awesome-llm-apps/rag_tutorials/agentic_rag_gpt5/README.md

🧠 Agentic RAG with GPT-5

An agentic RAG application built with the Agno framework, featuring GPT-5 and LanceDB for efficient knowledge retrieval and question answering.

✨ Features

• 🤖 GPT-5: Latest OpenAI model for intelligent responses
• 🗄️ LanceDB: Lightweight vector database for fast similarity search
• 🔍 Agentic RAG: Intelligent retrieval augmented generation
• 📝 Markdown Formatting: Beautiful, structured responses
• 🌐 Dynamic Knowledge: Add URLs to expand knowledge base
• ⚡ Real-time Streaming: Watch answers generate live
• 🎯 Clean Interface: Simplified UI without configuration complexity

🚀 Quick Start

Prerequisites

• Python 3.11+
• OpenAI API key with GPT-5 access

Installation

1. Clone and navigate to the project

   cd rag_tutorials/agentic_rag_gpt5
   

2. Install dependencies

   pip install -r requirements.txt
   

3. Set up your OpenAI API key

   export OPENAI_API_KEY="your-api-key-here"
   

   Or create a .env file:

   OPENAI_API_KEY=your-api-key-here
   

4. Run the application

   streamlit run agentic_rag_gpt5.py
   

🎯 How to Use

1. Enter your OpenAI API key in the sidebar
2. Add knowledge sources by entering URLs in the sidebar
3. Ask questions using the text area or suggested prompts
4. Watch answers stream in real-time with markdown formatting

Suggested Questions

• "What is Agno?" - Learn about the Agno framework and agents
• "Teams in Agno" - Understand how teams work in Agno
• "Build RAG system" - Get a step-by-step guide to building RAG systems

🏗️ Architecture

Core Components

• Agent: Orchestrates the entire Q&A process
• UrlKnowledge: Manages document loading from URLs
• LanceDb: Vector database for efficient similarity search
• OpenAIEmbedder: Converts text to embeddings
• OpenAIChat: GPT-5-nano model for generating responses

Data Flow

1. Knowledge Loading: URLs are processed and stored in LanceDB
2. Vector Search: OpenAI embeddings enable semantic search
3. Response Generation: GPT-5-nano processes information and generates answers
4. Streaming Output: Real-time display of formatted responses

🔧 Configuration

Database Settings

• Vector DB: LanceDB with local storage
• Table Name: agentic_rag_docs
• Search Type: Vector similarity search

📚 Knowledge Management

Adding Sources

• Use the sidebar to add new URLs
• Sources are automatically processed and indexed
• Current sources are displayed as numbered list

Default Knowledge

• Starts with Agno documentation: https://docs.agno.com/introduction/agents.md
• Expandable with any web-based documentation

🎨 UI Features

Sidebar

• API Key Management: Secure input for OpenAI credentials
• URL Addition: Dynamic knowledge base expansion
• Current Sources: Numbered list of loaded URLs

Main Interface

• Suggested Prompts: Quick access to common questions
• Query Input: Large text area for custom questions
• Real-time Streaming: Live answer generation
• Markdown Rendering: Beautiful formatted responses

🛠️ Technical Details

Dependencies

streamlit>=1.28.0
agno>=0.1.0
openai>=1.0.0
lancedb>=0.4.0
python-dotenv>=1.0.0

Key Features

• Event Filtering: Only shows RunResponseContent events for clean output
• Safe Attribute Access: Prevents errors from missing attributes
• Caching: Efficient resource loading with Streamlit caching
• Error Handling: Graceful handling of API and processing errors

🔍 Troubleshooting

Common Issues

ModelProviderError with max_tokens

• ✅ Fixed: Uses max_completion_tokens instead of max_tokens

Tool calls appearing in output

• ✅ Fixed: Filters to only show RunResponseContent events

Knowledge base not loading

• Check OpenAI API key is valid
• Ensure URLs are accessible
• Verify internet connection

Performance Tips

• Cache Resources: Knowledge base and agent are cached for efficiency
• Streaming: Real-time updates without blocking
• LanceDB: Fast local vector search without external dependencies

🎯 Use Cases

• Documentation Q&A: Ask questions about technical documentation
• Research Assistant: Get answers from multiple knowledge sources
• Learning Tool: Interactive exploration of complex topics
• Content Discovery: Find relevant information across multiple sources

Built with ❤️ using Agno, GPT-5, and LanceDB