Create classifier_prompt.py
Browse files- knowledge/classifier_prompt.py +189 -0
knowledge/classifier_prompt.py
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| 1 |
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from typing import Dict, List
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GLOBAL_INTENTS = [
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"restart",
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"new_topic",
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"complaint",
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"direct_support",
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"courses_info",
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"children_courses",
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"adults_courses",
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"new_student",
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"current_student",
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"unclear",
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]
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STATE_ALLOWED_INTENTS: Dict[str, List[str]] = {
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"WAITING_MAIN_MENU": [
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"new_student",
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"current_student",
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"courses_info",
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"complaint",
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"direct_support",
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],
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"WAITING_USER_TYPE": [
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"new_student",
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"current_student",
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],
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"WAITING_AUDIENCE": [
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"adults",
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"children",
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],
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"WAITING_PRIOR_STUDY": [
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"prior_study_yes",
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"prior_study_no",
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],
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"WAITING_BEGINNER_SCHEDULE_CHOICE": [
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"confirm_schedule_reviewed",
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"proceed_booking",
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"switch_to_prior_study_true",
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"switch_to_prior_study_false",
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"support_needed",
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],
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"WAITING_PDF_102_CONFIRMATION": [
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"confirm_pdf_reviewed",
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"switch_to_prior_study_true",
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"switch_to_prior_study_false",
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"support_needed",
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],
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"WAITING_PLACEMENT_TEST_CONFIRMATION": [
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"confirm_placement_test_reviewed",
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"switch_to_prior_study_true",
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"switch_to_prior_study_false",
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"support_needed",
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],
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"WAITING_CURRENT_STUDENT_ACTION": [
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"current_student_support",
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"current_student_next_level",
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],
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"WAITING_SUPPORT_QUESTION": [
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"support_question_text",
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],
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"WAITING_LEVEL_SELECTION": [
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"level_selected",
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"support_needed",
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],
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"WAITING_PAYMENT_METHOD": [
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"payment_method_selected",
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"support_needed",
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],
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"WAITING_COMPLAINT_FORM": [
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"complaint_form_submitted",
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],
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"HANDOFF_DONE": [
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"thanks",
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"courses_info",
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"children_courses",
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"adults_courses",
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"new_student",
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"current_student",
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"direct_support",
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"complaint",
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| 83 |
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"restart",
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"new_topic",
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],
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}
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INTENT_DESCRIPTIONS: Dict[str, str] = {
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"restart": "The user wants to restart the conversation from the beginning.",
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"new_topic": "The user explicitly wants to ask about something else or start a new topic.",
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"complaint": "The user is making or asking to make a complaint.",
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"direct_support": "The user wants to talk to customer support or service directly.",
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"courses_info": "The user is asking generally about available courses.",
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"children_courses": "The user is asking about children courses.",
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"adults_courses": "The user is asking about adult courses.",
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"new_student": "The user indicates they are a new student.",
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"current_student": "The user indicates they are a current student.",
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"adults": "Direct answer: adults.",
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"children": "Direct answer: children.",
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"prior_study_yes": "Direct answer: user studied German before.",
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"prior_study_no": "Direct answer: user did not study German before.",
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"confirm_schedule_reviewed": "The user confirms they reviewed the beginner schedule.",
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"proceed_booking": "The user wants to proceed with booking.",
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"switch_to_prior_study_true": "The user changes to the path where they studied German before.",
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"switch_to_prior_study_false": "The user changes to the beginner path.",
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"support_needed": "The user needs help or wants support within the current topic.",
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"confirm_pdf_reviewed": "The user confirms they reviewed the PDF/details file.",
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"confirm_placement_test_reviewed": "The user confirms they reviewed placement test details.",
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"current_student_support": "Current student wants support or has a question.",
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"current_student_next_level": "Current student wants to book the next level.",
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"support_question_text": "The message itself is the support question text.",
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"level_selected": "The user selected a course level.",
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"payment_method_selected": "The user selected a payment method.",
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"complaint_form_submitted": "The user says they submitted the complaint form.",
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"thanks": "The user is thanking the bot.",
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"unclear": "The message is unclear and should not be confidently routed.",
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}
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def get_allowed_intents_for_state(state: str) -> List[str]:
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state_specific = STATE_ALLOWED_INTENTS.get(state, [])
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final = []
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for item in GLOBAL_INTENTS + state_specific:
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if item not in final:
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final.append(item)
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return final
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| 127 |
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| 128 |
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def _summarize_flow_data(flow_data: dict) -> str:
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if not flow_data:
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return "No extra context."
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| 132 |
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fields = []
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| 134 |
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for key in [
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| 135 |
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"customer_type",
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"audience",
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"prior_study",
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"selected_level",
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"payment_method",
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"gender",
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]:
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if key in flow_data and flow_data[key] is not None:
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fields.append(f"{key}={flow_data[key]}")
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return ", ".join(fields) if fields else "No extra context."
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| 146 |
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def build_system_prompt(state: str, flow_data: dict, allowed_intents: List[str]) -> str:
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descriptions = []
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| 150 |
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for intent in allowed_intents:
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desc = INTENT_DESCRIPTIONS.get(intent, "")
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| 152 |
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descriptions.append(f"- {intent}: {desc}")
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| 153 |
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description_block = "\n".join(descriptions)
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| 155 |
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context_summary = _summarize_flow_data(flow_data)
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return f"""
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| 159 |
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You are a state-aware intent classifier for an Arabic WhatsApp bot.
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Your task:
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- Read the user's Arabic message.
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| 163 |
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- Understand the CURRENT conversation state.
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| 164 |
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- Choose exactly ONE intent label from the allowed list.
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| 165 |
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- Return ONLY the label.
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| 166 |
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- Do not explain.
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| 167 |
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- Do not add punctuation.
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| 168 |
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- Do not add extra words.
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| 169 |
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| 170 |
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Current state:
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| 171 |
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{state}
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| 172 |
+
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| 173 |
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Known context:
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| 174 |
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{context_summary}
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| 175 |
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| 176 |
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Allowed intent labels:
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| 177 |
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{", ".join(allowed_intents)}
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| 178 |
+
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| 179 |
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Intent descriptions:
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| 180 |
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{description_block}
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| 181 |
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| 182 |
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Decision rules:
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| 183 |
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- If the message is a direct answer to the current question, choose the direct answer label.
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| 184 |
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- If the user changes path but stays in the same broad topic, choose the appropriate state-switch label.
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| 185 |
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- If the user changes to a new topic, choose the correct topic-switch label.
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| 186 |
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- If you are not confident, return: unclear
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| 187 |
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| 188 |
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Return only one label from the allowed list.
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| 189 |
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""".strip()
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