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refactor: remove constants and put in config file

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removed all constants from all files and put them in src/control_backend/core/config.py
also removed some old mock agents that we don't use anymore

ref: N25B-236
This commit is contained in:
Pim Hutting
2025-11-05 13:43:57 +01:00
parent c6edad0bb4
commit 9e926178da
15 changed files with 136 additions and 73 deletions
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3
src/control_backend/agents/bdi/behaviours/belief_setter.py
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@@ -18,7 +18,8 @@ class BeliefSetterBehaviour(CyclicBehaviour):
logger = logging.getLogger("BDI/Belief Setter")
async def run(self):
msg = await self.receive(timeout=0.1)
t = settings.behaviour_settings.default_rcv_timeout
msg = await self.receive(timeout=t)
if msg:
self.logger.info(f"Received message {msg.body}")
self._process_message(msg)

3
src/control_backend/agents/bdi/behaviours/receive_llm_resp_behaviour.py
View File

@@ -13,7 +13,8 @@ class ReceiveLLMResponseBehaviour(CyclicBehaviour):
logger = logging.getLogger("BDI/LLM Reciever")
async def run(self):
msg = await self.receive(timeout=2)
t = settings.llm_settings.llm_response_rcv_timeout
msg = await self.receive(timeout=t)
if not msg:
return

3
src/control_backend/agents/bdi/behaviours/text_belief_extractor.py
View File

@@ -39,7 +39,8 @@ class BeliefFromText(CyclicBehaviour):
beliefs = {"mood": ["X"], "car": ["Y"]}
async def run(self):
msg = await self.receive(timeout=0.1)
t = settings.behaviour_settings.default_rcv_timeout
msg = await self.receive(timeout=t)
if msg:
sender = msg.sender.node
match sender:

3
src/control_backend/agents/belief_collector/behaviours/continuous_collect.py
View File

@@ -16,7 +16,8 @@ class ContinuousBeliefCollector(CyclicBehaviour):
"""
async def run(self):
msg = await self.receive(timeout=0.1) # Wait for 0.1s
t = settings.behaviour_settings.default_rcv_timeout
msg = await self.receive(timeout=t)
if msg:
await self._process_message(msg)

6
src/control_backend/agents/llm/llm.py
View File

@@ -35,7 +35,8 @@ class LLMAgent(Agent):
Receives SPADE messages and processes only those originating from the
configured BDI agent.
"""
msg = await self.receive(timeout=1)
t = settings.behaviour_settings.llm_response_rcv_timeout
msg = await self.receive(timeout=t)
if not msg:
return
@@ -78,7 +79,8 @@ class LLMAgent(Agent):
:param prompt: Input text prompt to pass to the LLM.
:return: LLM-generated content or fallback message.
"""
async with httpx.AsyncClient(timeout=120.0) as client:
t = settings.llm_settings.request_timeout_s
async with httpx.AsyncClient(timeout=t) as client:
# Example dynamic content for future (optional)
instructions = LLMInstructions()

0
src/control_backend/agents/mock_agents/__init__.py
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44
src/control_backend/agents/mock_agents/belief_text_mock.py
View File

@@ -1,44 +0,0 @@
import json
from spade.agent import Agent
from spade.behaviour import OneShotBehaviour
from spade.message import Message
from control_backend.core.config import settings
class BeliefTextAgent(Agent):
class SendOnceBehaviourBlfText(OneShotBehaviour):
async def run(self):
to_jid = (
settings.agent_settings.belief_collector_agent_name
+ "@"
+ settings.agent_settings.host
)
# Send multiple beliefs in one JSON payload
payload = {
"type": "belief_extraction_text",
"beliefs": {
"user_said": [
"hello test",
"Can you help me?",
"stop talking to me",
"No",
"Pepper do a dance",
]
},
}
msg = Message(to=to_jid)
msg.body = json.dumps(payload)
await self.send(msg)
print(f"Beliefs sent to {to_jid}!")
self.exit_code = "Job Finished!"
await self.agent.stop()
async def setup(self):
print("BeliefTextAgent started")
self.b = self.SendOnceBehaviourBlfText()
self.add_behaviour(self.b)

4
src/control_backend/agents/ri_command_agent.py
View File

@@ -22,9 +22,9 @@ class RICommandAgent(Agent):
self,
jid: str,
password: str,
port: int = 5222,
port: int = settings.agent_settings.default_spade_port,
verify_security: bool = False,
address="tcp://localhost:0000",
address=settings.zmq_settings.ri_command_address,
bind=False,
):
super().__init__(jid, password, port, verify_security)

8
src/control_backend/agents/ri_communication_agent.py
View File

@@ -21,9 +21,9 @@ class RICommunicationAgent(Agent):
self,
jid: str,
password: str,
port: int = 5222,
port: int = settings.agent_settings.default_spade_port,
verify_security: bool = False,
address="tcp://localhost:0000",
address=settings.zmq_settings.ri_command_address,
bind=False,
):
super().__init__(jid, password, port, verify_security)
@@ -58,13 +58,13 @@ class RICommunicationAgent(Agent):
# See what endpoint we received
match message["endpoint"]:
case "ping":
await asyncio.sleep(1)
await asyncio.sleep(settings.agent_settings.behaviour_settings.ping_sleep_s)
case _:
logger.info(
"Received message with topic different than ping, while ping expected."
)
async def setup(self, max_retries: int = 5):
async def setup(self, max_retries: int = settings.behaviour_settings.comm_setup_max_retries):
"""
Try to setup the communication agent, we have 5 retries in case we dont have a response yet.
"""

14
src/control_backend/agents/transcription/speech_recognizer.py
View File

@@ -10,6 +10,8 @@ import numpy as np
import torch
import whisper
from control_backend.core.config import settings
class SpeechRecognizer(abc.ABC):
def __init__(self, limit_output_length=True):
@@ -41,10 +43,10 @@ class SpeechRecognizer(abc.ABC):
:param audio: The audio sample (16 kHz) to use for length estimation.
:return: The estimated length of the transcribed audio in tokens.
"""
length_seconds = len(audio) / 16_000
length_seconds = len(audio) / settings.vad_settings.sample_rate_hz
length_minutes = length_seconds / 60
word_count = length_minutes * 300
token_count = word_count / 3 * 4
word_count = length_minutes * settings.behaviour_settings.transcription_words_per_minute
token_count = word_count / settings.behaviour_settings.transcription_words_per_token
return int(token_count)
def _get_decode_options(self, audio: np.ndarray) -> dict:
@@ -72,7 +74,7 @@ class MLXWhisperSpeechRecognizer(SpeechRecognizer):
def __init__(self, limit_output_length=True):
super().__init__(limit_output_length)
self.was_loaded = False
self.model_name = "mlx-community/whisper-small.en-mlx"
self.model_name = settings.speech_model_settings.mlx_model_name
def load_model(self):
if self.was_loaded:
@@ -99,7 +101,9 @@ class OpenAIWhisperSpeechRecognizer(SpeechRecognizer):
if self.model is not None:
return
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.model = whisper.load_model("small.en", device=device)
self.model = whisper.load_model(
settings.speech_model_settings.openai_model_name, device=device
)
def recognize_speech(self, audio: np.ndarray) -> str:
self.load_model()

3
src/control_backend/agents/transcription/transcription_agent.py
View File

@@ -31,9 +31,10 @@ class TranscriptionAgent(Agent):
class Transcribing(CyclicBehaviour):
def __init__(self, audio_in_socket: azmq.Socket):
super().__init__()
max_concurrent_tasks = settings.transcription_settings.max_concurrent_transcriptions
self.audio_in_socket = audio_in_socket
self.speech_recognizer = SpeechRecognizer.best_type()
self._concurrency = asyncio.Semaphore(3)
self._concurrency = asyncio.Semaphore(max_concurrent_tasks)
def warmup(self):
"""Load the transcription model into memory to speed up the first transcription."""

26
src/control_backend/agents/vad_agent.py
View File

@@ -20,7 +20,11 @@ class SocketPoller[T]:
multiple usages.
"""
def __init__(self, socket: azmq.Socket, timeout_ms: int = 100):
def __init__(
self,
socket: azmq.Socket,
timeout_ms: int = settings.behaviour_settings.socket_poller_timeout_ms,
):
"""
:param socket: The socket to poll and get data from.
:param timeout_ms: A timeout in milliseconds to wait for data.
@@ -49,12 +53,16 @@ class Streaming(CyclicBehaviour):
super().__init__()
self.audio_in_poller = SocketPoller[bytes](audio_in_socket)
self.model, _ = torch.hub.load(
repo_or_dir="snakers4/silero-vad", model="silero_vad", force_reload=False
repo_or_dir=settings.vad_settings.repo_or_dir,
model=settings.vad_settings.model_name,
force_reload=False,
)
self.audio_out_socket = audio_out_socket
self.audio_buffer = np.array([], dtype=np.float32)
self.i_since_speech = 100 # Used to allow small pauses in speech
self.i_since_speech = (
settings.behaviour_settings.vad_initial_since_speech
) # Used to allow small pauses in speech
async def run(self) -> None:
data = await self.audio_in_poller.poll()
@@ -62,15 +70,17 @@ class Streaming(CyclicBehaviour):
if len(self.audio_buffer) > 0:
logger.debug("No audio data received. Discarding buffer until new data arrives.")
self.audio_buffer = np.array([], dtype=np.float32)
self.i_since_speech = 100
self.i_since_speech = settings.behaviour_settings.vad_initial_since_speech
return
# copy otherwise Torch will be sad that it's immutable
chunk = np.frombuffer(data, dtype=np.float32).copy()
prob = self.model(torch.from_numpy(chunk), 16000).item()
prob = self.model(torch.from_numpy(chunk), settings.vad_settings.sample_rate_hz).item()
non_speech_patience = settings.behaviour_settings.vad_non_speech_patience_chunks
prob_threshold = settings.behaviour_settings.vad_prob_threshold
if prob > 0.5:
if self.i_since_speech > 3:
if prob > prob_threshold:
if self.i_since_speech > non_speech_patience:
logger.debug("Speech started.")
self.audio_buffer = np.append(self.audio_buffer, chunk)
self.i_since_speech = 0
@@ -78,7 +88,7 @@ class Streaming(CyclicBehaviour):
self.i_since_speech += 1
# prob < 0.5, so speech maybe ended. Wait a bit more before to be more certain
if self.i_since_speech <= 3:
if self.i_since_speech <= non_speech_patience:
self.audio_buffer = np.append(self.audio_buffer, chunk)
return