Source code for tests.system.weaviate.example_weaviate_openai
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from __future__ import annotations
import json
from pathlib import Path
import pendulum
from airflow.decorators import dag, setup, task, teardown
from airflow.providers.openai.operators.openai import OpenAIEmbeddingOperator
from airflow.providers.weaviate.hooks.weaviate import WeaviateHook
from airflow.providers.weaviate.operators.weaviate import WeaviateIngestOperator
[docs]COLLECTION_NAME = "Weaviate_openai_example_collection"
@dag(
schedule=None,
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
catchup=False,
tags=["example", "weaviate"],
)
[docs]def example_weaviate_openai():
"""
Example DAG which creates embeddings using OpenAIEmbeddingOperator and the uses WeaviateIngestOperator to insert embeddings to Weaviate .
"""
@setup
@task
def create_weaviate_collection():
"""
Example task to create collection without any Vectorizer. You're expected to provide custom vectors for your data.
"""
weaviate_hook = WeaviateHook()
# collection definition object. Weaviate's autoschema feature will infer properties when importing.
weaviate_hook.create_collection(COLLECTION_NAME)
@setup
@task
def get_data_to_embed():
data = json.load(Path("jeopardy_data_without_vectors.json").open())
return [item["Question"] for item in data]
data_to_embed = get_data_to_embed()
embed_data = OpenAIEmbeddingOperator.partial(
task_id="embedding_using_xcom_data",
conn_id="openai_default",
model="text-embedding-ada-002",
).expand(input_text=data_to_embed["return_value"])
@task
def update_vector_data_in_json(**kwargs):
ti = kwargs["ti"]
data = json.load(Path("jeopardy_data_without_vectors.json").open())
embedded_data = ti.xcom_pull(task_ids="embedding_using_xcom_data", key="return_value")
for i, vector in enumerate(embedded_data):
data[i]["Vector"] = vector
return data
update_vector_data_in_json = update_vector_data_in_json()
perform_ingestion = WeaviateIngestOperator(
task_id="perform_ingestion",
conn_id="weaviate_default",
collection_name=COLLECTION_NAME,
input_data=update_vector_data_in_json["return_value"],
)
embed_query = OpenAIEmbeddingOperator(
task_id="embed_query",
conn_id="openai_default",
input_text="biology",
model="text-embedding-ada-002",
)
@task
def query_weaviate(**kwargs):
ti = kwargs["ti"]
query_vector = ti.xcom_pull(task_ids="embed_query", key="return_value")
weaviate_hook = WeaviateHook()
properties = ["question", "answer", "category"]
response = weaviate_hook.query_with_vector(query_vector, COLLECTION_NAME, 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
"""
weaviate_hook = WeaviateHook()
# collection definition object. Weaviate's autoschema feature will infer properties when importing.
weaviate_hook.delete_collections([COLLECTION_NAME])
(
create_weaviate_collection()
>> embed_data
>> update_vector_data_in_json
>> perform_ingestion
>> embed_query
>> query_weaviate()
>> delete_weaviate_collection()
)
example_weaviate_openai()
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)