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README.md

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-# AI Assistant API
-
-## 🚀 Overview
-
-This project is an AI-powered assistant that uses FastAPI and FAISS for retrieval-augmented generation (RAG). It processes user queries using a vector database and evaluates responses with Opik.
-
-## 🛠️ Features
-
-- Upload and manage datasets
-- Query AI assistant with domain-specific constraints
-- Use FAISS for efficient document retrieval
-- Evaluate LLM responses using Opik
-
-## 📽️ Demo Video
-
-[🎥 Click here to watch the demo](https://drive.google.com/file/d/10h4VnTm_y5SBczI6NnoTuqRxyq55HAn5/view?usp=sharing)
-
-
-## 📦 Installation
-
-### Install Ollama
-
-Ollama is required for this project. Follow these steps to install it:
-
-```bash
-# For macOS
-brew install ollama
-
-# For Linux
-curl -fsSL https://ollama.ai/install.sh | sh
-
-# Verify installation
-ollama --version
-
-# Windows
-You can download from web https://ollama.com/
-```
-# Clone and Setup the Project
-## Clone the repository
-```
-git clone https://github.com/Komal-99/cyfuture_bot.git
-```
-
-## Navigate to the project directory
-```
-cd cyfuture_bot
-```
-
-## Install dependencies
-```
-pip install -r requirements.txt  # For Python projects
-yarn install  # For JavaScript projects
- ```
-
-🚀 Usage
-Start the Project
-Run the ```start.sh``` script to set up and launch the application:
-```
-chmod +x start.sh
-./start.sh
-
-```
-This script:
-
-Sets environment variables for optimization
-
-Starts Ollama in the background
-
-Pulls required models (deepseek-r1:7b, nomic-embed-text)
-
-Waits for Ollama to initialize
-
-### Launches the FastAPI server on http://127.0.0.1:7860
-###  Streamlit Application - http://127.0.0.1:8501
-
-## API Endpoints
-Upload Dataset
-```
-POST /upload_dataset/ #Upload an Excel dataset to be used for evaluation.
-```
-Run Evaluation
-```
-POST /run_evaluation/ #Evaluate the model's performance using Opik.
-
-```
-Query AI Assistant
-```
-GET /query/?input_text=your_question # Ask the assistant a question. The model retrieves relevant information and generates an answer based on indexed documents.
-
-```
-📂 Folder Structure
-
-```
-.
-├── AI_Agent/          # Datasource
-├── deepseek_cyfuture/ # DeepSeek Vector db
-├── .env               # Environment variables
-├── .gitignore         # Files to ignore in Git
-├── dataset.xlsx       # Sample dataset file
-├── Dockerfile         # Docker configuration
-├── requirements.txt   # Dependencies (Python projects)
-├── start.sh           # Startup script
-├── app.py             # Main application file
-├── README.md          # Project documentation
-
-```
-🤝 Contributing
-Contributions are welcome! Please follow these steps:
-
-Fork the repository
-
-Create a new branch (git checkout -b feature-branch)
-
-Commit your changes (git commit -m 'Add new feature')
-
-Push to the branch (git push origin feature-branch)
-
-Create a pull request
-
-📜 License
-This project is licensed under the MIT License - see the LICENSE file for details.
-
-📬 Contact
-For questions or issues, reach out:
-
-GitHub: https://github.com/Komal-99
-