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"""This module contains Google Vertex AI Generative AI operators."""
from __future__ import annotations
from typing import TYPE_CHECKING, Sequence
from airflow.providers.google.cloud.hooks.vertex_ai.generative_model import GenerativeModelHook
from airflow.providers.google.cloud.operators.cloud_base import GoogleCloudBaseOperator
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class PromptLanguageModelOperator(GoogleCloudBaseOperator):
"""
Uses the Vertex AI PaLM API to generate natural language text.
:param project_id: Required. The ID of the Google Cloud project that the
service belongs to.
:param location: Required. The ID of the Google Cloud location that the
service belongs to.
:param prompt: Required. Inputs or queries that a user or a program gives
to the Vertex AI PaLM API, in order to elicit a specific response.
:param pretrained_model: By default uses the pre-trained model `text-bison`,
optimized for performing natural language tasks such as classification,
summarization, extraction, content creation, and ideation.
:param temperature: Temperature controls the degree of randomness in token
selection. Defaults to 0.0.
:param max_output_tokens: Token limit determines the maximum amount of text
output. Defaults to 256.
:param top_p: Tokens are selected from most probable to least until the sum
of their probabilities equals the top_p value. Defaults to 0.8.
:param top_k: A top_k of 1 means the selected token is the most probable
among all tokens. Defaults to 0.4.
:param gcp_conn_id: The connection ID to use connecting to Google Cloud.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
"""
def __init__(
self,
*,
project_id: str,
location: str,
prompt: str,
pretrained_model: str = "text-bison",
temperature: float = 0.0,
max_output_tokens: int = 256,
top_p: float = 0.8,
top_k: int = 40,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.project_id = project_id
self.location = location
self.prompt = prompt
self.pretrained_model = pretrained_model
self.temperature = temperature
self.max_output_tokens = max_output_tokens
self.top_p = top_p
self.top_k = top_k
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context):
self.hook = GenerativeModelHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
self.log.info("Submitting prompt")
response = self.hook.prompt_language_model(
project_id=self.project_id,
location=self.location,
prompt=self.prompt,
pretrained_model=self.pretrained_model,
temperature=self.temperature,
max_output_tokens=self.max_output_tokens,
top_p=self.top_p,
top_k=self.top_k,
)
self.log.info("Model response: %s", response)
self.xcom_push(context, key="prompt_response", value=response)
return response
[docs]class GenerateTextEmbeddingsOperator(GoogleCloudBaseOperator):
"""
Uses the Vertex AI PaLM API to generate natural language text.
:param project_id: Required. The ID of the Google Cloud project that the
service belongs to.
:param location: Required. The ID of the Google Cloud location that the
service belongs to.
:param prompt: Required. Inputs or queries that a user or a program gives
to the Vertex AI PaLM API, in order to elicit a specific response.
:param pretrained_model: By default uses the pre-trained model `textembedding-gecko`,
optimized for performing text embeddings.
:param gcp_conn_id: The connection ID to use connecting to Google Cloud.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
"""
def __init__(
self,
*,
project_id: str,
location: str,
prompt: str,
pretrained_model: str = "textembedding-gecko",
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.project_id = project_id
self.location = location
self.prompt = prompt
self.pretrained_model = pretrained_model
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context):
self.hook = GenerativeModelHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
self.log.info("Generating text embeddings")
response = self.hook.generate_text_embeddings(
project_id=self.project_id,
location=self.location,
prompt=self.prompt,
pretrained_model=self.pretrained_model,
)
self.log.info("Model response: %s", response)
self.xcom_push(context, key="prompt_response", value=response)
return response
[docs]class PromptMultimodalModelOperator(GoogleCloudBaseOperator):
"""
Use the Vertex AI Gemini Pro foundation model to generate natural language text.
:param project_id: Required. The ID of the Google Cloud project that the
service belongs to.
:param location: Required. The ID of the Google Cloud location that the
service belongs to.
:param prompt: Required. Inputs or queries that a user or a program gives
to the Multi-modal model, in order to elicit a specific response.
:param pretrained_model: By default uses the pre-trained model `gemini-pro`,
supporting prompts with text-only input, including natural language
tasks, multi-turn text and code chat, and code generation. It can
output text and code.
:param gcp_conn_id: The connection ID to use connecting to Google Cloud.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
"""
def __init__(
self,
*,
project_id: str,
location: str,
prompt: str,
pretrained_model: str = "gemini-pro",
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.project_id = project_id
self.location = location
self.prompt = prompt
self.pretrained_model = pretrained_model
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context):
self.hook = GenerativeModelHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
response = self.hook.prompt_multimodal_model(
project_id=self.project_id,
location=self.location,
prompt=self.prompt,
pretrained_model=self.pretrained_model,
)
self.log.info("Model response: %s", response)
self.xcom_push(context, key="prompt_response", value=response)
return response