CYarros10 commented on code in PR #43008:
URL: https://github.com/apache/airflow/pull/43008#discussion_r1801154190
##########
providers/src/airflow/providers/google/cloud/operators/vertex_ai/generative_model.py:
##########
@@ -842,3 +838,155 @@ def execute(self, context: Context):
)
return response.summary_metrics
+
+
+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).
+ """
+
+ 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
+
+ def execute(self, context: Context):
+ self.hook = GenerativeModelHook(
+ gcp_conn_id=self.gcp_conn_id,
+ impersonation_chain=self.impersonation_chain,
+ )
+
+ response = 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", response)
+
+ return response
Review Comment:
Agreed!
##########
providers/src/airflow/providers/google/cloud/operators/vertex_ai/generative_model.py:
##########
@@ -842,3 +838,155 @@ def execute(self, context: Context):
)
return response.summary_metrics
+
+
+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).
+ """
+
+ 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
+
+ def execute(self, context: Context):
+ self.hook = GenerativeModelHook(
+ gcp_conn_id=self.gcp_conn_id,
+ impersonation_chain=self.impersonation_chain,
+ )
+
+ response = 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", response)
+
+ return response
+
+
+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).
+ """
+
+ 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
+
+ def execute(self, context: Context):
+ self.hook = GenerativeModelHook(
+ gcp_conn_id=self.gcp_conn_id,
+ impersonation_chain=self.impersonation_chain,
+ )
+ response = self.hook.generate_from_cached_content(
Review Comment:
Agreed!
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: commits-unsubscr...@airflow.apache.org
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org
