https://github.com/wahidpanda/ai-agent-analysis-of-cag-vs-rag
AI-powered agent is designed to compare the performance of these two cutting-edge approaches, providing insights into their strengths, weaknesses, and real-world applications.
https://github.com/wahidpanda/ai-agent-analysis-of-cag-vs-rag
ai-agent cag huggingface langchain llm open-source project python rag trending
Last synced: 8 months ago
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AI-powered agent is designed to compare the performance of these two cutting-edge approaches, providing insights into their strengths, weaknesses, and real-world applications.
- Host: GitHub
- URL: https://github.com/wahidpanda/ai-agent-analysis-of-cag-vs-rag
- Owner: wahidpanda
- License: mit
- Created: 2025-03-06T09:10:54.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-03-06T10:02:07.000Z (8 months ago)
- Last Synced: 2025-03-06T10:26:27.776Z (8 months ago)
- Topics: ai-agent, cag, huggingface, langchain, llm, open-source, project, python, rag, trending
- Language: Python
- Homepage: https://wahidpanda-ai-agent-analysis-of-cag-vs-rag-app-ufebht.streamlit.app/
- Size: 156 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# CAG Demonstrator Agent
This project implements a demonstrator agent that compares the Cache-Augmented Generation (CAG) Framework with traditional Retrieval-Augmented Generation (RAG) using various LLMs.
## Features
- Implements both CAG and RAG frameworks for comparison
- Supports multiple LLM providers (OpenAI, Anthropic, Google, Mistral, Groq)
- Measures and compares performance metrics
- Generates detailed comparison reports
- Uses efficient caching mechanisms for improved performance
- Real time CAG vs RAG comparison on streamlit
## Installation
1. Clone the repository:
```bash
git clone https://github.com/wahidpanda/AI-AGENT-Analysis-of-CAG-vs-RAG
cd CAG vs RAG Comparison AI AGENT
```
2. Create a virtual environment and activate it:
```bash
python -m venv venv
# On Windows
venv\Scripts\activate
# On Unix or MacOS
source venv/bin/activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Set up environment variables:
- Copy `.env.example` to `.env`
- Add your API keys for the LLM providers you want to use:
```
OPENAI_API_KEY=your_key_here
ANTHROPIC_API_KEY=your_key_here
MISTRAL_API_KEY=your_key_here
GROQ_API_KEY=your_key_here
GOOGLE_API_KEY=your_key_here
```
## Usage
Run the demonstrator:
```bash
python demonstrator.py
```
The demonstrator will:
1. Initialize both CAG and RAG frameworks
2. Run a series of comparison queries
3. Generate metrics and save results to the `Results` directory
## Project Structure
```
CAG/
├── cag_demo/ # Main package directory
│ ├── __init__.py
│ ├── cag_framework.py # CAG implementation
│ ├── rag_framework.py # RAG implementation
│ ├── llm_interface.py # LLM API interface
│ └── config.py # Configuration settings
├── Data/ # Data directory
│ ├── Preloaded_Contexts/ # CAG knowledge base
│ └── Retrieved_Documents/ # RAG document store
├── Results/ # Comparison results
├── demonstrator.py # Main demonstration script
├── requirements.txt # Project dependencies
└── .env.example # Environment variables template
```
## Framework Comparison
The demonstrator compares two approaches:
1. **Cache-Augmented Generation (CAG)**:
- Preloads and caches knowledge
- Eliminates real-time retrieval steps
- Reduces latency and improves response times
- Uses efficient memory management
2. **Retrieval-Augmented Generation (RAG)**:
- Traditional document retrieval approach
- Real-time document fetching
- Standard context processing
## Results
Results are saved in JSON format in the `Results` directory with the following information:
- Timestamp
- LLM model used
- Framework configurations
- Query responses
- Performance metrics
- Time comparisons
## Contributing
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## License
This project is licensed under the MIT License - see the LICENSE file for details.
## Acknowledgments
- Thanks to all contributors and the open-source community
- Inspired by advances in LLM architectures and retrieval techniques