Agentic RAG Revolutionizes AI Knowledge Systems Forever!

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AI Summary

Summary of Video Transcript

Concept Introduction

• The video introduces the concept of Agentic RAG (Retrieval Augmented Generation) and demonstrates how to use a framework called Fi Data to create Agentic RAG flows.

Traditional RAG vs. Agentic RAG

• Traditional RAG systems involve indexing documents into a vector database, which are then used to augment queries passed through a large language model to generate responses.
• Agentic RAG adds an agent (or agents) to the workflow, which can decompose queries, rewrite them, find relevant documents, check for hallucinations, and call other tools as needed.
• The purpose of Agentic RAG is to create a more responsive, accurate, and adaptable system.

Fi Data Framework

• Fi Data is a new framework for building AI agents and Agentic RAG systems.
• The video provides a walkthrough of setting up Fi Data and creating an agent using the framework.

Creating an Agent with Fi Data

• The presenter demonstrates how to create a web search agent using Fi Data, OpenAI’s GPT model, and the DuckDuckGo search engine.
• The agent is coded in Python and uses the Fi Agent library to import necessary components and set up the agent with specific tools and instructions.

Building an Agentic RAG System

• The video shows how to create an Agentic RAG system by setting up a knowledge base from a PDF file and using a vector database (LanceDB) for indexing.
• The agent is designed to only use the provided knowledge base for generating responses, ensuring accuracy and relevance to the given documents.

Example Use Case

• An example is provided where the agent is asked to suggest a three-course meal with a Thai dish as the main course.
• Initially, the agent provides a response that includes items not found in the knowledge base, demonstrating a traditional RAG system.
• By setting search_knowledge to true, the agent is transformed into an Agentic RAG system, ensuring it only uses the provided knowledge base for its responses.

Conclusion

• The video concludes with the successful creation of an Agentic RAG system using Fi Data, highlighting the benefits of query decomposition and the avoidance of hallucinations by large language models.

Detailed Instructions and URLs

• No specific CLI commands or URLs were provided in the transcript for extraction.