Source code for tests.system.providers.weaviate.example_weaviate_dynamic_mapping_dag
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import annotations
import pendulum
from airflow.decorators import dag, setup, task, teardown
from airflow.providers.weaviate.operators.weaviate import WeaviateIngestOperator
@dag(
schedule=None,
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
catchup=False,
tags=["example", "weaviate"],
)
[docs]def example_weaviate_dynamic_mapping_dag():
"""
Example DAG which uses WeaviateIngestOperator to insert embeddings to Weaviate using dynamic mapping"""
@setup
@task
def create_weaviate_class(data):
"""
Example task to create class without any Vectorizer. You're expected to provide custom vectors for your data.
"""
from airflow.providers.weaviate.hooks.weaviate import WeaviateHook
weaviate_hook = WeaviateHook()
# Class definition object. Weaviate's autoschema feature will infer properties when importing.
class_obj = {
"class": data[0],
"vectorizer": data[1],
}
weaviate_hook.create_class(class_obj)
@setup
@task
def get_data_to_ingest():
import json
from pathlib import Path
file1 = json.load(Path("jeopardy_data_with_vectors.json").open())
file2 = json.load(Path("jeopardy_data_without_vectors.json").open())
return [file1, file2]
get_data_to_ingest = get_data_to_ingest()
perform_ingestion = WeaviateIngestOperator.partial(
task_id="perform_ingestion",
conn_id="weaviate_default",
).expand(
class_name=["example1", "example2"],
input_data=get_data_to_ingest["return_value"],
)
@teardown
@task
def delete_weaviate_class(class_name):
"""
Example task to delete a weaviate class
"""
from airflow.providers.weaviate.hooks.weaviate import WeaviateHook
weaviate_hook = WeaviateHook()
# Class definition object. Weaviate's autoschema feature will infer properties when importing.
weaviate_hook.delete_classes([class_name])
(
create_weaviate_class.expand(data=[["example1", "none"], ["example2", "text2vec-openai"]])
>> perform_ingestion
>> delete_weaviate_class.expand(class_name=["example1", "example2"])
)
example_weaviate_dynamic_mapping_dag()
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)