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# to you under the Apache License, Version 2.0 (the
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"""This module contains Google Vertex AI Generative AI operators."""
from __future__ import annotations
from collections.abc import Sequence
from typing import TYPE_CHECKING
from airflow.exceptions import AirflowProviderDeprecationWarning
from airflow.providers.google.cloud.hooks.vertex_ai.generative_model import GenerativeModelHook
from airflow.providers.google.cloud.operators.cloud_base import GoogleCloudBaseOperator
from airflow.providers.google.common.deprecated import deprecated
if TYPE_CHECKING:
from airflow.utils.context import Context
@deprecated(
planned_removal_date="January 01, 2025",
use_instead="TextGenerationModelPredictOperator",
category=AirflowProviderDeprecationWarning,
)
[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 (templated).
:param location: Required. The ID of the Google Cloud location that the
service belongs to (templated).
: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 (templated).
: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).
"""
[docs] template_fields = ("location", "project_id", "impersonation_chain", "prompt")
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
@deprecated(
planned_removal_date="January 01, 2025",
use_instead="TextEmbeddingModelGetEmbeddingsOperator",
category=AirflowProviderDeprecationWarning,
)
[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 (templated).
:param location: Required. The ID of the Google Cloud location that the
service belongs to (templated).
: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 (templated).
: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).
"""
[docs] template_fields = ("location", "project_id", "impersonation_chain", "prompt")
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
@deprecated(
planned_removal_date="January 01, 2025",
use_instead="GenerativeModelGenerateContentOperator",
category=AirflowProviderDeprecationWarning,
)
[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 (templated).
:param location: Required. The ID of the Google Cloud location that the
service belongs to (templated).
: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 (templated).
:param generation_config: Optional. Generation configuration settings.
:param safety_settings: Optional. Per request settings for blocking unsafe content.
: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).
"""
[docs] template_fields = ("location", "project_id", "impersonation_chain", "prompt")
def __init__(
self,
*,
project_id: str,
location: str,
prompt: str,
generation_config: dict | None = None,
safety_settings: dict | None = None,
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.generation_config = generation_config
self.safety_settings = safety_settings
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,
generation_config=self.generation_config,
safety_settings=self.safety_settings,
pretrained_model=self.pretrained_model,
)
self.log.info("Model response: %s", response)
self.xcom_push(context, key="prompt_response", value=response)
return response
@deprecated(
planned_removal_date="January 01, 2025",
use_instead="GenerativeModelGenerateContentOperator",
category=AirflowProviderDeprecationWarning,
)
[docs]class TextGenerationModelPredictOperator(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 (templated).
:param location: Required. The ID of the Google Cloud location that the
service belongs to (templated).
: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 (templated).
: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).
"""
[docs] template_fields = ("location", "project_id", "impersonation_chain", "prompt")
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.text_generation_model_predict(
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="model_response", value=response)
return response
[docs]class TextEmbeddingModelGetEmbeddingsOperator(GoogleCloudBaseOperator):
"""
Uses the Vertex AI Embeddings API to generate embeddings based on prompt.
:param project_id: Required. The ID of the Google Cloud project that the
service belongs to (templated).
:param location: Required. The ID of the Google Cloud location that the
service belongs to (templated).
: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 (templated).
: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).
"""
[docs] template_fields = ("location", "project_id", "impersonation_chain", "prompt")
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.text_embedding_model_get_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="model_response", value=response)
return response
[docs]class GenerativeModelGenerateContentOperator(GoogleCloudBaseOperator):
"""
Use the Vertex AI Gemini Pro foundation model to generate content.
:param project_id: Required. The ID of the Google Cloud project that the
service belongs to (templated).
:param location: Required. The ID of the Google Cloud location that the
service belongs to (templated).
:param contents: Required. The multi-part content of a message that a user or a program
gives to the generative model, in order to elicit a specific response.
:param generation_config: Optional. Generation configuration settings.
:param safety_settings: Optional. Per request settings for blocking unsafe content.
:param tools: Optional. A list of tools available to the model during evaluation, such as a data store.
:param system_instruction: Optional. An instruction given to the model to guide its behavior.
: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).
"""
[docs] template_fields = ("location", "project_id", "impersonation_chain", "contents", "pretrained_model")
def __init__(
self,
*,
project_id: str,
location: str,
contents: list,
tools: list | None = None,
generation_config: dict | None = None,
safety_settings: dict | None = None,
system_instruction: str | None = None,
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.contents = contents
self.tools = tools
self.generation_config = generation_config
self.safety_settings = safety_settings
self.system_instruction = system_instruction
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.generative_model_generate_content(
project_id=self.project_id,
location=self.location,
contents=self.contents,
tools=self.tools,
generation_config=self.generation_config,
safety_settings=self.safety_settings,
system_instruction=self.system_instruction,
pretrained_model=self.pretrained_model,
)
self.log.info("Model response: %s", response)
self.xcom_push(context, key="model_response", value=response)
return response
[docs]class SupervisedFineTuningTrainOperator(GoogleCloudBaseOperator):
"""
Use the Supervised Fine Tuning API to create a tuning job.
: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 source_model: Required. A pre-trained model optimized for performing natural
language tasks such as classification, summarization, extraction, content
creation, and ideation.
:param train_dataset: Required. Cloud Storage URI of your training dataset. The dataset
must be formatted as a JSONL file. For best results, provide at least 100 to 500 examples.
:param tuned_model_display_name: Optional. Display name of the TunedModel. The name can be up
to 128 characters long and can consist of any UTF-8 characters.
:param validation_dataset: Optional. Cloud Storage URI of your training dataset. The dataset must be
formatted as a JSONL file. For best results, provide at least 100 to 500 examples.
:param epochs: Optional. To optimize performance on a specific dataset, try using a higher
epoch value. Increasing the number of epochs might improve results. However, be cautious
about over-fitting, especially when dealing with small datasets. If over-fitting occurs,
consider lowering the epoch number.
:param adapter_size: Optional. Adapter size for tuning.
:param learning_multiplier_rate: Optional. Multiplier for adjusting the default learning rate.
: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).
"""
[docs] template_fields = ("location", "project_id", "impersonation_chain", "train_dataset", "validation_dataset")
def __init__(
self,
*,
project_id: str,
location: str,
source_model: str,
train_dataset: str,
tuned_model_display_name: str | None = None,
validation_dataset: str | None = None,
epochs: int | None = None,
adapter_size: int | None = None,
learning_rate_multiplier: float | None = None,
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.source_model = source_model
self.train_dataset = train_dataset
self.tuned_model_display_name = tuned_model_display_name
self.validation_dataset = validation_dataset
self.epochs = epochs
self.adapter_size = adapter_size
self.learning_rate_multiplier = learning_rate_multiplier
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.supervised_fine_tuning_train(
project_id=self.project_id,
location=self.location,
source_model=self.source_model,
train_dataset=self.train_dataset,
validation_dataset=self.validation_dataset,
epochs=self.epochs,
adapter_size=self.adapter_size,
learning_rate_multiplier=self.learning_rate_multiplier,
tuned_model_display_name=self.tuned_model_display_name,
)
self.log.info("Tuned Model Name: %s", response.tuned_model_name)
self.log.info("Tuned Model Endpoint Name: %s", response.tuned_model_endpoint_name)
self.xcom_push(context, key="tuned_model_name", value=response.tuned_model_name)
self.xcom_push(context, key="tuned_model_endpoint_name", value=response.tuned_model_endpoint_name)
result = {
"tuned_model_name": response.tuned_model_name,
"tuned_model_endpoint_name": response.tuned_model_endpoint_name,
}
return result
[docs]class CountTokensOperator(GoogleCloudBaseOperator):
"""
Use the Vertex AI Count Tokens API to calculate the number of input tokens before sending a request to the Gemini API.
:param project_id: Required. The ID of the Google Cloud project that the
service belongs to (templated).
:param location: Required. The ID of the Google Cloud location that the
service belongs to (templated).
:param contents: Required. The multi-part content of a message that a user or a program
gives to the generative 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).
"""
[docs] template_fields = ("location", "project_id", "impersonation_chain", "contents", "pretrained_model")
def __init__(
self,
*,
project_id: str,
location: str,
contents: list,
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.contents = contents
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.count_tokens(
project_id=self.project_id,
location=self.location,
contents=self.contents,
pretrained_model=self.pretrained_model,
)
self.log.info("Total tokens: %s", response.total_tokens)
self.log.info("Total billable characters: %s", response.total_billable_characters)
self.xcom_push(context, key="total_tokens", value=response.total_tokens)
self.xcom_push(context, key="total_billable_characters", value=response.total_billable_characters)
[docs]class RunEvaluationOperator(GoogleCloudBaseOperator):
"""
Use the Rapid Evaluation API to evaluate a model.
: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 pretrained_model: Required. A pre-trained model optimized for performing natural
language tasks such as classification, summarization, extraction, content
creation, and ideation.
:param eval_dataset: Required. A fixed dataset for evaluating a model against. Adheres to Rapid Evaluation API.
:param metrics: Required. A list of evaluation metrics to be used in the experiment. Adheres to Rapid Evaluation API.
:param experiment_name: Required. The name of the evaluation experiment.
:param experiment_run_name: Required. The specific run name or ID for this experiment.
:param prompt_template: Required. The template used to format the model's prompts during evaluation. Adheres to Rapid Evaluation API.
:param generation_config: Optional. A dictionary containing generation parameters for the model.
:param safety_settings: Optional. A dictionary specifying harm category thresholds for blocking model outputs.
:param system_instruction: Optional. An instruction given to the model to guide its behavior.
:param tools: Optional. A list of tools available to the model during evaluation, such as a data store.
: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).
"""
[docs] template_fields = (
"location",
"project_id",
"impersonation_chain",
"pretrained_model",
"eval_dataset",
"prompt_template",
"experiment_name",
"experiment_run_name",
)
def __init__(
self,
*,
project_id: str,
location: str,
pretrained_model: str,
eval_dataset: dict,
metrics: list,
experiment_name: str,
experiment_run_name: str,
prompt_template: str,
generation_config: dict | None = None,
safety_settings: dict | None = None,
system_instruction: str | None = None,
tools: list | None = None,
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.pretrained_model = pretrained_model
self.eval_dataset = eval_dataset
self.metrics = metrics
self.experiment_name = experiment_name
self.experiment_run_name = experiment_run_name
self.prompt_template = prompt_template
self.generation_config = generation_config
self.safety_settings = safety_settings
self.system_instruction = system_instruction
self.tools = tools
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.run_evaluation(
project_id=self.project_id,
location=self.location,
pretrained_model=self.pretrained_model,
eval_dataset=self.eval_dataset,
metrics=self.metrics,
experiment_name=self.experiment_name,
experiment_run_name=self.experiment_run_name,
prompt_template=self.prompt_template,
generation_config=self.generation_config,
safety_settings=self.safety_settings,
system_instruction=self.system_instruction,
tools=self.tools,
)
return response.summary_metrics
[docs]class CreateCachedContentOperator(GoogleCloudBaseOperator):
"""
Create CachedContent to reduce the cost of requests that contain repeat content with high input token counts.
: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 model_name: Required. The name of the publisher model to use for cached content.
:param system_instruction: Developer set system instruction.
:param contents: The content to cache.
:param ttl_hours: The TTL for this resource in hours. The expiration time is computed: now + TTL.
Defaults to one hour.
:param display_name: The user-generated meaningful display name of the cached content
: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).
"""
[docs] template_fields = (
"location",
"project_id",
"impersonation_chain",
"model_name",
"contents",
"system_instruction",
)
def __init__(
self,
*,
project_id: str,
location: str,
model_name: str,
system_instruction: str | None = None,
contents: list | None = None,
ttl_hours: float = 1,
display_name: str | None = None,
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.model_name = model_name
self.system_instruction = system_instruction
self.contents = contents
self.ttl_hours = ttl_hours
self.display_name = display_name
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,
)
cached_content_name = self.hook.create_cached_content(
project_id=self.project_id,
location=self.location,
model_name=self.model_name,
system_instruction=self.system_instruction,
contents=self.contents,
ttl_hours=self.ttl_hours,
display_name=self.display_name,
)
self.log.info("Cached Content Name: %s", cached_content_name)
return cached_content_name
[docs]class GenerateFromCachedContentOperator(GoogleCloudBaseOperator):
"""
Generate a response from CachedContent.
: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 cached_content_name: Required. The name of the cached content resource.
:param contents: Required. The multi-part content of a message that a user or a program
gives to the generative model, in order to elicit a specific response.
:param generation_config: Optional. Generation configuration settings.
:param safety_settings: Optional. Per request settings for blocking unsafe content.
: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).
"""
[docs] template_fields = (
"location",
"project_id",
"impersonation_chain",
"cached_content_name",
"contents",
)
def __init__(
self,
*,
project_id: str,
location: str,
cached_content_name: str,
contents: list,
generation_config: dict | None = None,
safety_settings: dict | None = None,
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.cached_content_name = cached_content_name
self.contents = contents
self.generation_config = generation_config
self.safety_settings = safety_settings
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,
)
cached_content_text = self.hook.generate_from_cached_content(
project_id=self.project_id,
location=self.location,
cached_content_name=self.cached_content_name,
contents=self.contents,
generation_config=self.generation_config,
safety_settings=self.safety_settings,
)
self.log.info("Cached Content Response: %s", cached_content_text)
return cached_content_text