b3e9ee96fa
- Fix ambiguous fullwidth characters (commas, parentheses) in strings and comments - Replace Chinese comments with English equivalents - Fix unused imports with proper noqa annotations for intentional imports - Fix bare except clauses with specific exception types - Fix redefined variables and undefined names - Add ruff noqa annotations for generated protobuf files - Add lint and format check to GitHub Actions CI pipeline
89 lines
3.0 KiB
Python
89 lines
3.0 KiB
Python
"""
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Simple demo showing basic leann usage
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Run: uv run python examples/simple_demo.py
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"""
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import argparse
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from leann import LeannBuilder, LeannChat, LeannSearcher
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def main():
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parser = argparse.ArgumentParser(
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description="Simple demo of Leann with selectable embedding models."
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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="sentence-transformers/all-mpnet-base-v2",
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help="The embedding model to use, e.g., 'sentence-transformers/all-mpnet-base-v2' or 'text-embedding-ada-002'.",
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)
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args = parser.parse_args()
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print(f"=== Leann Simple Demo with {args.embedding_model} ===")
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print()
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# Sample knowledge base
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chunks = [
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"Machine learning is a subset of artificial intelligence that enables computers to learn without being explicitly programmed.",
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"Deep learning uses neural networks with multiple layers to process data and make decisions.",
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"Natural language processing helps computers understand and generate human language.",
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"Computer vision enables machines to interpret and understand visual information from images and videos.",
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"Reinforcement learning teaches agents to make decisions by receiving rewards or penalties for their actions.",
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"Data science combines statistics, programming, and domain expertise to extract insights from data.",
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"Big data refers to extremely large datasets that require special tools and techniques to process.",
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"Cloud computing provides on-demand access to computing resources over the internet.",
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]
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print("1. Building index (no embeddings stored)...")
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builder = LeannBuilder(
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embedding_model=args.embedding_model,
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backend_name="hnsw",
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)
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for chunk in chunks:
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builder.add_text(chunk)
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builder.build_index("demo_knowledge.leann")
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print()
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print("2. Searching with real-time embeddings...")
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searcher = LeannSearcher("demo_knowledge.leann")
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queries = [
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"What is machine learning?",
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"How does neural network work?",
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"Tell me about data processing",
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]
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for query in queries:
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print(f"Query: {query}")
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results = searcher.search(query, top_k=2)
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for i, result in enumerate(results, 1):
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print(f" {i}. Score: {result.score:.3f}")
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print(f" Text: {result.text[:100]}...")
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print()
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print("3. Interactive chat demo:")
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print(" (Note: Requires OpenAI API key for real responses)")
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chat = LeannChat("demo_knowledge.leann")
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# Demo questions
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demo_questions: list[str] = [
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"What is the difference between machine learning and deep learning?",
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"How is data science related to big data?",
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]
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for question in demo_questions:
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print(f" Q: {question}")
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response = chat.ask(question)
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print(f" A: {response}")
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print()
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print("Demo completed! Try running:")
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print(" uv run python examples/document_search.py")
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if __name__ == "__main__":
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main()
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