MongoDB RAG System Powered by Llama-3.1-8B-Instant

🛢️ MongoDB RAG System Powered by Llama-3.1-8B-Instant 🍃

MongoDB-RAG

📖 Overview

Mongodb-RAG is a Python project that implements a Retrieval Augmented Generation (RAG) system using MongoDB for storage and the powerful llama-3.1-8b-instant LLM for generation. This project demonstrates scalable, context-aware response generation by combining efficient document retrieval from MongoDB with state-of-the-art language modeling.

✨ Features

Retrieval Augmented Generation (RAG): Combines MongoDB-based retrieval with LLM-powered answer generation.
MongoDB Integration: Efficient storage and retrieval of documents and context.
Latest LLM: Utilizes llama-3.1-8b-instant for high-quality, instant responses.
Web Deployment Ready: Easily deployable to Render.
Clean Python Codebase: Modular Python scripts for easy understanding and customization.

📁 Folder Structure


├── __pycache__/
├── .gitignore
├── app.py
├── index.py
├── mongodb_database.py
├── query_generator.py
└── requirements.txt

app.py: Main application logic (entry point for web deployment).
index.py: Likely includes web server/router code.
mongodb_database.py: MongoDB connection and data handling logic.
query_generator.py: Core RAG logic for generating and retrieving responses.
requirements.txt: Python package dependencies.

🚀 Getting Started

✅ Prerequisites

Python 3.8+
MongoDB instance (local or Atlas)
(Optionally) Node.js & Render CLI for deployment

⚙️ Installation

Clone the repository

git clone https://github.com/YUGESHKARAN/Mongodb-RAG.git
cd Mongodb-RAG

Install dependencies
```
pip install -r requirements.txt
```
Configure environment
- Set up your MongoDB URI and any required environment variables.
Run the application
```
python app.py
```

☁️ Deployment

To deploy on Render:

Create a new Web Service on Render.
Connect your GitHub repository.
Set your build and start commands (typically, pip install -r requirements.txt and python app.py).
Add your environment variables (e.g.,MODEL_API_KEY, MongoDB URI) in the Render dashboard.
Deploy!

🌐 Usage

Access the service via the URL provided by Render after deployment.
The system retrieves relevant documents from MongoDB and generates responses using the llama-3.1-8b-instant LLM.

📊 Monitoring LLMs Logs

Using LangSmith to monitor LLMs logs, operations, token usage, costs, and responses.

🤝 Contributing

Contributions, suggestions, and improvements are welcome! Open an issue or pull request.

🙌 Acknowledgements

Maintained by YUGESHKARAN.