refactor: remove SPADE dependencies
Did not look at tests yet, this is a very non-final commit. ref: N25B-300
This commit is contained in:
@@ -1,67 +1,121 @@
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import logging
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import asyncio
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import json
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from collections.abc import Iterable
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import agentspeak
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from spade.behaviour import OneShotBehaviour
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from spade.message import Message
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from spade_bdi.bdi import BDIAgent
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import agentspeak.runtime
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import agentspeak.stdlib
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from control_backend.agents.base import BaseAgent
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from control_backend.core.agent_system import InternalMessage
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from control_backend.core.config import settings
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from .behaviours.belief_setter_behaviour import BeliefSetterBehaviour
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from .behaviours.receive_llm_resp_behaviour import ReceiveLLMResponseBehaviour
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from control_backend.schemas.ri_message import SpeechCommand
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class BDICoreAgent(BDIAgent):
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"""
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This is the Brain agent that does the belief inference with AgentSpeak.
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This is a continous process that happens automatically in the background.
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This class contains all the actions that can be called from AgentSpeak plans.
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It has the BeliefSetter behaviour and can aks and recieve requests from the LLM agent.
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"""
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logger = logging.getLogger(__package__).getChild(__name__)
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class BDICoreAgent(BaseAgent):
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def __init__(self, name: str, asl: str):
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super().__init__(name)
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self.asl_file = asl
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self.env = agentspeak.runtime.Environment()
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self.bdi_agent = None
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self.actions = agentspeak.stdlib.actions
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async def setup(self) -> None:
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"""
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Initializes belief behaviors and message routing.
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"""
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self.logger.info("BDICoreAgent setup started.")
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self.logger.debug("Setup started.")
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self.add_behaviour(BeliefSetterBehaviour())
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self.add_behaviour(ReceiveLLMResponseBehaviour())
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self._add_custom_actions()
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self.logger.info("BDICoreAgent setup complete.")
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await self._load_asl()
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def add_custom_actions(self, actions) -> None:
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# Start the BDI cycle loop
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await self.add_background_task(self._bdi_loop())
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self.logger.debug("Setup complete.")
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async def _load_asl(self):
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try:
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with open(self.asl_file) as source:
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self.bdi_agent = self.env.build_agent(source, self.actions)
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except FileNotFoundError:
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self.logger.warning(f"Could not find the specified ASL file at {self.asl_file}.")
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self.bdi_agent = agentspeak.runtime.Agent(self.env, self.name)
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async def _bdi_loop(self):
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"""Runs the AgentSpeak BDI loop."""
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while self._running:
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assert self.bdi_agent is not None
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self.bdi_agent.step()
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await asyncio.sleep(0.01)
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async def handle_message(self, msg: InternalMessage):
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"""
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Registers custom AgentSpeak actions callable from plans.
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Route incoming messages (Beliefs or LLM responses).
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"""
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sender = msg.sender
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@actions.add(".reply", 1)
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def _reply(agent: "BDICoreAgent", term, intention):
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match sender:
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case settings.agent_settings.bdi_belief_collector_name:
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self.logger.debug("Processing message from belief collector.")
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try:
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if msg.thread == "beliefs":
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beliefs = json.loads(msg.body)
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self._add_beliefs(beliefs)
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except Exception as e:
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self.logger.error(f"Error processing belief: {e}")
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case settings.agent_settings.llm_name:
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content = msg.body
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self.logger.info("Received LLM response: %s", content)
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# Forward to Robot Speech Agent
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cmd = SpeechCommand(data=content)
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out_msg = InternalMessage(
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to=settings.agent_settings.robot_speech_name,
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sender=self.name,
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body=cmd.model_dump_json(),
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)
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await self.send(out_msg)
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# TODO: test way of adding beliefs
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def _add_beliefs(self, beliefs: dict[str, list[str]]):
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if not beliefs:
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return
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for belief_name, args in beliefs.items():
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self._add_belief(belief_name, args)
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if belief_name == "user_said":
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self._add_belief("user_said")
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def _add_belief(self, belief_name: str, arguments: Iterable[str] = []):
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args = (agentspeak.Literal(arg) for arg in arguments)
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literal_belief = agentspeak.Literal(belief_name, args)
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assert self.bdi_agent is not None
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self.bdi_agent.call(
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agentspeak.Trigger.addition,
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agentspeak.GoalType.belief,
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literal_belief,
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agentspeak.runtime.Intention(),
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)
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self.logger.debug(f"Added belief {belief_name}({','.join(arguments)})")
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def _add_custom_actions(self) -> None:
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"""Add any custom actions here."""
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@self.actions.add(".reply", 1)
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def _reply(agent, term, intention):
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"""
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Sends text to the LLM (AgentSpeak action).
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Example: .reply("Hello LLM!")
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Sends text to the LLM.
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"""
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message_text = agentspeak.grounded(term.args[0], intention.scope)
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self.logger.debug("Reply action sending: %s", message_text)
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self._send_to_llm(str(message_text))
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asyncio.create_task(self._send_to_llm(str(message_text)))
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yield
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def _send_to_llm(self, text: str):
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async def _send_to_llm(self, text: str):
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"""
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Sends a text query to the LLM Agent asynchronously.
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Sends a text query to the LLM agent asynchronously.
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"""
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class SendBehaviour(OneShotBehaviour):
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async def run(self) -> None:
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msg = Message(
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to=settings.agent_settings.llm_name + "@" + settings.agent_settings.host,
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body=text,
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)
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await self.send(msg)
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self.agent.logger.info("Message sent to LLM agent: %s", text)
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self.add_behaviour(SendBehaviour())
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msg = InternalMessage(to=settings.agent_settings.llm_name, sender=self.name, body=text)
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await self.send(msg)
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self.logger.info("Message sent to LLM agent: %s", text)
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@@ -1,85 +0,0 @@
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import json
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from spade.agent import Message
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from spade.behaviour import CyclicBehaviour
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from spade_bdi.bdi import BDIAgent
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from control_backend.core.config import settings
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class BeliefSetterBehaviour(CyclicBehaviour):
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"""
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This is the behaviour that the BDI agent runs. This behaviour waits for incoming
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message and updates the agent's beliefs accordingly.
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"""
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agent: BDIAgent
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async def run(self):
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"""Polls for messages and processes them."""
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msg = await self.receive()
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self.agent.logger.debug(
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"Received message from %s with thread '%s' and body: %s",
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msg.sender,
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msg.thread,
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msg.body,
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)
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self._process_message(msg)
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def _process_message(self, message: Message):
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"""Routes the message to the correct processing function based on the sender."""
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sender = message.sender.node # removes host from jid and converts to str
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self.agent.logger.debug("Processing message from sender: %s", sender)
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match sender:
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case settings.agent_settings.bdi_belief_collector_name:
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self.agent.logger.debug(
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"Message is from the belief collector agent. Processing as belief message."
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)
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self._process_belief_message(message)
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case _:
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self.agent.logger.debug("Not the belief agent, discarding message")
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pass
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def _process_belief_message(self, message: Message):
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if not message.body:
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self.agent.logger.debug("Ignoring message with empty body from %s", message.sender.node)
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return
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match message.thread:
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case "beliefs":
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try:
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beliefs: dict[str, list[str]] = json.loads(message.body)
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self._set_beliefs(beliefs)
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except json.JSONDecodeError:
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self.agent.logger.error(
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"Could not decode beliefs from JSON. Message body: '%s'",
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message.body,
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exc_info=True,
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)
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case _:
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pass
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def _set_beliefs(self, beliefs: dict[str, list[str]]):
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"""Removes previous values for beliefs and updates them with the provided values."""
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if self.agent.bdi is None:
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self.agent.logger.warning("Cannot set beliefs; agent's BDI is not yet initialized.")
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return
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if not beliefs:
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self.agent.logger.debug("Received an empty set of beliefs. No beliefs were updated.")
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return
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# Set new beliefs (outdated beliefs are automatically removed)
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for belief, arguments in beliefs.items():
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self.agent.logger.debug("Setting belief %s with arguments %s", belief, arguments)
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self.agent.bdi.set_belief(belief, *arguments)
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# Special case: if there's a new user message, flag that we haven't responded yet
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if belief == "user_said":
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self.agent.bdi.set_belief("new_message")
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self.agent.logger.debug(
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"Detected 'user_said' belief, also setting 'new_message' belief."
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)
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self.agent.logger.info("Successfully updated %d beliefs.", len(beliefs))
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@@ -1,37 +0,0 @@
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from spade.behaviour import CyclicBehaviour
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from spade.message import Message
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from control_backend.core.config import settings
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from control_backend.schemas.ri_message import SpeechCommand
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class ReceiveLLMResponseBehaviour(CyclicBehaviour):
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"""
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Adds behavior to receive responses from the LLM Agent.
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"""
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async def run(self):
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msg = await self.receive()
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sender = msg.sender.node
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match sender:
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case settings.agent_settings.llm_name:
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content = msg.body
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self.agent.logger.info("Received LLM response: %s", content)
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speech_command = SpeechCommand(data=content)
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message = Message(
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to=settings.agent_settings.robot_speech_name
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+ "@"
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+ settings.agent_settings.host,
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sender=self.agent.jid,
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body=speech_command.model_dump_json(),
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)
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self.agent.logger.debug("Sending message: %s", message)
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await self.send(message)
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case _:
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self.agent.logger.debug("Discarding message from %s", sender)
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pass
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@@ -1,92 +0,0 @@
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import json
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from json import JSONDecodeError
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from spade.agent import Message
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from spade.behaviour import CyclicBehaviour
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from control_backend.core.config import settings
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class BeliefCollectorBehaviour(CyclicBehaviour):
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"""
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Continuously collects beliefs/emotions from extractor agents:
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Then we send a unified belief packet to the BDI agent.
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"""
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async def run(self):
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msg = await self.receive()
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await self._process_message(msg)
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async def _process_message(self, msg: Message):
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sender_node = msg.sender.node
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# Parse JSON payload
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try:
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payload = json.loads(msg.body)
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except JSONDecodeError as e:
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self.agent.logger.warning(
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"BeliefCollector: failed to parse JSON from %s. Body=%r Error=%s",
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sender_node,
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msg.body,
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e,
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)
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return
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msg_type = payload.get("type")
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# Prefer explicit 'type' field
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if msg_type == "belief_extraction_text" or sender_node == "bel_text_agent_mock":
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self.agent.logger.debug(
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"Message routed to _handle_belief_text (sender=%s)", sender_node
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)
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await self._handle_belief_text(payload, sender_node)
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# This is not implemented yet, but we keep the structure for future use
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elif msg_type == "emotion_extraction_text" or sender_node == "emo_text_agent_mock":
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self.agent.logger.debug("Message routed to _handle_emo_text (sender=%s)", sender_node)
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await self._handle_emo_text(payload, sender_node)
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else:
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self.agent.logger.warning(
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"Unrecognized message (sender=%s, type=%r). Ignoring.", sender_node, msg_type
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)
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async def _handle_belief_text(self, payload: dict, origin: str):
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"""
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Expected payload:
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{
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"type": "belief_extraction_text",
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"beliefs": {"user_said": ["Can you help me?"]}
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}
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"""
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beliefs = payload.get("beliefs", {})
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if not beliefs:
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self.agent.logger.debug("Received empty beliefs set.")
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return
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self.agent.logger.debug("Forwarding %d beliefs.", len(beliefs))
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for belief_name, belief_list in beliefs.items():
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for belief in belief_list:
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self.agent.logger.debug(" - %s %s", belief_name, str(belief))
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await self._send_beliefs_to_bdi(beliefs, origin=origin)
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async def _handle_emo_text(self, payload: dict, origin: str):
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"""TODO: implement (after we have emotional recogntion)"""
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pass
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async def _send_beliefs_to_bdi(self, beliefs: list[str], origin: str | None = None):
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"""
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Sends a unified belief packet to the BDI agent.
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"""
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if not beliefs:
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return
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to_jid = f"{settings.agent_settings.bdi_core_name}@{settings.agent_settings.host}"
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msg = Message(to=to_jid, sender=self.agent.jid, thread="beliefs")
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msg.body = json.dumps(beliefs)
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await self.send(msg)
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self.agent.logger.info("Sent %d belief(s) to BDI core.", len(beliefs))
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@@ -1,11 +1,88 @@
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from control_backend.agents.base import BaseAgent
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import json
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from .behaviours.belief_collector_behaviour import BeliefCollectorBehaviour
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from control_backend.agents.base import BaseAgent
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from control_backend.core.agent_system import InternalMessage
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from control_backend.core.config import settings
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class BDIBeliefCollectorAgent(BaseAgent):
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"""
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Continuously collects beliefs/emotions from extractor agents and forwards a
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unified belief packet to the BDI agent.
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"""
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async def setup(self):
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self.logger.info("BDIBeliefCollectorAgent starting (%s)", self.jid)
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# Attach the continuous collector behaviour (listens and forwards to BDI)
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self.add_behaviour(BeliefCollectorBehaviour())
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self.logger.info("BDIBeliefCollectorAgent ready.")
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self.logger.info("Setting up %s", self.name)
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async def handle_message(self, msg: InternalMessage):
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sender_node = msg.sender
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# Parse JSON payload
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try:
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payload = json.loads(msg.body)
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except Exception as e:
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self.logger.warning(
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"BeliefCollector: failed to parse JSON from %s. Body=%r Error=%s",
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sender_node,
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msg.body,
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e,
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)
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return
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msg_type = payload.get("type")
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# Prefer explicit 'type' field
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if msg_type == "belief_extraction_text":
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self.logger.debug("Message routed to _handle_belief_text (sender=%s)", sender_node)
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await self._handle_belief_text(payload, sender_node)
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# This is not implemented yet, but we keep the structure for future use
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elif msg_type == "emotion_extraction_text":
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self.logger.debug("Message routed to _handle_emo_text (sender=%s)", sender_node)
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await self._handle_emo_text(payload, sender_node)
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else:
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self.logger.warning(
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"Unrecognized message (sender=%s, type=%r). Ignoring.", sender_node, msg_type
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)
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async def _handle_belief_text(self, payload: dict, origin: str):
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"""
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Expected payload:
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{
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"type": "belief_extraction_text",
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"beliefs": {"user_said": ["Can you help me?"]}
|
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|
||||
}
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"""
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beliefs = payload.get("beliefs", {})
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if not beliefs:
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self.logger.debug("Received empty beliefs set.")
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return
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self.logger.debug("Forwarding %d beliefs.", len(beliefs))
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for belief_name, belief_list in beliefs.items():
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for belief in belief_list:
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self.logger.debug(" - %s %s", belief_name, str(belief))
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await self._send_beliefs_to_bdi(beliefs, origin=origin)
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async def _handle_emo_text(self, payload: dict, origin: str):
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"""TODO: implement (after we have emotional recognition)"""
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pass
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|
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async def _send_beliefs_to_bdi(self, beliefs: dict, origin: str | None = None):
|
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"""
|
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Sends a unified belief packet to the BDI agent.
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"""
|
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if not beliefs:
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return
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|
||||
msg = InternalMessage(
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to=settings.agent_settings.bdi_core_name,
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sender=self.name,
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body=json.dumps(beliefs),
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thread="beliefs",
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)
|
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|
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await self.send(msg)
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self.logger.info("Sent %d belief(s) to BDI core.", len(beliefs))
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|
||||
@@ -1,104 +0,0 @@
|
||||
import json
|
||||
import logging
|
||||
|
||||
from spade.behaviour import CyclicBehaviour
|
||||
from spade.message import Message
|
||||
|
||||
from control_backend.core.config import settings
|
||||
|
||||
|
||||
class TextBeliefExtractorBehaviour(CyclicBehaviour):
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 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()
|
||||
if msg is None:
|
||||
return
|
||||
|
||||
sender = msg.sender.node
|
||||
match sender:
|
||||
case settings.agent_settings.transcription_name:
|
||||
self.logger.debug("Received text from transcriber: %s", msg.body)
|
||||
await self._process_transcription_demo(msg.body)
|
||||
case _:
|
||||
self.logger.info("Discarding message from %s", sender)
|
||||
pass
|
||||
|
||||
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()
|
||||
|
||||
belief_message.to = (
|
||||
settings.agent_settings.bdi_belief_collector_name
|
||||
+ "@"
|
||||
+ settings.agent_settings.host
|
||||
)
|
||||
belief_message.body = response
|
||||
belief_message.thread = "beliefs"
|
||||
|
||||
await self.send(belief_message)
|
||||
self.agent.logger.info("Sent beliefs to BDI.")
|
||||
except json.JSONDecodeError:
|
||||
# Parsing failed, so the response is in the wrong format, log warning
|
||||
self.agent.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 = {"beliefs": {"user_said": [txt]}, "type": "belief_extraction_text"}
|
||||
payload = json.dumps(belief)
|
||||
belief_msg = Message()
|
||||
|
||||
belief_msg.to = (
|
||||
settings.agent_settings.bdi_belief_collector_name + "@" + settings.agent_settings.host
|
||||
)
|
||||
belief_msg.body = payload
|
||||
belief_msg.thread = "beliefs"
|
||||
|
||||
await self.send(belief_msg)
|
||||
self.logger.info("Sent %d beliefs to the belief collector.", len(belief["beliefs"]))
|
||||
@@ -1,8 +1,38 @@
|
||||
from control_backend.agents.base import BaseAgent
|
||||
import json
|
||||
|
||||
from .behaviours.text_belief_extractor_behaviour import TextBeliefExtractorBehaviour
|
||||
from control_backend.agents.base import BaseAgent
|
||||
from control_backend.core.agent_system import InternalMessage
|
||||
from control_backend.core.config import settings
|
||||
|
||||
|
||||
class TextBeliefExtractorAgent(BaseAgent):
|
||||
async def setup(self):
|
||||
self.add_behaviour(TextBeliefExtractorBehaviour())
|
||||
self.logger.info("Settting up %s.", self.name)
|
||||
# Setup LLM belief context if needed (currently demo is just passthrough)
|
||||
self.beliefs = {"mood": ["X"], "car": ["Y"]}
|
||||
|
||||
async def handle_message(self, msg: InternalMessage):
|
||||
sender = msg.sender
|
||||
if sender == settings.agent_settings.transcription_name:
|
||||
self.logger.debug("Received text from transcriber: %s", msg.body)
|
||||
await self._process_transcription_demo(msg.body)
|
||||
else:
|
||||
self.logger.info("Discarding message from %s", sender)
|
||||
|
||||
async def _process_transcription_demo(self, txt: str):
|
||||
"""
|
||||
Demo version to process the transcription input to beliefs.
|
||||
"""
|
||||
# For demo, just wrapping user text as user_said belief
|
||||
belief = {"beliefs": {"user_said": [txt]}, "type": "belief_extraction_text"}
|
||||
payload = json.dumps(belief)
|
||||
|
||||
belief_msg = InternalMessage(
|
||||
to=settings.agent_settings.bdi_belief_collector_name,
|
||||
sender=self.name,
|
||||
body=payload,
|
||||
thread="beliefs",
|
||||
)
|
||||
|
||||
await self.send(belief_msg)
|
||||
self.logger.info("Sent %d beliefs to the belief collector.", len(belief["beliefs"]))
|
||||
|
||||
Reference in New Issue
Block a user