docs: add docstrings to dated file handler

ref: N25B-401

.gitignore

@@ -273,6 +273,8 @@ experiment-*.log
 
 
 
+
+
 
 
 

src/control_backend/agents/actuation/robot_gesture_agent.py

@@ -1,5 +1,4 @@
 import json
-import logging
 
 import zmq
 import zmq.asyncio as azmq
@@ -9,8 +8,6 @@ from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
 from control_backend.schemas.ri_message import GestureCommand, RIEndpoint
 
-experiment_logger = logging.getLogger(settings.logging_settings.experiment_logger_name)
-
 
 class RobotGestureAgent(BaseAgent):
     """
@@ -114,7 +111,6 @@ class RobotGestureAgent(BaseAgent):
                         gesture_command.data,
                     )
                     return
-            experiment_logger.action("Gesture: %s", gesture_command.data)
             await self.pubsocket.send_json(gesture_command.model_dump())
         except Exception:
             self.logger.exception("Error processing internal message.")

src/control_backend/agents/bdi/bdi_core_agent.py

@@ -1,7 +1,6 @@
 import asyncio
 import copy
 import json
-import logging
 import time
 from collections.abc import Iterable
 
@@ -20,9 +19,6 @@ from control_backend.schemas.ri_message import GestureCommand, RIEndpoint, Speec
 DELIMITER = ";\n"  # TODO: temporary until we support lists in AgentSpeak
 
 
-experiment_logger = logging.getLogger(settings.logging_settings.experiment_logger_name)
-
-
 class BDICoreAgent(BaseAgent):
     """
     BDI Core Agent.
@@ -211,9 +207,6 @@ class BDICoreAgent(BaseAgent):
         else:
             term = agentspeak.Literal(name)
 
-        if name != "user_said":
-            experiment_logger.observation(f"Formed new belief: {name}{f'={args}' if args else ''}")
-
         self.bdi_agent.call(
             agentspeak.Trigger.addition,
             agentspeak.GoalType.belief,
@@ -251,9 +244,6 @@ class BDICoreAgent(BaseAgent):
             new_args = (agentspeak.Literal(arg) for arg in args)
             term = agentspeak.Literal(name, new_args)
 
-        if name != "user_said":
-            experiment_logger.observation(f"Removed belief: {name}{f'={args}' if args else ''}")
-
         result = self.bdi_agent.call(
             agentspeak.Trigger.removal,
             agentspeak.GoalType.belief,
@@ -396,8 +386,6 @@ class BDICoreAgent(BaseAgent):
                 body=str(message_text),
             )
 
-            experiment_logger.chat(str(message_text), extra={"role": "assistant"})
-
             self.add_behavior(self.send(chat_history_message))
 
             yield
@@ -453,7 +441,6 @@ class BDICoreAgent(BaseAgent):
             trigger_name = agentspeak.grounded(term.args[0], intention.scope)
 
             self.logger.debug("Started trigger %s", trigger_name)
-            experiment_logger.observation("Triggered: %s", trigger_name)
 
             msg = InternalMessage(
                 to=settings.agent_settings.user_interrupt_name,

src/control_backend/agents/bdi/bdi_program_manager.py

@@ -1,12 +1,10 @@
 import asyncio
 import json
-import logging
 
 import zmq
 from pydantic import ValidationError
 from zmq.asyncio import Context
 
-import control_backend
 from control_backend.agents import BaseAgent
 from control_backend.agents.bdi.agentspeak_generator import AgentSpeakGenerator
 from control_backend.core.config import settings
@@ -21,8 +19,6 @@ from control_backend.schemas.program import (
     Program,
 )
 
-experiment_logger = logging.getLogger(settings.logging_settings.experiment_logger_name)
-
 
 class BDIProgramManager(BaseAgent):
     """
@@ -245,18 +241,6 @@ class BDIProgramManager(BaseAgent):
         await self.send(extractor_msg)
         self.logger.debug("Sent message to extractor agent to clear history.")
 
-    @staticmethod
-    def _rollover_experiment_logs():
-        """
-        A new experiment program started; make a new experiment log file.
-        """
-        handlers = logging.getLogger("experiment").handlers
-        for handler in handlers:
-            if isinstance(handler, control_backend.logging.DatedFileHandler):
-                experiment_logger.action("Doing rollover...")
-                handler.do_rollover()
-                experiment_logger.debug("Finished rollover.")
-
     async def _receive_programs(self):
         """
         Continuous loop that receives program updates from the HTTP endpoint.
@@ -277,7 +261,6 @@ class BDIProgramManager(BaseAgent):
             self._initialize_internal_state(program)
             await self._send_program_to_user_interrupt(program)
             await self._send_clear_llm_history()
-            self._rollover_experiment_logs()
 
             await asyncio.gather(
                 self._create_agentspeak_and_send_to_bdi(program),

src/control_backend/agents/bdi/text_belief_extractor_agent.py

@@ -150,9 +150,6 @@ class TextBeliefExtractorAgent(BaseAgent):
             return
 
         available_beliefs = [b for b in belief_list.beliefs if isinstance(b, SemanticBelief)]
-        self._current_beliefs = BeliefState(
-            false={InternalBelief(name=b.name, arguments=None) for b in available_beliefs},
-        )
         self.belief_inferrer.available_beliefs = available_beliefs
         self.logger.debug(
             "Received %d semantic beliefs from the program manager: %s",
@@ -173,9 +170,6 @@ class TextBeliefExtractorAgent(BaseAgent):
         available_goals = {g for g in goals_list.goals if g.can_fail}
         available_goals -= self._force_completed_goals
         self.goal_inferrer.goals = available_goals
-        self._current_goal_completions = {
-            f"achieved_{AgentSpeakGenerator.slugify(goal)}": False for goal in available_goals
-        }
         self.logger.debug(
             "Received %d failable goals from the program manager: %s",
             len(available_goals),

src/control_backend/agents/communication/ri_communication_agent.py

@@ -55,7 +55,6 @@ class RICommunicationAgent(BaseAgent):
         self.connected = False
         self.gesture_agent: RobotGestureAgent | None = None
         self.speech_agent: RobotSpeechAgent | None = None
-        self.visual_emotion_recognition_agent: VisualEmotionRecognitionAgent | None = None
 
     async def setup(self):
         """
@@ -219,7 +218,6 @@ class RICommunicationAgent(BaseAgent):
                         socket_address=addr,
                         bind=bind,
                     )
-                    self.visual_emotion_recognition_agent = visual_emotion_agent
                     await visual_emotion_agent.start()
                 case _:
                     self.logger.warning("Unhandled negotiation id: %s", id)
@@ -326,9 +324,6 @@ class RICommunicationAgent(BaseAgent):
         if self.speech_agent is not None:
             await self.speech_agent.stop()
 
-        if self.visual_emotion_recognition_agent is not None:
-            await self.visual_emotion_recognition_agent.stop()
-
         if self.pub_socket is not None:
             self.pub_socket.close()
 
@@ -337,7 +332,6 @@ class RICommunicationAgent(BaseAgent):
             self.connected = True
 
     async def handle_message(self, msg: InternalMessage):
-        return 
         try:
             pause_command = PauseCommand.model_validate_json(msg.body)
             await self._req_socket.send_json(pause_command.model_dump())

src/control_backend/agents/llm/llm_agent.py

@@ -1,6 +1,5 @@
 import asyncio
 import json
-import logging
 import re
 import uuid
 from collections.abc import AsyncGenerator
@@ -15,8 +14,6 @@ from control_backend.core.config import settings
 from ...schemas.llm_prompt_message import LLMPromptMessage
 from .llm_instructions import LLMInstructions
 
-experiment_logger = logging.getLogger(settings.logging_settings.experiment_logger_name)
-
 
 class LLMAgent(BaseAgent):
     """
@@ -173,7 +170,7 @@ class LLMAgent(BaseAgent):
             *self.history,
         ]
 
-        message_id = str(uuid.uuid4())
+        message_id = str(uuid.uuid4())  # noqa
 
         try:
             full_message = ""
@@ -182,9 +179,10 @@ class LLMAgent(BaseAgent):
                 full_message += token
                 current_chunk += token
 
-                experiment_logger.chat(
+                self.logger.llm(
+                    "Received token: %s",
                     full_message,
-                    extra={"role": "assistant", "reference": message_id, "partial": True},
+                    extra={"reference": message_id},  # Used in the UI to update old logs
                 )
 
                 # Stream the message in chunks separated by punctuation.
@@ -199,11 +197,6 @@ class LLMAgent(BaseAgent):
             # Yield any remaining tail
             if current_chunk:
                 yield current_chunk
-
-            experiment_logger.chat(
-                full_message,
-                extra={"role": "assistant", "reference": message_id, "partial": False},
-            )
         except httpx.HTTPError as err:
             self.logger.error("HTTP error.", exc_info=err)
             yield "LLM service unavailable."
@@ -219,7 +212,7 @@ class LLMAgent(BaseAgent):
         :yield: Raw text tokens (deltas) from the SSE stream.
         :raises httpx.HTTPError: If the API returns a non-200 status.
         """
-        async with httpx.AsyncClient(timeout=httpx.Timeout(20.0)) as client:
+        async with httpx.AsyncClient() as client:
             async with client.stream(
                 "POST",
                 settings.llm_settings.local_llm_url,

src/control_backend/agents/perception/transcription_agent/transcription_agent.py

@@ -1,5 +1,4 @@
 import asyncio
-import logging
 
 import numpy as np
 import zmq
@@ -11,8 +10,6 @@ from control_backend.core.config import settings
 
 from .speech_recognizer import SpeechRecognizer
 
-experiment_logger = logging.getLogger(settings.logging_settings.experiment_logger_name)
-
 
 class TranscriptionAgent(BaseAgent):
     """
@@ -28,8 +25,6 @@ class TranscriptionAgent(BaseAgent):
     :ivar audio_in_socket: The ZMQ SUB socket instance.
     :ivar speech_recognizer: The speech recognition engine instance.
     :ivar _concurrency: Semaphore to limit concurrent transcriptions.
-    :ivar _current_speech_reference: The reference of the current user utterance, for synchronising
-        experiment logs.
     """
 
     def __init__(self, audio_in_address: str):
@@ -44,7 +39,6 @@ class TranscriptionAgent(BaseAgent):
         self.audio_in_socket: azmq.Socket | None = None
         self.speech_recognizer = None
         self._concurrency = None
-        self._current_speech_reference: str | None = None
 
     async def setup(self):
         """
@@ -69,10 +63,6 @@ class TranscriptionAgent(BaseAgent):
 
         self.logger.info("Finished setting up %s", self.name)
 
-    async def handle_message(self, msg: InternalMessage):
-        if msg.thread == "voice_activity":
-            self._current_speech_reference = msg.body
-
     async def stop(self):
         """
         Stop the agent and close sockets.
@@ -106,21 +96,20 @@ class TranscriptionAgent(BaseAgent):
 
     async def _share_transcription(self, transcription: str):
         """
-        Share a transcription to the other agents that depend on it, and to experiment logs.
+        Share a transcription to the other agents that depend on it.
 
         Currently sends to:
         - :attr:`settings.agent_settings.text_belief_extractor_name`
-        - The UI via the experiment logger
 
         :param transcription: The transcribed text.
         """
-        experiment_logger.chat(
+        receiver_names = [
-            transcription,
+            settings.agent_settings.text_belief_extractor_name,
-            extra={"role": "user", "reference": self._current_speech_reference, "partial": False},
+        ]
-        )
 
+        for receiver_name in receiver_names:
             message = InternalMessage(
-            to=settings.agent_settings.text_belief_extractor_name,
+                to=receiver_name,
                 sender=self.name,
                 body=transcription,
             )
@@ -140,9 +129,10 @@ class TranscriptionAgent(BaseAgent):
                 audio = np.frombuffer(audio_data, dtype=np.float32)
                 speech = await self._transcribe(audio)
                 if not speech:
-                    self.logger.debug("Nothing transcribed.")
+                    self.logger.info("Nothing transcribed.")
                     continue
 
+                self.logger.info("Transcribed speech: %s", speech)
                 await self._share_transcription(speech)
             except Exception as e:
                 self.logger.error(f"Error in transcription loop: {e}")

src/control_backend/agents/perception/vad_agent.py

@@ -1,6 +1,4 @@
 import asyncio
-import logging
-import uuid
 
 import numpy as np
 import torch
@@ -14,8 +12,6 @@ from control_backend.schemas.internal_message import InternalMessage
 from ...schemas.program_status import PROGRAM_STATUS, ProgramStatus
 from .transcription_agent.transcription_agent import TranscriptionAgent
 
-experiment_logger = logging.getLogger(settings.logging_settings.experiment_logger_name)
-
 
 class SocketPoller[T]:
     """
@@ -256,18 +252,6 @@ class VADAgent(BaseAgent):
             if prob > prob_threshold:
                 if self.i_since_speech > non_speech_patience + begin_silence_length:
                     self.logger.debug("Speech started.")
-                    reference = str(uuid.uuid4())
-                    experiment_logger.chat(
-                        "...",
-                        extra={"role": "user", "reference": reference, "partial": True},
-                    )
-                    await self.send(
-                        InternalMessage(
-                            to=settings.agent_settings.transcription_name,
-                            body=reference,
-                            thread="voice_activity",
-                        )
-                    )
                 self.audio_buffer = np.append(self.audio_buffer, chunk)
                 self.i_since_speech = 0
                 continue
@@ -285,10 +269,9 @@ class VADAgent(BaseAgent):
                 assert self.audio_out_socket is not None
                 await self.audio_out_socket.send(self.audio_buffer[: -2 * len(chunk)].tobytes())
 
-            # At this point, we know that there is no speech.
+            # At this point, we know that the speech has ended.
-            # Prepend the last few chunks that had no speech, for a more fluent boundary.
+            # Prepend the last chunk that had no speech, for a more fluent boundary
-            self.audio_buffer = np.append(self.audio_buffer, chunk)
+            self.audio_buffer = chunk
-            self.audio_buffer = self.audio_buffer[-begin_silence_length * len(chunk) :]
 
     async def handle_message(self, msg: InternalMessage):
         """

src/control_backend/agents/perception/visual_emotion_recognition_agent/visual_emotion_recognition_agent.py

@@ -97,7 +97,6 @@ class VisualEmotionRecognitionAgent(BaseAgent):
 
                 if frame_image is None:
                     # Could not decode image, skip this frame
-                    self.logger.warning("Received invalid video frame, skipping.")
                     continue
 
                 # Get the dominant emotion from each face

src/control_backend/agents/user_interrupt/user_interrupt_agent.py

@@ -1,5 +1,4 @@
 import json
-import logging
 
 import zmq
 from zmq.asyncio import Context
@@ -17,8 +16,6 @@ from control_backend.schemas.ri_message import (
     SpeechCommand,
 )
 
-experiment_logger = logging.getLogger(settings.logging_settings.experiment_logger_name)
-
 
 class UserInterruptAgent(BaseAgent):
     """
@@ -197,7 +194,6 @@ class UserInterruptAgent(BaseAgent):
             case "transition_phase":
                 new_phase_id = msg.body
                 self.logger.info(f"Phase transition detected: {new_phase_id}")
-                experiment_logger.observation("Transitioned to next phase.")
 
                 payload = {"type": "phase_update", "id": new_phase_id}
 
@@ -300,7 +296,6 @@ class UserInterruptAgent(BaseAgent):
 
         :param text_to_say: The string that the robot has to say.
         """
-        experiment_logger.chat(text_to_say, extra={"role": "assistant"})
         cmd = SpeechCommand(data=text_to_say, is_priority=True)
         out_msg = InternalMessage(
             to=settings.agent_settings.robot_speech_name,

src/control_backend/logging/dated_file_handler.py

@@ -12,12 +12,21 @@ class DatedFileHandler(FileHandler):
         super().__init__(**kwargs)
 
     def _make_filename(self) -> str:
+        """
+        Create the filename for the current logfile, using the configured file prefix and the
+        current date and time. If the directory does not exist, it gets created.
+
+        :return: A filepath.
+        """
         filepath = Path(f"{self._file_prefix}-{datetime.now():%Y%m%d-%H%M%S}.log")
         if not filepath.parent.is_dir():
             filepath.parent.mkdir(parents=True, exist_ok=True)
         return str(filepath)
 
     def do_rollover(self):
+        """
+        Close the current logfile and create a new one with the current date and time.
+        """
         self.acquire()
         try:
             if self.stream: