219 lines
8.5 KiB
Python
219 lines
8.5 KiB
Python
import json
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from app.ai.openai_client import OpenAIClient
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from app.config import OPENAI_API_KEY
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from app.schemas.onboarding import PatientOnboardingResult
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class ResultExtractionService:
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def __init__(self):
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self.openai = OpenAIClient()
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async def extract_result(
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self,
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session,
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state,
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conversation_history,
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) -> dict:
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current_state = json.loads(
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state.state_json
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)
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prompt = f"""
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You are a senior clinical data extraction engine at a healthcare facility.
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Your task is to convert the collected patient onboarding conversation and structured state
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into a clean, comprehensive, structured patient intake record for the attending doctor.
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CLINICAL RULES:
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1. You are NOT a doctor. Do NOT diagnose or add medical interpretations.
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2. Capture ALL patient-reported symptoms, onset/duration, severity, location, frequency.
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3. Capture ALL reported medical history, medications (with dosage/frequency), allergies (with reaction), surgical history, and previous episodes.
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4. Capture any functional impact (sleep disruption, appetite changes, weakness) in additional_information.
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5. Do NOT invent information. If something was not provided, leave it null or an empty list.
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PATIENT CONTEXT:
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{json.dumps({
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"id": session.patient_id,
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"name": session.patient_name,
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"age": session.age,
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"gender": session.gender,
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}, indent=2, ensure_ascii=False)}
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CATEGORY / SPECIALTY:
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{json.dumps({
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"type": session.category_type,
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"code": session.category_code,
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"name": session.category_name,
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}, indent=2, ensure_ascii=False)}
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CURRENT INTAKE STATE:
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{json.dumps(current_state, indent=2, ensure_ascii=False)}
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FULL CONVERSATION:
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{json.dumps(conversation_history, indent=2, ensure_ascii=False)}
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Return ONLY a valid JSON object matching this structure:
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{{
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"patient_id": "{session.patient_id}",
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"category": {{
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"type": "{session.category_type}",
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"code": "{session.category_code}",
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"name": "{session.category_name}"
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}},
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"chief_complaint": "primary reason for visit or main symptom",
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"symptoms": [
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{{
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"name": "symptom name",
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"duration": "duration or null",
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"severity": "severity or null",
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"location": "location or null",
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"description": "brief details or null"
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}}
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],
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"medical_history": [
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{{
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"condition": "condition name",
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"duration": "duration or null",
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"status": "active/past or null"
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}}
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],
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"medications": [
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{{
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"name": "medication name",
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"dosage": "dosage or null",
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"frequency": "frequency or null",
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"duration": "duration or null"
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}}
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],
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"allergies": [
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{{
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"name": "allergen name",
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"type": "drug/food/etc or null",
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"reaction": "reaction or null"
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}}
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],
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"surgical_history": [],
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"previous_episodes": [],
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"family_history": [],
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"dietary_habits": null,
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"additional_information": "any functional impact, previous episodes, or additional notes",
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"language": "{session.language}",
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"onboarding_completed": true
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}}
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"""
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from app.config import GEMINI_API_KEY, GEMINI_MODEL, ANTHROPIC_API_KEY, ANTHROPIC_MODEL
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# Primary: Google Gemini extraction
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if GEMINI_API_KEY and GEMINI_API_KEY.strip() not in ["your_gemini_key", "your_gemini_api_key", ""]:
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try:
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import asyncio
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from google import genai
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from google.genai import types
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client = genai.Client(api_key=GEMINI_API_KEY)
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config = types.GenerateContentConfig(response_mime_type="application/json")
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res = await asyncio.wait_for(
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client.aio.models.generate_content(
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model=GEMINI_MODEL or "gemini-1.5-flash",
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contents=prompt,
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config=config,
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),
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timeout=3.5
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)
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cleaned = res.text.replace("```json", "").replace("```", "").strip()
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parsed = json.loads(cleaned)
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validated = PatientOnboardingResult.model_validate(parsed)
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return validated.model_dump()
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except Exception as e:
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print(f"[Gemini Result Extraction Warning]: {e}")
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# Secondary: OpenAI extraction
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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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system_prompt=prompt,
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user_message="Generate the final structured patient onboarding result.",
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)
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cleaned = raw_response.strip()
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if cleaned.startswith("```json"):
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cleaned = cleaned[7:]
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if cleaned.startswith("```"):
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cleaned = cleaned[3:]
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if cleaned.endswith("```"):
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cleaned = cleaned[:-3]
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cleaned = cleaned.strip()
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parsed = json.loads(cleaned)
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validated = PatientOnboardingResult.model_validate(parsed)
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return validated.model_dump()
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except Exception as e:
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print(f"[OpenAI Result Extraction Warning]: {e}")
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# Tertiary: Anthropic Claude extraction
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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=800,
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system=prompt,
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messages=[{"role": "user", "content": "Generate structured onboarding result"}],
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)
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cleaned = res.content[0].text.replace("```json", "").replace("```", "").strip()
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parsed = json.loads(cleaned)
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validated = PatientOnboardingResult.model_validate(parsed)
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return validated.model_dump()
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except Exception as e:
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print(f"[Claude Result Extraction Error]: {e}")
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# Fallback structured builder from state
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chief_complaint = current_state.get("reason_for_visit")
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if isinstance(chief_complaint, dict):
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chief_complaint = chief_complaint.get("value")
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symptoms_raw = current_state.get("symptoms")
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symptoms_list = []
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if isinstance(symptoms_raw, dict):
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symptoms_list = symptoms_raw.get("items", [])
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elif isinstance(symptoms_raw, list):
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symptoms_list = symptoms_raw
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meds_raw = current_state.get("medications")
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meds_list = meds_raw.get("items", []) if isinstance(meds_raw, dict) else (meds_raw or [])
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allergies_raw = current_state.get("allergies")
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allergies_list = allergies_raw.get("items", []) if isinstance(allergies_raw, dict) else (allergies_raw or [])
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history_raw = current_state.get("medical_history")
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history_list = history_raw.get("items", []) if isinstance(history_raw, dict) else (history_raw or [])
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surgical_history = current_state.get("surgical_history", [])
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previous_episodes = current_state.get("previous_episodes", [])
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family_history = current_state.get("family_history", [])
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add_info = current_state.get("additional_information")
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fallback_result = {
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"patient_id": session.patient_id,
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"category": {
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"type": session.category_type,
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"code": session.category_code,
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"name": session.category_name,
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},
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"chief_complaint": chief_complaint or None,
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"symptoms": symptoms_list,
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"medical_history": history_list,
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"medications": meds_list,
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"allergies": allergies_list,
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"surgical_history": surgical_history if isinstance(surgical_history, list) else [],
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"previous_episodes": previous_episodes if isinstance(previous_episodes, list) else [],
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"family_history": family_history if isinstance(family_history, list) else [],
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"additional_information": add_info,
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"language": session.language,
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"onboarding_completed": True,
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}
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validated = PatientOnboardingResult.model_validate(fallback_result)
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return validated.model_dump()
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