vector-hybrid-search

Build hybrid search in Elasticsearch by mapping kNN vector fields alongside standard BM25 text fields. You ingest embeddings and text together, then query both retrieval methods simultaneously to rank semantic and lexical matches for RAG pipelines.

Integrate LangChain with Elasticsearch by using its vector store wrappers to store and query embeddings. You configure your embedding model, ingest chunked documents into an index, and use the retriever to fetch semantic matches for your RAG application.

Elasticsearch supports kNN indexing for production-grade RAG applications. It provides native vector storage and retrieval capabilities, allowing you to scale semantic queries while offering deployment choices and monitoring tools for optimization.

Optimize chunking and embedding strategies by evaluating your document structure and query needs. You select an embedding model, define chunk sizes that balance context and precision, and iteratively monitor retrieval accuracy to refine your vector storage configuration.

You can use LlamaIndex to manage retrievers and ingestion for an Elasticsearch vector database. It integrates with your mapping and deployment setup, allowing you to orchestrate document chunking, embedding generation, and semantic retrieval workflows.

Use hybrid search instead of pure semantic vector search when queries rely on specific keywords, names, or exact phrases. Combining BM25 lexical retrieval with vector search ensures precise term matching alongside contextual semantic understanding.