Build a RAG Agent Workflow in Python — From Retrieval to Cited Response

A complete Python RAG agent workflow needs: a query understanding component that reformulates natural language queries for optimal retrieval, a retrieval client that queries the vector store with proper connection management, a re-ranking component that improves precision on the top candidates, a citation metadata manager that attaches source information to retrieved content, an LLM generation component that produces citation-grounded outputs, an output validator that checks citation coverage, and an MCP interface that exposes the complete workflow to the orchestrating agent.

Structure the Python RAG agent workflow as a pipeline of composable components: each component is a Python class with a clearly defined input type, output type, and configuration. The pipeline orchestrator passes typed objects between components, making the flow testable and swappable. The workshop covers this component architecture and shows how each class plugs into the overall workflow.

Async retrieval in Python uses asyncio to run multiple retrieval operations concurrently: querying the vector store and episodic memory simultaneously rather than sequentially, running multiple re-ranking requests in parallel when evaluating several candidate documents, and overlapping the retrieval phase with early document processing. The workshop covers implementing async RAG retrieval without introducing race conditions in the citation metadata.

Testing a Python RAG agent workflow requires mocking the vector store (to test retrieval logic without requiring a live database), mocking the LLM client (to test citation parsing without live generation), and integration tests that verify the complete pipeline with a small test corpus. The workshop covers a pytest-based testing framework for RAG agent workflows that makes each component independently testable.

The Python RAG workflow integrates with MCP by wrapping the complete pipeline as an MCP server with a retrieval tool that accepts structured query parameters and returns structured results with citation metadata. Other agents in the multi-agent system invoke this MCP service rather than implementing their own retrieval. This centralized RAG service ensures consistent retrieval quality and citation standards across all agents.

Yes. The retrieval layer of the Python RAG agent workflow can query multiple vector stores and merge results with appropriate source attribution. This is useful when different document collections are stored in different vector databases or when combining a private knowledge base with a public one. The workshop covers multi-source retrieval with result merging and deduplication.