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Agentic RAG Using CrewAI & LangChain!

Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model using an external knowledge base attached through a vector database. It is okay until we have simple documents and use cases. But, with the introduction of AI agents, the researchers thought why not integrate these AI agents in our RAG pipeline. Because these AI agents are able to plan, come up with logic, reasoning, and they also know how to use tools whenever there is a requirement. This gave rise to so called 'Agentic RAG' AI Agent RAG, or Agentic RAG, utilizes intelligent agents and advanced LLMs to streamline and enhance the retrieval and generation process, making it more efficient compared to traditional RAG approach. Let's build a simple agentic RAG workflow using LangChain, CrewAI and Groq. The complete notebook code is here: https://github.com/pavanbelagatti/Age... The video is inspired by 'AI Anytime':    • Build an Agentic RAG using Crew AI