fix(llm): 分场景使用不同模型

- nvidia 激活、模型改为 step-3.7-flash → 用于日常生成/采集/优化等
- compliance_optimizer.py 显式指定 provider=sensenova → 合规审查走 deepseek-v4-flash
- _FALLBACK 同步更新为 step-3.7-flash
- 管理后台标签同步更新

platform/backend/app/core/nvidia_client.py

@@ -30,7 +30,7 @@ _API_KEYS = {
 # 代码级回退默认值（实际配置优先从 DB 读取）
 _FALLBACK = {
     "opencode-go": {"base_url": "https://opencode.ai/zen/go/v1", "model": "deepseek-v4-flash"},
-    "nvidia": {"base_url": "https://integrate.api.nvidia.com/v1", "model": "stepfun-ai/step-3.5-flash"},
+    "nvidia": {"base_url": "https://integrate.api.nvidia.com/v1", "model": "stepfun-ai/step-3.7-flash"},
     "sensenova": {"base_url": "https://token.sensenova.cn/v1", "model": "deepseek-v4-flash"},
 }
 

platform/frontend/admin.html

@@ -608,7 +608,7 @@
                     <el-col :span="12"><el-form-item label="供应商">
                         <el-select v-model="llmConfigForm.provider" style="width:100%">
                             <el-option label="opencode-go (deepseek-v4-flash)" value="opencode-go"></el-option>
-                            <el-option label="nvidia (step-3.5-flash)" value="nvidia"></el-option>
+                            <el-option label="nvidia (step-3.7-flash)" value="nvidia"></el-option>
                             <el-option label="sensenova (deepseek-v4-flash)" value="sensenova"></el-option>
                         </el-select>
                     </el-form-item></el-col>

scripts/compliance_optimizer.py

@@ -182,7 +182,7 @@ def polish_with_llm(html: str, platform: str, remaining_issues: Optional[List[Di
                 prompt = get_prompt("compliance_fix", issues_desc=issues_desc, html=html)
             else:
                 prompt = get_prompt("compliance_polish", html=html)
-            polished = call_llm(prompt, provider=None, temperature=temperature, max_tokens=max_tokens, system_prompt=system_prompt)
+            polished = call_llm(prompt, provider="sensenova", temperature=temperature, max_tokens=max_tokens, system_prompt=system_prompt)
             polished = clean_html_content(polished)
             polished = strip_ai_preface(polished)
             polished = strip_thinking_html(polished)