fix: report provider test upstream errors

backend-fastapi/ai_platform/providers/ollama_provider.py

@@ -376,6 +376,19 @@ class OllamaProvider(BaseLLMProvider):
 
     async def fetch_models_from_api(self) -> List[Dict[str, Any]]:
         """通过 /api/tags 端点拉取 Ollama 本地已安装的模型"""
+        base_url = (self.api_base or 'http://localhost:11434').rstrip('/')
+        # Ollama 的 /api/tags 在根路径，去掉 /v1
+        if base_url.endswith('/v1'):
+            base_url = base_url[:-3]
+
+        try:
+            return await self.fetch_models_from_api_strict()
+        except Exception as e:
+            logger.warning(f'在线拉取 Ollama 模型列表失败 ({base_url}): {e}')
+            return []
+
+    async def fetch_models_from_api_strict(self) -> List[Dict[str, Any]]:
+        """通过 Ollama /api/tags 严格拉取模型列表，失败时保留上游异常"""
         import httpx
 
         base_url = (self.api_base or 'http://localhost:11434').rstrip('/')
@@ -383,50 +396,45 @@ class OllamaProvider(BaseLLMProvider):
         if base_url.endswith('/v1'):
             base_url = base_url[:-3]
 
-        try:
-            async with httpx.AsyncClient(timeout=10) as client:
-                resp = await client.get(f'{base_url}/api/tags')
-                resp.raise_for_status()
-                data = resp.json()
+        async with httpx.AsyncClient(timeout=10) as client:
+            resp = await client.get(f'{base_url}/api/tags')
+            resp.raise_for_status()
+            data = resp.json()
 
-            results = []
-            for item in data.get('models', []):
-                name = item.get('name', '')
-                if not name:
-                    continue
+        results = []
+        for item in data.get('models', []):
+            name = item.get('name', '')
+            if not name:
+                continue
 
-                name_lower = name.lower()
-                if any(kw in name_lower for kw in self._RERANK_KEYWORDS):
-                    model_type = 'rerank'
-                elif any(kw in name_lower for kw in self._EMBEDDING_KEYWORDS):
-                    model_type = 'embedding'
-                else:
-                    model_type = 'chat'
+            name_lower = name.lower()
+            if any(kw in name_lower for kw in self._RERANK_KEYWORDS):
+                model_type = 'rerank'
+            elif any(kw in name_lower for kw in self._EMBEDDING_KEYWORDS):
+                model_type = 'embedding'
+            else:
+                model_type = 'chat'
 
-                # 从 size 推算显示名称
-                size_gb = round(item.get('size', 0) / (1024 ** 3), 1)
-                display = f'{name} ({size_gb}GB)' if size_gb > 0 else name
+            # 从 size 推算显示名称
+            size_gb = round(item.get('size', 0) / (1024 ** 3), 1)
+            display = f'{name} ({size_gb}GB)' if size_gb > 0 else name
 
-                results.append({
-                    'model_name': name,
-                    'display_name': display,
-                    'model_type': model_type,
-                    'max_tokens': 4096,
-                    'context_window': 4096,
-                    'supports_vision': False,
-                    'supports_function_call': model_type == 'chat',
-                    'input_price': 0,
-                    'output_price': 0,
-                })
+            results.append({
+                'model_name': name,
+                'display_name': display,
+                'model_type': model_type,
+                'max_tokens': 4096,
+                'context_window': 4096,
+                'supports_vision': False,
+                'supports_function_call': model_type == 'chat',
+                'input_price': 0,
+                'output_price': 0,
+            })
 
-            type_order = {'chat': 0, 'embedding': 1, 'rerank': 2}
-            results.sort(key=lambda m: (type_order.get(m['model_type'], 9), m['model_name']))
-            logger.info(f'从 Ollama ({base_url}) 拉取到 {len(results)} 个本地模型')
-            return results
-
-        except Exception as e:
-            logger.warning(f'在线拉取 Ollama 模型列表失败 ({base_url}): {e}')
-            return []
+        type_order = {'chat': 0, 'embedding': 1, 'rerank': 2}
+        results.sort(key=lambda m: (type_order.get(m['model_type'], 9), m['model_name']))
+        logger.info(f'从 Ollama ({base_url}) 拉取到 {len(results)} 个本地模型')
+        return results
     
     @classmethod
     def get_default_models(cls) -> List[Dict[str, Any]]: