- Full Traicie Selection Specialist Flow implemented
- Added Specialist basics for handling phases and automatically transferring data between state and output - Added QR-code generation for Magic Links
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Josako
@@ -22,6 +22,7 @@ from common.services.interaction.specialist_services import SpecialistServices
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from common.extensions import cache_manager
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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 common.utils.eveai_exceptions import EveAISpecialistExecutionError
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class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
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@@ -53,6 +54,13 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
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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_criteria_questions")
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self._add_state_result_relation("ko_criteria_scores")
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self._add_state_result_relation("competency_questions")
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self._add_state_result_relation("competency_scores")
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self._add_state_result_relation("personal_contact_data")
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def _instantiate_specialist(self):
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verbose = self.tuning
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@@ -89,16 +97,14 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
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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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case "ko_questions":
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contact_form = cache_manager.specialist_forms_config_cache.get_config("PERSONAL_CONTACT_FORM", "1.0")
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results = SpecialistResult.create_for_type(self.type, self.type_version,
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answer=f"We hebben de antwoorden op de KO criteria verwerkt. Je bent een geschikte kandidaat. Kan je je contactegevens doorgeven?",
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form_request=contact_form,
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phase="personal_contact_data")
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case "ko_question_evaluation":
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results = self.execute_ko_question_evaluation(arguments, formatted_context, citations)
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case "personal_contact_data":
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results = SpecialistResult.create_for_type(self.type, self.type_version,
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answer=f"We hebben de contactgegevens verwerkt. We nemen zo snel mogelijk contact met je op.",
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phase="candidate_selected")
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results = self.execute_personal_contact_data(arguments, formatted_context, citations)
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case "no_valid_candidate":
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results = self.execute_no_valid_candidate(arguments, formatted_context, citations)
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case "candidate_selected":
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results = self.execute_candidate_selected(arguments, formatted_context, citations)
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self.log_tuning(f"Traicie Selection Specialist execution ended", {"Results": results.model_dump() if results else "No info"})
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@@ -108,18 +114,30 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
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def execute_initial_state(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
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self.log_tuning("Traicie Selection Specialist initial_state_execution started", {})
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knockout_competencies = [
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{
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"title": c["title"],
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"description": c["description"]
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}
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for c in self.specialist.configuration.get("competencies", [])
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if c.get("is_knockout") is True
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]
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current_app.logger.debug(f"Specialist Competencies:\n{self.specialist.configuration.get("competencies", [])}")
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# Convert TONE_OF_VOICE en LANGUAGE_LEVEL lists tp strings usable by the LLM
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tone_of_voice_str = "\n\n".join([f"Name: {item['name']}\nDescription: {item['description']}\nWhen to use: {item['when_to_use']}" for item in TONE_OF_VOICE])
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language_level_str = "\n\n".join([f"Name: {item['name']}\nDescription: {item['description']}\nCEFR level: {item['cefr_level']}\nIdeal Target Audience: {item['ideal_audience']}" for item in LANGUAGE_LEVEL])
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ko_competencies = []
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for competency in self.specialist.configuration.get("competencies", []):
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if competency["is_knockout"] is True and competency["assess"] is True:
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current_app.logger.debug(f"Assessable Knockout competency: {competency}")
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ko_competencies.append({"title: ": competency["title"], "description": competency["description"]})
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tone_of_voice = self.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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current_app.logger.debug(f"Selected tone of voice: {selected_tone_of_voice}")
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tone_of_voice_context = f"{selected_tone_of_voice["description"]}"
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language_level = self.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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current_app.logger.debug(f"Selected language level: {selected_language_level}")
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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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"region": arguments.region,
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@@ -127,11 +145,11 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
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"start_date": arguments.start_date,
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"language": arguments.language,
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"interaction_mode": arguments.interaction_mode,
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'tone_of_voice': self.specialist.configuration.get('tone_of_voice', 'Professional & Neutral'),
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'tone_of_voice_context': tone_of_voice_str,
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'language_level': self.specialist.configuration.get('language_level', 'Standard'),
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'language_level_context': language_level_str,
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'ko_criteria': knockout_competencies,
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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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@@ -162,10 +180,69 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
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results = SpecialistResult.create_for_type(self.type, self.type_version,
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answer=f"We starten met een aantal KO Criteria vragen",
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form_request=ko_form,
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phase="ko_questions")
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phase="ko_question_evaluation")
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return results
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def execute_ko_question_evaluation(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
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self.log_tuning("Traicie Selection Specialist ko_question_evaluation started", {})
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# Check if the form has been returned (it should)
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if not arguments.form_values:
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raise EveAISpecialistExecutionError(self.tenant_id, self.specialist_id, self.session_id, "No form values returned")
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current_app.logger.debug(f"Form values: {arguments.form_values}")
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# Load the previous KO Questions
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previous_ko_questions = self.flow.state.ko_criteria_questions
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current_app.logger.debug(f"Previous KO Questions: {previous_ko_questions}")
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# Evaluate KO Criteria
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evaluation = "positive"
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for criterium, answer in arguments.form_values.items():
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for qa in previous_ko_questions:
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if qa.get("title") == criterium:
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if qa.get("answer_positive") != answer:
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evaluation = "negative"
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break
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if evaluation == "negative":
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break
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if evaluation == "negative":
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results = SpecialistResult.create_for_type(self.type, self.type_version,
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answer=f"We hebben de antwoorden op de KO criteria verwerkt. Je voldoet jammer genoeg niet aan de minimale vereisten voor deze job.",
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form_request=None,
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phase="no_valid_candidate")
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else:
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# Check if answers to questions are positive
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contact_form = cache_manager.specialist_forms_config_cache.get_config("PERSONAL_CONTACT_FORM", "1.0")
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results = SpecialistResult.create_for_type(self.type, self.type_version,
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answer=f"We hebben de antwoorden op de KO criteria verwerkt. Je bent een geschikte kandidaat. Kan je je contactegevens doorgeven?",
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form_request=contact_form,
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phase="personal_contact_data")
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return results
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def execute_personal_contact_data(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
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self.log_tuning("Traicie Selection Specialist personal_contact_data started", {})
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results = SpecialistResult.create_for_type(self.type, self.type_version,
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answer=f"We hebben de contactgegevens verwerkt. We nemen zo snel mogelijk contact met je op.",
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phase="candidate_selected")
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return results
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def execute_no_valid_candidate(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
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self.log_tuning("Traicie Selection Specialist no_valid_candidate started", {})
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results = SpecialistResult.create_for_type(self.type, self.type_version,
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answer=f"Je voldoet jammer genoeg niet aan de minimale vereisten voor deze job. Maar solliciteer gerust voor één van onze andere jobs.",
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phase="no_valid_candidate")
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def execute_candidate_selected(self, arguments: SpecialistArguments, formatted_context, citations) -> SpecialistResult:
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self.log_tuning("Traicie Selection Specialist candidate_selected started", {})
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results = SpecialistResult.create_for_type(self.type, self.type_version,
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answer=f"We hebben je contactgegegevens verwerkt. We nemen zo snel mogelijk contact met je op.",
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phase="candidate_selected")
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return results
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class SelectionInput(BaseModel):
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region: str = Field(..., alias="region")
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