- smaller changes to eveai.css to ensure background of selected buttons do not get all white and to ensure that the background of fiels in editable cells do not become white in a tabulator.

- The Role Definition Specialist now creates a new selection specialist upon completion
2025-06-01 10:09:34 +02:00
parent 578981c745
commit 81e754317a
6 changed files with 460 additions and 9 deletions
--- a/eveai_chat_workers/specialists/traicie/TRAICIE_ROLE_DEFINITION_SPECIALIST/1_2.py
+++ b/eveai_chat_workers/specialists/traicie/TRAICIE_ROLE_DEFINITION_SPECIALIST/1_2.py
@@ -0,0 +1,197 @@
+import asyncio
+import json
+from os import wait
+from typing import Optional, List
+
+from crewai.flow.flow import start, listen, and_
+from flask import current_app
+from pydantic import BaseModel, Field
+from sqlalchemy.exc import SQLAlchemyError
+
+from common.extensions import db
+from common.models.user import Tenant
+from common.models.interaction import Specialist
+from eveai_chat_workers.outputs.globals.basic_types.list_item import ListItem
+from eveai_chat_workers.specialists.crewai_base_specialist import CrewAIBaseSpecialistExecutor
+from eveai_chat_workers.specialists.specialist_typing import SpecialistResult, SpecialistArguments
+from eveai_chat_workers.outputs.traicie.competencies.competencies_v1_1 import Competencies
+from eveai_chat_workers.specialists.crewai_base_classes import EveAICrewAICrew, EveAICrewAIFlow, EveAIFlowState
+from common.services.interaction.specialist_services import SpecialistServices
+
+
+class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
+    """
+    type: TRAICIE_ROLE_DEFINITION_SPECIALIST
+    type_version: 1.0
+    Traicie Role Definition Specialist Executor class
+    """
+
+    def __init__(self, tenant_id, specialist_id, session_id, task_id, **kwargs):
+        self.role_definition_crew = None
+
+        super().__init__(tenant_id, specialist_id, session_id, task_id)
+
+        # Load the Tenant & set language
+        self.tenant = Tenant.query.get_or_404(tenant_id)
+
+    @property
+    def type(self) -> str:
+        return "TRAICIE_ROLE_DEFINITION_SPECIALIST"
+
+    @property
+    def type_version(self) -> str:
+        return "1.1"
+
+    def _config_task_agents(self):
+        self._add_task_agent("traicie_get_competencies_task", "traicie_hr_bp_agent")
+
+    def _config_pydantic_outputs(self):
+        self._add_pydantic_output("traicie_get_competencies_task", Competencies, "competencies")
+
+    def _instantiate_specialist(self):
+        verbose = self.tuning
+
+        role_definition_agents = [self.traicie_hr_bp_agent]
+        role_definition_tasks = [self.traicie_get_competencies_task]
+        self.role_definition_crew = EveAICrewAICrew(
+            self,
+            "Role Definition Crew",
+            agents=role_definition_agents,
+            tasks=role_definition_tasks,
+            verbose=verbose,
+        )
+
+        self.flow = RoleDefinitionFlow(
+            self,
+            self.role_definition_crew
+        )
+
+    def execute(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
+        self.log_tuning("Traicie Role Definition Specialist execution started", {})
+
+        flow_inputs = {
+            "vacancy_text": arguments.vacancy_text,
+            "role_name": arguments.role_name,
+            'role_reference': arguments.role_reference,
+        }
+
+        flow_results = self.flow.kickoff(inputs=flow_inputs)
+
+        flow_state = self.flow.state
+
+        results = RoleDefinitionSpecialistResult.create_for_type(self.type, self.type_version)
+        if flow_state.competencies:
+            results.competencies = flow_state.competencies
+
+        self.create_selection_specialist(arguments, flow_state.competencies)
+
+        self.log_tuning(f"Traicie Role Definition Specialist execution ended", {"Results": results.model_dump()})
+
+        return results
+
+    def create_selection_specialist(self, arguments: SpecialistArguments, competencies: List[ListItem]):
+        """This method creates a new TRAICIE_SELECTION_SPECIALIST specialist with the given competencies."""
+        current_app.logger.info(f"Creating selection with arguments: {arguments.model_dump()}")
+        selection_comptencies = []
+        for competency in competencies:
+            selection_competency = {
+                "title": competency.title,
+                "description": competency.description,
+                "assess": True,
+                "is_knockout": False,
+            }
+            selection_comptencies.append(selection_competency)
+
+        selection_config = {
+            "name": arguments.specialist_name,
+            "competencies": selection_comptencies,
+            "tone_of_voice": "Professional & Neutral",
+            "language_level": "Standard",
+            "role_reference": arguments.role_reference,
+        }
+        name = arguments.role_name
+        if len(name) > 50:
+            name = name[:47] + "..."
+
+        new_specialist = Specialist(
+            name=name,
+            description=f"Specialist for {arguments.role_name} role",
+            type="TRAICIE_SELECTION_SPECIALIST",
+            type_version="1.0",
+            tuning=False,
+            configuration=selection_config,
+        )
+        try:
+            db.session.add(new_specialist)
+            db.session.commit()
+        except SQLAlchemyError as e:
+            db.session.rollback()
+            current_app.logger.error(f"Error creating selection specialist: {str(e)}")
+            raise e
+
+        SpecialistServices.initialize_specialist(new_specialist.id, "TRAICIE_SELECTION_SPECIALIST", "1.0")
+
+
+
+
+class RoleDefinitionSpecialistInput(BaseModel):
+    role_name: str = Field(..., alias="role_name")
+    role_reference: Optional[str] = Field(..., alias="role_reference")
+    vacancy_text: Optional[str] = Field(None, alias="vacancy_text")
+
+
+class RoleDefinitionSpecialistResult(SpecialistResult):
+    competencies: Optional[List[ListItem]] = None
+
+
+class RoleDefFlowState(EveAIFlowState):
+    """Flow state for Traicie Role Definition specialist that automatically updates from task outputs"""
+    input: Optional[RoleDefinitionSpecialistInput] = None
+    competencies: Optional[List[ListItem]] = None
+
+
+class RoleDefinitionFlow(EveAICrewAIFlow[RoleDefFlowState]):
+    def __init__(self,
+                 specialist_executor: CrewAIBaseSpecialistExecutor,
+                 role_definitiion_crew: EveAICrewAICrew,
+                 **kwargs):
+        super().__init__(specialist_executor, "Traicie Role Definition Specialist Flow", **kwargs)
+        self.specialist_executor = specialist_executor
+        self.role_definition_crew = role_definitiion_crew
+        self.exception_raised = False
+
+    @start()
+    def process_inputs(self):
+        return ""
+
+    @listen(process_inputs)
+    async def execute_role_definition   (self):
+        inputs = self.state.input.model_dump()
+        try:
+            current_app.logger.debug("In execute_role_definition")
+            crew_output = await self.role_definition_crew.kickoff_async(inputs=inputs)
+            # Unfortunately, crew_output will only contain the output of the latest task.
+            # As we will only take into account the flow state, we need to ensure both competencies and criteria
+            # are copies to the flow state.
+            update = {}
+            for task in self.role_definition_crew.tasks:
+                current_app.logger.debug(f"Task {task.name} output:\n{task.output}")
+                if task.name == "traicie_get_competencies_task":
+                    # update["competencies"] = task.output.pydantic.competencies
+                    self.state.competencies = task.output.pydantic.competencies
+            # crew_output.pydantic = crew_output.pydantic.model_copy(update=update)
+            current_app.logger.debug(f"State after execute_role_definition: {self.state}")
+            current_app.logger.debug(f"State dump after execute_role_definition: {self.state.model_dump()}")
+            return crew_output
+        except Exception as e:
+            current_app.logger.error(f"CREW execute_role_definition Kickoff Error: {str(e)}")
+            self.exception_raised = True
+            raise e
+
+    async def kickoff_async(self, inputs=None):
+        current_app.logger.debug(f"Async kickoff {self.name}")
+        current_app.logger.debug(f"Inputs: {inputs}")
+        self.state.input = RoleDefinitionSpecialistInput.model_validate(inputs)
+        current_app.logger.debug(f"State: {self.state}")
+        result = await super().kickoff_async(inputs)
+        return self.state