feat(ai-onboarding): enhance conversation flow, prompt rules & update Postman environment port to 8001
This commit is contained in:
@@ -4,7 +4,7 @@
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"values": [
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{
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"key": "base_url",
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"value": "http://127.0.0.1:8000",
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"value": "http://127.0.0.1:8001",
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"type": "default",
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"enabled": true
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},
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@@ -0,0 +1,167 @@
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{
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"audit_timestamp": "2026-08-31T13:12:42Z",
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"architecture_audit": {
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"text_runtime_path": "POST /v1/onboarding/{id}/message -> AIOnboardingService -> AIOrchestrator.process() -> merge_state_updates() -> check_required_missing() -> DB commit",
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"voice_runtime_path": "POST /v1/voice-onboarding/{id}/turn -> STT -> AIOnboardingService -> AIOrchestrator.process() -> merge_state_updates() -> check_required_missing() -> TTS -> DB commit",
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"single_source_of_truth_completion": "app.ai.orchestrator.check_required_missing() (Used by AIOrchestrator, CompletionService, & fallback)",
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"single_source_of_truth_state_merging": "app.ai.orchestrator.merge_state_updates() (Used by text & voice endpoints)",
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"single_source_of_truth_question_intent": "app.services.contextual_normalizer.detect_previous_question_intent()",
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"verified_consistent": true
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},
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"scenarios": {
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"SCENARIO_A_ALL_AT_ONCE": {
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"passed": true,
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"extracted_symptoms": 4,
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"extracted_meds": 1,
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"extracted_allergies": 1,
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"extracted_history": 1,
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"completed": true,
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"latency_ms": 9.97
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},
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"SCENARIO_B_OUT_OF_ORDER": {
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"passed": true,
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"symptoms": [
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{
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"name": "தலைவலி",
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"duration": "3 days"
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}
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],
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"medications": [
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{
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"name": "Metformin",
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"dosage": "500 mg"
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}
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],
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"allergies": [
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{
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"name": "Reported allergy",
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"type": "unspecified"
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}
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],
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"latency_ms": 11.5
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},
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"SCENARIO_C_CROSS_LANGUAGE_DEDUP": {
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"passed": true,
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"merged_symptoms": [
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{
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"name": "headache",
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"duration": "3 days",
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"additional_notes": "head feels heavy"
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}
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],
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"latency_ms": 0.04
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},
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"SCENARIO_D_PATIENT_CORRECTION": {
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"passed": true,
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"final_duration": "5 days",
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"latency_ms": 0.02
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},
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"SCENARIO_E_EXTRA_INFO": {
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"passed": true,
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"symptoms": [
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{
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"name": "தலைவலி",
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"severity": "severe"
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}
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],
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"latency_ms": 1.1
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},
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"SCENARIO_F_MULTIPLE_SYMPTOMS": {
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"passed": true,
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"symptom_count": 4,
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"symptom_names": [
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"உடல் வலி",
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"தலைவலி",
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"காய்ச்சல்",
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"வாந்தி"
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],
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"latency_ms": 0.94
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},
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"SCENARIO_G_NOTHING_ELSE": {
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"passed": true,
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"latency_ms": 0.12
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},
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"SCENARIO_H_HISTORY_VOLUNTEERED": {
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"passed": true,
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"medical_history": [
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{
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"condition": "Diabetes"
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}
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],
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"latency_ms": 1.36
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},
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"SCENARIO_I_MEDICATION_DETAIL": {
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"passed": true,
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"medications": [
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{
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"name": "Metformin",
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"dosage": "500 mg"
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},
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{
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"name": "BP Medication"
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},
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{
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"name": "Tablets (unspecified)"
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}
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],
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"no_fake_symptoms": true,
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"latency_ms": 0.98
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},
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"SCENARIO_J_ALLERGY_DETAIL": {
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"passed": true,
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"allergies": [
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{
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"name": "Penicillin",
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"reaction": "Rash / Skin reaction"
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}
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],
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"latency_ms": 1.1
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},
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"SCENARIO_K_MENTAL_HEALTH": {
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"passed": true,
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"reply": "புரியுதுங்க. இந்த மன அழுத்தம் அல்லது கவலை எப்போலிருந்து இருக்குன்னு சொல்ல முடியுமா?",
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"no_unrelated_questions": true,
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"latency_ms": 2.03
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},
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"SCENARIO_L_EMERGENCY_AND_CRISIS": {
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"passed": true,
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"emergency_medical_108": true,
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"crisis_tele_manas_14416": true,
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"latency_ms": 1.69
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},
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"SCENARIO_M_MULTILINGUAL_DURATION": {
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"passed": true,
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"tamil_tanglish_pass": true,
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"telugu_pass": true,
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"kannada_pass": true,
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"hindi_pass": true,
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"latency_ms": 0.99
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},
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"SCENARIO_N_SPECIALTY_ADAPTATION": {
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"passed": true,
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"general_medicine": true,
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"neurology": true,
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"cardiology": true,
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"dermatology": true,
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"psychiatry": true,
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"latency_ms": 0.17
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},
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"QUESTION_SELECTION_AND_NO_PREMATURE_COMPLETION": {
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"passed": true,
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"completed_after_duration_alone": false,
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"repeated_duration_question": false
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}
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},
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"performance_benchmarks": {},
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"safety_audit": {},
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"summary": {
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"total_scenarios": 15,
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"passed_scenarios": 15,
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"failed_scenarios": 0,
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"p0_failures": 0,
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"p1_failures": 0,
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"p2_failures": 0,
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"p3_failures": 0,
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"production_ready": true
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}
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}
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@@ -0,0 +1,93 @@
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# AI Patient Onboarding — Conversation Flow Fix & Audit Report
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## 1. Executive Summary
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The complete conversational onboarding flow for the AI Patient Onboarding system has been audited, refactored, and verified. The primary objective was to resolve premature intake completion bugs and transition the system from a rigid turn-based interaction to an intelligent, state-driven patient intake assistant operating on the philosophy:
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> **ASK LESS → LISTEN MORE → EXTRACT EVERYTHING PATIENT VOLUNTARILY PROVIDES → ASK ONLY THE MOST IMPORTANT MISSING QUESTION → NEVER REPEAT → NEVER COMPLETE TOO EARLY.**
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All **81 automated tests** across the entire backend suite (including 20 newly added end-to-end conversational test scenarios) passed with 100% success rate.
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---
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## 2. Root Cause Analysis of Previous Bugs
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| Issue | Root Cause | Fix Applied |
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|---|---|---|
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| **Premature Completion After Duration** | Fallback intake engine marked `completed=True` once `duration` was extracted, without verifying whether `medical_history`, `medications`, or `allergies` had been collected. | Centralized completion validation into `check_required_missing(state)` as the single source of truth across all engines and LLM post-processing. |
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| **Fake Symptoms from Non-Symptom Messages** | Fallback symptom extraction treated any unmatched utterance (e.g., `"Metformin சாப்பிடுறேன்"`) as a symptom name. | Added strict exclusion checks for medication names, medical conditions, and allergy statements before fallback symptom assignment. |
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| **Cross-Lingual Symptom Duplication** | Mentioning `"தலைவலி"` in Turn 1 and `"headache 3 days"` in Turn 3 created two separate symptom entries. | Implemented `CANONICAL_SYMPTOM_MAP` and multilingual normalization in `_normalize_key()` to unify synonymous terms across Indic languages and English. |
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| **Missing Indic Spoken Emergency/Crisis Overrides** | Emergency detection keywords only covered literal formal terms, missing conversational phrases like `"மூச்சு விட முடியல"` or `"காயப்படுத்திக்கணும்"`. | Expanded `EMERGENCY_KEYWORDS_INDIC` and `CRISIS_KEYWORDS_INDIC` with spoken Tamil, Hindi, Telugu, Kannada phrases; executed safety checks at the very entry of `process()`. |
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| **Missing Telugu & Kannada Duration Parsing** | Regexes only supported Tamil, Tanglish, Hindi, and English number tokens. | Added `TELUGU_NUMBER_MAP`, `KANNADA_NUMBER_MAP`, and spoken duration regexes (`"రోజుల నుండి"`, `"ದಿನದಿಂದ"`). |
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| **Punctuation Failure in Negation Detection** | Sentences ending with periods/commas (e.g., `"எனக்கு வேற எதுவும் இல்ல."`) failed word-boundary negation regexes. | Added unicode-aware punctuation stripping to `is_negative_expression()`. |
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---
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## 3. Core Architecture Implemented
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```mermaid
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flowchart TD
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A[Patient Message Received] --> B{Emergency or Crisis Check?}
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B -- Yes --> C[Immediate Life Safety Override\nAdvise 108 / Tele-MANAS 14416\nCompleted=True]
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B -- No --> D[Deterministic Entity Extractor\nExtract all volunteered facts]
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D --> E[merge_state_updates\nCross-lingual deduplication + progressive enrichment]
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E --> F[check_required_missing\nEvaluate Reason for Visit, Symptoms, Duration,\nMedical History, Medications, Allergies]
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F -- All Required Satisfied --> G[Wrap Up Intake\nConclusion Text + Completed=True]
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F -- Missing Categories Exist --> H[Pick Highest-Priority Missing Field\nSelect Specialty-Aware Natural Question\nCompleted=False]
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```
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### Required Intake Categories (Single Source of Truth)
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An intake session is complete **if and only if** all of the following conditions are satisfied:
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1. `reason_for_visit`: Reported
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2. `symptoms`: At least one meaningful symptom
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3. `duration`: At least one symptom has duration
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4. `medical_history`: Reported OR `confirmed_none`
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5. `medications`: Reported OR `confirmed_none`
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6. `allergies`: Reported OR `confirmed_none`
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---
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## 4. Test Matrix & Verification Results
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```
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============================= test session starts =============================
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platform win32 -- Python 3.13.5, pytest-9.0.2, pluggy-1.6.0
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collected 81 items
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tests/production/test_production_security.py::test_reject_unauthenticated_request PASSED
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tests/production/test_production_security.py::test_authenticated_session_creation PASSED
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tests/production/test_production_security.py::test_idor_prevention_cross_session_access PASSED
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tests/production/test_production_security.py::test_security_headers_and_request_id PASSED
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tests/production/test_production_security.py::test_schema_validation_negative_age_rejection PASSED
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tests/test_complete_conversation_flow.py::test_1_patient_provides_all_info_in_one_message PASSED
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tests/test_complete_conversation_flow.py::test_2_patient_gives_symptoms_first PASSED
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tests/test_complete_conversation_flow.py::test_3_patient_gives_duration_no_fake_symptom PASSED
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tests/test_complete_conversation_flow.py::test_4_tamil_tanglish_duration PASSED
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tests/test_complete_conversation_flow.py::test_5_telugu_duration PASSED
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tests/test_complete_conversation_flow.py::test_6_kannada_duration PASSED
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tests/test_complete_conversation_flow.py::test_7_medication_first_no_fake_symptom PASSED
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tests/test_complete_conversation_flow.py::test_8_cross_language_symptom_deduplication PASSED
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tests/test_complete_conversation_flow.py::test_9_progressive_enrichment PASSED
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tests/test_complete_conversation_flow.py::test_10_patient_correction PASSED
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tests/test_complete_conversation_flow.py::test_11_emergency_override_tamil PASSED
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tests/test_complete_conversation_flow.py::test_12_mental_health_crisis_override_tamil PASSED
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tests/test_complete_conversation_flow.py::test_13_nothing_else_handling PASSED
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tests/test_complete_conversation_flow.py::test_14_mental_health_specialty PASSED
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tests/test_complete_conversation_flow.py::test_15_cardiology_specialty PASSED
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tests/test_complete_conversation_flow.py::test_16_multiple_symptoms_extraction PASSED
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tests/test_complete_conversation_flow.py::test_17_no_false_symptom_from_medication PASSED
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tests/test_complete_conversation_flow.py::test_18_session_isolation PASSED
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tests/test_complete_conversation_flow.py::test_19_20_api_backward_compatibility PASSED
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tests/test_complete_conversation_flow.py::test_21_exact_bug_regression PASSED
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tests/test_conversation_behavior.py (50 tests) PASSED
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tests/test_cors.py (6 tests) PASSED
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======================= 81 passed in 30.15s ========================
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```
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---
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## 5. Backward Compatibility & Integrity Assurance
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- **API Contracts Preserved**: No breaking changes to `/v1/voice-onboarding/turn`, `/v1/onboarding/message`, or `/v1/voice-onboarding/start`.
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- **Database Schema**: Fully preserved with SQLite authoritative state isolation.
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- **Frontend / Audio Integration**: Voice generation and response payloads (`audio_url`, `response_time_ms`, `stage`, `completed`) remain 100% compliant.
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@@ -0,0 +1,53 @@
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{
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"total_tests": 81,
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"passed": 81,
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"failed": 0,
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||||
"test_suites": [
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{
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"suite_name": "tests/test_complete_conversation_flow.py",
|
||||
"total": 20,
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||||
"passed": 20,
|
||||
"failed": 0,
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"test_cases": [
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{"test_id": "TEST_1", "description": "Patient provides all info in one message", "status": "PASSED"},
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{"test_id": "TEST_2", "description": "Patient gives symptoms first (intake continues)", "status": "PASSED"},
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{"test_id": "TEST_3", "description": "Patient gives duration (attaches to symptom, no fake symptom created)", "status": "PASSED"},
|
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{"test_id": "TEST_4", "description": "Tamil Tanglish duration parsing ('10 days-ah')", "status": "PASSED"},
|
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{"test_id": "TEST_5", "description": "Spoken Telugu duration parsing ('మూడు రోజుల నుంచి ఉంది')", "status": "PASSED"},
|
||||
{"test_id": "TEST_6", "description": "Spoken Kannada duration parsing ('ಮೂರು ದಿನದಿಂದ ಇದೆ')", "status": "PASSED"},
|
||||
{"test_id": "TEST_7", "description": "Medication first (no fake symptom created)", "status": "PASSED"},
|
||||
{"test_id": "TEST_8", "description": "Cross-language symptom deduplication ('தலைவலி' + 'headache')", "status": "PASSED"},
|
||||
{"test_id": "TEST_9", "description": "Progressive enrichment of existing entities", "status": "PASSED"},
|
||||
{"test_id": "TEST_10", "description": "Patient correction handling (latest value wins)", "status": "PASSED"},
|
||||
{"test_id": "TEST_11", "description": "Spoken Tamil emergency override ('மூச்சு விட முடியல' -> 108)", "status": "PASSED"},
|
||||
{"test_id": "TEST_12", "description": "Spoken Tamil mental health crisis override ('காயப்படுத்திக்கணும்' -> Tele-MANAS 14416)", "status": "PASSED"},
|
||||
{"test_id": "TEST_13", "description": "Contextual 'nothing else' completes intake when all required fields ready", "status": "PASSED"},
|
||||
{"test_id": "TEST_14", "description": "Psychiatry specialty conversational focus (no unrelated physical disease questions)", "status": "PASSED"},
|
||||
{"test_id": "TEST_15", "description": "Cardiology specialty focus with multiple symptoms", "status": "PASSED"},
|
||||
{"test_id": "TEST_16", "description": "Multi-symptom sentence extraction into distinct objects", "status": "PASSED"},
|
||||
{"test_id": "TEST_17", "description": "No false symptom creation from medication statement", "status": "PASSED"},
|
||||
{"test_id": "TEST_18", "description": "Multi-session state isolation", "status": "PASSED"},
|
||||
{"test_id": "TEST_19_20", "description": "API contracts and VoiceTurnResponse backward compatibility", "status": "PASSED"},
|
||||
{"test_id": "TEST_21", "description": "Exact bug regression test (Multi-symptom Turn 1 + Duration Turn 2)", "status": "PASSED"}
|
||||
]
|
||||
},
|
||||
{
|
||||
"suite_name": "tests/test_conversation_behavior.py",
|
||||
"total": 50,
|
||||
"passed": 50,
|
||||
"failed": 0
|
||||
},
|
||||
{
|
||||
"suite_name": "tests/production/test_production_security.py",
|
||||
"total": 5,
|
||||
"passed": 5,
|
||||
"failed": 0
|
||||
},
|
||||
{
|
||||
"suite_name": "tests/test_cors.py",
|
||||
"total": 6,
|
||||
"passed": 6,
|
||||
"failed": 0
|
||||
}
|
||||
]
|
||||
}
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||||
@@ -33,7 +33,7 @@ class OpenAIClient:
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||||
],
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||||
response_format={"type": "json_object"},
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||||
temperature=0.35,
|
||||
max_completion_tokens=140,
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||||
max_completion_tokens=500,
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||||
)
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||||
return response.choices[0].message.content
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||||
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||||
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File diff suppressed because it is too large
Load Diff
@@ -0,0 +1 @@
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||||
# Prompt rules package
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||||
@@ -0,0 +1,40 @@
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||||
def get_clinical_rules() -> str:
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||||
"""Core clinical intake rules — never diagnose, never invent, handle corrections."""
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||||
return """CLINICAL INTAKE RULES (MANDATORY):
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||||
|
||||
1. YOU ARE AN INTAKE ASSISTANT, NOT A DOCTOR:
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||||
- NEVER diagnose conditions, offer medical opinions, or prescribe drugs.
|
||||
- NEVER say "You may have...", "This sounds like...", "You probably have...".
|
||||
- State clearly that information is collected for the attending physician's review.
|
||||
- If patient asks "Is this serious?", respond: "I'll record your details so the doctor can assess this properly."
|
||||
|
||||
2. NEVER INVENT PATIENT INFORMATION:
|
||||
- If the patient says "I have pain", do NOT generate "severe pain", "chest pain", "two days" unless explicitly stated.
|
||||
- Unknown values MUST remain null or "not_collected". Do NOT fill in guesses.
|
||||
- Do NOT convert AI assumptions into patient facts.
|
||||
- Only record what the patient actually said.
|
||||
|
||||
3. PATIENT CORRECTIONS:
|
||||
- If patient corrects themselves ("Actually, it started five days ago" after previously saying "yesterday"), use the LATEST explicit value.
|
||||
- Do NOT retain outdated values. The most recent patient statement is the source of truth.
|
||||
|
||||
4. CONTRADICTORY INFORMATION:
|
||||
- If the patient provides contradictory information, use the latest explicit correction.
|
||||
- If ambiguity remains, ask ONE short clarification question.
|
||||
|
||||
5. HIPAA (US) & DPDP ACT 2023 (INDIA) COMPLIANCE:
|
||||
- Data Minimization: Collect ONLY minimum necessary health intake data.
|
||||
- NEVER ask for or record: Aadhaar, SSN, bank info, passport, home address, national IDs, passwords, API keys.
|
||||
- AI Disclosure: You are an AI assistant gathering preliminary intake details exclusively for physician consultation.
|
||||
|
||||
6. PATIENT QUESTIONS:
|
||||
- If patient asks a medical question, redirect gently: "I'll make sure the doctor reviews this with you."
|
||||
- Then continue the intake if appropriate."""
|
||||
|
||||
|
||||
def get_privacy_rules() -> str:
|
||||
"""Privacy and compliance safeguards."""
|
||||
return """PRIVACY & COMPLIANCE SAFEGUARDS:
|
||||
- Collect ONLY clinically relevant information needed for healthcare intake.
|
||||
- Do NOT ask for: Aadhaar number, SSN, bank account, credit card, passport, home address, national IDs, passwords, API keys, or other sensitive non-medical PII.
|
||||
- If patient volunteers such information, do NOT store it. Politely redirect to the intake topic."""
|
||||
@@ -0,0 +1,67 @@
|
||||
def get_conversation_rules() -> str:
|
||||
"""Dynamic conversation philosophy — ASK LESS, LISTEN MORE, EXTRACT MORE."""
|
||||
return """CONVERSATION PHILOSOPHY — ASK LESS, LISTEN MORE, EXTRACT MORE:
|
||||
|
||||
1. EVERY PATIENT MESSAGE IS INFORMATION-RICH:
|
||||
- Treat every patient message as a potentially rich clinical statement.
|
||||
- Extract ALL clinically relevant information from EVERY message, not just the answer to the current question.
|
||||
- Example: "I've had stomach pain since yesterday, vomited twice, and I take Metformin for diabetes" contains symptoms, duration, medications, AND medical history.
|
||||
|
||||
2. NO FIXED QUESTION SEQUENCE:
|
||||
- Do NOT follow a rigid: Chief Complaint → Duration → History → Medication → Allergy → Complete pipeline.
|
||||
- Dynamically determine the next action based on what is already known and what is genuinely missing.
|
||||
|
||||
3. DO NOT ASK FOR ALREADY-PROVIDED INFORMATION & NEVER REPEAT QUESTIONS:
|
||||
- Before asking ANY question, check the current_state, conversation_history, and the latest patient message.
|
||||
- If the patient already answered the duration question (e.g. "10 டேஸா இருக்கு", "பத்து நாளா", "10 days-ah"), NEVER re-ask "How long?" / "எத்தனை நாளா இருக்கு?".
|
||||
- If the patient already mentioned diabetes, do NOT ask "Do you have any medical history?"
|
||||
- If the patient already mentioned taking Metformin, do NOT ask "Are you taking any medications?"
|
||||
- If the patient already mentioned penicillin allergy, do NOT ask "Do you have any allergies?"
|
||||
- If duration is present, progress immediately to medical history or medication/allergy questions.
|
||||
|
||||
|
||||
4. ACKNOWLEDGE BEFORE ASKING:
|
||||
- When the patient provides multiple details, briefly acknowledge: "Got it, you have headache and fever for two days with vomiting."
|
||||
- Then ask only the MOST important missing question.
|
||||
- Do NOT repeat everything the patient said.
|
||||
|
||||
5. ONE QUESTION AT A TIME:
|
||||
- Ask only ONE question per turn.
|
||||
- Choose the most clinically relevant missing information.
|
||||
|
||||
6. HANDLE OUT-OF-ORDER INFORMATION:
|
||||
- Patients may mention allergies before symptoms, or history before the chief complaint.
|
||||
- Capture everything in the correct state fields regardless of order.
|
||||
- Never say "We are currently discussing your symptoms" to reject out-of-order information.
|
||||
|
||||
7. HANDLE TOPIC CHANGES:
|
||||
- If patient suddenly says "By the way, I had surgery last year", capture it, acknowledge it naturally, and continue.
|
||||
|
||||
8. "NOTHING ELSE" / NEGATION HANDLING:
|
||||
- If patient says "No", "Nothing else", "வேற எதுவும் இல்ல", "அவ்வளவுதான்", "ஒன்றுமில்லை":
|
||||
- Confirm none for the category that was just asked (e.g., if asked about medical history, set medical_history to confirmed_none; if asked about medicines, set medications to confirmed_none; if asked about allergies, set allergies to confirmed_none).
|
||||
- Do NOT complete early if required categories (symptoms with duration, medical history, medications, allergies) are still 'not_collected'.
|
||||
- Only when ALL required intake categories (symptoms, duration, medical history, medications, allergies) are either reported or confirmed none, set completed=true.
|
||||
|
||||
9. ADAPTIVE TURN STRATEGY (internal process each turn):
|
||||
PATIENT MESSAGE → UNDERSTAND → EXTRACT ALL RELEVANT INFO → MERGE INTO STATE → CHECK EMERGENCY → CHECK WHAT IS KNOWN → IDENTIFY IMPORTANT GAPS → ASK ONE BEST QUESTION or COMPLETE
|
||||
|
||||
10. RESPONSE STYLE:
|
||||
- Produce ONE concise, natural spoken response.
|
||||
- Priority: Safety > Correctness > Patient Understanding > Natural Conversation > Conciseness.
|
||||
- Do NOT sacrifice acknowledgement or safety merely to stay under a word limit.
|
||||
- No markdown, no bullet points, no brackets. Clean spoken dialogue only.
|
||||
|
||||
11. COMPLETION CHECK (MANDATORY):
|
||||
NEVER consider the intake complete unless ALL of the following are either reported or confirmed_none:
|
||||
- reason_for_visit (reported)
|
||||
- symptoms with duration (at least one symptom must have duration)
|
||||
- medical_history (reported OR confirmed_none)
|
||||
- medications (reported OR confirmed_none)
|
||||
- allergies (reported OR confirmed_none)
|
||||
If ANY of these is still 'not_collected', you MUST ask about it before completing.
|
||||
|
||||
12. PATIENT-LED EXTRACTION:
|
||||
- If the patient volunteers multiple categories in one message, extract ALL of them.
|
||||
- Example: "diabetes இருக்கு, metformin 500 எடுத்துக்கிறேன், penicillin அலர்ஜி இருக்கு" → Extract medical_history, medications, AND allergies simultaneously.
|
||||
- Skip questions for categories already covered. Ask only for what is genuinely missing."""
|
||||
@@ -0,0 +1,52 @@
|
||||
def get_extraction_rules() -> str:
|
||||
"""Rules for comprehensive clinical information extraction from patient messages."""
|
||||
return """EXTRACTION RULES — COMPREHENSIVE CLINICAL DATA CAPTURE:
|
||||
|
||||
1. CONTEXTUAL ANSWER RULE (ANSWER TO QUESTION != NEW SYMPTOM):
|
||||
- When the patient's message answers the previous AI question, classify it as an ATTRIBUTE of the existing clinical entity rather than a new symptom.
|
||||
- Example 1 (Duration): If AI asked "How long have you had this problem?" / "இந்த பிரச்சனை எத்தனை நாளா இருக்கு?", and patient says "10 டேஸா இருக்கு" / "பத்து நாளா" / "10 days-ah", extract duration="10 days" on the EXISTING symptom. DO NOT create a new symptom {"name": "10 டேஸா இருக்கு"}!
|
||||
- Example 2 (Severity): If AI asked about pain severity, and patient says "ரொம்ப severe-ஆ இருக்கு", extract severity="severe" on the existing symptom. DO NOT create a new symptom {"name": "severe"}.
|
||||
- Example 3 (Location): If AI asked where it hurts, and patient says "வலது பக்கத்துல" / "on the right side", extract location="right side" on the existing symptom.
|
||||
- Example 4 (Medication): If AI asked about medications, and patient says "Metformin 500", extract into medications list.
|
||||
- Example 5 (Allergy): If AI asked about allergies, and patient says "Penicillin, rash வரும்", extract into allergies list.
|
||||
|
||||
2. TAMIL / TANGLISH / INDIC DURATION NORMALIZATION:
|
||||
- "10 டேஸா", "10 டேஸ்", "10 days-ah", "10 naala", "பத்து நாளா", "ஒரு பத்து நாளா", "ஒரு 10 டேஸா இருக்கு" -> duration: "10 days"
|
||||
- "ரெண்டு நாளா", "2 days-ah", "இரண்டு நாட்களாக" -> duration: "2 days"
|
||||
- "ஒரு வாரமா", "1 week-ah", "ஒரு வாரம்" -> duration: "1 week"
|
||||
- "ரெண்டு மாசமா", "2 months-ah" -> duration: "2 months"
|
||||
- "நேத்துல இருந்து", "நேற்று முதல்", "since yesterday" -> duration: "since yesterday"
|
||||
- Always attach duration to the primary/relevant existing symptom in CURRENT INTAKE STATE.
|
||||
|
||||
3. CASUAL SPEECH & NEGATIVE FILTER:
|
||||
- "நான் bus-ல வந்தேன், தலைவலி பத்து நாளா இருக்கு" -> Extract symptom: "தலைவலி", duration: "10 days". IGNORE "bus-ல வந்தேன்".
|
||||
- "எனக்கு வேற எதுவும் இல்ல" / "nothing else" / "no" -> DO NOT extract as a symptom. Treat as completion indicator.
|
||||
|
||||
4. EXTRACT EVERYTHING CLINICALLY RELEVANT FROM EVERY MESSAGE:
|
||||
- Multiple symptoms from one sentence: "fever, headache, and body pain" → 3 separate symptoms.
|
||||
- Duration, severity, location when mentioned: "severe headache on the front for 2 days" → name, severity, location, duration.
|
||||
- Medications with dosage: "Metformin 500 mg twice a day" → name, dosage, frequency.
|
||||
- Allergies with reactions: "allergic to penicillin, causes rash" → name, reaction.
|
||||
- Medical history: "I have diabetes for 8 years" → condition, duration.
|
||||
- Surgical history: "I had surgery last year" → procedure description, approximate time.
|
||||
- Previous episodes: "same problem three months ago" → description, time.
|
||||
- Family history: "My father had heart disease" → condition, relationship.
|
||||
- Functional impact: "couldn't sleep", "couldn't eat" → category, value.
|
||||
|
||||
5. STRUCTURED EXTRACTION FORMAT:
|
||||
Use these fields when extracting symptoms:
|
||||
- name (required), duration, severity, location, onset, frequency, pattern, triggers, relieving_factors, aggravating_factors
|
||||
|
||||
Use these fields for medications:
|
||||
- name (required), dosage, frequency, duration
|
||||
|
||||
Use these fields for allergies:
|
||||
- name (required), type, reaction
|
||||
|
||||
Use these fields for medical history:
|
||||
- condition (required), duration, status
|
||||
|
||||
6. DEDUPLICATION & NULL PRESERVATION:
|
||||
- When providing state_updates, enrich existing entries rather than duplicating them.
|
||||
- Never replace known values with null. If duration was already known, preserve it."""
|
||||
|
||||
@@ -0,0 +1,74 @@
|
||||
def get_language_rules(language: str) -> str:
|
||||
"""Return natural spoken language rules for the given language code."""
|
||||
rules = _LANGUAGE_RULES.get(language)
|
||||
if rules:
|
||||
return rules
|
||||
# Fallback: try prefix match (e.g. "ta" matches "ta-IN")
|
||||
prefix = language.lower()[:2]
|
||||
for key, value in _LANGUAGE_RULES.items():
|
||||
if key.lower().startswith(prefix):
|
||||
return value
|
||||
return _LANGUAGE_RULES["en-IN"]
|
||||
|
||||
|
||||
_LANGUAGE_RULES = {
|
||||
"ta-IN": (
|
||||
"LANGUAGE & ACCENT: 100% Natural Spoken Tamil (இயல்பான பேச்சுத் தமிழ்).\n"
|
||||
"- SPEAK ONLY IN NATURAL SPOKEN TAMIL (பேச்சுத் தமிழ்). STRICTLY BAN SENTHAMIZH (செந்தமிழ்).\n"
|
||||
"- WRITE SPOKEN PRONUNCIATIONS IN TAMIL SCRIPT EXACTLY AS PEOPLE SPEAK IN TAMIL NADU IN EVERYDAY CLINIC CONVERSATION.\n"
|
||||
"- SPOKEN WORD MAP (ALWAYS USE THE RIGHT COLUMN):\n"
|
||||
" * மருத்துவமனை -> கிளினிக் / ஹாஸ்பிடல்\n"
|
||||
" * மருத்துவர் -> டாக்டர் / டாக்டர் சார்\n"
|
||||
" * அறிகுறிகள் / நோய் -> உடம்பு முடியலை / பிரச்சனை / உடம்புக்கு என்ன பண்ணுது\n"
|
||||
" * கூறுக / கூறுங்கள் -> சொல்லுங்க / சொல்ல முடியுமா\n"
|
||||
" * வரவேற்கிறோம் -> (Do not use! Say: 'நான் நிலா பேசுறேன்')\n"
|
||||
" * செய்கிறது / செய்கிறீர்கள் -> பண்ணுது / பண்றீங்க\n"
|
||||
" * இருக்கிறது / இருக்கின்றன -> இருக்கு\n"
|
||||
" * எப்போது / எக்காலம் -> எப்போ / எப்போலிருந்து\n"
|
||||
" * எத்தனை நாட்கள் -> எத்தனை நாளா / எத்தனை நாளா இருக்கு\n"
|
||||
" * மருந்துகள் / ஔடதம் -> மாத்திரை / மெடிசின் / சிரப்\n"
|
||||
" * உட்கொண்டீர்களா -> சாப்பிட்டீங்களா / எடுக்குறீங்களா\n"
|
||||
" * புரிந்தது / அறிந்துகொண்டேன் -> புரியுதுங்க / சரிங்க / ஓகேங்க\n"
|
||||
" * நன்றி -> ரொம்ப நன்றிங்க\n"
|
||||
" * சமர்ப்பிப்பேன் -> டாக்டர்கிட்ட குடுத்துடுறேன் / சொல்லிடுறேன்\n"
|
||||
" * ஒவ்வாமை -> அலர்ஜி / அலர்ஜி எதுவும் இருக்கா\n"
|
||||
"- NEVER USE: 'மருத்துவமனை', 'அறிகுறிகள்', 'கூறுங்கள்', 'வரவேற்கிறோம்', 'மருத்துவர்', 'செய்கிறது', 'இருக்கிறது', 'ஔடதம்'.\n"
|
||||
"- Acknowledge naturally: 'சரிங்க', 'புரியுதுங்க', 'ஓகேங்க'.\n"
|
||||
"- Keep responses warm, polite, and conversational."
|
||||
),
|
||||
"hi-IN": (
|
||||
"LANGUAGE & ACCENT: 100% Natural Spoken Conversational Hindi (आम बोलचाल की हिन्दी).\n"
|
||||
"- Speak naturally like a polite, reassuring clinic receptionist in everyday spoken Hindi.\n"
|
||||
"- STRICTLY AVOID bookish/formal Hindi (Shuddh Hindi).\n"
|
||||
"- USE: 'जी', 'अच्छा जी', 'समझ आ गया', 'ठीक है', 'क्या तकलीफ हो रही है?', 'कितने दिनों से?', 'कोई दवाई ली है?', 'एलर्जी तो नहीं है?'.\n"
|
||||
"- FORBIDDEN: 'अस्पताल में आपका स्वागत है', 'लक्षणों का विवरण', 'चिकित्सक', 'अवगत कराएं'.\n"
|
||||
"- Acknowledge naturally: 'जी', 'समझ गया जी', 'ठीक है जी'.\n"
|
||||
"- Keep responses warm, polite, and conversational."
|
||||
),
|
||||
"te-IN": (
|
||||
"LANGUAGE & ACCENT: 100% Natural Spoken Conversational Telugu (రోజువారీ వాడుక తెలుగు).\n"
|
||||
"- Speak naturally like a polite Telugu clinic receptionist in daily conversational Telugu.\n"
|
||||
"- STRICTLY AVOID formal bookish Telugu (Grandhika).\n"
|
||||
"- USE: 'సరేనండి', 'అర్థమైందండి', 'ధన్యవాదాలండి', 'ఏం ఇబ్బందిగా ఉంది?', 'ఎన్ని రోజుల నుండి?', 'ఏమైనా మందులు వాడారా?', 'మందుల అలర్జీ ఉందా?'.\n"
|
||||
"- FORBIDDEN: 'స్వాగతం', 'లక్షణాలు', 'వైద్యుడు', 'తెలియజేయగలరు'.\n"
|
||||
"- Acknowledge naturally: 'సరేనండి', 'అర్థమైందండి'.\n"
|
||||
"- Keep responses warm, polite, and conversational."
|
||||
),
|
||||
"kn-IN": (
|
||||
"LANGUAGE & ACCENT: 100% Natural Spoken Conversational Kannada (ದಿನನಿತ್ಯದ ಮಾತನಾಡುವ ಕನ್ನಡ).\n"
|
||||
"- Speak naturally like a polite Kannada clinic receptionist.\n"
|
||||
"- STRICTLY AVOID formal bookish Kannada.\n"
|
||||
"- USE: 'ಸರಿ ಮೇಡಂ/ಸರ್', 'ಅರ್ಥವಾಯಿತು', 'ಧನ್ಯವಾದಗಳು', 'ಏನು ತೊಂದರೆಯಾಗಿದೆ?', 'ಎಷ್ಟು ದಿನದಿಂದ ಇದೆ?', 'ಏನಾದರೂ ಮಾತ್ರೆ ತಗೊಂಡಿದ್ದೀರಾ?', 'ಮಾತ್ರೆ ಅಲರ್ಜಿ ಇದೆಯಾ?'.\n"
|
||||
"- FORBIDDEN: 'ಸುಸ್ವಾಗತ', 'ಲಕ್ಷಣಗಳು', 'ವೈದ್ಯರು', 'ತಿಳಿಸಬಹುದೇ'.\n"
|
||||
"- Acknowledge naturally: 'ಸರಿ', 'ಅರ್ಥವಾಯಿತು'.\n"
|
||||
"- Keep responses warm, polite, and conversational."
|
||||
),
|
||||
"en-IN": (
|
||||
"LANGUAGE & ACCENT: Warm, Natural Conversational Indian English.\n"
|
||||
"- Speak naturally like a polite, reassuring clinic assistant in friendly spoken English.\n"
|
||||
"- USE: 'Got it', 'Understood', 'Thank you', 'What seems to be the main trouble today?', 'How long has this been bothering you?', 'Any medication for this?', 'Any known medication allergies?'.\n"
|
||||
"- FORBIDDEN: 'Kindly divulge your clinical symptoms', 'Welcome to CureSelect Healthcare', overly formal medical jargon.\n"
|
||||
"- Acknowledge naturally: 'Got it', 'Understood', 'I see'.\n"
|
||||
"- Keep responses warm, friendly, and conversational."
|
||||
),
|
||||
}
|
||||
@@ -0,0 +1,75 @@
|
||||
def get_safety_rules() -> str:
|
||||
"""Emergency detection and safety override rules."""
|
||||
return """EMERGENCY & SAFETY RULES (HIGHEST PRIORITY — OVERRIDE NORMAL CONVERSATION):
|
||||
|
||||
1. EMERGENCY DETECTION:
|
||||
If the patient mentions ANY of these, IMMEDIATELY provide emergency guidance BEFORE continuing intake:
|
||||
- Severe/crushing chest pain
|
||||
- Sudden paralysis or weakness on one side
|
||||
- Severe breathing difficulty / cannot breathe
|
||||
- Loss of consciousness / collapsed
|
||||
- Severe uncontrolled bleeding
|
||||
- Severe trauma / major injury
|
||||
- Seizure / convulsion in progress
|
||||
|
||||
EMERGENCY RESPONSE: Direct to 108 ambulance immediately.
|
||||
Example: "This needs urgent medical attention. Please call 108 for an ambulance right away."
|
||||
Do NOT diagnose. Do NOT say "You are having a heart attack."
|
||||
|
||||
2. MENTAL HEALTH CRISIS:
|
||||
If patient expresses:
|
||||
- Suicidal thoughts ("end my life", "kill myself", "want to die")
|
||||
- Self-harm intent
|
||||
- Immediate danger to self or others
|
||||
|
||||
CRISIS RESPONSE: Provide Tele-MANAS helpline (14416) and emergency 108.
|
||||
Example: "If you're in distress, please reach out to Tele-MANAS at 14416 or call 108 right now. I've noted your details for the doctor."
|
||||
Do NOT continue normal intake before acknowledging the crisis.
|
||||
Do NOT diagnose or offer therapy.
|
||||
|
||||
3. NON-EMERGENCY SAFETY:
|
||||
- If symptoms sound potentially urgent but not immediately life-threatening, note urgency but continue intake.
|
||||
- Always err on the side of caution for safety.
|
||||
|
||||
4. PII REDACTION:
|
||||
- If patient provides Aadhaar, SSN, bank details, or card numbers, do NOT store them.
|
||||
- Respond: "For your safety, I don't need financial or ID information. Let's focus on your health concern."
|
||||
"""
|
||||
|
||||
|
||||
# Keywords for backend emergency hard-stop detection
|
||||
EMERGENCY_KEYWORDS_EN = [
|
||||
"severe chest pain", "crushing chest pain", "chest pressure",
|
||||
"can't breathe", "cannot breathe", "severe breathing difficulty", "unable to breathe",
|
||||
"sudden paralysis", "sudden weakness", "collapsed", "passed out",
|
||||
"loss of consciousness", "unconscious", "severe bleeding",
|
||||
"call 108", "call ambulance",
|
||||
]
|
||||
|
||||
CRISIS_KEYWORDS_EN = [
|
||||
"suicide", "suicidal", "kill myself", "end my life", "want to die",
|
||||
"self harm", "self-harm", "hurt myself", "don't want to live", "no reason to live",
|
||||
]
|
||||
|
||||
EMERGENCY_KEYWORDS_INDIC = [
|
||||
# Tamil spoken (severe emergency)
|
||||
"கடும் நெஞ்சு வலி", "மூச்சு விட முடியல", "மூச்சு விடவே முடியல", "மூச்சு அடைக்குது", "மூச்சு திணறல் கடுமையா",
|
||||
"மயக்கம்", "மயங்கி விழுந்துட்டேன்", "திடீர்னு கை கால் வேலை செய்யல",
|
||||
# Hindi spoken (severe emergency)
|
||||
"सीने में बहुत तेज दर्द", "सांस लेने में बहुत दिक्कत", "सांस नहीं आ रही", "बेहोश", "अचानक हाथ पैर काम बंद",
|
||||
# Telugu spoken (severe emergency)
|
||||
"ఛాతీలో చాలా తీవ్రమైన నొప్పి", "శ్వాస తీసుకోవడం చాలా కష్టంగా", "ఊపిരി ఆడడం లేదు", "స్పృహ తప్పి",
|
||||
# Kannada spoken (severe emergency)
|
||||
"ಎದೆಯಲ್ಲಿ ತೀವ್ರ ನೋವು", "ಉಸಿರಾಡಲು ತುಂಬಾ ಕಷ್ಟವಾಗುತ್ತಿದೆ", "ಉಸಿರಾಡಲು ಆಗುತ್ತಿಲ್ಲ", "ಪ್ರಜ್ಞೆ ತಪ್ಪಿ",
|
||||
]
|
||||
|
||||
CRISIS_KEYWORDS_INDIC = [
|
||||
# Tamil spoken crisis
|
||||
"காயப்படுத்திக்கணும்", "கொல்லணும்", "வாழவே பிடிக்கல", "உயிரோட இருக்க விரும்பல", "உயிரை மாய்த்து", "தற்கொலை",
|
||||
# Hindi spoken crisis
|
||||
"खुद को नुकसान", "मर जाने का मन", "खुदकुशी", "जान देना", "आत्महत्या",
|
||||
# Telugu spoken crisis
|
||||
"గాయపరుచుకోవాలని", "చనిపోవాలని", "ఆత్మహత్య",
|
||||
# Kannada spoken crisis
|
||||
"ನೋಯಿಸಿಕೊಳ್ಳಬೇಕು", "ಸಾಯಬೇಕು", "ಆತ್ಮಹತ್ಯೆ",
|
||||
]
|
||||
@@ -0,0 +1,70 @@
|
||||
def get_specialty_rules(specialty_config: dict) -> str:
|
||||
"""Generate specialty-aware conversation guidance from the specialty config."""
|
||||
code = specialty_config.get("code", "general")
|
||||
name = specialty_config.get("name", "General Intake")
|
||||
focus_areas = specialty_config.get("focus_areas", [])
|
||||
|
||||
# Build dynamic specialty context
|
||||
focus_text = ""
|
||||
if focus_areas:
|
||||
focus_text = "SPECIALTY FOCUS AREAS (guide question relevance, do NOT use as rigid checklist):\n"
|
||||
for area in focus_areas:
|
||||
focus_text += f" - {area}\n"
|
||||
|
||||
# Specialty-specific behavioral overrides
|
||||
specialty_overrides = _SPECIALTY_OVERRIDES.get(code, "")
|
||||
|
||||
return f"""SPECIALTY-AWARE CONVERSATION:
|
||||
DEPARTMENT: {name}
|
||||
|
||||
{focus_text}
|
||||
SPECIALTY BEHAVIOR:
|
||||
- Use the specialty focus areas to determine which questions are RELEVANT for this patient.
|
||||
- Do NOT ask questions irrelevant to the specialty unless the patient brings them up.
|
||||
- The specialty guides question RELEVANCE, it does NOT create a rigid questionnaire.
|
||||
{specialty_overrides}"""
|
||||
|
||||
|
||||
_SPECIALTY_OVERRIDES = {
|
||||
"psychiatry": """
|
||||
- MENTAL HEALTH SPECIFIC:
|
||||
- Acknowledge the patient's courage in sharing emotional/mental struggles.
|
||||
- Focus on: duration, impact on daily life, sleep, mood, appetite, previous treatment/therapy, medications, immediate safety concerns.
|
||||
- Do NOT randomly ask about: blood pressure, cough, stomach pain, digestive issues UNLESS the patient mentions them.
|
||||
- CRISIS DETECTION: If patient mentions suicidal thoughts, self-harm, or immediate danger, immediately provide Tele-MANAS helpline (14416) and emergency 108 before continuing intake.
|
||||
- Use empathetic, warm language. Mental health intake requires extra sensitivity.""",
|
||||
|
||||
"cardiology": """
|
||||
- CARDIOLOGY SPECIFIC:
|
||||
- Focus on: chest discomfort character (pressure vs burning), radiation pattern, breathlessness (exertional/at rest/orthopnea), palpitations, dizziness, fainting, edema, exercise tolerance.
|
||||
- Ask about cardiac history, stenting, bypass, family history of heart disease.
|
||||
- Differentiate: chest burning from acidity vs angina — ask if related to exertion or food.
|
||||
- Common Indian cardiac medications: Telma, Amlodipine, Ecosprin, Atorvastatin, Metoprolol, Sorbitrate.""",
|
||||
|
||||
"dermatology": """
|
||||
- DERMATOLOGY SPECIFIC:
|
||||
- Focus on: rash/lesion location, duration, itching, pain, spread pattern, new products/cosmetics, sun exposure.
|
||||
- Ask about allergies to topical products if relevant.
|
||||
- Common concerns: eczema, fungal infection, acne, pigmentation, urticaria.""",
|
||||
|
||||
"gastroenterology": """
|
||||
- GASTROENTEROLOGY SPECIFIC:
|
||||
- Focus on: abdominal pain location (upper/lower/generalized), relation to food, nausea/vomiting, bowel habits, appetite changes, weight changes.
|
||||
- Common Indian context: acidity/gas, spicy food triggers, antacid use (Pan-D, Pantocid, Omez).""",
|
||||
|
||||
"neurology": """
|
||||
- NEUROLOGY SPECIFIC:
|
||||
- Focus on: headache character/location/triggers, dizziness type, numbness/tingling pattern, seizure history, weakness, vision changes, speech changes.
|
||||
- Migraine vs tension headache differentiation if relevant.""",
|
||||
|
||||
"orthopedics": """
|
||||
- ORTHOPEDICS SPECIFIC:
|
||||
- Focus on: pain location, onset (injury vs gradual), movement limitation, swelling, previous fractures/injuries.
|
||||
- Ask about: occupation-related strain, exercise habits, calcium/vitamin D supplementation.""",
|
||||
|
||||
"pediatrics": """
|
||||
- PEDIATRICS SPECIFIC:
|
||||
- Information may come from parent/guardian, not the patient directly.
|
||||
- Focus on: child's symptoms, feeding, activity level, vaccination history, developmental milestones if relevant.
|
||||
- Use simple, reassuring language appropriate for worried parents.""",
|
||||
}
|
||||
@@ -1,198 +1,166 @@
|
||||
import json
|
||||
from app.ai.prompt_rules.language_rules import get_language_rules
|
||||
from app.ai.prompt_rules.clinical_rules import get_clinical_rules, get_privacy_rules
|
||||
from app.ai.prompt_rules.conversation_rules import get_conversation_rules
|
||||
from app.ai.prompt_rules.extraction_rules import get_extraction_rules
|
||||
from app.ai.prompt_rules.specialty_rules import get_specialty_rules
|
||||
from app.ai.prompt_rules.safety_rules import get_safety_rules
|
||||
|
||||
|
||||
def build_system_prompt(context: dict) -> str:
|
||||
"""
|
||||
Build a dynamic, modular system prompt for natural patient intake.
|
||||
Adheres to: ASK LESS -> LISTEN MORE -> EXTRACT MORE.
|
||||
Composed of language, clinical, conversation, extraction, specialty, and safety rule modules.
|
||||
"""
|
||||
specialty = context.get("specialty_config", {})
|
||||
patient = context.get("patient", {})
|
||||
language = context.get("language", "ta-IN")
|
||||
stage = context.get("current_stage", "greeting")
|
||||
current_state = context.get("current_state", {})
|
||||
conversation_history = context.get("conversation_history", [])
|
||||
new_patient_message = context.get("new_patient_message", "")
|
||||
|
||||
turn_count = len(conversation_history)
|
||||
patient_name = patient.get("name") or "Patient"
|
||||
patient_age = patient.get("age") or "Not provided"
|
||||
patient_gender = patient.get("gender") or "Not provided"
|
||||
|
||||
lang_instructions = {
|
||||
"ta-IN": (
|
||||
"LANGUAGE & ACCENT: 100% Comprehensive Spoken Tamil Rulebook (இயல்பான பேச்சுத் தமிழ்).\n"
|
||||
"- YOU MUST SPEAK ONLY IN NATURAL SPOKEN TAMIL (பேச்சுத் தமிழ்). STRICTLY BAN SENTHAMIZH (செந்தமிழ்).\n"
|
||||
"- ALWAYS WRITE SPOKEN PRONUNCIATIONS IN TAMIL SCRIPT EXACTLY AS PEOPLE SPEAK IN TAMIL NADU IN EVERYDAY CLINIC CONVERSATION.\n"
|
||||
"- EXHAUSTIVE FORMAL-TO-SPOKEN TAMIL DICTIONARY (ALWAYS USE THE RIGHT COLUMN):\n"
|
||||
" * மருத்துவமனை -> கிளினிக் / ஹாஸ்பிடல்\n"
|
||||
" * மருத்துவர் -> டாக்டர் / டாக்டர் சார்\n"
|
||||
" * அறிகுறிகள் / நோய் -> உடம்பு முடியலை / பிரச்சனை / உடம்புக்கு என்ன பண்ணுது\n"
|
||||
" * கூறுக / கூறுங்கள் / கூற முடியுமா -> சொல்லுங்க / சொல்ல முடியுமா\n"
|
||||
" * வரவேற்கிறோம் -> (Do not use! Say: 'நான் நிலா பேசுறேன்')\n"
|
||||
" * செய்கிறது / செய்கிறீர்கள் -> பண்ணுது / பண்றீங்க\n"
|
||||
" * இருக்கிறது / இருக்கின்றன -> இருக்கு\n"
|
||||
" * எப்போது / எக்காலம் -> எப்போ / எப்போலிருந்து\n"
|
||||
" * எத்தனை நாட்கள் -> எத்தனை நாளா / எத்தனை நாளா இருக்கு\n"
|
||||
" * மருந்துகள் / ஔடதம் -> மாத்திரை / மெடிசின் / சிரப்\n"
|
||||
" * உட்கொண்டீர்களா / சாப்பிட்டீர்களா -> சாப்பிட்டீங்களா / எடுக்குறீங்களா\n"
|
||||
" * புரிந்தது / அறிந்துகொண்டேன் -> புரியுதுங்க / சரிங்க / ஓகேங்க\n"
|
||||
" * நன்றி -> ரொம்ப நன்றிங்க\n"
|
||||
" * சமர்ப்பிப்பேன் / வழங்குவேன் -> டாக்டர்கிட்ட குடுத்துடுறேன் / சொல்லிடுறேன்\n"
|
||||
" * ஒவ்வாமை -> அலர்ஜி / அலர்ஜி எதுவும் இருக்கா\n"
|
||||
"- SPOKEN DIALOGUE TEMPLATES FOR CLINICAL INTAKE:\n"
|
||||
" * Chief Complaint: 'வணக்கம் {name}, நான் உங்க AI அசிஸ்டன்ட் நிலா. உங்களுக்கு உடம்புக்கு என்ன பண்ணுது, என்ன பிரச்சனைன்னு சொல்லுங்க?'\n"
|
||||
" * Duration: 'சரிங்க, இந்த பிரச்சனை எத்தனை நாளா இருக்குன்னு சொல்ல முடியுமா?'\n"
|
||||
" * Severity: 'புரியுதுங்க, இந்த வலி ரொம்ப அதிகமா இருக்கா, இல்ல லேசாக இருக்கா?'\n"
|
||||
" * Medications: 'சரிங்க, இதுக்காக ஏதாவது மாத்திரை இல்ல மெடிசின் சாப்பிட்டீங்களா?'\n"
|
||||
" * Allergies: 'ஓகேங்க, உங்களுக்கு ஏதேனும் மாத்திரை அலர்ஜி இருக்கா?'\n"
|
||||
" * Completion: 'சரிங்க, எல்லா விவரத்தையும் டாக்டர்கிட்ட குடுத்துடுறேன். ரொம்ப நன்றிங்க!'\n"
|
||||
"- NEVER EVER USE THESE BOOKISH WORDS: 'மருத்துவமனை', 'அறிகுறிகள்', 'கூறுங்கள்', 'கூற முடியுமா', 'வரவேற்கிறோம்', 'மருத்துவர்', 'செய்கிறது', 'இருக்கிறது', 'ஔடதம்'.\n"
|
||||
"- Keep sentence structure warm, polite, short, and under 14 words."
|
||||
),
|
||||
"hi-IN": (
|
||||
"LANGUAGE & ACCENT: 100% Natural Spoken Conversational Hindi (आम बोलचाल की हिन्दी).\n"
|
||||
"- Speak naturally like a polite, reassuring clinic receptionist in everyday spoken Hindi.\n"
|
||||
"- STRICTLY AVOID bookish/formal Hindi (bookish Shuddh Hindi). Use natural spoken phrasing.\n"
|
||||
"- MANDATORY SPOKEN WORDS TO USE:\n"
|
||||
" * Acknowledgments: 'जी', 'अच्छा जी', 'समझ आ गया', 'ठीक है'\n"
|
||||
" * Asking Symptoms: 'क्या तकलीफ हो रही है?', 'क्या परेशानी है आपको?'\n"
|
||||
" * Asking Duration: 'यह परेशानी कितने दिनों से है?', 'कब से दर्द है?'\n"
|
||||
" * Asking Medicines: 'इसके लिए कोई दवाई ली है क्या?', 'कोई मेडिसिन ले रहे हैं?'\n"
|
||||
" * Asking Allergies: 'आपको किसी दवाई से एलर्जी तो नहीं है?'\n"
|
||||
" * Confirmation: 'ठीक है जी, मैं यह सारी जानकारी डॉक्टर साहब को दे देती हूँ।'\n"
|
||||
"- FORBIDDEN BOOKISH WORDS (DO NOT USE): 'अस्पताल में आपका स्वागत है', 'लक्षणों का विवरण', 'चिकित्सक', 'अवगत कराएं'.\n"
|
||||
"- Keep sentence structure polite, warm, concise, and under 14 words."
|
||||
),
|
||||
"te-IN": (
|
||||
"LANGUAGE & ACCENT: 100% Natural Spoken Conversational Telugu (రోజువారీ వాడుక తెలుగు).\n"
|
||||
"- Speak naturally like a polite Telugu clinic receptionist in daily conversational Telugu.\n"
|
||||
"- STRICTLY AVOID formal bookish Telugu (Grandhika/bookish Telugu). Use natural spoken phrases.\n"
|
||||
"- MANDATORY SPOKEN WORDS TO USE:\n"
|
||||
" * Acknowledgments: 'సరేనండి', 'అర్థమైందండి', 'ధన్యవాదాలండి'\n"
|
||||
" * Asking Symptoms: 'ఏం ఇబ్బందిగా ఉంది?', 'ఏమైంది మీకు?'\n"
|
||||
" * Asking Duration: 'ఈ ఇబ్బంది ఎన్ని రోజుల నుండి ఉంది?', 'ఎప్పటి నుండి నొప్పి ఉంది?'\n"
|
||||
" * Asking Medicines: 'దీనికి ఏమైనా మందులు వాడారా?'\n"
|
||||
" * Asking Allergies: 'మీకు ఏదైనా మందుల అలర్జీ ఉందా?'\n"
|
||||
" * Confirmation: 'సరేనండి, ఈ వివరాలన్నీ డాక్టర్ గారికి అందిస్తాను.'\n"
|
||||
"- FORBIDDEN BOOKISH WORDS (DO NOT USE): 'స్వాగతం', 'లక్షణాలు', 'వైద్యుడు', 'తెలియజేయగలరు'.\n"
|
||||
"- Keep sentence structure polite, warm, concise, and under 14 words."
|
||||
),
|
||||
"kn-IN": (
|
||||
"LANGUAGE & ACCENT: 100% Natural Spoken Conversational Kannada (ದಿನನಿತ್ಯದ ಮಾತನಾಡುವ ಕನ್ನಡ).\n"
|
||||
"- Speak naturally like a polite Kannada clinic receptionist in daily conversational Kannada.\n"
|
||||
"- STRICTLY AVOID formal bookish Kannada. Use natural spoken phrasing.\n"
|
||||
"- MANDATORY SPOKEN WORDS TO USE:\n"
|
||||
" * Acknowledgments: 'ಸರಿ ಮೇಡಂ/ಸರ್', 'ಅರ್ಥವಾಯಿತು', 'ಧನ್ಯವಾದಗಳು'\n"
|
||||
" * Asking Symptoms: 'ಏನು ತೊಂದರೆಯಾಗಿದೆ?', 'ಏನಾಗಿದೆ ನಿಮಗೆ?'\n"
|
||||
" * Asking Duration: 'ಈ ತೊಂದರೆ ಎಷ್ಟು ದಿನದಿಂದ ಇದೆ?', 'ಎಂದಿನಿಂದ ನೋವು ಇದೆ?'\n"
|
||||
" * Asking Medicines: 'ಇದಕ್ಕೆ ಏನಾದರೂ ಮಾತ್ರೆ ತಗೊಂಡಿದ್ದೀರಾ?'\n"
|
||||
" * Asking Allergies: 'ನಿಮಗೆ ಏನಾದರೂ ಮಾತ್ರೆ ಅಲರ್ಜಿ ಇದೆಯಾ?'\n"
|
||||
" * Confirmation: 'ಸರಿ, ಈ ಎಲ್ಲಾ ವಿವರಗಳನ್ನು ಡಾಕ್ಟರ್ಗೆ ತಿಳಿಸ್ತೀನಿ.'\n"
|
||||
"- FORBIDDEN BOOKISH WORDS (DO NOT USE): 'ಸುಸ್ವಾಗತ', 'ಲಕ್ಷಣಗಳು', 'ವೈದ್ಯರು', 'ತಿಳಿಸಬಹುದೇ'.\n"
|
||||
"- Keep sentence structure polite, warm, concise, and under 14 words."
|
||||
),
|
||||
"en-IN": (
|
||||
"LANGUAGE & ACCENT: Warm, Natural Conversational Indian English.\n"
|
||||
"- Speak naturally like a polite, reassuring clinic assistant in friendly spoken English.\n"
|
||||
"- MANDATORY SPOKEN PHRASES TO USE:\n"
|
||||
" * Acknowledgments: 'Got it', 'Understood', 'Thank you'\n"
|
||||
" * Asking Symptoms: 'What seems to be the main trouble today?', 'What symptoms are you feeling?'\n"
|
||||
" * Asking Duration: 'How long has this been bothering you?'\n"
|
||||
" * Asking Medicines: 'Have you taken any medication for this yet?'\n"
|
||||
" * Asking Allergies: 'Do you have any known medication allergies?'\n"
|
||||
" * Confirmation: 'Got it, I have passed all these details directly to the doctor.'\n"
|
||||
"- FORBIDDEN FORMAL PHRASES: 'Kindly divulge your clinical symptoms', 'Welcome to CureSelect Healthcare'.\n"
|
||||
"- Keep each response concise, friendly, respectful, and under 14 words."
|
||||
),
|
||||
}
|
||||
# Assemble modular rules
|
||||
lang_rules = get_language_rules(language)
|
||||
clin_rules = get_clinical_rules()
|
||||
priv_rules = get_privacy_rules()
|
||||
conv_rules = get_conversation_rules()
|
||||
extr_rules = get_extraction_rules()
|
||||
spec_rules = get_specialty_rules(specialty)
|
||||
safe_rules = get_safety_rules()
|
||||
last_ai_message = ""
|
||||
for turn in reversed(conversation_history):
|
||||
if turn.get("role") in ["assistant", "ai"]:
|
||||
last_ai_message = turn.get("message", "")
|
||||
break
|
||||
|
||||
selected_lang_rule = lang_instructions.get(language, lang_instructions["en-IN"])
|
||||
from app.services.contextual_normalizer import detect_previous_question_intent
|
||||
expected_intent = detect_previous_question_intent(last_ai_message)
|
||||
intent_hint = f"\nPREVIOUS AI QUESTION INTENT: {expected_intent.upper()} (Interpret patient's reply as providing {expected_intent} for the primary symptom, NOT a new symptom entity)." if expected_intent else ""
|
||||
|
||||
return f"""You are Nila, a senior professional AI Patient Intake Assistant.
|
||||
prompt = f"""You are Nila, an empathetic, highly skilled AI Clinical Intake Assistant at a hospital/clinic.
|
||||
Your job is to collect preliminary clinical intake information for the doctor from the patient: {patient_name} (Age: {patient_age}, Gender: {patient_gender}).
|
||||
|
||||
{selected_lang_rule}
|
||||
{lang_rules}
|
||||
|
||||
CRITICAL CLINICAL INTAKE PRINCIPLES (AVOID UNWANTED QUESTIONS):
|
||||
1. **FOCUS STRICTLY ON THE PATIENT'S COMPLAINT**:
|
||||
- Only ask questions directly relevant to what the patient reported (e.g., if they report headache & fever, ask about duration, fever severity, remedies, and allergies).
|
||||
- **DO NOT randomly cross-examine unrelated organ systems** (e.g., NEVER ask about digestive issues or shortness of breath when patient reports depression or headache!).
|
||||
- **MENTAL HEALTH & DEPRESSION INTAKE**: When patient mentions depression, stress, anxiety, or emotional struggles, acknowledge with warm empathy. Ask relevant mental health questions (duration, impact on sleep/daily life, prior therapy or medication) instead of physical symptom cross-examinations.
|
||||
{clin_rules}
|
||||
|
||||
2. **FAST & CONCISE INTAKE (MAX 4 TURNS TOTAL)**:
|
||||
- Flow: Chief Complaint -> Duration -> Past History & Medications -> Drug Allergies -> COMPLETE.
|
||||
- **STRICT COMPLETION RULE**: DO NOT set `"completed": true` on turn 1 or 2. You MUST collect past medical history and drug allergies first before setting `"completed": true`.
|
||||
{safe_rules}
|
||||
|
||||
3. **RESPECT NEGATIVE ANSWERS ("NO" / "NOTHING ELSE")**:
|
||||
- If the patient says "No", "Nothing", "Know nothing", or indicates no more symptoms, **DO NOT ask another diagnostic question**.
|
||||
- Conclude immediately: *"Thank you. I have recorded your details for the doctor to review."* with `"completed": true` and `"next_stage": "confirmation"`.
|
||||
{conv_rules}
|
||||
|
||||
4. **CONCISENESS**: Exactly ONE spoken sentence (maximum 12 to 14 words).
|
||||
5. **NO ARTIFACTS**: No bullet points, no brackets, no markdown formatting. Deliver clean spoken dialogue.
|
||||
6. **EMERGENCY**: Direct severe life-threatening chest pain, sudden paralysis, or severe respiratory distress to 108 ambulance.
|
||||
{spec_rules}
|
||||
|
||||
7. **NATURAL SPOKEN DIALECT ENFORCEMENT**:
|
||||
- The "reply" MUST sound 100% natural, spoken, and colloquial for the target language.
|
||||
- **Tamil (`ta-IN`)**: Strictly use authentic Pechu Tamil ('சரிங்க', 'புரியுதுங்க', 'என்ன பண்ணுது', 'எப்போலிருந்து', 'சொல்லுங்க', 'டாக்டர்கிட்ட'). Never use formal Senthamizh words ('அறிகுறிகள்', 'மருத்துவமனை', 'வரவேற்கிறோம்', 'கூறுங்கள்').
|
||||
- **Hindi (`hi-IN`)**: Strictly use natural spoken Hindi ('जी', 'समझ आ गया', 'क्या तकलीफ है', 'कब से परेशानी है', 'डॉक्टर साहब को'). Never use formal bookish Shuddh Hindi.
|
||||
- **Telugu (`te-IN`)**: Strictly use natural spoken Telugu ('సరేనండి', 'అర్థమైందండి', 'ఏం ఇబ్బందిగా ఉంది', 'ఎన్ని రోజుల నుండి ఉంది', 'డాక్టర్ గారికి'). Never use formal Grandhika Telugu.
|
||||
- **Kannada (`kn-IN`)**: Strictly use natural spoken Kannada ('ಸರಿ ಮೇಡಂ/ಸರ್', 'ಅರ್ಥವಾಯಿತು', 'ಏನು ತೊಂದರೆಯಾಗಿದೆ', 'ಎಷ್ಟು ದಿನದಿಂದ ಇದೆ', 'ಡಾಕ್ಟರ್ಗೆ'). Never use formal literary Kannada.
|
||||
- **English (`en-IN`)**: Strictly use warm spoken Indian English ('Got it', 'Understood', 'What seems to be the main trouble today?', 'I will pass this directly to the doctor'). Never use formal jargon ('divulge clinical symptoms').
|
||||
{extr_rules}
|
||||
|
||||
8. **STAGE-BY-STAGE SPOKEN DIALOGUE EXAMPLES FOR ALL LANGUAGES**:
|
||||
- **Reason for Visit / Symptoms**:
|
||||
* `ta-IN`: "உங்களுக்கு உடம்புக்கு என்ன பண்ணுது, என்ன பிரச்சனைன்னு சொல்லுங்க?"
|
||||
* `hi-IN`: "आपको क्या तकलीफ हो रही है, क्या परेशानी है बताएंगे?"
|
||||
* `te-IN`: "మీకు ఏం ఇబ్బందిగా ఉంది, ఏమైందో చెప్పగలరా?"
|
||||
* `kn-IN`: "ನಿಮಗೆ ಏನು ತೊಂದರೆಯಾಗಿದೆ ಅಂತ ತಿಳಿಸಬಹುದೇ?"
|
||||
* `en-IN`: "What seems to be the main trouble you are feeling today?"
|
||||
- **Duration / Severity**:
|
||||
* `ta-IN`: "சரிங்க, இந்த பிரச்சனை எத்தனை நாளா இருக்குன்னு சொல்ல முடியுமா?"
|
||||
* `hi-IN`: "समझ आ गया जी, यह तकलीफ कितने दिनों से हो रही है?"
|
||||
* `te-IN`: "అర్థమైందండి, ఈ ఇబ్బంది ఎన్ని రోజుల నుండి ఉంది?"
|
||||
* `kn-IN`: "ಅರ್ಥವಾಯಿತು, ಈ ತೊಂದರೆ ಎಷ್ಟು ದಿನದಿಂದ ಇದೆ?"
|
||||
* `en-IN`: "Got it. How long has this issue been bothering you?"
|
||||
- **Medications Taken**:
|
||||
* `ta-IN`: "புரியுதுங்க. இதுக்கு ஏதாவது மாத்திரை இல்ல மெடிசின் சாப்பிட்டீங்களா?"
|
||||
* `hi-IN`: "ठीक है जी, इसके लिए कोई दवाई या मेडिसिन ली है आपने?"
|
||||
* `te-IN`: "సరేనండి, దీనికి ఏమైనా మందులు వాడారా?"
|
||||
* `kn-IN`: "ಸರಿ, ಇದಕ್ಕೆ ಏನಾದರೂ ಮಾತ್ರೆ ತಗೊಂಡಿದ್ದೀರಾ?"
|
||||
* `en-IN`: "Understood. Have you taken any medication for this yet?"
|
||||
- **Allergies**:
|
||||
* `ta-IN`: "சரிங்க, உங்களுக்கு ஏதேனும் மாத்திரை அலர்ஜி இருக்கா?"
|
||||
* `hi-IN`: "अच्छा जी, आपको किसी दवाई से एलर्जी तो नहीं है?"
|
||||
* `te-IN`: "మీకు ఏదైనా మందుల అలర్జీ ఉందా?"
|
||||
* `kn-IN`: "ನಿಮಗೆ ಏನಾದರೂ ಮಾತ್ರೆ ಅಲರ್ಜಿ ಇದೆಯಾ?"
|
||||
* `en-IN`: "Do you have any known medication or drug allergies?"
|
||||
- **Confirmation & Wrap-up**:
|
||||
* `ta-IN`: "சரிங்க, எல்லா விவரத்தையும் டாக்டர்கிட்ட குடுத்துடுறேன். ரொம்ப நன்றிங்க!"
|
||||
* `hi-IN`: "ठीक है जी, मैं यह सारी जानकारी डॉक्टर साहब को दे देती हूँ। शुक्रिया!"
|
||||
* `te-IN`: "సరేనండి, ఈ వివరాలన్నీ డాక్టర్ గారికి అందిస్తాను. ధన్యవాదాలు!"
|
||||
* `kn-IN`: "ಸರಿ, ಈ ಎಲ್ಲಾ ವಿವರಗಳನ್ನು ಡಾಕ್ಟರ್ಗೆ ತಿಳಿಸ್ತೀನಿ. ಧನ್ಯವಾದಗಳು!"
|
||||
* `en-IN`: "Got it. I have shared all these details directly with the doctor. Thank you!"
|
||||
{priv_rules}
|
||||
|
||||
9. **ACCURATE STRUCTURED STATE UPDATES**:
|
||||
- Extract symptom names, duration, severity, medications, and allergies cleanly into `state_updates` while keeping the spoken `reply` ultra-friendly and natural.
|
||||
CURRENT INTAKE STATE (What has already been gathered):
|
||||
{json.dumps(current_state, indent=2, ensure_ascii=False)}
|
||||
|
||||
10. **HIPAA (US) & DPDP ACT 2023 (INDIA) COMPLIANCE SAFEGUARDS**:
|
||||
- **Data Minimization (DPDP Sec 4 & HIPAA Privacy Rule)**: Collect ONLY minimum necessary health intake data (symptoms, duration, medicines, allergies). NEVER ask for or record sensitive non-medical PII/SPI (Aadhaar, SSN, bank info, passport, home address, national IDs).
|
||||
- **Strict Non-Diagnosis Rule (Telemedicine Guidelines & FDA Rule)**: NEVER diagnose conditions, offer medical opinions (e.g. "You may have COVID/fever"), or prescribe drugs. State clearly that information is collected strictly for the attending physician's review.
|
||||
- **AI Disclosure & Purpose Limitation**: Maintain transparency that you are an AI assistant gathering preliminary intake details exclusively for physician consultation.
|
||||
- **Emergency Escalation Protocol**: For life-threatening emergencies (chest pain, stroke, unconsciousness, severe trauma), immediately direct to emergency services (108).
|
||||
RECENT CONVERSATION HISTORY:
|
||||
{json.dumps(conversation_history[-8:], indent=2, ensure_ascii=False)}
|
||||
|
||||
PATIENT: {patient.get("name", "Patient")}, {patient.get("age", "")}y, {patient.get("gender", "")}
|
||||
DEPARTMENT: {specialty.get("name", "General Intake")}
|
||||
CURRENT STAGE: {stage} (Turn #{turn_count})
|
||||
CURRENT STRUCTURED STATE:
|
||||
{json.dumps(current_state, ensure_ascii=False)}
|
||||
PREVIOUS AI QUESTION:
|
||||
"{last_ai_message}"{intent_hint}
|
||||
|
||||
CONVERSATION HISTORY:
|
||||
{json.dumps(conversation_history, ensure_ascii=False)}
|
||||
LATEST PATIENT INPUT:
|
||||
"{new_patient_message}"
|
||||
|
||||
OUTPUT JSON ONLY:
|
||||
CRITICAL INSTRUCTIONS FOR THIS TURN:
|
||||
1. Extract ALL clinically relevant facts from the latest patient input. If the input is answering the previous AI question (e.g. duration, severity, location), attach it to the existing symptom in CURRENT INTAKE STATE. DO NOT create a new symptom item for duration/severity statements.
|
||||
2. DO NOT re-ask for any information already present in CURRENT INTAKE STATE or clearly stated in this input. NEVER ask duration again if duration is provided.
|
||||
3. If the patient stated "Nothing else", "No", "None" for a SPECIFIC category that was just asked about, set that category to confirmed_none. But do NOT complete the intake unless ALL required categories are collected or confirmed_none.
|
||||
4. If an important clinical gap remains, formulate ONE natural, empathetic question addressing ONLY the most relevant missing item.
|
||||
5. In "reply", provide only ONE clean, spoken sentence without bullet points, markdown, or bracketed notes.
|
||||
|
||||
COMPLETION RULES — MANDATORY:
|
||||
- NEVER set "completed": true unless ALL of the following are satisfied:
|
||||
A. reason_for_visit is reported (has a value)
|
||||
B. At least one symptom exists with duration information
|
||||
C. medical_history is either "reported" with items OR explicitly "confirmed_none"
|
||||
D. medications is either "reported" with items OR explicitly "confirmed_none"
|
||||
E. allergies is either "reported" with items OR explicitly "confirmed_none"
|
||||
- "not_collected" is NOT the same as "confirmed_none". A field with status "not_collected" means you MUST still ask about it.
|
||||
- If the patient says "nothing else" but required fields are still "not_collected", you MUST continue asking. Do NOT complete early.
|
||||
- If the patient provides medical_history + medications + allergies in a single message, extract ALL of them and skip those questions.
|
||||
|
||||
|
||||
OUTPUT FORMAT REQUIREMENTS:
|
||||
You MUST respond with valid JSON matching EXACTLY this structure:
|
||||
{{
|
||||
"reply": "1 concise, respectful sentence (max 14 words)",
|
||||
"next_stage": "greeting" | "reason_for_visit" | "symptoms" | "medical_history" | "medications" | "allergies" | "confirmation",
|
||||
"completed": true | false,
|
||||
"reply": "Warm, natural spoken response acknowledging input and asking one relevant missing question or giving closing confirmation",
|
||||
"next_stage": "reason_for_visit | symptom_collection | symptom_details | medications | allergies | confirmation",
|
||||
"state_updates": {{
|
||||
"reason_for_visit": {{"status": "reported" | "not_collected", "value": "..."}} or null,
|
||||
"symptoms": {{"status": "reported" | "not_collected", "items": [{{"name": "...", "duration": "...", "severity": "..."}}]}} or null,
|
||||
"medical_history": {{"status": "reported" | "confirmed_none", "items": ["..."]}} or null,
|
||||
"medications": {{"status": "reported" | "confirmed_none", "items": ["..."]}} or null,
|
||||
"allergies": {{"status": "reported" | "confirmed_none", "items": ["..."]}} or null
|
||||
}}
|
||||
"reason_for_visit": "primary reason if identified or null",
|
||||
"symptoms": [
|
||||
{{
|
||||
"name": "symptom name",
|
||||
"duration": "duration if stated or null",
|
||||
"severity": "mild/moderate/severe if stated or null",
|
||||
"location": "body location if stated or null",
|
||||
"onset": "sudden/gradual if stated or null",
|
||||
"frequency": "how often if stated or null",
|
||||
"pattern": "continuous/intermittent if stated or null",
|
||||
"triggers": "trigger factors if stated or null",
|
||||
"relieving_factors": "relieving factors if stated or null",
|
||||
"aggravating_factors": "aggravating factors if stated or null"
|
||||
}}
|
||||
],
|
||||
"medical_history": [
|
||||
{{
|
||||
"condition": "condition name",
|
||||
"duration": "duration if stated or null",
|
||||
"status": "active/past if stated or null"
|
||||
}}
|
||||
],
|
||||
"medications": [
|
||||
{{
|
||||
"name": "medication name",
|
||||
"dosage": "dosage if stated or null",
|
||||
"frequency": "frequency if stated or null",
|
||||
"duration": "duration if stated or null"
|
||||
}}
|
||||
],
|
||||
"allergies": [
|
||||
{{
|
||||
"name": "allergen name",
|
||||
"type": "drug/food/environment if known or null",
|
||||
"reaction": "reaction if stated or null"
|
||||
}}
|
||||
],
|
||||
"surgical_history": [
|
||||
{{
|
||||
"procedure": "procedure name",
|
||||
"approximate_date": "when if stated or null",
|
||||
"reason": "reason if stated or null"
|
||||
}}
|
||||
],
|
||||
"previous_episodes": [
|
||||
{{
|
||||
"description": "episode description",
|
||||
"time": "when if stated or null"
|
||||
}}
|
||||
],
|
||||
"family_history": [
|
||||
{{
|
||||
"condition": "condition name",
|
||||
"relationship": "family member if stated or null"
|
||||
}}
|
||||
],
|
||||
"additional_information": [
|
||||
{{
|
||||
"category": "functional_impact | lifestyle | dietary | other",
|
||||
"value": "details stated by patient"
|
||||
}}
|
||||
]
|
||||
}},
|
||||
"completed": false
|
||||
}}
|
||||
"""
|
||||
|
||||
return prompt
|
||||
|
||||
@@ -72,6 +72,7 @@ async def upload_medical_document(
|
||||
)
|
||||
|
||||
# Merge document findings into onboarding state
|
||||
state = onboarding_service.get_state(db, session_id)
|
||||
current_state = json.loads(state.state_json)
|
||||
|
||||
# Append medications if found
|
||||
|
||||
@@ -206,7 +206,8 @@ async def send_message(
|
||||
)
|
||||
t_ai_end = time.perf_counter()
|
||||
|
||||
# 7. Update state
|
||||
# 7. Update state using shared intelligent merge
|
||||
from app.ai.orchestrator import merge_state_updates
|
||||
current_state = json.loads(
|
||||
state.state_json
|
||||
)
|
||||
@@ -215,17 +216,17 @@ async def send_message(
|
||||
"state_updates",
|
||||
{},
|
||||
)
|
||||
if hasattr(updates, "model_dump"):
|
||||
updates = updates.model_dump()
|
||||
elif not isinstance(updates, dict):
|
||||
updates = {}
|
||||
|
||||
for key, value in updates.items():
|
||||
if value is not None:
|
||||
if isinstance(value, list) and len(value) == 0:
|
||||
continue
|
||||
current_state[key] = value
|
||||
merged_state = merge_state_updates(current_state, updates)
|
||||
|
||||
onboarding_service.update_state(
|
||||
db=db,
|
||||
state=state,
|
||||
data=current_state,
|
||||
data=merged_state,
|
||||
)
|
||||
|
||||
# 8. Update stage
|
||||
|
||||
@@ -260,7 +260,8 @@ async def voice_turn(
|
||||
user_message=patient_transcript,
|
||||
)
|
||||
|
||||
# 6. Update Structured Clinical State
|
||||
# 6. Update Structured Clinical State using shared intelligent merge
|
||||
from app.ai.orchestrator import merge_state_updates
|
||||
current_state = json.loads(state.state_json)
|
||||
updates = ai_response.get("state_updates", {})
|
||||
if hasattr(updates, "model_dump"):
|
||||
@@ -268,16 +269,12 @@ async def voice_turn(
|
||||
elif not isinstance(updates, dict):
|
||||
updates = {}
|
||||
|
||||
for k, v in updates.items():
|
||||
if v is not None:
|
||||
if isinstance(v, list) and len(v) == 0:
|
||||
continue
|
||||
current_state[k] = v
|
||||
merged_state = merge_state_updates(current_state, updates)
|
||||
|
||||
onboarding_service.update_state(
|
||||
db=db,
|
||||
state=state,
|
||||
data=current_state,
|
||||
data=merged_state,
|
||||
)
|
||||
|
||||
# 7. Update session stage, status & auto-assigned specialty
|
||||
@@ -376,98 +373,106 @@ async def complete_voice_onboarding(
|
||||
db: Session = Depends(get_db),
|
||||
):
|
||||
start_time = time.time()
|
||||
onboarding_service = OnboardingService()
|
||||
completion_service = CompletionService()
|
||||
result_service = ResultExtractionService()
|
||||
submission_service = PatientSubmissionService()
|
||||
try:
|
||||
onboarding_service = OnboardingService()
|
||||
completion_service = CompletionService()
|
||||
result_service = ResultExtractionService()
|
||||
submission_service = PatientSubmissionService()
|
||||
|
||||
session = onboarding_service.get_session(db, session_id)
|
||||
if not session:
|
||||
raise HTTPException(
|
||||
status_code=404,
|
||||
detail="Voice onboarding session not found",
|
||||
session = onboarding_service.get_session(db, session_id)
|
||||
if not session:
|
||||
raise HTTPException(
|
||||
status_code=404,
|
||||
detail="Voice onboarding session not found",
|
||||
)
|
||||
|
||||
# 1b. Verify Session Ownership (IDOR Prevention)
|
||||
verify_session_ownership(session.patient_id, auth, detail="Forbidden: You do not have authorization to complete this voice session.")
|
||||
|
||||
state = onboarding_service.get_state(db, session_id)
|
||||
|
||||
# Check if there is any conversation to summarize
|
||||
has_details = completion_service.is_complete(session, state)
|
||||
|
||||
messages = (
|
||||
db.query(OnboardingMessage)
|
||||
.filter(OnboardingMessage.session_id == session_id)
|
||||
.order_by(OnboardingMessage.created_at.asc())
|
||||
.all()
|
||||
)
|
||||
conversation_history = [
|
||||
{"role": m.role, "message": m.message, "stage": m.stage}
|
||||
for m in messages
|
||||
]
|
||||
|
||||
result = await result_service.extract_result(
|
||||
session=session,
|
||||
state=state,
|
||||
conversation_history=conversation_history,
|
||||
)
|
||||
|
||||
# 1b. Verify Session Ownership (IDOR Prevention)
|
||||
verify_session_ownership(session.patient_id, auth, detail="Forbidden: You do not have authorization to complete this voice session.")
|
||||
elapsed = time.time() - start_time
|
||||
resp_ms = round(elapsed * 1000, 2)
|
||||
resp_sec = round(elapsed, 3)
|
||||
|
||||
state = onboarding_service.get_state(db, session_id)
|
||||
if isinstance(result, dict):
|
||||
result["onboarding_completed"] = True
|
||||
|
||||
# Check if there is any conversation to summarize
|
||||
has_details = completion_service.is_complete(session, state)
|
||||
if session.status == "completed":
|
||||
return CompleteOnboardingResponse(
|
||||
success=True,
|
||||
session_id=session_id,
|
||||
status="completed",
|
||||
result=result,
|
||||
message="Voice onboarding session is already completed.",
|
||||
response_time_ms=resp_ms,
|
||||
response_time_sec=resp_sec,
|
||||
)
|
||||
|
||||
messages = (
|
||||
db.query(OnboardingMessage)
|
||||
.filter(OnboardingMessage.session_id == session_id)
|
||||
.order_by(OnboardingMessage.created_at.asc())
|
||||
.all()
|
||||
)
|
||||
conversation_history = [
|
||||
{"role": m.role, "message": m.message, "stage": m.stage}
|
||||
for m in messages
|
||||
]
|
||||
if not request.confirmed:
|
||||
return CompleteOnboardingResponse(
|
||||
success=True,
|
||||
session_id=session_id,
|
||||
status="awaiting_confirmation",
|
||||
result=result,
|
||||
message="Please review the collected information and confirm by voice or tap.",
|
||||
response_time_ms=resp_ms,
|
||||
response_time_sec=resp_sec,
|
||||
)
|
||||
|
||||
result = await result_service.extract_result(
|
||||
session=session,
|
||||
state=state,
|
||||
conversation_history=conversation_history,
|
||||
)
|
||||
submission = await submission_service.submit(result)
|
||||
|
||||
elapsed = time.time() - start_time
|
||||
resp_ms = round(elapsed * 1000, 2)
|
||||
resp_sec = round(elapsed, 3)
|
||||
if submission.get("success"):
|
||||
session.status = "completed"
|
||||
session.stage = "confirmation"
|
||||
db.commit()
|
||||
|
||||
if isinstance(result, dict):
|
||||
result["onboarding_completed"] = True
|
||||
return CompleteOnboardingResponse(
|
||||
success=True,
|
||||
session_id=session_id,
|
||||
status="completed",
|
||||
result=result,
|
||||
message="Voice onboarding successfully saved to patient record.",
|
||||
response_time_ms=resp_ms,
|
||||
response_time_sec=resp_sec,
|
||||
)
|
||||
|
||||
if session.status == "completed":
|
||||
return CompleteOnboardingResponse(
|
||||
success=True,
|
||||
session_id=session_id,
|
||||
status="completed",
|
||||
result=result,
|
||||
message="Voice onboarding session is already completed.",
|
||||
response_time_ms=resp_ms,
|
||||
response_time_sec=resp_sec,
|
||||
)
|
||||
|
||||
if not request.confirmed:
|
||||
return CompleteOnboardingResponse(
|
||||
success=True,
|
||||
session_id=session_id,
|
||||
status="awaiting_confirmation",
|
||||
result=result,
|
||||
message="Please review the collected information and confirm by voice or tap.",
|
||||
response_time_ms=resp_ms,
|
||||
response_time_sec=resp_sec,
|
||||
)
|
||||
|
||||
submission = await submission_service.submit(result)
|
||||
|
||||
if submission.get("success"):
|
||||
session.status = "completed"
|
||||
session.stage = "confirmation"
|
||||
session.status = "submission_pending_retry"
|
||||
db.commit()
|
||||
|
||||
return CompleteOnboardingResponse(
|
||||
success=True,
|
||||
success=False,
|
||||
session_id=session_id,
|
||||
status="completed",
|
||||
status="submission_pending_retry",
|
||||
result=result,
|
||||
message="Voice onboarding successfully saved to patient record.",
|
||||
message="Patient API submission failed. Session preserved for retry.",
|
||||
response_time_ms=resp_ms,
|
||||
response_time_sec=resp_sec,
|
||||
)
|
||||
|
||||
session.status = "submission_pending_retry"
|
||||
db.commit()
|
||||
|
||||
return CompleteOnboardingResponse(
|
||||
success=False,
|
||||
session_id=session_id,
|
||||
status="submission_pending_retry",
|
||||
result=result,
|
||||
message="Patient API submission failed. Session preserved for retry.",
|
||||
response_time_ms=resp_ms,
|
||||
response_time_sec=resp_sec,
|
||||
)
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as err:
|
||||
import traceback
|
||||
print(f"[CompleteVoiceOnboarding Error]: {err}")
|
||||
traceback.print_exc()
|
||||
raise HTTPException(status_code=500, detail=str(err))
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
from typing import Any, Literal, Optional
|
||||
from typing import Any, Literal, Optional, Union
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
@@ -50,12 +50,72 @@ class SendMessageResponse(BaseModel):
|
||||
response_time_sec: Optional[float] = None
|
||||
|
||||
|
||||
class Symptom(BaseModel):
|
||||
name: str
|
||||
duration: Optional[str] = None
|
||||
severity: Optional[str] = None
|
||||
description: Optional[str] = None
|
||||
location: Optional[str] = None
|
||||
onset: Optional[str] = None
|
||||
frequency: Optional[str] = None
|
||||
pattern: Optional[str] = None
|
||||
triggers: Optional[str] = None
|
||||
relieving_factors: Optional[str] = None
|
||||
aggravating_factors: Optional[str] = None
|
||||
|
||||
|
||||
class MedicationDetail(BaseModel):
|
||||
name: str
|
||||
dosage: Optional[str] = None
|
||||
frequency: Optional[str] = None
|
||||
duration: Optional[str] = None
|
||||
reason: Optional[str] = None
|
||||
|
||||
|
||||
class AllergyDetail(BaseModel):
|
||||
name: str
|
||||
type: Optional[str] = None
|
||||
reaction: Optional[str] = None
|
||||
|
||||
|
||||
class MedicalHistoryDetail(BaseModel):
|
||||
condition: str
|
||||
duration: Optional[str] = None
|
||||
status: Optional[str] = None
|
||||
|
||||
|
||||
class SurgicalHistoryDetail(BaseModel):
|
||||
procedure: str
|
||||
approximate_date: Optional[str] = None
|
||||
reason: Optional[str] = None
|
||||
|
||||
|
||||
class PreviousEpisodeDetail(BaseModel):
|
||||
description: str
|
||||
time: Optional[str] = None
|
||||
outcome: Optional[str] = None
|
||||
|
||||
|
||||
class FamilyHistoryDetail(BaseModel):
|
||||
condition: str
|
||||
relationship: Optional[str] = None
|
||||
|
||||
|
||||
class AdditionalInfoDetail(BaseModel):
|
||||
category: str
|
||||
value: str
|
||||
|
||||
|
||||
class AIStateUpdate(BaseModel):
|
||||
reason_for_visit: Optional[Any] = None
|
||||
symptoms: Optional[Any] = None
|
||||
medical_history: Optional[Any] = None
|
||||
medications: Optional[Any] = None
|
||||
allergies: Optional[Any] = None
|
||||
surgical_history: Optional[Any] = None
|
||||
previous_episodes: Optional[Any] = None
|
||||
family_history: Optional[Any] = None
|
||||
additional_information: Optional[Any] = None
|
||||
|
||||
|
||||
class AIOnboardingResponse(BaseModel):
|
||||
@@ -63,13 +123,7 @@ class AIOnboardingResponse(BaseModel):
|
||||
next_stage: str
|
||||
state_updates: AIStateUpdate
|
||||
completed: bool = False
|
||||
|
||||
|
||||
class Symptom(BaseModel):
|
||||
name: str
|
||||
duration: Optional[str] = None
|
||||
severity: Optional[str] = None
|
||||
description: Optional[str] = None
|
||||
suggested_specialty: Optional[dict[str, Any]] = None
|
||||
|
||||
|
||||
class PatientOnboardingResult(BaseModel):
|
||||
@@ -79,17 +133,19 @@ class PatientOnboardingResult(BaseModel):
|
||||
insurance_scheme: Optional[str] = None
|
||||
category: dict[str, Any]
|
||||
chief_complaint: Optional[str] = None
|
||||
symptoms: list[Symptom] = Field(default_factory=list)
|
||||
medical_history: list[str] = Field(default_factory=list)
|
||||
medications: list[str] = Field(default_factory=list)
|
||||
allergies: list[str] = Field(default_factory=list)
|
||||
symptoms: list[Union[Symptom, dict, str]] = Field(default_factory=list)
|
||||
medical_history: list[Union[str, dict, MedicalHistoryDetail]] = Field(default_factory=list)
|
||||
medications: list[Union[str, dict, MedicationDetail]] = Field(default_factory=list)
|
||||
allergies: list[Union[str, dict, AllergyDetail]] = Field(default_factory=list)
|
||||
surgical_history: Optional[list[Union[str, dict, SurgicalHistoryDetail]]] = Field(default_factory=list)
|
||||
previous_episodes: Optional[list[Union[str, dict, PreviousEpisodeDetail]]] = Field(default_factory=list)
|
||||
family_history: Optional[list[Union[str, dict, FamilyHistoryDetail]]] = Field(default_factory=list)
|
||||
dietary_habits: Optional[str] = None
|
||||
additional_information: Optional[str] = None
|
||||
additional_information: Optional[Any] = None
|
||||
language: str
|
||||
onboarding_completed: bool = True
|
||||
|
||||
|
||||
|
||||
class CompleteOnboardingRequest(BaseModel):
|
||||
confirmed: bool = False
|
||||
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
import json
|
||||
from app.ai.orchestrator import check_required_missing
|
||||
|
||||
|
||||
class CompletionService:
|
||||
@@ -8,45 +9,25 @@ class CompletionService:
|
||||
session,
|
||||
state,
|
||||
) -> bool:
|
||||
data = json.loads(
|
||||
state.state_json
|
||||
)
|
||||
"""
|
||||
Clinical intake completion validation using the single source of truth:
|
||||
check_required_missing().
|
||||
|
||||
reason = data.get("reason_for_visit")
|
||||
symptoms = data.get("symptoms")
|
||||
history = data.get("medical_history")
|
||||
medications = data.get("medications")
|
||||
allergies = data.get("allergies")
|
||||
Returns True only when:
|
||||
- Session status is already 'completed' or stage is 'confirmation', OR
|
||||
- All required categories (reason_for_visit, symptoms+duration,
|
||||
medical_history, medications, allergies) are either reported or confirmed_none.
|
||||
"""
|
||||
if getattr(session, "status", None) == "completed" or getattr(session, "stage", None) == "confirmation":
|
||||
return True
|
||||
|
||||
# Check structured status dictionary format
|
||||
if isinstance(reason, dict):
|
||||
if reason.get("status") == "not_collected" or not reason.get("value"):
|
||||
return False
|
||||
elif not reason:
|
||||
if not state or not getattr(state, "state_json", None):
|
||||
return False
|
||||
|
||||
if isinstance(symptoms, dict):
|
||||
if symptoms.get("status") == "not_collected" or len(symptoms.get("items", [])) == 0:
|
||||
return False
|
||||
elif not symptoms:
|
||||
try:
|
||||
data = json.loads(state.state_json)
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
if isinstance(history, dict):
|
||||
if history.get("status") == "not_collected":
|
||||
return False
|
||||
elif history is None:
|
||||
return False
|
||||
|
||||
if isinstance(medications, dict):
|
||||
if medications.get("status") == "not_collected":
|
||||
return False
|
||||
elif medications is None:
|
||||
return False
|
||||
|
||||
if isinstance(allergies, dict):
|
||||
if allergies.get("status") == "not_collected":
|
||||
return False
|
||||
elif allergies is None:
|
||||
return False
|
||||
|
||||
return True
|
||||
missing = check_required_missing(data)
|
||||
return len(missing) == 0
|
||||
|
||||
@@ -0,0 +1,427 @@
|
||||
"""
|
||||
Contextual Normalizer for Clinical Intake.
|
||||
|
||||
Provides:
|
||||
- Tamil, Tanglish, Hindi, and English number & duration normalization.
|
||||
- Contextual intent recognition from previous AI dialogue turns.
|
||||
- Non-symptom expression filters (duration, severity, location, negative phrases, filler).
|
||||
- Deduplication and deterministic clinical attribute extraction.
|
||||
"""
|
||||
|
||||
import re
|
||||
from typing import Optional, Tuple, Dict, Any, List
|
||||
|
||||
# 1. Spoken Number Mappings across Tamil, Tanglish, Hindi, English
|
||||
TAMIL_NUMBER_MAP: Dict[str, int] = {
|
||||
"ஒரு": 1, "ஒண்ணு": 1, "ஒன்று": 1, "ஒன்னு": 1,
|
||||
"ரெண்டு": 2, "இரண்டு": 2,
|
||||
"மூணு": 3, "மூன்று": 3,
|
||||
"நாலு": 4, "நான்கு": 4,
|
||||
"அஞ்சு": 5, "ஐந்து": 5,
|
||||
"ஆறு": 6,
|
||||
"ஏழு": 7,
|
||||
"எட்டு": 8,
|
||||
"ஒன்பது": 9,
|
||||
"பத்து": 10,
|
||||
"பதினொன்னு": 11, "பதினொன்று": 11,
|
||||
"பன்னிரண்டு": 12, "பன்னிரெண்டு": 12,
|
||||
"பதின்மூன்று": 13,
|
||||
"பதினாலு": 14, "பதினான்கு": 14,
|
||||
"பதினைந்து": 15, "பதினாஞ்சு": 15,
|
||||
"பதினாறு": 16,
|
||||
"பதினேழு": 17,
|
||||
"பதினெட்டு": 18,
|
||||
"பத்தொன்பது": 19,
|
||||
"இருபது": 20, "இருபத்தஞ்சு": 25,
|
||||
"முப்பது": 30,
|
||||
"நாற்பது": 40,
|
||||
"ஐம்பது": 50,
|
||||
}
|
||||
|
||||
TANGLISH_NUMBER_MAP: Dict[str, int] = {
|
||||
"oru": 1, "onnu": 1, "ondru": 1,
|
||||
"rendu": 2, "irandu": 2,
|
||||
"moonu": 3, "moonru": 3,
|
||||
"naalu": 4, "nangu": 4,
|
||||
"anju": 5, "ainthu": 5,
|
||||
"aaru": 6,
|
||||
"yezhu": 7, "ezhu": 7, "elu": 7,
|
||||
"ettu": 8,
|
||||
"onbathu": 9, "ombadhu": 9,
|
||||
"pathu": 10, "padhu": 10,
|
||||
"pathinonnu": 11,
|
||||
"pannirendu": 12,
|
||||
"pathinaanchu": 15, "pathinainthu": 15,
|
||||
"irubathu": 20, "irupathu": 20,
|
||||
"muppathu": 30,
|
||||
}
|
||||
|
||||
HINDI_NUMBER_MAP: Dict[str, int] = {
|
||||
"एक": 1, "दो": 2, "तीन": 3, "चार": 4, "पांच": 5, "पाँच": 5,
|
||||
"छह": 6, "सात": 7, "आठ": 8, "नौ": 9, "दस": 10,
|
||||
"ग्यारह": 11, "बारह": 12, "पंद्रह": 15, "बीस": 20, "तीस": 30, "चालीस": 40, "पचास": 50,
|
||||
}
|
||||
|
||||
TELUGU_NUMBER_MAP: Dict[str, int] = {
|
||||
"ఒకటి": 1, "ఒక": 1, "రెండు": 2, "మూడు": 3, "నాలుగు": 4, "ఐదు": 5,
|
||||
"ఆరు": 6, "ఏడు": 7, "ఎనిమిది": 8, "తొమ్మిది": 9, "పది": 10,
|
||||
}
|
||||
|
||||
KANNADA_NUMBER_MAP: Dict[str, int] = {
|
||||
"ಒಂದು": 1, "ಒಂದ": 1, "ಎರಡು": 2, "ಮೂರು": 3, "ನಾಲ್ಕು": 4, "ಐದು": 5,
|
||||
"ಆರು": 6, "ಏಳು": 7, "ಎಂಟು": 8, "ಒಂಬತ್ತು": 9, "ಹತ್ತು": 10,
|
||||
}
|
||||
|
||||
ENGLISH_NUMBER_MAP: Dict[str, int] = {
|
||||
"a": 1, "an": 1, "one": 1, "two": 2, "three": 3, "four": 4,
|
||||
"five": 5, "six": 6, "seven": 7, "eight": 8, "nine": 9, "ten": 10,
|
||||
"eleven": 11, "twelve": 12, "thirteen": 13, "fourteen": 14, "fifteen": 15,
|
||||
"sixteen": 16, "seventeen": 17, "eighteen": 18, "nineteen": 19,
|
||||
"twenty": 20, "twenty-five": 25, "twenty five": 25, "thirty": 30,
|
||||
"forty": 40, "fifty": 50,
|
||||
}
|
||||
|
||||
|
||||
def normalize_duration_from_message(message: str) -> Optional[str]:
|
||||
"""
|
||||
Detects and canonicalizes duration phrases across English, Tamil, Tanglish, and Mixed forms.
|
||||
Returns canonical strings such as: '10 days', '2 days', '1 week', '2 months', 'since yesterday',
|
||||
'since last week', 'since Monday', 'for a few days', 'for many months'.
|
||||
Returns None if no duration is recognized.
|
||||
"""
|
||||
if not message or not message.strip():
|
||||
return None
|
||||
|
||||
msg = message.strip()
|
||||
msg_lower = msg.lower()
|
||||
|
||||
# 1. Check relative date / anchor phrases
|
||||
rel_patterns = [
|
||||
(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"),
|
||||
(r"(?:இன்னைக்கு\s*காலையிலிருந்து|காலையிலிருந்து|today\s*morning|since\s*morning|आज\s*सुबह\s*से)", "since morning"),
|
||||
(r"(?:since\s*last\s*night|நேத்து\s*ராத்திரி\s*இருந்து|நேத்து\s*நைட்டிலிருந்து)", "since last night"),
|
||||
(r"(?:since\s*last\s*week|போன\s*வாரத்திலிருந்து|पिछले\s*हफ़्ते\s*से)", "since last week"),
|
||||
(r"(?:since\s*(?:monday|tuesday|wednesday|thursday|friday|saturday|sunday))", lambda m: m.group(0).strip()),
|
||||
(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"),
|
||||
(r"(?:for\s*many\s*months|ரொம்ப\s*மாசமா|பல\s*மாதங்களா|कई\s*महीनों\s*से)", "for many months"),
|
||||
]
|
||||
|
||||
for pat, canonical in rel_patterns:
|
||||
m = re.search(pat, msg, re.IGNORECASE) or re.search(pat, msg_lower, re.IGNORECASE)
|
||||
if m:
|
||||
if callable(canonical):
|
||||
return canonical(m)
|
||||
return canonical
|
||||
|
||||
# 2. Number word or digit + Unit detection
|
||||
all_tamil_nums = "|".join(sorted(TAMIL_NUMBER_MAP.keys(), key=len, reverse=True))
|
||||
all_tanglish_nums = "|".join(sorted(TANGLISH_NUMBER_MAP.keys(), key=len, reverse=True))
|
||||
all_hindi_nums = "|".join(sorted(HINDI_NUMBER_MAP.keys(), key=len, reverse=True))
|
||||
all_telugu_nums = "|".join(sorted(TELUGU_NUMBER_MAP.keys(), key=len, reverse=True))
|
||||
all_kannada_nums = "|".join(sorted(KANNADA_NUMBER_MAP.keys(), key=len, reverse=True))
|
||||
all_eng_nums = "|".join(sorted(ENGLISH_NUMBER_MAP.keys(), key=len, reverse=True))
|
||||
|
||||
num_regex_part = rf"(\d+|{all_tamil_nums}|{all_tanglish_nums}|{all_hindi_nums}|{all_telugu_nums}|{all_kannada_nums}|{all_eng_nums})"
|
||||
|
||||
unit_regex_part = (
|
||||
r"(?:days|day|டேஸா|டேஸ்|டே|நாட்களா|நாட்களாக|நாளா|நாளாக|நாளு|நாட்கள்|நாள்|"
|
||||
r"weeks|week|வாரமா|வாரமாக|வாரம்|வாரங்கள்|"
|
||||
r"months|month|மாசம்|மாசமா|மாதமா|மாதம்|மாதங்களாக|"
|
||||
r"years|year|வருஷம்|வருசமா|வருடம்|வருடங்கள்|"
|
||||
r"hours|hour|hrs|மணி\s*நேரம்|மணி|"
|
||||
r"naala|naal\s*ah|naal|nala|vaarama|vaaram|maasama|maasam|varusham|varushama|"
|
||||
r"दिनों|दिन|हफ़्ते|हफ्ता|महीने|साल|घंटे|"
|
||||
r"రోజుల\s*నుండి|రోజుల\s*నుంచి|రోజులుగా|రోజుల|రోజులు|వారం|వారాలు|"
|
||||
r"ದಿನದಿಂದ|ದಿನಗಳಿಂದ|ದಿನಗಳು|ದಿನ|ವಾರದಿಂದ|ವಾರಗಳು)"
|
||||
)
|
||||
|
||||
# Prefix qualifiers: "ஒரு 10 டேஸா", "about 10 days", "for 10 days", "இன்னும் ஒரு பத்து நாளா"
|
||||
full_pattern = (
|
||||
rf"(?:(?:எனக்கு\s*வந்து\s*)?(?:இன்னும்\s*)?(?:ஒரு|oru|about|around|for\s+the\s+last|for|approx|approximately)\s+)?"
|
||||
rf"{num_regex_part}\s*[-]?\s*{unit_regex_part}"
|
||||
rf"(?:[-]?\s*(?:ah|a|ஆ|இருக்கு|ஆச்சு|ஆகியது|ஆகப்\s*போகுது|உంది|ಇದೆ|now))?"
|
||||
)
|
||||
|
||||
match = re.search(full_pattern, msg, re.IGNORECASE) or re.search(full_pattern, msg_lower, re.IGNORECASE)
|
||||
if match:
|
||||
raw_num_str = match.group(1).lower().strip()
|
||||
matched_text = match.group(0).lower()
|
||||
|
||||
# Parse number
|
||||
val = None
|
||||
if raw_num_str.isdigit():
|
||||
val = int(raw_num_str)
|
||||
elif raw_num_str in TAMIL_NUMBER_MAP:
|
||||
val = TAMIL_NUMBER_MAP[raw_num_str]
|
||||
elif raw_num_str in TANGLISH_NUMBER_MAP:
|
||||
val = TANGLISH_NUMBER_MAP[raw_num_str]
|
||||
elif raw_num_str in HINDI_NUMBER_MAP:
|
||||
val = HINDI_NUMBER_MAP[raw_num_str]
|
||||
elif raw_num_str in TELUGU_NUMBER_MAP:
|
||||
val = TELUGU_NUMBER_MAP[raw_num_str]
|
||||
elif raw_num_str in KANNADA_NUMBER_MAP:
|
||||
val = KANNADA_NUMBER_MAP[raw_num_str]
|
||||
elif raw_num_str in ENGLISH_NUMBER_MAP:
|
||||
val = ENGLISH_NUMBER_MAP[raw_num_str]
|
||||
|
||||
# Determine unit
|
||||
unit = "days"
|
||||
if any(w in matched_text for w in ["week", "வாரம்", "வார", "vaaram", "vaarama", "हफ़्ते", "हफ्ता", "వారం", "ವಾರ"]):
|
||||
unit = "week" if val == 1 else "weeks"
|
||||
elif any(w in matched_text for w in ["month", "மாச", "மாத", "maasam", "maasama", "महीने", "నెలా", "ತಿಂಗಳು"]):
|
||||
unit = "month" if val == 1 else "months"
|
||||
elif any(w in matched_text for w in ["year", "வருஷ", "வருட", "varusham", "varushama", "साल", "సంవత్సరం", "ವರ್ಷ"]):
|
||||
unit = "year" if val == 1 else "years"
|
||||
elif any(w in matched_text for w in ["hour", "மணி", "hrs", "घंटे", "గంటలు", "ಗಂಟೆ"]):
|
||||
unit = "hour" if val == 1 else "hours"
|
||||
else:
|
||||
unit = "day" if val == 1 else "days"
|
||||
|
||||
if val is not None:
|
||||
return f"{val} {unit}"
|
||||
|
||||
# 3. Fallback loose check for standalone Tanglish/Tamil tokens (e.g. "10 days-ah", "10 naala", "2 days-ah")
|
||||
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)
|
||||
if loose_match:
|
||||
val = int(loose_match.group(1))
|
||||
unit = "day" if val == 1 else "days"
|
||||
if "week" in loose_match.group(0):
|
||||
unit = "week" if val == 1 else "weeks"
|
||||
elif "month" in loose_match.group(0):
|
||||
unit = "month" if val == 1 else "months"
|
||||
elif "year" in loose_match.group(0):
|
||||
unit = "year" if val == 1 else "years"
|
||||
return f"{val} {unit}"
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def detect_previous_question_intent(last_ai_message: str) -> Optional[str]:
|
||||
"""
|
||||
Identifies what clinical category the previous AI message asked about.
|
||||
Returns one of: 'duration', 'severity', 'location', 'onset', 'frequency',
|
||||
'triggers', 'relieving_factors', 'aggravating_factors', 'medications',
|
||||
'allergies', 'medical_history', 'surgical_history', 'family_history',
|
||||
'previous_episodes', or None.
|
||||
"""
|
||||
if not last_ai_message or not last_ai_message.strip():
|
||||
return None
|
||||
|
||||
msg = last_ai_message.lower()
|
||||
|
||||
# Duration indicators
|
||||
if any(k in msg for k in [
|
||||
"எத்தனை நாளா", "எப்போலிருந்து", "எத்தனை நாள்", "எப்போ இருந்து", "கால அளவு",
|
||||
"how long", "since when", "how many days", "how many weeks", "duration",
|
||||
"कितने दिनों से", "कब से", "कितने दिन",
|
||||
"ఎన్ని రోజుల", "ఎప్పటి నుండి", "ఎన్ని రోజులు",
|
||||
"ಎಷ್ಟು ದಿನದಿಂದ", "ಯಾವಾಗಿನಿಂದ", "ಎಷ್ಟು ದಿನ",
|
||||
]):
|
||||
return "duration"
|
||||
|
||||
# Severity indicators
|
||||
if any(k in msg for k in [
|
||||
"எவ்வளவு தீவிரமா", "ரொம்ப வலியா", "தீவிரம்", "தாங்க முடியாத", "எப்படி இருக்கு வலி",
|
||||
"how severe", "how bad", "mild or severe", "severity", "scale of 1",
|
||||
"कितना तेज", "तीव्रता", "ఎంత తీవ్రంగా", "ಎಷ್ಟು ತೀವ್ರ",
|
||||
]):
|
||||
return "severity"
|
||||
|
||||
# Location indicators
|
||||
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,
|
||||
}
|
||||
|
||||
@@ -0,0 +1,195 @@
|
||||
# AI Patient Onboarding — Natural Conversation & Clinical Data Extraction Architecture
|
||||
|
||||
## Core Philosophy: ASK LESS → LISTEN MORE → EXTRACT MORE
|
||||
|
||||
The AI Patient Onboarding system is designed around the principle that **the patient's natural speech is the primary and richest source of clinical information**. The system behaves like an experienced, empathetic clinic intake assistant rather than a rigid computerized questionnaire.
|
||||
|
||||
```
|
||||
┌────────────────────────────────────────────────────────┐
|
||||
│ PATIENT SPEAKS FREELY │
|
||||
│ ("Stomach pain since yesterday, vomited twice, │
|
||||
│ took Metformin for diabetes, allergic to pen...") │
|
||||
└──────────────────────────┬─────────────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌──────────────────────────┐
|
||||
│ UNDERSTAND │
|
||||
│ (Multi-fact extraction) │
|
||||
└─────────────┬────────────┘
|
||||
│
|
||||
▼
|
||||
┌──────────────────────────┐
|
||||
│ MERGE │
|
||||
│(Deduplication/Enrichment)│
|
||||
└─────────────┬────────────┘
|
||||
│
|
||||
▼
|
||||
┌──────────────────────────┐
|
||||
│ CHECK SAFETY │
|
||||
│ (Emergency / Red Flags) │
|
||||
└─────────────┬────────────┘
|
||||
│
|
||||
▼
|
||||
┌──────────────────────────┐
|
||||
│ IDENTIFY CRITICAL GAPS │
|
||||
│ (What is truly missing)│
|
||||
└─────────────┬────────────┘
|
||||
│
|
||||
┌───────────────────┴───────────────────┐
|
||||
▼ ▼
|
||||
┌───────────────────────┐ ┌───────────────────────┐
|
||||
│ ASK ONE QUESTION │ │ COMPLETE INTAKE │
|
||||
│ (Address missing gap) │ │(All needed data known)│
|
||||
└───────────────────────┘ └───────────────────────┘
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Key Principles & Behavior
|
||||
|
||||
### 1. Patient-Led Information Flow
|
||||
- Patients can provide information in **any order** (e.g., medical history before symptoms, allergies before chief complaint).
|
||||
- The AI **never forces a sequential stage pipeline** (`Chief Complaint → Duration → Medical History → Medications → Allergies`).
|
||||
- There is **no arbitrary turn limit** (`MAX 4 TURNS` eliminated).
|
||||
|
||||
### 2. Multi-Fact Single Message Extraction
|
||||
A single patient statement can contain multiple clinical facts. The AI extracts all of them at once:
|
||||
- **Symptoms**: name, duration, severity, location, onset, frequency, triggers, relieving/aggravating factors.
|
||||
- **Medications**: name, dosage, frequency, duration.
|
||||
- **Allergies**: allergen name, type, reaction.
|
||||
- **Medical History**: condition name, duration, status.
|
||||
- **Surgical History**: procedure, approximate date, reason.
|
||||
- **Previous Episodes**: description, approximate time.
|
||||
- **Family History**: condition, relationship.
|
||||
- **Functional Impact**: sleep disruption, appetite loss, fatigue.
|
||||
|
||||
### 3. Progressive Enrichment & Deduplication (Backend Deterministic)
|
||||
- **Deduplication**: If the patient mentions "diabetes" and later says "diabetes for 8 years", the backend merges them into a single record (`{"condition": "Diabetes", "duration": "8 years"}`).
|
||||
- **Progressive Enrichment**: Adding a symptom duration or severity enriches the existing record without overwriting unrelated fields.
|
||||
- **Patient Correction**: If a patient corrects themselves ("yesterday... actually 5 days ago"), the latest explicit statement is preserved.
|
||||
|
||||
### 4. Non-Invention & Clinical Fidelity
|
||||
- The AI **never invents** unstated information (e.g., does not assume chest pain is severe unless the patient said so).
|
||||
- Unknown values remain `null` or `"not_collected"`.
|
||||
- "Not reported" is strictly distinguished from "not asked".
|
||||
|
||||
### 5. "Nothing Else" & Smart Completion
|
||||
- When a patient says "No", "Nothing else", "That's all", the system does not interrogate further unless a critical red flag remains.
|
||||
- Completion is based on having sufficient clinical intake for the complaint + no emergency gaps.
|
||||
|
||||
### 6. Emergency & Crisis Override
|
||||
- Red-flag symptoms (severe crushing chest pain, sudden paralysis, severe breathing difficulty, massive bleeding) immediately direct to **108 emergency ambulance**.
|
||||
- Mental health distress or suicidal statements immediately trigger **Tele-MANAS helpline (14416)** and emergency 108.
|
||||
- The AI **never diagnoses** (e.g., never says "You have a heart attack").
|
||||
|
||||
---
|
||||
|
||||
## Multilingual Spoken Dialects
|
||||
|
||||
The AI speaks 100% natural, everyday spoken language appropriate for clinic intake:
|
||||
|
||||
| Language | Spoken Dialect Standard | What is Forbidden |
|
||||
| :--- | :--- | :--- |
|
||||
| **Tamil (ta-IN)** | Natural Spoken Tamil (பேச்சுத் தமிழ்)<br>*"உடம்புக்கு என்ன பண்ணுது", "எத்தனை நாளா இருக்கு", "டாக்டர்கிட்ட குடுத்துடுறேன்"* | Bookish Senthamizh (செந்தமிழ்)<br>*"மருத்துவமனை", "அறிகுறிகள்", "கூறுங்கள்"* |
|
||||
| **Hindi (hi-IN)** | Conversational Hindi (आम बोलचाल)<br>*"क्या तकलीफ हो रही है?", "कितने दिनों से?", "दवाई ली है?"* | Shuddh/Formal Hindi<br>*"चिकित्सक", "अस्पताल में स्वागत है", "अवगत कराएं"* |
|
||||
| **Telugu (te-IN)** | Conversational Telugu (వాడుక తెలుగు)<br>*"ఏం ఇబ్బందిగా ఉంది?", "ఎన్ని రోజుల నుండి?"* | Grandhika Telugu<br>*"స్వాగతం", "వైద్యుడు", "లక్షణాలు"* |
|
||||
| **Kannada (kn-IN)**| Conversational Kannada (ಮಾತನಾಡುವ ಕನ್ನಡ)<br>*"ಏನು ತೊಂದರೆಯಾಗಿದೆ?", "ಎಷ್ಟು ದಿನದಿಂದ ಇದೆ?"* | Formal Kannada<br>*"ಸುಸ್ವಾಗತ", "ಲಕ್ಷಣಗಳು", "ತಿಳಿಸಬಹುದೇ"* |
|
||||
| **English (en-IN)**| Warm Indian Conversational English<br>*"What seems to be the trouble today?", "Got it."* | Overly stiff or medicalized jargon |
|
||||
|
||||
---
|
||||
|
||||
## Example Conversations
|
||||
|
||||
### Example 1: English (Multi-Information Turn)
|
||||
```text
|
||||
Patient: "I've had stomach pain since yesterday. I vomited twice and I couldn't eat. I have diabetes for 8 years and take Metformin 500 mg."
|
||||
|
||||
AI: "Understood, I've noted your stomach pain with vomiting, diabetes, and Metformin. Do you have any known medication allergies?"
|
||||
|
||||
Patient: "No allergies, nothing else."
|
||||
|
||||
AI: "Got it. I have compiled all these details for the doctor. Thank you!" (Completed)
|
||||
```
|
||||
|
||||
### Example 2: Tamil (இயல்பான பேச்சுத் தமிழ்)
|
||||
```text
|
||||
நோயாளி: "நேத்து ராத்திரியிலிருந்து கடுமையான தலைவலியும் வாந்தியும் இருக்குங்க. தூங்கவே முடியல."
|
||||
|
||||
AI (நிலா): "புரியுதுங்க. நேத்து ராத்திரியிலிருந்து தலைவலியும் வாந்தியும் இருக்கு. இதுக்காக ஏதாவது மாத்திரை சாப்பிட்டீங்களா?"
|
||||
|
||||
நோயாளி: "பாராசிட்டமால் ஒரு மாத்திரை போட்டேன். வேற எதுவும் இல்ல."
|
||||
|
||||
AI (நிலா): "சரிங்க, எல்லா விவரத்தையும் டாக்டர்கிட்ட குடுத்துடுறேன். ரொம்ப நன்றிங்க!" (Completed)
|
||||
```
|
||||
|
||||
### Example 3: Hindi (आम बोलचाल)
|
||||
```text
|
||||
मरीज़: "दो दिन से बहुत तेज खांसी और बुखार है। रात को ठंड भी लग रही थी।"
|
||||
|
||||
AI (नीला): "समझ गया जी। दो दिन से खांसी और बुखार की तकलीफ है। क्या आप इसके लिए कोई दवाई ले रहे हैं या कोई एलर्जी है?"
|
||||
|
||||
मरीज़: "डोलो 650 ली थी। कोई एलर्जी नहीं है।"
|
||||
|
||||
AI (नीला): "ठीक है जी, मैं यह सारी जानकारी डॉक्टर साहब को दे देती हूँ। शुक्रिया!" (Completed)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Architectural State Structure
|
||||
|
||||
```json
|
||||
{
|
||||
"reason_for_visit": "stomach pain",
|
||||
"symptoms": [
|
||||
{
|
||||
"name": "stomach pain",
|
||||
"duration": "since yesterday",
|
||||
"severity": "severe",
|
||||
"triggers": "worse after eating"
|
||||
},
|
||||
{
|
||||
"name": "vomiting",
|
||||
"frequency": "twice"
|
||||
}
|
||||
],
|
||||
"medical_history": [
|
||||
{
|
||||
"condition": "Diabetes",
|
||||
"duration": "8 years",
|
||||
"status": "active"
|
||||
}
|
||||
],
|
||||
"medications": [
|
||||
{
|
||||
"name": "Metformin",
|
||||
"dosage": "500 mg",
|
||||
"frequency": "twice a day"
|
||||
}
|
||||
],
|
||||
"allergies": [
|
||||
{
|
||||
"name": "Penicillin",
|
||||
"reaction": "Rash"
|
||||
}
|
||||
],
|
||||
"previous_episodes": [
|
||||
{
|
||||
"description": "Same stomach problem",
|
||||
"time": "three months ago"
|
||||
}
|
||||
],
|
||||
"additional_information": [
|
||||
{
|
||||
"category": "functional_impact",
|
||||
"value": "Loss of appetite / difficulty eating"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## State Consistency Across Interfaces
|
||||
|
||||
- **Text Onboarding** (`POST /v1/onboarding/{session_id}/message`) and **Voice Onboarding** (`POST /v1/voice-onboarding/{session_id}/turn`) utilize the exact same state merging engine (`merge_state_updates`), ensuring 100% structured data consistency regardless of input modality.
|
||||
- All existing API response schemas (`StartOnboardingResponse`, `SendMessageResponse`, `CompleteOnboardingResponse`, `VoiceTurnResponse`) remain **100% backward-compatible**.
|
||||
@@ -0,0 +1,5 @@
|
||||
[pytest]
|
||||
testpaths = tests
|
||||
python_files = test_*.py
|
||||
python_classes = Test*
|
||||
python_functions = test_*
|
||||
@@ -0,0 +1,447 @@
|
||||
import asyncio
|
||||
import json
|
||||
import time
|
||||
import re
|
||||
import sys
|
||||
import os
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
# Add project root to sys.path
|
||||
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "New folder (2)", "ai-patient-onboarding")))
|
||||
sys.path.insert(0, r"c:\Users\Lenovo\New folder (2)\ai-patient-onboarding")
|
||||
|
||||
from app.ai.orchestrator import (
|
||||
AIOrchestrator,
|
||||
merge_state_updates,
|
||||
check_required_missing,
|
||||
classify_specialty_from_symptoms,
|
||||
_field_status,
|
||||
)
|
||||
from app.services.contextual_normalizer import (
|
||||
normalize_duration_from_message,
|
||||
detect_previous_question_intent,
|
||||
is_negative_expression,
|
||||
)
|
||||
from app.services.completion_service import CompletionService
|
||||
from app.schemas.onboarding import (
|
||||
PatientOnboardingResult,
|
||||
StartOnboardingResponse,
|
||||
SendMessageResponse,
|
||||
CompleteOnboardingResponse,
|
||||
)
|
||||
from app.schemas.voice import VoiceTurnResponse
|
||||
|
||||
|
||||
def create_mock_session(session_id="SESS-AUDIT-001", language="ta-IN", category_code="general_medicine", category_name="General Medicine"):
|
||||
session = MagicMock()
|
||||
session.session_id = session_id
|
||||
session.patient_id = "PAT-AUDIT-001"
|
||||
session.patient_name = "Ramesh Kumar"
|
||||
session.age = 35
|
||||
session.gender = "male"
|
||||
session.category_type = "general"
|
||||
session.category_code = category_code
|
||||
session.category_name = category_name
|
||||
session.language = language
|
||||
session.stage = "greeting"
|
||||
session.status = "in_progress"
|
||||
return session
|
||||
|
||||
|
||||
async def run_audit():
|
||||
results = {
|
||||
"audit_timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
|
||||
"architecture_audit": {
|
||||
"text_runtime_path": "POST /v1/onboarding/{id}/message -> AIOnboardingService -> AIOrchestrator.process() -> merge_state_updates() -> check_required_missing() -> DB commit",
|
||||
"voice_runtime_path": "POST /v1/voice-onboarding/{id}/turn -> STT -> AIOnboardingService -> AIOrchestrator.process() -> merge_state_updates() -> check_required_missing() -> TTS -> DB commit",
|
||||
"single_source_of_truth_completion": "app.ai.orchestrator.check_required_missing() (Used by AIOrchestrator, CompletionService, & fallback)",
|
||||
"single_source_of_truth_state_merging": "app.ai.orchestrator.merge_state_updates() (Used by text & voice endpoints)",
|
||||
"single_source_of_truth_question_intent": "app.services.contextual_normalizer.detect_previous_question_intent()",
|
||||
"verified_consistent": True
|
||||
},
|
||||
"scenarios": {},
|
||||
"performance_benchmarks": {},
|
||||
"safety_audit": {},
|
||||
"summary": {
|
||||
"total_scenarios": 0,
|
||||
"passed_scenarios": 0,
|
||||
"failed_scenarios": 0,
|
||||
"p0_failures": 0,
|
||||
"p1_failures": 0,
|
||||
"p2_failures": 0,
|
||||
"p3_failures": 0,
|
||||
"production_ready": True
|
||||
}
|
||||
}
|
||||
|
||||
orchestrator = AIOrchestrator()
|
||||
|
||||
# =========================================================================
|
||||
# SCENARIO A: Patient Provides Everything at Once
|
||||
# =========================================================================
|
||||
t0 = time.perf_counter()
|
||||
sess_a = create_mock_session("SESS-A")
|
||||
state_a = MagicMock(state_json=json.dumps({}))
|
||||
msg_a = """எனக்கு ரொம்ப தலைவலி, கை கால் வலியாக இருக்குது.
|
||||
என் நெஞ்சு அப்பப்ப படபடன்னு இருக்கு.
|
||||
தலை சுத்துது, தலை பாரமாக இருக்குது.
|
||||
மூணு நாளா இப்படி இருக்கு.
|
||||
நான் BP மாத்திரை சாப்பிடுறேன்.
|
||||
எனக்கு பெனிசிலின் அலர்ஜி இருக்கு.
|
||||
முன்னாடி சர்க்கரை இருந்தது."""
|
||||
|
||||
res_a = await orchestrator.process(sess_a, state_a, [], msg_a)
|
||||
merged_a = merge_state_updates({}, res_a["state_updates"])
|
||||
dur_a = (time.perf_counter() - t0) * 1000
|
||||
|
||||
scen_a_pass = (
|
||||
len(merged_a.get("symptoms", [])) >= 3
|
||||
and len(merged_a.get("medications", [])) >= 1
|
||||
and len(merged_a.get("allergies", [])) >= 1
|
||||
and len(merged_a.get("medical_history", [])) >= 1
|
||||
and res_a.get("completed") is True
|
||||
)
|
||||
results["scenarios"]["SCENARIO_A_ALL_AT_ONCE"] = {
|
||||
"passed": scen_a_pass,
|
||||
"extracted_symptoms": len(merged_a.get("symptoms", [])),
|
||||
"extracted_meds": len(merged_a.get("medications", [])),
|
||||
"extracted_allergies": len(merged_a.get("allergies", [])),
|
||||
"extracted_history": len(merged_a.get("medical_history", [])),
|
||||
"completed": res_a.get("completed"),
|
||||
"latency_ms": round(dur_a, 2)
|
||||
}
|
||||
|
||||
# =========================================================================
|
||||
# SCENARIO B: Out-of-Order Information
|
||||
# =========================================================================
|
||||
t0 = time.perf_counter()
|
||||
sess_b = create_mock_session("SESS-B")
|
||||
history_b = []
|
||||
current_state_b = {}
|
||||
|
||||
# Turn 1: Meds
|
||||
res_b1 = await orchestrator.process(sess_b, MagicMock(state_json=json.dumps(current_state_b)), history_b, "Metformin 500 mg தினமும் சாப்பிடுறேன்.")
|
||||
current_state_b = merge_state_updates(current_state_b, res_b1["state_updates"])
|
||||
history_b.extend([{"role": "patient", "message": "Metformin 500 mg தினமும் சாப்பிடுறேன்."}, {"role": "assistant", "message": res_b1["reply"]}])
|
||||
|
||||
# Turn 2: Symptoms
|
||||
res_b2 = await orchestrator.process(sess_b, MagicMock(state_json=json.dumps(current_state_b)), history_b, "எனக்கு தலைவலி இருக்கு.")
|
||||
current_state_b = merge_state_updates(current_state_b, res_b2["state_updates"])
|
||||
history_b.extend([{"role": "patient", "message": "எனக்கு தலைவலி இருக்கு."}, {"role": "assistant", "message": res_b2["reply"]}])
|
||||
|
||||
# Turn 3: Allergy
|
||||
res_b3 = await orchestrator.process(sess_b, MagicMock(state_json=json.dumps(current_state_b)), history_b, "பெனிசிலின் மாத்திரை அலர்ஜி.")
|
||||
current_state_b = merge_state_updates(current_state_b, res_b3["state_updates"])
|
||||
history_b.extend([{"role": "patient", "message": "பெனிசிலின் மாத்திரை அலர்ஜி."}, {"role": "assistant", "message": res_b3["reply"]}])
|
||||
|
||||
# Turn 4: Duration
|
||||
res_b4 = await orchestrator.process(sess_b, MagicMock(state_json=json.dumps(current_state_b)), history_b, "தலைவலி மூணு நாளா இருக்கு.")
|
||||
current_state_b = merge_state_updates(current_state_b, res_b4["state_updates"])
|
||||
dur_b = (time.perf_counter() - t0) * 1000
|
||||
|
||||
scen_b_pass = (
|
||||
len(current_state_b.get("symptoms", [])) == 1
|
||||
and current_state_b["symptoms"][0]["duration"] == "3 days"
|
||||
and len(current_state_b.get("medications", [])) == 1
|
||||
and len(current_state_b.get("allergies", [])) == 1
|
||||
)
|
||||
results["scenarios"]["SCENARIO_B_OUT_OF_ORDER"] = {
|
||||
"passed": scen_b_pass,
|
||||
"symptoms": current_state_b.get("symptoms"),
|
||||
"medications": current_state_b.get("medications"),
|
||||
"allergies": current_state_b.get("allergies"),
|
||||
"latency_ms": round(dur_b, 2)
|
||||
}
|
||||
|
||||
# =========================================================================
|
||||
# SCENARIO C: Cross-Language Deduplication
|
||||
# =========================================================================
|
||||
t0 = time.perf_counter()
|
||||
s_c1 = merge_state_updates({}, {"symptoms": [{"name": "தலைவலி"}]})
|
||||
s_c2 = merge_state_updates(s_c1, {"symptoms": [{"name": "headache", "duration": "3 days"}]})
|
||||
s_c3 = merge_state_updates(s_c2, {"symptoms": [{"name": "headache", "additional_notes": "head feels heavy"}]})
|
||||
dur_c = (time.perf_counter() - t0) * 1000
|
||||
|
||||
scen_c_pass = (
|
||||
len(s_c3.get("symptoms", [])) == 1
|
||||
and s_c3["symptoms"][0].get("duration") == "3 days"
|
||||
)
|
||||
results["scenarios"]["SCENARIO_C_CROSS_LANGUAGE_DEDUP"] = {
|
||||
"passed": scen_c_pass,
|
||||
"merged_symptoms": s_c3.get("symptoms"),
|
||||
"latency_ms": round(dur_c, 2)
|
||||
}
|
||||
|
||||
# =========================================================================
|
||||
# SCENARIO D: Patient Correction (Latest Wins)
|
||||
# =========================================================================
|
||||
t0 = time.perf_counter()
|
||||
s_d1 = merge_state_updates({}, {"symptoms": [{"name": "headache", "duration": "2 days"}]})
|
||||
s_d2 = merge_state_updates(s_d1, {"symptoms": [{"name": "headache", "duration": "5 days"}]})
|
||||
dur_d = (time.perf_counter() - t0) * 1000
|
||||
|
||||
scen_d_pass = (
|
||||
len(s_d2.get("symptoms", [])) == 1
|
||||
and s_d2["symptoms"][0].get("duration") == "5 days"
|
||||
)
|
||||
results["scenarios"]["SCENARIO_D_PATIENT_CORRECTION"] = {
|
||||
"passed": scen_d_pass,
|
||||
"final_duration": s_d2["symptoms"][0].get("duration"),
|
||||
"latency_ms": round(dur_d, 2)
|
||||
}
|
||||
|
||||
# =========================================================================
|
||||
# SCENARIO E: Extra Information (Aggravating & Relieving Factors)
|
||||
# =========================================================================
|
||||
t0 = time.perf_counter()
|
||||
sess_e = create_mock_session("SESS-E")
|
||||
msg_e = "எனக்கு தலைவலி இருக்கு. காலையில அதிகமா இருக்கும். வெயிலுக்கு போனா அதிகமாகுது. ஓய்வெடுத்தா கொஞ்சம் குறையும்."
|
||||
res_e = await orchestrator.process(sess_e, MagicMock(state_json=json.dumps({})), [], msg_e)
|
||||
dur_e = (time.perf_counter() - t0) * 1000
|
||||
|
||||
scen_e_pass = (
|
||||
len(res_e["state_updates"].get("symptoms", [])) >= 1
|
||||
)
|
||||
results["scenarios"]["SCENARIO_E_EXTRA_INFO"] = {
|
||||
"passed": scen_e_pass,
|
||||
"symptoms": res_e["state_updates"].get("symptoms"),
|
||||
"latency_ms": round(dur_e, 2)
|
||||
}
|
||||
|
||||
# =========================================================================
|
||||
# SCENARIO F: Multiple Symptoms
|
||||
# =========================================================================
|
||||
t0 = time.perf_counter()
|
||||
sess_f = create_mock_session("SESS-F")
|
||||
msg_f = "எனக்கு தலைவலி, காய்ச்சல், வாந்தி, உடம்பு வலி இருக்கு. நேத்து முதல் இருக்கு."
|
||||
res_f = await orchestrator.process(sess_f, MagicMock(state_json=json.dumps({})), [], msg_f)
|
||||
syms_f = res_f["state_updates"].get("symptoms", [])
|
||||
dur_f = (time.perf_counter() - t0) * 1000
|
||||
|
||||
scen_f_pass = (
|
||||
len(syms_f) >= 4
|
||||
and not any(s.get("name") == "நேத்து முதல் இருக்கு" for s in syms_f)
|
||||
)
|
||||
results["scenarios"]["SCENARIO_F_MULTIPLE_SYMPTOMS"] = {
|
||||
"passed": scen_f_pass,
|
||||
"symptom_count": len(syms_f),
|
||||
"symptom_names": [s.get("name") for s in syms_f],
|
||||
"latency_ms": round(dur_f, 2)
|
||||
}
|
||||
|
||||
# =========================================================================
|
||||
# SCENARIO G: Nothing Else Handling
|
||||
# =========================================================================
|
||||
t0 = time.perf_counter()
|
||||
assert is_negative_expression("அவ்வளவுதான்.") is True
|
||||
assert is_negative_expression("Nothing else.") is True
|
||||
assert is_negative_expression("வேற எதுவும் இல்லை.") is True
|
||||
assert is_negative_expression("No other problems.") is True
|
||||
dur_g = (time.perf_counter() - t0) * 1000
|
||||
|
||||
results["scenarios"]["SCENARIO_G_NOTHING_ELSE"] = {
|
||||
"passed": True,
|
||||
"latency_ms": round(dur_g, 2)
|
||||
}
|
||||
|
||||
# =========================================================================
|
||||
# SCENARIO H: Medical & Surgical History Volunteered
|
||||
# =========================================================================
|
||||
t0 = time.perf_counter()
|
||||
sess_h = create_mock_session("SESS-H")
|
||||
msg_h = "எனக்கு 8 வருஷமா diabetes இருக்கு. 5 வருஷத்துக்கு முன்னாடி operation பண்ணிருக்கேன்."
|
||||
res_h = await orchestrator.process(sess_h, MagicMock(state_json=json.dumps({})), [], msg_h)
|
||||
mh_h = res_h["state_updates"].get("medical_history", [])
|
||||
dur_h = (time.perf_counter() - t0) * 1000
|
||||
|
||||
scen_h_pass = (
|
||||
len(mh_h) >= 1
|
||||
and mh_h[0].get("condition") == "Diabetes"
|
||||
)
|
||||
results["scenarios"]["SCENARIO_H_HISTORY_VOLUNTEERED"] = {
|
||||
"passed": scen_h_pass,
|
||||
"medical_history": mh_h,
|
||||
"latency_ms": round(dur_h, 2)
|
||||
}
|
||||
|
||||
# =========================================================================
|
||||
# SCENARIO I: Structured Medication Detail
|
||||
# =========================================================================
|
||||
t0 = time.perf_counter()
|
||||
sess_i = create_mock_session("SESS-I")
|
||||
msg_i = "Metformin 500 mg காலை மாலை சாப்பிடுறேன். BP மாத்திரை 10 mg இரவு எடுக்குறேன்."
|
||||
res_i = await orchestrator.process(sess_i, MagicMock(state_json=json.dumps({})), [], msg_i)
|
||||
meds_i = res_i["state_updates"].get("medications", [])
|
||||
syms_i = res_i["state_updates"].get("symptoms", []) or []
|
||||
dur_i = (time.perf_counter() - t0) * 1000
|
||||
|
||||
scen_i_pass = (
|
||||
len(meds_i) >= 1
|
||||
and len(syms_i) == 0
|
||||
)
|
||||
results["scenarios"]["SCENARIO_I_MEDICATION_DETAIL"] = {
|
||||
"passed": scen_i_pass,
|
||||
"medications": meds_i,
|
||||
"no_fake_symptoms": len(syms_i) == 0,
|
||||
"latency_ms": round(dur_i, 2)
|
||||
}
|
||||
|
||||
# =========================================================================
|
||||
# SCENARIO J: Allergy Detail
|
||||
# =========================================================================
|
||||
t0 = time.perf_counter()
|
||||
sess_j = create_mock_session("SESS-J")
|
||||
msg_j = "Penicillin எடுத்தா உடம்பு முழுக்க rash வரும்."
|
||||
res_j = await orchestrator.process(sess_j, MagicMock(state_json=json.dumps({})), [], msg_j)
|
||||
algs_j = res_j["state_updates"].get("allergies") or []
|
||||
if isinstance(algs_j, dict):
|
||||
algs_j = algs_j.get("items", [])
|
||||
dur_j = (time.perf_counter() - t0) * 1000
|
||||
|
||||
scen_j_pass = (
|
||||
len(algs_j) >= 1
|
||||
and algs_j[0].get("name") == "Penicillin"
|
||||
)
|
||||
results["scenarios"]["SCENARIO_J_ALLERGY_DETAIL"] = {
|
||||
"passed": scen_j_pass,
|
||||
"allergies": algs_j,
|
||||
"latency_ms": round(dur_j, 2)
|
||||
}
|
||||
|
||||
# =========================================================================
|
||||
# SCENARIO K: Mental Health Adaptation
|
||||
# =========================================================================
|
||||
t0 = time.perf_counter()
|
||||
sess_k = create_mock_session("SESS-K", category_code="psychiatry", category_name="Psychiatry")
|
||||
msg_k = "எனக்கு ரொம்ப stress ஆகுது. தூக்கம் சரியா வரல. வேலை செய்யவே மனசு வரல."
|
||||
res_k = await orchestrator.process(sess_k, MagicMock(state_json=json.dumps({})), [], msg_k)
|
||||
reply_k = res_k.get("reply", "")
|
||||
dur_k = (time.perf_counter() - t0) * 1000
|
||||
|
||||
scen_k_pass = (
|
||||
not any(w in reply_k for w in ["இருமல்", "பிபி", "வயிற்று வலி", "ஆஸ்துமா"])
|
||||
)
|
||||
results["scenarios"]["SCENARIO_K_MENTAL_HEALTH"] = {
|
||||
"passed": scen_k_pass,
|
||||
"reply": reply_k,
|
||||
"no_unrelated_questions": scen_k_pass,
|
||||
"latency_ms": round(dur_k, 2)
|
||||
}
|
||||
|
||||
# =========================================================================
|
||||
# SCENARIO L: Emergency & Crisis Overrides
|
||||
# =========================================================================
|
||||
t0 = time.perf_counter()
|
||||
sess_l1 = create_mock_session("SESS-L1")
|
||||
res_l1 = await orchestrator.process(sess_l1, MagicMock(state_json=json.dumps({})), [], "மூச்சு விட முடியல.")
|
||||
|
||||
sess_l2 = create_mock_session("SESS-L2")
|
||||
res_l2 = await orchestrator.process(sess_l2, MagicMock(state_json=json.dumps({})), [], "திடீர்னு கை கால் வேலை செய்யல.")
|
||||
|
||||
sess_l3 = create_mock_session("SESS-L3")
|
||||
res_l3 = await orchestrator.process(sess_l3, MagicMock(state_json=json.dumps({})), [], "நான் என்னையே காயப்படுத்திக்கணும்னு தோணுது.")
|
||||
dur_l = (time.perf_counter() - t0) * 1000
|
||||
|
||||
scen_l_pass = (
|
||||
"108" in res_l1["reply"] and res_l1.get("completed") is True
|
||||
and "108" in res_l2["reply"] and res_l2.get("completed") is True
|
||||
and ("14416" in res_l3["reply"] or "Tele-MANAS" in res_l3["reply"]) and res_l3.get("completed") is True
|
||||
)
|
||||
results["scenarios"]["SCENARIO_L_EMERGENCY_AND_CRISIS"] = {
|
||||
"passed": scen_l_pass,
|
||||
"emergency_medical_108": "108" in res_l1["reply"] and "108" in res_l2["reply"],
|
||||
"crisis_tele_manas_14416": "14416" in res_l3["reply"] or "Tele-MANAS" in res_l3["reply"],
|
||||
"latency_ms": round(dur_l, 2)
|
||||
}
|
||||
|
||||
# =========================================================================
|
||||
# SCENARIO M: Multilingual Duration Normalization
|
||||
# =========================================================================
|
||||
t0 = time.perf_counter()
|
||||
dur_m1 = normalize_duration_from_message("10 டேஸா இருக்கு") == "10 days"
|
||||
dur_m2 = normalize_duration_from_message("10 days-ah இருக்கு") == "10 days"
|
||||
dur_m3 = normalize_duration_from_message("ஒரு பத்து நாளா இருக்கு") == "10 days"
|
||||
dur_m4 = normalize_duration_from_message("ரெண்டு நாளா") == "2 days"
|
||||
dur_m5 = normalize_duration_from_message("நேத்து ல இருந்து") == "since yesterday"
|
||||
dur_m6 = normalize_duration_from_message("மூணு நாளா") == "3 days"
|
||||
dur_m7 = normalize_duration_from_message("మూడు రోజుల నుంచి ఉంది") == "3 days"
|
||||
dur_m8 = normalize_duration_from_message("ಮೂರು ದಿನದಿಂದ ಇದೆ") == "3 days"
|
||||
dur_m9 = normalize_duration_from_message("तीन दिनों से") == "3 days"
|
||||
dur_m_total = (time.perf_counter() - t0) * 1000
|
||||
|
||||
scen_m_pass = all([dur_m1, dur_m2, dur_m3, dur_m4, dur_m5, dur_m6, dur_m7, dur_m8, dur_m9])
|
||||
results["scenarios"]["SCENARIO_M_MULTILINGUAL_DURATION"] = {
|
||||
"passed": scen_m_pass,
|
||||
"tamil_tanglish_pass": all([dur_m1, dur_m2, dur_m3, dur_m4, dur_m5, dur_m6]),
|
||||
"telugu_pass": dur_m7,
|
||||
"kannada_pass": dur_m8,
|
||||
"hindi_pass": dur_m9,
|
||||
"latency_ms": round(dur_m_total, 2)
|
||||
}
|
||||
|
||||
# =========================================================================
|
||||
# SCENARIO N: Specialty Adaptation
|
||||
# =========================================================================
|
||||
t0 = time.perf_counter()
|
||||
spec_gm = classify_specialty_from_symptoms("காய்ச்சல் இருக்கு.")["code"] == "general_medicine"
|
||||
spec_neuro = classify_specialty_from_symptoms("தலை சுத்துது.")["code"] == "neurology"
|
||||
spec_cardio = classify_specialty_from_symptoms("நெஞ்சு படபடன்னு இருக்கு.")["code"] == "cardiology"
|
||||
spec_derm = classify_specialty_from_symptoms("தோலில் அரிப்பு இருக்கு.")["code"] == "dermatology"
|
||||
spec_psych = classify_specialty_from_symptoms("எனக்கு anxiety இருக்கு.")["code"] == "psychiatry"
|
||||
dur_n = (time.perf_counter() - t0) * 1000
|
||||
|
||||
scen_n_pass = all([spec_gm, spec_neuro, spec_cardio, spec_derm, spec_psych])
|
||||
results["scenarios"]["SCENARIO_N_SPECIALTY_ADAPTATION"] = {
|
||||
"passed": scen_n_pass,
|
||||
"general_medicine": spec_gm,
|
||||
"neurology": spec_neuro,
|
||||
"cardiology": spec_cardio,
|
||||
"dermatology": spec_derm,
|
||||
"psychiatry": spec_psych,
|
||||
"latency_ms": round(dur_n, 2)
|
||||
}
|
||||
|
||||
# =========================================================================
|
||||
# QUESTION SELECTION & PREMATURE COMPLETION VERIFICATION
|
||||
# =========================================================================
|
||||
sess_q = create_mock_session("SESS-Q")
|
||||
msg_q1 = "எனக்கு ரொம்ப தலைவலி, கை கால் வலியாக இருக்குது. என் நெஞ்சு அப்பப்ப படபடன்னு இருக்குது. தலை சுத்துது, தலை பாரமாக இருக்குது."
|
||||
res_q1 = await orchestrator.process(sess_q, MagicMock(state_json=json.dumps({})), [], msg_q1)
|
||||
s_q1 = merge_state_updates({}, res_q1["state_updates"])
|
||||
|
||||
res_q2 = await orchestrator.process(
|
||||
sess_q, MagicMock(state_json=json.dumps(s_q1)),
|
||||
[{"role": "patient", "message": msg_q1}, {"role": "assistant", "message": res_q1["reply"]}],
|
||||
"பிரச்சனை வந்து ஒரு மூணு நாளா இருக்கு."
|
||||
)
|
||||
|
||||
q_sel_pass = (
|
||||
res_q2.get("completed") is False
|
||||
and "எத்தனை நாளா" not in res_q2.get("reply", "")
|
||||
)
|
||||
results["scenarios"]["QUESTION_SELECTION_AND_NO_PREMATURE_COMPLETION"] = {
|
||||
"passed": q_sel_pass,
|
||||
"completed_after_duration_alone": res_q2.get("completed"),
|
||||
"repeated_duration_question": "எத்தனை நாளா" in res_q2.get("reply", "")
|
||||
}
|
||||
|
||||
# Count passes / fails
|
||||
total = len(results["scenarios"])
|
||||
passed = sum(1 for s in results["scenarios"].values() if s.get("passed"))
|
||||
failed = total - passed
|
||||
|
||||
results["summary"]["total_scenarios"] = total
|
||||
results["summary"]["passed_scenarios"] = passed
|
||||
results["summary"]["failed_scenarios"] = failed
|
||||
results["summary"]["production_ready"] = (failed == 0)
|
||||
|
||||
# Save to JSON
|
||||
with open("FLOW_FINAL_AUDIT_RESULTS.json", "w", encoding="utf-8") as f:
|
||||
json.dump(results, f, indent=2, ensure_ascii=False)
|
||||
|
||||
print(f"AUDIT COMPLETE: {passed}/{total} scenarios PASSED. Production ready: {results['summary']['production_ready']}")
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(run_audit())
|
||||
@@ -0,0 +1,22 @@
|
||||
import sys
|
||||
import os
|
||||
if hasattr(sys.stdout, "reconfigure"):
|
||||
sys.stdout.reconfigure(encoding="utf-8")
|
||||
sys.path.insert(0, r"c:\Users\Lenovo\New folder (2)\ai-patient-onboarding")
|
||||
from app.services.contextual_normalizer import normalize_duration_from_message
|
||||
|
||||
test_cases = [
|
||||
("10 டேஸா இருக்கு", "10 days"),
|
||||
("10 days-ah இருக்கு", "10 days"),
|
||||
("ஒரு பத்து நாளா இருக்கு", "10 days"),
|
||||
("ரெண்டு நாளா", "2 days"),
|
||||
("நேத்து ல இருந்து", "since yesterday"),
|
||||
("மூணு நாளா", "3 days"),
|
||||
("మూడు రోజుల నుంచి ఉంది", "3 days"),
|
||||
("ಮೂರು ದಿನದಿಂದ ಇದೆ", "3 days"),
|
||||
("तीन दिनों से", "3 days")
|
||||
]
|
||||
|
||||
for text, expected in test_cases:
|
||||
res = normalize_duration_from_message(text)
|
||||
print(f"Text: '{text}' -> Got: '{res}' | Expected: '{expected}' | Pass: {res == expected}")
|
||||
@@ -1,32 +1,37 @@
|
||||
import httpx
|
||||
|
||||
client = httpx.Client(timeout=60.0)
|
||||
def main():
|
||||
client = httpx.Client(timeout=60.0)
|
||||
|
||||
start_res = client.post('http://127.0.0.1:8000/v1/voice-onboarding/start', json={
|
||||
'patient': {'id': 'PAT-INTAKE-5', 'first_name': 'Arun', 'last_name': 'Kumar'},
|
||||
'category': {'type': 'general', 'code': 'general_medicine', 'name': 'General Medicine'},
|
||||
'language': 'en-IN'
|
||||
}).json()
|
||||
start_res = client.post('http://127.0.0.1:8000/v1/voice-onboarding/start', json={
|
||||
'patient': {'id': 'PAT-INTAKE-5', 'first_name': 'Arun', 'last_name': 'Kumar'},
|
||||
'category': {'type': 'general', 'code': 'general_medicine', 'name': 'General Medicine'},
|
||||
'language': 'en-IN'
|
||||
}).json()
|
||||
|
||||
sid = start_res['session_id']
|
||||
print("=== DEBUGGING 5-STAGE DIALOGUE ===")
|
||||
print("Session ID:", sid)
|
||||
sid = start_res['session_id']
|
||||
print("=== DEBUGGING 5-STAGE DIALOGUE ===")
|
||||
print("Session ID:", sid)
|
||||
|
||||
dialogue = [
|
||||
"Chest pain and shortness of breath",
|
||||
"2 days, pain severity is 8 out of 10",
|
||||
"Yes, sweating and dizziness",
|
||||
"High blood pressure and Diabetes",
|
||||
"Taking Amlodipine, no drug allergies"
|
||||
]
|
||||
dialogue = [
|
||||
"Chest pain and shortness of breath",
|
||||
"2 days, pain severity is 8 out of 10",
|
||||
"Yes, sweating and dizziness",
|
||||
"High blood pressure and Diabetes",
|
||||
"Taking Amlodipine, no drug allergies"
|
||||
]
|
||||
|
||||
for i, turn_input in enumerate(dialogue, 1):
|
||||
res = client.post(f"http://127.0.0.1:8000/v1/voice-onboarding/{sid}/turn", data={"text_fallback": turn_input})
|
||||
print(f"Turn {i} Status Code: {res.status_code}")
|
||||
if res.status_code != 200:
|
||||
print("FULL ERROR BODY:", res.text)
|
||||
break
|
||||
data = res.json()
|
||||
print(f"Turn {i} Input: '{turn_input}'")
|
||||
print(f" --> Next Stage: {data['stage']} | Completed: {data['completed']}")
|
||||
print("-" * 70)
|
||||
for i, turn_input in enumerate(dialogue, 1):
|
||||
res = client.post(f"http://127.0.0.1:8000/v1/voice-onboarding/{sid}/turn", data={"text_fallback": turn_input})
|
||||
print(f"Turn {i} Status Code: {res.status_code}")
|
||||
if res.status_code != 200:
|
||||
print("FULL ERROR BODY:", res.text)
|
||||
break
|
||||
data = res.json()
|
||||
print(f"Turn {i} Input: '{turn_input}'")
|
||||
print(f" --> Next Stage: {data['stage']} | Completed: {data['completed']}")
|
||||
print("-" * 70)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
||||
@@ -1,30 +1,35 @@
|
||||
import httpx
|
||||
|
||||
client = httpx.Client(timeout=30.0)
|
||||
def main():
|
||||
client = httpx.Client(timeout=30.0)
|
||||
|
||||
test_cases = [
|
||||
("Cardiology Test", "எனக்கு நெஞ்சு வலி மற்றும் மூச்சு திணறல் இருக்கு"),
|
||||
("Dermatology Test", "தோல் அரிப்பு மற்றும் படை உள்ளது"),
|
||||
("Orthopedics Test", "மூட்டு வலி நடக்க முடியல"),
|
||||
("Neurology Test", "headache and dizziness for 3 days"),
|
||||
("Gastroenterology Test", "வயிற்று வலி மற்றும் வாந்தி"),
|
||||
("Pulmonology Test", "இருமல் மற்றும் சளி அதிகமாக உள்ளது"),
|
||||
("ENT Test", "காது வலி மற்றும் தொண்டை வலி")
|
||||
]
|
||||
test_cases = [
|
||||
("Cardiology Test", "எனக்கு நெஞ்சு வலி மற்றும் மூச்சு திணறல் இருக்கு"),
|
||||
("Dermatology Test", "தோல் அரிப்பு மற்றும் படை உள்ளது"),
|
||||
("Orthopedics Test", "மூட்டு வலி நடக்க முடியல"),
|
||||
("Neurology Test", "headache and dizziness for 3 days"),
|
||||
("Gastroenterology Test", "வயிற்று வலி மற்றும் வாந்தி"),
|
||||
("Pulmonology Test", "இருமல் மற்றும் சளி அதிகமாக உள்ளது"),
|
||||
("ENT Test", "காது வலி மற்றும் தொண்டை வலி")
|
||||
]
|
||||
|
||||
print("=== AUTOMATIC SPECIALTY TRIAGE ENGINE TEST ===")
|
||||
for label, symptom in test_cases:
|
||||
start_res = client.post("http://127.0.0.1:8000/v1/voice-onboarding/start", json={
|
||||
"patient": {"id": "PAT-AUTO", "first_name": "Arun", "last_name": "Kumar"},
|
||||
"category": {"type": "general", "code": "general_medicine", "name": "General Medicine"},
|
||||
"language": "ta-IN"
|
||||
})
|
||||
sid = start_res.json()["session_id"]
|
||||
print("=== AUTOMATIC SPECIALTY TRIAGE ENGINE TEST ===")
|
||||
for label, symptom in test_cases:
|
||||
start_res = client.post("http://127.0.0.1:8000/v1/voice-onboarding/start", json={
|
||||
"patient": {"id": "PAT-AUTO", "first_name": "Arun", "last_name": "Kumar"},
|
||||
"category": {"type": "general", "code": "general_medicine", "name": "General Medicine"},
|
||||
"language": "ta-IN"
|
||||
})
|
||||
sid = start_res.json()["session_id"]
|
||||
|
||||
turn_res = client.post(f"http://127.0.0.1:8000/v1/voice-onboarding/{sid}/turn", data={"text_fallback": symptom})
|
||||
spec = turn_res.json().get("suggested_specialty", {})
|
||||
turn_res = client.post(f"http://127.0.0.1:8000/v1/voice-onboarding/{sid}/turn", data={"text_fallback": symptom})
|
||||
spec = turn_res.json().get("suggested_specialty", {})
|
||||
|
||||
print(f"Label: {label}")
|
||||
print(f" Patient Symptom: '{symptom}'")
|
||||
print(f" --> Auto-Assigned Specialty: {spec.get('name')} (Code: {spec.get('code')})")
|
||||
print(f" --> Reasoning: {spec.get('reason')}\n")
|
||||
print(f"Label: {label}")
|
||||
print(f" Patient Symptom: '{symptom}'")
|
||||
print(f" --> Auto-Assigned Specialty: {spec.get('name')} (Code: {spec.get('code')})")
|
||||
print(f" --> Reasoning: {spec.get('reason')}\n")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
||||
@@ -1,98 +1,92 @@
|
||||
import urllib.request
|
||||
import urllib.parse
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
import time
|
||||
import pytest
|
||||
from fastapi.testclient import TestClient
|
||||
|
||||
BASE_URL = 'http://127.0.0.1:8000'
|
||||
VALID_KEY = 'skyheal_secure_api_key_2026'
|
||||
# Add project root to sys.path
|
||||
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")))
|
||||
|
||||
def http_post(url, body_dict=None, token=None, headers=None):
|
||||
if headers is None:
|
||||
headers = {'Content-Type': 'application/json'}
|
||||
else:
|
||||
headers = dict(headers)
|
||||
if 'Content-Type' not in headers:
|
||||
headers['Content-Type'] = 'application/json'
|
||||
from app.main import app
|
||||
|
||||
if token:
|
||||
headers['Authorization'] = f"Bearer {token}"
|
||||
client = TestClient(app)
|
||||
VALID_KEY = "skyheal_secure_api_key_2026"
|
||||
|
||||
data_bytes = json.dumps(body_dict).encode('utf-8') if body_dict is not None else b''
|
||||
req = urllib.request.Request(url, data=data_bytes, headers=headers, method='POST')
|
||||
try:
|
||||
with urllib.request.urlopen(req) as res:
|
||||
return res.status, json.loads(res.read().decode('utf-8')), res.headers
|
||||
except urllib.error.HTTPError as e:
|
||||
body = e.read().decode('utf-8', errors='ignore')
|
||||
try:
|
||||
parsed = json.loads(body)
|
||||
except:
|
||||
parsed = body
|
||||
return e.code, parsed, e.headers
|
||||
|
||||
def http_get(url, token=None, headers=None):
|
||||
if headers is None:
|
||||
headers = {}
|
||||
else:
|
||||
headers = dict(headers)
|
||||
if token:
|
||||
headers['Authorization'] = f"Bearer {token}"
|
||||
req = urllib.request.Request(url, headers=headers, method='GET')
|
||||
try:
|
||||
with urllib.request.urlopen(req) as res:
|
||||
ct = res.headers.get('Content-Type', '')
|
||||
body = res.read().decode('utf-8', errors='ignore')
|
||||
if 'json' in ct:
|
||||
return res.status, json.loads(body), res.headers
|
||||
return res.status, body, res.headers
|
||||
except urllib.error.HTTPError as e:
|
||||
body = e.read().decode('utf-8', errors='ignore')
|
||||
try:
|
||||
parsed = json.loads(body)
|
||||
except:
|
||||
parsed = body
|
||||
return e.code, parsed, e.headers
|
||||
def test_reject_unauthenticated_request():
|
||||
"""1. Reject Unauthenticated Request (401)"""
|
||||
res = client.post("/v1/onboarding/start", json={"patient": {"id": "P1"}})
|
||||
assert res.status_code == 401
|
||||
|
||||
|
||||
def test_authenticated_session_creation():
|
||||
"""2. Authenticated Session Creation (200)"""
|
||||
res = client.post(
|
||||
"/v1/onboarding/start",
|
||||
json={
|
||||
"patient": {"id": "PAT-PROD-01", "name": "Prod Patient", "age": 32, "gender": "female"},
|
||||
"category": {"type": "general", "code": "general_medicine", "name": "General Medicine"}
|
||||
},
|
||||
headers={"Authorization": "Bearer test_token_PAT-PROD-01"}
|
||||
)
|
||||
assert res.status_code == 200
|
||||
data = res.json()
|
||||
assert data.get("session_id") is not None
|
||||
|
||||
|
||||
def test_idor_prevention_cross_session_access():
|
||||
"""3. IDOR Prevention Cross-Session Access (403)"""
|
||||
# Create session as PAT-PROD-01
|
||||
start_res = client.post(
|
||||
"/v1/onboarding/start",
|
||||
json={
|
||||
"patient": {"id": "PAT-PROD-01", "name": "Prod Patient", "age": 32, "gender": "female"},
|
||||
"category": {"type": "general", "code": "general_medicine", "name": "General Medicine"}
|
||||
},
|
||||
headers={"Authorization": "Bearer test_token_PAT-PROD-01"}
|
||||
)
|
||||
s_id = start_res.json().get("session_id")
|
||||
assert s_id is not None
|
||||
|
||||
# Attempt access as PAT-PROD-02
|
||||
msg_res = client.post(
|
||||
f"/v1/onboarding/{s_id}/message",
|
||||
json={"message": "Unauthorized message"},
|
||||
headers={"Authorization": "Bearer test_token_PAT-PROD-02"}
|
||||
)
|
||||
assert msg_res.status_code == 403
|
||||
|
||||
|
||||
def test_security_headers_and_request_id():
|
||||
"""4. Security Headers & X-Request-ID Correlation"""
|
||||
res = client.get("/health")
|
||||
assert res.status_code == 200
|
||||
assert "X-Request-ID" in res.headers
|
||||
assert res.headers.get("X-Content-Type-Options") == "nosniff"
|
||||
assert res.headers.get("X-Frame-Options") == "DENY"
|
||||
|
||||
|
||||
def test_schema_validation_negative_age_rejection():
|
||||
"""5. Schema Validation Negative Age Rejection (422)"""
|
||||
res = client.post(
|
||||
"/v1/onboarding/start",
|
||||
json={
|
||||
"patient": {"id": "PAT-NEG", "name": "Invalid Age", "age": -10, "gender": "female"},
|
||||
"category": {"type": "general", "code": "general_medicine", "name": "General Medicine"}
|
||||
},
|
||||
headers={"Authorization": f"Bearer {VALID_KEY}"}
|
||||
)
|
||||
assert res.status_code == 422
|
||||
|
||||
|
||||
def run_automated_production_tests():
|
||||
test_results = []
|
||||
test_reject_unauthenticated_request()
|
||||
test_authenticated_session_creation()
|
||||
test_idor_prevention_cross_session_access()
|
||||
test_security_headers_and_request_id()
|
||||
test_schema_validation_negative_age_rejection()
|
||||
print("ALL PRODUCTION SECURITY TESTS PASSED SUCCESSFULLY!")
|
||||
|
||||
# 1. Test Authentication Security
|
||||
c_no_auth, _, _ = http_post(f'{BASE_URL}/v1/onboarding/start', {"patient": {"id": "P1"}})
|
||||
test_results.append({"test": "1. Reject Unauthenticated Request (401)", "passed": c_no_auth == 401, "status": c_no_auth})
|
||||
|
||||
c_auth, r_auth, _ = http_post(f'{BASE_URL}/v1/onboarding/start', {
|
||||
"patient": {"id": "PAT-PROD-01", "name": "Prod Patient", "age": 32, "gender": "female"},
|
||||
"category": {"type": "general", "code": "general_medicine", "name": "General Medicine"}
|
||||
}, token="test_token_PAT-PROD-01")
|
||||
s_id = r_auth.get("session_id") if isinstance(r_auth, dict) else None
|
||||
test_results.append({"test": "2. Authenticated Session Creation (200)", "passed": c_auth == 200, "status": c_auth})
|
||||
|
||||
# 2. Test IDOR Session Isolation
|
||||
if s_id:
|
||||
c_idor, r_idor, _ = http_post(f'{BASE_URL}/v1/onboarding/{s_id}/message', {"message": "Unauthorized message"}, token="test_token_PAT-PROD-02")
|
||||
test_results.append({"test": "3. IDOR Prevention Cross-Session Access (403)", "passed": c_idor == 403, "status": c_idor})
|
||||
|
||||
# 3. Test Response Security Headers & X-Request-ID
|
||||
c_hdr, _, hdrs = http_get(f'{BASE_URL}/health')
|
||||
req_id = hdrs.get("X-Request-ID")
|
||||
nosniff = hdrs.get("X-Content-Type-Options")
|
||||
frame = hdrs.get("X-Frame-Options")
|
||||
hdr_passed = req_id is not None and nosniff == "nosniff" and frame == "DENY"
|
||||
test_results.append({"test": "4. Security Headers & X-Request-ID Correlation", "passed": hdr_passed, "status": c_hdr})
|
||||
|
||||
# 4. Test Age Validation Schema Bounds
|
||||
c_age_neg, _, _ = http_post(f'{BASE_URL}/v1/onboarding/start', {
|
||||
"patient": {"id": "PAT-NEG", "name": "Invalid Age", "age": -10, "gender": "female"},
|
||||
"category": {"type": "general", "code": "general_medicine", "name": "General Medicine"}
|
||||
}, token=VALID_KEY)
|
||||
test_results.append({"test": "5. Schema Validation Negative Age Rejection (422)", "passed": c_age_neg == 422, "status": c_age_neg})
|
||||
|
||||
total = len(test_results)
|
||||
passed = sum(1 for t in test_results if t['passed'])
|
||||
print(f"AUTOMATED PRODUCTION TESTS COMPLETED: {passed}/{total} Passed.")
|
||||
|
||||
with open('tests_production_results.json', 'w', encoding='utf-8') as f:
|
||||
json.dump({"total": total, "passed": passed, "results": test_results}, f, indent=2)
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
run_automated_production_tests()
|
||||
|
||||
@@ -0,0 +1,386 @@
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
import asyncio
|
||||
import pytest
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
# Add project root to sys.path
|
||||
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
|
||||
|
||||
from app.ai.orchestrator import (
|
||||
AIOrchestrator,
|
||||
merge_state_updates,
|
||||
check_required_missing,
|
||||
classify_specialty_from_symptoms,
|
||||
_field_status,
|
||||
)
|
||||
from app.services.contextual_normalizer import (
|
||||
normalize_duration_from_message,
|
||||
detect_previous_question_intent,
|
||||
)
|
||||
from app.services.completion_service import CompletionService
|
||||
from app.schemas.onboarding import (
|
||||
PatientOnboardingResult,
|
||||
StartOnboardingResponse,
|
||||
SendMessageResponse,
|
||||
CompleteOnboardingResponse,
|
||||
)
|
||||
from app.schemas.voice import VoiceTurnResponse
|
||||
|
||||
|
||||
def get_mock_session():
|
||||
session = MagicMock()
|
||||
session.session_id = "ONB-FLOW-TEST-001"
|
||||
session.patient_id = "PAT-FLOW-001"
|
||||
session.patient_name = "Ramesh Kumar"
|
||||
session.age = 35
|
||||
session.gender = "male"
|
||||
session.category_type = "general"
|
||||
session.category_code = "general_medicine"
|
||||
session.category_name = "General Medicine"
|
||||
session.language = "ta-IN"
|
||||
session.stage = "greeting"
|
||||
session.status = "in_progress"
|
||||
return session
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 1: Patient Provides All Information in One Message
|
||||
# =====================================================================
|
||||
def test_1_patient_provides_all_info_in_one_message():
|
||||
async def run():
|
||||
mock_session = get_mock_session()
|
||||
orchestrator = AIOrchestrator()
|
||||
msg = "எனக்கு diabetes இருக்கு, metformin 500 mg தினமும் சாப்பிடுறேன், தலைவலி மூணு நாளா இருக்கு, penicillin allergy இருக்கு."
|
||||
state_mock = MagicMock(state_json=json.dumps({}))
|
||||
|
||||
res = await orchestrator.process(mock_session, state_mock, [], msg)
|
||||
merged = merge_state_updates({}, res["state_updates"])
|
||||
|
||||
assert len(merged.get("medical_history", [])) >= 1
|
||||
assert len(merged.get("medications", [])) >= 1
|
||||
assert len(merged.get("allergies", [])) >= 1
|
||||
assert len(merged.get("symptoms", [])) >= 1
|
||||
assert res.get("completed") is True
|
||||
|
||||
asyncio.run(run())
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 2: Patient Gives Symptoms First
|
||||
# =====================================================================
|
||||
def test_2_patient_gives_symptoms_first():
|
||||
async def run():
|
||||
mock_session = get_mock_session()
|
||||
orchestrator = AIOrchestrator()
|
||||
msg = "எனக்கு தலைவலி இருக்கு."
|
||||
state_mock = MagicMock(state_json=json.dumps({}))
|
||||
|
||||
res = await orchestrator.process(mock_session, state_mock, [], msg)
|
||||
assert res.get("completed") is False
|
||||
assert len(res.get("reply", "")) > 5
|
||||
|
||||
asyncio.run(run())
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 3: Patient Gives Duration (No Fake Symptom Created)
|
||||
# =====================================================================
|
||||
def test_3_patient_gives_duration_no_fake_symptom():
|
||||
async def run():
|
||||
mock_session = get_mock_session()
|
||||
orchestrator = AIOrchestrator()
|
||||
state_mock = MagicMock(state_json=json.dumps({
|
||||
"reason_for_visit": "தலைவலி",
|
||||
"symptoms": [{"name": "தலைவலி"}]
|
||||
}))
|
||||
|
||||
res = await orchestrator.process(
|
||||
mock_session, state_mock,
|
||||
[{"role": "patient", "message": "தலைவலி இருக்கு", "stage": "greeting"}],
|
||||
"மூணு நாளா இருக்கு."
|
||||
)
|
||||
merged = merge_state_updates({"symptoms": [{"name": "தலைவலி"}]}, res["state_updates"])
|
||||
syms = merged.get("symptoms", [])
|
||||
|
||||
assert len(syms) == 1
|
||||
assert syms[0]["name"] == "தலைவலி"
|
||||
assert syms[0]["duration"] == "3 days"
|
||||
assert res.get("completed") is False
|
||||
|
||||
asyncio.run(run())
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 4: Tamil Tanglish Duration
|
||||
# =====================================================================
|
||||
def test_4_tamil_tanglish_duration():
|
||||
assert normalize_duration_from_message("10 டேஸா இருக்கு.") == "10 days"
|
||||
assert normalize_duration_from_message("10 days-ah இருக்கு.") == "10 days"
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 5: Telugu Duration Support
|
||||
# =====================================================================
|
||||
def test_5_telugu_duration():
|
||||
assert normalize_duration_from_message("మూడు రోజుల నుంచి ఉంది") == "3 days"
|
||||
assert normalize_duration_from_message("3 రోజుల నుండి ఉంది") == "3 days"
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 6: Kannada Duration Support
|
||||
# =====================================================================
|
||||
def test_6_kannada_duration():
|
||||
assert normalize_duration_from_message("ಮೂರು ದಿನದಿಂದ ಇದೆ") == "3 days"
|
||||
assert normalize_duration_from_message("3 ದಿನದಿಂದ ಇದೆ") == "3 days"
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 7: Medication First (No Fake Symptom Created)
|
||||
# =====================================================================
|
||||
def test_7_medication_first_no_fake_symptom():
|
||||
async def run():
|
||||
mock_session = get_mock_session()
|
||||
orchestrator = AIOrchestrator()
|
||||
state_mock = MagicMock(state_json=json.dumps({}))
|
||||
|
||||
res = await orchestrator.process(mock_session, state_mock, [], "Metformin 500 mg தினமும் சாப்பிடுறேன்.")
|
||||
updates = res["state_updates"]
|
||||
|
||||
meds = updates.get("medications") or []
|
||||
syms = updates.get("symptoms") or []
|
||||
|
||||
if isinstance(meds, dict):
|
||||
meds = meds.get("items", [])
|
||||
|
||||
assert len(meds) == 1
|
||||
assert meds[0]["name"] == "Metformin"
|
||||
assert len(syms) == 0
|
||||
|
||||
asyncio.run(run())
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 8: Cross-Language Symptom Deduplication
|
||||
# =====================================================================
|
||||
def test_8_cross_language_symptom_deduplication():
|
||||
state1 = merge_state_updates({}, {"symptoms": [{"name": "தலைவலி"}]})
|
||||
state2 = merge_state_updates(state1, {"symptoms": [{"name": "headache", "duration": "3 days"}]})
|
||||
syms = state2.get("symptoms", [])
|
||||
|
||||
assert len(syms) == 1
|
||||
assert syms[0]["duration"] == "3 days"
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 9: Progressive Enrichment
|
||||
# =====================================================================
|
||||
def test_9_progressive_enrichment():
|
||||
s1 = merge_state_updates({}, {"medical_history": [{"condition": "Diabetes"}]})
|
||||
s2 = merge_state_updates(s1, {"medical_history": [{"condition": "Diabetes", "duration": "8 years"}]})
|
||||
mh = s2.get("medical_history", [])
|
||||
|
||||
assert len(mh) == 1
|
||||
assert mh[0]["condition"] == "Diabetes"
|
||||
assert mh[0]["duration"] == "8 years"
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 10: Patient Correction
|
||||
# =====================================================================
|
||||
def test_10_patient_correction():
|
||||
s1 = merge_state_updates({}, {"symptoms": [{"name": "headache", "duration": "3 days"}]})
|
||||
s2 = merge_state_updates(s1, {"symptoms": [{"name": "headache", "duration": "10 days"}]})
|
||||
syms = s2.get("symptoms", [])
|
||||
|
||||
assert len(syms) == 1
|
||||
assert syms[0]["duration"] == "10 days"
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 11: Emergency Override (Spoken Tamil)
|
||||
# =====================================================================
|
||||
def test_11_emergency_override_tamil():
|
||||
async def run():
|
||||
mock_session = get_mock_session()
|
||||
orchestrator = AIOrchestrator()
|
||||
state_mock = MagicMock(state_json=json.dumps({}))
|
||||
|
||||
res = await orchestrator.process(mock_session, state_mock, [], "மூச்சு விட முடியல")
|
||||
|
||||
assert "108" in res["reply"]
|
||||
assert res.get("completed") is True
|
||||
|
||||
asyncio.run(run())
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 12: Mental Health Crisis Override (Spoken Tamil)
|
||||
# =====================================================================
|
||||
def test_12_mental_health_crisis_override_tamil():
|
||||
async def run():
|
||||
mock_session = get_mock_session()
|
||||
orchestrator = AIOrchestrator()
|
||||
state_mock = MagicMock(state_json=json.dumps({}))
|
||||
|
||||
res = await orchestrator.process(mock_session, state_mock, [], "நான் என்னையே காயப்படுத்திக்கணும்னு தோணுது")
|
||||
|
||||
assert "14416" in res["reply"] or "Tele-MANAS" in res["reply"]
|
||||
assert res.get("completed") is True
|
||||
|
||||
asyncio.run(run())
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 13: Nothing Else Handling
|
||||
# =====================================================================
|
||||
def test_13_nothing_else_handling():
|
||||
async def run():
|
||||
mock_session = get_mock_session()
|
||||
orchestrator = AIOrchestrator()
|
||||
ready_state = {
|
||||
"reason_for_visit": "fever",
|
||||
"symptoms": [{"name": "fever", "duration": "2 days"}],
|
||||
"medical_history": {"status": "confirmed_none", "items": []},
|
||||
"medications": {"status": "confirmed_none", "items": []},
|
||||
"allergies": [],
|
||||
}
|
||||
state_mock = MagicMock(state_json=json.dumps(ready_state))
|
||||
|
||||
res = await orchestrator.process(
|
||||
mock_session, state_mock,
|
||||
[{"role": "assistant", "message": "Do you have any allergies?", "stage": "allergies"}],
|
||||
"வேற எதுவும் இல்லை"
|
||||
)
|
||||
|
||||
assert res.get("completed") is True
|
||||
|
||||
asyncio.run(run())
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 14: Mental Health Specialty Focus
|
||||
# =====================================================================
|
||||
def test_14_mental_health_specialty():
|
||||
async def run():
|
||||
mock_session = get_mock_session()
|
||||
mock_session.category_code = "psychiatry"
|
||||
orchestrator = AIOrchestrator()
|
||||
state_mock = MagicMock(state_json=json.dumps({}))
|
||||
|
||||
res = await orchestrator.process(mock_session, state_mock, [], "எனக்கு ரொம்ப stress, தூக்கம் சரியா வரல, இரண்டு வாரமா இப்படித்தான்.")
|
||||
reply = res["reply"]
|
||||
|
||||
assert not any(k in reply for k in ["இருமல்", "பிபி", "வயிற்று வலி"])
|
||||
|
||||
asyncio.run(run())
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 15: Cardiology Specialty Focus
|
||||
# =====================================================================
|
||||
def test_15_cardiology_specialty():
|
||||
async def run():
|
||||
mock_session = get_mock_session()
|
||||
mock_session.category_code = "cardiology"
|
||||
orchestrator = AIOrchestrator()
|
||||
state_mock = MagicMock(state_json=json.dumps({}))
|
||||
|
||||
res = await orchestrator.process(mock_session, state_mock, [], "நெஞ்சு படபடன்னு இருக்கு, நடக்கும்போது மூச்சு வாங்குது.")
|
||||
syms = res["state_updates"].get("symptoms", [])
|
||||
|
||||
assert len(syms) >= 2
|
||||
|
||||
asyncio.run(run())
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 16: Multiple Symptoms Extraction
|
||||
# =====================================================================
|
||||
def test_16_multiple_symptoms_extraction():
|
||||
async def run():
|
||||
mock_session = get_mock_session()
|
||||
orchestrator = AIOrchestrator()
|
||||
state_mock = MagicMock(state_json=json.dumps({}))
|
||||
|
||||
res = await orchestrator.process(mock_session, state_mock, [], "தலைவலி, கை கால் வலி, தலைசுற்றல், நெஞ்சு படபடப்பு.")
|
||||
syms = res["state_updates"].get("symptoms", [])
|
||||
|
||||
assert len(syms) >= 3
|
||||
|
||||
asyncio.run(run())
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 17: No False Symptom from Medication Statement
|
||||
# =====================================================================
|
||||
def test_17_no_false_symptom_from_medication():
|
||||
async def run():
|
||||
mock_session = get_mock_session()
|
||||
orchestrator = AIOrchestrator()
|
||||
state_mock = MagicMock(state_json=json.dumps({}))
|
||||
|
||||
res = await orchestrator.process(mock_session, state_mock, [], "நான் Metformin சாப்பிடுறேன்.")
|
||||
syms = res["state_updates"].get("symptoms") or []
|
||||
|
||||
assert len(syms) == 0
|
||||
|
||||
asyncio.run(run())
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 18: Session Isolation Verification
|
||||
# =====================================================================
|
||||
def test_18_session_isolation():
|
||||
state_a = merge_state_updates({}, {"symptoms": [{"name": "headache"}]})
|
||||
state_b = merge_state_updates({}, {"symptoms": [{"name": "fever"}]})
|
||||
|
||||
assert state_a["symptoms"][0]["name"] == "headache"
|
||||
assert state_b["symptoms"][0]["name"] == "fever"
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 19 & 20: API Response & Voice Turn Compatibility
|
||||
# =====================================================================
|
||||
def test_19_20_api_backward_compatibility():
|
||||
res = VoiceTurnResponse(
|
||||
success=True,
|
||||
session_id="SESS-001",
|
||||
patient_transcript="hello",
|
||||
ai_text="hi",
|
||||
stage="greeting",
|
||||
completed=False,
|
||||
audio_url="/v1/voice-onboarding/audio/123",
|
||||
response_time_ms=100.0,
|
||||
response_time_sec=0.1
|
||||
)
|
||||
assert res.success is True
|
||||
assert res.completed is False
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# TEST 21: Exact Bug Regression Test (Multi-Symptom + Duration Turn 2)
|
||||
# =====================================================================
|
||||
def test_21_exact_bug_regression():
|
||||
async def run():
|
||||
mock_session = get_mock_session()
|
||||
orchestrator = AIOrchestrator()
|
||||
msg1 = "எனக்கு ரொம்ப தலைவலி, கை கால் வலியாக இருக்குது. என் நெஞ்சு அப்பப்ப படபடன்னு இருக்குது. தலை சுத்துது, தலை பாரமாக இருக்குது."
|
||||
state_mock = MagicMock(state_json=json.dumps({}))
|
||||
|
||||
res1 = await orchestrator.process(mock_session, state_mock, [], msg1)
|
||||
s1 = merge_state_updates({}, res1["state_updates"])
|
||||
state_mock2 = MagicMock(state_json=json.dumps(s1))
|
||||
|
||||
res2 = await orchestrator.process(
|
||||
mock_session, state_mock2,
|
||||
[{"role": "patient", "message": msg1, "stage": "greeting"}, {"role": "assistant", "message": res1["reply"], "stage": res1["next_stage"]}],
|
||||
"பிரச்சனை வந்து ஒரு மூணு நாளா இருக்கு."
|
||||
)
|
||||
|
||||
# MUST NOT complete after duration answer alone
|
||||
assert res2.get("completed") is False
|
||||
assert "எத்தனை நாளா" not in res2["reply"]
|
||||
|
||||
asyncio.run(run())
|
||||
@@ -0,0 +1,977 @@
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
import pytest
|
||||
from unittest.mock import MagicMock, AsyncMock
|
||||
|
||||
# Add project root to sys.path
|
||||
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
|
||||
|
||||
from app.ai.orchestrator import (
|
||||
AIOrchestrator,
|
||||
merge_state_updates,
|
||||
classify_specialty_from_symptoms,
|
||||
check_required_missing,
|
||||
_field_status,
|
||||
_field_has_items,
|
||||
_any_symptom_has_duration,
|
||||
)
|
||||
from app.services.contextual_normalizer import normalize_duration_from_message, detect_previous_question_intent
|
||||
from app.ai.prompts import build_system_prompt
|
||||
from app.services.completion_service import CompletionService
|
||||
from app.schemas.onboarding import (
|
||||
PatientOnboardingResult,
|
||||
StartOnboardingResponse,
|
||||
SendMessageResponse,
|
||||
CompleteOnboardingResponse,
|
||||
CategoryContext,
|
||||
AIOnboardingResponse,
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_session():
|
||||
session = MagicMock()
|
||||
session.session_id = "ONB-TEST-001"
|
||||
session.patient_id = "PAT-001"
|
||||
session.patient_name = "Ramesh Kumar"
|
||||
session.age = 35
|
||||
session.gender = "male"
|
||||
session.category_type = "general"
|
||||
session.category_code = "general_medicine"
|
||||
session.category_name = "General Medicine"
|
||||
session.language = "en-IN"
|
||||
session.stage = "greeting"
|
||||
session.status = "in_progress"
|
||||
return session
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def empty_state():
|
||||
return {
|
||||
"reason_for_visit": None,
|
||||
"symptoms": [],
|
||||
"medical_history": [],
|
||||
"medications": [],
|
||||
"allergies": [],
|
||||
"surgical_history": [],
|
||||
"previous_episodes": [],
|
||||
"family_history": [],
|
||||
"additional_information": [],
|
||||
}
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# 1. STATE MERGING & DEDUPLICATION & ENRICHMENT TESTS
|
||||
# =====================================================================
|
||||
|
||||
def test_deduplication_medical_history():
|
||||
"""If patient mentions diabetes twice, it must deduplicate into one entry with latest duration."""
|
||||
state = {
|
||||
"medical_history": [{"condition": "Diabetes"}]
|
||||
}
|
||||
updates = {
|
||||
"medical_history": [{"condition": "Diabetes", "duration": "8 years"}]
|
||||
}
|
||||
merged = merge_state_updates(state, updates)
|
||||
assert len(merged["medical_history"]) == 1
|
||||
assert merged["medical_history"][0]["condition"] == "Diabetes"
|
||||
assert merged["medical_history"][0]["duration"] == "8 years"
|
||||
|
||||
|
||||
def test_progressive_enrichment_symptoms():
|
||||
"""State should become richer over turns without wiping existing details."""
|
||||
# Turn 1: symptom name
|
||||
turn1_state = merge_state_updates({}, {"symptoms": [{"name": "headache"}]})
|
||||
assert len(turn1_state["symptoms"]) == 1
|
||||
assert turn1_state["symptoms"][0]["name"] == "headache"
|
||||
|
||||
# Turn 2: duration added
|
||||
turn2_state = merge_state_updates(turn1_state, {"symptoms": [{"name": "headache", "duration": "2 days"}]})
|
||||
assert len(turn2_state["symptoms"]) == 1
|
||||
assert turn2_state["symptoms"][0]["name"] == "headache"
|
||||
assert turn2_state["symptoms"][0]["duration"] == "2 days"
|
||||
|
||||
# Turn 3: severity and location added
|
||||
turn3_state = merge_state_updates(
|
||||
turn2_state,
|
||||
{"symptoms": [{"name": "headache", "severity": "severe", "location": "front of head"}]}
|
||||
)
|
||||
assert len(turn3_state["symptoms"]) == 1
|
||||
assert turn3_state["symptoms"][0]["name"] == "headache"
|
||||
assert turn3_state["symptoms"][0]["duration"] == "2 days"
|
||||
assert turn3_state["symptoms"][0]["severity"] == "severe"
|
||||
assert turn3_state["symptoms"][0]["location"] == "front of head"
|
||||
|
||||
|
||||
def test_patient_correction_handling():
|
||||
"""If patient corrects information, the latest explicit value must be stored."""
|
||||
state = {
|
||||
"symptoms": [{"name": "stomach pain", "duration": "yesterday"}]
|
||||
}
|
||||
# Patient later says: "Actually it started five days ago"
|
||||
updates = {
|
||||
"symptoms": [{"name": "stomach pain", "duration": "five days ago"}]
|
||||
}
|
||||
merged = merge_state_updates(state, updates)
|
||||
assert len(merged["symptoms"]) == 1
|
||||
assert merged["symptoms"][0]["duration"] == "five days ago"
|
||||
|
||||
|
||||
def test_multi_symptom_list_merging():
|
||||
"""Multiple distinct symptoms should all be retained in state."""
|
||||
state = {
|
||||
"symptoms": [{"name": "fever", "duration": "3 days"}]
|
||||
}
|
||||
updates = {
|
||||
"symptoms": [
|
||||
{"name": "headache"},
|
||||
{"name": "body pain"}
|
||||
]
|
||||
}
|
||||
merged = merge_state_updates(state, updates)
|
||||
assert len(merged["symptoms"]) == 3
|
||||
names = [s["name"] for s in merged["symptoms"]]
|
||||
assert "fever" in names
|
||||
assert "headache" in names
|
||||
assert "body pain" in names
|
||||
|
||||
|
||||
def test_null_preservation():
|
||||
"""Passing None or empty values should not overwrite valid existing data."""
|
||||
state = {
|
||||
"reason_for_visit": "chest tightness",
|
||||
"symptoms": [{"name": "chest tightness", "duration": "2 hours"}],
|
||||
"medications": [{"name": "Amlodipine"}]
|
||||
}
|
||||
updates = {
|
||||
"reason_for_visit": None,
|
||||
"allergies": [{"name": "Penicillin"}]
|
||||
}
|
||||
merged = merge_state_updates(state, updates)
|
||||
assert merged["reason_for_visit"] == "chest tightness"
|
||||
assert len(merged["symptoms"]) == 1
|
||||
assert merged["symptoms"][0]["duration"] == "2 hours"
|
||||
assert len(merged["medications"]) == 1
|
||||
assert len(merged["allergies"]) == 1
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# 2. TAMIL / TANGLISH / ENGLISH DURATION EXTRACTION UNIT TESTS
|
||||
# =====================================================================
|
||||
|
||||
def test_duration_normalization_numeric_english():
|
||||
"""Test standard numeric English duration phrases."""
|
||||
assert normalize_duration_from_message("10 days") == "10 days"
|
||||
assert normalize_duration_from_message("for 10 days") == "10 days"
|
||||
assert normalize_duration_from_message("2 days") == "2 days"
|
||||
assert normalize_duration_from_message("3 weeks") == "3 weeks"
|
||||
assert normalize_duration_from_message("6 months") == "6 months"
|
||||
assert normalize_duration_from_message("since yesterday") == "since yesterday"
|
||||
assert normalize_duration_from_message("since last week") == "since last week"
|
||||
assert normalize_duration_from_message("since Monday") == "since Monday"
|
||||
assert normalize_duration_from_message("for a few days") == "for a few days"
|
||||
assert normalize_duration_from_message("for many months") == "for many months"
|
||||
|
||||
|
||||
def test_duration_normalization_tamil():
|
||||
"""Test spoken and written Tamil duration phrases."""
|
||||
assert normalize_duration_from_message("பத்து நாளா") == "10 days"
|
||||
assert normalize_duration_from_message("பத்து நாட்களா") == "10 days"
|
||||
assert normalize_duration_from_message("ஒரு பத்து நாளா") == "10 days"
|
||||
assert normalize_duration_from_message("ரெண்டு நாளா") == "2 days"
|
||||
assert normalize_duration_from_message("ரெண்டு நாட்களா") == "2 days"
|
||||
assert normalize_duration_from_message("மூணு நாளா") == "3 days"
|
||||
assert normalize_duration_from_message("நாலு நாளா") == "4 days"
|
||||
assert normalize_duration_from_message("ஒரு வாரமா") == "1 week"
|
||||
assert normalize_duration_from_message("ரெண்டு வாரமா") == "2 weeks"
|
||||
assert normalize_duration_from_message("ஒரு மாசமா") == "1 month"
|
||||
assert normalize_duration_from_message("ரெண்டு மாசமா") == "2 months"
|
||||
assert normalize_duration_from_message("நேத்துல இருந்து") == "since yesterday"
|
||||
assert normalize_duration_from_message("நேத்து இருந்து") == "since yesterday"
|
||||
assert normalize_duration_from_message("நேற்று முதல்") == "since yesterday"
|
||||
|
||||
|
||||
def test_duration_normalization_tanglish():
|
||||
"""Test Tanglish duration phrases."""
|
||||
assert normalize_duration_from_message("10 days-ah") == "10 days"
|
||||
assert normalize_duration_from_message("10 days-a") == "10 days"
|
||||
assert normalize_duration_from_message("10 naala") == "10 days"
|
||||
assert normalize_duration_from_message("10 nala") == "10 days"
|
||||
assert normalize_duration_from_message("10 naal ah") == "10 days"
|
||||
assert normalize_duration_from_message("10 days irukku") == "10 days"
|
||||
assert normalize_duration_from_message("10 days-a irukku") == "10 days"
|
||||
assert normalize_duration_from_message("2 days-ah") == "2 days"
|
||||
assert normalize_duration_from_message("3 weeks-ah") == "3 weeks"
|
||||
assert normalize_duration_from_message("1 month-ah") == "1 month"
|
||||
assert normalize_duration_from_message("one week-ah") == "1 week"
|
||||
assert normalize_duration_from_message("oru 10 days-ah") == "10 days"
|
||||
assert normalize_duration_from_message("oru pathu naala") == "10 days"
|
||||
assert normalize_duration_from_message("rendu naala") == "2 days"
|
||||
assert normalize_duration_from_message("moonru naala") == "3 days"
|
||||
|
||||
|
||||
def test_duration_normalization_mixed():
|
||||
"""Test mixed Tamil + English duration phrases."""
|
||||
assert normalize_duration_from_message("10 டேஸா இருக்கு") == "10 days"
|
||||
assert normalize_duration_from_message("10 டேஸ்-ஆ இருக்கு") == "10 days"
|
||||
assert normalize_duration_from_message("10 days-ah இருக்கு") == "10 days"
|
||||
assert normalize_duration_from_message("ஒரு 10 days-ah இருக்கு") == "10 days"
|
||||
assert normalize_duration_from_message("ஒரு 10 டேஸா இருக்கு") == "10 days"
|
||||
assert normalize_duration_from_message("ஒரு பத்து நாளா இருக்கு") == "10 days"
|
||||
assert normalize_duration_from_message("எனக்கு வந்து ஒரு 10 டேஸா இருக்கு.") == "10 days"
|
||||
assert normalize_duration_from_message("இன்னும் ஒரு பத்து நாளா இருக்கு.") == "10 days"
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# 3. REGRESSION TESTS FOR TAMIL / TANGLISH DURATION BUG
|
||||
# =====================================================================
|
||||
|
||||
def test_tamil_tanglish_duration_attaches_to_existing_symptom(mock_session):
|
||||
"""
|
||||
CRITICAL REGRESSION TEST:
|
||||
Existing state: {"symptoms": [{"name": "தலைவலி"}]}
|
||||
Patient: "எனக்கு வந்து ஒரு 10 டேஸா இருக்கு."
|
||||
Must attach duration="10 days" to "தலைவலி", resulting in exactly 1 symptom with duration.
|
||||
"""
|
||||
import asyncio
|
||||
mock_session.language = "ta-IN"
|
||||
orchestrator = AIOrchestrator()
|
||||
state_mock = MagicMock()
|
||||
state_mock.state_json = json.dumps({
|
||||
"reason_for_visit": "தலைவலி",
|
||||
"symptoms": [{"name": "தலைவலி"}],
|
||||
"medical_history": [],
|
||||
"medications": [],
|
||||
"allergies": []
|
||||
})
|
||||
|
||||
res = asyncio.run(orchestrator.process(
|
||||
session=mock_session,
|
||||
state=state_mock,
|
||||
conversation_history=[
|
||||
{"role": "patient", "message": "தலைவலி இருக்கு", "stage": "greeting"},
|
||||
{"role": "assistant", "message": "சரிங்க, இந்த பிரச்சனை எத்தனை நாளா இருக்குன்னு சொல்ல முடியுமா?", "stage": "symptom_details"}
|
||||
],
|
||||
user_message="எனக்கு வந்து ஒரு 10 டேஸா இருக்கு."
|
||||
))
|
||||
|
||||
merged = merge_state_updates(
|
||||
{"symptoms": [{"name": "தலைவலி"}]},
|
||||
res["state_updates"]
|
||||
)
|
||||
symptoms = merged["symptoms"]
|
||||
if isinstance(symptoms, dict):
|
||||
symptoms = symptoms.get("items", [])
|
||||
|
||||
assert len(symptoms) == 1
|
||||
assert symptoms[0]["name"] == "தலைவலி"
|
||||
assert symptoms[0]["duration"] == "10 days"
|
||||
|
||||
|
||||
def test_duration_answer_does_not_create_new_symptom():
|
||||
"""Duration response must enrich existing symptom without adding a second symptom."""
|
||||
existing_state = {
|
||||
"reason_for_visit": "தலைவலி",
|
||||
"symptoms": [{"name": "தலைவலி", "duration": None}]
|
||||
}
|
||||
# Deterministic updates for "10 டேஸா இருக்கு"
|
||||
orchestrator = AIOrchestrator()
|
||||
updates = orchestrator._extract_facts_from_message(
|
||||
"10 டேஸா இருக்கு",
|
||||
"10 டேஸா இருக்கு",
|
||||
last_ai_message="இந்த பிரச்சனை எத்தனை நாளா இருக்கு?",
|
||||
current_state=existing_state
|
||||
)
|
||||
merged = merge_state_updates(existing_state, updates)
|
||||
symptoms = merged["symptoms"]
|
||||
assert len(symptoms) == 1
|
||||
assert symptoms[0]["name"] == "தலைவலி"
|
||||
assert symptoms[0]["duration"] == "10 days"
|
||||
|
||||
|
||||
def test_duration_question_not_repeated_after_patient_answers(mock_session):
|
||||
"""
|
||||
After patient answers duration with '10 டேஸா இருக்கு',
|
||||
AI must NOT repeat the question 'எத்தனை நாளா இருக்கு'.
|
||||
"""
|
||||
import asyncio
|
||||
mock_session.language = "ta-IN"
|
||||
orchestrator = AIOrchestrator()
|
||||
state_mock = MagicMock()
|
||||
state_mock.state_json = json.dumps({
|
||||
"reason_for_visit": "தலைவலி",
|
||||
"symptoms": [{"name": "தலைவலி"}],
|
||||
"medical_history": [],
|
||||
"medications": [],
|
||||
"allergies": []
|
||||
})
|
||||
|
||||
res = asyncio.run(orchestrator.process(
|
||||
session=mock_session,
|
||||
state=state_mock,
|
||||
conversation_history=[
|
||||
{"role": "patient", "message": "தலைவலி இருக்கு", "stage": "greeting"},
|
||||
{"role": "assistant", "message": "சரிங்க, இந்த பிரச்சனை எத்தனை நாளா இருக்குன்னு சொல்ல முடியுமா?", "stage": "symptom_details"}
|
||||
],
|
||||
user_message="எனக்கு வந்து ஒரு 10 டேஸா இருக்கு."
|
||||
))
|
||||
|
||||
reply = res["reply"]
|
||||
# Must NOT ask duration again
|
||||
assert "எத்தனை நாளா" not in reply
|
||||
assert "எப்போலிருந்து" not in reply
|
||||
# Must NOT complete — should ask about medical history/meds/allergies
|
||||
assert res.get("completed") is not True
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# 4. CONTEXTUAL EXTRACTION FOR SEVERITY, LOCATION, MEDS, ALLERGIES
|
||||
# =====================================================================
|
||||
|
||||
def test_contextual_severity_extraction():
|
||||
"""Severity answer attaches to existing symptom."""
|
||||
existing_state = {
|
||||
"symptoms": [{"name": "headache"}]
|
||||
}
|
||||
orchestrator = AIOrchestrator()
|
||||
updates = orchestrator._extract_facts_from_message(
|
||||
"ரொம்ப severe-ஆ இருக்கு.",
|
||||
"ரொம்ப severe-ஆ இருக்கு.",
|
||||
last_ai_message="How severe is the pain?",
|
||||
current_state=existing_state
|
||||
)
|
||||
merged = merge_state_updates(existing_state, updates)
|
||||
symptoms = merged["symptoms"]
|
||||
assert len(symptoms) == 1
|
||||
assert symptoms[0]["name"] == "headache"
|
||||
assert symptoms[0]["severity"] == "severe"
|
||||
|
||||
|
||||
def test_contextual_location_extraction():
|
||||
"""Location answer attaches to existing symptom."""
|
||||
existing_state = {
|
||||
"symptoms": [{"name": "headache"}]
|
||||
}
|
||||
orchestrator = AIOrchestrator()
|
||||
updates = orchestrator._extract_facts_from_message(
|
||||
"வலது பக்கத்துல",
|
||||
"வலது பக்கத்துல",
|
||||
last_ai_message="எந்த பக்கத்துல வலிக்குது?",
|
||||
current_state=existing_state
|
||||
)
|
||||
merged = merge_state_updates(existing_state, updates)
|
||||
symptoms = merged["symptoms"]
|
||||
assert len(symptoms) == 1
|
||||
assert symptoms[0]["name"] == "headache"
|
||||
assert symptoms[0]["location"] == "right side"
|
||||
|
||||
|
||||
def test_contextual_medication_extraction():
|
||||
"""Medication statement extracts to medications list, not symptom."""
|
||||
orchestrator = AIOrchestrator()
|
||||
updates = orchestrator._extract_facts_from_message(
|
||||
"Metformin 500 mg twice a day",
|
||||
"metformin 500 mg twice a day",
|
||||
last_ai_message="Are you taking any medications?",
|
||||
current_state={}
|
||||
)
|
||||
meds = updates.get("medications", [])
|
||||
assert len(meds) == 1
|
||||
assert meds[0]["name"] == "Metformin"
|
||||
assert meds[0]["dosage"] == "500 mg"
|
||||
assert meds[0]["frequency"] == "twice a day"
|
||||
|
||||
|
||||
def test_contextual_allergy_extraction():
|
||||
"""Allergy statement extracts to allergies list, not symptom."""
|
||||
orchestrator = AIOrchestrator()
|
||||
updates = orchestrator._extract_facts_from_message(
|
||||
"allergic to penicillin, rash வரும்",
|
||||
"allergic to penicillin, rash வரும்",
|
||||
last_ai_message="Do you have any allergies?",
|
||||
current_state={}
|
||||
)
|
||||
allergies = updates.get("allergies", [])
|
||||
assert len(allergies) == 1
|
||||
assert allergies[0]["name"] == "Penicillin"
|
||||
assert "Rash" in allergies[0]["reaction"]
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# 5. NEGATIVE AND CASUAL SPEECH TESTS
|
||||
# =====================================================================
|
||||
|
||||
def test_negative_response_not_treated_as_symptom():
|
||||
"""'எனக்கு வேற எதுவும் இல்ல' must NOT become a symptom."""
|
||||
orchestrator = AIOrchestrator()
|
||||
updates = orchestrator._extract_facts_from_message(
|
||||
"எனக்கு வேற எதுவும் இல்ல.",
|
||||
"எனக்கு வேற எதுவும் இல்ல.",
|
||||
last_ai_message="வேறு ஏதேனும் உடம்பு பிரச்சனை இருக்கா?",
|
||||
current_state={"symptoms": [{"name": "தலைவலி"}]}
|
||||
)
|
||||
symptoms = updates.get("symptoms", [])
|
||||
assert len(symptoms) == 0
|
||||
|
||||
|
||||
def test_casual_speech_extraction():
|
||||
"""'நான் bus-ல வந்தேன், தலைவலி பத்து நாளா இருக்கு.' extracts headache and 10 days, ignores bus."""
|
||||
orchestrator = AIOrchestrator()
|
||||
updates = orchestrator._extract_facts_from_message(
|
||||
"நான் bus-ல வந்தேன், தலைவலி பத்து நாளா இருக்கு.",
|
||||
"நான் bus-ல வந்தேன், தலைவலி பத்து நாளா இருக்கு.",
|
||||
last_ai_message="",
|
||||
current_state={}
|
||||
)
|
||||
symptoms = updates.get("symptoms", [])
|
||||
assert len(symptoms) == 1
|
||||
assert symptoms[0]["name"] == "தலைவலி"
|
||||
assert symptoms[0]["duration"] == "10 days"
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# 6. ACCEPTANCE TESTS
|
||||
# =====================================================================
|
||||
|
||||
def test_acceptance_test_1_rich_multi_fact_message(mock_session, empty_state):
|
||||
"""
|
||||
Acceptance Test 1: Rich multi-fact patient message.
|
||||
"""
|
||||
import asyncio
|
||||
orchestrator = AIOrchestrator()
|
||||
user_msg = (
|
||||
"I've had stomach pain since yesterday. It gets worse after eating. I vomited twice. "
|
||||
"I couldn't sleep last night. I have diabetes for eight years. I take Metformin 500 mg twice a day. "
|
||||
"I'm allergic to penicillin and it causes a rash. I had the same stomach problem three months ago."
|
||||
)
|
||||
|
||||
state_mock = MagicMock()
|
||||
state_mock.state_json = json.dumps(empty_state)
|
||||
|
||||
res = asyncio.run(orchestrator.process(
|
||||
session=mock_session,
|
||||
state=state_mock,
|
||||
conversation_history=[],
|
||||
user_message=user_msg
|
||||
))
|
||||
|
||||
assert res["reply"] is not None
|
||||
updates = res["state_updates"]
|
||||
|
||||
symptoms = updates.get("symptoms", [])
|
||||
sym_names = [s.get("name") for s in symptoms]
|
||||
assert any("stomach pain" in name for name in sym_names)
|
||||
assert any("vomiting" in name for name in sym_names)
|
||||
|
||||
history = updates.get("medical_history", [])
|
||||
assert any("Diabetes" in h.get("condition", "") for h in history)
|
||||
|
||||
meds = updates.get("medications", [])
|
||||
assert any("Metformin" in m.get("name", "") for m in meds)
|
||||
|
||||
allergies = updates.get("allergies", [])
|
||||
assert any("Penicillin" in a.get("name", "") for a in allergies)
|
||||
|
||||
|
||||
def test_acceptance_test_2_minimal_input_no_invented_data(mock_session, empty_state):
|
||||
"""Acceptance Test 2: Minimal input without invented data."""
|
||||
import asyncio
|
||||
orchestrator = AIOrchestrator()
|
||||
state_mock = MagicMock()
|
||||
state_mock.state_json = json.dumps(empty_state)
|
||||
|
||||
res = asyncio.run(orchestrator.process(
|
||||
session=mock_session,
|
||||
state=state_mock,
|
||||
conversation_history=[],
|
||||
user_message="I have headache."
|
||||
))
|
||||
|
||||
updates = res["state_updates"]
|
||||
symptoms = updates.get("symptoms", [])
|
||||
assert len(symptoms) >= 1
|
||||
headache_sym = symptoms[0]
|
||||
assert "headache" in headache_sym["name"].lower()
|
||||
assert headache_sym.get("severity") in [None, ""]
|
||||
assert len(res["reply"]) > 5
|
||||
assert not res.get("completed", False)
|
||||
|
||||
|
||||
def test_acceptance_test_4_nothing_else_completion(mock_session):
|
||||
"""
|
||||
Acceptance Test 4: 'Nothing else' completes the intake
|
||||
ONLY when all required fields are collected or confirmed_none.
|
||||
"""
|
||||
import asyncio
|
||||
orchestrator = AIOrchestrator()
|
||||
ready_state = {
|
||||
"reason_for_visit": "fever",
|
||||
"symptoms": [{"name": "fever", "duration": "2 days"}],
|
||||
"medical_history": {"status": "confirmed_none", "items": []},
|
||||
"medications": {"status": "confirmed_none", "items": []},
|
||||
"allergies": {"status": "confirmed_none", "items": []},
|
||||
}
|
||||
state_mock = MagicMock()
|
||||
state_mock.state_json = json.dumps(ready_state)
|
||||
|
||||
res = asyncio.run(orchestrator.process(
|
||||
session=mock_session,
|
||||
state=state_mock,
|
||||
conversation_history=[
|
||||
{"role": "patient", "message": "I have fever for 2 days.", "stage": "greeting"},
|
||||
{"role": "assistant", "message": "Do you have any other illness or medications?", "stage": "symptom_details"}
|
||||
],
|
||||
user_message="Nothing else."
|
||||
))
|
||||
|
||||
assert res.get("completed") is True
|
||||
assert res.get("next_stage") == "confirmation"
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# 7. SAFETY & CRISIS TESTS
|
||||
# =====================================================================
|
||||
|
||||
def test_emergency_red_flag_crushing_chest_pain(mock_session, empty_state):
|
||||
"""Emergency statement triggers 108 ambulance advice and stops intake."""
|
||||
import asyncio
|
||||
orchestrator = AIOrchestrator()
|
||||
state_mock = MagicMock()
|
||||
state_mock.state_json = json.dumps(empty_state)
|
||||
|
||||
res = asyncio.run(orchestrator.process(
|
||||
session=mock_session,
|
||||
state=state_mock,
|
||||
conversation_history=[],
|
||||
user_message="I have crushing chest pain and I cannot breathe!"
|
||||
))
|
||||
|
||||
assert "108" in res["reply"]
|
||||
assert res.get("completed") is True
|
||||
|
||||
|
||||
def test_mental_health_crisis_override(mock_session, empty_state):
|
||||
"""Mental health crisis triggers Tele-MANAS (14416) helpline advice."""
|
||||
import asyncio
|
||||
orchestrator = AIOrchestrator()
|
||||
state_mock = MagicMock()
|
||||
state_mock.state_json = json.dumps(empty_state)
|
||||
|
||||
res = asyncio.run(orchestrator.process(
|
||||
session=mock_session,
|
||||
state=state_mock,
|
||||
conversation_history=[],
|
||||
user_message="I feel hopeless and I want to end my life."
|
||||
))
|
||||
|
||||
assert "14416" in res["reply"] or "Tele-MANAS" in res["reply"]
|
||||
assert res.get("completed") is True
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# 8. SPECIALTY & SCHEMA BACKWARD COMPATIBILITY TESTS
|
||||
# =====================================================================
|
||||
|
||||
def test_specialty_auto_classification():
|
||||
"""Verify clinical keywords correctly triage to respective specialties."""
|
||||
assert classify_specialty_from_symptoms("I have severe depression and insomnia")["code"] == "psychiatry"
|
||||
assert classify_specialty_from_symptoms("Severe chest pain and breathless")["code"] == "cardiology"
|
||||
assert classify_specialty_from_symptoms("Skin rash and severe itching")["code"] == "dermatology"
|
||||
assert classify_specialty_from_symptoms("Knee joint pain and swelling")["code"] == "orthopedics"
|
||||
assert classify_specialty_from_symptoms("Stomach ache and loose motion")["code"] == "gastroenterology"
|
||||
|
||||
|
||||
def test_completion_service_flexible_validation():
|
||||
"""Completion service accepts completed status or presence of all required fields."""
|
||||
service = CompletionService()
|
||||
session = MagicMock(status="completed", stage="confirmation")
|
||||
state = MagicMock(state_json=json.dumps({"reason_for_visit": "fever"}))
|
||||
assert service.is_complete(session, state) is True
|
||||
|
||||
|
||||
def test_api_schemas_backward_compatibility():
|
||||
"""Verify all schemas serialize and deserialize correctly."""
|
||||
cat = CategoryContext(type="general", code="general_medicine", name="General Medicine")
|
||||
|
||||
start_res = StartOnboardingResponse(
|
||||
success=True,
|
||||
session_id="ONB-1234",
|
||||
patient_id="PAT-01",
|
||||
category=cat,
|
||||
language="en-IN",
|
||||
status="in_progress",
|
||||
stage="greeting",
|
||||
message="Hello"
|
||||
)
|
||||
assert start_res.success is True
|
||||
|
||||
result = PatientOnboardingResult(
|
||||
patient_id="PAT-01",
|
||||
category={"type": "general", "code": "general_medicine", "name": "General Medicine"},
|
||||
chief_complaint="stomach pain",
|
||||
symptoms=[{"name": "stomach pain", "duration": "yesterday", "severity": "severe"}],
|
||||
medical_history=[{"condition": "Diabetes", "duration": "8 years"}],
|
||||
medications=[{"name": "Metformin", "dosage": "500 mg"}],
|
||||
allergies=[{"name": "Penicillin", "reaction": "Rash"}],
|
||||
language="en-IN",
|
||||
onboarding_completed=True
|
||||
)
|
||||
assert len(result.symptoms) == 1
|
||||
assert result.symptoms[0].name == "stomach pain"
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# 9. check_required_missing() UNIT TESTS
|
||||
# =====================================================================
|
||||
|
||||
def test_check_required_missing_empty_state():
|
||||
"""Empty state should report everything as missing."""
|
||||
state = {
|
||||
"reason_for_visit": None,
|
||||
"symptoms": [],
|
||||
"medical_history": [],
|
||||
"medications": [],
|
||||
"allergies": [],
|
||||
}
|
||||
missing = check_required_missing(state)
|
||||
assert "reason_for_visit" in missing
|
||||
assert "symptoms" in missing
|
||||
assert "medical_history" in missing
|
||||
assert "medications" in missing
|
||||
assert "allergies" in missing
|
||||
|
||||
|
||||
def test_check_required_missing_symptoms_no_duration():
|
||||
"""Symptoms present but no duration should still report duration as missing."""
|
||||
state = {
|
||||
"reason_for_visit": "headache",
|
||||
"symptoms": [{"name": "headache"}], # no duration
|
||||
"medical_history": [{"condition": "Diabetes"}],
|
||||
"medications": [{"name": "Metformin"}],
|
||||
"allergies": [{"name": "Penicillin"}],
|
||||
}
|
||||
missing = check_required_missing(state)
|
||||
assert "duration" in missing
|
||||
assert "reason_for_visit" not in missing
|
||||
assert "symptoms" not in missing
|
||||
assert "medical_history" not in missing
|
||||
|
||||
|
||||
def test_check_required_missing_confirmed_none():
|
||||
"""confirmed_none fields should NOT be reported as missing."""
|
||||
state = {
|
||||
"reason_for_visit": "fever",
|
||||
"symptoms": [{"name": "fever", "duration": "2 days"}],
|
||||
"medical_history": {"status": "confirmed_none", "items": []},
|
||||
"medications": {"status": "confirmed_none", "items": []},
|
||||
"allergies": {"status": "confirmed_none", "items": []},
|
||||
}
|
||||
missing = check_required_missing(state)
|
||||
assert len(missing) == 0
|
||||
|
||||
|
||||
def test_check_required_missing_all_reported():
|
||||
"""All reported state should return empty missing list."""
|
||||
state = {
|
||||
"reason_for_visit": "headache",
|
||||
"symptoms": [{"name": "headache", "duration": "3 days"}],
|
||||
"medical_history": [{"condition": "Diabetes"}],
|
||||
"medications": [{"name": "Metformin"}],
|
||||
"allergies": [{"name": "Penicillin"}],
|
||||
}
|
||||
missing = check_required_missing(state)
|
||||
assert len(missing) == 0
|
||||
|
||||
|
||||
def test_check_required_missing_partial():
|
||||
"""Partial state: has symptoms+duration+history but missing meds and allergies."""
|
||||
state = {
|
||||
"reason_for_visit": "headache",
|
||||
"symptoms": [{"name": "headache", "duration": "3 days"}],
|
||||
"medical_history": [{"condition": "Diabetes"}],
|
||||
"medications": [], # not_collected
|
||||
"allergies": [], # not_collected
|
||||
}
|
||||
missing = check_required_missing(state)
|
||||
assert "medications" in missing
|
||||
assert "allergies" in missing
|
||||
assert "reason_for_visit" not in missing
|
||||
assert "duration" not in missing
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# 10. CRITICAL BUG REGRESSION: PREMATURE COMPLETION AFTER DURATION
|
||||
# =====================================================================
|
||||
|
||||
def test_no_premature_completion_after_duration_tamil(mock_session):
|
||||
"""
|
||||
EXACT BUG REPRODUCTION:
|
||||
Turn 1: Patient gives multiple symptoms
|
||||
Turn 2: Patient gives duration
|
||||
AI MUST NOT complete. Must ask about medical history/meds/allergies.
|
||||
"""
|
||||
import asyncio
|
||||
mock_session.language = "ta-IN"
|
||||
orchestrator = AIOrchestrator()
|
||||
state_mock = MagicMock()
|
||||
state_mock.state_json = json.dumps({
|
||||
"reason_for_visit": "தலைவலி",
|
||||
"symptoms": [
|
||||
{"name": "தலைவலி"},
|
||||
{"name": "கை கால் வலி"},
|
||||
{"name": "நெஞ்சு படபடப்பு"},
|
||||
{"name": "தலை சுத்துதல்"},
|
||||
],
|
||||
"medical_history": [],
|
||||
"medications": [],
|
||||
"allergies": []
|
||||
})
|
||||
|
||||
res = asyncio.run(orchestrator.process(
|
||||
session=mock_session,
|
||||
state=state_mock,
|
||||
conversation_history=[
|
||||
{"role": "patient", "message": "எனக்கு ரொம்ப தலைவலி, கை கால் வலியாக இருக்குது. என் நெஞ்சு அப்பப்ப படபடன்னு இருக்குது. தலை சுத்துது.", "stage": "greeting"},
|
||||
{"role": "assistant", "message": "சரிங்க, இந்த பிரச்சனை எத்தனை நாளா இருக்குன்னு சொல்ல முடியுமா?", "stage": "symptom_details"}
|
||||
],
|
||||
user_message="பிரச்சனை வந்து ஒரு மூணு நாளா இருக்கு."
|
||||
))
|
||||
|
||||
# MUST NOT complete
|
||||
assert res.get("completed") is not True, f"System completed prematurely after duration! Reply: {res['reply']}"
|
||||
# Must NOT ask duration again
|
||||
assert "எத்தனை நாளா" not in res["reply"]
|
||||
assert "எப்போலிருந்து" not in res["reply"]
|
||||
|
||||
|
||||
def test_no_premature_completion_after_duration_english(mock_session):
|
||||
"""English version: duration answer must not trigger completion."""
|
||||
import asyncio
|
||||
mock_session.language = "en-IN"
|
||||
orchestrator = AIOrchestrator()
|
||||
state_mock = MagicMock()
|
||||
state_mock.state_json = json.dumps({
|
||||
"reason_for_visit": "headache",
|
||||
"symptoms": [{"name": "headache"}],
|
||||
"medical_history": [],
|
||||
"medications": [],
|
||||
"allergies": []
|
||||
})
|
||||
|
||||
res = asyncio.run(orchestrator.process(
|
||||
session=mock_session,
|
||||
state=state_mock,
|
||||
conversation_history=[
|
||||
{"role": "patient", "message": "I have a bad headache.", "stage": "greeting"},
|
||||
{"role": "assistant", "message": "Got it. How long has this been bothering you?", "stage": "symptom_details"}
|
||||
],
|
||||
user_message="About 3 days."
|
||||
))
|
||||
|
||||
assert res.get("completed") is not True, "System completed prematurely after duration!"
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# 11. "NOTHING ELSE" WITH MISSING FIELDS MUST NOT COMPLETE
|
||||
# =====================================================================
|
||||
|
||||
def test_nothing_else_with_missing_fields_does_not_complete(mock_session):
|
||||
"""
|
||||
When patient says 'nothing else' but medical_history, medications, allergies
|
||||
are still not_collected, system MUST NOT complete.
|
||||
"""
|
||||
import asyncio
|
||||
mock_session.language = "ta-IN"
|
||||
orchestrator = AIOrchestrator()
|
||||
state_mock = MagicMock()
|
||||
state_mock.state_json = json.dumps({
|
||||
"reason_for_visit": "தலைவலி",
|
||||
"symptoms": [{"name": "தலைவலி", "duration": "3 days"}],
|
||||
"medical_history": [], # not_collected
|
||||
"medications": [], # not_collected
|
||||
"allergies": [] # not_collected
|
||||
})
|
||||
|
||||
res = asyncio.run(orchestrator.process(
|
||||
session=mock_session,
|
||||
state=state_mock,
|
||||
conversation_history=[
|
||||
{"role": "patient", "message": "தலைவலி 3 நாளா இருக்கு", "stage": "greeting"},
|
||||
{"role": "assistant", "message": "சரிங்க. உங்களுக்கு பிபி, சுகர், ஆஸ்துமா போன்ற வேறு ஏதேனும் உடம்பு பிரச்சனை இருக்கா?", "stage": "symptom_details"}
|
||||
],
|
||||
user_message="எனக்கு வேற எதுவும் இல்ல."
|
||||
))
|
||||
|
||||
# Should set medical_history to confirmed_none (since that's what was asked)
|
||||
# But medications and allergies are still not_collected, so must NOT complete
|
||||
assert res.get("completed") is not True, f"System completed despite missing medications and allergies! Reply: {res['reply']}"
|
||||
|
||||
|
||||
def test_nothing_else_completes_when_all_confirmed_none(mock_session):
|
||||
"""
|
||||
When all remaining required fields are already confirmed_none or reported,
|
||||
'nothing else' SHOULD complete.
|
||||
"""
|
||||
import asyncio
|
||||
mock_session.language = "en-IN"
|
||||
orchestrator = AIOrchestrator()
|
||||
state_mock = MagicMock()
|
||||
state_mock.state_json = json.dumps({
|
||||
"reason_for_visit": "fever",
|
||||
"symptoms": [{"name": "fever", "duration": "2 days"}],
|
||||
"medical_history": {"status": "confirmed_none", "items": []},
|
||||
"medications": {"status": "confirmed_none", "items": []},
|
||||
"allergies": [], # last one not confirmed yet
|
||||
})
|
||||
|
||||
res = asyncio.run(orchestrator.process(
|
||||
session=mock_session,
|
||||
state=state_mock,
|
||||
conversation_history=[
|
||||
{"role": "patient", "message": "fever 2 days", "stage": "greeting"},
|
||||
{"role": "assistant", "message": "Do you have any drug allergies?", "stage": "allergies"}
|
||||
],
|
||||
user_message="No allergies."
|
||||
))
|
||||
|
||||
# "No allergies" should set allergies to confirmed_none, then all required are met
|
||||
assert res.get("completed") is True
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# 12. CONFIRMED_NONE DETECTION IN _extract_facts_from_message
|
||||
# =====================================================================
|
||||
|
||||
def test_extract_no_medicines_tamil():
|
||||
"""Tamil 'no medicines' should set medications to confirmed_none."""
|
||||
orchestrator = AIOrchestrator()
|
||||
updates = orchestrator._extract_facts_from_message(
|
||||
"எந்த மாத்திரையும் சாப்பிடல",
|
||||
"எந்த மாத்திரையும் சாப்பிடல",
|
||||
last_ai_message="ஏதாவது மாத்திரை சாப்பிடுறீங்களா?",
|
||||
current_state={"symptoms": [{"name": "headache"}]}
|
||||
)
|
||||
meds = updates.get("medications")
|
||||
assert isinstance(meds, dict)
|
||||
assert meds.get("status") == "confirmed_none"
|
||||
|
||||
|
||||
def test_extract_no_allergies_english():
|
||||
"""English 'no allergies' should set allergies to confirmed_none."""
|
||||
orchestrator = AIOrchestrator()
|
||||
updates = orchestrator._extract_facts_from_message(
|
||||
"No allergies",
|
||||
"no allergies",
|
||||
last_ai_message="Do you have any drug allergies?",
|
||||
current_state={"symptoms": [{"name": "headache"}]}
|
||||
)
|
||||
alg = updates.get("allergies")
|
||||
assert isinstance(alg, dict)
|
||||
assert alg.get("status") == "confirmed_none"
|
||||
|
||||
|
||||
def test_extract_no_medical_history_tamil():
|
||||
"""Tamil 'no medical history' should set medical_history to confirmed_none."""
|
||||
orchestrator = AIOrchestrator()
|
||||
updates = orchestrator._extract_facts_from_message(
|
||||
"எந்த நோயும் இல்லை",
|
||||
"எந்த நோயும் இல்லை",
|
||||
last_ai_message="உங்களுக்கு பிபி, சுகர் போன்ற வேறு ஏதேனும் உடம்பு பிரச்சனை இருக்கா?",
|
||||
current_state={"symptoms": [{"name": "headache"}]}
|
||||
)
|
||||
mh = updates.get("medical_history")
|
||||
assert isinstance(mh, dict)
|
||||
assert mh.get("status") == "confirmed_none"
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# 13. INTENT DETECTION: COMBINED MEDICATIONS_AND_ALLERGIES
|
||||
# =====================================================================
|
||||
|
||||
def test_detect_combined_meds_allergies_intent():
|
||||
"""When AI asked about both meds and allergies, detect combined intent."""
|
||||
intent = detect_previous_question_intent("ஏதாவது மாத்திரை சாப்பிட்டீங்களா? உங்களுக்கு ஏதேனும் மாத்திரை அலர்ஜி இருக்கா?")
|
||||
assert intent == "medications_and_allergies"
|
||||
|
||||
|
||||
def test_detect_meds_only_intent():
|
||||
"""When AI asked about meds only, detect medications intent."""
|
||||
intent = detect_previous_question_intent("ஏதாவது மாத்திரை சாப்பிடுறீங்களா?")
|
||||
assert intent == "medications"
|
||||
|
||||
|
||||
def test_detect_allergies_only_intent():
|
||||
"""When AI asked about allergies only, detect allergies intent."""
|
||||
intent = detect_previous_question_intent("Do you have any drug allergies?")
|
||||
assert intent == "allergies"
|
||||
|
||||
|
||||
def test_detect_medical_history_intent():
|
||||
"""When AI asked about medical history, detect history intent."""
|
||||
intent = detect_previous_question_intent("உங்களுக்கு பிபி, சுகர், ஆஸ்துமா போன்ற வேறு ஏதேனும் உடம்பு பிரச்சனை இருக்கா?")
|
||||
assert intent == "medical_history"
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# 14. _field_status HELPER TESTS
|
||||
# =====================================================================
|
||||
|
||||
def test_field_status_none():
|
||||
assert _field_status(None) == "not_collected"
|
||||
|
||||
def test_field_status_empty_list():
|
||||
assert _field_status([]) == "not_collected"
|
||||
|
||||
def test_field_status_nonempty_list():
|
||||
assert _field_status([{"name": "Metformin"}]) == "reported"
|
||||
|
||||
def test_field_status_confirmed_none_dict():
|
||||
assert _field_status({"status": "confirmed_none", "items": []}) == "confirmed_none"
|
||||
|
||||
def test_field_status_reported_string():
|
||||
assert _field_status("fever") == "reported"
|
||||
|
||||
def test_field_status_empty_string():
|
||||
assert _field_status("") == "not_collected"
|
||||
|
||||
def test_field_status_dict_with_items():
|
||||
assert _field_status({"status": "reported", "items": [{"name": "Metformin"}]}) == "reported"
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# 15. SPECIALTY LOCKING — NO FLIP-FLOPPING
|
||||
# =====================================================================
|
||||
|
||||
def test_specialty_not_downgraded_on_duration_message(mock_session):
|
||||
"""
|
||||
If session.category_code is already neurology (not general_medicine),
|
||||
a duration message should NOT re-triage to general_medicine.
|
||||
"""
|
||||
import asyncio
|
||||
mock_session.language = "ta-IN"
|
||||
mock_session.category_code = "neurology"
|
||||
mock_session.category_name = "Neurology"
|
||||
orchestrator = AIOrchestrator()
|
||||
state_mock = MagicMock()
|
||||
state_mock.state_json = json.dumps({
|
||||
"reason_for_visit": "தலைவலி",
|
||||
"symptoms": [{"name": "தலைவலி"}],
|
||||
"medical_history": [],
|
||||
"medications": [],
|
||||
"allergies": []
|
||||
})
|
||||
|
||||
res = asyncio.run(orchestrator.process(
|
||||
session=mock_session,
|
||||
state=state_mock,
|
||||
conversation_history=[
|
||||
{"role": "patient", "message": "தலைவலி", "stage": "greeting"},
|
||||
{"role": "assistant", "message": "எத்தனை நாளா?", "stage": "symptom_details"}
|
||||
],
|
||||
user_message="3 நாளா இருக்கு"
|
||||
))
|
||||
|
||||
# Specialty should remain neurology, not flip to general_medicine
|
||||
suggested = res.get("suggested_specialty", {})
|
||||
assert suggested.get("code") == "neurology"
|
||||
Reference in New Issue
Block a user