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🗃️ AI RAG Agent with Web Access

This script demonstrates how to build a Retrieval-Augmented Generation (RAG) agent with web access using GPT-4o in just 15 lines of Python code. The agent uses a PDF knowledge base and has the ability to search the web using DuckDuckGo.

Features

  • Creates a RAG agent using GPT-4o
  • Incorporates a PDF-based knowledge base
  • Uses LanceDB as the vector database for efficient similarity search
  • Includes web search capability through DuckDuckGo
  • Provides a playground interface for easy interaction

How to get Started?

  1. Clone the GitHub repository
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd awesome-llm-apps/rag_tutorials/agentic_rag
  1. Install the required dependencies:
pip install -r requirements.txt
  1. Get your OpenAI API Key
  • Sign up for an OpenAI account (or the LLM provider of your choice) and obtain your API key.
  • Set your OpenAI API key as an environment variable:
export OPENAI_API_KEY='your-api-key-here'
  1. Run the AI RAG Agent
python3 rag_agent.py
  1. Open your web browser and navigate to the URL provided in the console output to interact with the RAG agent through the playground interface.

How it works?

  1. Knowledge Base Creation: The script creates a knowledge base from a PDF file hosted online.
  2. Vector Database Setup: LanceDB is used as the vector database for efficient similarity search within the knowledge base.
  3. Agent Configuration: An AI agent is created using GPT-4o as the underlying model, with the PDF knowledge base and DuckDuckGo search tool.
  4. Playground Setup: A playground interface is set up for easy interaction with the RAG agent.