import uuid
from langchain_community.vectorstores import Qdrant
from qdrant_client import  models
from utils import setup_qdrant_client,setup_openai_embeddings


def embed_documents_into_qdrant(documents, api_key, qdrant_url, qdrant_api_key, collection_name="Lex-v1"):
    """Embed documents into Qdrant."""
    embeddings_model = setup_openai_embeddings(api_key)
    client = setup_qdrant_client(qdrant_url, qdrant_api_key)
    qdrant = Qdrant(client=client, collection_name=collection_name, embeddings=embeddings_model)
    try:
        qdrant.add_documents(documents)
    except Exception as e:
        print("Failed to embed documents:", e)

def embed_documents_with_unique_collection(documents, api_key, qdrant_url, qdrant_api_key, collection_name=None):
    """Embed documents into a unique Qdrant collection."""
    if not collection_name:
        collection_name = f"session-{uuid.uuid4()}"
        client = setup_qdrant_client(qdrant_url, qdrant_api_key)
        client.create_collection(
            collection_name=collection_name,
            vectors_config=models.VectorParams(size=1536, distance=models.Distance.COSINE)
        )
    embeddings_model = setup_openai_embeddings(api_key)
    qdrant = Qdrant(client=client, collection_name=collection_name, embeddings=embeddings_model)
    try:
        qdrant.add_documents(documents)
    except Exception as e:
        print("Failed to embed documents:", e)
    return collection_name