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"""
Quick test script for RAG system
Run this to verify everything is working
"""
import os
from dotenv import load_dotenv
from openai import OpenAI
from pathlib import Path
from rag_system import RAGSystem, QueryExpander
from evaluation import RAGEvaluator
# Load environment
load_dotenv(override=True)
openai_client = OpenAI()
print("="*60)
print("🧪 RAG System Quick Test")
print("="*60)
# Test 1: Query Expansion
print("\n1️⃣ Testing Query Expansion...")
try:
expander = QueryExpander(openai_client)
query = "What are your skills?"
expanded = expander.expand_query(query, num_variations=2)
print(f"✓ Original: {query}")
print(f"✓ Expanded to {len(expanded)} queries")
for i, q in enumerate(expanded[1:], 1):
print(f" {i}. {q}")
except Exception as e:
print(f"✗ Query expansion failed: {e}")
# Test 2: Document Loading
print("\n2️⃣ Testing Document Loading...")
try:
# Create simple test documents
test_docs = {
"doc1": "I have experience with Python, JavaScript, and SQL. I've worked on ML projects.",
"doc2": "My education includes a degree in Computer Science. I studied AI and databases.",
"doc3": "I'm passionate about building scalable systems and working with data."
}
rag_system = RAGSystem(openai_client, data_dir="data_test")
rag_system.load_knowledge_base(test_docs, chunk_size=100, overlap=20)
print("✓ RAG system initialized")
print(f"✓ Loaded {len(test_docs)} test documents")
except Exception as e:
print(f"✗ Document loading failed: {e}")
exit(1)
# Test 3: Retrieval Methods
print("\n3️⃣ Testing Retrieval Methods...")
test_query = "What programming languages?"
methods_to_test = ["bm25", "semantic", "hybrid", "hybrid_rerank"]
for method in methods_to_test:
try:
results = rag_system.retriever.retrieve(
test_query,
method=method,
top_k=2
)
print(f"✓ {method:15s}: Retrieved {len(results)} documents")
if results:
print(f" Top score: {results[0]['retrieval_score']:.4f}")
except Exception as e:
print(f"✗ {method:15s}: Failed - {e}")
# Test 4: End-to-End RAG Query
print("\n4️⃣ Testing End-to-End RAG Query...")
try:
system_prompt = "You are answering questions about a person's professional background."
response = rag_system.query(
"What programming languages do you know?",
system_prompt,
method="hybrid_rerank",
top_k=3
)
print("✓ Query successful!")
print(f"✓ Retrieved {len(response['context'])} context documents")
print(f"✓ Generated answer ({len(response['answer'])} characters)")
print(f"\nAnswer preview:\n{response['answer'][:200]}...")
except Exception as e:
print(f"✗ RAG query failed: {e}")
# Test 5: LLM-as-Judge
print("\n5️⃣ Testing LLM-as-Judge...")
try:
evaluator = RAGEvaluator(openai_client)
# Test relevance judgment
judge_result = evaluator.llm_as_judge_relevance(
query="What are your programming skills?",
document="I have experience with Python, JavaScript, and SQL.",
context="Professional background"
)
print("✓ LLM judge evaluation successful")
print(f" Relevance score: {judge_result['relevance_score']}/5")
print(f" Explanation: {judge_result['explanation']}")
except Exception as e:
print(f"✗ LLM judge failed: {e}")
# Summary
print("\n" + "="*60)
print("✅ All tests completed!")
print("="*60)
print("\n💡 Next steps:")
print(" 1. Add your linkedin.pdf to the me/ folder")
print(" 2. Edit me/summary.txt with your information")
print(" 3. Update NAME in app.py")
print(" 4. Run: python app.py")
print("\n📊 For full evaluation:")
print(" jupyter notebook demo_and_evaluation.ipynb")
print("="*60)
# Cleanup test data
print("\n🧹 Cleaning up test data...")
import shutil
if Path("data_test").exists():
shutil.rmtree("data_test")
print("✓ Test data cleaned up")
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