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)