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Add to unstable: logging, switch_act, and event_resolution component …
…using the same thought_chain for event resolution that we already use in the stable game master. PiperOrigin-RevId: 722650193 Change-Id: I6d5a9d9c7f5cf8012eb03f9ef44b17e8350054ae
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concordia/components/game_master/unstable/event_resolution.py
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| # Copyright 2023 DeepMind Technologies Limited. | ||
| # | ||
| # Licensed 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 | ||
| # | ||
| # https://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. | ||
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| """Component that helps a game master decide whose turn is next.""" | ||
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| from collections.abc import Callable, Mapping, Sequence | ||
| import types | ||
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| from concordia.components.agent import action_spec_ignored | ||
| from concordia.document import interactive_document | ||
| from concordia.language_model import language_model | ||
| from concordia.thought_chains import thought_chains | ||
| from concordia.typing import entity as entity_lib | ||
| from concordia.typing import entity_component | ||
| from concordia.typing import logging | ||
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| DEFAULT_RESOLUTION_PRE_ACT_KEY = '\nEvent' | ||
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| GET_ACTIVE_ENTITY_QUERY = ('Which entity just took an action?' | ||
| ' Respond using only the entity\'s name and no' | ||
| ' other words.') | ||
| GET_PUTATIVE_ACTION_QUERY = 'What is {name} attempting to do?' | ||
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| class EventResolution(entity_component.ContextComponent): | ||
| """A component that resolves and records events. | ||
| """ | ||
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| def __init__( | ||
| self, | ||
| model: language_model.LanguageModel, | ||
| event_resolution_steps: ( | ||
| Sequence[ | ||
| Callable[ | ||
| [interactive_document.InteractiveDocument, str, str], str | ||
| ] | ||
| ] | ||
| | None | ||
| ) = None, | ||
| components: Mapping[ | ||
| entity_component.ComponentName, str | ||
| ] = types.MappingProxyType({}), | ||
| pre_act_key: str = DEFAULT_RESOLUTION_PRE_ACT_KEY, | ||
| logging_channel: logging.LoggingChannel = logging.NoOpLoggingChannel, | ||
| ): | ||
| """Initializes the component. | ||
| Args: | ||
| model: The language model to use for the component. | ||
| event_resolution_steps: thinking steps for the event resolution component | ||
| to use whenever it converts putative events like action suggestions into | ||
| real events in the simulation. | ||
| components: The components to condition the answer on. This is a mapping | ||
| of the component name to a label to use in the prompt. | ||
| pre_act_key: Prefix to add to the output of the component when called | ||
| in `pre_act`. | ||
| logging_channel: The channel to use for debug logging. | ||
| Raises: | ||
| ValueError: If the component order is not None and contains duplicate | ||
| components. | ||
| """ | ||
| super().__init__() | ||
| self._model = model | ||
| self._event_resolution_steps = event_resolution_steps | ||
| self._components = dict(components) | ||
| self._pre_act_key = pre_act_key | ||
| self._logging_channel = logging_channel | ||
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| def get_named_component_pre_act_value(self, component_name: str) -> str: | ||
| """Returns the pre-act value of a named component of the parent entity.""" | ||
| return ( | ||
| self.get_entity().get_component( | ||
| component_name, type_=action_spec_ignored.ActionSpecIgnored | ||
| ).get_pre_act_value() | ||
| ) | ||
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| def pre_act( | ||
| self, | ||
| action_spec: entity_lib.ActionSpec, | ||
| ) -> str: | ||
| result = '' | ||
| prompt_to_log = '' | ||
| if action_spec.output_type == entity_lib.OutputType.RESOLVE: | ||
| entity_name = self.get_entity().name | ||
| prompt = interactive_document.InteractiveDocument(self._model) | ||
| component_states = '\n'.join([ | ||
| f"{entity_name}'s" | ||
| f' {prefix}:\n{self.get_named_component_pre_act_value(key)}' | ||
| for key, prefix in self._components.items() | ||
| ]) | ||
| prompt.statement(f'Statements:\n{component_states}\n') | ||
| active_entity_name = prompt.open_question( | ||
| GET_ACTIVE_ENTITY_QUERY) | ||
| putative_action = prompt.open_question( | ||
| GET_PUTATIVE_ACTION_QUERY.format(name=active_entity_name), | ||
| max_tokens=1200) | ||
| prompt, event_statement = thought_chains.run_chain_of_thought( | ||
| thoughts=self._event_resolution_steps, | ||
| premise=putative_action, | ||
| document=prompt, | ||
| active_player_name=active_entity_name, | ||
| ) | ||
| result = f'{self._pre_act_key}: {event_statement}' | ||
| prompt_to_log = prompt.view().text() | ||
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| self._logging_channel( | ||
| {'Key': self._pre_act_key, | ||
| 'Value': result, | ||
| 'Prompt': prompt_to_log}) | ||
| return result |
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