"""
Vector store implementation using Qdrant.
"""

from qdrant_client import QdrantClient
from qdrant_client.http import models as qdrant_models
from langchain.vectorstores import Qdrant
from config import QDRANT_HOST, QDRANT_PORT, QDRANT_COLLECTION_NAME
from embeddings import get_ollama_embeddings

def get_qdrant_client() -> QdrantClient:
    """
    Creates a Qdrant client connected to the local Qdrant instance.
    """
    return QdrantClient(host=QDRANT_HOST, port=QDRANT_PORT)

def ensure_collection(client: QdrantClient, collection_name: str, vector_size: int = 768) -> None:
    """
    Ensures that the specified collection exists in Qdrant.
    If it does not exist, it will be created with the given vector size.
    """
    if not client.has_collection(collection_name):
        client.recreate_collection(
            collection_name=collection_name,
            vectors_config=qdrant_models.VectorParams(
                size=vector_size,
                distance="Cosine"
            )
        )

def get_vector_store() -> Qdrant:
    """
    Returns a Qdrant vector store instance ready for use with LangChain.
    """
    client = get_qdrant_client()
    ensure_collection(client, QDRANT_COLLECTION_NAME)
    embeddings = get_ollama_embeddings()
    return Qdrant(
        client=client,
        collection_name=QDRANT_COLLECTION_NAME,
        embeddings=embeddings
    )