Building an Agent with RAG for Q&A
Explore how to build a question and answer agent using retrieval augmented generation with Llama Stack. Understand how to integrate a vector database to semantically search documents, configure the rag/knowledge_search tool, and enhance your AI assistant's ability to provide accurate, context-aware answers from dynamic knowledge sources without retraining the model.
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Previously, we set up a knowledge base by registering a vector database and ingesting documents into it using the RAG tool. This allowed us to search your data semantically. Now, it’s time to use that.
We’ll create an agent that uses the rag/knowledge_search tool to retrieve relevant chunks from our knowledge base at runtime, combine them with model reasoning, and generate a final response. This pattern is called retrieval-augmented generation, or RAG, and it’s one of the most powerful techniques for improving factual accuracy and grounding in LLM-powered applications.
Why use RAG agents?
Language models are limited by two things: their training data and their context window. They can’t remember new information after training, and ...