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.
How to build a semantic search engine using Couchbase and Azure OpenAI.
Build a PDF Chat App with LangChain, Google Gemini, Couchbase Vector Search, and Streamlit.
It connects Couchbase with LangChain, allowing you to store and retrieve embeddings efficiently for AI and machine learning workflows.
Yes, Couchbase supports multimodal data, allowing you to handle structured, semi-structured, and unstructured data alongside embeddings.
No, Couchbase’s native Full-Text Search (FTS) can handle similarity searches, eliminating the need for external tools.
Yes, Couchbase offers flexibility to deploy both on-premises and across major cloud platforms, adapting to your infrastructure needs.
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