style: apply ruff check and format

Made sure all ruff checks pass and formatted all files.

ref: N25B-224

src/control_backend/agents/bdi/bdi_core.py

@@ -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())

src/control_backend/agents/bdi/behaviours/belief_setter.py

@@ -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)

src/control_backend/agents/bdi/behaviours/receive_llm_resp_behaviour.py

@@ -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

src/control_backend/agents/bdi/behaviours/text_belief_extractor.py

@@ -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)

src/control_backend/agents/bdi/text_extractor.py

@@ -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)