Source code for tests.system.providers.weaviate.example_weaviate_using_hook

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from __future__ import annotations

import pendulum
from weaviate.classes.config import DataType, Property
from weaviate.collections.classes.config import Configure

from airflow.decorators import dag, task, teardown

[docs]COLLECTION_NAME = "QuestionWithOpenAIVectorizerUsingHook"
@dag( schedule=None, start_date=pendulum.datetime(2021, 1, 1, tz="UTC"), catchup=False, tags=["example", "weaviate"], )
[docs]def example_weaviate_dag_using_hook(): """Example Weaviate DAG demonstrating usage of the hook.""" @task() def create_collection_with_vectorizer(): """ Example task to create collection with OpenAI Vectorizer responsible for vectorining data using Weaviate cluster. """ from airflow.providers.weaviate.hooks.weaviate import WeaviateHook weaviate_hook = WeaviateHook() weaviate_hook.create_collection( COLLECTION_NAME, description="Information from a Jeopardy! question", properties=[ Property(name="question", description="The question", data_type=DataType.TEXT), Property(name="answer", description="The answer", data_type=DataType.TEXT), Property(name="category", description="The category", data_type=DataType.TEXT), ], vectorizer_config=Configure.Vectorizer.text2vec_openai(), ) @task() def create_collection_without_vectorizer(): """ 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( "QuestionWithoutVectorizerUsingHook", vectorizer_config=None, ) @task(trigger_rule="all_done") def store_data_without_vectors_in_xcom(): import json from pathlib import Path data = json.load(Path("jeopardy_data_without_vectors.json").open()) return data @task(trigger_rule="all_done") def store_data_with_vectors_in_xcom(): import json from pathlib import Path data = json.load(Path("jeopardy_data_with_vectors.json").open()) return data @task(trigger_rule="all_done") def batch_data_without_vectors(data: list): from airflow.providers.weaviate.hooks.weaviate import WeaviateHook weaviate_hook = WeaviateHook() weaviate_hook.batch_data(COLLECTION_NAME, data) @task(trigger_rule="all_done") def batch_data_with_vectors(data: list): from airflow.providers.weaviate.hooks.weaviate import WeaviateHook weaviate_hook = WeaviateHook() weaviate_hook.batch_data("QuestionWithoutVectorizerUsingHook", data) @teardown @task def delete_weaviate_collection_vector(): """ 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]) @teardown @task def delete_weaviate_collection_without_vector(): """ 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(["QuestionWithoutVectorizerUsingHook"]) data_with_vectors = store_data_with_vectors_in_xcom() ( create_collection_without_vectorizer() >> batch_data_with_vectors(data_with_vectors["return_value"]) >> delete_weaviate_collection_vector() ) data_without_vectors = store_data_without_vectors_in_xcom() ( create_collection_with_vectorizer() >> batch_data_without_vectors(data_without_vectors["return_value"]) >> delete_weaviate_collection_without_vector() )
example_weaviate_dag_using_hook() from tests.system.utils 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)

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