🧐 Agentic RAG with Reasoning

🎓 FREE Step-by-Step Tutorial

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A sophisticated RAG system that demonstrates an AI agent's step-by-step reasoning process using Agno, Gemini and OpenAI. This implementation allows users to add web sources, ask questions, and observe the agent's thought process in real-time with reasoning capabilities.

Features

1. Interactive Knowledge Base Management

• Add URLs dynamically for web content
• Default knowledge source: MCP vs A2A Protocol article
• Persistent vector database storage using LanceDB
• Session state tracking prevents duplicate URL loading

2. Transparent Reasoning Process

• Real-time display of the agent's thinking steps
• Side-by-side view of reasoning and final answer
• Clear visibility into the RAG process

3. Advanced RAG Capabilities

• Vector search using OpenAI embeddings for semantic matching
• Source attribution with citations

Agent Configuration

• Gemini 2.5 Flash for language processing
• OpenAI embedding model for vector search
• ReasoningTools for step-by-step analysis
• Customizable agent instructions
• Default knowledge source: MCP vs A2A Protocol article

Prerequisites

You'll need the following API keys:

1. Google API Key

• Sign up at aistudio.google.com
• Navigate to API Keys section
• Create a new API key

2. OpenAI API Key

• Sign up at platform.openai.com
• Navigate to API Keys section
• Generate a new API key

How to Run

1. Clone the Repository:

   git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
   cd rag_tutorials/agentic_rag_with_reasoning
   

2. Install the dependencies:

   pip install -r requirements.txt
   

3. Run the Application:

   streamlit run rag_reasoning_agent.py
   

4. Configure API Keys:

• Enter your Google API key in the first field
• Enter your OpenAI API key in the second field
• Both keys are required for the app to function

5. Use the Application:

• Default Knowledge Source: The app comes pre-loaded with the MCP vs A2A Protocol article
• Add Knowledge Sources: Use the sidebar to add URLs to your knowledge base
• Suggested Prompts: Click the prompt buttons (What is MCP?, MCP vs A2A, Agent Communication) for quick questions
• Ask Questions: Enter queries in the main input field
• View Reasoning: Watch the agent's thought process unfold in real-time in the left panel
• Get Answers: Receive comprehensive responses with source citations in the right panel

How It Works

The application uses a sophisticated RAG pipeline with Agno v2.0:

Knowledge Base Setup

• Documents are loaded from URLs using Agno's Knowledge class
• Text is automatically chunked and embedded using OpenAI's embedding model
• Vectors are stored in LanceDB for efficient retrieval
• Vector search enables semantic matching for relevant information
• URLs are tracked in session state to prevent duplicate loading

Agent Processing

• User queries trigger the agent's reasoning process
• ReasoningTools help the agent think step-by-step
• The agent searches the knowledge base for relevant information
• Gemini 2.5 Flash generates comprehensive answers with citations
• Streaming events provide real-time updates on reasoning and content

UI Flow

• Enter API keys → Knowledge base loads with default MCP vs A2A article → Use suggested prompts or ask custom questions
• Reasoning process displayed in left panel, answer generation in right panel
• Sources cited for transparency and verification
• All events streamed in real-time for better user experience