README.md

---
title: CompifAI
emoji: 💻
colorFrom: gray
colorTo: green
sdk: docker
pinned: false
license: apache-2.0
---

# Competence Standards RAG Chatbot

## 📋 Table of Contents

- [About the Project](#about-the-project)
- [Features](#features)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Configuration](#configuration)
- [Starting the Application](#starting-the-application)
- [Database Population](#database-population)
- [Data Management](#data-management)
- [Testing](#testing)

## 🎯 About the Project

This RAG chatbot is specifically designed to assist with competence standards in higher education settings. It provides intelligent responses based on a curated knowledge base of documents.

The system uses advanced natural language processing to understand queries and retrieve relevant information from the document corpus, providing contextually appropriate responses.

## ✨ Features

- **Interactive Chat Interface**: Built with Chainlit for an intuitive user experience
- **Vector Search**: Powered by Milvus for efficient similarity search
- **Advanced Embeddings**: Uses Nebius AI Qwen3-Embedding-8B model
- **Document Processing**: Supports multiple formats (PDF, DOCX, HTML)
- **Authentication**: Integrated with Chainlit's authentication system
- **Persistent Storage**: Database integration with PostgreSQL
- **Containerized Deployment**: Docker Compose setup for easy deployment
- **Testing Suite**: Comprehensive testing with RAGAS evaluation


## 🚀 Installation

1. **Clone the repository**
   ```bash
   git clone https://github.com/daniel-was-taken/prod-rag-chat.git
   cd prod-rag-chat
   ```

2. **Create a virtual environment**
   ```bash
   python -m venv venv
   
   # On Windows
   .\venv\Scripts\activate
   
   # On macOS/Linux
   source venv/bin/activate
   ```

3. **Install dependencies**
   ```bash
   pip install -r requirements.txt
   ```

## ⚙️ Configuration

1. **Create environment file**
   ```bash
   cp .env.example .env  # If available, or create manually
   ```

2. **Configure environment variables**
   Create a `.env` file in the root directory with the following variables:
   ```env
   # Nebius AI Configuration
   NEBIUS_API_KEY=your_nebius_api_key_here
   OPENAI_API_KEY=your_openai_api_key_here  # Fallback
   
   # Milvus Configuration
   MILVUS_URI=http://localhost:19530
   
   # Chainlit Configuration
   CHAINLIT_AUTH_SECRET=your_auth_secret_here
   
   # Database Configuration (if using PostgreSQL)
   DATABASE_URL=postgresql://user:password@localhost:5432/dbname
   ```

## 🎬 Starting the Application

### Method 1: Using Docker Compose (Recommended)

1. **Start the infrastructure services**
   ```bash
   docker-compose up -d
   ```
   This will start:
   - Milvus vector database
   - etcd (for Milvus coordination)
   - MinIO (for Milvus storage)

2. **Wait for services to be ready** 

3. **Start the Chainlit application**
   ```bash
   chainlit run app.py -w
   ```

### Method 2: Manual Setup

1. **Install and configure Milvus standalone**
   Follow the [Milvus installation guide](https://milvus.io/docs/install_standalone-docker.md)

2. **Start the application**
   ```bash
   chainlit run app.py -w
   ```

The application will be available at `http://localhost:8000`

## 📊 Database Population

The system automatically populates the vector database on first startup. However, you can manually manage the data:

### Automatic Population

The application automatically checks if the Milvus collection exists and has data. If not, it runs the population script automatically.

### Manual Population

To manually populate or repopulate the database:

```bash
python populate_db.py
```

### Adding New Documents

1. **Add documents to the data directory**
   ```bash
   # Place your documents in the data/ folder
   cp your_new_document.pdf data/
   cp your_new_document.docx data/
   ```

2. **Supported file formats:**
   - PDF files (`.pdf`)
   - Microsoft Word documents (`.docx`)
   - HTML files (`.html`)

3. **Repopulate the database**
   ```bash
   # Delete existing collection
   python delete_collection.py
   
   # Repopulate with new documents
   python populate_db.py
   ```

### Database Configuration

The population script uses the following configuration:

- **Embedding Model**: Qwen/Qwen3-Embedding-8B (4096 dimensions)
- **Chunk Size**: 1500 characters maximum
- **Combine Threshold**: 200 characters minimum
- **Batch Size**: 5 documents per batch
- **Collection Name**: `my_rag_collection`

### Document Processing Pipeline

1. **Loading**: Documents are loaded using UnstructuredLoader
2. **Cleaning**: Text is cleaned and normalized
3. **Chunking**: Documents are split into manageable chunks
4. **Embedding**: Chunks are converted to vector embeddings
5. **Storage**: Embeddings are stored in Milvus with metadata

## 🗃️ Data Management


### Deleting the Collection

```bash
python delete_collection.py
```


### Updating Documents

To update the document corpus:

1. Add/remove documents in the `data/` directory
2. Delete the existing collection: `python delete_collection.py`
3. Restart the application (it will automatically repopulate)

## 🧪 Testing

The project includes comprehensive testing:

### Running Unit Tests

```bash

# Run specific test files
python -m unittest tests/test_chainlit.py -v
```

### RAGAS Evaluation

Evaluate the RAG system performance:

```bash
# Run RAGAS evaluation
python tests/test_ragas.py

# Or use the Jupyter notebook
jupyter notebook tests/test_ragas.ipynb
```

### Manual Testing

Test individual components:

```bash
# Test vector search functionality
python tests/test_vector_search.py

```


## 📄 License

This project is licensed under the MIT license.