Optimize RAG with AI Agents & Vector Databases

About this video

• Video Title: Optimize RAG with AI Agents & Vector Databases
• Channel: IBM Technology
• Speakers: [No speaker names available in the transcript]
• Duration: 32:15

Overview

This video demonstrates how to optimize Retrieval-Augmented Generation (RAG) by integrating multiple AI agents with vector databases. It provides a step-by-step guide to building a multi-agent application that categorizes queries, retrieves relevant context from a vector database, and generates natural language responses. The tutorial covers setting up the project, configuring AI models (specifically using watsonx.ai), and implementing agents using the CrewAI framework.

Key takeaways

• Problem: Retrieval-augmented generation (RAG) can suffer from irrelevant or lacking context provided to the LLM, even with large amounts of data in a vector database.
• Solution: Integrate multiple AI agents to improve RAG performance. This involves query categorization, context retrieval, and natural language response generation.
• Tools Used: CrewAI for agent orchestration, watsonx.ai for LLM capabilities, and ChromaDB as a vector database.
• Project Setup: The video guides through cloning a repository, setting up dependencies for both UI (React, Express) and API (Python), and configuring environment variables for watsonx.ai integration.
• Agent Roles: Three primary agents are demonstrated:
  1. Categorization Agent: Analyzes user queries to determine the most relevant ChromaDB collection.
  2. Retriever Agent: Uses the category identified by the first agent to query the vector database (ChromaDB) with the help of a tool, retrieving relevant documents.
  3. Generation Agent: Takes the retrieved context and the original query, interpolates them into a custom prompt, and generates a natural language response.
• CrewAI Framework: The video showcases how to define agents with roles, goals, and backstories, assign tasks to them, and orchestrate their execution sequentially within a "crew."
• Tools and Functions: Agents can be equipped with tools (functions) to interact with external systems, such as querying the vector database.
• Customization: The process allows for customization of LLM models, parameters, prompts, and the overall agent pipeline to enhance application performance and tailor responses.
• Future Enhancements: Suggested improvements include routing queries to web search if they fall outside the vector database's scope and formatting the final response in HTML for better UI presentation.