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

@@ -0,0 +1,96 @@
+import asyncio
+import json
+import logging
+
+from spade.behaviour import CyclicBehaviour
+from spade.message import Message
+
+from control_backend.core.config import settings
+
+
+class BeliefFromText(CyclicBehaviour):
+    logger = logging.getLogger("Belief From Text")
+
+    # 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).
+    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.
+    """
+
+    # 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"]
+    }
+
+    async def run(self):
+        msg = await self.receive(timeout=0.1)
+        if msg:
+            sender = msg.sender.node
+            match sender:
+                # TODO: Change to Transcriber agent name once implemented
+                case settings.agent_settings.test_agent_name:
+                    self.logger.info("Received text from transcriber.")
+                    await self._process_transcription_demo(msg.body)
+                case _:
+                    self.logger.info("Received message from other agent.")
+                    pass
+        await asyncio.sleep(1)
+
+    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")
+        #response = self.send(prompt_msg)
+
+        # Mock response; response is beliefs in JSON format, it parses do dict[str,list[list[str]]]
+        response = '{"mood": [["happy"]]}'
+        # 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.thread = "beliefs"
+
+            await self.send(belief_message)
+            self.logger.info("Sent beliefs to BDI.")
+        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.
+        """
+        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.thread = "beliefs"
+
+        await self.send(belief_msg)
+        self.logger.info("Sent beliefs to Belief Collector.")

src/control_backend/agents/bdi/text_extractor.py

@@ -0,0 +1,9 @@
+from spade.agent import Agent
+
+from control_backend.agents.bdi.behaviours.text_belief_extractor import BeliefFromText
+
+
+class TBeliefExtractor(Agent):
+    async def setup(self):
+        self.b = BeliefFromText()
+        self.add_behaviour(self.b)

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_url: str = "http://localhost:1234/v1/chat/completions"
-    local_llm_model: str = "openai/gpt-oss-120b"
+    local_llm_model: str = "openai/gpt-oss-20b"
 
 class Settings(BaseSettings):
     app_title: str = "PepperPlus"

src/control_backend/main.py

@@ -13,6 +13,7 @@ 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.api.v1.router import api_router
 from control_backend.core.config import settings
 from control_backend.core.zmq_context import context
@@ -42,14 +43,25 @@ async def lifespan(app: FastAPI):
     )
     await ri_communication_agent.start()
 
-    
+    llm_agent = LLMAgent(
-    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)
+        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.text_belief_extractor_agent_name + '@' + settings.agent_settings.host,
+        settings.agent_settings.text_belief_extractor_agent_name,
+    )
+    await text_belief_extractor.start()
+
     _temp_vad_agent = VADAgent("tcp://localhost:5558", False)
     await _temp_vad_agent.start()