from pathlib import Path
from langchain_openai import OpenAIEmbeddings
from langchain_chroma import Chroma
from dotenv import load_dotenv
import os
import sqlite3

load_dotenv()


def test_load_embeddings():
    print("=== Testing Embeddings Load ===")

    base_dir = Path.cwd()
    chroma_dir = base_dir / "data" / "processed" / "embeddings" / "chroma"

    # Test SQLite connection directly
    print("\nTesting SQLite database:")
    try:
        conn = sqlite3.connect(str(chroma_dir / "chroma.sqlite3"))
        cursor = conn.cursor()
        # Check tables
        cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
        tables = cursor.fetchall()
        print(f"Found tables: {tables}")

        # Try to count records
        for table in tables:
            cursor.execute(f"SELECT COUNT(*) FROM {table[0]};")
            count = cursor.fetchone()[0]
            print(f"Table {table[0]}: {count} records")
    except Exception as e:
        print(f"SQLite Error: {str(e)}")
    finally:
        if 'conn' in locals():
            conn.close()

    # Now try ChromaDB with langchain collection
    print("\nTesting ChromaDB load:")
    try:
        embeddings = OpenAIEmbeddings(
            openai_api_key=os.getenv("OPENAI_API_KEY")
        )

        db = Chroma(
            persist_directory=str(chroma_dir),
            embedding_function=embeddings,
            collection_name="langchain"
        )
        print("\nChroma instance created")
        print(f"Collection names: {db._client.list_collections()}")

        # Try to get collection details
        collection = db._client.get_collection("langchain")
        print(f"\nCollection count: {collection.count()}")
        print(f"Collection peek: {collection.peek()}")

    except Exception as e:
        print(f"\nChroma Error: {str(e)}")


if __name__ == "__main__":
    test_load_embeddings()