README.md

metadata

title: RAG Pipeline Demo
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.25.0
app_file: app.py
pinned: false

🎓 University Knowledge Retrieval System

A comprehensive Retrieval-Augmented Generation (RAG) system for university data with advanced guardrails and experimental validation.

🌟 Features

• Interactive Chat Interface: Natural language queries about university data
• Advanced Guardrails: Enhanced input/output security, including blocking queries with Austrian social security numbers (SVNRs) and redacting them from responses.
• Experimental Dashboard: Comprehensive testing suite for RAG validation.
• Real Database: 6,000+ students, 1,300+ faculty, 2,600+ courses, with student records now including SVNRs for guardrail testing.
• Vector Search: Semantic search using ChromaDB and Sentence Transformers, with SVNRs intentionally included in student documents for output guardrail testing.

🚀 Quick Start

Prerequisites

• Python 3.8+
• Hugging Face API Token

Installation

1. Clone and setup:

git clone <repository-url>
cd projekt
pip install -r requirements.txt

2. Configure API Key (choose one):

   • Create secrets_local.py: HF = "your_hugging_face_token"
   • Or set environment variable: HF_TOKEN=your_token

3. Initialize Database and Vector Store: Run the setup scripts to create the SQLite database and populate the vector store.

python database/setup_db.py
python rag/build_vector_store.py

4. Run the application:

streamlit run app.py

📊 System Components

Chat Interface

• Natural language queries about university data
• Real-time RAG pipeline with source citations
• Input/output guardrails for security

Experimental Dashboard

Two comprehensive test suites:

1. Input Guardrails: Tests against malicious inputs (SQL injection, PII extraction)
2. Output Guardrails: Validates response quality and detects hallucinations

🏗️ Architecture

├── app.py                    # Main Streamlit application
├── experimental_dashboard.py # Experiment interface and system info
├── experiments/             # Test suites for RAG validation
│   ├── experiment_1_input_guardrails.py
│   ├── experiment_2_output_guardrails.py
│   ├── experiment_3_hyperparameters.py
│   └── experiment_4_context_window.py
├── database/               # SQLite university database
├── rag/                   # Vector store and retrieval
├── rails/                 # Input/output guardrails
├── model/                 # RAG model integration
└── guards/               # Security components

🔧 Configuration

Dependencies (requirements.txt):

• streamlit==1.37.0
• sentence-transformers==5.1.0
• chromadb==1.0.21
• Faker==15.3.4 (for database generation)
• huggingface-hub==0.34.4
• nltk, numpy, scikit-learn

🎯 Usage Examples

Student Queries:

• "What courses is Maria taking?"
• "Who are the students in computer science?"

Faculty Queries:

• "Who teaches in the engineering department?"
• "Show me all professors"

Course Queries:

• "What courses are available?"
• "Who teaches advanced mathematics?"

🧪 Running Experiments

Access via the "Experiments" tab in the web interface, or run individually:

cd experiments
python experiment_1_input_guardrails.py
python experiment_2_output_guardrails.py

🔒 Security Features

• Input Validation: SQL injection prevention, malicious prompt detection, blocking queries containing valid Austrian social security numbers (SVNRs).
• Output Filtering: PII redaction (including SVNRs), hallucination detection, relevance checking.
• Content Sanitization: Automatic cleaning of responses and database content.

📈 Database Statistics

• Students: 6,398 records with realistic personal data
• Faculty: 1,297 professors across multiple departments
• Courses: 2,600 courses linked to faculty
• Enrollments: 19,443 student-course relationships

🔑 API Requirements

Requires Hugging Face API access for:

• Text generation models
• Embedding models for semantic search
• Guardrail validation services