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"""This module contains a Google Cloud Vertex AI Generative Model hook."""
from __future__ import annotations
from typing import Any
import vertexai
from vertexai.generative_models import GenerativeModel
from vertexai.language_models import TextEmbeddingModel
from vertexai.preview import generative_models as preview_generative_model
from vertexai.preview.caching import CachedContent
from vertexai.preview.evaluation import EvalResult, EvalTask
from airflow.providers.google.common.hooks.base_google import PROVIDE_PROJECT_ID, GoogleBaseHook
[docs]
class GenerativeModelHook(GoogleBaseHook):
"""Hook for Google Cloud Vertex AI Generative Model APIs."""
[docs]
def get_text_embedding_model(self, pretrained_model: str):
"""Return a Model Garden Model object based on Text Embedding."""
model = TextEmbeddingModel.from_pretrained(pretrained_model)
return model
[docs]
def get_generative_model(
self,
pretrained_model: str,
system_instruction: Any | None = None,
generation_config: dict | None = None,
safety_settings: dict | None = None,
tools: list | None = None,
) -> GenerativeModel:
"""Return a Generative Model object."""
model = GenerativeModel(
model_name=pretrained_model,
system_instruction=system_instruction,
generation_config=generation_config,
safety_settings=safety_settings,
tools=tools,
)
return model
[docs]
def get_eval_task(
self,
dataset: dict,
metrics: list,
experiment: str,
) -> EvalTask:
"""Return an EvalTask object."""
eval_task = EvalTask(
dataset=dataset,
metrics=metrics,
experiment=experiment,
)
return eval_task
[docs]
def get_cached_context_model(
self,
cached_content_name: str,
) -> Any:
"""Return a Generative Model with Cached Context."""
cached_content = CachedContent(cached_content_name=cached_content_name)
cached_context_model = preview_generative_model.GenerativeModel.from_cached_content(cached_content)
return cached_context_model
@GoogleBaseHook.fallback_to_default_project_id
[docs]
def run_evaluation(
self,
pretrained_model: str,
eval_dataset: dict,
metrics: list,
experiment_name: str,
experiment_run_name: str,
prompt_template: str,
location: str,
generation_config: dict | None = None,
safety_settings: dict | None = None,
system_instruction: str | None = None,
tools: list | None = None,
project_id: str = PROVIDE_PROJECT_ID,
) -> EvalResult:
"""
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.
"""
vertexai.init(project=project_id, location=location, credentials=self.get_credentials())
model = self.get_generative_model(
pretrained_model=pretrained_model,
system_instruction=system_instruction,
generation_config=generation_config,
safety_settings=safety_settings,
tools=tools,
)
eval_task = self.get_eval_task(
dataset=eval_dataset,
metrics=metrics,
experiment=experiment_name,
)
eval_result = eval_task.evaluate(
model=model,
prompt_template=prompt_template,
experiment_run_name=experiment_run_name,
)
return eval_result