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"""
Comprehensive RAG Test
Tests all features: retrieval, LLM integration, memory, and source citations.
"""
from pathlib import Path
import sys
sys.path.insert(0, str(Path(__file__).parent))
from setup_retrieval import setup_retrieval_system
from rag.chain import UPBRAGChain
def test_rag_features():
"""Test all RAG chain features comprehensively."""
print("=" * 70)
print("COMPREHENSIVE RAG CHAIN TEST")
print("=" * 70)
# Initialize system
print("\n[1/5] Initializing retrieval system...")
retriever, vectorstore_manager, chunks = setup_retrieval_system(
vectorstore_path="vectorstore/faiss_index",
use_existing=True
)
print(f"System ready with {len(chunks)} chunks")
# Create RAG chain
print("\n[2/5] Creating RAG chain with GPT-4o-mini...")
rag_chain = UPBRAGChain(retriever, retrieval_method="hybrid")
print("RAG chain initialized")
# Test 1: Basic query with sources
print("\n[3/5] Testing basic query with source citations...")
print("-" * 70)
q1 = "¿Cuáles son las ingenierías disponibles en la UPB?"
print(f"Q: {q1}")
r1 = rag_chain.invoke(q1, include_sources=True)
print(f"\nA: {r1['answer']}")
if 'sources' in r1:
print(f"\nSources retrieved: {len(r1['sources'])}")
print("Categories:", set(s['category'] for s in r1['sources']))
# Test 2: Follow-up question (memory test)
print("\n[4/5] Testing conversation memory...")
print("-" * 70)
q2 = "¿Cuál de esas me recomiendas si me gusta programar?"
print(f"Q: {q2}")
r2 = rag_chain.invoke(q2, include_sources=False)
print(f"\nA: {r2['answer']}")
# Test 3: Specific question
print("\n[5/5] Testing specific information retrieval...")
print("-" * 70)
q3 = "¿Qué opciones de financiación tienen?"
print(f"Q: {q3}")
r3 = rag_chain.invoke(q3, include_sources=True)
print(f"\nA: {r3['answer']}")
if 'sources' in r3:
print(f"\nSources retrieved: {len(r3['sources'])}")
scholarships_sources = [s for s in r3['sources'] if s['category'] == 'scholarships']
print(f"Scholarship-related sources: {len(scholarships_sources)}")
# Show conversation summary
print("\n" + "=" * 70)
print("CONVERSATION HISTORY")
print("=" * 70)
print(rag_chain.get_history_summary())
# Summary
print("\n" + "=" * 70)
print("TEST SUMMARY")
print("=" * 70)
print("Features tested:")
print(" - Hybrid retrieval (BM25 + Vector with RRF)")
print(" - GPT-4o-mini LLM integration")
print(" - Conversation memory across multiple turns")
print(" - Source citation with metadata")
print(" - Spanish language responses")
print(" - Context-aware follow-up questions")
print("\nAll features working correctly!")
print("=" * 70)
if __name__ == "__main__":
test_rag_features()
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