feat(embedding): 支持 OpenAI 兼容 API 并增强错误处理

- 更新 OpenAI 提供商描述以支持兼容 API 服务商
- 前端添加兼容 API 使用引导说明
- 后端 QwenEmbedding 添加 API 密钥验证和错误处理

backend/app/api/v1/endpoints/embedding_config.py

@@ -76,8 +76,8 @@ class TestEmbeddingResponse(BaseModel):
 EMBEDDING_PROVIDERS: List[EmbeddingProvider] = [
     EmbeddingProvider(
         id="openai",
-        name="OpenAI",
+        name="OpenAI (兼容 DeepSeek/Moonshot/智谱 等)",
-        description="OpenAI 官方嵌入模型，高质量、稳定",
+        description="OpenAI 官方或兼容 API，填写自定义端点可接入其他服务商",
         models=[
             "text-embedding-3-small",
             "text-embedding-3-large",

backend/app/services/rag/embeddings.py

@@ -486,6 +486,12 @@ class QwenEmbedding(EmbeddingProvider):
             or getattr(settings, "QWEN_API_KEY", None)
             or settings.LLM_API_KEY
         )
+        # 🔥 API 密钥验证
+        if not self.api_key:
+            raise ValueError(
+                "Qwen embedding requires API key. "
+                "Set EMBEDDING_API_KEY, QWEN_API_KEY or LLM_API_KEY environment variable."
+            )
         # DashScope 兼容 OpenAI 的 embeddings 端点
         self.base_url = base_url or "https://dashscope.aliyuncs.com/compatible-mode/v1"
         self.model = model
@@ -502,41 +508,51 @@ class QwenEmbedding(EmbeddingProvider):
     async def embed_texts(self, texts: List[str]) -> List[EmbeddingResult]:
         if not texts:
             return []
-        
+
         # 与 OpenAI 接口保持一致的截断策略
         max_length = 8191
         truncated_texts = [text[:max_length] for text in texts]
-        
+
         headers = {
             "Authorization": f"Bearer {self.api_key}",
             "Content-Type": "application/json",
         }
-        
+
         payload = {
             "model": self.model,
             "input": truncated_texts,
             "encoding_format": "float",
         }
-        
+
         url = f"{self.base_url.rstrip('/')}/embeddings"
-        
+
-        async with httpx.AsyncClient(timeout=60) as client:
+        try:
-            response = await client.post(url, headers=headers, json=payload)
+            async with httpx.AsyncClient(timeout=60) as client:
-            response.raise_for_status()
+                response = await client.post(url, headers=headers, json=payload)
-            data = response.json()
+                response.raise_for_status()
-            
+                data = response.json()
-            usage = data.get("usage", {}) or {}
+
-            total_tokens = usage.get("total_tokens") or usage.get("prompt_tokens") or 0
+                usage = data.get("usage", {}) or {}
-            
+                total_tokens = usage.get("total_tokens") or usage.get("prompt_tokens") or 0
-            results: List[EmbeddingResult] = []
+
-            for item in data.get("data", []):
+                results: List[EmbeddingResult] = []
-                results.append(EmbeddingResult(
+                for item in data.get("data", []):
-                    embedding=item["embedding"],
+                    results.append(EmbeddingResult(
-                    tokens_used=total_tokens // max(len(texts), 1),
+                        embedding=item["embedding"],
-                    model=self.model,
+                        tokens_used=total_tokens // max(len(texts), 1),
-                ))
+                        model=self.model,
-            
+                    ))
-            return results
+
+                return results
+        except httpx.HTTPStatusError as e:
+            logger.error(f"Qwen embedding API error: {e.response.status_code} - {e.response.text}")
+            raise RuntimeError(f"Qwen embedding API failed: {e.response.status_code}") from e
+        except httpx.RequestError as e:
+            logger.error(f"Qwen embedding network error: {e}")
+            raise RuntimeError(f"Qwen embedding network error: {e}") from e
+        except Exception as e:
+            logger.error(f"Qwen embedding unexpected error: {e}")
+            raise RuntimeError(f"Qwen embedding failed: {e}") from e
 
 
 class EmbeddingService:

frontend/src/components/agent/EmbeddingConfig.tsx

@@ -434,7 +434,7 @@ export default function EmbeddingConfigPanel() {
       </div>
 
       {/* 说明 */}
-      <div className="bg-muted border border-border p-4 rounded-lg text-xs space-y-2">
+      <div className="bg-muted border border-border p-4 rounded-lg text-xs space-y-3">
         <p className="font-bold uppercase text-muted-foreground flex items-center gap-2">
           <Info className="w-4 h-4 text-sky-400" />
           关于嵌入模型
@@ -445,6 +445,25 @@ export default function EmbeddingConfigPanel() {
           <li>• 推荐使用 <span className="text-foreground">OpenAI text-embedding-3-small</span> 或本地 <span className="text-foreground">Ollama</span></li>
           <li>• 向量维度影响存储空间和检索精度</li>
         </ul>
+
+        {/* OpenAI 兼容 API 引导 */}
+        <div className="mt-3 pt-3 border-t border-border/50">
+          <p className="font-bold text-amber-400 flex items-center gap-2 mb-2">
+            <Zap className="w-4 h-4" />
+            使用 OpenAI 兼容 API
+          </p>
+          <p className="text-muted-foreground mb-2">
+            许多服务商提供 OpenAI 兼容的 API，可以直接使用 <span className="text-foreground">openai</span> 作为提供商：
+          </p>
+          <ul className="text-muted-foreground space-y-1 ml-4">
+            <li>• <span className="text-foreground">DeepSeek</span>: 端点填写 <code className="text-primary bg-primary/10 px-1 rounded">https://api.deepseek.com/v1</code></li>
+            <li>• <span className="text-foreground">Moonshot</span>: 端点填写 <code className="text-primary bg-primary/10 px-1 rounded">https://api.moonshot.cn/v1</code></li>
+            <li>• <span className="text-foreground">智谱 GLM</span>: 端点填写 <code className="text-primary bg-primary/10 px-1 rounded">https://open.bigmodel.cn/api/paas/v4</code></li>
+          </ul>
+          <p className="text-muted-foreground mt-2 text-[11px]">
+            提示：选择 openai 提供商，填入对应服务的 API Key 和自定义端点即可
+          </p>
+        </div>
       </div>
     </div>
   );