build: add Dockerfile

ref: N25B-280

.dockerignore

@@ -0,0 +1,14 @@
+.git
+.venv
+__pycache__/
+*.pyc
+.dockerignore
+Dockerfile
+README.md
+.gitlab-ci.yml
+.gitignore
+.pre-commit-config.yaml
+.githooks/
+test/
+.pytest_cache/
+.ruff_cache/

.githooks/check-commit-msg.sh

@@ -30,7 +30,7 @@ HEADER=$(head -n 1 "$COMMIT_MSG_FILE")
 
 # Check for Merge commits (covers 'git merge' and PR merges from GitHub/GitLab)
 # Examples: "Merge branch 'main' into ...", "Merge pull request #123 from ..."
-MERGE_PATTERN="^Merge (branch|pull request|tag) .*"
+MERGE_PATTERN="^Merge (remote-tracking )?(branch|pull request|tag) .*"
 if [[ "$HEADER" =~ $MERGE_PATTERN ]]; then
   echo -e "${GREEN}Merge commit detected by message content. Skipping validation.${NC}"
   exit 0

Dockerfile

@@ -0,0 +1,21 @@
+# Debian based image
+FROM ghcr.io/astral-sh/uv:0.9.8-trixie-slim
+
+WORKDIR /app
+
+ENV VIRTUAL_ENV=/app/.venv
+ENV PATH="$VIRTUAL_ENV/bin:$PATH"
+
+RUN apt-get update && apt-get install -y gcc=4:14.2.0-1 portaudio19-dev && apt-get install -y ca-certificates && rm -rf /var/lib/apt/lists/*
+
+COPY pyproject.toml uv.lock .python-version ./
+
+RUN uv sync
+
+COPY . .
+
+EXPOSE 8000
+
+ENV PYTHONPATH=src
+
+CMD [ "fastapi", "run", "src/control_backend/main.py" ]

src/control_backend/agents/bdi/bdi_core.py

@@ -1,3 +1,4 @@
+import json
 import logging
 
 import agentspeak
@@ -37,28 +38,66 @@ class BDICoreAgent(BDIAgent):
         Registers custom AgentSpeak actions callable from plans.
         """
 
-        @actions.add(".reply", 1)
+        @actions.add(".reply", 3)
         def _reply(agent: "BDICoreAgent", term, intention):
             """
             Sends text to the LLM (AgentSpeak action).
             Example: .reply("Hello LLM!")
             """
             message_text = agentspeak.grounded(term.args[0], intention.scope)
-            self.logger.debug("Reply action sending: %s", message_text)
+            norms = agentspeak.grounded(term.args[1], intention.scope)
+            goals = agentspeak.grounded(term.args[2], intention.scope)
 
-            self._send_to_llm(str(message_text))
+            self.logger.debug("Norms: %s", norms)
+            self.logger.debug("Goals: %s", goals)
+            self.logger.debug("User text: %s", message_text)
+
+            self._send_to_llm(str(message_text), str(norms), str(goals))
             yield
 
-    def _send_to_llm(self, text: str):
+        @actions.add(".reply_no_norms", 2)
+        def _reply_no_norms(agent: "BDICoreAgent", term, intention):
+            message_text = agentspeak.grounded(term.args[0], intention.scope)
+            goals = agentspeak.grounded(term.args[1], intention.scope)
+
+            self.logger.debug("Goals: %s", goals)
+            self.logger.debug("User text: %s", message_text)
+
+            self._send_to_llm(str(message_text), goals=str(goals))
+
+        @actions.add(".reply_no_goals", 2)
+        def _reply_no_goals(agent: "BDICoreAgent", term, intention):
+            message_text = agentspeak.grounded(term.args[0], intention.scope)
+            norms = agentspeak.grounded(term.args[1], intention.scope)
+
+            self.logger.debug("Norms: %s", norms)
+            self.logger.debug("User text: %s", message_text)
+
+            self._send_to_llm(str(message_text), norms=str(norms))
+
+        @actions.add(".reply_no_goals_no_norms", 1)
+        def _reply_no_goals_no_norms(agent: "BDICoreAgent", term, intention):
+            message_text = agentspeak.grounded(term.args[0], intention.scope)
+
+            self.logger.debug("User text: %s", message_text)
+
+            self._send_to_llm(message_text)
+
+    def _send_to_llm(self, text: str, norms: str = None, goals: str = None):
         """
         Sends a text query to the LLM Agent asynchronously.
         """
 
         class SendBehaviour(OneShotBehaviour):
             async def run(self) -> None:
+                message_dict = {
+                    "text": text,
+                    "norms": norms if norms else "",
+                    "goals": goals if goals else "",
+                }
                 msg = Message(
                     to=settings.agent_settings.llm_agent_name + "@" + settings.agent_settings.host,
-                    body=text,
+                    body=json.dumps(message_dict),
                 )
 
                 await self.send(msg)

src/control_backend/agents/bdi/bdi_program_manager/bdi_program_manager.py

@@ -0,0 +1,27 @@
+import zmq
+from zmq.asyncio import Context
+
+from control_backend.agents import BaseAgent
+from control_backend.core.config import settings
+
+from .receive_programs_behavior import ReceiveProgramsBehavior
+
+
+class BDIProgramManager(BaseAgent):
+    """
+    Will interpret programs received from the HTTP endpoint. Extracts norms, goals, triggers and
+    forwards them to the BDI as beliefs.
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.sub_socket = None
+
+    async def setup(self):
+        context = Context.instance()
+
+        self.sub_socket = context.socket(zmq.SUB)
+        self.sub_socket.connect(settings.zmq_settings.internal_sub_address)
+        self.sub_socket.subscribe("program")
+
+        self.add_behaviour(ReceiveProgramsBehavior())

src/control_backend/agents/bdi/bdi_program_manager/receive_programs_behavior.py

@@ -0,0 +1,59 @@
+import json
+
+from pydantic import ValidationError
+from spade.behaviour import CyclicBehaviour
+from spade.message import Message
+
+from control_backend.core.config import settings
+from control_backend.schemas.program import Program
+
+
+class ReceiveProgramsBehavior(CyclicBehaviour):
+    async def _receive(self) -> Program | None:
+        topic, body = await self.agent.sub_socket.recv_multipart()
+
+        try:
+            return Program.model_validate_json(body)
+        except ValidationError as e:
+            self.agent.logger.error("Received an invalid program.", exc_info=e)
+            return None
+
+    def _extract_norms(self, program: Program) -> str:
+        """First phase only for now, as a single newline delimited string."""
+        if not program.phases:
+            return ""
+        if not program.phases[0].phaseData.norms:
+            return ""
+        norm_values = [norm.value for norm in program.phases[0].phaseData.norms]
+        return "\n".join(norm_values)
+
+    def _extract_goals(self, program: Program) -> str:
+        """First phase only for now, as a single newline delimited string."""
+        if not program.phases:
+            return ""
+        if not program.phases[0].phaseData.goals:
+            return ""
+        goal_descriptions = [goal.description for goal in program.phases[0].phaseData.goals]
+        return "\n".join(goal_descriptions)
+
+    async def _send_to_bdi(self, program: Program):
+        temp_allowed_parts = {
+            "norms": [self._extract_norms(program)],
+            "goals": [self._extract_goals(program)],
+        }
+
+        message = Message(
+            to=settings.agent_settings.bdi_core_agent_name + "@" + settings.agent_settings.host,
+            sender=self.agent.jid,
+            body=json.dumps(temp_allowed_parts),
+            thread="beliefs",
+        )
+        await self.send(message)
+        self.agent.logger.debug("Sent new norms and goals to the BDI agent.")
+
+    async def run(self):
+        program = await self._receive()
+        if not program:
+            return
+
+        await self._send_to_bdi(program)

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

@@ -17,7 +17,9 @@ class BeliefSetterBehaviour(CyclicBehaviour):
 
     async def run(self):
         """Polls for messages and processes them."""
-        msg = await self.receive()
+        msg = await self.receive(timeout=1)
+        if not msg:
+            return
         self.agent.logger.debug(
             "Received message from %s with thread '%s' and body: %s",
             msg.sender,
@@ -37,8 +39,13 @@ class BeliefSetterBehaviour(CyclicBehaviour):
                     "Message is from the belief collector agent. Processing as belief message."
                 )
                 self._process_belief_message(message)
+            case settings.agent_settings.program_manager_agent_name:
+                self.agent.logger.debug(
+                    "Processing message from the program manager. Processing as belief message."
+                )
+                self._process_belief_message(message)
             case _:
-                self.agent.logger.debug("Not the belief agent, discarding message")
+                self.agent.logger.debug("Not from expected agents, discarding message")
                 pass
 
     def _process_belief_message(self, message: Message):

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

@@ -11,7 +11,9 @@ class ReceiveLLMResponseBehaviour(CyclicBehaviour):
     """
 
     async def run(self):
-        msg = await self.receive()
+        msg = await self.receive(timeout=1)
+        if not msg:
+            return
 
         sender = msg.sender.node
         match sender:

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

@@ -38,8 +38,8 @@ class BeliefFromText(CyclicBehaviour):
     beliefs = {"mood": ["X"], "car": ["Y"]}
 
     async def run(self):
-        msg = await self.receive()
+        msg = await self.receive(timeout=1)
-        if msg is None:
+        if not msg:
             return
 
         sender = msg.sender.node

src/control_backend/agents/bdi/rules.asl

@@ -1,3 +1,18 @@
-+new_message : user_said(Message) <-
+norms("").
+goals("").
+
++new_message : user_said(Message) & norms(Norms) & goals(Goals) <-
   -new_message;
-  .reply(Message).
+  .reply(Message, Norms, Goals).
+
+// +new_message : user_said(Message) & norms(Norms) <-
+//   -new_message;
+//   .reply_no_goals(Message, Norms).
+//
+// +new_message : user_said(Message) & goals(Goals) <-
+//   -new_message;
+//   .reply_no_norms(Message, Goals).
+//
+// +new_message : user_said(Message) <-
+//   -new_message;
+//   .reply_no_goals_no_norms(Message).

src/control_backend/agents/belief_collector/behaviours/continuous_collect.py

@@ -14,7 +14,9 @@ class ContinuousBeliefCollector(CyclicBehaviour):
     """
 
     async def run(self):
-        msg = await self.receive()
+        msg = await self.receive(timeout=1)
+        if not msg:
+            return
         await self._process_message(msg)
 
     async def _process_message(self, msg: Message):

src/control_backend/agents/llm/llm.py

@@ -30,7 +30,9 @@ class LLMAgent(BaseAgent):
             Receives SPADE messages and processes only those originating from the
             configured BDI agent.
             """
-            msg = await self.receive()
+            msg = await self.receive(timeout=1)
+            if not msg:
+                return
 
             sender = msg.sender.node
             self.agent.logger.debug(
@@ -50,9 +52,13 @@ class LLMAgent(BaseAgent):
             Forwards user text from the BDI to the LLM and replies with the generated text in chunks
             separated by punctuation.
             """
-            user_text = message.body
+            try:
+                message = json.loads(message.body)
+            except json.JSONDecodeError:
+                self.agent.logger.error("Could not process BDI message.", exc_info=True)
+
             # Consume the streaming generator and send a reply for every chunk
-            async for chunk in self._query_llm(user_text):
+            async for chunk in self._query_llm(message["text"], message["norms"], message["goals"]):
                 await self._reply(chunk)
             self.agent.logger.debug(
                 "Finished processing BDI message. Response sent in chunks to BDI Core Agent."
@@ -68,7 +74,7 @@ class LLMAgent(BaseAgent):
             )
             await self.send(reply)
 
-        async def _query_llm(self, prompt: str) -> AsyncGenerator[str]:
+        async def _query_llm(self, prompt: str, norms: str, goals: str) -> AsyncGenerator[str]:
             """
             Sends a chat completion request to the local LLM service and streams the response by
             yielding fragments separated by punctuation like.
@@ -76,15 +82,7 @@ class LLMAgent(BaseAgent):
             :param prompt: Input text prompt to pass to the LLM.
             :yield: Fragments of the LLM-generated content.
             """
-            instructions = LLMInstructions(
+            instructions = LLMInstructions(norms if norms else None, goals if goals else None)
-                "- Be friendly and respectful.\n"
-                "- Make the conversation feel natural and engaging.\n"
-                "- Speak like a pirate.\n"
-                "- When the user asks what you can do, tell them.",
-                "- Try to learn the user's name during conversation.\n"
-                "- Suggest playing a game of asking yes or no questions where you think of a word "
-                "and the user must guess it.",
-            )
             messages = [
                 {
                     "role": "developer",

src/control_backend/agents/llm/llm_instructions.py

@@ -6,10 +6,7 @@ class LLMInstructions:
 
     @staticmethod
     def default_norms() -> str:
-        return """
+        return "Be friendly and respectful.\nMake the conversation feel natural and engaging."
-        Be friendly and respectful.
-        Make the conversation feel natural and engaging.
-        """.strip()
 
     @staticmethod
     def default_goals() -> str:

src/control_backend/agents/ri_command_agent.py

@@ -54,7 +54,9 @@ class RICommandAgent(BaseAgent):
         """Behaviour for sending commands received from other Python agents."""
 
         async def run(self):
-            message: spade.agent.Message = await self.receive(timeout=0.1)
+            message: spade.agent.Message = await self.receive(timeout=1)
+            if not message:
+                return
             if message and message.to == self.agent.jid:
                 try:
                     speech_command = SpeechCommand.model_validate_json(message.body)

src/control_backend/agents/ri_communication_agent.py

@@ -21,10 +21,13 @@ class RICommunicationAgent(BaseAgent):
         password: str,
         port: int = 5222,
         verify_security: bool = False,
-        address="tcp://localhost:0000",
+        address=None,
-        bind=False,
+        bind=True,
     ):
         super().__init__(jid, password, port, verify_security)
+        if not address:
+            self.logger.critical("No address set for negotiations.")
+            raise Exception  # TODO: improve
         self._address = address
         self._bind = bind
 
@@ -119,10 +122,7 @@ class RICommunicationAgent(BaseAgent):
                     port = port_data["port"]
                     bind = port_data["bind"]
 
-                    if not bind:
+                    addr = f"tcp://{settings.zmq_settings.external_host}:{port}"
-                        addr = f"tcp://localhost:{port}"
-                    else:
-                        addr = f"tcp://*:{port}"
 
                     match id:
                         case "main":

src/control_backend/api/v1/endpoints/program.py

@@ -1,10 +1,10 @@
-import json
 import logging
 
 from fastapi import APIRouter, HTTPException, Request
+from pydantic import ValidationError
 
 from control_backend.schemas.message import Message
-from control_backend.schemas.program import Phase
+from control_backend.schemas.program import Program
 
 logger = logging.getLogger(__name__)
 router = APIRouter()
@@ -16,37 +16,20 @@ async def receive_message(program: Message, request: Request):
     Receives a BehaviorProgram as a stringified JSON list inside `message`.
     Converts it into real Phase objects.
     """
-    logger.info("Received raw program: ")
+    logger.debug("Received raw program: %s", program)
-    logger.debug("%s", program)
     raw_str = program.message  # This is the JSON string
 
-    # Convert Json into dict.
+    # Validate program
     try:
-        program_list = json.loads(raw_str)
+        program = Program.model_validate_json(raw_str)
-    except json.JSONDecodeError as e:
+    except ValidationError as e:
-        logger.error("Failed to decode program JSON: %s", e)
+        logger.error("Failed to validate program JSON: %s", e)
-        raise HTTPException(status_code=400, detail="Undecodeable Json string") from None
+        raise HTTPException(status_code=400, detail="Not a valid program") from None
-
-    # Validate Phases
-    try:
-        phases: list[Phase] = [Phase(**phase) for phase in program_list]
-    except Exception as e:
-        logger.error("❌ Failed to convert to Phase objects: %s", e)
-        raise HTTPException(status_code=400, detail="Non-Phase String") from None
-
-    logger.info(f"Succesfully recieved {len(phases)} Phase(s).")
-    for p in phases:
-        logger.info(
-            f"Phase {p.id}: "
-            f"{len(p.phaseData.norms)} norms, "
-            f"{len(p.phaseData.goals)} goals, "
-            f"{len(p.phaseData.triggers) if hasattr(p.phaseData, 'triggers') else 0} triggers"
-        )
 
     # send away
     topic = b"program"
-    body = json.dumps([p.model_dump() for p in phases]).encode("utf-8")
+    body = program.model_dump_json().encode()
     pub_socket = request.app.state.endpoints_pub_socket
     await pub_socket.send_multipart([topic, body])
 
-    return {"status": "Program parsed", "phase_count": len(phases)}
+    return {"status": "Program parsed"}

src/control_backend/core/config.py

@@ -1,3 +1,5 @@
+import os
+
 from pydantic import BaseModel
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
@@ -6,9 +8,11 @@ class ZMQSettings(BaseModel):
     internal_pub_address: str = "tcp://localhost:5560"
     internal_sub_address: str = "tcp://localhost:5561"
 
+    external_host: str = "0.0.0.0"
+
 
 class AgentSettings(BaseModel):
-    host: str = "localhost"
+    host: str = os.environ.get("XMPP_HOST", "localhost")
     bdi_core_agent_name: str = "bdi_core"
     belief_collector_agent_name: str = "belief_collector"
     text_belief_extractor_agent_name: str = "text_belief_extractor"
@@ -16,14 +20,15 @@ class AgentSettings(BaseModel):
     llm_agent_name: str = "llm_agent"
     test_agent_name: str = "test_agent"
     transcription_agent_name: str = "transcription_agent"
+    program_manager_agent_name: str = "program_manager"
 
     ri_communication_agent_name: str = "ri_communication_agent"
     ri_command_agent_name: str = "ri_command_agent"
 
 
 class LLMSettings(BaseModel):
-    local_llm_url: str = "http://localhost:1234/v1/chat/completions"
+    local_llm_url: str = os.environ.get("LLM_URL", "http://localhost:1234/v1/") + "chat/completions"
-    local_llm_model: str = "openai/gpt-oss-20b"
+    local_llm_model: str = os.environ.get("LLM_MODEL", "openai/gpt-oss-20b")
 
 
 class Settings(BaseSettings):

src/control_backend/main.py

@@ -1,5 +1,6 @@
 import contextlib
 import logging
+import os
 import threading
 
 import zmq
@@ -14,6 +15,7 @@ from control_backend.agents import (
     VADAgent,
 )
 from control_backend.agents.bdi import BDICoreAgent, TBeliefExtractorAgent
+from control_backend.agents.bdi.bdi_program_manager.bdi_program_manager import BDIProgramManager
 from control_backend.api.v1.router import api_router
 from control_backend.core.config import settings
 from control_backend.logging import setup_logging
@@ -48,7 +50,9 @@ async def lifespan(app: FastAPI):
     # --- APPLICATION STARTUP ---
     setup_logging()
     logger.info("%s is starting up.", app.title)
-    logger.warning("testing extra", extra={"extra1": "one", "extra2": "two"})
+    logger.info(
+        "LLM_URL: %s, LLM_MODEL: %s", os.environ.get("LLM_URL"), os.environ.get("LLM_MODEL")
+    )
 
     # Initiate sockets
     proxy_thread = threading.Thread(target=setup_sockets)
@@ -71,7 +75,7 @@ async def lifespan(app: FastAPI):
                 "jid": f"{settings.agent_settings.ri_communication_agent_name}"
                 f"@{settings.agent_settings.host}",
                 "password": settings.agent_settings.ri_communication_agent_name,
-                "address": "tcp://*:5555",
+                "address": f"tcp://{settings.zmq_settings.external_host}:5555",
                 "bind": True,
             },
         ),
@@ -113,21 +117,39 @@ async def lifespan(app: FastAPI):
         ),
         "VADAgent": (
             VADAgent,
-            {"audio_in_address": "tcp://localhost:5558", "audio_in_bind": False},
+            {
+                "audio_in_address": f"tcp://{settings.zmq_settings.external_host}:5558",
+                "audio_in_bind": True,
+            },
+        ),
+        "ProgramManager": (
+            BDIProgramManager,
+            {
+                "name": settings.agent_settings.program_manager_agent_name,
+                "jid": f"{settings.agent_settings.program_manager_agent_name}@"
+                f"{settings.agent_settings.host}",
+                "password": settings.agent_settings.program_manager_agent_name,
+            },
         ),
     }
 
+    vad_agent_instance = None
+
     for name, (agent_class, kwargs) in agents_to_start.items():
         try:
             logger.debug("Starting agent: %s", name)
             agent_instance = agent_class(**{k: v for k, v in kwargs.items() if k != "name"})
             await agent_instance.start()
+            if isinstance(agent_instance, VADAgent):
+                vad_agent_instance = agent_instance
             logger.info("Agent '%s' started successfully.", name)
         except Exception as e:
             logger.error("Failed to start agent '%s': %s", name, e, exc_info=True)
             # Consider if the application should continue if an agent fails to start.
             raise
 
+    await vad_agent_instance.streaming_behaviour.reset()
+
     logger.info("Application startup complete.")
 
     yield