Pinecone Assistant

Pinecone Assistant answers questions about your proprietary data. You upload files to an assistant, then chat with it or retrieve context snippets, and get back answers grounded in your data.

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What you can do

With Pinecone Assistant, you can:

• Prototype and deploy an AI assistant without building your own retrieval pipeline.
• Get context-aware answers about your proprietary data without training an LLM.
• Get answers grounded in your data, with references to the files each answer draws from.

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SDK support

You can use the Assistant API directly, through the Pinecone Python SDK, or through the Pinecone Node.js SDK.

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Workflow

These steps outline the Pinecone Assistant workflow, which you can follow in the Pinecone console or with the Pinecone API:

1. Create an assistant to answer questions about your documents.
2. Upload documents to your assistant. Your assistant manages chunking, embedding, and storage for you.
3. Chat with your assistant and receive responses as a JSON object or as a text stream. For each chat, your assistant queries a large language model (LLM) with context from your documents, so the LLM’s responses are grounded in them.
4. Evaluate the assistant’s responses for correctness and completeness.
5. Add instructions to tailor your assistant’s behavior and responses to specific use cases or requirements. Filter chat by file metadata to reduce latency and improve the accuracy of responses.
6. Retrieve context snippets to understand what relevant data snippets Pinecone Assistant is using to generate responses. You can use the retrieved snippets with your own LLM, RAG application, or agentic workflow.

To learn how Pinecone Assistant processes files and generates answers, see Assistant architecture.

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Resources

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Other Pinecone products