Add observe_and_summarize supporting agent

PiperOrigin-RevId: 700005751
Change-Id: I38008ecc2a08f328547d0456740745ef46fa0093

examples/modular/environment/supporting_agent_factory/observe_and_summarize_agent.py

@@ -0,0 +1,141 @@
+# Copyright 2024 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.
+
+"""An Agent Factory."""
+
+import datetime
+
+from concordia.agents import entity_agent_with_logging
+from concordia.associative_memory import associative_memory
+from concordia.associative_memory import formative_memories
+from concordia.clocks import game_clock
+from concordia.components import agent as agent_components
+from concordia.contrib.components.agent import observations_since_last_update
+from concordia.contrib.components.agent import situation_representation_via_narrative
+from concordia.language_model import language_model
+from concordia.memory_bank import legacy_associative_memory
+from concordia.utils import measurements as measurements_lib
+
+
+def _get_class_name(object_: object) -> str:
+  return object_.__class__.__name__
+
+
+def build_agent(
+    *,
+    config: formative_memories.AgentConfig,
+    model: language_model.LanguageModel,
+    memory: associative_memory.AssociativeMemory,
+    clock: game_clock.MultiIntervalClock,
+    update_time_interval: datetime.timedelta | None = None,
+) -> entity_agent_with_logging.EntityAgentWithLogging:
+  """Build an agent.
+
+  Args:
+    config: The agent config to use.
+    model: The language model to use.
+    memory: The agent's memory object.
+    clock: The clock to use.
+    update_time_interval: Agent calls update every time this interval passes.
+
+  Returns:
+    An agent.
+  """
+  del update_time_interval
+  if config.extras.get('main_character', False):
+    raise ValueError('This function is meant for a supporting character '
+                     'but it was called on a main character.')
+
+  agent_name = config.name
+
+  raw_memory = legacy_associative_memory.AssociativeMemoryBank(memory)
+
+  measurements = measurements_lib.Measurements()
+  instructions = agent_components.instructions.Instructions(
+      agent_name=agent_name,
+      logging_channel=measurements.get_channel('Instructions').on_next,
+  )
+
+  time_display = agent_components.report_function.ReportFunction(
+      function=clock.current_time_interval_str,
+      pre_act_key='\nCurrent time',
+      logging_channel=measurements.get_channel('TimeDisplay').on_next,
+  )
+
+  observation_label = '\nObservation'
+  observation = observations_since_last_update.ObservationsSinceLastUpdate(
+      model=model,
+      clock_now=clock.now,
+      pre_act_key=observation_label,
+      logging_channel=measurements.get_channel(
+          'ObservationsSinceLastUpdate').on_next,
+  )
+
+  situation_representation_label = (
+      f'\nQuestion: What situation is {agent_name} in right now?\nAnswer')
+  situation_representation = (
+      situation_representation_via_narrative.SituationRepresentation(
+          model=model,
+          clock_now=clock.now,
+          pre_act_key=situation_representation_label,
+          logging_channel=measurements.get_channel(
+              'SituationRepresentation'
+          ).on_next,
+      )
+  )
+
+  if config.goal:
+    goal_label = '\nOverarching goal'
+    overarching_goal = agent_components.constant.Constant(
+        state=config.goal,
+        pre_act_key=goal_label,
+        logging_channel=measurements.get_channel(goal_label).on_next)
+  else:
+    goal_label = None
+    overarching_goal = None
+
+  entity_components = (
+      # Components that provide pre_act context.
+      instructions,
+      time_display,
+      situation_representation,
+      observation,
+  )
+  components_of_agent = {_get_class_name(component): component
+                         for component in entity_components}
+  components_of_agent[
+      agent_components.memory_component.DEFAULT_MEMORY_COMPONENT_NAME] = (
+          agent_components.memory_component.MemoryComponent(raw_memory))
+
+  component_order = list(components_of_agent.keys())
+  if overarching_goal is not None:
+    components_of_agent[goal_label] = overarching_goal
+    # Place goal after the instructions.
+    component_order.insert(1, goal_label)
+
+  act_component = agent_components.concat_act_component.ConcatActComponent(
+      model=model,
+      clock=clock,
+      component_order=component_order,
+      logging_channel=measurements.get_channel('ActComponent').on_next,
+  )
+
+  agent = entity_agent_with_logging.EntityAgentWithLogging(
+      agent_name=agent_name,
+      act_component=act_component,
+      context_components=components_of_agent,
+      component_logging=measurements,
+  )
+
+  return agent