RAG System for Legislation Study with Gemini 2.5 Pro

System Architecture Overview

PDF Document → Text Extraction → Chunking → Embedding → Vector DB → RAG Pipeline → Gemini 2.5 Pro → Response with Citations & Confidence

1. Document Processing Pipeline

PDF Text Extraction

See document_processing.py

Intelligent Chunking Strategy

See document_processing.py

2. Vector Database Setup

Using Chroma DB (Local & Free)

See vector_database.py

3. RAG Pipeline Implementation

See rag_pipeline.py

4. Complete System Setup

See system_setup.py

5. Enhanced Features

Query Expansion

See enhanced_features.py

Citation Verification

See enhanced_features.py

6. Web Interface (Optional)

See web_interface.py

7. Cost Optimization Tips

• Batch embedding generation to reduce API calls
• Cache embeddings locally to avoid regenerating
• Use smaller chunks for more precise retrieval
• Implement query similarity to avoid duplicate processing
• Set up rate limiting for API calls

8. Evaluation Metrics

def evaluate_system(rag_system, test_questions: List[dict]):
    """Evaluate system performance"""
    metrics = {
        'accuracy': [],
        'confidence_calibration': [],
        'citation_accuracy': []
    }
    
    for qa_pair in test_questions:
        result = rag_system.ask_question(qa_pair['question'])
        
        # Compare with ground truth
        accuracy = calculate_answer_similarity(
            result['answer'], 
            qa_pair['expected_answer']
        )
        metrics['accuracy'].append(accuracy)
        
        # More evaluation logic...
    
    return metrics

This system provides a robust foundation for studying legislation with precise answers, proper citations, and confidence scoring. The modular design allows you to customize each component based on your specific needs and computational resources.