feat: 修复 H5 底部导航覆盖 + 更新项目进度文档

## H5 底部导航修复 (Bug #10) - 精简 App.vue，移除重复 tabbar，仅保留全局样式 - uni-page 设置 height: calc(100% - 50px) + overflow-y: auto - 内容区域精确停在底部导航上方，独立滚动不再叠加 - 恢复 custom-tab-bar 组件 ## 项目进度文档 - PROGRESS.md 更新至 10 个 Bug 修复 - 新增 H5 底部导航修复记录 - 新增历史变更条目
2026-05-12 20:24:42 +08:00
parent 69e164dcae
commit 7b62c2f8b4
125 changed files with 19725 additions and 728 deletions
@@ -2,5 +2,7 @@ from .openai import OpenAIProvider
 from .claude import ClaudeProvider
 from .deepl import DeepLProvider
 from .local import LocalProvider
+from .spark import SparkProvider
+from .sensenova import SensenovaProvider

-__all__ = ["OpenAIProvider", "ClaudeProvider", "DeepLProvider", "LocalProvider"]
+__all__ = ["OpenAIProvider", "ClaudeProvider", "DeepLProvider", "LocalProvider", "SparkProvider", "SensenovaProvider"]
@@ -32,8 +32,10 @@ class ClaudeProvider(AIProvider):
        content = await self._call(system, prompt)
        return {"translated_text": content, "provider": self.name}

-    async def reply(self, inquiry: str, context: Optional[Dict[str, Any]] = None, tone: str = "professional") -> Dict[str, Any]:
+    async def reply(self, inquiry: str, context: Optional[Dict[str, Any]] = None, tone: str = "professional", preference_context: Optional[str] = None) -> Dict[str, Any]:
        system = SYSTEM_PROMPTS["reply"]
+        if preference_context:
+            system += f"\nUser writing preference: {preference_context}"
        context_str = ""
        if context:
            for k, v in context.items():
@@ -43,8 +45,10 @@ class ClaudeProvider(AIProvider):
        content = await self._call(system, prompt)
        return {"reply": content, "provider": self.name}

-    async def generate_marketing(self, product_info: Dict[str, Any], target: str, style: str = "professional", language: str = "en") -> Dict[str, Any]:
+    async def generate_marketing(self, product_info: Dict[str, Any], target: str, style: str = "professional", language: str = "en", preference_context: Optional[str] = None) -> Dict[str, Any]:
        system = SYSTEM_PROMPTS["marketing"]
+        if preference_context:
+            system += f"\nUser preference: {preference_context}"
        info = json.dumps(product_info, ensure_ascii=False, indent=2)
        prompt = f"Product:\n{info}\n\nTarget: {target}\nStyle: {style}\nLanguage: {language}\n\nWrite marketing copy:"
        content = await self._call(system, prompt, max_tokens=1500)
@@ -14,17 +14,22 @@ class LocalProvider(AIProvider):
        result = await self._generate(prompt)
        return {"translated_text": result, "provider": self.name, "cost": 0.0}

-    async def reply(self, inquiry: str, context: Optional[Dict[str, Any]] = None, tone: str = "professional") -> Dict[str, Any]:
-        ctx = ""
+    async def reply(self, inquiry: str, context: Optional[Dict[str, Any]] = None, tone: str = "professional", preference_context: Optional[str] = None) -> Dict[str, Any]:
+        prompt = ""
+        if preference_context:
+            prompt += f"[User prefers: {preference_context}]\n"
        if context:
-            ctx = "\n".join(f"{k}: {v}" for k, v in context.items() if v)
-        prompt = f"{ctx}\nCustomer: {inquiry}\n\nWrite a {tone} reply:"
+            prompt += "\n".join(f"{k}: {v}" for k, v in context.items() if v) + "\n"
+        prompt += f"Customer: {inquiry}\n\nWrite a {tone} reply:"
        result = await self._generate(prompt)
        return {"reply": result, "provider": self.name, "cost": 0.0}

-    async def generate_marketing(self, product_info: Dict[str, Any], target: str, style: str = "professional", language: str = "en") -> Dict[str, Any]:
+    async def generate_marketing(self, product_info: Dict[str, Any], target: str, style: str = "professional", language: str = "en", preference_context: Optional[str] = None) -> Dict[str, Any]:
        info = json.dumps(product_info, ensure_ascii=False)
-        prompt = f"Product: {info}\nTarget: {target}\nStyle: {style}\nLanguage: {language}\n\nMarketing copy:"
+        prompt = ""
+        if preference_context:
+            prompt += f"[User prefers: {preference_context}]\n"
+        prompt += f"Product: {info}\nTarget: {target}\nStyle: {style}\nLanguage: {language}\n\nMarketing copy:"
        result = await self._generate(prompt, max_tokens=800)
        return {"content": result, "provider": self.name, "cost": 0.0}

@@ -19,8 +19,11 @@ SYSTEM_PROMPTS = {


 class OpenAIProvider(AIProvider):
-    def __init__(self, api_key: str, model: str = "gpt-4o"):
-        self.client = AsyncOpenAI(api_key=api_key)
+    def __init__(self, api_key: str, model: str = "gpt-4o", base_url: Optional[str] = None):
+        kwargs = {"api_key": api_key}
+        if base_url:
+            kwargs["base_url"] = base_url
+        self.client = AsyncOpenAI(**kwargs)
        self.model = model
        self._name = f"openai-{model}"
        self._pricing = {
@@ -39,8 +42,10 @@ class OpenAIProvider(AIProvider):
        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]:
+    async def reply(self, inquiry: str, context: Optional[Dict[str, Any]] = None, tone: str = "professional", preference_context: Optional[str] = None) -> Dict[str, Any]:
        system = SYSTEM_PROMPTS["reply"] + f"\nTone: {tone}"
+        if preference_context:
+            system += f"\nUser preference: {preference_context}"

        context_str = ""
        if context:
@@ -57,8 +62,10 @@ class OpenAIProvider(AIProvider):
        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]:
+    async def generate_marketing(self, product_info: Dict[str, Any], target: str, style: str = "professional", language: str = "en", preference_context: Optional[str] = None) -> Dict[str, Any]:
        system = SYSTEM_PROMPTS["marketing"] + f"\nStyle: {style}\nTarget audience: {target}\nLanguage: {language}"
+        if preference_context:
+            system += f"\nUser preference: {preference_context}"

        product_str = json.dumps(product_info, ensure_ascii=False, indent=2)
        prompt = f"Product information:\n{product_str}\n\nGenerate marketing copy:"
@@ -76,7 +83,7 @@ class OpenAIProvider(AIProvider):
        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:
+    async def _call(self, system: str, prompt: str, max_tokens: int = 3000, response_format: Optional[Dict] = None, model: Optional[str] = None) -> str:
        kwargs = {
            "model": model or self.model,
            "messages": [
@@ -90,7 +97,46 @@ class OpenAIProvider(AIProvider):
            kwargs["response_format"] = response_format

        resp = await self.client.chat.completions.create(**kwargs)
-        return resp.choices[0].message.content
+        content = resp.choices[0].message.content
+        
+        if content is None and hasattr(resp.choices[0].message, 'reasoning'):
+            reasoning = resp.choices[0].message.reasoning
+            if reasoning:
+                import re
+                final_output_patterns = [
+                    r'Final Output Generation[:：]\s*(.+?)(?:\n\n|$)',
+                    r'Final Output[:：]\s*(.+?)(?:\n\n|$)',
+                    r'7\.\s*Final Output Generation[:：]\s*(.+?)(?:\n\n|$)',
+                    r'翻译结果[：:]\s*(.+?)(?:\n\n|$)',
+                    r'最终输出[：:]\s*(.+?)(?:\n\n|$)',
+                ]
+                for pattern in final_output_patterns:
+                    match = re.search(pattern, reasoning, re.DOTALL)
+                    if match:
+                        content = match.group(1).strip()
+                        break
+                
+                if content is None:
+                    paragraphs = re.split(r'\n\n+', reasoning.strip())
+                    if paragraphs:
+                        for p in reversed(paragraphs):
+                            p = p.strip()
+                            if p and len(p) > 10:
+                                if not p.startswith('步骤') and not p.startswith('Step'):
+                                    content = p
+                                    break
+        
+        if content is None and hasattr(resp.choices[0].message, 'reasoning'):
+            reasoning = resp.choices[0].message.reasoning
+            if reasoning:
+                import re
+                cleaned = re.sub(r'^步骤\d+[：:].*$', '', reasoning, flags=re.MULTILINE)
+                cleaned = re.sub(r'^Step \d+[：:].*$', '', cleaned, flags=re.MULTILINE)
+                cleaned = re.sub(r'\n+', '\n', cleaned).strip()
+                if cleaned:
+                    content = cleaned
+        
+        return content

    @property
    def name(self) -> str:
@@ -0,0 +1,7 @@
+from app.ai.providers.openai import OpenAIProvider
+
+
+class SensenovaProvider(OpenAIProvider):
+    def __init__(self, api_key: str, model: str = "sensenova-6.7-flash-lite", base_url: str = "https://token.sensenova.cn/v1"):
+        super().__init__(api_key=api_key, model=model, base_url=base_url)
+        self._name = f"sensenova-{model}"
@@ -0,0 +1,87 @@
+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. "
+                 "Translate business terms accurately. Return ONLY the translated text.",
+    "reply": "You are an experienced foreign trade sales expert. Write professional, "
+             "clear business replies. Return ONLY the reply text.",
+    "marketing": "You are a creative copywriter for international trade. "
+                 "Return ONLY the marketing copy, no explanations.",
+    "extract": "Extract structured data from text. Return ONLY valid JSON.",
+}
+
+
+class SparkProvider(AIProvider):
+    def __init__(self, api_key: str, model: str = "astron-code-latest", base_url: str = None):
+        from app.config import settings
+        self.client = AsyncOpenAI(
+            api_key=api_key,
+            base_url=base_url or settings.IFLYTEK_API_BASE,
+        )
+        self.model = model
+        self._name = f"spark-{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: {context}"
+        prompt = f"Translate {f'from {source_lang} ' if source_lang and source_lang != 'auto' else ''}to {target_lang}:\n\n{text}"
+        content = await self._call(system, prompt)
+        return {"translated_text": content, "provider": self.name}
+
+    async def reply(self, inquiry: str, context: Optional[Dict[str, Any]] = None, tone: str = "professional", preference_context: Optional[str] = None) -> Dict[str, Any]:
+        system = SYSTEM_PROMPTS["reply"] + f"\nTone: {tone}"
+        if preference_context:
+            system += f"\nUser preference: {preference_context}"
+        ctx = ""
+        if context:
+            ctx = "\n".join(f"{k}: {v}" for k, v in context.items() if v)
+        prompt = f"{ctx}\nCustomer inquiry:\n{inquiry}\n\nWrite a reply:"
+        content = await self._call(system, prompt)
+        return {"reply": content, "provider": self.name}
+
+    async def generate_marketing(self, product_info: Dict[str, Any], target: str, style: str = "professional", language: str = "en", preference_context: Optional[str] = None) -> Dict[str, Any]:
+        system = SYSTEM_PROMPTS["marketing"] + f"\nStyle: {style}\nAudience: {target}\nLanguage: {language}"
+        if preference_context:
+            system += f"\nUser preference: {preference_context}"
+        info = json.dumps(product_info, ensure_ascii=False)
+        prompt = f"Product:\n{info}\n\nGenerate marketing copy:"
+        content = await self._call(system, prompt, max_tokens=1500)
+        return {"content": content, "provider": self.name}
+
+    async def extract_info(self, text: str, schema: Dict[str, Any]) -> Dict[str, Any]:
+        system = SYSTEM_PROMPTS["extract"]
+        prompt = f"Schema:\n{json.dumps(schema, indent=2)}\n\nText:\n{text}\n\nJSON:"
+        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}
+
+    async def _call(self, system: str, prompt: str, max_tokens: int = 1000, response_format: Optional[Dict] = None) -> str:
+        kwargs = {
+            "model": 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:
+        return 0.0