Source code for tests.system.weaviate.example_weaviate_without_vectorizer_dag

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from __future__ import annotations

import pendulum

from airflow.decorators import dag, setup, task, teardown
from airflow.providers.openai.operators.openai import OpenAIEmbeddingOperator
from airflow.providers.weaviate.operators.weaviate import WeaviateIngestOperator


[docs]
COLLECTION_NAME = "Weaviate_example_without_vectorizer_collection"



@dag(
    schedule=None,
    start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
    catchup=False,
    tags=["example", "weaviate"],
)

[docs]
def example_weaviate_without_vectorizer_dag():
    """
    Example DAG which uses WeaviateIngestOperator to insert embeddings to Weaviate without 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, vectorizer_config=None)

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

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

    data_to_ingest = get_data_without_vectors()

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

    embedd_query = OpenAIEmbeddingOperator(
        task_id="embedd_query",
        conn_id="openai_default",
        input_text="biology",
        model="text-embedding-ada-002",
    )

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

        ti = kwargs["ti"]
        query_vector = ti.xcom_pull(task_ids="embedd_query", key="return_value")
        weaviate_hook = WeaviateHook()
        properties = ["question", "answer", "category"]
        response = weaviate_hook.query_with_vector(
            query_vector, "Weaviate_example_without_vectorizer_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
        >> embedd_query
        >> query_weaviate()
        >> delete_weaviate_collection()
    )



example_weaviate_without_vectorizer_dag()


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

# Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)

[docs]
test_run = get_test_run(dag)