jrmccluskey commented on code in PR #35036: URL: https://github.com/apache/beam/pull/35036#discussion_r2109321204
########## sdks/python/apache_beam/ml/inference/gemini_inference.py: ########## @@ -0,0 +1,168 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +from collections.abc import Callable +from collections.abc import Iterable +from collections.abc import Sequence +from typing import Any +from typing import Optional + +from google import genai +from google.genai import errors + +from apache_beam.ml.inference import utils +from apache_beam.ml.inference.base import PredictionResult +from apache_beam.ml.inference.base import RemoteModelHandler + +LOGGER = logging.getLogger("GeminiModelHandler") + + +def _retry_on_appropriate_service_error(exception: Exception) -> bool: + """ + Retry filter that returns True if a returned HTTP error code is 5xx or 429. + This is used to retry remote requests that fail, most notably 429 + (throttling by the service) + + Args: + exception: the returned exception encountered during the request/response + loop. + + Returns: + boolean indication whether or not the exception is a ServerError (5xx) or + a 429 error. + """ + if not isinstance(exception, errors.APIError): + return False + return exception.code == 429 or exception.code >= 500 + + +def generate_from_string( + model_name: str, + batch: Sequence[str], + model: genai.Client, + inference_args: dict[str, Any]): + return model.models.generate_content( + model=model_name, contents=batch, **inference_args) + + +class GeminiModelHandler(RemoteModelHandler[Any, PredictionResult, + genai.Client]): + def __init__( + self, + model_name: str, + request_fn: Callable[[str, Sequence[Any], genai.Client, dict[str, Any]], + Any], + api_key: Optional[str] = None, + project: Optional[str] = None, + location: Optional[str] = None, + *, + min_batch_size: Optional[int] = None, + max_batch_size: Optional[int] = None, + max_batch_duration_secs: Optional[int] = None, + **kwargs): + """Implementation of the ModelHandler interface for Google Gemini. + **NOTE:** This API and its implementation are under development and + do not provide backward compatibility guarantees. + Gemini can be accessed through either the Vertex AI API or the Gemini + Developer API, and this handler chooses which to connect to based upon + the arguments provided. As written, this model handler operates solely on + string input. + + Args: + model_name: the Gemini model to send the request to + api_key: the Gemini Developer API key to use for the requests. Setting + this parameter sends requests for this job to the Gemini Developer API. + If this paramter is provided, do not set the project or location + parameters. + project: the GCP project to use for Vertex AI requests. Setting this + parameter routes requests to Vertex AI. If this paramter is provided, + location must also be provided and api_key should not be set. + location: the GCP project to use for Vertex AI requests. Setting this + parameter routes requests to Vertex AI. If this paramter is provided, + project must also be provided and api_key should not be set. + min_batch_size: optional. the minimum batch size to use when batching + inputs. + max_batch_size: optional. the maximum batch size to use when batching + inputs. + max_batch_duration_secs: optional. the maximum amount of time to buffer + a batch before emitting; used in streaming contexts. + """ + self._batching_kwargs = {} + self._env_vars = kwargs.get('env_vars', {}) + if min_batch_size is not None: + self._batching_kwargs["min_batch_size"] = min_batch_size + if max_batch_size is not None: + self._batching_kwargs["max_batch_size"] = max_batch_size + if max_batch_duration_secs is not None: + self._batching_kwargs["max_batch_duration_secs"] = max_batch_duration_secs + + self.model_name = model_name + self.request_fn = request_fn + + if api_key: + if project or location: + raise ValueError("project and location must be None if api_key is set") + self.api_key = api_key + self.use_vertex = False + else: + if project is None or location is None: + raise ValueError( + "project and location must both be provided if api_key is None") + self.project = project + self.location = location + self.use_vertex = True + + super().__init__( + namespace='GeminiModelHandler', + retry_filter=_retry_on_appropriate_service_error, + **kwargs) + + def create_client(self) -> genai.Client: + """Creates the GenAI client used to send requests. Creates a version for + the Vertex AI API or the Gemini Developer API based on the arguments + provided when the GeminiModelHandler class is instantiated. + """ + if self.use_vertex: + return genai.Client( + vertexai=True, project=self.project, location=self.location) + return genai.Client(api_key=self.api_key) + + def request( + self, + batch: Sequence[Any], + model: genai.Client, + inference_args: Optional[dict[str, Any]] = None + ) -> Iterable[PredictionResult]: + """ Sends a prediction request to a Gemini service containing a batch + of inputs and matches that input with the prediction response from + the endpoint as an iterable of PredictionResults. + + Args: + batch: a sequence of any values to be passed to the Gemini service. + Should be a list of strings. Review Comment: Ah yeah that was a missed line, I originally wrote this as string-only but decided it was better in the long-term to go ahead and make it more general. Let me update that docstring -- 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: github-unsubscr...@beam.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
