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style: apply ruff check and format

Browse Source 
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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4
src/control_backend/agents/bdi/bdi_core.py
View File

@@ -58,8 +58,8 @@ 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)

6
src/control_backend/agents/bdi/behaviours/belief_setter.py
View File

@@ -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)

4
src/control_backend/agents/bdi/behaviours/receive_llm_resp_behaviour.py
View File

@@ -9,7 +9,9 @@ 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:
@@ -20,7 +22,7 @@ class ReceiveLLMResponseBehaviour(CyclicBehaviour):
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

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)

32
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
# 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)
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?"]}
}
@@ -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)

2
src/control_backend/agents/belief_collector/belief_collector.py
View File

@@ -1,10 +1,12 @@
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)

24
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")
@@ -90,25 +90,21 @@ class LLMAgent(Agent):
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:

5
src/control_backend/api/v1/endpoints/command.py
View File

@@ -1,9 +1,9 @@
from fastapi import APIRouter, Request
import logging
from fastapi import APIRouter, Request
from zmq import Socket
from control_backend.schemas.ri_message import SpeechCommand, RIEndpoint
from control_backend.schemas.ri_message import SpeechCommand
logger = logging.getLogger(__name__)
@@ -18,5 +18,4 @@ async def receive_command(command: SpeechCommand, request: Request):
pub_socket: Socket = request.app.state.internal_comm_socket
pub_socket.send_multipart([topic, command.model_dump_json().encode()])
return {"status": "Command received"}

2
src/control_backend/api/v1/router.py
View File

@@ -1,6 +1,6 @@
from fastapi.routing import APIRouter
from control_backend.api.v1.endpoints import message, sse, command
from control_backend.api.v1.endpoints import command, message, sse
api_router = APIRouter()

2
src/control_backend/core/config.py
View File

@@ -24,6 +24,7 @@ class LLMSettings(BaseModel):
local_llm_url: str = "http://localhost:1234/v1/chat/completions"
local_llm_model: str = "openai/gpt-oss-20b"
class Settings(BaseSettings):
app_title: str = "PepperPlus"
@@ -37,4 +38,5 @@ class Settings(BaseSettings):
model_config = SettingsConfigDict(env_file=".env")
settings = Settings()

20
src/control_backend/main.py
View File

@@ -8,13 +8,14 @@ import zmq
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
# Internal imports
from control_backend.agents.ri_communication_agent import RICommunicationAgent
from control_backend.agents.bdi.bdi_core import BDICoreAgent
from control_backend.agents.vad_agent import VADAgent
from control_backend.agents.llm.llm import LLMAgent
from control_backend.agents.bdi.text_extractor import TBeliefExtractor
from control_backend.agents.belief_collector.belief_collector import BeliefCollectorAgent
from control_backend.agents.llm.llm import LLMAgent
# Internal imports
from control_backend.agents.ri_communication_agent import RICommunicationAgent
from control_backend.agents.vad_agent import VADAgent
from control_backend.api.v1.router import api_router
from control_backend.core.config import settings
from control_backend.core.zmq_context import context
@@ -34,7 +35,6 @@ async def lifespan(app: FastAPI):
app.state.internal_comm_socket = internal_comm_socket
logger.info("Internal publishing socket bound to %s", internal_comm_socket)
# Initiate agents
ri_communication_agent = RICommunicationAgent(
settings.agent_settings.ri_communication_agent_name + "@" + settings.agent_settings.host,
@@ -45,26 +45,28 @@ async def lifespan(app: FastAPI):
await ri_communication_agent.start()
llm_agent = LLMAgent(
settings.agent_settings.llm_agent_name + '@' + settings.agent_settings.host,
settings.agent_settings.llm_agent_name + "@" + settings.agent_settings.host,
settings.agent_settings.llm_agent_name,
)
await llm_agent.start()
bdi_core = BDICoreAgent(
settings.agent_settings.bdi_core_agent_name + '@' + settings.agent_settings.host,
settings.agent_settings.bdi_core_agent_name + "@" + settings.agent_settings.host,
settings.agent_settings.bdi_core_agent_name,
"src/control_backend/agents/bdi/rules.asl",
)
await bdi_core.start()
belief_collector = BeliefCollectorAgent(
settings.agent_settings.belief_collector_agent_name + '@' + settings.agent_settings.host,
settings.agent_settings.belief_collector_agent_name + "@" + settings.agent_settings.host,
settings.agent_settings.belief_collector_agent_name,
)
await belief_collector.start()
text_belief_extractor = TBeliefExtractor(
settings.agent_settings.text_belief_extractor_agent_name + '@' + settings.agent_settings.host,
settings.agent_settings.text_belief_extractor_agent_name
+ "@"
+ settings.agent_settings.host,
settings.agent_settings.text_belief_extractor_agent_name,
)
await text_belief_extractor.start()

4
src/control_backend/schemas/ri_message.py
View File

@@ -1,7 +1,7 @@
from enum import Enum
from typing import Any, Literal
from typing import Any
from pydantic import BaseModel, Field, ValidationError
from pydantic import BaseModel
class RIEndpoint(str, Enum):

8
test/integration/agents/test_ri_commands_agent.py
View File

@@ -1,10 +1,10 @@
import asyncio
import zmq
import json
import pytest
from unittest.mock import AsyncMock, MagicMock, patch
import pytest
import zmq
from control_backend.agents.ri_command_agent import RICommandAgent
from control_backend.schemas.ri_message import SpeechCommand
@pytest.mark.asyncio

12
test/integration/agents/test_ri_communication_agent.py
View File

@@ -1,6 +1,8 @@
import asyncio
from unittest.mock import ANY, AsyncMock, MagicMock, patch
import pytest
from unittest.mock import AsyncMock, MagicMock, patch, ANY
from control_backend.agents.ri_communication_agent import RICommunicationAgent
@@ -185,8 +187,8 @@ async def test_setup_creates_socket_and_negotiate_3(monkeypatch, caplog):
# Mock RICommandAgent agent startup
# We are sending wrong negotiation info to the communication agent, so we should retry and expect a
# better response, within a limited time.
# We are sending wrong negotiation info to the communication agent,
# so we should retry and expect a better response, within a limited time.
with patch(
"control_backend.agents.ri_communication_agent.RICommandAgent", autospec=True
) as MockCommandAgent:
@@ -358,8 +360,8 @@ async def test_setup_creates_socket_and_negotiate_7(monkeypatch, caplog):
# Mock RICommandAgent agent startup
# We are sending wrong negotiation info to the communication agent, so we should retry and expect a
# better response, within a limited time.
# We are sending wrong negotiation info to the communication agent,
# so we should retry and expect a better response, within a limited time.
with patch(
"control_backend.agents.ri_communication_agent.RICommandAgent", autospec=True
) as MockCommandAgent:

3
test/integration/api/endpoints/test_command_endpoint.py
View File

@@ -1,7 +1,8 @@
from unittest.mock import MagicMock
import pytest
from fastapi import FastAPI
from fastapi.testclient import TestClient
from unittest.mock import MagicMock
from control_backend.api.v1.endpoints import command
from control_backend.schemas.ri_message import SpeechCommand

16
test/integration/schemas/test_ri_message.py
View File

@@ -1,7 +1,8 @@
import pytest
from control_backend.schemas.ri_message import RIMessage, RIEndpoint, SpeechCommand
from pydantic import ValidationError
from control_backend.schemas.ri_message import RIEndpoint, RIMessage, SpeechCommand
def valid_command_1():
return SpeechCommand(data="Hallo?")
@@ -13,24 +14,13 @@ def invalid_command_1():
def test_valid_speech_command_1():
command = valid_command_1()
try:
RIMessage.model_validate(command)
SpeechCommand.model_validate(command)
assert True
except ValidationError:
assert False
def test_invalid_speech_command_1():
command = invalid_command_1()
passed_ri_message_validation = False
try:
# Should succeed, still.
RIMessage.model_validate(command)
passed_ri_message_validation = True
# Should fail.
with pytest.raises(ValidationError):
SpeechCommand.model_validate(command)
assert False
except ValidationError:
assert passed_ri_message_validation

1
test/unit/agents/bdi/behaviours/test_belief_setter.py
View File

@@ -203,6 +203,7 @@ def test_set_beliefs_success(belief_setter, mock_agent, caplog):
assert "Set belief is_hot with arguments ['kitchen']" in caplog.text
assert "Set belief door_opened with arguments ['front_door', 'back_door']" in caplog.text
# def test_responded_unset(belief_setter, mock_agent):
# # Arrange
# new_beliefs = {"user_said": ["message"]}

63
test/unit/agents/belief_collector/behaviours/test_continuous_collect.py
View File

@@ -1,10 +1,12 @@
import json
import logging
from unittest.mock import MagicMock, AsyncMock, call
from unittest.mock import AsyncMock, MagicMock
import pytest
from control_backend.agents.belief_collector.behaviours.continuous_collect import ContinuousBeliefCollector
from control_backend.agents.belief_collector.behaviours.continuous_collect import (
ContinuousBeliefCollector,
)
@pytest.fixture
def mock_agent(mocker):
@@ -13,6 +15,7 @@ def mock_agent(mocker):
agent.jid = "belief_collector_agent@test"
return agent
@pytest.fixture
def continuous_collector(mock_agent, mocker):
"""Fixture to create an instance of ContinuousBeliefCollector with a mocked agent."""
@@ -25,6 +28,7 @@ def continuous_collector(mock_agent, mocker):
collector.receive = AsyncMock()
return collector
@pytest.mark.asyncio
async def test_run_no_message_received(continuous_collector, mocker):
"""
@@ -40,6 +44,7 @@ async def test_run_no_message_received(continuous_collector, mocker):
# Assert
continuous_collector._process_message.assert_not_called()
@pytest.mark.asyncio
async def test_run_message_received(continuous_collector, mocker):
"""
@@ -56,6 +61,7 @@ async def test_run_message_received(continuous_collector, mocker):
# Assert
continuous_collector._process_message.assert_awaited_once_with(mock_msg)
@pytest.mark.asyncio
async def test_process_message_invalid(continuous_collector, mocker):
"""
@@ -67,7 +73,9 @@ async def test_process_message_invalid(continuous_collector, mocker):
msg.body = invalid_json
msg.sender = "belief_text_agent_mock@test"
logger_mock = mocker.patch("control_backend.agents.belief_collector.behaviours.continuous_collect.logger")
logger_mock = mocker.patch(
"control_backend.agents.belief_collector.behaviours.continuous_collect.logger"
)
# Act
await continuous_collector._process_message(msg)
@@ -75,6 +83,7 @@ async def test_process_message_invalid(continuous_collector, mocker):
# Assert
logger_mock.warning.assert_called_once()
def test_get_sender_from_message(continuous_collector):
"""
Test that _sender_node correctly extracts the sender node from the message JID.
@@ -89,6 +98,7 @@ def test_get_sender_from_message(continuous_collector):
# Assert
assert sender_node == "agent_node"
@pytest.mark.asyncio
async def test_routes_to_handle_belief_text_by_type(continuous_collector, mocker):
msg = MagicMock()
@@ -98,6 +108,7 @@ async def test_routes_to_handle_belief_text_by_type(continuous_collector, mocker
await continuous_collector._process_message(msg)
spy.assert_awaited_once()
@pytest.mark.asyncio
async def test_routes_to_handle_belief_text_by_sender(continuous_collector, mocker):
msg = MagicMock()
@@ -107,6 +118,7 @@ async def test_routes_to_handle_belief_text_by_sender(continuous_collector, mock
await continuous_collector._process_message(msg)
spy.assert_awaited_once()
@pytest.mark.asyncio
async def test_routes_to_handle_emo_text(continuous_collector, mocker):
msg = MagicMock()
@@ -116,50 +128,64 @@ async def test_routes_to_handle_emo_text(continuous_collector, mocker):
await continuous_collector._process_message(msg)
spy.assert_awaited_once()
@pytest.mark.asyncio
async def test_unrecognized_message_logs_info(continuous_collector, mocker):
msg = MagicMock()
msg.body = json.dumps({"type": "something_else"})
msg.sender = "x@test"
logger_mock = mocker.patch("control_backend.agents.belief_collector.behaviours.continuous_collect.logger")
logger_mock = mocker.patch(
"control_backend.agents.belief_collector.behaviours.continuous_collect.logger"
)
await continuous_collector._process_message(msg)
logger_mock.info.assert_any_call(
"BeliefCollector: unrecognized message (sender=%s, type=%r). Ignoring.", "x", "something_else"
"BeliefCollector: unrecognized message (sender=%s, type=%r). Ignoring.",
"x",
"something_else",
)
@pytest.mark.asyncio
async def test_belief_text_no_beliefs(continuous_collector, mocker):
msg_payload = {"type": "belief_extraction_text"} # no 'beliefs'
logger_mock = mocker.patch("control_backend.agents.belief_collector.behaviours.continuous_collect.logger")
logger_mock = mocker.patch(
"control_backend.agents.belief_collector.behaviours.continuous_collect.logger"
)
await continuous_collector._handle_belief_text(msg_payload, "origin_node")
logger_mock.info.assert_any_call("BeliefCollector: no beliefs to process.")
@pytest.mark.asyncio
async def test_belief_text_beliefs_not_dict(continuous_collector, mocker):
payload = {"type": "belief_extraction_text", "beliefs": ["not", "a", "dict"]}
logger_mock = mocker.patch("control_backend.agents.belief_collector.behaviours.continuous_collect.logger")
logger_mock = mocker.patch(
"control_backend.agents.belief_collector.behaviours.continuous_collect.logger"
)
await continuous_collector._handle_belief_text(payload, "origin")
logger_mock.warning.assert_any_call("BeliefCollector: 'beliefs' is not a dict: %r", ["not", "a", "dict"])
logger_mock.warning.assert_any_call(
"BeliefCollector: 'beliefs' is not a dict: %r", ["not", "a", "dict"]
)
@pytest.mark.asyncio
async def test_belief_text_values_not_lists(continuous_collector, mocker):
payload = {"type": "belief_extraction_text", "beliefs": {"user_said": "not-a-list"}}
logger_mock = mocker.patch("control_backend.agents.belief_collector.behaviours.continuous_collect.logger")
logger_mock = mocker.patch(
"control_backend.agents.belief_collector.behaviours.continuous_collect.logger"
)
await continuous_collector._handle_belief_text(payload, "origin")
logger_mock.warning.assert_any_call(
"BeliefCollector: 'beliefs' values are not all lists: %r", {"user_said": "not-a-list"}
)
@pytest.mark.asyncio
async def test_belief_text_happy_path_logs_items_and_sends(continuous_collector, mocker):
payload = {
"type": "belief_extraction_text",
"beliefs": {"user_said": ["hello test", "No"]}
}
# Your code calls self.send(..); patch it (or switch implementation to self.agent.send and patch that)
payload = {"type": "belief_extraction_text", "beliefs": {"user_said": ["hello test", "No"]}}
continuous_collector.send = AsyncMock()
logger_mock = mocker.patch("control_backend.agents.belief_collector.behaviours.continuous_collect.logger")
logger_mock = mocker.patch(
"control_backend.agents.belief_collector.behaviours.continuous_collect.logger"
)
await continuous_collector._handle_belief_text(payload, "belief_text_agent_mock")
logger_mock.info.assert_any_call("BeliefCollector: forwarding %d beliefs.", 1)
@@ -169,12 +195,14 @@ async def test_belief_text_happy_path_logs_items_and_sends(continuous_collector,
# make sure we attempted a send
continuous_collector.send.assert_awaited_once()
@pytest.mark.asyncio
async def test_send_beliefs_noop_on_empty(continuous_collector):
continuous_collector.send = AsyncMock()
await continuous_collector._send_beliefs_to_bdi([], origin="o")
continuous_collector.send.assert_not_awaited()
# @pytest.mark.asyncio
# async def test_send_beliefs_sends_json_packet(continuous_collector):
# # Patch .send and capture the message body
@@ -191,16 +219,19 @@ async def test_send_beliefs_noop_on_empty(continuous_collector):
# assert "belief_packet" in json.loads(sent["body"])["type"]
# assert json.loads(sent["body"])["beliefs"] == beliefs
def test_sender_node_no_sender_returns_literal(continuous_collector):
msg = MagicMock()
msg.sender = None
assert continuous_collector._sender_node(msg) == "no_sender"
def test_sender_node_without_at(continuous_collector):
msg = MagicMock()
msg.sender = "localpartonly"
assert continuous_collector._sender_node(msg) == "localpartonly"
@pytest.mark.asyncio
async def test_belief_text_coerces_non_strings(continuous_collector, mocker):
payload = {"type": "belief_extraction_text", "beliefs": {"user_said": [["hi", 123]]}}

4
test/unit/agents/transcription/test_speech_recognizer.py
View File

@@ -6,7 +6,7 @@ from control_backend.agents.transcription.speech_recognizer import OpenAIWhisper
def test_estimate_max_tokens():
"""Inputting one minute of audio, assuming 300 words per minute, expecting 400 tokens."""
audio = np.empty(shape=(60*16_000), dtype=np.float32)
audio = np.empty(shape=(60 * 16_000), dtype=np.float32)
actual = SpeechRecognizer._estimate_max_tokens(audio)
@@ -16,7 +16,7 @@ def test_estimate_max_tokens():
def test_get_decode_options():
"""Check whether the right decode options are given under different scenarios."""
audio = np.empty(shape=(60*16_000), dtype=np.float32)
audio = np.empty(shape=(60 * 16_000), dtype=np.float32)
# With the defaults, it should limit output length based on input size
recognizer = OpenAIWhisperSpeechRecognizer()