refactor: rename all agents and improve structure pt1
ref: N25B-257
This commit is contained in:
Björn Otgaar
@@ -1,161 +0,0 @@
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import json
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import re
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from collections.abc import AsyncGenerator
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import httpx
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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.agents import BaseAgent
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from control_backend.core.config import settings
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from .llm_instructions import LLMInstructions
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class LLMAgent(BaseAgent):
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"""
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Agent responsible for processing user text input and querying a locally
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hosted LLM for text generation. Receives messages from the BDI Core Agent
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and responds with processed LLM output.
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"""
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class ReceiveMessageBehaviour(CyclicBehaviour):
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"""
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Cyclic behaviour to continuously listen for incoming messages from
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the BDI Core Agent and handle them.
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"""
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async def run(self):
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"""
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Receives SPADE messages and processes only those originating from the
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configured BDI agent.
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"""
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msg = await self.receive()
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sender = msg.sender.node
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self.agent.logger.debug(
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"Received message: %s from %s",
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msg.body,
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sender,
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)
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if sender == settings.agent_settings.bdi_core_agent_name:
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self.agent.logger.debug("Processing message from BDI Core Agent")
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await self._process_bdi_message(msg)
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else:
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self.agent.logger.debug("Message ignored (not from BDI Core Agent)")
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async def _process_bdi_message(self, message: Message):
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"""
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Forwards user text from the BDI to the LLM and replies with the generated text in chunks
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separated by punctuation.
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"""
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user_text = message.body
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# Consume the streaming generator and send a reply for every chunk
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async for chunk in self._query_llm(user_text):
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await self._reply(chunk)
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self.agent.logger.debug(
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"Finished processing BDI message. Response sent in chunks to BDI Core Agent."
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)
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async def _reply(self, msg: str):
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"""
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Sends a response message back to the BDI Core Agent.
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"""
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reply = Message(
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to=settings.agent_settings.bdi_core_agent_name + "@" + settings.agent_settings.host,
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body=msg,
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)
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await self.send(reply)
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async def _query_llm(self, prompt: str) -> AsyncGenerator[str]:
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"""
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Sends a chat completion request to the local LLM service and streams the response by
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yielding fragments separated by punctuation like.
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:param prompt: Input text prompt to pass to the LLM.
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:yield: Fragments of the LLM-generated content.
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"""
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instructions = LLMInstructions(
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"- Be friendly and respectful.\n"
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"- Make the conversation feel natural and engaging.\n"
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"- Speak like a pirate.\n"
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"- When the user asks what you can do, tell them.",
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"- Try to learn the user's name during conversation.\n"
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"- Suggest playing a game of asking yes or no questions where you think of a word "
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"and the user must guess it.",
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)
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messages = [
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{
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"role": "developer",
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"content": instructions.build_developer_instruction(),
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},
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{
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"role": "user",
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"content": prompt,
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},
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]
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try:
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current_chunk = ""
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async for token in self._stream_query_llm(messages):
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current_chunk += token
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# Stream the message in chunks separated by punctuation.
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# We include the delimiter in the emitted chunk for natural flow.
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pattern = re.compile(r".*?(?:,|;|:|—|–|\.{3}|…|\.|\?|!)\s*", re.DOTALL)
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for m in pattern.finditer(current_chunk):
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chunk = m.group(0)
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if chunk:
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yield current_chunk
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current_chunk = ""
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# Yield any remaining tail
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if current_chunk:
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yield current_chunk
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except httpx.HTTPError as err:
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self.agent.logger.error("HTTP error.", exc_info=err)
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yield "LLM service unavailable."
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except Exception as err:
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self.agent.logger.error("Unexpected error.", exc_info=err)
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yield "Error processing the request."
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async def _stream_query_llm(self, messages) -> AsyncGenerator[str]:
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"""Raises httpx.HTTPError when the API gives an error."""
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async with httpx.AsyncClient(timeout=None) as client:
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async with client.stream(
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"POST",
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settings.llm_settings.local_llm_url,
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json={
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"model": settings.llm_settings.local_llm_model,
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"messages": messages,
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"temperature": 0.3,
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"stream": True,
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},
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) as response:
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response.raise_for_status()
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async for line in response.aiter_lines():
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if not line or not line.startswith("data: "):
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continue
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data = line[len("data: ") :]
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if data.strip() == "[DONE]":
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break
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try:
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event = json.loads(data)
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delta = event.get("choices", [{}])[0].get("delta", {}).get("content")
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if delta:
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yield delta
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except json.JSONDecodeError:
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self.agent.logger.error("Failed to parse LLM response: %s", data)
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async def setup(self):
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"""
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Sets up the SPADE behaviour to filter and process messages from the
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BDI Core Agent.
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"""
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behaviour = self.ReceiveMessageBehaviour()
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self.add_behaviour(behaviour)
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self.logger.info("LLMAgent setup complete")
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@@ -1,46 +0,0 @@
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class LLMInstructions:
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"""
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Defines structured instructions that are sent along with each request
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to the LLM to guide its behavior (norms, goals, etc.).
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"""
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@staticmethod
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def default_norms() -> str:
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return """
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Be friendly and respectful.
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Make the conversation feel natural and engaging.
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""".strip()
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@staticmethod
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def default_goals() -> str:
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return """
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Try to learn the user's name during conversation.
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""".strip()
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def __init__(self, norms: str | None = None, goals: str | None = None):
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self.norms = norms if norms is not None else self.default_norms()
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self.goals = goals if goals is not None else self.default_goals()
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def build_developer_instruction(self) -> str:
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"""
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Builds a multi-line formatted instruction string for the LLM.
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Includes only non-empty structured fields.
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"""
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sections = [
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"You are a Pepper robot engaging in natural human conversation.",
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"Keep responses between 1–3 sentences, unless told otherwise.\n",
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"You're given goals to reach. Reach them in order, but make the conversation feel "
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"natural. Some turns you should not try to achieve your goals.\n",
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]
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if self.norms:
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sections.append("Norms to follow:")
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sections.append(self.norms)
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sections.append("")
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if self.goals:
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sections.append("Goals to reach:")
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sections.append(self.goals)
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sections.append("")
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return "\n".join(sections).strip()
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