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Pinecone Assistant
Pinecone Assistant is a service that allow you to build production-grade chat and agent-based applications quickly.
Assistant quickstart
Create an AI assistant that answers complex questions about your proprietary data
Database quickstart
Set up a fully managed vector database for high-performance semantic search
Use cases
Pinecone Assistant is useful for a variety of tasks, especially for the following:- Prototyping and deploying an AI assistant quickly.
- Providing context-aware answers about your proprietary data without training an LLM.
- Retrieving answers grounded in your data, with references.
SDK support
You can use the Assistant API directly, through the Pinecone Python SDK, or through the Pinecone Node.js SDK.Workflow
You can use the Pinecone Assistant through the Pinecone console or Pinecone API.The following steps outline the general Pinecone Assistant workflow:
1
Create an assistant
Create an assistant to answer questions about your documents.
2
Upload documents
Upload documents to your assistant. Your assistant manages chunking, embedding, and storage for you.
3
Chat with an assistant
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 to ensure the LLM provides grounded responses.
4
Evaluate answers
Evaluate the assistant’s responses for correctness and completeness.
5
Optimize performance
Use custom instructions to tailor your assistant’s behavior and responses to specific use cases or requirements. Filter by metadata associated with files to reduce latency and improve the accuracy of responses.
6
Retrieve context snippets
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.
For information on how the Pinecone Assistant works, see Assistant architecture.