uniLLM Backend

A FastAPI-based backend service for the German Student Info Chatbot, providing RAG (Retrieval-Augmented Generation) capabilities with vector search and LLM integration.

Table of Contents

• Overview
• Prerequisites
• Installation
• Configuration
• Development
• API Endpoints
• Data Pipeline
• Deployment

Overview

The uniLLM backend is built with:

• FastAPI - Modern, fast web framework for building APIs
• LlamaIndex - RAG framework for document indexing and retrieval
• Qdrant - Vector database for semantic search
• OpenAI GPT-4 - Large language model for response generation

Prerequisites

• Python 3.8+
• Docker and Docker Compose
• Git

Installation

1. Clone the repository:

git clone https://github.com/rissalhedna/unillm.git
cd unillm-backend

2. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Start the required services:

docker compose up -d

This will start:

• PostgreSQL database on port 5432
• Qdrant vector database on port 6333

Configuration

Create a .env file in the root directory with the following variables:

# Database
DATABASE_URL=postgresql://postgres:postgres@localhost:5432/postgres

# Qdrant Vector Database
QDRANT_HOST=localhost
QDRANT_PORT=6333
QDRANT_URL=http://localhost:6333

# OpenAI API
OPENAI_API_KEY=your_openai_api_key_here

# Environment
DEV=dev  # or "prod" for production

Development

Local Development

1. Start the development server:

fastapi dev main.py

The API will be available at http://localhost:8000

2. Access the interactive API documentation:
   • Swagger UI: http://localhost:8000/docs
   • ReDoc: http://localhost:8000/redoc

Docker Development

1. Build and run with Docker:

docker compose up --build

Code Quality

Before committing changes, run pre-commit hooks:

pre-commit run --all-files

API Endpoints

Chat Endpoints

• POST /chat - Send a message to the chatbot
• GET /chats - Retrieve chat history
• GET /chats/{chat_id} - Get specific chat conversation

Health Check

• GET /health - API health status

Documentation

• GET /docs - Swagger UI documentation
• GET /redoc - ReDoc documentation

Data Pipeline

The backend includes a comprehensive data processing pipeline for managing the knowledge base:

Web Scraping

Located in /scripts folder:

• handbook_germany_crawler.py - Scrapes handbook-germany.de
• study_in_germany_crawler.py - Scrapes study-in-germany.de

Data Processing

The pipeline processes data through:

1. Web Scraping: Automated crawlers collect information from German study websites
2. Data Cleaning: Raw data is processed and cleaned
3. Text Chunking: Documents are split into appropriate chunks for embeddings
4. Vectorization: Text chunks are converted to embeddings using OpenAI
5. Storage: Embeddings are stored in Qdrant vector database

Running the Pipeline

1. Install browser dependencies:

playwright install chromium

2. Run scrapers:

cd scripts
python handbook_germany_crawler.py
python study_in_germany_crawler.py

3. Process the data using the notebook:

jupyter notebook notebooks/scraping_cleaning_pipeline.ipynb

Architecture

User Query → FastAPI → LlamaIndex → Qdrant (Vector Search) → OpenAI GPT-4 → Response

Key Components

• Query Processing: FastAPI receives and validates user queries
• RAG System: LlamaIndex orchestrates retrieval and generation
• Vector Search: Qdrant finds relevant document chunks
• Response Generation: OpenAI GPT-4 generates contextual responses
• Fallback System: Search engine fallback for queries outside knowledge base

Deployment

Production Deployment

1. Set environment variables for production
2. Use Docker for containerized deployment:

docker build -t unillm-backend .
docker run -p 8000:8000 unillm-backend

Deployment Platforms

• Railway: Simple deployment with database support
• Linode: Cost-effective VPS hosting
• Docker: Containerized deployment

Current Status

✅ Working Features:

• FastAPI backend with async support
• RAG system with Qdrant integration
• Document indexing and retrieval
• OpenAI GPT-4 integration
• Web scraping pipeline
• Docker containerization

🔧 Known Issues:

• Chat history persistence needs improvement
• Search engine fallback system in development

🚧 Upcoming Features:

• Enhanced search engine fallback
• CV matching functionality
• Improved caching system
• Rate limiting and authentication
• Monitoring and logging improvements

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests and pre-commit hooks
5. Submit a pull request

License

This project is licensed under the MIT License.