rag-agent

To build a retrieval-augmented generation agent, you need a deterministic pipeline that covers document preparation, embedding, vector storage, search tool definition, agent creation, and query validation to return grounded responses.

The content_and_artifact return pattern works by generating an answer while simultaneously returning the retrieved document chunks as artifacts, ensuring that every response includes explicit source citations for the user.

The best way to integrate a vector store for document chunking is to define a search tool within the agent pipeline that retrieves relevant embedded chunks, ensuring the vector store returns content_and_artifact patterns for source tracking.

Yes, you need safety guardrails to ensure sources are cited in your RAG pipeline, which validates queries and enforces the return of document artifacts alongside generated content to maintain response accuracy and traceability.

Yes, you can validate user queries deterministically before searching the vector store by incorporating a query validation step into the RAG agent pipeline, which ensures only grounded and relevant document searches are executed.

The limitation of a deterministic pipeline for document QA is its strict sequential flow from document preparation to agent creation, which may lack the adaptive reasoning needed for complex, multi-step conversational queries outside the vector store scope.