agentic_rag_with_reasoning

🧐 Agentic RAG with Reasoning

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

Features

1. Interactive Knowledge Base Management

• Upload documents to expand the knowledge base
• Add URLs dynamically for web content
• Persistent vector database storage using LanceDB

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

• Claude 3.5 Sonnet for language processing
• OpenAI embedding model for vector search
• ReasoningTools for step-by-step analysis
• Customizable agent instructions

Prerequisites

You'll need the following API keys:

1. Anthropic API Key

• Sign up at console.anthropic.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 Anthropic 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:

• Add Knowledge Sources: Use the sidebar to add URLs to your knowledge base
• Ask Questions: Enter queries in the main input field
• View Reasoning: Watch the agent's thought process unfold in real-time
• Get Answers: Receive comprehensive responses with source citations

How It Works

The application uses a sophisticated RAG pipeline:

Knowledge Base Setup

• Documents are loaded from URLs using WebBaseLoader
• Text is chunked and embedded using OpenAI's embedding model
• Vectors are stored in LanceDB for efficient retrieval
• Vector search enables semantic matching for relevant information

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
• Claude 4 Sonnet generates comprehensive answers with citations

UI Flow

• Enter API keys → Add knowledge sources → Ask questions
• Reasoning process and answer generation displayed side-by-side
• Sources cited for transparency and verification