Chat client changes
- Form values shown correct in MessageHistory of Chat client - Improements to CSS - Move css en js to assets directory - Introduce better Personal Contact Form & Professional Contact Form - Start working on actual Selection Specialist
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, Dict, Any
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from datetime import date
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from time import sleep
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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, EmailStr
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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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from common.extensions import cache_manager
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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: 1.1
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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 "1.1"
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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 Selection 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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#
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# flow_results = self.flow.kickoff(inputs=flow_inputs)
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#
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# flow_state = self.flow.state
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#
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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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for i in range(3):
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sleep(1)
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self.ept.send_update(self.task_id, "Traicie Selection Specialist Processing", {"name": f"Processing Iteration {i}"})
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# flow_results = asyncio.run(self.flow.kickoff_async(inputs=arguments.model_dump()))
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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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contact_form = cache_manager.specialist_forms_config_cache.get_config("PERSONAL_CONTACT_FORM", "1.0")
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current_app.logger.debug(f"Contact form: {contact_form}")
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results = SpecialistResult.create_for_type(self.type, self.type_version,
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answer=f"Antwoord op uw vraag: {arguments.question}",
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form_request=contact_form)
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self.log_tuning(f"Traicie Selection 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, self.type, self.type_version)
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class SelectionSpecialistInput(BaseModel):
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region: str = Field(..., alias="region")
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working_schedule: Optional[str] = Field(..., alias="working_schedule")
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start_date: Optional[date] = Field(None, alias="vacancy_text")
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language: Optional[str] = Field(None, alias="language")
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interaction_mode: Optional[str] = Field(None, alias="interaction_mode")
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question: Optional[str] = Field(None, alias="question")
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field_values: Optional[Dict[str, Any]] = Field(None, alias="field_values")
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class SelectionSpecialistKOCriteriumScore(BaseModel):
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criterium: Optional[str] = Field(None, alias="criterium")
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answer: Optional[str] = Field(None, alias="answer")
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score: Optional[int] = Field(None, alias="score")
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class SelectionSpecialistCompetencyScore(BaseModel):
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competency: Optional[str] = Field(None, alias="competency")
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answer: Optional[str] = Field(None, alias="answer")
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score: Optional[int] = Field(None, alias="score")
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class PersonalContactData(BaseModel):
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name: str = Field(..., description="Your name", alias="name")
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email: EmailStr = Field(..., description="Your Name", alias="email")
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phone: str = Field(..., description="Your Phone Number", alias="phone")
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address: Optional[str] = Field(None, description="Your Address", alias="address")
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zip: Optional[str] = Field(None, description="Postal Code", alias="zip")
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city: Optional[str] = Field(None, description="City", alias="city")
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country: Optional[str] = Field(None, description="Country", alias="country")
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consent: bool = Field(..., description="Consent", alias="consent")
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class SelectionSpecialistResult(SpecialistResult):
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ko_criteria_scores: Optional[List[SelectionSpecialistKOCriteriumScore]] = Field(
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None, alias="ko_criteria_scores"
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)
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competency_scores: Optional[List[SelectionSpecialistCompetencyScore]] = Field(
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None, alias="competency_scores"
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)
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personal_contact_data: Optional[PersonalContactData] = Field(
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None, alias="personal_contact_data"
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)
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class SelectionSpecialistFlowState(EveAIFlowState):
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"""Flow state for Traicie Role Definition specialist that automatically updates from task outputs"""
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input: Optional[SelectionSpecialistInput] = None
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ko_criteria_scores: Optional[List[SelectionSpecialistKOCriteriumScore]] = Field(
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None, alias="ko_criteria_scores"
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)
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competency_scores: Optional[List[SelectionSpecialistCompetencyScore]] = Field(
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None, alias="competency_scores"
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)
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personal_contact_data: Optional[PersonalContactData] = Field(
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None, alias="personal_contact_data"
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)
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phase: Optional[str] = Field(None, alias="phase")
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interaction_mode: Optional[str] = Field(None, alias="mode")
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class RoleDefinitionFlow(EveAICrewAIFlow[SelectionSpecialistFlowState]):
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def __init__(self,
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specialist_executor: CrewAIBaseSpecialistExecutor,
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role_definition_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_definition_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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