feat: integrate AgentSpeak with semantic belief extraction

ref: N25B-429

src/control_backend/agents/bdi/agentspeak_generator.py

@@ -332,7 +332,13 @@ class AgentSpeakGenerator:
 
     @_astify.register
     def _(self, sb: SemanticBelief) -> AstExpression:
-        return AstLiteral(f"semantic_{self._slugify_str(sb.description)}")
+        return AstLiteral(self.get_semantic_belief_slug(sb))
+
+    @staticmethod
+    def get_semantic_belief_slug(sb: SemanticBelief) -> str:
+        # If you need a method like this for other types, make a public slugify singledispatch for
+        #  all types.
+        return f"semantic_{AgentSpeakGenerator._slugify_str(sb.name)}"
 
     @_astify.register
     def _(self, ib: InferredBelief) -> AstExpression:

src/control_backend/agents/bdi/bdi_program_manager.py

@@ -1,3 +1,5 @@
+import asyncio
+
 import zmq
 from pydantic import ValidationError
 from zmq.asyncio import Context
@@ -5,8 +7,9 @@ from zmq.asyncio import Context
 from control_backend.agents import BaseAgent
 from control_backend.agents.bdi.agentspeak_generator import AgentSpeakGenerator
 from control_backend.core.config import settings
+from control_backend.schemas.belief_list import BeliefList
 from control_backend.schemas.internal_message import InternalMessage
-from control_backend.schemas.program import Program
+from control_backend.schemas.program import Belief, ConditionalNorm, InferredBelief, Program
 
 
 class BDIProgramManager(BaseAgent):
@@ -56,6 +59,45 @@ class BDIProgramManager(BaseAgent):
 
         await self.send(msg)
 
+    @staticmethod
+    def _extract_beliefs_from_program(program: Program) -> list[Belief]:
+        beliefs: list[Belief] = []
+
+        for phase in program.phases:
+            for norm in phase.norms:
+                if isinstance(norm, ConditionalNorm):
+                    beliefs += BDIProgramManager._extract_beliefs_from_belief(norm.condition)
+
+            for trigger in phase.triggers:
+                beliefs += BDIProgramManager._extract_beliefs_from_belief(trigger.condition)
+
+        return beliefs
+
+    @staticmethod
+    def _extract_beliefs_from_belief(belief: Belief) -> list[Belief]:
+        if isinstance(belief, InferredBelief):
+            return BDIProgramManager._extract_beliefs_from_belief(
+                belief.left
+            ) + BDIProgramManager._extract_beliefs_from_belief(belief.right)
+        return [belief]
+
+    async def _send_beliefs_to_semantic_belief_extractor(self, program: Program):
+        """
+        Extract beliefs from the program and send them to the Semantic Belief Extractor Agent.
+
+        :param program: The program received from the API.
+        """
+        beliefs = BeliefList(beliefs=self._extract_beliefs_from_program(program))
+
+        message = InternalMessage(
+            to=settings.agent_settings.text_belief_extractor_name,
+            sender=self.name,
+            body=beliefs.model_dump_json(),
+            thread="beliefs",
+        )
+
+        await self.send(message)
+
     async def _receive_programs(self):
         """
         Continuous loop that receives program updates from the HTTP endpoint.
@@ -72,7 +114,10 @@ class BDIProgramManager(BaseAgent):
                 self.logger.exception("Received an invalid program.")
                 continue
 
-            await self._create_agentspeak_and_send_to_bdi(program)
+            await asyncio.gather(
+                self._create_agentspeak_and_send_to_bdi(program),
+                self._send_beliefs_to_semantic_belief_extractor(program),
+            )
 
     async def setup(self):
         """

src/control_backend/agents/bdi/text_belief_extractor_agent.py

@@ -3,21 +3,16 @@ import json
 
 import httpx
 from pydantic import ValidationError
-from slugify import slugify
 
 from control_backend.agents.base import BaseAgent
+from control_backend.agents.bdi.agentspeak_generator import AgentSpeakGenerator
 from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
+from control_backend.schemas.belief_list import BeliefList
 from control_backend.schemas.belief_message import Belief as InternalBelief
 from control_backend.schemas.belief_message import BeliefMessage
 from control_backend.schemas.chat_history import ChatHistory, ChatMessage
-from control_backend.schemas.program import (
+from control_backend.schemas.program import SemanticBelief
-    Belief,
-    ConditionalNorm,
-    InferredBelief,
-    Program,
-    SemanticBelief,
-)
 
 
 class TextBeliefExtractorAgent(BaseAgent):
@@ -32,11 +27,12 @@ class TextBeliefExtractorAgent(BaseAgent):
     the message itself.
     """
 
-    def __init__(self, name: str):
+    def __init__(self, name: str, temperature: float = settings.llm_settings.code_temperature):
         super().__init__(name)
         self.beliefs: dict[str, bool] = {}
         self.available_beliefs: list[SemanticBelief] = []
         self.conversation = ChatHistory(messages=[])
+        self.temperature = temperature
 
     async def setup(self):
         """
@@ -85,45 +81,19 @@ class TextBeliefExtractorAgent(BaseAgent):
         :param msg: The received message from the program manager.
         """
         try:
-            program = Program.model_validate_json(msg.body)
+            belief_list = BeliefList.model_validate_json(msg.body)
         except ValidationError:
             self.logger.warning(
-                "Received message from program manager but it is not a valid program."
+                "Received message from program manager but it is not a valid list of beliefs."
             )
             return
 
-        self.logger.debug("Received a program from the program manager.")
+        self.available_beliefs = [b for b in belief_list.beliefs if isinstance(b, SemanticBelief)]
-
+        self.logger.debug(
-        self.available_beliefs = self._extract_basic_beliefs_from_program(program)
+            "Received %d beliefs from the program manager.",
-
+            len(self.available_beliefs),
-    # TODO Copied from an incomplete version of the program manager. Use that one instead.
-    @staticmethod
-    def _extract_basic_beliefs_from_program(program: Program) -> list[SemanticBelief]:
-        beliefs = []
-
-        for phase in program.phases:
-            for norm in phase.norms:
-                if isinstance(norm, ConditionalNorm):
-                    beliefs += TextBeliefExtractorAgent._extract_basic_beliefs_from_belief(
-                        norm.condition
         )
 
-            for trigger in phase.triggers:
-                beliefs += TextBeliefExtractorAgent._extract_basic_beliefs_from_belief(
-                    trigger.condition
-                )
-
-        return beliefs
-
-    # TODO Copied from an incomplete version of the program manager. Use that one instead.
-    @staticmethod
-    def _extract_basic_beliefs_from_belief(belief: Belief) -> list[SemanticBelief]:
-        if isinstance(belief, InferredBelief):
-            return TextBeliefExtractorAgent._extract_basic_beliefs_from_belief(
-                belief.left
-            ) + TextBeliefExtractorAgent._extract_basic_beliefs_from_belief(belief.right)
-        return [belief]
-
     async def _user_said(self, text: str):
         """
         Create a belief for the user's full speech.
@@ -207,8 +177,7 @@ class TextBeliefExtractorAgent(BaseAgent):
 
     @staticmethod
     def _create_belief_schema(belief: SemanticBelief) -> tuple[str, dict]:
-        # TODO: use real belief names
+        return AgentSpeakGenerator.get_semantic_belief_slug(belief), {
-        return belief.name or slugify(belief.description), {
             "type": ["boolean", "null"],
             "description": belief.description,
         }
@@ -237,10 +206,9 @@ class TextBeliefExtractorAgent(BaseAgent):
 
     @staticmethod
     def _format_beliefs(beliefs: list[SemanticBelief]):
-        # TODO: use real belief names
         return "\n".join(
             [
-                f"- {belief.name or slugify(belief.description)}: {belief.description}"
+                f"- {AgentSpeakGenerator.get_semantic_belief_slug(belief)}: {belief.description}"
                 for belief in beliefs
             ]
         )
@@ -267,7 +235,7 @@ Given the above conversation, what beliefs can be inferred?
 If there is no relevant information about a belief belief, give null.
 In case messages conflict, prefer using the most recent messages for inference.
 
-Choose from the following list of beliefs, formatted as (belief_name, description):
+Choose from the following list of beliefs, formatted as `- <belief_name>: <description>`:
 {self._format_beliefs(beliefs)}
 
 Respond with a JSON similar to the following, but with the property names as given above:
@@ -304,8 +272,7 @@ Respond with a JSON similar to the following, but with the property names as giv
 
         return None
 
-    @staticmethod
+    async def _query_llm(self, prompt: str, schema: dict) -> dict:
-    async def _query_llm(prompt: str, schema: dict) -> dict:
         """
         Query an LLM with the given prompt and schema, return an instance of a dict conforming to
         that schema.
@@ -333,7 +300,7 @@ Respond with a JSON similar to the following, but with the property names as giv
                         },
                     },
                     "reasoning_effort": "low",
-                    "temperature": settings.llm_settings.code_temperature,
+                    "temperature": self.temperature,
                     "stream": False,
                 },
                 timeout=None,
@@ -342,4 +309,5 @@ Respond with a JSON similar to the following, but with the property names as giv
 
             response_json = response.json()
             json_message = response_json["choices"][0]["message"]["content"]
-            return json.loads(json_message)
+            beliefs = json.loads(json_message)
+            return beliefs

src/control_backend/schemas/belief_list.py

@@ -0,0 +1,14 @@
+from pydantic import BaseModel
+
+from control_backend.schemas.program import Belief as ProgramBelief
+
+
+class BeliefList(BaseModel):
+    """
+    Represents a list of beliefs, separated from a program. Useful in agents which need to
+    communicate beliefs.
+
+    :ivar: beliefs: The list of beliefs.
+    """
+
+    beliefs: list[ProgramBelief]

src/control_backend/schemas/program.py

@@ -43,7 +43,6 @@ class SemanticBelief(ProgramElement):
     :ivar description: Description of how to form the belief, used by the LLM.
     """
 
-    name: str = ""
     description: str
 
 
@@ -113,10 +112,12 @@ class Goal(ProgramElement):
     for example when the achieving of the goal is dependent on the user's reply, this means
     that the achieved status will be set from somewhere else in the program.
 
+    :ivar description: A description of the goal, used to determine if it has been achieved.
     :ivar plan: The plan to execute.
     :ivar can_fail: Whether we can fail to achieve the goal after executing the plan.
     """
 
+    description: str
     plan: Plan
     can_fail: bool = True
 

test/unit/agents/bdi/test_bdi_core_agent.py

@@ -20,7 +20,7 @@ def mock_agentspeak_env():
 
 @pytest.fixture
 def agent():
-    agent = BDICoreAgent("bdi_agent", "dummy.asl")
+    agent = BDICoreAgent("bdi_agent")
     agent.send = AsyncMock()
     agent.bdi_agent = MagicMock()
     return agent
@@ -133,14 +133,14 @@ async def test_custom_actions(agent):
 
     # Invoke action
     mock_term = MagicMock()
-    mock_term.args = ["Hello", "Norm", "Goal"]
+    mock_term.args = ["Hello", "Norm"]
     mock_intention = MagicMock()
 
     # Run generator
     gen = action_fn(agent, mock_term, mock_intention)
     next(gen)  # Execute
 
-    agent._send_to_llm.assert_called_with("Hello", "Norm", "Goal")
+    agent._send_to_llm.assert_called_with("Hello", "Norm", "")
 
 
 def test_add_belief_sets_event(agent):

test/unit/agents/bdi/test_bdi_program_manager.py

@@ -32,6 +32,8 @@ def make_valid_program_json(norm="N1", goal="G1") -> str:
                     Goal(
                         id=uuid.uuid4(),
                         name=goal,
+                        description="This description can be used to determine whether the goal "
+                        "has been achieved.",
                         plan=Plan(
                             id=uuid.uuid4(),
                             name="Goal Plan",
@@ -75,6 +77,7 @@ async def test_receive_programs_valid_and_invalid():
     ]
 
     manager = BDIProgramManager(name="program_manager_test")
+    manager._internal_pub_socket = AsyncMock()
     manager.sub_socket = sub
     manager._create_agentspeak_and_send_to_bdi = AsyncMock()
 

test/unit/agents/bdi/test_text_belief_extractor.py

@@ -8,9 +8,11 @@ import pytest
 from control_backend.agents.bdi import TextBeliefExtractorAgent
 from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
+from control_backend.schemas.belief_list import BeliefList
 from control_backend.schemas.belief_message import BeliefMessage
 from control_backend.schemas.program import (
     ConditionalNorm,
+    KeywordBelief,
     LLMAction,
     Phase,
     Plan,
@@ -186,13 +188,31 @@ async def test_retry_query_llm_fail_immediately(agent):
 
 
 @pytest.mark.asyncio
-async def test_extracting_beliefs_from_program(agent, sample_program):
+async def test_extracting_semantic_beliefs(agent):
+    """
+    The Program Manager sends beliefs to this agent. Test whether the agent handles them correctly.
+    """
     assert len(agent.available_beliefs) == 0
+    beliefs = BeliefList(
+        beliefs=[
+            KeywordBelief(
+                id=uuid.uuid4(),
+                name="keyword_hello",
+                keyword="hello",
+            ),
+            SemanticBelief(
+                id=uuid.uuid4(), name="semantic_hello_1", description="Some semantic belief 1"
+            ),
+            SemanticBelief(
+                id=uuid.uuid4(), name="semantic_hello_2", description="Some semantic belief 2"
+            ),
+        ]
+    )
     await agent.handle_message(
         InternalMessage(
             to=settings.agent_settings.text_belief_extractor_name,
             sender=settings.agent_settings.bdi_program_manager_name,
-            body=sample_program.model_dump_json(),
+            body=beliefs.model_dump_json(),
         ),
     )
     assert len(agent.available_beliefs) == 2

test/unit/api/v1/endpoints/test_program_endpoint.py

@@ -43,6 +43,8 @@ def make_valid_program_dict():
                     Goal(
                         id=uuid.uuid4(),
                         name="Some goal",
+                        description="This description can be used to determine whether the goal "
+                        "has been achieved.",
                         plan=Plan(
                             id=uuid.uuid4(),
                             name="Goal Plan",

test/unit/schemas/test_ui_program_message.py

@@ -31,6 +31,7 @@ def base_goal() -> Goal:
     return Goal(
         id=uuid.uuid4(),
         name="testGoalName",
+        description="This description can be used to determine whether the goal has been achieved.",
         plan=Plan(
             id=uuid.uuid4(),
             name="testGoalPlanName",