Backup code/memory_utils_22.py

import os
import sys
import json
import time
import datetime
import shutil
import gradio as gr
from pprint import pprint
import traceback

# LlamaIndex Imports
from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex

# Local Imports setup
# Assuming this file is in 'utils/', we step back to find 'memory_bank'
bank_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../memory_bank')
sys.path.append(bank_path)

# Import functions from sibling/child modules
# Ensure these files exist in the appended path
try:
    from build_memory_index import build_memory_index
    from summarize_memory import summarize_memory, extract_session_summary, extract_semantic_memory
except ImportError:
    # Fallback or placeholder if running independently for testing
    print("Warning: Could not import memory build/summary modules.")
    def build_memory_index(*args, **kwargs): pass
    def summarize_memory(*args, **kwargs): return {}
    def extract_session_summary(*args, **kwargs): return {}
    def extract_semantic_memory(*args, **kwargs): return {}


PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))

# تشخیص خودکار حافظه دائمی ابری
BASE_DATA_DIR = (
    "/data" if os.path.exists("/data") else os.path.join(PROJECT_ROOT, "data")
)

# مسیر فایل json
MEMORY_FILE_PATH = os.path.join(
    BASE_DATA_DIR, "memories", "update_memory_0512_eng.json"
)

# مسیر ایندکس‌ها (هم‌سطح با پوشه memories)
INDEX_BASE_DIR = os.getenv(
    "EMMA_INDEX_DIR", os.path.join(BASE_DATA_DIR, "memory_index", "llamaindex")
)


def enter_name(name, memory, local_memory_qa, data_args, update_memory_index=True):
    """
    Legacy/Basic function to load user memory and initialize vector store.
    """
    cur_date = datetime.date.today().strftime("%Y-%m-%d")
    user_memory_index = None
    
    # Handle Gradio States
    if isinstance(data_args, gr.State): data_args = data_args.value
    if isinstance(memory, gr.State): memory = memory.value
    if isinstance(local_memory_qa, gr.State): local_memory_qa = local_memory_qa.value
    
    memory_dir = MEMORY_FILE_PATH
    
    if name in memory.keys():
        user_memory = memory[name]
        memory_index_path = os.path.join(data_args.memory_basic_dir, f'memory_index/{name}_index')
        os.makedirs(os.path.dirname(memory_index_path), exist_ok=True)
        
        if (not os.path.exists(memory_index_path)) or update_memory_index:
            print(f'Initializing memory index {memory_index_path}...')
        
            if os.path.exists(memory_index_path):
                shutil.rmtree(memory_index_path)
            # Initialize using the local QA object
            memory_index_path, _ = local_memory_qa.init_memory_vector_store(
                filepath=memory_dir, 
                vs_path=memory_index_path, 
                user_name=name, 
                cur_date=cur_date
            )                      
        
        user_memory_index = local_memory_qa.load_memory_index(memory_index_path) if memory_index_path else None
        msg = f"Welcome back, {name}!"
        return msg, user_memory, memory, name, user_memory_index
    else:
        memory[name] = {}
        memory[name].update({"name": name}) 
        msg = f"Welcome, new user {name}! I will remember your name, so next time we meet, I'll be able to call you by your name!"
        return msg, memory[name], memory, name, user_memory_index


def enter_name_llamaindex(name, memory, data_args, update_memory_index=True):
    """
    Load the user's session, episodic, and semantic memory indices.
    Compatible with LlamaIndex v0.10+.
    """
    sessions_memory = None
    episodic_memory = None
    semantic_memory = None

    print(f"[DEBUG] enter_name_llamaindex called with name={name!r}")

    if name not in memory:
        print(f"[DEBUG] user {name!r} not found in memory")
        return "User not found.", None, None, None, None

    user_memory = memory[name]
    # مسیر دقیق کاربر در پوشه llamaindex
    base_path = os.path.join(INDEX_BASE_DIR, name)
    sessions_path = os.path.join(base_path, "sessions")
    episodic_path = os.path.join(base_path, "episodic_memory")
    semantic_path = os.path.join(base_path, "semantic_memory")
    
    print(f"[DEBUG] base_path: {base_path}")
    print(f"[DEBUG] sessions_path: {sessions_path}")
    print(f"[DEBUG] episodic_path: {episodic_path}")
    print(f"[DEBUG] semantic_path: {semantic_path}")

    indices_exist = (
        os.path.isdir(sessions_path)
        and os.path.isdir(episodic_path)
        and os.path.isdir(semantic_path)
    )

    print(f"[DEBUG] indices_exist: {indices_exist}")
    #print(f"[DEBUG] update_memory_index: {update_memory_index}")

    if update_memory_index or not indices_exist:
        print(f"[DEBUG] Initializing memory indices for {name}...")

        # Important: build_memory_index must persist to these same absolute paths.
        build_memory_index(memory, data_args, name=name)

        print("[DEBUG] After build:")
        print(f"[DEBUG] sessions_path exists: {os.path.isdir(sessions_path)}")
        print(f"[DEBUG] episodic_path exists: {os.path.isdir(episodic_path)}")
        print(f"[DEBUG] semantic_path exists: {os.path.isdir(semantic_path)}")

    def load_index_safe(index_name, index_path):
        if not os.path.isdir(index_path):
            print(f"[DEBUG] {index_name} path does not exist: {index_path}")
            return None

        try:
            storage_context = StorageContext.from_defaults(
                persist_dir=index_path
            )
            index = load_index_from_storage(storage_context)
            print(
                f"[DEBUG] {index_name} loaded: "
                f"{type(index).__name__}, is_none={index is None}"
            )
            return index
        except Exception as exc:
            print(f"[ERROR] Could not load {index_name}: {exc}")
            traceback.print_exc()
            return None

    sessions_memory = load_index_safe("sessions_memory", sessions_path)
    episodic_memory = load_index_safe("episodic_memory", episodic_path)
    semantic_memory = load_index_safe("semantic_memory", semantic_path)

    print("[DEBUG] RETURN VALUES:")
    print(f"  hello_msg: Welcome back, {name}!")
    print(f"  user_memory type: {type(user_memory).__name__}")
    print(f"  sessions_memory type: {type(sessions_memory).__name__}")
    print(f"  episodic_memory type: {type(episodic_memory).__name__}")
    print(f"  semantic_memory type: {type(semantic_memory).__name__}")

    return (
        f"Welcome back, {name}!",
        user_memory,
        sessions_memory,
        episodic_memory,
        semantic_memory,
    )
    

def summarize_memory_event_personality(data_args, memory, user_name):
    """
    Summarizes the memory and returns the user-specific memory dict.
    """
    if isinstance(data_args, gr.State): data_args = data_args.value
    if isinstance(memory, gr.State): memory = memory.value
    
    memory_dir = MEMORY_FILE_PATH
    
    # Note: Ensure summarize_memory handles the language argument correctly (passed 'en' or similar)
    memory = summarize_memory(memory_dir, user_name, language=data_args.language)
    user_memory = memory[user_name] if user_name in memory.keys() else {}
    return user_memory


def save_local_memory(memory, history, user_name, data_args, new_conversation=False):
    """
    Saves user-model conversations into memory and adds episodic memory for each session.
    Handles both list-of-lists (old Gradio) and list-of-dicts (new Gradio/OpenAI) formats.
    """
    if isinstance(data_args, gr.State): data_args = data_args.value
    if isinstance(memory, gr.State): memory = memory.value

    memory_dir = MEMORY_FILE_PATH

    # 1. Initialize user memory with ALL required structures FIRST
    if user_name not in memory:
        memory[user_name] = {
            "sessions": [],
            "episodic_memory": [],
            "semantic_memory": {}
        }
    
    # 2. Ensure all sub-structures exist and are correct type
    memory[user_name].setdefault("sessions", [])
    memory[user_name].setdefault("episodic_memory", [])
    memory[user_name].setdefault("semantic_memory", {})

    # 3. Now safely check types
    if not isinstance(memory[user_name]["semantic_memory"], dict):
        memory[user_name]["semantic_memory"] = {}

    # Create new session or update existing one
    if new_conversation or not memory[user_name]["sessions"]:
        if new_conversation and memory[user_name]["sessions"]:
            # Logic to summarize previous session before starting new one could go here
            pass 

        # Create new session
        session = {
            "session_id": len(memory[user_name]["sessions"]),
            "date": time.strftime("%Y-%m-%d", time.localtime()),
            "conversation": []
        }
        memory[user_name]["sessions"].append(session)

    current_session = memory[user_name]["sessions"][-1]
    
    # --- Modified section to fix KeyError: 0 and handle History formats ---
    if not new_conversation and history:
        last_item = history[-1]
        
        # Case 1: New Format (List of Dicts)
        # In this format, history is linear. The last item is bot response, second to last is user query.
        if isinstance(last_item, dict):
            if len(history) >= 2:
                user_query = history[-2].get('content', '')
                bot_response = history[-1].get('content', '')
                
                # Verify roles to ensure correct pairing
                if history[-2].get('role') == 'user' and history[-1].get('role') == 'assistant':
                    current_session["conversation"].append({
                        'query': user_query, 
                        'response': bot_response
                    })
                
        # Case 2: Old Format (List of Lists/Tuples)
        elif isinstance(last_item, (list, tuple)):
            current_session["conversation"].append({
                'query': last_item[0], 
                'response': last_item[1]
            })
    # ----------------------------------------------------------------------

    # Optional: Update semantic memory in real-time
        memory[user_name]["semantic_memory"] = extract_semantic_memory(
            memory[user_name]["semantic_memory"], 
            current_session["conversation"]
     )
    
    semantic_memory_text = memory[user_name]["semantic_memory"]
    
    # Save to file
    # Ensure memory directory exists
    os.makedirs(os.path.dirname(memory_dir), exist_ok=True)
    
    with open(memory_dir, "w", encoding="utf-8") as f:
        json.dump(memory, f, ensure_ascii=False, indent=4)

    return memory, semantic_memory_text