LangChain Qdrant

LangChain Qdrant is the LangChain connector that stores and retrieves text or image embeddings inside Qdrant, an open-source, Rust-powered vector database optimized for high-dimensional nearest-neighbor search. When you call Qdrant.from_documents or Qdrant.from_embeddings, LangChain automatically chunks the input, generates embeddings with your chosen model, and upserts them—along with metadata—into Qdrant’s collections. At query time the integration converts the user prompt into an embedding, issues a HNSW or IVF-PQ similarity search, and returns the top-k vectors with millisecond latency. Built-in payload filters let you narrow results by tags like tenant, language, or timestamp, enabling multi-tenant Retrieval-Augmented Generation (RAG) pipelines without extra SQL. Because Qdrant persists to disk and streams updates over gRPC, LangChain apps can scale from a laptop to a Kubernetes cluster while maintaining ACID-style consistency. Version pinning and Docker images make deployment reproducible, and LangChain’s callback hooks expose search timing, token usage, and cost for end-to-end observability.