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#!/usr/bin/env python3
"""
Test script to check RAG system status and document retrieval.
"""
import asyncio
import sys
import os
from pathlib import Path
# Add backend directory to path
sys.path.append(str(Path(__file__).parent))
from rag.qdrant_client import QdrantManager
from rag.embeddings import EmbeddingGenerator
from dotenv import load_dotenv
load_dotenv()
async def check_system():
"""Check RAG system status."""
print("=" * 60)
print("RAG SYSTEM STATUS CHECK")
print("=" * 60)
# Check environment variables
print("\n1. Checking environment variables:")
print(f" OPENAI_API_KEY: {'[OK] Configured' if os.getenv('OPENAI_API_KEY') else '[ERROR] Missing'}")
print(f" QDRANT_URL: {os.getenv('QDRANT_URL', 'Not set')}")
print(f" BOOK_CONTENT_PATH: {os.getenv('BOOK_CONTENT_PATH', 'Not set')}")
# Initialize Qdrant manager
print("\n2. Connecting to Qdrant...")
try:
qdrant_manager = QdrantManager(
url=os.getenv("QDRANT_URL"),
api_key=os.getenv("QDRANT_API_KEY")
)
await qdrant_manager.initialize()
print(" [OK] Connected to Qdrant")
# List collections
collections = await qdrant_manager.list_collections()
print(f" Collections: {collections}")
# Get collection stats
stats = await qdrant_manager.get_collection_stats()
print(f" Collection stats: {stats}")
if stats.get("vector_count", 0) == 0:
print(" [WARNING] No documents found in collection!")
print("\n POSSIBLE SOLUTIONS:")
print(" 1. Run ingestion: python scripts/ingest.py --content-path ./book_content --force-reindex")
print(" 2. Check if BOOK_CONTENT_PATH is correct")
print(" 3. Verify documents exist at the specified path")
else:
print(f" [OK] Found {stats.get('vector_count', 0)} documents in collection")
# Test search
print("\n3. Testing document search...")
try:
# Initialize embedder
embedder = EmbeddingGenerator(
api_key=os.getenv("OPENAI_API_KEY"),
model="text-embedding-3-small"
)
# Generate query embedding
test_query = "What is humanoid robotics?"
query_result = await embedder.generate_embedding(test_query)
query_embedding = query_result["embedding"]
# Search for similar documents
search_results = await qdrant_manager.search_similar(
query_embedding=query_embedding,
limit=3,
score_threshold=0.1 # Very low threshold to get any results
)
print(f" Query: {test_query}")
print(f" Results found: {len(search_results)}")
if search_results:
print("\n Top results:")
for i, result in enumerate(search_results):
score = result.get("score", 0)
content = result.get("content", "")[:200] + "..."
file_name = result.get("metadata", {}).get("file_name", "unknown")
print(f"\n Result {i+1}:")
print(f" Score: {score:.4f}")
print(f" File: {file_name}")
print(f" Content: {content}")
else:
print(" [WARNING] No documents retrieved even with low threshold!")
except Exception as e:
print(f" [ERROR] Search test failed: {str(e)}")
await qdrant_manager.close()
except Exception as e:
print(f" [ERROR] Failed to connect to Qdrant: {str(e)}")
print("\n" + "=" * 60)
if __name__ == "__main__":
asyncio.run(check_system()) |