RAG with Pinecone
RAG is a framework for combining LLMs with an external vector database to generate more accurate and up-to-date responses. The Pinecone vector database lets you build RAG applications using vector search.
Leverage domain-specific and up-to-date data at lower cost for any scale and get 50% more accurate answers with RAG.
Scale with low cost
Supply unlimited knowledge to your AI applications with RAG without compromising performance at up to 50x lower cost.
Easy and reliable
Get started in a few clicks to apply RAG with enterprise-grade data security, support SLAs, and observability.
Learn how customers are using RAG with Pinecone
“To make our newest Notion AI products available to tens of millions of users worldwide we needed to support RAG over billions of documents while meeting strict performance, security, cost, and operational requirements. This simply wouldn’t be possible without Pinecone.”
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“Pinecone serverless opened up possibilities we hadn't considered before and allows us to invest even more in our long-term product capabilities.”
Director of Engineering, DISCO
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What developers can build with Pinecone
Customer service chatbots
Code generation copilot
Knowledge base Q&A
Resources for developers
Build RAG applications faster with Pinecone Canopy
Canopy is an open-source framework and context engine built on top of the Pinecone vector database so you can build and host your own production-ready chat assistant at any scale