vector_size = len(partial_embeddings[0])
        collection_name = collection_name or uuid.uuid4().hex
        distance_func = distance_func.upper()
        client = qdrant_client.QdrantClient(
            location=location,
            url=url,
            port=port,
            grpc_port=grpc_port,
            prefer_grpc=prefer_grpc,
            https=https,
            api_key=api_key,
            prefix=prefix,
            timeout=timeout,
            host=host,
            path=path,
            **kwargs,
        )
        try:
            # Skip any validation in case of forced collection recreate.
            if force_recreate:
                raise ValueError
            # Get the vector configuration of the existing collection and vector, if it
            # was specified. If the old configuration does not match the current one,
            # an exception is being thrown.
            collection_info = client.get_collection(collection_name=collection_name)
            current_vector_config = collection_info.config.params.vectors
            if isinstance(current_vector_config, dict) and vector_name is not None:
                if vector_name not in current_vector_config:
                    raise QdrantException(
                        f"Existing Qdrant collection {collection_name} does not "
                        f"contain vector named {vector_name}. Did you mean one of the "
                        f"existing vectors: {', '.join(current_vector_config.keys())}? "
                        f"If you want to recreate the collection, set `force_recreate` "
                        f"parameter to `True`."
                    )
                current_vector_config = current_vector_config.get(
                    vector_name
                )  # type: ignore[assignment]
            elif isinstance(current_vector_config, dict) and vector_name is None:
                raise QdrantException(