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디렉토리 플랫폼 템플릿

Lightweight Node.js library for low-latency on-device vector similarity search using HNSW and BM25 hybrid, with CRUD, metadata filtering, and persistence for edge RAG pipelines. Enables real-time semantic search without servers; more lightweight than cloud Qdrant.

PostgreSQL extension enabling fast full-text, faceted, and hybrid search over Postgres tables using the BM25 algorithm. Built on Tantivy for production-ready search with ACID guarantees and transactional consistency.

A search approach combining traditional keyword-based BM25 ranking with modern vector similarity search. By leveraging both lexical matching and semantic understanding, hybrid search provides superior retrieval quality through techniques like reciprocal rank fusion (RRF) to merge results from both methods.

Information retrieval using high-dimensional sparse vectors where most values are zero, typically based on term frequency methods like BM25. Sparse retrieval excels at exact keyword matching and is interpretable, often combined with dense retrieval in hybrid search systems for robust performance.

Search technique combining BM25 lexical search and semantic vector search using Reciprocal Rank Fusion (RRF) to merge results, balancing precision of keyword matching with contextual understanding of neural embeddings.