193 lines
7.4 KiB
Python
193 lines
7.4 KiB
Python
import os
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import sys
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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
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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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dotenv.load_dotenv()
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# Auto-detect user's mail path
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def get_mail_path():
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"""Get the mail path for the current user"""
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home_dir = os.path.expanduser("~")
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return os.path.join(home_dir, "Library", "Mail")
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def create_leann_index_from_multiple_sources(messages_dirs: List[Path], index_path: str = "mail_index.leann", max_count: int = -1, include_html: bool = False, embedding_model: str = "facebook/contriever"):
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"""
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Create LEANN index from multiple mail data sources.
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Args:
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messages_dirs: List of Path objects pointing to Messages directories
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index_path: Path to save the LEANN index
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max_count: Maximum number of emails to process per directory
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include_html: Whether to include HTML content in email processing
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"""
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print("Creating LEANN index from multiple mail data sources...")
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# Load documents using EmlxReader from local readers module
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from .readers import EmlxReader, find_all_messages_directories
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reader = EmlxReader(include_html=include_html)
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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 Messages directory
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for i, messages_dir in enumerate(messages_dirs):
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print(f"\nProcessing Messages directory {i+1}/{len(messages_dirs)}: {messages_dir}")
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try:
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documents = reader.load_data(messages_dir)
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if documents:
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print(f"Loaded {len(documents)} email documents from {messages_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 {messages_dir}")
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except Exception as e:
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print(f"Error processing {messages_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(f"\nTotal loaded {len(all_documents)} email documents from {len(messages_dirs)} directories")
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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 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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all_texts.append(node.get_content())
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print(f"Created {len(all_texts)} text chunks from {len(all_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=embedding_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)} email 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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llm_config={"type": "openai", "model": "gpt-4o"})
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print(f"You: {query}")
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import time
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start_time = time.time()
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chat_response = chat.ask(
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query,
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top_k=10,
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recompute_beighbor_embeddings=True,
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complexity=12,
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beam_width=1,
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)
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end_time = time.time()
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print(f"Time taken: {end_time - start_time} seconds")
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print(f"Leann: {chat_response}")
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async def main():
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# Parse command line arguments
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parser = argparse.ArgumentParser(description='LEANN Mail Reader - Create and query email index')
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parser.add_argument('--index-dir', type=str, default="./mail_index_leann_raw_text_all_dicts",
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help='Directory to store the LEANN index (default: ./mail_index_leann_raw_text_all_dicts)')
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parser.add_argument('--max-emails', type=int, default=1000,
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help='Maximum number of emails to process (-1 means all)')
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parser.add_argument('--query', type=str, default="Give me some funny advertisement about apple or other companies",
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help='Single query to run (default: runs example queries)')
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parser.add_argument('--include-html', action='store_true', default=False,
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help='Include HTML content in email processing (default: False)')
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parser.add_argument('--embedding-model', type=str, default="facebook/contriever",
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help='Embedding model to use (default: facebook/contriever)')
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args = parser.parse_args()
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print(f"args: {args}")
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# Automatically find all Messages directories under the current user's Mail directory
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from .readers import find_all_messages_directories
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mail_path = get_mail_path()
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print(f"Searching for email data in: {mail_path}")
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messages_dirs = find_all_messages_directories(mail_path)
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print('len(messages_dirs): ', len(messages_dirs))
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if not messages_dirs:
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print("No Messages directories found. Exiting.")
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return
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INDEX_DIR = Path(args.index_dir)
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INDEX_PATH = str(INDEX_DIR / "mail_documents.leann")
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print(f"Index directory: {INDEX_DIR}")
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print(f"Found {len(messages_dirs)} Messages directories.")
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# Create or load the LEANN index from all sources
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index_path = create_leann_index_from_multiple_sources(messages_dirs, INDEX_PATH, args.max_emails, args.include_html, args.embedding_model)
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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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"Hows Berkeley Graduate Student Instructor",
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"how's the icloud related advertisement saying",
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"Whats the number of class recommend to take per semester for incoming EECS students"
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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()) |