Convex Agents RAG

To ground agent responses, you apply retrieval-augmented generation to search through custom content and surface relevant documents to your agents. This ensures replies are grounded in known data rather than model hallucinations.

Semantic search enables agents to query a custom knowledge base and retrieve relevant documents based on meaning. It surfaces specific policy references or support docs directly to the agent for generation tasks.

You need to configure your Convex app using @convex-dev/agent and @convex-dev/rag. This setup allows you to ingest content into a namespace for search and generation tasks.

Yes, retrieval-augmented generation is ideal for FAQ automation and support chat scenarios. It allows agents to search specified knowledge bases and generate grounded answers from policy documents.

Content ingestion involves loading your custom data into a configured namespace within the Convex app. Once ingested, agents can perform semantic search across this content to ground their responses.