gemini-code-assist[bot] commented on code in PR #37917: URL: https://github.com/apache/beam/pull/37917#discussion_r2984171345
########## sdks/python/apache_beam/ml/inference/agent_development_kit.py: ########## @@ -0,0 +1,291 @@ +# +# 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. +# + +"""ModelHandler for running agents built with the Google Agent Development Kit. + +This module provides :class:`ADKAgentModelHandler`, a Beam +:class:`~apache_beam.ml.inference.base.ModelHandler` that wraps an ADK +:class:`google.adk.agents.llm_agent.LlmAgent` so it can be used with the +:class:`~apache_beam.ml.inference.base.RunInference` transform. + +Typical usage:: + + import apache_beam as beam + from apache_beam.ml.inference.base import RunInference + from apache_beam.ml.inference.agent_development_kit import ADKAgentModelHandler + from google.adk.agents import LlmAgent + + agent = LlmAgent( + name="my_agent", + model="gemini-2.0-flash", + instruction="You are a helpful assistant.", + ) + + with beam.Pipeline() as p: + results = ( + p + | beam.Create(["What is the capital of France?"]) + | RunInference(ADKAgentModelHandler(agent=agent)) + ) + +If your agent contains state that is not picklable (e.g. tool closures that +capture unpicklable objects), pass a zero-arg factory callable instead:: + + handler = ADKAgentModelHandler(agent=lambda: LlmAgent(...)) + +""" + +import asyncio +import logging +import uuid +from collections.abc import Callable +from collections.abc import Iterable +from collections.abc import Sequence +from typing import Any +from typing import Optional +from typing import Union + +from apache_beam.ml.inference.base import ModelHandler +from apache_beam.ml.inference.base import PredictionResult + +try: + from google.adk.agents import Agent + from google.adk.runners import Runner + from google.adk.sessions import BaseSessionService + from google.adk.sessions import InMemorySessionService + from google.genai.types import Content as genai_Content + from google.genai.types import Part as genai_Part + ADK_AVAILABLE = True +except ImportError: + ADK_AVAILABLE = False + genai_Content = Any # type: ignore[assignment, misc] + genai_Part = Any # type: ignore[assignment, misc] + +LOGGER = logging.getLogger("ADKAgentModelHandler") + +# Type alias for an agent or factory that produces one +_AgentOrFactory = Union["Agent", Callable[[], "Agent"]] + + +class ADKAgentModelHandler(ModelHandler[Union[str, genai_Content], + PredictionResult, + "Runner"]): + """ModelHandler for running ADK agents with the Beam RunInference transform. + + Accepts either a fully constructed :class:`google.adk.agents.Agent` or a + zero-arg factory callable that produces one. The factory form is useful when + the agent contains state that is not picklable and therefore cannot be + serialized alongside the pipeline graph. + + Each call to :meth:`run_inference` invokes the agent once per element in the + batch. By default every invocation uses a fresh, isolated session (stateless). + Stateful multi-turn conversations can be achieved by passing a ``session_id`` + key inside ``inference_args``; elements sharing the same ``session_id`` will + continue the same conversation history. + + Args: + agent: A pre-constructed :class:`~google.adk.agents.Agent` instance, or a + zero-arg callable that returns one. The callable form defers agent + construction to worker ``load_model`` time, which is useful when the + agent cannot be serialized. + app_name: The ADK application name used to namespace sessions. Defaults to + ``"beam_inference"``. + session_service_factory: Optional zero-arg callable returning a + :class:`~google.adk.sessions.BaseSessionService`. When ``None``, an + :class:`~google.adk.sessions.InMemorySessionService` is created + automatically. + min_batch_size: Optional minimum batch size. + max_batch_size: Optional maximum batch size. + max_batch_duration_secs: Optional maximum time to buffer a batch before + emitting; used in streaming contexts. + max_batch_weight: Optional maximum total weight of a batch. + element_size_fn: Optional function that returns the size (weight) of an + element. + """ + def __init__( + self, + agent: _AgentOrFactory, + app_name: str = "beam_inference", + session_service_factory: Optional[Callable[[], + "BaseSessionService"]] = None, + *, + min_batch_size: Optional[int] = None, + max_batch_size: Optional[int] = None, + max_batch_duration_secs: Optional[int] = None, + max_batch_weight: Optional[int] = None, + element_size_fn: Optional[Callable[[Any], int]] = None, + **kwargs): + if not ADK_AVAILABLE: + raise ImportError( + "google-adk is required to use ADKAgentModelHandler. " + "Install it with: pip install google-adk") + + if agent is None: + raise ValueError("'agent' must be an Agent instance or a callable.") + + self._agent_or_factory = agent + self._app_name = app_name + self._session_service_factory = session_service_factory + + super().__init__( + min_batch_size=min_batch_size, + max_batch_size=max_batch_size, + max_batch_duration_secs=max_batch_duration_secs, + max_batch_weight=max_batch_weight, + element_size_fn=element_size_fn, + **kwargs) + + def load_model(self) -> "Runner": + """Instantiates the ADK Runner on the worker. + + Resolves the agent (calling the factory if a callable was provided), then + creates a :class:`~google.adk.runners.Runner` backed by the configured + session service. + + Returns: + A fully initialised :class:`~google.adk.runners.Runner`. + """ + if callable(self._agent_or_factory) and not isinstance( + self._agent_or_factory, Agent): + agent = self._agent_or_factory() + else: + agent = self._agent_or_factory + + if self._session_service_factory is not None: + session_service = self._session_service_factory() + else: + session_service = InMemorySessionService() + + runner = Runner( + agent=agent, + app_name=self._app_name, + session_service=session_service, + ) + LOGGER.info( + "Loaded ADK Runner for agent '%s' (app_name='%s')", + agent.name, + self._app_name, + ) + return runner + + def run_inference( + self, + batch: Sequence[Union[str, genai_Content]], + model: "Runner", + inference_args: Optional[dict[str, Any]] = None, + ) -> Iterable[PredictionResult]: + """Runs the ADK agent on each element in the batch. + + Each element is sent to the agent as a new user turn. The final response + text from the agent is returned as the ``inference`` field of a + :class:`~apache_beam.ml.inference.base.PredictionResult`. + + Args: + batch: A sequence of inputs, each of which is either a ``str`` (the user + message text) or a :class:`google.genai.types.Content` object (for + richer multi-part messages). + model: The :class:`~google.adk.runners.Runner` returned by + :meth:`load_model`. + inference_args: Optional dict of extra arguments. Supported keys: + + - ``"session_id"`` (:class:`str`): If supplied, all elements in this + batch share this session ID, enabling stateful multi-turn + conversations. If omitted, each element receives a unique auto- + generated session ID. + - ``"user_id"`` (:class:`str`): The user identifier to pass to the + runner. Defaults to ``"beam_user"``. + + Returns: + An iterable of :class:`~apache_beam.ml.inference.base.PredictionResult`, + one per input element. + """ + if inference_args is None: + inference_args = {} + + user_id: str = inference_args.get("user_id", "beam_user") + agent_invocations = [] + elements_with_sessions = [] + + for element in batch: + session_id: str = inference_args.get("session_id", str(uuid.uuid4())) + + # Ensure a session exists for this invocation + model.session_service.create_session( + app_name=self._app_name, + user_id=user_id, + session_id=session_id, + ) Review Comment:  `model.session_service.create_session` raises a `SessionExistsError` if a session with the given ID already exists. When a `session_id` is provided via `inference_args`, it's shared across all elements in the batch. The current implementation calls `create_session` for each element, which will cause a `SessionExistsError` for the second element onwards, crashing the pipeline. To fix this, you should wrap the call in a `try...except SessionExistsError` block. This will gracefully handle cases where the session already exists, which is expected for stateful conversations using a shared session ID. You will also need to import `SessionExistsError` at the top of the file within the `try...except ImportError` block: ```python from google.adk.exceptions import SessionExistsError ``` ```suggestion try: model.session_service.create_session( app_name=self._app_name, user_id=user_id, session_id=session_id, ) except SessionExistsError: # It's okay if the session already exists for shared session IDs. pass ``` ########## sdks/python/apache_beam/ml/inference/agent_development_kit.py: ########## @@ -0,0 +1,291 @@ +# +# 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. +# + +"""ModelHandler for running agents built with the Google Agent Development Kit. + +This module provides :class:`ADKAgentModelHandler`, a Beam +:class:`~apache_beam.ml.inference.base.ModelHandler` that wraps an ADK +:class:`google.adk.agents.llm_agent.LlmAgent` so it can be used with the +:class:`~apache_beam.ml.inference.base.RunInference` transform. + +Typical usage:: + + import apache_beam as beam + from apache_beam.ml.inference.base import RunInference + from apache_beam.ml.inference.agent_development_kit import ADKAgentModelHandler + from google.adk.agents import LlmAgent + + agent = LlmAgent( + name="my_agent", + model="gemini-2.0-flash", + instruction="You are a helpful assistant.", + ) + + with beam.Pipeline() as p: + results = ( + p + | beam.Create(["What is the capital of France?"]) + | RunInference(ADKAgentModelHandler(agent=agent)) + ) + +If your agent contains state that is not picklable (e.g. tool closures that +capture unpicklable objects), pass a zero-arg factory callable instead:: + + handler = ADKAgentModelHandler(agent=lambda: LlmAgent(...)) + +""" + +import asyncio +import logging +import uuid +from collections.abc import Callable +from collections.abc import Iterable +from collections.abc import Sequence +from typing import Any +from typing import Optional +from typing import Union + +from apache_beam.ml.inference.base import ModelHandler +from apache_beam.ml.inference.base import PredictionResult + +try: + from google.adk.agents import Agent + from google.adk.runners import Runner + from google.adk.sessions import BaseSessionService + from google.adk.sessions import InMemorySessionService + from google.genai.types import Content as genai_Content + from google.genai.types import Part as genai_Part + ADK_AVAILABLE = True +except ImportError: + ADK_AVAILABLE = False + genai_Content = Any # type: ignore[assignment, misc] + genai_Part = Any # type: ignore[assignment, misc] + +LOGGER = logging.getLogger("ADKAgentModelHandler") + +# Type alias for an agent or factory that produces one +_AgentOrFactory = Union["Agent", Callable[[], "Agent"]] + + +class ADKAgentModelHandler(ModelHandler[Union[str, genai_Content], + PredictionResult, + "Runner"]): + """ModelHandler for running ADK agents with the Beam RunInference transform. + + Accepts either a fully constructed :class:`google.adk.agents.Agent` or a + zero-arg factory callable that produces one. The factory form is useful when + the agent contains state that is not picklable and therefore cannot be + serialized alongside the pipeline graph. + + Each call to :meth:`run_inference` invokes the agent once per element in the + batch. By default every invocation uses a fresh, isolated session (stateless). + Stateful multi-turn conversations can be achieved by passing a ``session_id`` + key inside ``inference_args``; elements sharing the same ``session_id`` will + continue the same conversation history. + + Args: + agent: A pre-constructed :class:`~google.adk.agents.Agent` instance, or a + zero-arg callable that returns one. The callable form defers agent + construction to worker ``load_model`` time, which is useful when the + agent cannot be serialized. + app_name: The ADK application name used to namespace sessions. Defaults to + ``"beam_inference"``. + session_service_factory: Optional zero-arg callable returning a + :class:`~google.adk.sessions.BaseSessionService`. When ``None``, an + :class:`~google.adk.sessions.InMemorySessionService` is created + automatically. + min_batch_size: Optional minimum batch size. + max_batch_size: Optional maximum batch size. + max_batch_duration_secs: Optional maximum time to buffer a batch before + emitting; used in streaming contexts. + max_batch_weight: Optional maximum total weight of a batch. + element_size_fn: Optional function that returns the size (weight) of an + element. + """ + def __init__( + self, + agent: _AgentOrFactory, + app_name: str = "beam_inference", + session_service_factory: Optional[Callable[[], + "BaseSessionService"]] = None, + *, + min_batch_size: Optional[int] = None, + max_batch_size: Optional[int] = None, + max_batch_duration_secs: Optional[int] = None, + max_batch_weight: Optional[int] = None, + element_size_fn: Optional[Callable[[Any], int]] = None, + **kwargs): + if not ADK_AVAILABLE: + raise ImportError( + "google-adk is required to use ADKAgentModelHandler. " + "Install it with: pip install google-adk") + + if agent is None: + raise ValueError("'agent' must be an Agent instance or a callable.") + + self._agent_or_factory = agent + self._app_name = app_name + self._session_service_factory = session_service_factory + + super().__init__( + min_batch_size=min_batch_size, + max_batch_size=max_batch_size, + max_batch_duration_secs=max_batch_duration_secs, + max_batch_weight=max_batch_weight, + element_size_fn=element_size_fn, + **kwargs) + + def load_model(self) -> "Runner": + """Instantiates the ADK Runner on the worker. + + Resolves the agent (calling the factory if a callable was provided), then + creates a :class:`~google.adk.runners.Runner` backed by the configured + session service. + + Returns: + A fully initialised :class:`~google.adk.runners.Runner`. + """ + if callable(self._agent_or_factory) and not isinstance( + self._agent_or_factory, Agent): + agent = self._agent_or_factory() + else: + agent = self._agent_or_factory + + if self._session_service_factory is not None: + session_service = self._session_service_factory() + else: + session_service = InMemorySessionService() + + runner = Runner( + agent=agent, + app_name=self._app_name, + session_service=session_service, + ) + LOGGER.info( + "Loaded ADK Runner for agent '%s' (app_name='%s')", + agent.name, + self._app_name, + ) + return runner + + def run_inference( + self, + batch: Sequence[Union[str, genai_Content]], + model: "Runner", + inference_args: Optional[dict[str, Any]] = None, + ) -> Iterable[PredictionResult]: + """Runs the ADK agent on each element in the batch. + + Each element is sent to the agent as a new user turn. The final response + text from the agent is returned as the ``inference`` field of a + :class:`~apache_beam.ml.inference.base.PredictionResult`. + + Args: + batch: A sequence of inputs, each of which is either a ``str`` (the user + message text) or a :class:`google.genai.types.Content` object (for + richer multi-part messages). + model: The :class:`~google.adk.runners.Runner` returned by + :meth:`load_model`. + inference_args: Optional dict of extra arguments. Supported keys: + + - ``"session_id"`` (:class:`str`): If supplied, all elements in this + batch share this session ID, enabling stateful multi-turn + conversations. If omitted, each element receives a unique auto- + generated session ID. + - ``"user_id"`` (:class:`str`): The user identifier to pass to the + runner. Defaults to ``"beam_user"``. + + Returns: + An iterable of :class:`~apache_beam.ml.inference.base.PredictionResult`, + one per input element. + """ + if inference_args is None: + inference_args = {} + + user_id: str = inference_args.get("user_id", "beam_user") + agent_invocations = [] + elements_with_sessions = [] + + for element in batch: + session_id: str = inference_args.get("session_id", str(uuid.uuid4())) + + # Ensure a session exists for this invocation + model.session_service.create_session( + app_name=self._app_name, + user_id=user_id, + session_id=session_id, + ) + + # Wrap plain strings in a Content object + if isinstance(element, str): + message = genai_Content(role="user", parts=[genai_Part(text=element)]) + else: + # Assume the caller has already constructed a types.Content object + message = element + + agent_invocations.append( + self._invoke_agent(model, user_id, session_id, message)) + elements_with_sessions.append(element) + + # Run all agent invocations concurrently + async def _run_concurrently(): + return await asyncio.gather(*agent_invocations) + + response_texts = asyncio.run(_run_concurrently()) + + results = [] + for i, element in enumerate(elements_with_sessions): + results.append( + PredictionResult( + example=element, + inference=response_texts[i], + model_id=model.agent.name, + )) + + return results + + @staticmethod + async def _invoke_agent( + runner: "Runner", + user_id: str, + session_id: str, + message: genai_Content, + ) -> Optional[str]: + """Drives the ADK event loop and returns the final response text. + + Args: + runner: The ADK Runner to invoke. + user_id: The user ID for this invocation. + session_id: The session ID for this invocation. + message: The :class:`google.genai.types.Content` to send. + + Returns: + The text of the agent's final response, or ``None`` if the agent + produced no final text response. + """ + async for event in runner.run_async( + user_id=user_id, + session_id=session_id, + new_message=message, + ): + if event.is_final_response(): + if event.content and event.content.parts: + return event.content.parts[0].text Review Comment:  The agent's final response may contain multiple text parts. This implementation only considers the text from the first part (`parts[0]`), which could lead to incomplete results. To handle multi-part responses correctly and idiomatically, you should use the `.text` property of the `content` object, which concatenates the text from all parts. This also simplifies the code. ```suggestion if event.content: return event.content.text ``` -- 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: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
