feat: implement AI-orchestrated result extraction and service connectivity tests
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@@ -3,7 +3,7 @@ import json
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from app.ai.openai_client import OpenAIClient
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from app.ai.prompts import build_system_prompt
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from app.ai.validator import validate_ai_response
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from app.config import OPENAI_API_KEY
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from app.config import OPENAI_API_KEY, GEMINI_API_KEY, GEMINI_MODEL, ANTHROPIC_API_KEY, ANTHROPIC_MODEL
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from app.specialties.registry import get_specialty_config
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@@ -49,7 +49,22 @@ class AIOrchestrator:
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prompt = build_system_prompt(context)
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# Real OpenAI Call if valid API key is set
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# 1. Primary: Google Gemini (High-Speed Multilingual Indic Reasoning ~0.8s)
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if GEMINI_API_KEY and GEMINI_API_KEY.strip() not in ["your_gemini_key", ""]:
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try:
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import google.generativeai as genai
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genai.configure(api_key=GEMINI_API_KEY)
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model = genai.GenerativeModel(GEMINI_MODEL or "gemini-3.6-flash")
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full_prompt = f"{prompt}\n\nPATIENT MESSAGE: {user_message}\n\nRespond strictly with JSON matching the schema above."
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res = await model.generate_content_async(full_prompt)
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raw_text = res.text.replace("```json", "").replace("```", "").strip()
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parsed = json.loads(raw_text)
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validated = validate_ai_response(parsed)
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return validated.model_dump()
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except Exception as e:
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print(f"[Gemini Orchestrator Error]: {e}")
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# 2. Secondary: OpenAI Call if valid API key is set
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if OPENAI_API_KEY and OPENAI_API_KEY.strip() not in ["your_openai_api_key", "your_openai_key", ""]:
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try:
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raw_response = await self.openai.generate_response(
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@@ -62,7 +77,25 @@ class AIOrchestrator:
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except Exception as e:
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print(f"[OpenAI Orchestrator Error]: {e}")
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# Fallback simulator for offline development with concise clinical flow
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# 3. Tertiary: Anthropic Claude
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if ANTHROPIC_API_KEY and ANTHROPIC_API_KEY.strip() not in ["your_anthropic_key", ""]:
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try:
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from anthropic import AsyncAnthropic
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client = AsyncAnthropic(api_key=ANTHROPIC_API_KEY)
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res = await client.messages.create(
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model=ANTHROPIC_MODEL or "claude-sonnet-4-5-20250929",
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max_tokens=600,
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system=prompt,
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messages=[{"role": "user", "content": user_message}],
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)
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raw_text = res.content[0].text.replace("```json", "").replace("```", "").strip()
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parsed = json.loads(raw_text)
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validated = validate_ai_response(parsed)
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return validated.model_dump()
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except Exception as e:
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print(f"[Claude Orchestrator Error]: {e}")
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# 4. Fallback simulator for offline development with localized language support
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parsed = self._simulate_specialty_response(session, specialty, user_message, len(conversation_history))
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validated = validate_ai_response(parsed)
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return validated.model_dump()
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@@ -70,13 +103,36 @@ class AIOrchestrator:
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def _simulate_specialty_response(self, session, specialty, user_message, turn_count=0):
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stage = session.stage
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code = specialty.get("code", "general")
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lang = (session.language or "ta-IN").lower()
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msg_lower = user_message.lower().strip()
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is_negative = msg_lower in ["no", "no more", "nothing", "nothing else", "know nothing", "none", "illness none"]
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is_negative = msg_lower in ["no", "no more", "nothing", "nothing else", "know nothing", "none", "illness none", "இல்லை", "ஒன்றுமில்லை", "नहीं"]
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# Fast conclusion if patient says "No" or already had 4 turns
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# Localized clinical phrases
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if lang.startswith("ta"):
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conclusion_text = "நன்றி. உங்கள் உடல்நல விவரங்கள் பதிவு செய்யப்பட்டுள்ளன. மருத்துவர் விரைவில் பரிசீலிப்பார்."
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symptom_q = "இந்த அறிகுறிகள் எவ்வளவு நாட்களாக இருக்கிறது, மற்றும் ஏதேனும் மாத்திரைகள் சாப்பிட்டீர்களா?"
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med_q = "நன்றி. உங்களுக்கு ஏதேனும் மருந்து ஒவ்வாமை (Allergy) அல்லது வேறு உடல்நல பிரச்சனைகள் உள்ளதா?"
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elif lang.startswith("hi"):
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conclusion_text = "धन्यवाद। आपकी स्वास्थ्य जानकारी दर्ज कर ली गई है। डॉक्टर जल्द ही समीक्षा करेंगे।"
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symptom_q = "आपको यह समस्या कितने दिनों से है और क्या आपने कोई दवाई ली है?"
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med_q = "धन्यवाद। क्या आपको किसी दवाई से एलर्जी या कोई अन्य बीमारी है?"
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elif lang.startswith("te"):
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conclusion_text = "ధన్యవాదాలు. మీ ఆరోగ్య వివరాలు నమోదు చేయబడ్డాయి."
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symptom_q = "ఈ సమస్య మీకు ఎన్ని రోజుల నుండి ఉంది మరియు ఏవైనా మందులు వాడారా?"
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med_q = "ధన్యవాదాలు. మీకు ఏవైనా మందుల పడనితనం (Allergy) ఉందా?"
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elif lang.startswith("kn"):
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conclusion_text = "ಧನ್ಯವಾದಗಳು. ನಿಮ್ಮ ಆರೋಗ್ಯದ ಮಾಹಿತಿ ದಾಖಲಾಗಿದೆ."
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symptom_q = "ಈ ಸಮಸ್ಯೆ ನಿಮಗೆ ಎಷ್ಟು ದಿನಗಳಿಂದ ಇದೆ ಮತ್ತು ಯಾವುದೇ ಮಾತ್ರೆಗಳನ್ನು ತೆಗೆದುಕೊಂಡಿದ್ದೀರಾ?"
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med_q = "ಧನ್ಯವಾದಗಳು. ನಿಮಗೆ ಯಾವುದೇ ಔಷಧಿ ಅಲರ್ಜಿ ಇದೆಯೇ?"
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else:
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conclusion_text = "Thank you. I have recorded your intake details for the doctor to review."
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symptom_q = "How long have you had these symptoms, and have you taken any medicines?"
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med_q = "Thank you. Do you have any known drug allergies or existing health conditions?"
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# Fast conclusion if patient says "No" or already had 6 turns
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if is_negative or turn_count >= 6:
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return {
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"reply": "Thank you. I have recorded your intake details for the doctor to review.",
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"reply": conclusion_text,
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"next_stage": "confirmation",
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"state_updates": {
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"reason_for_visit": None,
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@@ -89,12 +145,8 @@ class AIOrchestrator:
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}
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if stage == "greeting":
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if code == "cardiology":
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reply = "Could you please share what cardiac or chest symptoms you are experiencing?"
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else:
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reply = "Could you please describe what primary symptoms you are experiencing today?"
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return {
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"reply": reply,
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"reply": symptom_q,
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"next_stage": "reason_for_visit",
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"state_updates": {
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"reason_for_visit": user_message,
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@@ -106,9 +158,8 @@ class AIOrchestrator:
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"completed": False
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}
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elif stage == "reason_for_visit":
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reply = "How long have you had these symptoms, and have you taken any medicines?"
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return {
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"reply": reply,
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"reply": med_q,
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"next_stage": "medications",
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"state_updates": {
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"reason_for_visit": user_message,
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@@ -120,10 +171,9 @@ class AIOrchestrator:
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"completed": False
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}
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elif stage == "medications":
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reply = "Thank you. Do you have any known drug allergies or existing health conditions?"
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return {
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"reply": reply,
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"next_stage": "allergies",
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"reply": conclusion_text,
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"next_stage": "confirmation",
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"state_updates": {
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"reason_for_visit": None,
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"symptoms": [],
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@@ -135,7 +185,7 @@ class AIOrchestrator:
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}
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else:
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return {
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"reply": "Thank you. I have recorded your intake details for the doctor to review.",
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"reply": conclusion_text,
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"next_stage": "confirmation",
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"state_updates": {
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"reason_for_visit": None,
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@@ -55,7 +55,7 @@ class SarvamClient:
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return None
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lang_prefix = language_code.lower()[:2]
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speaker = "meera" if lang_prefix == "ta" else "arvind"
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speaker = "anushka" if lang_prefix in ["ta", "te", "hi"] else "karun"
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client = self._get_client()
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headers = {
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@@ -66,7 +66,7 @@ class SarvamClient:
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"inputs": [text],
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"target_language_code": language_code,
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"speaker": speaker,
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"model": "bulbul:v1",
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"model": "bulbul:v2",
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}
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try:
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res = await client.post(
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