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47 lines
1.7 KiB
Markdown
47 lines
1.7 KiB
Markdown
## 🗃️ AI RAG Agent with Web Access
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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.
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### Features
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- Creates a RAG agent using GPT-4o
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- Incorporates a PDF-based knowledge base
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- Uses LanceDB as the vector database for efficient similarity search
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- Includes web search capability through DuckDuckGo
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- Provides a playground interface for easy interaction
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### How to get Started?
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1. Clone the GitHub repository
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```bash
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git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
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cd awesome-llm-apps/rag_tutorials/agentic_rag
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```
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2. Install the required dependencies:
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```bash
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pip install -r requirements.txt
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```
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3. Get your OpenAI API Key
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- Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key.
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- Set your OpenAI API key as an environment variable:
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```bash
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export OPENAI_API_KEY='your-api-key-here'
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```
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4. Run the AI RAG Agent
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```bash
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python3 rag_agent.py
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```
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5. Open your web browser and navigate to the URL provided in the console output to interact with the RAG agent through the playground interface.
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### How it works?
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1. **Knowledge Base Creation:** The script creates a knowledge base from a PDF file hosted online.
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2. **Vector Database Setup:** LanceDB is used as the vector database for efficient similarity search within the knowledge base.
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3. **Agent Configuration:** An AI agent is created using GPT-4o as the underlying model, with the PDF knowledge base and DuckDuckGo search tool.
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4. **Playground Setup:** A playground interface is set up for easy interaction with the RAG agent.
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