LangChain

Use Couchbase for vector stores, document loading, LLM caching, semantic caching with search indexes, and chat history storage.

LangChain overview & features

Install the Couchbase LangChain package to integrate Couchbase capabilities into your applications. This allows for efficient vector storage, document loading, and caching of prompts and responses for LLMs. Semantic caching supports similarity-based retrieval and requires a search index and embeddings. Additionally, Couchbase can store chat message history for session management. Refer to the documentation for setup, examples, and API guidance for seamless implementation.

LangChain resources

Blogs
Latest updates on LangChain with Couchbase

  • LLM + RAG integration with Couchbase
  • New features regarding LangChain/Capella
  • How to build faster LLM apps, and build LLM apps faster
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Video
How to enhance your applications using AI, vector search, LangChain, and Couchbase for better solutions?
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Concept
What is LangChain?
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LangChain tutorials

Frequently asked questions

  • What is Couchbase LangChain integration?

    It connects Couchbase with LangChain, allowing you to store and retrieve embeddings efficiently for AI and machine learning workflows.

  • Can I use Couchbase for both structured and unstructured data with LangChain?

    Yes, Couchbase supports multimodal data, allowing you to handle structured, semi-structured, and unstructured data alongside embeddings.

  • Does Couchbase require additional tools for similarity searches?

    No, Couchbase’s native Full-Text Search (FTS) can handle similarity searches, eliminating the need for external tools.

  • Is Couchbase with LangChain suitable for on-premises and cloud-based AI solutions?

    Yes, Couchbase offers flexibility to deploy both on-premises and across major cloud platforms, adapting to your infrastructure needs.

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