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#
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from __future__ import annotations

from datetime import timedelta

import pendulum
from weaviate.classes.config import Configure

try:
    from airflow.sdk import dag, setup, task, teardown
except ImportError:
    # Airflow 2 path
    from airflow.decorators import dag, setup, task, teardown  # type: ignore[attr-defined,no-redef]
from airflow.providers.weaviate.operators.weaviate import WeaviateIngestOperator

COLLECTION_NAME = "Weaviate_with_vectorizer_example_collection"

default_args = {
    "retries": 5,
    "retry_delay": timedelta(seconds=15),
    "pool": "weaviate_pool",
}


@dag(
    schedule=None,
    start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
    default_args=default_args,
    catchup=False,
    tags=["example", "weaviate"],
)
def example_weaviate_vectorizer_dag():
    """
    Example DAG which uses WeaviateIngestOperator to insert embeddings to Weaviate with vectorizer and then query to verify the response .
    """

    @setup
    @task
    def create_weaviate_collection():
        """
        Example task to create collection without any Vectorizer. You're expected to provide custom vectors for your data.
        """
        from airflow.providers.weaviate.hooks.weaviate import WeaviateHook

        weaviate_hook = WeaviateHook()
        # collection definition object. Weaviate's autoschema feature will infer properties when importing.
        weaviate_hook.create_collection(
            COLLECTION_NAME,
            vector_config=Configure.Vectors.text2vec_openai(vectorize_collection_name=True),
        )

    @setup
    @task
    def get_data_to_ingest():
        import json
        from pathlib import Path

        data = json.load(Path("jeopardy_data_without_vectors.json").open())
        return data

    data_to_ingest = get_data_to_ingest()

    perform_ingestion = WeaviateIngestOperator(
        task_id="perform_ingestion",
        conn_id="weaviate_default",
        collection_name=COLLECTION_NAME,
        input_data=data_to_ingest["return_value"],
    )

    @task
    def query_weaviate():
        from airflow.providers.weaviate.hooks.weaviate import WeaviateHook

        weaviate_hook = WeaviateHook()
        properties = ["question", "answer", "category"]
        response = weaviate_hook.query_with_text(
            "biology", "Weaviate_with_vectorizer_example_collection", properties=properties
        )
        assert "In 1953 Watson & Crick built a model" in response.objects[0].properties["question"]

    @teardown
    @task
    def delete_weaviate_collection():
        """
        Example task to delete a weaviate collection
        """
        from airflow.providers.weaviate.hooks.weaviate import WeaviateHook

        weaviate_hook = WeaviateHook()
        # collection definition object. Weaviate's autoschema feature will infer properties when importing.

        weaviate_hook.delete_collections([COLLECTION_NAME])

    create_weaviate_collection() >> perform_ingestion >> query_weaviate() >> delete_weaviate_collection()


example_weaviate_vectorizer_dag()


from tests_common.test_utils.system_tests import get_test_run  # noqa: E402

# Needed to run the example DAG with pytest (see: contributing-docs/testing/system_tests.rst)
test_run = get_test_run(dag)
