首次提交by MimoCode
This commit is contained in:
tang1219
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from abc import ABC, abstractmethod
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from typing import Optional
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from pydantic import BaseModel
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class VisionResult(BaseModel):
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generic_name: Optional[str] = None
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brand_name: Optional[str] = None
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manufacturer: Optional[str] = None
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specification: Optional[str] = None
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class DateResult(BaseModel):
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production_date: Optional[str] = None
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expiry_date: Optional[str] = None
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class LeafletResult(BaseModel):
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indications: str
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adult_dose: str
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child_dose: Optional[str] = None
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contraindications: str
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notes: Optional[str] = None
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class VisionProvider(ABC):
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@abstractmethod
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async def recognize_medicine(self, image_bytes: bytes) -> VisionResult:
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pass
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@abstractmethod
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async def recognize_dates(self, image_bytes: bytes) -> DateResult:
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pass
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class TextProvider(ABC):
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@abstractmethod
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async def summarize_leaflet(self, text: str) -> LeafletResult:
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pass
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@abstractmethod
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async def natural_language_search(self, query: str, medicines: list) -> list:
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pass
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@@ -0,0 +1,34 @@
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from typing import Optional
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from app.ai.base import VisionProvider, TextProvider
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class AIManager:
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_instance = None
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def __new__(cls):
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if cls._instance is None:
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cls._instance = super().__new__(cls)
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cls._instance._initialized = False
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return cls._instance
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def __init__(self):
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if self._initialized:
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return
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self.vision_providers: dict[str, VisionProvider] = {}
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self.text_providers: dict[str, TextProvider] = {}
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self._initialized = True
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def register_vision_provider(self, name: str, provider: VisionProvider):
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self.vision_providers[name] = provider
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def register_text_provider(self, name: str, provider: TextProvider):
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self.text_providers[name] = provider
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def get_vision_provider(self, name: str) -> Optional[VisionProvider]:
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return self.vision_providers.get(name)
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def get_text_provider(self, name: str) -> Optional[TextProvider]:
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return self.text_providers.get(name)
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ai_manager = AIManager()
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@@ -0,0 +1,153 @@
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import base64
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import json
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from openai import AsyncOpenAI
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from app.ai.base import VisionProvider, TextProvider, VisionResult, DateResult, LeafletResult
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from app.config import settings
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class OpenAIVisionProvider(VisionProvider):
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def __init__(self):
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self.client = AsyncOpenAI(api_key=settings.OPENAI_API_KEY)
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self.model = settings.OPENAI_MODEL
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async def recognize_medicine(self, image_bytes: bytes) -> VisionResult:
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base64_image = base64.b64encode(image_bytes).decode('utf-8')
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response = await self.client.chat.completions.create(
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model=self.model,
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": """请识别这张药品包装图片中的信息，返回JSON格式：
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{
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"generic_name": "通用名称",
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"brand_name": "商品名称",
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"manufacturer": "生产厂家",
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"specification": "规格"
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}
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只提取图片中真实出现的内容，不要猜测。"""
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},
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{base64_image}"
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}
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}
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]
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}
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],
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response_format={"type": "json_object"}
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)
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result = json.loads(response.choices[0].message.content)
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return VisionResult(**result)
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async def recognize_dates(self, image_bytes: bytes) -> DateResult:
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base64_image = base64.b64encode(image_bytes).decode('utf-8')
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response = await self.client.chat.completions.create(
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model=self.model,
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": """请识别这张图片中的日期信息，返回JSON格式：
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{
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"production_date": "生产日期(YYYY-MM-DD格式，如果无法识别则为null)",
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"expiry_date": "有效期/过期日期(YYYY-MM-DD格式，如果无法识别则为null)"
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}
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只提取图片中真实出现的日期，不要猜测或推理。"""
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},
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{base64_image}"
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}
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}
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]
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}
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],
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response_format={"type": "json_object"}
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)
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result = json.loads(response.choices[0].message.content)
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return DateResult(**result)
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class OpenAITextProvider(TextProvider):
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def __init__(self):
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self.client = AsyncOpenAI(api_key=settings.OPENAI_API_KEY)
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self.model = "gpt-4"
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async def summarize_leaflet(self, text: str) -> LeafletResult:
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response = await self.client.chat.completions.create(
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model=self.model,
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messages=[
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{
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"role": "system",
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"content": "你是一个医疗信息提取助手。请从药品说明书中提取关键信息。"
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},
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{
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"role": "user",
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"content": f"""请从以下药品说明书中提取关键信息，返回JSON格式：
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{{
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"indications": "适应症",
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"adult_dose": "成人用法用量",
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"child_dose": "儿童用法用量(如果没有则为null)",
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"contraindications": "禁忌",
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"notes": "注意事项(如果有)"
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}}
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说明书内容：
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{text}"""
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}
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],
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response_format={"type": "json_object"}
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)
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result = json.loads(response.choices[0].message.content)
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return LeafletResult(**result)
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async def natural_language_search(self, query: str, medicines: list) -> list:
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medicines_text = "\n".join([
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f"- {m['name']}: {m.get('indications', '')}"
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for m in medicines
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])
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response = await self.client.chat.completions.create(
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model=self.model,
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messages=[
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{
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"role": "system",
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"content": "你是一个药品搜索助手。根据用户描述的症状，从药品列表中找出可能适用的药品。"
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},
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{
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"role": "user",
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"content": f"""用户描述：{query}
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可用药品列表：
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{medicines_text}
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请返回JSON格式的搜索结果：
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{{
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"results": [
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{{
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"medicine_id": 药品ID,
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"name": "药品名称",
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"reason": "匹配原因"
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}}
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]
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}}"""
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}
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],
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response_format={"type": "json_object"}
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)
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result = json.loads(response.choices[0].message.content)
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return result.get('results', [])
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