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A RAG system is just the beginning of harnessing the power of LLM. The next step is creating an intelligent Agent. In Agentic RAG the Agent makes use of available tools, strategies and LLM to generate response in a specialized way. Unlike a simple RAG, an Agent can dynamically choose between tools, routing strategy, etc.
A tailored Chatbot to reduce hallucinations and improve factuality.
Docker implementation of Llama Index Agentic RAG. Developing a RAG system requires multiple component such as LLM, Vector-DB, UI, etc. In this work we perform containerization of entire system.
Simple agents are good for 1-to-1 retrieval system. For more complex task we need multi steps reasoning loop. In a reasoning loop the agent can break down a complex task into subtasks and solve them step by step while maintaining a conversational memory.