Project Overview
RAG AI Assistant is a Retrieval-Augmented Generation system that provides intelligent document processing and context-aware responses.
Features
Document upload and processing
AI-powered question answering
Vector-based similarity search
REST API backend
Installation
Prerequisites
Python 3.8+
Git (for version control)
Setup
bash
git clone https://github.com/chandra-200546/RAG-based-AI-assistent
pip install -r requirements.txt
python main.py
Project Structure
text
RAG_AI_Assistant/
āāā backend/
ā āāā app/
ā āāā models/
ā āāā routes/
ā āāā main.py
ā āāā requirements.txt
āāā README.md
API Usage
Upload Documents
python
POST /api/documents/upload
Content-Type: multipart/form-data
Response:
{
"status": "success",
"document_id": "12345"
}
Ask Questions
python
POST /api/chat/query
{
"question": "What is in my documents?",
"document_ids": ["12345"]
}
Response:
{
"answer": "The response from AI",
"sources": ["document1.pdf"]
}
Configuration
Set up environment variables
Configure API keys
Adjust model parameters in config
Deployment
The application can be deployed using:
Docker containers
Traditional server deployment
Cloud platforms (AWS, GCP, Azure)
GitHub Repository
Your project is available at:
https://github.com/chandra-200546/RAG-based-AI-assistent
Contributing
Fork the repository
Create feature branch
Commit changes
Push to branch
Create Pull Request
License
MIT License - see LICENSE file for details
Support
For issues and questions:
Create GitHub issues
Check documentation
Contact maintainers