from chimeradb import KnowledgeGraph
    kg = KnowledgeGraph("my.db")
    
    # Semantic search - find by meaning
    results = kg.search("who works on language models?")
    
    # Graph traversal - follow relationships  
    employees = kg.traverse("acme", direction="incoming")
    
    # SQL analytics - aggregate data
    stats = kg.query("SELECT company, COUNT(*) FROM nodes...")

- RAG needs semantic search + relationship context

- AI agents need graph traversal + analytical queries

- Combine all three in a single SQL query

Zero infrastructure: One file, runs anywhere, 10-100x faster than SQLite for analytics.

Built on DuckDB + duckpgq + vss extensions. MIT licensed.

    pip install chimeradb