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

@@ -1,8 +1,10 @@
 import asyncio
-from spade.behaviour import CyclicBehaviour
-import logging
-from spade.message import Message
 import json
+import logging
+
+from spade.behaviour import CyclicBehaviour
+from spade.message import Message
+
 from control_backend.core.config import settings
 
 
@@ -11,8 +13,7 @@ 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 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.
@@ -22,14 +23,14 @@ class BeliefFromText(CyclicBehaviour):
     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):
     #    msg = await self.receive(timeout=0.1)
     #    self.beliefs = dict uit message
     #    send instruction prompt to LLM
 
-    beliefs: dict[str,list[str]]
+    beliefs: dict[str, list[str]]
     beliefs = {
         "mood": ["X"],
         "car": ["Y"]
@@ -48,14 +49,14 @@ class BeliefFromText(CyclicBehaviour):
                     self.logger.info("Received message from other agent.")
                     pass
         await asyncio.sleep(1)
-    
-    async def _process_transcription(self,text: str):
+
+    async def _process_transcription(self, text: str):
         text_prompt = f"Text: {text}"
-        
+
         beliefs_prompt = "These are the beliefs to be bound:\n"
         for belief, values in self.beliefs.items():
             beliefs_prompt += f"{belief}({', '.join(values)})\n"
-        
+
         prompt = text_prompt + beliefs_prompt
         self.logger.info(prompt)
         #prompt_msg = Message(to="LLMAgent@whatever")
@@ -66,26 +67,30 @@ class BeliefFromText(CyclicBehaviour):
         # Verify by trying to parse
         try:
             json.loads(response)
-            belief_message = Message(to=settings.agent_settings.bdi_core_agent_name + '@' + settings.agent_settings.host, body=response)
+            belief_message = Message(
+                to=settings.agent_settings.bdi_core_agent_name + '@' + settings.agent_settings.host,
+                body=response)
             belief_message.thread = "beliefs"
 
             await self.send(belief_message)
             self.logger.info("Sent beliefs to BDI.")
-        except:
-            #loading failed so the response is in wrong format, throw warning (let LLM respond to ask again?)
+        except json.JSONDecodeError:
+            # Parsing failed, so the response is in the wrong format, log warning
             self.logger.warning("Received LLM response in incorrect format.")
 
     async def _process_transcription_demo(self, txt: str):
         """
-        Demo version to process the transcription input to beliefs. For the demo only the belief 'user_said' is relevant so
-        this function simply makes a dict with key: "user_said", value: txt and passes this to the Belief Collector agent.
+        Demo version to process the transcription input to beliefs. For the demo only the belief
+        'user_said' is relevant, so this function simply makes a dict with key: "user_said",
+        value: txt and passes this to the Belief Collector agent.
         """
         belief = {"user_said": [[txt]]}
         payload = json.dumps(belief)
         # TODO: Change to belief collector
-        belief_msg = Message(to=settings.agent_settings.bdi_core_agent_name + '@' + settings.agent_settings.host, body=payload)
+        belief_msg = Message(to=settings.agent_settings.bdi_core_agent_name
+                             + '@' + settings.agent_settings.host,
+                             body=payload)
         belief_msg.thread = "beliefs"
-        
+
         await self.send(belief_msg)
         self.logger.info("Sent beliefs to Belief Collector.")
-        

src/control_backend/agents/bdi/test_agent.py

@@ -1,9 +1,10 @@
-import spade
 from spade.agent import Agent
 from spade.behaviour import OneShotBehaviour
 from spade.message import Message
+
 from control_backend.core.config import settings
 
+
 class SenderAgent(Agent):
     class InformBehav(OneShotBehaviour):
         async def run(self):

src/control_backend/agents/bdi/text_extractor.py

@@ -1,9 +1,8 @@
-import spade
 from spade.agent import Agent
-import logging
 
 from control_backend.agents.bdi.behaviours.text_belief_extractor import BeliefFromText
 
+
 class TBeliefExtractor(Agent):
     async def setup(self):
         self.b = BeliefFromText()

src/control_backend/core/config.py

@@ -10,6 +10,7 @@ class AgentSettings(BaseModel):
     host: str = "localhost"    
     bdi_core_agent_name: str = "bdi_core"
     belief_collector_agent_name: str = "belief_collector"
+    text_belief_extractor_agent_name: str = "text_belief_extractor"
     vad_agent_name: str = "vad_agent"
     llm_agent_name: str = "llm_agent"
     test_agent_name: str = "test_agent"
@@ -19,8 +20,8 @@ class AgentSettings(BaseModel):
 
 
 class LLMSettings(BaseModel):
-    local_llm_url: str = "http://145.107.82.68:1234/v1/chat/completions"
-    local_llm_model: str = "openai/gpt-oss-120b"
+    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"

src/control_backend/main.py

@@ -44,18 +44,29 @@ 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)
+    llm_agent = LLMAgent(
+        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, "src/control_backend/agents/bdi/rules.asl")
+    bdi_core = BDICoreAgent(
+        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()
 
-    text_belief_extractor = TBeliefExtractor(settings.agent_settings.belief_collector_agent_name + '@' + settings.agent_settings.host, "placehodler")
+    text_belief_extractor = TBeliefExtractor(
+        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()
 
-    test_agent = SenderAgent(settings.agent_settings.test_agent_name + '@' + settings.agent_settings.host, "placeholder")
+    test_agent = SenderAgent(
+        settings.agent_settings.test_agent_name + '@' + settings.agent_settings.host,
+        settings.agent_settings.test_agent_name
+    )
     await test_agent.start()
 
     _temp_vad_agent = VADAgent("tcp://localhost:5558", False)