mirror of
https://github.com/Shubhamsaboo/awesome-llm-apps.git
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5685c508fc
- Replaced UrlKnowledge with Knowledge for better document loading. - Enhanced session state management for URLs to prevent duplicates. - Updated placeholder URLs and query prompts for clarity. - Adjusted requirements.txt to specify a minimum version for the 'agno' package.
108 lines
3.2 KiB
Markdown
108 lines
3.2 KiB
Markdown
# 🧠 Agentic RAG with GPT-5
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An agentic RAG application built with the Agno framework, featuring GPT-5 and LanceDB for efficient knowledge retrieval and question answering.
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## ✨ Features
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- **🤖 GPT-5**: Latest OpenAI model for intelligent responses
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- **🗄️ LanceDB**: Lightweight vector database for fast similarity search
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- **🔍 Agentic RAG**: Intelligent retrieval augmented generation
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- **📝 Markdown Formatting**: Beautiful, structured responses
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- **🌐 Dynamic Knowledge**: Add URLs to expand knowledge base
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- **⚡ Real-time Streaming**: Watch answers generate live
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- **🎯 Clean Interface**: Simplified UI without configuration complexity
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## 🚀 Quick Start
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### Prerequisites
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- Python 3.11+
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- OpenAI API key with GPT-5 access
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### Installation
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1. **Clone and navigate to the project**
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```bash
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cd rag_tutorials/agentic_rag_gpt5
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```
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2. **Install dependencies**
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```bash
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pip install -r requirements.txt
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```
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3. **Set up your OpenAI API key**
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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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Or create a `.env` file:
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```
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OPENAI_API_KEY=your-api-key-here
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```
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4. **Run the application**
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```bash
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streamlit run agentic_rag_gpt5.py
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```
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## 🎯 How to Use
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1. **Enter your OpenAI API key** in the sidebar
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2. **Add knowledge sources** by entering URLs in the sidebar
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3. **Ask questions** using the text area or suggested prompts
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4. **Watch answers stream** in real-time with markdown formatting
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### Suggested Questions
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- **"What is Agno?"** - Learn about the Agno framework and agents
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- **"Teams in Agno"** - Understand how teams work in Agno
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- **"Build RAG system"** - Get a step-by-step guide to building RAG systems
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## 🏗️ Architecture
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### Core Components
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- **`Agent`**: Orchestrates the entire Q&A process
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- **`UrlKnowledge`**: Manages document loading from URLs
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- **`LanceDb`**: Vector database for efficient similarity search
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- **`OpenAIEmbedder`**: Converts text to embeddings
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- **`OpenAIChat`**: GPT-5-nano model for generating responses
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### Data Flow
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1. **Knowledge Loading**: URLs are processed and stored in LanceDB
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2. **Vector Search**: OpenAI embeddings enable semantic search
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3. **Response Generation**: GPT-5-nano processes information and generates answers
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4. **Streaming Output**: Real-time display of formatted responses
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## 🔧 Configuration
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### Database Settings
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- **Vector DB**: LanceDB with local storage
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- **Table Name**: `agentic_rag_docs`
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- **Search Type**: Vector similarity search
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## 📚 Knowledge Management
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### Adding Sources
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- Use the sidebar to add new URLs
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- Sources are automatically processed and indexed
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- Current sources are displayed as numbered list
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### Default Knowledge
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- Starts with Agno documentation: `https://docs.agno.com/introduction/agents.md`
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- Expandable with any web-based documentation
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## 🎨 UI Features
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### Sidebar
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- **API Key Management**: Secure input for OpenAI credentials
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- **URL Addition**: Dynamic knowledge base expansion
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- **Current Sources**: Numbered list of loaded URLs
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### Main Interface
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- **Suggested Prompts**: Quick access to common questions
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- **Query Input**: Large text area for custom questions
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- **Real-time Streaming**: Live answer generation
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- **Markdown Rendering**: Beautiful formatted responses
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