archived_scripts/example.py

"""
Example script demonstrating how to use the RAG system programmatically.
"""
import os
from config import settings
from dataset_loader import RAGBenchLoader
from vector_store import ChromaDBManager
from llm_client import GroqLLMClient, RAGPipeline
from trace_evaluator import TRACEEvaluator


def main():
    """Example usage of RAG system."""
    
    # Set your API key
    api_key = os.getenv("GROQ_API_KEY") or "your_api_key_here"
    
    if api_key == "your_api_key_here":
        print("Please set your GROQ_API_KEY in .env file or environment variable")
        return
    
    print("=" * 50)
    print("RAG System Example")
    print("=" * 50)
    
    # 1. Load dataset
    print("\n1. Loading dataset...")
    loader = RAGBenchLoader()
    dataset = loader.load_dataset("hotpotqa", split="train", max_samples=20)
    print(f"Loaded {len(dataset)} samples")
    
    # 2. Create vector store and collection
    print("\n2. Creating vector store...")
    vector_store = ChromaDBManager()
    
    collection_name = "example_collection"
    embedding_model = "emilyalsentzer/Bio_ClinicalBERT"
    chunking_strategy = "hybrid"
    
    print(f"Loading data into collection with {chunking_strategy} chunking...")
    vector_store.load_dataset_into_collection(
        collection_name=collection_name,
        embedding_model_name=embedding_model,
        chunking_strategy=chunking_strategy,
        dataset_data=dataset,
        chunk_size=512,
        overlap=50
    )
    
    # 3. Initialize LLM client
    print("\n3. Initializing LLM client...")
    llm_client = GroqLLMClient(
        api_key=api_key,
        model_name="llama-3.1-8b-instant",
        max_rpm=30,
        rate_limit_delay=2.0
    )
    
    # 4. Create RAG pipeline
    print("\n4. Creating RAG pipeline...")
    rag = RAGPipeline(llm_client, vector_store)
    
    # 5. Query the system
    print("\n5. Querying the system...")
    queries = [
        "What is machine learning?",
        "How does neural network work?",
        "What is deep learning?"
    ]
    
    for i, query in enumerate(queries, 1):
        print(f"\n--- Query {i}: {query} ---")
        result = rag.query(query, n_results=3)
        
        print(f"Response: {result['response']}")
        print(f"\nRetrieved {len(result['retrieved_documents'])} documents:")
        for j, doc in enumerate(result['retrieved_documents'], 1):
            print(f"\nDocument {j} (Distance: {doc.get('distance', 'N/A')}):")
            print(f"{doc['document'][:200]}...")
    
    # 6. Run evaluation
    print("\n6. Running TRACE evaluation...")
    evaluator = TRACEEvaluator(llm_client)
    
    # Prepare test cases
    test_cases = []
    test_samples = loader.get_test_data("hotpotqa", num_samples=5)
    
    for sample in test_samples:
        result = rag.query(sample["question"], n_results=5)
        test_cases.append({
            "query": sample["question"],
            "response": result["response"],
            "retrieved_documents": [doc["document"] for doc in result["retrieved_documents"]],
            "ground_truth": sample.get("answer", "")
        })
    
    results = evaluator.evaluate_batch(test_cases)
    
    print("\nTRACE Evaluation Results:")
    print(f"Utilization:  {results['utilization']:.3f}")
    print(f"Relevance:    {results['relevance']:.3f}")
    print(f"Adherence:    {results['adherence']:.3f}")
    print(f"Completeness: {results['completeness']:.3f}")
    print(f"Average:      {results['average']:.3f}")
    
    # 7. View chat history
    print("\n7. Chat History:")
    history = rag.get_chat_history()
    print(f"Total conversations: {len(history)}")
    
    print("\n" + "=" * 50)
    print("Example completed successfully!")
    print("=" * 50)


if __name__ == "__main__":
    main()