LangChain Vector Database

The LangChain vector database is any ANN engine — Chroma, Pinecone, Milvus, Elasticsearch, Qdrant — accessed through LangChain’s VectorStore interface for storing and querying multidimensional embeddings. After TextSplitter splits documents into chunks, embedder models convert the text into floating-point arrays; vector_db = Chroma.from_documents(docs, embedder) writes these arrays along with metadata. At query time, LangChain embeds the user’s query, performs a similarity search (.similarity_search or .max_marginal_relevance_search), and returns the first k documents to a chain or Retrieval-Augmented Generation (RAG) agent. Additional filters narrow results by tag, tenant, or timestamp, enabling SaaS use cases in multi-tenant scenarios. Because each vendor implements the same API — add, delete, update — teams can change databases by changing a single line of code, sacrificing speed, cost, and cloud availability. Callbacks expose latency and recall metrics, while persistent collections provide offline ingestion and real-time chat across billions of vectors, making LangChain’s vector database the foundation of scalable, real-world LLM applications.