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# 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
from datetime import timedelta
import pendulum
from weaviate.classes.config import Configure, DataType, Property
try:
from airflow.sdk import dag, task, teardown
except ImportError:
# Airflow 2 path
from airflow.decorators import dag, task, teardown # type: ignore[attr-defined,no-redef]
from airflow.providers.weaviate.operators.weaviate import (
WeaviateDocumentIngestOperator,
WeaviateIngestOperator,
)
COLLECTION_NAME = "QuestionWithoutVectorizerUsingOperator"
sample_data_with_vector = [
{
"Answer": "Liver",
"Category": "SCIENCE",
"Question": "This organ removes excess glucose from the blood & stores it as glycogen",
"Vector": [
-0.006632288,
-0.0042016874,
0.030541966,
],
},
{
"Answer": "Elephant",
"Category": "ANIMALS",
"Question": "It's the only living mammal in the order Proboseidea",
"Vector": [
-0.0166891,
-0.00092290324,
-0.0125168245,
],
},
{
"Answer": "the nose or snout",
"Category": "ANIMALS",
"Question": "The gavial looks very much like a crocodile except for this bodily feature",
"Vector": [
-0.015592773,
0.019883318,
0.017782344,
],
},
]
sample_data_without_vector = [
{
"Answer": "Liver",
"Category": "SCIENCE",
"Question": "This organ removes excess glucose from the blood & stores it as glycogen",
},
{
"Answer": "Elephant",
"Category": "ANIMALS",
"Question": "It's the only living mammal in the order Proboseidea",
},
{
"Answer": "the nose or snout",
"Category": "ANIMALS",
"Question": "The gavial looks very much like a crocodile except for this bodily feature",
},
]
def get_data_with_vectors(*args, **kwargs):
return sample_data_with_vector
def get_data_without_vectors(*args, **kwargs):
return sample_data_without_vector
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_using_operator():
"""
Example Weaviate DAG demonstrating usage of the operator.
"""
# Example tasks to create a Weaviate collection without vectorizers, store data with custom vectors in XCOM, and call
# WeaviateIngestOperator to ingest data with those custom vectors.
@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(COLLECTION_NAME, vector_config=Configure.Vectors.self_provided())
@task(trigger_rule="all_done")
def store_data_with_vectors_in_xcom():
return sample_data_with_vector
# [START howto_operator_weaviate_embedding_and_ingest_xcom_data_with_vectors]
batch_data_with_vectors_xcom_data = WeaviateIngestOperator(
task_id="batch_data_with_vectors_xcom_data",
conn_id="weaviate_default",
collection_name=COLLECTION_NAME,
input_data=store_data_with_vectors_in_xcom(),
trigger_rule="all_done",
)
# [END howto_operator_weaviate_embedding_and_ingest_xcom_data_with_vectors]
# [START howto_operator_weaviate_embedding_and_ingest_callable_data_with_vectors]
batch_data_with_vectors_callable_data = WeaviateIngestOperator(
task_id="batch_data_with_vectors_callable_data",
conn_id="weaviate_default",
collection_name=COLLECTION_NAME,
input_data=get_data_with_vectors(),
trigger_rule="all_done",
)
# [END howto_operator_weaviate_embedding_and_ingest_callable_data_with_vectors]
# Example tasks to create collection with OpenAI vectorizer, store data without vectors in XCOM, and call
# WeaviateIngestOperator to ingest data by internally generating OpenAI vectors while ingesting.
@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(
"QuestionWithOpenAIVectorizerUsingOperator",
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),
],
vector_config=Configure.Vectors.text2vec_openai(),
)
@task()
def create_collection_for_doc_data_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(
"QuestionWithOpenAIVectorizerUsingOperatorDocs",
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),
Property(name="docLink", description="URL for source document", data_type=DataType.TEXT),
],
vector_config=Configure.Vectors.text2vec_openai(),
)
@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_doc_data_without_vectors_in_xcom():
import json
from pathlib import Path
data = json.load(Path("jeopardy_doc_data_without_vectors.json").open())
return data
xcom_data_without_vectors = store_data_without_vectors_in_xcom()
xcom_doc_data_without_vectors = store_doc_data_without_vectors_in_xcom()
# [START howto_operator_weaviate_ingest_xcom_data_without_vectors]
batch_data_without_vectors_xcom_data = WeaviateIngestOperator(
task_id="batch_data_without_vectors_xcom_data",
conn_id="weaviate_default",
collection_name="QuestionWithOpenAIVectorizerUsingOperator",
input_data=xcom_data_without_vectors["return_value"],
trigger_rule="all_done",
)
# [END howto_operator_weaviate_ingest_xcom_data_without_vectors]
# [START howto_operator_weaviate_ingest_callable_data_without_vectors]
batch_data_without_vectors_callable_data = WeaviateIngestOperator(
task_id="batch_data_without_vectors_callable_data",
conn_id="weaviate_default",
collection_name="QuestionWithOpenAIVectorizerUsingOperator",
input_data=get_data_without_vectors(),
trigger_rule="all_done",
)
# [END howto_operator_weaviate_ingest_callable_data_without_vectors]
create_or_replace_document_objects_without_vectors = WeaviateDocumentIngestOperator(
task_id="create_or_replace_document_objects_without_vectors_xcom_data",
existing="replace",
document_column="docLink",
conn_id="weaviate_default",
collection_name="QuestionWithOpenAIVectorizerUsingOperatorDocs",
input_data=xcom_doc_data_without_vectors["return_value"],
trigger_rule="all_done",
)
@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(
[
"QuestionWithOpenAIVectorizerUsingOperator",
]
)
@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(
[
COLLECTION_NAME,
]
)
@teardown
@task
def delete_weaviate_docs_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(["QuestionWithOpenAIVectorizerUsingOperatorDocs"])
(
create_collection_without_vectorizer()
>> [batch_data_with_vectors_xcom_data, batch_data_with_vectors_callable_data]
>> delete_weaviate_collection_without_vector()
)
(
create_collection_for_doc_data_with_vectorizer()
>> [create_or_replace_document_objects_without_vectors]
>> delete_weaviate_docs_collection_without_vector()
)
(
create_collection_with_vectorizer()
>> [
batch_data_without_vectors_xcom_data,
batch_data_without_vectors_callable_data,
]
>> delete_weaviate_collection_vector()
)
example_weaviate_using_operator()
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)