Optimize RAG with AI Agents & Vector Databases

Optimize RAG with AI Agents & Vector Databases

---

---

AI Summary

Overview

• Integrating multiple AI agents for improved context retrieval in a chatbot application.

Steps Covered

1. Clone Repository: Download the provided repo to your local machine.
2. Setup UI:
   • Navigate to the UI directory.
   • Install dependencies using package manager.
   • Configure environment variables and set branding name.
3. Setup API:
   • Create a Python virtual environment and activate it.
   • Install necessary dependencies (e.g., CrewAI, watsonx.ai).
   • Configure environment variables for Watson Studio connection.
4. Service Initialization:
   • Start FastAPI, React UI, and Express server using the command line.
5. Agent Implementation:
   • Implement categorization, retrieval, and response generation agents using CrewAI.
   • Each agent utilizes LLM for processing user queries and retrieving context from VectorDB.
6. Query Processing:
   • Routes user queries to the appropriate agent based on category (technical, billing, account).
   • Uses specific functions to query the ChromaDB based on the categorized input.
   • Final outputs formatted as JSON responses to be sent back to the UI.

Key Components

• Carbon Components: Used for UI design, accessible through the Carbon Design System.
• VectorDB and ChromaDB: Databases used for storing and retrieving relevant documents.

Conclusion

• Summary of building an AI-powered multi-agent chatbot that categorizes queries and generates contextual responses. Experimentation with UI customization and agent functionalities is encouraged to enhance the application.