refactor: rename all agents and improve structure pt1
ref: N25B-257
This commit is contained in:
Björn Otgaar
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from .bdi_core import BDICoreAgent as BDICoreAgent
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from .text_extractor import TBeliefExtractorAgent as TBeliefExtractorAgent
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@@ -1,67 +0,0 @@
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import logging
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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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from control_backend.core.config import settings
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from .behaviours.belief_setter import BeliefSetterBehaviour
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from .behaviours.receive_llm_resp_behaviour import ReceiveLLMResponseBehaviour
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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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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.add_behaviour(BeliefSetterBehaviour())
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self.add_behaviour(ReceiveLLMResponseBehaviour())
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self.logger.info("BDICoreAgent setup complete.")
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def add_custom_actions(self, actions) -> None:
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"""
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Registers custom AgentSpeak actions callable from plans.
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"""
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@actions.add(".reply", 1)
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def _reply(agent: "BDICoreAgent", 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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"""
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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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yield
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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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"""
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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_agent_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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@@ -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.belief_collector_agent_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_agent_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.ri_command_agent_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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@@ -1,104 +0,0 @@
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import json
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import logging
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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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class BeliefFromText(CyclicBehaviour):
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logger = logging.getLogger(__name__)
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# TODO: LLM prompt nog hardcoded
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llm_instruction_prompt = """
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You are an information extraction assistent for a BDI agent. Your task is to extract values \
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from a user's text to bind a list of ungrounded beliefs. Rules:
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You will receive a JSON object with "beliefs" (a list of ungrounded AgentSpeak beliefs) \
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and "text" (user's transcript).
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Analyze the text to find values that sematically match the variables (X,Y,Z) in the beliefs.
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A single piece of text might contain multiple instances that match a belief.
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Respond ONLY with a single JSON object.
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The JSON object's keys should be the belief functors (e.g., "weather").
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The value for each key must be a list of lists.
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Each inner list must contain the extracted arguments (as strings) for one instance \
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of that belief.
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CRITICAL: If no information in the text matches a belief, DO NOT include that key \
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in your response.
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"""
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# on_start agent receives message containing the beliefs to look out for and
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# sets up the LLM with instruction prompt
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# async def on_start(self):
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# msg = await self.receive(timeout=0.1)
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# self.beliefs = dict uit message
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# send instruction prompt to LLM
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beliefs: dict[str, list[str]]
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beliefs = {"mood": ["X"], "car": ["Y"]}
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async def run(self):
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msg = await self.receive()
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if msg is None:
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return
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sender = msg.sender.node
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match sender:
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case settings.agent_settings.transcription_agent_name:
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self.logger.debug("Received text from transcriber: %s", msg.body)
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await self._process_transcription_demo(msg.body)
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case _:
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self.logger.info("Discarding message from %s", sender)
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pass
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async def _process_transcription(self, text: str):
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text_prompt = f"Text: {text}"
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beliefs_prompt = "These are the beliefs to be bound:\n"
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for belief, values in self.beliefs.items():
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beliefs_prompt += f"{belief}({', '.join(values)})\n"
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prompt = text_prompt + beliefs_prompt
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self.logger.info(prompt)
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# prompt_msg = Message(to="LLMAgent@whatever")
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# response = self.send(prompt_msg)
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# Mock response; response is beliefs in JSON format, it parses do dict[str,list[list[str]]]
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response = '{"mood": [["happy"]]}'
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# Verify by trying to parse
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try:
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json.loads(response)
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belief_message = Message()
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belief_message.to = (
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settings.agent_settings.belief_collector_agent_name
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+ "@"
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+ settings.agent_settings.host
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)
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belief_message.body = response
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belief_message.thread = "beliefs"
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await self.send(belief_message)
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self.agent.logger.info("Sent beliefs to BDI.")
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except json.JSONDecodeError:
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# Parsing failed, so the response is in the wrong format, log warning
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self.agent.logger.warning("Received LLM response in incorrect format.")
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async def _process_transcription_demo(self, txt: str):
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"""
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Demo version to process the transcription input to beliefs. For the demo only the belief
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'user_said' is relevant, so this function simply makes a dict with key: "user_said",
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value: txt and passes this to the Belief Collector agent.
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"""
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belief = {"beliefs": {"user_said": [txt]}, "type": "belief_extraction_text"}
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payload = json.dumps(belief)
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belief_msg = Message()
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belief_msg.to = (
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settings.agent_settings.belief_collector_agent_name + "@" + settings.agent_settings.host
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)
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belief_msg.body = payload
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belief_msg.thread = "beliefs"
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await self.send(belief_msg)
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self.logger.info("Sent %d beliefs to the belief collector.", len(belief["beliefs"]))
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@@ -1,3 +0,0 @@
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+new_message : user_said(Message) <-
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-new_message;
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.reply(Message).
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@@ -1,8 +0,0 @@
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from control_backend.agents.base import BaseAgent
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from .behaviours.text_belief_extractor import BeliefFromText
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class TBeliefExtractorAgent(BaseAgent):
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async def setup(self):
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self.add_behaviour(BeliefFromText())
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