- Bug fixes
- TRAICIE_KO_INTERVIEW_DEFINITION spacialist updated to new version - Edit Document Version now includes Catalog Tagging Fields - eveai_ordered_list_editor no longer includes Expand Button & Add Row doesn't submit - Active Period was not correctly returned in some cases in the license_period_services.py - Partner menu removed if not Super User
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Josako
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from datetime import datetime as dt, timezone as tz
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from typing import Optional, List, Dict
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import json
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import yaml
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from crewai.flow.flow import start, listen
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from flask import current_app
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from pydantic import BaseModel, Field
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from common.extensions import db, minio_client
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from common.models.interaction import Specialist, EveAIAsset
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from common.utils.minio_utils import MIB_CONVERTOR
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from common.utils.eveai_exceptions import EveAISpecialistExecutionError
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from common.utils.model_logging_utils import set_logging_information
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from eveai_chat_workers.definitions.language_level.language_level_v1_0 import LANGUAGE_LEVEL
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from eveai_chat_workers.definitions.tone_of_voice.tone_of_voice_v1_0 import TONE_OF_VOICE
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from eveai_chat_workers.outputs.traicie.knockout_questions.knockout_questions_v1_0 import KOQuestions, KOQuestion
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from eveai_chat_workers.specialists.crewai_base_classes import EveAICrewAICrew, EveAICrewAIFlow, EveAIFlowState
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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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class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
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"""
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type: TRAICIE_KO_INTERVIEW_DEFINITION_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.ko_def_crew = None
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super().__init__(tenant_id, specialist_id, session_id, task_id)
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@property
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def type(self) -> str:
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return "TRAICIE_KO_INTERVIEW_DEFINITION_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_ko_criteria_interview_definition_task", "traicie_hr_bp_agent")
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def _config_pydantic_outputs(self):
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self._add_pydantic_output("traicie_ko_criteria_interview_definition_task", KOQuestions, "ko_questions")
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def _config_state_result_relations(self):
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self._add_state_result_relation("ko_questions")
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def _instantiate_specialist(self):
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verbose = self.tuning
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ko_def_agents = [self.traicie_hr_bp_agent]
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ko_def_tasks = [self.traicie_ko_criteria_interview_definition_task]
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self.ko_def_crew = EveAICrewAICrew(
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self,
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"KO Criteria Interview Definition Crew",
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agents=ko_def_agents,
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tasks=ko_def_tasks,
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verbose=verbose,
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)
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self.flow = KOFlow(
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self,
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self.ko_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 KO Criteria Interview Definition Specialist execution started", {})
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if not self._cached_session.interactions:
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specialist_phase = "initial"
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else:
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specialist_phase = self._cached_session.interactions[-1].specialist_results.get('phase', 'initial')
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results = None
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match specialist_phase:
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case "initial":
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results = self.execute_initial_state(arguments, formatted_context, citations)
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self.log_tuning(f"Traicie KO Criteria Interview Definition Specialist execution ended",
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{"Results": results.model_dump() if results else "No info"})
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return results
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def execute_initial_state(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
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self.log_tuning("Traicie KO Criteria Interview Definition Specialist initial_state_execution started", {})
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selection_specialist = Specialist.query.get(arguments.specialist_id)
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if not selection_specialist:
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raise EveAISpecialistExecutionError(self.tenant_id, self.specialist_id, self.session_id,
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"No selection specialist found")
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if selection_specialist.type != "TRAICIE_SELECTION_SPECIALIST":
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raise EveAISpecialistExecutionError(self.tenant_id, self.specialist_id, self.session_id,
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"Specialist is no Selection Specialist")
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ko_competencies = []
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for competency in selection_specialist.configuration.get("competencies", []):
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if competency["is_knockout"] is True and competency["assess"] is True:
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ko_competencies.append({"title": competency["title"], "description": competency["description"]})
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tone_of_voice = selection_specialist.configuration.get('tone_of_voice', 'Professional & Neutral')
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selected_tone_of_voice = next(
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(item for item in TONE_OF_VOICE if item["name"] == tone_of_voice),
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None # fallback indien niet gevonden
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)
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tone_of_voice_context = f"{selected_tone_of_voice["description"]}"
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language_level = selection_specialist.configuration.get('language_level', 'Standard')
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selected_language_level = next(
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(item for item in LANGUAGE_LEVEL if item["name"] == language_level),
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None
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)
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language_level_context = (f"{selected_language_level['description']}, "
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f"corresponding to CEFR level {selected_language_level['cefr_level']}")
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flow_inputs = {
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'name': "Evie",
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'tone_of_voice': tone_of_voice,
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'tone_of_voice_context': tone_of_voice_context,
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'language_level': language_level,
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'language_level_context': language_level_context,
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'ko_criteria': ko_competencies,
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}
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flow_results = self.flow.kickoff(inputs=flow_inputs)
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new_type = "TRAICIE_KO_CRITERIA_QUESTIONS"
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# Controleer of we een KOQuestions object hebben of een lijst van KOQuestion objecten
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if hasattr(self.flow.state.ko_questions, 'to_json'):
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# Het is een KOQuestions object
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json_str = self.flow.state.ko_questions.to_json()
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elif isinstance(self.flow.state.ko_questions, list):
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# Het is een lijst van KOQuestion objecten
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# Maak een KOQuestions object en gebruik to_json daarop
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ko_questions_obj = KOQuestions.from_question_list(self.flow.state.ko_questions)
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json_str = ko_questions_obj.to_json()
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else:
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# Fallback voor het geval het een onverwacht type is
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current_app.logger.warning(f"Unexpected type for ko_questions: {type(self.flow.state.ko_questions)}")
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ko_questions_data = [q.model_dump() for q in self.flow.state.ko_questions]
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json_str = json.dumps(ko_questions_data, ensure_ascii=False, indent=2)
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try:
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asset = db.session.query(EveAIAsset).filter(
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EveAIAsset.type == new_type,
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EveAIAsset.type_version == "1.0.0",
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EveAIAsset.configuration.is_not(None),
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EveAIAsset.configuration.has_key('specialist_id'),
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EveAIAsset.configuration['specialist_id'].astext.cast(db.Integer) == selection_specialist.id
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).first()
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except (ValueError, TypeError) as e:
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current_app.logger.warning(f"Error casting specialist_id in asset configuration: {str(e)}")
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asset = None
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if not asset:
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asset = EveAIAsset(
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name=f"KO Criteria Form for specialist {selection_specialist.id}",
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type=new_type,
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type_version="1.0.0",
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system_metadata={
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"Creator Specialist Type": self.type,
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"Creator Specialist Type Version": self.type_version,
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"Creator Specialist ID": self.specialist_id
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},
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configuration={
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"specialist_id": selection_specialist.id,
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},
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)
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set_logging_information(asset, dt.now(tz=tz.utc))
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asset.last_used_at = asset.created_at
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else:
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asset.last_used_at = dt.now(tz=tz.utc)
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try:
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# Stap 1: Asset aanmaken maar nog niet committen
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db.session.add(asset)
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db.session.flush() # Geeft ons het ID zonder te committen
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# Stap 2: Upload naar MinIO (kan falen zonder database impact)
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bucket_name, object_name, file_size = minio_client.upload_asset_file(
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tenant_id=self.tenant_id,
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asset_id=asset.id,
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asset_type=new_type,
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file_type="json",
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file_data=json_str
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)
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# Stap 3: Storage metadata toevoegen
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asset.bucket_name = bucket_name
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asset.object_name = object_name
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asset.file_size = file_size / MIB_CONVERTOR
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asset.file_type = "json"
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# Stap 4: Token usage toevoegen
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asset.prompt_tokens = self.ko_def_crew.usage_metrics.prompt_tokens
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asset.completion_tokens = self.ko_def_crew.usage_metrics.completion_tokens
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# Alles in één keer committen
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db.session.commit()
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except Exception as e:
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current_app.logger.error(f"Error creating asset: {str(e)}")
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db.session.rollback()
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# Probeer MinIO cleanup als upload is gelukt maar database commit faalde
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try:
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if 'bucket_name' in locals() and 'object_name' in locals():
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minio_client.delete_object(bucket_name, object_name)
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except:
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pass # Log maar ga door met originele exception
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raise EveAISpecialistExecutionError(self.tenant_id, self.specialist_id, self.session_id,
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f"Failed to create asset: {str(e)}")
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results = SpecialistResult.create_for_type(self.type, self.type_version,
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answer=f"asset {asset.id} created for specialist {selection_specialist.id}",
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phase="finished",
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asset_id=asset.id,
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)
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return results
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class KODefInput(BaseModel):
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name: Optional[str] = Field(None, alias="name")
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tone_of_voice: Optional[str] = Field(None, alias="tone_of_voice")
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tone_of_voice_context: Optional[str] = Field(None, alias="tone_of_voice_context")
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language_level: Optional[str] = Field(None, alias="language_level")
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language_level_context: Optional[str] = Field(None, alias="language_level_context")
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ko_criteria: Optional[List[Dict[str, str]]] = Field(None, alias="ko_criteria")
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class KODefResult(SpecialistResult):
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asset_id: Optional[int] = Field(None, alias="asset_id")
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class KOFlowState(EveAIFlowState):
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"""Flow state for Traicie Role Definition specialist that automatically updates from task outputs"""
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input: Optional[KODefInput] = None
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ko_questions: Optional[List[KOQuestion]] = Field(None, alias="ko_questions")
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phase: Optional[str] = Field(None, alias="phase")
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class KOFlow(EveAICrewAIFlow[KOFlowState]):
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def __init__(self,
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specialist_executor: CrewAIBaseSpecialistExecutor,
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ko_def_crew: EveAICrewAICrew,
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**kwargs):
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super().__init__(specialist_executor, "Traicie KO Interview Definiton Specialist Flow", **kwargs)
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self.specialist_executor = specialist_executor
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self.ko_def_crew = ko_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_ko_def_definition(self):
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inputs = self.state.input.model_dump()
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try:
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crew_output = await self.ko_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.ko_def_crew.tasks:
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if task.name == "traicie_ko_criteria_interview_definition_task":
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# update["competencies"] = task.output.pydantic.competencies
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self.state.ko_questions = task.output.pydantic.ko_questions
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# crew_output.pydantic = crew_output.pydantic.model_copy(update=update)
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self.state.phase = "personal_contact_data"
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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_ko_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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self.state.input = KODefInput.model_validate(inputs)
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result = await super().kickoff_async(inputs)
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return self.state
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