text: texts=texts,    project_id="<my_project_id>",    region="<my_region>",    gcs_bucket_uri="<my_gcs_bucket>",    index_id="<my_matching_engine_index_id>",    endpoint_id="<my_matching_engine_endpoint_id>",)vector_store.add_texts(texts=texts)vector_store.similarity_search("lunch", k=2)Create Index and deploy it to an Endpointâ€‹Imports, Constants and Configsâ€‹# Installing dependencies.pip install tensorflow \            google-cloud-aiplatform \            tensorflow-hub \            tensorflow-textimport osimport jsonfrom google.cloud import aiplatformimport tensorflow_hub as hubimport tensorflow_textPROJECT_ID = "<my_project_id>"REGION = "<my_region>"VPC_NETWORK = "<my_vpc_network_name>"PEERING_RANGE_NAME = "ann-langchain-me-range"  # Name for creating the VPC peering.BUCKET_URI = "gs://<bucket_uri>"# The number of dimensions for the tensorflow universal sentence encoder.# If other embedder is used, the dimensions would probably need to change.DIMENSIONS = 512DISPLAY_NAME = "index-test-name"EMBEDDING_DIR = f"{BUCKET_URI}/banana"DEPLOYED_INDEX_ID = "endpoint-test-name"PROJECT_NUMBER = !gcloud projects list --filter="PROJECT_ID:'{PROJECT_ID}'" --format='value(PROJECT_NUMBER)'PROJECT_NUMBER = PROJECT_NUMBER[0]VPC_NETWORK_FULL = f"projects/{PROJECT_NUMBER}/global/networks/{VPC_NETWORK}"# Change this if you need the VPC to be created.CREATE_VPC = False# Set the project id gcloud config set project {PROJECT_ID}# Remove the if condition to run the encapsulated codeif