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Merge branch 'dev' into refactor/logging

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Kasper
2025-11-04 20:54:03 +01:00
parent a98018ddda da30c392aa
commit 270df83fab
22 changed files with 359 additions and 136 deletions
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15
src/control_backend/agents/bdi/behaviours/text_belief_extractor.py
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@@ -9,18 +9,23 @@ from control_backend.core.config import settings
class BeliefFromText(CyclicBehaviour):
# 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).
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.
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
# 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