Files
awesome-llm-apps/rag_tutorials/agentic_rag_with reasoning/README.md
ShubhamSaboo 3fd0513ba4 Added new demo
2025-06-05 20:44:01 -05:00

99 lines
2.8 KiB
Markdown

# 🧐 Agentic RAG with Reasoning
A sophisticated RAG system that demonstrates an AI agent's step-by-step reasoning process using Agno, Claude and Cohere. 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
- Hybrid search combining keyword and semantic matching
- Cohere reranking for improved relevance
- Source attribution with citations
## Agent Configuration
- Claude 3.5 Sonnet for language processing
- Cohere embedding and reranking models
- 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. Cohere API Key
- Sign up at dashboard.cohere.ai
- Navigate to API Keys section
- Generate a new API key
## How to Run
1. **Clone the Repository**:
```bash
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd rag_tutorials/agentic_rag
```
2. **Install the dependencies**:
```bash
pip install -r requirements.txt
```
3. **Run the Application:**
```bash
streamlit run agentic_rag.py
```
4. **Configure API Keys:**
- Enter your Anthropic API key in the first field
- Enter your Cohere 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 Cohere's embedding model
- Vectors are stored in LanceDB for efficient retrieval
- Hybrid search enables both keyword and semantic matching
### 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 3.5 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