147 lines
4.9 KiB
Python
147 lines
4.9 KiB
Python
import argparse
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import asyncio
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from pathlib import Path
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import dotenv
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from leann.api import LeannBuilder, LeannChat
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from llama_index.core import SimpleDirectoryReader
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from llama_index.core.node_parser import SentenceSplitter
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dotenv.load_dotenv()
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async def main(args):
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INDEX_DIR = Path(args.index_dir)
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INDEX_PATH = str(INDEX_DIR / "pdf_documents.leann")
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if not INDEX_DIR.exists():
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node_parser = SentenceSplitter(
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chunk_size=256, chunk_overlap=128, separator=" ", paragraph_separator="\n\n"
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)
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print("Loading documents...")
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documents = SimpleDirectoryReader(
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args.data_dir,
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recursive=True,
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encoding="utf-8",
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required_exts=[".pdf", ".txt", ".md"],
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).load_data(show_progress=True)
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print("Documents loaded.")
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all_texts = []
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for doc in documents:
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nodes = node_parser.get_nodes_from_documents([doc])
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if nodes:
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all_texts.extend(node.get_content() for node in nodes)
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print("--- Index directory not found, building new index ---")
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print("\n[PHASE 1] Building Leann index...")
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# LeannBuilder now automatically detects normalized embeddings and sets appropriate distance metric
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print(f"Using {args.embedding_model} with {args.embedding_mode} mode")
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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=args.embedding_model,
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embedding_mode=args.embedding_mode,
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# distance_metric is automatically set based on 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"Loaded {len(all_texts)} text chunks from documents.")
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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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print("\n[PHASE 2] Starting Leann chat session...")
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# Build llm_config based on command line arguments
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if args.llm == "simulated":
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llm_config = {"type": "simulated"}
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elif args.llm == "ollama":
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llm_config = {"type": "ollama", "model": args.model, "host": args.host}
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elif args.llm == "hf":
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llm_config = {"type": "hf", "model": args.model}
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elif args.llm == "openai":
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llm_config = {"type": "openai", "model": args.model}
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else:
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raise ValueError(f"Unknown LLM type: {args.llm}")
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print(f"Using LLM: {args.llm} with model: {args.model if args.llm != 'simulated' else 'N/A'}")
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chat = LeannChat(index_path=INDEX_PATH, llm_config=llm_config)
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# query = (
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# "什么是盘古大模型以及盘古开发过程中遇到了什么阴暗面,任务令一般在什么城市颁发"
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# )
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query = args.query
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print(f"You: {query}")
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chat_response = chat.ask(query, top_k=20, recompute_embeddings=True, complexity=32)
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print(f"Leann chat response: \033[36m{chat_response}\033[0m")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Run Leann Chat with various LLM backends.")
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parser.add_argument(
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"--llm",
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type=str,
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default="openai",
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choices=["simulated", "ollama", "hf", "openai"],
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help="The LLM backend to use.",
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)
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parser.add_argument(
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"--model",
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type=str,
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default="gpt-4o",
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help="The model name to use (e.g., 'llama3:8b' for ollama, 'deepseek-ai/deepseek-llm-7b-chat' for hf, 'gpt-4o' for openai).",
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)
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parser.add_argument(
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"--embedding-model",
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type=str,
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default="facebook/contriever",
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help="The embedding model to use (e.g., 'facebook/contriever', 'text-embedding-3-small').",
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)
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parser.add_argument(
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"--embedding-mode",
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type=str,
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default="sentence-transformers",
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choices=["sentence-transformers", "openai", "mlx"],
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help="The embedding backend mode.",
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)
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parser.add_argument(
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"--host",
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type=str,
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default="http://localhost:11434",
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help="The host for the Ollama API.",
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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="./test_doc_files",
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help="Directory where the Leann index will be stored.",
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)
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parser.add_argument(
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"--data-dir",
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type=str,
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default="examples/data",
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help="Directory containing documents to index (PDF, TXT, MD files).",
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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="Based on the paper, what are the main techniques LEANN explores to reduce the storage overhead and DLPM explore to achieve Fairness and Efiiciency trade-off?",
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help="The query to ask the Leann chat system.",
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)
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args = parser.parse_args()
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asyncio.run(main(args))
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