import os import asyncio import dotenv from pathlib import Path from typing import List, Any, Optional from leann.api import LeannBuilder, LeannSearcher, LeannChat from llama_index.core.node_parser import SentenceSplitter import requests import time dotenv.load_dotenv() def create_leann_index_from_multiple_wechat_exports(export_dirs: List[Path], index_path: str = "wechat_history_index.leann", max_count: int = -1): """ Create LEANN index from multiple WeChat export data sources. Args: export_dirs: List of Path objects pointing to WeChat export directories index_path: Path to save the LEANN index max_count: Maximum number of chat entries to process per export """ print("Creating LEANN index from multiple WeChat export data sources...") # Load documents using WeChatHistoryReader from history_data from history_data.wechat_history import WeChatHistoryReader reader = WeChatHistoryReader() INDEX_DIR = Path(index_path).parent if not INDEX_DIR.exists(): print(f"--- Index directory not found, building new index ---") all_documents = [] total_processed = 0 # Process each WeChat export directory for i, export_dir in enumerate(export_dirs): print(f"\nProcessing WeChat export {i+1}/{len(export_dirs)}: {export_dir}") try: documents = reader.load_data( wechat_export_dir=str(export_dir), max_count=max_count, concatenate_messages=False # Disable concatenation - one message per document ) if documents: print(f"Loaded {len(documents)} chat documents from {export_dir}") all_documents.extend(documents) total_processed += len(documents) # Check if we've reached the max count if max_count > 0 and total_processed >= max_count: print(f"Reached max count of {max_count} documents") break else: print(f"No documents loaded from {export_dir}") except Exception as e: print(f"Error processing {export_dir}: {e}") continue if not all_documents: print("No documents loaded from any source. Exiting.") return None print(f"\nTotal loaded {len(all_documents)} chat documents from {len(export_dirs)} exports") # Create text splitter with 256 chunk size text_splitter = SentenceSplitter(chunk_size=256, chunk_overlap=25) # Convert Documents to text strings and chunk them all_texts = [] for doc in all_documents: # Split the document into chunks nodes = text_splitter.get_nodes_from_documents([doc]) for node in nodes: all_texts.append(node.get_content()) print(f"Created {len(all_texts)} text chunks from {len(all_documents)} documents") # Create LEANN index directory print(f"--- Index directory not found, building new index ---") INDEX_DIR.mkdir(exist_ok=True) print(f"--- Building new LEANN index ---") print(f"\n[PHASE 1] Building Leann index...") # Use HNSW backend for better macOS compatibility builder = LeannBuilder( backend_name="hnsw", embedding_model="Qwen/Qwen3-Embedding-0.6B", graph_degree=32, complexity=64, is_compact=True, is_recompute=True, num_threads=1 # Force single-threaded mode ) print(f"Adding {len(all_texts)} chat chunks to index...") for chunk_text in all_texts: builder.add_text(chunk_text) builder.build_index(index_path) print(f"\nLEANN index built at {index_path}!") else: print(f"--- Using existing index at {INDEX_DIR} ---") return index_path def create_leann_index(export_dir: str = None, index_path: str = "wechat_history_index.leann", max_count: int = 1000): """ Create LEANN index from WeChat chat history data. Args: export_dir: Path to the WeChat export directory (optional, uses default if None) index_path: Path to save the LEANN index max_count: Maximum number of chat entries to process """ print("Creating LEANN index from WeChat chat history data...") INDEX_DIR = Path(index_path).parent if not INDEX_DIR.exists(): print(f"--- Index directory not found, building new index ---") INDEX_DIR.mkdir(exist_ok=True) print(f"--- Building new LEANN index ---") print(f"\n[PHASE 1] Building Leann index...") # Load documents using WeChatHistoryReader from history_data from history_data.wechat_history import WeChatHistoryReader reader = WeChatHistoryReader() documents = reader.load_data( wechat_export_dir=export_dir, max_count=max_count, concatenate_messages=False # Disable concatenation - one message per document ) if not documents: print("No documents loaded. Exiting.") return None print(f"Loaded {len(documents)} chat documents") # Create text splitter with 256 chunk size text_splitter = SentenceSplitter(chunk_size=256, chunk_overlap=25) # Convert Documents to text strings and chunk them all_texts = [] for doc in documents: # Split the document into chunks nodes = text_splitter.get_nodes_from_documents([doc]) for node in nodes: all_texts.append(node.get_content()) print(f"Created {len(all_texts)} text chunks from {len(documents)} documents") # Create LEANN index directory print(f"--- Index directory not found, building new index ---") INDEX_DIR.mkdir(exist_ok=True) print(f"--- Building new LEANN index ---") print(f"\n[PHASE 1] Building Leann index...") # Use HNSW backend for better macOS compatibility builder = LeannBuilder( backend_name="hnsw", embedding_model="mlx-community/Qwen3-Embedding-0.6B-4bit-DWQ", # MLX-optimized model graph_degree=32, complexity=64, is_compact=True, is_recompute=True, num_threads=1 # Force single-threaded mode ) print(f"Adding {len(all_texts)} chat chunks to index...") for chunk_text in all_texts: builder.add_text(chunk_text) builder.build_index(index_path) print(f"\nLEANN index built at {index_path}!") else: print(f"--- Using existing index at {INDEX_DIR} ---") return index_path async def query_leann_index(index_path: str, query: str): """ Query the LEANN index. Args: index_path: Path to the LEANN index query: The query string """ print(f"\n[PHASE 2] Starting Leann chat session...") chat = LeannChat(index_path=index_path) print(f"You: {query}") chat_response = chat.ask( query, top_k=5, recompute_beighbor_embeddings=True, complexity=32, beam_width=1, llm_config={ "type": "openai", "model": "gpt-4o", "api_key": os.getenv("OPENAI_API_KEY"), }, llm_kwargs={ "temperature": 0.0, "max_tokens": 1000 } ) print(f"Leann: {chat_response}") async def main(): """Main function with integrated WeChat export functionality.""" # Initialize WeChat reader with export capabilities from history_data.wechat_history import WeChatHistoryReader reader = WeChatHistoryReader() # Find existing exports or create new ones using the centralized method export_dirs = reader.find_or_export_wechat_data("./wechat_export_direct") if not export_dirs: print("Failed to find or export WeChat data. Exiting.") return INDEX_DIR = Path("./wechat_history_index_leann_test") INDEX_PATH = str(INDEX_DIR / "wechat_history.leann") # Create or load the LEANN index from all sources index_path = create_leann_index_from_multiple_wechat_exports(export_dirs, INDEX_PATH, max_count=5000) if index_path: # Example queries queries = [ "我想买魔术师约翰逊的球衣，给我一些对应聊天记录?", ] for query in queries: print("\n" + "="*60) await query_leann_index(index_path, query) if __name__ == "__main__": asyncio.run(main())