Agentic RAG using GoLang
Agentic RAG takes Retrieval-Augmented Generation one step further. In traditional RAG, retrieval is a fixed step — fetching context from a knowledge base before generating a response. In Agentic RAG, however, the data source itself becomes a tool in the agent’s toolkit. The agent autonomously decides when and how to query it based on the conversation history, user intent, and reasoning chain, alongside other tools like summarizers or planners. This transforms RAG from a static retrieval process into a dynamic, decision-driven system. ...