Vector-based semantic memory with hybrid search and query expansion.

Provides persistent vector storage, local embedding generation, BM25 keyword scoring, hybrid (embedding + BM25) search, and rule-based query expansion for improved recall.

Main types

• [VectorStore] — Trait for storing and querying embedding vectors.
• [FileVectorStore] — File-backed persistent vector store.
• [LocalEmbedding] — Local TF-IDF-based embedding provider.
• [HybridSearcher] — Combines embedding similarity with BM25 keyword scoring.
• [Bm25Index] — BM25 inverted index for keyword-based retrieval.
• [QueryExpander] — Trait for expanding queries to improve search recall.
• [RagPipeline] — Retrieval-Augmented Generation pipeline for knowledge base search.