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Version: 2.0

Vectara and LangChain

LangChain serves as an orchestration framework for building LLM-powered applications. Vectara's integration into LangChain as a vectorstore empowers developers to utilize its robust semantic retrieval engine and end-to-end RAG capabilities without having to integrate additional components like a vector database, an embedding model, or even an LLM. All of this can be done through Vectara, while taking advantage of additional capabilities provided in the LangChain ecosystem.

Integration benefits​

  • Provides semantic search and full RAG pipelines.
  • Vectara RAG-as-a-Service removes the need to integrate with and maintain additional components like embedding model, vector database, hybrid search or the LLM itself.
  • Offers tight integration with the LangChain ecosystem for low latency and reduced cost.
  • Enables enterprise grade scalability, security, and privacy features right out of the box.

This example notebook includes code examples showing a few ways to use Vectara in a LangChain application, including RAG, semantic search, as well as Self Query.