from app.services.document_processor import get_embedding
from app.core.database import search_points
import sys

def debug_search(query):
    print(f"--- Debugging Search for: '{query}' ---")
    
    # 1. Generate Embedding
    print("Generating embedding...")
    try:
        vector = get_embedding(query)
        print("Embedding generated successfully.")
    except Exception as e:
        print(f"FAILED to generate embedding: {e}")
        return

    # 2. Search Qdrant
    print("Searching Qdrant...")
    results = search_points(vector, limit=3)
    
    print(f"Found {len(results)} matches.")
    
    if not results:
        print("NO MATCHES FOUND. Check Qdrant connection or data.")
        return

    for i, hit in enumerate(results):
        score = hit.get("score", "N/A")
        payload = hit.get("payload", {})
        source = payload.get("source", "Unknown")
        text = payload.get("text", "")[:200] # Show first 200 chars
        
        print(f"\nMatch #{i+1} (Score: {score}):")
        print(f"Source: {source}")
        print(f"Text Snippet: {text}...")

if __name__ == "__main__":
    query = "What is Physical AI?"
    if len(sys.argv) > 1:
        query = sys.argv[1]
    debug_search(query)