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style: apply ruff check and format

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Made sure all ruff checks pass and formatted all files.

ref: N25B-224
This commit is contained in:
Kasper
2025-11-02 19:45:01 +01:00
parent 657c300bc7
commit 48c9746417
25 changed files with 199 additions and 143 deletions
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6
src/control_backend/agents/bdi/bdi_core.py
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@@ -58,11 +58,11 @@ class BDICoreAgent(BDIAgent):
class SendBehaviour(OneShotBehaviour):
async def run(self) -> None:
msg = Message(
to= settings.agent_settings.llm_agent_name + '@' + settings.agent_settings.host,
body= text
to=settings.agent_settings.llm_agent_name + "@" + settings.agent_settings.host,
body=text,
)
await self.send(msg)
self.agent.logger.info("Message sent to LLM: %s", text)
self.add_behaviour(SendBehaviour())
self.add_behaviour(SendBehaviour())

6
src/control_backend/agents/bdi/behaviours/belief_setter.py
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@@ -3,7 +3,7 @@ import logging
from spade.agent import Message
from spade.behaviour import CyclicBehaviour
from spade_bdi.bdi import BDIAgent, BeliefNotInitiated
from spade_bdi.bdi import BDIAgent
from control_backend.core.config import settings
@@ -23,7 +23,6 @@ class BeliefSetterBehaviour(CyclicBehaviour):
self.logger.info(f"Received message {msg.body}")
self._process_message(msg)
def _process_message(self, message: Message):
sender = message.sender.node # removes host from jid and converts to str
self.logger.debug("Sender: %s", sender)
@@ -61,6 +60,7 @@ class BeliefSetterBehaviour(CyclicBehaviour):
self.agent.bdi.set_belief(belief, *arguments)
# Special case: if there's a new user message, flag that we haven't responded yet
if belief == "user_said": self.agent.bdi.set_belief("new_message")
if belief == "user_said":
self.agent.bdi.set_belief("new_message")
self.logger.info("Set belief %s with arguments %s", belief, arguments)

8
src/control_backend/agents/bdi/behaviours/receive_llm_resp_behaviour.py
View File

@@ -9,18 +9,20 @@ class ReceiveLLMResponseBehaviour(CyclicBehaviour):
"""
Adds behavior to receive responses from the LLM Agent.
"""
logger = logging.getLogger("BDI/LLM Reciever")
async def run(self):
msg = await self.receive(timeout=2)
if not msg:
return
sender = msg.sender.node
sender = msg.sender.node
match sender:
case settings.agent_settings.llm_agent_name:
content = msg.body
self.logger.info("Received LLM response: %s", content)
#Here the BDI can pass the message back as a response
# Here the BDI can pass the message back as a response
case _:
self.logger.debug("Not from the llm, discarding message")
pass
pass

40
src/control_backend/agents/bdi/behaviours/text_belief_extractor.py
View File

@@ -13,28 +13,30 @@ class BeliefFromText(CyclicBehaviour):
# TODO: LLM prompt nog hardcoded
llm_instruction_prompt = """
You are an information extraction assistent for a BDI agent. Your task is to extract values from a user's text to bind a list of ungrounded beliefs. Rules:
You will receive a JSON object with "beliefs" (a list of ungrounded AgentSpeak beliefs) and "text" (user's transcript).
You are an information extraction assistent for a BDI agent. Your task is to extract values \
from a user's text to bind a list of ungrounded beliefs. Rules:
You will receive a JSON object with "beliefs" (a list of ungrounded AgentSpeak beliefs) \
and "text" (user's transcript).
Analyze the text to find values that sematically match the variables (X,Y,Z) in the beliefs.
A single piece of text might contain multiple instances that match a belief.
Respond ONLY with a single JSON object.
The JSON object's keys should be the belief functors (e.g., "weather").
The value for each key must be a list of lists.
Each inner list must contain the extracted arguments (as strings) for one instance of that belief.
CRITICAL: If no information in the text matches a belief, DO NOT include that key in your response.
Each inner list must contain the extracted arguments (as strings) for one instance \
of that belief.
CRITICAL: If no information in the text matches a belief, DO NOT include that key \
in your response.
"""
# on_start agent receives message containing the beliefs to look out for and sets up the LLM with instruction prompt
#async def on_start(self):
# on_start agent receives message containing the beliefs to look out for and
# sets up the LLM with instruction prompt
# async def on_start(self):
# msg = await self.receive(timeout=0.1)
# self.beliefs = dict uit message
# send instruction prompt to LLM
beliefs: dict[str, list[str]]
beliefs = {
"mood": ["X"],
"car": ["Y"]
}
beliefs = {"mood": ["X"], "car": ["Y"]}
async def run(self):
msg = await self.receive(timeout=0.1)
@@ -58,8 +60,8 @@ class BeliefFromText(CyclicBehaviour):
prompt = text_prompt + beliefs_prompt
self.logger.info(prompt)
#prompt_msg = Message(to="LLMAgent@whatever")
#response = self.send(prompt_msg)
# prompt_msg = Message(to="LLMAgent@whatever")
# response = self.send(prompt_msg)
# Mock response; response is beliefs in JSON format, it parses do dict[str,list[list[str]]]
response = '{"mood": [["happy"]]}'
@@ -67,8 +69,9 @@ class BeliefFromText(CyclicBehaviour):
try:
json.loads(response)
belief_message = Message(
to=settings.agent_settings.bdi_core_agent_name + '@' + settings.agent_settings.host,
body=response)
to=settings.agent_settings.bdi_core_agent_name + "@" + settings.agent_settings.host,
body=response,
)
belief_message.thread = "beliefs"
await self.send(belief_message)
@@ -85,9 +88,12 @@ class BeliefFromText(CyclicBehaviour):
"""
belief = {"beliefs": {"user_said": [txt]}, "type": "belief_extraction_text"}
payload = json.dumps(belief)
belief_msg = Message(to=settings.agent_settings.belief_collector_agent_name
+ '@' + settings.agent_settings.host,
body=payload)
belief_msg = Message(
to=settings.agent_settings.belief_collector_agent_name
+ "@"
+ settings.agent_settings.host,
body=payload,
)
belief_msg.thread = "beliefs"
await self.send(belief_msg)

2
src/control_backend/agents/bdi/text_extractor.py
View File

@@ -6,4 +6,4 @@ from control_backend.agents.bdi.behaviours.text_belief_extractor import BeliefFr
class TBeliefExtractor(Agent):
async def setup(self):
self.b = BeliefFromText()
self.add_behaviour(self.b)
self.add_behaviour(self.b)

38
src/control_backend/agents/belief_collector/behaviours/continuous_collect.py
View File

@@ -1,11 +1,14 @@
import json
import logging
from spade.behaviour import CyclicBehaviour
from spade.agent import Message
from spade.behaviour import CyclicBehaviour
from control_backend.core.config import settings
logger = logging.getLogger(__name__)
class ContinuousBeliefCollector(CyclicBehaviour):
"""
Continuously collects beliefs/emotions from extractor agents:
@@ -17,7 +20,6 @@ class ContinuousBeliefCollector(CyclicBehaviour):
if msg:
await self._process_message(msg)
async def _process_message(self, msg: Message):
sender_node = self._sender_node(msg)
@@ -27,7 +29,9 @@ class ContinuousBeliefCollector(CyclicBehaviour):
except Exception as e:
logger.warning(
"BeliefCollector: failed to parse JSON from %s. Body=%r Error=%s",
sender_node, msg.body, e
sender_node,
msg.body,
e,
)
return
@@ -35,16 +39,21 @@ class ContinuousBeliefCollector(CyclicBehaviour):
# Prefer explicit 'type' field
if msg_type == "belief_extraction_text" or sender_node == "belief_text_agent_mock":
logger.info("BeliefCollector: message routed to _handle_belief_text (sender=%s)", sender_node)
logger.info(
"BeliefCollector: message routed to _handle_belief_text (sender=%s)", sender_node
)
await self._handle_belief_text(payload, sender_node)
#This is not implemented yet, but we keep the structure for future use
elif msg_type == "emotion_extraction_text" or sender_node == "emo_text_agent_mock":
logger.info("BeliefCollector: message routed to _handle_emo_text (sender=%s)", sender_node)
# This is not implemented yet, but we keep the structure for future use
elif msg_type == "emotion_extraction_text" or sender_node == "emo_text_agent_mock":
logger.info(
"BeliefCollector: message routed to _handle_emo_text (sender=%s)", sender_node
)
await self._handle_emo_text(payload, sender_node)
else:
logger.info(
"BeliefCollector: unrecognized message (sender=%s, type=%r). Ignoring.",
sender_node, msg_type
sender_node,
msg_type,
)
@staticmethod
@@ -56,13 +65,12 @@ class ContinuousBeliefCollector(CyclicBehaviour):
s = str(msg.sender) if msg.sender is not None else "no_sender"
return s.split("@", 1)[0] if "@" in s else s
async def _handle_belief_text(self, payload: dict, origin: str):
"""
Expected payload:
{
"type": "belief_extraction_text",
"beliefs": {"user_said": ["hello"","Can you help me?","stop talking to me","No","Pepper do a dance"]}
"beliefs": {"user_said": ["Can you help me?"]}
}
@@ -72,11 +80,11 @@ class ContinuousBeliefCollector(CyclicBehaviour):
if not beliefs:
logger.info("BeliefCollector: no beliefs to process.")
return
if not isinstance(beliefs, dict):
logger.warning("BeliefCollector: 'beliefs' is not a dict: %r", beliefs)
return
if not all(isinstance(v, list) for v in beliefs.values()):
logger.warning("BeliefCollector: 'beliefs' values are not all lists: %r", beliefs)
return
@@ -84,17 +92,14 @@ class ContinuousBeliefCollector(CyclicBehaviour):
logger.info("BeliefCollector: forwarding %d beliefs.", len(beliefs))
for belief_name, belief_list in beliefs.items():
for belief in belief_list:
logger.info(" - %s %s", belief_name,str(belief))
logger.info(" - %s %s", belief_name, str(belief))
await self._send_beliefs_to_bdi(beliefs, origin=origin)
async def _handle_emo_text(self, payload: dict, origin: str):
"""TODO: implement (after we have emotional recogntion)"""
pass
async def _send_beliefs_to_bdi(self, beliefs: list[str], origin: str | None = None):
"""
Sends a unified belief packet to the BDI agent.
@@ -107,6 +112,5 @@ class ContinuousBeliefCollector(CyclicBehaviour):
msg = Message(to=to_jid, sender=self.agent.jid, thread="beliefs")
msg.body = json.dumps(beliefs)
await self.send(msg)
logger.info("BeliefCollector: sent %d belief(s) to BDI at %s", len(beliefs), to_jid)

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

@@ -1,13 +1,15 @@
import logging
from spade.agent import Agent
from .behaviours.continuous_collect import ContinuousBeliefCollector
logger = logging.getLogger(__name__)
class BeliefCollectorAgent(Agent):
async def setup(self):
logger.info("BeliefCollectorAgent starting (%s)", self.jid)
# Attach the continuous collector behaviour (listens and forwards to BDI)
self.add_behaviour(ContinuousBeliefCollector())
logger.info("BeliefCollectorAgent ready.")
logger.info("BeliefCollectorAgent ready.")

28
src/control_backend/agents/llm/llm.py
View File

@@ -65,8 +65,8 @@ class LLMAgent(Agent):
Sends a response message back to the BDI Core Agent.
"""
reply = Message(
to=settings.agent_settings.bdi_core_agent_name + '@' + settings.agent_settings.host,
body=msg
to=settings.agent_settings.bdi_core_agent_name + "@" + settings.agent_settings.host,
body=msg,
)
await self.send(reply)
self.agent.logger.info("Reply sent to BDI Core Agent")
@@ -80,35 +80,31 @@ class LLMAgent(Agent):
"""
async with httpx.AsyncClient(timeout=120.0) as client:
# Example dynamic content for future (optional)
instructions = LLMInstructions()
developer_instruction = instructions.build_developer_instruction()
response = await client.post(
settings.llm_settings.local_llm_url,
headers={"Content-Type": "application/json"},
json={
"model": settings.llm_settings.local_llm_model,
"messages": [
{
"role": "developer",
"content": developer_instruction
},
{
"role": "user",
"content": prompt
}
{"role": "developer", "content": developer_instruction},
{"role": "user", "content": prompt},
],
"temperature": 0.3
"temperature": 0.3,
},
)
try:
response.raise_for_status()
data: dict[str, Any] = response.json()
return data.get("choices", [{}])[0].get(
"message", {}
).get("content", "No response")
return (
data.get("choices", [{}])[0]
.get("message", {})
.get("content", "No response")
)
except httpx.HTTPError as err:
self.agent.logger.error("HTTP error: %s", err)
return "LLM service unavailable."

19
src/control_backend/agents/mock_agents/belief_text_mock.py
View File

@@ -1,18 +1,33 @@
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 = f"{settings.agent_settings.belief_collector_agent_name}@{settings.agent_settings.host}"
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"]}
"beliefs": {
"user_said": [
"hello test",
"Can you help me?",
"stop talking to me",
"No",
"Pepper do a dance",
]
},
}
msg = Message(to=to_jid)

3
src/control_backend/agents/ri_command_agent.py
View File

@@ -1,8 +1,9 @@
import json
import logging
import zmq
from spade.agent import Agent
from spade.behaviour import CyclicBehaviour
import zmq
from control_backend.core.config import settings
from control_backend.core.zmq_context import context

11
src/control_backend/agents/ri_communication_agent.py
View File

@@ -1,14 +1,13 @@
import asyncio
import json
import logging
import zmq
from spade.agent import Agent
from spade.behaviour import CyclicBehaviour
import zmq
from control_backend.agents.ri_command_agent import RICommandAgent
from control_backend.core.config import settings
from control_backend.core.zmq_context import context
from control_backend.schemas.message import Message
from control_backend.agents.ri_command_agent import RICommandAgent
logger = logging.getLogger(__name__)
@@ -47,7 +46,7 @@ class RICommunicationAgent(Agent):
message = await asyncio.wait_for(self.agent.req_socket.recv_json(), timeout=3.0)
# We didnt get a reply :(
except asyncio.TimeoutError as e:
except TimeoutError:
logger.info("No ping retrieved in 3 seconds, killing myself.")
self.kill()
@@ -88,7 +87,7 @@ class RICommunicationAgent(Agent):
try:
received_message = await asyncio.wait_for(self.req_socket.recv_json(), timeout=20.0)
except asyncio.TimeoutError:
except TimeoutError:
logger.warning(
"No connection established in 20 seconds (attempt %d/%d)",
retries + 1,

18
src/control_backend/agents/transcription/speech_recognizer.py
View File

@@ -75,7 +75,8 @@ class MLXWhisperSpeechRecognizer(SpeechRecognizer):
self.model_name = "mlx-community/whisper-small.en-mlx"
def load_model(self):
if self.was_loaded: return
if self.was_loaded:
return
# There appears to be no dedicated mechanism to preload a model, but this `get_model` does
# store it in memory for later usage
ModelHolder.get_model(self.model_name, mx.float16)
@@ -83,9 +84,9 @@ class MLXWhisperSpeechRecognizer(SpeechRecognizer):
def recognize_speech(self, audio: np.ndarray) -> str:
self.load_model()
return mlx_whisper.transcribe(audio,
path_or_hf_repo=self.model_name,
decode_options=self._get_decode_options(audio))["text"]
return mlx_whisper.transcribe(
audio, path_or_hf_repo=self.model_name, decode_options=self._get_decode_options(audio)
)["text"]
return mlx_whisper.transcribe(audio, path_or_hf_repo=self.model_name)["text"].strip()
@@ -95,12 +96,13 @@ class OpenAIWhisperSpeechRecognizer(SpeechRecognizer):
self.model = None
def load_model(self):
if self.model is not None: return
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)
def recognize_speech(self, audio: np.ndarray) -> str:
self.load_model()
return whisper.transcribe(self.model,
audio,
decode_options=self._get_decode_options(audio))["text"]
return whisper.transcribe(
self.model, audio, decode_options=self._get_decode_options(audio)
)["text"]

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

@@ -47,7 +47,8 @@ class TranscriptionAgent(Agent):
"""Share a transcription to the other agents that depend on it."""
receiver_jids = [
settings.agent_settings.text_belief_extractor_agent_name
+ '@' + settings.agent_settings.host,
+ "@"
+ settings.agent_settings.host,
] # Set message receivers here
for receiver_jid in receiver_jids: