# app/qdrant_client.py
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams
from app.config import settings

# OpenAI text-embedding-3-small produces 1536-dimensional vectors
EMBEDDING_DIMENSION = 1536

# Initialize Qdrant client
qdrant_client = QdrantClient(
    url=settings.QDRANT_URL,
    api_key=settings.QDRANT_API_KEY,
)

COLLECTION_NAME = "book_embeddings"


def init_qdrant_collection(recreate: bool = False):
    """Initialize Qdrant collection if it doesn't exist (or recreate if flagged)"""
    try:
        # Check if collection exists
        collections = qdrant_client.get_collections().collections
        collection_names = [col.name for col in collections]

        if recreate and COLLECTION_NAME in collection_names:
            qdrant_client.delete_collection(collection_name=COLLECTION_NAME)
            print(f"Deleted existing Qdrant collection: {COLLECTION_NAME} (for dimension fix)")

        if COLLECTION_NAME not in collection_names:
            # Create collection with vector configuration
            qdrant_client.create_collection(
                collection_name=COLLECTION_NAME,
                vectors_config=VectorParams(
                    size=EMBEDDING_DIMENSION,  # OpenAI text-embedding-3-small dimension
                    distance=Distance.COSINE
                )
            )
            print(f"Created Qdrant collection: {COLLECTION_NAME}")
        else:
            # Verify dimensions match (optional safety check)
            info = qdrant_client.get_collection(COLLECTION_NAME)
            if info.config.params.vectors.size != EMBEDDING_DIMENSION:
                raise ValueError(
                    f"Collection {COLLECTION_NAME} has wrong size {info.config.params.vectors.size}; "
                    f"expected {EMBEDDING_DIMENSION}. Recreate with flag."
                )
            print(f"Qdrant collection already exists with correct dims: {COLLECTION_NAME}")
    except Exception as e:
        print(f"Warning: Could not initialize Qdrant collection: {e}")


def get_qdrant_client():
    """Dependency to get Qdrant client"""
    return qdrant_client