repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
|---|---|---|---|---|---|
ai-agent-book | chapter3/memobase/agent.py | .py | """
Memobase Agent Implementation with Kimi K3 Model
Advanced memory management for LOCOMO benchmark
"""
import json
import logging
import time
import hashlib
import pickle
from typing import List, Dict, Any, Optional, Tuple
from dataclasses import dataclass, field
from datetime import datetime
from pathlib import Pat... | 581 | 21,972 |
ai-agent-book | chapter3/memobase/test_evaluate_generic_empty_query.py | .py | """Regression tests: benchmark evaluation must not raise ZeroDivisionError on
tasks with an empty/whitespace query (e.g. loaded from tasks.json) that fall
through to the generic evaluator."""
import os
import sys
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from locomo_benchmark import BenchmarkTask... | 39 | 1,336 |
ai-agent-book | chapter3/contextual-retrieval/quickstart.py | .py | #!/usr/bin/env python3
"""Quick start script to test the Contextual Retrieval System
This script provides a quick way to test contextual retrieval
with a sample document and see the improvements.
"""
import logging
from pathlib import Path
from config import Config
from contextual_chunking import ContextualChunker
fr... | 163 | 6,034 |
ai-agent-book | chapter3/contextual-retrieval/contextual_tools.py | .py | """Enhanced tools for contextual retrieval with BM25 and semantic search
Educational implementation showing how contextual chunks improve both
BM25 (lexical) and embedding (semantic) retrieval.
"""
import json
import logging
import requests
import numpy as np
from typing import Dict, Any, List, Optional, Tuple
from d... | 659 | 27,521 |
ai-agent-book | chapter3/contextual-retrieval/main.py | .py | """Main entry point for Agentic RAG system"""
import os
import json
import logging
import argparse
from typing import Optional
from config import Config, KnowledgeBaseType
from agent import AgenticRAG
from chunking import DocumentIndexer
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %... | 293 | 10,328 |
ai-agent-book | chapter3/contextual-retrieval/campaign.py | .py | #!/usr/bin/env python3
"""Canonical live plain-vs-contextual retrieval campaign (Experiment 3-10)."""
from __future__ import annotations
import argparse
import concurrent.futures
import json
import os
import statistics
import sys
import time
from pathlib import Path
from typing import Any, Dict, List, Sequence
impor... | 284 | 13,270 |
ai-agent-book | chapter3/contextual-retrieval/contextual_chunking.py | .py | """Contextual Chunking Module - Educational implementation of Anthropic's Contextual Retrieval
This module demonstrates the key insight from Anthropic's research:
- Traditional RAG destroys context by chunking documents
- Contextual Retrieval prepends chunk-specific context before embedding
- This preserves semantic m... | 537 | 22,249 |
ai-agent-book | chapter3/contextual-retrieval/test_history_limit_zero.py | .py | """conversation_history_limit=0 must omit history, not include all via [-0:]."""
from types import SimpleNamespace
from agent import AgenticRAG
def test_history_limit_zero_omits_history():
agent = object.__new__(AgenticRAG)
agent.config = SimpleNamespace(
agent=SimpleNamespace(conversation_history_li... | 40 | 1,290 |
ai-agent-book | chapter3/contextual-retrieval/demo.py | .py | #!/usr/bin/env python3
"""Interactive demo of the Contextual Retrieval System
This script provides an interactive demonstration showing:
1. How chunks lose context in traditional RAG
2. How contextual retrieval solves this problem
3. Side-by-side comparison of retrieval quality
"""
import json
import logging
from pat... | 411 | 15,894 |
ai-agent-book | chapter3/contextual-retrieval/test_simple.py | .py | #!/usr/bin/env python3
"""Simple test script for Agentic RAG system"""
import os
import json
from pathlib import Path
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
def _basic_functionality():
"""Test basic functionality of the system"""
print("🧪 Testing Agentic RAG System")
p... | 196 | 6,778 |
ai-agent-book | chapter3/contextual-retrieval/compare_retrieval.py | .py | #!/usr/bin/env python3
"""上下文感知检索对比评测（实验 3-10）
本脚本用可控的对比实验量化“上下文感知检索”相较传统分块的检索召回提升：
同一批文本块分别以两种方式建立 BM25 索引——
* 无上下文（plain） ：只索引原始文本块 metadata.original_text
* 有上下文（contextual）：索引 LLM 生成的前缀 + 原始文本块（content 字段）
然后在同一评测集上比较 recall@k（命中率：前 k 个结果中是否含有相关文本块）。
这正是 Anthropic “Contextual Retrieval” 的核心主张：为文本块补上上下文前缀，
能同... | 345 | 15,372 |
ai-agent-book | chapter3/contextual-retrieval/contextual_agent.py | .py | """Contextual Agentic RAG System
This module extends the base AgenticRAG to use contextual retrieval,
demonstrating the improved answer quality from better retrieval.
"""
import json
import logging
from typing import List, Dict, Any, Optional, Generator
from datetime import datetime
from agent import AgenticRAG, Mes... | 357 | 14,559 |
ai-agent-book | chapter3/contextual-retrieval/config.py | .py | """Configuration for Agentic RAG System"""
import os
from dataclasses import dataclass, field
from typing import Optional, Dict, Any
from enum import Enum
from dotenv import load_dotenv
load_dotenv()
def _openrouter_model_id(model: Optional[str]) -> str:
"""Map a provider-native model name to an OpenRouter mode... | 260 | 9,156 |
ai-agent-book | chapter3/contextual-retrieval/tools.py | .py | """Tools for knowledge base interaction"""
import json
import logging
import requests
from typing import Dict, Any, List, Optional
from dataclasses import dataclass
from config import KnowledgeBaseConfig, KnowledgeBaseType
logger = logging.getLogger(__name__)
@dataclass
class SearchResult:
"""Search result fro... | 519 | 20,397 |
ai-agent-book | chapter3/contextual-retrieval/chunking.py | .py | """Document chunking and indexing script"""
import os
import json
import hashlib
import logging
import requests
from typing import List, Dict, Any, Optional, Tuple
from pathlib import Path
from datetime import datetime
from config import ChunkingConfig, KnowledgeBaseConfig, KnowledgeBaseType
logging.basicConfig(leve... | 424 | 15,888 |
ai-agent-book | chapter3/contextual-retrieval/agent.py | .py | """Agentic RAG System with ReAct Pattern"""
import json
import logging
from typing import List, Dict, Any, Optional, Generator
from dataclasses import dataclass, field
from datetime import datetime
from openai import OpenAI
from config import Config, LLMConfig, AgentConfig
from tools import KnowledgeBaseTools, get_to... | 391 | 16,057 |
ai-agent-book | chapter3/contextual-retrieval/index_local_laws_contextual.py | .py | """Script to chunk and index local legal documents using Contextual Retrieval
This script:
1. Cleans up existing indexes
2. Reads legal documents from local laws directory
3. Chunks them with paragraph-aware boundaries (soft limit 1024, hard limit 2048)
4. Generates contextual descriptions for each chunk
5. Indexes ... | 589 | 24,573 |
ai-agent-book | chapter3/contextual-retrieval/evaluation/evaluate.py | .py | """Evaluation framework for Agentic RAG system"""
import json
import logging
import time
from typing import List, Dict, Any, Optional
from pathlib import Path
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from config import Config
from agent import AgenticRAG
logg... | 397 | 17,231 |
ai-agent-book | chapter3/structured-index/download_sample.py | .py | """
Script to download sample technical documentation for testing.
Since the full Intel manual is very large, this creates a sample document.
"""
import requests
from pathlib import Path
import json
def create_sample_intel_doc():
"""Create a sample Intel architecture documentation for testing."""
sample... | 298 | 13,821 |
ai-agent-book | chapter3/structured-index/document_processor.py | .py | """
Document processor for handling various file formats.
Specializes in processing technical documentation like Intel manuals.
"""
import re
from pathlib import Path
from typing import List, Optional, Dict, Any
import pypdf
import pdfplumber
from bs4 import BeautifulSoup
import markdown
from loguru import logger
impo... | 292 | 10,551 |
ai-agent-book | chapter3/structured-index/hybrid_retriever.py | .py | """Hybrid Structured Retriever for RAPTOR Hierarchical Trees and GraphRAG Knowledge Graphs.
Merges RAPTOR tree summary nodes and GraphRAG entity-relation summaries into a unified retrieval index.
Performs Reciprocal Rank Fusion (RRF) scoring and evidence citation tracking across hierarchical and graph chunks.
"""
fro... | 515 | 20,827 |
ai-agent-book | chapter3/structured-index/main.py | .py | """
结构化索引工具的主入口：构建 / 查询 RAPTOR 与 GraphRAG 索引，或运行离线对比演示。
说明：RAPTOR、GraphRAG 的**索引构建**需要调用 LLM（实体抽取、递归摘要），因此
build / query 依赖 OPENAI_API_KEY 及相应重型依赖（umap、sentence-transformers 等）。
若只想直观理解「结构化索引解决了扁平检索的什么问题」，可运行无需 API 的 `demo` 子命令。
"""
import argparse
import asyncio
from pathlib import Path
import json
import sys
from ... | 203 | 8,889 |
ai-agent-book | chapter3/structured-index/test_indexing.py | .py | """
Test script for structured indexing with sample Intel x86 instruction documentation.
"""
import asyncio
from pathlib import Path
from loguru import logger
from config import get_raptor_config, get_graphrag_config
from raptor_indexer import RaptorIndexer
from graphrag_indexer import GraphRAGIndexer
from document_p... | 193 | 6,335 |
ai-agent-book | chapter3/structured-index/campaign.py | .py | #!/usr/bin/env python3
"""Real Intel SDM RAPTOR-vs-GraphRAG campaign for Experiment 3-7.
This campaign deliberately does not use the hand-authored offline demo. It
extracts a bounded, pinned set of pages from Intel's current Volume 1 PDF,
builds hierarchical summaries and entity relationships with live Ark calls,
ans... | 561 | 27,215 |
ai-agent-book | chapter3/structured-index/test_hybrid_retriever.py | .py | """Unit tests for HybridStructuredRetriever covering core requirements and edge cases."""
import numpy as np
import pytest
from hybrid_retriever import HybridStructuredRetriever, SearchResult
def test_relation_target_included_in_text_content():
"""Verify that relation matching includes target entity name."""
... | 143 | 4,919 |
ai-agent-book | chapter3/structured-index/test_graphrag_search_top_k.py | .py | """Regression test: GraphRAGIndexer.search must return empty list for non-positive top_k."""
import importlib
import sys
import types
from contextlib import contextmanager
from dataclasses import dataclass
import numpy as np
import pytest
class STStub:
def __init__(self, *args, **kwargs):
self.encode_ca... | 169 | 4,982 |
ai-agent-book | chapter3/structured-index/api_service.py | .py | """
HTTP API service for querying RAPTOR and GraphRAG indexes.
"""
from fastapi import FastAPI, HTTPException, BackgroundTasks, UploadFile, File
from fastapi.responses import JSONResponse
from pydantic import BaseModel, Field
from typing import List, Dict, Any, Optional, Literal
from pathlib import Path
import asyncio... | 392 | 13,677 |
ai-agent-book | chapter3/structured-index/structured_vs_flat_demo.py | .py | """
结构化索引 vs 扁平检索：离线对比演示。
本模块不依赖 OpenAI / 向量模型 / 网络，纯 Python + networkx 即可运行。
它用一个手工整理的「Intel x86 SIMD 指令集」小知识库，直观对比两条检索路线：
* 扁平检索（Flat）：把每个知识点当成互相独立的文本块，按词面相似度打分召回。
这是传统 RAG「文档分块 + 向量检索」的抽象——只能返回零散片段。
* 结构化检索（Structured）：
- GraphRAG 式的实体-关系图：沿关系边做多跳遍历，能回答扁平检索答不了的
「A 通过什么和 B 相连」这类关系性问题（对应书中「多跳关系... | 300 | 14,406 |
ai-agent-book | chapter3/structured-index/raptor_indexer.py | .py | """
RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval) implementation.
This creates a hierarchical tree structure with recursive summarization.
"""
import os
import json
import pickle
from pathlib import Path
from typing import List, Dict, Any, Optional, Tuple
from dataclasses import dataclass, asd... | 325 | 12,699 |
ai-agent-book | chapter3/structured-index/graphrag_indexer.py | .py | """
GraphRAG (Graph-based Retrieval Augmented Generation) implementation.
This creates a knowledge graph with entities, relationships, and community detection.
"""
import os
import json
import pickle
from pathlib import Path
from typing import List, Dict, Any, Optional, Tuple, Set
from dataclasses import dataclass, as... | 597 | 25,225 |
ai-agent-book | chapter3/structured-index/test_raptor_chunk_step.py | .py | """Regression: equal chunk_size/overlap must not crash range() with step 0."""
import sys
import types
from dataclasses import dataclass
def _stub_raptor_deps() -> None:
mods = [
"tiktoken",
"tqdm",
"umap",
"openai",
"sentence_transformers",
"loguru",
"sklea... | 65 | 1,790 |
ai-agent-book | chapter3/structured-index/config.py | .py | """
Configuration for structured index project.
"""
import os
from pathlib import Path
from typing import Optional
from dataclasses import dataclass
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
def _openrouter_model_id(model) -> str:
"""Map a provider-native model name to an OpenRou... | 181 | 7,061 |
ai-agent-book | chapter3/user-memory/quickstart.py | .py | #!/usr/bin/env python3
"""
Quick start script for User Memory System with Separated Architecture
Demonstrates conversation-based memory processing
"""
import os
import sys
import time
from dotenv import load_dotenv
from conversational_agent import ConversationalAgent, ConversationConfig
from background_memory_processo... | 200 | 7,504 |
ai-agent-book | chapter3/user-memory/test_json_cards_string_payload.py | .py | """Regression test for JSON cards tool payload parsing."""
import os
import sys
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from agent import UserMemoryAgent
from config import MemoryMode
def test_json_cards_add_memory_parses_stringified_json():
captured = {}
class FakeMemoryManager:
... | 38 | 1,288 |
ai-agent-book | chapter3/user-memory/main.py | .py | #!/usr/bin/env python3
"""
Main entry point for User Memory System with Separated Architecture
Conversational agent handles dialogue, background processor handles memory
"""
import os
import sys
import json
import logging
import argparse
import time
from pathlib import Path
from typing import Optional
from conversatio... | 957 | 37,712 |
ai-agent-book | chapter3/user-memory/locomo_benchmark.py | .py | """
LOCOMO Benchmark Integration for User Memory System
Based on: https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/LOCOMO
"""
import json
import os
import logging
import time
from typing import Dict, List, Any, Tuple
from dataclasses import dataclass, asdict
from datetime import datetime
import requests
from a... | 550 | 21,215 |
ai-agent-book | chapter3/user-memory/test_search_history_limit_zero.py | .py | """Regression test for ConversationHistory.search_history limit=0 handling.
Proves contract: Requesting limit<=0 from search_history returns an empty list
without executing text or vector search.
Locks out bug where limit=0 appended the first match before breaking, returning 1 result instead of 0.
"""
import sys
from... | 39 | 1,381 |
ai-agent-book | chapter3/user-memory/test_background_processor_loop.py | .py | """
Regression test for https://github.com/bojieli/ai-agent-book/issues/181
BackgroundMemoryProcessor entered an infinite processing loop because:
1. Its ConversationHistory instance loaded the history file once at startup
and never reloaded, so turns saved by the main agent's separate instance
were invisible ->... | 88 | 3,295 |
ai-agent-book | chapter3/user-memory/memory_manager.py | .py | """
Memory Manager module for handling different memory mechanisms
"""
import json
import os
import uuid
from datetime import datetime
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass, field, asdict
from abc import ABC, abstractmethod
import logging
from config import Config, Memor... | 800 | 31,417 |
ai-agent-book | chapter3/user-memory/test_consolidate_created_at.py | .py | import pytest
from memory_manager import NotesMemoryManager, MemoryNote
def test_consolidate_memories_preserves_earliest_created_at():
"""Verify deduplicating identical memory notes retains the earliest created_at timestamp."""
mgr = NotesMemoryManager(user_id="test_created_at_user")
mgr.notes = [
... | 32 | 1,134 |
ai-agent-book | chapter3/user-memory/demo_conversation_processing.py | .py | #!/usr/bin/env python3
"""
Demonstration of conversation-based memory processing
Shows how memory operations are triggered per conversation round
"""
import os
import sys
from dotenv import load_dotenv
from conversational_agent import ConversationalAgent, ConversationConfig
from background_memory_processor import Back... | 267 | 8,835 |
ai-agent-book | chapter3/user-memory/run_evaluation.py | .py | #!/usr/bin/env python3
"""Live sequential-memory campaign for Experiments 3-1 and 3-2.
Unlike the offline keyword fixture, this runner sends every historical session
to a real memory writer one at a time. From session two onward the writer is
given only the previous *memory state* and the new session; prior raw sessi... | 558 | 23,587 |
ai-agent-book | chapter3/user-memory/memory_operation_formatter.py | .py | """
Formatter for memory operations output
Provides consistent formatting for memory operation lists
"""
from typing import List, Dict, Any
import json
def format_memory_operations(operations: List[Dict[str, Any]], verbose: bool = False) -> str:
"""
Format memory operations for display
Args:
... | 143 | 3,975 |
ai-agent-book | chapter3/user-memory/test_format_memory_operations_memory_id.py | .py | import pytest
from memory_operation_formatter import format_memory_operations
def test_format_memory_operations_includes_memory_id_when_content_present():
operations = [
{
"action": "update",
"memory_id": "mem_101",
"content": "User prefers dark mode.",
"rea... | 17 | 517 |
ai-agent-book | chapter3/user-memory/conversational_agent.py | .py | """
Conversational Agent - Focuses purely on conversation without direct memory management
Memory updates are handled by a separate background process
"""
import json
import logging
import os
from typing import List, Dict, Any, Optional
from dataclasses import dataclass
from datetime import datetime
import uuid
from o... | 306 | 12,409 |
ai-agent-book | chapter3/user-memory/test_evaluation_memory_view.py | .py | """Regression coverage for evaluation menu memory display."""
import builtins
import types
from pathlib import Path
def test_evaluation_option_two_prints_memory_manager_context(monkeypatch, capsys):
monkeypatch.syspath_prepend(str(Path(__file__).parent))
import main
from config import MemoryMode
cla... | 64 | 2,256 |
ai-agent-book | chapter3/user-memory/conversation_history.py | .py | """
Conversation History Management with optional Dify integration for vector search
"""
import json
import os
import logging
from typing import List, Dict, Any, Optional
from dataclasses import dataclass, asdict
from datetime import datetime
import requests
from config import Config
logger = logging.getLogger(__name... | 353 | 12,028 |
ai-agent-book | chapter3/user-memory/config.py | .py | """
Configuration module for User Memory System
"""
import os
from typing import Optional, Dict, Any
from dotenv import load_dotenv
from enum import Enum
# Load environment variables
load_dotenv()
def _reasoning_safe_temperature(model, requested=1.0):
"""Reasoning models (Kimi K3, GPT-5, ...) only accept temper... | 232 | 9,852 |
ai-agent-book | chapter3/user-memory/test_makedirs_bare_filename.py | .py | """Regression tests: os.makedirs(os.path.dirname(p)) must not raise
FileNotFoundError when p is a bare filename with no directory component
(e.g. LOG_FILE=debug.log, or an empty storage dir env var)."""
import os
import sys
import pytest
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from config impo... | 44 | 1,834 |
ai-agent-book | chapter3/user-memory/test_session_scoped_history.py | .py | """Regression tests for issue #493's cross-session history leak."""
import os
import sys
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from conversational_agent import ConversationConfig, ConversationalAgent
from conversation_history import ConversationHistory, ConversationTurn
class StubMemoryMan... | 82 | 2,563 |
ai-agent-book | chapter3/user-memory/agent.py | .py | """
User Memory Agent with Kimi K3 and React pattern
Following the system-hint project's tool-based approach
"""
import json
import os
import sys
import logging
from typing import List, Dict, Any, Optional, Tuple
from dataclasses import dataclass, field
from datetime import datetime
import uuid
from openai import Open... | 970 | 42,788 |
ai-agent-book | chapter3/user-memory/memory_cli.py | .py | #!/usr/bin/env python3
"""
用户记忆离线命令行工具 (memory_cli)
这是一个**离线**的记忆运维 CLI，直接操作 memory_manager 的持久化存储，
无需任何大模型 API，即可演示用户记忆系统的完整生命周期：
提取（手动写入）→ 存储 → 更新 → 去重 / 版本化冲突消解 → 跨会话回忆。
与 main.py 的分工：
* main.py —— 完整的对话 / 后台记忆处理 / 评测流程，需要 LLM API。
* memory_cli.py —— 单条记忆的增删查改与整理逻辑，纯本地可运行，
便于在没有 API Key 的情况下检验存储、去重与冲突消解的行... | 299 | 13,953 |
ai-agent-book | chapter3/user-memory/test_recent_turns_limit_zero.py | .py | """get_recent_turns(limit=0) must return [], not the full history."""
import os
import sys
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from config import Config
from conversation_history import ConversationHistory, ConversationTurn
def test_limit_zero_returns_empty(tmp_path, monkeypatch):
mon... | 32 | 1,209 |
ai-agent-book | chapter3/user-memory/background_memory_processor.py | .py | """
Background Memory Processor - Analyzes conversation context and updates memories
Runs separately from the main conversational agent
"""
import json
import logging
import threading
import time
from typing import List, Dict, Any, Optional, Tuple
from dataclasses import dataclass, field
from datetime import datetime
... | 650 | 28,766 |
Open-Assistant | oasst-shared/tests/test_oasst_api_client.py | .py | from typing import Any
from unittest import mock
from uuid import uuid4
import aiohttp
import pytest
from oasst_shared.api_client import OasstApiClient
from oasst_shared.exceptions import OasstError, OasstErrorCode
from oasst_shared.schemas import protocol as protocol_schema
@pytest.fixture
def oasst_api_client_mock... | 130 | 3,945 |
Open-Assistant | oasst-shared/oasst_shared/utils.py | .py | import hashlib
import time
from datetime import datetime, timezone
from functools import wraps
from loguru import logger
DELETED_USER_DISPLAY_NAME = "Deleted User"
DELETED_USER_ID_PREFIX = "deleted_"
def utcnow() -> datetime:
"""Return the current utc date and time with tzinfo set to UTC."""
return datetime... | 96 | 2,842 |
Open-Assistant | oasst-shared/oasst_shared/model_configs.py | .py | import pydantic
class ModelConfig(pydantic.BaseModel):
model_id: str
max_input_length: int = 512
max_total_length: int = 1024
quantized: bool = False
@property
def is_llama(self) -> bool:
return "llama" in self.model_id.lower()
@property
def is_lorem(self) -> bool:
re... | 159 | 5,311 |
Open-Assistant | oasst-shared/oasst_shared/api_client.py | .py | """API Client for interacting with the OASST backend."""
import enum
import typing as t
from http import HTTPStatus
from typing import Optional, Type
from uuid import UUID
import aiohttp
from loguru import logger
from oasst_shared.exceptions.oasst_api_error import OasstError, OasstErrorCode
from oasst_shared.schemas i... | 157 | 6,707 |
Open-Assistant | oasst-shared/oasst_shared/exceptions/oasst_api_error.py | .py | from enum import IntEnum
from http import HTTPStatus
class OasstErrorCode(IntEnum):
"""
Error codes of the Open-Assistant backend API.
Ranges:
0-1000: general errors
1000-2000: tasks endpoint
2000-3000: prompt_repository, task_repository, user_repository
3000-4000: external res... | 109 | 3,108 |
Open-Assistant | oasst-shared/oasst_shared/schemas/inference.py | .py | import enum
import platform
import random
import uuid
from datetime import datetime
from typing import Annotated, Literal, Union
import psutil
import pydantic
import pynvml
from oasst_shared.model_configs import ModelConfig
INFERENCE_PROTOCOL_VERSION = "1"
class WorkerGpuInfo(pydantic.BaseModel):
name: str
... | 415 | 11,332 |
Open-Assistant | oasst-shared/oasst_shared/schemas/protocol.py | .py | import enum
from datetime import datetime
from typing import List, Literal, Optional, Union
from uuid import UUID, uuid4
import pydantic
from oasst_shared.exceptions import OasstErrorCode
from pydantic import BaseModel, Field, conint, conlist, constr
class TaskRequestType(str, enum.Enum):
random = "random"
s... | 620 | 16,916 |
Open-Assistant | oasst-data/examples/filter_messages.py | .py | import argparse
import json
from oasst_data import read_message_list, write_messages
from oasst_data.schemas import ExportMessageNode
from oasst_data.writer import open_jsonl_write
def parse_args():
parser = argparse.ArgumentParser(description="filter_messages")
parser.add_argument(
"input_file_name"... | 154 | 4,515 |
Open-Assistant | oasst-data/examples/split_dataset.py | .py | import argparse
import random
from oasst_data import read_message_list, write_messages
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--val_percent",
type=int,
default=5,
)
parser.add_argument(
"input_file_name",
type=str,
he... | 65 | 1,830 |
Open-Assistant | oasst-data/examples/clean_dataset.py | .py | import argparse
from collections import OrderedDict
import pandas
from oasst_data.reader import read_message_trees
from oasst_data.schemas import ExportMessageNode, ExportMessageTree
from oasst_data.traversal import visit_messages_depth_first
from oasst_data.writer import write_message_trees
def parse_args():
pa... | 107 | 3,358 |
Open-Assistant | oasst-data/examples/filter_trees.py | .py | import argparse
from oasst_data import read_message_trees, write_message_trees
from oasst_data.schemas import ExportMessageTree
from oasst_data.traversal import visit_messages_depth_first
def parse_args():
parser = argparse.ArgumentParser(description="filter_tres")
parser.add_argument(
"input_file_na... | 57 | 1,748 |
Open-Assistant | oasst-data/examples/tree_to_messages.py | .py | import argparse
from oasst_data import ExportMessageNode, read_message_trees, visit_messages_depth_first, write_messages
def parse_args():
parser = argparse.ArgumentParser(description="tree_to_messages")
parser.add_argument(
"input_file_name",
type=str,
help="path to input .jsonl or .... | 50 | 1,593 |
Open-Assistant | oasst-data/oasst_data/writer.py | .py | import gzip
import json
from datetime import datetime
from pathlib import Path
from typing import Iterable, TextIO
from oasst_data.schemas import ExportMessageNode, ExportMessageTree
def default_serializer(obj):
"""JSON serializer for objects not serializable by default json code"""
if isinstance(obj, dateti... | 68 | 1,799 |
Open-Assistant | oasst-data/oasst_data/__init__.py | .py | from oasst_data.reader import (
read_dataset_message_trees,
read_dataset_messages,
read_message_list,
read_message_tree_list,
read_message_trees,
read_messages,
)
from oasst_data.schemas import (
ExportMessageEvent,
ExportMessageEventEmoji,
ExportMessageEventRanking,
ExportMessag... | 46 | 1,201 |
Open-Assistant | oasst-data/oasst_data/schemas.py | .py | from __future__ import annotations
from datetime import datetime
from typing import Literal, Optional
from pydantic import BaseModel, conint
class LabelAvgValue(BaseModel):
value: float | None
count: int
LabelValues = dict[str, LabelAvgValue]
class ExportMessageEvent(BaseModel):
type: str
user_i... | 96 | 2,329 |
Open-Assistant | oasst-data/oasst_data/traversal.py | .py | from typing import Callable, Optional
from .schemas import ExportMessageNode
def visit_threads_depth_first(
node: ExportMessageNode,
visitor: Callable[[list[ExportMessageNode]], None],
predicate: Optional[Callable[[list[ExportMessageNode]], bool]] = None,
parents: list[ExportMessageNode] = None,
):
... | 36 | 1,079 |
Open-Assistant | oasst-data/oasst_data/reader.py | .py | import gzip
import json
from pathlib import Path
from typing import Callable, Iterable, Optional, TextIO
import pydantic
from datasets import load_dataset
from .schemas import ExportMessageNode, ExportMessageTree
def open_jsonl_read(input_file_path: str | Path) -> TextIO:
if not isinstance(input_file_path, Path... | 130 | 4,084 |
Open-Assistant | notebooks/data-augmentation/anthropic/trainer.py | .py | import numpy as np
from datasets import load_dataset
from sklearn.metrics import f1_score
from torch.utils.data import Dataset
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
MAXLEN = 128
BATCH_SIZE = 128
MODEL = "roberta-base"
LABEL2ID = {
"__casual__": 0,
... | 89 | 2,779 |
Open-Assistant | text-frontend/__main__.py | .py | """Simple REPL frontend."""
import http
import random
import requests
import typer
app = typer.Typer()
# debug constants
USER = {"id": "1234", "display_name": "John Doe", "auth_method": "local"}
def _random_message_id():
return str(random.randint(1000, 9999))
def _render_message(message: dict) -> str:
... | 311 | 12,803 |
Open-Assistant | text-frontend/auto_main.py | .py | """Simple REPL frontend."""
import http
import random
from uuid import uuid4
import requests
import typer
from faker import Faker
app = typer.Typer()
fake = Faker()
def _random_message_id():
return str(uuid4())
def _render_message(message: dict) -> str:
"""Render a message to the user."""
if message[... | 270 | 11,134 |
Open-Assistant | model/model_eval/utils.py | .py | import json
import os
import numpy as np
def load_sampling_data(path):
"""
Load sampling data and ensure appropriate keys are present.
"""
if os.path.exists(path):
data = json.load(open(path))
else:
raise FileNotFoundError(f"Sampling data {path} not found")
if "prompts" not ... | 47 | 1,173 |
Open-Assistant | model/model_eval/eval_datasets.py | .py | from collections import defaultdict
import torch
from model_training.custom_datasets.ranking_collator import RankingDataCollator
from torch.utils.data import DataLoader, Dataset
def get_sampling_dataloader(data, tokenizer, max_length, batch_size):
collate_fn = SamplingDataCollator(tokenizer, max_length=max_lengt... | 95 | 2,915 |
Open-Assistant | model/model_eval/rejection_sampling.py | .py | import argparse
import model_training.models.reward_model # noqa: F401 (registers reward model for AutoModel loading)
import numpy as np
import torch
from eval_datasets import RejectionSamplingDataset, SamplingDataCollator
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassifica... | 72 | 2,887 |
Open-Assistant | model/model_eval/sampling_score.py | .py | import argparse
import json
import model_training.models.reward_model # noqa: F401 (registers reward model for AutoModel loading)
import numpy as np
import pandas as pd
import torch
from eval_datasets import get_sampling_dataloader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from utils ... | 69 | 2,701 |
Open-Assistant | model/model_eval/eval_rm.py | .py | import argparse
from collections import defaultdict
import numpy as np
import torch
from model_training.custom_datasets.rank_datasets import HellaSwagDataset, HFDataset, SHPDataset
from model_training.custom_datasets.ranking_collator import RankingDataCollator
from model_training.metrics import RewardMetrics
from torc... | 101 | 4,022 |
Open-Assistant | model/model_eval/manual/sampling_report.py | .py | import argparse
import gzip
import json
import random
import re
from collections import OrderedDict
from datetime import datetime
from pathlib import Path
from typing import Any, Optional
import pydantic
import torch
from model_training.models.peft_modeling import load_peft_model
from tqdm import tqdm
from transformer... | 387 | 13,964 |
Open-Assistant | model/model_eval/manual/subsample_dataset.py | .py | import argparse
import gzip
import json
import random
from pathlib import Path
from typing import Optional
import pydantic
from oasst_data import ExportMessageTree
def load_message_trees(
input_file_path: str | Path,
lang_codes: list[str],
tree_state: str,
max_length: Optional[int] = None,
) -> list[... | 127 | 3,565 |
Open-Assistant | model/model_eval/manual/create_synth_import.py | .py | import argparse
import json
import random
import re
import sys
from uuid import uuid4
import pydantic
from oasst_data import ExportMessageNode, ExportMessageTree
from sampling_report import SamplingReport
def filter_text(s: str) -> str:
m = re.search(
r"\</?prefix\>|\<human\>|\<\|endoftext\|\>|\<\|prompt... | 106 | 3,359 |
Open-Assistant | model/pretokenizer/indexed_dataset.py | .py | # copied from https://github.com/epfLLM/Megatron-LLM/blob/main/megatron/data/indexed_dataset.py
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# copied from fairseq/fairseq/data/indexed_d... | 566 | 18,879 |
Open-Assistant | model/pretokenizer/pretokenize.py | .py | import argparse
import json
import random
from enum import IntEnum
from pathlib import Path
from subprocess import run
import indexed_dataset
import numpy as np
import torch
from model_training.custom_datasets.formatting import DatasetEntryLm, DatasetEntrySft, Role
from model_training.utils.utils import _strtobool, ge... | 432 | 14,361 |
Open-Assistant | model/pretokenizer/tokenizer.py | .py | # copied from https://github.com/epfLLM/Megatron-LLM/blob/main/megatron/tokenizer/tokenizer.py
# (only keeping _FalconTokenizer & _SentencePieceTokenizer)
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Megatron tokenizers."""
from abc import ABC, abstractmethod
def build_tokenizer(args):
"""... | 332 | 9,948 |
Open-Assistant | model/pretokenizer/create_hf_tokenizer_config.py | .py | import argparse
from distutils.util import strtobool as strtoboolint
import transformers
from tokenizer import build_tokenizer
from transformers.utils import cached_file
def strtobool(s: str) -> bool:
return bool(strtoboolint(s))
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(... | 113 | 4,528 |
Open-Assistant | model/model_training/trainer_rl.py | .py | import argparse
import math
import os
import random
from argparse import Namespace
from typing import Sequence
import numpy as np
import torch
import transformers
import tritonclient.grpc as client_util
import trlx
from model_training.custom_datasets.formatting import QA_SPECIAL_TOKENS, format_pairs
from model_trainin... | 201 | 7,469 |
Open-Assistant | model/model_training/trainer_rm.py | .py | import argparse
import logging
import os
from typing import Callable, Literal, Optional, Sequence, Union
import datasets
import torch
from model_training.custom_datasets.ranking_collator import RankingDataCollator
from model_training.efficiency_utils import fuse_gelu
from model_training.metrics import RewardMetrics
fr... | 335 | 12,775 |
Open-Assistant | model/model_training/check_dataset_counts.py | .py | import argparse
from collections import Counter
from enum import Enum
from pathlib import Path
from typing import Any
import pandas as pd
import yaml
from langdetect import DetectorFactory, detect
from model_training.custom_datasets.formatting import DatasetEntrySft
from model_training.utils.utils import _strtobool, g... | 165 | 6,716 |
Open-Assistant | model/model_training/check_dataset_appearances.py | .py | """
This script should help to detect any keywords or other unwanted appearances in the datasets
RUN WITH:
python check_dataset_appearances.py -d <datasets> --cache_dir <path-to-cache-dir> --mode <one of sft, rm, rl>
e.g.:
python check_dataset_appearances.py -d gpt4all webgpt --cache_dir .cache --mode sft
python chec... | 130 | 5,620 |
Open-Assistant | model/model_training/metrics.py | .py | import numpy as np
from scipy import stats as st
RM_METRICS = ["accuracy", "kendalltau", "spearmanr"]
def reward_accuracy(eval_pred):
logits = eval_pred.predictions
labels = eval_pred.label_ids
pos_scores, neg_scores = [], []
for b_logits, b_labels in zip(logits, labels):
b_labels = b_labels[... | 98 | 3,167 |
Open-Assistant | model/model_training/efficiency_utils.py | .py | import functools
import torch
from transformers.activations import FastGELUActivation, GELUActivation, NewGELUActivation, QuickGELUActivation
def rsetattr(obj, attr, val):
pre, _, post = attr.rpartition(".")
return setattr(rgetattr(obj, pre) if pre else obj, post, val)
def rgetattr(obj, attr, *args):
d... | 56 | 1,734 |
Open-Assistant | model/model_training/trainer_sft.py | .py | #!/usr/bin/env python3
import argparse
import logging
import os
from functools import partial
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import datasets
import torch
# from model_training.custom_datasets.formatting import DatasetEntry
from model_training.custom_datasets.dialogue_co... | 482 | 18,125 |
Open-Assistant | model/model_training/to_triton.py | .py | import os
from argparse import Namespace
from string import Template
import torch
import transformers
from torch import nn
from trainer_rl import argument_parsing
from utils.utils import get_model
class SFTLogitsModel(nn.Module):
def __init__(self, model):
super().__init__()
self.model = model
... | 125 | 3,568 |
Open-Assistant | model/model_training/tools/augment_oasst.py | .py | """
Augment oasst dataset with sft generated results
You can use augment new response using a model with bad response, ie non SFT model
had to do this in a quick fashion, please tolerate the hackiness in the code
"""
import json
import os
# so far load_oasst_export is pretty deterministic in thread orde... | 248 | 9,614 |
Open-Assistant | model/model_training/tools/model_cli.py | .py | #!/usr/bin/env python3
import argparse
import time
import torch
import transformers
from model_training.custom_datasets.formatting import QA_SPECIAL_TOKENS, format_pairs, format_system_prefix
from model_training.models import get_specific_model
from model_training.utils.utils import _strtobool
from tokenizers import p... | 122 | 4,776 |
Open-Assistant | model/model_training/tools/model_chat.py | .py | #!/usr/bin/env python3
"""
A very simple script to test model locally
"""
import argparse
from enum import Enum
from typing import List, Tuple
import torch
from model_training.custom_datasets.formatting import QA_SPECIAL_TOKENS
from model_training.utils.utils import _strtobool
from tokenizers import pre_tokenizers
... | 151 | 6,244 |
Open-Assistant | model/model_training/tools/check_oasst_export.py | .py | import argparse
from oasst_data import ExportMessageTree, read_message_tree_list, visit_messages_depth_first
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("input_file_path", type=str, help=".jsonl or jsonl.gz OA export")
parser.add_argument("--lang", type=str, help="comma separ... | 62 | 2,090 |
Open-Assistant | model/model_training/tools/sample_rm_data.py | .py | """
Recursive method to traverse down the conversation tree
Use fastlangid for language identification :
>> pip install fastlangid
"""
import glob
import json
import random
import sys
from collections import defaultdict
from copy import deepcopy
from fastlangid.langid import LID
langid = LID()
total_ran... | 189 | 5,739 |
Open-Assistant | model/model_training/tools/export_model.py | .py | import argparse
import sys
import model_training.models.reward_model # noqa: F401 make sure reward model is registered for AutoModel
import torch
from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer
def parse_args():
parser = argparse.ArgumentParser()
parser.add_a... | 116 | 4,735 |
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