Initial commit: TradeMate 外贸小助手 MVP

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
2026-05-08 18:17:12 +08:00
commit c6206787da
121 changed files with 11743 additions and 0 deletions
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+from typing import Dict, Any, Optional
+import json
+from openai import AsyncOpenAI
+from app.ai.base import AIProvider
+
+
+SYSTEM_PROMPTS = {
+    "translate": "You are a professional translator specialized in foreign trade and e-commerce. "
+                 "Accurately translate business terms like MOQ, FOB, CIF, lead time, etc. "
+                 "Return ONLY the translated text, no explanations.",
+    "reply": "You are an experienced foreign trade sales expert. Write professional, "
+             "clear business replies. Be concise but warm. Include relevant details "
+             "naturally. Return ONLY the reply text, no explanations.",
+    "marketing": "You are a creative copywriter for international trade. Write compelling "
+                 "marketing content that drives action. Adapt to the target audience's culture. "
+                 "Return ONLY the copy, no explanations.",
+    "extract": "You extract structured data from text. Return ONLY valid JSON matching the requested schema.",
+}
+
+
+class OpenAIProvider(AIProvider):
+    def __init__(self, api_key: str, model: str = "gpt-4o"):
+        self.client = AsyncOpenAI(api_key=api_key)
+        self.model = model
+        self._name = f"openai-{model}"
+        self._pricing = {
+            "gpt-4o": {"input": 0.01, "output": 0.03},
+            "gpt-4o-mini": {"input": 0.0015, "output": 0.006},
+        }
+        self._cheap_model = "gpt-4o-mini" if model == "gpt-4o" else model
+
+    async def translate(self, text: str, source_lang: Optional[str], target_lang: str, context: Optional[str] = None) -> Dict[str, Any]:
+        system = SYSTEM_PROMPTS["translate"]
+        if context:
+            system += f"\nContext: this is about {context}"
+        if source_lang and source_lang != "auto":
+            system += f"\nSource language: {source_lang}"
+
+        content = await self._call(system, f"Translate to {target_lang}:\n\n{text}", model=self._cheap_model)
+        return {"translated_text": content, "provider": self.name, "model": self.model}
+
+    async def reply(self, inquiry: str, context: Optional[Dict[str, Any]] = None, tone: str = "professional") -> Dict[str, Any]:
+        system = SYSTEM_PROMPTS["reply"] + f"\nTone: {tone}"
+
+        context_str = ""
+        if context:
+            if context.get("product"):
+                context_str += f"Product: {context['product']}\n"
+            if context.get("price"):
+                context_str += f"Price: {context['price']}\n"
+            if context.get("customer_history"):
+                context_str += f"Customer history: {context['customer_history']}\n"
+            if context.get("conversation_history"):
+                context_str += f"Previous messages: {context['conversation_history']}\n"
+
+        prompt = f"{context_str}\nCustomer inquiry:\n{inquiry}\n\nWrite a reply:"
+        content = await self._call(system, prompt)
+        return {"reply": content, "provider": self.name, "model": self.model}
+
+    async def generate_marketing(self, product_info: Dict[str, Any], target: str, style: str = "professional", language: str = "en") -> Dict[str, Any]:
+        system = SYSTEM_PROMPTS["marketing"] + f"\nStyle: {style}\nTarget audience: {target}\nLanguage: {language}"
+
+        product_str = json.dumps(product_info, ensure_ascii=False, indent=2)
+        prompt = f"Product information:\n{product_str}\n\nGenerate marketing copy:"
+        content = await self._call(system, prompt)
+        return {"content": content, "provider": self.name, "model": self.model}
+
+    async def extract_info(self, text: str, schema: Dict[str, Any]) -> Dict[str, Any]:
+        system = SYSTEM_PROMPTS["extract"]
+        schema_str = json.dumps(schema, indent=2)
+        prompt = f"Schema:\n{schema_str}\n\nText:\n{text}\n\nExtracted JSON:"
+        content = await self._call(system, prompt, response_format={"type": "json_object"})
+        try:
+            data = json.loads(content)
+            return {"data": data, "confidence": 0.9, "provider": self.name}
+        except json.JSONDecodeError:
+            return {"data": {}, "confidence": 0.0, "provider": self.name, "error": "parse_failed"}
+
+    async def _call(self, system: str, prompt: str, max_tokens: int = 1000, response_format: Optional[Dict] = None, model: Optional[str] = None) -> str:
+        kwargs = {
+            "model": model or self.model,
+            "messages": [
+                {"role": "system", "content": system},
+                {"role": "user", "content": prompt},
+            ],
+            "max_tokens": max_tokens,
+            "temperature": 0.7,
+        }
+        if response_format:
+            kwargs["response_format"] = response_format
+
+        resp = await self.client.chat.completions.create(**kwargs)
+        return resp.choices[0].message.content
+
+    @property
+    def name(self) -> str:
+        return self._name
+
+    @property
+    def cost_per_1k_tokens(self) -> float:
+        p = self._pricing.get(self.model, {"input": 0.01, "output": 0.03})
+        return (p["input"] + p["output"]) / 2