320 lines
10 KiB
Python
320 lines
10 KiB
Python
import os
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import asyncio
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import dotenv
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import argparse
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from pathlib import Path
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from typing import List, Any, Optional
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from leann.api import LeannBuilder, LeannSearcher, LeannChat
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from llama_index.core.node_parser import SentenceSplitter
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import requests
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import time
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dotenv.load_dotenv()
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# Default WeChat export directory
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DEFAULT_WECHAT_EXPORT_DIR = "./wechat_export_direct"
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def create_leann_index_from_multiple_wechat_exports(
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export_dirs: List[Path],
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index_path: str = "wechat_history_index.leann",
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max_count: int = -1,
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):
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"""
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Create LEANN index from multiple WeChat export data sources.
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Args:
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export_dirs: List of Path objects pointing to WeChat export directories
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index_path: Path to save the LEANN index
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max_count: Maximum number of chat entries to process per export
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"""
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print("Creating LEANN index from multiple WeChat export data sources...")
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# Load documents using WeChatHistoryReader from history_data
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from history_data.wechat_history import WeChatHistoryReader
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reader = WeChatHistoryReader()
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INDEX_DIR = Path(index_path).parent
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if not INDEX_DIR.exists():
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print(f"--- Index directory not found, building new index ---")
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all_documents = []
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total_processed = 0
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# Process each WeChat export directory
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for i, export_dir in enumerate(export_dirs):
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print(
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f"\nProcessing WeChat export {i + 1}/{len(export_dirs)}: {export_dir}"
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)
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try:
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documents = reader.load_data(
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wechat_export_dir=str(export_dir),
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max_count=max_count,
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concatenate_messages=True, # Disable concatenation - one message per document
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)
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if documents:
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print(f"Loaded {len(documents)} chat documents from {export_dir}")
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all_documents.extend(documents)
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total_processed += len(documents)
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# Check if we've reached the max count
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if max_count > 0 and total_processed >= max_count:
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print(f"Reached max count of {max_count} documents")
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break
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else:
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print(f"No documents loaded from {export_dir}")
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except Exception as e:
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print(f"Error processing {export_dir}: {e}")
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continue
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if not all_documents:
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print("No documents loaded from any source. Exiting.")
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return None
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print(
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f"\nTotal loaded {len(all_documents)} chat documents from {len(export_dirs)} exports and starting to split them into chunks"
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)
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# Create text splitter with 256 chunk size
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text_splitter = SentenceSplitter(chunk_size=256, chunk_overlap=128)
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# Convert Documents to text strings and chunk them
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all_texts = []
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for doc in all_documents:
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# Split the document into chunks
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nodes = text_splitter.get_nodes_from_documents([doc])
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for node in nodes:
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text = '[Contact] means the message is from: ' + doc.metadata["contact_name"] + '\n' + node.get_content()
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all_texts.append(text)
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print(
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f"Finished splitting {len(all_documents)} documents into {len(all_texts)} text chunks"
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)
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# Create LEANN index directory
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print(f"--- Index directory not found, building new index ---")
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INDEX_DIR.mkdir(exist_ok=True)
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print(f"--- Building new LEANN index ---")
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print(f"\n[PHASE 1] Building Leann index...")
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# Use HNSW backend for better macOS compatibility
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builder = LeannBuilder(
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backend_name="hnsw",
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embedding_model="Qwen/Qwen3-Embedding-0.6B",
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graph_degree=32,
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complexity=64,
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is_compact=True,
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is_recompute=True,
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num_threads=1, # Force single-threaded mode
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)
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print(f"Adding {len(all_texts)} chat chunks to index...")
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for chunk_text in all_texts:
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builder.add_text(chunk_text)
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builder.build_index(index_path)
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print(f"\nLEANN index built at {index_path}!")
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else:
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print(f"--- Using existing index at {INDEX_DIR} ---")
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return index_path
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def create_leann_index(
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export_dir: str = None,
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index_path: str = "wechat_history_index.leann",
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max_count: int = 1000,
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):
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"""
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Create LEANN index from WeChat chat history data.
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Args:
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export_dir: Path to the WeChat export directory (optional, uses default if None)
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index_path: Path to save the LEANN index
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max_count: Maximum number of chat entries to process
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"""
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print("Creating LEANN index from WeChat chat history data...")
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INDEX_DIR = Path(index_path).parent
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if not INDEX_DIR.exists():
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print(f"--- Index directory not found, building new index ---")
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INDEX_DIR.mkdir(exist_ok=True)
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print(f"--- Building new LEANN index ---")
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print(f"\n[PHASE 1] Building Leann index...")
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# Load documents using WeChatHistoryReader from history_data
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from history_data.wechat_history import WeChatHistoryReader
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reader = WeChatHistoryReader()
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documents = reader.load_data(
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wechat_export_dir=export_dir,
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max_count=max_count,
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concatenate_messages=False, # Disable concatenation - one message per document
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)
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if not documents:
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print("No documents loaded. Exiting.")
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return None
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print(f"Loaded {len(documents)} chat documents")
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# Create text splitter with 256 chunk size
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text_splitter = SentenceSplitter(chunk_size=256, chunk_overlap=25)
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# Convert Documents to text strings and chunk them
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all_texts = []
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for doc in documents:
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# Split the document into chunks
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nodes = text_splitter.get_nodes_from_documents([doc])
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for node in nodes:
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all_texts.append(node.get_content())
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print(f"Created {len(all_texts)} text chunks from {len(documents)} documents")
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# Create LEANN index directory
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print(f"--- Index directory not found, building new index ---")
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INDEX_DIR.mkdir(exist_ok=True)
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print(f"--- Building new LEANN index ---")
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print(f"\n[PHASE 1] Building Leann index...")
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# Use HNSW backend for better macOS compatibility
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builder = LeannBuilder(
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backend_name="hnsw",
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embedding_model="mlx-community/Qwen3-Embedding-0.6B-4bit-DWQ", # MLX-optimized model
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graph_degree=32,
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complexity=64,
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is_compact=True,
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is_recompute=True,
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num_threads=1, # Force single-threaded mode
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)
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print(f"Adding {len(all_texts)} chat chunks to index...")
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for chunk_text in all_texts:
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builder.add_text(chunk_text)
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builder.build_index(index_path)
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print(f"\nLEANN index built at {index_path}!")
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else:
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print(f"--- Using existing index at {INDEX_DIR} ---")
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return index_path
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async def query_leann_index(index_path: str, query: str):
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"""
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Query the LEANN index.
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Args:
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index_path: Path to the LEANN index
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query: The query string
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"""
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print(f"\n[PHASE 2] Starting Leann chat session...")
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chat = LeannChat(index_path=index_path)
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print(f"You: {query}")
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chat_response = chat.ask(
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query,
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top_k=20,
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recompute_beighbor_embeddings=True,
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complexity=16,
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beam_width=1,
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llm_config={
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"type": "openai",
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"model": "gpt-4o",
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"api_key": os.getenv("OPENAI_API_KEY"),
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},
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llm_kwargs={"temperature": 0.0, "max_tokens": 1000},
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)
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print(f"Leann: {chat_response}")
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async def main():
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"""Main function with integrated WeChat export functionality."""
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# Parse command line arguments
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parser = argparse.ArgumentParser(
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description="LEANN WeChat History Reader - Create and query WeChat chat history index"
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)
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parser.add_argument(
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"--export-dir",
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type=str,
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default=DEFAULT_WECHAT_EXPORT_DIR,
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help=f"Directory to store WeChat exports (default: {DEFAULT_WECHAT_EXPORT_DIR})",
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)
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parser.add_argument(
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"--index-dir",
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type=str,
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default="./wechat_history_magic_test_11Debug_new",
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help="Directory to store the LEANN index (default: ./wechat_history_index_leann_test)",
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)
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parser.add_argument(
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"--max-entries",
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type=int,
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default=50,
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help="Maximum number of chat entries to process (default: 5000)",
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)
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parser.add_argument(
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"--query",
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type=str,
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default=None,
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help="Single query to run (default: runs example queries)",
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)
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parser.add_argument(
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"--force-export",
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action="store_true",
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default=False,
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help="Force re-export of WeChat data even if exports exist",
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)
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args = parser.parse_args()
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INDEX_DIR = Path(args.index_dir)
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INDEX_PATH = str(INDEX_DIR / "wechat_history.leann")
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print(f"Using WeChat export directory: {args.export_dir}")
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print(f"Index directory: {INDEX_DIR}")
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print(f"Max entries: {args.max_entries}")
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# Initialize WeChat reader with export capabilities
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from history_data.wechat_history import WeChatHistoryReader
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reader = WeChatHistoryReader()
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# Find existing exports or create new ones using the centralized method
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export_dirs = reader.find_or_export_wechat_data(args.export_dir)
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if not export_dirs:
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print("Failed to find or export WeChat data. Exiting.")
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return
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# Create or load the LEANN index from all sources
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index_path = create_leann_index_from_multiple_wechat_exports(
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export_dirs, INDEX_PATH, max_count=args.max_entries
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)
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if index_path:
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if args.query:
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# Run single query
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await query_leann_index(index_path, args.query)
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else:
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# Example queries
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queries = [
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"我想买魔术师约翰逊的球衣，给我一些对应聊天记录?",
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]
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for query in queries:
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print("\n" + "=" * 60)
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await query_leann_index(index_path, query)
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if __name__ == "__main__":
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asyncio.run(main())
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