- Move global config files to globals iso global folder, as the name global conflicts with python language
- Creation of Traicie Vancancy Definition specialist - Allow to invoke non-interaction specialists from withing Evie's mgmt interface (eveai_app) - Improvements to crewai specialized classes - Introduction to json editor for showing specialists arguments and results in a better way - Introduction of more complex pagination (adding extra arguments) by adding a global 'get_pagination_html' - Allow follow-up of ChatSession / Specialist execution - Improvement in logging of Specialists (but needs to be finished)
This commit is contained in:
Josako
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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 crewai import Process
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from flask import current_app
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from gevent import sleep
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from pydantic import BaseModel, Field
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from common.extensions import cache_manager
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from common.models.user import Tenant
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from common.utils.business_event_context import current_event
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from eveai_chat_workers.outputs.globals.basic_types.list_item import ListItem
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from eveai_chat_workers.retrievers.retriever_typing import RetrieverArguments
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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_0 import BehaviouralCompetencies
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from eveai_chat_workers.outputs.traicie.knockout_criteria.knockout_criteria_v1_0 import KnockoutCriteria
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from eveai_chat_workers.specialists.crewai_base_classes import EveAICrewAICrew, EveAICrewAIFlow, EveAIFlowState
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from common.utils.pydantic_utils import flatten_pydantic_model
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class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
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"""
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type: TRAICIE_VACANCY_DEFINITION_SPECIALIST
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type_version: 1.0
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Traicie Vacancy 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.vac_def_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_VACANCY_DEFINITION_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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self._add_task_agent("traicie_get_ko_criteria_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", BehaviouralCompetencies, "competencies")
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self._add_pydantic_output("traicie_get_ko_criteria_task", KnockoutCriteria, "criteria")
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def _instantiate_specialist(self):
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verbose = self.tuning
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vac_def_agents = [self.traicie_hr_bp_agent]
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vac_def_tasks = [self.traicie_get_competencies_task, self.traicie_get_ko_criteria_task]
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self.vac_def_crew = EveAICrewAICrew(
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self,
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"Vacancy Definition Crew",
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agents=vac_def_agents,
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tasks=vac_def_tasks,
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verbose=verbose,
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)
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self.flow = VacancyDefinitionFlow(
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self,
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self.vac_def_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 Vacancy Definition Specialist execution started", {})
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flow_inputs = {
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"vacancy_text": arguments.vacancy_text,
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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 = VacancyDefinitionSpecialistResult.create_for_type(self.type, self.type_version)
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update_data = {}
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if flow_state.competencies:
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results.competencies = flow_state.competencies
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if flow_state.criteria:
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results.criteria = flow_state.criteria
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self.log_tuning(f"Traicie Vacancy Definition Specialist execution ended", {"Results": results.model_dump()})
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return results
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class VacancyDefinitionSpecialistInput(BaseModel):
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vacancy_text: Optional[str] = Field(None, alias="vacancy_text")
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class VacancyDefinitionSpecialistResult(SpecialistResult):
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competencies: Optional[List[ListItem]] = None
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criteria: Optional[List[ListItem]] = None
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class VacancyDefFlowState(EveAIFlowState):
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"""Flow state for Traicie Vacancy Definition specialist that automatically updates from task outputs"""
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input: Optional[VacancyDefinitionSpecialistInput] = None
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competencies: Optional[List[ListItem]] = None
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criteria: Optional[List[ListItem]] = None
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class VacancyDefinitionFlow(EveAICrewAIFlow[VacancyDefFlowState]):
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def __init__(self,
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specialist_executor: CrewAIBaseSpecialistExecutor,
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vac_def_crew: EveAICrewAICrew,
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**kwargs):
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super().__init__(specialist_executor, "Traicie Vacancy Definition Specialist Flow", **kwargs)
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self.specialist_executor = specialist_executor
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self.vac_def_crew = vac_def_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_vac_def(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_vac_def")
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crew_output = await self.vac_def_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.vac_def_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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elif task.name == "traicie_get_ko_criteria_task":
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# update["criteria"] = task.output.pydantic.criteria
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self.state.criteria = task.output.pydantic.criteria
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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_vac_def: {self.state}")
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current_app.logger.debug(f"State dump after execute_vac_def: {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_vac_def 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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self.state.input = VacancyDefinitionSpecialistInput.model_validate(inputs)
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result = await super().kickoff_async(inputs)
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return self.state
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