refactor: reorgnize all examples/ and test/
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Andy Lee
188
apps/wechat_rag.py
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188
apps/wechat_rag.py
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"""
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WeChat History RAG example using the unified interface.
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Supports WeChat chat history export and search.
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"""
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import subprocess
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import sys
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from pathlib import Path
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# Add parent directory to path for imports
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sys.path.insert(0, str(Path(__file__).parent))
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from base_rag_example import BaseRAGExample
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from .history_data.wechat_history import WeChatHistoryReader
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class WeChatRAG(BaseRAGExample):
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"""RAG example for WeChat chat history."""
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def __init__(self):
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# Set default values BEFORE calling super().__init__
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self.max_items_default = 50 # Match original default
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self.embedding_model_default = (
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"sentence-transformers/all-MiniLM-L6-v2" # Fast 384-dim model
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)
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super().__init__(
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name="WeChat History",
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description="Process and query WeChat chat history with LEANN",
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default_index_name="wechat_history_magic_test_11Debug_new",
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)
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def _add_specific_arguments(self, parser):
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"""Add WeChat-specific arguments."""
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wechat_group = parser.add_argument_group("WeChat Parameters")
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wechat_group.add_argument(
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"--export-dir",
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type=str,
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default="./wechat_export",
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help="Directory to store WeChat exports (default: ./wechat_export)",
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)
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wechat_group.add_argument(
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"--force-export",
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action="store_true",
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help="Force re-export of WeChat data even if exports exist",
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)
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wechat_group.add_argument(
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"--chunk-size", type=int, default=192, help="Text chunk size (default: 192)"
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)
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wechat_group.add_argument(
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"--chunk-overlap", type=int, default=64, help="Text chunk overlap (default: 64)"
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)
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def _export_wechat_data(self, export_dir: Path) -> bool:
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"""Export WeChat data using wechattweak-cli."""
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print("Exporting WeChat data...")
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# Check if WeChat is running
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try:
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result = subprocess.run(["pgrep", "WeChat"], capture_output=True, text=True)
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if result.returncode != 0:
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print("WeChat is not running. Please start WeChat first.")
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return False
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except Exception:
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pass # pgrep might not be available on all systems
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# Create export directory
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export_dir.mkdir(parents=True, exist_ok=True)
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# Run export command
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cmd = ["packages/wechat-exporter/wechattweak-cli", "export", str(export_dir)]
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try:
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print(f"Running: {' '.join(cmd)}")
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result = subprocess.run(cmd, capture_output=True, text=True)
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if result.returncode == 0:
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print("WeChat data exported successfully!")
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return True
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else:
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print(f"Export failed: {result.stderr}")
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return False
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except FileNotFoundError:
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print("\nError: wechattweak-cli not found!")
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print("Please install it first:")
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print(" sudo packages/wechat-exporter/wechattweak-cli install")
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return False
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except Exception as e:
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print(f"Export error: {e}")
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return False
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async def load_data(self, args) -> list[str]:
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"""Load WeChat history and convert to text chunks."""
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# Initialize WeChat reader with export capabilities
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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. Trying to find any existing exports...")
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# Try to find any existing exports in common locations
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export_dirs = reader.find_wechat_export_dirs()
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if not export_dirs:
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print("No WeChat data found. Please ensure WeChat exports exist.")
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return []
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# Load documents from all found export directories
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all_documents = []
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total_processed = 0
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for i, export_dir in enumerate(export_dirs):
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print(f"\nProcessing WeChat export {i + 1}/{len(export_dirs)}: {export_dir}")
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try:
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# Apply max_items limit per export
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max_per_export = -1
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if args.max_items > 0:
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remaining = args.max_items - total_processed
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if remaining <= 0:
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break
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max_per_export = remaining
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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_per_export,
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concatenate_messages=True, # Enable message concatenation for better context
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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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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 []
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print(f"\nTotal loaded {len(all_documents)} chat documents from {len(export_dirs)} exports")
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# Convert to text chunks with contact information
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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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from llama_index.core.node_parser import SentenceSplitter
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text_splitter = SentenceSplitter(
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chunk_size=args.chunk_size, chunk_overlap=args.chunk_overlap
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)
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nodes = text_splitter.get_nodes_from_documents([doc])
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for node in nodes:
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# Add contact information to each chunk
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contact_name = doc.metadata.get("contact_name", "Unknown")
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text = f"[Contact] means the message is from: {contact_name}\n" + node.get_content()
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all_texts.append(text)
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print(f"Created {len(all_texts)} text chunks from {len(all_documents)} documents")
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return all_texts
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if __name__ == "__main__":
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import asyncio
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# Check platform
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if sys.platform != "darwin":
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print("\n⚠️ Warning: WeChat export is only supported on macOS")
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print(" You can still query existing exports on other platforms\n")
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# Example queries for WeChat RAG
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print("\n💬 WeChat History RAG Example")
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print("=" * 50)
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print("\nExample queries you can try:")
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print("- 'Show me conversations about travel plans'")
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print("- 'Find group chats about weekend activities'")
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print("- '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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print("- 'What did we discuss about the project last month?'")
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print("\nNote: WeChat must be running for export to work\n")
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rag = WeChatRAG()
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asyncio.run(rag.run())
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