- Introduction of TRACIE_KO_INTERVIEW_DEFINITION_SPECIALIST
- Re-introduction of EveAIAsset - Make translation services resistent for situation with and without current_event defined. - Ensure first question is asked in eveai_chat_client - Start of version 1.4.0 of TRAICIE_SELECTION_SPECIALIST
This commit is contained in:
Josako
@@ -0,0 +1,308 @@
|
||||
from datetime import datetime as dt, timezone as tz
|
||||
from typing import Optional, List, Dict
|
||||
|
||||
import json
|
||||
import yaml
|
||||
from crewai.flow.flow import start, listen
|
||||
from flask import current_app
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from common.extensions import db, minio_client
|
||||
from common.models.interaction import Specialist, EveAIAsset
|
||||
from common.utils.eveai_exceptions import EveAISpecialistExecutionError
|
||||
from common.utils.model_logging_utils import set_logging_information
|
||||
from eveai_chat_workers.definitions.language_level.language_level_v1_0 import LANGUAGE_LEVEL
|
||||
from eveai_chat_workers.definitions.tone_of_voice.tone_of_voice_v1_0 import TONE_OF_VOICE
|
||||
from eveai_chat_workers.outputs.traicie.knockout_questions.knockout_questions_v1_0 import KOQuestions, KOQuestion
|
||||
from eveai_chat_workers.specialists.crewai_base_classes import EveAICrewAICrew, EveAICrewAIFlow, EveAIFlowState
|
||||
from eveai_chat_workers.specialists.crewai_base_specialist import CrewAIBaseSpecialistExecutor
|
||||
from eveai_chat_workers.specialists.specialist_typing import SpecialistResult, SpecialistArguments
|
||||
|
||||
|
||||
class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
|
||||
"""
|
||||
type: TRAICIE_KO_INTERVIEW_DEFINITION_SPECIALIST
|
||||
type_version: 1.0
|
||||
Traicie Selection Specialist Executor class
|
||||
"""
|
||||
|
||||
def __init__(self, tenant_id, specialist_id, session_id, task_id, **kwargs):
|
||||
self.ko_def_crew = None
|
||||
|
||||
super().__init__(tenant_id, specialist_id, session_id, task_id)
|
||||
|
||||
|
||||
@property
|
||||
def type(self) -> str:
|
||||
return "TRAICIE_KO_INTERVIEW_DEFINITION_SPECIALIST"
|
||||
|
||||
@property
|
||||
def type_version(self) -> str:
|
||||
return "1.0"
|
||||
|
||||
def _config_task_agents(self):
|
||||
self._add_task_agent("traicie_ko_criteria_interview_definition_task", "traicie_recruiter_agent")
|
||||
|
||||
def _config_pydantic_outputs(self):
|
||||
self._add_pydantic_output("traicie_ko_criteria_interview_definition_task", KOQuestions, "ko_questions")
|
||||
|
||||
def _config_state_result_relations(self):
|
||||
self._add_state_result_relation("ko_questions")
|
||||
|
||||
def _instantiate_specialist(self):
|
||||
verbose = self.tuning
|
||||
|
||||
ko_def_agents = [self.traicie_recruiter_agent]
|
||||
ko_def_tasks = [self.traicie_ko_criteria_interview_definition_task]
|
||||
self.ko_def_crew = EveAICrewAICrew(
|
||||
self,
|
||||
"KO Criteria Interview Definition Crew",
|
||||
agents=ko_def_agents,
|
||||
tasks=ko_def_tasks,
|
||||
verbose=verbose,
|
||||
)
|
||||
|
||||
self.flow = KOFlow(
|
||||
self,
|
||||
self.ko_def_crew
|
||||
)
|
||||
|
||||
def execute(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
|
||||
self.log_tuning("Traicie KO Criteria Interview Definition Specialist execution started", {})
|
||||
|
||||
current_app.logger.debug(f"Arguments: {arguments.model_dump()}")
|
||||
current_app.logger.debug(f"Formatted Context: {formatted_context}")
|
||||
current_app.logger.debug(f"Formatted History: {self._formatted_history}")
|
||||
current_app.logger.debug(f"Cached Chat Session: {self._cached_session}")
|
||||
|
||||
if not self._cached_session.interactions:
|
||||
specialist_phase = "initial"
|
||||
else:
|
||||
specialist_phase = self._cached_session.interactions[-1].specialist_results.get('phase', 'initial')
|
||||
|
||||
results = None
|
||||
|
||||
match specialist_phase:
|
||||
case "initial":
|
||||
results = self.execute_initial_state(arguments, formatted_context, citations)
|
||||
|
||||
self.log_tuning(f"Traicie KO Criteria Interview Definition Specialist execution ended",
|
||||
{"Results": results.model_dump() if results else "No info"})
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def execute_initial_state(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
|
||||
self.log_tuning("Traicie KO Criteria Interview Definition Specialist initial_state_execution started", {})
|
||||
|
||||
selection_specialist = Specialist.query.get(arguments.specialist_id)
|
||||
if not selection_specialist:
|
||||
raise EveAISpecialistExecutionError(self.tenant_id, self.specialist_id, self.session_id,
|
||||
"No selection specialist found")
|
||||
if selection_specialist.type != "TRAICIE_SELECTION_SPECIALIST":
|
||||
raise EveAISpecialistExecutionError(self.tenant_id, self.specialist_id, self.session_id,
|
||||
"Specialist is no Selection Specialist")
|
||||
|
||||
current_app.logger.debug(f"Specialist Competencies:\n"
|
||||
f"{selection_specialist.configuration.get("competencies", [])}")
|
||||
|
||||
ko_competencies = []
|
||||
for competency in selection_specialist.configuration.get("competencies", []):
|
||||
if competency["is_knockout"] is True and competency["assess"] is True:
|
||||
current_app.logger.debug(f"Assessable Knockout competency: {competency}")
|
||||
ko_competencies.append({"title": competency["title"], "description": competency["description"]})
|
||||
|
||||
tone_of_voice = selection_specialist.configuration.get('tone_of_voice', 'Professional & Neutral')
|
||||
selected_tone_of_voice = next(
|
||||
(item for item in TONE_OF_VOICE if item["name"] == tone_of_voice),
|
||||
None # fallback indien niet gevonden
|
||||
)
|
||||
current_app.logger.debug(f"Selected tone of voice: {selected_tone_of_voice}")
|
||||
tone_of_voice_context = f"{selected_tone_of_voice["description"]}"
|
||||
|
||||
language_level = selection_specialist.configuration.get('language_level', 'Standard')
|
||||
selected_language_level = next(
|
||||
(item for item in LANGUAGE_LEVEL if item["name"] == language_level),
|
||||
None
|
||||
)
|
||||
current_app.logger.debug(f"Selected language level: {selected_language_level}")
|
||||
language_level_context = (f"{selected_language_level['description']}, "
|
||||
f"corresponding to CEFR level {selected_language_level['cefr_level']}")
|
||||
|
||||
flow_inputs = {
|
||||
'tone_of_voice': tone_of_voice,
|
||||
'tone_of_voice_context': tone_of_voice_context,
|
||||
'language_level': language_level,
|
||||
'language_level_context': language_level_context,
|
||||
'ko_criteria': ko_competencies,
|
||||
}
|
||||
|
||||
flow_results = self.flow.kickoff(inputs=flow_inputs)
|
||||
current_app.logger.debug(f"Flow results: {flow_results}")
|
||||
current_app.logger.debug(f"Flow state: {self.flow.state}")
|
||||
|
||||
new_type = "TRAICIE_KO_CRITERIA_QUESTIONS"
|
||||
|
||||
current_app.logger.debug(f"KO Criteria Questions:\n {self.flow.state.ko_questions}")
|
||||
# Controleer of we een KOQuestions object hebben of een lijst van KOQuestion objecten
|
||||
if hasattr(self.flow.state.ko_questions, 'to_json'):
|
||||
# Het is een KOQuestions object
|
||||
json_str = self.flow.state.ko_questions.to_json()
|
||||
elif isinstance(self.flow.state.ko_questions, list):
|
||||
# Het is een lijst van KOQuestion objecten
|
||||
# Maak een KOQuestions object en gebruik to_json daarop
|
||||
ko_questions_obj = KOQuestions.from_question_list(self.flow.state.ko_questions)
|
||||
json_str = ko_questions_obj.to_json()
|
||||
else:
|
||||
# Fallback voor het geval het een onverwacht type is
|
||||
current_app.logger.warning(f"Unexpected type for ko_questions: {type(self.flow.state.ko_questions)}")
|
||||
ko_questions_data = [q.model_dump() for q in self.flow.state.ko_questions]
|
||||
json_str = json.dumps(ko_questions_data, ensure_ascii=False, indent=2)
|
||||
|
||||
current_app.logger.debug(f"KO Criteria Questions json style:\n {json_str}")
|
||||
|
||||
try:
|
||||
asset = db.session.query(EveAIAsset).filter(
|
||||
EveAIAsset.type == new_type,
|
||||
EveAIAsset.type_version == "1.0.0",
|
||||
EveAIAsset.configuration.is_not(None),
|
||||
EveAIAsset.configuration.has_key('specialist_id'),
|
||||
EveAIAsset.configuration['specialist_id'].astext.cast(db.Integer) == selection_specialist.id
|
||||
).first()
|
||||
except (ValueError, TypeError) as e:
|
||||
current_app.logger.warning(f"Error casting specialist_id in asset configuration: {str(e)}")
|
||||
asset = None
|
||||
|
||||
if not asset:
|
||||
asset = EveAIAsset(
|
||||
name=f"KO Criteria Form for specialist {selection_specialist.id}",
|
||||
type=new_type,
|
||||
type_version="1.0.0",
|
||||
system_metadata={
|
||||
"Creator Specialist Type": self.type,
|
||||
"Creator Specialist Type Version": self.type_version,
|
||||
"Creator Specialist ID": self.specialist_id
|
||||
},
|
||||
configuration={
|
||||
"specialist_id": selection_specialist.id,
|
||||
},
|
||||
)
|
||||
set_logging_information(asset, dt.now(tz=tz.utc))
|
||||
asset.last_used_at = asset.created_at
|
||||
else:
|
||||
asset.last_used_at = dt.now(tz=tz.utc)
|
||||
|
||||
try:
|
||||
# Stap 1: Asset aanmaken maar nog niet committen
|
||||
db.session.add(asset)
|
||||
db.session.flush() # Geeft ons het ID zonder te committen
|
||||
|
||||
# Stap 2: Upload naar MinIO (kan falen zonder database impact)
|
||||
bucket_name, object_name, file_size = minio_client.upload_asset_file(
|
||||
tenant_id=self.tenant_id,
|
||||
asset_id=asset.id,
|
||||
asset_type=new_type,
|
||||
file_type="json",
|
||||
file_data=json_str
|
||||
)
|
||||
|
||||
# Stap 3: Storage metadata toevoegen
|
||||
asset.bucket_name = bucket_name
|
||||
asset.object_name = object_name
|
||||
asset.file_size = file_size
|
||||
asset.file_type = "json"
|
||||
|
||||
# Stap 4: Token usage toevoegen
|
||||
asset.prompt_tokens = self.ko_def_crew.usage_metrics.prompt_tokens
|
||||
asset.completion_tokens = self.ko_def_crew.usage_metrics.completion_tokens
|
||||
|
||||
# Alles in één keer committen
|
||||
db.session.commit()
|
||||
|
||||
except Exception as e:
|
||||
current_app.logger.error(f"Error creating asset: {str(e)}")
|
||||
db.session.rollback()
|
||||
# Probeer MinIO cleanup als upload is gelukt maar database commit faalde
|
||||
try:
|
||||
if 'bucket_name' in locals() and 'object_name' in locals():
|
||||
minio_client.delete_object(bucket_name, object_name)
|
||||
except:
|
||||
pass # Log maar ga door met originele exception
|
||||
raise EveAISpecialistExecutionError(self.tenant_id, self.specialist_id, self.session_id,
|
||||
f"Failed to create asset: {str(e)}")
|
||||
|
||||
results = SpecialistResult.create_for_type(self.type, self.type_version,
|
||||
answer=f"asset {asset.id} created for specialist {selection_specialist.id}",
|
||||
phase="finished",
|
||||
asset_id=asset.id,
|
||||
)
|
||||
|
||||
|
||||
return results
|
||||
|
||||
|
||||
class KODefInput(BaseModel):
|
||||
tone_of_voice: Optional[str] = Field(None, alias="tone_of_voice")
|
||||
tone_of_voice_context: Optional[str] = Field(None, alias="tone_of_voice_context")
|
||||
language_level: Optional[str] = Field(None, alias="language_level")
|
||||
language_level_context: Optional[str] = Field(None, alias="language_level_context")
|
||||
ko_criteria: Optional[List[Dict[str, str]]] = Field(None, alias="ko_criteria")
|
||||
|
||||
|
||||
class KODefResult(SpecialistResult):
|
||||
asset_id: Optional[int] = Field(None, alias="asset_id")
|
||||
|
||||
|
||||
class KOFlowState(EveAIFlowState):
|
||||
"""Flow state for Traicie Role Definition specialist that automatically updates from task outputs"""
|
||||
input: Optional[KODefInput] = None
|
||||
ko_questions: Optional[List[KOQuestion]] = Field(None, alias="ko_questions")
|
||||
phase: Optional[str] = Field(None, alias="phase")
|
||||
|
||||
|
||||
class KOFlow(EveAICrewAIFlow[KOFlowState]):
|
||||
def __init__(self,
|
||||
specialist_executor: CrewAIBaseSpecialistExecutor,
|
||||
ko_def_crew: EveAICrewAICrew,
|
||||
**kwargs):
|
||||
super().__init__(specialist_executor, "Traicie KO Interview Definiton Specialist Flow", **kwargs)
|
||||
self.specialist_executor = specialist_executor
|
||||
self.ko_def_crew = ko_def_crew
|
||||
self.exception_raised = False
|
||||
|
||||
@start()
|
||||
def process_inputs(self):
|
||||
return ""
|
||||
|
||||
@listen(process_inputs)
|
||||
async def execute_ko_def_definition(self):
|
||||
inputs = self.state.input.model_dump()
|
||||
try:
|
||||
current_app.logger.debug("Run execute_ko_interview_definition")
|
||||
crew_output = await self.ko_def_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.ko_def_crew.tasks:
|
||||
current_app.logger.debug(f"Task {task.name} output:\n{task.output}")
|
||||
if task.name == "traicie_ko_criteria_interview_definition_task":
|
||||
# update["competencies"] = task.output.pydantic.competencies
|
||||
self.state.ko_questions = task.output.pydantic.ko_questions
|
||||
# crew_output.pydantic = crew_output.pydantic.model_copy(update=update)
|
||||
self.state.phase = "personal_contact_data"
|
||||
current_app.logger.debug(f"State after execute_ko_def_definition: {self.state}")
|
||||
current_app.logger.debug(f"State dump after execute_ko_def_definition: {self.state.model_dump()}")
|
||||
return crew_output
|
||||
except Exception as e:
|
||||
current_app.logger.error(f"CREW execute_ko_def 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 = KODefInput.model_validate(inputs)
|
||||
current_app.logger.debug(f"State: {self.state}")
|
||||
result = await super().kickoff_async(inputs)
|
||||
return self.state
|
||||
@@ -181,8 +181,8 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
|
||||
answer = f"Let's start our selection process by asking you a few important questions."
|
||||
|
||||
if arguments.language != 'en':
|
||||
TranslationServices.translate_config(ko_form, "fields", arguments.language)
|
||||
TranslationServices.translate(answer, arguments.language)
|
||||
TranslationServices.translate_config(self.tenant_id, ko_form, "fields", arguments.language)
|
||||
TranslationServices.translate(self.tenant_id, answer, arguments.language)
|
||||
|
||||
|
||||
results = SpecialistResult.create_for_type(self.type, self.type_version,
|
||||
@@ -234,7 +234,8 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
|
||||
if arguments.language != 'nl':
|
||||
answer = TranslationServices.translate(answer, arguments.language)
|
||||
if arguments.language != 'en':
|
||||
contact_form = TranslationServices.translate_config(contact_form, "fields", arguments.language)
|
||||
contact_form = TranslationServices.translate_config(self.tenant_id, contact_form, "fields",
|
||||
arguments.language)
|
||||
results = SpecialistResult.create_for_type(self.type, self.type_version,
|
||||
answer=answer,
|
||||
form_request=contact_form,
|
||||
|
||||
@@ -0,0 +1,370 @@
|
||||
import asyncio
|
||||
import json
|
||||
from os import wait
|
||||
from typing import Optional, List, Dict, Any
|
||||
from datetime import date
|
||||
from time import sleep
|
||||
from crewai.flow.flow import start, listen, and_
|
||||
from flask import current_app
|
||||
from pydantic import BaseModel, Field, EmailStr
|
||||
from sqlalchemy.exc import SQLAlchemyError
|
||||
|
||||
from common.extensions import db
|
||||
from common.models.user import Tenant
|
||||
from common.models.interaction import Specialist
|
||||
from common.services.utils.translation_services import TranslationServices
|
||||
from eveai_chat_workers.outputs.globals.basic_types.list_item import ListItem
|
||||
from eveai_chat_workers.outputs.traicie.knockout_questions.knockout_questions_v1_0 import KOQuestions, KOQuestion
|
||||
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
|
||||
from common.extensions import cache_manager
|
||||
from eveai_chat_workers.definitions.language_level.language_level_v1_0 import LANGUAGE_LEVEL
|
||||
from eveai_chat_workers.definitions.tone_of_voice.tone_of_voice_v1_0 import TONE_OF_VOICE
|
||||
from common.utils.eveai_exceptions import EveAISpecialistExecutionError
|
||||
|
||||
|
||||
class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
|
||||
"""
|
||||
type: TRAICIE_SELECTION_SPECIALIST
|
||||
type_version: 1.1
|
||||
Traicie Selection 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_SELECTION_SPECIALIST"
|
||||
|
||||
@property
|
||||
def type_version(self) -> str:
|
||||
return "1.3"
|
||||
|
||||
def _config_task_agents(self):
|
||||
self._add_task_agent("traicie_ko_criteria_interview_definition_task", "traicie_recruiter_agent")
|
||||
|
||||
def _config_pydantic_outputs(self):
|
||||
self._add_pydantic_output("traicie_ko_criteria_interview_definition_task", KOQuestions, "ko_questions")
|
||||
|
||||
def _config_state_result_relations(self):
|
||||
self._add_state_result_relation("ko_criteria_questions")
|
||||
self._add_state_result_relation("ko_criteria_scores")
|
||||
self._add_state_result_relation("competency_questions")
|
||||
self._add_state_result_relation("competency_scores")
|
||||
self._add_state_result_relation("personal_contact_data")
|
||||
|
||||
def _instantiate_specialist(self):
|
||||
verbose = self.tuning
|
||||
|
||||
ko_def_agents = [self.traicie_recruiter_agent]
|
||||
ko_def_tasks = [self.traicie_ko_criteria_interview_definition_task]
|
||||
self.ko_def_crew = EveAICrewAICrew(
|
||||
self,
|
||||
"KO Criteria Interview Definition Crew",
|
||||
agents=ko_def_agents,
|
||||
tasks=ko_def_tasks,
|
||||
verbose=verbose,
|
||||
)
|
||||
|
||||
self.flow = SelectionFlow(
|
||||
self,
|
||||
self.ko_def_crew
|
||||
)
|
||||
|
||||
def execute(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
|
||||
self.log_tuning("Traicie Selection Specialist execution started", {})
|
||||
|
||||
current_app.logger.debug(f"Arguments: {arguments.model_dump()}")
|
||||
current_app.logger.debug(f"Formatted Context: {formatted_context}")
|
||||
current_app.logger.debug(f"Formatted History: {self._formatted_history}")
|
||||
current_app.logger.debug(f"Cached Chat Session: {self._cached_session}")
|
||||
|
||||
if not self._cached_session.interactions:
|
||||
specialist_phase = "initial"
|
||||
else:
|
||||
specialist_phase = self._cached_session.interactions[-1].specialist_results.get('phase', 'initial')
|
||||
|
||||
results = None
|
||||
|
||||
match specialist_phase:
|
||||
case "initial":
|
||||
results = self.execute_initial_state(arguments, formatted_context, citations)
|
||||
case "ko_question_evaluation":
|
||||
results = self.execute_ko_question_evaluation(arguments, formatted_context, citations)
|
||||
case "personal_contact_data":
|
||||
results = self.execute_personal_contact_data(arguments, formatted_context, citations)
|
||||
case "no_valid_candidate":
|
||||
results = self.execute_no_valid_candidate(arguments, formatted_context, citations)
|
||||
case "candidate_selected":
|
||||
results = self.execute_candidate_selected(arguments, formatted_context, citations)
|
||||
|
||||
self.log_tuning(f"Traicie Selection Specialist execution ended", {"Results": results.model_dump() if results else "No info"})
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def execute_initial_state(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
|
||||
self.log_tuning("Traicie Selection Specialist initial_state execution started", {})
|
||||
|
||||
current_app.logger.debug(f"Specialist Competencies:\n{self.specialist.configuration.get("competencies", [])}")
|
||||
|
||||
ko_competencies = []
|
||||
for competency in self.specialist.configuration.get("competencies", []):
|
||||
if competency["is_knockout"] is True and competency["assess"] is True:
|
||||
current_app.logger.debug(f"Assessable Knockout competency: {competency}")
|
||||
ko_competencies.append({"title: ": competency["title"], "description": competency["description"]})
|
||||
|
||||
tone_of_voice = self.specialist.configuration.get('tone_of_voice', 'Professional & Neutral')
|
||||
selected_tone_of_voice = next(
|
||||
(item for item in TONE_OF_VOICE if item["name"] == tone_of_voice),
|
||||
None # fallback indien niet gevonden
|
||||
)
|
||||
current_app.logger.debug(f"Selected tone of voice: {selected_tone_of_voice}")
|
||||
tone_of_voice_context = f"{selected_tone_of_voice["description"]}"
|
||||
|
||||
language_level = self.specialist.configuration.get('language_level', 'Standard')
|
||||
selected_language_level = next(
|
||||
(item for item in LANGUAGE_LEVEL if item["name"] == language_level),
|
||||
None
|
||||
)
|
||||
current_app.logger.debug(f"Selected language level: {selected_language_level}")
|
||||
language_level_context = (f"{selected_language_level['description']}, "
|
||||
f"corresponding to CEFR level {selected_language_level['cefr_level']}")
|
||||
|
||||
flow_inputs = {
|
||||
"region": arguments.region,
|
||||
"working_schedule": arguments.working_schedule,
|
||||
"start_date": arguments.start_date,
|
||||
"language": arguments.language,
|
||||
"interaction_mode": arguments.interaction_mode,
|
||||
'tone_of_voice': tone_of_voice,
|
||||
'tone_of_voice_context': tone_of_voice_context,
|
||||
'language_level': language_level,
|
||||
'language_level_context': language_level_context,
|
||||
'ko_criteria': ko_competencies,
|
||||
}
|
||||
|
||||
flow_results = self.flow.kickoff(inputs=flow_inputs)
|
||||
|
||||
current_app.logger.debug(f"Flow results: {flow_results}")
|
||||
|
||||
current_app.logger.debug(f"Flow state: {self.flow.state}")
|
||||
|
||||
fields = {}
|
||||
for ko_question in self.flow.state.ko_criteria_questions:
|
||||
fields[ko_question.title] = {
|
||||
"name": ko_question.title,
|
||||
"description": ko_question.title,
|
||||
"context": ko_question.question,
|
||||
"type": "options",
|
||||
"required": True,
|
||||
"allowed_values": [ko_question.answer_positive, ko_question.answer_negative]
|
||||
}
|
||||
|
||||
ko_form = {
|
||||
"type": "KO_CRITERIA_FORM",
|
||||
"version": "1.0.0",
|
||||
"name": "Starter Questions",
|
||||
"icon": "verified",
|
||||
"fields": fields,
|
||||
}
|
||||
|
||||
answer = f"Let's start our selection process by asking you a few important questions."
|
||||
|
||||
if arguments.language != 'en':
|
||||
TranslationServices.translate_config(self.tenant_id, ko_form, "fields", arguments.language)
|
||||
TranslationServices.translate(self.tenant_id, answer, arguments.language)
|
||||
|
||||
|
||||
results = SpecialistResult.create_for_type(self.type, self.type_version,
|
||||
answer=answer,
|
||||
form_request=ko_form,
|
||||
phase="ko_question_evaluation")
|
||||
|
||||
return results
|
||||
|
||||
def execute_ko_question_evaluation(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
|
||||
self.log_tuning("Traicie Selection Specialist ko_question_evaluation started", {})
|
||||
|
||||
# Check if the form has been returned (it should)
|
||||
if not arguments.form_values:
|
||||
raise EveAISpecialistExecutionError(self.tenant_id, self.specialist_id, self.session_id, "No form values returned")
|
||||
current_app.logger.debug(f"Form values: {arguments.form_values}")
|
||||
|
||||
# Load the previous KO Questions
|
||||
previous_ko_questions = self.flow.state.ko_criteria_questions
|
||||
current_app.logger.debug(f"Previous KO Questions: {previous_ko_questions}")
|
||||
|
||||
# Evaluate KO Criteria
|
||||
evaluation = "positive"
|
||||
for criterium, answer in arguments.form_values.items():
|
||||
for qa in previous_ko_questions:
|
||||
if qa.get("title") == criterium:
|
||||
if qa.get("answer_positive") != answer:
|
||||
evaluation = "negative"
|
||||
break
|
||||
if evaluation == "negative":
|
||||
break
|
||||
|
||||
if evaluation == "negative":
|
||||
answer = (f"We hebben de antwoorden op onze eerste vragen verwerkt. Je voldoet jammer genoeg niet aan de "
|
||||
f"minimale vereisten voor deze job.")
|
||||
if arguments.language != 'nl':
|
||||
answer = TranslationServices.translate(answer, arguments.language)
|
||||
|
||||
results = SpecialistResult.create_for_type(self.type, self.type_version,
|
||||
answer=answer,
|
||||
form_request=None,
|
||||
phase="no_valid_candidate")
|
||||
else:
|
||||
answer = (f"We hebben de antwoorden op de KO criteria verwerkt. Je bent een geschikte kandidaat. "
|
||||
f"Ben je bereid je contactgegevens door te geven, zodat we je kunnen contacteren voor een verder "
|
||||
f"gesprek?")
|
||||
# Check if answers to questions are positive
|
||||
contact_form = cache_manager.specialist_forms_config_cache.get_config("PERSONAL_CONTACT_FORM", "1.0")
|
||||
if arguments.language != 'nl':
|
||||
answer = TranslationServices.translate(answer, arguments.language)
|
||||
if arguments.language != 'en':
|
||||
contact_form = TranslationServices.translate_config(self.tenant_id, contact_form, "fields", arguments.language)
|
||||
results = SpecialistResult.create_for_type(self.type, self.type_version,
|
||||
answer=answer,
|
||||
form_request=contact_form,
|
||||
phase="personal_contact_data")
|
||||
|
||||
return results
|
||||
|
||||
def execute_personal_contact_data(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
|
||||
self.log_tuning("Traicie Selection Specialist personal_contact_data started", {})
|
||||
|
||||
results = SpecialistResult.create_for_type(self.type, self.type_version,
|
||||
answer=f"We hebben de contactgegevens verwerkt. We nemen zo snel mogelijk contact met je op.",
|
||||
phase="candidate_selected")
|
||||
return results
|
||||
|
||||
def execute_no_valid_candidate(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
|
||||
self.log_tuning("Traicie Selection Specialist no_valid_candidate started", {})
|
||||
results = SpecialistResult.create_for_type(self.type, self.type_version,
|
||||
answer=f"Je voldoet jammer genoeg niet aan de minimale vereisten voor deze job. Maar solliciteer gerust voor één van onze andere jobs.",
|
||||
phase="no_valid_candidate")
|
||||
|
||||
def execute_candidate_selected(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
|
||||
self.log_tuning("Traicie Selection Specialist candidate_selected started", {})
|
||||
results = SpecialistResult.create_for_type(self.type, self.type_version,
|
||||
answer=f"We hebben je contactgegegevens verwerkt. We nemen zo snel mogelijk contact met je op.",
|
||||
phase="candidate_selected")
|
||||
return results
|
||||
|
||||
|
||||
class SelectionInput(BaseModel):
|
||||
region: str = Field(..., alias="region")
|
||||
working_schedule: Optional[str] = Field(..., alias="working_schedule")
|
||||
start_date: Optional[date] = Field(None, alias="vacancy_text")
|
||||
language: Optional[str] = Field(None, alias="language")
|
||||
interaction_mode: Optional[str] = Field(None, alias="interaction_mode")
|
||||
tone_of_voice: Optional[str] = Field(None, alias="tone_of_voice")
|
||||
tone_of_voice_context: Optional[str] = Field(None, alias="tone_of_voice_context")
|
||||
language_level: Optional[str] = Field(None, alias="language_level")
|
||||
language_level_context: Optional[str] = Field(None, alias="language_level_context")
|
||||
ko_criteria: Optional[List[Dict[str, str]]] = Field(None, alias="ko_criteria")
|
||||
question: Optional[str] = Field(None, alias="question")
|
||||
field_values: Optional[Dict[str, Any]] = Field(None, alias="field_values")
|
||||
|
||||
|
||||
class SelectionKOCriteriumScore(BaseModel):
|
||||
criterium: Optional[str] = Field(None, alias="criterium")
|
||||
answer: Optional[str] = Field(None, alias="answer")
|
||||
score: Optional[int] = Field(None, alias="score")
|
||||
|
||||
|
||||
class SelectionCompetencyScore(BaseModel):
|
||||
competency: Optional[str] = Field(None, alias="competency")
|
||||
answer: Optional[str] = Field(None, alias="answer")
|
||||
score: Optional[int] = Field(None, alias="score")
|
||||
|
||||
|
||||
class PersonalContactData(BaseModel):
|
||||
name: str = Field(..., description="Your name", alias="name")
|
||||
email: EmailStr = Field(..., description="Your Name", alias="email")
|
||||
phone: str = Field(..., description="Your Phone Number", alias="phone")
|
||||
address: Optional[str] = Field(None, description="Your Address", alias="address")
|
||||
zip: Optional[str] = Field(None, description="Postal Code", alias="zip")
|
||||
city: Optional[str] = Field(None, description="City", alias="city")
|
||||
country: Optional[str] = Field(None, description="Country", alias="country")
|
||||
consent: bool = Field(..., description="Consent", alias="consent")
|
||||
|
||||
|
||||
class SelectionResult(SpecialistResult):
|
||||
ko_criteria_questions: Optional[List[ListItem]] = Field(None, alias="ko_criteria_questions")
|
||||
ko_criteria_scores: Optional[List[SelectionKOCriteriumScore]] = Field(None, alias="ko_criteria_scores")
|
||||
competency_questions: Optional[List[ListItem]] = Field(None, alias="competency_questions")
|
||||
competency_scores: Optional[List[SelectionCompetencyScore]] = Field(None, alias="competency_scores")
|
||||
personal_contact_data: Optional[PersonalContactData] = Field(None, alias="personal_contact_data")
|
||||
|
||||
|
||||
class SelectionFlowState(EveAIFlowState):
|
||||
"""Flow state for Traicie Role Definition specialist that automatically updates from task outputs"""
|
||||
input: Optional[SelectionInput] = None
|
||||
ko_criteria_questions: Optional[List[KOQuestion]] = Field(None, alias="ko_criteria_questions")
|
||||
ko_criteria_scores: Optional[List[SelectionKOCriteriumScore]] = Field(None, alias="ko_criteria_scores")
|
||||
competency_questions: Optional[List[ListItem]] = Field(None, alias="competency_questions")
|
||||
competency_scores: Optional[List[SelectionCompetencyScore]] = Field(None, alias="competency_scores")
|
||||
personal_contact_data: Optional[PersonalContactData] = Field(None, alias="personal_contact_data")
|
||||
phase: Optional[str] = Field(None, alias="phase")
|
||||
interaction_mode: Optional[str] = Field(None, alias="mode")
|
||||
|
||||
|
||||
class SelectionFlow(EveAICrewAIFlow[SelectionFlowState]):
|
||||
def __init__(self,
|
||||
specialist_executor: CrewAIBaseSpecialistExecutor,
|
||||
ko_def_crew: EveAICrewAICrew,
|
||||
**kwargs):
|
||||
super().__init__(specialist_executor, "Traicie Role Definition Specialist Flow", **kwargs)
|
||||
self.specialist_executor = specialist_executor
|
||||
self.ko_def_crew = ko_def_crew
|
||||
self.exception_raised = False
|
||||
|
||||
@start()
|
||||
def process_inputs(self):
|
||||
return ""
|
||||
|
||||
@listen(process_inputs)
|
||||
async def execute_ko_def_definition(self):
|
||||
inputs = self.state.input.model_dump()
|
||||
try:
|
||||
current_app.logger.debug("execute_ko_interview_definition")
|
||||
crew_output = await self.ko_def_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.ko_def_crew.tasks:
|
||||
current_app.logger.debug(f"Task {task.name} output:\n{task.output}")
|
||||
if task.name == "traicie_ko_criteria_interview_definition_task":
|
||||
# update["competencies"] = task.output.pydantic.competencies
|
||||
self.state.ko_criteria_questions = task.output.pydantic.ko_questions
|
||||
# crew_output.pydantic = crew_output.pydantic.model_copy(update=update)
|
||||
self.state.phase = "personal_contact_data"
|
||||
current_app.logger.debug(f"State after execute_ko_def_definition: {self.state}")
|
||||
current_app.logger.debug(f"State dump after execute_ko_def_definition: {self.state.model_dump()}")
|
||||
return crew_output
|
||||
except Exception as e:
|
||||
current_app.logger.error(f"CREW execute_ko_def 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 = SelectionInput.model_validate(inputs)
|
||||
current_app.logger.debug(f"State: {self.state}")
|
||||
result = await super().kickoff_async(inputs)
|
||||
return self.state
|
||||
Reference in New Issue
Block a user