LangChain Weaviate

LangChain Weaviate is an adapter that connects the LangChain framework to Weaviate, an open-source GraphQL vector database. Using Weaviate.from_documents() , you ingest chunked text or images, generate attachments, and store them as vectorized objects along with rich JSON metadata. Queries use Weaviate’s BM25 + HNSW hybrid search via GraphQL or REST, enabling millisecond-long similarity searches, semantic filtering, and cross-reference traversal. The adapter takes into account class schemas, leases, and API keys, and pushes batch updates to support fast ingestion. At runtime, LangChain inlines the user’s prompt, executes the query or aggregate call, and returns the top-k results to the chain or Retrieval-Augmented Generation (RAG) agent. Because the wrapper implements LangChain’s VectorStore interface, replacing Weaviate with Chroma or Pinecone — or vice versa — requires a single line of Python. Observability callbacks expose latency and recall metrics, while Weaviate’s built-in modules (generative, nearText, nearImage) open up advanced multimodal use cases, making LangChain Weaviate a production-ready foundation for scalable, real-world LLM applications.