feat(ai-onboarding): enhance conversation flow, prompt rules & update Postman environment port to 8001
This commit is contained in:
@@ -1,4 +1,5 @@
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import json
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from app.ai.orchestrator import check_required_missing
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class CompletionService:
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@@ -8,45 +9,25 @@ class CompletionService:
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session,
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state,
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) -> bool:
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data = json.loads(
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state.state_json
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)
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"""
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Clinical intake completion validation using the single source of truth:
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check_required_missing().
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reason = data.get("reason_for_visit")
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symptoms = data.get("symptoms")
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history = data.get("medical_history")
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medications = data.get("medications")
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allergies = data.get("allergies")
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Returns True only when:
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- Session status is already 'completed' or stage is 'confirmation', OR
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- All required categories (reason_for_visit, symptoms+duration,
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medical_history, medications, allergies) are either reported or confirmed_none.
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"""
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if getattr(session, "status", None) == "completed" or getattr(session, "stage", None) == "confirmation":
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return True
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# Check structured status dictionary format
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if isinstance(reason, dict):
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if reason.get("status") == "not_collected" or not reason.get("value"):
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return False
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elif not reason:
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if not state or not getattr(state, "state_json", None):
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return False
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if isinstance(symptoms, dict):
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if symptoms.get("status") == "not_collected" or len(symptoms.get("items", [])) == 0:
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return False
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elif not symptoms:
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try:
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data = json.loads(state.state_json)
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except Exception:
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return False
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if isinstance(history, dict):
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if history.get("status") == "not_collected":
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return False
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elif history is None:
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return False
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if isinstance(medications, dict):
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if medications.get("status") == "not_collected":
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return False
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elif medications is None:
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return False
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if isinstance(allergies, dict):
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if allergies.get("status") == "not_collected":
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return False
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elif allergies is None:
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return False
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return True
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missing = check_required_missing(data)
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return len(missing) == 0
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@@ -0,0 +1,427 @@
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"""
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Contextual Normalizer for Clinical Intake.
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Provides:
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- Tamil, Tanglish, Hindi, and English number & duration normalization.
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- Contextual intent recognition from previous AI dialogue turns.
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- Non-symptom expression filters (duration, severity, location, negative phrases, filler).
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- Deduplication and deterministic clinical attribute extraction.
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"""
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import re
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from typing import Optional, Tuple, Dict, Any, List
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# 1. Spoken Number Mappings across Tamil, Tanglish, Hindi, English
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TAMIL_NUMBER_MAP: Dict[str, int] = {
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"ஒரு": 1, "ஒண்ணு": 1, "ஒன்று": 1, "ஒன்னு": 1,
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"ரெண்டு": 2, "இரண்டு": 2,
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"மூணு": 3, "மூன்று": 3,
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"நாலு": 4, "நான்கு": 4,
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"அஞ்சு": 5, "ஐந்து": 5,
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"ஆறு": 6,
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"ஏழு": 7,
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"எட்டு": 8,
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"ஒன்பது": 9,
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"பத்து": 10,
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"பதினொன்னு": 11, "பதினொன்று": 11,
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"பன்னிரண்டு": 12, "பன்னிரெண்டு": 12,
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"பதின்மூன்று": 13,
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"பதினாலு": 14, "பதினான்கு": 14,
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"பதினைந்து": 15, "பதினாஞ்சு": 15,
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"பதினாறு": 16,
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"பதினேழு": 17,
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"பதினெட்டு": 18,
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"பத்தொன்பது": 19,
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"இருபது": 20, "இருபத்தஞ்சு": 25,
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"முப்பது": 30,
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"நாற்பது": 40,
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"ஐம்பது": 50,
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}
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TANGLISH_NUMBER_MAP: Dict[str, int] = {
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"oru": 1, "onnu": 1, "ondru": 1,
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"rendu": 2, "irandu": 2,
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"moonu": 3, "moonru": 3,
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"naalu": 4, "nangu": 4,
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"anju": 5, "ainthu": 5,
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"aaru": 6,
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"yezhu": 7, "ezhu": 7, "elu": 7,
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"ettu": 8,
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"onbathu": 9, "ombadhu": 9,
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"pathu": 10, "padhu": 10,
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"pathinonnu": 11,
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"pannirendu": 12,
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"pathinaanchu": 15, "pathinainthu": 15,
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"irubathu": 20, "irupathu": 20,
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"muppathu": 30,
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}
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HINDI_NUMBER_MAP: Dict[str, int] = {
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"एक": 1, "दो": 2, "तीन": 3, "चार": 4, "पांच": 5, "पाँच": 5,
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"छह": 6, "सात": 7, "आठ": 8, "नौ": 9, "दस": 10,
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"ग्यारह": 11, "बारह": 12, "पंद्रह": 15, "बीस": 20, "तीस": 30, "चालीस": 40, "पचास": 50,
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}
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TELUGU_NUMBER_MAP: Dict[str, int] = {
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"ఒకటి": 1, "ఒక": 1, "రెండు": 2, "మూడు": 3, "నాలుగు": 4, "ఐదు": 5,
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"ఆరు": 6, "ఏడు": 7, "ఎనిమిది": 8, "తొమ్మిది": 9, "పది": 10,
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}
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KANNADA_NUMBER_MAP: Dict[str, int] = {
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"ಒಂದು": 1, "ಒಂದ": 1, "ಎರಡು": 2, "ಮೂರು": 3, "ನಾಲ್ಕು": 4, "ಐದು": 5,
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"ಆರು": 6, "ಏಳು": 7, "ಎಂಟು": 8, "ಒಂಬತ್ತು": 9, "ಹತ್ತು": 10,
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}
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ENGLISH_NUMBER_MAP: Dict[str, int] = {
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"a": 1, "an": 1, "one": 1, "two": 2, "three": 3, "four": 4,
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"five": 5, "six": 6, "seven": 7, "eight": 8, "nine": 9, "ten": 10,
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"eleven": 11, "twelve": 12, "thirteen": 13, "fourteen": 14, "fifteen": 15,
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"sixteen": 16, "seventeen": 17, "eighteen": 18, "nineteen": 19,
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"twenty": 20, "twenty-five": 25, "twenty five": 25, "thirty": 30,
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"forty": 40, "fifty": 50,
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}
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def normalize_duration_from_message(message: str) -> Optional[str]:
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"""
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Detects and canonicalizes duration phrases across English, Tamil, Tanglish, and Mixed forms.
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Returns canonical strings such as: '10 days', '2 days', '1 week', '2 months', 'since yesterday',
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'since last week', 'since Monday', 'for a few days', 'for many months'.
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Returns None if no duration is recognized.
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"""
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if not message or not message.strip():
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return None
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msg = message.strip()
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msg_lower = msg.lower()
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# 1. Check relative date / anchor phrases
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rel_patterns = [
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(r"(?:நேற்றிலிருந்து|நேத்து\s*ல\s*இருந்து|நேத்துல\s*இருந்து|நேத்து\s*இருந்து|நேற்று\s*முதல்|since\s*yesterday|कल\s*से|निन्नೆಯಿಂದ|నిన్నటి\s*నుండి)", "since yesterday"),
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(r"(?:இன்னைக்கு\s*காலையிலிருந்து|காலையிலிருந்து|today\s*morning|since\s*morning|आज\s*सुबह\s*से)", "since morning"),
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(r"(?:since\s*last\s*night|நேத்து\s*ராத்திரி\s*இருந்து|நேத்து\s*நைட்டிலிருந்து)", "since last night"),
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(r"(?:since\s*last\s*week|போன\s*வாரத்திலிருந்து|पिछले\s*हफ़्ते\s*से)", "since last week"),
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(r"(?:since\s*(?:monday|tuesday|wednesday|thursday|friday|saturday|sunday))", lambda m: m.group(0).strip()),
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(r"(?:for\s*a\s*few\s*days|few\s*days|சில\s*நாட்களா|கொஞ்ச\s*நாளா|कुछ\s*दिनों\s*से)", "for a few days"),
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(r"(?:for\s*many\s*months|ரொம்ப\s*மாசமா|பல\s*மாதங்களா|कई\s*महीनों\s*से)", "for many months"),
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]
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for pat, canonical in rel_patterns:
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m = re.search(pat, msg, re.IGNORECASE) or re.search(pat, msg_lower, re.IGNORECASE)
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if m:
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if callable(canonical):
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return canonical(m)
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return canonical
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# 2. Number word or digit + Unit detection
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all_tamil_nums = "|".join(sorted(TAMIL_NUMBER_MAP.keys(), key=len, reverse=True))
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all_tanglish_nums = "|".join(sorted(TANGLISH_NUMBER_MAP.keys(), key=len, reverse=True))
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all_hindi_nums = "|".join(sorted(HINDI_NUMBER_MAP.keys(), key=len, reverse=True))
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all_telugu_nums = "|".join(sorted(TELUGU_NUMBER_MAP.keys(), key=len, reverse=True))
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all_kannada_nums = "|".join(sorted(KANNADA_NUMBER_MAP.keys(), key=len, reverse=True))
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all_eng_nums = "|".join(sorted(ENGLISH_NUMBER_MAP.keys(), key=len, reverse=True))
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num_regex_part = rf"(\d+|{all_tamil_nums}|{all_tanglish_nums}|{all_hindi_nums}|{all_telugu_nums}|{all_kannada_nums}|{all_eng_nums})"
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unit_regex_part = (
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r"(?:days|day|டேஸா|டேஸ்|டே|நாட்களா|நாட்களாக|நாளா|நாளாக|நாளு|நாட்கள்|நாள்|"
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r"weeks|week|வாரமா|வாரமாக|வாரம்|வாரங்கள்|"
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r"months|month|மாசம்|மாசமா|மாதமா|மாதம்|மாதங்களாக|"
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r"years|year|வருஷம்|வருசமா|வருடம்|வருடங்கள்|"
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r"hours|hour|hrs|மணி\s*நேரம்|மணி|"
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r"naala|naal\s*ah|naal|nala|vaarama|vaaram|maasama|maasam|varusham|varushama|"
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r"दिनों|दिन|हफ़्ते|हफ्ता|महीने|साल|घंटे|"
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r"రోజుల\s*నుండి|రోజుల\s*నుంచి|రోజులుగా|రోజుల|రోజులు|వారం|వారాలు|"
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r"ದಿನದಿಂದ|ದಿನಗಳಿಂದ|ದಿನಗಳು|ದಿನ|ವಾರದಿಂದ|ವಾರಗಳು)"
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)
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# Prefix qualifiers: "ஒரு 10 டேஸா", "about 10 days", "for 10 days", "இன்னும் ஒரு பத்து நாளா"
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full_pattern = (
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rf"(?:(?:எனக்கு\s*வந்து\s*)?(?:இன்னும்\s*)?(?:ஒரு|oru|about|around|for\s+the\s+last|for|approx|approximately)\s+)?"
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rf"{num_regex_part}\s*[-]?\s*{unit_regex_part}"
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rf"(?:[-]?\s*(?:ah|a|ஆ|இருக்கு|ஆச்சு|ஆகியது|ஆகப்\s*போகுது|உంది|ಇದೆ|now))?"
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)
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match = re.search(full_pattern, msg, re.IGNORECASE) or re.search(full_pattern, msg_lower, re.IGNORECASE)
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if match:
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raw_num_str = match.group(1).lower().strip()
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matched_text = match.group(0).lower()
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# Parse number
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val = None
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if raw_num_str.isdigit():
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val = int(raw_num_str)
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elif raw_num_str in TAMIL_NUMBER_MAP:
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val = TAMIL_NUMBER_MAP[raw_num_str]
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elif raw_num_str in TANGLISH_NUMBER_MAP:
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val = TANGLISH_NUMBER_MAP[raw_num_str]
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elif raw_num_str in HINDI_NUMBER_MAP:
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val = HINDI_NUMBER_MAP[raw_num_str]
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elif raw_num_str in TELUGU_NUMBER_MAP:
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val = TELUGU_NUMBER_MAP[raw_num_str]
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elif raw_num_str in KANNADA_NUMBER_MAP:
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val = KANNADA_NUMBER_MAP[raw_num_str]
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elif raw_num_str in ENGLISH_NUMBER_MAP:
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val = ENGLISH_NUMBER_MAP[raw_num_str]
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# Determine unit
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unit = "days"
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if any(w in matched_text for w in ["week", "வாரம்", "வார", "vaaram", "vaarama", "हफ़्ते", "हफ्ता", "వారం", "ವಾರ"]):
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unit = "week" if val == 1 else "weeks"
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elif any(w in matched_text for w in ["month", "மாச", "மாத", "maasam", "maasama", "महीने", "నెలా", "ತಿಂಗಳು"]):
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unit = "month" if val == 1 else "months"
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elif any(w in matched_text for w in ["year", "வருஷ", "வருட", "varusham", "varushama", "साल", "సంవత్సరం", "ವರ್ಷ"]):
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unit = "year" if val == 1 else "years"
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elif any(w in matched_text for w in ["hour", "மணி", "hrs", "घंटे", "గంటలు", "ಗಂಟೆ"]):
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unit = "hour" if val == 1 else "hours"
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else:
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unit = "day" if val == 1 else "days"
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if val is not None:
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return f"{val} {unit}"
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# 3. Fallback loose check for standalone Tanglish/Tamil tokens (e.g. "10 days-ah", "10 naala", "2 days-ah")
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loose_match = re.search(r"(\d+)\s*[-]?\s*(?:days?|naal[a]?|nal[a]?|weeks?|months?|years?|டேஸா?|டேஸ்|நாளா?|நாட்களா?)(?:[-]?\s*(?:ah|a|இருக்கு))?", msg_lower)
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if loose_match:
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val = int(loose_match.group(1))
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unit = "day" if val == 1 else "days"
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if "week" in loose_match.group(0):
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unit = "week" if val == 1 else "weeks"
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elif "month" in loose_match.group(0):
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unit = "month" if val == 1 else "months"
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elif "year" in loose_match.group(0):
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unit = "year" if val == 1 else "years"
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return f"{val} {unit}"
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return None
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def detect_previous_question_intent(last_ai_message: str) -> Optional[str]:
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"""
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Identifies what clinical category the previous AI message asked about.
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Returns one of: 'duration', 'severity', 'location', 'onset', 'frequency',
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'triggers', 'relieving_factors', 'aggravating_factors', 'medications',
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'allergies', 'medical_history', 'surgical_history', 'family_history',
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'previous_episodes', or None.
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"""
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if not last_ai_message or not last_ai_message.strip():
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return None
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msg = last_ai_message.lower()
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# Duration indicators
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if any(k in msg for k in [
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"எத்தனை நாளா", "எப்போலிருந்து", "எத்தனை நாள்", "எப்போ இருந்து", "கால அளவு",
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"how long", "since when", "how many days", "how many weeks", "duration",
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"कितने दिनों से", "कब से", "कितने दिन",
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"ఎన్ని రోజుల", "ఎప్పటి నుండి", "ఎన్ని రోజులు",
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"ಎಷ್ಟು ದಿನದಿಂದ", "ಯಾವಾಗಿನಿಂದ", "ಎಷ್ಟು ದಿನ",
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]):
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return "duration"
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# Severity indicators
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if any(k in msg for k in [
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"எவ்வளவு தீவிரமா", "ரொம்ப வலியா", "தீவிரம்", "தாங்க முடியாத", "எப்படி இருக்கு வலி",
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"how severe", "how bad", "mild or severe", "severity", "scale of 1",
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"कितना तेज", "तीव्रता", "ఎంత తీవ్రంగా", "ಎಷ್ಟು ತೀವ್ರ",
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]):
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return "severity"
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# Location indicators
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if any(k in msg for k in [
|
||||
"எந்த இடத்துல", "எந்த பக்கத்துல", "எங்கே வலிக்குது", "எங்கு", "எந்த பகுதி",
|
||||
"where does it hurt", "which side", "location of the pain", "where is",
|
||||
"कहाँ दर्द", "किस जगह", "ఎక్కడ నొప్పి", "ಯಾವ ಜಾಗದಲ್ಲಿ",
|
||||
]):
|
||||
return "location"
|
||||
|
||||
# Onset indicators
|
||||
if any(k in msg for k in [
|
||||
"திடீர்னு", "படிப்படியா", "எப்படி ஆரம்பிச்சது", "onset", "sudden", "gradual", "how did it start",
|
||||
"अचानक", "धीरे-धीरे",
|
||||
]):
|
||||
return "onset"
|
||||
|
||||
# Frequency indicators
|
||||
if any(k in msg for k in [
|
||||
"எத்தனை வாட்டி", "எத்தனை முறை", "அடிக்கடி", "frequency", "how often", "how many times",
|
||||
"कितनी बार", "ఎన్ని సార్లు", "ಎಷ್ಟು ಬಾರಿ",
|
||||
]):
|
||||
return "frequency"
|
||||
|
||||
# Triggers / Aggravating / Relieving indicators
|
||||
if any(k in msg for k in [
|
||||
"எப்போ அதிகமாகுது", "எப்போ குறையுது", "சாப்பிட்ட பிறகு", "trigger", "worse", "better", "relieve", "aggravate",
|
||||
]):
|
||||
return "triggers"
|
||||
|
||||
# Combined Medications + Allergies indicators (asked together in one question)
|
||||
has_med_kw = any(k in msg for k in [
|
||||
"மாத்திரை", "மருந்து", "மெடிசின்", "medication", "medicine", "tablets", "taking anything", "taken any",
|
||||
"दवाई", "दवा", "మందులు", "ಔಷಧ",
|
||||
])
|
||||
has_alg_kw = any(k in msg for k in [
|
||||
"அலர்ஜி", "ஒவ்வாமை", "allergy", "allergies", "allergic",
|
||||
"एलर्जी", "అలర్జీ", "ಅಲರ್ಜಿ",
|
||||
])
|
||||
if has_med_kw and has_alg_kw:
|
||||
return "medications_and_allergies"
|
||||
|
||||
# Medications indicators
|
||||
if has_med_kw or any(k in msg for k in [
|
||||
"டேப்லெட்", "எடுத்துக்குறீங்களா", "சாப்பிட்டீங்களா", "சாப்பிடுறீங்களா",
|
||||
"prescribed", "மாத்திரை சாப்பிடுறீங்களா",
|
||||
"మాత్ర", "ಮಾತ್ರೆ",
|
||||
]):
|
||||
return "medications"
|
||||
|
||||
# Allergies indicators
|
||||
if has_alg_kw or any(k in msg for k in [
|
||||
"ஏதேனும் அலர்ஜி", "மாத்திரை அலர்ஜி",
|
||||
"reaction to any medicine",
|
||||
]):
|
||||
return "allergies"
|
||||
|
||||
# Medical History indicators
|
||||
if any(k in msg for k in [
|
||||
"பிபி", "சுகர்", "ஆஸ்துமா", "முந்தைய", "உடம்பு பிரச்சனை", "வேறு ஏதேனும்",
|
||||
"medical history", "chronic condition", "past illness", "diabetes", "hypertension",
|
||||
"पुरानी बीमारी", "बीपी", "शुगर", "పాత ఆరోగ్య సమస్యలు", "హಳೆಯ ತೊಂದರೆ",
|
||||
]):
|
||||
return "medical_history"
|
||||
|
||||
# Surgical History indicators
|
||||
if any(k in msg for k in [
|
||||
"ஆபரேஷன்", "அறுவை சிகிச்சை", "surgery", "operation", "surgical history", "ऑपरेशन",
|
||||
]):
|
||||
return "surgical_history"
|
||||
|
||||
# Family History indicators
|
||||
if any(k in msg for k in [
|
||||
"குடும்பத்துல", "குடும்ப பின்னணி", "family history", "family members", "parivarik",
|
||||
]):
|
||||
return "family_history"
|
||||
|
||||
# Previous Episodes indicators
|
||||
if any(k in msg for k in [
|
||||
"இதுக்கு முன்னாடி", "முன்னாடி வந்திருக்கா", "previous episode", "had this before", "same problem before",
|
||||
]):
|
||||
return "previous_episodes"
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def is_duration_expression(text: str) -> bool:
|
||||
"""Check if the text is primarily a duration statement rather than a symptom name."""
|
||||
if not text:
|
||||
return False
|
||||
norm = normalize_duration_from_message(text)
|
||||
if norm is not None:
|
||||
clean = re.sub(r"[^\w\s\u0B80-\u0BFF]", "", text.lower()).strip()
|
||||
words = clean.split()
|
||||
if len(words) <= 8:
|
||||
return True
|
||||
filler_words = {"எனக்கு", "வந்து", "ஒரு", "இன்னும்", "இருக்கு", "ஆச்சு", "ஆகுது", "ஆ", "for", "me", "it", "is", "about", "around", "since", "from", "now"}
|
||||
non_filler = [w for w in words if w not in filler_words]
|
||||
if len(non_filler) <= 3:
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def is_severity_expression(text: str) -> bool:
|
||||
"""Check if the text is primarily a severity descriptor."""
|
||||
if not text:
|
||||
return False
|
||||
t_lower = text.lower().strip()
|
||||
patterns = [
|
||||
"mild", "moderate", "severe", "very severe", "unbearable", "high",
|
||||
"ரொம்ப severe", "ரொம்ப அதிகம்", "மிதமான", "தாங்க முடியாத", "அதிகமா இருக்கு", "ரொம்ப வலி",
|
||||
"बहुत तेज", "हल्का", "कम", "చాలా తీవ్రంగా", "తక్కువగా", "ತುಂಬಾ ಜಾಸ್ತಿ",
|
||||
]
|
||||
return any(p in t_lower for p in patterns) and len(text.split()) <= 6
|
||||
|
||||
|
||||
def is_location_expression(text: str) -> bool:
|
||||
"""Check if the text is primarily a body location descriptor."""
|
||||
if not text:
|
||||
return False
|
||||
t_lower = text.lower().strip()
|
||||
patterns = [
|
||||
"right side", "left side", "front", "back", "lower abdomen", "upper abdomen", "both sides",
|
||||
"வலது பக்கம்", "இடது பக்கம்", "வலது பக்கத்துல", "இடது பக்கத்துல", "முன் பக்கம்", "பின் பக்கம்", "அடிவயிறு",
|
||||
"दाएं तरफ", "बाएं तरफ", "పీట", "కుడి వైపు", "ఎడమ వైపు", "ಬಲಗಡೆ", "ಎಡಗಡೆ",
|
||||
]
|
||||
return any(p in t_lower for p in patterns) and len(text.split()) <= 6
|
||||
|
||||
|
||||
def is_negative_expression(text: str) -> bool:
|
||||
"""Check if text is an explicit negation / nothing else."""
|
||||
if not text:
|
||||
return False
|
||||
t_clean = re.sub(r"[^\w\s\u0B80-\u0BFF\u0900-\u097F\u0C00-\u0C7F\u0C80-\u0CFF]", "", text.lower()).strip()
|
||||
words = t_clean.split()
|
||||
if not words:
|
||||
return False
|
||||
|
||||
negative_keywords = [
|
||||
"no", "no more", "nothing", "nothing else", "none", "no illness", "no problem", "that's all", "thats all",
|
||||
"இல்லை", "இல்லைங்க", "இல்ல", "ஒன்றுமில்லை", "ஒன்னுமில்ல", "ஏதுமில்லை", "அவ்வளவுதான்", "வேற எதுவும் இல்ல",
|
||||
"எனக்கு வேற எதுவும் இல்ல", "வேறு எதுவும் இல்லை", "வேறு எதுவும் இல்ல", "வேற எதுவும் இல்லை",
|
||||
"नहीं", "कुछ नहीं", "कोई नहीं", "बस यही है", "और कुछ नहीं",
|
||||
"లేదు", "ఏమీ లేదు", "లేదండి", "ఇంతేనండి", "ఇంకేమీ లేదు",
|
||||
"ಇಲ್ಲ", "ಇಲ್ಲವೇ ಇಲ್ಲ", "ಏನೂ ಇಲ್ಲ", "ಅಷ್ಟೇ",
|
||||
"ഇല്ല", "ഒന്നുമില്ല",
|
||||
]
|
||||
|
||||
for k in negative_keywords:
|
||||
k_clean = re.sub(r"[^\w\s\u0B80-\u0BFF\u0900-\u097F\u0C00-\u0C7F\u0C80-\u0CFF]", "", k.lower()).strip()
|
||||
if k_clean == t_clean:
|
||||
return True
|
||||
if len(words) <= 6:
|
||||
pattern = rf"(?:^|\s){re.escape(k_clean)}(?:\s|$)"
|
||||
if re.search(pattern, t_clean):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def is_casual_filler(text: str) -> bool:
|
||||
"""Identify casual filler statements that should not be extracted as clinical entities."""
|
||||
if not text or not str(text).strip():
|
||||
return False
|
||||
t_lower = str(text).lower().strip()
|
||||
filler_patterns = [
|
||||
r"^(?:சரி\s*சார்\s*சரி\s*சார்|சரி\s*சார்|சரிங்க|ஓகே\s*சார்|ஓகேங்க|ok\s*sir|ok\s*mam|okay|yes|yeah|sure)$",
|
||||
r"^(?:நான்\s*bus-?ல\s*வந்தேன்|auto-?ல\s*வந்தேன்|நடந்து\s*வந்தேன்)$",
|
||||
r"^(?:hello|hi|vanakkam|namaste)$",
|
||||
]
|
||||
return any(re.search(p, t_lower) for p in filler_patterns)
|
||||
|
||||
|
||||
def extract_severity_from_message(msg: str) -> Optional[str]:
|
||||
"""Extract normalized severity string."""
|
||||
m_lower = msg.lower()
|
||||
if any(k in m_lower for k in ["very severe", "unbearable", "ரொம்ப அதிகம்", "தாங்க முடியாத", "बहुत तेज"]):
|
||||
return "severe"
|
||||
if any(k in m_lower for k in ["severe", "ரொம்ப severe", "அதிகமா", "तेज"]):
|
||||
return "severe"
|
||||
if any(k in m_lower for k in ["moderate", "medium", "மிதமான", "मध्यम"]):
|
||||
return "moderate"
|
||||
if any(k in m_lower for k in ["mild", "light", "லேசா", "हल्का"]):
|
||||
return "mild"
|
||||
return None
|
||||
|
||||
|
||||
def extract_location_from_message(msg: str) -> Optional[str]:
|
||||
"""Extract normalized location string."""
|
||||
m_lower = msg.lower()
|
||||
if any(k in m_lower for k in ["right side", "வலது பக்கம்", "வலது பக்கத்துல", "दाएं"]):
|
||||
return "right side"
|
||||
if any(k in m_lower for k in ["left side", "இடது பக்கம்", "இடது பக்கத்துல", "बाएं"]):
|
||||
return "left side"
|
||||
if any(k in m_lower for k in ["front", "முன் பக்கம்"]):
|
||||
return "front"
|
||||
if any(k in m_lower for k in ["back", "பின் பக்கம்"]):
|
||||
return "back"
|
||||
if any(k in m_lower for k in ["lower abdomen", "அடிவயிறு"]):
|
||||
return "lower abdomen"
|
||||
return None
|
||||
@@ -57,7 +57,11 @@ class OnboardingService:
|
||||
"allergies": {
|
||||
"status": "not_collected",
|
||||
"items": []
|
||||
}
|
||||
},
|
||||
"surgical_history": [],
|
||||
"previous_episodes": [],
|
||||
"family_history": [],
|
||||
"additional_information": []
|
||||
}
|
||||
|
||||
state = OnboardingState(
|
||||
|
||||
@@ -20,37 +20,40 @@ class ResultExtractionService:
|
||||
)
|
||||
|
||||
prompt = f"""
|
||||
You are a hospital patient onboarding data extraction assistant.
|
||||
You are a senior clinical data extraction engine at a healthcare facility.
|
||||
|
||||
Your task is to convert the completed onboarding information
|
||||
into structured patient intake data.
|
||||
Your task is to convert the collected patient onboarding conversation and structured state
|
||||
into a clean, comprehensive, structured patient intake record for the attending doctor.
|
||||
|
||||
You are NOT a doctor.
|
||||
Do not diagnose the patient.
|
||||
Do not add information that was not provided.
|
||||
CLINICAL RULES:
|
||||
1. You are NOT a doctor. Do NOT diagnose or add medical interpretations.
|
||||
2. Capture ALL patient-reported symptoms, onset/duration, severity, location, frequency.
|
||||
3. Capture ALL reported medical history, medications (with dosage/frequency), allergies (with reaction), surgical history, and previous episodes.
|
||||
4. Capture any functional impact (sleep disruption, appetite changes, weakness) in additional_information.
|
||||
5. Do NOT invent information. If something was not provided, leave it null or an empty list.
|
||||
|
||||
PATIENT:
|
||||
PATIENT CONTEXT:
|
||||
{json.dumps({
|
||||
"id": session.patient_id,
|
||||
"name": session.patient_name,
|
||||
"age": session.age,
|
||||
"gender": session.gender,
|
||||
}, indent=2)}
|
||||
}, indent=2, ensure_ascii=False)}
|
||||
|
||||
CATEGORY:
|
||||
CATEGORY / SPECIALTY:
|
||||
{json.dumps({
|
||||
"type": session.category_type,
|
||||
"code": session.category_code,
|
||||
"name": session.category_name,
|
||||
}, indent=2)}
|
||||
}, indent=2, ensure_ascii=False)}
|
||||
|
||||
CURRENT STATE:
|
||||
{json.dumps(current_state, indent=2)}
|
||||
CURRENT INTAKE STATE:
|
||||
{json.dumps(current_state, indent=2, ensure_ascii=False)}
|
||||
|
||||
CONVERSATION:
|
||||
{json.dumps(conversation_history, indent=2)}
|
||||
FULL CONVERSATION:
|
||||
{json.dumps(conversation_history, indent=2, ensure_ascii=False)}
|
||||
|
||||
Return ONLY JSON conforming to this exact structure:
|
||||
Return ONLY a valid JSON object matching this structure:
|
||||
{{
|
||||
"patient_id": "{session.patient_id}",
|
||||
"category": {{
|
||||
@@ -58,19 +61,43 @@ Return ONLY JSON conforming to this exact structure:
|
||||
"code": "{session.category_code}",
|
||||
"name": "{session.category_name}"
|
||||
}},
|
||||
"chief_complaint": "string or null",
|
||||
"chief_complaint": "primary reason for visit or main symptom",
|
||||
"symptoms": [
|
||||
{{
|
||||
"name": "string",
|
||||
"duration": "string or null",
|
||||
"severity": "string or null",
|
||||
"description": "string or null"
|
||||
"name": "symptom name",
|
||||
"duration": "duration or null",
|
||||
"severity": "severity or null",
|
||||
"location": "location or null",
|
||||
"description": "brief details or null"
|
||||
}}
|
||||
],
|
||||
"medical_history": [],
|
||||
"medications": [],
|
||||
"allergies": [],
|
||||
"additional_information": "string or null",
|
||||
"medical_history": [
|
||||
{{
|
||||
"condition": "condition name",
|
||||
"duration": "duration or null",
|
||||
"status": "active/past or null"
|
||||
}}
|
||||
],
|
||||
"medications": [
|
||||
{{
|
||||
"name": "medication name",
|
||||
"dosage": "dosage or null",
|
||||
"frequency": "frequency or null",
|
||||
"duration": "duration or null"
|
||||
}}
|
||||
],
|
||||
"allergies": [
|
||||
{{
|
||||
"name": "allergen name",
|
||||
"type": "drug/food/etc or null",
|
||||
"reaction": "reaction or null"
|
||||
}}
|
||||
],
|
||||
"surgical_history": [],
|
||||
"previous_episodes": [],
|
||||
"family_history": [],
|
||||
"dietary_habits": null,
|
||||
"additional_information": "any functional impact, previous episodes, or additional notes",
|
||||
"language": "{session.language}",
|
||||
"onboarding_completed": true
|
||||
}}
|
||||
@@ -78,7 +105,7 @@ Return ONLY JSON conforming to this exact structure:
|
||||
|
||||
from app.config import GEMINI_API_KEY, GEMINI_MODEL, ANTHROPIC_API_KEY, ANTHROPIC_MODEL
|
||||
|
||||
# Primary: Google Gemini extraction (Ultra-fast ~1s)
|
||||
# Primary: Google Gemini extraction
|
||||
if GEMINI_API_KEY and GEMINI_API_KEY.strip() not in ["your_gemini_key", "your_gemini_api_key", ""]:
|
||||
try:
|
||||
import asyncio
|
||||
@@ -136,7 +163,7 @@ Return ONLY JSON conforming to this exact structure:
|
||||
except Exception as e:
|
||||
print(f"[Claude Result Extraction Error]: {e}")
|
||||
|
||||
# Fallback simulator from state (strictly without fake dummy strings)
|
||||
# Fallback structured builder from state
|
||||
chief_complaint = current_state.get("reason_for_visit")
|
||||
if isinstance(chief_complaint, dict):
|
||||
chief_complaint = chief_complaint.get("value")
|
||||
@@ -157,6 +184,11 @@ Return ONLY JSON conforming to this exact structure:
|
||||
history_raw = current_state.get("medical_history")
|
||||
history_list = history_raw.get("items", []) if isinstance(history_raw, dict) else (history_raw or [])
|
||||
|
||||
surgical_history = current_state.get("surgical_history", [])
|
||||
previous_episodes = current_state.get("previous_episodes", [])
|
||||
family_history = current_state.get("family_history", [])
|
||||
add_info = current_state.get("additional_information")
|
||||
|
||||
fallback_result = {
|
||||
"patient_id": session.patient_id,
|
||||
"category": {
|
||||
@@ -169,7 +201,10 @@ Return ONLY JSON conforming to this exact structure:
|
||||
"medical_history": history_list,
|
||||
"medications": meds_list,
|
||||
"allergies": allergies_list,
|
||||
"additional_information": None,
|
||||
"surgical_history": surgical_history if isinstance(surgical_history, list) else [],
|
||||
"previous_episodes": previous_episodes if isinstance(previous_episodes, list) else [],
|
||||
"family_history": family_history if isinstance(family_history, list) else [],
|
||||
"additional_information": add_info,
|
||||
"language": session.language,
|
||||
"onboarding_completed": True,
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user