TradeMate Dev
c6206787da
项目结构: - backend/ Python FastAPI 后端 - uni-app/ uni-app跨端前端 - docs/ 设计文档 - docker-compose.yml Docker编排 - nginx/scripts/systemd 运维配置 已完成功能: - 用户认证 (JWT) - 智能翻译 + 回复建议 - 营销素材生成 - 客户管理 + 沉默检测 - 报价单管理 - 产品库管理 - 汇率换算 - 推送通知 (uni-push) - WhatsApp Webhook框架 - Celery定时任务
103 lines
4.9 KiB
Python
103 lines
4.9 KiB
Python
from typing import Dict, Any, Optional
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import json
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from openai import AsyncOpenAI
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from app.ai.base import AIProvider
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SYSTEM_PROMPTS = {
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"translate": "You are a professional translator specialized in foreign trade and e-commerce. "
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"Accurately translate business terms like MOQ, FOB, CIF, lead time, etc. "
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"Return ONLY the translated text, no explanations.",
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"reply": "You are an experienced foreign trade sales expert. Write professional, "
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"clear business replies. Be concise but warm. Include relevant details "
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"naturally. Return ONLY the reply text, no explanations.",
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"marketing": "You are a creative copywriter for international trade. Write compelling "
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"marketing content that drives action. Adapt to the target audience's culture. "
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"Return ONLY the copy, no explanations.",
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"extract": "You extract structured data from text. Return ONLY valid JSON matching the requested schema.",
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}
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class OpenAIProvider(AIProvider):
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def __init__(self, api_key: str, model: str = "gpt-4o"):
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self.client = AsyncOpenAI(api_key=api_key)
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self.model = model
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self._name = f"openai-{model}"
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self._pricing = {
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"gpt-4o": {"input": 0.01, "output": 0.03},
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"gpt-4o-mini": {"input": 0.0015, "output": 0.006},
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}
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self._cheap_model = "gpt-4o-mini" if model == "gpt-4o" else model
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async def translate(self, text: str, source_lang: Optional[str], target_lang: str, context: Optional[str] = None) -> Dict[str, Any]:
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system = SYSTEM_PROMPTS["translate"]
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if context:
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system += f"\nContext: this is about {context}"
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if source_lang and source_lang != "auto":
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system += f"\nSource language: {source_lang}"
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content = await self._call(system, f"Translate to {target_lang}:\n\n{text}", model=self._cheap_model)
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return {"translated_text": content, "provider": self.name, "model": self.model}
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async def reply(self, inquiry: str, context: Optional[Dict[str, Any]] = None, tone: str = "professional") -> Dict[str, Any]:
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system = SYSTEM_PROMPTS["reply"] + f"\nTone: {tone}"
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context_str = ""
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if context:
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if context.get("product"):
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context_str += f"Product: {context['product']}\n"
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if context.get("price"):
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context_str += f"Price: {context['price']}\n"
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if context.get("customer_history"):
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context_str += f"Customer history: {context['customer_history']}\n"
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if context.get("conversation_history"):
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context_str += f"Previous messages: {context['conversation_history']}\n"
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prompt = f"{context_str}\nCustomer inquiry:\n{inquiry}\n\nWrite a reply:"
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content = await self._call(system, prompt)
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return {"reply": content, "provider": self.name, "model": self.model}
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async def generate_marketing(self, product_info: Dict[str, Any], target: str, style: str = "professional", language: str = "en") -> Dict[str, Any]:
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system = SYSTEM_PROMPTS["marketing"] + f"\nStyle: {style}\nTarget audience: {target}\nLanguage: {language}"
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product_str = json.dumps(product_info, ensure_ascii=False, indent=2)
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prompt = f"Product information:\n{product_str}\n\nGenerate marketing copy:"
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content = await self._call(system, prompt)
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return {"content": content, "provider": self.name, "model": self.model}
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async def extract_info(self, text: str, schema: Dict[str, Any]) -> Dict[str, Any]:
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system = SYSTEM_PROMPTS["extract"]
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schema_str = json.dumps(schema, indent=2)
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prompt = f"Schema:\n{schema_str}\n\nText:\n{text}\n\nExtracted JSON:"
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content = await self._call(system, prompt, response_format={"type": "json_object"})
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try:
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data = json.loads(content)
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return {"data": data, "confidence": 0.9, "provider": self.name}
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except json.JSONDecodeError:
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return {"data": {}, "confidence": 0.0, "provider": self.name, "error": "parse_failed"}
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async def _call(self, system: str, prompt: str, max_tokens: int = 1000, response_format: Optional[Dict] = None, model: Optional[str] = None) -> str:
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kwargs = {
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"model": model or self.model,
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"messages": [
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{"role": "system", "content": system},
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{"role": "user", "content": prompt},
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],
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"max_tokens": max_tokens,
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"temperature": 0.7,
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}
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if response_format:
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kwargs["response_format"] = response_format
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resp = await self.client.chat.completions.create(**kwargs)
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return resp.choices[0].message.content
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@property
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def name(self) -> str:
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return self._name
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@property
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def cost_per_1k_tokens(self) -> float:
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p = self._pricing.get(self.model, {"input": 0.01, "output": 0.03})
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return (p["input"] + p["output"]) / 2
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