LangChain Elasticsearch

LangChain Elasticsearch is a vector storage adapter that connects the LangChain framework with Elasticsearch’s dense vector and KNN search capabilities. With just ElasticsearchStore.from_documents(), developers chunk text, generate embeddings, and index them as dense_vector fields using Elasticsearch’s HNSW engine for millisecond-scale similarity queries. During a Retrieval-Augmented Generation (RAG) call, LangChain embeds the user’s query, runs a knn search via the REST API, and returns the top-k documents with metadata to justify the LLM response. The adapter supports cloud and self-hosted clusters, API key or AWS-SigV4 authentication, and optional BM25 + vector hybrid ranking. Because it implements LangChain’s standard vector storage interface, teams can replace Elasticsearch with Pinecone or Milvus by changing a single line of code. Built-in filters, field mappings, and index patterns make it easy to secure multi-tenant data, while callbacks expose search latency and recall metrics, turning existing Elasticsearch deployments into a high-performance foundation for generative AI.