code: def example_weaviate_dag_using_hook():
      """Example Weaviate DAG demonstrating usage of the hook."""
  
      @task()
      def create_class_with_vectorizer():
          """
          Example task to create class with OpenAI Vectorizer responsible for vectorining data using Weaviate cluster.
          """
          from airflow.providers.weaviate.hooks.weaviate import (
              WeaviateHook,
          )
  
          weaviate_hook = WeaviateHook()
          class_obj = {
              "class": "QuestionWithOpenAIVectorizerUsingHook",
              "description": "Information from a Jeopardy! question",  # description of the class
              "properties": [
                  {
                      "dataType": ["text"],
                      "description": "The question",
                      "name": "question",
                  },
                  {
                      "dataType": ["text"],
                      "description": "The answer",
                      "name": "answer",
                  },
                  {
                      "dataType": ["text"],
                      "description": "The category",
                      "name": "category",
                  },
              ],
              "vectorizer": "text2vec-openai",
          }
          weaviate_hook.create_class(class_obj)
  
      @task()
      def create_class_without_vectorizer():
          """
          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": "QuestionWithoutVectorizerUsingHook",
              "vectorizer": "none",
          }
          weaviate_hook.create_class(class_obj)
  
      @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(
              "QuestionWithOpenAIVectorizerUsingHook", 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_class_Vector():
          """
          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(
              ["QuestionWithOpenAIVectorizerUsingHook"]
          )
  
      @teardown
      @task
      def delete_weaviate_class_without_Vector():
          """
          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(
              ["QuestionWithoutVectorizerUsingHook"]
          )
  
      data_with_vectors = store_data_with_vectors_in_xcom()
      (
          create_class_without_vectorizer()
          >> batch_data_with_vectors(data_with_vectors["return_value"])
          >> delete_weaviate_class_Vector()
      )
  
      data_without_vectors = store_data_without_vectors_in_xcom()
      (
          create_class_with_vectorizer()
          >> batch_data_without_vectors(
              data_without_vectors["return_value"]
          )
          >> delete_weaviate_class_without_Vector()
      )
  
imports: from __future__ import annotations
  import pendulum
  from airflow.decorators import dag, task, teardown
  from tests.system.utils import get_test_run
label_desc: Use image node_id 1 for calling a global function with example usage: example_weaviate_dag_using_hook() and returns: data, data