RAG Agent FAQs

Question 1

What is RAG Agent and what does it do?

Accepted Answer

RAG Agent is a production-ready system that combines a LangGraph agent with Model Context Protocol (MCP) integration. It's designed to build reliable, context-aware AI agents capable of semantic search, grounded responses, and automated quality evaluation.

Question 2

How does RAG Agent ensure responses are accurate and grounded?

Accepted Answer

It leverages semantic search using OpenAI embeddings and MongoDB Atlas Vector Search to find relevant context. Responses are then formulated using the COSTAR prompting framework to ensure they are grounded in the retrieved information, minimizing hallucinations. Automated RAGAS-based evaluation further assesses answer quality and relevancy.

Question 3

What role does Model Context Protocol (MCP) play in RAG Agent?

Accepted Answer

MCP provides a standardized protocol for exposing tools and functionalities to the LangGraph agent. This enables modular, reusable components for agent communication and interaction, making the system highly extensible and interoperable.

Question 4

What technologies are integrated into the RAG Agent architecture?

Accepted Answer

RAG Agent integrates key technologies like LangChain/LangGraph for agent orchestration, Model Context Protocol (MCP) for standardized tool access, OpenAI for embeddings, MongoDB Atlas for vector storage and search, and RAGAS for automated evaluation metrics.

Question 5

Can I use RAG Agent with my own documents or knowledge base?

Accepted Answer

Yes, RAG Agent is designed for custom knowledge bases. It includes a document indexing workflow to process your own documents, generate embeddings, and store them in MongoDB Atlas for efficient semantic retrieval by the agent.

RAG Agent

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