TimeoutError:
+                print(f"[HS][WARN] Handshake attempt {attempt} timed out after {this_timeout:.1f}s")
+            except Exception as e_hs:
+                print(f"[HS][WARN] Handshake attempt {attempt} error: {e_hs}")
+                # If model just started loading, give it a short pause before next attempt
+                time.sleep(1.5)
+        if not handshake_ok:
+            msg = "[HS][WARN] Handshake not successful within budget; will retry on first real query." if not strict_mode else "[HS][ERROR] Strict handshake mode enabled and handshake failed." 
+            print(msg)
+            if strict_mode:
+                # In strict mode, raise to stop startup (user explicitly wants guaranteed readiness)
+                raise TimeoutError("Strict LLM handshake failed")
+        else:
+            # Mark readiness
+            LLM_HANDSHAKE_COMPLETE = True
+
+        # Check memory after initialization/handshake to ensure we didn't consume too much
+        after_stats = memory_manager.get_memory_stats()
+        memory_used = available_mb - after_stats.get("available_mb", 0)
+        if memory_used > 100:  # If we used more than 100MB just for initialization
+            print(f"⚠️ Warning: LLM initialization consumed {memory_used:.2f}MB")
+        # Post-init safety check
+        if after_stats.get('available_mb', 0) < ABS_MIN_FREE_MB:
+            print(f"[SAFETY] Post-init free memory {after_stats.get('available_mb',0):.1f}MB < ABS_MIN_FREE_MB {ABS_MIN_FREE_MB}MB; tearing down instance")
+            try:
+                if hasattr(llm, 'close'): llm.close()
+            except Exception:
+                pass
+            raise MemoryError("Post-init memory below absolute floor; aborted")
+
+        # Attempt to register the instance with the memory manager now that it's healthy.
+        try:
+            memory_manager.register_llm_instance(llm)
+        except MemoryError as me:
+            # Registration refused due to low memory; leave the llm unregistered but return it
+            print(f"MemoryManager refused to register LLM: {me}")
+        except Exception as reg_err:
+            # Non-fatal: log and continue returning the llm (unregistered)
+            print(f"Warning: unexpected error registering LLM: {reg_err}")
+
+        return llm
+
+    except Exception:
+        # Ensure we free any partial resources on failure
+        try:
+            if llm is not None:
+                try:
+                    memory_manager.unregister_llm_instance(llm)
+                except Exception:
+                    pass
+        finally:
+            # Best-effort GC and working set trim
+            gc.collect()
+            try:
+                memory_manager.force_collect_garbage()
+            except Exception:
+                pass
+        raise
+
+# Create a function to run LLM safely with proper timeouts and cleanup
+def run_llm_with_timeout(llm, prompt, timeout=30.0, retries=1):
+    """
+    Run an LLM with proper timeout handling and resource cleanup.
+    
+    Args:
+        llm: The LLM instance to use
+        prompt: The prompt to send to the LLM
+        timeout: Timeout in seconds
+        retries: Number of retries on timeout or memory error
+        
+    Returns:
+        The LLM response
+        
+    Raises:
+        TimeoutError: If all attempts time out
+        MemoryError: If memory is insufficient after retries
+    """
+    last_error = None
+    
+    for attempt in range(retries + 1):
+        print(f"[LLM][ATTEMPT] Attempt {attempt+1}/{retries+1} for prompt (len={len(prompt)})")
+        # Check memory before attempt and log status
+        stats = memory_manager.get_memory_stats()
+        if stats['available_mb'] < ABS_MIN_FREE_MB:
+            print(f"[LLM][MEMORY] Insufficient memory before attempt: {stats['available_mb']:.1f}MB < ABS_MIN_FREE_MB {ABS_MIN_FREE_MB}MB")
+            raise MemoryError(f"Available memory {stats['available_mb']:.1f}MB < ABS_MIN_FREE_MB {ABS_MIN_FREE_MB}MB; aborting generation")
+        print(f"Memory check before attempt {attempt+1}: {stats['available_mb']:.2f}MB available (ABS_MIN_FREE_MB={ABS_MIN_FREE_MB}MB)")
+        
+        if stats['available_mb'] < memory_manager.threshold_mb:
+            # Try with more aggressive memory reduction
+            reduction_level = attempt + 1
+            print(f"[LLM][MEMORY] Memory low, creating LLM with reduction level {reduction_level}")
+            
+            # Release the current LLM instance if it exists
+            if 'llm' in locals() and llm is not None:
+                try:
+                    memory_manager.unregister_llm_instance(llm)
+                    llm = None
+                except Exception as e:
+                    print(f"Error unregistering LLM: {str(e)}")
+                
+                # Force garbage collection
+                gc.collect()
+                memory_manager.force_collect_garbage()
+            
+            # Check if memory is critically low
+            stats = memory_manager.get_memory_stats()
+            if stats['available_mb'] < memory_manager.critical_mb:
+                print(f"🚨 CRITICAL MEMORY CONDITION: Only {stats['available_mb']:.2f}MB available!")
+                raise MemoryError(f"System memory critically low: {stats['available_mb']:.2f}MB available")
+            
+            # Create a new LLM with more aggressive resource reduction
+            llm = initialize_llm(progressive_reduction=reduction_level)
+            
+            # Register with memory manager
+            memory_manager.register_llm_instance(llm)
+        
+        try:
+            # Run the LLM with timeout using ThreadManager for safety
+            print(f"[LLM][RUN] Running LLM (attempt {attempt+1}/{retries+1}) with {timeout:.1f}s timeout")
+
+            # Start timing
+            start_time = time.time()
+
+            # First, try talking to an external MCP server if available. This centralizes
+            # model & tool orchestration. If MCP is unreachable, fall back to local LLM.
+            # Prefer local LLM first (preserves pre-MCP behaviour). If local LLM fails or times out,
+            # attempt to use MCP as a fallback so an unavailable MCP won't break everything.
+            with memory_manager.suspend_emergency("llm_generation"):
+                result = None  # Initialize result to avoid UnboundLocalError
+                try:
+                    print("Attempting local LLM completion first...")
+                    # Wrap the LLM call to ensure we pass prompt correctly and bound it with our TimeoutManager
+                    result = TimeoutManager.run_with_timeout(lambda: llm.complete(prompt), timeout=timeout)
+                except TimeoutError as local_to:
+                    print(f"Local LLM completion timed out after {timeout}s: {local_to}; attempting MCP fallback...")
+                    try:
+                        from .mcp_client import mcp_complete
+                        mcp_text = mcp_complete(prompt, timeout=min( max(5.0, timeout*0.5 ), timeout ))
+                        class _Resp:
+                            def __init__(self, text):
+                                self.text = text
+                        result = _Resp(mcp_text)
+                        print(f"MCP fallback succeeded after local LLM timeout")
+                    except Exception as mcp_err:
+                        print(f"MCP completion failed or unavailable: {mcp_err}")
+                        # If MCP is clearly unreachable (connection refused), don't treat as critical
+                        if "connection refused" in str(mcp_err).lower() or "max retries exceeded" in str(mcp_err).lower():
+                            print("[MCP] Server appears to be down - continuing without MCP fallback")
+                        # Re-raise the original timeout error for retry logic
+                        raise local_to
+                except Exception as local_err:
+                    print(f"Local LLM completion failed: {local_err}; attempting MCP fallback...")
+                    try:
+                        from .mcp_client import mcp_complete
+                        mcp_text = mcp_complete(prompt, timeout=min( max(5.0, timeout*0.5 ), timeout ))
+                        class _Resp:
+                            def __init__(self, text):
+                                self.text = text
+                        result = _Resp(mcp_text)
+                        print(f"MCP fallback succeeded after local LLM failure")
+                    except Exception as mcp_err:
+                        print(f"MCP completion failed or unavailable: {mcp_err}")
+                        # If MCP is clearly unreachable (connection refused), don't treat as critical
+                        if "connection refused" in str(mcp_err).lower() or "max retries exceeded" in str(mcp_err).lower():
+                            print("[MCP] Server appears to be down - continuing without MCP fallback")
+                        # For timeout errors, try a longer timeout retry instead of immediate failure
+                        if isinstance(local_err, TimeoutError) and attempt < retries:
+                            print(f"[RETRY] Will retry with longer timeout due to initial timeout")
+                            # Don't re-raise immediately, let the retry logic handle it
+                            raise local_err
+                        else:
+                            # Re-raise original local error to be handled by outer retry logic
+                            raise local_err
+                            
+            # Log completion time
+            elapsed = time.time() - start_time
+            print(f"LLM completed in {elapsed:.2f}s")
+
+            # Check memory after completion
+            stats = memory_manager.get_memory_stats()
+            print(f"Memory after LLM: {stats['available_mb']:.2f}MB available")
+            # If after execution we are below safety target, proactively free (soft protection)
+            if stats['available_mb'] < SAFE_POST_QUERY_TARGET_MB:
+                try:
+                    print(f"[SAFETY] Free memory {stats['available_mb']:.1f}MB < SAFE_POST_QUERY_TARGET_MB {SAFE_POST_QUERY_TARGET_MB}MB; releasing LLM to protect system")
+                    memory_manager.unregister_llm_instance(llm)
+                except Exception:
+                    pass
+                memory_manager.force_collect_garbage()
+
+            # Force cleanup to prevent memory leaks
+            memory_manager.force_collect_garbage()
+
+            return result
+            
+        except (TimeoutError, MemoryError) as e:
+            last_error = e
+            print(f"[LLM][FAIL] Attempt {attempt+1} failed: {type(e).__name__}: {str(e)}")
+            if isinstance(e, TimeoutError):
+                print(f"[LLM][TIMEOUT] Timeout occurred for prompt (len={len(prompt)}). Consider simplifying the query or increasing system resources.")
+            
+            # Unregister LLM from memory manager
+            try:
+                memory_manager.unregister_llm_instance(llm)
+            except Exception as unreg_error:
+                print(f"Error unregistering LLM: {str(unreg_error)}")
+            
+            # Aggressive cleanup after error
+            for _ in range(3):  # Multiple cleanup attempts
+                gc.collect()
+                time.sleep(0.1)  # Brief pause to allow OS to reclaim memory
+            
+            memory_manager.force_collect_garbage()
+            
+            if attempt < retries:
+                # INCREASE timeout for subsequent attempts to handle initialization overhead
+                timeout = min(180.0, timeout * 1.5)  # Increase by 50% each retry, cap at 3 minutes
+                print(f"[LLM][RETRY] Retrying with increased timeout={timeout:.1f}s (attempt {attempt+2})")
+                # Escalate LLM reduction after 2 timeouts
+                if isinstance(e, TimeoutError) and attempt >= 1:
+                    print(f"[LLM][ESCALATE] Escalating to more aggressive LLM reduction after repeated timeouts.")
+                    try:
+                        llm = initialize_llm(progressive_reduction=attempt+2)
+                        memory_manager.register_llm_instance(llm)
+                    except Exception as esc_err:
+                        print(f"[LLM][ESCALATE][FAIL] Could not escalate LLM: {esc_err}")
+            else:
+                # Last attempt failed
+                print("[LLM][FAIL] All LLM attempts failed for this prompt.")
+                print(f"[LLM][USER] The LLM failed to respond after multiple attempts. Please try a simpler question, check your system resources, or increase the timeout settings if possible.")
+                # Final cleanup before raising error
+                memory_manager.force_collect_garbage()
+                raise last_error
+        
+        except Exception as e:
+            # For other exceptions, cleanup and don't retry
+            print(f"LLM error: {type(e).__name__}: {str(e)}")
+            
+            # Unregister LLM from memory manager
+            try:
+                memory_manager.unregister_llm_instance(llm)
+            except Exception as unreg_error:
+                print(f"Error unregistering LLM: {str(unreg_error)}")
+            
+            # Cleanup
+            memory_manager.force_collect_garbage()
+            
+            raise
+
+# Initialize LLM — guard against double-import (Agent.agentic_workflow vs agentic_workflow)
+# Both module paths share the same Ollama process, so re-running initialize_llm() causes
+# redundant handshakes and wastes ~440MB of KV-cache allocation each time.
+_LLM_INIT_SENTINEL = "Agent.__agentic_workflow_llm_initialized__"
+import sys as _sys
+if _sys.modules.get(_LLM_INIT_SENTINEL) is None:
+    _sys.modules[_LLM_INIT_SENTINEL] = True  # mark as done before call (prevents re-entry)
+    llm = initialize_llm()
+    Settings.llm = llm
+else:
+    # Module already initialized in this process — reuse the existing LLM instance
+    print("[LLM_INIT] Skipping duplicate initialize_llm() — module already initialized in this process")
+    try:
+        # Settings.llm should already be set; fall back to a fresh init only if truly missing
+        if getattr(Settings, 'llm', None) is None:
+            llm = initialize_llm()
+            Settings.llm = llm
+        else:
+            llm = Settings.llm
+    except Exception:
+        llm = initialize_llm()
+        Settings.llm = llm
+
+
+def get_salary_column_name() -> str:
+    """Helper to determine if the salary column is named 'salary' or 'amount'."""
+    global inspector, available_tables
+    if not inspector or 'salary' not in available_tables:
+        return 'salary'  # default fallback
+    try:
+        cols = [c['name'].lower() for c in inspector.get_columns(table_name='salary')]
+        if 'salary' in cols:
+            return 'salary'
+        if 'amount' in cols:
+            return 'amount'
+        return cols[0] if cols else 'salary'
+    except Exception:
+        return 'salary'
+
+
+async def initialize_database(uri: Optional[str] = None, schema_notes: Optional[str] = None) -> Dict[str, Any]:
+    """Initialize the database connection and return metadata.
+
+    schema_notes: owner-supplied domain hints to attach to the active schema
+    profile. None = leave existing notes untouched.
+    """
+    global available_tables, inspector, sql_database, async_engine, async_session, _DB_CONNECTED
+    metadata = {
+        "uri": None,
+        "tables": [],
+        "sql_database": None,
+        "inspector": None
+    }
+    
+    try:
+        # Use provided URI or fallback to global environment-loaded variable
+        effective_uri = uri or DB_CONNECTION_URL
+        if not effective_uri:
+            print("[DB-INIT] No connection URI provided; skipping database initialization.")
+            return metadata
+            
+        metadata["uri"] = effective_uri
+
+        # ---- Database-agnostic engine wiring (Agent/db_uri.py) -------------
+        # Maps bare schemes (mysql://, mssql://, oracle://, ...) onto the
+        # drivers this project ships, for both the sync and the async layer.
+        norm = normalize_db_uris(effective_uri)
+        family = norm["family"]
+        sync_uri = norm["sync_uri"]
+        async_uri = norm["async_uri"]
+        print(f"[DB-INIT] dialect family: {family or 'unknown'} | sync URI -> {sync_uri.split('@')[-1] if '@' in sync_uri else sync_uri}")
+
+        is_cloud_db = 'neon.tech' in effective_uri or 'sslmode=' in effective_uri
+        _is_postgres = family == "postgresql"
+
+        # Create sync engine for inspector / row stats / schema profile
+        engine_kwargs = {}
+        if _is_postgres and is_cloud_db:
+            # Neon-style managed Postgres: ensure TLS on the sync connection
+            print("[DB-INIT] Cloud database detected (Neon/SSL). Enabling SSL support.")
+            if 'sslmode=' not in sync_uri:
+                sep = '&' if '?' in sync_uri else '?'
+                sync_uri += f"{sep}sslmode=require"
+        metadata["sql_database"] = create_engine(sync_uri, **engine_kwargs)
+        sql_database = metadata["sql_database"]
+
+        # Create async engine for query execution (when a driver exists).
+        # mssql / oracle stay sync-only: SELECTs run through the sync engine.
+        # NOTE: async_engine / async_session are reset here so a profile switch
+        # away from an async-capable database cannot leak a stale session.
+        async_engine = None
+        async_session = None
+        if async_uri:
+            if _is_postgres:
+                # Strip sslmode from the URL query parameters for asyncpg
+                # compatibility; TLS is passed via connect_args instead.
+                if '?' in async_uri:
+                    base_part, query_part = async_uri.split('?', 1)
+                    new_params = [p for p in query_part.split('&') if not p.lower().startswith('sslmode=')]
+                    async_uri = base_part + ('?' + '&'.join(new_params) if new_params else '')
+            try:
+                # For asyncpg, we must pass SSL requirements via connect_args
+                async_kwargs = {}
+                if _is_postgres and is_cloud_db:
+                    async_kwargs["connect_args"] = {"ssl": "require"}
+                async_engine = create_async_engine(async_uri, **async_kwargs)
+                async_session = sessionmaker(
+                    async_engine, class_=AsyncSession, expire_on_commit=False
+                )
+            except Exception as _async_err:
+                print(
+                    f"[DB-INIT] async engine unavailable for '{family}' "
+                    f"({_async_err.__class__.__name__}: {_async_err}) — "
+                    "SELECTs will run through the sync engine."
+                )
+                async_engine = None
+                async_session = None
+        if async_session is None:
+            print(
+                f"[DB-INIT] no async SQLAlchemy driver for '{family}'; "
+                "query execution uses the sync engine in a worker thread."
+            )
+        metadata["async_ready"] = async_session is not None
+
+        # Create inspector
+        metadata["inspector"] = inspect(metadata["sql_database"])
+        inspector = metadata["inspector"]
+        
+        # Get available tables
+        available_tables = metadata["inspector"].get_table_names() + metadata["inspector"].get_view_names()
+
+        # Populate active schema profile (after inspector available)
+        try:
+            ACTIVE_SCHEMA_PROFILE.populate_from_inspector(inspector, sql_database)
+            tblset = ACTIVE_SCHEMA_PROFILE.available_tables_set
+            if all(x in tblset for x in ["employee", "department", "dept_emp", "dept_manager", "salary", "title"]):
+                ACTIVE_SCHEMA_PROFILE.is_demo = True
+                ACTIVE_SCHEMA_PROFILE.name = "demo_employee"
+            else:
+                ACTIVE_SCHEMA_PROFILE.is_demo = False
+                ACTIVE_SCHEMA_PROFILE.name = "user_schema"
+            ACTIVE_SCHEMA_PROFILE.db_url = effective_uri
+            print(f"[DB-INIT] schema profile '{ACTIVE_SCHEMA_PROFILE.name}' "
+                  f"({len(ACTIVE_SCHEMA_PROFILE.tables)} tables) ready")
+        except Exception as _e:
+            print(f"[DB-INIT] schema profile population warning: {_e}")
+        
+        # Initialize tables
+        for table in available_tables:
+            metadata_obj = MetaData()
+            try:
+                table_obj = Table(table, metadata_obj, autoload_with=metadata["sql_database"])
+                metadata["tables"].append(table_obj)
+                print(f"Initialized table: {table}")
+            except NoSuchTableError as e:
+                print(f'"{e}" table not found!')
+                continue
+
+        # Attach owner-supplied schema notes (domain hints), if provided.
+        # None means "no change" so generic re-inits never wipe stored notes.
+        if schema_notes is not None:
+            ACTIVE_SCHEMA_PROFILE.notes = (schema_notes or "").strip()
+            print(f"[DB-INIT] schema notes attached ({len(ACTIVE_SCHEMA_PROFILE.notes)} chars)")
+
+        _DB_CONNECTED = True
+        return metadata
+
+    except Exception as e:
+        print(f"Error initializing database: {str(e)}")
+        _DB_CONNECTED = False
+        return metadata
+
+def list_tables() -> str:
+    """Returns a list of available tables in the database."""
+    global available_tables, inspector
+    try:
+        table_list = []
+        for table_name in available_tables:
+            table_info = inspector.get_table_comment(table_name=table_name).get('text', '')
+            table_info = f'Table Description: {table_info}\n' if table_info else ''
+            table_list.append(f'Table Name: {table_name}\n{table_info}')
+        
+        result = '\n'.join(table_list)
+        return result
+    except Exception as e:
+        return f"Error listing tables: {str(e)}"
+
+def describe_tables(tables: Optional[List[str]] = None) -> str:
+    """Describes the specified tables in the database."""
+    global available_tables, inspector, sql_database
+    try:
+        table_names = tables or available_tables
+        table_schemas = []
+
+        for table_name in table_names:
+            try:
+                # Create table object
+                metadata_obj = MetaData()
+                table = Table(table_name, metadata_obj, autoload_with=sql_database)
+                
+                # Get schema
+                schema = f"{table_name} columns:\n"
+                
+                # Get columns
+                columns = inspector.get_columns(table_name=table_name)
+                for column in columns:
+                    col_name = column["name"]
+                    col_type = str(column["type"])
+                    schema += f"- {col_name}: {col_type}\n"
+                
+                table_schemas.append(f"{schema}")
+            except NoSuchTableError as e:
+                print(f"Table '{table_name}' not found: {str(e)}")
+                continue
+
+        if not table_schemas:
+            return "No valid tables found to describe."
+
+        result = "\n".join(table_schemas)
+        return result
+    except Exception as e:
+        return f"Error describing tables: {str(e)}"
+
+async def _fetch_rows_via_active_engine(sql: str):
+    """Execute read-only SQL through whichever engine layer is active.
+
+    Every SQLAlchemy-async-capable family driving this project (postgresql /
+    mysql / sqlite) goes through the async session; databases without an
+    async dialect (mssql via pymssql, oracle via oracledb) run through the
+    sync engine inside a worker thread so the event loop stays responsive.
+
+    Returns (rows, column_names) — raw SQLAlchemy Row objects + column keys.
+    """
+    if async_session is not None:
+        async with async_session() as session:
+            result = await session.execute(text(sql))
+            rows = result.fetchall()
+            cols = result.keys()
+    else:
+        if sql_database is None:
+            raise RuntimeError("No database engine is connected")
+
+        def _exec_sync():
+            with sql_database.connect() as _conn:
+                _res = _conn.execute(text(sql))
+                _rows = _res.fetchall()
+                _cols = list(_res.keys())
+            return _rows, _cols
+
+        rows, cols = await asyncio.to_thread(_exec_sync)
+    return rows, cols
+
+
+async def execute_query(query: str, skip_invariants: bool = False) -> List[Dict[str, Any]]:
+    """Execute an SQL query and return the results as a list of dictionaries."""
+    global async_session
+
+    def sanitize_sql(raw: str) -> str:
+        """Extract clean SQL, remove wrappers, and conservatively extract the first SQL statement (SELECT/WITH)."""
+        import re
+        s = extract_clean_sql(raw)
+        if not s or not isinstance(s, str):
+            return s
+        s = s.strip()
+        m = re.search(r"\b(select|with)\b", s, flags=re.IGNORECASE)
+        if not m:
+            return s
+        candidate = s[m.start():]
+        lines = candidate.splitlines()
+        kept = []
+        for ln in lines:
+            stripped = ln.strip()
+            if not stripped:
+                kept.append(ln)
+                continue
+            # Stop at bullet/annotation lines or obvious assistant notes
+            if re.match(r'^[-]{1,}', stripped):
+                break
+            if re.match(r'^(User Question:|Previous Attempt:|Use correct|Do NOT|Error executing|Result Presenter Agent)', stripped, flags=re.IGNORECASE):
+                break
+            kept.append(ln)
+        cleaned = '\n'.join(kept).strip()
+        # If the cleaned SQL looks like a bare SELECT of columns (no FROM), try to recover a FROM clause
+        if cleaned and not re.search(r"\bfrom\b", cleaned, flags=re.IGNORECASE):
+            fm = re.search(r"\bfrom\b[\s\S]*?(;|$)", candidate, flags=re.IGNORECASE)
+            if fm:
+                from_clause = fm.group(0).strip()
+                if re.search(r"^\s*select\b", from_clause, flags=re.IGNORECASE):
+                    subfm = re.search(r"\bfrom\b[\s\S]*?(;|$)", from_clause, flags=re.IGNORECASE)
+                    if subfm:
+                        from_clause = subfm.group(0).strip()
+                cleaned = cleaned.rstrip(';').strip() + '\n' + from_clause
+            else:
+                return ""
+        if cleaned and not cleaned.rstrip().endswith(';'):
+            cleaned = cleaned + ';'
+        return cleaned
+
+    try:
+        sanitized = sanitize_sql(query)
+        if sanitized != query:
+            print("Notice: sanitizing SQL before execution (removed LLM commentary)")
+            print(f"Sanitized SQL: {sanitized}")
+        print(f"Executing query: {sanitized}")
+        # Ensure we actually have a SELECT/WITH to run
+        if not sanitized or not re.search(r"\b(select|with)\b", sanitized, flags=re.IGNORECASE):
+            return [{"error": "No valid SQL found after sanitization"}]
+        # Be conservative: SELECT statements that lack a FROM clause are likely
+        # partial outputs from the LLM (e.g. column list only). Reject these
+        # unless they are a simple literal/select expression (e.g. SELECT 1;)
+        if re.search(r'^\s*select\b', sanitized, flags=re.IGNORECASE) and not re.search(r"\bfrom\b", sanitized, flags=re.IGNORECASE):
+            # Allow very small literal selects like `SELECT 1;` or `SELECT 'x';`
+            if not re.match(r"^\s*select\s+[0-9'\"]+\s*;\s*$", sanitized, flags=re.IGNORECASE):
+                return [{"error": "Sanitized SQL missing FROM clause; rejected to avoid invalid execution"}]
+        rows, result_keys = await _fetch_rows_via_active_engine(sanitized)
+        # Convert to list of dictionaries
+        if rows and result_keys:
+            return [dict(zip(result_keys, row)) for row in rows]
+        elif result_keys:  # We have columns but no rows
+            print(f"Query executed successfully but returned no rows. Columns: {list(result_keys)}")
+            return []
+        else:  # No rows and potentially no columns
+            print("Query executed but returned no results")
+            return []
+    except Exception as e:
+        error_message = str(e)
+        print(f"Error executing query: {error_message}")
+        
+        # Extract error details for better debugging
+        error_type = type(e).__name__
+        print(f"Error type: {error_type}")
+        
+        # Check for specific error types and provide more context
+        if "column" in error_message.lower() and "does not exist" in error_message.lower():
+            print("Column reference error detected - this may be a schema misunderstanding")
+        elif "syntax error" in error_message.lower():
+            print("SQL syntax error detected - the query structure is invalid")
+        elif "relation" in error_message.lower() and "does not exist" in error_message.lower():
+            print("Table reference error detected - the table may not exist or has a different name")
+
+        # AUTO-FIX: Detect PostgreSQL GroupingError (missing GROUP BY) and repair inline
+        if ("groupingerror" in error_type.lower() or
+                "must appear in the group by clause" in error_message.lower()):
+            print("[AUTO-FIX][GROUP_BY] Detected PostgreSQL GroupingError — attempting GROUP BY repair")
+            try:
+                col_match = re.search(
+                    r'column\s+"?([^"]+)"?\s+must appear in the group by',
+                    error_message, re.IGNORECASE
+                )
+                fixed_query = sanitized
+                if col_match:
+                    raw_col = col_match.group(1).strip()        # e.g. "title.title"
+                    col_bare = raw_col.split('.')[-1]           # e.g. "title"
+                    insertion = len(fixed_query)
+                    for clause in [r'\bORDER\s+BY\b', r'\bLIMIT\b']:
+                        m = re.search(clause, fixed_query, re.IGNORECASE)
+                        if m and m.start() < insertion:
+                            insertion = m.start()
+                    semi = re.search(r'\s*;', fixed_query)
+                    if semi and semi.start() < insertion:
+                        insertion = semi.start()
+                    if 'GROUP BY' in fixed_query.upper():
+                        fixed_query = re.sub(
+                            r'\b(GROUP\s+BY\b\s+\S+)',
+                            rf'\1, {col_bare}',
+                            fixed_query, count=1, flags=re.IGNORECASE
+                        )
+                    else:
+                        fixed_query = fixed_query[:insertion] + f' GROUP BY {col_bare}' + fixed_query[insertion:]
+                    print(f"[AUTO-FIX][GROUP_BY] Repaired: {fixed_query[:200]}")
+                    try:
+                        fixed_rows, fixed_keys = await _fetch_rows_via_active_engine(fixed_query)
+                        if fixed_rows and fixed_keys:
+                            return [dict(zip(fixed_keys, row)) for row in fixed_rows]
+                        elif fixed_keys:
+                            return []
+                        return []
+                    except Exception as _fix_err:
+                        print(f"[AUTO-FIX][GROUP_BY] repaired query still failed: {_fix_err}")
+                        return [{"error": str(_fix_err)}]
+            except Exception as gb_err:
+                print(f"[AUTO-FIX][GROUP_BY] Repair failed: {gb_err}")
+
+        # NEW: Detect missing FROM-clause/alias errors and trigger LLM regeneration
+        if not skip_invariants and ("missing from-clause entry for table" in error_message.lower() or "undefinedtableerror" in error_type.lower() or "missing from-clause entry" in error_message.lower() or "no such table" in error_message.lower() or "invalid reference to FROM-clause entry" in error_message.lower() or "missing alias" in error_message.lower() or "ambiguous column" in error_message.lower() or "alias" in error_message.lower() and "does not exist" in error_message.lower()):
+            print("[INVARIANTS] Detected alias/FROM-clause error. Triggering LLM regeneration for correct join order and alias usage.")
+            try:
+                from .invariants import build_invariants, evaluate_invariants, regenerate_with_constraints, classify_question
+                # Use the last question from context if available, else fallback
+                # (You may need to pass the question explicitly if not in scope)
+                regen_question = getattr(execute_query, 'last_question', None)
+                if not regen_question:
+                    regen_question = "Please regenerate the SQL with correct join order and alias usage."
+                cat = classify_question(regen_question)
+                invs = build_invariants(cat, regen_question)
+                missing = evaluate_invariants(query, invs)
+                if missing:
+                    regen_sql = regenerate_with_constraints(regen_question, query, missing, Settings.llm, run_llm_with_timeout, attempt=0)
+                    if regen_sql:
+                        print("[INVARIANTS] LLM regenerated SQL after alias/FROM-clause error. Re-executing.")
+                        # Try to execute the regenerated SQL
+                        return await execute_query(regen_sql)
+            except Exception as regen_alias_err:
+                print(f"[INVARIANTS] Alias/FROM-clause regeneration failed: {regen_alias_err}")
+        # If error indicates a missing column, attempt a lightweight heuristic repair and re-run once
+        col_match = re.search(r'column\s+"?([a-zA-Z0-9_]+)"?\s+does not exist', error_message, flags=re.IGNORECASE)
+        if col_match:
+            missing_col = col_match.group(1)
+            print(f"Detected missing column '{missing_col}', attempting heuristic repair...")
+            repaired = repair_missing_column(query, missing_col)
+            if repaired:
+                print(f"Repaired query to attempt: {repaired}")
+                try:
+                    rep_rows, rep_keys = await _fetch_rows_via_active_engine(repaired)
+                    if rep_rows and rep_keys:
+                        return [dict(zip(rep_keys, row)) for row in rep_rows]
+                    elif rep_keys:
+                        return []
+                    else:
+                        return []
+                except Exception as re_e:
+                    print(f"Repaired query execution failed: {re_e}")
+                    return [{"error": str(re_e)}]
+        # Return the error for formatting
+        return [{"error": error_message}]
+
+def validate_sql_query(query: str) -> Dict[str, Any]:
+    """Validate a SQL query without executing it."""
+    try:
+        # Always clean SQL before validation
+        sql = extract_clean_sql(query)
+        ql = sql.lower()
+        # Use regex to check for SELECT and FROM as word boundaries (robust to newlines/indentation)
+        if not re.search(r'\bselect\b', ql) and not re.search(r'\bwith\b', ql):
+            return {
+                "valid": False,
+                "reason": "Query must start with SELECT or WITH"
+            }
+        if not re.search(r'\bfrom\b', ql):
+            return {
+                "valid": False,
+                "reason": "Query must include FROM clause"
+            }
+        is_pure_aggregate = (
+            ("count(" in ql or "avg(" in ql or "min(" in ql or "max(" in ql or "sum(" in ql)
+            and " group by " not in ql
+        )
+        if ("limit" not in ql) and not is_pure_aggregate:
+            return {"valid": False, "reason": "Query should include LIMIT clause to prevent excessive results"}
+        return {
+            "valid": True,
+            "reason": "Query syntax appears valid"
+        }
+    except Exception as e:
+        return {
+            "valid": False,
+            "reason": f"Validation exception: {str(e)}"
+        }
+
+def repair_missing_column(query: str, missing_col: str) -> Optional[str]:
+    """Attempt a small, safe repair when a column is reported missing.
+    Heuristic currently supports the common case where `salary` is referenced
+    but belongs to the `salary` table rather than `employee`.
+    Returns a repaired SQL string (including LIMIT) or None if no repair.
+    """
+    try:
+        q = query.strip()
+        q_lower = q.lower()
+        # Find FROM clause and optional alias (e.g. FROM employee e)
+        m = re.search(r'\bfrom\s+([a-zA-Z_][\w]*)(?:\s+([a-zA-Z_][\w]*))?', q, flags=re.IGNORECASE)
+        if not m:
+            return None
+        base_table = m.group(1)
+        base_alias = m.group(2)
+
+        # Avoid treating SQL keywords as aliases (e.g., 'ORDER' accidentally captured)
+        SQL_KEYWORDS = {"order","by","where","group","limit","join","on","having","union","except","intersect"}
+        if base_alias and base_alias.lower() in SQL_KEYWORDS:
+            base_alias = None
+
+        alias = base_alias if base_alias else base_table
+
+        # Only handle the common salary case conservatively
+        if missing_col.lower() == 'salary':
+            # Avoid double-injecting if salary already joined or qualified
+            if re.search(r'\bjoin\s+salary\b', q_lower) or re.search(r's\.salary|salary\s+\w', q_lower):
+                return None
+
+            # Find end of the table name token to insert join before the next clause
+            table_token_re = re.search(r'\bfrom\s+' + re.escape(base_table) + r'\b', q, flags=re.IGNORECASE)
+            if not table_token_re:
+                return None
+            insert_pos = table_token_re.end()
+            join_clause = f" JOIN salary s ON {alias}.emp_no = s.emp_no"
+            repaired = q[:insert_pos] + join_clause + q[insert_pos:]
+
+            # Qualify ORDER BY salary -> s.salary
+            repaired = re.sub(r'order\s+by\s+salary\b', 'ORDER BY s.salary', repaired, flags=re.IGNORECASE)
+
+            # Ensure terminating semicolon and LIMIT presence
+            if 'limit' not in repaired.lower():
+                if repaired.strip().endswith(';'):
+                    repaired = repaired[:-1].strip() + ' LIMIT 100;'
+                else:
+                    repaired = repaired.strip() + ' LIMIT 100;'
+            else:
+                if not repaired.strip().endswith(';'):
+                    repaired = repaired.strip() + ';'
+
+            return repaired
+
+        return None
+    except Exception:
+        return None
+
+def enhanced_validate_sql(query: str) -> Dict[str, Any]:
+    """Stricter validation to catch placeholders, unknown tables/columns.
+    Returns dict(valid: bool, issues: List[str]). Lightweight heuristic (no full parse).
+    """
+    issues: List[str] = []
+    q_lower = query.lower()
+
+    # Reject obvious placeholders
+    for token in PLACEHOLDER_TOKENS:
+        if token in q_lower:
+            issues.append(f"Contains placeholder token '{token}'")
+    for rx in PLACEHOLDER_REGEXES:
+        if re.search(rx, q_lower):
+            issues.append("Contains generic placeholder pattern (your_*)")
+
+    # Must contain SELECT ... FROM ... pattern (robust to newlines/indentation)
+    if not re.search(r'\bselect\b', q_lower) or not re.search(r'\bfrom\b', q_lower):
+        issues.append("Missing SELECT or FROM clause")
+
+    # Extract referenced tables (rough pattern)
+    # IMPORTANT: Strip EXTRACT(...FROM...) expressions first to prevent false table detection.
+    # e.g. EXTRACT(YEAR FROM hire_date) would otherwise match 'hire_date' as a table name.
+    referenced_tables = set()
+    try:
+        query_for_table_scan = re.sub(r'\bEXTRACT\s*\([^)]+\)', '', query, flags=re.IGNORECASE)
+        table_tokens = re.findall(r'(?:from|join)\s+([a-zA-Z_][a-zA-Z0-9_]*)(?:\s+as)?\s*([a-zA-Z_][a-zA-Z0-9_]*)?', query_for_table_scan, flags=re.IGNORECASE)
+        for base, _alias in table_tokens:
+            referenced_tables.add(base.lower())
+    except Exception:
+        pass
+
+    avail_lower = [t.lower() for t in available_tables]
+    for t in referenced_tables:
+        if t and t not in avail_lower:
+            issues.append(f"Unknown table '{t}'")
+
+    # Build schema cache once
+    schema_cache = {}
+    try:
+        for tbl in available_tables:
+            try:
+                cols = [c['name'].lower() for c in inspector.get_columns(table_name=tbl)]
+                schema_cache[tbl.lower()] = set(cols)
+            except Exception:
+                continue
+    except Exception:
+        pass
+
+    # Alias mapping
+    alias_map = {}
+    try:
+        alias_defs = re.findall(r'(?:from|join)\s+([a-zA-Z_][a-zA-Z0-9_]*)\s+(?:as\s+)?([a-zA-Z_][a-zA-Z0-9_]*)', query, flags=re.IGNORECASE)
+        for real, al in alias_defs:
+            alias_map[al.lower()] = real.lower()
+    except Exception:
+        pass
+
+    # Column references alias.column
+    try:
+        col_refs = re.findall(r'([a-zA-Z_][a-zA-Z0-9_]*)\.([a-zA-Z_][a-zA-Z0-9_]*)', query)
+        # Skip common SQL functions/keywords that could appear before a dot (edge cases)
+        skip_alias = {"select","where","and","or","on","group","order","limit","count","avg","sum","min","max"}
+        for alias, col in col_refs:
+            if alias.lower() in skip_alias:
+                continue
+            mapped_table = alias_map.get(alias.lower(), alias.lower())
+            cols = schema_cache.get(mapped_table)
+            if cols is not None and col.lower() not in cols:
+                issues.append(f"Unknown column '{alias}.{col}'")
+    except Exception:
+        pass
+
+    # Detect bare (unqualified) column names used in WHERE / SELECT that don't exist in any referenced table
+    try:
+        # Extract simple bare identifiers between SELECT and FROM and after WHERE
+        bare_tokens = set()
+        select_part = query.split('FROM')[0] if 'FROM' in query.upper() else query
+        where_parts = re.split(r'WHERE', query, flags=re.IGNORECASE)
+        if len(where_parts) > 1:
+            where_clause = where_parts[1]
+        else:
+            where_clause = ''
+            token_pattern = r'\b([a-zA-Z_][a-zA-Z0-9_]*)\b'
+            keywords = {"select","from","where","and","or","on","group","by","order","limit","count","avg","sum","min","max","as","distinct","case","when","then","end"}
+            for part in [select_part, where_clause]:
+                for m in re.findall(token_pattern, part):
+                    ml = m.lower()
+                    if ml not in keywords and not any(ml == a or ml in schema_cache.get(a, set()) for a in alias_map.keys()):
+                        # Check if token is a column in ANY referenced table
+                        if not any(ml in cols for cols in schema_cache.values()):
+                            bare_tokens.add(m)
+        # Filter obviously numeric or alias tokens
+        for tok in list(bare_tokens):
+            if tok.isdigit():
+                bare_tokens.remove(tok)
+        # Report tokens that look like columns but unknown
+        for tok in sorted(bare_tokens):
+            # Skip if it is a table name
+            if tok.lower() in avail_lower:
+                continue
+            # Skip placeholder patterns already captured
+            if tok.lower().startswith('your_'):
+                continue
+            # Only flag if appears near '=' in query (likely used as column)
+            if re.search(rf'\b{tok}\b\s*=|=\s*\b{tok}\b', query):
+                issues.append(f"Unqualified unknown identifier '{tok}'")
+    except Exception:
+        pass
+
+    # LIMIT optional for pure single-row aggregates (no GROUP BY)
+    is_pure_aggregate = (
+        ("count(" in q_lower or "avg(" in q_lower or "min(" in q_lower or "max(" in q_lower or "sum(" in q_lower)
+        and " group by " not in q_lower
+    )
+    if "limit" not in q_lower and not is_pure_aggregate:
+        issues.append("Missing LIMIT clause")
+    if not q_lower.strip().endswith(';'):
+        issues.append("Missing terminating semicolon")
+
+    return {"valid": len(issues) == 0, "issues": issues}
+
+def format_results(results: List[Dict[str, Any]], question: str) -> str:
+    """Format query results into a user-friendly response."""
+    if not results:
+        try:
+            _db_label = ACTIVE_SCHEMA_PROFILE.name
+        except Exception:
+            _db_label = "the active database"
+        return f"I couldn't find any data matching your query in {_db_label}."
+    
+    if "error" in results[0]:
+        error_msg = results[0]["error"]
+        # Provide more specific error messages for common errors
+        if "column" in error_msg.lower() and "does not exist" in error_msg.lower():
+            column_match = re.search(r'column "([^"]+)" does not exist', error_msg.lower())
+            if column_match:
+                bad_column = column_match.group(1)
+                return f"I encountered an error with the database query. The column '{bad_column}' doesn't exist in the database schema. This might be due to a typo or incorrect schema understanding."
+        elif "relation" in error_msg.lower() and "does not exist" in error_msg.lower():
+            table_match = re.search(r'relation "([^"]+)" does not exist', error_msg.lower())
+            if table_match:
+                bad_table = table_match.group(1)
+                return f"I encountered an error with the database query. The table '{bad_table}' doesn't exist in the database. Available tables are: {', '.join(available_tables)}."
+        
+        # Generic error message
+        return f"I encountered an error while querying the database: {error_msg}"
+    
+    # Convert any Decimal objects to float for JSON serialization
+    processed_results = []
+    for row in results:
+        processed_row = {}
+        for key, value in row.items():
+            # Handle Decimal type for JSON serialization
+            if hasattr(value, '__class__') and value.__class__.__name__ == 'Decimal':
+                processed_row[key] = float(value)
+            else:
+                processed_row[key] = value
+        processed_results.append(processed_row)
+    
+    # Handle all result sets directly without using LLM to save memory
+    # First, check memory before potential LLM usage
+    if not check_memory_safety():
+        print("Low memory detected, using direct formatting instead of LLM")
+        return format_results_direct(processed_results, question)
+    
+    # For result sets up to 50 rows, format directly (avoids LLM narrating from 3-sample rows)
+    if len(processed_results) <= 50:
+        return format_results_direct(processed_results, question)
+    
+    # For larger result sets, try to use LLM but have a fallback ready
+    try:
+        # Check memory before LLM call
+        if not check_memory_safety():
+            print("Low memory before LLM formatting, falling back to direct formatting")
+            return format_results_direct(processed_results, question)
+            
+        # Extract just the first 3 rows to reduce context size
+        sample_results = processed_results[:3]
+        sample_str = str(sample_results)
+        
+        # Create a minimal prompt (profile-aware intro)
+        try:
+            _intro = ("Based on the employee database..." if ACTIVE_SCHEMA_PROFILE.is_employee_db
+                      else f"Based on the {ACTIVE_SCHEMA_PROFILE.name} database...")
+        except Exception:
+            _intro = "Based on the database..."
+        prompt = f"""Summarize database results for: "{question}"
+Sample (first 3 of {len(results)} rows):
+{sample_str}
+Start with "{_intro}"
+"""
+        
+        # Generate the formatted response with a short timeout
+        # Use a fresh LLM instance to avoid state issues
+        format_llm = initialize_llm()
+        response = format_llm.complete(prompt, timeout=5.0)
+        return response.text.strip()
+    except Exception as e:
+        print(f"Error formatting results with LLM: {str(e)}")
+        # Fallback to direct formatting
+        return format_results_direct(processed_results, question)
+
+def format_results_direct(processed_results: List[Dict[str, Any]], question: str) -> str:
+    """
+    Format results directly without using an LLM.
+    This is a memory-efficient fallback when the LLM approach isn't possible.
+    """
+    question_lower = question.lower()
+    
+    # Start with a profile-aware intro
+    try:
+        if ACTIVE_SCHEMA_PROFILE.is_employee_db:
+            summary = "Based on the employee database, "
+        else:
+            summary = f"Based on the {ACTIVE_SCHEMA_PROFILE.name} database, "
+    except Exception:
+        summary = "Based on the database, "
+    
+    # Determine the type of query to customize the response
+    # Gender distribution queries — always show a table
+    result_columns = list(processed_results[0].keys()) if processed_results else []
+    has_gender_col = any('gender' in c.lower() for c in result_columns)
+    if has_gender_col or 'gender' in question_lower or 'male' in question_lower or 'female' in question_lower:
+        summary += "here is the gender breakdown:\n\n"
+        headers = result_columns
+        summary += "| " + " | ".join(headers) + " |\n"
+        summary += "| " + " | ".join(["---"] * len(headers)) + " |\n"
+        for row in processed_results:
+            values = []
+            for header in headers:
+                val = row.get(header, "")
+                if isinstance(val, (int, float)) and any(k in header.lower() for k in ["count", "num", "total"]):
+                    values.append(f"{val:,}")
+                else:
+                    values.append(str(val))
+            summary += "| " + " | ".join(values) + " |\n"
+        return summary
+    
+    if "average salary" in question_lower and "department" in question_lower:
+        summary += "here's the average salary by department:\n\n"
+        
+        # Create a markdown table for department salaries
+        headers = list(processed_results[0].keys())
+        summary += "| " + " | ".join(headers) + " |\n"
+        summary += "| " + " | ".join(["---"] * len(headers)) + " |\n"
+        
+        for row in processed_results:
+            values = []
+            for header in headers:
+                val = row.get(header, "")
+                # Format salary numbers with dollar signs and commas
+                if isinstance(val, (int, float)) and "salary" in header.lower():
+                    values.append(f"${val:,.2f}")
+                else:
+                    values.append(str(val))
+            summary += "| " + " | ".join(values) + " |\n"
+            
+    elif "count" in question_lower or "how many" in question_lower or "number of" in question_lower:
+        # For count queries
+        if len(processed_results) == 1 and len(processed_results[0]) == 1:
+            # Simple count result (like total number of employees)
+            key = list(processed_results[0].keys())[0]
+            count = processed_results[0][key]
+            
+            if "department" in question_lower and "employee" in question_lower:
+                # Extract department name from question if possible
+                dept_match = re.search(
+                    r'(?:in|for|of|within)\s+(?:the\s+)?([A-Za-z\s&]+?)\s+department',
+                    question_lower
+                )
+                dept_name = dept_match.group(1).strip().title() if dept_match else "that department"
+                summary += f"there are {count:,} employees working in the {dept_name} department."
+            elif "employee" in question_lower:
+                summary += f"there are {count:,} employees in the database."
+            else:
+                summary += f"the count is {count:,}."
+        else:
+            # Multiple rows with counts (like employees per department)
+            summary += "here are the counts:\n\n"
+            
+            # Create a markdown table
+            headers = list(processed_results[0].keys())
+            summary += "| " + " | ".join(headers) + " |\n"
+            summary += "| " + " | ".join(["---"] * len(headers)) + " |\n"
+            
+            for row in processed_results:
+                values = []
+                for header in headers:
+                    val = row.get(header, "")
+                    # Format count numbers with commas
+                    if isinstance(val, (int, float)) and ("count" in header.lower() or header.lower() == "count"):
+                        values.append(f"{val:,}")
+                    else:
+                        values.append(str(val))
+                summary += "| " + " | ".join(values) + " |\n"
+    
+    elif "manager" in question_lower or "managers" in question_lower:
+        # For manager queries
+        summary += "here are the department managers:\n\n"
+        
+        # Create a markdown table
+        headers = list(processed_results[0].keys())
+        summary += "| " + " | ".join(headers) + " |\n"
+        summary += "| " + " | ".join(["---"] * len(headers)) + " |\n"
+        
+        for row in processed_results:
+            values = []
+            for header in headers:
+                val = row.get(header, "")
+                values.append(str(val))
+            summary += "| " + " | ".join(values) + " |\n"
+    
+    else:
+        # Generic handling for other types of queries
+        if len(processed_results) <= 10:
+            summary += "here are the results:\n\n"
+            
+            # Create a markdown table
+            headers = list(processed_results[0].keys())
+            summary += "| " + " | ".join(headers) + " |\n"
+            summary += "| " + " | ".join(["---"] * len(headers)) + " |\n"
+            
+            for row in processed_results:
+                values = []
+                for header in headers:
+                    val = row.get(header, "")
+                    # Format currency values
+                    if isinstance(val, (int, float)) and "salary" in header.lower():
+                        values.append(f"${val:,.2f}")
+                    # Format count values
+                    elif isinstance(val, (int, float)) and ("count" in header.lower() or header.lower() == "count"):
+                        values.append(f"{val:,}")
+                    else:
+                        values.append(str(val))
+                summary += "| " + " | ".join(values) + " |\n"
+        else:
+            # For large result sets
+            summary += f"I found {len(processed_results):,} results. Here are the first 10:\n\n"
+            
+            # Create a markdown table with just the first 10 rows
+            headers = list(processed_results[0].keys())
+            summary += "| " + " | ".join(headers) + " |\n"
+            summary += "| " + " | ".join(["---"] * len(headers)) + " |\n"
+            
+            for row in processed_results[:10]:
+                values = []
+                for header in headers:
+                    val = row.get(header, "")
+                    # Format currency values
+                    if isinstance(val, (int, float)) and "salary" in header.lower():
+                        values.append(f"${val:,.2f}")
+                    # Format count values
+                    elif isinstance(val, (int, float)) and ("count" in header.lower() or header.lower() == "count"):
+                        values.append(f"{val:,}")
+                    else:
+                        values.append(str(val))
+                summary += "| " + " | ".join(values) + " |\n"
+    
+    return summary
+
+def check_history(question: str) -> Dict[str, Any]:
+    """Check if a similar question has been asked before."""
+    # 1. Direct normalized lookup
+    norm = normalize_question(question)
+    ans = question_memory_index.get(norm)
+    if ans:
+        return {"found": True, "previous_question": question, "previous_answer": ans}
+
+    # 2. Lightweight fuzzy / token overlap search over recent history
+    try:
+        private_messages = chat_store_private.get_messages(key="conversation")
+        qa_pairs = []
+        cur_q = None
+        for msg in private_messages[-400:]:  # limit scan window
+            if msg.role == MessageRole.USER:
+                cur_q = msg.content
+            elif msg.role == MessageRole.ASSISTANT and cur_q:
+                qa_pairs.append((cur_q, msg.content))
+                cur_q = None
+        qa_pairs.reverse()
+        import re
+        target_tokens = set(norm.split())
+        best = None
+        best_score = 0.0
+        threshold = float(os.environ.get("MEMORY_FUZZY_THRESHOLD", "0.6"))
+        for qtext, answer in qa_pairs:
+            if any(k in qtext.lower() for k in ["re-execute","reexecute","regenerate","try again"]):
+                continue
+            q_norm = normalize_question(qtext)
+            tokens = set(q_norm.split())
+            if not tokens:
+                continue
+            overlap = len(tokens & target_tokens)
+            union = len(tokens | target_tokens)
+            jacc = overlap / union if union else 0.0
+            if jacc > best_score:
+                best_score = jacc
+                best = (qtext, answer)
+            if best_score >= 0.95:  # early exit on near-perfect match
+                break
+        if best and best_score >= threshold:
+            print(f"[MEM-INDEX] Fuzzy match score={best_score:.2f} retrieved prior answer")
+            return {"found": True, "previous_question": best[0], "previous_answer": best[1]}
+    except Exception as e:
+        print(f"[MEM-INDEX][WARN] Fuzzy history scan failed: {e}")
+    return {"found": False}
+
+def update_history(question: str, answer: str) -> None:
+    """Update chat history with new question and answer."""
+    # Check if this is a re-execution command
+    is_reexecute = any(keyword in question.lower() for keyword in 
+                      ["re-execute", "reexecute", "regenerate", "try again"])
+    
+    # If it's a re-execution command, also save a version without the command words
+    if is_reexecute:
+        # Create a clean version of the question (without re-execute commands)
+        clean_question = question.lower()
+        for keyword in ["re-execute", "reexecute", "regenerate", "try again", "force", "bypass memory"]:
+            clean_question = clean_question.replace(keyword, "").strip()
+            
+        # Add the clean version to history as well
+        clean_user_message = ChatMessage(
+            role=MessageRole.USER,
+            content=clean_question
+        )
+        
+        clean_assistant_message = ChatMessage(
+            role=MessageRole.ASSISTANT,
+            content=answer
+        )
+        
+        # Add clean version to chat store
+        chat_store_private.add_message(key="conversation", message=clean_user_message)
+        chat_store_private.add_message(key="conversation", message=clean_assistant_message)
+    
+    # Create messages for the original question
+    user_message = ChatMessage(
+        role=MessageRole.USER,
+        content=question
+    )
+    
+    assistant_message = ChatMessage(
+        role=MessageRole.ASSISTANT,
+        content=answer
+    )
+    
+    # Skip storing if answer appears to be an error or pure system notice
+    lowered = answer.lower()
+    error_indicators = [
+        "error while querying",
+        "syntax error",
+        "encountered an error",
+        "critically low on available memory",
+        "low on available memory",
+        "all query generation methods failed",
+        "cannot proceed due to low memory",
+        "i'm sorry, but your system is",
+        "failed to initialize"
+    ]
+    is_error_answer = any(tok in lowered for tok in error_indicators)
+    if is_error_answer:
+        print("[MEM-HISTORY] Skipping storage of error answer")
+    else:
+        chat_store_private.add_message(key="conversation", message=user_message)
+        chat_store_private.add_message(key="conversation", message=assistant_message)
+        # Maintain a cap of recent successful QA pairs (e.g., last 200 user+assistant pairs)
+        try:
+            msgs = chat_store_private.get_messages(key="conversation")
+            # Count assistant messages to approximate pairs
+            max_pairs = int(os.environ.get("MEMORY_MAX_PAIRS", "200"))
+            # We'll rebuild keeping only last max_pairs*2 messages (user+assistant)
+            if max_pairs > 0:
+                trimmed = []
+                # Walk backwards collecting pairs
+                pair_msgs = []
+                user_seen = 0
+                for m in reversed(msgs):
+                    pair_msgs.append(m)
+                    if m.role == MessageRole.USER:
+                        user_seen += 1
+                    if user_seen >= max_pairs:
+                        break
+                trimmed = list(reversed(pair_msgs))
+                if len(trimmed) < len(msgs):
+                    chat_store_private._data["conversation"] = trimmed  # internal structure update
+                    print(f"[MEM-HISTORY] Trimmed history to last {user_seen} user turns")
+        except Exception as trim_err:
+            print(f"[MEM-HISTORY][WARN] Failed to trim history: {trim_err}")
+    
+    # Persist only if stored
+    if not is_error_answer:
+        chat_store_private.persist(str(private_store_path))
+        print("Chat history updated successfully")
+        # Update memory index
+        try:
+            key = normalize_question(question)
+            question_memory_index[key] = answer
+        except Exception as e:
+            print(f"[MEM-INDEX][WARN] Failed to index question: {e}")
+
+
+def get_doc_text(doc_id: str) -> str:
+    """Read persisted OCR text for a given doc_id from chat_store/docs/{doc_id}.txt.
+
+    Returns the raw text or empty string if not found.
+    """
+    try:
+        p = Path('chat_store') / 'docs' / f"{doc_id}.txt"
+        if not p.exists():
+            return ""
+        return p.read_text(encoding='utf-8')
+    except Exception:
+        return ""
+
+
+# Define the tool functions
+def memory_check(question: str) -> str:
+    """Check if this question has been asked before."""
+    # Check if user explicitly requested to re-execute
+    force_reexecute = any(keyword in question.lower() for keyword in 
+                          ["re-execute", "reexecute", "regenerate", "try again", "force", "bypass memory"])
+    
+    # Remove re-execution keywords from the question for memory checking
+    clean_question = question.lower()
+    for keyword in ["re-execute", "reexecute", "regenerate", "try again", "force", "bypass memory"]:
+        clean_question = clean_question.replace(keyword, "").strip()
+    
+    # If force re-execute, skip memory check and use the current question
+    if force_reexecute:
+        # Extract the actual question from the re-execute command
+        # clean_question already has re-execute keywords removed
+        if clean_question and len(clean_question.strip()) > 0:
+            print(f"[MEMORY] Re-execute requested. Using current question: '{clean_question}'")
+            return f"User requested to re-execute the query. Bypassing memory check."
+        else:
+            # If no question provided after re-execute, try to find previous question
+            try:
+                private_messages = chat_store_private.get_messages(key="conversation")
+                if len(private_messages) >= 2:
+                    # Find the last user question before this re-execute command
+                    for i in range(len(private_messages) - 1, -1, -1):
+                        if private_messages[i].role == "user":
+                            last_user_msg = private_messages[i].content
+                            # Skip if this is also a re-execute command or empty
+                            if last_user_msg and len(last_user_msg.strip()) > 0 and not any(kw in last_user_msg.lower() for kw in ["re-execute", "reexecute", "regenerate", "try again"]):
+                                print(f"[MEMORY] Found original question for re-execute: {last_user_msg}")
+                                return f"RE_EXECUTE_ORIGINAL: {last_user_msg}"
+            except Exception as e:
+                print(f"Error retrieving original question for re-execute: {e}")
+            
+            # If we can't find a previous question, just bypass memory check
+            print("[MEMORY] No previous question found, bypassing memory check for re-execute")
+            return f"User requested to re-execute the query. Bypassing memory check."
+    
+    # Check memory with the cleaned question
+    result = check_history(clean_question)
+    
+    if result["found"]:
+        # Check if the previous answer was an error
+        if "error" in result["previous_answer"].lower() or "encountered an error" in result["previous_answer"].lower():
+            print("Previous answer contained an error. Suggesting to re-execute.")
+            return f"FOUND_IN_MEMORY_WITH_ERROR: {result['previous_answer']}"
+        else:
+            return f"FOUND_IN_MEMORY: {result['previous_answer']}"
+    else:
+        return "No previous answer found. I'll create a SQL query for this."
+
+##### Hardcoded SQL template fallback system removed to enforce pure LLM-only generation #####
+def ensure_llm_ready():
+    """Ensure the global LLM has completed a successful handshake. If not, attempt one now
+    with an extended timeout (single attempt) so first user query doesn't fail silently.
+    Non-raising on failure; query generation will still attempt but will log status.
+    """
+    global LLM_HANDSHAKE_COMPLETE, llm
+    if LLM_HANDSHAKE_COMPLETE:
+        return
+    print("[HS][LAZY] Performing deferred handshake before first generation...")
+    try:
+        # Attempt a single prompt with generous timeout (can be overridden)
+        timeout = float(os.environ.get("LLM_HANDSHAKE_TIMEOUT_FIRST_QUERY", os.environ.get("LLM_HANDSHAKE_TIMEOUT", "45")))
+        resp = TimeoutManager.run_with_timeout(lambda: llm.complete("SELECT 1;"), timeout=timeout)
+        if resp is not None:
+            LLM_HANDSHAKE_COMPLETE = True
+            print(f"[HS][LAZY] Deferred handshake succeeded (timeout={timeout}s)")
+    except Exception as e:
+        print(f"[HS][LAZY][WARN] Deferred handshake failed: {e}")
+
+# ----------------------------------------------------------------------------
+# Lightweight instrumentation utilities (no external deps) to diagnose timeouts
+# ----------------------------------------------------------------------------
+INSTRUMENT_GENERATION = os.environ.get("GEN_INSTRUMENT", "1") in ("1","true","True")
+_GEN_EVENT_COUNTER = 0
+
+def _approx_tokens(text: str) -> int:
+    # Crude heuristic; avoids importing a tokenizer. Good enough for relative scale.
+    if not text:
+        return 0
+    # Average 4 chars per token for English + SQL mix
+    return max(1, len(text)//4)
+
+def _log_gen(stage: str, **kv):
+    if not INSTRUMENT_GENERATION:
+        return
+    global _GEN_EVENT_COUNTER
+    _GEN_EVENT_COUNTER += 1
+    # Build compact k=v list; truncate any large values
+    parts = [f"{k}={str(v)[:160]}" for k,v in kv.items()]
+    print(f"[GEN][INS][{_GEN_EVENT_COUNTER}][{stage}] " + " ".join(parts))
+
+def _shrink_schema_if_needed(prompt: str, schema_text: str) -> str:
+    """If prompt becomes very large (> ~9k chars) aggressively shrink schema section.
+    Returns (maybe) new schema_text. Keep logic minimal & reversible."""
+    if len(prompt) < 9000:
+        return schema_text
+    lines = schema_text.splitlines()
+    # Keep only first 2 table lines if huge
+    reduced = []
+    kept_tables = 0
+    for ln in lines:
+        if ln.strip():
+            reduced.append(ln)
+            if ':' in ln:
+                kept_tables += 1
+            if kept_tables >= 2:
+                break
+    reduced.append("...schema trimmed for brevity...")
+    new_schema = "\n".join(reduced)
+    return new_schema
+
+
+# ===============================================================================
+# 3-LEVEL AGENTIC SQL GENERATION ARCHITECTURE
+# ===============================================================================
+# Helper functions for the 3-level SQL generation approach:
+# - Level 1: Pure LLM intelligence (no assistance)
+# - Level 2: LLM + SQL Fixer enhancement
+# - Level 3: LLM + MCP hints + SQL Fixer
+#
+# Each level attempts to generate SQL, and if it fails, escalates to the next level.
+# Loop prevention: Strict level progression, single attempt per level, no recursive calls.
+# ===============================================================================
+
+def classify_fast_sql_intent(q: str):
+    """
+    Classify user question to determine if it matches a fast SQL (MCP) intent pattern.
+    Returns (tool_name, tool_args) or (None, None) if no match.
+    
+    This function extracts the intent classification logic from query_craft to make it reusable.
+    """
+    ql = q.lower()
+    patterns = [
+        # Put salary range FIRST to avoid conflicts with other patterns
+        (r"salary range.*department|maximum and minimum salary.*department", ("fast_sql.salary_range_department", {})),
+        # DISABLED - NO HARDCODED QUERIES: (r"gender pay gap|pay gap|salary gap.*(male|female)|salary gap.*department|salary.*difference.*male.*female|pay.*difference.*male.*female", ("fast_sql.gender_pay_gap", {})),
+        (r"highest average salary|department .*highest average salary|which department has the highest average salary", ("fast_sql.department_highest_avg_salary", {})),
+        # Make top paid pattern more specific to avoid false matches
+        (r"top \d+ .*paid|highest[ -]paid employees|top paid employees", ("fast_sql.top_paid_employees", {})),
+        (r"employee count by department|employees per department|how many employees .*department|count of employees .*department", ("fast_sql.employee_count_by_department", {})),
+        (r"list departments|show departments|what departments", ("fast_sql.list_departments", {})),
+        (r"how many employees|total employees|employee count", ("fast_sql.count_employees", {})),
+        (r"highest and lowest salary|highest.*lowest salary|lowest.*highest salary", ("fast_sql.salary_extremes", {})),
+        (r"department managers|list managers per department|managers of each department", ("fast_sql.department_manager_listing", {})),
+    ]
+    for pat, result in patterns:
+        try:
+            m = re.search(pat, ql)
+        except Exception:
+            m = None
+        if m:
+            if result[0] == 'fast_sql.top_paid_employees':
+                nm = re.search(r"top\s+(\d+)", ql)
+                top_n = int(nm.group(1)) if nm else 10
+                return result[0], {"top_n": top_n, "question": q}
+            if result[0] == 'fast_sql.salary_range_department':
+                dm = re.search(r"salary range .*department (?:for|of) ([A-Za-z &'-]+)", ql)
+                dept_name = dm.group(1).strip().title() if dm else 'Development'
+                return result[0], {"department_name": dept_name, "question": q}
+            return result[0], {"question": q}
+    return None, None
+
+
+def _mcp_generic_schema_hint(question: str, _fast_log=None):
+    """
+    Dynamic-schema Level 3 grounding: query the generic MCP tools
+    (schema.list_tables / schema.describe_table / schema.sample_data) and
+    build a verified schema+sample hint from the LIVE database. This replaces
+    the employee-specific fast_sql skeletons for any user-uploaded database.
+
+    Returns a text block (not raw SQL) used as grounding context in prompts.
+    """
+    def _log(stage: str, **kv):
+        if _fast_log:
+            _fast_log(stage, **kv)
+
+    try:
+        from .mcp_client import mcp_call_tool
+
+        def _call(name, args, timeout=30.0):
+            ctx = {"tool_call": {"name": name, "args": args or {}}}
+            resp = mcp_call_tool(prompt=f"(generic schema hint) {name}", timeout=timeout, context=ctx)
+            return resp.get('tool_result') if isinstance(resp, dict) else None
+
+        # 1) Tables in the live DB
+        list_res = _call("schema.list_tables", {})
+        tables = list_res.get("tables") if isinstance(list_res, dict) else None
+        if not tables:
+            _log('GENERIC_HINT', result="no tables returned")
+            return None
+
+        # 2) Pick the tables whose names appear in the question (cheap keyword match)
+        ql = question.lower()
+        matched = [t for t in tables if t and (t.lower().replace('_', ' ') in ql or t.lower() in ql)]
+        if not matched:
+            # Fall back to FK-neighbors heuristic: take up to 3 largest tables
+            matched = tables[:3]
+
+        # 3) Describe + sample each matched table (bounded)
+        sections = []
+        for t in matched[:5]:
+            desc = _call("schema.describe_table", {"table": t})
+            cols = desc.get("columns") if isinstance(desc, dict) else None
+            if not cols:
+                continue
+            sample = _call("schema.sample_data", {"table": t, "limit": 2})
+            rows = sample.get("rows") if isinstance(sample, dict) else None
+            section = f"{t}({', '.join(map(str, cols))})"
+            if rows:
+                section += f"  e.g. rows: {rows[:2]}"
+            sections.append(section)
+
+        if not sections:
+            _log('GENERIC_HINT', result="describe failed")
+            return None
+
+        hint = (
+            "VERIFIED LIVE SCHEMA (from actual database inspection):\n"
+            + "\n".join(f"- {s}" for s in sections)
+            + "\nUse ONLY these tables/columns unless another table is required to join them."
+        )
+        _owner_notes = _schema_notes_block()
+        if _owner_notes:
+            hint += "\n\n" + _owner_notes
+        _log('GENERIC_HINT', tables=matched, chars=len(hint))
+        return hint
+    except Exception as e:
+        _log('GENERIC_HINT_FAIL', error=str(e))
+        return None
+
+
+def get_mcp_hint_if_available(question: str):
+    """
+    Attempt to retrieve a SQL hint from MCP server based on question classification.
+    Returns SQL string as a hint (not to be used directly), or None if unavailable.
+    
+    This is Level 3 assistance - MCP provides hints to guide the LLM, not hardcoded SQL.
+    """
+    ENABLE_FAST_SQL = os.environ.get("ENABLE_MCP_FAST_TOOLS", "1") not in ("0", "false", "False")
+    FAST_SQL_LOG = os.environ.get("FAST_SQL_LOG", "1") not in ("0", "false", "False")
+    
+    def _fast_log(stage: str, **kv):
+        if FAST_SQL_LOG:
+            parts = [f"{k}={str(v)[:140]}" for k, v in kv.items()]
+            print(f"[FAST][{stage}] " + " ".join(parts))
+    
+    if not ENABLE_FAST_SQL:
+        return None
+
+    # Dynamic schema support: the 14 fast_sql.* tools are purpose-built for the
+    # employee demo schema. For user schemas, ground Level 3 with live schema +
+    # sample data from the generic MCP tools instead.
+    if not getattr(ACTIVE_SCHEMA_PROFILE, "is_employee_db", False):
+        return _mcp_generic_schema_hint(question, _fast_log)
+    
+    tool_name, tool_args = classify_fast_sql_intent(question)
+    if not tool_name:
+        return None
+    
+    _fast_log('INTENT', tool=tool_name, args=tool_args)
+    try:
+        from .mcp_client import mcp_call_tool
+        ctx = {"tool_call": {"name": tool_name, "args": tool_args or {}}}
+        start_fast = time.time()
+        resp = mcp_call_tool(prompt=f"(fast sql) {question}", timeout=45.0, context=ctx)
+        dur = round(time.time() - start_fast, 3)
+        tool_res = resp.get('tool_result') if isinstance(resp, dict) else None
+        if isinstance(tool_res, dict) and 'sql' in tool_res:
+            sql = tool_res['sql']
+            _fast_log('TOOL_SQL', ms=int(dur*1000), sql_preview=sql[:160])
+            # Remember for the deterministic post-3-level fallback (see the
+            # "All 3 levels failed" block): if every LLM call fails due to
+            # provider outage/credits, this schema-true SQL still answers.
+            global LAST_MCP_HINT_SQL
+            LAST_MCP_HINT_SQL = sql
+            return sql  # Return as hint
+        else:
+            _fast_log('NO_SQL', raw=tool_res)
+            return None
+    except Exception as fast_err:
+        _fast_log('FALLBACK', error=str(fast_err))
+        return None
+
+
+def is_sql_valid(sql: str, question: str = None) -> bool:
+    """
+    Validation check for generated SQL with both syntactic and semantic checks.
+    
+    Args:
+        sql: The SQL query to validate
+        question: Optional user question for semantic validation
+        
+    Returns:
+        True if SQL appears valid both syntactically and semantically, False otherwise.
+    """
+    if not sql or not isinstance(sql, str):
+        return False
+        
+    # [LEVEL 1] Call enhanced_validate_sql for strict schema and column checking
+    validation = enhanced_validate_sql(sql)
+    if not validation["valid"]:
+        print(f"[VALIDATION] Rejected by enhanced_validate_sql: {validation['issues']}")
+        return False
+
+    sql_lower = sql.lower().strip()
+    
+    # Quick placeholder check
+    PLACEHOLDER_TOKENS = ["<", ">", "...", "___", "placeholder", "example", "your_"]
+    PLACEHOLDER_REGEXES = [
+        r"<[A-Z_]+>",  # <TABLE_NAME>, <COLUMN>
+        r"\b(table_name|column_name|your_\w+)\b"
+    ]
+    
+    for token in PLACEHOLDER_TOKENS:
+        if token in sql_lower:
+            return False
+    
+    for regex in PLACEHOLDER_REGEXES:
+        if re.search(regex, sql, re.IGNORECASE):
+            return False
+    
+    # Reject hardcoded department codes (D001-D009) - indicates incomplete query
+    # These suggest LLM didn't join to department table to filter by name
+    if ACTIVE_SCHEMA_PROFILE.is_employee_db and re.search(r"'D00[0-9]'", sql, re.IGNORECASE):
+        print("[VALIDATION] Rejected: Contains hardcoded department code (needs dept_name filter)")
+        return False
+
+    # STRICT SCHEMA CHECK: Reject common hallucinations before they hit the DB
+    # employee (e) does NOT have dept_no or salary
+    if ACTIVE_SCHEMA_PROFILE.is_employee_db:
+        if re.search(r'\b[eE]\.dept_no\b|employee\.dept_no\b', sql, re.IGNORECASE):
+            print("[VALIDATION] Rejected: Table 'employee' lacks 'dept_no'. Bridging through 'dept_emp' is required.")
+            return False
+        if re.search(r'\b[eE]\.salary\b|employee\.salary\b', sql, re.IGNORECASE):
+            print("[VALIDATION] Rejected: Table 'employee' lacks 'salary'. Join with 'salary' table is required.")
+            return False
+
+    # MANAGER JOIN CHECK: Note that dept_manager should be used for manager queries.
+    # We do NOT hard-reject here — adjust_manager_query() in apply_semantic_adjusters
+    # will regenerate the SQL using generate_manager_list_sql() if dept_manager is absent.
+    # Hard-rejecting here blocks all 3 levels before the adjuster ever gets a chance to fix it.
+    if (ACTIVE_SCHEMA_PROFILE.is_employee_db and question
+            and any(k in question.lower() for k in ["manager", "managers", "who manages"])):
+        if 'dept_manager' not in sql.lower() and 'manager_name' not in sql.lower():
+            print("[VALIDATION] Warning: Manager query missing dept_manager — adjuster will fix after this level succeeds.")
+
+
+    # ===== SEMANTIC VALIDATION =====
+    # Check if question asks for specific department but SQL doesn't filter by it
+    if question and ACTIVE_SCHEMA_PROFILE.is_employee_db:
+        print(f"[VALIDATION DEBUG] Checking semantic validation for question: {question[:100]}")
+        question_lower = question.lower()
+        
+        # Department-specific query patterns
+        dept_patterns = [
+            (r'\bin\s+(development|finance|sales|marketing|research|production|hr|human\s+resources|quality\s+management|customer\s+service)\s+(department|dept)\b', 1),
+            (r'\b(development|finance|sales|marketing|research|production|hr|human\s+resources|quality\s+management|customer\s+service)\s+department\b', 1),
+        ]
+        
+        for pattern, group_idx in dept_patterns:
+            match = re.search(pattern, question_lower, re.IGNORECASE)
+            if match:
+                dept_name = match.group(group_idx).strip()
+                print(f"[VALIDATION DEBUG] Found department mention: '{dept_name}'")
+                # Normalization
+                dept_map = {
+                    'hr': 'Human Resources',
+                    'human resources': 'Human Resources',
+                    'quality management': 'Quality Management',
+                    'customer service': 'Customer Service'
+                }
+                dept_name_proper = dept_map.get(dept_name.lower(), dept_name.title())
+                print(f"[VALIDATION DEBUG] Normalized to: '{dept_name_proper}'")
+                print(f"[VALIDATION DEBUG] Checking if '{dept_name_proper.lower()}' is in SQL")
+                print(f"[VALIDATION DEBUG] SQL being checked (first 200 chars): {sql_lower[:200]}")
+                
+                # Check if SQL contains this department name as a filter
+                if dept_name_proper.lower() not in sql_lower:
+                    print(f"[VALIDATION] Rejected: Question asks for '{dept_name_proper}' but SQL doesn't filter by it")
+                    print(f"[VALIDATION] This likely returns ALL departments instead of just '{dept_name_proper}'")
+                    return False
+                else:
+                    print(f"[VALIDATION DEBUG] Department filter found in SQL - validation passed")
+                break  # First matching pattern is sufficient — no need to check remaining patterns
+    else:
+        print("[VALIDATION DEBUG] No question provided for semantic validation")
+    
+    # ===== ALIAS VALIDATION =====
+    # Check if SQL references aliases that aren't defined in FROM/JOIN clauses
+    # Extract defined aliases from FROM and JOIN clauses
+    defined_aliases = set()
+    
+    # Match FROM table alias and JOIN table alias patterns
+    from_pattern = r'\bFROM\s+(\w+)\s+(?:AS\s+)?(\w+)\b'
+    join_pattern = r'\bJOIN\s+(\w+)\s+(?:AS\s+)?(\w+)\b'
+    
+    # Exclude SQL keywords and common column names from alias matching
+    _SQL_KEYWORDS = {'where', 'group', 'order', 'limit', 'having', 'join', 'on',
+                     'and', 'or', 'not', 'in', 'as', 'by', 'select', 'from', 'set',
+                     'emp_no', 'dept_no', 'from_date', 'to_date', 'first_name', 'last_name'}
+    # Strip EXTRACT(... FROM ...) to avoid matching column names as tables
+    _sql_for_alias = re.sub(r'\bEXTRACT\s*\([^)]+\)', 'EXTRACT_EXPR', sql, flags=re.IGNORECASE)
+    _sql_for_alias_lower = _sql_for_alias.lower()
+
+    for match in re.finditer(from_pattern, _sql_for_alias, re.IGNORECASE):
+        table_name = match.group(1).lower()
+        alias = match.group(2).lower()
+        # Skip SQL keywords mistakenly matched as aliases
+        if alias in _SQL_KEYWORDS or table_name in _SQL_KEYWORDS:
+            continue
+        if alias != table_name:
+            defined_aliases.add(alias)
+            print(f"[VALIDATION DEBUG] Found alias '{alias}' for table '{table_name}'")
+
+    for match in re.finditer(join_pattern, _sql_for_alias, re.IGNORECASE):
+        table_name = match.group(1).lower()
+        alias = match.group(2).lower()
+        if alias in _SQL_KEYWORDS or table_name in _SQL_KEYWORDS:
+            continue
+        if alias != table_name:
+            defined_aliases.add(alias)
+            print(f"[VALIDATION DEBUG] Found alias '{alias}' for table '{table_name}'")
+    
+    # NEW: Handle comma-separated joins like FROM table1 t1, table2 t2
+    # Only look at the part BEFORE the first JOIN or WHERE to avoid condition noise
+    comma_from_pattern = r'\bFROM\s+(.*?)(?=\bJOIN\b|\bWHERE\b|$)'
+    comma_match = re.search(comma_from_pattern, _sql_for_alias, re.IGNORECASE | re.DOTALL)
+    if comma_match:
+        from_clause = comma_match.group(1)
+        # Find all pairs of word+word (table alias)
+        alias_pairs = re.findall(r'(\w+)\s+(?:AS\s+)?(\w+)', from_clause, re.IGNORECASE)
+        for table_name, alias in alias_pairs:
+            t_low = table_name.lower()
+            a_low = alias.lower()
+            if t_low in _SQL_KEYWORDS or a_low in _SQL_KEYWORDS:
+                continue
+            defined_aliases.add(a_low)
+            print(f"[VALIDATION DEBUG] Found comma-join alias '{a_low}'")
+    
+    # Find all alias references (pattern: alias.column)
+    alias_ref_pattern = r'\b([a-z_]\w*)\.(\w+)\b'
+    used_aliases = set()
+    
+    for match in re.finditer(alias_ref_pattern, sql_lower, re.IGNORECASE):
+        alias = match.group(1)
+        # Skip if it's a table name (not an alias) — dynamic: use the live profile's
+        # table set so this works for any user-uploaded schema, not just employees
+        _skip_tables = ACTIVE_SCHEMA_PROFILE.available_tables_set or {
+            'employee', 'department', 'salary', 'dept_emp', 'dept_manager', 'title', 'titles'}
+        if alias not in _skip_tables:
+            used_aliases.add(alias)
+    
+    # Check for undefined aliases
+    undefined_aliases = used_aliases - defined_aliases
+    if undefined_aliases:
+        print(f"[VALIDATION] Rejected: SQL references undefined aliases: {undefined_aliases}")
+        print(f"[VALIDATION] Defined aliases: {defined_aliases}")
+        print(f"[VALIDATION] Used aliases: {used_aliases}")
+        return False
+    
+    # Basic structure check
+    if not sql.strip().endswith(';'):
+        return False
+    
+    return True
+
+
+def level_1_pure_llm(question: str, schema_text: str, relevant_tables: list, 
+                     base_llm, hints: str = "", timeout: float = 35.0):
+    """
+    Level 1: Pure LLM intelligence with no assistance.
+    
+    Args:
+        question: User's natural language question
+        schema_text: Formatted schema information
+        relevant_tables: List of relevant table names
+        base_llm: The LLM instance to use
+        timeout: Timeout for LLM call in seconds
+        
+    Returns:
+        Valid SQL string or None if generation fails
+    """
+    print("[LEVEL_1] Attempting pure LLM generation (no hints, no assistance)")
+    
+    _CURRENT_ROW_RULE_EMP = "ALWAYS filter to_date = '9999-01-01' to get current records in salary, dept_emp, and dept_manager."
+    prompt = (
+        f"{_dynamic_schema_block(schema_text)}\n\n"
+        f"You are an SQL expert. Convert the natural language question into a SINGLE valid PostgreSQL query.\n"
+        f"Question: {question}\n"
+        f"Available tables: {', '.join(relevant_tables)}\n"
+        f"Table schemas (columns):\n{schema_text}\n"
+        f"Rules: Do NOT invent column names. {_current_row_rules(_CURRENT_ROW_RULE_EMP)} "
+        f"Append LIMIT 100 ONLY if a specific count (like 'top 10') is not requested. "
+        f"The query must end with a single semicolon. Return ONLY raw SQL."
+        f"Be precise with column names and table joins."
+    )
+    
+    try:
+        start_t = time.time()
+        raw = base_llm.complete(prompt=prompt)
+        gen_dur = time.time() - start_t
+        text_out = raw.text if hasattr(raw, 'text') else str(raw)
+        print(f"[LEVEL_1] LLM responded in {gen_dur:.2f}s")
+        
+        # Extract SQL from code fences if present
+        sql_query = text_out.strip()
+        if '```' in sql_query:
+            parts = re.split(r"```(?:sql)?", sql_query, flags=re.IGNORECASE)
+            sql_query = '\n'.join(p for p in parts if 'select' in p.lower())
+        if sql_query.lower().startswith('sql'):
+            sql_query = sql_query[3:].strip()
+        
+        # Clean and validate with semantic checks
+        sql_query = extract_clean_sql(sql_query)
+        if is_sql_valid(sql_query, question):
+            print(f"[LEVEL_1] ✓ Generated valid SQL: {sql_query[:100]}...")
+            return sql_query
+        else:
+            print(f"[LEVEL_1] ✗ Generated SQL failed validation. SQL: {sql_query}")
+            return None
+            
+    except Exception as e:
+        print(f"[LEVEL_1] ✗ Failed with error: {type(e).__name__}: {e}")
+        return None
+
+
+def generate_micro_gen_hint(question: str) -> str:
+    """
+    Generate micro-gen SQL hint based on question pattern.
+    This is called LAZILY inside Level 2, only when Level 1 fails.
+    
+    Args:
+        question: User's natural language question
+        
+    Returns:
+        SQL hint string or None if no pattern matches
+    """
+    question_lower = question.lower()
+    
+    # Salary range by department pattern
+    if re.search(r'\b(maximum and minimum|max.*min|min.*max|salary range)\b.*\b(development|dept|department)\b', question_lower):
+        print("[MICRO-GEN][LAZY] Detected salary range in department question")
+        try:
+            # Extract department name from question
+            dept_name = "Development"  # Default
+            if "development" in question_lower:
+                dept_name = "Development"
+            elif "sales" in question_lower:
+                dept_name = "Sales"
+            elif "marketing" in question_lower:
+                dept_name = "Marketing"
+            elif "finance" in question_lower:
+                dept_name = "Finance"
+            elif "human resources" in question_lower:
+                dept_name = "Human Resources"
+            elif "research" in question_lower:
+                dept_name = "Research"
+            elif "production" in question_lower:
+                dept_name = "Production"
+            
+            # Smart Hint: If question asks for EACH/ALL/Distribution, don't filter by a single department
+            is_global = any(k in question_lower for k in ["each", "all", "every", "distribution", "per department", "by department"])
+            
+            # Build SQL hint
+            where_clause = f"WHERE d.dept_name = '{dept_name}' " if not is_global else " "
+            hint_sql = (
+                f"SELECT d.dept_name, MAX(s.salary) AS max_salary, MIN(s.salary) AS min_salary "
+                f"FROM salary s "
+                f"JOIN employee e ON s.emp_no = e.emp_no "
+                f"JOIN dept_emp de ON e.emp_no = de.emp_no "
+                f"JOIN department d ON de.dept_no = d.dept_no "
+                f"{where_clause}"
+                f"AND s.to_date = '9999-01-01' "
+                f"AND de.to_date = '9999-01-01' "
+                f"{'GROUP BY d.dept_name' if is_global else ''};"
+            )
+            if is_global:
+                print(f"[MICRO-GEN][LAZY] Generated GLOBAL hint (no department filter)")
+            else:
+                print(f"[MICRO-GEN][LAZY] Generated hint for {dept_name}")
+            return hint_sql
+        except Exception as e:
+            print(f"[MICRO-GEN][LAZY] Failed: {e}")
+            return None
+    
+    # Add more patterns here as needed
+    # ...
+    
+    return None
+
+
+def level_2_llm_with_fixer(question: str, schema_text: str, relevant_tables: list,
+                           base_llm, hints: str = "", timeout: float = 45.0):
+    """
+    Level 2: LLM + Micro-gen hints + SQL Fixer enhancement.
+    
+    This level LAZILY generates schema-aware hints from micro-generators
+    (only when Level 1 fails and Level 2 is invoked).
+    Then applies fix_common_sql_errors for repair.
+    
+    Args:
+        question: User's natural language question
+        schema_text: Formatted schema information
+        relevant_tables: List of relevant table names
+        base_llm: The LLM instance to use
+        timeout: Timeout for LLM call in seconds
+        
+    Returns:
+        Valid SQL string or None if generation fails
+    """
+    print("[LEVEL_2] Attempting LLM + Micro-gen hints + SQL Fixer")
+    
+    # LAZY GENERATION: Only generate micro-hint NOW (not before Level 1)
+    micro_gen_hint = generate_micro_gen_hint(question)
+    
+    # Build hint text if micro-gen provided one
+    hint_text = ""
+    if micro_gen_hint:
+        hint_text = (
+            f"\n💡 MICRO-GEN HINT (schema-aware pattern):\n{micro_gen_hint}\n\n"
+            f"Use this as a guide but adapt column/table names to match the actual schema.\n"
+        )
+        print(f"[LEVEL_2] Micro-gen hint available: {micro_gen_hint[:100]}...")
+    else:
+        print("[LEVEL_2] No micro-gen hint available, using enhanced prompt only")
+    
+    _CURRENT_ROW_RULE_EMP = "ALWAYS filter to_date = '9999-01-01' to get current records in salary, dept_emp, and dept_manager."
+    # Enhanced prompt with optional micro-gen hint
+    prompt = (
+        f"{_dynamic_schema_block(schema_text)}\n\n"
+        f"{hint_text}"
+        f"Question: {question}\n"
+        f"Tables: {', '.join(relevant_tables)}\n"
+        f"Table schemas:\n{schema_text}\n"
+        f"Rules: {_current_row_rules(_CURRENT_ROW_RULE_EMP)} "
+        f"Append LIMIT 100 ONLY if a specific count (like 'top 10') is not requested. "
+        f"The query must end with a single semicolon. Return ONLY SQL (PostgreSQL)."
+        f"Be precise with column names and table joins."
+    )
+    
+    try:
+        start_t = time.time()
+        raw = base_llm.complete(prompt=prompt)
+        gen_dur = time.time() - start_t
+        text_out = raw.text if hasattr(raw, 'text') else str(raw)
+        print(f"[LEVEL_2] LLM responded in {gen_dur:.2f}s")
+        
+        # Extract SQL from code fences if present
+        sql_query = text_out.strip()
+        if '```' in sql_query:
+            parts = re.split(r"```(?:sql)?", sql_query, flags=re.IGNORECASE)
+            sql_query = '\n'.join(p for p in parts if 'select' in p.lower())
+        if sql_query.lower().startswith('sql'):
+            sql_query = sql_query[3:].strip()
+        
+        # Clean and apply fixer
+        sql_query = extract_clean_sql(sql_query)
+        sql_query = fix_common_sql_errors(sql_query)
+        
+        # Additional validation
+        base_val = validate_sql_query(sql_query)
+        if not base_val['valid']:
+            sql_query = fix_query(sql_query, base_val['reason'])
+        
+        if is_sql_valid(sql_query, question):
+            print(f"[LEVEL_2] ✓ Generated and fixed valid SQL: {sql_query[:100]}...")
+            return sql_query
+        else:
+            print(f"[LEVEL_2] ✗ Generated SQL failed validation after fixes")
+            return None
+            
+    except Exception as e:
+        print(f"[LEVEL_2] ✗ Failed with error: {type(e).__name__}: {e}")
+        return None
+
+
+def level_3_mcp_hints_llm_fixer(question: str, schema_text: str, relevant_tables: list,
+                                base_llm, hints: str = "", timeout: float = 55.0):
+    """
+    Level 3: LLM + MCP hints + SQL Fixer (MCP provides hints to guide the LLM).
+    
+    This is the final escalation level. MCP tools provide SQL patterns as hints,
+    which are injected into the prompt to guide the LLM's generation.
+    
+    Args:
+        question: User's natural language question
+        schema_text: Formatted schema information
+        relevant_tables: List of relevant table names
+        base_llm: The LLM instance to use
+        timeout: Timeout for LLM call in seconds
+        
+    Returns:
+        Valid SQL string or None if generation fails
+    """
+    print("[LEVEL_3] Attempting LLM + MCP hints + SQL Fixer")
+    
+    # Try to get MCP hint
+    mcp_hint = get_mcp_hint_if_available(question)
+    
+    # Construct hint text
+    micro_hint_text = ""
+    if mcp_hint:
+        micro_hint_text = (
+            f"\n🎯 HINT FROM MCP - GROUNDING CONTEXT:\n{mcp_hint}\n\n"
+            f"Use this verified context (schema, sample data or SQL pattern) as guidance but adapt as needed for the schema.\n"
+        )
+        print(f"[LEVEL_3] MCP hint available: {mcp_hint[:100]}...")
+    else:
+        print("[LEVEL_3] No MCP hint available, proceeding with enhanced prompt")
+    
+    # Enhanced prompt with MCP hint if available
+    prompt = (
+        f"{_dynamic_schema_block(schema_text)}\n\n"
+        f"{micro_hint_text}"
+        f"You are an SQL expert. Convert the natural language question into a SINGLE valid PostgreSQL query.\n"
+        f"Question: {question}\n"
+        f"Available tables: {', '.join(relevant_tables)}\n"
+        f"Table schemas (columns):\n{schema_text}\n"
+        f"Rules: Do NOT invent column names. Use existing columns from provided schemas. "
+        f"Use the LIMIT value requested in the question, or default to LIMIT 100; if none is mentioned. "
+        f"End with a semicolon. Return ONLY raw SQL.\n"
+        f"CRITICAL: If a hint is provided above, use it as a pattern guide but ensure it matches the actual schema."
+    )
+    
+    try:
+        start_t = time.time()
+        raw = base_llm.complete(prompt=prompt)
+        gen_dur = time.time() - start_t
+        text_out = raw.text if hasattr(raw, 'text') else str(raw)
+        print(f"[LEVEL_3] LLM responded in {gen_dur:.2f}s")
+        
+        # Extract SQL from code fences if present
+        sql_query = text_out.strip()
+        if '```' in sql_query:
+            parts = re.split(r"```(?:sql)?", sql_query, flags=re.IGNORECASE)
+            sql_query = '\n'.join(p for p in parts if 'select' in p.lower())
+        if sql_query.lower().startswith('sql'):
+            sql_query = sql_query[3:].strip()
+        
+        # Clean and apply comprehensive fixes
+        sql_query = extract_clean_sql(sql_query)
+        sql_query = fix_common_sql_errors(sql_query)
+        
+        # Additional validation and fixes
+        base_val = validate_sql_query(sql_query)
+        if not base_val['valid']:
+            sql_query = fix_query(sql_query, base_val['reason'])
+        
+        if is_sql_valid(sql_query, question):
+            print(f"[LEVEL_3] ✓ Generated valid SQL with MCP guidance: {sql_query[:100]}...")
+            return sql_query
+        else:
+            print(f"[LEVEL_3] ✗ Generated SQL failed validation after all fixes")
+            return None
+            
+    except Exception as e:
+        print(f"[LEVEL_3] ✗ Failed with error: {type(e).__name__}: {e}")
+        return None
+
+
+def query_craft(question: str) -> str:
+    """Generate SQL via LLM (no hardcoded templates) with minimal retries and semantic post-fixes.
+    Reconstructed clean version after refactor issues.
+    """
+    # Make sure LLM handshake succeeded or attempt deferred handshake now
+    ensure_llm_ready()
+    print(f"[QUERY_CRAFT] Received question: {question}")
+    question_lower = question.lower()
+
+    # ---------------- DISABLED: Old MCP tool dispatch (replaced by 3-level architecture) -----------------
+    # The 3-level architecture handles MCP hints in Level 3 via get_mcp_hint_if_available()
+    # This old code was causing duplicate MCP calls and wasting ~31s
+    ENABLE_FAST_SQL = False  # FORCE DISABLED to prevent duplicate MCP calls
+    FAST_SQL_LOG = os.environ.get("FAST_SQL_LOG", "1") not in ("0","false","False")
+
+    def _fast_log(stage: str, **kv):
+        if FAST_SQL_LOG:
+            parts = [f"{k}={str(v)[:140]}" for k,v in kv.items()]
+            print(f"[FAST][{stage}] " + " ".join(parts))
+
+    def classify_fast_sql_intent(q: str):
+        ql = q.lower()
+        patterns = [
+            # Put salary range FIRST to avoid conflicts with other patterns
+            (r"salary range.*department|maximum and minimum salary.*department", ("fast_sql.salary_range_department", {})),
+            # DISABLED - NO HARDCODED QUERIES: (r"gender pay gap|pay gap|salary gap.*(male|female)|salary gap.*department|salary.*difference.*male.*female|pay.*difference.*male.*female", ("fast_sql.gender_pay_gap", {})),
+            (r"highest average salary|department .*highest average salary|which department has the highest average salary", ("fast_sql.department_highest_avg_salary", {})),
+            # Make top paid pattern more specific to avoid false matches
+            (r"top \d+ .*paid|highest[- ]paid employees|top paid employees", ("fast_sql.top_paid_employees", {})),
+            (r"employee count by department|employees per department|how many employees .*department|count of employees .*department", ("fast_sql.employee_count_by_department", {})),
+            (r"list departments|show departments|what departments", ("fast_sql.list_departments", {})),
+            (r"how many employees|total employees|employee count", ("fast_sql.count_employees", {})),
+            (r"highest and lowest salary|highest.*lowest salary|lowest.*highest salary", ("fast_sql.salary_extremes", {})),
+            (r"department managers|list managers per department|managers of each department", ("fast_sql.department_manager_listing", {})),
+        ]
+        for pat, result in patterns:
+            try:
+                m = re.search(pat, ql)
+            except Exception:
+                m = None
+            if m:
+                if result[0] == 'fast_sql.top_paid_employees':
+                    nm = re.search(r"top\s+(\d+)", ql)
+                    top_n = int(nm.group(1)) if nm else 10
+                    return result[0], {"top_n": top_n, "question": q}
+                if result[0] == 'fast_sql.salary_range_department':
+                    dm = re.search(r"salary range .*department (?:for|of) ([A-Za-z &'-]+)", ql)
+                    dept_name = dm.group(1).strip().title() if dm else 'Development'
+                    return result[0], {"department_name": dept_name, "question": q}
+                return result[0], {"question": q}
+        return None, None
+
+    micro_hint = None
+    if ENABLE_FAST_SQL:
+        tool_name, tool_args = classify_fast_sql_intent(question)
+        if tool_name:
+            _fast_log('INTENT', tool=tool_name, args=tool_args)
+            try:
+                from .mcp_client import mcp_call_tool
+                ctx = {"tool_call": {"name": tool_name, "args": tool_args or {}}}
+                start_fast = time.time()
+                resp = mcp_call_tool(prompt=f"(fast sql) {question}", timeout=45.0, context=ctx)
+                dur = round(time.time() - start_fast, 3)
+                tool_res = resp.get('tool_result') if isinstance(resp, dict) else None
+                if isinstance(tool_res, dict) and 'sql' in tool_res:
+                    sql = tool_res['sql']
+                    _fast_log('TOOL_SQL', ms=int(dur*1000), sql_preview=sql[:160])
+                    # Use as a hint, not as a direct return
+                    micro_hint = sql
+                else:
+                    _fast_log('NO_SQL', raw=tool_res)
+            except Exception as fast_err:
+                _fast_log('FALLBACK', error=str(fast_err))
+                # EARLY SHORT-CIRCUIT for salary extremes: use as hint only
+                if tool_name == 'fast_sql.salary_extremes':
+                    try:
+                        if 'salary' in available_tables:
+                            try:
+                                cols = [c['name'] for c in inspector.get_columns(table_name='salary')]
+                                scol = 'salary' if 'salary' in cols else (cols[0] if cols else 'salary')
+                            except Exception:
+                                scol = 'salary'
+                            parts = [f"SELECT MAX(s.{scol}) AS highest_salary, MIN(s.{scol}) AS lowest_salary", "FROM salary s"]
+                            try:
+                                if any(c['name'] == 'to_date' for c in inspector.get_columns(table_name='salary')):
+                                    parts.append("WHERE s.to_date IS NULL")
+                            except Exception:
+                                pass
+                            agg_sql = " ".join(parts)
+                            if not agg_sql.strip().endswith(';'):
+                                agg_sql += ';'
+                            _fast_log('EARLY_EXTREMES', sql=agg_sql)
+                            micro_hint = agg_sql
+                    except Exception as early_err:
+                        _fast_log('EARLY_EXTREMES_FAIL', err=str(early_err))
+                # EARLY SHORT-CIRCUIT for salary range by department: use as hint only
+                elif tool_name == 'fast_sql.salary_range_department':
+                    try:
+                        if 'salary' in available_tables and 'department' in available_tables:
+                            dept_name = tool_args.get('department_name', 'Development')
+                            try:
+                                cols = [c['name'] for c in inspector.get_columns(table_name='salary')]
+                                scol = 'salary' if 'salary' in cols else (cols[0] if cols else 'salary')
+                            except Exception:
+                                scol = 'salary'
+                            parts = [
+                                f"SELECT MAX(s.{scol}) AS max_salary, MIN(s.{scol}) AS min_salary",
+                                "FROM salary s",
+                                "JOIN employee e ON s.emp_no = e.emp_no",
+                                "JOIN dept_emp de ON e.emp_no = de.emp_no",
+                                "JOIN department d ON de.dept_no = d.dept_no",
+                                f"WHERE d.dept_name = '{dept_name}'"
+                            ]
+                            try:
+                                if any(c['name'] == 'to_date' for c in inspector.get_columns(table_name='salary')):
+                                    parts.append("AND s.to_date IS NULL")
+                                if any(c['name'] == 'to_date' for c in inspector.get_columns(table_name='dept_emp')):
+                                    parts.append("AND de.to_date IS NULL")
+                            except Exception:
+                                pass
+                            range_sql = " ".join(parts)
+                            if not range_sql.strip().endswith(';'):
+                                range_sql += ';'
+                            _fast_log('EARLY_RANGE_DEPT', sql=range_sql, dept=dept_name)
+                            micro_hint = range_sql
+                    except Exception as early_err:
+                        _fast_log('EARLY_RANGE_DEPT_FAIL', err=str(early_err))
+                # proceed to normal path
+
+
+    # Infer relevant tables heuristically from question.
+    # Employee-keyword routing only applies to the demo employee schema; for
+    # user schemas we use a keyword-to-table-name matcher over the LIVE tables.
+    table_set = set()
+    if ACTIVE_SCHEMA_PROFILE.is_employee_db:
+        if any(k in question_lower for k in ["manager", "managers"]):
+            table_set.update(["department", "dept_manager", "employee"])
+        if any(k in question_lower for k in ["average salary", "avg salary", "salary"]):
+            table_set.update(["salary", "dept_emp", "department", "employee"])
+        if "department" in question_lower:
+            table_set.add("department")
+        if any(k in question_lower for k in ["engineer", "title", "analyst", "developer", "staff"]):
+            table_set.add("title")
+        if not table_set:
+            # default minimal context
+            table_set.add("employee")
+    else:
+        # Generic matcher: match question keywords against live table names
+        for t in available_tables:
+            t_norm = (t or "").replace('_', ' ').lower().strip()
+            if t_norm and len(t_norm) > 3 and t_norm.rstrip('s') in question_lower:
+                table_set.add(t)
+        # Also consider FK-reachable neighbors of matched tables
+        if table_set and ACTIVE_SCHEMA_PROFILE.tables:
+            for matched in list(table_set):
+                for fk in ACTIVE_SCHEMA_PROFILE.fk_by_table.get(matched, []) or []:
+                    ref = fk.get("referred_table")
+                    if ref and ref in available_tables:
+                        table_set.add(ref)
+        if not table_set:
+            table_set.update(ACTIVE_SCHEMA_PROFILE.tables[:4])
+    # Keep only existing tables
+    relevant_tables = [t for t in table_set if t in available_tables]
+    if not relevant_tables:
+        relevant_tables = available_tables[:4]  # small slice fallback
+
+    # Build lightweight schema snippets for relevant tables (column names only) to reduce hallucinated columns
+    def _collect_schema_snippets(tables: List[str], per_table_col_cap: int = 15) -> List[str]:
+        out: List[str] = []
+        try:
+            for tbl in tables:
+                try:
+                    cols = [c['name'] for c in inspector.get_columns(table_name=tbl)]
+                    if cols:
+                        shown = cols[:per_table_col_cap]
+                        out.append(f"{tbl}: {', '.join(shown)}")
+                except Exception:
+                    continue
+        except Exception:
+            pass
+        return out
+
+    schema_snippets = _collect_schema_snippets(relevant_tables)
+    schema_text = "\n".join(schema_snippets) if schema_snippets else "(schema unavailable)"
+
+    # ---------------- DISABLED: Plan Phase (not needed by 3-level architecture) ----------------
+    # The 3-level architecture generates SQL directly without needing a separate planning step
+    # This was adding ~25s overhead
+    ENABLE_PLAN_PHASE = False  # FORCE DISABLED to eliminate redundant LLM call
+    plan_dict: Optional[Dict[str, Any]] = None
+    if ENABLE_PLAN_PHASE:
+        plan_prompt = (
+            "You are a planning assistant. Given a natural language question, output ONLY JSON describing the SQL plan.\n"
+            "Keys: tables (list of table names), joins (list of join conditions like 'a.col = b.col'), filters (list of simple predicates),"
+            " group_by (list of columns), metrics (list of aggregate expressions), order_by (optional string), limit (int or null).\n"
+            "Do not include explanatory text. Return strict JSON only.\n"
+            f"Question: {question}\nJSON:"
+        )
+        try:
+            # Short timeout plan step to avoid long stall
+            _plan_start = time.time()
+            plan_resp = Settings.llm.complete(plan_prompt, timeout=10.0)
+            _plan_dur = time.time() - _plan_start
+            _log_gen("plan", dur_s=round(_plan_dur,2), prompt_chars=len(plan_prompt), est_tokens=_approx_tokens(plan_prompt))
+            raw_plan = plan_resp.text if hasattr(plan_resp, 'text') else str(plan_resp)
+            # Extract first JSON object
+            import json as _json, re as _re
+            m = _re.search(r"\{.*\}", raw_plan, flags=_re.DOTALL)
+            if m:
+                candidate = m.group(0)
+                try:
+                    plan_dict = _json.loads(candidate)
+                except Exception:
+                    plan_dict = None
+            # Basic validation
+            if plan_dict:
+                ptables = plan_dict.get('tables') or []
+                if not isinstance(ptables, list):
+                    plan_dict = None
+                else:
+                    # Ensure tables exist
+                    cleaned = [t for t in ptables if isinstance(t, str) and t in available_tables]
+                    if not cleaned:
+                        plan_dict = None
+                    else:
+                        plan_dict['tables'] = cleaned
+                        # Replace relevant_tables with planned subset to shrink prompt
+                        relevant_tables = cleaned
+                        schema_snippets = _collect_schema_snippets(relevant_tables, per_table_col_cap=12)
+                        schema_text = "\n".join(schema_snippets) if schema_snippets else "(schema unavailable)"
+                        print(f"[PLAN] Accepted plan tables={cleaned} joins={plan_dict.get('joins')} metrics={plan_dict.get('metrics')}")
+            else:
+                print("[PLAN][WARN] Could not parse plan JSON; continuing without plan phase")
+        except Exception as plan_err:
+            print(f"[PLAN][WARN] Plan phase failed: {plan_err}")
+
+    def _build_sql_prompt_from_plan(plan: Dict[str, Any]) -> str:
+        """Construct a concise prompt using the validated plan dict."""
+        return (
+            f"You are an expert SQL generator. Use ONLY the provided plan and schema to write ONE PostgreSQL query.\n"
+            f"Question: {question}\n"
+            f"Plan tables: {', '.join(plan.get('tables', []))}\n"
+            f"Plan joins: {plan.get('joins', [])}\n"
+            f"Plan filters: {plan.get('filters', [])}\n"
+            f"Plan group_by: {plan.get('group_by', [])}\n"
+            f"Plan metrics: {plan.get('metrics', [])}\n"
+            f"Plan order_by: {plan.get('order_by', '')}\n"
+            f"Plan limit: {plan.get('limit', 100)}\n"
+            f"Table schemas (columns):\n{schema_text}\n"
+            "Rules: Do NOT invent columns. Apply all joins & filters. Ensure current rows by filtering to_date IS NULL where relevant."
+            " End with LIMIT 100; Return ONLY raw SQL."
+        )
+
+    # Lightweight hints (pure text, not SQL)
+    hints = []
+    if "manager" in question_lower:
+        hints.append("Join department d, dept_manager dm, employee e")
+    if "average" in question_lower and "salary" in question_lower:
+        # Check if a specific department is mentioned — give a targeted hint
+        _dept_names = ["marketing", "sales", "development", "research", "finance",
+                       "human resources", "production", "quality management", "customer service"]
+        _mentioned_dept = next((d.title() for d in _dept_names if d in question_lower), None)
+        if _mentioned_dept:
+            hints.append(
+                f"Filter by specific department: FROM salary s "
+                f"JOIN dept_emp de ON s.emp_no = de.emp_no "
+                f"JOIN department d ON de.dept_no = d.dept_no "
+                f"WHERE d.dept_name = '{_mentioned_dept}' AND s.to_date IS NULL AND de.to_date IS NULL. "
+                f"dept_emp has NO dept_name column — you MUST join department d to filter by name."
+            )
+        else:
+            hints.append("AVG(s.salary) per department; join salary s, dept_emp de, department d; dept_emp has NO dept_name — join department d to get name")
+    if "count" in question_lower or "how many" in question_lower:
+        hints.append("Use COUNT(*) or COUNT(DISTINCT ...) with LIMIT 100")
+    # Explicit richer hint for employee count by department to encourage correct join early
+    if ("department" in question_lower and any(k in question_lower for k in ["how many", "count", "number of"]) and any(k in question_lower for k in ["employee", "employees"])):
+        hints.append("Join department d with dept_emp de ON d.dept_no = de.dept_no; filter current rows de.to_date IS NULL; SELECT d.dept_name, COUNT(*) AS employee_count GROUP BY d.dept_name ORDER BY employee_count DESC")
+    if "engineer" in question_lower:
+        hints.append("Filter title table for specific engineer title t.title")
+    query_hints = '; '.join(hints)
+
+    # Normalized title extraction (heuristic)
+    normalized_title = None
+    title_map = [
+        (r"senior engineers?", "Senior Engineer"),
+        (r"staff engineers?", "Staff Engineer"),
+        (r"assistant engineers?", "Assistant Engineer"),
+        (r"engineers?", "Engineer"),
+        (r"analysts?", "Analyst"),
+        (r"developers?", "Developer"),
+    ]
+    for pat, canon in title_map:
+        if re.search(pat, question_lower):
+            normalized_title = canon
+            break
+
+    # Detect explicit top-paid / salary ranking questions to provide a stronger textual hint
+    is_salary_rank = False
+    try:
+        if re.search(r"top\s+\d+.*paid", question_lower) or re.search(r"highest[- ]paid", question_lower) or ("top" in question_lower and "paid" in question_lower):
+            is_salary_rank = True
+    except Exception:
+        is_salary_rank = False
+
+    salary_rank_hint = ""
+    if is_salary_rank or "salary" in question_lower:
+        col = get_salary_column_name()
+        salary_rank_hint = (
+            f"\nNote: Salaries are stored in table `salary` using column `{col}`. "
+            f"You MUST join `salary` s ON e.emp_no = s.emp_no. "
+            f"Always filter s.to_date IS NULL for current pay."
+        )
+        # Inject directly into question for maximum visibility to small models
+        question = f"{question}\n\n# HINT: {salary_rank_hint}"
+
+    # Quick targeted micro-generator fallback: for employee-count-by-department
+    # questions, try the small focused generator first to avoid multi-attempt LLM
+    # failures that result in the generic "Unable to generate SQL" error.
+    try:
+        try:
+            cat = classify_question(question)
+        except Exception:
+            cat = None
+        if cat == "EMP_COUNT_BY_DEPT":
+            try:
+                # Check if question asks about a SPECIFIC department by name
+                _dept_match = re.search(
+                    r'(?:in|for|of|within|at)\s+(?:the\s+)?([A-Za-z\s&]+?)\s+department',
+                    question_lower
+                )
+                _specific_dept_name = None
+                if _dept_match:
+                    _candidate = _dept_match.group(1).strip().title()
+                    # Validate against cached real department names (populated at DB init)
+                    # This automatically excludes generic words like 'each', 'every', etc.
+                    for _rd in _cached_dept_names:
+                        if _candidate.lower() == _rd.lower():
+                            _specific_dept_name = _rd  # Use exact DB casing
+                            break
+                if _specific_dept_name:
+                    _specific_hint = _build_sql(
+                        select_cols=["COUNT(DISTINCT de.emp_no) AS employee_count"],
+                        base_table="dept_emp de",
+                        joins=["JOIN department d ON de.dept_no = d.dept_no"],
+                        where=[f"d.dept_name = '{_specific_dept_name}'", "de.to_date IS NULL"]
+                    )
+                    print(f"[MICRO-HINT] Injecting specific-department employee count hint for: {_specific_dept_name}")
+                    question = f"{question}\n\n# HINT: The following SQL may help, but do not copy blindly.\n{_specific_hint}"
+                else:
+                    # Generic all-departments query — use the existing micro-generator
+                    rescue_sql = generate_employee_count_by_department_sql(question)
+                    if rescue_sql:
+                        print("[MICRO-HINT] Using micro-generated employee-count-by-department SQL as LLM hint only.")
+                        question = f"{question}\n\n# HINT: The following SQL may help, but do not copy blindly.\n{rescue_sql}"
+            except Exception as micro_err:
+                print(f"[MICRO-HINT] Micro-generator failed: {micro_err}")
+    except Exception:
+        # Non-fatal; continue to normal LLM generation
+        pass
+
+    base_llm = Settings.llm
+    # If this is a straightforward top-N salary ranking question, try a safe micro-generator
+    # that builds a correct SQL using the actual inspector/schema to avoid hallucinated columns.
+    try:
+        if is_salary_rank:
+            from math import floor
+            def try_micro_top_paid():
+                m = re.search(r"top\s+(\d+)", question.lower())
+                n = int(m.group(1)) if m else 10
+                if 'salary' not in available_tables or 'employee' not in available_tables:
+                    return None
+                try:
+                    cols = [c['name'] for c in inspector.get_columns(table_name='salary')]
+                    if 'salary' in cols:
+                        col_name = 'salary'
+                    elif 'amount' in cols:
+                        col_name = 'amount'
+                    elif cols:
+                        col_name = cols[0]
+                    else:
+                        col_name = 'salary'
+                except Exception:
+                    col_name = 'salary'
+                def _local_build_sql(select_cols, base_table, joins=None, where=None,
+                                     group_by=None, order_by=None, limit=None):
+                    parts = []
+                    parts.append("SELECT " + ", ".join(select_cols))
+                    parts.append("FROM " + base_table)
+                    if joins:
+                        for j in joins:
+                            parts.append(j)
+                    if where:
+                        parts.append("WHERE " + " AND ".join(where))
+                    if group_by:
+                        parts.append("GROUP BY " + ", ".join(group_by))
+                    if order_by:
+                        parts.append("ORDER BY " + order_by)
+                    if limit:
+                        parts.append(f"LIMIT {limit}")
+                    sql = " ".join(parts)
+                    if not sql.strip().endswith(';'):
+                        sql = sql + ';'
+                    return sql
+
+                select_cols = [f"e.emp_no", f"e.first_name", f"e.last_name", f"s.{col_name} AS salary"]
+                joins = ["JOIN salary s ON e.emp_no = s.emp_no"]
+                where = ["s.to_date IS NULL"]
+                order_by = f"s.{col_name} DESC"
+                sql = _local_build_sql(select_cols=select_cols, base_table="employee e", joins=joins, where=where, order_by=order_by, limit=n)
+                return sql
+            micro_sql = try_micro_top_paid()
+            if micro_sql:
+                print("[MICRO-HINT] Using micro-generated top-paid SQL as LLM hint only.")
+                # Inject as hint to LLM prompt, do not return directly
+                question = f"{question}\n\n# HINT: The following SQL may help, but do not copy blindly.\n{micro_sql}"
+        # Small helper to assemble SQL from runtime pieces (avoids storing static SQL templates)
+        def _build_sql(select_cols: List[str], base_table: str, joins: Optional[List[str]] = None,
+                       where: Optional[List[str]] = None, group_by: Optional[List[str]] = None,
+                       order_by: Optional[str] = None, limit: Optional[int] = None) -> str:
+            parts: List[str] = []
+            parts.append("SELECT " + ", ".join(select_cols))
+            parts.append("FROM " + base_table)
+            if joins:
+                for j in joins:
+                    parts.append(j)
+            if where:
+                parts.append("WHERE " + " AND ".join(where))
+            if group_by:
+                parts.append("GROUP BY " + ", ".join(group_by))
+            if order_by:
+                parts.append("ORDER BY " + order_by)
+            if limit:
+                parts.append(f"LIMIT {limit}")
+            sql = " ".join(parts)
+            if not sql.strip().endswith(';'):
+                sql = sql + ';'
+            return sql
+
+        # Additional micro-generators for common question types
+        # 1) highest and lowest salary across all departments
+        if re.search(r"highest\s+and\s+lowest\s+salary|highest.*lowest|lowest.*highest", question.lower()):
+            def gen_high_low():
+                if 'salary' not in available_tables:
+                    return None
+                try:
+                    cols = [c['name'] for c in inspector.get_columns(table_name='salary')]
+                    col = 'salary' if 'salary' in cols else (cols[0] if cols else 'salary')
+                except Exception:
+                    col = 'salary'
+                select_cols = [f"MAX(s.{col}) AS highest_salary", f"MIN(s.{col}) AS lowest_salary"]
+                return _build_sql(select_cols=select_cols, base_table="salary s", where=["s.to_date IS NULL"], limit=None)
+            v = gen_high_low()
+            if v:
+                print('[MICRO-HINT] Built high/low salary micro SQL proposal (used as LLM hint only)')
+                question = f"{question}\n\n# HINT: The following SQL may help, but do not copy blindly.\n{v}"
+
+        # 2) department with highest average salary
+        if re.search(r"which department has the highest average salary|department.*highest average salary", question.lower()):
+            def gen_dept_highest_avg():
+                if not all(t in available_tables for t in ['department','dept_emp','salary']):
+                    return None
+                try:
+                    cols = [c['name'] for c in inspector.get_columns(table_name='salary')]
+                    col = 'salary' if 'salary' in cols else (cols[0] if cols else 'salary')
+                except Exception:
+                    col = 'salary'
+                select_cols = ["d.dept_name", f"AVG(s.{col}) AS avg_salary"]
+                joins = ["JOIN dept_emp de ON d.dept_no = de.dept_no", "JOIN salary s ON de.emp_no = s.emp_no"]
+                where = ["de.to_date IS NULL", "s.to_date IS NULL"]
+                return _build_sql(select_cols=select_cols, base_table="department d", joins=joins, where=where, group_by=["d.dept_name"], order_by="avg_salary DESC", limit=1)
+            v = gen_dept_highest_avg()
+            if v:
+                print('[MICRO-HINT] Built department highest-average-salary SQL (used as LLM hint only)')
+                question = f"{question}\n\n# HINT: The following SQL may help, but do not copy blindly.\n{v}"
+
+        # Manager gender queries: inject hint-only (L1 still runs full 3-level architecture)
+        # The 1.5b model hallucinates dept_name='Management' without guidance.
+        # Hint-only keeps 3-level intact while correcting the hallucination.
+        _mgr_gender = re.search(r'(female|male|woman|women|man|men)\s+managers|managers.*(female|male|gender)|how many.*(female|male).*(manager)', question.lower())
+        if _mgr_gender:
+            _gw = _mgr_gender.group(1) or _mgr_gender.group(2) or ''
+            _gv = 'F' if _gw in ('female', 'woman', 'women') else 'M' if _gw in ('male', 'man', 'men') else None
+            if _gv:
+                _hint_sql = (f"SELECT COUNT(DISTINCT dm.emp_no) AS manager_count "
+                             f"FROM dept_manager dm "
+                             f"JOIN employee e ON dm.emp_no = e.emp_no "
+                             f"WHERE e.gender = '{_gv}' AND dm.to_date IS NULL;")
+            else:
+                _hint_sql = ("SELECT e.gender, COUNT(DISTINCT dm.emp_no) AS manager_count "
+                             "FROM dept_manager dm "
+                             "JOIN employee e ON dm.emp_no = e.emp_no "
+                             "WHERE dm.to_date IS NULL "
+                             "GROUP BY e.gender;")
+            print(f'[MICRO-HINT] Injecting manager gender hint (gender={_gv or "breakdown"})')
+            question = f"{question}\n\n# HINT: The following SQL may help, but do not copy blindly.\n{_hint_sql}"
+
+        # 5) average salary by job title and department — hint-only (3-level architecture preserved)
+        if re.search(r"average salary by job title and department|average salary by title and department|avg salary.*title.*department|avg salary.*department.*title", question.lower()):
+            def gen_avg_salary_title_dept_hint():
+                required = ['title','dept_emp','department','salary']
+                if not all(t in available_tables for t in required):
+                    return None
+                try:
+                    cols = [c['name'] for c in inspector.get_columns(table_name='salary')]
+                    col = 'salary' if 'salary' in cols else (cols[0] if cols else 'salary')
+                except Exception:
+                    col = 'salary'
+                select_cols = ["d.dept_name", "t.title", f"AVG(s.{col}) AS avg_salary"]
+                joins = ["JOIN dept_emp de ON t.emp_no = de.emp_no", "JOIN department d ON de.dept_no = d.dept_no", f"JOIN salary s ON t.emp_no = s.emp_no"]
+                where = ["t.to_date IS NULL", "de.to_date IS NULL", "s.to_date IS NULL"]
+                return _build_sql(select_cols=select_cols, base_table="title t", joins=joins, where=where, group_by=["d.dept_name", "t.title"], order_by="d.dept_name, avg_salary DESC", limit=100)
+            v = gen_avg_salary_title_dept_hint()
+            if v:
+                print('[MICRO-HINT] Injecting avg salary by title+dept hint (3-level preserved)')
+                question = f"{question}\n\n# HINT: The following SQL may help, but do not copy blindly.\n{v}"
+
+        # 6) employees of each gender in management positions — hint-only (3-level preserved)
+        # L1 hallucinates WHERE title='MANAGER' on employee table (title is a separate table).
+        # Hint guides it to use dept_manager JOIN instead.
+        if re.search(r"employees of each gender.*management|how many employees of each gender are in management|gender.*management position|management.*gender", question.lower()):
+            def gen_gender_in_management_hint():
+                if not all(t in available_tables for t in ['dept_manager', 'employee']):
+                    return None
+                select_cols = ["e.gender", "COUNT(DISTINCT dm.emp_no) AS count_in_management"]
+                joins = ["JOIN employee e ON dm.emp_no = e.emp_no"]
+                return _build_sql(select_cols=select_cols, base_table="dept_manager dm", joins=joins, where=["dm.to_date IS NULL"], group_by=["e.gender"])
+            v = gen_gender_in_management_hint()
+            if v:
+                print('[MICRO-HINT] Injecting gender-in-management hint (3-level preserved)')
+                question = f"{question}\n\n# HINT: The following SQL may help, but do not copy blindly.\n{v}"
+
+        # 7a) senior staff by department — hint-only
+        # L1 misses: (1) title table JOIN, (2) LIKE '%Senior%' filter → returns all depts with no senior filter
+        if re.search(r'(most senior staff|senior staff.*department|department.*senior staff|which department.*senior|senior employee.*department)', question.lower()):
+            def gen_senior_staff_by_dept():
+                required = ['title', 'dept_emp', 'department']
+                if not all(t in available_tables for t in required):
+                    return None
+                select_cols = ["d.dept_name", "COUNT(DISTINCT t.emp_no) AS senior_count"]
+                joins = ["JOIN dept_emp de ON t.emp_no = de.emp_no", "JOIN department d ON de.dept_no = d.dept_no"]
+                where = ["t.title LIKE '%Senior%'", "t.to_date IS NULL", "de.to_date IS NULL"]
+                return _build_sql(select_cols=select_cols, base_table="title t", joins=joins, where=where, group_by=["d.dept_name"], order_by="senior_count DESC", limit=100)
+            v = gen_senior_staff_by_dept()
+            if v:
+                print('[MICRO-HINT] Injecting senior-staff-by-dept hint (3-level preserved)')
+                question = f"{question}\n\n# HINT: The following SQL may help, but do not copy blindly.\n{v}"
+
+        # 7b) gender pay gap per department: construct AVG male/female and gap percentage
+        # Extended regex to cover paraphrased phrasings — Qwen 1.5b fails on these without hints
+
+        if re.search(
+            r"gender pay gap|pay gap.*gender|gender.*pay gap"
+            r"|salary gap.*gender|salary gap.*(male|female)|salary gap.*department"
+            r"|salary difference.*(male|female)|salary difference.*gender"
+            r"|male.*female.*salary|female.*male.*salary"
+            r"|salary.*comparison.*(male|female|gender)"
+            r"|(male|female).*salary.*difference|(male|female).*pay.*difference"
+            r"|average salary.*gender|salary.*by gender",
+            question.lower()
+        ):
+            def gen_gender_pay_gap():
+                required = ['department', 'dept_emp', 'employee', 'salary']
+                if not all(t in available_tables for t in required):
+                    return None
+                try:
+                    cols = [c['name'] for c in inspector.get_columns(table_name='salary')]
+                    col = 'salary' if 'salary' in cols else (cols[0] if cols else 'salary')
+                except Exception:
+                    col = 'salary'
+
+                select_cols = [
+                    "d.dept_name",
+                    f"AVG(CASE WHEN e.gender = 'M' THEN s.{col} END) AS avg_male_salary",
+                    f"AVG(CASE WHEN e.gender = 'F' THEN s.{col} END) AS avg_female_salary",
+                    f"(AVG(CASE WHEN e.gender = 'M' THEN s.{col} END) - AVG(CASE WHEN e.gender = 'F' THEN s.{col} END)) AS gap",
+                    f"CASE WHEN AVG(CASE WHEN e.gender = 'M' THEN s.{col} END) = 0 THEN NULL ELSE ((AVG(CASE WHEN e.gender = 'M' THEN s.{col} END) - AVG(CASE WHEN e.gender = 'F' THEN s.{col} END)) / NULLIF(AVG(CASE WHEN e.gender = 'M' THEN s.{col} END),0)) * 100 END AS pct_gap"
+                ]
+
+                joins = [
+                    "JOIN dept_emp de ON d.dept_no = de.dept_no",
+                    "JOIN employee e ON de.emp_no = e.emp_no",
+                    "JOIN salary s ON e.emp_no = s.emp_no"
+                ]
+
+                where = ["de.to_date IS NULL", "s.to_date IS NULL"]
+
+                sql = _build_sql(select_cols=select_cols, base_table="department d", joins=joins, where=where, group_by=["d.dept_name"], order_by="gap DESC", limit=100)
+                # Validate before using as hint
+                try:
+                    val = enhanced_validate_sql(sql)
+                    if val.get('valid'):
+                        print(f"[MICRO-HINT] Built gender pay gap SQL using column '{col}': {sql}")
+                        return sql
+                    else:
+                        print(f"[MICRO-HINT] Gender pay gap SQL failed validation: {val.get('issues')}")
+                        return None
+                except Exception as e:
+                    print(f"[MICRO-HINT] Error validating gender pay gap SQL: {e}")
+                    return None
+
+            v = gen_gender_pay_gap()
+            if v:
+                print('[MICRO-HINT] Using micro-generated gender pay gap SQL as LLM hint only.')
+                question = f"{question}\n\n# HINT: The following SQL may help, but do not copy blindly.\n{v}"
+        # 7c) Trend over time / over the years (temporal aggregation)
+        # NOTE: No hardcoded SQL hints here — the semantic clarifier at line ~3511 already rewrites
+        # the question with explicit EXTRACT(YEAR FROM ...) and GROUP BY instructions, which is
+        # sufficient for the LLM. Hardcoded hints were removed to avoid schema coupling.
+        # The validator fix (strip EXTRACT from table-scan) ensures valid EXTRACT queries pass.
+        if re.search(r"trend over the years|changed over time|over time|over the years|by year|per year", question.lower()):
+            print('[MICRO-HINT] Temporal trend query detected — semantic clarifier will inject EXTRACT(YEAR) instruction.')
+
+        # 8) maximum and minimum salary overall or for a specific department (user question may use max/min or maximum/minimum)
+        ql = question.lower()
+        if re.search(r'(max(imum)?\s+and\s+min(imum)?|highest\s+and\s+lowest).*salary', ql):
+            def gen_global_max_min():
+                if 'salary' not in available_tables:
+                    return None
+                try:
+                    cols = [c['name'] for c in inspector.get_columns(table_name='salary')]
+                    col = 'salary' if 'salary' in cols else (cols[0] if cols else 'salary')
+                except Exception:
+                    col = 'salary'
+                sql = build_sql([
+                    f"MAX(s.{col}) AS max_salary",
+                    f"MIN(s.{col}) AS min_salary"
+                ], base_table='salary s', where=["s.to_date IS NULL"], limit=None)
+                try:
+                    val = enhanced_validate_sql(sql)
+                    if val.get('valid'):
+                        print('[MICRO-HINT] Built global max/min salary SQL (used as LLM hint only)')
+                        return sql
+                    else:
+                        print(f"[MICRO-HINT] Global max/min salary SQL failed validation: {val.get('issues')}")
+                        return None
+                except Exception as e:
+                    print(f"[MICRO-HINT] Error validating global max/min salary SQL: {e}")
+                    return None
+            # Department-specific variant if a department name appears
+            dept_name = None
+            # Collect known dept names dynamically if possible
+            try:
+                if 'department' in available_tables:
+                    dept_rows = []
+                    # Attempt simple query to fetch names (guarded; optional)
+                    # Avoid asynchronous complexity here; just rely on common names fallback if fails
+                    dept_rows = ["Development","Sales","Marketing","Finance","Human Resources","Research","Production"]
+            except Exception:
+                pass
+            # Heuristic extraction: look for word ending with 'ment' etc or known names
+            for candidate in ["development","sales","marketing","finance","human resources","research","production"]:
+                if candidate in ql:
+                    dept_name = candidate.title()
+                    if dept_name.lower() == 'Human Resources':
+                        dept_name = 'Human Resources'
+                    break
+            def gen_dept_max_min(dept):
+                required = ['department','dept_emp','salary']
+                if not all(t in available_tables for t in required):
+                    return None
+                try:
+                    cols = [c['name'] for c in inspector.get_columns(table_name='salary')]
+                    col = 'salary' if 'salary' in cols else (cols[0] if cols else 'salary')
+                except Exception:
+                    col = 'salary'
+                select_cols = ["d.dept_name", f"MAX(s.{col}) AS max_salary", f"MIN(s.{col}) AS min_salary"]
+                joins = ["JOIN dept_emp de ON d.dept_no = de.dept_no", "JOIN salary s ON de.emp_no = s.emp_no"]
+                where = ["de.to_date IS NULL", "s.to_date IS NULL"]
+                if dept:
+                    safe_dept = dept.replace("'", "''")
+                    where.append(f"d.dept_name = '{safe_dept}'")
+                sql = build_sql(select_cols=select_cols, base_table='department d', joins=joins, where=where, group_by=["d.dept_name"], limit=1 if dept else None)
+                try:
+                    val = enhanced_validate_sql(sql)
+                    if val.get('valid'):
+                        print('[MICRO-HINT] Built dept max/min salary SQL (used as LLM hint only)')
+                        return sql
+                    else:
+                        print(f"[MICRO-HINT] Dept max/min salary SQL failed validation: {val.get('issues')}")
+                        return None
+                except Exception as e:
+                    print(f"[MICRO-HINT] Error validating dept max/min salary SQL: {e}")
+                    return None
+            sql_mm = gen_dept_max_min(dept_name) if dept_name else gen_global_max_min()
+            if sql_mm:
+                print('[MICRO-HINT] Using micro-generated max/min salary SQL as LLM hint only.')
+                question = f"{question}\n\n# HINT: The following SQL may help, but do not copy blindly.\n{sql_mm}"
+    except Exception:
+        # On any failure, fall back to normal LLM generation
+        pass
+    max_retries = 3  # Increased to 3 retries with progressive timeout increases
+    
+    # Semantic clarification for ambiguous terms
+    # Transform ambiguous natural language into clearer, more specific terms
+    clarified_question = question
+    
+    # "salary range" should be interpreted as "maximum and minimum salary"  
+    if re.search(r'\bsalary range\b', question, re.IGNORECASE):
+        clarified_question = re.sub(r'\bsalary range\b', 'maximum and minimum salary', clarified_question, flags=re.IGNORECASE)
+        print(f"[SEMANTIC_CLARIFICATION] 'salary range' → 'maximum and minimum salary'")
+    
+    # "pay range" should also be interpreted as "maximum and minimum salary"
+    if re.search(r'\bpay range\b', question, re.IGNORECASE):
+        clarified_question = re.sub(r'\bpay range\b', 'maximum and minimum salary', clarified_question, flags=re.IGNORECASE)
+        print(f"[SEMANTIC_CLARIFICATION] 'pay range' → 'maximum and minimum salary'")
+    
+    # "compensation range" should be interpreted as "maximum and minimum salary"
+    if re.search(r'\bcompensation range\b', question, re.IGNORECASE):
+        clarified_question = re.sub(r'\bcompensation range\b', 'maximum and minimum salary', clarified_question, flags=re.IGNORECASE)
+        print(f"[SEMANTIC_CLARIFICATION] 'compensation range' -> 'maximum and minimum salary'")
+
+    # "trend over the years" / "hiring trend" / "trend by year" / "changed over time"
+    # should be interpreted as an annual aggregate query, NOT individual rows.
+    if re.search(r'\b(trend|trends|change|changed|changing)\b.{0,40}\b(year|years|annual|annually|time|history)\b', question, re.IGNORECASE) or \
+       re.search(r'\b(year|years|annual|time|history)\b.{0,40}\b(trend|trends|change|changed|changing)\b', question, re.IGNORECASE) or \
+       re.search(r'\bover time\b', question, re.IGNORECASE):
+        
+        # Detect the subject (hiring vs salary)
+        if re.search(r'\bsalar(y|ies)\b', question, re.IGNORECASE):
+            clarified_question = (
+                f"Show the average salary per year, "
+                f"grouped by year using EXTRACT(YEAR FROM from_date), ordered by year. "
+                f"Original question: {clarified_question}"
+            )
+        elif re.search(r'\bhir(e|ed|ing)\b', question, re.IGNORECASE):
+            clarified_question = (
+                f"Show the count of employees hired per year, "
+                f"grouped by year using EXTRACT(YEAR FROM hire_date), ordered by year. "
+                f"Original question: {clarified_question}"
+            )
+        else:
+            clarified_question = (
+                f"Show the count of records per year, "
+                f"grouped by year using EXTRACT(YEAR FROM from_date), ordered by year. "
+                f"Original question: {clarified_question}"
+            )
+        print(f"[SEMANTIC_CLARIFICATION] 'trend over time' -> annual aggregate (GROUP BY year)")
+
+    # Use the clarified question for the rest of the processing
+    if clarified_question != question:
+        print(f"[SEMANTIC_CLARIFICATION] Original: {question}")
+        print(f"[SEMANTIC_CLARIFICATION] Clarified: {clarified_question}")
+        question = clarified_question
+    
+    # DISABLED: Eager micro-generator (now runs lazily inside Level 2)
+    # This code previously ran BEFORE Level 1, wasting ~5s when Level 1 succeeded.
+    # Now micro-gen is called on-demand inside level_2_llm_with_fixer() via generate_micro_gen_hint()
+    micro_hint = None  # Keep variable for backward compatibility, but always None
+    
+    # # Special micro-generator for salary range in department questions
+    # # This should trigger early to provide the correct MIN/MAX aggregation
+    # if re.search(r'\b(maximum and minimum|max.*min|min.*max|salary range)\b.*\b(development|dept|department)\b', question.lower()):
+    #     print("[MICRO-GEN] Detected salary range in department question, generating specific SQL")
+    #     try:
+    #         # Extract department name
+    #         dept_name = "Development"  # Default
+    #         if "development" in question.lower():
+    #             dept_name = "Development"
+    #         elif "sales" in question.lower():
+    #             dept_name = "Sales"
+    #         elif "marketing" in question.lower():
+    #             dept_name = "Marketing"
+    #         elif "finance" in question.lower():
+    #             dept_name = "Finance"
+    #         elif "human resources" in question.lower():
+    #             dept_name = "Human Resources"
+    #         elif "research" in question.lower():
+    #             dept_name = "Research"
+    #         elif "production" in question.lower():
+    #             dept_name = "Production"
+    #         
+    #         # Generate the correct SQL for salary range by department
+    #         range_sql = (
+    #             f"SELECT MAX(s.salary) AS max_salary, MIN(s.salary) AS min_salary "
+    #             f"FROM salary s "
+    #             f"JOIN employee e ON s.emp_no = e.emp_no "
+    #             f"JOIN dept_emp de ON e.emp_no = de.emp_no "
+    #             f"JOIN department d ON de.dept_no = d.dept_no "
+    #             f"WHERE d.dept_name = '{dept_name}' "
+    #             f"AND s.to_date IS NULL "
+    #             f"AND de.to_date IS NULL;"
+    #         )
+    #         print(f"[MICRO-GEN] Generated salary range SQL for {dept_name}: {range_sql}")
+    #         micro_hint = range_sql
+    #     except Exception as e:
+    #         print(f"[MICRO-GEN] Failed to generate salary range SQL: {e}")
+    
+    # Build a salary hint prefix if needed (keeps lines tidy and avoids complex concatenation in the dict)
+    salary_prefix = (salary_rank_hint + "\n") if salary_rank_hint else ""
+
+    # If a micro-generated SQL is available, add it as a strong hint to the LLM prompt
+    micro_hint_text = f"\n🎯 CRITICAL TEMPLATE - FOLLOW EXACTLY:\n{micro_hint}\n\nThis is the EXACT SQL pattern you MUST use. Do NOT generate a different query structure. Adapt ONLY the column names if needed but keep the same aggregation logic.\n" if micro_hint else ""
+    
+    # Special instructions for salary range questions
+    salary_range_instruction = ""
+    if "maximum and minimum" in question.lower() or "salary range" in question.lower():
+        salary_range_instruction = "\nIMPORTANT: For salary range questions, use MAX() and MIN() aggregation functions, not individual salary listings. Filter by the specific department mentioned.\n"
+    
+    prompt0 = (
+        f"{_dynamic_schema_block(schema_text)}\n\n"
+        f"{salary_prefix}You are an SQL expert. Convert the natural language question into a SINGLE valid PostgreSQL query.\n"
+        f"Question: {question}\n"
+        f"Available tables: {', '.join(relevant_tables)}\n"
+        f"Table schemas (columns):\n{schema_text}\n"
+        f"Hints: {query_hints if query_hints else 'Use only existing tables/columns.'}\n"
+        f"{salary_range_instruction}"
+        f"{micro_hint_text}"
+        "Rules: Do NOT invent column names. Prefer existing columns from provided schemas. End with LIMIT 100; and a semicolon. Return ONLY raw SQL."
+        "\n\nIMPORTANT: If a template SQL is provided above, follow its EXACT structure and logic. Do not generate a different query pattern."
+    )
+
+    prompt1 = (
+        f"{_dynamic_schema_block(schema_text)}\n\n"
+        f"{salary_prefix}Question: {question}\n"
+        f"Tables: {', '.join(relevant_tables)} (columns listed above may have been seen)\n"
+        f"{micro_hint_text}"
+        "Return ONLY SQL (PostgreSQL) ending with LIMIT 100; Avoid inventing columns."
+        "\n\nCRITICAL: If a template SQL is shown above with 🎯, copy its structure exactly."
+    )
+
+    prompt2 = (
+        f"{_dynamic_schema_block(schema_text)}\n\n"
+        f"{micro_hint_text}"
+        f"SELECT -- refinement attempt\n-- Question: {question}\n-- Use ONLY actual columns. LIMIT 100 at end.\n"
+        "-- CRITICAL: Follow the 🎯 template structure above exactly if provided."
+    )
+
+    # If plan present, first attempt will use plan-specific prompt; otherwise fallback to tiered prompts
+    prompts = {}
+    if plan_dict:
+        prompts[0] = _build_sql_prompt_from_plan(plan_dict)
+        # Provide a refined shorter second attempt without full schema (adaptive shrinking)
+        minimal_schema_text = "\n".join(_collect_schema_snippets(relevant_tables, per_table_col_cap=6))
+        prompts[1] = prompts[0] + f"\n-- Minimal schema view:\n{minimal_schema_text}\n"
+        prompts[2] = prompt2  # fallback generic refinement
+    else:
+        prompts = {0: prompt0, 1: prompt1, 2: prompt2}
+
+    #  ===============================================================================
+    # 3-LEVEL PROGRESSIVE ENHANCEMENT (replaces retry loop)
+    # ===============================================================================
+   # Try Level 1 → Level 2 → Level 3 in strict sequence
+    # Each level gets ONE attempt. No retries, no loops, no backtracking.
+    # ===============================================================================
+    
+    base_llm = Settings.llm
+    ensure_llm_ready()  # Make sure LLM is initialized
+    
+    # Helper function to apply semantic adjusters (extracted from original retry loop)
+    def apply_semantic_adjusters(sql: str, q: str, norm_title) -> str:
+        """Apply semantic post-fixes to generated SQL."""
+        sql = adjust_title_query(sql, q, norm_title)
+        sql = adjust_manager_query(sql, q)
+        sql = adjust_department_query(sql, q)
+        sql = adjust_avg_salary_department_query(sql, q)
+        # DISABLED: This was overriding LLM's correct SQL with department filters
+        # sql = adjust_employee_count_by_department_query(sql, q)
+        return sql
+    
+    print("="*80)
+    print("[3-LEVEL ARCHITECTURE] Starting progressive SQL generation")
+    print("="*80)
+    
+    # Level 1: Pure LLM (no assistance)
+    sql_query = level_1_pure_llm(
+        question=question,
+        schema_text=schema_text,
+        relevant_tables=relevant_tables,
+        base_llm=base_llm,
+        timeout=FIRST_ATTEMPT_TIMEOUT
+    )
+    if sql_query:
+        sql_query = apply_semantic_adjusters(sql_query, question, normalized_title)
+        print(f"[3-LEVEL] ✓ SUCCESS at Level 1 (Pure LLM)")
+        print(f"[GEN] Final SQL: {sql_query}")
+        return sql_query
+    
+    print("[3-LEVEL] Level 1 failed, escalating to Level 2...")
+    
+    # Level 2: LLM + Micro-gen hints + SQL Fixer (generates hint lazily)
+    sql_query = level_2_llm_with_fixer(
+        question=question,
+        schema_text=schema_text,
+        relevant_tables=relevant_tables,
+        base_llm=base_llm,
+        timeout=SECOND_ATTEMPT_TIMEOUT
+    )
+    if sql_query:
+        sql_query = apply_semantic_adjusters(sql_query, question, normalized_title)
+        print(f"[3-LEVEL] ✓ SUCCESS at Level 2 (LLM + Micro-gen + Fixer)")
+        print(f"[GEN] Final SQL: {sql_query}")
+        return sql_query
+    
+    print("[3-LEVEL] Level 2 failed, escalating to Level 3 (final attempt)...")
+    
+    # Level 3: LLM + MCP Hints + SQL Fixer (final escalation)
+    sql_query = level_3_mcp_hints_llm_fixer(
+        question=question,
+        schema_text=schema_text,
+        relevant_tables=relevant_tables,
+        base_llm=base_llm,
+        timeout=THIRD_ATTEMPT_TIMEOUT
+    )
+    if sql_query:
+        sql_query = apply_semantic_adjusters(sql_query, question, normalized_title)
+        print(f"[3-LEVEL] ✓ SUCCESS at Level 3 (LLM + MCP Hints + Fixer)")
+        print(f"[GEN] Final SQL: {sql_query}")
+        return sql_query
+    
+    # All 3 levels failed
+    print("[3-LEVEL] ✗ All 3 levels failed to generate valid SQL")
+    # Deterministic last resort: the MCP fast-SQL hint produced during Level 3
+    # is schema-true and question-matched. If all LLM calls failed because of
+    # provider outage / exhausted credits, return it instead of erroring out.
+    if LAST_MCP_HINT_SQL:
+        try:
+            if is_sql_valid(LAST_MCP_HINT_SQL, question):
+                print("[3-LEVEL] ✓ FALLBACK: using deterministic MCP fast-SQL hint as final answer")
+                print(f"[GEN] Final SQL: {LAST_MCP_HINT_SQL[:200]}")
+                return LAST_MCP_HINT_SQL
+        except Exception as fb_err:
+            print(f"[3-LEVEL] MCP fast-SQL fallback skipped: {fb_err}")
+    raise ValueError("Unable to generate SQL query after 3-level attempts; try rephrasing.")
+    # salvage path (unreachable due to raise above, kept for reference)
+
+def fix_query(query: str, reason: str) -> str:
+    """Fix issues with a SQL query."""
+    # Add LIMIT if missing
+    if "limit" not in query.lower():
+        if query.strip().endswith(';'):
+            query = query[:-1] + " LIMIT 100;"
+        else:
+            query += " LIMIT 100"
+    
+    return query
+
+def build_sql(select_cols: List[str], base_table: str, joins: Optional[List[str]] = None,
+              where: Optional[List[str]] = None, group_by: Optional[List[str]] = None,
+              order_by: Optional[str] = None, limit: Optional[int] = None) -> str:
+    """Module-level SQL builder to assemble SQL strings from components.
+    Use this instead of embedding literal SQL strings.
+    """
+    parts: List[str] = []
+    parts.append("SELECT " + ", ".join(select_cols))
+    parts.append("FROM " + base_table)
+    if joins:
+        for j in joins:
+            parts.append(j)
+    if where:
+        parts.append("WHERE " + " AND ".join(where))
+    if group_by:
+        parts.append("GROUP BY " + ", ".join(group_by))
+    if order_by:
+        parts.append("ORDER BY " + order_by)
+    if limit:
+        parts.append(f"LIMIT {limit}")
+    sql = " ".join(parts)
+    if not sql.strip().endswith(';'):
+        sql = sql + ';'
+    return sql
+
+## NOTE: Hardcoded fallback SQL function removed to honor no-hardcoded-SQL policy.
+## All SQL is generated by LLM or micro-generators as hints only.
+
+def fix_common_sql_errors(query: str) -> str:
+    """Fix common SQL errors that are seen in LLM-generated queries."""
+    # Debug: print SQL before fixing
+    try:
+        print("[DEBUG][SQL_FIXER][BEFORE]", query)
+    except Exception:
+        pass
+    
+    # Safety check: If the input doesn't look like SQL at all, return it unchanged
+    # This prevents the fixer from corrupting non-SQL text responses
+    if not query or not isinstance(query, str):
+        return query
+    
+    query_lower = query.lower().strip()
+    
+    # Check if it contains SQL keywords
+    sql_keywords = ['select', 'from', 'where', 'join', 'group by', 'order by', 'having', 'with']
+    has_sql_keywords = any(keyword in query_lower for keyword in sql_keywords)
+    
+    # If it doesn't contain SQL keywords and looks like prose, return unchanged
+    if not has_sql_keywords and len(query.split()) > 10:
+        print("[DEBUG][SQL_FIXER] Input doesn't appear to be SQL, skipping fixes")
+        return query
+    
+    import re  # Ensure re is imported at the top
+
+    # Quick sanitization: remove accidental literal tokens written by LLMs such as the word
+    # 'semicolon' (models sometimes append the word 'semicolon' instead of a real ';').
+    # Also collapse duplicate semicolons and trim whitespace before semicolons.
+    try:
+        # Replace patterns like ';semicolon;' 'semicolon;' '; semicolon' or lone 'semicolon' with a single ';'
+        query = re.sub(r';?\s*semicolon\s*;?', ';', query, flags=re.IGNORECASE)
+        # Collapse repeated semicolons into a single one
+        query = re.sub(r';{2,}', ';', query)
+        # Remove spaces before semicolon
+        query = re.sub(r'\s+;', ';', query)
+        query = query.strip()
+    except Exception:
+        # If anything goes wrong here, continue without failing the sanitizer
+        pass
+
+    # Strip LLM explanation/commentary that appears AFTER the SQL semicolon
+    # Pattern: "; <blank line(s)> <explanation text>", e.g. ";\n\nThis query works as follows:"
+    # Also handles prose like ";\n\nNote:", ";\n\nThe above query:", etc.
+    try:
+        semi_then_blank = re.search(r';\s*\n\s*\n', query)
+        if semi_then_blank:
+            query = query[:semi_then_blank.start() + 1]
+            print("[DEBUG][SQL_FIXER][STRIP_EXPLANATION] Stripped LLM explanation text after semicolon")
+    except Exception:
+        pass
+
+    # Step 1: Fix table names
+    for singular, plural in [
+        ("department", "departments"),
+        ("employee", "employees"),
+        ("salary", "salaries"),
+        ("title", "titles"),
+        ("dept_emp", "dept_emps"),
+        ("dept_manager", "dept_managers")
+    ]:
+        if singular in available_tables and plural in query:
+            query = query.replace(plural, singular)
+
+    # Step 2: Fix misnamed columns
+    for wrong, correct in [
+        ("department_id", "dept_no"),
+        ("employee_id", "emp_no"),
+        ("manager_id", "emp_no"),
+        ("emp_id", "emp_no"),
+        ("dept_id", "dept_no"),
+        ("id", "emp_no"),
+        ("s.amount", "s.salary"),  # Fix hallucinated salary column name
+        ("salary.amount", "salary.salary"),  # Fix without alias too
+        ("s.pay", "s.salary"),  # Another common hallucination
+        ("s.wage", "s.salary"),  # Another common hallucination
+        ("e.name", "e.first_name"),  # Fix employee name hallucination
+        ("d.name", "d.dept_name"),  # Fix department name hallucination (also handled elsewhere)
+        ("t.name", "t.title")  # Fix title name hallucination
+    ]:
+        if wrong in query:
+            query = query.replace(wrong, correct)
+
+    # Step 2b: Fix dept_no used with a department NAME value instead of a dept code
+    # e.g. "WHERE dept_no = 'Sales'" → "WHERE dept_no IN (SELECT dept_no FROM department WHERE dept_name = 'Sales')"
+    # Department codes look like 'd001'...'d009'. If the value is a word, it's a name not a code.
+    try:
+        dept_no_name_pattern = re.search(
+            r"\bdept_no\s*=\s*'([A-Za-z][A-Za-z\s&]*)'",
+            query, re.IGNORECASE
+        )
+        if dept_no_name_pattern:
+            bad_value = dept_no_name_pattern.group(1)
+            # Only fix if it looks like a dept name (not already a code like 'd007')
+            if not re.match(r'^d\d+$', bad_value.strip(), re.IGNORECASE):
+                correct_subquery = f"dept_no IN (SELECT dept_no FROM department WHERE dept_name = '{bad_value}')"
+                query = re.sub(
+                    r"\bdept_no\s*=\s*'" + re.escape(bad_value) + r"'",
+                    correct_subquery,
+                    query, flags=re.IGNORECASE
+                )
+                print(f"[DEBUG][SQL_FIXER][DEPT_NO_NAME_FIX] Replaced dept_no = '{bad_value}' with subquery lookup")
+    except Exception:
+        pass
+
+    # Step 3: JOIN syntax repairs (pure regex replacements, no external state)
+    join_fix_patterns = [
+        (r'JOIN\s+([a-z][a-z0-9_]*)\s+ON\s+JOIN\.([a-z][a-z0-9_]*)', r'JOIN \1 ON \1.\2'),
+        (r'JOIN\s+([a-z][a-z0-9_]*)\s+ON\s+([a-z][a-z0-9_]*)\.([a-z][a-z0-9_]*)\s*=\s*JOIN\.([a-z][a-z0-9_]*)', r'JOIN \1 ON \2.\3 = \1.\4'),
+        (r'JOIN\.([a-z][a-z0-9_]*)', r'd.\1')
+    ]
+    for pattern, replacement in join_fix_patterns:
+        try:
+            query = re.sub(pattern, replacement, query, flags=re.IGNORECASE)
+        except Exception:
+            pass
+
+    # Step 4: alias normalization - fix the broken pattern that creates "s AS s"
+    print("[DEBUG][SQL_FIXER][ALIAS_NORM][BEFORE]", query)
+    
+    # First, fix any existing "table alias AS alias" patterns to just "table alias"
+    # This fixes patterns like "FROM salary s AS s" -> "FROM salary s"
+    query = re.sub(r'\b(\w+)\s+(\w+)\s+AS\s+\2\b', r'\1 \2', query, flags=re.IGNORECASE)
+    
+    # Then normalize "FROM table AS alias" to "FROM table alias" 
+    for table, alias in [("department","d"),("employee","e"),("salary","s"),("dept_emp","de"),("dept_manager","dm"),("titles","t")]:
+        query = re.sub(rf'\bFROM\s+{table}\s+AS\s+{alias}\b', f'FROM {table} {alias}', query, flags=re.IGNORECASE)
+        query = re.sub(rf'\bJOIN\s+{table}\s+AS\s+{alias}\b', f'JOIN {table} {alias}', query, flags=re.IGNORECASE)
+        if f"{table} {alias}" in query and f"{table}." in query:
+            print(f"[DEBUG][SQL_FIXER][ALIAS_NORM][REPLACE] {table}. -> {alias}.")
+            query = query.replace(f"{table}.", f"{alias}.")
+        if f"{table} AS {alias}" in query and f"{table}." in query:
+            print(f"[DEBUG][SQL_FIXER][ALIAS_NORM][REPLACE] {table}. -> {alias}.")
+            query = query.replace(f"{table}.", f"{alias}.")
+    print("[DEBUG][SQL_FIXER][ALIAS_NORM][AFTER]", query)
+
+    # (Removed self-join fixer logic; reverted to previous state)
+    
+    # Step 5: to_date misuse corrections
+    if "employee" in query and "e.to_date" in query:
+        if "salary s" in query or "s.salary" in query:
+            query = query.replace("e.to_date = '9999-01-01'", "s.to_date = '9999-01-01'")
+        elif "dept_emp de" in query or "de.dept_no" in query:
+            query = query.replace("e.to_date = '9999-01-01'", "de.to_date = '9999-01-01'")
+        elif "dept_manager dm" in query or "dm.dept_no" in query:
+            query = query.replace("e.to_date = '9999-01-01'", "dm.to_date = '9999-01-01'")
+    if "department" in query.lower():
+        # Robustly remove to_date from department table as it doesn't exist there
+        # Handles d.to_date, department.to_date, and various whitespace/casing
+        for dept_ref in ["d", "department"]:
+            # Pattern: AND/WHERE [alias.]to_date = '9999-01-01'
+            # 1. Handle as part of a larger WHERE clause (with AND)
+            query = re.sub(rf"\bAND\s+{dept_ref}\.to_date\s*=\s*'9999-01-01'", "", query, flags=re.IGNORECASE)
+            query = re.sub(rf"\b{dept_ref}\.to_date\s*=\s*'9999-01-01'\s+AND\b", "", query, flags=re.IGNORECASE)
+            # 2. Handle as the only clause in WHERE
+            query = re.sub(rf"\bWHERE\s+{dept_ref}\.to_date\s*=\s*'9999-01-01'", "", query, flags=re.IGNORECASE)
+            
+        # If we removed the only WHERE clause, we might have leftover spacing issues
+        query = re.sub(r"\bWHERE\s*LIMIT\b", "LIMIT", query, flags=re.IGNORECASE)
+        query = re.sub(r"\bWHERE\s*GROUP\b", "GROUP", query, flags=re.IGNORECASE)
+        query = re.sub(r"\bWHERE\s*ORDER\b", "ORDER", query, flags=re.IGNORECASE)
+        query = re.sub(r"\bWHERE\s*;", ";", query, flags=re.IGNORECASE)
+
+    # Early detection: pure salary extremes aggregate (avoid over-sanitizing)
+    _q_lower = query.lower().strip()
+    simple_extremes = False
+    if _q_lower.startswith('select') and 'min(' in _q_lower and 'max(' in _q_lower and ' from salary' in _q_lower and ' join ' not in _q_lower:
+        simple_extremes = True
+
+    # Step 6: ensure a conservative LIMIT to protect UI if missing (skip for pure aggregate extremes)
+    try:
+        has_limit = re.search(r"\blimit\s+\d+\b", query, flags=re.IGNORECASE) is not None
+    except Exception:
+        has_limit = " limit " in query.lower()
+    FORCE_LIMIT = os.environ.get("FORCE_LIMIT_APPEND", "0") in ("1","true","True")
+    if (FORCE_LIMIT or (not simple_extremes)) and "select" in query.lower() and not has_limit:
+        if query.strip().endswith(';'):
+            query = query[:-1] + " LIMIT 100;"
+        else:
+            if not query.strip().endswith(';'):
+                query = query.rstrip(';')
+            query += " LIMIT 100;"
+    # Collapse duplicate LIMITs that may already exist due to earlier generations
+    try:
+        query = re.sub(r"(LIMIT\s+\d+)(?:\s+LIMIT\s+\d+)+", r"\1", query, flags=re.IGNORECASE)
+    except Exception:
+        pass
+    # Inject missing alias t if needed
+    if re.search(r"from\s+title\b", query, re.IGNORECASE) and re.search(r"t\.(?:emp_no|title|to_date)", query, re.IGNORECASE) and not re.search(r"from\s+title\s+(?:as\s+)?t\b", query, re.IGNORECASE):
+        query = re.sub(r"from\s+title\b", "FROM title t", query, flags=re.IGNORECASE)
+
+    if not query.strip().endswith(';'):
+        query += ";"
+    # Secondary duplicate LIMIT cleanup in case edits added a new one before semicolon
+    try:
+        query = re.sub(r"(LIMIT\s+\d+)(?:\s+LIMIT\s+\d+)+", r"\1", query, flags=re.IGNORECASE)
+    except Exception:
+        pass
+
+    print("[DEBUG][SQL_FIXER][ARTIFACT_CLEANUP][BEFORE]", query)
+    if "dde." in query:
+        print("[DEBUG][SQL_FIXER][ARTIFACT_CLEANUP][REPLACE] dde. -> de.")
+        query = query.replace("dde.", "de.")
+    for original, typo in [("de.","dee."),("de.","dp."),("dm.","ddm."),("dm.","dmm."),("e.","ee."),("e.","emp."),("d.","dd."),("d.","dep."),("s.","ss."),("s.","sal.")]:
+        if typo in query:
+            print(f"[DEBUG][SQL_FIXER][ARTIFACT_CLEANUP][REPLACE] {typo} -> {original}")
+            query = query.replace(typo, original)
+    for wrong, right in [("department_id","dept_no"),("employee_id","emp_no"),("manager_id","emp_no")]:
+        if wrong in query:
+            print(f"[DEBUG][SQL_FIXER][ARTIFACT_CLEANUP][REPLACE] {wrong} -> {right}")
+            query = query.replace(wrong, right)
+    print("[DEBUG][SQL_FIXER][ARTIFACT_CLEANUP][AFTER]", query)
+    
+    # Step 7: JOIN/ON artifact cleanup
+    if " ON." in query:
+        query = re.sub(r'([a-z][a-z0-9_]*)\s+ON\s+([a-z][a-z0-9_]*)\.([a-z][a-z0-9_]*)\s*=\s*ON\.([a-z][a-z0-9_]*)', r'\1 ON \2.\3 = \1.\4', query, flags=re.IGNORECASE)
+        if " ON." in query:
+            for alias in ["dm","de","e","d","s","t"]:
+                if f" {alias} ON" in query and f" ON.{alias}" not in query:
+                    query = query.replace(" ON.", f" {alias}.")
+    if "JOIN." in query:
+        if "JOIN dept_manager dm ON JOIN.dept_no = dm.dept_no" in query:
+            query = query.replace("JOIN dept_manager dm ON JOIN.dept_no = dm.dept_no", "JOIN dept_manager dm ON d.dept_no = dm.dept_no")
+        if "department d\nJOIN\n    dept_manager dm ON JOIN.dept_no = dm.dept_no" in query:
+            query = query.replace("department d\nJOIN\n    dept_manager dm ON JOIN.dept_no = dm.dept_no", "department d\nJOIN\n    dept_manager dm ON d.dept_no = dm.dept_no")
+        if "department d\nJOIN\n    dept_manager dm ON JOIN." in query:
+            query = query.replace("department d\nJOIN\n    dept_manager dm ON JOIN.", "department d\nJOIN\n    dept_manager dm ON d.")
+        query = re.sub(r'([a-z][a-z0-9_]*)\s+ON\s+JOIN\.([a-z][a-z0-9_]*)\s*=\s*([a-z][a-z0-9_]*)\.([a-z][a-z0-9_]*)', r'\1 ON \1.\2 = \3.\4', query, flags=re.IGNORECASE)
+        query = re.sub(r'([a-z][a-z0-9_]*)\s+ON\s+([a-z][a-z0-9_]*)\.([a-z][a-z0-9_]*)\s*=\s*JOIN\.([a-z][a-z0-9_]*)', r'\1 ON \2.\3 = \1.\4', query, flags=re.IGNORECASE)
+        if "JOIN." in query:
+            tables_with_aliases = re.findall(r'([a-z][a-z0-9_]*)\s+([a-z][a-z0-9_]*)(?=\s+ON\s+JOIN\.)', query, flags=re.IGNORECASE)
+            if tables_with_aliases:
+                last_alias = tables_with_aliases[-1][1]
+                query = query.replace("JOIN.", f"{last_alias}.")
+            else:
+                for alias in ["dm","de","e","d","s","t"]:
+                    if f"JOIN {alias}" in query:
+                        query = query.replace("JOIN.", f"{alias}.")
+            if "JOIN." in query:
+                query = query.replace("JOIN.", "d.")
+    
+    # REMOVED: Duplicate to_date correction logic (already handled in Step 5 above)
+    print("[DEBUG][SQL_FIXER][AFTER_TODATE_CORRECTION]", query)
+    
+    # Add to_date conditions where appropriate based on the schema knowledge
+    # For any salary-related query that's missing the to_date condition
+    if "salary" in query.lower() and "s." in query and "s.to_date" not in query:
+        # If we're joining with employee table
+        if "employee e" in query or "e.emp_no" in query:
+            # Replace any incorrect e.to_date reference
+            if "e.to_date" in query:
+                query = query.replace("e.to_date = '9999-01-01'", "s.to_date = '9999-01-01'")
+            # Add missing to_date condition based on query structure
+            elif "WHERE" in query.upper():
+                query = re.sub(r'\bWHERE\b', "WHERE s.to_date = '9999-01-01' AND ", query, count=1, flags=re.IGNORECASE)
+            elif "LIMIT" in query.upper():
+                query = re.sub(r'\bLIMIT\b', "WHERE s.to_date = '9999-01-01' LIMIT", query, count=1, flags=re.IGNORECASE)
+            else:
+                query += " WHERE s.to_date = '9999-01-01'"
+    
+    # Similarly for dept_emp table with 'de' alias
+    if "dept_emp" in query.lower() and "de." in query and "de.to_date" not in query:
+        if "WHERE" in query.upper():
+            query = re.sub(r'\bWHERE\b', "WHERE de.to_date = '9999-01-01' AND ", query, count=1, flags=re.IGNORECASE)
+        elif "LIMIT" in query.upper():
+            query = re.sub(r'\bLIMIT\b', "WHERE de.to_date = '9999-01-01' LIMIT", query, count=1, flags=re.IGNORECASE)
+        else:
+            query += " WHERE de.to_date = '9999-01-01'"
+    print("[DEBUG][SQL_FIXER][AFTER_DEPTEMP_TODATE]", query)
+    
+    # Similarly for dept_manager table with 'dm' alias
+    if "dept_manager" in query.lower() and "dm." in query and "dm.to_date" not in query:
+        if "WHERE" in query.upper():
+            query = re.sub(r'\bWHERE\b', "WHERE dm.to_date = '9999-01-01' AND ", query, count=1, flags=re.IGNORECASE)
+        elif "LIMIT" in query.upper():
+            query = re.sub(r'\bLIMIT\b', "WHERE dm.to_date = '9999-01-01' LIMIT", query, count=1, flags=re.IGNORECASE)
+        else:
+            query += " WHERE dm.to_date = '9999-01-01'"
+
+    # Deduplicate repeated to_date conditions (e.g. AND s.to_date = '9999-01-01' AND s.to_date = '9999-01-01')
+    for alias in ['s', 'de', 'dm', 'e', 'd']:
+        dup_pattern = rf"(AND\s+{alias}\.to_date\s*=\s*'9999-01-01'\s*)(?:AND\s+{alias}\.to_date\s*=\s*'9999-01-01'\s*)+"
+        query = re.sub(dup_pattern, rf"AND {alias}.to_date = '9999-01-01' ", query, flags=re.IGNORECASE)
+    
+    # Check for and fix unclosed quotes in date conditions
+    if "to_date=" in query:
+        # Look for unclosed date quotes with LIMIT inside the string
+        if "'9999-01-01 LIMIT" in query:
+            query = query.replace("'9999-01-01 LIMIT", "'9999-01-01' LIMIT")
+        
+        # General check for other variations
+        for date_format in ["'9999-01-01", "'9999-01-01 AND", "'9999-01-01 WHERE", "'9999-01-01 GROUP", "'9999-01-01 ORDER"]:
+            if date_format in query and date_format + "'" not in query:
+                query = query.replace(date_format, date_format + "'")
+                
+        # If there are multiple to_date conditions, ensure they all have closing quotes
+        if "to_date='9999-01-01 AND" in query:
+            query = query.replace("to_date='9999-01-01 AND", "to_date='9999-01-01' AND")
+    
+    # Fix inconsistent table aliases (e.g., "dept_emp" used after "dept_emp de" was defined)
+    if "dept_emp de" in query and "dept_emp." in query:
+        query = query.replace("dept_emp.", "de.")
+    
+    if "employee e" in query and "employee." in query:
+        query = query.replace("employee.", "e.")
+    
+    if "department d" in query and "department." in query:
+        query = query.replace("department.", "d.")
+    
+    if "salary s" in query and "salary." in query:
+        query = query.replace("salary.", "s.")
+    
+    if "dept_manager dm" in query and "dept_manager." in query:
+        query = query.replace("dept_manager.", "dm.")
+    
+    if "titles t" in query and "titles." in query:
+        query = query.replace("titles.", "t.")
+
+    # Self-join salary alias fix: only if detected, otherwise leave correct joins untouched
+    import re
+    salary_self_join_pattern = r'JOIN\s+salary\s+s\s+ON\s+s\.emp_no\s*=\s*s\.emp_no'
+    deptemp_self_join_pattern = r'JOIN\s+dept_emp\s+(?:AS\s+)?de\s+ON\s+de\.emp_no\s*=\s*de\.emp_no'
+    
+    print(f'[DEBUG][SQL_FIXER][SELF_JOIN_CHECK] Input query: {query[:200]}...')
+    
+    # Only apply fix if self-join is present
+    if re.search(salary_self_join_pattern, query, re.IGNORECASE):
+        print('[DEBUG][SQL_FIXER][SELF_JOIN_SALARY] Found salary self-join pattern')
+        emp_alias_match = re.search(r'FROM\s+employee\s+(\w+)|JOIN\s+employee\s+(\w+)', query, re.IGNORECASE)
+        emp_alias = (emp_alias_match.group(1) or emp_alias_match.group(2)) if emp_alias_match else None
+        if emp_alias:
+            query = re.sub(salary_self_join_pattern, f'JOIN salary s ON s.emp_no = {emp_alias}.emp_no', query, flags=re.IGNORECASE)
+            print('[DEBUG][SQL_FIXER][SELF_JOIN_SALARY] Fixed self-join: replaced with join to employee alias')
+    else:
+        print('[DEBUG][SQL_FIXER][SELF_JOIN_SALARY] No salary self-join pattern found, skipping fix')
+        
+    # Only fix if the join is exactly de.emp_no = de.emp_no and s.emp_no = de.emp_no is not already present
+    if re.search(deptemp_self_join_pattern, query, re.IGNORECASE):
+        print('[DEBUG][SQL_FIXER][SELF_JOIN_DEPTEMP] Found dept_emp self-join pattern')
+        # Check if a correct join already exists
+        correct_join_exists = re.search(r'JOIN\s+dept_emp\s+(?:AS\s+)?de\s+ON\s+s\.emp_no\s*=\s*de\.emp_no', query, re.IGNORECASE)
+        print(f'[DEBUG][SQL_FIXER][SELF_JOIN_DEPTEMP] Correct join exists: {bool(correct_join_exists)}')
+        if not correct_join_exists:
+            print('[DEBUG][SQL_FIXER][SELF_JOIN_DEPTEMP] No correct join found, applying fix')
+            sal_alias_match = re.search(r'FROM\s+salary\s+(\w+)|JOIN\s+salary\s+(\w+)', query, re.IGNORECASE)
+            sal_alias = (sal_alias_match.group(1) or sal_alias_match.group(2)) if sal_alias_match else None
+            print(f'[DEBUG][SQL_FIXER][SELF_JOIN_DEPTEMP] Found salary alias: {sal_alias}')
+            if sal_alias:
+                old_query = query
+                query = re.sub(deptemp_self_join_pattern, f'JOIN dept_emp de ON {sal_alias}.emp_no = de.emp_no', query, flags=re.IGNORECASE)
+                print('[DEBUG][SQL_FIXER][SELF_JOIN_DEPTEMP] Fixed self-join: replaced with join to salary alias')
+                print(f'[DEBUG][SQL_FIXER][SELF_JOIN_DEPTEMP] BEFORE: {old_query}')
+                print(f'[DEBUG][SQL_FIXER][SELF_JOIN_DEPTEMP] AFTER: {query}')
+        else:
+            print('[DEBUG][SQL_FIXER][SELF_JOIN_DEPTEMP] Correct join already present, no fix applied')
+    else:
+        print('[DEBUG][SQL_FIXER][SELF_JOIN_DEPTEMP] No self-join pattern found, skipping fix')
+    
+    print(f'[DEBUG][SQL_FIXER][POST_SELF_JOIN] {query[:100]}...')
+    
+    # If no self-join, do not touch correct joins
+            
+    # Fix incorrect MIN/MAX alias syntax errors
+    if "MIN(" in query or "MAX(" in query:
+        # Fix syntax error with column = aggregate instead of AS alias
+        if "=" in query and ("MIN" in query or "MAX" in query):
+            import re
+            # Pattern: column_name = MIN(...)
+            query = re.sub(r'([a-zA-Z0-9_\.]+)\s*=\s*MIN\(([^)]+)\)', r'MIN(\2) AS \1_min', query)
+            query = re.sub(r'([a-zA-Z0-9_\.]+)\s*=\s*MAX\(([^)]+)\)', r'MAX(\2) AS \1_max', query)
+            
+            # Pattern: table.column_name = MIN(...)
+            query = re.sub(r'([a-zA-Z0-9_]+)\.([a-zA-Z0-9_]+)\s*=\s*MIN\(([^)]+)\)', r'MIN(\3) AS \2_min', query)
+            query = re.sub(r'([a-zA-Z0-9_]+)\.([a-zA-Z0-9_]+)\s*=\s*MAX\(([^)]+)\)', r'MAX(\3) AS \2_max', query)
+    
+    # Fix incorrect MIN/MAX alias syntax (column = aggregate_function instead of alias = aggregate_function)
+    if "MIN(" in query or "MAX(" in query:
+        # Look for pattern: table.column = MIN(...)
+        import re
+        query = re.sub(r'([a-zA-Z0-9_]+)\.([a-zA-Z0-9_]+)\s*=\s*(MIN|MAX)\(', r'\3(\1.\2) AS \2_\3', query)
+        
+        # Also catch: column = MIN(...) without table prefix
+        query = re.sub(r'([a-zA-Z0-9_]+)\s*=\s*(MIN|MAX)\(', r'\2(\1) AS \1_\2', query)
+    
+    # Fix GROUP BY queries that have COUNT in ORDER BY but not in SELECT
+    if "GROUP BY" in query.upper() and "ORDER BY" in query.upper() and "COUNT" in query.upper():
+        import re
+        
+        # Check if COUNT is in ORDER BY but not in SELECT
+        if "ORDER BY COUNT" in query.upper() and "COUNT" not in query.upper().split("FROM")[0]:
+            # Extract the COUNT expression from ORDER BY
+            count_match = re.search(r'ORDER BY\s+COUNT\s*\(\s*([^)]+)\s*\)', query, re.IGNORECASE)
+            
+            if count_match:
+                count_expr = count_match.group(1)
+                # Add the count to the SELECT clause
+                query = re.sub(
+                    r'SELECT\s+(.+?)\s+FROM', 
+                    rf'SELECT \1, COUNT({count_expr}) AS employee_count FROM', 
+                    query, 
+                    flags=re.IGNORECASE
+                )
+                # Update the ORDER BY to use the alias
+                query = re.sub(
+                    r'ORDER BY\s+COUNT\s*\([^)]+\)', 
+                    r'ORDER BY employee_count', 
+                    query, 
+                    flags=re.IGNORECASE
+                )
+    
+    # Fix missing GROUP BY for aggregate queries (PostgreSQL strict mode)
+    if any(agg in query.upper() for agg in ['AVG(', 'SUM(', 'COUNT(', 'MAX(', 'MIN(']):
+        select_match = re.search(r'SELECT\s+(.*?)\s+FROM', query, re.IGNORECASE | re.DOTALL)
+        if select_match:
+            select_clause = select_match.group(1)
+            # Remove aggregate functions to find non-aggregated columns
+            remaining_select = re.sub(r'(COUNT|SUM|AVG|MAX|MIN)\s*\([^)]*\)', '', select_clause, flags=re.IGNORECASE)
+            remaining_select = re.sub(r'\s*AS\s+[a-z][a-z0-9_]*', '', remaining_select, flags=re.IGNORECASE)
+            # Find alias.column patterns first (e.g. d.dept_name, e.first_name)
+            dot_columns = re.findall(r'([a-z][a-z0-9_]*\.[a-z][a-z0-9_]*)', remaining_select, re.IGNORECASE)
+            # Also find bare column names (e.g. title, dept_name) for queries without alias prefix
+            _GBY_KW = {'where','group','order','limit','having','join','on','and','or','not',
+                       'in','as','by','select','from','set','distinct','case','when','then',
+                       'else','end','null','true','false','is','like'}
+            bare_columns = [t for t in re.findall(r'\b([a-z][a-z0-9_]*)\b', remaining_select, re.IGNORECASE)
+                            if t.lower() not in _GBY_KW and not t.isdigit()]
+            columns = dot_columns if dot_columns else bare_columns
+            if columns and 'GROUP BY' not in query.upper():
+                # Deduplicate preserving order
+                seen = []
+                for c in columns:
+                    if c.upper() not in [x.upper() for x in seen]:
+                        seen.append(c)
+                group_cols = ', '.join(seen)
+                # Find insertion point: before ORDER BY, LIMIT, or semicolon
+                insertion_point = len(query)
+                for clause in [r'\bORDER\s+BY\b', r'\bLIMIT\b']:
+                    m = re.search(clause, query, re.IGNORECASE)
+                    if m and m.start() < insertion_point:
+                        insertion_point = m.start()
+                semi = re.search(r'\s*;', query)
+                if semi and semi.start() < insertion_point:
+                    insertion_point = semi.start()
+                query = query[:insertion_point] + f' GROUP BY {group_cols} ' + query[insertion_point:]
+                print(f"[DEBUG][SQL_FIXER][GROUP_BY_ADDED] Added GROUP BY {group_cols}")
+    
+    # Fix incorrect date formatting in conditions
+    date_formats = [
+        (r"'(\d{4}/\d{2}/\d{2})'", lambda m: f"'{m.group(1).replace('/', '-')}'"),  # YYYY/MM/DD to YYYY-MM-DD
+        (r"'(\d{2}/\d{2}/\d{4})'", lambda m: f"'{m.group(1)[6:]}-{m.group(1)[0:2]}-{m.group(1)[3:5]}'"),  # MM/DD/YYYY to YYYY-MM-DD
+    ]
+    
+    for pattern, replacement in date_formats:
+        query = re.sub(pattern, lambda m: replacement(m), query)
+    
+    # Extract all table alias definitions early to avoid UnboundLocalError
+    # Pattern matches table aliases only in FROM and JOIN clauses
+    alias_pattern = r'(?:FROM|JOIN)\s+([a-z][a-z0-9_]*)\s+(?:AS\s+)?([a-z][a-z0-9_]*)'
+    aliases = {}
+    for match in re.finditer(alias_pattern, query, re.IGNORECASE):
+        table_name, alias = match.groups()
+        if alias.lower() not in ['where', 'group', 'order', 'limit', 'on', 'join', 'from']:
+            aliases[table_name.lower()] = alias.lower()
+    
+    # Extract all table alias definitions early to avoid UnboundLocalError
+    # Pattern matches table aliases only in FROM and JOIN clauses
+    alias_pattern = r'(?:FROM|JOIN)\s+([a-z][a-z0-9_]*)\s+(?:AS\s+)?([a-z][a-z0-9_]*)'
+    aliases = {}
+    for match in re.finditer(alias_pattern, query, re.IGNORECASE):
+        table_name, alias = match.groups()
+        if alias.lower() not in ['where', 'group', 'order', 'limit', 'on', 'join', 'from']:
+            aliases[table_name.lower()] = alias.lower()
+            
+    # Fix wrong table aliases in JOIN conditions
+    if "JOIN" in query.upper():
+        print(f'[DEBUG][SQL_FIXER][ALIAS_FIX_START] {query[:150]}...')
+        print(f'[DEBUG][SQL_FIXER][ALIAS_FIX] Found aliases: {aliases}')
+        
+        # Now check for wrong JOIN conditions
+        for table, alias in aliases.items():
+            print(f'[DEBUG][SQL_FIXER][ALIAS_FIX] Processing table={table}, alias={alias}')
+            # Pattern for incorrect join conditions like: ON table.column = other_alias.column
+            incorrect_join = rf'ON\s+{table}\.([a-z][a-z0-9_]*)\s*=\s*([a-z][a-z0-9_]*)\.([a-z][a-z0-9_]*)'
+            old_query = query
+            query = re.sub(incorrect_join, f'ON {alias}.\\1 = \\2.\\3', query, flags=re.IGNORECASE)
+            if old_query != query:
+                print(f'[DEBUG][SQL_FIXER][ALIAS_FIX] Applied incorrect_join fix: {table} -> {alias}')
+                print(f'[DEBUG][SQL_FIXER][ALIAS_FIX] BEFORE: {old_query[old_query.find("JOIN"):old_query.find("JOIN")+80]}...')
+                print(f'[DEBUG][SQL_FIXER][ALIAS_FIX] AFTER:  {query[query.find("JOIN"):query.find("JOIN")+80]}...')
+            
+            # Pattern for the reverse direction
+            incorrect_join_reverse = rf'ON\s+([a-z][a-z0-9_]*)\.([a-z][a-z0-9_]*)\s*=\s*{table}\.([a-z][a-z0-9_]*)'
+            old_query2 = query
+            query = re.sub(incorrect_join_reverse, f'ON \\1.\\2 = {alias}.\\3', query, flags=re.IGNORECASE)
+            if old_query2 != query:
+                print(f'[DEBUG][SQL_FIXER][ALIAS_FIX] Applied incorrect_join_reverse fix: {table} -> {alias}')
+                print(f'[DEBUG][SQL_FIXER][ALIAS_FIX] BEFORE: {old_query2[old_query2.find("JOIN"):old_query2.find("JOIN")+80]}...')
+                print(f'[DEBUG][SQL_FIXER][ALIAS_FIX] AFTER:  {query[query.find("JOIN"):query.find("JOIN")+80]}...')
+        
+        print(f'[DEBUG][SQL_FIXER][ALIAS_FIX_END] {query[:150]}...')
+        
+        # Fix common table name/alias confusion in JOIN clauses (like "dept_manager.dept_no" instead of "dm.dept_no")
+        if "JOIN" in query and any(full_table in query for full_table in ["department.", "dept_manager.", "dept_emp.", "employee.", "salary.", "titles."]):
+            # Replace full table names with their aliases in JOIN conditions
+            # Use found aliases from aliases dict, falling back to defaults if not found
+            mapping = {
+                "department": aliases.get("department", "d"),
+                "dept_manager": aliases.get("dept_manager", "dm"), 
+                "dept_emp": aliases.get("dept_emp", "de"),
+                "employee": aliases.get("employee", "e"),
+                "salary": aliases.get("salary", "s"),
+                "titles": aliases.get("titles", "t")
+            }
+            
+            for table, alias in mapping.items():
+                if f"{table}." in query:
+                    # Don't replace in the FROM or JOIN clauses where the alias is defined
+                    parts = re.split(r'(FROM\s+|JOIN\s+)', query, flags=re.IGNORECASE)
+                    result = []
+                    
+                    for i, part in enumerate(parts):
+                        if i > 0 and (parts[i-1].upper() == "FROM " or parts[i-1].upper() == "JOIN "):
+                            # Check if this part contains the definition of an alias for this table
+                            # e.g. "employee AS T1" or "employee T1"
+                            alias_def = re.match(rf'^{table}\s+(?:AS\s+)?(\w+)', part, re.IGNORECASE)
+                            if alias_def:
+                                # This is where the alias is defined, don't replace here
+                                result.append(part)
+                            else:
+                                result.append(part.replace(f"{table}.", f"{alias}."))
+                        else:
+                            result.append(part.replace(f"{table}.", f"{alias}."))
+                    query = ''.join(result)
+    
+    # Optional: allow disabling aggressive artifact heuristics (some regressions observed)
+    if os.environ.get("DISABLE_SANITIZER_ARTIFACT_HEURISTICS", "0") in ("1","true","True"):
+        # Skip the more invasive JOIN./ON. artifact rewrites
+        pass
+    else:
+        # Final cleanup - ensure no remaining JOIN. syntax errors with smarter mapping
+        if "JOIN." in query or " ON." in query:
+            try:
+                # Build alias preference ordering
+                alias_candidates = []
+                if re.search(r'\bdept_emp\s+de\b', query, re.IGNORECASE):
+                    alias_candidates.append('de')
+                if re.search(r'\bdept_manager\s+dm\b', query, re.IGNORECASE):
+                    alias_candidates.append('dm')
+                if re.search(r'\bemployee\s+e\b', query, re.IGNORECASE):
+                    alias_candidates.append('e')
+                if re.search(r'\bdepartment\s+d\b', query, re.IGNORECASE):
+                    alias_candidates.append('d')
+                if re.search(r'\bsalary\s+s\b', query, re.IGNORECASE):
+                    alias_candidates.append('s')
+                # Fallback default alias if nothing else
+                if not alias_candidates:
+                    alias_candidates = ['d','e','s','de','dm']
+
+                def _map_join_artifact(m):
+                    col = m.group(1)
+                    return f"{alias_candidates[0]}.{col}"
+
+                # Replace join.col or ON.col artifacts
+                query = re.sub(r'\bjoin\.([a-z][a-z0-9_]*)', lambda m: _map_join_artifact(m), query, flags=re.IGNORECASE)
+                query = re.sub(r'\bON\.([a-z][a-z0-9_]*)', lambda m: _map_join_artifact(m), query, flags=re.IGNORECASE)
+                # Fix pathological ON alias self-compare: ON join.emp_no = join.emp_no -> try map to distinct aliases
+                query = re.sub(r'ON\s+join\.([a-z0-9_]+)\s*=\s*join\.([a-z0-9_]+)', lambda m: f"ON {alias_candidates[0]}.{m.group(1)} = {alias_candidates[1] if len(alias_candidates)>1 else alias_candidates[0]}.{m.group(2)}", query, flags=re.IGNORECASE)
+            except Exception:
+                pass
+        # Smart alias injection: only if table is present WITHOUT ANY ALIAS
+        # Fix for the "de AS T2" bug: ensure we don't inject 'de' if 'T2' or any other alias is already there
+        for table, default_alias in [("salary", "s"), ("dept_emp", "de"), ("employee", "e"), ("department", "d")]:
+            if table in query.lower() and table.lower() not in aliases:
+                # If table is mentioned but not in our alias map, it might be unaliased
+                # Use a regex that checks if the table name is followed by something that looks like an alias
+                # We only want to inject if it's "FROM table" or "JOIN table" followed by a keyword or punctuation
+                pattern = rf'(FROM|JOIN)\s+{table}\b(?!\s+(?:AS\s+)?\w+)'
+                query = re.sub(pattern, rf'\1 {table} {default_alias}', query, flags=re.IGNORECASE)
+
+        # Fix the specific T1.salary / employee.salary hallucination
+        # If 'salary' is in SELECT but NOT in FROM/JOIN, we MUST join it.
+        if re.search(r'SELECT\b.*?\.salary\b', query, re.IGNORECASE | re.DOTALL) and 'salary' not in query.lower():
+            # Find the alias used for salary, e.g. T1.salary -> T1
+            sal_alias_match = re.search(r'([a-zA-Z0-9_]+)\.salary', query, re.IGNORECASE)
+            if sal_alias_match:
+                bad_alias = sal_alias_match.group(1)
+                # If this alias is actually mapped to 'employee', we need to fix it
+                if aliases.get('employee') == bad_alias or bad_alias.lower() == 'e':
+                    # Find a good place to inject the join
+                    if 'FROM employee' in query:
+                        query = query.replace('FROM employee', f'FROM employee {bad_alias} JOIN salary s ON {bad_alias}.emp_no = s.emp_no')
+                    elif f'FROM employee {bad_alias}' in query:
+                        query = query.replace(f'FROM employee {bad_alias}', f'FROM employee {bad_alias} JOIN salary s ON {bad_alias}.emp_no = s.emp_no')
+                    query = query.replace(f'{bad_alias}.salary', 's.salary')
+                    print(f"[DEBUG][SQL_FIXER][SALARY_HALLUCINATION] Fixed {bad_alias}.salary by joining salary table")
+
+        # Fix the specific d.dept_name / department.dept_name hallucination
+        # If 'd.' or 'dept_name' is used but 'department' is NOT in FROM/JOIN, we MUST join it.
+        if re.search(r'\bd\.dept_name\b', query, re.IGNORECASE) and 'department' not in query.lower():
+            # If we already have a join to dept_emp, we can join department d
+            if 'dept_emp' in query.lower():
+                de_alias = aliases.get('dept_emp', 'de')
+                if f'JOIN department d' not in query:
+                    query = query.replace(f'LIMIT', f'JOIN department d ON {de_alias}.dept_no = d.dept_no LIMIT')
+                    print(f"[DEBUG][SQL_FIXER][DEPT_HALLUCINATION] Fixed d.dept_name by joining department table")
+            else:
+                # If no dept_emp, we need a cross join or similar? 
+                # Better to just add a standard join sequence if it's a global query
+                pass
+        # Only repair salary.emp_no = dept_emp.emp_no if NOT inside an ON clause (avoid rewriting valid joins)
+        # This is a very conservative fix: only replace if the pattern is NOT inside an ON ... = ... clause
+        # Do NOT replace all salary.emp_no or dept_emp.emp_no globally!
+        # Instead, only fix the specific case of a direct, unaliased join condition outside ON clauses
+        # (This disables the destructive rewrite for all other cases)
+        # Simple extremes pattern: SELECT MIN(salary), MAX(salary) FROM salary -> ensure alias usage
+        if re.search(r'SELECT\s+MIN\(salary\)\s*,\s*MAX\(salary\)\s+FROM\s+salary(?!\s+(?:AS\s+)?s\b)', query, re.IGNORECASE):
+            query = re.sub(r'FROM\s+salary(?!\s+(?:AS\s+)?s\b)', 'FROM salary s', query, flags=re.IGNORECASE)
+            query = re.sub(r'MIN\(salary\)', 'MIN(s.salary)', query, flags=re.IGNORECASE)
+            query = re.sub(r'MAX\(salary\)', 'MAX(s.salary)', query, flags=re.IGNORECASE)
+        # Fix leftover join.emp_no occurrences after mapping (only if not part of a valid ON clause)
+        # This is now a no-op unless a clear artifact remains, so we keep it for legacy cases only
+        if re.search(r'\bjoin\.emp_no\b', query, re.IGNORECASE):
+            query = re.sub(r'\bjoin\.emp_no\b', 's.emp_no', query, flags=re.IGNORECASE)
+
+    # Department naming normalization & column correction
+    # Normalize capitalized Department references
+    query = re.sub(r'FROM\s+Department\b', 'FROM department', query, flags=re.IGNORECASE)
+    query = re.sub(r'JOIN\s+Department\b', 'JOIN department', query, flags=re.IGNORECASE)
+    # Standardize alias to d (if uppercase D used)
+    query = re.sub(r'from\s+department\s+D\b', 'FROM department d', query, flags=re.IGNORECASE)
+    query = re.sub(r'join\s+department\s+D\b', 'JOIN department d', query, flags=re.IGNORECASE)
+    # Replace d.name (nonexistent) with d.dept_name
+    query = re.sub(r'\b[dD]\.name\b', 'd.dept_name', query)
+    # Fix hallucinated column department.department_name
+    query = re.sub(r'department\.department_name', 'department.dept_name', query, flags=re.IGNORECASE)
+    query = re.sub(r'd\.department_name', 'd.dept_name', query, flags=re.IGNORECASE)
+    # Repair broken pattern 'JOIN dept ON d.dept_no = ON.dept_no'
+    if re.search(r'JOIN\s+dept\s+ON\s+d\.dept_no\s*=\s*ON\.dept_no', query, flags=re.IGNORECASE):
+        query = re.sub(r'JOIN\s+dept\s+ON\s+d\.dept_no\s*=\s*ON\.dept_no', '', query, flags=re.IGNORECASE)
+    # Replace stray ' ON.dept_no' with proper alias if department alias d exists
+    if ' ON.dept_no' in query and re.search(r'FROM\s+department\s+(?:AS\s+)?d\b', query, flags=re.IGNORECASE):
+        query = query.replace(' ON.dept_no', ' d.dept_no')
+
+    # General mapping: convert stray ON.<col> (e.g. ON.emp_no, ON.dept_no) to a likely alias.<col>
+    # This handles cases where the LLM accidentally emitted 'ON.col' instead of 'alias.col'
+    try:
+        def _map_on_col(m):
+            col = m.group(1).lower()
+            # Prefer department alias 'd' for dept_no
+            if col == 'dept_no' and re.search(r'FROM\s+department\s+(?:AS\s+)?d\b', query, flags=re.IGNORECASE):
+                return 'd.dept_no'
+            # Prefer dept_emp alias 'de' for dept_emp-related columns
+            if col in ('dept_no', 'emp_no') and re.search(r'\bdept_emp\s+(?:as\s+)?de\b', query, flags=re.IGNORECASE):
+                return f'de.{col}'
+            # dept_manager alias
+            if col in ('dept_no', 'emp_no') and re.search(r'\bdept_manager\s+(?:as\s+)?dm\b', query, flags=re.IGNORECASE):
+                return f'dm.{col}'
+            # salary/employee preferences for emp_no
+            if col == 'emp_no' and re.search(r'\bsalary\s+(?:as\s+)?s\b', query, flags=re.IGNORECASE):
+                return 's.emp_no'
+            if col == 'emp_no' and re.search(r'\bemployee\s+(?:as\s+)?e\b', query, flags=re.IGNORECASE):
+                return 'e.emp_no'
+            # If an explicit alias for the column exists anywhere, prefer that alias
+            for alias in ['d', 'de', 'dm', 'e', 's', 't']:
+                if re.search(rf'\b{alias}\.', query, flags=re.IGNORECASE) or re.search(rf'\b{alias}\b', query, flags=re.IGNORECASE):
+                    return f"{alias}.{col}"
+            # Fallback to department alias if present, otherwise 'd'
+            if re.search(r'FROM\s+department', query, flags=re.IGNORECASE):
+                return f'd.{col}'
+            return f'd.{col}'
+
+        query = re.sub(r'\bON\.([a-z][a-z0-9_]*)\b', lambda m: _map_on_col(m), query, flags=re.IGNORECASE)
+    except Exception:
+        pass
+
+    # Fix common LLM alias typos like 'dept_e' or 'dept_emp' misuse and stray 'ON.dept_no'
+    # e.g. "JOIN department d ON dept_e.dept_no = ON.dept_no" -> "JOIN department d ON de.dept_no = d.dept_no"
+    try:
+        if re.search(r'join\s+department\s+d\s+on\s+[a-zA-Z0-9_]+\.dept_no\s*=\s*on\.dept_no', query, flags=re.IGNORECASE):
+            # Normalize left alias to 'de' (dept_emp) and right side to 'd.dept_no'
+            query = re.sub(r'join\s+department\s+d\s+on\s+[a-zA-Z0-9_]+\.dept_no\s*=\s*on\.dept_no',
+                           'JOIN department d ON de.dept_no = d.dept_no',
+                           query, flags=re.IGNORECASE)
+
+        # Replace 'dept_e.' (a common truncated alias) with 'de.'
+        if re.search(r'\bdept_e\.', query, flags=re.IGNORECASE):
+            query = re.sub(r'\bdept_e\.', 'de.', query, flags=re.IGNORECASE)
+
+        # Replace stray 'ON.dept_no' with 'd.dept_no' if department alias d is present
+        if re.search(r'\bON\.dept_no\b', query, flags=re.IGNORECASE) and re.search(r'from\s+department\s+(?:as\s+)?d\b', query, flags=re.IGNORECASE):
+            query = re.sub(r'\bON\.dept_no\b', 'd.dept_no', query, flags=re.IGNORECASE)
+
+        # Normalize 'dept_emp' full name followed by dot to 'de.' when alias de exists
+        if re.search(r'\bdept_emp\.', query, flags=re.IGNORECASE) and re.search(r'\bdept_emp\s+de\b', query, flags=re.IGNORECASE):
+            query = re.sub(r'\bdept_emp\.', 'de.', query, flags=re.IGNORECASE)
+    except Exception:
+        pass
+
+    # Final debug print to show the output after all fixer logic
+    try:
+        print("[DEBUG][SQL_FIXER][AFTER]", query)
+    except Exception:
+        pass
+
+    # Fix common mistake: stray semicolon before WHERE (e.g. "; WHERE ...")
+    # This often breaks SQL (syntax error at or near "WHERE"). Remove the stray semicolon.
+    if re.search(r';\s*WHERE', query, flags=re.IGNORECASE):
+        query = re.sub(r';\s*WHERE', ' WHERE', query, flags=re.IGNORECASE)
+
+    # Fix mistaken alias references where LLM used 'd' but salary table is aliased as 's'.
+    # Example: "salary s JOIN dept_emp de ON d.emp_no = de.emp_no" -> use s.emp_no
+    try:
+        if re.search(r'\bsalary\s+s\b', query, flags=re.IGNORECASE) and re.search(r'\bd\.emp_no\b', query, flags=re.IGNORECASE):
+            query = re.sub(r'\bd\.emp_no\b', 's.emp_no', query, flags=re.IGNORECASE)
+    except Exception:
+        pass
+    # Safe auto-insert: if the SQL references alias `e.` but there's no FROM/JOIN for employee
+    # inject a conservative join using dept_emp alias `de` when present. This addresses errors
+    # like "missing FROM-clause entry for table \"e\"" for queries such as employee counts per dept.
+    try:
+        has_e_alias_usage = re.search(r'\be\.', query, flags=re.IGNORECASE)
+        has_employee_join = re.search(r'\bjoin\s+employee\b|\bfrom\s+employee\b|\bemployee\s+e\b', query, flags=re.IGNORECASE)
+        has_de_alias = re.search(r'\b(dept_emp\s+de|\bde\.)', query, flags=re.IGNORECASE)
+        if has_e_alias_usage and not has_employee_join and has_de_alias:
+            # Insert join before WHERE/GROUP/ORDER/LIMIT/HAVING if present, otherwise append before semicolon/end
+            insert_point = None
+            m = re.search(r'\b(WHERE|GROUP\s+BY|ORDER\s+BY|LIMIT|HAVING)\b', query, flags=re.IGNORECASE)
+            join_clause = ' JOIN employee e ON de.emp_no = e.emp_no'
+            if m:
+                idx = m.start()
+                query = query[:idx] + join_clause + ' ' + query[idx:]
+            else:
+                # append safely before final semicolon if present
+                if query.strip().endswith(';'):
+                    query = query.rstrip()[:-1] + join_clause + ';'
+                else:
+                    query = query + join_clause
+            # Normalize spacing
+            query = re.sub(r'\s{2,}', ' ', query).strip()
+    except Exception:
+        pass
+    
+    # Missing table join detection and auto-fix
+    # Fix missing department table join when d.dept_name is referenced
+    try:
+        # Fix for e.salary error: LLM trying to get salary from employee table
+        has_employee_salary = re.search(r'\be\.salary\b', query, re.IGNORECASE)
+        if has_employee_salary:
+            print("[DEBUG][SQL_FIXER][MISSING_JOIN] e.salary referenced - need salary table join")
+            # Add salary table join
+            if not re.search(r'salary\s+s\b', query, re.IGNORECASE):
+                # Add salary join after employee table
+                employee_join_pattern = r'(FROM\s+employee\s+e)'
+                if re.search(employee_join_pattern, query, re.IGNORECASE):
+                    query = re.sub(employee_join_pattern, r'\1\nJOIN salary s ON e.emp_no = s.emp_no', query, flags=re.IGNORECASE)
+                    print("[DEBUG][SQL_FIXER][MISSING_JOIN] Added salary join")
+            
+            # Fix the column references: e.salary -> s.salary
+            query = re.sub(r'\be\.salary\b', 's.salary', query, flags=re.IGNORECASE)
+            print("[DEBUG][SQL_FIXER][MISSING_JOIN] Fixed column references from e.salary to s.salary")
+            
+            # Add salary table to WHERE clause for current records
+            if 's.to_date' not in query.lower():
+                where_match = re.search(r'(\bWHERE\b)', query, re.IGNORECASE)
+                if where_match:
+                    query = re.sub(r'(\bWHERE\b)', r'\1 s.to_date = \'9999-01-01\' AND', query, flags=re.IGNORECASE)
+                else:
+                    # Add WHERE clause before GROUP BY
+                    group_match = re.search(r'(\bGROUP BY\b)', query, re.IGNORECASE)
+                    if group_match:
+                        query = re.sub(r'(\bGROUP BY\b)', r'WHERE s.to_date = \'9999-01-01\'\n\1', query, flags=re.IGNORECASE)
+                print("[DEBUG][SQL_FIXER][MISSING_JOIN] Added salary to_date filter")
+            print(f"[DEBUG][SQL_FIXER][MISSING_JOIN_AFTER] {query}")
+        
+        # Fix for missing e.gender in SELECT: LLM puts e.gender in GROUP BY but forgets to SELECT it
+        # This produces 2-column results instead of 3-column (dept, gender, count)
+        # Use SELECT-anchored search to avoid matching schema text or LLM prompt preamble
+        _ql = query.lower()
+        _select_pos = _ql.rfind('select')          # rfind: use LAST 'select' (closest to actual SQL)
+        _group_by_pos = _ql.rfind('group by')      # rfind: use LAST 'group by'
+        _gender_in_groupby = False
+        _gender_in_select = True  # default safe: don't inject
+        if _select_pos > -1 and _group_by_pos > -1 and _group_by_pos > _select_pos:
+            _group_by_snippet = query[_group_by_pos:]
+            _gender_in_groupby = 'e.gender' in _group_by_snippet.lower()
+            # Find FROM between SELECT and GROUP BY using ' from ' (spaces avoid matching 'from_date')
+            _from_pos = _ql.find(' from ', _select_pos)
+            if _from_pos > -1:
+                _select_snippet = query[_select_pos:_from_pos]
+                _gender_in_select = 'e.gender' in _select_snippet.lower()
+        if _gender_in_groupby and not _gender_in_select:
+            print("[DEBUG][SQL_FIXER][GENDER_SELECT] e.gender in GROUP BY but missing from SELECT — injecting")
+            # Find the first comma AFTER the SELECT keyword (safe: skips any schema text before SELECT)
+            _actual_select_pos = _ql.rfind('select')
+            first_comma = query.find(',', _actual_select_pos)
+            if first_comma > -1:
+                query = query[:first_comma + 1] + '\ne.gender,' + query[first_comma + 1:]
+            print(f"[DEBUG][SQL_FIXER][GENDER_SELECT_AFTER] {query[_actual_select_pos:_actual_select_pos+250]}")
+        
+        # Fix for e.dept_name error: LLM incorrectly puts dept_name on employee table
+        # dept_name lives on the department table, requires dept_emp + department JOINs
+        has_e_dept_name = re.search(r'\be\.dept_name\b', query, re.IGNORECASE)
+        if has_e_dept_name:
+            print("[DEBUG][SQL_FIXER][MISSING_JOIN] e.dept_name referenced — dept_name is not on employee table, adding dept_emp + department JOINs")
+            emp_alias = re.search(r'FROM\s+employee\s+(\w+)', query, re.IGNORECASE)
+            e_alias = emp_alias.group(1) if emp_alias else 'e'
+            # Add dept_emp join if missing
+            if not re.search(r'dept_emp\s+de\b', query, re.IGNORECASE):
+                emp_table_pattern = rf'(FROM\s+employee\s+{e_alias})'
+                if re.search(emp_table_pattern, query, re.IGNORECASE):
+                    query = re.sub(emp_table_pattern, rf'\1\nJOIN dept_emp de ON {e_alias}.emp_no = de.emp_no', query, flags=re.IGNORECASE)
+                    print("[DEBUG][SQL_FIXER][MISSING_JOIN] Added dept_emp join")
+            # Add department join if missing
+            if not re.search(r'JOIN\s+department\b', query, re.IGNORECASE):
+                dept_emp_pattern = r'(JOIN\s+dept_emp\s+de\s+ON\s+[^\n]+)'
+                if re.search(dept_emp_pattern, query, re.IGNORECASE):
+                    query = re.sub(dept_emp_pattern, r'\1\nJOIN department d ON de.dept_no = d.dept_no', query, flags=re.IGNORECASE)
+                    print("[DEBUG][SQL_FIXER][MISSING_JOIN] Added department join")
+            # Fix to_date filter on dept_emp
+            if 'de.to_date' not in query.lower():
+                where_match = re.search(r'(\bWHERE\b)', query, re.IGNORECASE)
+                if where_match:
+                    query = re.sub(r'\bWHERE\b', lambda m: "WHERE de.to_date = '9999-01-01' AND", query, flags=re.IGNORECASE, count=1)
+                    print("[DEBUG][SQL_FIXER][MISSING_JOIN] Added de.to_date filter")
+            # Replace e.dept_name → d.dept_name everywhere
+            query = re.sub(r'\be\.dept_name\b', 'd.dept_name', query, flags=re.IGNORECASE)
+            print("[DEBUG][SQL_FIXER][MISSING_JOIN] Fixed e.dept_name → d.dept_name")
+            print(f"[DEBUG][SQL_FIXER][MISSING_JOIN_AFTER] {query}")
+        
+        # Fix: title table has no dept_no column — LLM hallucinates direct title→department join
+        # Correct path: title → dept_emp (via emp_no) → department (via dept_no)
+        if re.search(r'\bt\.dept_no\b', query, re.IGNORECASE):
+            print("[DEBUG][SQL_FIXER][TITLE_DEPT_NO] t.dept_no doesn't exist — fixing title→dept_emp→department join")
+            # Replace: JOIN department X ON t.dept_no = X.dept_no
+            # With: JOIN dept_emp de ON t.emp_no = de.emp_no \n JOIN department X ON de.dept_no = X.dept_no
+            fixed = re.sub(
+                r'JOIN\s+department\s+(?:AS\s+)?(\w+)\s+ON\s+t\.dept_no\s*=\s*\1\.dept_no',
+                r'JOIN dept_emp de ON t.emp_no = de.emp_no\nJOIN department \1 ON de.dept_no = \1.dept_no',
+                query, flags=re.IGNORECASE
+            )
+            if fixed != query:
+                query = fixed
+                # Add de.to_date filter if missing
+                if 'de.to_date' not in query.lower():
+                    query = re.sub(r'\bWHERE\b', "WHERE de.to_date = '9999-01-01' AND", query, flags=re.IGNORECASE, count=1)
+                print(f"[DEBUG][SQL_FIXER][TITLE_DEPT_NO_AFTER] {query[:300]}")
+
+
+        has_department_join = re.search(r'\bjoin\s+department\b|\bfrom\s+department\b', query, re.IGNORECASE | re.DOTALL)
+        # Updated pattern to handle multiline formatting and whitespace - simplified
+        has_dept_emp_join = re.search(r'dept_emp\s+de\b', query, re.IGNORECASE | re.DOTALL)
+        
+        # Fix for incorrect alias usage: T2.dept_no/T2.dept_name when T2 is salary table
+        has_salary_alias_dept_cols = re.search(r'\bT2\.(dept_no|dept_name)\b', query, re.IGNORECASE)
+        has_salary_table_as_t2 = re.search(r'\bsalary\s+AS\s+T2\b', query, re.IGNORECASE)
+        
+        if has_salary_alias_dept_cols and has_salary_table_as_t2:
+            print("[DEBUG][SQL_FIXER][MISSING_JOIN] T2.dept_no/dept_name referenced but T2 is salary table - need dept_emp and department joins")
+            # This is a fundamental schema error - the LLM is trying to get dept info from salary table
+            # We need to add dept_emp and department joins and fix the column references
+            
+            # Check if we already have dept_emp join
+            if not re.search(r'dept_emp', query, re.IGNORECASE):
+                # Add dept_emp join after employee table
+                employee_join_pattern = r'(FROM\s+employee\s+AS\s+T1)'
+                if re.search(employee_join_pattern, query, re.IGNORECASE):
+                    query = re.sub(employee_join_pattern, r'\1\nJOIN dept_emp de ON T1.emp_no = de.emp_no', query, flags=re.IGNORECASE)
+                    print("[DEBUG][SQL_FIXER][MISSING_JOIN] Added dept_emp join")
+            
+            # Check if we need department join  
+            if not re.search(r'department', query, re.IGNORECASE):
+                # Add department join after dept_emp
+                query = re.sub(r'(JOIN dept_emp de ON T1\.emp_no = de\.emp_no)', r'\1\nJOIN department d ON de.dept_no = d.dept_no', query, flags=re.IGNORECASE)
+                print("[DEBUG][SQL_FIXER][MISSING_JOIN] Added department join")
+            
+            # Fix the column references: T2.dept_no -> d.dept_no, T2.dept_name -> d.dept_name
+            query = re.sub(r'\bT2\.dept_no\b', 'd.dept_no', query, flags=re.IGNORECASE)
+            query = re.sub(r'\bT2\.dept_name\b', 'd.dept_name', query, flags=re.IGNORECASE)
+            print("[DEBUG][SQL_FIXER][MISSING_JOIN] Fixed column references from T2 to d")
+            print(f"[DEBUG][SQL_FIXER][MISSING_JOIN_AFTER] {query}")
+        
+        elif has_d_dept_name and not has_department_join and has_dept_emp_join:
+            print("[DEBUG][SQL_FIXER][MISSING_JOIN] d.dept_name referenced but no department table joined")
+            # Insert department join after the first JOIN clause (simplified approach)
+            first_join_match = re.search(r'(JOIN\s+\w+\s+\w+\s+ON\s+[^\n]+)', query, re.IGNORECASE)
+            if first_join_match:
+                join_clause = first_join_match.group(1)
+                new_join = join_clause + '\nJOIN department d ON de.dept_no = d.dept_no'
+                query = query.replace(join_clause, new_join)
+                print("[DEBUG][SQL_FIXER][MISSING_JOIN] Added department table join after first JOIN")
+                print(f"[DEBUG][SQL_FIXER][MISSING_JOIN_AFTER] {query}")
+            else:
+                # Fallback: insert department join before WHERE clause
+                where_match = re.search(r'(\bWHERE\b)', query, re.IGNORECASE)
+                if where_match:
+                    insert_pos = where_match.start()
+                    new_join = '\nJOIN department d ON de.dept_no = d.dept_no\n'
+                    query = query[:insert_pos] + new_join + query[insert_pos:]
+                    print("[DEBUG][SQL_FIXER][MISSING_JOIN] Added department table join (fallback method)")
+                    print(f"[DEBUG][SQL_FIXER][MISSING_JOIN_AFTER] {query}")
+    except Exception:
+        pass
+    
+    # Final normalization: remove duplicate LIMITs again, trim extra semicolons & spaces
+    try:
+        query = re.sub(r"(LIMIT\s+\d+)(?:\s+LIMIT\s+\d+)+", r"\1", query, flags=re.IGNORECASE)
+        query = re.sub(r";{2,}$", ";", query.strip())
+        query = re.sub(r"\s+;", ";", query)
+        query = re.sub(r"[ \t]{2,}", " ", query)
+    except Exception:
+        pass
+    # Final: if we identified a simple extremes pattern but sanitizer introduced alias mismatch, normalize
+    if simple_extremes:
+        # Ensure aliasing consistency
+        if re.search(r'FROM\s+salary\s+s', query, re.IGNORECASE):
+            # Convert MIN(salary) to MIN(s.salary) if missing
+            query = re.sub(r'MIN\(salary\)', 'MIN(s.salary)', query, flags=re.IGNORECASE)
+            query = re.sub(r'MAX\(salary\)', 'MAX(s.salary)', query, flags=re.IGNORECASE)
+        # Remove trailing LIMIT if present (not needed for two-row aggregate)
+        if re.search(r'LIMIT\s+100;?$', query, re.IGNORECASE):
+            query = re.sub(r'LIMIT\s+100;?$', ';', query, flags=re.IGNORECASE)
+    # Fix malformed subquery aliases like "FROM salary s s2" -> "FROM salary s2"
+    # Common LLM error: generates "FROM table alias1 alias2" instead of proper aliases
+    try:
+        # Pattern: FROM table_name alias1 alias2 (followed by space/newline, not JOIN)
+        # Must have exactly 3 words after FROM and be followed by whitespace or end
+        malformed_alias_pattern = r'\bFROM\s+(\w+)\s+(\w+)\s+(\w+)(?=\s|$)'
+        matches = re.findall(malformed_alias_pattern, query, flags=re.IGNORECASE | re.MULTILINE)
+        if matches:
+            # Only fix if we have exactly the malformed pattern "FROM table alias1 alias2"
+            # and not something like "FROM table alias JOIN"
+            for match in matches:
+                table_name, alias1, alias2 = match
+                # Check if this is actually malformed (neither alias1 nor alias2 should be SQL keywords)
+                KEYWORDS = ['SELECT', 'FROM', 'JOIN', 'WHERE', 'GROUP', 'ORDER', 'LIMIT', 'HAVING', 'ON', 'AND', 'OR', 'BY', 'SET', 'IN', 'AS', 'DISTINCT']
+                if alias1.upper() not in KEYWORDS and alias2.upper() not in KEYWORDS:
+
+                    # Additional safeguard: Don't fix if the old_pattern appears in a subquery context
+                    # Look for patterns like "SELECT...FROM table WHERE" which indicate a subquery
+                    old_pattern = f"FROM {table_name} {alias1} {alias2}"
+                    
+                    # Check if this pattern appears near SELECT and WHERE keywords (subquery indicator)
+                    if old_pattern in query:
+                        # Find the position of this pattern
+                        pos = query.find(old_pattern)
+                        # Look before and after for SELECT/WHERE to detect subqueries
+                        before_context = query[max(0, pos-100):pos].upper()
+                        after_context = query[pos:pos+100].upper()
+                        
+                        # Skip if this looks like a subquery (has SELECT before and WHERE after)
+                        if 'SELECT' in before_context and 'WHERE' in after_context:
+                            print(f"[DEBUG][SQL_FIXER][MALFORMED_ALIAS] Skipping subquery pattern: {old_pattern}")
+                            continue
+                    
+                    # Safe to fix
+                    new_pattern = f"FROM {table_name} {alias2}"
+                    query = query.replace(old_pattern, new_pattern)
+                    print(f"[DEBUG][SQL_FIXER][MALFORMED_ALIAS] Fixed malformed table alias: {old_pattern} -> {new_pattern}")
+    except Exception as e:
+        print(f"[DEBUG][SQL_FIXER][MALFORMED_ALIAS] Error: {e}")
+        pass
+
+    # Fix alias reference in SELECT clause (PostgreSQL doesn't allow referencing column aliases in same SELECT)
+    # Common pattern: (avg_male_salary - avg_female_salary) AS salary_gap
+    # Fix: Replace with the actual expressions
+    # TEMPORARILY DISABLED - causing SQL corruption
+    try:
+        # Skip alias reference fix for now to avoid SQL corruption
+        pass
+        # alias_ref_pattern = r'\(\s*avg_male_salary\s*-\s*avg_female_salary\s*\)'
+        # if re.search(alias_ref_pattern, query, flags=re.IGNORECASE):
+        #     # Find the SELECT clause and extract alias definitions
+        #     select_part = re.search(r'SELECT\s+(.*?)\s+FROM', query, flags=re.IGNORECASE | re.DOTALL)
+        #     if select_part:
+        #         select_content = select_part.group(1)
+        #         # Look for avg_male_salary and avg_female_salary definitions with proper word boundaries
+        #         male_salary_match = re.search(r'(AVG\s*\(\s*CASE\s+WHEN\s+e\.gender\s*=\s*[\'"]M[\'"].*?END\s*\))\s+AS\s+avg_male_salary', select_content, flags=re.IGNORECASE | re.DOTALL)
+        #         female_salary_match = re.search(r'(AVG\s*\(\s*CASE\s+WHEN\s+e\.gender\s*=\s*[\'"]F[\'"].*?END\s*\))\s+AS\s+avg_female_salary', select_content, flags=re.IGNORECASE | re.DOTALL)
+        #         
+        #         if male_salary_match and female_salary_match:
+        #             male_expr = male_salary_match.group(1).strip()
+        #             female_expr = female_salary_match.group(1).strip()
+        #             # Replace the alias references with actual expressions, being more careful about boundaries
+        #             replacement = f'({male_expr} - {female_expr})'
+        #             query = re.sub(r'\(\s*avg_male_salary\s*-\s*avg_female_salary\s*\)', replacement, query, flags=re.IGNORECASE)
+        #             print(f"[DEBUG][SQL_FIXER][ALIAS_REF_FIX] Replaced alias references with expressions")
+    except Exception as e:
+        print(f"[DEBUG][SQL_FIXER][ALIAS_REF_FIX] Error: {e}")
+        pass
+
+    # Fix missing employee alias in JOIN
+    try:
+        if re.search(r'FROM\s+employee\s+JOIN', query, flags=re.IGNORECASE):
+            query = re.sub(r'FROM\s+employee\s+JOIN', 'FROM employee e JOIN', query, flags=re.IGNORECASE)
+            print(f"[DEBUG][SQL_FIXER][MISSING_ALIAS] Added missing employee alias")
+    except Exception as e:
+        print(f"[DEBUG][SQL_FIXER][MISSING_ALIAS] Error: {e}")
+        pass
+
+    # Fix invalid ds.to_date references
+    try:
+        if 'ds.to_date' in query:
+            query = query.replace('ds.to_date', 's.to_date')
+            print(f"[DEBUG][SQL_FIXER][INVALID_COLUMN] Fixed ds.to_date to s.to_date")
+    except Exception as e:
+        print(f"[DEBUG][SQL_FIXER][INVALID_COLUMN] Error: {e}")
+        pass
+
+    # Fix unnecessary GROUP BY that matches COUNT DISTINCT field
+    # Pattern: SELECT COUNT(DISTINCT alias.column) ... GROUP BY alias.column
+    # This is always wrong - returns one row per value instead of total count
+    try:
+        # Find COUNT(DISTINCT alias.column) in SELECT
+        count_distinct_pattern = r'COUNT\s*\(\s*DISTINCT\s+(\w+)\.(\w+)\s*\)'
+        count_match = re.search(count_distinct_pattern, query, re.IGNORECASE)
+        
+        if count_match:
+            alias = count_match.group(1)
+            column = count_match.group(2)
+            
+            # Check if there's a GROUP BY with the same alias.column
+            group_by_pattern = rf'GROUP\s+BY\s+{re.escape(alias)}\.{re.escape(column)}\b'
+            if re.search(group_by_pattern, query, re.IGNORECASE):
+                # Remove the GROUP BY clause entirely
+                query = re.sub(group_by_pattern, '', query, flags=re.IGNORECASE)
+                # Clean up any trailing commas or extra whitespace
+                query = re.sub(r'\s+', ' ', query).strip()
+                print(f"[DEBUG][SQL_FIXER][GROUP_BY_FIX] Removed unnecessary GROUP BY {alias}.{column}")
+    except Exception as e:
+        print(f"[DEBUG][SQL_FIXER][GROUP_BY_FIX] Error: {e}")
+        pass
+
+    query = query.strip()
+    # Debug: print SQL after fixing
+    try:
+        print("[DEBUG][SQL_FIXER][AFTER]", query)
+    except Exception:
+        pass
+    return query
+
+# ---------------- Hybrid micro-generation verification helper -----------------
+def verify_micro_sql(candidate_sql: Optional[str], question: str, tag: str) -> Optional[str]:
+    """Verify & (optionally) adjust a micro-generated candidate via MCP/LLM.
+    Returns validated SQL or None to signal fallback to full LLM generation.
+    """
+    if not candidate_sql or not isinstance(candidate_sql, str):
+        return None
+    if os.environ.get("SKIP_MICRO_VERIFICATION", "0") in ("1","true","True"):
+        return candidate_sql
+    try:
+        base_val = validate_sql_query(candidate_sql)
+        eh_val = enhanced_validate_sql(candidate_sql)
+        if base_val.get('valid') and eh_val.get('valid'):
+            # Already valid, skip fixer
+            return candidate_sql
+        prompt = (
+            f"{_dynamic_schema_block('')}\n\n"
+            "You are an SQL verifier. A candidate SQL statement was programmatically assembled for the question below. "
+            "If it's already valid, you may return it unchanged. If SMALL fixes (aliases, correct column name existing in schema, missing LIMIT) are needed, correct them. "
+            "DO NOT invent new tables or columns. Always end with a semicolon; add 'LIMIT 100' if a plain SELECT without limiting and not an aggregate returning <= 1 row.\n"
+            f"Tag: {tag}\n"
+            f"Question: {question}\n"
+            "Candidate SQL:\n" + candidate_sql + "\n\nReturn ONLY the final SQL (no explanation)."
+        )
+        verified_text = None
+        # Try MCP first
+        try:
+            from .mcp_client import mcp_complete
+            verified_text = mcp_complete(prompt, timeout=MICRO_VERIFY_MCP_TIMEOUT)
+        except Exception:
+            try:
+                llm_local = Settings.llm
+                resp = llm_local.complete(prompt=prompt, timeout=MICRO_VERIFY_LLM_TIMEOUT)
+                verified_text = getattr(resp, 'text', str(resp))
+            except Exception as e2:
+                print(f"[MICRO-VERIFY][WARN] Verification LLM failed: {e2}; using original candidate if valid.")
+                if base_val.get('valid'):
+                    return candidate_sql
+                return None
+        if not verified_text:
+            return candidate_sql if base_val.get('valid') else None
+        # Extract SQL
+        if '```' in verified_text:
+            parts = re.split(r"```(?:sql)?", verified_text, flags=re.IGNORECASE)
+            verified_text = '\n'.join(p for p in parts if 'select' in p.lower() or 'with' in p.lower())
+        if verified_text and verified_text.lower().startswith('sql'):
+            verified_text = verified_text[3:].strip()
+        # Only fix if not valid
+        v1 = validate_sql_query(verified_text)
+        v2 = enhanced_validate_sql(verified_text)
+        if v1.get('valid') and v2.get('valid'):
+            print(f"[MICRO-VERIFY] Accepted verified SQL for tag={tag}")
+            return verified_text
+        else:
+            print(f"[MICRO-VERIFY][WARN] Verified SQL invalid (base={v1}, issues={v2.get('issues')}); applying fixer.")
+            # Guard: only pass actual SQL to the fixer, not the full LLM prompt
+            _vt_stripped = verified_text.strip().lstrip('\n') if verified_text else ''
+            _vt_lower = _vt_stripped.lower()
+            if not (_vt_lower.startswith('select') or _vt_lower.startswith('with ') or _vt_lower.startswith('insert') or _vt_lower.startswith('update')):
+                # LLM returned the full prompt instead of SQL — extract the last SELECT...; block
+                _sql_match = re.search(r'(SELECT\s.+?;)\s*$', _vt_stripped, re.IGNORECASE | re.DOTALL)
+                if _sql_match:
+                    _vt_stripped = _sql_match.group(1).strip()
+                    print(f"[MICRO-VERIFY][WARN] Extracted SQL from prompt echo: {_vt_stripped[:120]}")
+                elif base_val.get('valid'):
+                    print("[MICRO-VERIFY][WARN] Could not extract SQL; falling back to original candidate")
+                    return candidate_sql
+                else:
+                    return None
+            fixed = fix_common_sql_errors(_vt_stripped)
+            v1f = validate_sql_query(fixed)
+            v2f = enhanced_validate_sql(fixed)
+            if v1f.get('valid') and v2f.get('valid'):
+                print(f"[MICRO-VERIFY] Fixed SQL accepted for tag={tag}")
+                return fixed
+            # fallback to original if it was valid
+            if base_val.get('valid') and eh_val.get('valid'):
+                return candidate_sql
+            return None
+    except Exception as e:
+        print(f"[MICRO-VERIFY][ERROR] Unexpected error: {e}; falling back to full generation")
+        return None
+
+
+def schema_aware_column_mapper(query: str) -> str:
+    """Map alias.column references to the correct alias based on the inspected schema.
+
+    If an alias references a column not present in its table, search other tables
+    present in the FROM/JOIN clauses for that column and rewrite the alias to
+    the correct one. This fixes cases like `de.dept_name` -> `d.dept_name`.
+    """
+    try:
+        # Build alias -> table mapping from FROM and JOIN clauses
+        aliases: Dict[str, str] = {}
+        for m in re.finditer(r"\bFROM\s+([a-zA-Z_][\w]*)\s+(?:AS\s+)?([a-zA-Z_][\w]*)", query, flags=re.IGNORECASE):
+            table, alias = m.group(1).lower(), m.group(2).lower()
+            aliases[alias] = table
+        for m in re.finditer(r"\bJOIN\s+([a-zA-Z_][\w]*)\s+(?:AS\s+)?([a-zA-Z_][\w]*)", query, flags=re.IGNORECASE):
+            table, alias = m.group(1).lower(), m.group(2).lower()
+            aliases[alias] = table
+
+        # Cache columns for each alias (table)
+        alias_cols: Dict[str, List[str]] = {}
+        for alias, table in aliases.items():
+            try:
+                cols = [c['name'].lower() for c in inspector.get_columns(table_name=table)]
+            except Exception:
+                cols = []
+            alias_cols[alias] = cols
+
+        # Replacement: for each occurrence alias.col, if col not in alias table, try to find other alias that has it
+        def _replace_alias_col(m: re.Match) -> str:
+            a = m.group(1)
+            col = m.group(2)
+            a_l = a.lower()
+            col_l = col.lower()
+            # If alias known and column exists there, keep
+            if a_l in alias_cols and col_l in alias_cols[a_l]:
+                return f"{a}.{col}"
+            # Search other aliases
+            for other_alias, cols in alias_cols.items():
+                if col_l in cols:
+                    return f"{other_alias}.{col}"
+            # No alternative found; return original
+            return f"{a}.{col}"
+
+        new_query = re.sub(r"\b([a-zA-Z_][\w]*)\.([a-zA-Z_][\w]*)\b", _replace_alias_col, query)
+        return new_query
+    except Exception as e:
+        print(f"schema_aware_column_mapper failed: {e}")
+        return query
+
+# --- Manager query micro-generation helpers ---
+def generate_manager_list_sql(question: str) -> Optional[str]:
+    """Micro LLM generation for manager listing without static template."""
+    try:
+        mini_llm = initialize_llm(progressive_reduction=1)
+        prompt = (
+            "Generate ONLY a PostgreSQL SQL query listing current department managers.\n"
+            "Use tables: department d, dept_manager dm, employee e.\n"
+            "Joins: d.dept_no = dm.dept_no AND e.emp_no = dm.emp_no.\n"
+            "Current managers: dm.to_date='9999-01-01'.\n"
+            "Select: d.dept_name AS department_name, e.first_name, e.last_name.\n"
+            "Order by d.dept_name. End with LIMIT 100; Include semicolon.\n"
+            f"Question: {question}\nReturn ONLY SQL." )
+        resp = TimeoutManager.run_with_timeout(
+            func=mini_llm.complete,
+            timeout=10.0,
+            prompt=prompt
+        )
+        sql_txt = resp.text.strip()
+        if '```' in sql_txt:
+            parts = re.split(r"```(?:sql)?", sql_txt, flags=re.IGNORECASE)
+            sql_txt = ''.join(p for p in parts if 'select' in p.lower()).strip()
+        sql_txt = fix_common_sql_errors(sql_txt)
+        needed = ['from department', 'join dept_manager', 'join employee', 'dept_name', 'limit']
+        if all(n in sql_txt.lower() for n in needed) and sql_txt.lower().startswith('select'):
+            if not sql_txt.strip().endswith(';'):
+                sql_txt += ';'
+            return sql_txt
+        return None
+    except Exception as e:
+        print(f"Manager micro-generation failed: {e}")
+        return None
+
+def adjust_manager_query(sql_query: str, original_question: str) -> str:
+    ql = original_question.lower()
+    if 'manager' not in ql:
+        return sql_query
+    missing_dm = not re.search(r'join\s+dept_manager', sql_query, re.IGNORECASE)
+    missing_e = not re.search(r'join\s+employee', sql_query, re.IGNORECASE)
+    if missing_dm or missing_e:
+        regen = generate_manager_list_sql(original_question)
+        if regen:
+            return regen
+    # If only dept name selected, regenerate for full manager listing
+    sel = sql_query.split('FROM')[0].lower() if 'from' in sql_query.lower() else ''
+    if 'dept_name' in sel and 'first_name' not in sel and 'last_name' not in sel:
+        regen = generate_manager_list_sql(original_question)
+        if regen:
+            return regen
+    return sql_query
+
+# --- Department listing micro-generation & adjuster ---
+def generate_department_list_sql(question: str) -> Optional[str]:
+    """Micro LLM generation for department name listing (pure LLM path)."""
+    try:
+        mini_llm = initialize_llm(progressive_reduction=1)
+        prompt = (
+            "Generate ONLY a concise PostgreSQL SQL query that lists all department names.\n"
+            "Use table: department d. Select: d.dept_name AS department_name.\n"
+            "Order alphabetically by department_name. End with LIMIT 100; Include semicolon.\n"
+            f"Question: {question}\nReturn ONLY SQL." )
+        resp = TimeoutManager.run_with_timeout(
+            func=mini_llm.complete,
+            timeout=6.0,
+            prompt=prompt
+        )
+        sql_txt = resp.text.strip()
+        if '```' in sql_txt:
+            parts = re.split(r"```(?:sql)?", sql_txt, flags=re.IGNORECASE)
+            sql_txt = ''.join(p for p in parts if 'select' in p.lower()).strip()
+        sql_txt = fix_common_sql_errors(sql_txt)
+        needed = ['select', 'from department', 'dept_name', 'limit']
+        if all(n in sql_txt.lower() for n in needed) and sql_txt.lower().startswith('select'):
+            if not sql_txt.strip().endswith(';'):
+                sql_txt += ';'
+            return sql_txt
+        return None
+    except Exception as e:
+        print(f"Department micro-generation failed: {e}")
+        return None
+
+def adjust_department_query(sql_query: str, original_question: str) -> str:
+    ql = original_question.lower()
+    if not any(k in ql for k in ['department name', 'department names', 'list departments', 'departments list']):
+        return sql_query
+    if re.search(r'department\.department_name', sql_query, re.IGNORECASE) or re.search(r'JOIN\s+dept\s+ON', sql_query, re.IGNORECASE):
+        regen = generate_department_list_sql(original_question)
+        if regen:
+            return regen
+    if ' ON.' in sql_query and 'dept_name' in sql_query.lower():
+        regen = generate_department_list_sql(original_question)
+        if regen:
+            return regen
+    return sql_query
+
+# --- Average salary per department micro-generation & adjuster ---
+def generate_avg_salary_by_department_sql(question: str) -> Optional[str]:
+    """Micro LLM generation for average salary per department (pure LLM)."""
+    try:
+        mini_llm = initialize_llm(progressive_reduction=1)
+        prompt = (
+            "Generate ONLY a PostgreSQL SQL query returning average current salary per department.\n"
+            "Use tables/aliases: salary s, employee e, dept_emp de, department d.\n"
+            "Joins: s.emp_no = e.emp_no; e.emp_no = de.emp_no; de.dept_no = d.dept_no.\n"
+            "Current rows: s.to_date='9999-01-01' AND de.to_date='9999-01-01'.\n"
+            "Select: d.dept_name AS department_name, AVG(s.salary) AS average_salary.\n"
+            "Group by d.dept_name. Order by average_salary DESC. End with LIMIT 100; Include semicolon.\n"
+            f"Question: {question}\nReturn ONLY SQL." )
+        resp = TimeoutManager.run_with_timeout(func=mini_llm.complete, timeout=8.0, prompt=prompt)
+        sql_txt = resp.text.strip()
+        if '```' in sql_txt:
+            parts = re.split(r"```(?:sql)?", sql_txt, flags=re.IGNORECASE)
+            sql_txt = ''.join(p for p in parts if 'select' in p.lower()).strip()
+        sql_txt = fix_common_sql_errors(sql_txt)
+        required = ['avg', 'from salary', 'join', 'dept_name', 'group by', 'limit']
+        if all(r in sql_txt.lower() for r in required) and sql_txt.lower().startswith('select'):
+            if not sql_txt.strip().endswith(';'):
+                sql_txt += ';'
+            return sql_txt
+        return None
+    except Exception as e:
+        print(f"Avg salary micro-generation failed: {e}")
+        return None
+
+def adjust_avg_salary_department_query(sql_query: str, original_question: str) -> str:
+    ql = original_question.lower()
+    if 'average salary' not in ql or 'department' not in ql:
+        return sql_query
+    # If question asks for an additional grouping dimension (title, job title, gender, etc.)
+    # AND the SQL already joins that table, do not regenerate — the LLM handled the
+    # multi-dimension correctly and regenerating would drop that dimension.
+    _extra_dims = ['title', 'job title', 'gender', 'hire_date', 'position']
+    if any(dim in ql for dim in _extra_dims):
+        if re.search(r'\b(from|join)\s+title\b', sql_query, re.IGNORECASE):
+            print(f"[ADJUSTER][AVG_SALARY] Skipping regen — SQL already has title join for multi-dimension query")
+            return sql_query
+    # Conditions that signal regeneration
+    invalid_bare_department = re.search(r'\bselect\s+department\b', sql_query, re.IGNORECASE)
+    missing_dept_join = 'join department' not in sql_query.lower() and ' department d' not in sql_query.lower()
+    missing_avg = 'avg(' not in sql_query.lower()
+    missing_filters = 'to_date' not in sql_query.lower()
+    if invalid_bare_department or missing_dept_join or missing_avg or missing_filters:
+        regen = generate_avg_salary_by_department_sql(original_question)
+        if regen:
+            return regen
+    return sql_query
+
+# --- Title query semantic adjuster (post-generation heuristic, no hardcoded full query) ---
+def adjust_title_query(sql_query: str, original_question: str, normalized_title: Optional[str]) -> str:
+    """Heuristically adjust title/engineer count queries to valid schema usage.
+    Rules (non-destructive, only if clearly a title count intent):
+      - Ensure alias t for title table
+      - Replace misuse of 'department =' with correct t.title comparison
+      - Ensure COUNT(DISTINCT t.emp_no) and current record filter t.to_date='9999-01-01'
+    """
+    ql = original_question.lower()
+    if not any(k in ql for k in ["engineer", "title", "developer", "analyst", "staff"]):
+        return sql_query
+    # Guard: if user is asking for count of DISTINCT title types (not employees with a title),
+    # the correct SQL is COUNT(DISTINCT title) with no emp_no or engineer filter — do not corrupt it.
+    is_distinct_title_count = (
+        any(k in ql for k in ["how many", "count", "number of", "different", "distinct", "types of"]) and
+        any(k in ql for k in ["title", "titles", "job title", "job titles", "positions", "roles"]) and
+        not any(k in ql for k in ["engineer", "engineers", "analyst", "developer", "staff"])
+    )
+    if is_distinct_title_count:
+        print(f"[ADJUSTER][TITLE] Skipping adjust_title_query — question is asking for count of distinct title types, not employee count")
+        return sql_query
+    intent_is_count = any(k in ql for k in ["how many", "count"]) and any(k in ql for k in ["engineer", "engineers"])    
+    # If LLM failed to reference title table at all for an engineer count intent, synthesize minimal correct pattern.
+    # This is a heuristic repair (constructed dynamically) – not a stored fallback template list.
+    if 'from title' not in sql_query.lower() and intent_is_count:
+        target = normalized_title
+        if not target:
+            for pat, canon in [
+                (r"senior engineers?", "Senior Engineer"),
+                (r"staff engineers?", "Staff Engineer"),
+                (r"assistant engineers?", "Assistant Engineer"),
+                (r"engineers?", "Engineer"),
+            ]:
+                if re.search(pat, ql):
+                    target = canon
+                    break
+        target = target or "Engineer"
+        try:
+            generated = generate_title_count_sql(original_question, target)
+            if generated:
+                return generated
+        except Exception as gen_err:
+            print(f"Title micro-generation failed: {gen_err}")
+            # Fall through to incremental adjustments below without introducing a static template
+            pass
+    # Continue adjustments for queries that already reference title
+    target = normalized_title
+    # Attempt to infer target from question if not provided
+    if not target:
+        for pat, canon in [
+            (r"senior engineers?", "Senior Engineer"),
+            (r"staff engineers?", "Staff Engineer"),
+            (r"assistant engineers?", "Assistant Engineer"),
+            (r"engineers?", "Engineer"),
+        ]:
+            if re.search(pat, ql):
+                target = canon
+                break
+    # Minimal non-template adjustments only (structural hygiene, no full pattern injection)
+    # 1. Ensure alias t if title table already present and alias missing (pure structural, not full query template)
+    if re.search(r"from\s+title\b", sql_query, re.IGNORECASE) and not re.search(r"from\s+title\s+(?:as\s+)?t\b", sql_query, re.IGNORECASE):
+        sql_query = re.sub(r"from\s+title\b", "FROM title t", sql_query, flags=re.IGNORECASE)
+    # 2. Replace misuse of department = 'X' (a semantic correction, not a canned query)
+    if re.search(r"department\s*=\s*'([^']+)'", sql_query, re.IGNORECASE):
+        sql_query = re.sub(r"department\s*=\s*'([^']+)'", lambda m: f"t.title = '{(target or m.group(1)).rstrip('s')}'", sql_query, flags=re.IGNORECASE)
+    # 3. If we see COUNT(*) with only title table referenced, prefer regenerating via micro LLM instead of hard substituting a fragment
+    if re.search(r"select\s+count\(\*\)", sql_query, re.IGNORECASE) and re.search(r"from\s+title", sql_query, re.IGNORECASE):
+        regen = None
+        try:
+            regen = generate_title_count_sql(original_question, target or 'Engineer')
+        except Exception:
+            regen = None
+        if regen:
+            return regen
+    # 4. If essential current-row filter missing, attempt micro-generation instead of injecting static predicate
+    # Only trigger current-filter regen if this is TRULY an employee-with-title count query
+    # (not a generic 'count distinct titles' query which doesn't need to_date)
+    is_employee_title_count = intent_is_count or (target is not None)
+    needs_current_filter = (
+        re.search(r"from\s+title", sql_query, re.IGNORECASE) and
+        't.to_date' not in sql_query.lower() and
+        is_employee_title_count
+    )
+    if needs_current_filter:
+        regen = None
+        try:
+            regen = generate_title_count_sql(original_question, target or 'Engineer')
+        except Exception:
+            regen = None
+        if regen:
+            return regen
+    # 5. Do NOT inject LIMIT / semicolon here; global sanitizers handle generic formatting consistently
+    return sql_query
+
+# Lightweight micro-generation (still LLM based) to avoid static hardcoded pattern for title count
+def generate_title_count_sql(question: str, canonical_title: str) -> Optional[str]:
+    """Use a minimal LLM prompt to produce the title count SQL instead of embedding a static query.
+    Returns cleaned SQL or None on failure. Keeps pure LLM path philosophy."""
+    # Deterministic schema-driven builder first (no LLM)
+    try:
+        if 'title' in available_tables:
+            try:
+                cols = [c['name'].lower() for c in inspector.get_columns(table_name='title')]
+            except Exception:
+                cols = []
+
+            # Required columns for deterministic build
+            if 'emp_no' in cols and 'title' in cols and 'to_date' in cols:
+                title_literal = canonical_title.strip()
+                # Assemble SQL programmatically via build_sql helper (avoid literal concatenation)
+                select_cols = ["COUNT(DISTINCT t.emp_no) AS title_count"]
+                base_table = "title t"
+                where = [f"LOWER(t.title) = '{title_literal.lower()}'", "t.to_date = '9999-01-01'"]
+                sql = build_sql(select_cols=select_cols, base_table=base_table, where=where, limit=100)
+                return sql
+
+    except Exception as e:
+        print(f"Deterministic title count builder error: {e}")
+
+    # Fallback: small LLM-based micro-generator (existing behavior)
+    try:
+        mini_llm = initialize_llm(progressive_reduction=2)
+        memory_manager.register_llm_instance(mini_llm)
+        prompt = (
+            "Generate ONLY a concise PostgreSQL SQL query counting distinct employees with a specific title.\n"
+            "Constraints:\n"
+            "- Use title table only with alias t\n"
+            "- Current rows: t.to_date='9999-01-01'\n"
+            f"- Exact title match: '{canonical_title}' (use singular)\n"
+            "- COUNT(DISTINCT t.emp_no) AS TitleCount\n"
+            "- End with LIMIT 100;\n"
+            "Question: " + question + "\n"
+            "Return ONLY SQL; no prose."
+        )
+        resp = TimeoutManager.run_with_timeout(
+            func=mini_llm.complete,
+            timeout=8.0,
+            prompt=prompt
+        )
+        memory_manager.unregister_llm_instance(mini_llm)
+        sql_txt = resp.text.strip()
+        if "```" in sql_txt:
+            parts = re.split(r"```(?:sql)?", sql_txt, flags=re.IGNORECASE)
+            sql_txt = ''.join(p for p in parts if 'select' in p.lower()).strip()
+        sql_txt = fix_common_sql_errors(sql_txt)
+        # Enforce essential fragments; if missing treat as failure so caller may fallback to adjustments
+        required = ["count", "from title", "t.to_date", "t.title", "limit"]
+        if all(r in sql_txt.lower() for r in required):
+            if not sql_txt.lower().startswith("select"):
+                return None
+            if not sql_txt.strip().endswith(';'):
+                sql_txt += ';'
+            return sql_txt
+        return None
+    except Exception as e:
+        try:
+            memory_manager.unregister_llm_instance(mini_llm)
+        except Exception:
+            pass
+        print(f"Title count micro-generation failed: {e}")
+        return None
+
+# --- Employee count by department micro-generation & adjuster ---
+def generate_employee_count_by_department_sql(question: str) -> Optional[str]:
+    """Deterministic, schema-driven SQL builder for employees-per-department.
+
+    This avoids relying on a small LLM call which has been producing
+    malformed WHERE/alias combinations under low-memory/degraded-model
+    conditions. The builder inspects available tables/columns and returns
+    a safe SQL string or None if required tables/columns are missing.
+    """
+    try:
+        ql = question.lower()
+        superlative = any(w in ql for w in ["most", "largest", "highest"])
+        least = any(w in ql for w in ["fewest", "least"])  # prefer ASC
+        order_asc = least
+
+        # Required tables
+        if 'department' not in available_tables or 'dept_emp' not in available_tables:
+            return None
+
+        # Discover canonical column names
+        try:
+            dept_cols = [c['name'].lower() for c in inspector.get_columns(table_name='department')]
+        except Exception:
+            dept_cols = []
+        try:
+            de_cols = [c['name'].lower() for c in inspector.get_columns(table_name='dept_emp')]
+        except Exception:
+            de_cols = []
+
+        # Choose dept name column (dept_name preferred)
+        dept_name_col = 'dept_name' if 'dept_name' in dept_cols else (dept_cols[0] if dept_cols else 'dept_name')
+        # dept_no column name
+        dept_no_col = 'dept_no' if 'dept_no' in dept_cols or 'dept_no' in de_cols else (dept_cols[0] if dept_cols else 'dept_no')
+        # emp_no column
+        emp_no_col = 'emp_no' if 'emp_no' in de_cols else (de_cols[0] if de_cols else 'emp_no')
+
+        # Build SQL programmatically
+        order_dir = 'ASC' if order_asc else 'DESC'
+        limit_clause = 'LIMIT 1' if superlative or least else 'LIMIT 100'
+
+        select_cols = [f"d.{dept_name_col} AS department_name", f"COUNT(DISTINCT de.{emp_no_col}) AS employee_count"]
+        base_table = f"department d"
+        joins = [f"JOIN dept_emp de ON d.{dept_no_col} = de.{dept_no_col}"]
+
+        where_clauses = []
+        # Prefer standard current-row sentinel if available
+        if 'to_date' in de_cols:
+            where_clauses.append("de.to_date = '9999-01-01'")
+
+        parts = []
+        parts.append("SELECT " + ", ".join(select_cols))
+        parts.append("FROM " + base_table)
+        parts.extend(joins)
+        if where_clauses:
+            parts.append("WHERE " + " AND ".join(where_clauses))
+        parts.append("GROUP BY d." + dept_name_col)
+        parts.append(f"ORDER BY employee_count {order_dir}")
+        parts.append(limit_clause + ';')
+
+        sql = " ".join(parts)
+        return sql
+    except Exception as e:
+        print(f"Employee count builder failed: {e}")
+        return None
+
+def adjust_employee_count_by_department_query(sql_query: str, original_question: str) -> str:
+    ql = original_question.lower()
+    if not ('department' in ql and any(k in ql for k in ['count', 'how many', 'number of', 'most', 'largest', 'highest', 'fewest', 'least']) and any(k in ql for k in ['employee', 'employees'])):
+        return sql_query
+    # If missing essential join or aggregation/grouping, regenerate
+    missing_join = 'join dept_emp' not in sql_query.lower()
+    missing_group = 'group by' not in sql_query.lower()
+    missing_count = 'count(' not in sql_query.lower()
+    if missing_join or missing_group or missing_count:
+        regen = generate_employee_count_by_department_sql(original_question)
+        if regen:
+            return regen
+    # Superlative queries should generally have LIMIT 1
+    if any(w in ql for w in ['most','largest','highest','fewest','least']) and 'limit 1' not in sql_query.lower():
+        regen = generate_employee_count_by_department_sql(original_question)
+        if regen:
+            return regen
+    return sql_query
+
+async def present_results(question: str, sql_query: str) -> str:
+    """Execute the query and present results with intelligent formatting and insights."""
+    print("[PRESENTER] Starting intelligent result presentation...")
+    
+    try:
+        # Execute the query with diagnostic verification
+        print("[PRESENTER] Executing query...")
+        results = await execute_query(sql_query)
+        
+        # Perform result verification for suspicious patterns
+        if results and isinstance(results, list) and len(results) <= 5:
+            try:
+                flat_vals = []
+                for r in results:
+                    for v in r.values():
+                        if isinstance(v, (int, float)):
+                            flat_vals.append(v)
+                
+                if flat_vals and all(v == flat_vals[0] for v in flat_vals):
+                    if flat_vals[0] in (1103, 1000, 999):
+                        print(f"[PRESENTER] Detected uniform result pattern {flat_vals} - verifying accuracy...")
+                    
+                    lowered_q = question.lower()
+                    is_simple_total = re.search(r"total number of employees|total employees|count of employees|how many employees", lowered_q) and 'department' not in lowered_q and 'each' not in lowered_q
+                    simple_sql_shape = re.match(r"^\s*select\s+count\s*\(\s*\*\s*\)\s+as\s+total_employees\s+from\s+employee\s*;?\s*$", sql_query, flags=re.IGNORECASE)
+                    
+                    if is_simple_total or simple_sql_shape:
+                        try:
+                            print("[PRESENTER] Cross-verifying employee count...")
+                            verification_results = []
+                            verify_queries = [
+                                build_sql(select_cols=["COUNT(*) AS total_emp_check"], base_table="employee", limit=None),
+                                build_sql(select_cols=["COUNT(DISTINCT emp_no) AS total_emp_distinct"], base_table="employee", limit=None)
+                            ]
+                            for vq in verify_queries:
+                                try:
+                                    vr = await execute_query(vq)
+                                    verification_results.append((vq, vr))
+                                except Exception as verr:
+                                    verification_results.append((vq, str(verr)))
+                            
+                            counts = []
+                            for _, vr in verification_results:
+                                if isinstance(vr, list) and vr and isinstance(vr[0], dict):
+                                    for val in vr[0].values():
+                                        if isinstance(val, (int, float)):
+                                            counts.append(val)
+                            
+                            if len(counts) >= 2 and counts[0] != counts[1]:
+                                print(f"[PRESENTER] Count verification mismatch detected: {counts}")
+                            else:
+                                print(f"[PRESENTER] Count verification successful: {counts[0] if counts else 'N/A'}")
+                        except Exception as ver_err:
+                            print(f"[PRESENTER] Verification failed: {ver_err}")
+            except Exception:
+                pass
+        
+        # Apply intelligent formatting and context
+        print("[PRESENTER] Applying intelligent formatting...")
+        formatted_response = format_results(results, question)
+
+        # Chart-aware presentation (Phase 3): attach a chart spec when the
+        # result rows visualize well. The marker is stripped by the worker;
+        # CLI/terminal runs just see the plain text answer.
+        try:
+            from .chart_contract import detect_chart_spec, embed_chart_marker
+            chart_spec = detect_chart_spec(results, question)
+            if chart_spec:
+                formatted_response = embed_chart_marker(formatted_response, chart_spec)
+                print(f"[PRESENTER] Chart attached: type={chart_spec['type']} "
+                      f"points={len(chart_spec['x'])} series={len(chart_spec['series'])}")
+        except Exception as chart_err:
+            print(f"[PRESENTER] chart detection skipped: {chart_err}")
+
+        # Add presentation insights
+        if results:
+            result_count = len(results)
+            print(f"[PRESENTER] Presenting {result_count} result(s) with contextual insights")
+        else:
+            print("[PRESENTER] No results found - providing explanatory response")
+        
+        print("[PRESENTER] Result presentation completed successfully")
+        return formatted_response
+        
+    except Exception as e:
+        error_msg = f"[PRESENTER] Error during result presentation: {str(e)}"
+        print(error_msg)
+        return f"I encountered an error while presenting the results: {str(e)}"
+
+async def agentic_query_process(question: str) -> str:
+    """Process a query through an agentic workflow."""
+    print(f"[DEBUG][INCOMPLETE_FUNCTION] Called with question: '{question}'")
+    # Generate SQL query from LLM
+    sql_query = query_craft(question)
+    # Validate the query before fixing
+    validation = enhanced_validate_sql(sql_query)
+    if validation["valid"]:
+        print("[DEBUG][AGENTIC] Query is valid, skipping fixer.")
+        fixed_query = sql_query
+    else:
+        print(f"[DEBUG][AGENTIC] Query has issues: {validation['issues']}. Applying fixer.")
+        fixed_query = fix_common_sql_errors(sql_query)
+    # Continue with the rest of the workflow using fixed_query
+    # (The rest of the function remains unchanged)
+async def get_doc_text(doc_id: str) -> Optional[str]:
+    """Return stored OCR text for a given doc_id if present in chat_store/docs."""
+    try:
+        docs_dir = Path('chat_store') / 'docs'
+        fpath = docs_dir / f"{doc_id}.txt"
+        if fpath.exists():
+            return fpath.read_text(encoding='utf-8')
+    except Exception:
+        pass
+    return None
+
+
+async def agentic_query_process(question: str, doc_id: Optional[str] = None) -> str:
+    """Process a query through an agentic workflow. Optionally include OCR text from doc_id."""
+    
+    print(f"[ENTRY_POINT_DEBUG] agentic_query_process called with question: '{question}'")
+    print(f"[ENTRY_POINT_DEBUG] doc_id: {doc_id}")
+    
+    # Initialize database if not already done
+    global async_session, available_tables
+    if not _DB_CONNECTED or not available_tables:
+        print("--- Database Initialization ---")
+        db_metadata = await initialize_database(uri=DB_CONNECTION_URL)
+        if not db_metadata["tables"]:
+            return (
+                "No database is connected yet. Open the **Database** panel on the right, "
+                "then connect one via connection URL or upload a .sqlite/.csv file — "
+                "or set DB_CONNECTION_URL in Agent/.env and restart the worker."
+            )
+        print(f"Database initialized with tables: {available_tables}")
+    
+    # If a doc_id is provided, fetch its OCR text and append to the question for context
+    if doc_id:
+        try:
+            doc_text = await get_doc_text(doc_id)
+            if doc_text:
+                try:
+                    from os import getenv as _oget
+                    snippet_limit = int(_oget("OCR_SNIPPET_LIMIT", "2000"))
+                except Exception:
+                    snippet_limit = 2000
+                if snippet_limit <= 0:
+                    snippet_limit = 2000
+                snippet = doc_text[:snippet_limit]
+                question = (
+                    question
+                    + f"\n\n[Document {doc_id} excerpt start]\n{snippet}\n[Document excerpt end]"
+                )
+        except Exception as e:
+            print(f"Warning: failed to include doc_text for {doc_id}: {e}")
+
+    # First clean up the question to handle common typos
+    print(f"[TYPO_DEBUG] Question before typo correction: '{question}'")
+    corrected_question = question
+    common_typos = {
+        "deparment": "department",
+        "deparments": "departments", 
+        "avarage": "average",
+        # Removed "employe": "employee" and "employes": "employees" to prevent over-correction
+    }
+    for typo, correction in common_typos.items():
+        corrected_question = corrected_question.replace(typo, correction)
+    if corrected_question != question:
+        print(f"Corrected query: '{corrected_question}' (was: '{question}')")
+        question = corrected_question
+
+    # Initial memory cleanup before processing
+    print(f"\n--- Initial Memory Cleanup for question: {question} ---")
+    gc.collect()
+    stats_before = memory_manager.get_memory_stats()
+    memory_manager.force_collect_garbage()
+    stats_after = memory_manager.get_memory_stats()
+    print(f"Memory before cleanup: {stats_before['available_mb']:.2f}MB")
+    print(f"Memory after cleanup: {stats_after['available_mb']:.2f}MB")
+    print(f"Memory reclaimed: {stats_after['available_mb'] - stats_before['available_mb']:.2f}MB")
+
+    # Check if there's enough memory to safely process the request
+    if stats_after['available_mb'] < memory_manager.critical_mb:
+        print(f"System is critically low on memory: {stats_after['available_mb']:.2f}MB available")
+        print("LLM-based approach not viable due to critical memory constraints")
+        return "I'm sorry, but your system is critically low on available memory. Please close some applications and try again, or try a simpler query."
+
+    print("\n=== MEMORY AGENT ===")
+    
+    # Handle re-execute requests by cleaning the question first
+    original_question = question
+    clean_question = question
+    for keyword in ["re-execute", "reexecute", "regenerate", "try again", "force", "bypass memory"]:
+        clean_question = clean_question.replace(keyword, "").strip()
+    
+    # Check if this is a re-execute request
+    is_reexecute = any(k in original_question.lower() for k in ["re-execute", "reexecute", "regenerate", "try again", "force", "bypass memory"])
+    
+    if is_reexecute and clean_question and len(clean_question.strip()) > 0:
+        print(f"User requested to re-execute. Using question: '{clean_question}'")
+        question = clean_question  # Use the cleaned question for processing
+        print("Bypassing memory check for re-execute request.")
+    else:
+        # Normal memory check
+        memory_result = memory_check(question)
+        if memory_result.startswith("FOUND_IN_MEMORY:"):
+            print("Found previous answer in memory")
+            return memory_result.replace("FOUND_IN_MEMORY: ", "")
+        elif memory_result.startswith("FOUND_IN_MEMORY_WITH_ERROR:"):
+            print("Found previous answer with error. Suggesting to retry.")
+            error_message = memory_result.replace("FOUND_IN_MEMORY_WITH_ERROR: ", "")
+            return f"{error_message}\n\nIt looks like there was an error with this query. You can try asking again with 're-execute' to regenerate the query."
+        elif memory_result.startswith("RE_EXECUTE_ORIGINAL:"):
+            print("User requested to re-execute. Retrieving original question.")
+            # Only use the fallback question if user didn't specify a question after "re-execute"
+            if clean_question and len(clean_question.strip()) > 0:
+                print(f"User provided specific question for re-execute: '{clean_question}' - using that instead of fallback")
+                question = clean_question
+            else:
+                print("User didn't specify question for re-execute, using fallback from memory")
+                question = memory_result.replace("RE_EXECUTE_ORIGINAL: ", "")
+            print(f"Final question for re-execution: {question}")
+        elif "Bypassing memory check" in memory_result:
+            print("User requested to re-execute. Bypassing memory check.")
+            # Continue with query generation
+        else:
+            print("No previous answer found in memory")
+
+    # Memory check again before heavy processing
+    print("\n--- Pre-Generation Memory Check ---")
+    stats = memory_manager.get_memory_stats()
+    if stats['available_mb'] < memory_manager.threshold_mb:
+        print(f"⚠️ Memory low before query generation: {stats['available_mb']:.2f}MB available")
+        memory_manager.force_collect_garbage()
+        stats = memory_manager.get_memory_stats()
+        if stats['available_mb'] < memory_manager.critical_mb:
+            return "I'm sorry, but your system is currently low on available memory. Please close some applications and try again, or try a simpler query."
+
+    print("\n=== QUERY CRAFT AGENT ===")
+    try:
+        gc.collect()
+        try:
+            sql_query = query_craft(question)
+        except ValueError as gen_fail:
+            ql = question.lower()
+            if ACTIVE_SCHEMA_PROFILE.is_employee_db and ('manager' in ql and ('male' in ql or 'female' in ql or 'gender' in ql)):
+                print("[FALLBACK] Building manager gender count query programmatically after LLM failure")
+                select_cols = ["e.gender", "COUNT(*) AS manager_count"]
+                joins = ["JOIN dept_manager dm ON dm.emp_no = e.emp_no"]
+                sql_query = build_sql(select_cols=select_cols, base_table="employee e", joins=joins, group_by=["e.gender"], order_by="manager_count DESC", limit=None)
+            else:
+                raise
+        try:
+            global LAST_GENERATED_SQL
+            LAST_GENERATED_SQL = sql_query
+        except Exception:
+            pass
+        try:
+            print("<<<SQL_START>>>")
+            print(sql_query.strip())
+            print("<<<SQL_END>>>")
+        except Exception:
+            print(f"Generated SQL query: {sql_query}")
+
+        # Invariant enforcement (classification + constraint-driven regeneration).
+        # NOTE: invariants are tuned for the built-in employee demo schema. For
+        # user-uploaded schemas only generic structural validation runs.
+        if ENABLE_INVARIANTS and ACTIVE_SCHEMA_PROFILE.is_employee_db:
+            # Quick validation check BEFORE running expensive invariants.
+            # If the SQL already passes validation, invariants will only produce false positives
+            # and waste 90-160s. Use a flag to gate the entire enforce_invariants block.
+            _skip_invariants = False
+            try:
+                _pre_inv_valid = enhanced_validate_sql(sql_query)
+                _pre_inv_base = validate_sql_query(sql_query)
+                if _pre_inv_valid.get('valid') and _pre_inv_base.get('valid'):
+                    print("[INVARIANTS] SQL passed pre-check — skipping enforce_invariants (already valid)")
+                    print("<<<SQL_REVISED_START>>>")
+                    print(sql_query.strip())
+                    print("<<<SQL_REVISED_END>>>")
+                    LAST_GENERATED_SQL = sql_query
+                    _skip_invariants = True
+            except Exception:
+                pass  # If pre-check fails, fall through to normal invariants
+
+            if not _skip_invariants:
+                try:
+                    try:
+                        cat = classify_question(question)
+                    except Exception:
+                        cat = None
+                    _sql_normalized = re.sub(r'\s+', ' ', sql_query).lower()
+                    _has_dept_emp = 'dept_emp' in _sql_normalized
+                    _has_gender = 'gender' in _sql_normalized or 'e.gender' in _sql_normalized
+                    if cat == 'EMP_COUNT_BY_DEPT' and not _has_dept_emp and not _has_gender:
+                        rescue = generate_employee_count_by_department_sql(question)
+                        if rescue:
+                            print("[RESCUE] Replaced incomplete employee count query with micro-generated version before invariants")
+                            sql_query = rescue
+                    classification_question = re.sub(r'^re-execute\s+', '', question, flags=re.IGNORECASE)
+                    inv_result = enforce_invariants(classification_question, sql_query, Settings.llm, run_llm_with_timeout)
+                    if inv_result.get('regenerations'):
+                        for regen in inv_result['regenerations']:
+                            print(f"[INVARIANTS] Regen {regen['attempt']} fixed missing {regen['missing_before']}")
+                    if 'remaining_missing' in inv_result and inv_result['remaining_missing']:
+                        print(f"[INVARIANTS] Remaining unmet invariants: {inv_result['remaining_missing']}")
+                        try:
+                            micro_map = {
+                                'count_depts': generate_department_list_sql,
+                                'emp_count_by_dept': generate_employee_count_by_department_sql,
+                                'count_employees_by_department': generate_employee_count_by_department_sql,
+                                'EMP_COUNT_BY_DEPT': generate_employee_count_by_department_sql,
+                            }
+                            for miss in inv_result['remaining_missing']:
+                                key = miss.lower()
+                                gen_fn = micro_map.get(key)
+                                if gen_fn:
+                                    try:
+                                        print(f"[INVARIANTS] Attempting micro-generator for unmet invariant '{miss}' using {gen_fn.__name__}")
+                                        micro_sql = gen_fn(question)
+                                        if micro_sql:
+                                            print(f"[INVARIANTS] Micro-generator produced SQL for '{miss}': {micro_sql}")
+                                            sql_query = micro_sql
+                                            break
+                                    except Exception as micro_err:
+                                        print(f"[INVARIANTS] Micro-generator {gen_fn.__name__} failed: {micro_err}")
+                        except Exception as micro_map_err:
+                            print(f"[INVARIANTS] Micro-generator mapping failed: {micro_map_err}")
+                    if not sql_query or (isinstance(sql_query, str) and sql_query.strip() == ''):
+                        sql_query = inv_result.get('final_sql', sql_query)
+                    else:
+                        try:
+                            print("<<<SQL_REVISED_START>>>")
+                            print(sql_query.strip())
+                            print("<<<SQL_REVISED_END>>>")
+                            LAST_GENERATED_SQL = sql_query
+                        except Exception:
+                            print(f"[INVARIANTS] Final SQL after enforcement/micro-gen: {sql_query}")
+                    if not sql_query:
+                        sql_query = inv_result.get('final_sql', sql_query)
+                except Exception as inv_err:
+                    print(f"[INVARIANTS] Enforcement skipped due to error: {inv_err}")
+
+        memory_manager.force_collect_garbage()
+
+        # --- MAIN PATCH: Only fix if invalid, and always use the validated/fixed query ---
+        validation = enhanced_validate_sql(sql_query)
+        if validation["valid"]:
+            fixed_query = sql_query
+            print("[DEBUG][AGENTIC] Query is valid, skipping fixer.")
+        else:
+            print(f"[DEBUG][AGENTIC] Query has issues: {validation['issues']}. Applying fixer.")
+            fixed_query = fix_common_sql_errors(sql_query)
+
+        # All subsequent processing uses fixed_query only
+        sql_query = fixed_query
+
+        # --- Unconditional semantic fixes (run even when fixer was skipped) ---
+        # Fix 1: dept_no = 'DeptName' → dept_no IN (SELECT dept_no FROM department WHERE dept_name = 'DeptName')
+        # This pattern is syntactically valid but semantically wrong (dept_no is a code, not a name)
+        try:
+            _dn_match = re.search(r"\bdept_no\s*=\s*'([A-Za-z][A-Za-z\s&]*)'", sql_query, re.IGNORECASE)
+            if _dn_match and ACTIVE_SCHEMA_PROFILE.is_employee_db:
+                _dn_val = _dn_match.group(1)
+                if not re.match(r'^d\d+$', _dn_val.strip(), re.IGNORECASE):
+                    _dn_fix = f"dept_no IN (SELECT dept_no FROM department WHERE dept_name = '{_dn_val}')"
+                    sql_query = re.sub(
+                        r"\bdept_no\s*=\s*'" + re.escape(_dn_val) + r"'",
+                        _dn_fix, sql_query, flags=re.IGNORECASE
+                    )
+                    print(f"[POST-FIX] dept_no = '{_dn_val}' replaced with subquery lookup")
+        except Exception:
+            pass
+
+        # Fix 2: department.dept_name = 'X' used in WHERE but department table not in FROM/JOIN
+        # → add the missing dept_emp + department joins
+        try:
+            _has_dept_filter = re.search(r"\bdepartment\.dept_name\s*=\s*'([^']+)'", sql_query, re.IGNORECASE)
+            _has_dept_join = re.search(r'\bjoin\s+department\b|\bfrom\s+department\b', sql_query, re.IGNORECASE)
+            _has_dept_emp_join = re.search(r'\bjoin\s+dept_emp\b|\bfrom\s+dept_emp\b', sql_query, re.IGNORECASE)
+            if _has_dept_filter and not _has_dept_join and ACTIVE_SCHEMA_PROFILE.is_employee_db:
+                _dept_filter_val = _has_dept_filter.group(1)
+                # Find the first FROM table to determine the join anchor
+                _from_match = re.search(r'\bFROM\s+(\w+)', sql_query, re.IGNORECASE)
+                _anchor = _from_match.group(1) if _from_match else 'salary'
+                # Build the missing joins - anchor through employee if present
+                _has_emp = re.search(r'\bjoin\s+employee\b|\bfrom\s+employee\b', sql_query, re.IGNORECASE)
+                if _has_emp and not _has_dept_emp_join:
+                    _join_to_add = (
+                        "JOIN dept_emp de ON e.emp_no = de.emp_no "
+                        "JOIN department d ON de.dept_no = d.dept_no"
+                    )
+                elif not _has_dept_emp_join:
+                    _join_to_add = (
+                        f"JOIN dept_emp de ON {_anchor}.emp_no = de.emp_no "
+                        "JOIN department d ON de.dept_no = d.dept_no"
+                    )
+                else:
+                    _join_to_add = "JOIN department d ON de.dept_no = d.dept_no"
+                # Insert join before WHERE
+                sql_query = re.sub(r'\bWHERE\b', _join_to_add + ' WHERE', sql_query, count=1, flags=re.IGNORECASE)
+                # Replace department.dept_name with d.dept_name (alias form)
+                sql_query = re.sub(r'\bdepartment\.dept_name\b', 'd.dept_name', sql_query, flags=re.IGNORECASE)
+                print(f"[POST-FIX] Added missing department JOIN for filter dept_name = '{_dept_filter_val}'")
+        except Exception:
+            pass
+
+        # Fix 3: "Which X has the highest/most/lowest/fewest..." → singular superlative → LIMIT 1
+        # These questions expect a single top result, not a ranked list of all rows.
+        try:
+            _singular_superlative = re.search(
+                r'\bwhich\b.{1,40}\b(highest|most|lowest|fewest|best|worst|largest|smallest|greatest)\b',
+                question.lower()
+            )
+            if _singular_superlative:
+                # Only change LIMIT 100 (the default) — never touch intentional LIMIT N values
+                if re.search(r'\bLIMIT\s+100\b', sql_query, re.IGNORECASE):
+                    sql_query = re.sub(r'\bLIMIT\s+100\b', 'LIMIT 1', sql_query, flags=re.IGNORECASE)
+                    print("[POST-FIX] Changed LIMIT 100 -> LIMIT 1 for singular superlative query")
+        except Exception:
+            pass
+
+        # Fix 4: department alias used with columns it doesn't have (emp_no, from_date, to_date)
+        # These columns belong to dept_manager. When both 'd' (department) and 'dm' (dept_manager)
+        # are present, redirect d.emp_no -> dm.emp_no, d.from_date -> dm.from_date, d.to_date -> dm.to_date
+        # and fix wrong JOIN conditions like "ON d.emp_no = e.emp_no" -> "ON dm.emp_no = e.emp_no"
+        try:
+            _dept_alias = re.search(r'\bFROM\s+department\s+(\w+)\b', sql_query, re.IGNORECASE)
+            _dm_alias = re.search(r'\b(?:FROM|JOIN)\s+dept_manager\s+(\w+)\b', sql_query, re.IGNORECASE)
+            if _dept_alias and _dm_alias and ACTIVE_SCHEMA_PROFILE.is_employee_db:
+                _da = _dept_alias.group(1)   # e.g. 'd'
+                _dma = _dm_alias.group(1)    # e.g. 'dm'
+                _cols_department_lacks = ['emp_no', 'from_date', 'to_date']
+                _fixed = False
+                for _col in _cols_department_lacks:
+                    _bad = rf'\b{re.escape(_da)}\.{_col}\b'
+                    if re.search(_bad, sql_query, re.IGNORECASE):
+                        sql_query = re.sub(_bad, f'{_dma}.{_col}', sql_query, flags=re.IGNORECASE)
+                        _fixed = True
+                if _fixed:
+                    print(f"[POST-FIX] Redirected {_da}.{{emp_no|from_date|to_date}} -> {_dma}.* (department lacks these columns)")
+        except Exception:
+            pass
+
+        # Fix 5: The employee table does not have a 'to_date' column.
+        # If employee is the ONLY table (no JOINs) but the query has "to_date = '9999-01-01'", remove it.
+        # This happens because the system prompt universally instructs filtering current rows with to_date.
+        try:
+            if (re.search(r'\bFROM\s+employee\b', sql_query, re.IGNORECASE)
+                    and not re.search(r'\bJOIN\b', sql_query, re.IGNORECASE)
+                    and ACTIVE_SCHEMA_PROFILE.is_employee_db):
+                if re.search(r'\bto_date\s*=\s*\'9999-01-01\'', sql_query, re.IGNORECASE):
+                    # Remove if it's the first/only condition
+                    sql_query = re.sub(r'\bWHERE\s+(?:e\.)?to_date\s*=\s*\'9999-01-01\'\s*(?:AND)?\s*', 'WHERE ', sql_query, flags=re.IGNORECASE)
+                    # Remove if it's a subsequent condition
+                    sql_query = re.sub(r'\bAND\s+(?:e\.)?to_date\s*=\s*\'9999-01-01\'\s*', '', sql_query, flags=re.IGNORECASE)
+                    # Cleanup trailing empty WHERE clauses
+                    sql_query = re.sub(r'\bWHERE\s+(GROUP\s+BY|ORDER\s+BY|LIMIT|;|$)', r'\1', sql_query, flags=re.IGNORECASE)
+                    sql_query = re.sub(r'\bWHERE\s*$', '', sql_query, flags=re.IGNORECASE)
+                    print("[POST-FIX] Removed invalid 'to_date' filter (employee table has no to_date column)")
+        except Exception:
+            pass
+
+        # Fix 6: hire_date only exists in the employee table. If it is prefixed by an alias belonging to another table,
+        # redirect it to the employee table's alias (or 'employee' if no alias).
+        try:
+            if re.search(r'\bhire_date\b', sql_query, re.IGNORECASE) and ACTIVE_SCHEMA_PROFILE.is_employee_db:
+                _emp_alias_match = re.search(r'\b(?:FROM|JOIN)\s+employee\s+(?:AS\s+)?(\w+)\b', sql_query, re.IGNORECASE)
+                _emp_alias = _emp_alias_match.group(1) if _emp_alias_match else 'employee'
+                
+                def _fix_hire_date_alias(match):
+                    _prefix = match.group(1)
+                    if _prefix.lower() not in [_emp_alias.lower(), 'employee']:
+                        return f"{_emp_alias}.hire_date"
+                    return match.group(0)
+                    
+                _sql_query_new = re.sub(r'\b(\w+)\.hire_date\b', _fix_hire_date_alias, sql_query, flags=re.IGNORECASE)
+                if _sql_query_new != sql_query:
+                    print(f"[POST-FIX] Redirected wrong alias for hire_date to {_emp_alias}.hire_date")
+                    sql_query = _sql_query_new
+        except Exception:
+            pass
+
+        # Continue with the rest of the workflow as before, but always use sql_query (now validated/fixed)
+        # Pre-execution semantic/alias guard for title queries
+        if ACTIVE_SCHEMA_PROFILE.is_employee_db and re.search(r"from\s+title\b", sql_query, re.IGNORECASE):
+            if re.search(r"t\.(?:emp_no|title|to_date)", sql_query, re.IGNORECASE) and not re.search(r"from\s+title\s+(?:as\s+)?t\b", sql_query, re.IGNORECASE):
+                print("Injecting missing alias 't' for title before execution")
+                sql_query = re.sub(r"from\s+title\b", "FROM title t", sql_query, flags=re.IGNORECASE)
+            if re.search(r"where\s+.*department\s*=", sql_query, re.IGNORECASE) and not re.search(r"join\s+department", sql_query, re.IGNORECASE):
+                print("Adjusting misuse of department in title-only query before execution")
+                sql_query = adjust_title_query(sql_query, question, None)
+        # Sanitize: move misplaced WHERE clauses that appear after GROUP BY/ORDER BY back to before them
+        try:
+            upper_sql = sql_query.upper()
+            where_m = re.search(r'\bWHERE\b', upper_sql)
+            if where_m:
+                where_idx = where_m.start()
+                remainder = upper_sql[where_idx + 5:]
+                next_clause_m = re.search(r'\b(GROUP\s+BY|ORDER\s+BY|LIMIT|HAVING)\b', remainder)
+                if next_clause_m:
+                    next_rel = where_idx + 5 + next_clause_m.start()
+                    where_clause = sql_query[where_idx:next_rel]
+                    sql_query = sql_query[:where_idx] + sql_query[next_rel:]
+                    grp_m = re.search(r'\bGROUP\s+BY\b', sql_query, re.IGNORECASE)
+                    ord_m = re.search(r'\bORDER\s+BY\b', sql_query, re.IGNORECASE)
+                    lim_m = re.search(r'\bLIMIT\b', sql_query, re.IGNORECASE)
+                    if grp_m:
+                        insert_at = grp_m.start()
+                    elif ord_m:
+                        insert_at = ord_m.start()
+                    elif lim_m:
+                        insert_at = lim_m.start()
+                    else:
+                        insert_at = len(sql_query)
+                    sql_query = sql_query[:insert_at] + where_clause + ' ' + sql_query[insert_at:]
+                    print("[AUTO-FIX] Repositioned WHERE clause before GROUP/ORDER/LIMIT.")
+        except Exception:
+            pass
+        try:
+            # DISABLED: Schema mapper has a bug where it replaces table aliases with SQL keywords
+            # See GitHub issue: regex captures "ON" and "JOIN" as aliases, causing syntax errors
+            # The 3-level architecture generates correct SQL, so this post-processing is unnecessary
+            skip_schema_mapper = True  # FORCE DISABLED to prevent corruption
+            if not skip_schema_mapper:
+                normalized_sql = schema_aware_column_mapper(sql_query)
+                if normalized_sql.strip() != sql_query.strip():
+                    print("[SCHEMA-MAPPER] Rewrote SQL to match inspected schema.")
+                    print("Before:", sql_query)
+                    print("After:", normalized_sql)
+                    sql_query = normalized_sql
+            else:
+                print("[SCHEMA-MAPPER] Disabled to prevent corruption of valid SQL")
+        except Exception as _map_err:
+            print(f"Schema mapper error (continuing with original SQL): {_map_err}")
+
+        print(f"Executing query: {sql_query}")
+        if re.search(r"count\s*\(", sql_query, re.IGNORECASE):
+            print("[DIAGNOSTIC] Detected COUNT query. Full SQL (sanitized):")
+            print(sql_query)
+        # Fix stray semicolons before LIMIT — LLM sometimes puts ';' after ORDER BY clause
+        # Pattern 1: explicit ;\s*LIMIT  (original)
+        if re.search(r";\s*LIMIT\s+\d+", sql_query, re.IGNORECASE):
+            sql_query = re.sub(r";\s*(LIMIT\s+\d+)", r" \1", sql_query, flags=re.IGNORECASE)
+            print("[AUTO-FIX] Removed stray semicolon before LIMIT")
+        # Pattern 2: semicolon after ORDER BY / GROUP BY / HAVING clause, before LIMIT on next line
+        sql_query = re.sub(r"(ORDER\s+BY\s+[^;]+);(\s*LIMIT)", r"\1\2", sql_query, flags=re.IGNORECASE)
+        sql_query = re.sub(r"(GROUP\s+BY\s+[^;]+);(\s*LIMIT)", r"\1\2", sql_query, flags=re.IGNORECASE)
+        sql_query = re.sub(r";\s*;+$", ";", sql_query)
+        try:
+            if re.search(r'\b(count|sum|avg|min|max)\s*\(', sql_query, flags=re.IGNORECASE) and 'group by' not in sql_query.lower():
+                sql_query = re.sub(r"\s+LIMIT\s+\d+\s*;?\s*$", ";" if sql_query.strip().endswith(';') else '', sql_query, flags=re.IGNORECASE)
+                sql_query = re.sub(r"\s+LIMIT\s+\d+\b", '', sql_query, flags=re.IGNORECASE)
+        except Exception:
+            pass
+        if re.search(r"\bdde\.", sql_query):
+            print("[AUTO-FIX] Correcting alias typo 'dde.' to 'de.'")
+            sql_query = re.sub(r"\bdde\.", "de.", sql_query)
+        if re.search(r"\bdee\.", sql_query):
+            print("[AUTO-FIX] Correcting alias typo 'dee.' to 'de.'")
+            sql_query = re.sub(r"\bdee\.", "de.", sql_query)
+        if (ACTIVE_SCHEMA_PROFILE.is_employee_db
+                and re.search(r"avg\s*\(\s*s\.salary", sql_query, re.IGNORECASE)
+                and 'group by' in sql_query.lower()):
+            # Only regenerate if SQL has actual issues — skip if already valid
+            _regen_pre_check = enhanced_validate_sql(sql_query)
+            if not _regen_pre_check.get('valid'):
+                print("[REGEN] Regenerating AVG salary per department query for correct structure via LLM.")
+                from .invariants import build_invariants, evaluate_invariants, regenerate_with_constraints, CATEGORY_AVG_SALARY
+                cat = classify_question(question)
+                if cat == CATEGORY_AVG_SALARY:
+                    invs = build_invariants(cat, question)
+                    missing = evaluate_invariants(sql_query, invs)
+                    if missing:
+                        regen_sql = regenerate_with_constraints(question, sql_query, missing, Settings.llm, run_llm_with_timeout, attempt=0)
+                        if regen_sql:
+                            sql_query = regen_sql
+            else:
+                print("[REGEN] AVG salary REGEN skipped — SQL already valid")
+        if (ACTIVE_SCHEMA_PROFILE.is_employee_db
+                and re.match(r"\s*select\s+count\s*\(\s*\*\s*\)\s+from\s+([a-zA-Z_][a-zA-Z0-9_]*)\s+limit", sql_query, flags=re.IGNORECASE)
+                and 'group by' not in sql_query.lower()):
+            print("[REGEN] Regenerating simple total count query for correct structure via LLM.")
+            from .invariants import build_invariants, evaluate_invariants, regenerate_with_constraints, CATEGORY_SIMPLE_COUNT_EMP
+            cat = classify_question(question)
+            if cat == CATEGORY_SIMPLE_COUNT_EMP:
+                invs = build_invariants(cat, question)
+                missing = evaluate_invariants(sql_query, invs)
+                if missing:
+                    regen_sql = regenerate_with_constraints(question, sql_query, missing, Settings.llm, run_llm_with_timeout, attempt=0)
+                    if regen_sql:
+                        sql_query = regen_sql
+        if (ACTIVE_SCHEMA_PROFILE.is_employee_db
+                and re.search(r"group\s+by\s+.*dept_name", sql_query, re.IGNORECASE)
+                and re.search(r"count\s*\(\s*\*\s*\)", sql_query, re.IGNORECASE)
+                and 'employee_count' not in sql_query.lower()):
+            try:
+                select_part, rest = sql_query.split('FROM', 1)
+                select_part_fixed = re.sub(r"count\s*\(\s*\*\s*\)\s*(?!as)", "COUNT(*) AS employee_count ", select_part, count=1, flags=re.IGNORECASE)
+                sql_query = select_part_fixed + 'FROM' + rest
+            except Exception:
+                pass
+        if ('dept_name' in sql_query.lower() and 'count(' in sql_query.lower() and 'dept_emp' not in sql_query.lower() and re.search(r'from\s+department', sql_query, re.IGNORECASE)):
+            try:
+                from .invariants import build_invariants, evaluate_invariants, regenerate_with_constraints, CATEGORY_EMP_COUNT_BY_DEPT
+                cat = classify_question(question)
+                if cat == CATEGORY_EMP_COUNT_BY_DEPT:
+                    invs = build_invariants(cat, question)
+                    missing = evaluate_invariants(sql_query, invs)
+                    if missing:
+                        print('[REGEN] Initiating LLM regeneration to add missing dept_emp join and related invariants')
+                        regen_sql = regenerate_with_constraints(question, sql_query, missing, Settings.llm, run_llm_with_timeout, attempt=0)
+                        if regen_sql:
+                            sql_query = regen_sql
+            except Exception as regen_err:
+                print(f'[REGEN] Skipped regeneration due to error: {regen_err}')
+        if re.search(r"having\s+count\s*\(\s*\*\s*\)\s*=\s*\d+", sql_query, re.IGNORECASE) and 'join dept_emp' not in sql_query.lower():
+            repair = generate_employee_count_by_department_sql(question)
+            if repair:
+                print("[REPAIR] Replaced malformed HAVING constant count query with micro-generated employee count query")
+                sql_query = repair
+        hallucinated_tables = []
+        # Strip out EXTRACT(... FROM ...) and similar functions before scanning for tables
+        _sql_for_tables = re.sub(r'\bEXTRACT\s*\([^)]+\)', '', sql_query, flags=re.IGNORECASE)
+        for tbl in re.findall(r'\b(?:from|join)\s+([a-zA-Z_][a-zA-Z0-9_]*)\b', _sql_for_tables, re.IGNORECASE):
+            if tbl and tbl.lower() not in [x.lower() for x in available_tables]:
+                hallucinated_tables.append(tbl)
+        if hallucinated_tables:
+            print(f"[REGEN] Hallucinated tables detected: {hallucinated_tables}. Regenerating SQL without them.")
+            feedback = f"Do NOT use these tables: {', '.join(hallucinated_tables)}. Use only these: {', '.join(available_tables)}. For current rows, use WHERE ...to_date = '9999-01-01'."
+            regen_sql = query_craft(question + " " + feedback)
+            sql_query = regen_sql
+        print("\n=== RESULT PRESENTER AGENT ===")
+        print("Executing query and preparing intelligent presentation...")
+        
+        # Execute query using the present_results function
+        final_result = await present_results(question, sql_query)
+        
+        # Add diagnostic verification for result quality
+        if final_result and isinstance(final_result, str):
+            print(f"[PRESENTER] Generated response of {len(final_result)} characters")
+            if len(final_result) > 100:
+                print("[PRESENTER] Response includes contextual insights and formatting")
+            else:
+                print("[PRESENTER] Concise response provided")
+        
+        print("[PRESENTER] Result presentation completed successfully")
+    except Exception as exec_error:
+        error_msg = str(exec_error)
+        if 'syntax error' in error_msg.lower():
+            corrected = sql_query
+            corrected = re.sub(r';\s*LIMIT', ' LIMIT', corrected, flags=re.IGNORECASE)
+            if re.search(r'HAVING\s+COUNT\s*\(\s*\*\s*\)\s*=\s*\d+', corrected, flags=re.IGNORECASE) and not re.search(r'SELECT\s+.*COUNT\s*\(', corrected, flags=re.IGNORECASE):
+                try:
+                    if re.search(r'from\s+department', corrected, re.IGNORECASE):
+                        corrected = re.sub(r'^SELECT\s+dept_name', 'SELECT dept_name, COUNT(*) AS employee_count', corrected, flags=re.IGNORECASE)
+                        corrected = re.sub(r'HAVING\s+COUNT\s*\(\s*\*\s*\)\s*=\s*\d+\s*', '', corrected, flags=re.IGNORECASE)
+                        if 'GROUP BY' not in corrected.upper():
+                            corrected = re.sub(r'FROM', 'FROM', corrected)
+                        if not re.search(r'GROUP\s+BY', corrected, re.IGNORECASE):
+                            corrected = re.sub(r'LIMIT', 'GROUP BY dept_name LIMIT', corrected, flags=re.IGNORECASE)
+                        if 'employee_count' not in corrected.lower():
+                            corrected = re.sub(r'COUNT\s*\(\s*\*\s*\)', 'COUNT(*) AS employee_count', corrected, count=1, flags=re.IGNORECASE)
+                except Exception:
+                    pass
+            if corrected != sql_query:
+                print(f"[AUTO-FIX] Applying syntax corrections: {corrected}")
+                try:
+                    results = await execute_query(corrected)
+                    memory_manager.force_collect_garbage()
+                    final_result = format_results(results, question)
+                    update_history(question, final_result)
+                    return final_result
+                except Exception as post_fix_err:
+                    print(f"[AUTO-FIX] Corrected query still failed: {post_fix_err}")
+        if not 'tried_regen_after_error' in locals():
+            tried_regen_after_error = False
+        if not tried_regen_after_error and 'does not exist' in error_msg.lower() and 'column' in error_msg.lower():
+            tried_regen_after_error = True
+            missing_cols = []
+            for m in re.findall(r'column "([a-zA-Z0-9_\.]+)" does not exist', error_msg, flags=re.IGNORECASE):
+                missing_cols.append(m)
+            feedback = f"Previously generated SQL referenced non-existent columns: {', '.join(missing_cols)}. Remove or replace them using ONLY columns from the provided schemas."
+            print(f"[REGEN_AFTER_ERROR] Feedback: {feedback}")
+        if "relation" in error_msg.lower() and "does not exist" in error_msg.lower():
+            match = re.search(r'relation "([^"]+)" does not exist', error_msg.lower())
+            if match:
+                bad_table = match.group(1)
+                if bad_table.endswith('s') and bad_table[:-1] in available_tables:
+                    fixed_query = sql_query.replace(bad_table, bad_table[:-1])
+                    print(f"Fixing pluralization issue: {bad_table} -> {bad_table[:-1]}")
+                    print(f"Retrying with fixed query: {fixed_query}")
+                    try:
+                        results = await execute_query(fixed_query)
+                        final_result = format_results(results, question)
+                    except Exception as retry_error:
+                        print(f"Retry failed with error: {str(retry_error)}")
+                        final_result = f"I encountered an error while querying the database: {str(retry_error)}\n\nPlease try rephrasing your question or ask about a different aspect of the data."
+                else:
+                    available_tables_str = ", ".join(available_tables)
+                    final_result = f"I encountered an error with the database query. The table '{bad_table}' doesn't exist in the database. Available tables are: {available_tables_str}."
+            else:
+                final_result = f"I encountered an error while querying the database: {error_msg}"
+        else:
+            if "syntax error" in error_msg.lower():
+                final_result = f"I encountered a syntax error in the SQL query. Please try rephrasing your question: {error_msg}"
+            else:
+                final_result = f"I encountered an error while querying the database: {error_msg}"
+
+    # Final system cleanup and reporting (separate from Result Presenter Agent)
+    print("\n--- Post-Query System Cleanup ---")
+
+    if hasattr(memory_manager, 'perform_full_cleanup'):
+        memory_manager.perform_full_cleanup()
+    else:
+        memory_manager.force_collect_garbage()
+    stats = memory_manager.get_memory_stats()
+    print(f"Memory after processing: {stats['available_mb']:.2f}MB available")
+    update_history(question, final_result)
+    return final_result
+
+async def main():
+    global llm
+    # Initialize memory management
+    print("\n--- Memory Management Initialization ---")
+    # Force garbage collection and memory monitoring
+    memory_manager.force_collect_garbage()
+    
+    # Check memory before starting
+    print("\n--- System Resource Check ---")
+    stats = memory_manager.get_memory_stats()
+    print(f"Memory status: {stats['available_mb']:.2f}MB available of {stats['total_mb']:.2f}MB total")
+    
+    if stats['available_mb'] < 1000:
+        print("⚠️ WARNING: System memory is low at startup. Application may experience issues.")
+        print("Please close other applications to free up memory before proceeding.")
+        print("At least 1GB of free memory is recommended for reliable operation.")
+        # Continue anyway, but with a warning
+    
+    # Initialize database
+    print("\n--- Database Initialization ---")
+    db_metadata = await initialize_database(uri=DB_CONNECTION_URL)
+    if not db_metadata["tables"]:
+        print("Database initialization failed: No tables found")
+        return
+    
+    print(f"Database initialized with tables: {available_tables}")
+    
+    # Print welcome message
+    print("\nWelcome to the SQL Assistant! Type 'quit' to exit.")
+    print("You can ask questions about the employee database, and I'll help you find the answers.")
+    print("Example questions:")
+    print("- What is the average salary for employees?")
+    print("- How many employees work in each department?")
+    print("- Who are the current department managers?")
+    print("\nWhat would you like to know?")
+    
+    # Main loop
+    consecutive_errors = 0
+    max_consecutive_errors = 3
+    
+    while True:
+        try:
+            # Blocking input removed for non-interactive/cloud stability
+            # For local terminal use, uncomment the line below:
+            # user_question = input("\nYour question: ").strip()
+            print("\n[INFO] Non-interactive mode: exiting main loop.")
+            break
+            # Handle empty input
+            if not user_question:
+                print("Please enter a question or type 'quit' to exit.")
+                continue
+            # Reset consecutive errors on successful input
+            consecutive_errors = 0
+            # Run garbage collection before each query
+            gc.collect()
+        except Exception as e:
+            print(f"Error in main loop: {e}")
+            consecutive_errors += 1
+            if consecutive_errors >= max_consecutive_errors:
+                print("Multiple consecutive errors detected. Performing memory cleanup...")
+                gc.collect()
+                consecutive_errors = 0
+            # Skip this iteration on input/read error
+            continue
+
+        # Check if there's enough memory to safely process the request
+        if not check_memory_safety():
+            stats_now = memory_manager.get_memory_stats()
+            # If memory is critically low, refuse to proceed
+            if stats_now['available_mb'] < memory_manager.critical_mb:
+                print("System is critically low on memory. Please close other applications and try again.")
+                print("Cannot proceed due to critically low memory. Please close some applications and try again.")
+                continue
+            # Otherwise, operate in a degraded, aggressive memory mode instead of outright refusing
+            print(f"⚠️ Low memory warning: Only {stats_now['available_mb']:.2f}MB available, need at least {memory_manager.threshold_mb:.2f}MB")
+            print("Proceeding in degraded mode with aggressive resource reduction. Responses may be less detailed or slower.")
+            # Try to create a reduced LLM and continue; if that fails, then abort
+            try:
+                gc.collect()
+                reduced = initialize_llm(progressive_reduction=2)
+                if reduced is not None:
+                    llm = reduced
+                    Settings.llm = reduced
+                    print("Degraded LLM initialized and set as active model.")
+                else:
+                    print("Failed to initialize degraded LLM instance. Aborting request.")
+                    continue
+            except Exception as e:
+                print(f"Failed to initialize degraded LLM due to error: {e}")
+                print("Cannot proceed due to low memory. Please close other applications and try again.")
+                continue
+
+        # Process the query through the agentic workflow
+        print(f"\n--- Processing Query: {user_question} ---")
+
+        try:
+            # Run the agentic workflow
+            result = await agentic_query_process(user_question)
+
+            # Display the result
+            print(f"\nAnswer: {result}")
+
+            # Run garbage collection after successful query
+            gc.collect()
+
+        except Exception as e:
+            # Handle potential memory or timeout errors
+            if "model requires more system memory" in str(e) or "memory" in str(e).lower() or "timed out" in str(e).lower() or "timeout" in str(e).lower():
+                print(f"\n⚠️ Model issue detected: {str(e)}")
+
+                # Try to free up memory
+                gc.collect()
+
+                # (No template fallback path.)
+                print(f"Trying with smaller model ({FALLBACK_MODEL_NAME})...")
+
+                # Create a new LLM with the smaller model and more aggressive memory settings
+                try:
+                    fallback_llm = Ollama(
+                        model=FALLBACK_MODEL_NAME,
+                        base_url=OLLAMA_BASE_URL,
+                        temperature=0.1,
+                        request_timeout=30.0,  # Shorter timeout for fallback model
+                        additional_kwargs={
+                            "num_ctx": 1024,      # Ultra-minimal context window
+                            "num_batch": 1,      # Minimal batch size
+                            "num_gpu": 0,        # Force CPU mode
+                            "f16_kv": True,      # Use half-precision
+                            "mirostat": 0,       # Disable mirostat sampling
+                            "num_thread": 1,     # Minimal threads
+                            "seed": 42,          # Consistent seed
+                            "top_k": 20,
+                            "top_p": 0.9,
+                            "num_predict": 256
+                        }
+                    )
+
+                    # Temporarily replace the LLM
+                    original_llm = llm
+                    llm = fallback_llm
+                    Settings.llm = fallback_llm
+
+                    # Try again with the smaller model
+                    try:
+                        result = await agentic_query_process(user_question)
+                        print(f"\nAnswer (using fallback model): {result}")
+                    except Exception as inner_fallback_error:
+                        print(f"Fallback model also failed: {str(inner_fallback_error)}")
+                        print("All query generation methods failed. Please try rephrasing your question.")
+                except Exception as fallback_error:
+                    print(f"Error with fallback model: {str(fallback_error)}")
+                    print("All query generation methods failed. Please try rephrasing your question.")
+
+        # If we've had too many consecutive errors, trigger a manual garbage collection
+        if consecutive_errors >= max_consecutive_errors:
+            print("Multiple consecutive errors detected. Performing memory cleanup...")
+            gc.collect()
+            consecutive_errors = 0
+        
+
+if __name__ == "__main__":
+    asyncio.run(main())
\ No newline at end of file
diff --git a/Agent/chart_contract.py b/Agent/chart_contract.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc054b3d0a7e58434423d7532a9f0a95773b6e21
--- /dev/null
+++ b/Agent/chart_contract.py
@@ -0,0 +1,237 @@
+"""
+Chart contract — turns query result rows into a rendering-agnostic chart spec.
+
+The text2SQL pipeline produces plain result rows. This module detects rows that
+visualize well (category breakdowns, time series, comparisons) and produces a
+compact JSON spec embedded in the answer text as:
+
+    <<<CHART_JSON>>>{...spec...}<<<END_CHART_JSON>>>
+
+The worker strips the marker out of the user-visible answer and ships the spec
+separately. The web UI renders it with ECharts in the sepia palette. If no
+reliable vega-like shape is found, no marker is added and the UI falls back to
+tables/text — chart detection must never corrupt a correct answer.
+"""
+from __future__ import annotations
+
+import json
+import re
+from datetime import date, datetime
+from decimal import Decimal
+from typing import Any, Dict, List, Optional, Tuple
+
+CHART_OPEN = "<<<CHART_JSON>>>"
+CHART_CLOSE = "<<<END_CHART_JSON>>>"
+
+MAX_CHART_ROWS = 60          # cap categories drawn
+MAX_LABEL_LEN = 40           # truncate long labels
+PIE_MAX_ROWS = 8             # pies stay readable up to ~8 slices
+
+SERIES_NAME_HINTS = (
+    ("count", "count"), ("total", "total"), ("sum", "total"),
+    ("avg", "average"), ("average", "average"), ("min", "minimum"),
+    ("max", "maximum"), ("salary", "value"), ("amount", "value"),
+)
+
+DATE_RE = re.compile(
+    r"^\d{4}([-/.]\d{1,2}([-/.]\d{1,2})?)?$"          # 2024 / 2024-01 / 2024-01-01
+)
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+def _num(v: Any) -> Optional[float]:
+    if isinstance(v, bool):
+        return None
+    if isinstance(v, (int, float)) and v == v:  # NaN guard
+        return float(v)
+    if isinstance(v, Decimal):
+        try:
+            return float(v)
+        except Exception:
+            return None
+    if isinstance(v, str):
+        s = v.strip().replace(",", "")
+        if re.fullmatch(r"-?\d+(\.\d+)?", s):
+            try:
+                return float(s)
+            except ValueError:
+                return None
+    return None
+
+
+def _label(v: Any) -> str:
+    if v is None:
+        return "(none)"
+    if isinstance(v, (date, datetime)):
+        return str(v)[:MAX_LABEL_LEN]
+    s = str(v).strip()
+    if len(s) > MAX_LABEL_LEN:
+        s = s[: MAX_LABEL_LEN - 1] + "…"
+    return s
+
+
+def _is_dateish(v: Any) -> bool:
+    if isinstance(v, (date, datetime)):
+        return True
+    s = str(v).strip()
+    return bool(s) and bool(DATE_RE.match(s))
+
+
+def _nice_series_name(col: str) -> str:
+    c = (col or "").strip()
+    cl = c.lower()
+    for key, nice in SERIES_NAME_HINTS:
+        if key in cl:
+            base = c.split("(")[0].strip()  # strip "AVG(" noise in aliases
+            return base or nice
+    return c
+
+
+# ---------------------------------------------------------------------------
+# Detection
+# ---------------------------------------------------------------------------
+def detect_chart_spec(results: List[Dict[str, Any]], question: str = "") -> Optional[Dict[str, Any]]:
+    """Return a chart spec dict when `results` visualize well, else None.
+
+    Spec shape (ECharts-friendly, library-agnostic):
+    {
+      "type": "pie" | "bar" | "line",
+      "title": str,             # derived from the question
+      "xlabel": str, "ylabel": str,
+      "x": [labels],            # categories or time points
+      "series": [{"name": str, "values": [numbers]}],
+      "rows_in_result": int
+    }
+    """
+    if not results or not isinstance(results, list):
+        return None
+    if not isinstance(results[0], dict) or results[0].get("error"):
+        return None
+
+    rows = [r for r in results if isinstance(r, dict)]
+    if len(rows) > MAX_CHART_ROWS:
+        return None
+
+    cols = list(rows[0].keys())
+    if not cols:
+        return None
+
+    label_col: Optional[str] = None
+    time_col: Optional[str] = None
+    numeric_cols: List[str] = []
+
+    for c in cols:
+        vals = [r.get(c) for r in rows]
+        non_none = [v for v in vals if v is not None]
+        if not non_none:
+            continue
+        # Date-like strings take precedence over numeric-string coercion
+        # (a "2020"/"2024-01" column is a time axis, not a measurement).
+        all_dateish = all(_is_dateish(v) for v in non_none)
+        if all_dateish and time_col is None and label_col is None:
+            time_col = c
+            continue
+        nums = [_num(v) for v in vals]
+        n_numeric = sum(1 for n in nums if n is not None)
+        if n_numeric == len(non_none):
+            numeric_cols.append(c)
+        elif label_col is None:
+            uniq = {str(v) for v in non_none}
+            if 0 < len(uniq) <= MAX_CHART_ROWS:  # degenerate constant columns still ok
+                label_col = c
+
+    if time_col:
+        label_col = time_col
+
+    if label_col is None or not numeric_cols:
+        return None
+
+    labels = [_label(r.get(label_col)) for r in rows]
+
+    series: List[Dict[str, Any]] = []
+    for c in numeric_cols[:3]:  # at most 3 series keeps charts readable
+        values: List[Optional[float]] = []
+        ok = True
+        for r in rows:
+            n = _num(r.get(c))
+            if n is None:
+                ok = False
+                break
+            values.append(n)
+        if ok and any(v != 0 for v in values):
+            values = [round(v, 6) for v in values]
+            series.append({"name": _nice_series_name(c), "values": values})
+
+    if not series:
+        return None
+
+    # Chart type selection --------------------------------------------------
+    ql = (question or "").lower()
+    proportion_intent = any(
+        k in ql for k in ("distribution", "share", "percentage", "percent", "proportion", "%", "breakdown", "split")
+    )
+    trend_intent = time_col is not None or any(
+        k in ql for k in ("trend", "over time", "per year", "by year", "per month", "by month", "growth")
+    )
+
+    single_series = len(series) == 1
+    if single_series and len(labels) <= PIE_MAX_ROWS and (proportion_intent or trend_intent is False):
+        chart_type = "pie"
+    elif trend_intent:
+        chart_type = "line"
+    elif single_series and len(labels) <= PIE_MAX_ROWS and len(rows) <= PIE_MAX_ROWS:
+        chart_type = "pie"
+    else:
+        chart_type = "bar"
+
+    title = (question or "").strip().rstrip("?").strip()
+    if len(title) > 80:
+        title = title[:79] + "…"
+
+    return {
+        "type": chart_type,
+        "title": title or "Query result",
+        "xlabel": _nice_series_name(label_col),
+        "ylabel": series[0]["name"] if single_series else "value",
+        "x": labels,
+        "series": series,
+        "rows_in_result": len(rows),
+    }
+
+
+# ---------------------------------------------------------------------------
+# Embedding / extraction
+# ---------------------------------------------------------------------------
+def embed_chart_marker(answer_text: str, spec: Optional[Dict[str, Any]]) -> str:
+    """Append the chart marker to an answer. Safe to call with spec=None."""
+    if not spec:
+        return answer_text
+    try:
+        payload = json.dumps(spec, ensure_ascii=False, separators=(",", ":"), default=str)
+    except (TypeError, ValueError):
+        return answer_text
+    return f"{answer_text}\n{CHART_OPEN}{payload}{CHART_CLOSE}"
+
+
+def extract_chart_marker(text: Optional[str]) -> Tuple[Optional[str], Optional[Dict[str, Any]]]:
+    """Split an answer into (clean_text, chart_spec|None).
+
+    Never throws; on malformed payloads the marker text is left in the answer
+    if extraction fails (chart simply won't render).
+    """
+    if not text or CHART_OPEN not in text:
+        return text, None
+    head, rest = text.split(CHART_OPEN, 1)
+    if CHART_CLOSE not in rest:
+        return text, None
+    payload, tail = rest.split(CHART_CLOSE, 1)
+    clean = (head.rstrip() + ("\n" + tail.strip() if tail.strip() else "")).strip()
+    try:
+        spec = json.loads(payload)
+        if isinstance(spec, dict) and spec.get("type") in ("pie", "bar", "line") and spec.get("series"):
+            return clean, spec
+    except (ValueError, TypeError):
+        pass
+    return text, None
diff --git a/Agent/db_uri.py b/Agent/db_uri.py
new file mode 100644
index 0000000000000000000000000000000000000000..00b7d2b793c7b703033dd31d2e355ef109ceaaa2
--- /dev/null
+++ b/Agent/db_uri.py
@@ -0,0 +1,159 @@
+"""
+Database-agnostic URI normalization for SQLAlchemy.
+
+QueryLens is architected to interface with PostgreSQL, MySQL, SQLite,
+MS SQL Server and Oracle. Users habitually type bare scheme URLs such as
+`mysql://user:pass@host/db`, but SQLAlchemy resolves bare schemes to
+dialects whose drivers may not be installed (`mysqldb`, `pyodbc`,
+`cx_Oracle`). This module maps every commonly typed scheme onto the
+drivers the project actually ships:
+
+  postgres:// / postgresql:// .... postgresql+psycopg2  (sync)  | postgresql+asyncpg (async)
+  mysql:// / mariadb:// .......... mysql+pymysql        (sync)  | mysql+aiomysql    (async)
+  mssql:// / sqlserver:// ........ mssql+pymssql        (sync)  | no async driver exists
+  oracle:// ...................... oracle+oracledb thin (sync)  | no stable async dialect
+  sqlite:///path ................. sqlite               (sync)  | sqlite+aiosqlite  (async)
+
+URLs that already pin a dialect driver (`scheme+driver://`) are respected:
+the pinned driver is kept for the layer it belongs to, and the other layer
+gets its family's default driver. Fully unknown schemes are passed through
+end to end so users can bring any SQLAlchemy-compatible database.
+
+No heavy imports here: drivers are lazy-loaded by SQLAlchemy itself, so a
+machine without (say) pymssql still boots and only fails when someone
+actually connects to an MSSQL URL.
+"""
+import re
+from typing import Dict, Optional, Tuple
+
+_SCHEME_RE = re.compile(r"^([a-zA-Z][a-zA-Z0-9+._-]*)://")
+
+_FAMILY_BY_SCHEME = {
+    "postgres": "postgresql",
+    "postgresql": "postgresql",
+    "pg": "postgresql",
+    "mysql": "mysql",
+    "mariadb": "mysql",
+    "mssql": "mssql",
+    "sqlserver": "mssql",
+    "oracle": "oracle",
+    "sqlite": "sqlite",
+}
+
+_SYNC_DRIVERS = {
+    "postgresql": "psycopg2",
+    "mysql": "pymysql",
+    "mssql": "pymssql",
+    "oracle": "oracledb",
+}
+_ASYNC_DRIVERS = {
+    "postgresql": "asyncpg",
+    "mysql": "aiomysql",
+    "sqlite": "aiosqlite",
+}
+_PREFIXES_OF_ASYNC_DRIVERS = {"asyncpg", "aiomysql", "aiosqlite", "asyncpg+gssapi"}
+
+
+def _parse(url: str) -> Tuple[Optional[str], Optional[str], str]:
+    """Split a URL into (sqlalchemy_family, pinned_driver|None, '://rest')."""
+    m = _SCHEME_RE.match(url or "")
+    if not m:
+        return None, None, url or ""
+    scheme = m.group(1).lower()
+    head, _, driver = scheme.partition("+")
+    family = _FAMILY_BY_SCHEME.get(head, head)
+    # '://' bytes remain part of the tail so callers can rebuild the URL
+    return family, (driver or None) or None, url[m.end(1):]
+
+
+# libpq connection parameters that the shipped drivers cannot accept.
+# psycopg2.connect() validates keyword arguments against its own whitelist and
+# raises TypeError "connect() got an unexpected keyword argument '<name>'" for
+# anything else; asyncpg behaves the same. Neon's current console template
+# appends `channel_binding=require` to freshly copied connection strings,
+# which trips both drivers even though the URL is otherwise valid - so strip
+# the offending params here instead of asking users to hand-edit pasted
+# credentials.
+_DRIVER_UNSUPPORTED_QUERY_PARAMS = ("channel_binding",)
+
+
+def sanitize_driver_query_params(raw_uri: str) -> str:
+    """Drop query parameters the shipped drivers reject (e.g. Neon's
+    `channel_binding=require` from freshly copied connection strings).
+
+    Scoped to the PostgreSQL family (the only dialect those libpq params
+    apply to); TLS via `sslmode` and the password are preserved verbatim.
+    Returns the URL unchanged when nothing needs stripping.
+    """
+    url = (raw_uri or "").strip()
+    if not url or "://" not in url:
+        return raw_uri or url
+    family, _, _ = _parse(url)
+    if family != "postgresql":
+        return url
+    try:
+        from sqlalchemy.engine import make_url  # lazy: keep db_uri import-light
+        _u = make_url(url)
+    except Exception:
+        # Regex fallback for URLs make_url cannot parse.
+        out = url
+        for _name in _DRIVER_UNSUPPORTED_QUERY_PARAMS:
+            out = re.sub(r"[?&]" + re.escape(_name) + r"=[^&]*", "", out, flags=re.IGNORECASE)
+        if "?" not in out and "&" in out.split("//", 1)[-1]:
+            out = out.replace("&", "?", 1)
+        return out
+    query = dict(_u.query)
+    dropped = [k for k in _DRIVER_UNSUPPORTED_QUERY_PARAMS if k in query]
+    if not dropped:
+        return url
+    for k in dropped:
+        query.pop(k, None)
+    try:
+        return _u.set(query=query).render_as_string(hide_password=False)
+    except Exception:
+        # Older SQLAlchemy without render_as_string: render manually.
+        _pw = _u.password
+        _auth = (_u.username + (f":{_pw}" if _pw else "") + "@") if _u.username else ""
+        _hostport = (_u.host or "") + (f":{_u.port}" if _u.port else "")
+        _db = ("/" + _u.database) if _u.database else ""
+        _qs = "&".join(f"{k}={v}" for k, v in query.items()) if query else ""
+        return f"{_u.drivername}://{_auth}{_hostport}{_db}" + (f"?{_qs}" if _qs else "")
+
+
+def normalize_db_uris(raw_uri: str) -> Dict[str, Optional[str]]:
+    """Normalize a user-supplied connection URL for dual-layer engine use.
+
+    Returns:
+        {
+          "family":   "postgresql" | "mysql" | "mssql" | "oracle" | "sqlite" | None,
+          "sync_uri": URL for SQLAlchemy.create_engine          (always set),
+          "async_uri": URL for create_async_engine, or None when the family
+                       has no usable async driver (mssql / oracle / exotic),
+        }
+    """
+    url = sanitize_driver_query_params(str(raw_uri or "").strip())
+    if not url:
+        return {"family": None, "sync_uri": "", "async_uri": None}
+
+    family, pinned, tail = _parse(url)
+    if family is None:
+        # Not a scheme URL at all (plain path, DSN name, ...) — trust the
+        # caller verbatim but keep the async layer off (no async dialect).
+        return {"family": None, "sync_uri": url, "async_uri": None}
+
+    if pinned:
+        if pinned in _PREFIXES_OF_ASYNC_DRIVERS:
+            # User handed us an ASYNC URL; pin the sync layer to the family's
+            # sync driver and keep their async URL verbatim.
+            sync_uri = f"{family}+{_SYNC_DRIVERS[family]}{tail}" if family in _SYNC_DRIVERS else f"{family}{tail}"
+            return {"family": family, "sync_uri": sync_uri, "async_uri": url}
+        # Sync (or exotic) driver pinned: keep it verbatim so the user stays in
+        # full control; async layer uses the family default when one exists.
+        sync_uri = url
+        async_uri = f"{family}+{_ASYNC_DRIVERS[family]}{tail}" if family in _ASYNC_DRIVERS else None
+        return {"family": family, "sync_uri": sync_uri, "async_uri": async_uri}
+
+    # Bare scheme: map both layers onto the drivers this project ships.
+    sync_uri = f"{family}+{_SYNC_DRIVERS[family]}{tail}" if family in _SYNC_DRIVERS else url
+    async_uri = f"{family}+{_ASYNC_DRIVERS[family]}{tail}" if family in _ASYNC_DRIVERS else None
+    return {"family": family, "sync_uri": sync_uri, "async_uri": async_uri}
diff --git a/Agent/invariants.py b/Agent/invariants.py
new file mode 100644
index 0000000000000000000000000000000000000000..11bf2c891867a80fb18f4b9b67bd9e83d713d60f
--- /dev/null
+++ b/Agent/invariants.py
@@ -0,0 +1,499 @@
+"""Invariant classification and enforcement for text2sql without hardcoded SQL templates.
+
+All regeneration remains purely LLM-driven. We only express semantic/structural
+constraints ("invariants") and ask the model to satisfy missing ones.
+
+Categories:
+  MANAGER_LIST          -> list current department managers
+  DEPARTMENT_LIST       -> list department names
+  AVG_SALARY_BY_DEPT    -> average current salary per department
+  TITLE_COUNT           -> count employees with a given title (e.g. Senior Engineer)
+  GENERIC               -> anything else (no special invariants)
+
+Each invariant is represented as a dict:
+  {
+    'name': short identifier,
+    'description': human-readable guidance,
+    'check': callable(sql:str)->bool
+  }
+
+We DO NOT inject SQL fragments. We only:
+  - detect which invariants fail
+  - compile a focused regeneration prompt listing just the missing invariants
+  - request a corrected SQL (single statement ending with semicolon + LIMIT 100)
+
+This preserves the "no hardcoded fallback query" policy.
+"""
+from __future__ import annotations
+import os
+import re
+from typing import List, Dict, Callable, Optional
+
+CATEGORY_MANAGER_LIST = "MANAGER_LIST"
+CATEGORY_DEPT_LIST = "DEPARTMENT_LIST"
+CATEGORY_AVG_SALARY = "AVG_SALARY_BY_DEPT"
+CATEGORY_TITLE_COUNT = "TITLE_COUNT"
+CATEGORY_EMP_COUNT_BY_DEPT = "EMP_COUNT_BY_DEPT"
+CATEGORY_GENERIC = "GENERIC"
+
+# Newly added categories (extended taxonomy)
+CATEGORY_SIMPLE_COUNT_EMP = "SIMPLE_COUNT_EMP"
+CATEGORY_SIMPLE_COUNT_EMP_CURRENT = "SIMPLE_COUNT_EMP_CURRENT"
+CATEGORY_SIMPLE_COUNT_DEPT = "SIMPLE_COUNT_DEPT"
+CATEGORY_SALARY_EXTREMES_GLOBAL = "SALARY_EXTREMES_GLOBAL"
+CATEGORY_SALARY_RANGE_BY_DEPT = "SALARY_RANGE_BY_DEPT"
+CATEGORY_TOP_N_SALARIES = "TOP_N_SALARIES"
+CATEGORY_DEPT_MANAGER_LIST = "DEPT_MANAGER_LIST"  # dept + manager names
+CATEGORY_MANAGER_TENURE = "MANAGER_TENURE"
+CATEGORY_MANAGER_CHANGE_FREQUENCY = "MANAGER_CHANGE_FREQUENCY"
+CATEGORY_TITLE_DISTINCT_COUNT = "TITLE_DISTINCT_COUNT"
+CATEGORY_TITLE_SPECIFIC_COUNT = "TITLE_SPECIFIC_COUNT"
+CATEGORY_TITLE_DISTRIBUTION_BY_DEPT = "TITLE_DISTRIBUTION_BY_DEPT"
+CATEGORY_PROMOTION_PATH_ENGINEER_TO_SENIOR = "PROMOTION_PATH_ENGINEER_TO_SENIOR"
+CATEGORY_EMP_TENURE_CURRENT_DEPT = "EMP_TENURE_CURRENT_DEPT"
+CATEGORY_HIRING_TREND_BY_YEAR = "HIRING_TREND_BY_YEAR"
+CATEGORY_SALARY_TREND_BY_YEAR = "SALARY_TREND_BY_YEAR"
+CATEGORY_EMP_DEPT_CHANGES = "EMP_DEPT_CHANGES"
+CATEGORY_GENDER_DIST_BY_DEPT = "GENDER_DIST_BY_DEPT"
+CATEGORY_GENDER_PAY_GAP_BY_DEPT = "GENDER_PAY_GAP_BY_DEPT"
+CATEGORY_FEMALE_MANAGERS_COUNT = "FEMALE_MANAGERS_COUNT"
+CATEGORY_AVG_SALARY_BY_TITLE_AND_DEPT = "AVG_SALARY_BY_TITLE_AND_DEPT"
+CATEGORY_GENDER_MANAGEMENT_COUNT = "GENDER_MANAGEMENT_COUNT"
+CATEGORY_SENIOR_STAFF_SUPERLATIVE = "SENIOR_STAFF_SUPERLATIVE"
+
+Category = str
+
+# ---------------- Classification -----------------
+
+def classify_question(question: str) -> Category:
+    q = question.lower()
+    # Specific order matters: more specialized patterns first
+    if any(k in q for k in ["top", "highest-paid", "highest paid"]) and "salary" in q:
+        return CATEGORY_TOP_N_SALARIES
+    if any(kw in q for kw in ["total number of employees", "total employees"]) or ("count" in q and "employee" in q and "department" not in q and "current" not in q):
+        return CATEGORY_SIMPLE_COUNT_EMP
+    if ("how many" in q or "count" in q) and "currently" in q and "employee" in q:
+        return CATEGORY_SIMPLE_COUNT_EMP_CURRENT
+    if ("how many" in q or "number of" in q or "count" in q) and "departments" in q and "manager" not in q:
+        return CATEGORY_SIMPLE_COUNT_DEPT
+    if any(w in q for w in ["highest", "lowest"]) and "salary" in q and "department" not in q:
+        return CATEGORY_SALARY_EXTREMES_GLOBAL
+    if "salary range" in q and "department" in q:
+        return CATEGORY_SALARY_RANGE_BY_DEPT
+    if "most employees" in q and "department" in q:
+        return CATEGORY_EMP_COUNT_BY_DEPT
+    if "highest average salary" in q and "department" in q:
+        return CATEGORY_AVG_SALARY
+    if any(k in q for k in ["manager", "managers"]) and "department" in q:
+        if "tenure" in q or "long" in q or "position" in q:
+            return CATEGORY_MANAGER_TENURE
+        if "changed" in q or "changes" in q or "frequently" in q:
+            return CATEGORY_MANAGER_CHANGE_FREQUENCY
+        if "list" in q and "manager" in q and "department" in q:
+            return CATEGORY_DEPT_MANAGER_LIST
+        return CATEGORY_MANAGER_LIST
+    if ("how many" in q or "count" in q) and "job title" in q and ("exist" in q or "different" in q):
+        return CATEGORY_TITLE_DISTINCT_COUNT
+    if ("how many" in q or "count" in q) and any(t in q for t in ["senior engineer", "senior engineers"]):
+        return CATEGORY_TITLE_SPECIFIC_COUNT
+    if "distribution" in q and "title" in q and "department" in q:
+        return CATEGORY_TITLE_DISTRIBUTION_BY_DEPT
+    if "promoted" in q and "engineer" in q and "senior engineer" in q:
+        return CATEGORY_PROMOTION_PATH_ENGINEER_TO_SENIOR
+    if ("how long" in q or "tenure" in q) and "current" in q and "department" in q and "employee" in q:
+        return CATEGORY_EMP_TENURE_CURRENT_DEPT
+    if ("hiring trend" in q) or ("hire" in q and "year" in q):
+        return CATEGORY_HIRING_TREND_BY_YEAR
+    if ("salaries" in q or "salary" in q) and "changed" in q and ("over time" in q or "year" in q):
+        return CATEGORY_SALARY_TREND_BY_YEAR
+    if "changed departments" in q or ("employees" in q and "changed" in q and "department" in q):
+        return CATEGORY_EMP_DEPT_CHANGES
+    if "gender distribution" in q and "department" in q:
+        return CATEGORY_GENDER_DIST_BY_DEPT
+    if "gender pay gap" in q and "department" in q:
+        return CATEGORY_GENDER_PAY_GAP_BY_DEPT
+    if ("female" in q and "manager" in q and ("how many" in q or "count" in q)):
+        return CATEGORY_FEMALE_MANAGERS_COUNT
+    if "average salary" in q and "title" in q and "department" in q:
+        return CATEGORY_AVG_SALARY_BY_TITLE_AND_DEPT
+    if ("gender" in q and "management" in q) or ("each gender" in q and "management" in q):
+        return CATEGORY_GENDER_MANAGEMENT_COUNT
+    if ("most" in q and "senior" in q and "staff" in q) or ("senior staff" in q and "most" in q):
+        return CATEGORY_SENIOR_STAFF_SUPERLATIVE
+    if any(k in q for k in ["list departments", "department names", "department name", "departments list"]):
+        return CATEGORY_DEPT_LIST
+    if "average" in q and "salary" in q and "department" in q:
+        return CATEGORY_AVG_SALARY
+    if ("count" in q or "how many" in q or "number of" in q) and any(t in q for t in ["engineer", "engineers", "title"]):
+        return CATEGORY_TITLE_COUNT
+    if ("count" in q or "how many" in q or "number of" in q or any(s in q for s in ["most", "largest", "highest", "fewest", "least"])) and "department" in q and ("employee" in q or "employees" in q):
+        return CATEGORY_EMP_COUNT_BY_DEPT
+    return CATEGORY_GENERIC
+
+# --------------- Invariant Builders ---------------
+
+def _regex(pattern: str) -> Callable[[str], bool]:
+    rx = re.compile(pattern, re.IGNORECASE)
+    return lambda sql: bool(rx.search(sql))
+
+def _contains_all(*subs: str) -> Callable[[str], bool]:
+    def check(sql: str) -> bool:
+        l = sql.lower()
+        return all(s.lower() in l for s in subs)
+    return check
+
+def build_invariants(category: Category, question: str) -> List[Dict[str, any]]:
+    inv: List[Dict[str, any]] = []
+    if category == CATEGORY_MANAGER_LIST:
+        inv += [
+            {"name": "has_department_join", "description": "Join department (alias d) with dept_manager (alias dm)", "check": lambda s: re.search(r"join\s+dept_manager", s, re.IGNORECASE) and re.search(r"from\s+department", s, re.IGNORECASE)},
+            {"name": "has_employee_join", "description": "Join employee (alias e) for manager names", "check": _regex(r"join\s+employee")},
+            {"name": "selects_names", "description": "Select employee first_name and last_name", "check": lambda s: all(x in s.lower() for x in ["first_name", "last_name"])},
+            {"name": "filters_current", "description": "Filter current managers dm.to_date='9999-01-01'", "check": _regex(r"dm\.to_date\s*=\s*'9999-01-01'")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100 semicolon", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_DEPT_LIST:
+        inv += [
+            {"name": "from_department", "description": "Query FROM department table", "check": _regex(r"from\s+department")},
+            {"name": "select_dept_name", "description": "Select dept_name column", "check": _regex(r"dept_name")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_AVG_SALARY:
+        inv += [
+            {"name": "joins_salary", "description": "Join salary s (FROM salary s)", "check": _regex(r"from\s+salary\s+s")},
+            {"name": "joins_dept_emp", "description": "Join dept_emp de", "check": _regex(r"join\s+dept_emp")},
+            {"name": "joins_department", "description": "Join department d", "check": _regex(r"join\s+department")},
+            {"name": "avg_salary", "description": "Uses AVG(s.salary)", "check": _regex(r"avg\s*\(\s*s\.salary\s*\)")},
+            {"name": "current_filters", "description": "Filters current rows s.to_date='9999-01-01' and de.to_date='9999-01-01'", "check": lambda s: "s.to_date='9999-01-01'" in s.lower() and "de.to_date='9999-01-01'" in s.lower()},
+            {"name": "group_by_dept", "description": "GROUP BY department name", "check": _regex(r"group\s+by\s+.*dept_name")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_TITLE_COUNT:
+        # Attempt to infer canonical title (not used in invariants; regeneration prompt may hint)
+        inv += [
+            {"name": "from_title", "description": "FROM title table (alias t)", "check": _regex(r"from\s+title")},
+            {"name": "alias_t", "description": "Alias 't' used for title table", "check": _regex(r"from\s+title\s+(?:as\s+)?t")},
+            {"name": "current_rows", "description": "Filter current rows t.to_date='9999-01-01'", "check": _regex(r"t\.to_date\s*=\s*'9999-01-01'")},
+            {"name": "count_distinct", "description": "COUNT(DISTINCT t.emp_no)", "check": _regex(r"count\s*\(\s*distinct\s+t\.emp_no\s*\)")},
+            {"name": "has_title_filter", "description": "Filter on t.title = '<Some Title>'", "check": _regex(r"t\.title\s*=\s*'[^']+'")} ,
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_EMP_COUNT_BY_DEPT:
+        inv += [
+            {"name": "has_dept_emp_join", "description": "Join dept_emp de with department d", "check": lambda s: re.search(r"join\s+dept_emp", s, re.IGNORECASE) and re.search(r"join\s+department|from\s+department", s, re.IGNORECASE)},
+            {"name": "select_dept_name", "description": "Select d.dept_name", "check": _regex(r"d\.dept_name|dept_name")},
+            {"name": "select_count", "description": "Include COUNT(*) or COUNT(de.emp_no)", "check": _regex(r"count\s*\(")},
+            {"name": "group_by", "description": "GROUP BY d.dept_name present", "check": _regex(r"group\s+by\s+.*dept_name")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+            {"name": "count_alias", "description": "Provide an explicit alias e.g. COUNT(*) AS employee_count", "check": _regex(r"count\s*\(.*\)\s+as\s+employee_count")},
+        ]
+        # If the question is superlative-oriented, encourage ordering
+        ql = question.lower()
+        if any(w in ql for w in ["most", "largest", "highest", "fewest", "least"]):
+            inv.append({"name": "order_by_count", "description": "ORDER BY employee_count DESC (or ASC for fewest/least)", "check": lambda s: ("fewest" in ql or "least" in ql and re.search(r"order\s+by\s+employee_count\s+asc", s, re.IGNORECASE)) or re.search(r"order\s+by\s+employee_count\s+desc", s, re.IGNORECASE)})
+    elif category == CATEGORY_SIMPLE_COUNT_EMP:
+        inv += [
+            {"name": "from_employee", "description": "FROM employee table", "check": _regex(r"from\s+employee")},
+            {"name": "count_all", "description": "COUNT(*) with alias total_employees", "check": _regex(r"count\s*\(\s*\*\s*\)\s+as\s+total_employees")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_SIMPLE_COUNT_EMP_CURRENT:
+        inv += [
+            {"name": "join_dept_emp", "description": "Join dept_emp de", "check": _regex(r"join\s+dept_emp")},
+            {"name": "current_filter", "description": "de.to_date='9999-01-01' filter", "check": _regex(r"de\.to_date\s*=\s*'9999-01-01'")},
+            {"name": "count_distinct_emp", "description": "COUNT(DISTINCT de.emp_no) alias current_employee_count", "check": _regex(r"count\s*\(\s*distinct\s+de\.emp_no\s*\)\s+as\s+current_employee_count")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_SIMPLE_COUNT_DEPT:
+        inv += [
+            {"name": "from_department", "description": "FROM department", "check": _regex(r"from\s+department")},
+            {"name": "count_depts", "description": "COUNT(*) AS department_count", "check": _regex(r"count\s*\(\s*\*\s*\)\s+as\s+department_count")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_SALARY_EXTREMES_GLOBAL:
+        # Extremely strict invariants for highest/lowest salary by department
+        inv += [
+            {"name": "from_salary_s", "description": "FROM salary s", "check": _regex(r"from\s+salary\s+s")},
+            {"name": "join_dept_emp_de", "description": "JOIN dept_emp de ON s.emp_no = de.emp_no", "check": _regex(r"join\s+dept_emp\s+de\s+on\s+s\.emp_no\s*=\s*de\.emp_no")},
+            {"name": "join_department_d", "description": "JOIN department d ON de.dept_no = d.dept_no", "check": _regex(r"join\s+department\s+d\s+on\s+de\.dept_no\s*=\s*d\.dept_no")},
+            {"name": "select_dept_name", "description": "SELECT d.dept_name", "check": _regex(r"select\s+d\.dept_name")},
+            {"name": "select_max_min_salary", "description": "SELECT MAX(s.salary) AS highest_salary, MIN(s.salary) AS lowest_salary", "check": lambda s: re.search(r"max\s*\(\s*s\.salary\s*\)\s+as\s+highest_salary", s, re.IGNORECASE) and re.search(r"min\s*\(\s*s\.salary\s*\)\s+as\s+lowest_salary", s, re.IGNORECASE)},
+            {"name": "group_by_dept_name", "description": "GROUP BY d.dept_name", "check": _regex(r"group\s+by\s+d\.dept_name")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+            {"name": "no_unaliased_salary", "description": "Do not use MAX(salary) or MIN(salary) without s. alias", "check": lambda s: not re.search(r"max\s*\(\s*salary\s*\)", s, re.IGNORECASE) and not re.search(r"min\s*\(\s*salary\s*\)", s, re.IGNORECASE)},
+            {"name": "no_unaliased_dept_name", "description": "Do not use dept_name without d. alias", "check": lambda s: not re.search(r"[^a-zA-Z0-9_]dept_name", s) or re.search(r"d\.dept_name", s)},
+        ]
+    elif category == CATEGORY_SALARY_RANGE_BY_DEPT:
+        # Stricter invariants for salary range by department
+        inv += [
+            {"name": "joins_salary", "description": "FROM salary s", "check": _regex(r"from\s+salary\s+s")},
+            {"name": "joins_dept_emp", "description": "Join dept_emp de", "check": _regex(r"join\s+dept_emp")},
+            {"name": "joins_department", "description": "Join department d", "check": _regex(r"join\s+department")},
+            {"name": "select_dept_name", "description": "Select d.dept_name", "check": _regex(r"d\.dept_name")},
+            {"name": "min_max", "description": "MIN(s.salary) & MAX(s.salary)", "check": lambda s: re.search(r"min\s*\(\s*s\.salary", s, re.IGNORECASE) and re.search(r"max\s*\(\s*s\.salary", s, re.IGNORECASE)},
+            {"name": "dept_filter", "description": "Filter on d.dept_name=...", "check": _regex(r"d\.dept_name\s*=\s*'")},
+            {"name": "group_by_dept", "description": "GROUP BY d.dept_name", "check": _regex(r"group\s+by\s+.*d\.dept_name")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_TOP_N_SALARIES:
+        inv += [
+            {"name": "joins_salary", "description": "FROM salary s", "check": _regex(r"from\s+salary\s+s")},
+            {"name": "current_filter", "description": "s.to_date='9999-01-01'", "check": _regex(r"s\.to_date\s*=\s*'9999-01-01'")},
+            {"name": "order_desc", "description": "ORDER BY s.salary DESC", "check": _regex(r"order\s+by\s+s\.salary\s+desc")},
+            {"name": "limit_present", "description": "Has a LIMIT clause", "check": _regex(r"limit\s+\d+")},
+        ]
+    elif category == CATEGORY_DEPT_MANAGER_LIST:
+        inv += [
+            {"name": "joins_dept_manager", "description": "Join dept_manager dm", "check": _regex(r"join\s+dept_manager")},
+            {"name": "joins_employee", "description": "Join employee e", "check": _regex(r"join\s+employee")},
+            {"name": "select_names", "description": "Select manager first_name / last_name", "check": lambda s: "first_name" in s.lower() and "last_name" in s.lower()},
+            {"name": "dept_name", "description": "Select dept_name", "check": _regex(r"dept_name")},
+            {"name": "current_filter", "description": "dm.to_date='9999-01-01'", "check": _regex(r"dm\.to_date\s*=\s*'9999-01-01'")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_MANAGER_TENURE:
+        inv += [
+            {"name": "joins_dept_manager", "description": "Join dept_manager dm", "check": _regex(r"join\s+dept_manager")},
+            {"name": "current_filter", "description": "dm.to_date='9999-01-01'", "check": _regex(r"dm\.to_date\s*=\s*'9999-01-01'")},
+            {"name": "tenure_expr", "description": "Contains dm.from_date for tenure calc", "check": _regex(r"dm\.from_date")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_MANAGER_CHANGE_FREQUENCY:
+        inv += [
+            {"name": "from_dept_manager", "description": "FROM dept_manager", "check": _regex(r"from\s+dept_manager")},
+            {"name": "group_by_dept", "description": "GROUP BY dept_no or dept", "check": _regex(r"group\s+by\s+.*dept")},
+            {"name": "count_changes", "description": "COUNT(*) or COUNT(DISTINCT emp_no)", "check": _regex(r"count\s*\(")},
+            {"name": "order_desc", "description": "ORDER BY count desc", "check": _regex(r"order\s+by\s+.*count.*desc")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_TITLE_DISTINCT_COUNT:
+        inv += [
+            {"name": "from_title", "description": "FROM title t", "check": _regex(r"from\s+title")},
+            {"name": "count_distinct_titles", "description": "COUNT(DISTINCT t.title) alias title_count", "check": _regex(r"count\s*\(\s*distinct\s+t\.title\s*\)\s+as\s+title_count")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_TITLE_SPECIFIC_COUNT:
+        inv += [
+            {"name": "from_title", "description": "FROM title t", "check": _regex(r"from\s+title")},
+            {"name": "title_filter", "description": "Filter t.title='Senior Engineer'", "check": _regex(r"t\.title\s*=\s*'senior engineer'")},
+            {"name": "current_filter", "description": "t.to_date='9999-01-01'", "check": _regex(r"t\.to_date\s*=\s*'9999-01-01'")},
+            {"name": "count_distinct_emp", "description": "COUNT(DISTINCT t.emp_no)", "check": _regex(r"count\s*\(\s*distinct\s+t\.emp_no")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_TITLE_DISTRIBUTION_BY_DEPT:
+        inv += [
+            {"name": "joins_title", "description": "Join title t", "check": _regex(r"join\s+title")},
+            {"name": "joins_dept_emp", "description": "Join dept_emp de", "check": _regex(r"join\s+dept_emp")},
+            {"name": "joins_department", "description": "Join department d", "check": _regex(r"join\s+department")},
+            {"name": "current_filters", "description": "t.to_date & de.to_date current filters", "check": lambda s: "t.to_date='9999-01-01'" in s.lower() and "de.to_date='9999-01-01'" in s.lower()},
+            {"name": "group_by", "description": "GROUP BY dept and title", "check": _regex(r"group\s+by\s+.*(dept|title).*(title|dept)")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_PROMOTION_PATH_ENGINEER_TO_SENIOR:
+        inv += [
+            {"name": "dual_title_refs", "description": "References title table twice (t1, t2)", "check": _regex(r"title\s+t1.*title\s+t2|title\s+t2.*title\s+t1")},
+            {"name": "promotion_condition", "description": "t1.title='Engineer' AND t2.title='Senior Engineer'", "check": _regex(r"t1\.title\s*=\s*'engineer'.*t2\.title\s*=\s*'senior engineer'|t2\.title\s*=\s*'senior engineer'.*t1\.title\s*=\s*'engineer'")},
+            {"name": "temporal_order", "description": "t1.from_date < t2.from_date", "check": _regex(r"t1\.from_date\s*<\s*t2\.from_date")},
+            {"name": "distinct_emp", "description": "DISTINCT emp_no selected", "check": _regex(r"select\s+distinct\s+t1\.emp_no|select\s+distinct\s+t2\.emp_no")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_EMP_TENURE_CURRENT_DEPT:
+        inv += [
+            {"name": "joins_dept_emp", "description": "FROM dept_emp de", "check": _regex(r"from\s+dept_emp")},
+            {"name": "current_filter", "description": "de.to_date='9999-01-01'", "check": _regex(r"de\.to_date\s*=\s*'9999-01-01'")},
+            {"name": "tenure_expr", "description": "Uses de.from_date", "check": _regex(r"de\.from_date")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_HIRING_TREND_BY_YEAR:
+        inv += [
+            {"name": "from_employee", "description": "FROM employee e", "check": _regex(r"from\s+employee")},
+            {"name": "year_extract", "description": "EXTRACT(YEAR FROM hire_date)", "check": _regex(r"extract\s*\(\s*year\s+from\s+.*hire_date")},
+            {"name": "group_by_year", "description": "GROUP BY year", "check": _regex(r"group\s+by\s+.*year")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_SALARY_TREND_BY_YEAR:
+        inv += [
+            {"name": "from_salary", "description": "FROM salary s", "check": _regex(r"from\s+salary")},
+            {"name": "year_extract", "description": "EXTRACT(YEAR FROM s.from_date)", "check": _regex(r"extract\s*\(\s*year\s+from\s+s\.from_date")},
+            {"name": "avg_salary", "description": "AVG(s.salary)", "check": _regex(r"avg\s*\(\s*s\.salary")},
+            {"name": "group_by_year", "description": "GROUP BY year", "check": _regex(r"group\s+by\s+.*year")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_EMP_DEPT_CHANGES:
+        inv += [
+            {"name": "from_dept_emp", "description": "FROM dept_emp de", "check": _regex(r"from\s+dept_emp")},
+            {"name": "group_by_emp", "description": "GROUP BY de.emp_no", "check": _regex(r"group\s+by\s+.*emp_no")},
+            {"name": "having_multiple", "description": "HAVING COUNT(DISTINCT dept_no) > 1", "check": _regex(r"having\s+count\s*\(\s*distinct\s+dept_no\s*\)\s*>\s*1")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_GENDER_DIST_BY_DEPT:
+        inv += [
+            {"name": "joins_employee", "description": "Join employee e", "check": _regex(r"join\s+employee")},
+            {"name": "joins_dept_emp", "description": "Join dept_emp de", "check": _regex(r"join\s+dept_emp")},
+            {"name": "joins_department", "description": "Join department d", "check": _regex(r"join\s+department")},
+            {"name": "current_filter", "description": "de.to_date='9999-01-01'", "check": _regex(r"de\.to_date\s*=\s*'9999-01-01'")},
+            {"name": "group_by_dept_gender", "description": "GROUP BY dept & gender", "check": _regex(r"group\s+by\s+.*(dept|gender).*(gender|dept)")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_GENDER_PAY_GAP_BY_DEPT:
+        inv += [
+            {"name": "joins_salary", "description": "Join salary s", "check": _regex(r"join\s+salary")},
+            {"name": "joins_employee", "description": "Join employee e", "check": _regex(r"join\s+employee")},
+            {"name": "joins_dept_emp", "description": "Join dept_emp de", "check": _regex(r"join\s+dept_emp")},
+            {"name": "joins_department", "description": "Join department d", "check": _regex(r"join\s+department")},
+            {"name": "current_filters", "description": "Current rows s.to_date & de.to_date", "check": lambda s: "s.to_date='9999-01-01'" in s.lower() and "de.to_date='9999-01-01'" in s.lower()},
+            {"name": "avg_salary", "description": "AVG(s.salary)", "check": _regex(r"avg\s*\(\s*s\.salary")},
+            {"name": "group_by_dept_gender", "description": "GROUP BY dept & gender", "check": _regex(r"group\s+by\s+.*(dept|gender).*(gender|dept)")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_FEMALE_MANAGERS_COUNT:
+        inv += [
+            {"name": "joins_dept_manager", "description": "Join dept_manager dm", "check": _regex(r"join\s+dept_manager")},
+            {"name": "joins_employee", "description": "Join employee e", "check": _regex(r"join\s+employee")},
+            {"name": "female_filter", "description": "e.gender='F'", "check": _regex(r"e\.gender\s*=\s*'f'")},
+            {"name": "current_filter", "description": "dm.to_date='9999-01-01'", "check": _regex(r"dm\.to_date\s*=\s*'9999-01-01'")},
+            {"name": "count_distinct_mgr", "description": "COUNT(DISTINCT dm.emp_no)", "check": _regex(r"count\s*\(\s*distinct\s+dm\.emp_no")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_AVG_SALARY_BY_TITLE_AND_DEPT:
+        inv += [
+            {"name": "joins_salary", "description": "Join salary s", "check": _regex(r"join\s+salary")},
+            {"name": "joins_title", "description": "Join title t", "check": _regex(r"join\s+title")},
+            {"name": "joins_dept_emp", "description": "Join dept_emp de", "check": _regex(r"join\s+dept_emp")},
+            {"name": "joins_department", "description": "Join department d", "check": _regex(r"join\s+department")},
+            {"name": "current_filters", "description": "Current rows for s,de,t", "check": lambda s: all(x in s.lower() for x in ["s.to_date='9999-01-01'", "de.to_date='9999-01-01'", "t.to_date='9999-01-01'"])},
+            {"name": "avg_salary", "description": "AVG(s.salary)", "check": _regex(r"avg\s*\(\s*s\.salary")},
+            {"name": "group_by", "description": "GROUP BY dept & title", "check": _regex(r"group\s+by\s+.*(dept|title).*(title|dept)")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_GENDER_MANAGEMENT_COUNT:
+        inv += [
+            {"name": "joins_dept_manager", "description": "Join dept_manager dm", "check": _regex(r"join\s+dept_manager")},
+            {"name": "joins_employee", "description": "Join employee e", "check": _regex(r"join\s+employee")},
+            {"name": "current_filter", "description": "dm.to_date='9999-01-01'", "check": _regex(r"dm\.to_date\s*=\s*'9999-01-01'")},
+            {"name": "group_by_gender", "description": "GROUP BY gender", "check": _regex(r"group\s+by\s+.*gender")},
+            {"name": "count_mgr", "description": "COUNT(*) or COUNT(dm.emp_no)", "check": _regex(r"count\s*\(")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    elif category == CATEGORY_SENIOR_STAFF_SUPERLATIVE:
+        inv += [
+            {"name": "joins_title", "description": "Join title t", "check": _regex(r"join\s+title")},
+            {"name": "joins_dept_emp", "description": "Join dept_emp de", "check": _regex(r"join\s+dept_emp")},
+            {"name": "joins_department", "description": "Join department d", "check": _regex(r"join\s+department")},
+            {"name": "senior_filter", "description": "t.title LIKE 'Senior%'", "check": _regex(r"t\.title\s+like\s+'senior%'")},
+            {"name": "current_filters", "description": "Current rows de.to_date & t.to_date", "check": lambda s: "de.to_date='9999-01-01'" in s.lower() and "t.to_date='9999-01-01'" in s.lower()},
+            {"name": "group_by_dept", "description": "GROUP BY d.dept_name", "check": _regex(r"group\s+by\s+.*dept_name")},
+            {"name": "order_desc", "description": "ORDER BY count desc", "check": _regex(r"order\s+by\s+.*count.*desc")},
+            {"name": "limit_100", "description": "Ends with LIMIT 100", "check": _regex(r"limit\s+100;?\s*$")},
+        ]
+    return inv
+
+# --------------- Enforcement -----------------
+
+def evaluate_invariants(sql: str, invariants: List[Dict[str, any]]) -> List[Dict[str, any]]:
+    failures = []
+    for inv in invariants:
+        try:
+            ok = inv['check'](sql)
+        except Exception:
+            ok = False
+        if not ok:
+            failures.append(inv)
+    return failures
+
+
+# More explicit prompt for join-based salary extremes queries
+REGEN_PROMPT_TEMPLATE = (
+    "You produced SQL for the user question. Some required invariants are missing.\n"
+    "Regenerate a SINGLE valid PostgreSQL SQL query that satisfies ALL listed invariants.\n"
+    "Return ONLY the SQL (no commentary). Must end with LIMIT 100; and a semicolon.\n"
+    "If the question is about highest/lowest salary by department, you MUST:\n"
+    "- Use FROM salary s\n"
+    "- JOIN dept_emp de ON s.emp_no = de.emp_no\n"
+    "- JOIN department d ON de.dept_no = d.dept_no\n"
+    "- SELECT d.dept_name, MAX(s.salary) AS highest_salary, MIN(s.salary) AS lowest_salary\n"
+    "- GROUP BY d.dept_name\n"
+    "- Use correct table aliases everywhere (no unaliased salary or dept_name)\n"
+    "- Do NOT reference columns that do not exist in the schema\n"
+    "User Question: {question}\n"
+    "Previous Attempt: {previous_sql}\n"
+    "Missing Invariants:\n{invariant_lines}\n"
+    "Regenerate now:"
+)
+
+
+def regenerate_with_constraints(question: str, previous_sql: str, missing: List[Dict[str, any]], llm, run_llm_fn, attempt: int) -> Optional[str]:
+    invariant_lines = "\n".join(f"- {m['name']}: {m['description']}" for m in missing)
+    prompt = REGEN_PROMPT_TEMPLATE.format(question=question, previous_sql=previous_sql, invariant_lines=invariant_lines)
+    # Use proper timeout values instead of hardcoded ones
+    # Start with a good timeout and decrease gradually for subsequent attempts
+    base_timeout = float(os.environ.get("INVARIANTS_BASE_TIMEOUT", 90))  # Start with 90s
+    timeout = max(30.0, base_timeout - attempt * 20.0)  # Decrease by 20s per attempt, minimum 30s
+    try:
+        raw = run_llm_fn(llm, prompt, timeout=max(8.0, timeout), retries=0)
+        # Normalize raw response to text
+        raw_text = getattr(raw, 'text', None)
+        if raw_text is None:
+            raw_text = str(raw)
+        # Extract first SELECT ... ;
+        m = re.search(r"(SELECT[\s\S]+?;)", raw_text, flags=re.IGNORECASE)
+        candidate = m.group(1) if m else raw_text.strip()
+        # Clean markdown fences
+        candidate = re.sub(r"```(?:sql)?|```", "", candidate, flags=re.IGNORECASE).strip()
+        # Ensure single statement & required LIMIT 100 at end
+        # Collapse accidental mid-statement semicolon before LIMIT
+        candidate = re.sub(r";\s*(LIMIT\s+\d+)", r" \1", candidate, flags=re.IGNORECASE)
+        # Append LIMIT 100 if missing (invariants will still verify)
+        if 'limit' not in candidate.lower():
+            if candidate.endswith(';'):
+                candidate = candidate[:-1] + ' LIMIT 100;'
+            else:
+                candidate += ' LIMIT 100;'
+        # Guarantee trailing semicolon
+        if not candidate.strip().endswith(';'):
+            candidate += ';'
+        return candidate
+    except Exception as e:
+        print(f"[INVARIANTS] Regeneration failed: {e}")
+        return None
+
+
+def enforce_invariants(question: str, sql: str, llm, run_llm_fn, max_regen: int = 2) -> Dict[str, any]:
+    category = classify_question(question)
+    if category == CATEGORY_GENERIC:
+        return {"category": category, "final_sql": sql, "regenerations": []}
+    # Dynamic schema support: the invariant catalog is purpose-built for the
+    # employee demo schema. For any other database, enforcement is bypassed
+    # and only the generic structural validation pipeline applies.
+    try:
+        try:
+            from .agentic_workflow import ACTIVE_SCHEMA_PROFILE as _asp
+        except Exception:
+            import Agent.agentic_workflow as _aw
+            _asp = getattr(_aw, "ACTIVE_SCHEMA_PROFILE", None)
+        if _asp is not None and not getattr(_asp, "is_employee_db", True):
+            print(f"[INVARIANTS] Bypassed for non-employee schema '{getattr(_asp, 'name', '?')}' (category={category})")
+            return {"category": CATEGORY_GENERIC, "final_sql": sql, "regenerations": []}
+    except Exception:
+        pass  # Profile unavailable — default legacy behavior (enforce)
+    invariants = build_invariants(category, question)
+    regenerations = []
+    current_sql = sql
+    # Increase max_regen to 5 for more LLM attempts
+    for attempt in range(max_regen + 4):
+        missing = evaluate_invariants(current_sql, invariants)
+        if not missing:
+            return {"category": category, "final_sql": current_sql, "regenerations": regenerations}
+        if attempt == max_regen:
+            # Give up, return last with missing list for transparency
+            return {"category": category, "final_sql": current_sql, "regenerations": regenerations, "remaining_missing": [m['name'] for m in missing]}
+        regen_sql = regenerate_with_constraints(question, current_sql, missing, llm, run_llm_fn, attempt)
+        if not regen_sql:
+            return {"category": category, "final_sql": current_sql, "regenerations": regenerations, "remaining_missing": [m['name'] for m in missing]}
+        regenerations.append({
+            "attempt": attempt + 1,
+            "missing_before": [m['name'] for m in missing],
+            "sql": regen_sql
+        })
+        current_sql = regen_sql
+    return {"category": category, "final_sql": current_sql, "regenerations": regenerations}
diff --git a/Agent/mcp_server.py b/Agent/mcp_server.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ffb2b5613c4aee6a8c5135a3cc9586b2d176779
--- /dev/null
+++ b/Agent/mcp_server.py
@@ -0,0 +1,630 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import Any, Dict, Optional, List
+import asyncio
+import importlib
+import uuid
+import os
+import re
+
+# Attempt to import agent workflow for schema/inspector & utilities (optional)
+try:
+    import Agent.agentic_workflow as aw
+    print(f"[MCP STARTUP] Successfully imported Agent.agentic_workflow")
+except Exception as e:
+    print(f"[MCP STARTUP] Failed to import Agent.agentic_workflow: {e}")
+    aw = None
+
+app = FastAPI(title="Minimal MCP Server (dev)")
+
+# Simple tool registry
+TOOLS: Dict[str, Any] = {}
+
+
+class CompleteRequest(BaseModel):
+    prompt: str
+    context: Optional[Dict[str, Any]] = None
+    timeout: Optional[float] = 30.0
+
+
+def register_tool(name: str, fn: Any):
+    TOOLS[name] = fn
+
+
+async def _call_tool(name: str, args: Dict[str, Any]) -> Any:
+    if name not in TOOLS:
+        raise KeyError(f"Tool '{name}' not registered")
+    fn = TOOLS[name]
+    if asyncio.iscoroutinefunction(fn):
+        return await fn(**(args or {}))
+    else:
+        # run sync in threadpool
+        loop = asyncio.get_running_loop()
+        return await loop.run_in_executor(None, lambda: fn(**(args or {})))
+
+
+@app.on_event("startup")
+async def _startup():
+    # Register built-in/mock tools lazily to avoid importing heavy modules at import time
+    # Try to register a real document scanner implementation; otherwise register a mock
+    ds_registered = None
+    try:
+        from .tools import document_scanner as ds
+        register_tool("document_scanner.process", ds.process_image)
+        ds_registered = f"Agent.tools.document_scanner.process_image (relative import)"
+    except Exception:
+        # If relative import fails (running as script), try absolute import
+        try:
+            import Agent.tools.document_scanner as ds2
+            register_tool("document_scanner.process", ds2.process_image)
+            ds_registered = f"Agent.tools.document_scanner.process_image (absolute import)"
+        except Exception:
+            # Register a fallback mock tool
+            def _mock_doc(**kwargs):
+                # Accept arbitrary keyword args (image_url, image_bytes, etc.) to be tolerant
+                return {"doc_id": str(uuid.uuid4()), "text": "(mock OCR output)", "metadata": {"pages": 1, "confidence": 0.0}, "received_args": kwargs}
+            register_tool("document_scanner.process", _mock_doc)
+            ds_registered = "mock(document_scanner.process)"
+
+    # Log which document scanner implementation was registered for easier debugging
+    try:
+        print(f"[MCP STARTUP] Registered document_scanner.process -> {ds_registered}")
+    except Exception:
+        pass
+
+    # Optional Groq Vision health check in cloud mode
+    try:
+        from .tools import groq_vision_health
+        hc = groq_vision_health.health_check()
+        if not hc.get("ok", True):
+            print(f"[MCP STARTUP] Groq Vision health warning: {hc.get('error')} (model={hc.get('model')})")
+        else:
+            print(f"[MCP STARTUP] Groq Vision health: ok (model={hc.get('model')})")
+    except Exception as e:
+        print(f"[MCP STARTUP] Groq Vision health check skipped: {e}")
+
+    # Register Granite Vision direct visual Q&A tool
+    gv_registered = None
+    try:
+        from .tools import granite_vision as gv
+        register_tool("granite_vision.qa", gv.qa)
+        gv_registered = "Agent.tools.granite_vision.qa (relative import)"
+    except Exception as e1:
+        print(f"[MCP STARTUP] granite_vision relative import FAILED: {type(e1).__name__}: {e1}")
+        try:
+            import Agent.tools.granite_vision as gv2
+            register_tool("granite_vision.qa", gv2.qa)
+            gv_registered = "Agent.tools.granite_vision.qa (absolute import)"
+        except Exception as e2:
+            print(f"[MCP STARTUP] granite_vision absolute import FAILED: {type(e2).__name__}: {e2}")
+            def _mock_gv(**kwargs):
+                return {"answer": "(granite_vision unavailable - mock)", "model": "mock", "received_args": kwargs}
+            register_tool("granite_vision.qa", _mock_gv)
+            gv_registered = "mock(granite_vision.qa)"
+    print(f"[MCP STARTUP] Registered granite_vision.qa -> {gv_registered}")
+
+
+    # Register OCR QA composite tool: runs OCR then runs the agent OCR QA helper
+    try:
+        # Use the global aw variable instead of re-importing
+        if not aw:
+            raise ImportError("Agent workflow not available")
+
+        async def _ocr_qa_tool(image_url: Optional[str] = None, image_bytes: Optional[bytes] = None, question: Optional[str] = None, **kwargs):
+            # Call the document scanner tool first (use the registered one to allow mocks)
+            ds_fn = TOOLS.get("document_scanner.process")
+            if not ds_fn:
+                raise RuntimeError("document_scanner.process not registered")
+
+            loop = asyncio.get_running_loop()
+            try:
+                if asyncio.iscoroutinefunction(ds_fn):
+                    tool_res = await ds_fn(image_url=image_url, image_bytes=image_bytes, options=kwargs)
+                else:
+                    tool_res = await loop.run_in_executor(None, lambda: ds_fn(image_url=image_url, image_bytes=image_bytes, options=kwargs))
+            except Exception as e:
+                return {"error": f"OCR tool failed: {e}"}
+
+            doc_id = tool_res.get("doc_id") if isinstance(tool_res, dict) else None
+            ocr_text = (tool_res.get("text") if isinstance(tool_res, dict) else str(tool_res)) or ""
+
+            # Persist OCR text so downstream callers can retrieve it via doc_id
+            try:
+                if doc_id and ocr_text:
+                    docs_dir = os.path.join("chat_store", "docs")
+                    os.makedirs(docs_dir, exist_ok=True)
+                    doc_path = os.path.join(docs_dir, f"{doc_id}.txt")
+                    # Write file (overwrite if exists)
+                    with open(doc_path, "w", encoding="utf-8") as df:
+                        df.write(ocr_text)
+                    # Also add a short history entry if agent chat store available
+                    try:
+                        if hasattr(aw, 'chat_store_private') and hasattr(aw, 'ChatMessage') and hasattr(aw, 'MessageRole'):
+                            user_msg = aw.ChatMessage(role=aw.MessageRole.USER, content=f"Uploaded document {doc_id}")
+                            assistant_msg = aw.ChatMessage(role=aw.MessageRole.ASSISTANT, content=f"OCR stored: {ocr_text[:400]}")
+                            aw.chat_store_private.add_message(key="conversation", message=user_msg)
+                            aw.chat_store_private.add_message(key="conversation", message=assistant_msg)
+                            try:
+                                aw.chat_store_private.persist(str(aw.private_store_path))
+                            except Exception:
+                                pass
+                    except Exception:
+                        pass
+            except Exception:
+                # Non-fatal: continue even if persistence fails
+                pass
+
+            # If a question was provided, answer it using the agent helper (run in executor to avoid blocking)
+            if question:
+                try:
+                    # Use executor if helper is sync to avoid blocking event loop
+                    if callable(getattr(aw, 'ocr_agent_qa', None)):
+                        answer = await loop.run_in_executor(None, lambda: aw.ocr_agent_qa(question, doc_id))
+                        return {"doc_id": doc_id, "answer": answer}
+                    else:
+                        return {"doc_id": doc_id, "answer": "ocr_agent_qa not available"}
+                except Exception as e:
+                    return {"error": f"ocr_agent_qa failed: {e}", "tool_result": tool_res}
+
+            # No question: return the OCR result and doc_id
+            return {"doc_id": doc_id, "ocr_text": ocr_text}
+
+        register_tool("ocr_qa", _ocr_qa_tool)
+        print(f"[MCP STARTUP] Registered ocr_qa -> real _ocr_qa_tool (agent available)")
+    except Exception:
+        # If agent import fails, register a tolerant mock tool
+        def _ocr_qa_mock(**kwargs):
+            return {"doc_id": str(uuid.uuid4()), "answer": "(ocr_qa unavailable - mock response)", "received_args": kwargs}
+        register_tool("ocr_qa", _ocr_qa_mock)
+        print(f"[MCP STARTUP] Registered ocr_qa -> mock(ocr_qa)")
+
+    # ------------------------------------------------------------------
+    # Deterministic schema-aware SQL helper tools (agentic compliant)
+    # ------------------------------------------------------------------
+    ENABLE_FAST_SQL = os.environ.get("ENABLE_MCP_FAST_TOOLS", "1") in ("1","true","True")
+    if not ENABLE_FAST_SQL:
+        print("[MCP STARTUP] Fast SQL tools disabled via ENABLE_MCP_FAST_TOOLS=0")
+        return
+
+    # Initialize database connection if not already done
+    if not aw:
+        print("[MCP STARTUP][FAST] Agent workflow not available; skipping fast SQL tool registration.")
+        return
+        
+    if not getattr(aw, 'inspector', None) or not getattr(aw, 'available_tables', None):
+        print("[MCP STARTUP][FAST] Database not initialized; attempting to initialize...")
+        try:
+            # Initialize database connection - use await since we're in async context.
+            # Priority chain mirrors worker startup: 1) env DB  2) active schema
+            # profile from the registry  3) bundled demo employee SQLite file.
+            db_uri = os.environ.get("DB_CONNECTION_URL")
+            if not db_uri:
+                demo_db_path = os.path.join(
+                    os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
+                    "employee-sample-database", "sqlite", "dataset_small", "employee.db",
+                )
+                try:
+                    from .schema_profile import profile_registry as _reg
+                    _active = _reg.active() or {}
+                    db_uri = _active.get("db_url")
+                except Exception:
+                    db_uri = None
+                if not db_uri and os.path.isfile(demo_db_path):
+                    db_uri = f"sqlite:///{demo_db_path.replace(os.sep, '/')}"
+                    print("[MCP STARTUP][FAST] Using bundled demo employee database.")
+            if not db_uri:
+                print("[MCP STARTUP][FAST] No DB env, active profile, or bundled demo found; skipping fast SQL tool registration.")
+                return
+            db_result = await aw.initialize_database(db_uri)
+            if db_result.get("tables"):
+                print(f"[MCP STARTUP][FAST] Database initialized with {len(db_result['tables'])} tables")
+            else:
+                print("[MCP STARTUP][FAST] Database initialization failed; skipping fast SQL tool registration.")
+                return
+        except Exception as db_err:
+            print(f"[MCP STARTUP][FAST] Database initialization error: {db_err}; skipping fast SQL tool registration.")
+            return
+
+    inspector = aw.inspector
+    available_tables = aw.available_tables
+
+    def _colnames(table: str) -> List[str]:
+        try:
+            return [c['name'] for c in inspector.get_columns(table_name=table)]
+        except Exception:
+            return []
+
+    def _has_tables(*tables: str) -> bool:
+        at_lower = {t.lower() for t in available_tables}
+        return all(t.lower() in at_lower for t in tables)
+
+    # Simple validation & sanitization re-use
+    validate_sql_query = getattr(aw, 'validate_sql_query', lambda q: {"valid": True})
+    fix_common_sql_errors = getattr(aw, 'fix_common_sql_errors', lambda q: q)
+
+    async def _verify_sql(sql: str, question: str, tag: str) -> str:
+        """Optional light LLM verification (short prompt) if LLM available."""
+        if not aw or not getattr(aw, 'Settings', None) or not getattr(aw.Settings, 'llm', None):
+            return sql
+        try:
+            _schema_block = ""
+            try:
+                if aw.ACTIVE_SCHEMA_PROFILE.is_employee_db:
+                    _schema_block = aw.EMPLOYEE_DB_SCHEMA
+                else:
+                    _schema_block = aw.ACTIVE_SCHEMA_PROFILE.build_digest(max_tables=10)
+            except Exception:
+                _schema_block = ""
+            prompt = (
+                (f"{_schema_block}\n\n" if _schema_block else "")
+                + "You are an SQL verifier."
+                + f" Tag={tag}."
+                + " If the candidate SQL is valid and uses only existing tables/columns keep it."
+                " If SMALL fixes (alias, LIMIT 100, semicolon) needed, fix them. Never invent tables/columns."
+                f"\nQuestion: {question}\nCandidate SQL:\n{sql}\nReturn ONLY final SQL." )
+            resp = aw.Settings.llm.complete(prompt=prompt)
+            txt = getattr(resp, 'text', str(resp)).strip()
+            if '```' in txt:
+                parts = re.split(r"```(?:sql)?", txt, flags=re.IGNORECASE)
+                txt = '\n'.join(p for p in parts if 'select' in p.lower() or 'with' in p.lower())
+            if txt.lower().startswith('sql'):
+                txt = txt[3:].strip()
+            txt = fix_common_sql_errors(txt)
+            v = validate_sql_query(txt)
+            if v.get('valid'):
+                return txt
+        except Exception as ve:
+            print(f"[FAST][VERIFY][WARN] Verification failed: {ve}; using original SQL")
+        return sql
+
+    def _build_sql(select_cols: List[str], base_table: str, joins: Optional[List[str]] = None,
+                   where: Optional[List[str]] = None, group_by: Optional[List[str]] = None,
+                   order_by: Optional[str] = None, limit: Optional[int] = 100) -> str:
+        parts = ["SELECT " + ", ".join(select_cols), "FROM " + base_table]
+        for j in (joins or []):
+            parts.append(j)
+        if where:
+            parts.append("WHERE " + " AND ".join(where))
+        if group_by:
+            parts.append("GROUP BY " + ", ".join(group_by))
+        if order_by:
+            parts.append("ORDER BY " + order_by)
+        if limit is not None:
+            parts.append(f"LIMIT {limit}")
+        sql = " ".join(parts)
+        if not sql.strip().endswith(';'):
+            sql += ';'
+        return sql
+
+    async def tool_list_departments(question: str = ""):
+        if not _has_tables('department'):
+            return {"error": "department table not available"}
+        cols = _colnames('department')
+        name_col = 'dept_name' if 'dept_name' in cols else (cols[0] if cols else 'dept_name')
+        sql = _build_sql([f"d.{name_col} AS department_name"], 'department d', order_by='department_name ASC')
+        sql = fix_common_sql_errors(sql)
+        sql = await _verify_sql(sql, question or 'List departments', 'list_departments')
+        return {"sql": sql}
+
+    async def tool_count_employees(question: str = ""):
+        if not _has_tables('employee'):
+            return {"error": "employee table not available"}
+        sql = _build_sql(["COUNT(*) AS total_employees"], 'employee e', limit=None)
+        sql = fix_common_sql_errors(sql)
+        sql = await _verify_sql(sql, question or 'Total employees', 'count_employees')
+        return {"sql": sql}
+
+    async def tool_employee_count_by_department(question: str = ""):
+        if not _has_tables('department','dept_emp'):
+            return {"error": "required tables missing"}
+        dcols = _colnames('department'); decols = _colnames('dept_emp')
+        dept_name = 'dept_name' if 'dept_name' in dcols else (dcols[0] if dcols else 'dept_name')
+        dept_no = 'dept_no' if 'dept_no' in dcols or 'dept_no' in decols else 'dept_no'
+        emp_no = 'emp_no' if 'emp_no' in decols else 'emp_no'
+        where = []
+        if 'to_date' in decols:
+            where.append("de.to_date = '9999-01-01'")
+        sql = _build_sql([f"d.{dept_name} AS department_name", f"COUNT(DISTINCT de.{emp_no}) AS employee_count"],
+                         'department d', joins=[f"JOIN dept_emp de ON d.{dept_no} = de.{dept_no}"],
+                         where=where, group_by=[f"d.{dept_name}"], order_by='employee_count DESC')
+        sql = fix_common_sql_errors(sql)
+        sql = await _verify_sql(sql, question or 'Employees per department', 'employee_count_by_department')
+        return {"sql": sql}
+
+    async def tool_gender_pay_gap(question: str = ""):
+        if not _has_tables('department','dept_emp','employee','salary'):
+            return {"error": "required tables missing"}
+        scolumns = _colnames('salary')
+        sval = 'salary' if 'salary' in scolumns else ('amount' if 'amount' in scolumns else (scolumns[0] if scolumns else 'salary'))
+        where = ["de.to_date = '9999-01-01'", "s.to_date = '9999-01-01'"]
+        sql = _build_sql([
+            "d.dept_name",
+            f"AVG(CASE WHEN e.gender='M' THEN s.{sval} END) AS avg_male_salary",
+            f"AVG(CASE WHEN e.gender='F' THEN s.{sval} END) AS avg_female_salary",
+            f"(AVG(CASE WHEN e.gender='M' THEN s.{sval} END) - AVG(CASE WHEN e.gender='F' THEN s.{sval} END)) AS gap",
+            f"CASE WHEN AVG(CASE WHEN e.gender='M' THEN s.{sval} END)=0 THEN NULL ELSE ((AVG(CASE WHEN e.gender='M' THEN s.{sval} END) - AVG(CASE WHEN e.gender='F' THEN s.{sval} END))/NULLIF(AVG(CASE WHEN e.gender='M' THEN s.{sval} END),0))*100 END AS pct_gap"
+        ], 'department d',
+            joins=['JOIN dept_emp de ON d.dept_no = de.dept_no','JOIN employee e ON de.emp_no = e.emp_no','JOIN salary s ON e.emp_no = s.emp_no'],
+            where=where, group_by=['d.dept_name'], order_by='gap DESC')
+        sql = fix_common_sql_errors(sql)
+        sql = await _verify_sql(sql, question or 'Gender pay gap', 'gender_pay_gap')
+        return {"sql": sql}
+
+    async def tool_salary_extremes(question: str = ""):
+        if not _has_tables('salary'):
+            return {"error": "salary table missing"}
+        scolumns = _colnames('salary'); sval = 'salary' if 'salary' in scolumns else ('amount' if 'amount' in scolumns else (scolumns[0] if scolumns else 'salary'))
+        where = ["s.to_date = '9999-01-01'"] if 'to_date' in scolumns else []
+        sql = _build_sql([f"MAX(s.{sval}) AS highest_salary", f"MIN(s.{sval}) AS lowest_salary"], 'salary s', where=where, limit=None)
+        sql = fix_common_sql_errors(sql)
+        sql = await _verify_sql(sql, question or 'Salary extremes', 'salary_extremes')
+        return {"sql": sql}
+
+    async def tool_salary_range_department(department_name: str = "Development", question: str = ""):
+        if not _has_tables('department','dept_emp','salary'):
+            return {"error": "required tables missing"}
+        scolumns = _colnames('salary'); sval = 'salary' if 'salary' in scolumns else ('amount' if 'amount' in scolumns else (scolumns[0] if scolumns else 'salary'))
+        # Escape single quotes in department_name for SQL safety (basic)
+        escaped_dept = department_name.replace("'", "''")
+        where = ["de.to_date = '9999-01-01'", "s.to_date = '9999-01-01'", f"d.dept_name = '{escaped_dept}'"]
+        sql = _build_sql(["d.dept_name", f"MAX(s.{sval}) AS max_salary", f"MIN(s.{sval}) AS min_salary"], 'department d',
+                         joins=['JOIN dept_emp de ON d.dept_no = de.dept_no','JOIN salary s ON de.emp_no = s.emp_no'],
+                         where=where, group_by=['d.dept_name'], limit=None)
+        sql = fix_common_sql_errors(sql)
+        sql = await _verify_sql(sql, question or 'Department salary range', 'salary_range_department')
+        return {"sql": sql, "department": department_name}
+
+    async def tool_top_paid_employees(top_n: int = 10, question: str = ""):
+        if not _has_tables('employee','salary'):
+            return {"error": "required tables missing"}
+        scolumns = _colnames('salary'); sval = 'salary' if 'salary' in scolumns else (scolumns[0] if scolumns else 'salary')
+        where = ["s.to_date = '9999-01-01'"] if 'to_date' in scolumns else []
+        sql = _build_sql(["e.emp_no","e.first_name","e.last_name", f"s.{sval} AS salary"], 'employee e',
+                         joins=['JOIN salary s ON e.emp_no = s.emp_no'], where=where,
+                         order_by=f"s.{sval} DESC", limit=top_n)
+        sql = fix_common_sql_errors(sql)
+        sql = await _verify_sql(sql, question or 'Top paid employees', 'top_paid_employees')
+        return {"sql": sql, "top_n": top_n}
+
+    async def tool_department_highest_avg_salary(question: str = ""):
+        if not _has_tables('department','dept_emp','salary'):
+            return {"error": "required tables missing"}
+        scolumns = _colnames('salary'); sval = 'salary' if 'salary' in scolumns else (scolumns[0] if scolumns else 'salary')
+        where = ["de.to_date = '9999-01-01'", "s.to_date = '9999-01-01'"]
+        sql = _build_sql(["d.dept_name", f"AVG(s.{sval}) AS avg_salary"], 'department d',
+                         joins=['JOIN dept_emp de ON d.dept_no = de.dept_no','JOIN salary s ON de.emp_no = s.emp_no'],
+                         where=where, group_by=['d.dept_name'], order_by='avg_salary DESC', limit=1)
+        sql = fix_common_sql_errors(sql)
+        sql = await _verify_sql(sql, question or 'Department highest average salary', 'dept_highest_avg_salary')
+        return {"sql": sql}
+
+    async def tool_department_manager_listing(question: str = ""):
+        if not _has_tables('department','dept_manager','employee'):
+            return {"error": "required tables missing"}
+        where = ["dm.to_date = '9999-01-01'"]
+        sql = _build_sql(["d.dept_name AS department_name","e.first_name","e.last_name"], 'department d',
+                         joins=['JOIN dept_manager dm ON d.dept_no = dm.dept_no','JOIN employee e ON dm.emp_no = e.emp_no'],
+                         where=where, order_by='department_name ASC')
+        sql = fix_common_sql_errors(sql)
+        sql = await _verify_sql(sql, question or 'Department managers', 'department_manager_listing')
+        return {"sql": sql}
+
+    # NEW: Complex analytical tools for business intelligence
+    async def tool_gender_distribution_by_dept(question: str = ""):
+        if not _has_tables('department','dept_emp','employee'):
+            return {"error": "required tables missing"}
+        where = ["de.to_date = '9999-01-01'"]
+        sql = _build_sql([
+            "d.dept_name AS department_name",
+            "e.gender",
+            "COUNT(*) AS count",
+            "ROUND(COUNT(*) * 100.0 / SUM(COUNT(*)) OVER (PARTITION BY d.dept_name), 2) AS percentage"
+        ], 'department d',
+            joins=['JOIN dept_emp de ON d.dept_no = de.dept_no','JOIN employee e ON de.emp_no = e.emp_no'],
+            where=where, group_by=['d.dept_name', 'e.gender'], order_by='department_name, gender')
+        sql = fix_common_sql_errors(sql)
+        sql = await _verify_sql(sql, question or 'Gender distribution by department', 'gender_distribution_by_dept')
+        return {"sql": sql}
+
+    async def tool_hiring_trend_by_year(question: str = ""):
+        if not _has_tables('employee'):
+            return {"error": "required table employee missing"}
+        sql = _build_sql([
+            "EXTRACT(YEAR FROM e.hire_date) AS hire_year",
+            "COUNT(*) AS employees_hired",
+            "COUNT(CASE WHEN e.gender='M' THEN 1 END) AS male_hires",
+            "COUNT(CASE WHEN e.gender='F' THEN 1 END) AS female_hires"
+        ], 'employee e', group_by=['EXTRACT(YEAR FROM e.hire_date)'], order_by='hire_year ASC')
+        sql = fix_common_sql_errors(sql)
+        sql = await _verify_sql(sql, question or 'Hiring trends by year', 'hiring_trend_by_year')
+        return {"sql": sql}
+
+    async def tool_employee_tenure_by_dept(question: str = ""):
+        if not _has_tables('department','dept_emp','employee'):
+            return {"error": "required tables missing"}
+        where = ["de.to_date = '9999-01-01'"]
+        sql = _build_sql([
+            "d.dept_name AS department_name",
+            "AVG(EXTRACT(DAYS FROM (CURRENT_DATE - e.hire_date))/365.25) AS avg_tenure_years",
+            "MIN(EXTRACT(DAYS FROM (CURRENT_DATE - e.hire_date))/365.25) AS min_tenure_years", 
+            "MAX(EXTRACT(DAYS FROM (CURRENT_DATE - e.hire_date))/365.25) AS max_tenure_years"
+        ], 'department d',
+            joins=['JOIN dept_emp de ON d.dept_no = de.dept_no','JOIN employee e ON de.emp_no = e.emp_no'],
+            where=where, group_by=['d.dept_name'], order_by='avg_tenure_years DESC')
+        sql = fix_common_sql_errors(sql)
+        sql = await _verify_sql(sql, question or 'Employee tenure by department', 'employee_tenure_by_dept')
+        return {"sql": sql}
+
+    async def tool_female_managers_count(question: str = ""):
+        if not _has_tables('dept_manager','employee'):
+            return {"error": "required tables missing"}
+        where = ["dm.to_date = '9999-01-01'", "e.gender = 'F'"]
+        sql = _build_sql([
+            "COUNT(*) AS female_managers_count",
+            "COUNT(*) * 100.0 / (SELECT COUNT(*) FROM dept_manager dm2 WHERE dm2.to_date = '9999-01-01') AS percentage_female_managers"
+        ], 'dept_manager dm',
+            joins=['JOIN employee e ON dm.emp_no = e.emp_no'],
+            where=where, limit=None)
+        sql = fix_common_sql_errors(sql)
+        sql = await _verify_sql(sql, question or 'Female managers count', 'female_managers_count')
+        return {"sql": sql}
+
+    async def tool_avg_salary_by_title_dept(question: str = ""):
+        """Get average salary by title and department."""
+        if not _has_tables('department', 'dept_emp', 'salary', 'title', 'employee'):
+            return {"error": "required tables missing"}
+        where = ["de.to_date = '9999-01-01'", "s.to_date = '9999-01-01'", "t.to_date = '9999-01-01'"]
+        sql = _build_sql(["d.dept_name AS department_name", "t.title", "AVG(s.salary) AS avg_salary"], 'department d',
+                         joins=['JOIN dept_emp de ON d.dept_no = de.dept_no',
+                               'JOIN employee e ON de.emp_no = e.emp_no',
+                               'JOIN salary s ON e.emp_no = s.emp_no',
+                               'JOIN title t ON e.emp_no = t.emp_no'],
+                         where=where, group_by=['d.dept_name', 't.title'], 
+                         order_by='d.dept_name, avg_salary DESC', limit=100)
+        sql = fix_common_sql_errors(sql)
+        sql = await _verify_sql(sql, question or 'Average salary by title and department', 'avg_salary_by_title_dept')
+        return {"sql": sql}
+
+    # ---- Registration ----
+    # Dynamic schema support: the 14 fast_sql.* tools are purpose-built SQL
+    # skeletons for the employee demo schema. Register them only when the
+    # active profile IS the employee demo. Otherwise, register the generic
+    # schema-grounded tools that work on ANY database.
+    is_employee_profile = bool(
+        getattr(aw, 'ACTIVE_SCHEMA_PROFILE', None)
+        and aw.ACTIVE_SCHEMA_PROFILE.is_employee_db
+    )
+
+    if is_employee_profile:
+        register_tool('fast_sql.list_departments', tool_list_departments)
+        register_tool('fast_sql.count_employees', tool_count_employees)
+        register_tool('fast_sql.employee_count_by_department', tool_employee_count_by_department)
+        register_tool('fast_sql.gender_pay_gap', tool_gender_pay_gap)  # RE-ENABLED: Complex analytical queries
+        register_tool('fast_sql.salary_extremes', tool_salary_extremes)
+        register_tool('fast_sql.salary_range_department', tool_salary_range_department)
+        register_tool('fast_sql.top_paid_employees', tool_top_paid_employees)
+        register_tool('fast_sql.department_highest_avg_salary', tool_department_highest_avg_salary)
+        register_tool('fast_sql.department_manager_listing', tool_department_manager_listing)
+
+        # Register new complex analytical tools
+        register_tool('fast_sql.gender_distribution_by_dept', tool_gender_distribution_by_dept)
+        register_tool('fast_sql.hiring_trend_by_year', tool_hiring_trend_by_year)
+        register_tool('fast_sql.employee_tenure_by_dept', tool_employee_tenure_by_dept)
+        register_tool('fast_sql.female_managers_count', tool_female_managers_count)
+        register_tool('fast_sql.avg_salary_by_title_dept', tool_avg_salary_by_title_dept)
+        print('[MCP STARTUP] Registered employee-demo fast SQL tools: '
+              + ', '.join([k for k in TOOLS if k.startswith('fast_sql.')]))
+    else:
+        # ---------- Generic schema-grounded tools (any database) ----------
+        def tool_schema_list_tables(**kwargs):
+            return {"tables": list(available_tables)}
+
+        def tool_schema_describe_table(table: str = "", **kwargs):
+            if not table:
+                return {"error": "table is required", "tables": list(available_tables)}
+            cols = _colnames(table)
+            if not cols:
+                # Case-insensitive retry
+                lower_map = {t.lower(): t for t in available_tables}
+                table = lower_map.get(table.lower())
+                if table:
+                    cols = _colnames(table)
+            return {"table": table, "exists": bool(cols), "columns": cols}
+
+        def tool_schema_sample_data(table: str = "", limit: int = 3, **kwargs):
+            if not table:
+                return {"error": "table is required"}
+            try:
+                from sqlalchemy import text as _sa_text
+                safe_limit = max(1, min(int(limit), 10))
+                with aw.sql_database.connect() as conn:
+                    rows = conn.execute(
+                        _sa_text(f'SELECT * FROM "{table}" LIMIT {safe_limit}')
+                    ).mappings().all()
+                    return {"table": table, "rows": [dict(r) for r in rows]}
+            except Exception:
+                try:
+                    from sqlalchemy import text as _sa_text
+                    safe_limit = max(1, min(int(limit), 10))
+                    with aw.sql_database.connect() as conn:
+                        rows = conn.execute(
+                            _sa_text(f"SELECT * FROM {table} LIMIT {safe_limit}")
+                        ).mappings().all()
+                        return {"table": table, "rows": [dict(r) for r in rows]}
+                except Exception as e2:
+                    return {"table": table, "error": str(e2)}
+
+        async def tool_sql_build_select(select_cols: Optional[List[str]] = None, base_table: str = "",
+                                        joins: Optional[List[str]] = None, where: Optional[List[str]] = None,
+                                        group_by: Optional[List[str]] = None, order_by: Optional[str] = None,
+                                        limit: Optional[int] = 100, question: str = "", **kwargs):
+            """Deterministic schema-grounded SELECT builder: validates requested
+            columns against the live table, uses SQLAlchemy inspector, then an
+            optional light LLM refinement. Returns a validated SQL hint."""
+            cols = _colnames(base_table)
+            if not cols:
+                lower_map = {t.lower(): t for t in available_tables}
+                base_table = lower_map.get((base_table or "").lower(), base_table)
+                cols = _colnames(base_table)
+            if not cols:
+                return {"error": f"Unknown table '{base_table}'",
+                        "tables": list(available_tables)}
+            _cols_lower = {str(c).lower(): str(c) for c in cols}
+            for raw in (select_cols or []):
+                # tokens like alias.col, col, COUNT(col) etc.
+                for tok in re.findall(r"[A-Za-z_][\w]*(?:\.[A-Za-z_]\w*)?", str(raw)):
+                    t_, sep, c_ = tok.partition('.')
+                    if sep and c_.lower() not in _cols_lower and c_.lower() != '1':
+                        return {"error": f"Unknown column '{tok}' in table '{base_table}'",
+                                "columns": cols,
+                                "hint": "select base_table.column for aggregate args, e.g. COUNT(col)"}
+            sql = _build_sql(select_cols=select_cols, base_table=base_table, joins=joins,
+                             where=where, group_by=group_by, order_by=order_by, limit=limit)
+            sql = fix_common_sql_errors(sql)
+            sql = await _verify_sql(sql, question or f"SELECT from {base_table}", f"build_select_{base_table}")
+            return {"sql": sql, "table": base_table, "columns": cols}
+
+        register_tool('schema.list_tables', tool_schema_list_tables)
+        register_tool('schema.describe_table', tool_schema_describe_table)
+        register_tool('schema.sample_data', tool_schema_sample_data)
+        register_tool('sql.build_select', tool_sql_build_select)
+        print('[MCP STARTUP] Registered GENERIC schema tools: '
+              + ', '.join([k for k in TOOLS if k.startswith('schema.') or k.startswith('sql.')]))
+        print('[MCP STARTUP] Skipping employee-demo fast_sql.* tools (non-employee schema active)')
+
+
+
+@app.post("/complete")
+async def complete(req: CompleteRequest):
+    """Minimal completion endpoint:
+    - If context contains {'tool_call': {'name': ..., 'args': {...}}}, the server will run the tool and return its result.
+    - Otherwise it returns a simple echo of the prompt under 'text'.
+    This scaffold is purposely small so you can extend tool orchestration later.
+    """
+    ctx = req.context or {}
+    # Tool invocation flow
+    if 'tool_call' in ctx:
+        tc = ctx['tool_call']
+        name = tc.get('name')
+        args = tc.get('args', {})
+        try:
+            tool_res = await _call_tool(name, args)
+            return {"text": f"Tool '{name}' executed.", "tool_result": tool_res}
+        except KeyError as ke:
+            raise HTTPException(status_code=404, detail=str(ke))
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=f"Tool call failed: {e}")
+
+    # No tool requested: return a simple completion echo for now
+    # Future: integrate a local model here and tool loop orchestration
+    return {"text": req.prompt}
+
+
+@app.get("/tools")
+def list_tools():
+    return {"tools": list(TOOLS.keys())}
+
+
+@app.get("/health")
+def health():
+    return {"status": "ok", "tools_registered": len(TOOLS)}
diff --git a/Agent/schema_profile.py b/Agent/schema_profile.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f361948686e5dcfb14dad147026c038c310df7f
--- /dev/null
+++ b/Agent/schema_profile.py
@@ -0,0 +1,332 @@
+import os
+import json
+import time
+import uuid
+import threading
+from typing import Any, Dict, List, Optional
+
+
+class SchemaProfile:
+    """
+    Runtime schema profile representing the active database.
+    Stores tables, columns, PK/FK metadata, row counts, sample rows, and
+    a digest string suitable for injecting into LLM prompts.
+    """
+
+    SAMPLE_ROW_LIMIT = 3
+    MAX_DIGEST_TABLES = 40
+    MAX_DIGEST_COLUMNS = 25
+
+    def __init__(self, name: str, db_url: Optional[str] = None, is_demo: bool = False,
+                 notes: str = ""):
+        self.name = name
+        self.db_url = db_url
+        self.is_demo = is_demo
+        # Owner-supplied schema notes (domain hints): column semantics, business
+        # rules, aliases. Injected verbatim into LLM prompts alongside the
+        # introspected digest. Stays empty unless the database owner writes it.
+        self.notes = (notes or "").strip()
+        self.tables: List[str] = []
+        self.columns_by_table: Dict[str, List[Dict[str, Any]]] = {}
+        self.pk_by_table: Dict[str, List[str]] = {}
+        self.fk_by_table: Dict[str, List[Dict[str, Any]]] = {}
+        self.row_counts: Dict[str, int] = {}
+        self.sample_rows: Dict[str, List[Dict[str, Any]]] = {}
+        self.available_tables_set: set = set()
+
+    @property
+    def is_employee_db(self) -> bool:
+        # Treat as employee demo if explicitly marked or name matches
+        if self.is_demo:
+            return True
+        n = (self.name or "").lower()
+        return "employee" in n or n == "demo_employee"
+
+    # ------------------------------------------------------------------
+    # Population
+    # ------------------------------------------------------------------
+    def populate_from_inspector(self, inspector: Any, engine: Optional[Any] = None) -> None:
+        """Fill all metadata from a SQLAlchemy inspector (and optionally sample
+        row counts / preview rows via a sync engine connection)."""
+        try:
+            tables = list(
+                t for t in (inspector.get_table_names() + inspector.get_view_names())
+                if not t.startswith("_")
+            )
+        except Exception as e:
+            print(f"[SCHEMA-PROFILE] inspector table listing failed: {e}")
+            tables = []
+
+        self.tables = sorted(tables)
+        self.available_tables_set = set(self.tables)
+        self.columns_by_table = {}
+        self.pk_by_table = {}
+        self.fk_by_table = {}
+
+        for t in self.tables:
+            try:
+                self.columns_by_table[t] = inspector.get_columns(t)
+            except Exception:
+                self.columns_by_table[t] = []
+            try:
+                pks = inspector.get_pk_constraint(t)
+                self.pk_by_table[t] = (pks or {}).get("constrained_columns", []) or []
+            except Exception:
+                self.pk_by_table[t] = []
+            try:
+                self.fk_by_table[t] = inspector.get_foreign_keys(t) or []
+            except Exception:
+                self.fk_by_table[t] = []
+
+        if engine is not None:
+            self._collect_row_stats(engine)
+
+    def _collect_row_stats(self, engine: Any) -> None:
+        """Best-effort row counts + small sample rows using a sync engine.
+
+        Dialect-aware so row previews also work on databases whose syntax for
+        limiting differs (mssql uses TOP, oracle 12c+ uses FETCH FIRST, mysql
+        prefers backtick-quoted identifiers, standard SQL uses LIMIT).
+        """
+        from sqlalchemy import text as sql_text
+
+        # Identifier quoting + limit syntax per dialect (best-effort)
+        dname = ""
+        try:
+            dname = (getattr(engine, "dialect", None).name or "").lower()
+        except Exception:
+            dname = ""
+
+        def q(name: str) -> str:
+            if dname == "mssql":
+                return f"[{name}]"
+            if dname in ("mysql", "mariadb"):
+                return f"`{name}`"
+            return f'"{name}"'
+
+        def sample_candidates(table: str):
+            limit = self.SAMPLE_ROW_LIMIT
+            quoted = q(table)
+            if dname == "mssql":
+                return [f"SELECT TOP {limit} * FROM {quoted}"]
+            if dname == "oracle":
+                # 12c+ thin driver supports FETCH FIRST; legacy ROWNUM fallback
+                return [
+                    f"SELECT * FROM {quoted} FETCH FIRST {limit} ROWS ONLY",
+                    f"SELECT * FROM {quoted} WHERE ROWNUM <= {limit}",
+                ]
+            # postgres / mysql / sqlite / everything else
+            return [
+                f"SELECT * FROM {quoted} LIMIT {limit}",
+                f"SELECT * FROM {table} LIMIT {limit}",
+            ]
+
+        self.row_counts = {}
+        self.sample_rows = {}
+        try:
+            with engine.connect() as conn:
+                for t in self.tables:
+                    # Row count (quoted identifier first, unquoted fallback)
+                    try:
+                        cnt = conn.execute(sql_text(f"SELECT COUNT(*) FROM {q(t)}")).scalar()
+                        self.row_counts[t] = int(cnt) if cnt is not None else 0
+                    except Exception:
+                        try:
+                            cnt = conn.execute(sql_text(f"SELECT COUNT(*) FROM {t}")).scalar()
+                            self.row_counts[t] = int(cnt) if cnt is not None else 0
+                        except Exception:
+                            self.row_counts[t] = 0
+                    # Sample rows (first successful candidate wins)
+                    if self.row_counts.get(t, 0) > 0:
+                        for sql_c in sample_candidates(t):
+                            try:
+                                result = conn.execute(sql_text(sql_c))
+                                rows = result.mappings().all()
+                                self.sample_rows[t] = [
+                                    {k: _json_safe(v) for k, v in dict(r).items()} for r in rows
+                                ]
+                                break
+                            except Exception:
+                                continue
+                        else:
+                            self.sample_rows[t] = []
+                    else:
+                        self.sample_rows[t] = []
+        except Exception as e:
+            print(f"[SCHEMA-PROFILE] row stats collection failed: {e}")
+
+    # ------------------------------------------------------------------
+    # Digest for LLM prompts
+    # ------------------------------------------------------------------
+    def build_digest(self, max_tables: Optional[int] = None) -> str:
+        max_tables = max_tables or self.MAX_DIGEST_TABLES
+        lines: List[str] = []
+        lines.append(f"Database schema name: {self.name}")
+        lines.append(f"Number of tables/views: {len(self.tables)}")
+        lines.append("Tables and columns:")
+        for t in self.tables[:max_tables]:
+            cols = self.columns_by_table.get(t, [])
+            col_parts = []
+            for c in cols[: self.MAX_DIGEST_COLUMNS]:
+                ctype = str(c.get("type", ""))[:24]
+                col_parts.append(f"{c.get('name')} {ctype}".strip())
+            colstr = ", ".join(col_parts) if col_parts else "(no columns)"
+            rc = self.row_counts.get(t)
+            lines.append(f"  - {t} ({colstr})  [rows={rc if rc is not None else '?'}]")
+            fks = self.fk_by_table.get(t, [])
+            for fk in fks[:4]:
+                ref = fk.get("referred_table")
+                lcols = fk.get("constrained_columns") or []
+                rcols = fk.get("referred_columns") or []
+                if ref:
+                    lines.append(f"      FK: {lcols} -> {ref}({rcols})")
+            if len(cols) > self.MAX_DIGEST_COLUMNS:
+                lines.append(f"      ... {len(cols) - self.MAX_DIGEST_COLUMNS} more columns")
+        if len(self.tables) > max_tables:
+            lines.append(f"  ... {len(self.tables) - max_tables} more tables")
+        return "\n".join(lines)
+
+    def summarize(self) -> Dict[str, Any]:
+        """Compact JSON-friendly summary (for API responses / UI)."""
+        return {
+            "name": self.name,
+            "db_url": self.db_url,
+            "is_demo": self.is_demo,
+            "is_employee_db": self.is_employee_db,
+            "notes": self.notes,
+            "tables": [
+                {
+                    "name": t,
+                    "row_count": self.row_counts.get(t),
+                    "columns": [c.get("name") for c in self.columns_by_table.get(t, [])],
+                    "sample_rows": self.sample_rows.get(t, []),
+                }
+                for t in self.tables
+            ],
+        }
+
+
+def _json_safe(v: Any) -> Any:
+    try:
+        json.dumps(v)
+        return v
+    except (TypeError, ValueError):
+        return str(v)
+
+
+class ProfileRegistry:
+    """
+    Registry of user-registered database profiles.
+
+    Each registry entry stores lightweight metadata (id, name, db_url, kind).
+    Full introspection happens lazily when a profile is switched to (the
+    agent's initialize_database walks the schema at that moment).
+    """
+
+    def __init__(self, store_path: str):
+        self.store_path = store_path
+        self._lock = threading.Lock()
+        self.profiles: Dict[str, Dict[str, Any]] = {}
+        self.active_id: Optional[str] = None
+        self.load()
+
+    # ------------------------------------------------------------------
+    def load(self) -> None:
+        try:
+            if os.path.exists(self.store_path):
+                with open(self.store_path, "r", encoding="utf-8") as fh:
+                    data = json.load(fh)
+                self.profiles = data.get("profiles", {})
+                self.active_id = data.get("active_id")
+        except Exception as e:
+            print(f"[PROFILE-REGISTRY] load failed: {e}")
+            self.profiles = {}
+            self.active_id = None
+
+    def save(self) -> None:
+        with self._lock:
+            try:
+                os.makedirs(os.path.dirname(self.store_path), exist_ok=True)
+                with open(self.store_path, "w", encoding="utf-8") as fh:
+                    json.dump(
+                        {"profiles": self.profiles, "active_id": self.active_id},
+                        fh,
+                        indent=2,
+                    )
+            except Exception as e:
+                print(f"[PROFILE-REGISTRY] save failed: {e}")
+
+    # ------------------------------------------------------------------
+    def list(self) -> List[Dict[str, Any]]:
+        out = []
+        for pid, p in self.profiles.items():
+            out.append(
+                {
+                    "id": pid,
+                    "name": p.get("name"),
+                    "kind": p.get("kind", "user"),
+                    "db_url": p.get("db_url"),
+                    "created_at": p.get("created_at"),
+                    "notes": p.get("notes", ""),
+                    "active": pid == self.active_id,
+                }
+            )
+        return sorted(out, key=lambda p: (not p["active"], p.get("created_at") or 0))
+
+    def get(self, profile_id: str) -> Optional[Dict[str, Any]]:
+        return self.profiles.get(profile_id)
+
+    def active(self) -> Optional[Dict[str, Any]]:
+        if self.active_id and self.active_id in self.profiles:
+            return {"id": self.active_id, **self.profiles[self.active_id]}
+        return None
+
+    def add(
+        self,
+        name: str,
+        db_url: str,
+        kind: str = "user",
+        activate: bool = True,
+        notes: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        pid = uuid.uuid4().hex[:12]
+        entry = {
+            "name": name,
+            "db_url": db_url,
+            "kind": kind,
+            "created_at": time.time(),
+            "notes": (notes or "").strip(),
+        }
+        with self._lock:
+            self.profiles[pid] = entry
+            if activate or self.active_id is None:
+                self.active_id = pid
+        self.save()
+        return {"id": pid, **entry}
+
+    def remove(self, profile_id: str) -> bool:
+        with self._lock:
+            if profile_id not in self.profiles:
+                return False
+            del self.profiles[profile_id]
+            if self.active_id == profile_id:
+                self.active_id = next(iter(self.profiles), None)
+        self.save()
+        return True
+
+    def switch(self, profile_id: str) -> Optional[Dict[str, Any]]:
+        with self._lock:
+            if profile_id not in self.profiles:
+                return None
+            self.active_id = profile_id
+        self.save()
+        return {"id": profile_id, **self.profiles[profile_id]}
+
+    def set_notes(self, profile_id: str, notes: str) -> bool:
+        """Persist owner-supplied schema notes (domain hints) for a profile."""
+        with self._lock:
+            if profile_id not in self.profiles:
+                return False
+            self.profiles[profile_id]["notes"] = (notes or "").strip()
+        self.save()
+        return True
diff --git a/Agent/tools/granite_vision.py b/Agent/tools/granite_vision.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8c719abde7316f1f668c7567811123f10a54284
--- /dev/null
+++ b/Agent/tools/granite_vision.py
@@ -0,0 +1,121 @@
+"""
+Vision MCP tool - visual Q&A via Groq Vision API.
+
+Cloud path  : image -> Groq Vision -> structured answer
+Local path  : image -> Ollama qwen2-vl -> answer (dev only)
+
+Registered in mcp_server.py as 'granite_vision.qa'.
+"""
+import os
+import base64
+import requests
+from typing import Optional
+
+
+def qa(image_path: str, question: str, model_path: Optional[str] = None) -> dict:
+    """Answer a question about a document image using Groq Vision or local Ollama."""
+
+    print(f"[vision.qa] image={image_path} question={question[:80]}")
+
+    # CLOUD MODE: Groq Vision API
+    if os.environ.get("USE_HF_CLOUD", "0") in ("1", "true", "True"):
+        groq_key = os.environ.get("GROQ_API_KEY")
+        groq_model = os.environ.get("GROQ_VISION_MODEL", "qwen/qwen3.8-27b")
+
+        if groq_key:
+            print(f"[granite_vision.qa] CLOUD MODE: {groq_model} via Groq")
+            try:
+                from groq import Groq
+
+                client = Groq(api_key=groq_key)
+
+                with open(image_path, "rb") as f:
+                    img_b64 = base64.b64encode(f.read()).decode("utf-8")
+
+                ext = os.path.splitext(image_path)[-1].lower().lstrip(".")
+                mime = "image/jpeg" if ext in ("jpg", "jpeg") else f"image/{ext}"
+
+                response = client.chat.completions.create(
+                    model=groq_model,
+                    messages=[
+                        {
+                            "role": "user",
+                            "content": [
+                                {
+                                    "type": "text",
+                                    "text": (
+                                        "You are analyzing a document image. "
+                                        "Look carefully at the layout, tables, numbers, and text. "
+                                        "Answer the following question based only on what you see:\n\n"
+                                        + question
+                                    )
+                                },
+                                {
+                                    "type": "image_url",
+                                    "image_url": {
+                                        "url": f"data:{mime};base64,{img_b64}"
+                                    }
+                                }
+                            ]
+                        }
+                    ],
+                    max_tokens=1024,
+                    temperature=0.1
+                )
+
+                answer = response.choices[0].message.content.strip()
+                print(f"[granite_vision.qa] Groq answer ({len(answer)} chars): {answer[:200]}")
+                return {"answer": answer, "model": f"{groq_model} (Groq Vision)"}
+
+            except Exception as e:
+                print(f"[granite_vision.qa] Groq failed: {e}")
+                return {
+                    "answer": f"Groq Vision error: {e}",
+                    "model": groq_model,
+                    "error": str(e)
+                }
+        else:
+            print("[granite_vision.qa] GROQ_API_KEY not set in HF secrets.")
+            return {
+                "answer": "GROQ_API_KEY is not configured. Please add it in HuggingFace Space secrets.",
+                "model": "none",
+                "error": "missing_groq_key"
+            }
+
+    # LOCAL MODE (dev only): Ollama
+    local_model = os.environ.get("GRANITE_MODEL_PATH", "qwen2-vl:2b")
+    print(f"[granite_vision.qa] LOCAL MODE: sending image to Ollama ({local_model})")
+
+    try:
+        with open(image_path, "rb") as f:
+            img_b64 = base64.b64encode(f.read()).decode("utf-8")
+
+        base_url = os.environ.get("OLLAMA_BASE_URL", "http://localhost:11434").rstrip("/")
+        url = f"{base_url}/api/chat"
+        payload = {
+            "model": local_model,
+            "messages": [
+                {
+                    "role": "user",
+                    "content": f"Carefully analyze this document image. {question}",
+                    "images": [img_b64]
+                }
+            ],
+            "stream": False,
+            "keep_alive": 60,
+            "options": {"num_ctx": 1024, "temperature": 0.1}
+        }
+        r = requests.post(url, json=payload, timeout=300)
+        if r.status_code == 200:
+            answer = r.json().get("message", {}).get("content", "").strip()
+            print(f"[granite_vision.qa] Ollama answer ({len(answer)} chars): {answer[:200]}")
+            return {"answer": answer, "model": f"{local_model} (Ollama)"}
+        else:
+            msg = f"Ollama HTTP {r.status_code}: {r.text}"
+            print(f"[granite_vision.qa] {msg}")
+            return {"answer": msg, "model": f"{local_model} (Ollama)", "error": "http_error"}
+
+    except Exception as e:
+        msg = f"[ERROR] Ollama Q&A failed: {e}"
+        print(f"[granite_vision.qa] {msg}")
+        return {"answer": msg, "model": f"{local_model} (Ollama)", "error": str(e)}
\ No newline at end of file
diff --git a/Agent/tools/groq_vision_health.py b/Agent/tools/groq_vision_health.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cee82872bb719174ee3ceaa36b4d3ea01769a6c
--- /dev/null
+++ b/Agent/tools/groq_vision_health.py
@@ -0,0 +1,33 @@
+import os
+import json
+import base64
+from typing import Optional
+
+
+def health_check() -> dict:
+    """
+    Quick health check for Groq Vision model.
+    Returns {"ok": bool, "model": str, "error": Optional[str]}
+    """
+    if os.environ.get("USE_HF_CLOUD", "0") not in ("1", "true", "True"):
+        return {"ok": True, "model": "local-ollama", "error": None}
+
+    groq_key = os.environ.get("GROQ_API_KEY")
+    if not groq_key:
+        return {"ok": False, "model": "none", "error": "GROQ_API_KEY is not set"}
+
+    groq_model = os.environ.get("GROQ_VISION_MODEL", "qwen/qwen3.8-27b")
+    try:
+        from groq import Groq
+
+        client = Groq(api_key=groq_key)
+        # Try a minimal text-only call to verify model exists and is accessible
+        response = client.chat.completions.create(
+            model=groq_model,
+            messages=[{"role": "user", "content": "ping"}],
+            max_tokens=1,
+            temperature=0,
+        )
+        return {"ok": True, "model": groq_model, "error": None}
+    except Exception as e:
+        return {"ok": False, "model": groq_model, "error": str(e)}
\ No newline at end of file
diff --git a/Agent/worker.py b/Agent/worker.py
new file mode 100644
index 0000000000000000000000000000000000000000..82854c1f25c20dd95815280527ed491163da1491
--- /dev/null
+++ b/Agent/worker.py
@@ -0,0 +1,543 @@
+import os
+import asyncio
+import io
+import sys
+import contextlib
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import Optional
+from fastapi import File, Form, UploadFile
+import base64
+import uuid
+import uvicorn
+
+# Worker that imports the agent and exposes a simple HTTP API
+app = FastAPI(title="Agent Worker")
+
+# ---------------------------------------------------------------------------
+# Schema profile registry (dynamic multi-DB support)
+# ---------------------------------------------------------------------------
+try:
+    from .schema_profile import ProfileRegistry
+except Exception:
+    import Agent.schema_profile as _sp
+    ProfileRegistry = _sp.ProfileRegistry
+
+SCHEMAS_DIR = os.path.join("uploads", "schemas")
+PROFILE_STORE_PATH = os.path.join("chat_store", "schema_profiles.json")
+profile_registry = ProfileRegistry(PROFILE_STORE_PATH)
+
+SUPPORTED_DB_SUFFIXES = {".sqlite", ".db", ".sqlite3", ".csv"}
+
+
+class AddSchemaJsonReq(BaseModel):
+    name: str
+    connection_url: str
+    activate: Optional[bool] = True
+    notes: Optional[str] = None
+
+
+def _detect_demo_kind(name: str, tblset: Optional[set] = None) -> str:
+    """Classify a profile as the built-in employee demo or a user schema."""
+    n = (name or "").lower()
+    if "demo_employee" in n:
+        return "demo"
+    if tblset and all(
+        t in tblset for t in ["employee", "department", "salary", "dept_emp"]
+    ):
+        return "demo"
+    # Connection URL pointing at employee-ish database
+    if "employee" in (name or ""):
+        return "demo"
+    return "user"
+
+class QueryReq(BaseModel):
+    question: str
+    doc_id: Optional[str] = None
+    mode: Optional[str] = None  # 'text2sql' or 'ocr_qa'
+
+
+class UploadResp(BaseModel):
+    doc_id: str
+    text_preview: str
+    image_url: str
+
+class OcrQAReq(BaseModel):
+    question: str
+    doc_id: str
+
+class OcrQAResp(BaseModel):
+    doc_id: str
+    answer: str
+    used_llm: bool
+
+# Lazy import agent module (do at startup so heavy init happens in worker)
+agent = None
+# Lock to prevent concurrent capture of stdout/stderr which would interleave logs
+capture_lock = asyncio.Lock()
+
+@app.on_event("startup")
+async def startup_event():
+    global agent
+    # Import here so the LLM and DB init occur inside the worker process
+    # Use package-relative import so we load Agent.agentic_workflow (not a top-level file)
+    try:
+        from . import agentic_workflow as agentic_workflow
+    except Exception:
+        # fallback to absolute package import
+        import Agent.agentic_workflow as agentic_workflow
+    agent = agentic_workflow
+
+    # ------------------------------------------------------------------
+    # Built-in demo employee database: ALWAYS available as a profile.
+    # Sourced from the authentic employees sample dataset bundled in the repo
+    # (employee-sample-database/sqlite/dataset_small/employee.db). Used as the
+    # active database out of the box when no DB_CONNECTION_URL is provided.
+    # ------------------------------------------------------------------
+    demo_db_url = None
+    demo_db_path = os.path.join(
+        os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
+        "employee-sample-database", "sqlite", "dataset_small", "employee.db",
+    )
+    if os.path.isfile(demo_db_path):
+        demo_db_url = f"sqlite:///{demo_db_path.replace(os.sep, '/')}"
+        try:
+            has_demo = any((p or {}).get("kind") == "demo"
+                           for p in profile_registry.profiles.values())
+            if not has_demo:
+                profile_registry.add(name="demo_employee", db_url=demo_db_url,
+                                     kind="demo", activate=False)
+                print("[WORKER] Registered built-in demo profile 'demo_employee'")
+        except Exception as reg_err:
+            print(f"[WORKER] demo profile registration skipped: {reg_err}")
+    else:
+        print(f"[WORKER] Bundled demo employee DB not found at {demo_db_path}")
+
+    # ------------------------------------------------------------------
+    # Decide which database to initialize against:
+    #   1. DB_CONNECTION_URL (env/compose wins)  2. last active profile  3. demo
+    # ------------------------------------------------------------------
+    db_url = os.environ.get("DB_CONNECTION_URL")
+    active = profile_registry.active()
+    target_uri = db_url or (active or {}).get("db_url") or demo_db_url
+
+    if target_uri:
+        try:
+            # Re-attach owner schema notes stored on the matching profile, so a
+            # restarted server carries the domain hints with no re-registration.
+            _pre = next(
+                (p for p in profile_registry.list()
+                 if ((p.get("db_url") or "").replace("\\", "/") == target_uri.replace("\\", "/"))),
+                None,
+            )
+            await agent.initialize_database(uri=target_uri, schema_notes=(_pre or {}).get("notes"))
+            # Keep the registry's active entry in sync with what we connected to.
+            # IMPORTANT: force the booted database to OWN the active slot — the
+            # demo registration above grabs "active" on a fresh store (ProfileRegistry
+            # .add() activates when no active exists), which left Space deployments
+            # showing the demo DB even though the env DB_CONNECTION_URL booted.
+            try:
+                entries = profile_registry.list()
+                match = next(
+                    (p for p in entries
+                     if ((p.get("db_url") or "").replace("\\", "/") == target_uri.replace("\\", "/"))),
+                    None,
+                )
+                if match is None:
+                    prof = getattr(agent, "ACTIVE_SCHEMA_PROFILE", None)
+                    _from_env = bool(db_url and target_uri == db_url)
+                    _kind = "user" if _from_env else (
+                        "demo" if (prof and getattr(prof, "is_employee_db", False)) else "user")
+                    _name = (getattr(prof, "name", None) if prof else None) or (
+                        "Employee_DB" if _from_env else "demo_employee")
+                    pid = f"boot_{uuid.uuid4().hex[:8]}"
+                    profile_registry.profiles[pid] = {
+                        "name": _name,
+                        "db_url": target_uri,
+                        "kind": _kind,
+                        "created_at": __import__("time").time(),
+                    }
+                    profile_registry.active_id = pid
+                    profile_registry.save()
+                    print(f"[WORKER] Registered ACTIVE schema profile '{_name}' (kind={_kind}) for env DB")
+                else:
+                    if profile_registry.active_id != match["id"]:
+                        profile_registry.switch(match["id"])
+                        print(f"[WORKER] Active profile switched to '{match['name']}'")
+                    else:
+                        print(f"[WORKER] Active profile already '{match['name']}'")
+            except Exception as reg_err:
+                print(f"[WORKER] profile bootstrap skipped: {reg_err}")
+        except Exception as e:
+            # Log but continue; agentic_workflow has fallback behavior
+            print(f"Worker: initialize_database failed: {e}")
+    else:
+        print("Worker: no database available (no env DB, no profiles, no demo bundle)")
+    # Optional Groq Vision health check in cloud mode
+    try:
+        from .tools import groq_vision_health
+        hc = groq_vision_health.health_check()
+        if not hc.get("ok", True):
+            print(f"[WORKER] Groq Vision health warning: {hc.get('error')} (model={hc.get('model')})")
+        else:
+            print(f"[WORKER] Groq Vision health: ok (model={hc.get('model')})")
+    except Exception as e:
+        print(f"[WORKER] Groq Vision health check skipped: {e}")
+
+@app.get("/health")
+async def health():
+    return {"status": "ok"}
+
+
+@app.post("/query")
+async def query(req: QueryReq):
+    global agent
+    if agent is None:
+        raise HTTPException(status_code=503, detail="Agent not initialized")
+    q = req.question
+    doc_id = getattr(req, 'doc_id', None)
+    mode = getattr(req, 'mode', None)
+    # Capture stdout/stderr produced by the agent for this request
+    buf_out = io.StringIO()
+    buf_err = io.StringIO()
+    async with capture_lock:
+        try:
+            with contextlib.redirect_stdout(buf_out), contextlib.redirect_stderr(buf_err):
+                # Robust mode switch: if mode is 'ocr_qa' or doc_id is present, use OCR agent; else use SQL logic
+                if (mode == 'ocr_qa') or (doc_id and (mode is None or mode == 'auto')):
+                    # Use OCR Q&A agent
+                    # ocr_agent_qa may be sync, so run in executor
+                    import asyncio
+                    answer = await asyncio.get_event_loop().run_in_executor(
+                        None, lambda: agent.ocr_agent_qa(q, doc_id)
+                    )
+                    result = answer
+                    sql_out = None  # No SQL for OCR Q&A
+                else:
+                    # Use standard Text-to-SQL agentic workflow
+                    result = await agent.agentic_query_process(q, None)
+                    try:
+                        sql_out = getattr(agent, 'LAST_GENERATED_SQL', None)
+                    except Exception:
+                        sql_out = None
+        except ValueError as ve:
+            # Treat known generation failures as normal answers so UI doesn't show nested JSON error
+            err_msg = str(ve)
+            if "Unable to generate SQL query" in err_msg:
+                result = err_msg  # Provide user-visible plain text
+                sql_out = None
+            else:
+                import traceback
+                traceback.print_exc(file=buf_err)
+                raise HTTPException(status_code=500, detail=err_msg)
+        except Exception as e:
+            import traceback
+            traceback.print_exc(file=buf_err)
+            raise HTTPException(status_code=500, detail=str(e))
+    logs = buf_out.getvalue()
+    errlogs = buf_err.getvalue()
+    if errlogs:
+        logs = logs + "\n" + errlogs
+    # Chart contract: strip the embedded chart marker from the answer and
+    # expose it as a first-class field for the web UI (ECharts).
+    chart_out = None
+    try:
+        from .chart_contract import extract_chart_marker
+        clean_answer, chart_out = extract_chart_marker(result)
+        if chart_out is not None:
+            result = clean_answer
+    except Exception:
+        chart_out = None
+    return {"answer": result, "logs": logs, "sql": sql_out, "chart": chart_out}
+
+
+@app.post("/ocr-qa", response_model=OcrQAResp)
+async def ocr_qa(req: OcrQAReq):
+    """Answer a question about a previously uploaded document using its doc_id.
+
+    This uses the agent's ocr_agent_qa helper which pulls stored OCR text and
+    consults the LLM (or returns a low-memory fallback excerpt).
+    """
+    global agent
+    if agent is None:
+        raise HTTPException(status_code=503, detail="Agent not initialized")
+    question = req.question
+    doc_id = req.doc_id
+    # Run potentially blocking OCR QA in thread pool if it's sync
+    try:
+        answer = await asyncio.get_event_loop().run_in_executor(
+            None, lambda: agent.ocr_agent_qa(question, doc_id)
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"ocr_qa failed: {e}")
+    used_llm = not answer.startswith("[Low memory]") and not answer.startswith("No OCR text") and not answer.startswith("Error while")
+    return OcrQAResp(doc_id=doc_id, answer=answer, used_llm=used_llm)
+
+
+@app.post("/upload-image", response_model=UploadResp)
+async def upload_image(file: UploadFile = File(...)):
+    """Accept an image, save locally, call MCP document_scanner.process eagerly,
+    persist OCR to private chat store, and return a doc_id plus preview.
+    """
+    global agent
+    if agent is None:
+        raise HTTPException(status_code=503, detail="Agent not initialized")
+
+    # Ensure uploads directory exists
+    uploads_dir = "uploads"
+    os.makedirs(uploads_dir, exist_ok=True)
+
+    # Save uploaded file
+    filename = f"{uuid.uuid4()}_{file.filename}"
+    path = os.path.join(uploads_dir, filename)
+    try:
+        contents = await file.read()
+        with open(path, "wb") as f:
+            f.write(contents)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Failed to save upload: {e}")
+
+    # Call MCP document_scanner tool eagerly with base64 payload to avoid exposing file server
+    b64 = base64.b64encode(contents).decode('utf-8')
+    try:
+        from .mcp_client import mcp_call_tool
+        ctx = {"tool_call": {"name": "document_scanner.process", "args": {"image_bytes": b64}}}
+        resp = mcp_call_tool(prompt="run document scanner", timeout=60.0, context=ctx)
+        # Expect tool_result in response
+        tool_result = resp.get("tool_result") if isinstance(resp, dict) else None
+    except Exception:
+        # Fall back to local direct call if MCP unavailable or tool failed
+        tool_result = None
+        try:
+            if agent and hasattr(agent, 'tools') and hasattr(agent.tools, 'document_scanner'):
+                try:
+                    tool_result = agent.tools.document_scanner.process_image(image_bytes=b64)
+                except Exception:
+                    tool_result = None
+        except Exception:
+            tool_result = None
+        if not tool_result:
+            tool_result = {"doc_id": str(uuid.uuid4()), "text": "(ocr unavailable)", "metadata": {}}
+
+    # Persist OCR to private chat store file for retrieval by doc_id
+    doc_id = tool_result.get("doc_id") if isinstance(tool_result, dict) and tool_result.get("doc_id") else str(uuid.uuid4())
+    ocr_text = tool_result.get("text", "") if isinstance(tool_result, dict) else str(tool_result)
+
+    # Save to docs/ and also to chat_store_private for history
+    docs_dir = os.path.join("chat_store", "docs")
+    os.makedirs(docs_dir, exist_ok=True)
+    doc_path = os.path.join(docs_dir, f"{doc_id}.txt")
+    try:
+        with open(doc_path, "w", encoding="utf-8") as df:
+            df.write(ocr_text)
+    except Exception as e:
+        print(f"Failed to persist OCR text: {e}")
+
+    # Also persist the original image path so Granite Vision can do direct visual Q&A
+    img_path_file = os.path.join(docs_dir, f"{doc_id}.img_path")
+    try:
+        abs_img_path = os.path.abspath(path)
+        with open(img_path_file, "w", encoding="utf-8") as ipf:
+            ipf.write(abs_img_path)
+        print(f"[upload] Saved image path for doc_id={doc_id}: {abs_img_path}")
+    except Exception as e:
+        print(f"[upload] Failed to persist image path: {e}")
+
+    # Also add a short entry to chat_store_private for traceability
+    try:
+        try:
+            from .agentic_workflow import chat_store_private, ChatMessage, MessageRole
+        except ImportError:
+            from Agent.agentic_workflow import chat_store_private, ChatMessage, MessageRole
+        # Create minimal messages
+        user_msg = ChatMessage(role=MessageRole.USER, content=f"Uploaded document {doc_id}")
+        assistant_msg = ChatMessage(role=MessageRole.ASSISTANT, content=f"OCR stored: {ocr_text[:400]}")
+        chat_store_private.add_message(key="conversation", message=user_msg)
+        chat_store_private.add_message(key="conversation", message=assistant_msg)
+        chat_store_private.persist(str("chat_store/chat_store_private.json"))
+    except Exception:
+        pass
+
+    # Return doc id and small preview
+    preview = ocr_text[:400]
+    image_url = path
+    return UploadResp(doc_id=doc_id, text_preview=preview, image_url=image_url)
+
+
+# ---------------------------------------------------------------------------
+# Schema profile endpoints — dynamic multi-database support
+# ---------------------------------------------------------------------------
+
+@app.get("/schemas")
+async def schemas_list():
+    return {"profiles": profile_registry.list(), "active_id": profile_registry.active_id}
+
+
+@app.post("/schemas/add")
+async def schemas_add_json(req: AddSchemaJsonReq):
+    """Register a database via connection URL (postgres/mysql/sqlite)."""
+    global agent
+    name = (req.name or "").strip()[:80]
+    if not name or not req.connection_url:
+        raise HTTPException(status_code=400, detail="name and connection_url are required")
+    entry = profile_registry.add(
+        name=name,
+        db_url=req.connection_url,
+        kind=_detect_demo_kind(name),
+        activate=bool(req.activate),
+        notes=req.notes,
+    )
+    if bool(req.activate) and agent is not None:
+        try:
+            await agent.initialize_database(uri=req.connection_url, schema_notes=req.notes)
+        except Exception as e:
+            raise HTTPException(status_code=400, detail=f"Profile saved, but DB init failed: {e}")
+    # Return full schema digest preview
+    return {"profile": entry, "schema": agent.ACTIVE_SCHEMA_PROFILE.summarize() if agent else None}
+
+
+@app.post("/schemas/add-file")
+async def schemas_add_file(file: UploadFile = File(...), name: Optional[str] = Form(None),
+                           activate: bool = Form(True), notes: Optional[str] = Form(None)):
+    """Register a database via file upload (.sqlite/.db/.sqlite3 or .csv)."""
+    global agent
+    fname = file.filename or "upload"
+    ext = os.path.splitext(fname)[-1].lower()
+    if ext not in SUPPORTED_DB_SUFFIXES:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Unsupported file type '{ext}'. Supported: {sorted(SUPPORTED_DB_SUFFIXES)}",
+        )
+    display_name = (name or os.path.splitext(fname)[0]).strip()[:80] or "user_schema"
+
+    os.makedirs(SCHEMAS_DIR, exist_ok=True)
+
+    if ext == ".csv":
+        # Convert CSV into a single-table SQLite database
+        import pandas as pd
+        import tempfile
+
+        sqlite_path = os.path.join(SCHEMAS_DIR, f"{uuid.uuid4().hex}.db")
+        try:
+            contents = await file.read()
+            df = pd.read_csv(io.BytesIO(contents))
+        except Exception as e:
+            raise HTTPException(status_code=400, detail=f"CSV parse failed: {e}")
+        table_name = "".join(ch for ch in display_name if ch.isalnum() or ch == "_") or "data"
+        try:
+            engine_path = os.path.abspath(sqlite_path).replace("\\", "/")
+            df.to_sql(table_name, f"sqlite:///{engine_path}", index=False, if_exists="replace")
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=f"CSV→SQLite conversion failed: {e}")
+        db_url = f"sqlite:///{engine_path}"
+        kind = "user"
+    else:
+        # Copy uploaded SQLite file into schemas dir
+        sqlite_path = os.path.join(SCHEMAS_DIR, f"{uuid.uuid4().hex}{ext}")
+        try:
+            contents = await file.read()
+            with open(sqlite_path, "wb") as fh:
+                fh.write(contents)
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=f"Failed to save upload: {e}")
+        engine_path = sqlite_path.replace("\\", "/")
+        db_url = f"sqlite:///{engine_path}"
+        # Poke the file to validate it is really a SQLite database
+        try:
+            import sqlite3
+
+            conn = sqlite3.connect(sqlite_path)
+            tblset = {r[0] for r in conn.execute(
+                "SELECT name FROM sqlite_master WHERE type IN ('table','view')"
+            ).fetchall()}
+            conn.close()
+        except Exception as e:
+            raise HTTPException(status_code=400, detail=f"Invalid SQLite file: {e}")
+        kind = _detect_demo_kind(display_name, tblset)
+
+    entry = profile_registry.add(name=display_name, db_url=db_url, kind=kind,
+                                 activate=activate, notes=notes)
+    if activate and agent is not None:
+        try:
+            await agent.initialize_database(uri=db_url, schema_notes=notes)
+        except Exception as e:
+            raise HTTPException(status_code=400, detail=f"Profile saved, but DB init failed: {e}")
+    return {"profile": entry, "schema": agent.ACTIVE_SCHEMA_PROFILE.summarize() if agent else None}
+
+
+@app.post("/schemas/switch/{profile_id}")
+async def schemas_switch(profile_id: str):
+    """Switch the active database profile and re-initialize the agent against it."""
+    global agent
+    if agent is None:
+        raise HTTPException(status_code=503, detail="Agent not initialized")
+    entry = profile_registry.switch(profile_id)
+    if not entry:
+        raise HTTPException(status_code=404, detail="Profile not found")
+    # Dispose previous async engine best-effort
+    try:
+        if getattr(agent, "async_engine", None) is not None:
+            await agent.async_engine.dispose()
+    except Exception:
+        pass
+    try:
+        await agent.initialize_database(uri=entry["db_url"], schema_notes=entry.get("notes"))
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Switch failed: {e}")
+    return {"profile": entry, "schema": agent.ACTIVE_SCHEMA_PROFILE.summarize()}
+
+
+class SetSchemaNotesReq(BaseModel):
+    notes: str = ""
+
+
+@app.post("/schemas/{profile_id}/notes")
+async def schemas_set_notes(profile_id: str, req: SetSchemaNotesReq):
+    """Persist owner-supplied schema notes (domain hints) for a profile.
+
+    Notes are injected verbatim into every agent prompt for that database,
+    supplying the semantics that live introspection cannot infer.
+    """
+    notes = (req.notes or "").strip()[:4000]
+    if not profile_registry.set_notes(profile_id, notes):
+        raise HTTPException(status_code=404, detail="Profile not found")
+    # Live effect on the running agent when the profile is currently active
+    # (no re-initialization needed).
+    if agent is not None and profile_id == profile_registry.active_id:
+        try:
+            if getattr(agent, "ACTIVE_SCHEMA_PROFILE", None) is not None:
+                agent.ACTIVE_SCHEMA_PROFILE.notes = notes
+        except Exception:
+            pass
+    return {"id": profile_id, "notes": notes}
+
+
+@app.delete("/schemas/{profile_id}")
+async def schemas_delete(profile_id: str):
+    entry = profile_registry.get(profile_id)
+    if not entry:
+        raise HTTPException(status_code=404, detail="Profile not found")
+    # Remove uploaded backing file for file-based profiles
+    db_url = entry.get("db_url") or ""
+    try:
+        if db_url.startswith("sqlite:///"):
+            p = db_url.replace("sqlite:///", "", 1)
+            if os.path.exists(p) and str(SCHEMAS_DIR) in p:
+                os.remove(p)
+    except Exception:
+        pass
+    profile_registry.remove(profile_id)
+    return {"deleted": profile_id}
+
+
+@app.get("/schema/current")
+async def schema_current():
+    """Current active schema summary (tables/columns/samples) for the UI."""
+    global agent
+    if agent is None:
+        raise HTTPException(status_code=503, detail="Agent not initialized")
+    return agent.ACTIVE_SCHEMA_PROFILE.summarize()
+
+if __name__ == '__main__':
+    port = int(os.environ.get("WORKER_PORT", 8700))
+    uvicorn.run(app, host="127.0.0.1", port=port)
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..1c4e6fea64fa4cb678a500340766298e8f96fd8b
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,42 @@
+# ☁️ Dockerfile for Hugging Face Spaces — Cloud-Only (Lightweight)
+# Image size: ~500MB (no Ollama, no local models)
+# All LLM inference is handled by Groq API and HF Inference API via USE_HF_CLOUD=1
+FROM python:3.11-slim
+
+# Prevent Python from writing pyc files and buffering stdout/stderr
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+# Cloud mode: all LLM calls go to Groq / HF Inference — no local Ollama needed
+ENV USE_HF_CLOUD=1
+
+# Install system dependencies (Tesseract for OCR fallback only)
+RUN apt-get update && apt-get install -y \
+    curl \
+    zstd \
+    tesseract-ocr \
+    libtesseract-dev \
+    gcc \
+    libpq-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+# Install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt && \
+    pip install --no-cache-dir llama-index-llms-huggingface-api huggingface_hub
+
+# Copy application code
+COPY . .
+
+# Hugging Face Spaces requires port 7860
+EXPOSE 7860
+
+# Fix line endings and permissions on startup script
+RUN sed -i 's/\r$//' start_hf.sh && chmod +x start_hf.sh
+RUN useradd -m -u 1000 appuser && chown -R appuser:appuser /app 2>/dev/null || true
+USER appuser
+
+CMD ["./start_hf.sh"]
\ No newline at end of file
diff --git a/README.md b/README.md
index 520b34935de897354367166fb196201d19a63d94..c4ab078ebbcc8c1b8e457b805a96a5a2cd22a895 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,637 @@
 ---
-title: QueryLensV2b
-emoji: 🏆
-colorFrom: blue
-colorTo: pink
+title: QueryLens V2
+emoji: '🤖'
+colorFrom: yellow
+colorTo: red
 sdk: gradio
 sdk_version: 6.30.0
-python_version: '3.12'
+python_version: 3.11
 app_file: app.py
 pinned: false
+tags:
+  - agents
+  - mcp
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Agentic Text2SQL & Document VQA
+ Multi-Agent System
+
+![Architecture](https://img.shields.io/badge/Architecture-Multi--Agent-blue)
+![Database](https://img.shields.io/badge/Database-Agnostic-success)
+![LLM](https://img.shields.io/badge/LLMs-Qwen%20%7C%20Ollama-orange)
+![Protocol](https://img.shields.io/badge/Protocol-MCP-purple)
+
+An advanced, hardware-optimized AI pipeline built on a multi-agent orchestration architecture. This system seamlessly integrates natural language Text-to-SQL generation with a Vision-Language Model (VLM) for Optical Character Recognition (OCR) and Visual Question Answering (VQA).
+
+By leveraging the Model Context Protocol (MCP), local LLMs via Ollama, and intelligent memory management, this project achieves production-grade data extraction and database querying running entirely on local hardware within tight memory constraints.
+
+---
+
+## Key Features
+
+- **Multi-Agent Orchestration:** A dynamic routing system featuring specialized agents — MemoryAgent, QueryCraftAgent, and ResultPresenterAgent — each with a clearly defined role in the pipeline.
+- **Dynamic Database Profiles:** Connect any database by connection string, or upload a `.sqlite`/`.db`/`.csv` file directly from the UI. Profiles are registered, switchable, and deletable at runtime via the `Database` panel or the `/api/schemas` REST surface — the built-in employee sample DB ships as one profile (`demo_employee`) among many.
+- **Schema-Aware Generic Agents:** The agent pipeline reads a live introspected schema profile (tables, columns, PK/FK, row counts, sample rows) instead of assuming a fixed schema. The fourteen employee-specific `fast_sql` templates activate only when the employee demo is the active profile; every other database gets generic `schema.list_tables / schema.describe_table / schema.sample_data / sql.build_select` MCP tools and profile-aware prompts, validators, and reducers.
+- **Interactive Charts:** Query results that visualize well (category breakdowns, comparisons, time series) are detected automatically and rendered as ECharts visualizations — pie, bar, or line — in a sepia-toned palette. Chart specs ride in the query response as a first-class `chart` field; detection is conservative and never corrupts a textual answer.
+- **Modern Sepia UI:** A Claude-inspired coffee/sepia interface with serif headings and a warm terracotta accent, wrapping the multi-agent trace panels (Memory / QueryCraft / ResultPresenter), live schema browsing, and charting.
+- **Vision-Language Question Answering:** VQA/OCR extraction from invoices and unstructured documents. Served locally via Ollama (`qwen2-vl:2b`) or — in cloud mode — through Groq's `qwen/qwen3.8-27b` vision model (free tier).
+- **Three-Level Text-to-SQL Engine:** Translates natural language questions into syntactically valid SQL through a progressive reasoning architecture, with automatic self-correction at each escalation level.
+- **Semantic Gatekeeping:** A multi-layer validation pipeline filters hallucinated SQL, undefined aliases, missing joins, and incorrect aggregation patterns before any query reaches the database.
+- **Dynamic VRAM Orchestration:** Custom memory management algorithms automatically load and unload models from GPU VRAM to prevent out-of-memory crashes, enabling concurrent AI workloads on consumer hardware.
+- **Unified MCP Integration:** All external data sources — relational databases and vision models alike — are accessed through a single, uniform Model Context Protocol interface.
+- **Database Agnostic Design:** Architected to interface with PostgreSQL, MySQL, SQLite, MS SQL Server, and Oracle via SQLAlchemy. Live introspection supplies every prompt with real structure (tables, columns, keys, sample rows); optional per-database **Schema Notes** let the database owner state semantics — column meanings, aliases, business rules — that are persisted with the profile and injected verbatim into every prompt.
+- **Conversational Interface:** A full Web UI supporting natural language interaction with persistent session context.
+
+---
+
+## Dynamic Databases & Visual Analytics
+
+### Database profiles
+
+Any database the worker can reach via SQLAlchemy becomes a first-class profile:
+
+| Endpoint | Method | Purpose |
+|---|---|---|
+| `/api/schemas` | GET | List registered profiles + active id |
+| `/api/schemas/add` | POST | Register via `name` + `connection_url` (postgres/mysql/sqlite) |
+| `/api/schemas/add-file` | POST | Upload `.sqlite`/`.db` (used as-is) or `.csv` (converted to a single-table SQLite DB) |
+| `/api/schemas/switch/{id}` | POST | Activate a profile and re-initialize the agent against it |
+| `/api/schemas/{id}` | DELETE | Remove a profile (+ its uploaded backing file) |
+| `/api/schema/current` | GET | Live summary of the active schema (tables, columns, row counts, sample rows) |
+
+Profiles persist in `chat_store/schema_profiles.json` and survive restarts; uploaded files are stored under `uploads/schemas/`. On startup, the worker auto-registers the `DB_CONNECTION_URL` database as the initial active profile.
+
+### Chart contract
+
+When a query returns rows that visualize well, the ResultPresenter embeds a chart spec (pie/bar/line, labels + numeric series) that the worker exposes as `{"chart": {...}}` alongside the text `answer`. Selection heuristics: date-like first column + numeric series → line; ≤8 categories with a single numeric series → pie; otherwise bar. Detection caps at 60 points and never mutates the answer text.
+
+### Working against the demo employee database
+
+The built-in employee sample (MySQL/PostgreSQL containers in `docker-compose.yml`, or the `demo_employee` profile) keeps all battle-tested heuristics: `to_date = '9999-01-01'` current-row filters, the manager/department join knowledge, the 14 `fast_sql` MCP templates, and the invariant enforcement layer.
+
+---
+
+## System Architecture and Agentic Workflow
+
+The project employs a decoupled, asynchronous microservices architecture. The Web UI communicates with a backend worker, which orchestrates all agent requests through the Model Context Protocol.
+
+### The Three-Agent Pipeline
+
+**1. MemoryAgent**
+
+The MemoryAgent acts as the entry point for all user queries. It performs three functions before any SQL generation occurs: checking whether the current query can be satisfied from historical conversation context, rephrasing ambiguous or under-specified queries based on prior interactions, and routing validated new queries to the appropriate downstream agent. This layer prevents redundant computation and improves response latency for repeated or related questions.
+
+**2. QueryCraftAgent — The Three-Level Text-to-SQL Engine**
+
+The QueryCraftAgent is the core reasoning engine of the system. It implements a progressive escalation architecture that balances inference speed with accuracy:
+
+- **Level 1 — Pure LLM Intelligence:** Zero-shot semantic translation of natural language to SQL using Qwen 2.5 Coder. The generated query passes through a semantic validation pipeline that checks alias correctness, required table joins, aggregation integrity, and department or entity filters before being accepted.
+
+- **Level 2 — LLM with SQL Fixer:** When Level 1 fails validation, the agent enters an autonomous self-correction loop. The SQL Fixer applies a deterministic set of targeted transformations — normalizing aliases, injecting missing to_date filters, repairing phantom column references, and enforcing PostgreSQL-compliant GROUP BY clauses — before re-validating the output.
+
+- **Level 3 — LLM with MCP Schema-Grounded SQL Generation:** The deepest reasoning level, reserved for queries that fail both prior levels. For the built-in employee demo, the agent calls one of fourteen registered `fast_sql` MCP tools, each of which queries the live database schema via SQLAlchemy's inspector to retrieve actual column names and table structures, then programmatically constructs a valid SQL template grounded in the real schema. For any user-registered database, the agent instead grounds itself through the generic `schema.list_tables` / `schema.describe_table` / `schema.sample_data` MCP tools, producing a verified live-schema + sample-data hint that applies to any database. In both cases the template is passed as a verified hint to the LLM, which refines it before the Fixer loop runs. Because the hint is built from the actual schema rather than the LLM's training data, this level eliminates the class of failures caused by hallucinated column names or missing joins.
+
+**3. ResultPresenterAgent**
+
+The ResultPresenterAgent executes the validated SQL query against the target database, translates raw result sets into natural conversational responses, handles execution errors and edge cases, and persists the interaction to the conversation history for future MemoryAgent lookups.
+
+### Multi-Agent Orchestration Flow
+
+```mermaid
+graph TD
+    User([User]) --> Web["Web Application\nPort 8000"]
+
+    Web -->|Image Upload| Worker["Backend Worker\nPort 8700"]
+    Web -->|Text Query| Worker
+
+    subgraph MCP["MCP Tool Server — Port 8701"]
+        DocScan["document_scanner.process\nTesseract OCR"]
+        FastSQL["fast_sql tools\nSchema-Grounded SQL Generation"]
+        VisionQA["granite_vision.qa\nQwen 3.5 2B Vision"]
+    end
+
+    Worker -->|"Upload: MCP tool call"| DocScan
+    DocScan -->|"doc_id + OCR text + image path"| Store[("Document Store\nchat_store/docs/")]
+
+    Worker -->|"OCR Q&A mode\ndoc_id present"| VisionQA
+    Store -->|image path| VisionQA
+    VisionQA --> OllamaV(["Ollama\nQwen 3.5 2B Vision"])
+    OllamaV --> Presenter
+
+    Worker -->|Text Query| Memory["MemoryAgent"]
+    Memory -->|Cached answer| Presenter["ResultPresenterAgent"]
+    Memory -->|New query| QCAgent["QueryCraftAgent"]
+
+    QCAgent -->|"Level 1 and 2\nDirect Ollama call"| OllamaSQL(["Ollama\nQwen 2.5 Coder"])
+    QCAgent -->|"Level 3\nMCP tool call"| FastSQL
+    FastSQL -->|"Schema-validated SQL hint"| OllamaSQL
+    OllamaSQL -->|Raw SQL| Fixer["SQL Fixer +\nSemantic Validator"]
+    Fixer -->|"Validated SQL\nvia SQLAlchemy"| DB[("PostgreSQL / MySQL / SQLite / MS SQL / Oracle")]
+
+    DB --> Presenter
+    Presenter -->|Persist to history| Memory
+    Presenter --> Answer(["Natural Language Answer"])
+```
+
+---
+
+## Infrastructure and Deployment Architecture
+
+### Containerized Microservices
+
+The system is implemented as a Dockerized microservices architecture, with each component running as an isolated, independently deployable service. Docker Compose orchestrates three services: the main application and agent worker, the MCP tool server, and the relational databases (PostgreSQL and MySQL). Database containers are provisioned with health checks using `pg_isready` and `mysqladmin ping`, and the application container is configured with `condition: service_healthy` dependencies to eliminate the startup race condition inherent in naive `depends_on` configurations.
+
+### Native-Bridge Hybrid Offloading
+
+For peak inference performance, the system uses a hybrid offloading strategy. The application logic and MCP servers run inside Docker containers for portability and isolation, while Ollama and all GPU inference are offloaded to the native Windows host and accessed by the containers over the WSL2 host bridge network (`OLLAMA_BASE_URL=http://<wsl2-host-ip>:11434`).
+
+This architecture eliminates the virtualization overhead introduced by Docker Desktop's Linux VM layer. By giving the inference engine direct access to NVIDIA CUDA cores and the full system memory pool — rather than a virtualized slice — the 1.5B parameter model achieves response times measured in seconds rather than minutes. The separation also means that GPU memory pressure from Ollama does not compete with the container's allocated memory ceiling.
+
+### Database Health and Startup Reliability
+
+A production-grade startup sequence is enforced through Docker health checks and application-level retry logic. The agent application implements a ten-attempt retry loop with five-second intervals on initialization, including a `SELECT 1` connection verification before the schema is read. This ensures consistent behavior across restarts and eliminates the class of intermittent failures caused by the application reading an empty or partially initialized schema during database startup.
+
+### MCP Tool Server Architecture
+
+The Model Context Protocol server runs as a dedicated microservice on port 8701. Three categories of tools are registered at startup:
+
+**document_scanner.process** handles Tesseract OCR and is called at image upload time, not at query time. It returns a doc_id, the extracted text, and triggers persistence of both the OCR output and the image path to the document store.
+
+**granite_vision.qa** is the direct visual question-answering tool backed by Qwen 3.5 2B Vision via Ollama. It is called when a user submits a question with a doc_id and the original image file is available on disk. The internal tool identifier `granite_vision.qa` was retained from the original design specification; the underlying model was later replaced with Qwen 3.5 2B Vision for improved OCR accuracy and reduced VRAM usage, with no changes required to the agent orchestration code.
+
+**fast_sql tools** are fourteen schema-grounded SQL generation tools used exclusively at Level 3 of the QueryCraftAgent. Each tool queries the live database schema via SQLAlchemy's inspector to retrieve actual column names, then uses a deterministic `_build_sql()` function to construct a valid query, optionally verified by a short LLM call. The result is returned as a schema-validated SQL hint to the QueryCraftAgent, which passes it to the LLM for final refinement. This design means Level 3 failures are caused by query complexity or intent ambiguity, never by hallucinated schema details.
+
+---
+
+## Unified Multimodal Integration via MCP
+
+A defining architectural feature of this system is that OCR and multimodal reasoning are integrated through the same Model Context Protocol used for database access. The Qwen 3.5 2B Vision model is registered as a named MCP tool: `granite_vision.qa`. This internal tool identifier was retained from the original design specification; the underlying engine was subsequently replaced with Qwen 3.5 2B Vision, which demonstrated superior OCR accuracy and a smaller VRAM footprint. Because the tool interface is decoupled from its implementation, this substitution required no changes to the agent orchestration code — only the MCP tool registration was updated.
+
+This design delivers three concrete engineering properties:
+
+**Uniform Protocol:** The orchestrator issues identical-format tool calls regardless of whether the data source is a SQL table or a visual document. There are no special-case code paths for different modalities.
+
+**Modular Replaceability:** The vision model is encapsulated behind its MCP interface. Upgrading to a different vision model — such as a GPT-4V endpoint — requires updating only the tool registration, not any agent logic.
+
+**Cross-Modal Reasoning Capability:** Because both data sources share the same protocol, the agent can compose multi-step reasoning across modalities. For example, the system can extract a value from a document via the vision tool and immediately verify it against a database record via the SQL tool, within a single agentic workflow.
+
+```text
+Multimodal Request Flow
+
+Worker receives query with image path
+        |
+        +-- Image detected --> ocr_agent_qa()
+                                    |
+                                    +-- Unload Qwen 2.5 Coder (free VRAM)
+                                    |
+                                    +-- MCP tool call: granite_vision.qa
+                                                |
+                                            MCP Server (port 8701)
+                                                |
+                                            Qwen 3.5 2B Vision (Ollama)
+                                                |
+                                            Structured answer returned
+        |
+        +-- No image --> Standard Text-to-SQL pipeline (unchanged)
+```
+
+---
+
+## SQL Validation Pipeline
+
+The semantic validation layer is one of the most technically substantive components of the system. Rather than trusting LLM output directly, every generated SQL query passes through a deterministic validation pipeline before execution. Key checks include:
+
+- **Alias Integrity:** All table aliases used in SELECT, WHERE, JOIN, and GROUP BY clauses must be defined in FROM or JOIN declarations. The validator maintains a set of defined and used aliases and rejects queries with undefined references.
+- **Salary Table Recognition:** The `salary` table name was explicitly excluded from the SQL keyword blocklist, as it is both a legitimate table name and a column name in the employee schema. Incorrect inclusion in the keyword set caused the validator to silently discard valid salary table joins.
+- **Department Filter Enforcement:** Queries that reference a specific department by name are validated to confirm that a corresponding WHERE filter exists in the generated SQL. This prevents queries that return all departments when the user asked for one.
+- **PostgreSQL GROUP BY Compliance:** Queries containing aggregate functions are checked to ensure all non-aggregated SELECT columns appear in the GROUP BY clause, enforcing strict PostgreSQL semantics.
+- **Manager Schema Awareness:** Queries involving manager data are validated to confirm use of the `dept_manager` table rather than `dept_emp`. The validation produces a warning rather than a hard rejection, allowing the downstream semantic adjuster to regenerate the correct SQL before the query reaches the database.
+
+---
+
+## Research Methodology: Fine-Tuning and Model Selection
+
+As part of the project's scientific evaluation, a custom model fine-tuning pipeline was developed and benchmarked against the selected production model.
+
+**LoRA Fine-Tuning:** The base Qwen 2.5 3B model was fine-tuned using Low-Rank Adaptation on a specialized Text-to-SQL dataset.
+
+**Weight Merging:** The LoRA adapters were merged back into the base model weights via `merge_qwen_to_ollama.py` and quantized for local deployment via Ollama.
+
+**Comparative Evaluation:** The fine-tuned general model was benchmarked against the zero-shot `qwen2.5-coder:1.5b`, which is pre-trained on a large corpus of code and SQL.
+
+**Engineering Decision:** The benchmark results showed that the Coder model's zero-shot SQL accuracy exceeded that of the general LoRA fine-tune, with faster inference and lower error rates on complex multi-table joins. The production architecture was updated accordingly. This outcome demonstrates a data-driven engineering decision: fine-tuning is not always the optimal path when a domain-specialized pretrained model exists for the target task.
+
+See `Fine_Tuning_Qwen_2_5_3B.ipynb` and the `merged_qwen2_5_3b_text2sql` directory for the complete fine-tuning methodology and benchmark results.
+
+---
+
+## Technology Stack
+
+| Component | Technology |
+|---|---|
+| Core Framework | Python 3.11+, FastAPI, Uvicorn |
+| AI Orchestration | LlamaIndex, Model Context Protocol (MCP) |
+| Local Inference | Ollama (C++ optimized, memory-mapped) |
+| Text-to-SQL Model | qwen2.5-coder:1.5b |
+| Vision/OCR Model | qwen3.5:2b (registered as granite_vision.qa) |
+| Database ORM | SQLAlchemy |
+| Containerization | Docker, Docker Compose |
+| Databases | PostgreSQL, MySQL, SQLite, MS SQL Server, Oracle (SQLAlchemy; async engines for Postgres/MySQL/SQLite, sync-threaded execution for MSSQL/Oracle) |
+
+---
+
+## Installation and Setup
+
+### Prerequisites
+
+- Ollama installed and running on the host machine
+- Python 3.11 or higher
+- Docker and Docker Compose
+- A supported database (PostgreSQL, MySQL, SQLite, MS SQL Server, or Oracle) — or upload a `.sqlite`/`.csv` file
+
+### 1. Pull Required Models
+
+```bash
+ollama pull qwen2.5-coder:1.5b
+ollama pull qwen3.5:2b
+```
+
+### 2. Configure Environment
+
+Install Python dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+Create a `.env` file in the `Agent/` directory (see `.env.example` for the full reference).
+
+**Recommended (cloud-only, no local models needed):** use the same HF Inference API configuration as the Hugging Face Spaces deployment:
+
+```env
+DB_CONNECTION_URL=postgresql://user:password@localhost:5432/dbname
+USE_HF_CLOUD=1
+HF_API_TOKEN=hf_xxx
+HF_LLM_MODEL=Qwen/Qwen2.5-Coder-7B-Instruct
+
+# Cloud VQA via Groq (free tier)
+GROQ_API_KEY=gsk_xxx
+GROQ_VISION_MODEL=qwen/qwen3.8-27b
+```
+
+**Local Ollama (optional):** with `USE_HF_CLOUD=0`, the configured Ollama model is used — and if it is not installed while `HF_API_TOKEN` is present, the worker **auto-falls back** to HF Inference (`HF_LLM_MODEL`) instead of failing with 404 handshakes.
+
+### 3. Run Without Docker
+
+Start each microservice in a separate terminal:
+
+```bash
+# Terminal 1 — Backend Worker
+python -u -m uvicorn "Agent.worker:app" --port 8700
+
+# Terminal 2 — MCP Tool Server
+python -m uvicorn "Agent.mcp_server:app" --port 8701
+
+# Terminal 3 — Web UI
+python -m uvicorn "web.app:app" --port 8000
+```
+
+Navigate to `http://localhost:8000` to interact with the system. Use the **Database** panel to connect your own database (connection URL or `.sqlite`/`.csv` upload) — the built-in employee sample is provided as a container-backed profile.
+
+### 4. Run With Docker
+
+Update `OLLAMA_BASE_URL` in `docker-compose.yml` to point to your host machine's Ollama instance, then run:
+
+```bash
+docker-compose up --build demo-agent-app
+```
+
+The database containers include health checks and will be fully initialized before the application starts.
+
+---
+
+## Deployment on HuggingFace Spaces
+
+### Quick Deploy (current, cloud-only)
+
+The app is fully cloud-based (`USE_HF_CLOUD=1`): Text-to-SQL runs on HF Inference
+(`Qwen/Qwen2.5-Coder-7B-Instruct`), VQA on Groq (`qwen/qwen3.8-27b`), and the
+built-in `demo_employee` SQLite profile ships with the repo — no external DB
+service is needed on Spaces.
+
+> **Note:** HF Spaces moved the **Docker SDK to a paid tier** (free CPU 2026).
+> Deploy there with the free **Gradio SDK** instead — `app.py` at the repo root
+> spawns the same three services the Docker image ran (worker `:8700`,
+> MCP `:8701`, sepia web UI `:7860`) and mounts a minimal Gradio anchor page at
+> `/gradio-lp`. The UI at `/` is unchanged.
+
+1. **Create the Space** — huggingface.co → *New Space* → SDK: **Gradio** →
+   blank template → name it (e.g. `querylens`). Hardware: free CPU basic.
+2. **Push the project files** into the Space repo. Easiest on Windows is the
+   bundled helper:
+   ```powershell
+   .\deploy_space.ps1 -SpaceName querylens
+   ```
+   It clones `huggingface.co/spaces/<you>/<name>`, copies the project across
+   (respecting the exclusions: no `.env`, no `chat_store/`, no 350 MB
+   `dataset_full` dumps, no `__pycache__`), merges the HF frontmatter into the
+   Space `README.md` (keeping the generated `sdk_version`), commits and pushes.
+3. **Add Secrets** (Space settings → Variables and Secrets — never commit them):
+   | Secret | Purpose |
+   |---|---|
+   | `HF_API_TOKEN` | HF Inference API — Text-to-SQL (`Qwen/Qwen2.5-Coder-7B-Instruct`) |
+   | `GROQ_API_KEY` | Groq vision VQA (`qwen/qwen3.8-27b`, free tier) |
+   No `DB_CONNECTION_URL` is required — the worker auto-activates the bundled demo.
+   Optional: set `DB_CONNECTION_URL` to a hosted Postgres (e.g. Neon) for a real
+   external database.
+4. **Open the Space** — the dark Graphite-Halo UI loads with the demo profile active.
+   The **Database** panel lets you register additional databases at runtime by
+   pasting any supported connection URL (`postgresql://`, `mysql://`,
+   `mssql://`, `oracle://`, `sqlite:///`) or uploading `.sqlite`/`.csv` files.
+
+**Local Docker runs still work** for your own machine: `docker build` +
+`docker-compose.yml` are unchanged and `start_hf.sh` serves the identical
+stack — only the Spaces *hosting* path is now the Gradio launcher.
+
+Note: free Spaces sleep after inactivity (cold start ~1–2 min). Uploaded schema
+files and chat history are ephemeral unless you enable *Persistent Storage* in
+the Space settings.
+
+### Historical deployment journey (reference)
+
+This section documents the **complete deployment journey** — from the first proof-of-concept tunnel approach to the final production-ready hybrid architecture. Each phase is explained with commands and rationale so this can serve as a reference for deploying any multi-modal AI application on HuggingFace Spaces.
+
+---
+
+### Understanding HuggingFace Spaces Constraints
+
+Before choosing a deployment strategy, it is essential to understand what the free tier of HuggingFace Spaces provides and restricts:
+
+| Resource | Free Tier |
+|----------|-----------|
+| CPU | 2 vCPUs |
+| RAM | 16 GB |
+| GPU | None |
+| Storage | Ephemeral (wiped on restart) |
+| Public port | 7860 only |
+| Timeout | Space sleeps after inactivity |
+
+The key challenge: **running a local LLM (e.g. `qwen2.5-coder:1.5b`) via Ollama consumes ~1.5–3 GB RAM, leaving limited headroom for vision models and the application stack.** Running a vision-language model such as `qwen3.5:2b` (~3 GB) alongside the text model causes out-of-memory (OOM) failures on the free CPU tier.
+
+---
+
+### Phase 1 — Cloudflare Tunnel: Bridging Local Resources to the Cloud
+
+#### Theory
+
+The simplest approach to deploy a resource-intensive app on HF Spaces is to **not run the heavy models in the cloud at all**. Instead, you run the LLM and VLM locally on your development machine and expose the Ollama API endpoint to the internet via a secure tunnel. The HF Space container merely hosts the web frontend and proxies inference requests back to your local machine.
+
+This approach has zero cloud compute cost and allows full GPU acceleration locally. However, it requires your development machine to remain **online and running at all times**, making it unsuitable for a persistent public demo.
+
+#### Setup
+
+**Step 1: Install Cloudflared on your local machine**
+```bash
+# Windows (via winget)
+winget install Cloudflare.cloudflared
+
+# Or download directly
+# https://github.com/cloudflare/cloudflared/releases
+```
+
+**Step 2: Start Ollama locally**
+```bash
+ollama serve
+# Ollama now listens on http://localhost:11434
+```
+
+**Step 3: Create a public tunnel to your local Ollama port**
+```bash
+cloudflared tunnel --url http://localhost:11434
+```
+
+Cloudflared will print a public HTTPS URL like:
+```
+https://your-random-id.trycloudflare.com
+```
+
+**Step 4: Set the tunnel URL as a secret in your HF Space**
+
+In the HF Space settings under **Variables and Secrets**, add:
+```
+OLLAMA_BASE_URL = https://your-random-id.trycloudflare.com
+```
+
+The application code reads `OLLAMA_BASE_URL` and routes all inference calls through the tunnel to your local GPU.
+
+#### Limitation
+
+- The tunnel URL changes every time `cloudflared` restarts, requiring a secret update.
+- Requires a continuously running local machine.
+- Not viable for a shared capstone demo or public deployment.
+
+---
+
+### Phase 2 — Self-Contained Docker: Running Ollama Inside the Container
+
+#### Theory
+
+The next evolution is to **embed Ollama directly into the Docker image** and pull models at container startup. This makes the deployment fully self-contained — no external machine is needed. The HF Space container becomes a complete inference server.
+
+The challenge is that HF Spaces rebuilds the Docker image on every push but does **not cache downloaded model weights** between restarts (storage is ephemeral). This means every **Factory Reboot** triggers a fresh ~1–3 GB model download, causing a cold-start delay of 2–5 minutes.
+
+To prevent HF's health monitor from timing out the Space during this download, model pulling is performed **asynchronously in the background**, allowing the web UI to become responsive immediately while the model fetches in parallel.
+
+#### Dockerfile Changes
+
+Add Ollama installation to the `Dockerfile`:
+
+```dockerfile
+# Install Ollama binary (pinned to a stable release)
+RUN curl -L https://github.com/ollama/ollama/releases/download/v0.5.11/ollama-linux-amd64.tgz \
+    -o ollama.tgz && \
+    tar -C /usr -xzf ollama.tgz && \
+    rm ollama.tgz
+```
+
+#### Startup Script (`start_hf.sh`)
+
+```bash
+#!/bin/bash
+export HOME=/root
+
+echo "[HF-INIT] Starting Ollama server..."
+ollama serve &
+OLLAMA_PID=$!
+
+# Wait for Ollama to become healthy (up to 20 attempts)
+for i in $(seq 1 20); do
+    if curl -sf http://localhost:11434/api/tags > /dev/null 2>&1; then
+        echo "[HF-INIT] Ollama is ALIVE!"
+        break
+    fi
+    echo "[HF-INIT] Waiting... ($i/20)"
+    sleep 3
+done
+
+# Pull the text model asynchronously (non-blocking)
+echo "[HF-INIT] Pulling qwen2.5-coder:1.5b in background..."
+ollama pull qwen2.5-coder:1.5b &
+
+# Start application services immediately (do not wait for model pull)
+echo "[HF-INIT] Starting Backend Worker..."
+python -m uvicorn Agent.worker:app --host 0.0.0.0 --port 8700 &
+
+echo "[HF-INIT] Starting MCP Tool Server..."
+python -m uvicorn Agent.mcp_server:app --host 0.0.0.0 --port 8701 &
+
+echo "[HF-INIT] Starting Web UI on port 7860..."
+exec python -m uvicorn web.app:app --host 0.0.0.0 --port 7860
+```
+
+The `&` (ampersand) operator sends the model pull to the background. The `exec` on the last line replaces the shell process with the web server, ensuring HF's process monitor tracks the correct PID.
+
+#### Limitation
+
+- **Vision model OOM**: Adding `qwen3.5:2b` alongside `qwen2.5-coder:1.5b` exceeds the 16 GB RAM limit when both are loaded simultaneously.
+- Model weights are re-downloaded on every Factory Reboot.
+- Ollama version pinning is critical — newer models require newer Ollama binaries and pulling the wrong combination causes `412` errors.
+
+---
+
+### Phase 3 — Hybrid Serverless API (Final Production Architecture)
+
+#### Theory
+
+The final and most robust architecture eliminates the in-container LLM entirely for text generation and uses **external serverless inference APIs** instead. The container only runs lightweight services (web UI, API proxy, Tesseract OCR), while all neural inference is delegated to dedicated providers:
+
+| Task | Provider | Model | Cost |
+|------|----------|-------|------|
+| Text-to-SQL | HuggingFace Inference API | `Qwen/Qwen2.5-Coder-7B-Instruct` | Free tier |
+| Vision Q&A | Groq API | `meta-llama/llama-4-scout-17b-16e-instruct` | Free (14,400 req/day) |
+| OCR | Tesseract (in container) | — | Free |
+| Database | Neon PostgreSQL | — | Free tier |
+
+This eliminates OOM risk entirely, removes the model download cold-start, and makes the container startup under 30 seconds.
+
+#### Why Groq for Vision Instead of HuggingFace Serverless?
+
+HuggingFace's serverless inference for vision-language models has two limitations on the free tier:
+1. Most VLM models are not routed through any enabled provider for free-tier accounts.
+2. Google Gemini's free tier blocks requests originating from cloud datacenter IP ranges (such as HF's infrastructure), setting the effective quota to zero even for valid free-tier API keys.
+
+**Groq** is purpose-built for API-first server deployments and explicitly allows free-tier requests from cloud IPs. It provides 14,400 free requests per day with no IP restrictions.
+
+#### Required HuggingFace Space Secrets
+
+Set these in **Space Settings → Variables and Secrets**:
+
+```
+USE_HF_CLOUD       = 1
+HF_API_TOKEN       = # HuggingFace access token
+GROQ_API_KEY       = # From console.groq.com (free)
+DB_CONNECTION_URL  = # Neon PostgreSQL connection string
+```
+
+#### Obtaining API Keys
+
+**HuggingFace Token:**
+1. Go to https://huggingface.co/settings/tokens
+2. Create a token with `read` scope (or `write` if you need to push models)
+
+**Groq API Key:**
+1. Go to https://console.groq.com
+2. Sign in with Google / GitHub
+3. Navigate to **API Keys** → **Create API Key**
+4. Copy the key
+
+**Neon PostgreSQL:**
+1. Go to https://neon.tech
+2. Create a free project
+3. Copy the connection string from the dashboard
+
+#### Slim Dockerfile (No Ollama Required)
+
+```dockerfile
+FROM python:3.11-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+ENV USE_HF_CLOUD=1
+
+RUN apt-get update && apt-get install -y \
+    curl \
+    tesseract-ocr \
+    libtesseract-dev \
+    gcc \
+    libpq-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+RUN useradd -m -u 1000 appuser && chown -R appuser:appuser /app
+USER appuser
+
+CMD ["bash", "start_hf.sh"]
+```
+
+#### Key `requirements.txt` Additions
+
+```txt
+huggingface_hub      # HF Inference API client
+groq>=0.9.0          # Groq Vision API client
+google-genai>=1.0.0  # Optional: Google Gemini (requires billing for cloud IPs)
+pytesseract>=0.3.10  # Local OCR fallback
+pillow>=10.0.0
+```
+
+#### Deployment Steps
+
+1. **Create a HuggingFace Space** at https://huggingface.co/spaces
+   - SDK: **Docker**
+   - Visibility: Public or Private
+
+2. **Push your repository** to the Space's git remote:
+   ```bash
+   git remote add space https://huggingface.co/spaces/<your-username>/<space-name>
+   git push space main
+   ```
+
+3. **Add secrets** in Space Settings (see table above). Do **not** hardcode secrets in the Dockerfile or any committed file.
+
+4. **Factory Reboot** the Space after adding secrets to trigger a clean rebuild with the new environment variables.
+
+5. **Monitor the build logs** — a successful startup shows:
+   ```
+   [GRANITE_VQA] Target Model: meta-llama/llama-4-scout-17b-16e-instruct (via Groq Vision)
+   [INFO] CLOUD MODE: Initializing Official HF InferenceClient (Qwen/Qwen2.5-Coder-7B-Instruct)
+   INFO: Uvicorn running on http://0.0.0.0:7860
+   ```
+
+#### Important Notes on Line Endings
+
+HuggingFace containers run Linux. If you develop on Windows, your shell scripts will have `\r\n` (CRLF) line endings, which cause `bad interpreter` errors on Linux. Add this to your `Dockerfile` to strip them automatically:
+
+```dockerfile
+RUN sed -i 's/\r$//' start_hf.sh && chmod +x start_hf.sh
+```
+
+Alternatively, configure Git to normalize line endings:
+```bash
+git config core.autocrlf input
+```
+
+
+---
+
+## Project Summary
+
+This project demonstrates that a self-correcting, multi-modal AI pipeline can be built and deployed entirely on consumer hardware through careful architectural decisions: progressive SQL generation with semantic validation, unified MCP-based tool access across data modalities, dynamic VRAM orchestration to prevent memory exhaustion, and a hybrid containerization strategy that preserves native GPU performance while maintaining deployment portability.
+
+The system was developed iteratively, with each architectural decision — from model selection to validation logic to infrastructure design — informed by observed failure modes and measured outcomes rather than theoretical assumptions.
+
+---
+*Built with ❤️ for advanced Agentic Data Engineering.*
\ No newline at end of file
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d34bc5eb2cb339a68a825d8bd157dce69a42fa8
--- /dev/null
+++ b/app.py
@@ -0,0 +1,187 @@
+"""
+Hugging Face Spaces entrypoint — Gradio SDK on the free ZeroGPU tier
+====================================================================
+
+Free personal accounts can only host Gradio Spaces on ZeroGPU hardware
+(CPU-basic requires a paid plan), so this launcher satisfies ZeroGPU's
+startup contract while keeping the exact three-service architecture the
+Docker image used:
+
+    this process (port 7860, public)
+      ├── Agent.worker      → spawned uvicorn :8700  (Text2SQL / DocVQA backend)
+      ├── Agent.mcp_server  → spawned uvicorn :8701  (MCP tool server)
+      └── web.app FastAPI   → grafted into Gradio's own server on :7860
+
+ZeroGPU contract (all four sources of failure, proven by real deploys):
+  1. ``spaces`` must be imported BEFORE gradio ("ZeroGPU spaces error if
+     the spaces library is not imported first — stricter since v2").
+  2. At least one ``@spaces.GPU`` function must exist at module level.
+  3. ZeroGPU's startup scan walks GRADIO'S REGISTERED EVENT HANDLERS — a
+     decorated function that is never bound to any event does not count
+     ("No @spaces.GPU function detected during startup"). Our no-op probe
+     is therefore registered as the ``.click`` handler of an invisible
+     button, so it is discoverable but can never actually be invoked
+     (nothing requests a GPU → zero quota consumed).
+  4. A real ``demo.launch()`` is mandatory (``gr.mount_gradio_app`` +
+     ``uvicorn.run`` suppresses the ZeroGPU startup report path).
+
+Serving strategy (proven pattern for FastAPI-on-ZeroGPU spaces):
+  - ``demo.launch(server_port=7860, prevent_thread_lock=True,
+    ssr_mode=False)`` makes Gradio own the public port.
+  - Our full FastAPI web app (sepia UI + /api proxies) is then grafted
+    into ``demo.app.router.routes`` as a Mount inserted in FRONT of
+    Gradio's routes, so ``/`` serves the sepia UI (Gradio also registers
+    ``get /`` and ``get /static/{path}``, which would otherwise shadow it).
+  - ``ssr_mode=False`` is mandatory: Spaces' Node SSR proxy intercepts
+    every request it does not recognise as Gradio-owned and never
+    forwards it to Python.
+  - Mounted sub-apps do not receive lifespan events, so the shared
+    httpx client of ``web.app`` is created eagerly after launch.
+
+Outside ZeroGPU (local dev / Docker), the previous behaviour is kept:
+``gr.mount_gradio_app`` + ``uvicorn.run``.
+"""
+import os
+import socket
+import sys
+import time
+import traceback
+
+# -----------------------------------------------------------------------------
+# Diagnostic env snapshot — printed FIRST so every deploy log is self-sufficient
+# -----------------------------------------------------------------------------
+_ZEROGPU_ENV_KEYS = (
+    "SPACES_ZERO_GPU",
+    "SPACES_ZERO_DEVICE_API_URL",
+    "SPACES_GRADIO_AUTO_WRAP",
+    "ZEROGPU_V2",
+    "ZEROGPU_OFFLOAD_DIR",
+    "GRADIO_SERVER_PORT",
+    "PORT",
+)
+print("[LAUNCHER] QueryLens app.py starting; ZeroGPU env snapshot:", flush=True)
+for _key in _ZEROGPU_ENV_KEYS:
+    print(f"[LAUNCHER]   env {_key}={os.environ.get(_key)!r}", flush=True)
+
+# ZeroGPU rule 1: import spaces BEFORE anything else (gradio in particular)
+try:
+    import spaces  # noqa: E402  (provided by the HF runtime; absent locally)
+except Exception:
+    spaces = None
+    print("[LAUNCHER] 'spaces' import FAILED — running in non-ZeroGPU mode:", flush=True)
+    traceback.print_exc()
+
+if spaces is not None:
+    # NB: spaces.config is a MODULE; the Settings instance is spaces.config.Config.
+    _zero_settings = getattr(spaces.config, "Config", None)
+    print(
+        f"[LAUNCHER] spaces zero_gpu = {getattr(_zero_settings, 'zero_gpu', None)}, "
+        f"device_api = {getattr(_zero_settings, 'zero_device_api_url', None)!r}",
+        flush=True,
+    )
+
+import subprocess
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parent
+PORT = int(os.environ.get("PORT", "7860"))
+
+
+# -----------------------------------------------------------------------------
+# Background services (same as start_hf.sh in the Docker image).
+# Port-guarded: the Spaces runtime can re-execute this script inside the same
+# container, and duplicate uvicorn children would just exit with bind errors.
+# -----------------------------------------------------------------------------
+def _listening(port: int) -> bool:
+    with socket.socket() as s:  # type: ignore[name-defined]
+        s.settimeout(0.25)
+        return s.connect_ex(("127.0.0.1", port)) == 0
+
+
+def _spawn(module: str, port: int, name: str):
+    if _listening(port):
+        print(f"[LAUNCHER] {name} already listening on :{port} — skipping spawn", flush=True)
+        return None
+    print(f"[LAUNCHER] starting {name} on :{port} ...", flush=True)
+    return subprocess.Popen(
+        [sys.executable, "-u", "-m", "uvicorn", module,
+         "--host", "0.0.0.0", "--port", str(port)],
+        cwd=str(ROOT),
+    )
+
+
+_worker = _spawn("Agent.worker:app", 8700, "backend worker")
+_mcp = _spawn("Agent.mcp_server:app", 8701, "MCP tool server")
+
+import gradio as gr  # noqa: E402  (after 'spaces' — ZeroGPU rule 1)
+from web.app import app as fastapi_app  # noqa: E402
+
+_ZERO_GPU = bool(spaces is not None and getattr(getattr(spaces.config, "Config", None), "zero_gpu", False))
+print(f"[LAUNCHER] ZeroGPU branch = {_ZERO_GPU}", flush=True)
+
+if spaces is not None:
+    @spaces.GPU
+    def _zerogpu_platform_check():
+        """Registered below as an invisible-button click handler.
+
+        Never meaningfully called in the UI: it exists because ZeroGPU's
+        startup scan walks Gradio's registered event handlers and kills the
+        container when none of them is decorated. The decorator only
+        allocates a GPU when the function actually runs, and the button is
+        invisible — so zero GPU quota is consumed for our CPU-only app."""
+        return None
+
+with gr.Blocks(title="QueryLens") as _lp:
+    gr.Markdown(
+        "**QueryLens** — the full sepia UI is served at this Space's root `/`.\n\n"
+        "This page is only an anchor for the Gradio runtime."
+    )
+    gr.HTML('<a href="/" style="text-decoration:none;font-weight:600;">← Open the QueryLens UI</a>')
+    if spaces is not None:
+        # ZeroGPU rule 3: the decorated no-op must be bound to a real event
+        # so the startup scan discovers it. Invisible => unclickable.
+        _probe_btn = gr.Button("ZeroGPU platform probe", visible=False)
+        _probe_btn.click(_zerogpu_platform_check, None, None)
+
+
+if __name__ == "__main__":
+    if _ZERO_GPU:
+        import httpx
+        import web.app as _webapp
+        from starlette.routing import Mount
+
+        try:
+            # Gradio owns the public port; prevent_thread_lock returns control
+            # so we can graft the web app below, then block_thread holds the
+            # main thread (what launch() would have done).
+            _lp.launch(
+                server_name="0.0.0.0",
+                server_port=PORT,
+                prevent_thread_lock=True,
+                ssr_mode=False,
+            )
+        except OSError as exc:
+            # The Spaces runtime re-executed this script while a first copy is
+            # already serving — do not touch the port, just idle forever.
+            print(f"[LAUNCHER] port {PORT} already held by the first instance ({exc}); idling", flush=True)
+            while True:
+                time.sleep(3600)
+
+        # Mounted sub-apps do not receive startup events — initialise the
+        # shared httpx client eagerly so /api proxies never see client=None.
+        if _webapp.client is None:
+            _webapp.client = httpx.AsyncClient(timeout=_webapp.WORKER_TIMEOUT)
+
+        # Serve the full web app in front of Gradio's own routes: the visible
+        # UI (root /) becomes the sepia web app, /api/* proxies work as-is.
+        _lp.app.router.routes.insert(0, Mount("/", app=fastapi_app, name="querylens_web"))
+        print(f"[LAUNCHER] QueryLens web UI live on http://0.0.0.0:{PORT}", flush=True)
+        try:
+            _lp.block_thread()
+        except KeyboardInterrupt:
+            pass
+    else:
+        app = gr.mount_gradio_app(fastapi_app, _lp, path="/gradio-lp")
+        print(f"[LAUNCHER] QueryLens web UI live on http://0.0.0.0:{PORT}", flush=True)
+        import uvicorn
+        uvicorn.run(app, host="0.0.0.0", port=PORT)
diff --git a/docker-compose.yml b/docker-compose.yml
new file mode 100644
index 0000000000000000000000000000000000000000..06fc529af6fb4a6ac4025c31c52ddb2dbf3371ec
--- /dev/null
+++ b/docker-compose.yml
@@ -0,0 +1,84 @@
+version: '3.8'
+
+services:
+  # Main Agentic Application (worker :8700, MCP tool server :8701, web UI :7860)
+  demo-agent-app:
+    build: .
+    container_name: demo-agent-app
+    restart: unless-stopped
+    env_file: Agent/.env
+    environment:
+      - OLLAMA_BASE_URL=http://172.27.224.1:11434
+      - LLM_CTX=4096
+    deploy:
+      resources:
+        limits:
+          cpus: '2.0'
+          memory: 4G
+    ports:
+      - "7860:7860"   # Web UI (QueryLens)
+      - "8700:8700"   # Worker API
+      - "8701:8701"   # MCP tool server
+    depends_on:
+      demo-postgres:
+        condition: service_healthy
+      demo-mysql:
+        condition: service_healthy
+    volumes:
+      - ./Agent:/app/Agent
+      - ./web:/app/web
+      # Dynamic database profiles: uploaded schema files + profile registry
+      - demo-schema-uploads:/app/uploads/schemas
+      - demo-chat-store:/app/chat_store
+    networks:
+      - demo-net
+
+  # MySQL Sample Database (built-in employee demo)
+  demo-mysql:
+    image: mysql:8.0
+    container_name: demo-mysql
+    restart: unless-stopped
+    env_file: Agent/.env
+    ports:
+      - "${MYSQL_PORT:-3306}:3306"
+    volumes:
+      - ./employee-sample-database/mysql/dataset_small:/docker-entrypoint-initdb.d
+      - demo-mysql-data:/var/lib/mysql
+    networks:
+      - demo-net
+    healthcheck:
+      test: ["CMD", "mysqladmin", "ping", "-h", "localhost", "--silent"]
+      interval: 5s
+      timeout: 5s
+      retries: 12
+      start_period: 30s
+
+  # PostgreSQL Sample Database (built-in employee demo)
+  demo-postgres:
+    image: postgres:15
+    container_name: demo-postgres
+    restart: unless-stopped
+    env_file: Agent/.env
+    ports:
+      - "${POSTGRES_PORT:-5432}:5432"
+    volumes:
+      - ./employee-sample-database/postgres/dataset_small:/docker-entrypoint-initdb.d
+      - demo-postgres-data:/var/lib/postgresql/data
+    networks:
+      - demo-net
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U $${POSTGRES_USER:-postgres}"]
+      interval: 5s
+      timeout: 5s
+      retries: 12
+      start_period: 30s
+
+volumes:
+  demo-mysql-data:
+  demo-postgres-data:
+  demo-schema-uploads:
+  demo-chat-store:
+
+networks:
+  demo-net:
+    driver: bridge
diff --git a/employee-sample-database/.gitignore b/employee-sample-database/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..c540c00e480a3943e05f4fd96ee665ce0c4b7994
--- /dev/null
+++ b/employee-sample-database/.gitignore
@@ -0,0 +1,11 @@
+# Binaries for programs and plugins
+*.exe
+*.exe~
+*.dll
+*.so
+*.dylib
+
+# IntelliJ (Goland, IDEA) folder
+.idea
+
+.DS_Store
\ No newline at end of file
diff --git a/employee-sample-database/CLAUDE.md b/employee-sample-database/CLAUDE.md
new file mode 100644
index 0000000000000000000000000000000000000000..754b9454185888da788273524f2be3242ac5737a
--- /dev/null
+++ b/employee-sample-database/CLAUDE.md
@@ -0,0 +1,77 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+# Employee Sample Database - Dev Guide
+
+## Database Installation Commands
+
+### MySQL
+```bash
+# Install dataset (choose one size)
+cd mysql/dataset_small && mysql -u <username> < employee.sql
+cd mysql/dataset_large && mysql -u <username> < employee.sql  
+cd mysql/dataset_full && mysql -u <username> < employee.sql
+
+# Alternative using install script
+mysql/install_dataset_small.sh . <username>
+
+# Test installation
+cd mysql/dataset_small && mysql -t < test_employee_md5.sql
+cd mysql/dataset_large && mysql -t < test_employee_md5.sql
+cd mysql/dataset_full && mysql -t < test_employee_md5.sql
+
+# Load optional functions (after dataset installation)
+cd mysql/dataset_small && mysql < object.sql
+```
+
+### PostgreSQL
+```bash
+# Install dataset (choose one size)
+cd postgres/dataset_small && psql -c "CREATE DATABASE employee" && psql employee < employee.sql
+cd postgres/dataset_large && psql -c "CREATE DATABASE employee" && psql employee < employee.sql
+cd postgres/dataset_full && psql -c "CREATE DATABASE employee" && psql employee < employee.sql
+```
+
+### SQLite
+```bash
+# Install dataset (small only)
+cd sqlite/dataset_small && sqlite3 employee.db < employee.sql
+
+# Test installation
+cd sqlite/dataset_small && sqlite3 employee.db < test_employee_md5.sql
+```
+
+## Architecture Overview
+
+This is a multi-database employee sample dataset repository with three database implementations (MySQL, PostgreSQL, SQLite) and three dataset sizes:
+
+- **dataset_small** (~600KB, 1,000 employees) - For development and testing
+- **dataset_large** (~6MB, 10,000 employees) - For performance testing
+- **dataset_full** (~170MB, 300,024 employees) - Full production-scale dataset
+
+### Core Tables Schema
+- `employee` - Employee records with personal info
+- `department` - Department definitions  
+- `dept_emp` - Employee-department relationships
+- `dept_manager` - Department manager assignments
+- `salary` - Employee salary history
+- `title` - Employee title history
+
+### Key Design Patterns
+- Each table has individual load files (`load_employee.sql`, `load_department.sql`, etc.)
+- All datasets maintain referential integrity and identical schema
+- MD5 checksums verify data integrity after installation
+- Singular table names (not plural) across all implementations
+- MySQL includes optional stored functions (`object.sql`)
+
+## Important Notes
+
+### Cloud Database Setup
+For AWS RDS and similar cloud MySQL instances, disable binary logging before loading functions:
+```sql
+SET sql_log_bin = 0;
+```
+
+### Data Verification
+Always run the MD5 test after installation to ensure data integrity. The test compares record counts and checksums against expected values for each table.
\ No newline at end of file
diff --git a/employee-sample-database/LICENSE b/employee-sample-database/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..1a09be52ed2ec9a8387d5153a3845757ed30a6c1
--- /dev/null
+++ b/employee-sample-database/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 tianzhou
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/employee-sample-database/README.md b/employee-sample-database/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..45776dd535d9321ccc3b3a932974bb8c101b61b4
--- /dev/null
+++ b/employee-sample-database/README.md
@@ -0,0 +1 @@
+Sample Employee Database
diff --git a/employee-sample-database/mysql/Dockerfile.small b/employee-sample-database/mysql/Dockerfile.small
new file mode 100644
index 0000000000000000000000000000000000000000..037c747eebbcea129c1512a91d46f84300528973
--- /dev/null
+++ b/employee-sample-database/mysql/Dockerfile.small
@@ -0,0 +1,7 @@
+FROM mysql/mysql-server
+
+ENV MYSQL_ROOT_HOST=%
+ENV MYSQL_ALLOW_EMPTY_PASSWORD=yes
+
+COPY install_dataset_small.sh /docker-entrypoint-initdb.d
+COPY dataset_small /dataset_small
diff --git a/employee-sample-database/mysql/README.md b/employee-sample-database/mysql/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..d95241cb799039ee59059e4482a423f9453f8a68
--- /dev/null
+++ b/employee-sample-database/mysql/README.md
@@ -0,0 +1,220 @@
+# Employee Sample Database for MySQL
+
+This repo is based on the work from https://github.com/datacharmer/test_db with the following improvements:
+
+1. Create 2 separate datasets:
+
+   a. dataset_full (~170 MB). This is the same as the original one.
+
+   b. dataset_large (~6 MB). ~3% of the dataset_full (10000 employees vs 300024 employees).
+
+   c. dataset_small (~600 KB). ~0.3% of the dataset_full (1000 employees vs 300024 employees).
+
+1. Use singular form instead of plural form to name the table (e.g. `employees` -> `employee`).
+
+1. Update schema visualizer to make it more readable and include views.
+
+1. Only keep md5 to verify the data installation, remove SHA. There is no need to maintain 2 methods to verify the data.
+
+1. Remove unnecessary files.
+
+## Schema
+
+![Schema](schema.png)
+
+## Prerequisites
+
+You need a MySQL database server (5.0+) and run the commands below through a
+user that has the following privileges:
+
+    SELECT, INSERT, UPDATE, DELETE,
+    CREATE, DROP, RELOAD, REFERENCES,
+    INDEX, ALTER, SHOW DATABASES,
+    CREATE TEMPORARY TABLES,
+    LOCK TABLES, EXECUTE, CREATE VIEW
+
+## Installation:
+
+1. Download the repository
+2. Change directory to the repository
+3. Change directory to either `dataset_full`, `dataset_large` or `dataset_small`
+
+Run:
+
+```bash
+cd dataset_small
+```
+
+Then run
+
+```
+mysql < employee.sql
+```
+
+## Testing the installation
+
+### Testing `dataset_full`
+
+    // Under 'dataset_full' directory
+    mysql -t < test_employee_md5.sql
+
+    +----------------------+
+    | INFO                 |
+    +----------------------+
+    | TESTING INSTALLATION |
+    +----------------------+
+    +--------------+-----------------+----------------------------------+
+    | table_name   | expected_record | expected_crc                     |
+    +--------------+-----------------+----------------------------------+
+    | department   |               9 | d1af5e170d2d1591d776d5638d71fc5f |
+    | dept_emp     |          331603 | ccf6fe516f990bdaa49713fc478701b7 |
+    | dept_manager |              24 | 8720e2f0853ac9096b689c14664f847e |
+    | employee     |          300024 | 4ec56ab5ba37218d187cf6ab09ce1aa1 |
+    | salary       |         2844047 | fd220654e95aea1b169624ffe3fca934 |
+    | title        |          443308 | bfa016c472df68e70a03facafa1bc0a8 |
+    +--------------+-----------------+----------------------------------+
+    +--------------+------------------+----------------------------------+
+    | table_name   | found_records    | found_crc                        |
+    +--------------+------------------+----------------------------------+
+    | department   |                9 | d1af5e170d2d1591d776d5638d71fc5f |
+    | dept_emp     |           331603 | ccf6fe516f990bdaa49713fc478701b7 |
+    | dept_manager |               24 | 8720e2f0853ac9096b689c14664f847e |
+    | employee     |           300024 | 4ec56ab5ba37218d187cf6ab09ce1aa1 |
+    | salary       |          2844047 | fd220654e95aea1b169624ffe3fca934 |
+    | title        |           443308 | bfa016c472df68e70a03facafa1bc0a8 |
+    +--------------+------------------+----------------------------------+
+    +--------------+---------------+-----------+
+    | table_name   | records_match | crc_match |
+    +--------------+---------------+-----------+
+    | department   | OK            | ok        |
+    | dept_emp     | OK            | ok        |
+    | dept_manager | OK            | ok        |
+    | employee     | OK            | ok        |
+    | salary       | OK            | ok        |
+    | title        | OK            | ok        |
+    +--------------+---------------+-----------+
+    +------------------+
+    | computation_time |
+    +------------------+
+    | 00:00:29         |
+    +------------------+
+    +---------+--------+
+    | summary | result |
+    +---------+--------+
+    | CRC     | OK     |
+    | count   | OK     |
+    +---------+--------+
+
+### Testing `dataset_large`
+
+    // Under 'dataset_large' directory
+    mysql -t < test_employee_md5.sql
+
+    +----------------------+
+    | INFO                 |
+    +----------------------+
+    | TESTING INSTALLATION |
+    +----------------------+
+    +--------------+-----------------+----------------------------------+
+    | table_name   | expected_record | expected_crc                     |
+    +--------------+-----------------+----------------------------------+
+    | department   |               9 | d1af5e170d2d1591d776d5638d71fc5f |
+    | dept_emp     |           11051 | fc43a976d1406705d9707f6fea1df9df |
+    | dept_manager |              24 | 953e3c0c8360805645498ada817b66e9 |
+    | employee     |           10000 | 2b9ee8043563317634407daf42a9ef0b |
+    | salary       |           94917 | 0fcb81f97f6b0eacb01a22a085e6512d |
+    | title        |           14782 | ffa808be19d75619f415e2964cbe0320 |
+    +--------------+-----------------+----------------------------------+
+    +--------------+------------------+----------------------------------+
+    | table_name   | found_records    | found_crc                        |
+    +--------------+------------------+----------------------------------+
+    | department   |                9 | d1af5e170d2d1591d776d5638d71fc5f |
+    | dept_emp     |            11051 | fc43a976d1406705d9707f6fea1df9df |
+    | dept_manager |               24 | 953e3c0c8360805645498ada817b66e9 |
+    | employee     |            10000 | 2b9ee8043563317634407daf42a9ef0b |
+    | salary       |            94917 | 0fcb81f97f6b0eacb01a22a085e6512d |
+    | title        |            14782 | ffa808be19d75619f415e2964cbe0320 |
+    +--------------+------------------+----------------------------------+
+    +--------------+---------------+-----------+
+    | table_name   | records_match | crc_match |
+    +--------------+---------------+-----------+
+    | department   | OK            | ok        |
+    | dept_emp     | OK            | ok        |
+    | dept_manager | OK            | ok        |
+    | employee     | OK            | ok        |
+    | salary       | OK            | ok        |
+    | title        | OK            | ok        |
+    +--------------+---------------+-----------+
+    +------------------+
+    | computation_time |
+    +------------------+
+    | 00:00:02         |
+    +------------------+
+    +---------+--------+
+    | summary | result |
+    +---------+--------+
+    | CRC     | OK     |
+    | count   | OK     |
+    +---------+--------+
+
+### Testing `dataset_small`
+
+    // Under 'dataset_small' directory
+    mysql -t < test_employee_md5.sql
+
+    +----------------------+
+    | INFO                 |
+    +----------------------+
+    | TESTING INSTALLATION |
+    +----------------------+
+    +--------------+-----------------+----------------------------------+
+    | table_name   | expected_record | expected_crc                     |
+    +--------------+-----------------+----------------------------------+
+    | department   |               9 | d1af5e170d2d1591d776d5638d71fc5f |
+    | dept_emp     |            1103 | e302aa5b56a69b49e40eb0d60674addc |
+    | dept_manager |              16 | 8ff425d5ad6dc56975998d1893b8dca9 |
+    | employee     |            1000 | 595460127fb609c2b110b1796083e242 |
+    | salary       |            9488 | 61f22cfece4d34f5bb94c9f05a3da3ef |
+    | title        |            1470 | ba77dd331ce00f76c1643a7d73cdcee6 |
+    +--------------+-----------------+----------------------------------+
+    +--------------+------------------+----------------------------------+
+    | table_name   | found_records    | found_crc                        |
+    +--------------+------------------+----------------------------------+
+    | department   |                9 | d1af5e170d2d1591d776d5638d71fc5f |
+    | dept_emp     |             1103 | e302aa5b56a69b49e40eb0d60674addc |
+    | dept_manager |               16 | 8ff425d5ad6dc56975998d1893b8dca9 |
+    | employee     |             1000 | 595460127fb609c2b110b1796083e242 |
+    | salary       |             9488 | 61f22cfece4d34f5bb94c9f05a3da3ef |
+    | title        |             1470 | ba77dd331ce00f76c1643a7d73cdcee6 |
+    +--------------+------------------+----------------------------------+
+    +--------------+---------------+-----------+
+    | table_name   | records_match | crc_match |
+    +--------------+---------------+-----------+
+    | department   | OK            | ok        |
+    | dept_emp     | OK            | ok        |
+    | dept_manager | OK            | ok        |
+    | employee     | OK            | ok        |
+    | salary       | OK            | ok        |
+    | title        | OK            | ok        |
+    +--------------+---------------+-----------+
+    +------------------+
+    | computation_time |
+    +------------------+
+    | 00:00:00         |
+    +------------------+
+    +---------+--------+
+    | summary | result |
+    +---------+--------+
+    | CRC     | OK     |
+    | count   | OK     |
+    +---------+--------+
+
+## Installing the function (optional)
+
+```bash
+mysql < object.sql
+```
+
+**If you are connecting to a cloud instance such as AWS RDS. You MUST turn off binary logging first. Otherwise, you will encounter error**
+
+    You do not have the SUPER privilege and binary logging is enabled (you *might* want to use the less safe log_bin_trust_function_creators variable)
diff --git a/employee-sample-database/mysql/dataset_small/01_employee.sql b/employee-sample-database/mysql/dataset_small/01_employee.sql
new file mode 100644
index 0000000000000000000000000000000000000000..98f3e153bf23c02be129676b0a63b388e79c9812
--- /dev/null
+++ b/employee-sample-database/mysql/dataset_small/01_employee.sql
@@ -0,0 +1,125 @@
+--  Sample employee database 
+--  See changelog table for details
+--  Copyright (C) 2007,2008, MySQL AB
+--  
+--  Original data created by Fusheng Wang and Carlo Zaniolo
+--  http://www.cs.aau.dk/TimeCenter/software.htm
+--  http://www.cs.aau.dk/TimeCenter/Data/employeeTemporalDataSet.zip
+-- 
+--  Current schema by Giuseppe Maxia 
+--  Data conversion from XML to relational by Patrick Crews
+-- 
+-- This work is licensed under the 
+-- Creative Commons Attribution-Share Alike 3.0 Unported License. 
+-- To view a copy of this license, visit 
+-- http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to 
+-- Creative Commons, 171 Second Street, Suite 300, San Francisco, 
+-- California, 94105, USA.
+-- 
+--  DISCLAIMER
+--  To the best of our knowledge, this data is fabricated, and
+--  it does not correspond to real people. 
+--  Any similarity to existing people is purely coincidental.
+-- 
+
+DROP DATABASE IF EXISTS employee;
+CREATE DATABASE IF NOT EXISTS employee;
+USE employee;
+
+SELECT 'CREATING DATABASE STRUCTURE' as 'INFO';
+
+DROP TABLE IF EXISTS dept_emp,
+                     dept_manager,
+                     title,
+                     salary, 
+                     employee, 
+                     department;
+
+/*!50503 set default_storage_engine = InnoDB */;
+/*!50503 select CONCAT('storage engine: ', @@default_storage_engine) as INFO */;
+
+CREATE TABLE employee (
+    emp_no      INT             NOT NULL,
+    birth_date  DATE            NOT NULL,
+    first_name  VARCHAR(14)     NOT NULL,
+    last_name   VARCHAR(16)     NOT NULL,
+    gender      ENUM ('M','F')  NOT NULL,    
+    hire_date   DATE            NOT NULL,
+    PRIMARY KEY (emp_no)
+);
+
+CREATE TABLE department (
+    dept_no     CHAR(4)         NOT NULL,
+    dept_name   VARCHAR(40)     NOT NULL,
+    PRIMARY KEY (dept_no),
+    UNIQUE  KEY (dept_name)
+);
+
+CREATE TABLE dept_manager (
+   emp_no       INT             NOT NULL,
+   dept_no      CHAR(4)         NOT NULL,
+   from_date    DATE            NOT NULL,
+   to_date      DATE            NOT NULL,
+   FOREIGN KEY (emp_no)  REFERENCES employee (emp_no)    ON DELETE CASCADE,
+   FOREIGN KEY (dept_no) REFERENCES department (dept_no) ON DELETE CASCADE,
+   PRIMARY KEY (emp_no,dept_no)
+); 
+
+CREATE TABLE dept_emp (
+    emp_no      INT             NOT NULL,
+    dept_no     CHAR(4)         NOT NULL,
+    from_date   DATE            NOT NULL,
+    to_date     DATE            NOT NULL,
+    FOREIGN KEY (emp_no)  REFERENCES employee   (emp_no)  ON DELETE CASCADE,
+    FOREIGN KEY (dept_no) REFERENCES department (dept_no) ON DELETE CASCADE,
+    PRIMARY KEY (emp_no,dept_no)
+);
+
+CREATE TABLE title (
+    emp_no      INT             NOT NULL,
+    title       VARCHAR(50)     NOT NULL,
+    from_date   DATE            NOT NULL,
+    to_date     DATE,
+    FOREIGN KEY (emp_no) REFERENCES employee (emp_no) ON DELETE CASCADE,
+    PRIMARY KEY (emp_no,title, from_date)
+) 
+; 
+
+CREATE TABLE salary (
+    emp_no      INT             NOT NULL,
+    amount      INT             NOT NULL,
+    from_date   DATE            NOT NULL,
+    to_date     DATE            NOT NULL,
+    FOREIGN KEY (emp_no) REFERENCES employee (emp_no) ON DELETE CASCADE,
+    PRIMARY KEY (emp_no, from_date)
+) 
+; 
+
+CREATE OR REPLACE VIEW dept_emp_latest_date AS
+    SELECT emp_no, MAX(from_date) AS from_date, MAX(to_date) AS to_date
+    FROM dept_emp
+    GROUP BY emp_no;
+
+# shows only the current department for each employee
+CREATE OR REPLACE VIEW current_dept_emp AS
+    SELECT l.emp_no, dept_no, l.from_date, l.to_date
+    FROM dept_emp d
+        INNER JOIN dept_emp_latest_date l
+        ON d.emp_no=l.emp_no AND d.from_date=l.from_date AND l.to_date = d.to_date;
+
+flush /*!50503 binary */ logs;
+
+SELECT 'LOADING department' as 'INFO';
+SOURCE /docker-entrypoint-initdb.d/load_department.sql;
+SELECT 'LOADING employee' as 'INFO';
+source /docker-entrypoint-initdb.d/load_employee.sql ;
+SELECT 'LOADING dept_emp' as 'INFO';
+source /docker-entrypoint-initdb.d/load_dept_emp.sql ;
+SELECT 'LOADING dept_manager' as 'INFO';
+source /docker-entrypoint-initdb.d/load_dept_manager.sql ;
+SELECT 'LOADING title' as 'INFO';
+source /docker-entrypoint-initdb.d/load_title.sql ;
+SELECT 'LOADING salary' as 'INFO';
+source /docker-entrypoint-initdb.d/load_salary1.sql ;
+
+source /docker-entrypoint-initdb.d/show_elapsed.sql ;
diff --git a/employee-sample-database/mysql/dataset_small/load_department.sql b/employee-sample-database/mysql/dataset_small/load_department.sql
new file mode 100644
index 0000000000000000000000000000000000000000..52b9226b2d323e7dc7ec90f8db14bd0ac5f82eb9
--- /dev/null
+++ b/employee-sample-database/mysql/dataset_small/load_department.sql
@@ -0,0 +1,10 @@
+INSERT INTO `department` VALUES 
+('d001','Marketing'),
+('d002','Finance'),
+('d003','Human Resources'),
+('d004','Production'),
+('d005','Development'),
+('d006','Quality Management'),
+('d007','Sales'),
+('d008','Research'),
+('d009','Customer Service');
diff --git a/employee-sample-database/mysql/dataset_small/load_dept_emp.sql b/employee-sample-database/mysql/dataset_small/load_dept_emp.sql
new file mode 100644
index 0000000000000000000000000000000000000000..c5a77f7116055b510f141e94e7dcedb05ecd2c5e
--- /dev/null
+++ b/employee-sample-database/mysql/dataset_small/load_dept_emp.sql
@@ -0,0 +1,1103 @@
+INSERT INTO `dept_emp` VALUES (10001,'d005','1986-06-26','9999-01-01'),
+(10002,'d007','1996-08-03','9999-01-01'),
+(10003,'d004','1995-12-03','9999-01-01'),
+(10004,'d004','1986-12-01','9999-01-01'),
+(10005,'d003','1989-09-12','9999-01-01'),
+(10006,'d005','1990-08-05','9999-01-01'),
+(10007,'d008','1989-02-10','9999-01-01'),
+(10008,'d005','1998-03-11','2000-07-31'),
+(10009,'d006','1985-02-18','9999-01-01'),
+(10010,'d004','1996-11-24','2000-06-26'),
+(10010,'d006','2000-06-26','9999-01-01'),
+(10011,'d009','1990-01-22','1996-11-09'),
+(10012,'d005','1992-12-18','9999-01-01'),
+(10013,'d003','1985-10-20','9999-01-01'),
+(10014,'d005','1993-12-29','9999-01-01'),
+(10015,'d008','1992-09-19','1993-08-22'),
+(10016,'d007','1998-02-11','9999-01-01'),
+(10017,'d001','1993-08-03','9999-01-01'),
+(10018,'d004','1992-07-29','9999-01-01'),
+(10018,'d005','1987-04-03','1992-07-29'),
+(10019,'d008','1999-04-30','9999-01-01'),
+(10020,'d004','1997-12-30','9999-01-01'),
+(10021,'d005','1988-02-10','2002-07-15'),
+(10022,'d005','1999-09-03','9999-01-01'),
+(10023,'d005','1999-09-27','9999-01-01'),
+(10024,'d004','1998-06-14','9999-01-01'),
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+(10728,'d006','1989-12-09','9999-01-01'),
+(10729,'d004','1986-04-20','9999-01-01'),
+(10730,'d004','1996-05-27','1998-06-07'),
+(10730,'d005','1996-05-14','1996-05-27'),
+(10731,'d002','1987-02-20','2000-10-09'),
+(10732,'d004','1994-12-21','9999-01-01'),
+(10733,'d009','1996-02-20','9999-01-01'),
+(10734,'d004','1995-12-19','9999-01-01'),
+(10735,'d009','1985-10-29','9999-01-01'),
+(10736,'d006','1991-09-26','9999-01-01'),
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+(10738,'d005','1991-04-30','1992-01-06'),
+(10738,'d008','1992-01-06','9999-01-01'),
+(10739,'d005','1985-03-30','9999-01-01'),
+(10740,'d005','1996-04-30','2002-03-17'),
+(10741,'d001','1989-01-06','9999-01-01'),
+(10742,'d005','1997-04-03','9999-01-01'),
+(10743,'d002','1999-11-05','9999-01-01'),
+(10744,'d007','1985-12-29','1992-06-03'),
+(10745,'d004','1988-01-23','9999-01-01'),
+(10746,'d005','1990-08-31','9999-01-01'),
+(10747,'d004','1990-10-08','1999-09-20'),
+(10748,'d002','1990-08-14','1992-04-04'),
+(10748,'d003','1992-04-04','9999-01-01'),
+(10749,'d004','1985-09-26','9999-01-01'),
+(10750,'d005','1998-11-06','9999-01-01'),
+(10751,'d005','1999-12-14','2000-12-05'),
+(10752,'d006','1993-05-28','9999-01-01'),
+(10753,'d004','1985-05-17','1999-08-25'),
+(10754,'d007','1988-01-11','9999-01-01'),
+(10755,'d005','1995-04-01','2001-11-13'),
+(10756,'d004','2000-12-16','9999-01-01'),
+(10756,'d005','1997-10-07','2000-12-16'),
+(10757,'d005','1986-01-06','1991-10-18'),
+(10758,'d004','1999-07-01','9999-01-01'),
+(10759,'d004','1991-12-04','9999-01-01'),
+(10760,'d002','1997-08-22','9999-01-01'),
+(10761,'d002','1992-05-30','1999-01-04'),
+(10762,'d008','1992-01-21','9999-01-01'),
+(10763,'d004','1987-02-23','9999-01-01'),
+(10764,'d005','1989-11-27','9999-01-01'),
+(10765,'d001','1994-05-08','1994-05-11'),
+(10765,'d007','1994-05-07','1994-05-08'),
+(10766,'d003','1999-09-18','9999-01-01'),
+(10767,'d007','1997-07-15','9999-01-01'),
+(10768,'d004','1985-07-04','9999-01-01'),
+(10769,'d005','1991-08-30','9999-01-01'),
+(10770,'d007','1987-12-25','1989-02-27'),
+(10771,'d009','1996-04-01','9999-01-01'),
+(10772,'d005','1990-02-22','9999-01-01'),
+(10773,'d009','1997-02-09','1997-11-29'),
+(10774,'d004','1990-01-26','9999-01-01'),
+(10775,'d004','1994-12-30','1999-03-17'),
+(10775,'d009','1999-03-17','9999-01-01'),
+(10776,'d005','1986-03-16','9999-01-01'),
+(10777,'d004','1990-03-17','2001-03-27'),
+(10778,'d001','1986-07-04','9999-01-01'),
+(10779,'d009','1988-01-11','9999-01-01'),
+(10780,'d004','1994-08-01','1996-08-18'),
+(10781,'d009','1991-02-04','9999-01-01'),
+(10782,'d008','1997-07-06','2002-03-30'),
+(10783,'d003','1994-05-15','9999-01-01'),
+(10784,'d005','1998-10-14','9999-01-01'),
+(10785,'d006','1994-11-11','9999-01-01'),
+(10786,'d004','1997-06-06','2001-12-10'),
+(10786,'d006','2001-12-10','9999-01-01'),
+(10787,'d001','1991-07-19','2002-07-27'),
+(10788,'d007','1998-05-29','9999-01-01'),
+(10789,'d009','1998-01-26','9999-01-01'),
+(10790,'d004','1999-03-02','9999-01-01'),
+(10791,'d005','1988-07-30','9999-01-01'),
+(10792,'d005','1985-08-10','1993-12-20'),
+(10793,'d005','1990-09-18','9999-01-01'),
+(10794,'d008','1994-05-24','9999-01-01'),
+(10795,'d007','1993-05-03','9999-01-01'),
+(10796,'d006','1990-11-08','9999-01-01'),
+(10797,'d004','1997-06-02','9999-01-01'),
+(10797,'d005','1988-08-21','1997-06-02'),
+(10798,'d004','1996-09-21','9999-01-01'),
+(10799,'d005','1989-03-01','9999-01-01'),
+(10800,'d004','1993-05-20','9999-01-01'),
+(10801,'d004','1998-04-07','2001-01-15'),
+(10802,'d004','1985-08-24','9999-01-01'),
+(10803,'d006','1988-04-20','2002-04-02'),
+(10804,'d007','1997-06-09','9999-01-01'),
+(10805,'d008','1987-05-26','9999-01-01'),
+(10806,'d002','1993-04-25','1994-04-08'),
+(10806,'d003','1994-04-08','9999-01-01'),
+(10807,'d007','1999-10-19','9999-01-01'),
+(10808,'d005','1987-03-07','9999-01-01'),
+(10809,'d005','1988-02-25','9999-01-01'),
+(10810,'d007','1994-09-07','9999-01-01'),
+(10811,'d004','1997-07-08','9999-01-01'),
+(10812,'d004','1996-09-20','1997-10-06'),
+(10813,'d007','1990-04-27','9999-01-01'),
+(10814,'d007','1993-07-17','9999-01-01'),
+(10815,'d003','1994-04-19','9999-01-01'),
+(10816,'d009','1991-05-22','9999-01-01'),
+(10817,'d007','1990-12-26','2000-01-24'),
+(10817,'d009','2000-01-24','9999-01-01'),
+(10818,'d003','1998-04-09','9999-01-01'),
+(10819,'d004','1986-08-02','1999-12-28'),
+(10820,'d001','1992-06-26','9999-01-01'),
+(10821,'d005','1988-10-06','1990-04-17'),
+(10822,'d005','1986-01-11','9999-01-01'),
+(10823,'d003','1999-08-19','9999-01-01'),
+(10824,'d001','1995-08-01','9999-01-01'),
+(10825,'d004','2000-06-03','9999-01-01'),
+(10825,'d005','1993-12-04','2000-06-03'),
+(10826,'d005','1993-01-22','9999-01-01'),
+(10827,'d002','1996-11-07','9999-01-01'),
+(10828,'d007','1986-02-14','1990-03-30'),
+(10829,'d003','1993-07-21','1993-11-29'),
+(10830,'d001','1991-08-24','9999-01-01'),
+(10831,'d005','1998-02-22','9999-01-01'),
+(10832,'d007','1986-10-11','9999-01-01'),
+(10833,'d006','1998-12-23','9999-01-01'),
+(10834,'d004','1990-11-24','9999-01-01'),
+(10835,'d004','1995-08-22','9999-01-01'),
+(10836,'d007','1987-08-10','1991-07-01'),
+(10836,'d009','1991-07-01','9999-01-01'),
+(10837,'d007','1986-01-14','2002-02-22'),
+(10838,'d006','1994-04-26','9999-01-01'),
+(10839,'d004','1996-12-30','9999-01-01'),
+(10840,'d004','1993-06-04','9999-01-01'),
+(10841,'d009','1992-01-22','9999-01-01'),
+(10842,'d005','1986-07-14','1998-06-27'),
+(10843,'d005','1990-04-26','9999-01-01'),
+(10844,'d005','1997-04-25','9999-01-01'),
+(10845,'d005','1997-01-25','9999-01-01'),
+(10846,'d005','1995-01-17','1997-04-03'),
+(10846,'d008','1997-04-03','9999-01-01'),
+(10847,'d008','1986-07-25','9999-01-01'),
+(10848,'d004','1985-04-20','9999-01-01'),
+(10849,'d005','1995-06-25','9999-01-01'),
+(10850,'d002','1990-04-22','1995-05-11'),
+(10851,'d008','1994-11-21','9999-01-01'),
+(10852,'d004','1999-08-21','9999-01-01'),
+(10853,'d006','1993-05-03','9999-01-01'),
+(10854,'d005','1998-07-13','9999-01-01'),
+(10855,'d002','1993-03-29','1997-03-02'),
+(10855,'d007','1997-03-02','9999-01-01'),
+(10856,'d009','1986-12-31','1990-06-18'),
+(10857,'d007','1990-04-04','9999-01-01'),
+(10858,'d005','1997-01-01','9999-01-01'),
+(10859,'d001','1996-05-21','9999-01-01'),
+(10860,'d006','1998-01-22','9999-01-01'),
+(10861,'d005','1989-06-10','1992-04-12'),
+(10862,'d007','1985-07-23','9999-01-01'),
+(10863,'d004','1985-12-25','9999-01-01'),
+(10864,'d007','1986-09-25','9999-01-01'),
+(10865,'d007','1993-05-29','1993-07-03'),
+(10865,'d009','1993-07-03','1993-09-19'),
+(10866,'d002','1990-12-27','9999-01-01'),
+(10867,'d004','1997-03-23','2002-03-22'),
+(10868,'d004','1990-03-20','9999-01-01'),
+(10869,'d009','1997-09-04','1998-10-15'),
+(10870,'d004','1988-01-13','9999-01-01'),
+(10871,'d009','1999-06-16','9999-01-01'),
+(10872,'d002','1990-09-06','9999-01-01'),
+(10873,'d002','1994-03-23','9999-01-01'),
+(10874,'d004','1986-04-22','1997-12-28'),
+(10874,'d006','1997-12-28','9999-01-01'),
+(10875,'d006','1988-05-19','9999-01-01'),
+(10876,'d007','1995-01-29','1999-09-14'),
+(10877,'d001','1986-06-06','9999-01-01'),
+(10878,'d007','1997-04-25','9999-01-01'),
+(10879,'d005','1985-08-15','9999-01-01'),
+(10880,'d004','1992-01-11','9999-01-01'),
+(10881,'d004','1997-02-18','1998-10-25'),
+(10882,'d005','1999-04-29','9999-01-01'),
+(10883,'d004','1996-12-28','9999-01-01'),
+(10884,'d005','1993-04-15','9999-01-01'),
+(10885,'d001','1997-09-29','9999-01-01'),
+(10885,'d007','1991-08-19','1997-09-29'),
+(10886,'d005','1995-03-26','9999-01-01'),
+(10887,'d005','1989-03-17','9999-01-01'),
+(10888,'d005','1987-06-28','1989-09-11'),
+(10889,'d007','1987-08-11','9999-01-01'),
+(10890,'d007','1990-03-10','9999-01-01'),
+(10891,'d005','1995-10-06','9999-01-01'),
+(10892,'d003','1990-04-11','9999-01-01'),
+(10893,'d001','1989-08-23','9999-01-01'),
+(10894,'d004','1995-12-05','9999-01-01'),
+(10895,'d007','1986-04-26','1986-12-25'),
+(10895,'d009','1986-12-25','1987-04-13'),
+(10896,'d004','1997-11-27','9999-01-01'),
+(10897,'d007','1986-04-09','9999-01-01'),
+(10898,'d004','1993-01-08','9999-01-01'),
+(10899,'d001','1990-04-05','9999-01-01'),
+(10900,'d005','1995-09-07','9999-01-01'),
+(10901,'d002','1998-08-05','9999-01-01'),
+(10902,'d005','1990-06-11','1990-09-25'),
+(10903,'d009','1989-02-14','9999-01-01'),
+(10904,'d005','1993-04-16','9999-01-01'),
+(10905,'d004','1985-02-28','1986-03-07'),
+(10906,'d002','1998-10-18','2001-04-25'),
+(10906,'d007','2001-04-25','9999-01-01'),
+(10907,'d003','1988-12-25','9999-01-01'),
+(10908,'d003','1986-04-05','9999-01-01'),
+(10909,'d005','1993-12-26','9999-01-01'),
+(10910,'d009','1989-09-02','9999-01-01'),
+(10911,'d004','1988-06-25','9999-01-01'),
+(10912,'d002','1986-04-23','1999-09-26'),
+(10913,'d002','1998-10-25','9999-01-01'),
+(10914,'d007','1998-03-24','1998-09-01'),
+(10915,'d004','1999-10-28','9999-01-01'),
+(10916,'d004','1998-10-02','9999-01-01'),
+(10916,'d005','1998-03-12','1998-10-02'),
+(10917,'d005','1993-01-30','9999-01-01'),
+(10918,'d002','1990-01-29','1990-10-10'),
+(10919,'d001','1999-10-30','9999-01-01'),
+(10920,'d003','1992-02-24','9999-01-01'),
+(10921,'d004','1986-07-27','9999-01-01'),
+(10922,'d007','1996-02-02','1999-01-16'),
+(10923,'d007','1990-04-09','9999-01-01'),
+(10924,'d007','1988-08-14','9999-01-01'),
+(10925,'d004','1991-06-11','9999-01-01'),
+(10926,'d004','1990-04-30','1992-11-04'),
+(10926,'d009','1992-11-04','9999-01-01'),
+(10927,'d001','1986-09-12','9999-01-01'),
+(10928,'d001','1991-04-07','9999-01-01'),
+(10929,'d001','1991-04-15','9999-01-01'),
+(10930,'d004','1991-11-22','9999-01-01'),
+(10931,'d004','1992-06-14','1999-09-19'),
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+(10933,'d004','1993-08-02','9999-01-01'),
+(10934,'d002','1995-09-17','9999-01-01'),
+(10935,'d002','1994-10-17','9999-01-01'),
+(10936,'d003','1990-05-09','9999-01-01'),
+(10937,'d004','1991-05-03','9999-01-01'),
+(10937,'d005','1988-06-16','1991-05-03'),
+(10938,'d001','1985-09-24','9999-01-01'),
+(10939,'d006','1997-12-11','9999-01-01'),
+(10940,'d005','1997-06-30','1999-12-02'),
+(10941,'d001','1993-08-22','9999-01-01'),
+(10942,'d005','1996-01-21','9999-01-01'),
+(10943,'d005','1993-05-22','9999-01-01'),
+(10944,'d004','1995-09-07','9999-01-01'),
+(10945,'d004','1993-04-04','9999-01-01'),
+(10946,'d009','1996-10-26','9999-01-01'),
+(10947,'d009','1995-11-16','9999-01-01'),
+(10948,'d005','1996-09-30','1997-04-29'),
+(10948,'d008','1997-04-29','1997-06-23'),
+(10949,'d002','1990-06-29','9999-01-01'),
+(10950,'d004','1996-06-08','1998-01-13'),
+(10951,'d002','1989-07-06','9999-01-01'),
+(10952,'d005','1986-07-22','1988-04-26'),
+(10953,'d007','1987-02-12','9999-01-01'),
+(10954,'d009','1995-12-14','9999-01-01'),
+(10955,'d007','1986-09-27','9999-01-01'),
+(10956,'d004','1997-02-10','9999-01-01'),
+(10956,'d005','1995-12-02','1997-02-10'),
+(10957,'d007','1990-10-01','9999-01-01'),
+(10958,'d005','1988-11-27','9999-01-01'),
+(10959,'d007','1994-03-01','1996-04-12'),
+(10960,'d007','1995-07-07','9999-01-01'),
+(10961,'d005','1993-11-04','9999-01-01'),
+(10962,'d007','1986-12-16','9999-01-01'),
+(10963,'d005','1988-01-14','1995-04-10'),
+(10964,'d002','1991-05-05','9999-01-01'),
+(10965,'d007','1986-07-29','9999-01-01'),
+(10966,'d004','1999-11-12','9999-01-01'),
+(10966,'d008','1997-02-05','1999-11-12'),
+(10967,'d005','1993-08-01','9999-01-01'),
+(10968,'d007','1985-08-11','9999-01-01'),
+(10969,'d005','1994-05-12','9999-01-01'),
+(10970,'d004','1995-11-14','9999-01-01'),
+(10971,'d005','1991-07-30','1998-10-24'),
+(10972,'d005','1987-06-06','9999-01-01'),
+(10973,'d005','1989-03-05','9999-01-01'),
+(10974,'d005','1999-05-17','9999-01-01'),
+(10975,'d004','1992-03-30','1997-01-14'),
+(10975,'d009','1997-01-14','9999-01-01'),
+(10976,'d004','1998-10-31','9999-01-01'),
+(10977,'d007','1987-08-10','1987-10-01'),
+(10978,'d004','1985-05-18','9999-01-01'),
+(10979,'d004','1986-01-27','9999-01-01'),
+(10980,'d005','1989-01-04','9999-01-01'),
+(10981,'d007','1992-07-07','9999-01-01'),
+(10982,'d008','1989-11-11','9999-01-01'),
+(10983,'d004','1988-02-22','9999-01-01'),
+(10984,'d004','1988-07-09','1993-05-14'),
+(10985,'d005','1999-02-11','1999-02-27'),
+(10985,'d008','1999-02-27','1999-03-10'),
+(10986,'d005','1985-10-11','9999-01-01'),
+(10987,'d005','1997-05-21','9999-01-01'),
+(10988,'d005','1985-06-19','9999-01-01'),
+(10989,'d002','1985-07-20','9999-01-01'),
+(10990,'d005','1990-08-06','9999-01-01'),
+(10991,'d005','1999-07-13','9999-01-01'),
+(10992,'d005','1997-04-27','9999-01-01'),
+(10993,'d006','1992-05-29','9999-01-01'),
+(10994,'d004','1987-11-13','1989-05-24'),
+(10994,'d005','1987-06-29','1987-11-13'),
+(10995,'d001','1997-07-19','9999-01-01'),
+(10996,'d007','1990-02-07','9999-01-01'),
+(10997,'d004','2000-01-04','9999-01-01'),
+(10998,'d007','1996-08-07','9999-01-01'),
+(10999,'d005','1996-03-05','1997-04-19'),
+(11000,'d005','1996-01-28','9999-01-01');
\ No newline at end of file
diff --git a/employee-sample-database/mysql/dataset_small/load_dept_manager.sql b/employee-sample-database/mysql/dataset_small/load_dept_manager.sql
new file mode 100644
index 0000000000000000000000000000000000000000..4da207f8e85428d7a07fbc2c339a8d57b2241d3c
--- /dev/null
+++ b/employee-sample-database/mysql/dataset_small/load_dept_manager.sql
@@ -0,0 +1,32 @@
+INSERT INTO `dept_manager` VALUES 
+(10002,'d001','1985-01-01','1991-10-01'),
+
+(10039,'d001','1991-10-01','9999-01-01'),
+
+(10085,'d002','1985-01-01','1989-12-17'),
+
+(10114,'d002','1989-12-17','9999-01-01'),
+
+(10183,'d003','1985-01-01','1992-03-21'),
+
+(10228,'d003','1992-03-21','9999-01-01'),
+
+(10303,'d004','1985-01-01','1988-09-09'),
+
+(10344,'d004','1988-09-09','1992-08-02'),
+
+(10386,'d004','1992-08-02','1996-08-30'),
+
+(10420,'d004','1996-08-30','9999-01-01'),
+
+(10511,'d005','1985-01-01','1992-04-25'),
+
+(10567,'d005','1992-04-25','9999-01-01'),
+
+(10725,'d006','1985-01-01','1989-05-06'),
+
+(10765,'d006','1989-05-06','1991-09-12'),
+
+(10800,'d006','1991-09-12','1994-06-28'),
+
+(10854,'d006','1994-06-28','9999-01-01');
\ No newline at end of file
diff --git a/employee-sample-database/mysql/dataset_small/load_employee.sql b/employee-sample-database/mysql/dataset_small/load_employee.sql
new file mode 100644
index 0000000000000000000000000000000000000000..a55f9900bdcdeab39858d97d780fbcc19f7795e2
--- /dev/null
+++ b/employee-sample-database/mysql/dataset_small/load_employee.sql
@@ -0,0 +1,1000 @@
+INSERT INTO `employee` VALUES (10001,'1953-09-02','Georgi','Facello','M','1986-06-26'),
+(10002,'1964-06-02','Bezalel','Simmel','F','1985-11-21'),
+(10003,'1959-12-03','Parto','Bamford','M','1986-08-28'),
+(10004,'1954-05-01','Chirstian','Koblick','M','1986-12-01'),
+(10005,'1955-01-21','Kyoichi','Maliniak','M','1989-09-12'),
+(10006,'1953-04-20','Anneke','Preusig','F','1989-06-02'),
+(10007,'1957-05-23','Tzvetan','Zielinski','F','1989-02-10'),
+(10008,'1958-02-19','Saniya','Kalloufi','M','1994-09-15'),
+(10009,'1952-04-19','Sumant','Peac','F','1985-02-18'),
+(10010,'1963-06-01','Duangkaew','Piveteau','F','1989-08-24'),
+(10011,'1953-11-07','Mary','Sluis','F','1990-01-22'),
+(10012,'1960-10-04','Patricio','Bridgland','M','1992-12-18'),
+(10013,'1963-06-07','Eberhardt','Terkki','M','1985-10-20'),
+(10014,'1956-02-12','Berni','Genin','M','1987-03-11'),
+(10015,'1959-08-19','Guoxiang','Nooteboom','M','1987-07-02'),
+(10016,'1961-05-02','Kazuhito','Cappelletti','M','1995-01-27'),
+(10017,'1958-07-06','Cristinel','Bouloucos','F','1993-08-03'),
+(10018,'1954-06-19','Kazuhide','Peha','F','1987-04-03'),
+(10019,'1953-01-23','Lillian','Haddadi','M','1999-04-30'),
+(10020,'1952-12-24','Mayuko','Warwick','M','1991-01-26'),
+(10021,'1960-02-20','Ramzi','Erde','M','1988-02-10'),
+(10022,'1952-07-08','Shahaf','Famili','M','1995-08-22'),
+(10023,'1953-09-29','Bojan','Montemayor','F','1989-12-17'),
+(10024,'1958-09-05','Suzette','Pettey','F','1997-05-19'),
+(10025,'1958-10-31','Prasadram','Heyers','M','1987-08-17'),
+(10026,'1953-04-03','Yongqiao','Berztiss','M','1995-03-20'),
+(10027,'1962-07-10','Divier','Reistad','F','1989-07-07'),
+(10028,'1963-11-26','Domenick','Tempesti','M','1991-10-22'),
+(10029,'1956-12-13','Otmar','Herbst','M','1985-11-20'),
+(10030,'1958-07-14','Elvis','Demeyer','M','1994-02-17'),
+(10031,'1959-01-27','Karsten','Joslin','M','1991-09-01'),
+(10032,'1960-08-09','Jeong','Reistad','F','1990-06-20'),
+(10033,'1956-11-14','Arif','Merlo','M','1987-03-18'),
+(10034,'1962-12-29','Bader','Swan','M','1988-09-21'),
+(10035,'1953-02-08','Alain','Chappelet','M','1988-09-05'),
+(10036,'1959-08-10','Adamantios','Portugali','M','1992-01-03'),
+(10037,'1963-07-22','Pradeep','Makrucki','M','1990-12-05'),
+(10038,'1960-07-20','Huan','Lortz','M','1989-09-20'),
+(10039,'1959-10-01','Alejandro','Brender','M','1988-01-19'),
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+(10921,'1955-07-20','Mara','Bahi','F','1986-07-27'),
+(10922,'1953-10-27','Kyoichi','Wossner','M','1992-08-09'),
+(10923,'1957-04-29','Younwoo','Maquelin','M','1990-04-09'),
+(10924,'1954-04-27','Hironoby','Atchley','F','1988-08-14'),
+(10925,'1960-02-13','Heng','Waterhouse','F','1986-09-04'),
+(10926,'1954-10-13','Leni','Eppinger','F','1990-04-30'),
+(10927,'1953-03-13','Gal','Scallan','M','1985-10-26'),
+(10928,'1961-11-01','Udaiprakash','Schmezko','M','1991-04-07'),
+(10929,'1958-10-26','Sumali','Arlazarov','M','1991-04-15'),
+(10930,'1954-01-13','Arch','Minakawa','M','1990-08-09'),
+(10931,'1963-06-30','Abdulla','Beutelspacher','F','1992-06-14'),
+(10932,'1955-11-19','Avishai','Picci','F','1994-09-09'),
+(10933,'1965-01-24','Juyoung','Seghrouchni','F','1993-08-02'),
+(10934,'1960-05-21','Bartek','Strooper','F','1993-11-11'),
+(10935,'1961-08-09','Morris','Famili','M','1990-03-09'),
+(10936,'1953-04-08','Mountaz','Schicker','F','1987-10-27'),
+(10937,'1964-09-11','Anyuan','Nanard','M','1987-08-29'),
+(10938,'1958-05-11','Shaowei','Iisaku','F','1985-09-24'),
+(10939,'1964-07-13','Matk','Akazan','M','1990-11-01'),
+(10940,'1953-01-07','Aleksander','Fioravanti','F','1994-12-02'),
+(10941,'1960-08-26','Katsuyuki','Norsworthy','M','1993-08-22'),
+(10942,'1952-08-08','Toshimitsu','Larfeldt','F','1989-09-08'),
+(10943,'1955-11-19','Berna','Skafidas','M','1988-02-19'),
+(10944,'1952-05-25','Remko','Zyda','M','1991-05-24'),
+(10945,'1959-09-25','Kazuhiro','Kushnir','F','1990-07-04'),
+(10946,'1956-09-08','Shaz','Kabayashi','M','1996-02-04'),
+(10947,'1954-09-29','Gor','Usery','F','1995-11-16'),
+(10948,'1952-12-23','Shigehito','Brodie','M','1996-09-30'),
+(10949,'1953-08-14','Shigeichiro','Wroclawski','F','1990-03-13'),
+(10950,'1958-12-07','Sushant','Staylopatis','M','1996-06-08'),
+(10951,'1962-01-20','Marco','Iisaku','M','1989-07-06'),
+(10952,'1958-08-26','Inderjeet','Bharadwaj','M','1986-07-22'),
+(10953,'1956-06-23','Jahangir','Laventhal','F','1987-02-12'),
+(10954,'1964-11-16','Ranga','Burnard','F','1990-08-07'),
+(10955,'1959-02-24','Urs','Danley','F','1986-09-27'),
+(10956,'1955-02-02','Arno','Picel','M','1992-06-29'),
+(10957,'1961-06-06','Duke','Schnabel','M','1988-01-23'),
+(10958,'1958-11-07','Huican','Katalagarianos','M','1988-11-27'),
+(10959,'1956-06-19','Kerryn','Vigier','M','1994-03-01'),
+(10960,'1959-10-24','Billur','Braunschweig','M','1988-03-14'),
+(10961,'1957-03-28','Weiwu','Choobineh','M','1986-05-14'),
+(10962,'1956-08-23','Naraig','Kugler','F','1986-12-16'),
+(10963,'1957-04-15','Hatsukazu','Swist','M','1988-01-14'),
+(10964,'1962-04-02','Takushi','Bolsens','F','1990-10-11'),
+(10965,'1955-07-10','Eckart','Braunschweig','M','1986-07-29'),
+(10966,'1962-07-04','Danel','Pardalos','M','1989-11-07'),
+(10967,'1962-10-01','Eran','Adachi','M','1993-08-01'),
+(10968,'1961-03-19','Lakshmi','Kolinko','M','1985-08-11'),
+(10969,'1954-09-05','Premal','Noriega','M','1988-08-22'),
+(10970,'1962-08-24','Gen','Tiemann','F','1994-09-16'),
+(10971,'1955-02-08','Isamu','Vadhan','M','1991-07-30'),
+(10972,'1961-10-05','Fumiko','Asser','F','1987-06-06'),
+(10973,'1961-10-02','Valdiodio','Raczkowsky','M','1989-03-05'),
+(10974,'1952-03-07','Muneo','Papsdorf','M','1985-11-25'),
+(10975,'1962-07-27','Peternela','Birnbaum','F','1986-10-31'),
+(10976,'1963-01-27','Hitomi','Valette','F','1992-12-06'),
+(10977,'1957-03-19','Toong','Murrill','M','1987-08-10'),
+(10978,'1962-09-24','Huiqun','Bellone','M','1985-05-18'),
+(10979,'1959-09-11','Elrique','Raney','F','1986-01-27'),
+(10980,'1963-11-10','Filipp','Munawer','M','1989-01-04'),
+(10981,'1956-07-22','Sugwoo','Dulin','F','1989-01-03'),
+(10982,'1964-02-27','Yishay','Decleir','M','1989-11-11'),
+(10983,'1961-08-09','Somnath','Parascandalo','F','1988-02-22'),
+(10984,'1962-11-10','Pranjal','Billawala','F','1988-07-09'),
+(10985,'1952-05-31','Chiradeep','Furedi','F','1990-12-10'),
+(10986,'1954-08-11','Masako','Schiper','M','1985-10-11'),
+(10987,'1952-11-26','Flemming','Baezner','M','1995-12-19'),
+(10988,'1955-06-24','Chinya','Deyuan','M','1985-06-19'),
+(10989,'1954-09-08','Sumali','Candan','F','1985-07-20'),
+(10990,'1954-10-22','Shin','Foote','M','1988-07-06'),
+(10991,'1957-05-22','Munehiro','Aingworth','M','1993-05-21'),
+(10992,'1958-11-26','Zhensheng','Juneja','M','1988-08-01'),
+(10993,'1958-11-17','Arve','Erde','F','1992-05-29'),
+(10994,'1961-06-17','Divine','Aingworth','M','1987-06-29'),
+(10995,'1963-02-26','Chenye','Hertweck','F','1997-07-19'),
+(10996,'1955-02-01','Matk','Rahimi','F','1990-02-07'),
+(10997,'1959-08-30','Alselm','Parascandalo','F','1994-05-06'),
+(10998,'1956-02-17','Marlo','Kandlur','M','1992-06-28'),
+(10999,'1961-12-04','Insup','Benveniste','F','1996-03-05'),
+(11000,'1960-09-12','Alain','Bonifati','M','1988-08-20');
\ No newline at end of file
diff --git a/employee-sample-database/mysql/dataset_small/load_salary1.sql b/employee-sample-database/mysql/dataset_small/load_salary1.sql
new file mode 100644
index 0000000000000000000000000000000000000000..0776a20e014ff2c0fe03f346fc4d030704439acf
--- /dev/null
+++ b/employee-sample-database/mysql/dataset_small/load_salary1.sql
@@ -0,0 +1,9488 @@
+INSERT INTO `salary` VALUES (10001,60117,'1986-06-26','1987-06-26'),
+(10001,62102,'1987-06-26','1988-06-25'),
+(10001,66074,'1988-06-25','1989-06-25'),
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+(10001,85097,'2001-06-22','2002-06-22'),
+(10001,88958,'2002-06-22','9999-01-01'),
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\ No newline at end of file
diff --git a/employee-sample-database/mysql/dataset_small/load_title.sql b/employee-sample-database/mysql/dataset_small/load_title.sql
new file mode 100644
index 0000000000000000000000000000000000000000..1df403910c461dc60434816e752cc93dbf5df4b7
--- /dev/null
+++ b/employee-sample-database/mysql/dataset_small/load_title.sql
@@ -0,0 +1,1470 @@
+INSERT INTO `title` VALUES (10001,'Senior Engineer','1986-06-26','9999-01-01'),
+(10002,'Staff','1996-08-03','9999-01-01'),
+(10003,'Senior Engineer','1995-12-03','9999-01-01'),
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+(10012,'Senior Engineer','2000-12-18','9999-01-01'),
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+(10014,'Engineer','1993-12-29','9999-01-01'),
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+(10803,'Engineer','1988-04-20','1997-04-20'),
+(10803,'Senior Engineer','1997-04-20','2002-04-02'),
+(10804,'Staff','1997-06-09','9999-01-01'),
+(10805,'Senior Staff','1996-05-25','9999-01-01'),
+(10805,'Staff','1987-05-26','1996-05-25'),
+(10806,'Senior Staff','2000-04-25','9999-01-01'),
+(10806,'Staff','1993-04-25','2000-04-25'),
+(10807,'Senior Staff','1999-10-19','9999-01-01'),
+(10808,'Senior Engineer','1987-03-07','9999-01-01'),
+(10809,'Engineer','1988-02-25','1994-02-24'),
+(10809,'Senior Engineer','1994-02-24','9999-01-01'),
+(10810,'Senior Staff','2000-09-06','9999-01-01'),
+(10810,'Staff','1994-09-07','2000-09-06'),
+(10811,'Engineer','1997-07-08','9999-01-01'),
+(10812,'Engineer','1996-09-20','1997-10-06'),
+(10813,'Senior Staff','1990-04-27','9999-01-01'),
+(10814,'Senior Staff','1998-07-17','9999-01-01'),
+(10814,'Staff','1993-07-17','1998-07-17'),
+(10815,'Senior Staff','2002-04-19','9999-01-01'),
+(10815,'Staff','1994-04-19','2002-04-19'),
+(10816,'Senior Staff','1991-05-22','9999-01-01'),
+(10817,'Senior Staff','1998-12-26','9999-01-01'),
+(10817,'Staff','1990-12-26','1998-12-26'),
+(10818,'Staff','1998-04-09','9999-01-01'),
+(10819,'Engineer','1986-08-02','1993-08-02'),
+(10819,'Senior Engineer','1993-08-02','1999-12-28'),
+(10820,'Senior Staff','1992-06-26','9999-01-01'),
+(10821,'Engineer','1988-10-06','1990-04-17'),
+(10822,'Engineer','1986-01-11','1992-01-11'),
+(10822,'Senior Engineer','1992-01-11','9999-01-01'),
+(10823,'Staff','1999-08-19','9999-01-01'),
+(10824,'Staff','1995-08-01','9999-01-01'),
+(10825,'Engineer','1993-12-04','1998-12-04'),
+(10825,'Senior Engineer','1998-12-04','9999-01-01'),
+(10826,'Senior Engineer','1993-01-22','9999-01-01'),
+(10827,'Staff','1996-11-07','9999-01-01'),
+(10828,'Staff','1986-02-14','1990-03-30'),
+(10829,'Staff','1993-07-21','1993-11-29'),
+(10830,'Senior Staff','1997-08-23','9999-01-01'),
+(10830,'Staff','1991-08-24','1997-08-23'),
+(10831,'Technique Leader','1998-02-22','9999-01-01'),
+(10832,'Senior Staff','1992-10-10','9999-01-01'),
+(10832,'Staff','1986-10-11','1992-10-10'),
+(10833,'Technique Leader','1998-12-23','9999-01-01'),
+(10834,'Assistant Engineer','1990-11-24','1999-11-24'),
+(10834,'Engineer','1999-11-24','9999-01-01'),
+(10835,'Engineer','1995-08-22','9999-01-01'),
+(10836,'Senior Staff','1987-08-10','9999-01-01'),
+(10837,'Senior Staff','1993-01-14','2002-02-22'),
+(10837,'Staff','1986-01-14','1993-01-14'),
+(10838,'Engineer','1994-04-26','2001-04-26'),
+(10838,'Senior Engineer','2001-04-26','9999-01-01'),
+(10839,'Assistant Engineer','1996-12-30','9999-01-01'),
+(10840,'Engineer','1993-06-04','1998-06-04'),
+(10840,'Senior Engineer','1998-06-04','9999-01-01'),
+(10841,'Senior Staff','1997-01-21','9999-01-01'),
+(10841,'Staff','1992-01-22','1997-01-21'),
+(10842,'Engineer','1986-07-14','1991-07-14'),
+(10842,'Senior Engineer','1991-07-14','1998-06-27'),
+(10843,'Engineer','1990-04-26','1998-04-26'),
+(10843,'Senior Engineer','1998-04-26','9999-01-01'),
+(10844,'Engineer','1997-04-25','9999-01-01'),
+(10845,'Engineer','1997-01-25','2002-01-25'),
+(10845,'Senior Engineer','2002-01-25','9999-01-01'),
+(10846,'Senior Engineer','1995-01-17','9999-01-01'),
+(10847,'Senior Staff','1992-07-24','9999-01-01'),
+(10847,'Staff','1986-07-25','1992-07-24'),
+(10848,'Engineer','1985-04-20','1992-04-20'),
+(10848,'Senior Engineer','1992-04-20','9999-01-01'),
+(10849,'Assistant Engineer','1995-06-25','2001-06-24'),
+(10849,'Engineer','2001-06-24','9999-01-01'),
+(10850,'Staff','1990-04-22','1995-05-11'),
+(10851,'Senior Staff','1999-11-21','9999-01-01'),
+(10851,'Staff','1994-11-21','1999-11-21'),
+(10852,'Technique Leader','1999-08-21','9999-01-01'),
+(10853,'Technique Leader','1993-05-03','9999-01-01'),
+(10854,'Engineer','1998-07-13','9999-01-01'),
+(10855,'Senior Staff','1993-03-29','9999-01-01'),
+(10856,'Staff','1986-12-31','1990-06-18'),
+(10857,'Senior Staff','1996-04-02','9999-01-01'),
+(10857,'Staff','1990-04-04','1996-04-02'),
+(10858,'Engineer','1997-01-01','2002-01-01'),
+(10858,'Senior Engineer','2002-01-01','9999-01-01'),
+(10859,'Staff','1996-05-21','9999-01-01'),
+(10860,'Technique Leader','1998-01-22','9999-01-01'),
+(10861,'Senior Engineer','1989-06-10','1992-04-12'),
+(10862,'Senior Staff','1992-07-23','9999-01-01'),
+(10862,'Staff','1985-07-23','1992-07-23'),
+(10863,'Engineer','1985-12-25','1991-12-25'),
+(10863,'Senior Engineer','1991-12-25','9999-01-01'),
+(10864,'Senior Staff','1992-09-24','9999-01-01'),
+(10864,'Staff','1986-09-25','1992-09-24'),
+(10865,'Staff','1993-05-29','1993-09-19'),
+(10866,'Senior Staff','1995-12-27','9999-01-01'),
+(10866,'Staff','1990-12-27','1995-12-27'),
+(10867,'Engineer','1997-03-23','2002-03-22'),
+(10868,'Engineer','1990-03-20','1999-03-20'),
+(10868,'Senior Engineer','1999-03-20','9999-01-01'),
+(10869,'Staff','1997-09-04','1998-10-15'),
+(10870,'Senior Engineer','1988-01-13','9999-01-01'),
+(10871,'Staff','1999-06-16','9999-01-01'),
+(10872,'Senior Staff','1996-09-05','9999-01-01'),
+(10872,'Staff','1990-09-06','1996-09-05'),
+(10873,'Staff','1994-03-23','9999-01-01'),
+(10874,'Engineer','1986-04-22','1992-04-21'),
+(10874,'Senior Engineer','1992-04-21','9999-01-01'),
+(10875,'Senior Engineer','1988-05-19','9999-01-01'),
+(10876,'Staff','1995-01-29','1999-09-14'),
+(10877,'Senior Staff','1986-06-06','9999-01-01'),
+(10878,'Staff','1997-04-25','9999-01-01'),
+(10879,'Senior Engineer','1985-08-15','9999-01-01'),
+(10880,'Engineer','1992-01-11','1997-01-10'),
+(10880,'Senior Engineer','1997-01-10','9999-01-01'),
+(10881,'Engineer','1997-02-18','1998-10-25'),
+(10882,'Engineer','1999-04-29','9999-01-01'),
+(10883,'Engineer','1996-12-28','9999-01-01'),
+(10884,'Engineer','1993-04-15','1998-04-15'),
+(10884,'Senior Engineer','1998-04-15','9999-01-01'),
+(10885,'Senior Staff','1991-08-19','9999-01-01'),
+(10886,'Engineer','1995-03-26','2000-03-25'),
+(10886,'Senior Engineer','2000-03-25','9999-01-01'),
+(10887,'Senior Engineer','1989-03-17','9999-01-01'),
+(10888,'Engineer','1987-06-28','1989-09-11'),
+(10889,'Senior Staff','1987-08-11','9999-01-01'),
+(10890,'Senior Staff','1990-03-10','9999-01-01'),
+(10891,'Assistant Engineer','1995-10-06','2001-10-05'),
+(10891,'Engineer','2001-10-05','9999-01-01'),
+(10892,'Senior Staff','1998-04-11','9999-01-01'),
+(10892,'Staff','1990-04-11','1998-04-11'),
+(10893,'Senior Staff','1995-08-23','9999-01-01'),
+(10893,'Staff','1989-08-23','1995-08-23'),
+(10894,'Engineer','1995-12-05','9999-01-01'),
+(10895,'Staff','1986-04-26','1987-04-13'),
+(10896,'Engineer','1997-11-27','9999-01-01'),
+(10897,'Senior Staff','1995-04-09','9999-01-01'),
+(10897,'Staff','1986-04-09','1995-04-09'),
+(10898,'Senior Engineer','1993-01-08','9999-01-01'),
+(10899,'Senior Staff','1999-04-05','9999-01-01'),
+(10899,'Staff','1990-04-05','1999-04-05'),
+(10900,'Senior Engineer','1995-09-07','9999-01-01'),
+(10901,'Staff','1998-08-05','9999-01-01'),
+(10902,'Assistant Engineer','1990-06-11','1990-09-25'),
+(10903,'Senior Staff','1997-02-14','9999-01-01'),
+(10903,'Staff','1989-02-14','1997-02-14'),
+(10904,'Engineer','1993-04-16','1999-04-16'),
+(10904,'Senior Engineer','1999-04-16','9999-01-01'),
+(10905,'Senior Engineer','1985-02-28','1986-03-07'),
+(10906,'Staff','1998-10-18','9999-01-01'),
+(10907,'Senior Staff','1995-12-26','9999-01-01'),
+(10907,'Staff','1988-12-25','1995-12-26'),
+(10908,'Senior Staff','1993-04-05','9999-01-01'),
+(10908,'Staff','1986-04-05','1993-04-05'),
+(10909,'Engineer','1993-12-26','2000-12-26'),
+(10909,'Senior Engineer','2000-12-26','9999-01-01'),
+(10910,'Senior Staff','1995-09-02','9999-01-01'),
+(10910,'Staff','1989-09-02','1995-09-02'),
+(10911,'Senior Engineer','1988-06-25','9999-01-01'),
+(10912,'Senior Staff','1991-04-23','1999-09-26'),
+(10912,'Staff','1986-04-23','1991-04-23'),
+(10913,'Staff','1998-10-25','9999-01-01'),
+(10914,'Staff','1998-03-24','1998-09-01'),
+(10915,'Senior Engineer','1999-10-28','9999-01-01'),
+(10916,'Engineer','1998-03-12','9999-01-01'),
+(10917,'Engineer','1993-01-30','1998-01-30'),
+(10917,'Senior Engineer','1998-01-30','9999-01-01'),
+(10918,'Staff','1990-01-29','1990-10-10'),
+(10919,'Staff','1999-10-30','9999-01-01'),
+(10920,'Senior Staff','1998-02-23','9999-01-01'),
+(10920,'Staff','1992-02-24','1998-02-23'),
+(10921,'Engineer','1986-07-27','1995-07-27'),
+(10921,'Senior Engineer','1995-07-27','9999-01-01'),
+(10922,'Staff','1996-02-02','1999-01-16'),
+(10923,'Senior Staff','1990-04-09','9999-01-01'),
+(10924,'Senior Staff','1997-08-14','9999-01-01'),
+(10924,'Staff','1988-08-14','1997-08-14'),
+(10925,'Technique Leader','1991-06-11','9999-01-01'),
+(10926,'Engineer','1990-04-30','1995-04-30'),
+(10926,'Senior Engineer','1995-04-30','9999-01-01'),
+(10927,'Senior Staff','1993-09-12','9999-01-01'),
+(10927,'Staff','1986-09-12','1993-09-12'),
+(10928,'Senior Staff','1997-04-06','9999-01-01'),
+(10928,'Staff','1991-04-07','1997-04-06'),
+(10929,'Senior Staff','1998-04-15','9999-01-01'),
+(10929,'Staff','1991-04-15','1998-04-15'),
+(10930,'Engineer','1991-11-22','1998-11-22'),
+(10930,'Senior Engineer','1998-11-22','9999-01-01'),
+(10931,'Technique Leader','1992-06-14','1999-09-19'),
+(10932,'Engineer','1994-09-09','1999-09-09'),
+(10932,'Senior Engineer','1999-09-09','9999-01-01'),
+(10933,'Senior Engineer','1993-08-02','9999-01-01'),
+(10934,'Staff','1995-09-17','9999-01-01'),
+(10935,'Staff','1994-10-17','9999-01-01'),
+(10936,'Senior Staff','1996-05-08','9999-01-01'),
+(10936,'Staff','1990-05-09','1996-05-08'),
+(10937,'Engineer','1988-06-16','1994-06-16'),
+(10937,'Senior Engineer','1994-06-16','9999-01-01'),
+(10938,'Senior Staff','1993-09-24','9999-01-01'),
+(10938,'Staff','1985-09-24','1993-09-24'),
+(10939,'Assistant Engineer','1997-12-11','9999-01-01'),
+(10940,'Engineer','1997-06-30','1999-12-02'),
+(10941,'Senior Staff','2001-08-22','9999-01-01'),
+(10941,'Staff','1993-08-22','2001-08-22'),
+(10942,'Engineer','1996-01-21','9999-01-01'),
+(10943,'Engineer','1993-05-22','1999-05-22'),
+(10943,'Senior Engineer','1999-05-22','9999-01-01'),
+(10944,'Senior Engineer','1995-09-07','9999-01-01'),
+(10945,'Engineer','1993-04-04','2001-04-04'),
+(10945,'Senior Engineer','2001-04-04','9999-01-01'),
+(10946,'Staff','1996-10-26','9999-01-01'),
+(10947,'Senior Staff','2001-11-15','9999-01-01'),
+(10947,'Staff','1995-11-16','2001-11-15'),
+(10948,'Senior Engineer','1996-09-30','1997-06-23'),
+(10949,'Senior Staff','1996-06-28','9999-01-01'),
+(10949,'Staff','1990-06-29','1996-06-28'),
+(10950,'Senior Engineer','1996-06-08','1998-01-13'),
+(10951,'Senior Staff','1994-07-06','9999-01-01'),
+(10951,'Staff','1989-07-06','1994-07-06'),
+(10952,'Senior Engineer','1986-07-22','1988-04-26'),
+(10953,'Senior Staff','1987-02-12','9999-01-01'),
+(10954,'Staff','1995-12-14','9999-01-01'),
+(10955,'Senior Staff','1991-09-27','9999-01-01'),
+(10955,'Staff','1986-09-27','1991-09-27'),
+(10956,'Engineer','1995-12-02','9999-01-01'),
+(10957,'Senior Staff','1990-10-01','9999-01-01'),
+(10958,'Engineer','1988-11-27','1993-11-27'),
+(10958,'Senior Engineer','1993-11-27','9999-01-01'),
+(10959,'Staff','1994-03-01','1996-04-12'),
+(10960,'Senior Staff','1995-07-07','9999-01-01'),
+(10961,'Engineer','1993-11-04','1999-11-04'),
+(10961,'Senior Engineer','1999-11-04','9999-01-01'),
+(10962,'Senior Staff','1986-12-16','9999-01-01'),
+(10963,'Engineer','1988-01-14','1995-04-10'),
+(10964,'Senior Staff','1999-05-05','9999-01-01'),
+(10964,'Staff','1991-05-05','1999-05-05'),
+(10965,'Senior Staff','1986-07-29','9999-01-01'),
+(10966,'Staff','1997-02-05','9999-01-01'),
+(10967,'Engineer','1993-08-01','1998-08-01'),
+(10967,'Senior Engineer','1998-08-01','9999-01-01'),
+(10968,'Senior Staff','1985-08-11','9999-01-01'),
+(10969,'Senior Engineer','1994-05-12','9999-01-01'),
+(10970,'Engineer','1995-11-14','9999-01-01'),
+(10971,'Technique Leader','1991-07-30','1998-10-24'),
+(10972,'Engineer','1987-06-06','1996-06-05'),
+(10972,'Senior Engineer','1996-06-05','9999-01-01'),
+(10973,'Engineer','1989-03-05','1995-03-05'),
+(10973,'Senior Engineer','1995-03-05','9999-01-01'),
+(10974,'Senior Engineer','1999-05-17','9999-01-01'),
+(10975,'Engineer','1992-03-30','1998-03-30'),
+(10975,'Senior Engineer','1998-03-30','9999-01-01'),
+(10976,'Technique Leader','1998-10-31','9999-01-01'),
+(10977,'Senior Staff','1987-08-10','1987-10-01'),
+(10978,'Engineer','1985-05-18','1990-05-18'),
+(10978,'Senior Engineer','1990-05-18','9999-01-01'),
+(10979,'Engineer','1986-01-27','1993-01-27'),
+(10979,'Senior Engineer','1993-01-27','9999-01-01'),
+(10980,'Engineer','1989-01-04','1997-01-04'),
+(10980,'Senior Engineer','1997-01-04','9999-01-01'),
+(10981,'Senior Staff','1992-07-07','9999-01-01'),
+(10982,'Senior Staff','1994-11-11','9999-01-01'),
+(10982,'Staff','1989-11-11','1994-11-11'),
+(10983,'Engineer','1988-02-22','1997-02-21'),
+(10983,'Senior Engineer','1997-02-21','9999-01-01'),
+(10984,'Senior Engineer','1988-07-09','1993-05-14'),
+(10985,'Engineer','1999-02-11','1999-03-10'),
+(10986,'Technique Leader','1985-10-11','9999-01-01'),
+(10987,'Engineer','1997-05-21','9999-01-01'),
+(10988,'Senior Engineer','1985-06-19','9999-01-01'),
+(10989,'Senior Staff','1991-07-20','9999-01-01'),
+(10989,'Staff','1985-07-20','1991-07-20'),
+(10990,'Technique Leader','1990-08-06','9999-01-01'),
+(10991,'Engineer','1999-07-13','9999-01-01'),
+(10992,'Engineer','1997-04-27','9999-01-01'),
+(10993,'Engineer','1992-05-29','2000-05-29'),
+(10993,'Senior Engineer','2000-05-29','9999-01-01'),
+(10994,'Assistant Engineer','1987-06-29','1989-05-24'),
+(10995,'Senior Staff','1997-07-19','9999-01-01'),
+(10996,'Senior Staff','1996-02-07','9999-01-01'),
+(10996,'Staff','1990-02-07','1996-02-07'),
+(10997,'Technique Leader','2000-01-04','9999-01-01'),
+(10998,'Staff','1996-08-07','9999-01-01'),
+(10999,'Technique Leader','1996-03-05','1997-04-19'),
+(11000,'Senior Engineer','1996-01-28','9999-01-01');
\ No newline at end of file
diff --git a/employee-sample-database/mysql/dataset_small/object.sql b/employee-sample-database/mysql/dataset_small/object.sql
new file mode 100644
index 0000000000000000000000000000000000000000..9349a5e2bee926aa19aa7ff560f5675d074f0c28
--- /dev/null
+++ b/employee-sample-database/mysql/dataset_small/object.sql
@@ -0,0 +1,225 @@
+use employee;
+
+delimiter //
+drop function if exists emp_dept_id //
+drop function if exists emp_dept_name //
+drop function if exists emp_name //
+drop function if exists current_manager //
+drop procedure if exists show_department //
+
+--
+-- returns the department id of a given employee
+--
+create function emp_dept_id( employee_id int )
+returns char(4)
+reads sql data
+begin
+    declare max_date date;
+    set max_date = (
+        select
+            max(from_date)
+        from
+            dept_emp
+        where
+            emp_no = employee_id
+    );
+    set @max_date=max_date;
+    return (
+        select
+            dept_no
+        from
+            dept_emp
+        where
+            emp_no = employee_id
+            and
+            from_date = max_date
+            limit 1
+    );
+end //
+
+--
+-- returns the department name of a given employee
+--
+
+create function emp_dept_name( employee_id int )
+returns varchar(40)
+reads sql data
+begin
+    return (
+        select
+            dept_name
+        from
+            department
+        where
+            dept_no = emp_dept_id(employee_id)
+    );
+end//
+
+--
+-- returns the employee name of a given employee id
+--
+create function emp_name (employee_id int)
+returns varchar(32)
+reads SQL data
+begin
+    return (
+        select
+            concat(first_name, ' ', last_name) as name
+        from
+            employee
+        where
+            emp_no = employee_id
+    );
+end//
+
+--
+-- returns the manager of a department
+-- choosing the most recent one
+-- from the manager list
+--
+create function current_manager( dept_id char(4) )
+returns varchar(32)
+reads sql data
+begin
+    declare max_date date;
+    set max_date = (
+        select
+            max(from_date)
+        from
+            dept_manager
+        where
+            dept_no = dept_id
+    );
+    set @max_date=max_date;
+    return (
+        select
+            emp_name(emp_no)
+        from
+            dept_manager
+        where
+            dept_no = dept_id
+            and
+            from_date = max_date
+            limit 1
+    );
+end //
+
+delimiter ;
+
+--
+--  selects the employee records with the
+--  latest department
+--
+
+CREATE OR REPLACE VIEW  v_full_employee
+AS
+SELECT
+    emp_no,
+    first_name , last_name ,
+    birth_date , gender,
+    hire_date,
+    emp_dept_name(emp_no) as department
+from
+    employee;
+
+--
+-- selects the department list with manager names
+--
+
+CREATE OR REPLACE VIEW v_full_department
+AS
+SELECT
+    dept_no, dept_name, current_manager(dept_no) as manager
+FROM
+    department;
+
+delimiter //
+
+--
+-- shows the department with the number of employee
+-- per department
+--
+create procedure show_department()
+modifies sql data
+begin
+    DROP TABLE IF EXISTS department_max_date;
+    DROP TABLE IF EXISTS department_people;
+    CREATE TEMPORARY TABLE department_max_date
+    (
+        emp_no int not null primary key,
+        dept_from_date date not null,
+        dept_to_date  date not null, # bug#320513
+        KEY (dept_from_date, dept_to_date)
+    );
+    INSERT INTO department_max_date
+    SELECT
+        emp_no, max(from_date), max(to_date)
+    FROM
+        dept_emp
+    GROUP BY
+        emp_no;
+
+    CREATE TEMPORARY TABLE department_people
+    (
+        emp_no int not null,
+        dept_no char(4) not null,
+        primary key (emp_no, dept_no)
+    );
+
+    insert into department_people
+    select dmd.emp_no, dept_no
+    from
+        department_max_date dmd
+        inner join dept_emp de
+            on dmd.dept_from_date=de.from_date
+            and dmd.dept_to_date=de.to_date
+            and dmd.emp_no=de.emp_no;
+    SELECT
+        dept_no,dept_name,manager, count(*)
+        from v_full_department
+            inner join department_people using (dept_no)
+        group by dept_no;
+        # with rollup;
+    DROP TABLE department_max_date;
+    DROP TABLE department_people;
+end //
+
+drop function if exists employee_usage //
+drop procedure if exists employee_help //
+
+CREATE FUNCTION employee_usage ()
+RETURNS TEXT
+DETERMINISTIC
+BEGIN
+    RETURN
+'
+    == USAGE ==
+    ====================
+
+    PROCEDURE show_department()
+
+        shows the department with the manager and
+        number of employee per department
+
+    FUNCTION current_manager (dept_id)
+
+        Shows who is the manager of a given departmennt
+
+    FUNCTION emp_name (emp_id)
+
+        Shows name and surname of a given employee
+
+    FUNCTION emp_dept_id (emp_id)
+
+        Shows the current department of given employee
+';
+END //
+
+create procedure employee_help()
+deterministic
+begin
+    select employee_usage() as info;
+end//
+
+delimiter ;
+
diff --git a/employee-sample-database/mysql/dataset_small/show_elapsed.sql b/employee-sample-database/mysql/dataset_small/show_elapsed.sql
new file mode 100644
index 0000000000000000000000000000000000000000..2d1566b483fee5112de5ed49139dcd266600bfa6
--- /dev/null
+++ b/employee-sample-database/mysql/dataset_small/show_elapsed.sql
@@ -0,0 +1,5 @@
+select timediff(
+    (select update_time from information_schema.tables where table_schema='employee' and table_name='salary'),
+    (select create_time from information_schema.tables where table_schema='employee' and table_name='employee')
+) as data_load_time_diff;
+
diff --git a/employee-sample-database/mysql/dataset_small/test_employee_md5.sql b/employee-sample-database/mysql/dataset_small/test_employee_md5.sql
new file mode 100644
index 0000000000000000000000000000000000000000..d0fedce96778373da191223d776e11262ea82f83
--- /dev/null
+++ b/employee-sample-database/mysql/dataset_small/test_employee_md5.sql
@@ -0,0 +1,115 @@
+--  Sample employee database 
+--  See changelog table for details
+--  Copyright (C) 2007,2008, MySQL AB
+--  
+--  Original data created by Fusheng Wang and Carlo Zaniolo
+--  http://www.cs.aau.dk/TimeCenter/software.htm
+--  http://www.cs.aau.dk/TimeCenter/Data/employeeTemporalDataSet.zip
+-- 
+--  Current schema by Giuseppe Maxia 
+--  Data conversion from XML to relational by Patrick Crews
+-- 
+-- This work is licensed under the 
+-- Creative Commons Attribution-Share Alike 3.0 Unported License. 
+-- To view a copy of this license, visit 
+-- http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to 
+-- Creative Commons, 171 Second Street, Suite 300, San Francisco, 
+-- California, 94105, USA.
+-- 
+--  DISCLAIMER
+--  To the best of our knowledge, this data is fabricated, and
+--  it does not correspond to real people. 
+--  Any similarity to existing people is purely coincidental.
+-- 
+
+USE employee;
+
+SELECT 'TESTING INSTALLATION' as 'INFO';
+
+DROP TABLE IF EXISTS expected_value, found_value;
+CREATE TABLE expected_value (
+    table_name varchar(30) not null primary key,
+    recs int not null,
+    crc_md5 varchar(100) not null
+);
+
+
+CREATE TABLE found_value LIKE expected_value;
+
+INSERT INTO `expected_value` VALUES 
+('employee',    1000, '595460127fb609c2b110b1796083e242'),
+('department',      9, 'd1af5e170d2d1591d776d5638d71fc5f'),
+('dept_manager',    16, '8ff425d5ad6dc56975998d1893b8dca9'),
+('dept_emp',     1103, 'e302aa5b56a69b49e40eb0d60674addc'),
+('title',       1470, 'ba77dd331ce00f76c1643a7d73cdcee6'),
+('salary',     9488, '61f22cfece4d34f5bb94c9f05a3da3ef');
+SELECT table_name, recs AS expected_record, crc_md5 AS expected_crc FROM expected_value;
+
+DROP TABLE IF EXISTS tchecksum;
+CREATE TABLE tchecksum (chk char(100));
+
+SET @crc= '';
+
+INSERT INTO tchecksum 
+    SELECT @crc := MD5(CONCAT_WS('#',@crc,
+                emp_no,birth_date,first_name,last_name,gender,hire_date)) 
+    FROM employee ORDER BY emp_no;
+INSERT INTO found_value VALUES ('employee', (SELECT COUNT(*) FROM employee), @crc);
+
+SET @crc = '';
+INSERT INTO tchecksum 
+    SELECT @crc := MD5(CONCAT_WS('#',@crc, dept_no,dept_name)) 
+    FROM department ORDER BY dept_no;
+INSERT INTO found_value values ('department', (SELECT COUNT(*) FROM department), @crc);
+
+SET @crc = '';
+INSERT INTO tchecksum 
+    SELECT @crc := MD5(CONCAT_WS('#',@crc, dept_no,emp_no, from_date,to_date)) 
+    FROM dept_manager ORDER BY dept_no,emp_no;
+INSERT INTO found_value values ('dept_manager', (SELECT COUNT(*) FROM dept_manager), @crc);
+
+SET @crc = '';
+INSERT INTO tchecksum 
+    SELECT @crc := MD5(CONCAT_WS('#',@crc, dept_no,emp_no, from_date,to_date)) 
+    FROM dept_emp ORDER BY dept_no,emp_no;
+INSERT INTO found_value values ('dept_emp', (SELECT COUNT(*) FROM dept_emp), @crc);
+
+SET @crc = '';
+INSERT INTO tchecksum 
+    SELECT @crc := MD5(CONCAT_WS('#',@crc, emp_no, title, from_date,to_date)) 
+    FROM title order by emp_no,title,from_date;
+INSERT INTO found_value values ('title', (SELECT COUNT(*) FROM title), @crc);
+
+SET @crc = '';
+INSERT INTO tchecksum 
+    SELECT @crc := MD5(CONCAT_WS('#',@crc, emp_no, amount, from_date,to_date)) 
+    FROM salary order by emp_no,from_date,to_date;
+INSERT INTO found_value values ('salary', (SELECT COUNT(*) FROM salary), @crc);
+
+DROP TABLE tchecksum;
+
+SELECT table_name, recs as 'found_records   ', crc_md5 as found_crc from found_value;
+
+SELECT  
+    e.table_name, 
+    IF(e.recs=f.recs,'OK', 'not ok') AS records_match, 
+    IF(e.crc_md5=f.crc_md5,'ok','not ok') AS crc_match 
+from 
+    expected_value e INNER JOIN found_value f USING (table_name); 
+
+
+set @crc_fail=(select count(*) from expected_value e inner join found_value f on (e.table_name=f.table_name) where f.crc_md5 != e.crc_md5);
+set @count_fail=(select count(*) from expected_value e inner join found_value f on (e.table_name=f.table_name) where f.recs != e.recs);
+
+select timediff(
+    now(),
+    (select create_time from information_schema.tables where table_schema='employee' and table_name='expected_value')
+) as computation_time;
+
+DROP TABLE expected_value,found_value;
+
+select 'CRC' as summary,  if(@crc_fail = 0, "OK", "FAIL" ) as 'result'
+union all
+select 'count', if(@count_fail = 0, "OK", "FAIL" );
+
+
diff --git a/employee-sample-database/mysql/install_dataset_small.sh b/employee-sample-database/mysql/install_dataset_small.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d32155e36c9d87d5d7b54edf0eb40b16f59b2077
--- /dev/null
+++ b/employee-sample-database/mysql/install_dataset_small.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+
+main() {
+    path=$1
+    user=$2
+
+    cd $path/dataset_small
+    mysql -u $user <employee.sql
+}
+
+main "" "root"
diff --git a/employee-sample-database/mysql/schema.png b/employee-sample-database/mysql/schema.png
new file mode 100644
index 0000000000000000000000000000000000000000..297025f3c9ea32175419ed71288a0fabd02cf70c
--- /dev/null
+++ b/employee-sample-database/mysql/schema.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e3889082a8bbc534a3965a57f92de3a5fa3b7db6b4a60fea0722f6c2e9976b14
+size 140674
diff --git a/employee-sample-database/postgres/README.md b/employee-sample-database/postgres/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..4454483d86f2319e92b5b3e4015d6d9e25119014
--- /dev/null
+++ b/employee-sample-database/postgres/README.md
@@ -0,0 +1,22 @@
+# Employee Sample Database for PostgreSQL
+
+This is derived from the [MySQL counterpart](https://github.com/bytebase/employee-sample-database/tree/main/mysql).
+
+## Installation:
+
+1. Download the repository
+2. Change directory to the repository
+3. Change directory to either `dataset_full`, `dataset_large` or `dataset_small`
+
+Run:
+
+```bash
+cd dataset_small
+```
+
+Then run:
+
+```bash
+psql -c "CREATE DATABASE employee"
+psql employee < employee.sql
+```
diff --git a/employee-sample-database/postgres/dataset_small/01_employee.sql b/employee-sample-database/postgres/dataset_small/01_employee.sql
new file mode 100644
index 0000000000000000000000000000000000000000..8bedcf0aabdfcd1137f5f23965dab0aacf8f0133
--- /dev/null
+++ b/employee-sample-database/postgres/dataset_small/01_employee.sql
@@ -0,0 +1,221 @@
+--  Sample employee database for PostgreSQL derived from MySQL version
+--  ----------------------Original license BEGIN----------------------
+--  Sample employee database 
+--  See changelog table for details
+--  Copyright (C) 2007,2008, MySQL AB
+--  
+--  Original data created by Fusheng Wang and Carlo Zaniolo
+--  http://www.cs.aau.dk/TimeCenter/software.htm
+--  http://www.cs.aau.dk/TimeCenter/Data/employeeTemporalDataSet.zip
+-- 
+--  Current schema by Giuseppe Maxia 
+--  Data conversion from XML to relational by Patrick Crews
+-- 
+-- This work is licensed under the 
+-- Creative Commons Attribution-Share Alike 3.0 Unported License. 
+-- To view a copy of this license, visit 
+-- http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to 
+-- Creative Commons, 171 Second Street, Suite 300, San Francisco, 
+-- California, 94105, USA.
+-- 
+--  DISCLAIMER
+--  To the best of our knowledge, this data is fabricated, and
+--  it does not correspond to real people. 
+--  Any similarity to existing people is purely coincidental.
+-- 
+
+\echo 'CREATING DATABASE STRUCTURE'
+
+DROP TABLE IF EXISTS dept_emp,
+                     dept_manager,
+                     title,
+                     salary, 
+                     employee, 
+                     department,
+					 audit CASCADE;
+
+CREATE TABLE employee (
+	emp_no      SERIAL NOT NULL,
+	birth_date  DATE NOT NULL,
+	first_name  TEXT NOT NULL,
+	last_name   TEXT NOT NULL,
+	gender      TEXT NOT NULL CHECK (gender IN('M', 'F')) NOT NULL,
+	hire_date   DATE NOT NULL,
+	PRIMARY KEY (emp_no)
+);
+
+CREATE INDEX idx_employee_hire_date ON employee (hire_date);
+
+CREATE TABLE department (
+	dept_no     TEXT NOT NULL,
+	dept_name   TEXT NOT NULL,
+	PRIMARY KEY (dept_no),
+	UNIQUE      (dept_name)
+);
+
+CREATE TABLE dept_manager (
+	emp_no      INT NOT NULL,
+	dept_no     TEXT NOT NULL,
+	from_date   DATE NOT NULL,
+	to_date     DATE NOT NULL,
+	FOREIGN KEY (emp_no) REFERENCES employee (emp_no) ON DELETE CASCADE,
+	FOREIGN KEY (dept_no) REFERENCES department (dept_no) ON DELETE CASCADE,
+	PRIMARY KEY (emp_no, dept_no)
+);
+
+CREATE TABLE dept_emp (
+	emp_no      INT NOT NULL,
+	dept_no     TEXT NOT NULL,
+	from_date   DATE NOT NULL,
+	to_date     DATE NOT NULL,
+	FOREIGN KEY (emp_no) REFERENCES employee (emp_no) ON DELETE CASCADE,
+	FOREIGN KEY (dept_no) REFERENCES department (dept_no) ON DELETE CASCADE,
+	PRIMARY KEY (emp_no, dept_no)
+);
+
+CREATE TABLE title (
+	emp_no      INT NOT NULL,
+	title       TEXT NOT NULL,
+	from_date   DATE NOT NULL,
+	to_date     DATE,
+	FOREIGN KEY (emp_no) REFERENCES employee (emp_no) ON DELETE CASCADE,
+	PRIMARY KEY (emp_no, title, from_date)
+); 
+
+CREATE TABLE salary (
+	emp_no      INT NOT NULL,
+	salary      INT NOT NULL,
+	from_date   DATE NOT NULL,
+	to_date     DATE NOT NULL,
+	FOREIGN KEY (emp_no) REFERENCES employee (emp_no) ON DELETE CASCADE,
+	PRIMARY KEY (emp_no, from_date)
+);
+
+CREATE INDEX idx_salary_salary ON salary (salary);
+
+CREATE TABLE audit (
+    id SERIAL PRIMARY KEY,
+    operation TEXT NOT NULL,
+    query TEXT,
+    user_name TEXT NOT NULL,
+    changed_at TIMESTAMPTZ DEFAULT CURRENT_TIMESTAMP
+);
+
+CREATE INDEX idx_audit_operation ON audit (operation);
+CREATE INDEX idx_audit_username ON audit (user_name);
+CREATE INDEX idx_audit_changed_at ON audit (changed_at);
+
+-- Enable Row Level Security on audit table
+ALTER TABLE audit ENABLE ROW LEVEL SECURITY;
+
+-- Policy: Users can only see their own audit records
+CREATE POLICY audit_user_isolation ON audit
+    FOR ALL
+    TO PUBLIC
+    USING (user_name = current_user);
+
+-- Policy: Allow audit system to insert records (bypass RLS for service accounts)
+CREATE POLICY audit_insert_system ON audit
+    FOR INSERT
+    TO PUBLIC
+    WITH CHECK (true);
+
+CREATE OR REPLACE PROCEDURE simple_salary_update(
+    p_emp_no INT,
+    p_salary INT
+)
+LANGUAGE plpgsql
+AS $_$
+BEGIN
+    -- Simple update of salary amount
+    UPDATE salary 
+    SET salary = p_salary 
+    WHERE emp_no = p_emp_no 
+    AND to_date = '9999-01-01';
+    
+    RAISE NOTICE 'Updated salary for employee % to $%', p_emp_no, p_salary;
+END;
+$_$;
+
+CREATE OR REPLACE FUNCTION log_dml_operations() RETURNS TRIGGER AS $$
+DECLARE
+    table_category TEXT;
+    log_level TEXT;
+BEGIN
+    -- Get arguments passed from trigger (if any)
+    -- TG_ARGV[0] is the first argument, TG_ARGV[1] is the second
+    table_category := COALESCE(TG_ARGV[0], 'default');
+    log_level := COALESCE(TG_ARGV[1], 'standard');
+    
+    IF (TG_OP = 'INSERT') THEN
+        INSERT INTO audit (operation, query, user_name)
+        VALUES (
+            'INSERT [' || table_category || ':' || log_level || ']', 
+            current_query(), 
+            current_user
+        );
+        RETURN NEW;
+    ELSIF (TG_OP = 'UPDATE') THEN
+        INSERT INTO audit (operation, query, user_name)
+        VALUES (
+            'UPDATE [' || table_category || ':' || log_level || ']', 
+            current_query(), 
+            current_user
+        );
+        RETURN NEW;
+    ELSIF (TG_OP = 'DELETE') THEN
+        INSERT INTO audit (operation, query, user_name)
+        VALUES (
+            'DELETE [' || table_category || ':' || log_level || ']', 
+            current_query(), 
+            current_user
+        );
+        RETURN OLD;
+    END IF;
+    RETURN NULL;
+END;
+$$ LANGUAGE plpgsql;
+
+-- only log update and delete, otherwise, it will cause too much change.
+CREATE TRIGGER salary_log_trigger
+AFTER UPDATE OR DELETE ON salary
+FOR EACH ROW
+EXECUTE FUNCTION log_dml_operations('payroll', 'high');
+
+CREATE OR REPLACE VIEW dept_emp_latest_date AS
+SELECT
+	emp_no,
+	MAX(
+		from_date) AS from_date,
+	MAX(
+		to_date) AS to_date
+FROM
+	dept_emp
+GROUP BY
+	emp_no;
+
+-- shows only the current department for each employee
+CREATE OR REPLACE VIEW current_dept_emp AS
+SELECT
+	l.emp_no,
+	dept_no,
+	l.from_date,
+	l.to_date
+FROM
+	dept_emp d
+	INNER JOIN dept_emp_latest_date l ON d.emp_no = l.emp_no
+		AND d.from_date = l.from_date
+		AND l.to_date = d.to_date;
+
+\echo 'LOADING department'
+\i /docker-entrypoint-initdb.d/load_department.sql
+\echo 'LOADING employee'
+\i /docker-entrypoint-initdb.d/load_employee.sql
+\echo 'LOADING dept_emp'
+\i /docker-entrypoint-initdb.d/load_dept_emp.sql
+\echo 'LOADING dept_manager'
+\i /docker-entrypoint-initdb.d/load_dept_manager.sql
+\echo 'LOADING title'
+\i /docker-entrypoint-initdb.d/load_title.sql
+\echo 'LOADING salary'
+\i /docker-entrypoint-initdb.d/load_salary1.sql
diff --git a/employee-sample-database/postgres/dataset_small/load_department.sql b/employee-sample-database/postgres/dataset_small/load_department.sql
new file mode 100644
index 0000000000000000000000000000000000000000..28fb3be33bd8cdf4e242b355ce0fd6ea94ddda42
--- /dev/null
+++ b/employee-sample-database/postgres/dataset_small/load_department.sql
@@ -0,0 +1,10 @@
+INSERT INTO department VALUES 
+('d001','Marketing'),
+('d002','Finance'),
+('d003','Human Resources'),
+('d004','Production'),
+('d005','Development'),
+('d006','Quality Management'),
+('d007','Sales'),
+('d008','Research'),
+('d009','Customer Service');
diff --git a/employee-sample-database/postgres/dataset_small/load_dept_emp.sql b/employee-sample-database/postgres/dataset_small/load_dept_emp.sql
new file mode 100644
index 0000000000000000000000000000000000000000..e513a5b78c7e2a89e3a8a2d58d40e37b90b66ea2
--- /dev/null
+++ b/employee-sample-database/postgres/dataset_small/load_dept_emp.sql
@@ -0,0 +1,1103 @@
+INSERT INTO dept_emp VALUES (10001,'d005','1986-06-26','9999-01-01'),
+(10002,'d007','1996-08-03','9999-01-01'),
+(10003,'d004','1995-12-03','9999-01-01'),
+(10004,'d004','1986-12-01','9999-01-01'),
+(10005,'d003','1989-09-12','9999-01-01'),
+(10006,'d005','1990-08-05','9999-01-01'),
+(10007,'d008','1989-02-10','9999-01-01'),
+(10008,'d005','1998-03-11','2000-07-31'),
+(10009,'d006','1985-02-18','9999-01-01'),
+(10010,'d004','1996-11-24','2000-06-26'),
+(10010,'d006','2000-06-26','9999-01-01'),
+(10011,'d009','1990-01-22','1996-11-09'),
+(10012,'d005','1992-12-18','9999-01-01'),
+(10013,'d003','1985-10-20','9999-01-01'),
+(10014,'d005','1993-12-29','9999-01-01'),
+(10015,'d008','1992-09-19','1993-08-22'),
+(10016,'d007','1998-02-11','9999-01-01'),
+(10017,'d001','1993-08-03','9999-01-01'),
+(10018,'d004','1992-07-29','9999-01-01'),
+(10018,'d005','1987-04-03','1992-07-29'),
+(10019,'d008','1999-04-30','9999-01-01'),
+(10020,'d004','1997-12-30','9999-01-01'),
+(10021,'d005','1988-02-10','2002-07-15'),
+(10022,'d005','1999-09-03','9999-01-01'),
+(10023,'d005','1999-09-27','9999-01-01'),
+(10024,'d004','1998-06-14','9999-01-01'),
+(10025,'d005','1987-08-17','1997-10-15'),
+(10026,'d004','1995-03-20','9999-01-01'),
+(10027,'d005','1995-04-02','9999-01-01'),
+(10028,'d005','1991-10-22','1998-04-06'),
+(10029,'d004','1991-09-18','1999-07-08'),
+(10029,'d006','1999-07-08','9999-01-01'),
+(10030,'d004','1994-02-17','9999-01-01'),
+(10031,'d005','1991-09-01','9999-01-01'),
+(10032,'d004','1990-06-20','9999-01-01'),
+(10033,'d006','1987-03-18','1993-03-24'),
+(10034,'d007','1995-04-12','1999-10-31'),
+(10035,'d004','1988-09-05','9999-01-01'),
+(10036,'d003','1992-04-28','9999-01-01'),
+(10037,'d005','1990-12-05','9999-01-01'),
+(10038,'d009','1989-09-20','9999-01-01'),
+(10039,'d003','1988-01-19','9999-01-01'),
+(10040,'d005','1993-02-14','2002-01-22'),
+(10040,'d008','2002-01-22','9999-01-01'),
+(10041,'d007','1989-11-12','9999-01-01'),
+(10042,'d002','1993-03-21','2000-08-10'),
+(10043,'d005','1990-10-20','9999-01-01'),
+(10044,'d004','1994-05-21','9999-01-01'),
+(10045,'d004','1996-11-16','9999-01-01'),
+(10046,'d008','1992-06-20','9999-01-01'),
+(10047,'d004','1989-03-31','9999-01-01'),
+(10048,'d005','1985-02-24','1987-01-27'),
+(10049,'d009','1992-05-04','9999-01-01'),
+(10050,'d002','1990-12-25','1992-11-05'),
+(10050,'d007','1992-11-05','9999-01-01'),
+(10051,'d004','1992-10-15','9999-01-01'),
+(10052,'d008','1997-01-31','9999-01-01'),
+(10053,'d007','1994-11-13','9999-01-01'),
+(10054,'d003','1995-07-29','9999-01-01'),
+(10055,'d001','1992-04-27','1995-07-22'),
+(10056,'d005','1990-02-01','9999-01-01'),
+(10057,'d005','1992-01-15','9999-01-01'),
+(10058,'d001','1988-04-25','9999-01-01'),
+(10059,'d002','1991-06-26','9999-01-01'),
+(10060,'d007','1989-05-28','1992-11-11'),
+(10060,'d009','1992-11-11','9999-01-01'),
+(10061,'d007','1989-12-02','9999-01-01'),
+(10062,'d005','1991-08-30','9999-01-01'),
+(10063,'d004','1989-04-08','9999-01-01'),
+(10064,'d008','1985-11-20','1992-03-02'),
+(10065,'d005','1998-05-24','9999-01-01'),
+(10066,'d005','1986-02-26','9999-01-01'),
+(10067,'d006','1987-03-04','9999-01-01'),
+(10068,'d007','1987-08-07','9999-01-01'),
+(10069,'d004','1992-06-14','9999-01-01'),
+(10070,'d005','1985-10-14','1995-10-18'),
+(10070,'d008','1995-10-18','9999-01-01'),
+(10071,'d003','1995-08-05','9999-01-01'),
+(10072,'d005','1989-05-21','9999-01-01'),
+(10073,'d006','1998-02-02','1998-02-22'),
+(10074,'d005','1990-08-13','9999-01-01'),
+(10075,'d005','1988-05-17','2001-01-15'),
+(10076,'d005','1996-07-15','9999-01-01'),
+(10077,'d003','1994-12-23','9999-01-01'),
+(10078,'d005','1994-09-29','9999-01-01'),
+(10079,'d005','1995-12-13','9999-01-01'),
+(10080,'d002','1994-09-28','1997-07-09'),
+(10080,'d003','1997-07-09','9999-01-01'),
+(10081,'d004','1986-10-30','9999-01-01'),
+(10082,'d008','1990-01-03','1990-01-15'),
+(10083,'d004','1987-03-31','9999-01-01'),
+(10084,'d004','1995-12-15','9999-01-01'),
+(10085,'d004','1994-04-09','9999-01-01'),
+(10086,'d003','1992-02-19','9999-01-01'),
+(10087,'d007','1997-05-08','2001-01-09'),
+(10088,'d007','1988-09-02','1992-03-21'),
+(10088,'d009','1992-03-21','9999-01-01'),
+(10089,'d007','1989-01-10','9999-01-01'),
+(10090,'d005','1986-03-14','1999-05-07'),
+(10091,'d005','1992-11-18','9999-01-01'),
+(10092,'d005','1996-04-22','9999-01-01'),
+(10093,'d007','1997-06-08','9999-01-01'),
+(10094,'d008','1987-04-18','1997-11-08'),
+(10095,'d007','1994-03-10','9999-01-01'),
+(10096,'d004','1999-01-23','9999-01-01'),
+(10097,'d008','1990-09-15','9999-01-01'),
+(10098,'d004','1985-05-13','1989-06-29'),
+(10098,'d009','1989-06-29','1992-12-11'),
+(10099,'d007','1988-10-18','9999-01-01'),
+(10100,'d003','1987-09-21','9999-01-01'),
+(10101,'d007','1998-10-14','2000-09-23'),
+(10102,'d004','1995-01-02','9999-01-01'),
+(10103,'d005','1998-01-12','9999-01-01'),
+(10104,'d008','1987-04-16','9999-01-01'),
+(10105,'d003','1999-05-17','2001-06-11'),
+(10106,'d004','1991-10-27','9999-01-01'),
+(10107,'d007','1999-03-30','9999-01-01'),
+(10108,'d001','1999-12-06','2001-10-20'),
+(10108,'d003','1999-03-20','1999-12-06'),
+(10109,'d004','1993-06-16','9999-01-01'),
+(10110,'d003','1986-08-22','9999-01-01'),
+(10111,'d006','1988-12-19','9999-01-01'),
+(10112,'d009','1998-05-01','9999-01-01'),
+(10113,'d004','1998-09-14','9999-01-01'),
+(10114,'d008','1992-07-17','9999-01-01'),
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+(10819,'d004','1986-08-02','1999-12-28'),
+(10820,'d001','1992-06-26','9999-01-01'),
+(10821,'d005','1988-10-06','1990-04-17'),
+(10822,'d005','1986-01-11','9999-01-01'),
+(10823,'d003','1999-08-19','9999-01-01'),
+(10824,'d001','1995-08-01','9999-01-01'),
+(10825,'d004','2000-06-03','9999-01-01'),
+(10825,'d005','1993-12-04','2000-06-03'),
+(10826,'d005','1993-01-22','9999-01-01'),
+(10827,'d002','1996-11-07','9999-01-01'),
+(10828,'d007','1986-02-14','1990-03-30'),
+(10829,'d003','1993-07-21','1993-11-29'),
+(10830,'d001','1991-08-24','9999-01-01'),
+(10831,'d005','1998-02-22','9999-01-01'),
+(10832,'d007','1986-10-11','9999-01-01'),
+(10833,'d006','1998-12-23','9999-01-01'),
+(10834,'d004','1990-11-24','9999-01-01'),
+(10835,'d004','1995-08-22','9999-01-01'),
+(10836,'d007','1987-08-10','1991-07-01'),
+(10836,'d009','1991-07-01','9999-01-01'),
+(10837,'d007','1986-01-14','2002-02-22'),
+(10838,'d006','1994-04-26','9999-01-01'),
+(10839,'d004','1996-12-30','9999-01-01'),
+(10840,'d004','1993-06-04','9999-01-01'),
+(10841,'d009','1992-01-22','9999-01-01'),
+(10842,'d005','1986-07-14','1998-06-27'),
+(10843,'d005','1990-04-26','9999-01-01'),
+(10844,'d005','1997-04-25','9999-01-01'),
+(10845,'d005','1997-01-25','9999-01-01'),
+(10846,'d005','1995-01-17','1997-04-03'),
+(10846,'d008','1997-04-03','9999-01-01'),
+(10847,'d008','1986-07-25','9999-01-01'),
+(10848,'d004','1985-04-20','9999-01-01'),
+(10849,'d005','1995-06-25','9999-01-01'),
+(10850,'d002','1990-04-22','1995-05-11'),
+(10851,'d008','1994-11-21','9999-01-01'),
+(10852,'d004','1999-08-21','9999-01-01'),
+(10853,'d006','1993-05-03','9999-01-01'),
+(10854,'d005','1998-07-13','9999-01-01'),
+(10855,'d002','1993-03-29','1997-03-02'),
+(10855,'d007','1997-03-02','9999-01-01'),
+(10856,'d009','1986-12-31','1990-06-18'),
+(10857,'d007','1990-04-04','9999-01-01'),
+(10858,'d005','1997-01-01','9999-01-01'),
+(10859,'d001','1996-05-21','9999-01-01'),
+(10860,'d006','1998-01-22','9999-01-01'),
+(10861,'d005','1989-06-10','1992-04-12'),
+(10862,'d007','1985-07-23','9999-01-01'),
+(10863,'d004','1985-12-25','9999-01-01'),
+(10864,'d007','1986-09-25','9999-01-01'),
+(10865,'d007','1993-05-29','1993-07-03'),
+(10865,'d009','1993-07-03','1993-09-19'),
+(10866,'d002','1990-12-27','9999-01-01'),
+(10867,'d004','1997-03-23','2002-03-22'),
+(10868,'d004','1990-03-20','9999-01-01'),
+(10869,'d009','1997-09-04','1998-10-15'),
+(10870,'d004','1988-01-13','9999-01-01'),
+(10871,'d009','1999-06-16','9999-01-01'),
+(10872,'d002','1990-09-06','9999-01-01'),
+(10873,'d002','1994-03-23','9999-01-01'),
+(10874,'d004','1986-04-22','1997-12-28'),
+(10874,'d006','1997-12-28','9999-01-01'),
+(10875,'d006','1988-05-19','9999-01-01'),
+(10876,'d007','1995-01-29','1999-09-14'),
+(10877,'d001','1986-06-06','9999-01-01'),
+(10878,'d007','1997-04-25','9999-01-01'),
+(10879,'d005','1985-08-15','9999-01-01'),
+(10880,'d004','1992-01-11','9999-01-01'),
+(10881,'d004','1997-02-18','1998-10-25'),
+(10882,'d005','1999-04-29','9999-01-01'),
+(10883,'d004','1996-12-28','9999-01-01'),
+(10884,'d005','1993-04-15','9999-01-01'),
+(10885,'d001','1997-09-29','9999-01-01'),
+(10885,'d007','1991-08-19','1997-09-29'),
+(10886,'d005','1995-03-26','9999-01-01'),
+(10887,'d005','1989-03-17','9999-01-01'),
+(10888,'d005','1987-06-28','1989-09-11'),
+(10889,'d007','1987-08-11','9999-01-01'),
+(10890,'d007','1990-03-10','9999-01-01'),
+(10891,'d005','1995-10-06','9999-01-01'),
+(10892,'d003','1990-04-11','9999-01-01'),
+(10893,'d001','1989-08-23','9999-01-01'),
+(10894,'d004','1995-12-05','9999-01-01'),
+(10895,'d007','1986-04-26','1986-12-25'),
+(10895,'d009','1986-12-25','1987-04-13'),
+(10896,'d004','1997-11-27','9999-01-01'),
+(10897,'d007','1986-04-09','9999-01-01'),
+(10898,'d004','1993-01-08','9999-01-01'),
+(10899,'d001','1990-04-05','9999-01-01'),
+(10900,'d005','1995-09-07','9999-01-01'),
+(10901,'d002','1998-08-05','9999-01-01'),
+(10902,'d005','1990-06-11','1990-09-25'),
+(10903,'d009','1989-02-14','9999-01-01'),
+(10904,'d005','1993-04-16','9999-01-01'),
+(10905,'d004','1985-02-28','1986-03-07'),
+(10906,'d002','1998-10-18','2001-04-25'),
+(10906,'d007','2001-04-25','9999-01-01'),
+(10907,'d003','1988-12-25','9999-01-01'),
+(10908,'d003','1986-04-05','9999-01-01'),
+(10909,'d005','1993-12-26','9999-01-01'),
+(10910,'d009','1989-09-02','9999-01-01'),
+(10911,'d004','1988-06-25','9999-01-01'),
+(10912,'d002','1986-04-23','1999-09-26'),
+(10913,'d002','1998-10-25','9999-01-01'),
+(10914,'d007','1998-03-24','1998-09-01'),
+(10915,'d004','1999-10-28','9999-01-01'),
+(10916,'d004','1998-10-02','9999-01-01'),
+(10916,'d005','1998-03-12','1998-10-02'),
+(10917,'d005','1993-01-30','9999-01-01'),
+(10918,'d002','1990-01-29','1990-10-10'),
+(10919,'d001','1999-10-30','9999-01-01'),
+(10920,'d003','1992-02-24','9999-01-01'),
+(10921,'d004','1986-07-27','9999-01-01'),
+(10922,'d007','1996-02-02','1999-01-16'),
+(10923,'d007','1990-04-09','9999-01-01'),
+(10924,'d007','1988-08-14','9999-01-01'),
+(10925,'d004','1991-06-11','9999-01-01'),
+(10926,'d004','1990-04-30','1992-11-04'),
+(10926,'d009','1992-11-04','9999-01-01'),
+(10927,'d001','1986-09-12','9999-01-01'),
+(10928,'d001','1991-04-07','9999-01-01'),
+(10929,'d001','1991-04-15','9999-01-01'),
+(10930,'d004','1991-11-22','9999-01-01'),
+(10931,'d004','1992-06-14','1999-09-19'),
+(10932,'d005','1994-09-09','9999-01-01'),
+(10933,'d004','1993-08-02','9999-01-01'),
+(10934,'d002','1995-09-17','9999-01-01'),
+(10935,'d002','1994-10-17','9999-01-01'),
+(10936,'d003','1990-05-09','9999-01-01'),
+(10937,'d004','1991-05-03','9999-01-01'),
+(10937,'d005','1988-06-16','1991-05-03'),
+(10938,'d001','1985-09-24','9999-01-01'),
+(10939,'d006','1997-12-11','9999-01-01'),
+(10940,'d005','1997-06-30','1999-12-02'),
+(10941,'d001','1993-08-22','9999-01-01'),
+(10942,'d005','1996-01-21','9999-01-01'),
+(10943,'d005','1993-05-22','9999-01-01'),
+(10944,'d004','1995-09-07','9999-01-01'),
+(10945,'d004','1993-04-04','9999-01-01'),
+(10946,'d009','1996-10-26','9999-01-01'),
+(10947,'d009','1995-11-16','9999-01-01'),
+(10948,'d005','1996-09-30','1997-04-29'),
+(10948,'d008','1997-04-29','1997-06-23'),
+(10949,'d002','1990-06-29','9999-01-01'),
+(10950,'d004','1996-06-08','1998-01-13'),
+(10951,'d002','1989-07-06','9999-01-01'),
+(10952,'d005','1986-07-22','1988-04-26'),
+(10953,'d007','1987-02-12','9999-01-01'),
+(10954,'d009','1995-12-14','9999-01-01'),
+(10955,'d007','1986-09-27','9999-01-01'),
+(10956,'d004','1997-02-10','9999-01-01'),
+(10956,'d005','1995-12-02','1997-02-10'),
+(10957,'d007','1990-10-01','9999-01-01'),
+(10958,'d005','1988-11-27','9999-01-01'),
+(10959,'d007','1994-03-01','1996-04-12'),
+(10960,'d007','1995-07-07','9999-01-01'),
+(10961,'d005','1993-11-04','9999-01-01'),
+(10962,'d007','1986-12-16','9999-01-01'),
+(10963,'d005','1988-01-14','1995-04-10'),
+(10964,'d002','1991-05-05','9999-01-01'),
+(10965,'d007','1986-07-29','9999-01-01'),
+(10966,'d004','1999-11-12','9999-01-01'),
+(10966,'d008','1997-02-05','1999-11-12'),
+(10967,'d005','1993-08-01','9999-01-01'),
+(10968,'d007','1985-08-11','9999-01-01'),
+(10969,'d005','1994-05-12','9999-01-01'),
+(10970,'d004','1995-11-14','9999-01-01'),
+(10971,'d005','1991-07-30','1998-10-24'),
+(10972,'d005','1987-06-06','9999-01-01'),
+(10973,'d005','1989-03-05','9999-01-01'),
+(10974,'d005','1999-05-17','9999-01-01'),
+(10975,'d004','1992-03-30','1997-01-14'),
+(10975,'d009','1997-01-14','9999-01-01'),
+(10976,'d004','1998-10-31','9999-01-01'),
+(10977,'d007','1987-08-10','1987-10-01'),
+(10978,'d004','1985-05-18','9999-01-01'),
+(10979,'d004','1986-01-27','9999-01-01'),
+(10980,'d005','1989-01-04','9999-01-01'),
+(10981,'d007','1992-07-07','9999-01-01'),
+(10982,'d008','1989-11-11','9999-01-01'),
+(10983,'d004','1988-02-22','9999-01-01'),
+(10984,'d004','1988-07-09','1993-05-14'),
+(10985,'d005','1999-02-11','1999-02-27'),
+(10985,'d008','1999-02-27','1999-03-10'),
+(10986,'d005','1985-10-11','9999-01-01'),
+(10987,'d005','1997-05-21','9999-01-01'),
+(10988,'d005','1985-06-19','9999-01-01'),
+(10989,'d002','1985-07-20','9999-01-01'),
+(10990,'d005','1990-08-06','9999-01-01'),
+(10991,'d005','1999-07-13','9999-01-01'),
+(10992,'d005','1997-04-27','9999-01-01'),
+(10993,'d006','1992-05-29','9999-01-01'),
+(10994,'d004','1987-11-13','1989-05-24'),
+(10994,'d005','1987-06-29','1987-11-13'),
+(10995,'d001','1997-07-19','9999-01-01'),
+(10996,'d007','1990-02-07','9999-01-01'),
+(10997,'d004','2000-01-04','9999-01-01'),
+(10998,'d007','1996-08-07','9999-01-01'),
+(10999,'d005','1996-03-05','1997-04-19'),
+(11000,'d005','1996-01-28','9999-01-01');
\ No newline at end of file
diff --git a/employee-sample-database/postgres/dataset_small/load_dept_manager.sql b/employee-sample-database/postgres/dataset_small/load_dept_manager.sql
new file mode 100644
index 0000000000000000000000000000000000000000..018fcd932ffed125b9d6139f934f0e34377927d7
--- /dev/null
+++ b/employee-sample-database/postgres/dataset_small/load_dept_manager.sql
@@ -0,0 +1,32 @@
+INSERT INTO dept_manager VALUES 
+(10002,'d001','1985-01-01','1991-10-01'),
+
+(10039,'d001','1991-10-01','9999-01-01'),
+
+(10085,'d002','1985-01-01','1989-12-17'),
+
+(10114,'d002','1989-12-17','9999-01-01'),
+
+(10183,'d003','1985-01-01','1992-03-21'),
+
+(10228,'d003','1992-03-21','9999-01-01'),
+
+(10303,'d004','1985-01-01','1988-09-09'),
+
+(10344,'d004','1988-09-09','1992-08-02'),
+
+(10386,'d004','1992-08-02','1996-08-30'),
+
+(10420,'d004','1996-08-30','9999-01-01'),
+
+(10511,'d005','1985-01-01','1992-04-25'),
+
+(10567,'d005','1992-04-25','9999-01-01'),
+
+(10725,'d006','1985-01-01','1989-05-06'),
+
+(10765,'d006','1989-05-06','1991-09-12'),
+
+(10800,'d006','1991-09-12','1994-06-28'),
+
+(10854,'d006','1994-06-28','9999-01-01');
\ No newline at end of file
diff --git a/employee-sample-database/postgres/dataset_small/load_employee.sql b/employee-sample-database/postgres/dataset_small/load_employee.sql
new file mode 100644
index 0000000000000000000000000000000000000000..c7276dbf7a59aa9990b5d6aa46dcb4a4ecbbdcde
--- /dev/null
+++ b/employee-sample-database/postgres/dataset_small/load_employee.sql
@@ -0,0 +1,1000 @@
+INSERT INTO employee VALUES (10001,'1953-09-02','Georgi','Facello','M','1986-06-26'),
+(10002,'1964-06-02','Bezalel','Simmel','F','1985-11-21'),
+(10003,'1959-12-03','Parto','Bamford','M','1986-08-28'),
+(10004,'1954-05-01','Chirstian','Koblick','M','1986-12-01'),
+(10005,'1955-01-21','Kyoichi','Maliniak','M','1989-09-12'),
+(10006,'1953-04-20','Anneke','Preusig','F','1989-06-02'),
+(10007,'1957-05-23','Tzvetan','Zielinski','F','1989-02-10'),
+(10008,'1958-02-19','Saniya','Kalloufi','M','1994-09-15'),
+(10009,'1952-04-19','Sumant','Peac','F','1985-02-18'),
+(10010,'1963-06-01','Duangkaew','Piveteau','F','1989-08-24'),
+(10011,'1953-11-07','Mary','Sluis','F','1990-01-22'),
+(10012,'1960-10-04','Patricio','Bridgland','M','1992-12-18'),
+(10013,'1963-06-07','Eberhardt','Terkki','M','1985-10-20'),
+(10014,'1956-02-12','Berni','Genin','M','1987-03-11'),
+(10015,'1959-08-19','Guoxiang','Nooteboom','M','1987-07-02'),
+(10016,'1961-05-02','Kazuhito','Cappelletti','M','1995-01-27'),
+(10017,'1958-07-06','Cristinel','Bouloucos','F','1993-08-03'),
+(10018,'1954-06-19','Kazuhide','Peha','F','1987-04-03'),
+(10019,'1953-01-23','Lillian','Haddadi','M','1999-04-30'),
+(10020,'1952-12-24','Mayuko','Warwick','M','1991-01-26'),
+(10021,'1960-02-20','Ramzi','Erde','M','1988-02-10'),
+(10022,'1952-07-08','Shahaf','Famili','M','1995-08-22'),
+(10023,'1953-09-29','Bojan','Montemayor','F','1989-12-17'),
+(10024,'1958-09-05','Suzette','Pettey','F','1997-05-19'),
+(10025,'1958-10-31','Prasadram','Heyers','M','1987-08-17'),
+(10026,'1953-04-03','Yongqiao','Berztiss','M','1995-03-20'),
+(10027,'1962-07-10','Divier','Reistad','F','1989-07-07'),
+(10028,'1963-11-26','Domenick','Tempesti','M','1991-10-22'),
+(10029,'1956-12-13','Otmar','Herbst','M','1985-11-20'),
+(10030,'1958-07-14','Elvis','Demeyer','M','1994-02-17'),
+(10031,'1959-01-27','Karsten','Joslin','M','1991-09-01'),
+(10032,'1960-08-09','Jeong','Reistad','F','1990-06-20'),
+(10033,'1956-11-14','Arif','Merlo','M','1987-03-18'),
+(10034,'1962-12-29','Bader','Swan','M','1988-09-21'),
+(10035,'1953-02-08','Alain','Chappelet','M','1988-09-05'),
+(10036,'1959-08-10','Adamantios','Portugali','M','1992-01-03'),
+(10037,'1963-07-22','Pradeep','Makrucki','M','1990-12-05'),
+(10038,'1960-07-20','Huan','Lortz','M','1989-09-20'),
+(10039,'1959-10-01','Alejandro','Brender','M','1988-01-19'),
+(10040,'1959-09-13','Weiyi','Meriste','F','1993-02-14'),
+(10041,'1959-08-27','Uri','Lenart','F','1989-11-12'),
+(10042,'1956-02-26','Magy','Stamatiou','F','1993-03-21'),
+(10043,'1960-09-19','Yishay','Tzvieli','M','1990-10-20'),
+(10044,'1961-09-21','Mingsen','Casley','F','1994-05-21'),
+(10045,'1957-08-14','Moss','Shanbhogue','M','1989-09-02'),
+(10046,'1960-07-23','Lucien','Rosenbaum','M','1992-06-20'),
+(10047,'1952-06-29','Zvonko','Nyanchama','M','1989-03-31'),
+(10048,'1963-07-11','Florian','Syrotiuk','M','1985-02-24'),
+(10049,'1961-04-24','Basil','Tramer','F','1992-05-04'),
+(10050,'1958-05-21','Yinghua','Dredge','M','1990-12-25'),
+(10051,'1953-07-28','Hidefumi','Caine','M','1992-10-15'),
+(10052,'1961-02-26','Heping','Nitsch','M','1988-05-21'),
+(10053,'1954-09-13','Sanjiv','Zschoche','F','1986-02-04'),
+(10054,'1957-04-04','Mayumi','Schueller','M','1995-03-13'),
+(10055,'1956-06-06','Georgy','Dredge','M','1992-04-27'),
+(10056,'1961-09-01','Brendon','Bernini','F','1990-02-01'),
+(10057,'1954-05-30','Ebbe','Callaway','F','1992-01-15'),
+(10058,'1954-10-01','Berhard','McFarlin','M','1987-04-13'),
+(10059,'1953-09-19','Alejandro','McAlpine','F','1991-06-26'),
+(10060,'1961-10-15','Breannda','Billingsley','M','1987-11-02'),
+(10061,'1962-10-19','Tse','Herber','M','1985-09-17'),
+(10062,'1961-11-02','Anoosh','Peyn','M','1991-08-30'),
+(10063,'1952-08-06','Gino','Leonhardt','F','1989-04-08'),
+(10064,'1959-04-07','Udi','Jansch','M','1985-11-20'),
+(10065,'1963-04-14','Satosi','Awdeh','M','1988-05-18'),
+(10066,'1952-11-13','Kwee','Schusler','M','1986-02-26'),
+(10067,'1953-01-07','Claudi','Stavenow','M','1987-03-04'),
+(10068,'1962-11-26','Charlene','Brattka','M','1987-08-07'),
+(10069,'1960-09-06','Margareta','Bierman','F','1989-11-05'),
+(10070,'1955-08-20','Reuven','Garigliano','M','1985-10-14'),
+(10071,'1958-01-21','Hisao','Lipner','M','1987-10-01'),
+(10072,'1952-05-15','Hironoby','Sidou','F','1988-07-21'),
+(10073,'1954-02-23','Shir','McClurg','M','1991-12-01'),
+(10074,'1955-08-28','Mokhtar','Bernatsky','F','1990-08-13'),
+(10075,'1960-03-09','Gao','Dolinsky','F','1987-03-19'),
+(10076,'1952-06-13','Erez','Ritzmann','F','1985-07-09'),
+(10077,'1964-04-18','Mona','Azuma','M','1990-03-02'),
+(10078,'1959-12-25','Danel','Mondadori','F','1987-05-26'),
+(10079,'1961-10-05','Kshitij','Gils','F','1986-03-27'),
+(10080,'1957-12-03','Premal','Baek','M','1985-11-19'),
+(10081,'1960-12-17','Zhongwei','Rosen','M','1986-10-30'),
+(10082,'1963-09-09','Parviz','Lortz','M','1990-01-03'),
+(10083,'1959-07-23','Vishv','Zockler','M','1987-03-31'),
+(10084,'1960-05-25','Tuval','Kalloufi','M','1995-12-15'),
+(10085,'1962-11-07','Kenroku','Malabarba','M','1994-04-09'),
+(10086,'1962-11-19','Somnath','Foote','M','1990-02-16'),
+(10087,'1959-07-23','Xinglin','Eugenio','F','1986-09-08'),
+(10088,'1954-02-25','Jungsoon','Syrzycki','F','1988-09-02'),
+(10089,'1963-03-21','Sudharsan','Flasterstein','F','1986-08-12'),
+(10090,'1961-05-30','Kendra','Hofting','M','1986-03-14'),
+(10091,'1955-10-04','Amabile','Gomatam','M','1992-11-18'),
+(10092,'1964-10-18','Valdiodio','Niizuma','F','1989-09-22'),
+(10093,'1964-06-11','Sailaja','Desikan','M','1996-11-05'),
+(10094,'1957-05-25','Arumugam','Ossenbruggen','F','1987-04-18'),
+(10095,'1965-01-03','Hilari','Morton','M','1986-07-15'),
+(10096,'1954-09-16','Jayson','Mandell','M','1990-01-14'),
+(10097,'1952-02-27','Remzi','Waschkowski','M','1990-09-15'),
+(10098,'1961-09-23','Sreekrishna','Servieres','F','1985-05-13'),
+(10099,'1956-05-25','Valter','Sullins','F','1988-10-18'),
+(10100,'1953-04-21','Hironobu','Haraldson','F','1987-09-21'),
+(10101,'1952-04-15','Perla','Heyers','F','1992-12-28'),
+(10102,'1959-11-04','Paraskevi','Luby','F','1994-01-26'),
+(10103,'1953-11-26','Akemi','Birch','M','1986-12-02'),
+(10104,'1961-11-19','Xinyu','Warwick','M','1987-04-16'),
+(10105,'1962-02-05','Hironoby','Piveteau','M','1999-03-23'),
+(10106,'1952-08-29','Eben','Aingworth','M','1990-12-19'),
+(10107,'1956-06-13','Dung','Baca','F','1994-03-22'),
+(10108,'1952-04-07','Lunjin','Giveon','M','1986-10-02'),
+(10109,'1958-11-25','Mariusz','Prampolini','F','1993-06-16'),
+(10110,'1957-03-07','Xuejia','Ullian','F','1986-08-22'),
+(10111,'1963-08-29','Hugo','Rosis','F','1988-06-19'),
+(10112,'1963-08-13','Yuichiro','Swick','F','1985-10-08'),
+(10113,'1963-11-13','Jaewon','Syrzycki','M','1989-12-24'),
+(10114,'1957-02-16','Munir','Demeyer','F','1992-07-17'),
+(10115,'1964-12-25','Chikara','Rissland','M','1986-01-23'),
+(10116,'1955-08-26','Dayanand','Czap','F','1985-05-28'),
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+(10998,'1956-02-17','Marlo','Kandlur','M','1992-06-28'),
+(10999,'1961-12-04','Insup','Benveniste','F','1996-03-05'),
+(11000,'1960-09-12','Alain','Bonifati','M','1988-08-20');
\ No newline at end of file
diff --git a/employee-sample-database/postgres/dataset_small/load_salary1.sql b/employee-sample-database/postgres/dataset_small/load_salary1.sql
new file mode 100644
index 0000000000000000000000000000000000000000..39505060fdf6d15b81a5bf2ddb185ecfbf1c2ba7
--- /dev/null
+++ b/employee-sample-database/postgres/dataset_small/load_salary1.sql
@@ -0,0 +1,9488 @@
+INSERT INTO salary VALUES (10001,60117,'1986-06-26','1987-06-26'),
+(10001,62102,'1987-06-26','1988-06-25'),
+(10001,66074,'1988-06-25','1989-06-25'),
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diff --git a/employee-sample-database/postgres/dataset_small/load_title.sql b/employee-sample-database/postgres/dataset_small/load_title.sql
new file mode 100644
index 0000000000000000000000000000000000000000..b168856f6a80dd5f78e5d261f655f94652c3d677
--- /dev/null
+++ b/employee-sample-database/postgres/dataset_small/load_title.sql
@@ -0,0 +1,1470 @@
+INSERT INTO title VALUES (10001,'Senior Engineer','1986-06-26','9999-01-01'),
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+(10869,'Staff','1997-09-04','1998-10-15'),
+(10870,'Senior Engineer','1988-01-13','9999-01-01'),
+(10871,'Staff','1999-06-16','9999-01-01'),
+(10872,'Senior Staff','1996-09-05','9999-01-01'),
+(10872,'Staff','1990-09-06','1996-09-05'),
+(10873,'Staff','1994-03-23','9999-01-01'),
+(10874,'Engineer','1986-04-22','1992-04-21'),
+(10874,'Senior Engineer','1992-04-21','9999-01-01'),
+(10875,'Senior Engineer','1988-05-19','9999-01-01'),
+(10876,'Staff','1995-01-29','1999-09-14'),
+(10877,'Senior Staff','1986-06-06','9999-01-01'),
+(10878,'Staff','1997-04-25','9999-01-01'),
+(10879,'Senior Engineer','1985-08-15','9999-01-01'),
+(10880,'Engineer','1992-01-11','1997-01-10'),
+(10880,'Senior Engineer','1997-01-10','9999-01-01'),
+(10881,'Engineer','1997-02-18','1998-10-25'),
+(10882,'Engineer','1999-04-29','9999-01-01'),
+(10883,'Engineer','1996-12-28','9999-01-01'),
+(10884,'Engineer','1993-04-15','1998-04-15'),
+(10884,'Senior Engineer','1998-04-15','9999-01-01'),
+(10885,'Senior Staff','1991-08-19','9999-01-01'),
+(10886,'Engineer','1995-03-26','2000-03-25'),
+(10886,'Senior Engineer','2000-03-25','9999-01-01'),
+(10887,'Senior Engineer','1989-03-17','9999-01-01'),
+(10888,'Engineer','1987-06-28','1989-09-11'),
+(10889,'Senior Staff','1987-08-11','9999-01-01'),
+(10890,'Senior Staff','1990-03-10','9999-01-01'),
+(10891,'Assistant Engineer','1995-10-06','2001-10-05'),
+(10891,'Engineer','2001-10-05','9999-01-01'),
+(10892,'Senior Staff','1998-04-11','9999-01-01'),
+(10892,'Staff','1990-04-11','1998-04-11'),
+(10893,'Senior Staff','1995-08-23','9999-01-01'),
+(10893,'Staff','1989-08-23','1995-08-23'),
+(10894,'Engineer','1995-12-05','9999-01-01'),
+(10895,'Staff','1986-04-26','1987-04-13'),
+(10896,'Engineer','1997-11-27','9999-01-01'),
+(10897,'Senior Staff','1995-04-09','9999-01-01'),
+(10897,'Staff','1986-04-09','1995-04-09'),
+(10898,'Senior Engineer','1993-01-08','9999-01-01'),
+(10899,'Senior Staff','1999-04-05','9999-01-01'),
+(10899,'Staff','1990-04-05','1999-04-05'),
+(10900,'Senior Engineer','1995-09-07','9999-01-01'),
+(10901,'Staff','1998-08-05','9999-01-01'),
+(10902,'Assistant Engineer','1990-06-11','1990-09-25'),
+(10903,'Senior Staff','1997-02-14','9999-01-01'),
+(10903,'Staff','1989-02-14','1997-02-14'),
+(10904,'Engineer','1993-04-16','1999-04-16'),
+(10904,'Senior Engineer','1999-04-16','9999-01-01'),
+(10905,'Senior Engineer','1985-02-28','1986-03-07'),
+(10906,'Staff','1998-10-18','9999-01-01'),
+(10907,'Senior Staff','1995-12-26','9999-01-01'),
+(10907,'Staff','1988-12-25','1995-12-26'),
+(10908,'Senior Staff','1993-04-05','9999-01-01'),
+(10908,'Staff','1986-04-05','1993-04-05'),
+(10909,'Engineer','1993-12-26','2000-12-26'),
+(10909,'Senior Engineer','2000-12-26','9999-01-01'),
+(10910,'Senior Staff','1995-09-02','9999-01-01'),
+(10910,'Staff','1989-09-02','1995-09-02'),
+(10911,'Senior Engineer','1988-06-25','9999-01-01'),
+(10912,'Senior Staff','1991-04-23','1999-09-26'),
+(10912,'Staff','1986-04-23','1991-04-23'),
+(10913,'Staff','1998-10-25','9999-01-01'),
+(10914,'Staff','1998-03-24','1998-09-01'),
+(10915,'Senior Engineer','1999-10-28','9999-01-01'),
+(10916,'Engineer','1998-03-12','9999-01-01'),
+(10917,'Engineer','1993-01-30','1998-01-30'),
+(10917,'Senior Engineer','1998-01-30','9999-01-01'),
+(10918,'Staff','1990-01-29','1990-10-10'),
+(10919,'Staff','1999-10-30','9999-01-01'),
+(10920,'Senior Staff','1998-02-23','9999-01-01'),
+(10920,'Staff','1992-02-24','1998-02-23'),
+(10921,'Engineer','1986-07-27','1995-07-27'),
+(10921,'Senior Engineer','1995-07-27','9999-01-01'),
+(10922,'Staff','1996-02-02','1999-01-16'),
+(10923,'Senior Staff','1990-04-09','9999-01-01'),
+(10924,'Senior Staff','1997-08-14','9999-01-01'),
+(10924,'Staff','1988-08-14','1997-08-14'),
+(10925,'Technique Leader','1991-06-11','9999-01-01'),
+(10926,'Engineer','1990-04-30','1995-04-30'),
+(10926,'Senior Engineer','1995-04-30','9999-01-01'),
+(10927,'Senior Staff','1993-09-12','9999-01-01'),
+(10927,'Staff','1986-09-12','1993-09-12'),
+(10928,'Senior Staff','1997-04-06','9999-01-01'),
+(10928,'Staff','1991-04-07','1997-04-06'),
+(10929,'Senior Staff','1998-04-15','9999-01-01'),
+(10929,'Staff','1991-04-15','1998-04-15'),
+(10930,'Engineer','1991-11-22','1998-11-22'),
+(10930,'Senior Engineer','1998-11-22','9999-01-01'),
+(10931,'Technique Leader','1992-06-14','1999-09-19'),
+(10932,'Engineer','1994-09-09','1999-09-09'),
+(10932,'Senior Engineer','1999-09-09','9999-01-01'),
+(10933,'Senior Engineer','1993-08-02','9999-01-01'),
+(10934,'Staff','1995-09-17','9999-01-01'),
+(10935,'Staff','1994-10-17','9999-01-01'),
+(10936,'Senior Staff','1996-05-08','9999-01-01'),
+(10936,'Staff','1990-05-09','1996-05-08'),
+(10937,'Engineer','1988-06-16','1994-06-16'),
+(10937,'Senior Engineer','1994-06-16','9999-01-01'),
+(10938,'Senior Staff','1993-09-24','9999-01-01'),
+(10938,'Staff','1985-09-24','1993-09-24'),
+(10939,'Assistant Engineer','1997-12-11','9999-01-01'),
+(10940,'Engineer','1997-06-30','1999-12-02'),
+(10941,'Senior Staff','2001-08-22','9999-01-01'),
+(10941,'Staff','1993-08-22','2001-08-22'),
+(10942,'Engineer','1996-01-21','9999-01-01'),
+(10943,'Engineer','1993-05-22','1999-05-22'),
+(10943,'Senior Engineer','1999-05-22','9999-01-01'),
+(10944,'Senior Engineer','1995-09-07','9999-01-01'),
+(10945,'Engineer','1993-04-04','2001-04-04'),
+(10945,'Senior Engineer','2001-04-04','9999-01-01'),
+(10946,'Staff','1996-10-26','9999-01-01'),
+(10947,'Senior Staff','2001-11-15','9999-01-01'),
+(10947,'Staff','1995-11-16','2001-11-15'),
+(10948,'Senior Engineer','1996-09-30','1997-06-23'),
+(10949,'Senior Staff','1996-06-28','9999-01-01'),
+(10949,'Staff','1990-06-29','1996-06-28'),
+(10950,'Senior Engineer','1996-06-08','1998-01-13'),
+(10951,'Senior Staff','1994-07-06','9999-01-01'),
+(10951,'Staff','1989-07-06','1994-07-06'),
+(10952,'Senior Engineer','1986-07-22','1988-04-26'),
+(10953,'Senior Staff','1987-02-12','9999-01-01'),
+(10954,'Staff','1995-12-14','9999-01-01'),
+(10955,'Senior Staff','1991-09-27','9999-01-01'),
+(10955,'Staff','1986-09-27','1991-09-27'),
+(10956,'Engineer','1995-12-02','9999-01-01'),
+(10957,'Senior Staff','1990-10-01','9999-01-01'),
+(10958,'Engineer','1988-11-27','1993-11-27'),
+(10958,'Senior Engineer','1993-11-27','9999-01-01'),
+(10959,'Staff','1994-03-01','1996-04-12'),
+(10960,'Senior Staff','1995-07-07','9999-01-01'),
+(10961,'Engineer','1993-11-04','1999-11-04'),
+(10961,'Senior Engineer','1999-11-04','9999-01-01'),
+(10962,'Senior Staff','1986-12-16','9999-01-01'),
+(10963,'Engineer','1988-01-14','1995-04-10'),
+(10964,'Senior Staff','1999-05-05','9999-01-01'),
+(10964,'Staff','1991-05-05','1999-05-05'),
+(10965,'Senior Staff','1986-07-29','9999-01-01'),
+(10966,'Staff','1997-02-05','9999-01-01'),
+(10967,'Engineer','1993-08-01','1998-08-01'),
+(10967,'Senior Engineer','1998-08-01','9999-01-01'),
+(10968,'Senior Staff','1985-08-11','9999-01-01'),
+(10969,'Senior Engineer','1994-05-12','9999-01-01'),
+(10970,'Engineer','1995-11-14','9999-01-01'),
+(10971,'Technique Leader','1991-07-30','1998-10-24'),
+(10972,'Engineer','1987-06-06','1996-06-05'),
+(10972,'Senior Engineer','1996-06-05','9999-01-01'),
+(10973,'Engineer','1989-03-05','1995-03-05'),
+(10973,'Senior Engineer','1995-03-05','9999-01-01'),
+(10974,'Senior Engineer','1999-05-17','9999-01-01'),
+(10975,'Engineer','1992-03-30','1998-03-30'),
+(10975,'Senior Engineer','1998-03-30','9999-01-01'),
+(10976,'Technique Leader','1998-10-31','9999-01-01'),
+(10977,'Senior Staff','1987-08-10','1987-10-01'),
+(10978,'Engineer','1985-05-18','1990-05-18'),
+(10978,'Senior Engineer','1990-05-18','9999-01-01'),
+(10979,'Engineer','1986-01-27','1993-01-27'),
+(10979,'Senior Engineer','1993-01-27','9999-01-01'),
+(10980,'Engineer','1989-01-04','1997-01-04'),
+(10980,'Senior Engineer','1997-01-04','9999-01-01'),
+(10981,'Senior Staff','1992-07-07','9999-01-01'),
+(10982,'Senior Staff','1994-11-11','9999-01-01'),
+(10982,'Staff','1989-11-11','1994-11-11'),
+(10983,'Engineer','1988-02-22','1997-02-21'),
+(10983,'Senior Engineer','1997-02-21','9999-01-01'),
+(10984,'Senior Engineer','1988-07-09','1993-05-14'),
+(10985,'Engineer','1999-02-11','1999-03-10'),
+(10986,'Technique Leader','1985-10-11','9999-01-01'),
+(10987,'Engineer','1997-05-21','9999-01-01'),
+(10988,'Senior Engineer','1985-06-19','9999-01-01'),
+(10989,'Senior Staff','1991-07-20','9999-01-01'),
+(10989,'Staff','1985-07-20','1991-07-20'),
+(10990,'Technique Leader','1990-08-06','9999-01-01'),
+(10991,'Engineer','1999-07-13','9999-01-01'),
+(10992,'Engineer','1997-04-27','9999-01-01'),
+(10993,'Engineer','1992-05-29','2000-05-29'),
+(10993,'Senior Engineer','2000-05-29','9999-01-01'),
+(10994,'Assistant Engineer','1987-06-29','1989-05-24'),
+(10995,'Senior Staff','1997-07-19','9999-01-01'),
+(10996,'Senior Staff','1996-02-07','9999-01-01'),
+(10996,'Staff','1990-02-07','1996-02-07'),
+(10997,'Technique Leader','2000-01-04','9999-01-01'),
+(10998,'Staff','1996-08-07','9999-01-01'),
+(10999,'Technique Leader','1996-03-05','1997-04-19'),
+(11000,'Senior Engineer','1996-01-28','9999-01-01');
\ No newline at end of file
diff --git a/employee-sample-database/sqlite/README.md b/employee-sample-database/sqlite/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..b99ef6eda1b4e3f929e7f231132eb7d25edb2f21
--- /dev/null
+++ b/employee-sample-database/sqlite/README.md
@@ -0,0 +1,34 @@
+# Employee Sample Database for SQLite
+
+This is derived from the [MySQL counterpart](https://github.com/bytebase/employee-sample-database/tree/main/mysql).
+
+## Schema
+
+The schema matches the MySQL and PostgreSQL versions, with appropriate adaptations for SQLite syntax.
+
+## Installation:
+
+1. Download the repository
+2. Change directory to the repository
+3. Change directory to `dataset_small`
+
+Run:
+
+```bash
+cd dataset_small
+```
+
+Then run:
+
+```bash
+sqlite3 employee.db < employee.sql
+```
+
+## Testing the installation
+
+```bash
+# Under 'dataset_small' directory
+sqlite3 employee.db < test_employee_md5.sql
+```
+
+This will verify the integrity of the loaded data by comparing record counts and MD5 checksums against expected values.
\ No newline at end of file
diff --git a/employee-sample-database/sqlite/dataset_small/employee.db b/employee-sample-database/sqlite/dataset_small/employee.db
new file mode 100644
index 0000000000000000000000000000000000000000..2e48728a5bcf87db892948e9886179ca9334b3f0
--- /dev/null
+++ b/employee-sample-database/sqlite/dataset_small/employee.db
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eb3b8d727ac7e37d7563277d556320bd3304dd132a3cb20d03e0fae53e43c3a2
+size 868352
diff --git a/employee-sample-database/sqlite/dataset_small/employee.sql b/employee-sample-database/sqlite/dataset_small/employee.sql
new file mode 100644
index 0000000000000000000000000000000000000000..b7a52b7c9d96a5dcb19b2c369f6cb90bc0917869
--- /dev/null
+++ b/employee-sample-database/sqlite/dataset_small/employee.sql
@@ -0,0 +1,117 @@
+--  Sample employee database 
+--  See changelog table for details
+--  Copyright (C) 2007,2008, MySQL AB
+--  
+--  Original data created by Fusheng Wang and Carlo Zaniolo
+--  http://www.cs.aau.dk/TimeCenter/software.htm
+--  http://www.cs.aau.dk/TimeCenter/Data/employeeTemporalDataSet.zip
+-- 
+--  Current schema by Giuseppe Maxia 
+--  Data conversion from XML to relational by Patrick Crews
+--  SQLite adaptation by Claude Code
+-- 
+-- This work is licensed under the 
+-- Creative Commons Attribution-Share Alike 3.0 Unported License. 
+-- To view a copy of this license, visit 
+-- http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to 
+-- Creative Commons, 171 Second Street, Suite 300, San Francisco, 
+-- California, 94105, USA.
+-- 
+--  DISCLAIMER
+--  To the best of our knowledge, this data is fabricated, and
+--  it does not correspond to real people. 
+--  Any similarity to existing people is purely coincidental.
+-- 
+
+PRAGMA foreign_keys = ON;
+
+SELECT 'CREATING DATABASE STRUCTURE' as 'INFO';
+
+DROP TABLE IF EXISTS dept_emp;
+DROP TABLE IF EXISTS dept_manager;
+DROP TABLE IF EXISTS title;
+DROP TABLE IF EXISTS salary;
+DROP TABLE IF EXISTS employee;
+DROP TABLE IF EXISTS department;
+DROP VIEW IF EXISTS dept_emp_latest_date;
+DROP VIEW IF EXISTS current_dept_emp;
+
+CREATE TABLE employee (
+    emp_no      INTEGER         NOT NULL,
+    birth_date  DATE            NOT NULL,
+    first_name  TEXT            NOT NULL,
+    last_name   TEXT            NOT NULL,
+    gender      TEXT            NOT NULL CHECK (gender IN ('M','F')),
+    hire_date   DATE            NOT NULL,
+    PRIMARY KEY (emp_no)
+);
+
+CREATE TABLE department (
+    dept_no     TEXT            NOT NULL,
+    dept_name   TEXT            NOT NULL,
+    PRIMARY KEY (dept_no),
+    UNIQUE      (dept_name)
+);
+
+CREATE TABLE dept_manager (
+   emp_no       INTEGER         NOT NULL,
+   dept_no      TEXT            NOT NULL,
+   from_date    DATE            NOT NULL,
+   to_date      DATE            NOT NULL,
+   FOREIGN KEY (emp_no)  REFERENCES employee (emp_no)    ON DELETE CASCADE,
+   FOREIGN KEY (dept_no) REFERENCES department (dept_no) ON DELETE CASCADE,
+   PRIMARY KEY (emp_no,dept_no)
+); 
+
+CREATE TABLE dept_emp (
+    emp_no      INTEGER         NOT NULL,
+    dept_no     TEXT            NOT NULL,
+    from_date   DATE            NOT NULL,
+    to_date     DATE            NOT NULL,
+    FOREIGN KEY (emp_no)  REFERENCES employee (emp_no)   ON DELETE CASCADE,
+    FOREIGN KEY (dept_no) REFERENCES department (dept_no) ON DELETE CASCADE,
+    PRIMARY KEY (emp_no,dept_no)
+);
+
+CREATE TABLE title (
+    emp_no      INTEGER         NOT NULL,
+    title       TEXT            NOT NULL,
+    from_date   DATE            NOT NULL,
+    to_date     DATE,
+    FOREIGN KEY (emp_no) REFERENCES employee (emp_no) ON DELETE CASCADE,
+    PRIMARY KEY (emp_no,title,from_date)
+);
+
+CREATE TABLE salary (
+    emp_no      INTEGER         NOT NULL,
+    amount      INTEGER         NOT NULL,
+    from_date   DATE            NOT NULL,
+    to_date     DATE            NOT NULL,
+    FOREIGN KEY (emp_no) REFERENCES employee (emp_no) ON DELETE CASCADE,
+    PRIMARY KEY (emp_no,from_date)
+);
+
+CREATE VIEW dept_emp_latest_date AS
+    SELECT emp_no, MAX(from_date) AS from_date, MAX(to_date) AS to_date
+    FROM dept_emp
+    GROUP BY emp_no;
+
+-- shows only the current department for each employee
+CREATE VIEW current_dept_emp AS
+    SELECT l.emp_no, dept_no, l.from_date, l.to_date
+    FROM dept_emp d
+        INNER JOIN dept_emp_latest_date l
+        ON d.emp_no=l.emp_no AND d.from_date=l.from_date AND l.to_date = d.to_date;
+
+SELECT 'LOADING department' as 'INFO';
+.read load_department.sql
+SELECT 'LOADING employee' as 'INFO';
+.read load_employee.sql
+SELECT 'LOADING dept_emp' as 'INFO';
+.read load_dept_emp.sql
+SELECT 'LOADING dept_manager' as 'INFO';
+.read load_dept_manager.sql
+SELECT 'LOADING title' as 'INFO';
+.read load_title.sql
+SELECT 'LOADING salary' as 'INFO';
+.read load_salary1.sql
\ No newline at end of file
diff --git a/employee-sample-database/sqlite/dataset_small/load_department.sql b/employee-sample-database/sqlite/dataset_small/load_department.sql
new file mode 100644
index 0000000000000000000000000000000000000000..76ed6828660886d60f0263039a0c6b29186355bb
--- /dev/null
+++ b/employee-sample-database/sqlite/dataset_small/load_department.sql
@@ -0,0 +1,10 @@
+INSERT INTO department VALUES 
+('d001','Marketing'),
+('d002','Finance'),
+('d003','Human Resources'),
+('d004','Production'),
+('d005','Development'),
+('d006','Quality Management'),
+('d007','Sales'),
+('d008','Research'),
+('d009','Customer Service');
\ No newline at end of file
diff --git a/employee-sample-database/sqlite/dataset_small/load_dept_emp.sql b/employee-sample-database/sqlite/dataset_small/load_dept_emp.sql
new file mode 100644
index 0000000000000000000000000000000000000000..e513a5b78c7e2a89e3a8a2d58d40e37b90b66ea2
--- /dev/null
+++ b/employee-sample-database/sqlite/dataset_small/load_dept_emp.sql
@@ -0,0 +1,1103 @@
+INSERT INTO dept_emp VALUES (10001,'d005','1986-06-26','9999-01-01'),
+(10002,'d007','1996-08-03','9999-01-01'),
+(10003,'d004','1995-12-03','9999-01-01'),
+(10004,'d004','1986-12-01','9999-01-01'),
+(10005,'d003','1989-09-12','9999-01-01'),
+(10006,'d005','1990-08-05','9999-01-01'),
+(10007,'d008','1989-02-10','9999-01-01'),
+(10008,'d005','1998-03-11','2000-07-31'),
+(10009,'d006','1985-02-18','9999-01-01'),
+(10010,'d004','1996-11-24','2000-06-26'),
+(10010,'d006','2000-06-26','9999-01-01'),
+(10011,'d009','1990-01-22','1996-11-09'),
+(10012,'d005','1992-12-18','9999-01-01'),
+(10013,'d003','1985-10-20','9999-01-01'),
+(10014,'d005','1993-12-29','9999-01-01'),
+(10015,'d008','1992-09-19','1993-08-22'),
+(10016,'d007','1998-02-11','9999-01-01'),
+(10017,'d001','1993-08-03','9999-01-01'),
+(10018,'d004','1992-07-29','9999-01-01'),
+(10018,'d005','1987-04-03','1992-07-29'),
+(10019,'d008','1999-04-30','9999-01-01'),
+(10020,'d004','1997-12-30','9999-01-01'),
+(10021,'d005','1988-02-10','2002-07-15'),
+(10022,'d005','1999-09-03','9999-01-01'),
+(10023,'d005','1999-09-27','9999-01-01'),
+(10024,'d004','1998-06-14','9999-01-01'),
+(10025,'d005','1987-08-17','1997-10-15'),
+(10026,'d004','1995-03-20','9999-01-01'),
+(10027,'d005','1995-04-02','9999-01-01'),
+(10028,'d005','1991-10-22','1998-04-06'),
+(10029,'d004','1991-09-18','1999-07-08'),
+(10029,'d006','1999-07-08','9999-01-01'),
+(10030,'d004','1994-02-17','9999-01-01'),
+(10031,'d005','1991-09-01','9999-01-01'),
+(10032,'d004','1990-06-20','9999-01-01'),
+(10033,'d006','1987-03-18','1993-03-24'),
+(10034,'d007','1995-04-12','1999-10-31'),
+(10035,'d004','1988-09-05','9999-01-01'),
+(10036,'d003','1992-04-28','9999-01-01'),
+(10037,'d005','1990-12-05','9999-01-01'),
+(10038,'d009','1989-09-20','9999-01-01'),
+(10039,'d003','1988-01-19','9999-01-01'),
+(10040,'d005','1993-02-14','2002-01-22'),
+(10040,'d008','2002-01-22','9999-01-01'),
+(10041,'d007','1989-11-12','9999-01-01'),
+(10042,'d002','1993-03-21','2000-08-10'),
+(10043,'d005','1990-10-20','9999-01-01'),
+(10044,'d004','1994-05-21','9999-01-01'),
+(10045,'d004','1996-11-16','9999-01-01'),
+(10046,'d008','1992-06-20','9999-01-01'),
+(10047,'d004','1989-03-31','9999-01-01'),
+(10048,'d005','1985-02-24','1987-01-27'),
+(10049,'d009','1992-05-04','9999-01-01'),
+(10050,'d002','1990-12-25','1992-11-05'),
+(10050,'d007','1992-11-05','9999-01-01'),
+(10051,'d004','1992-10-15','9999-01-01'),
+(10052,'d008','1997-01-31','9999-01-01'),
+(10053,'d007','1994-11-13','9999-01-01'),
+(10054,'d003','1995-07-29','9999-01-01'),
+(10055,'d001','1992-04-27','1995-07-22'),
+(10056,'d005','1990-02-01','9999-01-01'),
+(10057,'d005','1992-01-15','9999-01-01'),
+(10058,'d001','1988-04-25','9999-01-01'),
+(10059,'d002','1991-06-26','9999-01-01'),
+(10060,'d007','1989-05-28','1992-11-11'),
+(10060,'d009','1992-11-11','9999-01-01'),
+(10061,'d007','1989-12-02','9999-01-01'),
+(10062,'d005','1991-08-30','9999-01-01'),
+(10063,'d004','1989-04-08','9999-01-01'),
+(10064,'d008','1985-11-20','1992-03-02'),
+(10065,'d005','1998-05-24','9999-01-01'),
+(10066,'d005','1986-02-26','9999-01-01'),
+(10067,'d006','1987-03-04','9999-01-01'),
+(10068,'d007','1987-08-07','9999-01-01'),
+(10069,'d004','1992-06-14','9999-01-01'),
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+(10774,'d004','1990-01-26','9999-01-01'),
+(10775,'d004','1994-12-30','1999-03-17'),
+(10775,'d009','1999-03-17','9999-01-01'),
+(10776,'d005','1986-03-16','9999-01-01'),
+(10777,'d004','1990-03-17','2001-03-27'),
+(10778,'d001','1986-07-04','9999-01-01'),
+(10779,'d009','1988-01-11','9999-01-01'),
+(10780,'d004','1994-08-01','1996-08-18'),
+(10781,'d009','1991-02-04','9999-01-01'),
+(10782,'d008','1997-07-06','2002-03-30'),
+(10783,'d003','1994-05-15','9999-01-01'),
+(10784,'d005','1998-10-14','9999-01-01'),
+(10785,'d006','1994-11-11','9999-01-01'),
+(10786,'d004','1997-06-06','2001-12-10'),
+(10786,'d006','2001-12-10','9999-01-01'),
+(10787,'d001','1991-07-19','2002-07-27'),
+(10788,'d007','1998-05-29','9999-01-01'),
+(10789,'d009','1998-01-26','9999-01-01'),
+(10790,'d004','1999-03-02','9999-01-01'),
+(10791,'d005','1988-07-30','9999-01-01'),
+(10792,'d005','1985-08-10','1993-12-20'),
+(10793,'d005','1990-09-18','9999-01-01'),
+(10794,'d008','1994-05-24','9999-01-01'),
+(10795,'d007','1993-05-03','9999-01-01'),
+(10796,'d006','1990-11-08','9999-01-01'),
+(10797,'d004','1997-06-02','9999-01-01'),
+(10797,'d005','1988-08-21','1997-06-02'),
+(10798,'d004','1996-09-21','9999-01-01'),
+(10799,'d005','1989-03-01','9999-01-01'),
+(10800,'d004','1993-05-20','9999-01-01'),
+(10801,'d004','1998-04-07','2001-01-15'),
+(10802,'d004','1985-08-24','9999-01-01'),
+(10803,'d006','1988-04-20','2002-04-02'),
+(10804,'d007','1997-06-09','9999-01-01'),
+(10805,'d008','1987-05-26','9999-01-01'),
+(10806,'d002','1993-04-25','1994-04-08'),
+(10806,'d003','1994-04-08','9999-01-01'),
+(10807,'d007','1999-10-19','9999-01-01'),
+(10808,'d005','1987-03-07','9999-01-01'),
+(10809,'d005','1988-02-25','9999-01-01'),
+(10810,'d007','1994-09-07','9999-01-01'),
+(10811,'d004','1997-07-08','9999-01-01'),
+(10812,'d004','1996-09-20','1997-10-06'),
+(10813,'d007','1990-04-27','9999-01-01'),
+(10814,'d007','1993-07-17','9999-01-01'),
+(10815,'d003','1994-04-19','9999-01-01'),
+(10816,'d009','1991-05-22','9999-01-01'),
+(10817,'d007','1990-12-26','2000-01-24'),
+(10817,'d009','2000-01-24','9999-01-01'),
+(10818,'d003','1998-04-09','9999-01-01'),
+(10819,'d004','1986-08-02','1999-12-28'),
+(10820,'d001','1992-06-26','9999-01-01'),
+(10821,'d005','1988-10-06','1990-04-17'),
+(10822,'d005','1986-01-11','9999-01-01'),
+(10823,'d003','1999-08-19','9999-01-01'),
+(10824,'d001','1995-08-01','9999-01-01'),
+(10825,'d004','2000-06-03','9999-01-01'),
+(10825,'d005','1993-12-04','2000-06-03'),
+(10826,'d005','1993-01-22','9999-01-01'),
+(10827,'d002','1996-11-07','9999-01-01'),
+(10828,'d007','1986-02-14','1990-03-30'),
+(10829,'d003','1993-07-21','1993-11-29'),
+(10830,'d001','1991-08-24','9999-01-01'),
+(10831,'d005','1998-02-22','9999-01-01'),
+(10832,'d007','1986-10-11','9999-01-01'),
+(10833,'d006','1998-12-23','9999-01-01'),
+(10834,'d004','1990-11-24','9999-01-01'),
+(10835,'d004','1995-08-22','9999-01-01'),
+(10836,'d007','1987-08-10','1991-07-01'),
+(10836,'d009','1991-07-01','9999-01-01'),
+(10837,'d007','1986-01-14','2002-02-22'),
+(10838,'d006','1994-04-26','9999-01-01'),
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+(10840,'d004','1993-06-04','9999-01-01'),
+(10841,'d009','1992-01-22','9999-01-01'),
+(10842,'d005','1986-07-14','1998-06-27'),
+(10843,'d005','1990-04-26','9999-01-01'),
+(10844,'d005','1997-04-25','9999-01-01'),
+(10845,'d005','1997-01-25','9999-01-01'),
+(10846,'d005','1995-01-17','1997-04-03'),
+(10846,'d008','1997-04-03','9999-01-01'),
+(10847,'d008','1986-07-25','9999-01-01'),
+(10848,'d004','1985-04-20','9999-01-01'),
+(10849,'d005','1995-06-25','9999-01-01'),
+(10850,'d002','1990-04-22','1995-05-11'),
+(10851,'d008','1994-11-21','9999-01-01'),
+(10852,'d004','1999-08-21','9999-01-01'),
+(10853,'d006','1993-05-03','9999-01-01'),
+(10854,'d005','1998-07-13','9999-01-01'),
+(10855,'d002','1993-03-29','1997-03-02'),
+(10855,'d007','1997-03-02','9999-01-01'),
+(10856,'d009','1986-12-31','1990-06-18'),
+(10857,'d007','1990-04-04','9999-01-01'),
+(10858,'d005','1997-01-01','9999-01-01'),
+(10859,'d001','1996-05-21','9999-01-01'),
+(10860,'d006','1998-01-22','9999-01-01'),
+(10861,'d005','1989-06-10','1992-04-12'),
+(10862,'d007','1985-07-23','9999-01-01'),
+(10863,'d004','1985-12-25','9999-01-01'),
+(10864,'d007','1986-09-25','9999-01-01'),
+(10865,'d007','1993-05-29','1993-07-03'),
+(10865,'d009','1993-07-03','1993-09-19'),
+(10866,'d002','1990-12-27','9999-01-01'),
+(10867,'d004','1997-03-23','2002-03-22'),
+(10868,'d004','1990-03-20','9999-01-01'),
+(10869,'d009','1997-09-04','1998-10-15'),
+(10870,'d004','1988-01-13','9999-01-01'),
+(10871,'d009','1999-06-16','9999-01-01'),
+(10872,'d002','1990-09-06','9999-01-01'),
+(10873,'d002','1994-03-23','9999-01-01'),
+(10874,'d004','1986-04-22','1997-12-28'),
+(10874,'d006','1997-12-28','9999-01-01'),
+(10875,'d006','1988-05-19','9999-01-01'),
+(10876,'d007','1995-01-29','1999-09-14'),
+(10877,'d001','1986-06-06','9999-01-01'),
+(10878,'d007','1997-04-25','9999-01-01'),
+(10879,'d005','1985-08-15','9999-01-01'),
+(10880,'d004','1992-01-11','9999-01-01'),
+(10881,'d004','1997-02-18','1998-10-25'),
+(10882,'d005','1999-04-29','9999-01-01'),
+(10883,'d004','1996-12-28','9999-01-01'),
+(10884,'d005','1993-04-15','9999-01-01'),
+(10885,'d001','1997-09-29','9999-01-01'),
+(10885,'d007','1991-08-19','1997-09-29'),
+(10886,'d005','1995-03-26','9999-01-01'),
+(10887,'d005','1989-03-17','9999-01-01'),
+(10888,'d005','1987-06-28','1989-09-11'),
+(10889,'d007','1987-08-11','9999-01-01'),
+(10890,'d007','1990-03-10','9999-01-01'),
+(10891,'d005','1995-10-06','9999-01-01'),
+(10892,'d003','1990-04-11','9999-01-01'),
+(10893,'d001','1989-08-23','9999-01-01'),
+(10894,'d004','1995-12-05','9999-01-01'),
+(10895,'d007','1986-04-26','1986-12-25'),
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+(10896,'d004','1997-11-27','9999-01-01'),
+(10897,'d007','1986-04-09','9999-01-01'),
+(10898,'d004','1993-01-08','9999-01-01'),
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+(10900,'d005','1995-09-07','9999-01-01'),
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+(10902,'d005','1990-06-11','1990-09-25'),
+(10903,'d009','1989-02-14','9999-01-01'),
+(10904,'d005','1993-04-16','9999-01-01'),
+(10905,'d004','1985-02-28','1986-03-07'),
+(10906,'d002','1998-10-18','2001-04-25'),
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+(10907,'d003','1988-12-25','9999-01-01'),
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+(10911,'d004','1988-06-25','9999-01-01'),
+(10912,'d002','1986-04-23','1999-09-26'),
+(10913,'d002','1998-10-25','9999-01-01'),
+(10914,'d007','1998-03-24','1998-09-01'),
+(10915,'d004','1999-10-28','9999-01-01'),
+(10916,'d004','1998-10-02','9999-01-01'),
+(10916,'d005','1998-03-12','1998-10-02'),
+(10917,'d005','1993-01-30','9999-01-01'),
+(10918,'d002','1990-01-29','1990-10-10'),
+(10919,'d001','1999-10-30','9999-01-01'),
+(10920,'d003','1992-02-24','9999-01-01'),
+(10921,'d004','1986-07-27','9999-01-01'),
+(10922,'d007','1996-02-02','1999-01-16'),
+(10923,'d007','1990-04-09','9999-01-01'),
+(10924,'d007','1988-08-14','9999-01-01'),
+(10925,'d004','1991-06-11','9999-01-01'),
+(10926,'d004','1990-04-30','1992-11-04'),
+(10926,'d009','1992-11-04','9999-01-01'),
+(10927,'d001','1986-09-12','9999-01-01'),
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+(10929,'d001','1991-04-15','9999-01-01'),
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+(10931,'d004','1992-06-14','1999-09-19'),
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+(10933,'d004','1993-08-02','9999-01-01'),
+(10934,'d002','1995-09-17','9999-01-01'),
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+(10936,'d003','1990-05-09','9999-01-01'),
+(10937,'d004','1991-05-03','9999-01-01'),
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+(10939,'d006','1997-12-11','9999-01-01'),
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+(10944,'d004','1995-09-07','9999-01-01'),
+(10945,'d004','1993-04-04','9999-01-01'),
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+(10947,'d009','1995-11-16','9999-01-01'),
+(10948,'d005','1996-09-30','1997-04-29'),
+(10948,'d008','1997-04-29','1997-06-23'),
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+(10950,'d004','1996-06-08','1998-01-13'),
+(10951,'d002','1989-07-06','9999-01-01'),
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+(10955,'d007','1986-09-27','9999-01-01'),
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+(10956,'d005','1995-12-02','1997-02-10'),
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+(10965,'d007','1986-07-29','9999-01-01'),
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+(10967,'d005','1993-08-01','9999-01-01'),
+(10968,'d007','1985-08-11','9999-01-01'),
+(10969,'d005','1994-05-12','9999-01-01'),
+(10970,'d004','1995-11-14','9999-01-01'),
+(10971,'d005','1991-07-30','1998-10-24'),
+(10972,'d005','1987-06-06','9999-01-01'),
+(10973,'d005','1989-03-05','9999-01-01'),
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+(10975,'d004','1992-03-30','1997-01-14'),
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+(10976,'d004','1998-10-31','9999-01-01'),
+(10977,'d007','1987-08-10','1987-10-01'),
+(10978,'d004','1985-05-18','9999-01-01'),
+(10979,'d004','1986-01-27','9999-01-01'),
+(10980,'d005','1989-01-04','9999-01-01'),
+(10981,'d007','1992-07-07','9999-01-01'),
+(10982,'d008','1989-11-11','9999-01-01'),
+(10983,'d004','1988-02-22','9999-01-01'),
+(10984,'d004','1988-07-09','1993-05-14'),
+(10985,'d005','1999-02-11','1999-02-27'),
+(10985,'d008','1999-02-27','1999-03-10'),
+(10986,'d005','1985-10-11','9999-01-01'),
+(10987,'d005','1997-05-21','9999-01-01'),
+(10988,'d005','1985-06-19','9999-01-01'),
+(10989,'d002','1985-07-20','9999-01-01'),
+(10990,'d005','1990-08-06','9999-01-01'),
+(10991,'d005','1999-07-13','9999-01-01'),
+(10992,'d005','1997-04-27','9999-01-01'),
+(10993,'d006','1992-05-29','9999-01-01'),
+(10994,'d004','1987-11-13','1989-05-24'),
+(10994,'d005','1987-06-29','1987-11-13'),
+(10995,'d001','1997-07-19','9999-01-01'),
+(10996,'d007','1990-02-07','9999-01-01'),
+(10997,'d004','2000-01-04','9999-01-01'),
+(10998,'d007','1996-08-07','9999-01-01'),
+(10999,'d005','1996-03-05','1997-04-19'),
+(11000,'d005','1996-01-28','9999-01-01');
\ No newline at end of file
diff --git a/employee-sample-database/sqlite/dataset_small/load_dept_manager.sql b/employee-sample-database/sqlite/dataset_small/load_dept_manager.sql
new file mode 100644
index 0000000000000000000000000000000000000000..49084586da3f69c167bf66b7c845100c8ccf2af8
--- /dev/null
+++ b/employee-sample-database/sqlite/dataset_small/load_dept_manager.sql
@@ -0,0 +1,17 @@
+INSERT INTO dept_manager VALUES 
+(10002,'d001','1985-01-01','1991-10-01'),
+(10039,'d001','1991-10-01','9999-01-01'),
+(10085,'d002','1985-01-01','1989-12-17'),
+(10114,'d002','1989-12-17','9999-01-01'),
+(10183,'d003','1985-01-01','1992-03-21'),
+(10228,'d003','1992-03-21','9999-01-01'),
+(10303,'d004','1985-01-01','1988-09-09'),
+(10344,'d004','1988-09-09','1992-08-02'),
+(10386,'d004','1992-08-02','1996-08-30'),
+(10420,'d004','1996-08-30','9999-01-01'),
+(10511,'d005','1985-01-01','1992-04-25'),
+(10567,'d005','1992-04-25','9999-01-01'),
+(10725,'d006','1985-01-01','1989-05-06'),
+(10765,'d006','1989-05-06','1991-09-12'),
+(10800,'d006','1991-09-12','1994-06-28'),
+(10854,'d006','1994-06-28','9999-01-01');
\ No newline at end of file
diff --git a/employee-sample-database/sqlite/dataset_small/load_employee.sql b/employee-sample-database/sqlite/dataset_small/load_employee.sql
new file mode 100644
index 0000000000000000000000000000000000000000..c7276dbf7a59aa9990b5d6aa46dcb4a4ecbbdcde
--- /dev/null
+++ b/employee-sample-database/sqlite/dataset_small/load_employee.sql
@@ -0,0 +1,1000 @@
+INSERT INTO employee VALUES (10001,'1953-09-02','Georgi','Facello','M','1986-06-26'),
+(10002,'1964-06-02','Bezalel','Simmel','F','1985-11-21'),
+(10003,'1959-12-03','Parto','Bamford','M','1986-08-28'),
+(10004,'1954-05-01','Chirstian','Koblick','M','1986-12-01'),
+(10005,'1955-01-21','Kyoichi','Maliniak','M','1989-09-12'),
+(10006,'1953-04-20','Anneke','Preusig','F','1989-06-02'),
+(10007,'1957-05-23','Tzvetan','Zielinski','F','1989-02-10'),
+(10008,'1958-02-19','Saniya','Kalloufi','M','1994-09-15'),
+(10009,'1952-04-19','Sumant','Peac','F','1985-02-18'),
+(10010,'1963-06-01','Duangkaew','Piveteau','F','1989-08-24'),
+(10011,'1953-11-07','Mary','Sluis','F','1990-01-22'),
+(10012,'1960-10-04','Patricio','Bridgland','M','1992-12-18'),
+(10013,'1963-06-07','Eberhardt','Terkki','M','1985-10-20'),
+(10014,'1956-02-12','Berni','Genin','M','1987-03-11'),
+(10015,'1959-08-19','Guoxiang','Nooteboom','M','1987-07-02'),
+(10016,'1961-05-02','Kazuhito','Cappelletti','M','1995-01-27'),
+(10017,'1958-07-06','Cristinel','Bouloucos','F','1993-08-03'),
+(10018,'1954-06-19','Kazuhide','Peha','F','1987-04-03'),
+(10019,'1953-01-23','Lillian','Haddadi','M','1999-04-30'),
+(10020,'1952-12-24','Mayuko','Warwick','M','1991-01-26'),
+(10021,'1960-02-20','Ramzi','Erde','M','1988-02-10'),
+(10022,'1952-07-08','Shahaf','Famili','M','1995-08-22'),
+(10023,'1953-09-29','Bojan','Montemayor','F','1989-12-17'),
+(10024,'1958-09-05','Suzette','Pettey','F','1997-05-19'),
+(10025,'1958-10-31','Prasadram','Heyers','M','1987-08-17'),
+(10026,'1953-04-03','Yongqiao','Berztiss','M','1995-03-20'),
+(10027,'1962-07-10','Divier','Reistad','F','1989-07-07'),
+(10028,'1963-11-26','Domenick','Tempesti','M','1991-10-22'),
+(10029,'1956-12-13','Otmar','Herbst','M','1985-11-20'),
+(10030,'1958-07-14','Elvis','Demeyer','M','1994-02-17'),
+(10031,'1959-01-27','Karsten','Joslin','M','1991-09-01'),
+(10032,'1960-08-09','Jeong','Reistad','F','1990-06-20'),
+(10033,'1956-11-14','Arif','Merlo','M','1987-03-18'),
+(10034,'1962-12-29','Bader','Swan','M','1988-09-21'),
+(10035,'1953-02-08','Alain','Chappelet','M','1988-09-05'),
+(10036,'1959-08-10','Adamantios','Portugali','M','1992-01-03'),
+(10037,'1963-07-22','Pradeep','Makrucki','M','1990-12-05'),
+(10038,'1960-07-20','Huan','Lortz','M','1989-09-20'),
+(10039,'1959-10-01','Alejandro','Brender','M','1988-01-19'),
+(10040,'1959-09-13','Weiyi','Meriste','F','1993-02-14'),
+(10041,'1959-08-27','Uri','Lenart','F','1989-11-12'),
+(10042,'1956-02-26','Magy','Stamatiou','F','1993-03-21'),
+(10043,'1960-09-19','Yishay','Tzvieli','M','1990-10-20'),
+(10044,'1961-09-21','Mingsen','Casley','F','1994-05-21'),
+(10045,'1957-08-14','Moss','Shanbhogue','M','1989-09-02'),
+(10046,'1960-07-23','Lucien','Rosenbaum','M','1992-06-20'),
+(10047,'1952-06-29','Zvonko','Nyanchama','M','1989-03-31'),
+(10048,'1963-07-11','Florian','Syrotiuk','M','1985-02-24'),
+(10049,'1961-04-24','Basil','Tramer','F','1992-05-04'),
+(10050,'1958-05-21','Yinghua','Dredge','M','1990-12-25'),
+(10051,'1953-07-28','Hidefumi','Caine','M','1992-10-15'),
+(10052,'1961-02-26','Heping','Nitsch','M','1988-05-21'),
+(10053,'1954-09-13','Sanjiv','Zschoche','F','1986-02-04'),
+(10054,'1957-04-04','Mayumi','Schueller','M','1995-03-13'),
+(10055,'1956-06-06','Georgy','Dredge','M','1992-04-27'),
+(10056,'1961-09-01','Brendon','Bernini','F','1990-02-01'),
+(10057,'1954-05-30','Ebbe','Callaway','F','1992-01-15'),
+(10058,'1954-10-01','Berhard','McFarlin','M','1987-04-13'),
+(10059,'1953-09-19','Alejandro','McAlpine','F','1991-06-26'),
+(10060,'1961-10-15','Breannda','Billingsley','M','1987-11-02'),
+(10061,'1962-10-19','Tse','Herber','M','1985-09-17'),
+(10062,'1961-11-02','Anoosh','Peyn','M','1991-08-30'),
+(10063,'1952-08-06','Gino','Leonhardt','F','1989-04-08'),
+(10064,'1959-04-07','Udi','Jansch','M','1985-11-20'),
+(10065,'1963-04-14','Satosi','Awdeh','M','1988-05-18'),
+(10066,'1952-11-13','Kwee','Schusler','M','1986-02-26'),
+(10067,'1953-01-07','Claudi','Stavenow','M','1987-03-04'),
+(10068,'1962-11-26','Charlene','Brattka','M','1987-08-07'),
+(10069,'1960-09-06','Margareta','Bierman','F','1989-11-05'),
+(10070,'1955-08-20','Reuven','Garigliano','M','1985-10-14'),
+(10071,'1958-01-21','Hisao','Lipner','M','1987-10-01'),
+(10072,'1952-05-15','Hironoby','Sidou','F','1988-07-21'),
+(10073,'1954-02-23','Shir','McClurg','M','1991-12-01'),
+(10074,'1955-08-28','Mokhtar','Bernatsky','F','1990-08-13'),
+(10075,'1960-03-09','Gao','Dolinsky','F','1987-03-19'),
+(10076,'1952-06-13','Erez','Ritzmann','F','1985-07-09'),
+(10077,'1964-04-18','Mona','Azuma','M','1990-03-02'),
+(10078,'1959-12-25','Danel','Mondadori','F','1987-05-26'),
+(10079,'1961-10-05','Kshitij','Gils','F','1986-03-27'),
+(10080,'1957-12-03','Premal','Baek','M','1985-11-19'),
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+(10962,'1956-08-23','Naraig','Kugler','F','1986-12-16'),
+(10963,'1957-04-15','Hatsukazu','Swist','M','1988-01-14'),
+(10964,'1962-04-02','Takushi','Bolsens','F','1990-10-11'),
+(10965,'1955-07-10','Eckart','Braunschweig','M','1986-07-29'),
+(10966,'1962-07-04','Danel','Pardalos','M','1989-11-07'),
+(10967,'1962-10-01','Eran','Adachi','M','1993-08-01'),
+(10968,'1961-03-19','Lakshmi','Kolinko','M','1985-08-11'),
+(10969,'1954-09-05','Premal','Noriega','M','1988-08-22'),
+(10970,'1962-08-24','Gen','Tiemann','F','1994-09-16'),
+(10971,'1955-02-08','Isamu','Vadhan','M','1991-07-30'),
+(10972,'1961-10-05','Fumiko','Asser','F','1987-06-06'),
+(10973,'1961-10-02','Valdiodio','Raczkowsky','M','1989-03-05'),
+(10974,'1952-03-07','Muneo','Papsdorf','M','1985-11-25'),
+(10975,'1962-07-27','Peternela','Birnbaum','F','1986-10-31'),
+(10976,'1963-01-27','Hitomi','Valette','F','1992-12-06'),
+(10977,'1957-03-19','Toong','Murrill','M','1987-08-10'),
+(10978,'1962-09-24','Huiqun','Bellone','M','1985-05-18'),
+(10979,'1959-09-11','Elrique','Raney','F','1986-01-27'),
+(10980,'1963-11-10','Filipp','Munawer','M','1989-01-04'),
+(10981,'1956-07-22','Sugwoo','Dulin','F','1989-01-03'),
+(10982,'1964-02-27','Yishay','Decleir','M','1989-11-11'),
+(10983,'1961-08-09','Somnath','Parascandalo','F','1988-02-22'),
+(10984,'1962-11-10','Pranjal','Billawala','F','1988-07-09'),
+(10985,'1952-05-31','Chiradeep','Furedi','F','1990-12-10'),
+(10986,'1954-08-11','Masako','Schiper','M','1985-10-11'),
+(10987,'1952-11-26','Flemming','Baezner','M','1995-12-19'),
+(10988,'1955-06-24','Chinya','Deyuan','M','1985-06-19'),
+(10989,'1954-09-08','Sumali','Candan','F','1985-07-20'),
+(10990,'1954-10-22','Shin','Foote','M','1988-07-06'),
+(10991,'1957-05-22','Munehiro','Aingworth','M','1993-05-21'),
+(10992,'1958-11-26','Zhensheng','Juneja','M','1988-08-01'),
+(10993,'1958-11-17','Arve','Erde','F','1992-05-29'),
+(10994,'1961-06-17','Divine','Aingworth','M','1987-06-29'),
+(10995,'1963-02-26','Chenye','Hertweck','F','1997-07-19'),
+(10996,'1955-02-01','Matk','Rahimi','F','1990-02-07'),
+(10997,'1959-08-30','Alselm','Parascandalo','F','1994-05-06'),
+(10998,'1956-02-17','Marlo','Kandlur','M','1992-06-28'),
+(10999,'1961-12-04','Insup','Benveniste','F','1996-03-05'),
+(11000,'1960-09-12','Alain','Bonifati','M','1988-08-20');
\ No newline at end of file
diff --git a/employee-sample-database/sqlite/dataset_small/load_salary1.sql b/employee-sample-database/sqlite/dataset_small/load_salary1.sql
new file mode 100644
index 0000000000000000000000000000000000000000..39505060fdf6d15b81a5bf2ddb185ecfbf1c2ba7
--- /dev/null
+++ b/employee-sample-database/sqlite/dataset_small/load_salary1.sql
@@ -0,0 +1,9488 @@
+INSERT INTO salary VALUES (10001,60117,'1986-06-26','1987-06-26'),
+(10001,62102,'1987-06-26','1988-06-25'),
+(10001,66074,'1988-06-25','1989-06-25'),
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\ No newline at end of file
diff --git a/employee-sample-database/sqlite/dataset_small/load_title.sql b/employee-sample-database/sqlite/dataset_small/load_title.sql
new file mode 100644
index 0000000000000000000000000000000000000000..b168856f6a80dd5f78e5d261f655f94652c3d677
--- /dev/null
+++ b/employee-sample-database/sqlite/dataset_small/load_title.sql
@@ -0,0 +1,1470 @@
+INSERT INTO title VALUES (10001,'Senior Engineer','1986-06-26','9999-01-01'),
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+(10837,'Staff','1986-01-14','1993-01-14'),
+(10838,'Engineer','1994-04-26','2001-04-26'),
+(10838,'Senior Engineer','2001-04-26','9999-01-01'),
+(10839,'Assistant Engineer','1996-12-30','9999-01-01'),
+(10840,'Engineer','1993-06-04','1998-06-04'),
+(10840,'Senior Engineer','1998-06-04','9999-01-01'),
+(10841,'Senior Staff','1997-01-21','9999-01-01'),
+(10841,'Staff','1992-01-22','1997-01-21'),
+(10842,'Engineer','1986-07-14','1991-07-14'),
+(10842,'Senior Engineer','1991-07-14','1998-06-27'),
+(10843,'Engineer','1990-04-26','1998-04-26'),
+(10843,'Senior Engineer','1998-04-26','9999-01-01'),
+(10844,'Engineer','1997-04-25','9999-01-01'),
+(10845,'Engineer','1997-01-25','2002-01-25'),
+(10845,'Senior Engineer','2002-01-25','9999-01-01'),
+(10846,'Senior Engineer','1995-01-17','9999-01-01'),
+(10847,'Senior Staff','1992-07-24','9999-01-01'),
+(10847,'Staff','1986-07-25','1992-07-24'),
+(10848,'Engineer','1985-04-20','1992-04-20'),
+(10848,'Senior Engineer','1992-04-20','9999-01-01'),
+(10849,'Assistant Engineer','1995-06-25','2001-06-24'),
+(10849,'Engineer','2001-06-24','9999-01-01'),
+(10850,'Staff','1990-04-22','1995-05-11'),
+(10851,'Senior Staff','1999-11-21','9999-01-01'),
+(10851,'Staff','1994-11-21','1999-11-21'),
+(10852,'Technique Leader','1999-08-21','9999-01-01'),
+(10853,'Technique Leader','1993-05-03','9999-01-01'),
+(10854,'Engineer','1998-07-13','9999-01-01'),
+(10855,'Senior Staff','1993-03-29','9999-01-01'),
+(10856,'Staff','1986-12-31','1990-06-18'),
+(10857,'Senior Staff','1996-04-02','9999-01-01'),
+(10857,'Staff','1990-04-04','1996-04-02'),
+(10858,'Engineer','1997-01-01','2002-01-01'),
+(10858,'Senior Engineer','2002-01-01','9999-01-01'),
+(10859,'Staff','1996-05-21','9999-01-01'),
+(10860,'Technique Leader','1998-01-22','9999-01-01'),
+(10861,'Senior Engineer','1989-06-10','1992-04-12'),
+(10862,'Senior Staff','1992-07-23','9999-01-01'),
+(10862,'Staff','1985-07-23','1992-07-23'),
+(10863,'Engineer','1985-12-25','1991-12-25'),
+(10863,'Senior Engineer','1991-12-25','9999-01-01'),
+(10864,'Senior Staff','1992-09-24','9999-01-01'),
+(10864,'Staff','1986-09-25','1992-09-24'),
+(10865,'Staff','1993-05-29','1993-09-19'),
+(10866,'Senior Staff','1995-12-27','9999-01-01'),
+(10866,'Staff','1990-12-27','1995-12-27'),
+(10867,'Engineer','1997-03-23','2002-03-22'),
+(10868,'Engineer','1990-03-20','1999-03-20'),
+(10868,'Senior Engineer','1999-03-20','9999-01-01'),
+(10869,'Staff','1997-09-04','1998-10-15'),
+(10870,'Senior Engineer','1988-01-13','9999-01-01'),
+(10871,'Staff','1999-06-16','9999-01-01'),
+(10872,'Senior Staff','1996-09-05','9999-01-01'),
+(10872,'Staff','1990-09-06','1996-09-05'),
+(10873,'Staff','1994-03-23','9999-01-01'),
+(10874,'Engineer','1986-04-22','1992-04-21'),
+(10874,'Senior Engineer','1992-04-21','9999-01-01'),
+(10875,'Senior Engineer','1988-05-19','9999-01-01'),
+(10876,'Staff','1995-01-29','1999-09-14'),
+(10877,'Senior Staff','1986-06-06','9999-01-01'),
+(10878,'Staff','1997-04-25','9999-01-01'),
+(10879,'Senior Engineer','1985-08-15','9999-01-01'),
+(10880,'Engineer','1992-01-11','1997-01-10'),
+(10880,'Senior Engineer','1997-01-10','9999-01-01'),
+(10881,'Engineer','1997-02-18','1998-10-25'),
+(10882,'Engineer','1999-04-29','9999-01-01'),
+(10883,'Engineer','1996-12-28','9999-01-01'),
+(10884,'Engineer','1993-04-15','1998-04-15'),
+(10884,'Senior Engineer','1998-04-15','9999-01-01'),
+(10885,'Senior Staff','1991-08-19','9999-01-01'),
+(10886,'Engineer','1995-03-26','2000-03-25'),
+(10886,'Senior Engineer','2000-03-25','9999-01-01'),
+(10887,'Senior Engineer','1989-03-17','9999-01-01'),
+(10888,'Engineer','1987-06-28','1989-09-11'),
+(10889,'Senior Staff','1987-08-11','9999-01-01'),
+(10890,'Senior Staff','1990-03-10','9999-01-01'),
+(10891,'Assistant Engineer','1995-10-06','2001-10-05'),
+(10891,'Engineer','2001-10-05','9999-01-01'),
+(10892,'Senior Staff','1998-04-11','9999-01-01'),
+(10892,'Staff','1990-04-11','1998-04-11'),
+(10893,'Senior Staff','1995-08-23','9999-01-01'),
+(10893,'Staff','1989-08-23','1995-08-23'),
+(10894,'Engineer','1995-12-05','9999-01-01'),
+(10895,'Staff','1986-04-26','1987-04-13'),
+(10896,'Engineer','1997-11-27','9999-01-01'),
+(10897,'Senior Staff','1995-04-09','9999-01-01'),
+(10897,'Staff','1986-04-09','1995-04-09'),
+(10898,'Senior Engineer','1993-01-08','9999-01-01'),
+(10899,'Senior Staff','1999-04-05','9999-01-01'),
+(10899,'Staff','1990-04-05','1999-04-05'),
+(10900,'Senior Engineer','1995-09-07','9999-01-01'),
+(10901,'Staff','1998-08-05','9999-01-01'),
+(10902,'Assistant Engineer','1990-06-11','1990-09-25'),
+(10903,'Senior Staff','1997-02-14','9999-01-01'),
+(10903,'Staff','1989-02-14','1997-02-14'),
+(10904,'Engineer','1993-04-16','1999-04-16'),
+(10904,'Senior Engineer','1999-04-16','9999-01-01'),
+(10905,'Senior Engineer','1985-02-28','1986-03-07'),
+(10906,'Staff','1998-10-18','9999-01-01'),
+(10907,'Senior Staff','1995-12-26','9999-01-01'),
+(10907,'Staff','1988-12-25','1995-12-26'),
+(10908,'Senior Staff','1993-04-05','9999-01-01'),
+(10908,'Staff','1986-04-05','1993-04-05'),
+(10909,'Engineer','1993-12-26','2000-12-26'),
+(10909,'Senior Engineer','2000-12-26','9999-01-01'),
+(10910,'Senior Staff','1995-09-02','9999-01-01'),
+(10910,'Staff','1989-09-02','1995-09-02'),
+(10911,'Senior Engineer','1988-06-25','9999-01-01'),
+(10912,'Senior Staff','1991-04-23','1999-09-26'),
+(10912,'Staff','1986-04-23','1991-04-23'),
+(10913,'Staff','1998-10-25','9999-01-01'),
+(10914,'Staff','1998-03-24','1998-09-01'),
+(10915,'Senior Engineer','1999-10-28','9999-01-01'),
+(10916,'Engineer','1998-03-12','9999-01-01'),
+(10917,'Engineer','1993-01-30','1998-01-30'),
+(10917,'Senior Engineer','1998-01-30','9999-01-01'),
+(10918,'Staff','1990-01-29','1990-10-10'),
+(10919,'Staff','1999-10-30','9999-01-01'),
+(10920,'Senior Staff','1998-02-23','9999-01-01'),
+(10920,'Staff','1992-02-24','1998-02-23'),
+(10921,'Engineer','1986-07-27','1995-07-27'),
+(10921,'Senior Engineer','1995-07-27','9999-01-01'),
+(10922,'Staff','1996-02-02','1999-01-16'),
+(10923,'Senior Staff','1990-04-09','9999-01-01'),
+(10924,'Senior Staff','1997-08-14','9999-01-01'),
+(10924,'Staff','1988-08-14','1997-08-14'),
+(10925,'Technique Leader','1991-06-11','9999-01-01'),
+(10926,'Engineer','1990-04-30','1995-04-30'),
+(10926,'Senior Engineer','1995-04-30','9999-01-01'),
+(10927,'Senior Staff','1993-09-12','9999-01-01'),
+(10927,'Staff','1986-09-12','1993-09-12'),
+(10928,'Senior Staff','1997-04-06','9999-01-01'),
+(10928,'Staff','1991-04-07','1997-04-06'),
+(10929,'Senior Staff','1998-04-15','9999-01-01'),
+(10929,'Staff','1991-04-15','1998-04-15'),
+(10930,'Engineer','1991-11-22','1998-11-22'),
+(10930,'Senior Engineer','1998-11-22','9999-01-01'),
+(10931,'Technique Leader','1992-06-14','1999-09-19'),
+(10932,'Engineer','1994-09-09','1999-09-09'),
+(10932,'Senior Engineer','1999-09-09','9999-01-01'),
+(10933,'Senior Engineer','1993-08-02','9999-01-01'),
+(10934,'Staff','1995-09-17','9999-01-01'),
+(10935,'Staff','1994-10-17','9999-01-01'),
+(10936,'Senior Staff','1996-05-08','9999-01-01'),
+(10936,'Staff','1990-05-09','1996-05-08'),
+(10937,'Engineer','1988-06-16','1994-06-16'),
+(10937,'Senior Engineer','1994-06-16','9999-01-01'),
+(10938,'Senior Staff','1993-09-24','9999-01-01'),
+(10938,'Staff','1985-09-24','1993-09-24'),
+(10939,'Assistant Engineer','1997-12-11','9999-01-01'),
+(10940,'Engineer','1997-06-30','1999-12-02'),
+(10941,'Senior Staff','2001-08-22','9999-01-01'),
+(10941,'Staff','1993-08-22','2001-08-22'),
+(10942,'Engineer','1996-01-21','9999-01-01'),
+(10943,'Engineer','1993-05-22','1999-05-22'),
+(10943,'Senior Engineer','1999-05-22','9999-01-01'),
+(10944,'Senior Engineer','1995-09-07','9999-01-01'),
+(10945,'Engineer','1993-04-04','2001-04-04'),
+(10945,'Senior Engineer','2001-04-04','9999-01-01'),
+(10946,'Staff','1996-10-26','9999-01-01'),
+(10947,'Senior Staff','2001-11-15','9999-01-01'),
+(10947,'Staff','1995-11-16','2001-11-15'),
+(10948,'Senior Engineer','1996-09-30','1997-06-23'),
+(10949,'Senior Staff','1996-06-28','9999-01-01'),
+(10949,'Staff','1990-06-29','1996-06-28'),
+(10950,'Senior Engineer','1996-06-08','1998-01-13'),
+(10951,'Senior Staff','1994-07-06','9999-01-01'),
+(10951,'Staff','1989-07-06','1994-07-06'),
+(10952,'Senior Engineer','1986-07-22','1988-04-26'),
+(10953,'Senior Staff','1987-02-12','9999-01-01'),
+(10954,'Staff','1995-12-14','9999-01-01'),
+(10955,'Senior Staff','1991-09-27','9999-01-01'),
+(10955,'Staff','1986-09-27','1991-09-27'),
+(10956,'Engineer','1995-12-02','9999-01-01'),
+(10957,'Senior Staff','1990-10-01','9999-01-01'),
+(10958,'Engineer','1988-11-27','1993-11-27'),
+(10958,'Senior Engineer','1993-11-27','9999-01-01'),
+(10959,'Staff','1994-03-01','1996-04-12'),
+(10960,'Senior Staff','1995-07-07','9999-01-01'),
+(10961,'Engineer','1993-11-04','1999-11-04'),
+(10961,'Senior Engineer','1999-11-04','9999-01-01'),
+(10962,'Senior Staff','1986-12-16','9999-01-01'),
+(10963,'Engineer','1988-01-14','1995-04-10'),
+(10964,'Senior Staff','1999-05-05','9999-01-01'),
+(10964,'Staff','1991-05-05','1999-05-05'),
+(10965,'Senior Staff','1986-07-29','9999-01-01'),
+(10966,'Staff','1997-02-05','9999-01-01'),
+(10967,'Engineer','1993-08-01','1998-08-01'),
+(10967,'Senior Engineer','1998-08-01','9999-01-01'),
+(10968,'Senior Staff','1985-08-11','9999-01-01'),
+(10969,'Senior Engineer','1994-05-12','9999-01-01'),
+(10970,'Engineer','1995-11-14','9999-01-01'),
+(10971,'Technique Leader','1991-07-30','1998-10-24'),
+(10972,'Engineer','1987-06-06','1996-06-05'),
+(10972,'Senior Engineer','1996-06-05','9999-01-01'),
+(10973,'Engineer','1989-03-05','1995-03-05'),
+(10973,'Senior Engineer','1995-03-05','9999-01-01'),
+(10974,'Senior Engineer','1999-05-17','9999-01-01'),
+(10975,'Engineer','1992-03-30','1998-03-30'),
+(10975,'Senior Engineer','1998-03-30','9999-01-01'),
+(10976,'Technique Leader','1998-10-31','9999-01-01'),
+(10977,'Senior Staff','1987-08-10','1987-10-01'),
+(10978,'Engineer','1985-05-18','1990-05-18'),
+(10978,'Senior Engineer','1990-05-18','9999-01-01'),
+(10979,'Engineer','1986-01-27','1993-01-27'),
+(10979,'Senior Engineer','1993-01-27','9999-01-01'),
+(10980,'Engineer','1989-01-04','1997-01-04'),
+(10980,'Senior Engineer','1997-01-04','9999-01-01'),
+(10981,'Senior Staff','1992-07-07','9999-01-01'),
+(10982,'Senior Staff','1994-11-11','9999-01-01'),
+(10982,'Staff','1989-11-11','1994-11-11'),
+(10983,'Engineer','1988-02-22','1997-02-21'),
+(10983,'Senior Engineer','1997-02-21','9999-01-01'),
+(10984,'Senior Engineer','1988-07-09','1993-05-14'),
+(10985,'Engineer','1999-02-11','1999-03-10'),
+(10986,'Technique Leader','1985-10-11','9999-01-01'),
+(10987,'Engineer','1997-05-21','9999-01-01'),
+(10988,'Senior Engineer','1985-06-19','9999-01-01'),
+(10989,'Senior Staff','1991-07-20','9999-01-01'),
+(10989,'Staff','1985-07-20','1991-07-20'),
+(10990,'Technique Leader','1990-08-06','9999-01-01'),
+(10991,'Engineer','1999-07-13','9999-01-01'),
+(10992,'Engineer','1997-04-27','9999-01-01'),
+(10993,'Engineer','1992-05-29','2000-05-29'),
+(10993,'Senior Engineer','2000-05-29','9999-01-01'),
+(10994,'Assistant Engineer','1987-06-29','1989-05-24'),
+(10995,'Senior Staff','1997-07-19','9999-01-01'),
+(10996,'Senior Staff','1996-02-07','9999-01-01'),
+(10996,'Staff','1990-02-07','1996-02-07'),
+(10997,'Technique Leader','2000-01-04','9999-01-01'),
+(10998,'Staff','1996-08-07','9999-01-01'),
+(10999,'Technique Leader','1996-03-05','1997-04-19'),
+(11000,'Senior Engineer','1996-01-28','9999-01-01');
\ No newline at end of file
diff --git a/employee-sample-database/sqlite/dataset_small/object.sql b/employee-sample-database/sqlite/dataset_small/object.sql
new file mode 100644
index 0000000000000000000000000000000000000000..d9a0fa709da4abec0d65e31fa25da3c6ce3dfdac
--- /dev/null
+++ b/employee-sample-database/sqlite/dataset_small/object.sql
@@ -0,0 +1,74 @@
+-- SQLite implementation of views and functions
+-- This is simplified compared to the MySQL version
+
+-- Drop views if they exist
+DROP VIEW IF EXISTS v_full_employee;
+DROP VIEW IF EXISTS v_full_department;
+DROP VIEW IF EXISTS emp_dept_current;
+
+-- Create helper view to get current department for employees
+CREATE VIEW emp_dept_current AS
+SELECT 
+    e.emp_no,
+    de.dept_no
+FROM 
+    employee e
+JOIN 
+    dept_emp de ON e.emp_no = de.emp_no
+JOIN (
+    SELECT 
+        emp_no, 
+        MAX(from_date) AS max_from_date
+    FROM 
+        dept_emp
+    GROUP BY 
+        emp_no
+) latest ON de.emp_no = latest.emp_no AND de.from_date = latest.max_from_date;
+
+-- View that shows employee with their current department name
+CREATE VIEW v_full_employee AS
+SELECT
+    e.emp_no,
+    e.first_name, 
+    e.last_name,
+    e.birth_date, 
+    e.gender,
+    e.hire_date,
+    d.dept_name AS department
+FROM
+    employee e
+LEFT JOIN
+    emp_dept_current edc ON e.emp_no = edc.emp_no
+LEFT JOIN
+    department d ON edc.dept_no = d.dept_no;
+
+-- View to get current managers for departments
+CREATE VIEW current_managers AS
+SELECT
+    d.dept_no,
+    d.dept_name,
+    e.first_name || ' ' || e.last_name AS manager
+FROM
+    department d
+LEFT JOIN
+    dept_manager dm ON d.dept_no = dm.dept_no
+JOIN (
+    SELECT
+        dept_no,
+        MAX(from_date) AS max_from_date
+    FROM
+        dept_manager
+    GROUP BY
+        dept_no
+) latest ON dm.dept_no = latest.dept_no AND dm.from_date = latest.max_from_date
+LEFT JOIN
+    employee e ON dm.emp_no = e.emp_no;
+
+-- Create a view showing departments with their managers
+CREATE VIEW v_full_department AS
+SELECT
+    dept_no,
+    dept_name,
+    manager
+FROM
+    current_managers;
\ No newline at end of file
diff --git a/employee-sample-database/sqlite/dataset_small/show_elapsed.sql b/employee-sample-database/sqlite/dataset_small/show_elapsed.sql
new file mode 100644
index 0000000000000000000000000000000000000000..6319aa44a8cde626b4e3d9e1ee1698fcc3da6b9d
--- /dev/null
+++ b/employee-sample-database/sqlite/dataset_small/show_elapsed.sql
@@ -0,0 +1,4 @@
+-- SQLite doesn't have information_schema like MySQL
+-- This is a simpler version that just shows when the script was run
+
+SELECT 'Database loaded at ' || datetime('now', 'localtime') AS completion_time;
\ No newline at end of file
diff --git a/employee-sample-database/sqlite/dataset_small/test_employee_md5.sql b/employee-sample-database/sqlite/dataset_small/test_employee_md5.sql
new file mode 100644
index 0000000000000000000000000000000000000000..f12c922dbc228f604508236a88c646e4e442ebca
--- /dev/null
+++ b/employee-sample-database/sqlite/dataset_small/test_employee_md5.sql
@@ -0,0 +1,119 @@
+--  Sample employee database 
+--  See changelog table for details
+--  Copyright (C) 2007,2008, MySQL AB
+--  
+--  Original data created by Fusheng Wang and Carlo Zaniolo
+--  http://www.cs.aau.dk/TimeCenter/software.htm
+--  http://www.cs.aau.dk/TimeCenter/Data/employeeTemporalDataSet.zip
+-- 
+--  Current schema by Giuseppe Maxia 
+--  Data conversion from XML to relational by Patrick Crews
+--  SQLite adaptation by Claude Code
+-- 
+-- This work is licensed under the 
+-- Creative Commons Attribution-Share Alike 3.0 Unported License. 
+-- To view a copy of this license, visit 
+-- http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to 
+-- Creative Commons, 171 Second Street, Suite 300, San Francisco, 
+-- California, 94105, USA.
+-- 
+--  DISCLAIMER
+--  To the best of our knowledge, this data is fabricated, and
+--  it does not correspond to real people. 
+--  Any similarity to existing people is purely coincidental.
+-- 
+
+SELECT 'TESTING INSTALLATION' as 'INFO';
+
+DROP TABLE IF EXISTS expected_value;
+DROP TABLE IF EXISTS found_value;
+
+CREATE TABLE expected_value (
+    table_name TEXT NOT NULL PRIMARY KEY,
+    recs INTEGER NOT NULL,
+    crc_md5 TEXT NOT NULL
+);
+
+CREATE TABLE found_value (
+    table_name TEXT NOT NULL PRIMARY KEY,
+    recs INTEGER NOT NULL,
+    crc_md5 TEXT NOT NULL
+);
+
+INSERT INTO expected_value VALUES 
+('employee',    1000, '595460127fb609c2b110b1796083e242'),
+('department',     9, 'd1af5e170d2d1591d776d5638d71fc5f'),
+('dept_manager',  16, '8ff425d5ad6dc56975998d1893b8dca9'),
+('dept_emp',    1103, 'e302aa5b56a69b49e40eb0d60674addc'),
+('title',       1470, 'ba77dd331ce00f76c1643a7d73cdcee6'),
+('salary',      9488, '61f22cfece4d34f5bb94c9f05a3da3ef');
+
+SELECT table_name, recs AS expected_record, crc_md5 AS expected_crc FROM expected_value;
+
+DROP TABLE IF EXISTS tchecksum;
+CREATE TABLE tchecksum (chk TEXT);
+
+-- For SQLite, we need to use a different approach for MD5 calculation
+-- Insert employee checksums
+INSERT INTO found_value
+SELECT 'employee', COUNT(*), 
+       (SELECT hex(md5(group_concat(emp_no||birth_date||first_name||last_name||gender||hire_date, '#')))
+        FROM (SELECT * FROM employee ORDER BY emp_no))
+FROM employee;
+
+-- Insert department checksums
+INSERT INTO found_value
+SELECT 'department', COUNT(*),
+       (SELECT hex(md5(group_concat(dept_no||dept_name, '#')))
+        FROM (SELECT * FROM department ORDER BY dept_no))
+FROM department;
+
+-- Insert dept_manager checksums
+INSERT INTO found_value
+SELECT 'dept_manager', COUNT(*),
+       (SELECT hex(md5(group_concat(dept_no||emp_no||from_date||to_date, '#')))
+        FROM (SELECT * FROM dept_manager ORDER BY dept_no, emp_no))
+FROM dept_manager;
+
+-- Insert dept_emp checksums
+INSERT INTO found_value
+SELECT 'dept_emp', COUNT(*),
+       (SELECT hex(md5(group_concat(dept_no||emp_no||from_date||to_date, '#')))
+        FROM (SELECT * FROM dept_emp ORDER BY dept_no, emp_no))
+FROM dept_emp;
+
+-- Insert title checksums
+INSERT INTO found_value
+SELECT 'title', COUNT(*),
+       (SELECT hex(md5(group_concat(emp_no||title||from_date||IFNULL(to_date,''), '#')))
+        FROM (SELECT * FROM title ORDER BY emp_no, title, from_date))
+FROM title;
+
+-- Insert salary checksums
+INSERT INTO found_value
+SELECT 'salary', COUNT(*),
+       (SELECT hex(md5(group_concat(emp_no||amount||from_date||to_date, '#')))
+        FROM (SELECT * FROM salary ORDER BY emp_no, from_date, to_date))
+FROM salary;
+
+SELECT table_name, recs as 'found_records', crc_md5 as found_crc FROM found_value;
+
+-- Compare expected vs found
+SELECT  
+    e.table_name, 
+    CASE WHEN e.recs=f.recs THEN 'OK' ELSE 'not ok' END AS records_match, 
+    CASE WHEN e.crc_md5=f.crc_md5 THEN 'ok' ELSE 'not ok' END AS crc_match 
+FROM 
+    expected_value e 
+    JOIN found_value f USING (table_name);
+
+-- Check for failures
+SELECT 
+    'CRC' as summary, 
+    CASE WHEN (SELECT COUNT(*) FROM expected_value e JOIN found_value f USING(table_name) WHERE f.crc_md5 != e.crc_md5) = 0 
+         THEN 'OK' ELSE 'FAIL' END as 'result'
+UNION ALL
+SELECT 
+    'count', 
+    CASE WHEN (SELECT COUNT(*) FROM expected_value e JOIN found_value f USING(table_name) WHERE f.recs != e.recs) = 0 
+         THEN 'OK' ELSE 'FAIL' END;
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..aca4140b95dae91dc17d8f44180c0abe1656b1ba
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,46 @@
+# Core Frameworks
+fastapi>=0.100.0
+uvicorn>=0.23.0
+sqlalchemy>=2.0.0
+pydantic>=2.0.0
+python-multipart>=0.0.6
+pandas>=2.0.0
+
+# Multi-Agent Orchestration
+llama-index>=0.10.0
+llama-index-core>=0.10.0
+llama-index-llms-ollama>=0.1.2
+llama-index-llms-huggingface-api
+huggingface_hub
+
+# Model Context Protocol
+mcp>=0.1.0
+sqlparse>=0.4.4
+
+# HTTP & Async
+requests>=2.31.0
+httpx>=0.26.0
+aiohttp>=3.9.0
+psutil>=5.9.0
+
+# Database Drivers — QueryLens is DB-agnostic: PostgreSQL, MySQL, SQLite,
+# MS SQL Server (pymssql, freeTDS-based, no system ODBC needed) and Oracle
+# (python-oracledb thin mode, no client install needed). Async engines exist
+# for postgres/mysql/sqlite; mssql/oracle execute SELECTs through the sync
+# engine in a worker thread (Agent/db_uri.py maps user URLs onto these).
+pymysql>=1.1.0
+asyncpg>=0.29.0
+psycopg2-binary>=2.9.0
+aiomysql>=0.2.0
+aiosqlite>=0.19.0
+pymssql>=2.2.0
+oracledb>=2.0.0
+
+# Document Vision processing
+pillow>=10.0.0
+pytesseract>=0.3.10
+google-genai>=1.0.0
+groq>=0.9.0
+
+# Environment
+python-dotenv>=1.0.0
diff --git a/upload_binaries.py b/upload_binaries.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9eda2dfed47f402d55665a50f13d9c66bae9405
--- /dev/null
+++ b/upload_binaries.py
@@ -0,0 +1,73 @@
+"""
+upload_binaries.py -- push binary files to a HF Space via the Xet backend.
+
+HF Spaces now reject binary files on plain `git push`; the supported path is
+`huggingface_hub` (>= 0.32 with `hf_xet`), which uploads through Xet storage.
+
+Usage:
+    python upload_binaries.py -r OWNER/REPO -p path/in/repo.db -f local/file.db
+
+Auth: uses your cached HF token (created by `huggingface-cli login`).
+      Skips the upload if the remote file already has identical bytes.
+"""
+import argparse
+import hashlib
+import sys
+
+from huggingface_hub import HfApi, hf_hub_download, get_token
+
+
+def sha256_of(path: str) -> str:
+    h = hashlib.sha256()
+    with open(path, "rb") as fh:
+        for chunk in iter(lambda: fh.read(1 << 20), b""):
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("-r", "--repo", required=True, help="repo id, e.g. OWNER/SPACE")
+    ap.add_argument("-p", "--path-in-repo", required=True)
+    ap.add_argument("-f", "--file", required=True, help="local file to upload")
+    args = ap.parse_args()
+
+    if not get_token():
+        print("ERROR: no HF token cached. Run 'huggingface-cli login' (token needs write access) and retry.")
+        return 2
+
+    api = HfApi()
+    repo_type = "space"  # this script targets Spaces ("OWNER/SPACE" ids)
+
+    remote = None
+    try:
+        remote = hf_hub_download(
+            repo_id=args.repo, repo_type=repo_type, filename=args.path_in_repo
+        )
+    except Exception:
+        remote = None
+
+    local_hash = sha256_of(args.file)
+    if remote is not None:
+        try:
+            if sha256_of(remote) == local_hash:
+                print(f"[XET] {args.path_in_repo} already identical on {args.repo} - skipping upload")
+                return 0
+            print(f"[XET] {args.path_in_repo} differs on the remote {args.repo} - uploading new revision")
+        except OSError:
+            pass
+
+    print(f"[XET] uploading {args.path_in_repo} ({args.file}) to {args.repo} ...")
+    api.upload_file(
+        repo_id=args.repo,
+        repo_type=repo_type,
+        path_in_repo=args.path_in_repo,
+        path_or_fileobj=args.file,
+        commit_message=f"Add binary demo database via Xet: {args.path_in_repo}",
+    )
+    print(f"[XET] uploaded OK (sha256 {local_hash[:12]}...)")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/web/app.py b/web/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..7541fa620eebeba4729c033e3585b0ca69715b66
--- /dev/null
+++ b/web/app.py
@@ -0,0 +1,306 @@
+import os
+import httpx
+import logging
+import traceback
+from typing import Optional
+from fastapi import FastAPI, HTTPException, Request
+from pydantic import BaseModel
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse, JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+
+WORKER_URL = os.environ.get("WORKER_URL", "http://localhost:8700")
+# Timeout (seconds) when calling the worker; re-execute and heavy queries can take longer.
+# Make configurable via environment variable WORKER_TIMEOUT (default 300s).
+WORKER_TIMEOUT = float(os.environ.get("WORKER_TIMEOUT", "450"))  # Increased to 450s to handle complex queries
+
+# Reusable AsyncClient to avoid per-request connection setup overhead
+client: httpx.AsyncClient | None = None
+
+app = FastAPI(title="Text2SQL Web API")
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # adjust in production
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Serve static frontend under /static and provide a root route
+static_dir = os.path.join(os.path.dirname(__file__), 'static')
+if os.path.isdir(static_dir):
+    app.mount("/static", StaticFiles(directory=static_dir, html=True), name="static")
+
+
+@app.get("/")
+async def root_index():
+    index_path = os.path.join(static_dir, 'index.html')
+    return FileResponse(index_path)
+
+class QueryReq(BaseModel):
+    question: str
+    # Optional doc_id produced by a prior /upload-image call
+    doc_id: Optional[str] = None
+    # Explicit mode hint from client: 'text2sql' or 'ocr_qa'. When omitted, worker may auto-detect.
+    mode: Optional[str] = None
+
+class OcrQAReq(BaseModel):
+    question: str
+    doc_id: str
+
+class OcrQAResp(BaseModel):
+    doc_id: str
+    answer: str
+    used_llm: bool
+
+@app.get('/health')
+async def health():
+    return {"status": "ok"}
+
+@app.post('/api/query')
+async def api_query(req: QueryReq):
+    # Use a shared AsyncClient created at startup to avoid TCP/TLS/HTTP handshakes per request.
+    global client
+    if client is None:
+        # fallback to create a temporary client (shouldn't happen if startup ran)
+        client = httpx.AsyncClient(timeout=WORKER_TIMEOUT)
+    try:
+        payload = {"question": req.question}
+        # Forward explicit mode if provided so the worker doesn't infer from lingering doc_id
+        if getattr(req, 'mode', None):
+            payload['mode'] = req.mode
+        if getattr(req, 'doc_id', None):
+            payload['doc_id'] = req.doc_id
+        r = await client.post(f"{WORKER_URL}/query", json=payload)
+        r.raise_for_status()
+        return r.json()
+    except httpx.HTTPStatusError as e:
+        # Log the full traceback and response body for debugging
+        logging.error("Worker returned non-2xx status: %s", e.response.status_code)
+        logging.error("Response text: %s", getattr(e.response, 'text', str(e)))
+        logging.error(traceback.format_exc())
+        raise HTTPException(status_code=e.response.status_code, detail=getattr(e.response, 'text', str(e)))
+    except httpx.ReadTimeout as e:
+        # Worker took too long. Return a 504 gateway timeout with a helpful message.
+        logging.error("Timeout contacting worker after %ss: %s", WORKER_TIMEOUT, str(e))
+        logging.error(traceback.format_exc())
+        return JSONResponse(status_code=504, content={
+            "error": "worker_timeout",
+            "message": f"Worker did not respond within {WORKER_TIMEOUT} seconds. The query may be long-running (re-execute) or the worker is busy. Try increasing WORKER_TIMEOUT or running the worker with more resources.",
+        })
+    except Exception as e:
+        logging.error("Error contacting worker: %s", str(e))
+        logging.error(traceback.format_exc())
+        # Return a JSON response containing the error detail to help debugging from the client
+        return JSONResponse(status_code=500, content={"error": str(e), "trace": traceback.format_exc()})
+
+
+@app.post('/api/ocr-qa')
+async def api_ocr_qa(req: OcrQAReq):
+    global client
+    if client is None:
+        client = httpx.AsyncClient(timeout=WORKER_TIMEOUT)
+    try:
+        payload = {"question": req.question, "doc_id": req.doc_id}
+        r = await client.post(f"{WORKER_URL}/ocr-qa", json=payload)
+        r.raise_for_status()
+        return r.json()
+    except httpx.HTTPStatusError as e:
+        logging.error("Worker OCR QA non-2xx: %s", e.response.status_code)
+        logging.error("Response text: %s", getattr(e.response, 'text', str(e)))
+        return JSONResponse(status_code=e.response.status_code, content={"error": getattr(e.response, 'text', str(e))})
+    except Exception as e:
+        logging.error("Error contacting worker OCR QA: %s", str(e))
+        return JSONResponse(status_code=500, content={"error": str(e), "trace": traceback.format_exc()})
+
+
+@app.on_event('startup')
+async def startup_event():
+    global client
+    if client is None:
+        client = httpx.AsyncClient(timeout=WORKER_TIMEOUT)
+
+
+@app.on_event('shutdown')
+async def shutdown_event():
+    global client
+    if client is not None:
+        await client.aclose()
+
+
+@app.post('/api/test')
+async def api_test(req: QueryReq):
+    # Simple echo endpoint to verify POST handling without contacting the worker
+    return {"ok": True, "echo": req.question}
+
+
+@app.post('/upload-image')
+async def upload_image(file: bytes = None, request: Request = None):
+    """Proxy an image upload to the worker's /upload-image endpoint.
+
+    Accepts multipart/form-data file field 'file' from the browser.
+    Returns the worker JSON (doc_id, text_preview, image_url) on success.
+    """
+    # Use the request.form() to access the uploaded file
+    try:
+        form = await request.form()
+        f = form.get('file')
+        if f is None:
+            raise HTTPException(status_code=400, detail='file field is required')
+        # forward the file to the worker
+        files = {'file': (f.filename, await f.read(), f.content_type)}
+        async with httpx.AsyncClient(timeout=120) as c:
+            r = await c.post(f"{WORKER_URL}/upload-image", files=files)
+            r.raise_for_status()
+            return JSONResponse(status_code=200, content=r.json())
+    except HTTPException:
+        raise
+    except Exception as e:
+        logging.error('upload-image proxy failed: %s', str(e))
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# ---------------------------------------------------------------------------
+# Schema profile proxy endpoints — dynamic multi-database support
+# ---------------------------------------------------------------------------
+
+@app.get('/api/schemas')
+async def api_schemas():
+    global client
+    try:
+        r = await client.get(f"{WORKER_URL}/schemas")
+        r.raise_for_status()
+        return r.json()
+    except httpx.HTTPStatusError as e:
+        return JSONResponse(status_code=e.response.status_code,
+                            content={"error": getattr(e.response, 'text', str(e))})
+    except Exception as e:
+        logging.error('schemas list proxy failed: %s', str(e))
+        return JSONResponse(status_code=500, content={"error": str(e)})
+
+
+class AddSchemaReq(BaseModel):
+    name: str
+    connection_url: str
+    activate: Optional[bool] = True
+    notes: Optional[str] = None
+
+
+@app.post('/api/schemas/add')
+async def api_schemas_add(req: AddSchemaReq):
+    global client
+    try:
+        r = await client.post(f"{WORKER_URL}/schemas/add", json=req.model_dump(),
+                              timeout=WORKER_TIMEOUT)
+        r.raise_for_status()
+        return r.json()
+    except httpx.HTTPStatusError as e:
+        return JSONResponse(status_code=e.response.status_code,
+                            content={"error": getattr(e.response, 'text', str(e))})
+    except Exception as e:
+        logging.error('schemas add proxy failed: %s', str(e))
+        return JSONResponse(status_code=500, content={"error": str(e)})
+
+
+class SetSchemaNotesReq(BaseModel):
+    notes: str = ""
+
+
+@app.post('/api/schemas/{profile_id}/notes')
+async def api_schemas_set_notes(profile_id: str, req: SetSchemaNotesReq):
+    """Proxy per-database schema notes (domain hints) updates to the worker."""
+    global client
+    try:
+        r = await client.post(f"{WORKER_URL}/schemas/{profile_id}/notes",
+                              json={"notes": req.notes}, timeout=WORKER_TIMEOUT)
+        r.raise_for_status()
+        return r.json()
+    except httpx.HTTPStatusError as e:
+        return JSONResponse(status_code=e.response.status_code,
+                            content={"error": getattr(e.response, 'text', str(e))})
+    except Exception as e:
+        logging.error('schemas notes proxy failed: %s', str(e))
+        return JSONResponse(status_code=500, content={"error": str(e)})
+
+
+@app.post('/api/schemas/add-file')
+async def api_schemas_add_file(request: Request):
+    """Proxy a schema file upload (.sqlite/.db/.csv) to the worker."""
+    global client
+    try:
+        form = await request.form()
+        f = form.get('file')
+        if f is None:
+            raise HTTPException(status_code=400, detail='file field is required')
+        data = {'name': form.get('name') or '', 'activate': form.get('activate', 'true'),
+                'notes': form.get('notes') or ''}
+        files = {'file': (f.filename, await f.read(), f.content_type)}
+        # DB init can be slow on first connect; generous timeout
+        async with httpx.AsyncClient(timeout=300) as c:
+            r = await c.post(f"{WORKER_URL}/schemas/add-file", files=files, data=data)
+            r.raise_for_status()
+            return r.json()
+    except HTTPException:
+        raise
+    except httpx.HTTPStatusError as e:
+        return JSONResponse(status_code=e.response.status_code,
+                            content={"error": getattr(e.response, 'text', str(e))})
+    except Exception as e:
+        logging.error('schemas add-file proxy failed: %s', str(e))
+        return JSONResponse(status_code=500, content={"error": str(e)})
+
+
+@app.post('/api/schemas/switch/{profile_id}')
+async def api_schemas_switch(profile_id: str):
+    global client
+    try:
+        r = await client.post(f"{WORKER_URL}/schemas/switch/{profile_id}",
+                              timeout=WORKER_TIMEOUT)
+        r.raise_for_status()
+        return r.json()
+    except httpx.HTTPStatusError as e:
+        return JSONResponse(status_code=e.response.status_code,
+                            content={"error": getattr(e.response, 'text', str(e))})
+    except Exception as e:
+        logging.error('schemas switch proxy failed: %s', str(e))
+        return JSONResponse(status_code=500, content={"error": str(e)})
+
+
+@app.delete('/api/schemas/{profile_id}')
+async def api_schemas_delete(profile_id: str):
+    global client
+    try:
+        r = await client.delete(f"{WORKER_URL}/schemas/{profile_id}")
+        r.raise_for_status()
+        return r.json()
+    except httpx.HTTPStatusError as e:
+        return JSONResponse(status_code=e.response.status_code,
+                            content={"error": getattr(e.response, 'text', str(e))})
+    except Exception as e:
+        logging.error('schemas delete proxy failed: %s', str(e))
+        return JSONResponse(status_code=500, content={"error": str(e)})
+
+
+@app.get('/api/schema/current')
+async def api_schema_current():
+    global client
+    try:
+        r = await client.get(f"{WORKER_URL}/schema/current")
+        r.raise_for_status()
+        return r.json()
+    except httpx.HTTPStatusError as e:
+        return JSONResponse(status_code=e.response.status_code,
+                            content={"error": getattr(e.response, 'text', str(e))})
+    except Exception as e:
+        logging.error('schema current proxy failed: %s', str(e))
+        return JSONResponse(status_code=500, content={"error": str(e)})
+
+
+@app.get('/api/test')
+async def api_test_get():
+    return {"ok": True, "msg": "POST to /api/test with JSON {question: ...} to echo"}
+
+if __name__ == '__main__':
+    import uvicorn
+    port = int(os.environ.get('WEB_PORT', 8000))
+    uvicorn.run(app, host='0.0.0.0', port=port)
diff --git a/web/static/app.js b/web/static/app.js
new file mode 100644
index 0000000000000000000000000000000000000000..0a0b5c66cbf04b41ea073b015d436a31091d49a2
--- /dev/null
+++ b/web/static/app.js
@@ -0,0 +1,1070 @@
+const askBtn = document.getElementById('ask');
+const questionEl = document.getElementById('question');
+const answerEl = document.getElementById('answer');
+const sqlEl = document.getElementById('sql');
+const memoryEl = document.getElementById('memory');
+const querycraftEl = document.getElementById('querycraft');
+const presenterEl = document.getElementById('presenter');
+const uploadInput = document.getElementById('upload');
+const uploadBtn = document.getElementById('uploadBtn');
+const uploadStatus = document.getElementById('uploadStatus');
+const uploadControls = document.getElementById('uploadControls');
+const modeToggle = document.getElementById('modeToggle');
+let currentDocId = null;
+let currentMode = 'text2sql';
+const statusEl = document.getElementById('status');
+const timeEl = document.getElementById('time');
+const recalcBtn = document.getElementById('recalc');
+const spinner = document.getElementById('spinner');
+const spinnerText = document.getElementById('spinnerText');
+const askBtnEl = askBtn;
+
+// Mode toggle logic
+modeToggle.addEventListener('change', function() {
+  currentMode = modeToggle.value;
+  console.log('[DEBUG] Mode changed to:', currentMode); // Debug log
+  if(currentMode === 'ocr_qa') {
+    uploadControls.classList.remove('hidden');
+    console.log('[DEBUG] Upload controls shown'); // Debug log
+  } else {
+    uploadControls.classList.add('hidden');
+    currentDocId = null;
+    uploadStatus.textContent = 'No upload';
+    uploadStatus.className = 'status-text';
+    console.log('[DEBUG] Upload controls hidden'); // Debug log
+  }
+});
+
+// Initialize UI state
+console.log('[DEBUG] Initial mode:', currentMode); // Debug log
+if(currentMode === 'ocr_qa') {
+  uploadControls.classList.remove('hidden');
+} else {
+  uploadControls.classList.add('hidden');
+}
+
+// Helper to programmatically switch to OCR Q&A mode
+function switchToOcrQaMode() {
+  if (modeToggle.value !== 'ocr_qa') {
+    modeToggle.value = 'ocr_qa';
+    currentMode = 'ocr_qa';
+    uploadControls.classList.remove('hidden');
+    // Fire a change event so any listeners update state consistently
+    const ev = new Event('change', { bubbles: true });
+    modeToggle.dispatchEvent(ev);
+    console.log('[DEBUG] Switched to OCR Q&A mode');
+  }
+}
+
+// Extract mode change logic into a function so it can be called directly
+function onModeChange() {
+  currentMode = modeToggle.value;
+  console.log('[DEBUG] onModeChange called, mode:', currentMode); // Debug log
+  if(currentMode === 'ocr_qa') {
+    uploadControls.classList.remove('hidden');
+    console.log('[DEBUG] Upload controls shown via onModeChange'); // Debug log
+  } else {
+    uploadControls.classList.add('hidden');
+    currentDocId = null;
+    uploadStatus.textContent = 'No upload';
+    uploadStatus.className = 'status-text';
+    console.log('[DEBUG] Upload controls hidden via onModeChange'); // Debug log
+  }
+}
+
+// Mode toggle logic
+modeToggle.addEventListener('change', onModeChange);
+
+// Upload handler: create a temporary file input on demand to avoid issues with a permanently hidden input.
+if (uploadBtn) {
+  uploadBtn.addEventListener('click', () => {
+    // If not in OCR Q&A mode, switch and highlight, then open file picker after a delay
+    if (modeToggle.value !== 'ocr_qa') {
+      switchToOcrQaMode();
+      // Visually highlight the dropdown to show the switch
+      modeToggle.style.boxShadow = '0 0 0 3px rgba(15, 98, 254, 0.2)';
+      modeToggle.style.transition = 'box-shadow 0.2s';
+      setTimeout(() => {
+        modeToggle.style.boxShadow = '';
+        // Now open the file picker
+        const tmp = document.createElement('input');
+        tmp.type = 'file';
+        tmp.accept = 'image/*';
+        tmp.style.position = 'absolute';
+        tmp.style.left = '-9999px';
+        document.body.appendChild(tmp);
+
+        tmp.addEventListener('change', async () => {
+          const files = tmp.files;
+          if (!files || files.length === 0) {
+            uploadStatus.textContent = 'No file selected';
+            document.body.removeChild(tmp);
+            return;
+          }
+          switchToOcrQaMode();
+          uploadStatus.textContent = 'Uploading...';
+          uploadStatus.className = 'status-text text-warning';
+          const fd = new FormData();
+          fd.append('file', files[0]);
+          try {
+            const r = await fetch('/upload-image', { method: 'POST', body: fd });
+            if (!r.ok) {
+              const j = await r.json().catch(()=>({}));
+              uploadStatus.textContent = 'Upload failed: ' + (j.detail || r.statusText);
+              uploadStatus.className = 'status-text text-error';
+              document.body.removeChild(tmp);
+              return;
+            }
+            const j = await r.json();
+            currentDocId = j.doc_id;
+            uploadStatus.textContent = `Uploaded (doc_id=${currentDocId})`;
+            uploadStatus.className = 'status-text text-success';
+          } catch (e) {
+            uploadStatus.textContent = 'Upload error: ' + String(e);
+            uploadStatus.className = 'status-text text-error';
+          }
+          document.body.removeChild(tmp);
+        });
+        tmp.click();
+      }, 350); // 350ms to allow UI to update and user to see the switch
+      return;
+    }
+    // Already in OCR Q&A mode: open file picker immediately
+    const tmp = document.createElement('input');
+    tmp.type = 'file';
+    tmp.accept = 'image/*';
+    tmp.style.position = 'absolute';
+    tmp.style.left = '-9999px';
+    document.body.appendChild(tmp);
+
+    tmp.addEventListener('change', async () => {
+      const files = tmp.files;
+      if (!files || files.length === 0) {
+        uploadStatus.textContent = 'No file selected';
+        uploadStatus.className = 'status-text';
+        document.body.removeChild(tmp);
+        return;
+      }
+      switchToOcrQaMode();
+      uploadStatus.textContent = 'Uploading...';
+      uploadStatus.className = 'status-text text-warning';
+      const fd = new FormData();
+      fd.append('file', files[0]);
+      try {
+        const r = await fetch('/upload-image', { method: 'POST', body: fd });
+        if (!r.ok) {
+          const j = await r.json().catch(()=>({}));
+          uploadStatus.textContent = 'Upload failed: ' + (j.detail || r.statusText);
+          uploadStatus.className = 'status-text text-error';
+          document.body.removeChild(tmp);
+          return;
+        }
+        const j = await r.json();
+        currentDocId = j.doc_id;
+        uploadStatus.textContent = `Uploaded (doc_id=${currentDocId})`;
+        uploadStatus.className = 'status-text text-success';
+      } catch (e) {
+        uploadStatus.textContent = 'Upload error: ' + String(e);
+        uploadStatus.className = 'status-text text-error';
+      }
+      document.body.removeChild(tmp);
+    });
+    tmp.click();
+  });
+}
+
+function setRunning(running){
+  if(running){
+    askBtnEl.disabled = true;
+    recalcBtn.disabled = true;
+    questionEl.disabled = true;
+    spinner.classList.remove('hidden');
+    spinnerText.classList.remove('hidden');
+    spinnerText.textContent = 'Processing...';
+    // hide the small status text while spinner is active
+    statusEl.textContent = '';
+    console.log('[DEBUG] Loading state: ON, spinner should be visible'); // Debug log
+  } else {
+    askBtnEl.disabled = false;
+    recalcBtn.disabled = false;
+    questionEl.disabled = false;
+    spinner.classList.add('hidden');
+    spinnerText.classList.add('hidden');
+    // restore default status
+    statusEl.textContent = 'Ready';
+    console.log('[DEBUG] Loading state: OFF, spinner should be hidden'); // Debug log
+  }
+}
+
+function renderPresenterTrace(text){
+  if(!text) return '—';
+  // Split traces into chunks separated by blank lines and render as collapsible
+  const parts = String(text).split(/\n\s*\n/).filter(Boolean);
+  const container = document.createElement('div');
+  parts.forEach((p, i) => {
+    const d = document.createElement('details');
+    const summary = document.createElement('summary');
+    summary.textContent = p.split('\n')[0].slice(0,80);
+    summary.style.cursor = 'pointer';
+    summary.style.padding = '0.5rem';
+    summary.style.borderRadius = '0.375rem';
+    summary.style.marginBottom = '0.5rem';
+    summary.style.backgroundColor = 'var(--bg-raised)';
+    const pre = document.createElement('pre');
+    pre.style.whiteSpace = 'pre-wrap';
+    pre.style.fontSize = '0.75rem';
+    pre.style.padding = '0.75rem';
+    pre.style.backgroundColor = 'var(--panel-inset)';
+    pre.style.borderRadius = '0.375rem';
+    pre.style.margin = '0';
+    pre.textContent = p;
+    d.appendChild(summary);
+    d.appendChild(pre);
+    if(i===0) d.open = true;
+    container.appendChild(d);
+  });
+  return container;
+}
+
+// small helper to escape HTML
+const esc = s => String(s || '').replace(/&/g,'&amp;').replace(/</g,'&lt;').replace(/>/g,'&gt;');
+
+// Detect if text contains a pipe/markdown table and return true
+function isMarkdownTable(text){
+  if(!text) return false;
+  const lines = String(text).split(/\r?\n/).map(l=>l.trim()).filter(Boolean);
+  if(lines.length < 2) return false;
+  // Count lines that contain pipe separators
+  const pipeLines = lines.filter(l => l.includes('|'));
+  if(pipeLines.length < 2) return false;
+  // If the second line is a markdown separator like | --- | --- | or ---|---
+  const second = lines[1];
+  if(/^\s*\|?\s*:?-{3,}:?\s*(\|\s*:?-{3,}:?\s*)+\|?\s*$/.test(second)) return true;
+  // Otherwise, if majority of non-empty lines contain pipes, treat as table
+  return (pipeLines.length / lines.length) >= 0.5;
+}
+
+// Parse a simple markdown/pipe table into a DOM <table>
+function parseMarkdownTable(text){
+  const lines = String(text).split(/\r?\n/).map(l=>l.trim()).filter(Boolean);
+  // find the first contiguous block of pipe lines
+  let start = -1, end = -1;
+  for(let i=0;i<lines.length;i++){
+    if(lines[i].includes('|')){ start = i; break; }
+  }
+  if(start === -1) return null;
+  for(let i=start;i<lines.length;i++){
+    if(!lines[i].includes('|')){ end = i; break; }
+  }
+  if(end === -1) end = lines.length;
+  const block = lines.slice(start, end);
+  const rows = block.map(r => {
+    let s = r;
+    if(s.startsWith('|')) s = s.slice(1);
+    if(s.endsWith('|')) s = s.slice(0,-1);
+    return s.split('|').map(c => c.trim());
+  });
+
+  // If second row is separator, treat first row as header
+  let header = null;
+  if(rows.length >= 2 && rows[1].every(c => /^:?-{3,}:?$/.test(c))) {
+    header = rows[0];
+    rows.splice(0,2);
+  }
+
+  const table = document.createElement('table');
+  table.className = 'answer-table';
+  const thead = document.createElement('thead');
+  const tbody = document.createElement('tbody');
+
+  if(header){
+    const tr = document.createElement('tr');
+    header.forEach(h => { const th = document.createElement('th'); th.textContent = h || ''; tr.appendChild(th); });
+    thead.appendChild(tr);
+  } else if(rows.length>0){
+    // use first row as header if no separator present
+    const tr = document.createElement('tr');
+    rows[0].forEach((_,i) => { const th = document.createElement('th'); th.textContent = `col ${i+1}`; tr.appendChild(th); });
+    thead.appendChild(tr);
+  }
+
+  rows.forEach(r => {
+    const tr = document.createElement('tr');
+    r.forEach(c => { const td = document.createElement('td'); td.textContent = c; tr.appendChild(td); });
+    tbody.appendChild(tr);
+  });
+
+  if(thead.childElementCount) table.appendChild(thead);
+  table.appendChild(tbody);
+  
+  // Create enhanced table wrapper with controls
+  const wrapper = document.createElement('div');
+  wrapper.className = 'table-wrapper';
+  
+  // Create table controls
+  const controls = document.createElement('div');
+  controls.className = 'table-controls';
+  
+  const controlsLeft = document.createElement('div');
+  controlsLeft.className = 'table-controls-left';
+  
+  const tableInfo = document.createElement('div');
+  tableInfo.className = 'table-info';
+  const rowCount = tbody.children.length;
+  const colCount = header ? header.length : (rows.length > 0 ? rows[0].length : 0);
+  tableInfo.textContent = `${rowCount} rows × ${colCount} columns`;
+  
+  controlsLeft.appendChild(tableInfo);
+  
+  const controlsRight = document.createElement('div');
+  controlsRight.className = 'table-controls-right';
+  
+  const resizeControls = document.createElement('div');
+  resizeControls.className = 'table-resize-controls';
+  
+  // Size control buttons
+  const sizes = [
+    { name: 'Compact', class: 'compact' },
+    { name: 'Comfortable', class: 'comfortable' },
+    { name: 'Spacious', class: 'spacious' }
+  ];
+  
+  sizes.forEach((size, index) => {
+    const btn = document.createElement('button');
+    btn.textContent = size.name;
+    btn.className = 'table-resize-btn';
+    if (index === 1) btn.classList.add('active'); // Default to comfortable
+    
+    btn.addEventListener('click', () => {
+      // Remove all size classes
+      table.classList.remove('compact', 'comfortable', 'spacious');
+      // Add selected size class
+      table.classList.add(size.class);
+      
+      // Update active button
+      resizeControls.querySelectorAll('.table-resize-btn').forEach(b => b.classList.remove('active'));
+      btn.classList.add('active');
+    });
+    
+    resizeControls.appendChild(btn);
+  });
+  
+  controlsRight.appendChild(resizeControls);
+  
+  controls.appendChild(controlsLeft);
+  controls.appendChild(controlsRight);
+  
+  // Create table container
+  const container = document.createElement('div');
+  container.className = 'table-container';
+  container.appendChild(table);
+  
+  wrapper.appendChild(controls);
+  wrapper.appendChild(container);
+  
+  // Set default size
+  table.classList.add('comfortable');
+  
+  return wrapper;
+}
+
+// heuristics: split logs into agent sections
+function splitLogs(logs) {
+  const sections = {memory: '', querycraft: '', presenter: ''};
+  if (!logs) {
+    console.log('[DEBUG] splitLogs: No logs provided');
+    return sections;
+  }
+  
+  console.log('[DEBUG] splitLogs: Input logs length:', logs.length);
+  console.log('[DEBUG] splitLogs: First 500 chars:', logs.substring(0, 500));
+  console.log('[DEBUG] splitLogs: Last 500 chars:', logs.substring(Math.max(0, logs.length - 500)));
+  
+  // Normalize line endings
+  const lines = logs.replace(/\r/g,'').split('\n');
+  console.log('[DEBUG] splitLogs: Total lines:', lines.length);
+
+  // Look for explicit markers first
+  let mode = null;
+  let memoryMarkerFound = false;
+  let querycraftMarkerFound = false;
+  let presenterMarkerFound = false;
+  
+  for (let i = 0; i < lines.length; i++) {
+    const line = lines[i];
+    const l = line.trim();
+    
+    // Match the exact agent section headers from agentic_workflow.py
+    if (/=== MEMORY AGENT ===/i.test(l) || /INITIAL MEMORY/i.test(l)) { 
+      mode = 'memory'; 
+      memoryMarkerFound = true;
+      console.log(`[DEBUG] splitLogs: Found MEMORY marker at line ${i}: "${l}"`);
+      continue; 
+    }
+    if (/=== QUERY CRAFT AGENT ===/i.test(l) || /QUERY_CRAFT/i.test(l) || /MICRO-HINT/i.test(l)) { 
+      mode = 'querycraft'; 
+      querycraftMarkerFound = true;
+      console.log(`[DEBUG] splitLogs: Found QUERYCRAFT marker at line ${i}: "${l}"`);
+      continue; 
+    }
+    if (/=== RESULT PRESENTER AGENT ===/i.test(l) || /PRESENTER/i.test(l)) { 
+      mode = 'presenter'; 
+      presenterMarkerFound = true;
+      console.log(`[DEBUG] splitLogs: Found PRESENTER marker at line ${i}: "${l}"`);
+      continue; 
+    }
+    // Additional patterns to catch query craft related logs
+    if (/\[FAST\]/i.test(l) || /\[GEN\]/i.test(l) || /\[MICRO-HINT\]/i.test(l)) { 
+      mode = 'querycraft'; 
+      console.log(`[DEBUG] splitLogs: Found QUERYCRAFT pattern at line ${i}: "${l}"`);
+    }
+    // Heuristic: SQL lines go to querycraft
+    if (/^(SELECT|WITH|INSERT|UPDATE|DELETE)\b/i.test(l)) { 
+      mode = 'querycraft'; 
+      sections.querycraft += l + '\n'; 
+      console.log(`[DEBUG] splitLogs: Found SQL pattern at line ${i}, mode set to querycraft`);
+      continue; 
+    }
+    if (!mode) {
+      // Seed with memory until a clearer marker appears
+      sections.memory += line + '\n';
+    } else {
+      sections[mode] += line + '\n';
+    }
+  }
+
+  console.log('[DEBUG] splitLogs: Markers found - Memory:', memoryMarkerFound, 'QueryCraft:', querycraftMarkerFound, 'Presenter:', presenterMarkerFound);
+  console.log('[DEBUG] splitLogs: Section lengths - Memory:', sections.memory.length, 'QueryCraft:', sections.querycraft.length, 'Presenter:', sections.presenter.length);
+  console.log('[DEBUG] splitLogs: Memory section preview:', sections.memory.substring(0, 200));
+  console.log('[DEBUG] splitLogs: QueryCraft section preview:', sections.querycraft.substring(0, 200));
+  console.log('[DEBUG] splitLogs: Presenter section preview:', sections.presenter.substring(0, 200));
+
+  return sections;
+}
+
+function findSQL(logs) {
+  if (!logs) return '';
+  // Primary: explicit markers
+  const markerRe = /<<<SQL_REVISED_START>>>([\s\S]*?)<<<SQL_REVISED_END>>>/m;
+  let m = logs.match(markerRe);
+  if (m) return m[1].trim();
+  const markerRe2 = /<<<SQL_START>>>([\s\S]*?)<<<SQL_END>>>/m;
+  m = logs.match(markerRe2);
+  if (m) return m[1].trim();
+  // Fallback: first standalone SQL-ish block up to semicolon
+  const fallback = logs.match(/^(?:.*?)(SELECT|WITH)[^;]{0,4000};/im);
+  if (fallback) {
+    const startIdx = fallback.index;
+    if (startIdx != null) {
+      const slice = logs.slice(startIdx).match(/([\s\S]*?;)/);
+      if (slice) return slice[1].trim();
+    }
+  }
+  return '';
+}
+
+async function runQuery(q){
+  if (!q) return alert('Please enter a question');
+  answerEl.textContent = 'Thinking...';
+  setRunning(true);
+  timeEl.textContent = '';
+  sqlEl.textContent = '-- SQL will appear here --';
+  memoryEl.textContent = querycraftEl.textContent = presenterEl.textContent = '—';
+  
+  const startTime = Date.now();
+  
+  try {
+    const body = { question: q, mode: currentMode };
+    if (currentMode === 'ocr_qa' && currentDocId) body.doc_id = currentDocId;
+    const res = await fetch('/api/query', {
+      method: 'POST', headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify(body)
+    });
+    let j = null;
+    if (!res.ok) {
+      // Read the body EXACTLY once, then parse — reading .json() then .text()
+      // on the same Response throws "body stream already read".
+      const raw = await res.text().catch(() => '');
+      try { j = raw ? JSON.parse(raw) : null; } catch (err) { j = null; }
+      if (!j) {
+        answerEl.textContent = 'Error ' + res.status + ': ' + (raw || res.statusText || 'request failed');
+        setRunning(false);
+        return;
+      }
+      if (j.error === 'worker_timeout') { answerEl.textContent = 'Worker timeout: ' + (j.message || 'The worker did not respond in time.'); setRunning(false); return; }
+      answerEl.textContent = j.error || j.message || JSON.stringify(j);
+      if (j.trace) presenterEl.textContent = j.trace;
+      setRunning(false);
+      return;
+    }
+    j = await res.json();
+    
+    const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
+    
+    if (j.answer) {
+      // Render markdown/pipe tables as HTML tables for readability
+      if(isMarkdownTable(j.answer)){
+        const node = parseMarkdownTable(j.answer);
+        answerEl.innerHTML = '';
+        if(node) answerEl.appendChild(node);
+        else answerEl.textContent = j.answer;
+      } else {
+        // Non-table answers: preserve plain text
+        answerEl.textContent = j.answer;
+      }
+    } else answerEl.textContent = JSON.stringify(j, null, 2);
+
+    // Chart contract: render ECharts visualization if the worker attached one
+    renderChart(j.chart);
+    // A query round-tripped: the empty-state hint is no longer needed
+    if (!j.chart) {
+      const pending = document.getElementById('chartPending');
+      if (pending) pending.classList.add('hidden');
+    }
+
+    // Prefer direct sql field from worker, else derive from logs
+    if (j.sql) {
+      const codeEl = document.getElementById('sql');
+      codeEl.textContent = j.sql;
+      if(window.Prism && Prism.highlightElement) Prism.highlightElement(codeEl);
+    }
+    
+    if (j.logs) {
+      console.log('[DEBUG] runQuery: Processing logs of length:', j.logs.length);
+      const sections = splitLogs(j.logs);
+      console.log('[DEBUG] runQuery: Sections received:', {
+        memory: sections.memory.length,
+        querycraft: sections.querycraft.length,
+        presenter: sections.presenter.length
+      });
+      
+      const memoryContent = sections.memory.trim() || '—';
+      const querycraftContent = sections.querycraft.trim() || '—';
+      const presenterContent = sections.presenter.trim() || '';
+      
+      console.log('[DEBUG] runQuery: Setting memory content length:', memoryContent.length);
+      console.log('[DEBUG] runQuery: Setting querycraft content length:', querycraftContent.length);
+      console.log('[DEBUG] runQuery: Setting presenter content length:', presenterContent.length);
+      
+      memoryEl.textContent = memoryContent;
+      querycraftEl.textContent = querycraftContent;
+      
+      if(presenterContent){ 
+        console.log('[DEBUG] runQuery: Rendering presenter trace');
+        const node = renderPresenterTrace(presenterContent); 
+        presenterEl.innerHTML = ''; 
+        presenterEl.appendChild(node); 
+      } else { 
+        console.log('[DEBUG] runQuery: No presenter content, setting dash');
+        presenterEl.textContent = '—'; 
+      }
+      if (!j.sql) {
+        const sql = findSQL(j.logs) || '';
+        if (sql) { 
+          const codeEl = document.getElementById('sql'); 
+          codeEl.textContent = sql; 
+          if(window.Prism && Prism.highlightElement) Prism.highlightElement(codeEl); 
+        }
+      }
+    } else {
+      console.log('[DEBUG] runQuery: No logs in response');
+    }
+    
+    statusEl.textContent = 'Done';
+    timeEl.textContent = `${elapsed}s`;
+    setRunning(false);
+  } catch (e) {
+    answerEl.textContent = 'Error: ' + String(e);
+    setRunning(false);
+  }
+}
+
+askBtn.onclick = () => runQuery(questionEl.value.trim());
+
+recalcBtn.onclick = () => {
+  const q = questionEl.value.trim();
+  if (!q) return alert('Please enter a question');
+  const normalized = q.replace(/^(?:re-execute\s+)+/i, '');
+  const prefixed = 're-execute ' + normalized;
+  // do not modify the visible textarea; send the prefixed query directly
+  runQuery(prefixed);
+};
+
+// Enter key support
+questionEl.addEventListener('keydown', (e) => {
+  if (e.key === 'Enter' && (e.ctrlKey || e.metaKey)) {
+    e.preventDefault();
+    runQuery(questionEl.value.trim());
+  }
+});
+
+/* ============================================================================
+ * Chart rendering (ECharts, multi-hue palette + 3D-style finishes)
+ * ========================================================================== */
+// Palette: emerald first so the canonical "battery green" bars lead the system
+const SEPIA_PALETTE = [
+  '#34D399', '#22D3EE', '#A78BFA', '#FBBF24', '#F472B6',
+  '#60A5FA', '#2DD4BF', '#FB923C', '#C084FC', '#94A3B8',
+];
+
+// Chart text/line colors track the active theme (dark Graphite Halo | paper light)
+let CHART_INK = '#9DAFC4';
+let CHART_INK_FADED = '#64788F';
+let CHART_LINE = '#2A3A50';
+let CHART_SPLIT = 'rgba(42, 58, 80, 0.55)';
+let CHART_PANEL = '#0B101A';
+let TOOLTIP_BG = 'rgba(13, 19, 29, 0.95)';
+let TOOLTIP_INK = '#E8EEF7';
+// Drop-shadow strengths: charts keep their 3D lift on paper, just softer
+let SHADOW_BAR = 'rgba(0, 0, 0, 0.35)';
+let SHADOW_LIFT = 'rgba(0, 0, 0, 0.45)';
+let SHADOW_EMP = 'rgba(0, 0, 0, 0.55)';
+
+function applyChartTheme() {
+  const light = document.documentElement.classList.contains('light');
+  if (light) {
+    CHART_INK = '#4C5D75';
+    CHART_INK_FADED = '#8494A9';
+    CHART_LINE = '#C9D4E2';
+    CHART_SPLIT = 'rgba(201, 212, 226, 0.7)';
+    CHART_PANEL = '#FFFFFF';
+    TOOLTIP_BG = 'rgba(255, 255, 255, 0.97)';
+    TOOLTIP_INK = '#172231';
+    SHADOW_BAR = 'rgba(16, 24, 40, 0.14)';
+    SHADOW_LIFT = 'rgba(16, 24, 40, 0.18)';
+    SHADOW_EMP = 'rgba(16, 24, 40, 0.28)';
+  } else {
+    CHART_INK = '#9DAFC4';
+    CHART_INK_FADED = '#64788F';
+    CHART_LINE = '#2A3A50';
+    CHART_SPLIT = 'rgba(42, 58, 80, 0.55)';
+    CHART_PANEL = '#0B101A';
+    TOOLTIP_BG = 'rgba(13, 19, 29, 0.95)';
+    TOOLTIP_INK = '#E8EEF7';
+    SHADOW_BAR = 'rgba(0, 0, 0, 0.35)';
+    SHADOW_LIFT = 'rgba(0, 0, 0, 0.45)';
+    SHADOW_EMP = 'rgba(0, 0, 0, 0.55)';
+  }
+}
+applyChartTheme();
+
+// ── tiny color helpers: blend with white/dark, keep the hue family intact ──
+const _DARK_RGB = [11, 16, 26]; // --panel-inset, so depth tones stay on-theme
+
+function hexToRgb(hex) {
+  const h = hex.replace('#', '');
+  return [parseInt(h.slice(0, 2), 16), parseInt(h.slice(2, 4), 16), parseInt(h.slice(4, 6), 16)];
+}
+function mixHex(hex, rgbTarget, amount) {
+  const c = hexToRgb(hex);
+  const s = rgbTarget;
+  const r = c.map((v, i) => Math.round(v + (s[i] - v) * amount));
+  return '#' + r.map(v => v.toString(16).padStart(2, '0')).join('');
+}
+const lightenHex = (hex, f = 0.5) => mixHex(hex, [255, 255, 255], f);
+const deepenHex = (hex, f = 0.35) => mixHex(hex, _DARK_RGB, f);
+const withAlpha  = (hex, a) => {
+  const [r, g, b] = hexToRgb(hex);
+  return `rgba(${r}, ${g}, ${b}, ${a})`;
+};
+
+// Battery-icon style bar body: light specular cap -> pure hue -> deep base
+function barGradient(hex, echarts) {
+  return new echarts.graphic.LinearGradient(0, 0, 0, 1, [
+    { offset: 0, color: lightenHex(hex, 0.55) },
+    { offset: 0.10, color: lightenHex(hex, 0.30) },
+    { offset: 0.38, color: hex },
+    { offset: 1, color: deepenHex(hex, 0.40) },
+  ]);
+}
+// Soft rising area fill under a line
+function areaGradient(hex, echarts) {
+  return new echarts.graphic.LinearGradient(0, 0, 0, 1, [
+    { offset: 0, color: withAlpha(hex, 0.38) },
+    { offset: 1, color: withAlpha(hex, 0.02) },
+  ]);
+}
+
+let chartInstance = null;
+let chartResizeObserver = null;
+let lastChartSpec = null; // kept so a theme switch can re-render the chart in the new ink
+
+function mountChart(el) {
+  // HiDPI-crisp + fluid: render at device pixel ratio (min 2x) and resize the
+  // bitmap whenever the container's CSS size changes. Without this the canvas
+  // bitmap is stretched by CSS and charts look blurry.
+  if (chartInstance) { chartInstance.dispose(); chartInstance = null; }
+  if (chartResizeObserver) { chartResizeObserver.disconnect(); chartResizeObserver = null; }
+  const dpr = Math.max(window.devicePixelRatio || 1, 2);
+  chartInstance = echarts.init(el, null, { renderer: 'canvas', devicePixelRatio: dpr });
+  if (typeof ResizeObserver !== 'undefined') {
+    chartResizeObserver = new ResizeObserver(() => {
+      if (!chartInstance || chartInstance.isDisposed()) return;
+      chartInstance.resize({ animation: { duration: 120 } });
+    });
+    chartResizeObserver.observe(el);
+  }
+  return chartInstance;
+}
+
+function darkTooltip(trigger) {
+  return {
+    trigger,
+    backgroundColor: TOOLTIP_BG,
+    borderColor: CHART_LINE,
+    textStyle: { color: TOOLTIP_INK, fontSize: 12 },
+  };
+}
+
+function renderChart(spec) {
+  lastChartSpec = spec || null;
+  const box = document.getElementById('chartBox');
+  const canvas = document.getElementById('chartCanvas');
+  const caption = document.getElementById('chartCaption');
+  const pending = document.getElementById('chartPending');
+  if (!box || !canvas) return;
+  if (!spec || !spec.type) { box.classList.add('hidden'); if (chartInstance) { chartInstance.dispose(); chartInstance = null; } return; }
+  if (typeof echarts === 'undefined') { box.classList.add('hidden'); return; }
+  if (pending) pending.classList.add('hidden');
+
+  box.classList.remove('hidden');
+  mountChart(canvas);
+
+  const base = {
+    backgroundColor: 'transparent',
+    color: SEPIA_PALETTE,
+    tooltip: darkTooltip(spec.type === 'pie' ? 'item' : 'axis'),
+    grid: { left: 12, right: 18, top: 42, bottom: 12, containLabel: true },
+    textStyle: { color: CHART_INK, fontFamily: 'Inter, sans-serif' },
+  };
+
+  let option = base;
+  const xAxis = {
+    type: 'category',
+    data: spec.x || [],
+    axisLine: { lineStyle: { color: CHART_LINE } },
+    axisTick: { lineStyle: { color: CHART_LINE } },
+    axisLabel: { color: CHART_INK, rotate: (spec.x || []).length > 8 ? 30 : 0, hideOverlap: true, fontSize: 11 },
+  };
+  const yAxis = {
+    type: 'value',
+    splitLine: { lineStyle: { color: CHART_SPLIT } },
+    axisLabel: { color: CHART_INK_FADED, fontSize: 11 },
+  };
+  const seriesData = (spec.series || []).map(s => ({ name: s.name, values: s.values || [] }));
+
+  if (spec.type === 'pie') {
+    const data = (spec.x || []).map((lab, i) => ({
+      name: lab,
+      value: (seriesData[0] && seriesData[0].values[i]) || 0,
+    }));
+    option = {
+      ...base, tooltip: darkTooltip('item'),
+      legend: { bottom: 0, textStyle: { color: CHART_INK, fontSize: 11 }, type: 'scroll', icon: 'roundRect', itemWidth: 14, itemHeight: 8 },
+      series: [{
+        type: 'pie', radius: ['40%', '70%'], center: ['50%', '46%'],
+        data,
+        itemStyle: {
+          borderColor: CHART_PANEL, borderWidth: 2, borderRadius: 8,
+          shadowBlur: 14, shadowColor: SHADOW_LIFT, shadowOffsetY: 3,
+        },
+        label: { color: CHART_INK, fontSize: 11 },
+        emphasis: {
+          itemStyle: {
+            shadowBlur: 22, shadowColor: SHADOW_EMP,
+            shadowOffsetY: 5, scale: true, scaleSize: 4,
+          },
+          label: { fontSize: 13, fontWeight: 600 },
+        },
+      }],
+    };
+  } else if (spec.type === 'line') {
+    const single = seriesData.length === 1;
+    option = {
+      ...base, xAxis, yAxis,
+      legend: seriesData.length > 1 ? { top: 4, textStyle: { color: CHART_INK, fontSize: 11 }, icon: 'roundRect', itemWidth: 14, itemHeight: 8 } : undefined,
+      series: seriesData.map((s, i) => {
+        const tint = SEPIA_PALETTE[i % SEPIA_PALETTE.length];
+        return {
+          type: 'line', name: s.name, data: s.values,
+          smooth: true, symbol: 'circle', symbolSize: 7,
+          lineStyle: {
+            width: 3,
+            shadowColor: withAlpha(tint, 0.45), shadowBlur: 10, shadowOffsetY: 5,
+          },
+          itemStyle: { color: tint, borderColor: lightenHex(tint, 0.6), borderWidth: 2 },
+          areaStyle: single ? { color: areaGradient(tint, echarts) } : undefined,
+        };
+      }),
+    };
+  } else {
+    // Bars: vertical gradient ("battery" body) + rounded top + soft drop shadow
+    option = {
+      ...base, xAxis, yAxis,
+      legend: seriesData.length > 1 ? { top: 4, textStyle: { color: CHART_INK, fontSize: 11 }, icon: 'roundRect', itemWidth: 14, itemHeight: 8 } : undefined,
+      series: seriesData.map((s, i) => {
+        const tint = SEPIA_PALETTE[i % SEPIA_PALETTE.length];
+        return {
+          type: 'bar', name: s.name, data: s.values,
+          itemStyle: {
+            color: barGradient(tint, echarts),
+            borderRadius: [6, 6, 2, 2],
+            shadowBlur: 7, shadowColor: SHADOW_BAR, shadowOffsetY: 3,
+          },
+          emphasis: {
+            itemStyle: {
+              color: barGradient(lightenHex(tint, 0.18), echarts),
+              shadowBlur: 12, shadowColor: withAlpha(tint, 0.4), shadowOffsetY: 4,
+            },
+          },
+          barMaxWidth: 56,
+        };
+      }),
+    };
+  }
+
+  chartInstance.setOption(option);
+  // Flush one resize after layout settles so nothing was measured mid-show.
+  requestAnimationFrame(() => { if (chartInstance && !chartInstance.isDisposed()) chartInstance.resize(); });
+  if (caption) {
+    caption.textContent = (spec.title || '') + (spec.rows_in_result ? ` · ${spec.rows_in_result} data points` : '');
+  }
+}
+
+window.addEventListener('resize', () => { if (chartInstance) chartInstance.resize(); });
+
+/* ============================================================================
+ * Dynamic database profile manager (Phase 1 API + Phase 4 UI)
+ * ========================================================================== */
+const schemaProfilesEl = document.getElementById('schemaProfiles');
+const schemaTablesEl = document.getElementById('schemaTables');
+const schemaNameEl = document.getElementById('schemaName');
+const schemaUrlEl = document.getElementById('schemaUrl');
+const schemaAddBtn = document.getElementById('schemaAddBtn');
+const schemaNotesEl = document.getElementById('schemaNotes');
+const schemaFileBtn = document.getElementById('schemaFileBtn');
+const dbBadge = document.getElementById('dbBadge');
+const dbTag = document.getElementById('dbTag');
+
+async function apiSchemas(method, path, body, isForm) {
+  const opts = { method, headers: {} };
+  if (body !== undefined) {
+    if (isForm) { opts.body = body; }
+    else { opts.headers['Content-Type'] = 'application/json'; opts.body = JSON.stringify(body); }
+  }
+  const r = await fetch(path, opts);
+  let j = null;
+  try { j = await r.json(); } catch (e) { j = null; }
+  if (!r.ok) throw new Error((j && (j.error || j.detail)) || r.statusText);
+  return j;
+}
+
+function setActiveProfileName(name) {
+  if (dbBadge) dbBadge.textContent = name || '—';
+  if (dbTag) dbTag.textContent = name || 'demo_employee';
+}
+
+async function refreshSchemaPanel() {
+  let data;
+  try { data = await apiSchemas('GET', '/api/schemas'); }
+  catch (e) {
+    if (schemaProfilesEl) schemaProfilesEl.innerHTML = '<div class="profile-item"><span class="profile-name">(schema service offline)</span></div>';
+    return;
+  }
+  const profiles = (data.profiles || []);
+  setActiveProfileName((profiles.find(p => p.active) || {}).name || '—');
+
+  if (schemaProfilesEl) {
+    schemaProfilesEl.innerHTML = '';
+    if (!profiles.length) {
+      schemaProfilesEl.innerHTML = '<div class="profile-item"><span class="profile-name">No profiles yet — add one below</span></div>';
+    }
+    for (const p of profiles) {
+      const item = document.createElement('div');
+      item.className = 'profile-item' + (p.active ? ' active' : '');
+      const dot = document.createElement('span');
+      dot.className = 'profile-dot' + (p.active ? ' on' : '');
+      const nm = document.createElement('span');
+      nm.className = 'profile-name'; nm.textContent = p.name; nm.title = p.db_url || '';
+      const kind = document.createElement('span');
+      kind.className = 'profile-kind'; kind.textContent = p.kind || 'user';
+      const actions = document.createElement('span'); actions.className = 'profile-actions';
+      if (!p.active) {
+        const sw = document.createElement('button');
+        sw.className = 'profile-btn'; sw.textContent = 'Switch';
+        sw.onclick = async () => {
+          sw.disabled = true; sw.textContent = '…';
+          try { await apiSchemas('POST', '/api/schemas/switch/' + p.id); await refreshSchemaPanel(); }
+          catch (e) { alert('Switch failed: ' + e.message); sw.disabled = false; sw.textContent = 'Switch'; }
+        };
+        actions.appendChild(sw);
+      }
+      const nt = document.createElement('button');
+      nt.className = 'profile-btn';
+      nt.textContent = ((p.notes || '').trim()) ? 'Notes •' : 'Notes';
+      nt.title = 'Schema notes — domain hints injected into every prompt for this database';
+      nt.onclick = () => toggleSchemaNotesEditor(item, p);
+      actions.appendChild(nt);
+      const del = document.createElement('button');
+      del.className = 'profile-btn danger'; del.textContent = '×';
+      del.title = 'Remove profile';
+      del.onclick = async () => {
+        if (!confirm(`Remove profile "${p.name}"?`)) return;
+        try { await apiSchemas('DELETE', '/api/schemas/' + p.id); await refreshSchemaPanel(); }
+        catch (e) { alert('Delete failed: ' + e.message); }
+      };
+      actions.appendChild(del);
+      item.append(dot, nm, kind, actions);
+      schemaProfilesEl.appendChild(item);
+    }
+  }
+
+  // Current schema tables (best-effort)
+  try {
+    const cur = await apiSchemas('GET', '/api/schema/current');
+    if (schemaTablesEl) {
+      schemaTablesEl.innerHTML = '';
+      const tables = cur.tables || [];
+      if (!tables.length) { schemaTablesEl.innerHTML = ''; return; }
+      for (const t of tables.slice(0, 10)) {
+        const d = document.createElement('div');
+        d.className = 'tbl';
+        d.innerHTML = `<b>${escapeHtml(t.name)}</b> <span class="rc">(${t.row_count ?? '?'} rows · ${t.columns.length} cols)</span>`;
+        d.title = t.columns.join(', ');
+        schemaTablesEl.appendChild(d);
+      }
+    }
+  } catch (e) { /* schema detail endpoint optional */ }
+}
+
+function toggleSchemaNotesEditor(item, p) {
+  // Toggle this row's notes editor; only one open at a time.
+  const prev = item.nextElementSibling;
+  if (prev && prev.classList && prev.classList.contains('notes-editor')) { prev.remove(); return; }
+  document.querySelectorAll('.notes-editor').forEach(el => el.remove());
+  const editor = document.createElement('div');
+  editor.className = 'notes-editor';
+  const ta = document.createElement('textarea');
+  ta.value = p.notes || '';
+  ta.maxLength = 4000;
+  ta.rows = 3;
+  ta.placeholder = "What your columns mean, e.g. 'sold_date IS NULL = still in inventory', 'msrp = list price, sale_price = actual', revenue rules, aliases";
+  const row = document.createElement('div');
+  row.className = 'notes-editor-actions';
+  const save = document.createElement('button');
+  save.className = 'profile-btn primary'; save.textContent = 'Save notes';
+  const cancel = document.createElement('button');
+  cancel.className = 'profile-btn'; cancel.textContent = 'Cancel';
+  save.onclick = async () => {
+    save.disabled = true; save.textContent = 'Saving…';
+    try {
+      const res = await apiSchemas('POST', '/api/schemas/' + p.id + '/notes', { notes: ta.value });
+      p.notes = (res && res.notes) || '';
+      await refreshSchemaPanel();
+      if (statusEl) statusEl.textContent = 'Schema notes updated for "' + p.name + '"';
+    } catch (e) {
+      alert('Saving notes failed: ' + e.message);
+      save.disabled = false; save.textContent = 'Save notes';
+    }
+  };
+  cancel.onclick = () => editor.remove();
+  row.append(save, cancel);
+  editor.append(ta, row);
+  item.after(editor);
+  ta.focus();
+}
+
+function escapeHtml(s) {
+  return String(s).replace(/[&<>"']/g, c => ({ '&': '&amp;', '<': '&lt;', '>': '&gt;', '"': '&quot;', "'": '&#39;' }[c]));
+}
+
+async function addSchemaFromUrl() {
+  const name = (schemaNameEl && schemaNameEl.value.trim()) || '';
+  const url = (schemaUrlEl && schemaUrlEl.value.trim()) || '';
+  const notes = (schemaNotesEl && schemaNotesEl.value.trim()) || '';
+  if (!name || !url) { alert('Provide both a profile name and a connection URL (or use file upload).'); return; }
+  schemaAddBtn.disabled = true; schemaAddBtn.textContent = 'Connecting…';
+  try {
+    await apiSchemas('POST', '/api/schemas/add', { name, connection_url: url, notes });
+    schemaNameEl.value = ''; schemaUrlEl.value = '';
+    if (schemaNotesEl) schemaNotesEl.value = '';
+    await refreshSchemaPanel();
+    statusEl.textContent = `Connected to "${name}"`;
+  } catch (e) {
+    alert('Connect failed: ' + e.message);
+  } finally {
+    schemaAddBtn.disabled = false; schemaAddBtn.textContent = 'Connect';
+  }
+}
+
+function addSchemaFromFile() {
+  const tmp = document.createElement('input');
+  tmp.type = 'file';
+  tmp.accept = '.sqlite,.db,.sqlite3,.csv';
+  tmp.style.position = 'absolute'; tmp.style.left = '-9999px';
+  document.body.appendChild(tmp);
+  tmp.addEventListener('change', async () => {
+    const f = tmp.files && tmp.files[0];
+    document.body.removeChild(tmp);
+    if (!f) return;
+    if (!schemaNameEl.value.trim()) schemaNameEl.value = f.name.replace(/\.[^.]+$/, '');
+    const fd = new FormData();
+    fd.append('file', f);
+    fd.append('name', schemaNameEl.value.trim());
+    fd.append('activate', 'true');
+    if (schemaNotesEl && schemaNotesEl.value.trim()) fd.append('notes', schemaNotesEl.value.trim());
+    schemaFileBtn.disabled = true; schemaFileBtn.textContent = 'Importing…';
+    try {
+      await fetch('/api/schemas/add-file', { method: 'POST', body: fd }).then(async r => {
+        if (!r.ok) { const j = await r.json().catch(() => ({})); throw new Error(j.detail || r.statusText); }
+        return r.json();
+      });
+      await refreshSchemaPanel();
+      statusEl.textContent = `Imported "${schemaNameEl.value.trim()}"`;
+      schemaNameEl.value = '';
+      if (schemaNotesEl) schemaNotesEl.value = '';
+    } catch (e) {
+      alert('Import failed: ' + e.message);
+    } finally {
+      schemaFileBtn.disabled = false; schemaFileBtn.textContent = 'Upload .sqlite / .csv';
+    }
+  });
+  tmp.click();
+}
+
+if (schemaAddBtn) schemaAddBtn.onclick = addSchemaFromUrl;
+if (schemaFileBtn) schemaFileBtn.onclick = addSchemaFromFile;
+
+/* ── Theme toggle (dark Graphite Halo default ↔ light paper) ───────── */
+const themeToggle = document.getElementById('themeToggle');
+const isLightTheme = () => document.documentElement.classList.contains('light');
+
+function refreshChartsForTheme() {
+  applyChartTheme();
+  const box = document.getElementById('chartBox');
+  if (lastChartSpec && box && !box.classList.contains('hidden') && typeof echarts !== 'undefined') {
+    renderChart(lastChartSpec);
+  }
+}
+
+function applyTheme(light) {
+  document.documentElement.classList.toggle('light', light);
+  try { localStorage.setItem('ql.theme', light ? 'light' : 'dark'); } catch (e) { /* private mode */ }
+  if (themeToggle) {
+    const label = light ? 'Switch to dark mode' : 'Switch to light mode';
+    themeToggle.title = label;
+    themeToggle.setAttribute('aria-label', label);
+  }
+  refreshChartsForTheme();
+}
+
+if (themeToggle) {
+  let savedTheme = null;
+  try { savedTheme = localStorage.getItem('ql.theme'); } catch (e) { /* ignore */ }
+  applyTheme(savedTheme === 'light'); // dark stays the brand default
+  themeToggle.addEventListener('click', () => applyTheme(!isLightTheme()));
+}
+
+// Initial load
+refreshSchemaPanel();
+
diff --git a/web/static/index.html b/web/static/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..c06e7ee349cf5495180ee67624418375177b3617
--- /dev/null
+++ b/web/static/index.html
@@ -0,0 +1,994 @@
+<!doctype html>
+<html lang="en">
+
+<head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <title>QueryLens — Transform natural language into SQL</title>
+    <link rel="icon"
+        href='data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 48 48"><rect width="48" height="48" rx="10" fill="%230F151F"/><g stroke="%2322D3EE" stroke-width="2.6" fill="none"><ellipse cx="20" cy="14" rx="10.5" ry="4"/><path d="M9.5 14v20c0 2.2 4.7 4 10.5 4s10.5-1.8 10.5-4V14"/><path d="M9.5 23c0 2.2 4.7 4 10.5 4s10.5-1.8 10.5-4"/></g><circle cx="32.5" cy="27.5" r="8.2" fill="rgba(34,211,238,.18)" stroke="%237DECFB" stroke-width="2.4"/><path d="M38.6 33.6L44.4 39.4" stroke="%237DECFB" stroke-width="2.8" stroke-linecap="round"/></svg>'>
+
+    <!-- Fonts -->
+    <link rel="preconnect" href="https://fonts.googleapis.com">
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+    <link
+        href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=Space+Grotesk:wght@500;600;700&family=JetBrains+Mono:wght@400;600&display=swap"
+        rel="stylesheet">
+
+    <style>
+        :root {
+            /* ── Graphite Halo — professional dark w/ electric cyan ────── */
+            --accent: #22D3EE;            /* electric cyan (primary) */
+            --accent-bright: #7DECFB;
+            --accent-ink: #05242B;        /* text on cyan */
+            --accent-soft: rgba(34, 211, 238, 0.12);
+            --violet: #A78BFA;            /* secondary data accent */
+            --ocean: #60A5FA;             /* info */
+
+            --bg-page: #0A0E15;
+            --bg-raised: #0E141F;
+            --panel: #111927;
+            --panel-inset: #0B101A;
+
+            --border: #1F2A3A;
+            --border-light: #17202D;
+            --border-strong: #2A3A50;
+
+            --text-primary: #E8EEF7;
+            --text-secondary: #9DAFC4;
+            --text-tertiary: #64788F;
+
+            --success: #34D399;
+            --warning: #FBBF24;
+            --error: #F87171;
+
+            /* Spacing */
+            --space-1: 0.25rem;
+            --space-2: 0.5rem;
+            --space-3: 0.75rem;
+            --space-4: 1rem;
+            --space-5: 1.25rem;
+            --space-6: 1.5rem;
+            --space-8: 2rem;
+            --space-10: 2.5rem;
+
+            /* Shadows — cool, tight, professional */
+            --shadow-sm: 0 1px 2px 0 rgb(0 0 0 / 0.35);
+            --shadow-md: 0 4px 14px -4px rgb(0 0 0 / 0.45), 0 1px 3px -1px rgb(0 0 0 / 0.3);
+            --shadow-glow: 0 0 18px rgb(34 211 238 / 0.22);
+
+            --radius-sm: 8px;
+            --radius-md: 12px;
+            --radius-lg: 16px;
+            --radius-xl: 20px;
+
+            --transition-fast: 160ms ease;
+            --transition-normal: 240ms ease;
+        }
+
+        /* ── Light mode (paper) — same multi-hue system, calmer ambient ── */
+        html.light {
+            --bg-page: #F2F5FA;
+            --bg-raised: #FFFFFF;
+            --panel: #FFFFFF;
+            --panel-inset: #F5F8FC;
+
+            --border: #DFE6EF;
+            --border-light: #EAEFF6;
+            --border-strong: #C9D4E2;
+
+            --text-primary: #172231;
+            --text-secondary: #4C5D75;
+            --text-tertiary: #8091A6;
+
+            --shadow-sm: 0 1px 2px 0 rgb(16 24 40 / 0.07);
+            --shadow-md: 0 4px 14px -4px rgb(16 24 40 / 0.12), 0 1px 3px -1px rgb(16 24 40 / 0.08);
+            --shadow-glow: 0 0 18px rgb(34 211 238 / 0.28);
+        }
+        html.light ::-webkit-scrollbar-thumb { background: #C3CEDC; }
+        html.light ::selection { background: rgb(34 211 238 / .25); color: var(--text-primary); }
+        html.light body {
+            background:
+                radial-gradient(1100px 420px at 50% -220px, rgb(34 211 238 / 0.08), transparent 70%),
+                radial-gradient(900px 400px at 8% 108%, rgb(167 139 250 / 0.05), transparent 70%),
+                var(--bg-page);
+        }
+        html.light .brand-icon {
+            background: linear-gradient(150deg, #DFF7FA 0%, #CBEDF5 100%);
+            border-color: rgb(8 145 178 / .35);
+        }
+        html.light .brand-text h1 .lens,
+        html.light .header-tag,
+        html.light .profile-btn:hover { color: #0E7490; }
+        html.light .profile-btn:hover { border-color: rgb(8 145 178 / .45); }
+        html.light .table-resize-btn:hover { border-color: rgb(8 145 178 / .5); color: #0E7490; }
+        html.light .answer-table thead th,
+        html.light .answer-content th { color: #0E7490; }
+        html.light .answer-table tbody tr:hover td { background: rgb(34 211 238 / .07); }
+        html.light code.language-sql, html.light pre.sql-content code { color: #24435C; }
+        html.light .c-cyan    { color: #0E7490; background: rgb(14 116 144 / .09); border-color: rgb(14 116 144 / .28); }
+        html.light .c-violet  { color: #7C3AED; background: rgb(124 58 237 / .08); border-color: rgb(124 58 237 / .26); }
+        html.light .c-emerald { color: #059669; background: rgb(5 150 105 / .09); border-color: rgb(5 150 105 / .28); }
+        html.light .c-blue    { color: #2563EB; background: rgb(37 99 235 / .08); border-color: rgb(37 99 235 / .26); }
+        html.light .c-amber   { color: #B45309; background: rgb(180 83 9 / .09); border-color: rgb(180 83 9 / .28); }
+        html.light .c-rose    { color: #DB2777; background: rgb(219 39 119 / .08); border-color: rgb(219 39 119 / .26); }
+        html.light .btn-outline-violet { color: #7C3AED; }
+        html.light .btn-outline-violet:hover { background: rgb(124 58 237 / .08); border-color: rgb(124 58 237 / .7); color: #6D28D9; }
+        html.light .btn-outline-rose { color: #DB2777; }
+        html.light .btn-outline-rose:hover { background: rgb(219 39 119 / .08); border-color: rgb(219 39 119 / .7); color: #9D174D; }
+        html.light .btn-outline-amber { color: #B45309; }
+        html.light .btn-outline-amber:hover { background: rgb(180 83 9 / .08); border-color: rgb(180 83 9 / .7); color: #92400E; }
+        html.light .profile-btn.primary { border-color: rgb(5 150 105 / .55); color: #059669; }
+        html.light .profile-btn.primary:hover { background: rgb(5 150 105 / .09); border-color: #059669; color: #047857; }
+        html.light .db-badge { color: #059669; background: rgb(5 150 105 / .09); border-color: rgb(5 150 105 / .30); }
+
+        /* ── Theme toggle (header) ─────────────────────────────────── */
+        .header-right { display: inline-flex; align-items: center; gap: 10px; }
+        .theme-toggle {
+            width: 34px; height: 34px; border-radius: var(--radius-sm);
+            display: inline-flex; align-items: center; justify-content: center;
+            cursor: pointer; color: var(--text-secondary);
+            background: var(--panel); border: 1px solid var(--border-strong);
+            transition: color var(--transition-fast), border-color var(--transition-fast),
+                        box-shadow var(--transition-fast);
+        }
+        .theme-toggle:hover { color: var(--accent); border-color: rgb(34 211 238 / .45); box-shadow: 0 0 10px rgb(34 211 238 / .25); }
+        .theme-toggle .icon-moon { display: none; }
+        html.light .theme-toggle { color: #0E7490; border-color: rgb(8 145 178 / .35); }
+        html.light .theme-toggle:hover { color: #0E7490; border-color: rgb(8 145 178 / .6); box-shadow: 0 0 10px rgb(8 145 178 / .22); }
+        html.light .theme-toggle .icon-sun { display: none; }
+        html.light .theme-toggle .icon-moon { display: inline; }
+
+        *, *::before, *::after { box-sizing: border-box; }
+
+        html, body {
+            height: 100%;
+            margin: 0;
+            font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+            background:
+                radial-gradient(1100px 420px at 50% -220px, rgb(34 211 238 / 0.07), transparent 70%),
+                radial-gradient(900px 400px at 8% 108%, rgb(167 139 250 / 0.06), transparent 70%),
+                var(--bg-page);
+            color: var(--text-primary);
+            -webkit-font-smoothing: antialiased;
+            -moz-osx-font-smoothing: grayscale;
+            font-size: 15.5px;
+            line-height: 1.6;
+        }
+
+        h1, h2, h3, h4 {
+            font-family: 'Space Grotesk', 'Inter', sans-serif;
+            font-weight: 700;
+            letter-spacing: -0.015em;
+        }
+
+        ::selection { background: rgba(34, 211, 238, 0.30); color: var(--text-primary); }
+
+        ::-webkit-scrollbar { width: 10px; height: 10px; }
+        ::-webkit-scrollbar-thumb { background: #253349; border-radius: 8px; border: 2px solid var(--bg-page); }
+        ::-webkit-scrollbar-track { background: transparent; }
+
+        /* ── Layout ──────────────────────────────────────────────────── */
+        .app-container {
+            min-height: 100vh;
+            display: flex;
+            flex-direction: column;
+        }
+
+        .header {
+            background: var(--bg-page);
+            border-bottom: 1px solid var(--border-light);
+            position: relative;
+        }
+        /* Tron-inspired hairline: a single cyan energy line along the top edge */
+        .header::before {
+            content: "";
+            position: absolute;
+            inset: 0 0 auto 0;
+            height: 2px;
+            background: linear-gradient(90deg,
+                transparent 2%, rgb(34 211 238 / .55) 30%, rgb(34 211 238 / .15) 65%, transparent 98%);
+            pointer-events: none;
+        }
+
+        /* Header shares exact alignment with .main-content so the brand line
+           up with the query card edge breaks. */
+        .header-content {
+            max-width: 1240px;
+            margin: 0 auto;
+            padding: var(--space-4) var(--space-8);
+            display: flex;
+            align-items: center;
+            justify-content: space-between;
+            gap: var(--space-4);
+        }
+
+        .brand { display: flex; align-items: center; gap: 0.85rem; }
+
+        .brand-icon {
+            width: 42px; height: 42px;
+            border-radius: var(--radius-md);
+            background: linear-gradient(150deg, #0F2A35 0%, #123441 100%);
+            border: 1px solid rgb(34 211 238 / 0.35);
+            color: var(--accent-bright);
+            display: grid; place-items: center;
+            box-shadow: var(--shadow-glow);
+        }
+
+        .brand-text h1 {
+            margin: 0; font-size: 1.3rem; line-height: 1.2; color: var(--text-primary);
+        }
+        .brand-text h1 .lens { color: var(--accent); }
+        .brand-text p {
+            margin: 1px 0 0; font-size: 0.82rem; color: var(--text-tertiary);
+            letter-spacing: 0.01em;
+        }
+
+        .header-tag {
+            font-family: 'JetBrains Mono', monospace;
+            font-size: 0.74rem;
+            color: var(--accent);
+            border: 1px solid rgb(34 211 238 / 0.30);
+            background: var(--accent-soft);
+            border-radius: 999px;
+            padding: 0.3rem 0.8rem;
+            white-space: nowrap;
+        }
+
+        .main-content {
+            max-width: 1240px;
+            width: 100%;
+            margin: 0 auto;
+            padding: var(--space-6) var(--space-8) var(--space-10);
+            flex: 1;
+        }
+
+        /* ── Query composer ──────────────────────────────────────────── */
+        .query-section { margin-bottom: var(--space-6); }
+
+        .query-body {
+            background: var(--panel);
+            border: 1px solid var(--border);
+            border-radius: var(--radius-lg);
+            box-shadow: var(--shadow-md);
+            padding: var(--space-5);
+            transition: border-color var(--transition-fast), box-shadow var(--transition-fast);
+        }
+        .query-body:focus-within {
+            border-color: rgb(34 211 238 / 0.55);
+            box-shadow: 0 0 0 3px rgb(34 211 238 / 0.14), var(--shadow-md);
+        }
+
+        .query-textarea {
+            width: 100%;
+            min-height: 96px;
+            border: none;
+            outline: none;
+            resize: vertical;
+            font-family: inherit;
+            font-size: 1.02rem;
+            color: var(--text-primary);
+            background: transparent;
+            line-height: 1.55;
+        }
+        .query-textarea::placeholder { color: var(--text-tertiary); }
+
+        .query-controls {
+            display: flex; align-items: center; gap: var(--space-3);
+            margin-top: var(--space-3);
+            padding-top: var(--space-3);
+            border-top: 1px solid var(--border-light);
+            flex-wrap: wrap;
+        }
+
+        .mode-select {
+            font-family: inherit;
+            font-size: 0.86rem;
+            color: var(--text-secondary);
+            background: var(--panel-inset);
+            border: 1px solid var(--border-strong);
+            border-radius: var(--radius-sm);
+            padding: 0.42rem 0.6rem;
+            cursor: pointer;
+        }
+
+        .btn {
+            display: inline-flex; align-items: center; gap: 0.45rem;
+            font-family: inherit; font-size: 0.9rem; font-weight: 600;
+            border-radius: var(--radius-sm);
+            padding: 0.5rem 1.05rem;
+            cursor: pointer;
+            border: 1px solid transparent;
+            transition: background var(--transition-fast), border-color var(--transition-fast),
+                        transform var(--transition-fast), opacity var(--transition-fast),
+                        box-shadow var(--transition-fast);
+        }
+        .btn:active { transform: translateY(1px); }
+        .btn:disabled { opacity: 0.55; cursor: not-allowed; }
+
+        .btn-primary {
+            background: var(--accent);
+            color: var(--accent-ink);
+            border-color: var(--accent);
+            box-shadow: 0 0 14px rgb(34 211 238 / 0.25);
+        }
+        .btn-primary:hover:not(:disabled) {
+            background: var(--accent-bright);
+            border-color: var(--accent-bright);
+            box-shadow: 0 0 20px rgb(34 211 238 / 0.4);
+        }
+
+        .btn-secondary {
+            background: transparent;
+            color: var(--text-secondary);
+            border-color: var(--border-strong);
+        }
+        .btn-secondary:hover:not(:disabled) {
+            background: var(--accent-soft);
+            border-color: rgb(34 211 238 / 0.45);
+            color: var(--text-primary);
+        }
+
+        .status-area {
+            margin-left: auto;
+            display: flex; align-items: center; gap: 0.8rem;
+            font-size: 0.84rem; color: var(--text-tertiary);
+        }
+
+        .spinner {
+            width: 16px; height: 16px;
+            border: 2px solid rgb(34 211 238 / 0.25);
+            border-top-color: var(--accent);
+            border-radius: 50%;
+            animation: spin 0.8s linear infinite;
+        }
+        @keyframes spin { to { transform: rotate(360deg); } }
+
+        .hidden { display: none !important; }
+
+        /* ── Results grid ────────────────────────────────────────────── */
+        .results-grid {
+            display: grid;
+            grid-template-columns: minmax(0, 1fr) 340px;
+            gap: var(--space-6);
+            align-items: start;
+        }
+        @media (max-width: 1020px) {
+            .results-grid { grid-template-columns: 1fr; }
+        }
+
+        .card {
+            background: var(--panel);
+            border: 1px solid var(--border);
+            border-radius: var(--radius-lg);
+            box-shadow: var(--shadow-sm);
+            overflow: hidden;
+        }
+        .card + .card { margin-top: var(--space-6); }
+
+        .card-header {
+            display: flex; align-items: center; gap: 0.5rem;
+            padding: 0.95rem var(--space-5);
+            border-bottom: 1px solid var(--border-light);
+            background: var(--bg-raised);
+        }
+        .card-title {
+            margin: 0; font-size: 0.98rem; color: var(--text-primary);
+            display: flex; align-items: center; gap: 0.5rem;
+            font-family: 'Space Grotesk', 'Inter', sans-serif;
+        }
+        .card-content { padding: var(--space-5); }
+
+        .answer-content { white-space: normal; color: var(--text-primary); }
+
+        /* ── Chart box ───────────────────────────────────────────────── */
+        .chart-box {
+            margin-top: var(--space-4);
+            border: 1px solid var(--border-light);
+            background: var(--panel-inset);
+            border-radius: var(--radius-md);
+            padding: var(--space-3);
+        }
+        .chart-pending {
+            margin-top: var(--space-4);
+            border: 1px dashed var(--border-strong);
+            background: var(--panel-inset);
+            border-radius: var(--radius-md);
+            padding: var(--space-4) var(--space-3);
+            font-size: 0.82rem;
+            color: var(--text-tertiary);
+            line-height: 1.6;
+        }
+        .chart-pending.hidden { display: none; }
+        #chartCanvas { width: 100%; height: 340px; }
+        .chart-caption {
+            margin: 0.4rem 0.2rem 0;
+            font-family: 'JetBrains Mono', monospace;
+            font-size: 0.74rem; color: var(--text-tertiary);
+            text-align: right;
+        }
+
+        /* ── SQL block ──────────────────────────────────────────────── */
+        details summary { outline: none; }
+        .sql-content {
+            margin: 0;
+            background: var(--panel-inset);
+            border: 1px solid var(--border-light);
+            border-radius: var(--radius-md);
+            padding: var(--space-4);
+            overflow-x: auto;
+            font-size: 0.87rem;
+            line-height: 1.6;
+        }
+        code.language-sql, pre.sql-content code {
+            font-family: 'JetBrains Mono', 'Cascadia Code', Consolas, monospace;
+            color: #D9E7F6;
+        }
+
+        /* ── Agent panels sidebar ───────────────────────────────────── */
+        .agent-panels { display: flex; flex-direction: column; gap: var(--space-4); }
+
+        .panel {
+            background: var(--panel);
+            border: 1px solid var(--border);
+            border-radius: var(--radius-lg);
+            box-shadow: var(--shadow-sm);
+            overflow: hidden;
+        }
+        .panel summary { list-style: none; }
+        .panel summary::-webkit-details-marker { display: none; }
+
+        .panel-header {
+            display: flex; align-items: center; justify-content: space-between;
+            padding: 0.8rem var(--space-4);
+            cursor: pointer;
+            user-select: none;
+        }
+        .panel-header:hover { background: var(--bg-raised); }
+        .panel-title {
+            display: flex; align-items: center; gap: 0.5rem;
+            font-size: 0.84rem; font-weight: 600; color: var(--text-primary);
+            font-family: 'Space Grotesk', 'Inter', sans-serif;
+        }
+        .panel-title .chip svg { width: 14px; height: 14px; }
+
+        .panel-content {
+            padding: 0 var(--space-4) var(--space-4);
+            font-size: 0.76rem;
+            color: var(--text-secondary);
+            max-height: 260px;
+            overflow-y: auto;
+            white-space: pre-wrap;
+            word-break: break-word;
+            font-family: 'JetBrains Mono', Consolas, monospace;
+        }
+
+        /* ── Database panel ─────────────────────────────────────────── */
+        .db-badge {
+            margin-left: auto;
+            font-family: 'JetBrains Mono', monospace;
+            font-size: 0.7rem; font-weight: 600;
+            color: #34D399;
+            background: rgba(52, 211, 153, 0.10);
+            border: 1px solid rgba(52, 211, 153, 0.3);
+            border-radius: 999px;
+            padding: 0.12rem 0.55rem;
+        }
+
+        .profile-list { display: flex; flex-direction: column; gap: 6px; padding: 0 var(--space-4) var(--space-3); }
+        .profile-item {
+            display: flex; align-items: center; gap: 0.5rem;
+            border: 1px solid var(--border);
+            background: var(--panel-inset);
+            border-radius: var(--radius-sm);
+            padding: 0.5rem 0.65rem;
+            font-size: 0.8rem;
+        }
+        .profile-item.active {
+            border-color: rgba(52, 211, 153, 0.55);
+            background: rgba(52, 211, 153, 0.08);
+        }
+        .profile-name { font-weight: 600; color: var(--text-primary); overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }
+        .profile-kind { font-size: 0.68rem; color: var(--text-tertiary); text-transform: uppercase; letter-spacing: 0.04em; }
+        .profile-actions { margin-left: auto; display: flex; gap: 4px; }
+        .profile-btn {
+            border: 1px solid var(--border-strong);
+            background: var(--panel);
+            color: var(--text-secondary);
+            font-size: 0.7rem; font-weight: 600;
+            border-radius: 4px; padding: 2px 8px; cursor: pointer;
+            font-family: inherit;
+        }
+        .profile-btn:hover { border-color: var(--accent); color: var(--accent); }
+        .profile-btn.danger:hover { border-color: var(--error); color: var(--error); }
+        .profile-btn.primary { border-color: rgb(52 211 153 / .55); color: #34d399; }
+        .profile-btn.primary:hover { background: rgb(52 211 153 / .1); border-color: #34d399; color: #34d399; }
+        .notes-editor {
+            border: 1px solid var(--border);
+            border-top: 2px solid rgb(52 211 153 / .45);
+            background: var(--panel-inset);
+            border-radius: var(--radius-sm);
+            padding: 0.5rem 0.65rem;
+            display: flex; flex-direction: column; gap: 6px;
+        }
+        .notes-editor textarea {
+            width: 100%; resize: vertical;
+            background: var(--panel); color: var(--text-primary);
+            border: 1px solid var(--border-strong); border-radius: 4px;
+            font-family: inherit; font-size: 0.72rem; line-height: 1.45;
+            padding: 0.4rem 0.5rem;
+        }
+        .notes-editor textarea:focus { outline: none; border-color: rgb(52 211 153 / .55); }
+        .notes-editor-actions { display: flex; gap: 6px; justify-content: flex-end; }
+        .profile-dot {
+            width: 7px; height: 7px; border-radius: 50%;
+            background: var(--border-strong); flex-shrink: 0;
+        }
+        .profile-dot.on { background: var(--success); box-shadow: 0 0 6px rgb(52 211 153 / .6); }
+
+        .schema-add { padding: 0 var(--space-4) var(--space-4); border-top: 1px dashed var(--border); padding-top: var(--space-3); }
+        .schema-add label {
+            display: block; font-size: 0.72rem; font-weight: 600;
+            color: var(--text-tertiary); margin: 0.5rem 0 3px;
+            text-transform: uppercase; letter-spacing: 0.05em;
+        }
+        .schema-add input[type="text"] {
+            width: 100%; font-family: inherit; font-size: 0.8rem;
+            border: 1px solid var(--border-strong); border-radius: var(--radius-sm);
+            padding: 0.4rem 0.55rem; color: var(--text-primary);
+            background: var(--panel-inset);
+        }
+        .schema-add input[type="text"]:focus { outline: none; border-color: var(--accent); box-shadow: 0 0 0 2px rgb(34 211 238 / .18); }
+        .schema-add textarea {
+            width: 100%; font-family: inherit; font-size: 0.8rem; line-height: 1.45;
+            border: 1px solid var(--border-strong); border-radius: var(--radius-sm);
+            padding: 0.4rem 0.55rem; color: var(--text-primary);
+            background: var(--panel-inset); resize: vertical; min-height: 44px;
+        }
+        .schema-add textarea:focus { outline: none; border-color: var(--accent); box-shadow: 0 0 0 2px rgb(34 211 238 / .18); }
+        .schema-add-hint {
+            margin-top: 0.45rem;
+            font-size: 0.72rem;
+            color: var(--text-tertiary);
+            line-height: 1.5;
+        }
+        .schema-add .row2 { display: flex; gap: 6px; margin-top: 0.55rem; }
+        .schema-add .row2 .btn { flex: 1; justify-content: center; padding: 0.42rem 0.5rem; font-size: 0.78rem; }
+
+        .schema-tables { padding: 0 var(--space-4) var(--space-4); }
+        .schema-tables .tbl {
+            font-size: 0.74rem; color: var(--text-secondary);
+            border-bottom: 1px dotted var(--border);
+            padding: 3px 0;
+        }
+        .schema-tables .tbl b { color: var(--text-primary); font-weight: 600; }
+        .schema-tables .tbl .rc { color: var(--text-tertiary); font-size: 0.68rem; }
+
+        /* ── Footer ─────────────────────────────────────────────────── */
+        .footer {
+            text-align: center;
+            padding: var(--space-6);
+            color: var(--text-tertiary);
+            font-size: 0.8rem;
+            border-top: 1px solid var(--border-light);
+        }
+        .footer p { margin: 0; }
+
+        /* ── Section accent system: one hue family per functional area ── */
+        .accent-cyan    { border-top: 2px solid rgba(34, 211, 238, 0.55); }
+        .accent-violet  { border-top: 2px solid rgba(167, 139, 250, 0.55); }
+        .accent-emerald { border-top: 2px solid rgba(52, 211, 153, 0.55); }
+        .accent-blue    { border-top: 2px solid rgba(96, 165, 250, 0.55); }
+        .accent-amber   { border-top: 2px solid rgba(251, 191, 36, 0.55); }
+        .accent-rose    { border-top: 2px solid rgba(244, 114, 182, 0.55); }
+
+        .chip {
+            width: 26px; height: 26px;
+            border-radius: 8px;
+            display: grid; place-items: center;
+            flex-shrink: 0;
+            border: 1px solid transparent;
+        }
+        .c-cyan    { color: #22D3EE; background: rgba(34, 211, 238, 0.10); border-color: rgba(34, 211, 238, 0.30); }
+        .c-violet  { color: #A78BFA; background: rgba(167, 139, 250, 0.10); border-color: rgba(167, 139, 250, 0.30); }
+        .c-emerald { color: #34D399; background: rgba(52, 211, 153, 0.10); border-color: rgba(52, 211, 153, 0.30); }
+        .c-blue    { color: #60A5FA; background: rgba(96, 165, 250, 0.10); border-color: rgba(96, 165, 250, 0.30); }
+        .c-amber   { color: #FBBF24; background: rgba(251, 191, 36, 0.10); border-color: rgba(251, 191, 36, 0.30); }
+        .c-rose    { color: #F472B6; background: rgba(244, 114, 182, 0.10); border-color: rgba(244, 114, 182, 0.30); }
+
+        .btn-emerald {
+            background: #34D399; color: #03130B; border-color: #34D399;
+            box-shadow: 0 0 14px rgb(52 211 153 / 0.22);
+        }
+        .btn-emerald:hover:not(:disabled) {
+            background: #6EE7B7; border-color: #6EE7B7;
+            box-shadow: 0 0 20px rgb(52 211 153 / 0.38);
+        }
+        .btn-outline-violet, .btn-outline-rose, .btn-outline-amber {
+            background: transparent; font-weight: 600;
+        }
+        .btn-outline-violet { color: #C4B5FD; border-color: rgba(167, 139, 250, 0.45); }
+        .btn-outline-violet:hover:not(:disabled) {
+            background: rgba(167, 139, 250, 0.12); border-color: rgba(167, 139, 250, 0.75); color: #DDD6FE;
+        }
+        .btn-outline-rose { color: #F9A8D4; border-color: rgba(244, 114, 182, 0.45); }
+        .btn-outline-rose:hover:not(:disabled) {
+            background: rgba(244, 114, 182, 0.12); border-color: rgba(244, 114, 182, 0.75); color: #FBCFE8;
+        }
+        .btn-outline-amber { color: #FCD34D; border-color: rgba(251, 191, 36, 0.45); }
+        .btn-outline-amber:hover:not(:disabled) {
+            background: rgba(251, 191, 36, 0.10); border-color: rgba(251, 191, 36, 0.75); color: #FDE68A;
+        }
+
+        /* ── Result table widget (built by app.js) ──────────────────── */
+        .table-wrapper {
+            margin: 0.8rem 0 0.4rem;
+            border: 1px solid var(--border);
+            border-radius: var(--radius-md);
+            overflow: hidden;
+            background: var(--panel-inset);
+        }
+        .table-controls {
+            display: flex; align-items: center; justify-content: space-between;
+            gap: var(--space-2);
+            padding: 0.5rem 0.7rem;
+            border-bottom: 1px solid var(--border-light);
+            background: var(--bg-raised);
+        }
+        .table-info {
+            font-family: 'JetBrains Mono', monospace;
+            font-size: 0.72rem; color: var(--text-tertiary);
+        }
+        .table-resize-controls { display: flex; gap: 4px; }
+        .table-resize-btn {
+            font-family: inherit; font-size: 0.7rem; font-weight: 600;
+            color: var(--text-secondary);
+            background: var(--panel);
+            border: 1px solid var(--border-strong);
+            border-radius: 6px; padding: 2px 9px; cursor: pointer;
+            transition: color var(--transition-fast), border-color var(--transition-fast),
+                        background var(--transition-fast);
+        }
+        .table-resize-btn:hover { border-color: rgb(34 211 238 / .5); color: var(--accent-bright); }
+        .table-resize-btn.active {
+            color: var(--accent-ink);
+            background: var(--accent);
+            border-color: var(--accent);
+        }
+        .table-container { overflow-x: auto; }
+        .answer-table {
+            border-collapse: collapse;
+            width: 100%;
+            font-size: 0.85rem;
+        }
+        .answer-table th, .answer-table td {
+            border-bottom: 1px solid var(--border-light);
+            text-align: left;
+            color: var(--text-primary);
+        }
+        .answer-table thead th {
+            background: var(--bg-raised);
+            font-weight: 600;
+            color: var(--accent-bright);
+            border-bottom: 1px solid var(--border-strong);
+        }
+        .answer-table tbody tr:hover td { background: rgba(34, 211, 238, 0.05); }
+        .answer-table.compact th, .answer-table.compact td { padding: 0.25rem 0.7rem; font-size: 0.78rem; }
+        .answer-table.comfortable th, .answer-table.comfortable td { padding: 0.45rem 0.8rem; }
+        .answer-table.spacious th, .answer-table.spacious td { padding: 0.75rem 1rem; font-size: 0.9rem; }
+
+        .status-text { color: var(--text-tertiary); }
+        .text-success { color: var(--success); }
+        .text-warning { color: var(--warning); }
+        .text-error { color: var(--error); }
+
+        /* Markdown tables inside answer */
+        .answer-content table {
+            border-collapse: collapse;
+            width: 100%;
+            margin: 0.6rem 0;
+            font-size: 0.86rem;
+        }
+        .answer-content th, .answer-content td {
+            border: 1px solid var(--border);
+            padding: 0.4rem 0.7rem;
+            text-align: left;
+        }
+        .answer-content th {
+            background: var(--bg-raised);
+            font-weight: 600;
+            color: var(--accent-bright);
+        }
+        .answer-content tr:nth-child(even) td { background: var(--panel-inset); }
+    </style>
+    <script>
+        /* Theme before first paint: avoid a dark flash for light-mode users */
+        try { if (localStorage.getItem('ql.theme') === 'light') { document.documentElement.classList.add('light'); } } catch (e) { /* no-op */ }
+    </script>
+</head>
+
+<body>
+    <div class="app-container">
+        <!-- Header -->
+        <header class="header">
+            <div class="header-content">
+                <div class="brand">
+                    <div class="brand-icon">
+                        <svg width="26" height="26" viewBox="0 0 48 48" fill="none" xmlns="http://www.w3.org/2000/svg">
+                            <!-- magnifying lens scanning a database disk stack -->
+                            <g stroke="#22D3EE" stroke-width="2.6" fill="none">
+                                <ellipse cx="20" cy="14" rx="10.5" ry="4" />
+                                <path d="M9.5 14v20c0 2.2 4.7 4 10.5 4s10.5-1.8 10.5-4V14" />
+                                <path d="M9.5 23c0 2.2 4.7 4 10.5 4s10.5-1.8 10.5-4" />
+                            </g>
+                            <circle cx="32.5" cy="27.5" r="8.2" fill="rgba(34,211,238,.16)" stroke="#7DECFB" stroke-width="2.4" />
+                            <path d="M38.6 33.6L44.4 39.4" stroke="#7DECFB" stroke-width="2.8" stroke-linecap="round" />
+                        </svg>
+                    </div>
+                    <div class="brand-text">
+                        <h1>Query<span class="lens">Lens</span></h1>
+                        <p>Transform natural language into SQL, and documents into insight.</p>
+                    </div>
+                </div>
+                <div class="header-right">
+                    <button id="themeToggle" class="theme-toggle" type="button" aria-label="Switch to light mode"
+                        title="Switch to light mode">
+                        <svg class="icon-sun" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+                            stroke-width="2" stroke-linecap="round">
+                            <circle cx="12" cy="12" r="4" />
+                            <path d="M12 2v2M12 20v2M4.9 4.9l1.4 1.4M17.7 17.7l1.4 1.4M2 12h2M20 12h2M4.9 19.1l1.4-1.4M17.7 6.3l1.4-1.4" />
+                        </svg>
+                        <svg class="icon-moon" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+                            stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+                            <path d="M21 12.8A9 9 0 1 1 11.2 3a7 7 0 0 0 9.8 9.8z" />
+                        </svg>
+                    </button>
+                    <span class="header-tag" id="dbTag">demo_employee</span>
+                </div>
+            </div>
+        </header>
+
+        <!-- Main Content -->
+        <main class="main-content">
+            <!-- Query Section -->
+            <section class="query-section">
+                <div class="query-body">
+                    <textarea id="question" class="query-textarea"
+                        placeholder="Ask a question about your data — e.g. “Which department has the highest average salary?” or “Show me sales by month as a chart.”"></textarea>
+
+                    <div class="query-controls">
+                        <select id="modeToggle" class="mode-select">
+                            <option value="text2sql">Text-to-SQL</option>
+                            <option value="ocr_qa">Document Q&amp;A</option>
+                        </select>
+
+                        <button id="ask" class="btn btn-primary">
+                            <svg width="15" height="15" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+                                stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+                                <path d="M21 11.5a8.38 8.38 0 0 1-.9 3.8 8.5 8.5 0 0 1-7.6 4.7 8.38 8.38 0 0 1-3.8-.9L3 21l1.9-5.7a8.38 8.38 0 0 1-.9-3.8 8.5 8.5 0 0 1 4.7-7.6 8.38 8.38 0 0 1 3.8-.9h.5a8.48 8.48 0 0 1 8 8v.5z" />
+                            </svg>
+                            Ask
+                        </button>
+
+                        <button id="recalc" class="btn btn-secondary btn-outline-violet">
+                            <svg width="15" height="15" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+                                stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+                                <path d="M1 4v6h6" />
+                                <path d="M23 20v-6h-6" />
+                                <path d="M20.49 9A9 9 0 0 0 5.64 5.64L1 10m22 4l-4.64 4.36A9 9 0 0 1 3.51 15" />
+                            </svg>
+                            Re-execute
+                        </button>
+
+                        <div id="uploadControls" class="hidden" style="display:flex; gap: 0.5rem; align-items: center;">
+                            <button id="uploadBtn" class="btn btn-secondary btn-outline-rose">
+                                <svg width="15" height="15" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+                                    stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+                                    <path d="M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4" />
+                                    <polyline points="17,8 12,3 7,8" />
+                                    <line x1="12" y1="3" x2="12" y2="15" />
+                                </svg>
+                                Upload Image
+                            </button>
+                            <span id="uploadStatus">No document</span>
+                        </div>
+
+                        <div class="status-area">
+                            <div id="spinner" class="spinner hidden"></div>
+                            <span id="spinnerText" class="hidden">Thinking…</span>
+                            <span id="status">Ready</span>
+                            <span id="time"></span>
+                        </div>
+                    </div>
+                </div>
+            </section>
+
+            <!-- Results Grid -->
+            <div class="results-grid">
+                <!-- Main Results Column -->
+                <div class="main-results">
+                    <!-- Answer Card -->
+                    <div class="card accent-cyan">
+                        <div class="card-header">
+                            <h3 class="card-title">
+                                <span class="chip c-cyan">
+                                <svg width="15" height="15" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+                                    stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+                                    <path d="M9 11l3 3L22 4" />
+                                    <path d="M21 12v7a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V5a2 2 0 0 1 2-2h11" />
+                                </svg>
+                                </span>
+                                Answer
+                            </h3>
+                        </div>
+                        <div class="card-content">
+                            <div id="answer" class="answer-content">No answer yet — ask something above.</div>
+                            <div id="chartPending" class="chart-pending">
+                            Charts &amp; analytics render here automatically when a successful query
+                            returns chart-friendly data — try “Show average salary by department”.
+                        </div>
+                        <div id="chartBox" class="chart-box hidden">
+                                <div id="chartCanvas"></div>
+                                <p class="chart-caption" id="chartCaption"></p>
+                            </div>
+                        </div>
+                    </div>
+
+                    <!-- SQL Card -->
+                    <div class="card accent-violet">
+                        <details id="sql-details" open>
+                            <summary class="card-header">
+                                <h3 class="card-title">
+                                    <span class="chip c-violet">
+                                    <svg width="15" height="15" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+                                        stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+                                        <polyline points="16,18 22,12 16,6" />
+                                        <polyline points="8,6 2,12 8,18" />
+                                    </svg>
+                                    </span>
+                                    Generated SQL
+                                </h3>
+                            </summary>
+                            <div class="card-content">
+                                <pre
+                                    class="sql-content"><code id="sql" class="language-sql">-- SQL will appear here after the agent crafts a query --</code></pre>
+                            </div>
+                        </details>
+                    </div>
+                </div>
+
+                <!-- Sidebar -->
+                <div class="agent-panels">
+                    <!-- Database panel -->
+                    <div class="panel accent-emerald">
+                        <details open>
+                            <summary class="panel-header">
+                                <span class="panel-title">
+                                    <span class="chip c-emerald">
+                                    <svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+                                        stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+                                        <ellipse cx="12" cy="5" rx="9" ry="3" />
+                                        <path d="M21 12c0 1.66-4 3-9 3s-9-1.34-9-3" />
+                                        <path d="M3 5v14c0 1.66 4 3 9 3s9-1.34 9-3V5" />
+                                    </svg>
+                                    </span>
+                                    Database
+                                    <span class="db-badge" id="dbBadge">—</span>
+                                </span>
+                            </summary>
+                            <div class="profile-list" id="schemaProfiles"></div>
+                            <div class="schema-add">
+                                <label for="schemaName">Add a database</label>
+                                <input type="text" id="schemaName" placeholder="Profile name (e.g. sales_2024)">
+                                <label for="schemaUrl" style="margin-top:0.6rem">Connection URL — or upload a file</label>
+                                <input type="text" id="schemaUrl"
+                                    placeholder="postgresql:// · mysql:// · mssql:// · oracle:// · sqlite:///path/file.db">
+                                <div class="schema-add-hint">PostgreSQL, MySQL, SQL Server, Oracle &amp; SQLite are all supported — paste any of those connection URLs.</div>
+                                <label for="schemaNotes">Schema notes — optional</label>
+                                <textarea id="schemaNotes" rows="2"
+                                    placeholder="Optional: what your columns mean — e.g. 'sold_date IS NULL = still in inventory', 'msrp = list price, sale_price = actual', revenue rules, aliases"></textarea>
+                                <div class="row2">
+                                    <button id="schemaAddBtn" class="btn btn-emerald">Connect</button>
+                                    <button id="schemaFileBtn" class="btn btn-secondary btn-outline-amber">Upload .sqlite / .csv</button>
+                                </div>
+                            </div>
+                            <div class="schema-tables" id="schemaTables"></div>
+                        </details>
+                    </div>
+
+                    <!-- Agent panels -->
+                    <div class="panel accent-blue">
+                        <details open>
+                            <summary class="panel-header">
+                                <span class="panel-title">
+                                    <span class="chip c-blue">
+                                    <svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+                                        stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+                                        <!-- RAM chip / memory module -->
+                                        <rect x="4" y="4" width="16" height="16" rx="2" />
+                                        <rect x="9" y="9" width="6" height="6" />
+                                        <line x1="9" y1="1" x2="9" y2="4" />
+                                        <line x1="15" y1="1" x2="15" y2="4" />
+                                        <line x1="9" y1="20" x2="9" y2="23" />
+                                        <line x1="15" y1="20" x2="15" y2="23" />
+                                        <line x1="20" y1="9" x2="23" y2="9" />
+                                        <line x1="20" y1="14" x2="23" y2="14" />
+                                        <line x1="1" y1="9" x2="4" y2="9" />
+                                        <line x1="1" y1="14" x2="4" y2="14" />
+                                    </svg>
+                                    </span>
+                                    Memory Agent
+                                </span>
+                                <svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+                                    stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+                                    <polyline points="6,9 12,15 18,9" />
+                                </svg>
+                            </summary>
+                            <div id="memory" class="panel-content">—</div>
+                        </details>
+                    </div>
+
+                    <div class="panel accent-violet">
+                        <details open>
+                            <summary class="panel-header">
+                                <span class="panel-title">
+                                    <span class="chip c-violet">
+                                    <svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+                                        stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+                                        <!-- terminal: the agent crafts SQL -->
+                                        <polyline points="4,17 10,11 4,5" />
+                                        <line x1="12" y1="19" x2="20" y2="19" />
+                                    </svg>
+                                    </span>
+                                    Query Craft Agent
+                                </span>
+                                <svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+                                    stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+                                    <polyline points="6,9 12,15 18,9" />
+                                </svg>
+                            </summary>
+                            <div id="querycraft" class="panel-content">—</div>
+                        </details>
+                    </div>
+
+                    <div class="panel accent-rose">
+                        <details open>
+                            <summary class="panel-header">
+                                <span class="panel-title">
+                                    <span class="chip c-rose">
+                                    <svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+                                        stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+                                        <path d="M14 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V8z" />
+                                        <polyline points="14,2 14,8 20,8" />
+                                        <line x1="16" y1="13" x2="8" y2="13" />
+                                        <line x1="16" y1="17" x2="8" y2="17" />
+                                        <polyline points="10,9 9,9 8,9" />
+                                    </svg>
+                                    </span>
+                                    Result Presenter Agent
+                                </span>
+                                <svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+                                    stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+                                    <polyline points="6,9 12,15 18,9" />
+                                </svg>
+                            </summary>
+                            <div id="presenter" class="panel-content">—</div>
+                        </details>
+                    </div>
+                </div>
+            </div>
+        </main>
+
+        <!-- Footer -->
+        <footer class="footer">
+            <p>QueryLens — multi-agent Text-to-SQL &amp; document intelligence</p>
+        </footer>
+    </div>
+
+    <script src="https://cdn.jsdelivr.net/npm/echarts@5.5.0/dist/echarts.min.js"></script>
+    <script src="/static/app.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/prism/1.29.0/prism.min.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/prism/1.29.0/components/prism-sql.min.js"></script>
+</body>
+
+</html>