- Add a new 'system' type to dynamic forms, first one defined = 'tenant_make'
- Add active field to Specialist model - Improve Specialists view - Propagate make for Role Definition Specialist to Selection Specialist (make is defined at the role level) - Ensure a make with a given name can only be defined once
This commit is contained in:
Josako
@@ -0,0 +1,198 @@
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import asyncio
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import json
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from os import wait
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from typing import Optional, List
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from crewai.flow.flow import start, listen, and_
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from flask import current_app
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from pydantic import BaseModel, Field
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from sqlalchemy.exc import SQLAlchemyError
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from common.extensions import db
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from common.models.user import Tenant
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from common.models.interaction import Specialist
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from eveai_chat_workers.outputs.globals.basic_types.list_item import ListItem
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from eveai_chat_workers.specialists.crewai_base_specialist import CrewAIBaseSpecialistExecutor
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from eveai_chat_workers.specialists.specialist_typing import SpecialistResult, SpecialistArguments
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from eveai_chat_workers.outputs.traicie.competencies.competencies_v1_1 import Competencies
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from eveai_chat_workers.specialists.crewai_base_classes import EveAICrewAICrew, EveAICrewAIFlow, EveAIFlowState
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from common.services.interaction.specialist_services import SpecialistServices
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class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
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"""
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type: TRAICIE_ROLE_DEFINITION_SPECIALIST
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type_version: 1.0
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Traicie Role Definition Specialist Executor class
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"""
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def __init__(self, tenant_id, specialist_id, session_id, task_id, **kwargs):
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self.role_definition_crew = None
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super().__init__(tenant_id, specialist_id, session_id, task_id)
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# Load the Tenant & set language
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self.tenant = Tenant.query.get_or_404(tenant_id)
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@property
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def type(self) -> str:
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return "TRAICIE_ROLE_DEFINITION_SPECIALIST"
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@property
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def type_version(self) -> str:
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return "1.3"
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def _config_task_agents(self):
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self._add_task_agent("traicie_get_competencies_task", "traicie_hr_bp_agent")
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def _config_pydantic_outputs(self):
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self._add_pydantic_output("traicie_get_competencies_task", Competencies, "competencies")
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def _instantiate_specialist(self):
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verbose = self.tuning
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role_definition_agents = [self.traicie_hr_bp_agent]
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role_definition_tasks = [self.traicie_get_competencies_task]
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self.role_definition_crew = EveAICrewAICrew(
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self,
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"Role Definition Crew",
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agents=role_definition_agents,
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tasks=role_definition_tasks,
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verbose=verbose,
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)
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self.flow = RoleDefinitionFlow(
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self,
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self.role_definition_crew
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)
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def execute(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
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self.log_tuning("Traicie Role Definition Specialist execution started", {})
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flow_inputs = {
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"vacancy_text": arguments.vacancy_text,
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"role_name": arguments.role_name,
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'role_reference': arguments.role_reference,
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}
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flow_results = self.flow.kickoff(inputs=flow_inputs)
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flow_state = self.flow.state
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results = RoleDefinitionSpecialistResult.create_for_type(self.type, self.type_version)
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if flow_state.competencies:
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results.competencies = flow_state.competencies
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self.create_selection_specialist(arguments, flow_state.competencies)
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self.log_tuning(f"Traicie Role Definition Specialist execution ended", {"Results": results.model_dump()})
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return results
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def create_selection_specialist(self, arguments: SpecialistArguments, competencies: List[ListItem]):
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"""This method creates a new TRAICIE_SELECTION_SPECIALIST specialist with the given competencies."""
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current_app.logger.info(f"Creating selection with arguments: {arguments.model_dump()}")
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selection_comptencies = []
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for competency in competencies:
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selection_competency = {
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"title": competency.title,
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"description": competency.description,
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"assess": True,
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"is_knockout": False,
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}
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selection_comptencies.append(selection_competency)
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selection_config = {
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"name": arguments.specialist_name,
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"competencies": selection_comptencies,
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"tone_of_voice": "Professional & Neutral",
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"language_level": "Standard",
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"role_reference": arguments.role_reference,
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"make": arguments.make,
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}
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name = arguments.role_name
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if len(name) > 50:
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name = name[:47] + "..."
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new_specialist = Specialist(
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name=name,
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description=f"Specialist for {arguments.role_name} role",
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type="TRAICIE_SELECTION_SPECIALIST",
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type_version="1.1",
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tuning=False,
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configuration=selection_config,
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)
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try:
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db.session.add(new_specialist)
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db.session.commit()
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except SQLAlchemyError as e:
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db.session.rollback()
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current_app.logger.error(f"Error creating selection specialist: {str(e)}")
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raise e
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SpecialistServices.initialize_specialist(new_specialist.id, "TRAICIE_SELECTION_SPECIALIST", "1.0")
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class RoleDefinitionSpecialistInput(BaseModel):
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role_name: str = Field(..., alias="role_name")
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role_reference: Optional[str] = Field(..., alias="role_reference")
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vacancy_text: Optional[str] = Field(None, alias="vacancy_text")
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class RoleDefinitionSpecialistResult(SpecialistResult):
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competencies: Optional[List[ListItem]] = None
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class RoleDefFlowState(EveAIFlowState):
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"""Flow state for Traicie Role Definition specialist that automatically updates from task outputs"""
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input: Optional[RoleDefinitionSpecialistInput] = None
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competencies: Optional[List[ListItem]] = None
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class RoleDefinitionFlow(EveAICrewAIFlow[RoleDefFlowState]):
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def __init__(self,
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specialist_executor: CrewAIBaseSpecialistExecutor,
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role_definitiion_crew: EveAICrewAICrew,
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**kwargs):
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super().__init__(specialist_executor, "Traicie Role Definition Specialist Flow", **kwargs)
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self.specialist_executor = specialist_executor
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self.role_definition_crew = role_definitiion_crew
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self.exception_raised = False
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@start()
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def process_inputs(self):
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return ""
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@listen(process_inputs)
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async def execute_role_definition (self):
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inputs = self.state.input.model_dump()
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try:
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current_app.logger.debug("In execute_role_definition")
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crew_output = await self.role_definition_crew.kickoff_async(inputs=inputs)
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# Unfortunately, crew_output will only contain the output of the latest task.
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# As we will only take into account the flow state, we need to ensure both competencies and criteria
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# are copies to the flow state.
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update = {}
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for task in self.role_definition_crew.tasks:
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current_app.logger.debug(f"Task {task.name} output:\n{task.output}")
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if task.name == "traicie_get_competencies_task":
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# update["competencies"] = task.output.pydantic.competencies
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self.state.competencies = task.output.pydantic.competencies
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# crew_output.pydantic = crew_output.pydantic.model_copy(update=update)
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current_app.logger.debug(f"State after execute_role_definition: {self.state}")
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current_app.logger.debug(f"State dump after execute_role_definition: {self.state.model_dump()}")
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return crew_output
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except Exception as e:
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current_app.logger.error(f"CREW execute_role_definition Kickoff Error: {str(e)}")
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self.exception_raised = True
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raise e
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async def kickoff_async(self, inputs=None):
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current_app.logger.debug(f"Async kickoff {self.name}")
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current_app.logger.debug(f"Inputs: {inputs}")
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self.state.input = RoleDefinitionSpecialistInput.model_validate(inputs)
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current_app.logger.debug(f"State: {self.state}")
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result = await super().kickoff_async(inputs)
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return self.state
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@@ -0,0 +1,197 @@
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import asyncio
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import json
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from os import wait
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from typing import Optional, List
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from crewai.flow.flow import start, listen, and_
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from flask import current_app
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from pydantic import BaseModel, Field
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from sqlalchemy.exc import SQLAlchemyError
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from common.extensions import db
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from common.models.user import Tenant
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from common.models.interaction import Specialist
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from eveai_chat_workers.outputs.globals.basic_types.list_item import ListItem
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from eveai_chat_workers.specialists.crewai_base_specialist import CrewAIBaseSpecialistExecutor
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from eveai_chat_workers.specialists.specialist_typing import SpecialistResult, SpecialistArguments
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from eveai_chat_workers.outputs.traicie.competencies.competencies_v1_1 import Competencies
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from eveai_chat_workers.specialists.crewai_base_classes import EveAICrewAICrew, EveAICrewAIFlow, EveAIFlowState
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from common.services.interaction.specialist_services import SpecialistServices
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class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
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"""
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type: TRAICIE_SELECTION_SPECIALIST
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type_version: 1.0
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Traicie Selection Specialist Executor class
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"""
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def __init__(self, tenant_id, specialist_id, session_id, task_id, **kwargs):
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self.role_definition_crew = None
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super().__init__(tenant_id, specialist_id, session_id, task_id)
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# Load the Tenant & set language
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self.tenant = Tenant.query.get_or_404(tenant_id)
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@property
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def type(self) -> str:
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return "TRAICIE_SELECTION_SPECIALIST"
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@property
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def type_version(self) -> str:
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return "1.0"
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def _config_task_agents(self):
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self._add_task_agent("traicie_get_competencies_task", "traicie_hr_bp_agent")
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def _config_pydantic_outputs(self):
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self._add_pydantic_output("traicie_get_competencies_task", Competencies, "competencies")
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def _instantiate_specialist(self):
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verbose = self.tuning
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role_definition_agents = [self.traicie_hr_bp_agent]
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role_definition_tasks = [self.traicie_get_competencies_task]
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self.role_definition_crew = EveAICrewAICrew(
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self,
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"Role Definition Crew",
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agents=role_definition_agents,
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tasks=role_definition_tasks,
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verbose=verbose,
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)
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self.flow = RoleDefinitionFlow(
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self,
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self.role_definition_crew
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)
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def execute(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
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self.log_tuning("Traicie Role Definition Specialist execution started", {})
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flow_inputs = {
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"vacancy_text": arguments.vacancy_text,
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"role_name": arguments.role_name,
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'role_reference': arguments.role_reference,
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}
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flow_results = self.flow.kickoff(inputs=flow_inputs)
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flow_state = self.flow.state
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results = RoleDefinitionSpecialistResult.create_for_type(self.type, self.type_version)
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if flow_state.competencies:
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results.competencies = flow_state.competencies
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self.create_selection_specialist(arguments, flow_state.competencies)
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self.log_tuning(f"Traicie Role Definition Specialist execution ended", {"Results": results.model_dump()})
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return results
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def create_selection_specialist(self, arguments: SpecialistArguments, competencies: List[ListItem]):
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"""This method creates a new TRAICIE_SELECTION_SPECIALIST specialist with the given competencies."""
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current_app.logger.info(f"Creating selection with arguments: {arguments.model_dump()}")
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selection_comptencies = []
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for competency in competencies:
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selection_competency = {
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"title": competency.title,
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"description": competency.description,
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"assess": True,
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"is_knockout": False,
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}
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selection_comptencies.append(selection_competency)
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selection_config = {
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"name": arguments.specialist_name,
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"competencies": selection_comptencies,
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"tone_of_voice": "Professional & Neutral",
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"language_level": "Standard",
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"role_reference": arguments.role_reference,
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}
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name = arguments.role_name
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if len(name) > 50:
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name = name[:47] + "..."
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new_specialist = Specialist(
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name=name,
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description=f"Specialist for {arguments.role_name} role",
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type="TRAICIE_SELECTION_SPECIALIST",
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type_version="1.0",
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tuning=False,
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configuration=selection_config,
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)
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try:
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db.session.add(new_specialist)
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db.session.commit()
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except SQLAlchemyError as e:
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db.session.rollback()
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current_app.logger.error(f"Error creating selection specialist: {str(e)}")
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raise e
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SpecialistServices.initialize_specialist(new_specialist.id, "TRAICIE_SELECTION_SPECIALIST", "1.0")
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class RoleDefinitionSpecialistInput(BaseModel):
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role_name: str = Field(..., alias="role_name")
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role_reference: Optional[str] = Field(..., alias="role_reference")
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vacancy_text: Optional[str] = Field(None, alias="vacancy_text")
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class RoleDefinitionSpecialistResult(SpecialistResult):
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competencies: Optional[List[ListItem]] = None
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class RoleDefFlowState(EveAIFlowState):
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"""Flow state for Traicie Role Definition specialist that automatically updates from task outputs"""
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input: Optional[RoleDefinitionSpecialistInput] = None
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competencies: Optional[List[ListItem]] = None
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class RoleDefinitionFlow(EveAICrewAIFlow[RoleDefFlowState]):
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def __init__(self,
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specialist_executor: CrewAIBaseSpecialistExecutor,
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role_definitiion_crew: EveAICrewAICrew,
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**kwargs):
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super().__init__(specialist_executor, "Traicie Role Definition Specialist Flow", **kwargs)
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self.specialist_executor = specialist_executor
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self.role_definition_crew = role_definitiion_crew
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self.exception_raised = False
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@start()
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def process_inputs(self):
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return ""
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@listen(process_inputs)
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async def execute_role_definition (self):
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inputs = self.state.input.model_dump()
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try:
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current_app.logger.debug("In execute_role_definition")
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crew_output = await self.role_definition_crew.kickoff_async(inputs=inputs)
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# Unfortunately, crew_output will only contain the output of the latest task.
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# As we will only take into account the flow state, we need to ensure both competencies and criteria
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# are copies to the flow state.
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update = {}
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for task in self.role_definition_crew.tasks:
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current_app.logger.debug(f"Task {task.name} output:\n{task.output}")
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if task.name == "traicie_get_competencies_task":
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# update["competencies"] = task.output.pydantic.competencies
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self.state.competencies = task.output.pydantic.competencies
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# crew_output.pydantic = crew_output.pydantic.model_copy(update=update)
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current_app.logger.debug(f"State after execute_role_definition: {self.state}")
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current_app.logger.debug(f"State dump after execute_role_definition: {self.state.model_dump()}")
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return crew_output
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except Exception as e:
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current_app.logger.error(f"CREW execute_role_definition Kickoff Error: {str(e)}")
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self.exception_raised = True
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raise e
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async def kickoff_async(self, inputs=None):
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current_app.logger.debug(f"Async kickoff {self.name}")
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current_app.logger.debug(f"Inputs: {inputs}")
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self.state.input = RoleDefinitionSpecialistInput.model_validate(inputs)
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current_app.logger.debug(f"State: {self.state}")
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result = await super().kickoff_async(inputs)
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return self.state
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Reference in New Issue
Block a user