feat(core): 添加高级LLM参数配置功能
- 在模型配置中引入 `llmParams` 字段，允许用户为每个模型单独设置高级LLM参数 - 更新文档，详细说明 `llmParams` 的使用方法及示例 - 在模型管理器中实现对 `llmParams` 的验证和处理逻辑 - 增强 LLMService 以支持从 `llmParams` 中提取参数，优化模型行为控制 - 更新 UI 组件以支持高级参数的配置和展示，提升用户体验
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@@ -13,6 +13,9 @@ VITE_DEEPSEEK_API_KEY=your_deepseek_api_key
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# SiliconFlow API配置
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VITE_SILICONFLOW_API_KEY=your_siliconflow_api_key
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# 智谱 API配置
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VITE_ZHIPU_API_KEY=your_zhipu_api_key
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# 自定义API配置（可选）
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VITE_CUSTOM_API_KEY=your_custom_api_key
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VITE_CUSTOM_API_BASE_URL=your_custom_api_base_url
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@@ -133,6 +133,10 @@ services:
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- DeepSeek (DeepSeek-V3)
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- 自定义API（OpenAI兼容接口）
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除了API密钥，您还可以在模型配置界面为每个模型单独设置高级LLM参数。这些参数通过一个名为 `llmParams` 的字段进行配置，它允许您以键值对的形式指定LLM SDK支持的任何参数，从而更精细地控制模型行为。
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例如，您可以这样配置：`"llmParams": { "temperature": 0.5, "max_tokens": 1024 }`。
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有关 `llmParams` 的更详细说明和特定提供商的示例（如OpenAI的 `timeout`，Gemini的 `maxOutputTokens` 等），请参阅 [技术开发指南](docs/technical-development-guide.md)。
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### 方式二：通过环境变量配置
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Docker部署时通过 `-e` 参数配置环境变量：
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```bash
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@@ -36,6 +36,7 @@ src/
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- **多服务商兼容**: Provider标识区分
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- **敏感信息管理**: 环境变量+本地加密存储
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- **用户自管理API密钥**: 避免后端开销，保持应用简单性
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- **参数透明化**: 只使用用户明确配置的参数，不设置默认值避免误解
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---
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@@ -68,6 +69,7 @@ try {
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2. **测试隔离**: 使用动态唯一标识符避免冲突
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3. **错误场景**: 覆盖网络错误、无效Token等异常
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4. **状态管理**: 独立测试数据库、正确清理状态
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5. **参数测试**: 为每个LLM参数创建独立测试用例，确保参数兼容性
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### 测试模板
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```js
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@@ -81,6 +83,32 @@ describe("功能测试", () => {
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await expect(func()).rejects.toThrow("预期错误");
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});
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});
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// LLM参数测试最佳实践
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describe("Individual Parameter Tests", () => {
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// 为每个advancedParameterDefinitions中的参数创建独立测试
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it("should accept valid temperature", async () => {
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await modelManager.updateModel(configKey, {
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// ... 基础配置
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llmParams: { temperature: 0.3 } // 只测试一个参数
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});
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const response = await llmService.sendMessage(messages, configKey);
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expect(response).toBeDefined();
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});
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// 组合参数测试
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it("should handle multiple parameters together", async () => {
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await modelManager.updateModel(configKey, {
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// ... 基础配置
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llmParams: {
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temperature: 0.6,
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max_tokens: 150,
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top_p: 0.9
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}
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});
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});
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});
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```
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---
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@@ -126,6 +154,7 @@ const handlers = {
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| UI配置导入验证不充分 | 中 | ✅ 已修复 |
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| 数据迁移竞态条件 | 中 | ✅ 已修复 |
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| 测试覆盖缺失 | 高 | ✅ 已修复 |
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| LLM参数默认值误导用户 | 中 | ✅ 已修复 |
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#### 关键修复示例
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```ts
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@@ -143,6 +172,20 @@ for (const [key, value] of Object.entries(typedData.userSettings)) {
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}
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await this.storage.setItem(key, value);
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}
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// LLM参数透明化处理 (2024-12-20)
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// ❌ 旧版本：自动设置默认值
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if (completionConfig.temperature === undefined) {
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completionConfig.temperature = 0.7; // 可能误导用户
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}
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// ✅ 新版本：只使用用户明确配置的参数
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const completionConfig: any = {
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model: modelConfig.defaultModel,
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messages: formattedMessages,
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...restLlmParams // 只传递用户配置的参数
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};
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// 不设置任何默认值，让API服务商使用其默认配置
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```
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---
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@@ -0,0 +1,384 @@
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# LLM高级参数配置指南
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## 概述
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`llmParams` 功能允许您为每个模型配置详细的参数，以精确控制LLM的行为。本系统采用**智能参数分类**和**透明化传递**机制，确保参数配置的专业性和可靠性。
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## 🔧 核心设计原则
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### 1. 参数透明化 (2024.12.20 更新)
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- **不设置默认值**: 系统不会自动添加任何默认值，避免用户误解
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- **直接传递**: 用户配置什么参数就传递什么参数
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- **SDK原生**: 依赖各LLM服务商的SDK默认行为
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### 2. 智能参数分类
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- **按提供商过滤**: UI自动根据模型类型显示相关参数
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- **避免混淆**: OpenAI类型模型只显示OpenAI参数，Gemini模型只显示Gemini参数
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- **参数隔离**: 不同提供商的参数互不干扰
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### 3. 扩展性保证
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- **自定义参数**: 支持任意SDK兼容的自定义参数
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- **未来兼容**: 新参数无需修改核心代码即可使用
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- **类型保持**: 保持参数原始类型和结构
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## 🚀 参数生效机制
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### OpenAI兼容提供商 (OpenAI, DeepSeek, Zhipu, SiliconFlow, Custom)
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#### 参数传递流程
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```typescript
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// 1. 分离特殊参数
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const {
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timeout, // 客户端配置参数
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model, // 避免覆盖主模型配置
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messages, // 避免覆盖主消息
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...restLlmParams // 所有其他参数
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} = modelConfig.llmParams || {};
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// 2. 创建客户端实例
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const openai = new OpenAI({
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apiKey,
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baseURL,
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timeout: timeout || (isStream ? 90000 : 60000), // 仅timeout有特殊处理
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maxRetries: isStream ? 2 : 3
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});
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// 3. 构建API请求 - 无默认值设置
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const completionConfig = {
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model: modelConfig.defaultModel,
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messages: formattedMessages,
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...restLlmParams // 直接传递所有其他参数
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};
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// 4. 发送请求
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const response = await openai.chat.completions.create(completionConfig);
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```
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#### 支持的参数
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| 参数名 | 类型 | 范围 | 说明 |
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|--------|------|------|------|
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| `timeout` | integer | ≥1000 | 请求超时(毫秒) - 客户端配置 |
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| `temperature` | number | 0.0-2.0 | 控制输出随机性 |
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| `max_tokens` | integer | ≥1 | 最大生成token数量 |
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| `top_p` | number | 0.0-1.0 | 核心采样参数 |
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| `presence_penalty` | number | -2.0-2.0 | 存在惩罚 |
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| `frequency_penalty` | number | -2.0-2.0 | 频率惩罚 |
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| `stop` | array | - | 停止序列 |
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| `seed` | integer | - | 随机种子 |
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| `stream` | boolean | - | 流式输出（系统自动处理） |
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### Gemini提供商
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#### 参数传递流程
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```typescript
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// 1. 分离已知参数和未知参数
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const {
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temperature,
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maxOutputTokens,
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topP,
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topK,
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candidateCount,
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stopSequences,
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...otherSafeParams // 未知参数也会传递
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} = modelConfig.llmParams || {};
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// 2. 构建生成配置 - 无默认值设置
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const generationConfig = { ...otherSafeParams };
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// 3. 仅添加用户明确配置的参数
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if (temperature !== undefined) {
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generationConfig.temperature = temperature;
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}
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if (maxOutputTokens !== undefined) {
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generationConfig.maxOutputTokens = maxOutputTokens;
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}
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// ... 其他参数类似处理
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// 4. 创建聊天会话
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const chat = model.startChat({
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history: formatHistory(messages),
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...(Object.keys(generationConfig).length > 0 && { generationConfig })
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});
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```
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#### 支持的参数
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| 参数名 | 类型 | 范围 | 说明 |
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|--------|------|------|------|
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| `temperature` | number | 0.0-2.0 | 控制输出随机性 |
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| `maxOutputTokens` | integer | ≥1 | 最大输出token数量 |
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| `topP` | number | 0.0-1.0 | 核心采样参数 |
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| `topK` | integer | ≥1 | Top-K采样 |
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| `candidateCount` | integer | 1-8 | 候选响应数量 |
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| `stopSequences` | array | - | 停止序列数组 |
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## 🎯 UI智能参数管理
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### 参数类型自动识别
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系统会根据模型的`provider`字段自动显示相关参数：
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```typescript
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// 根据provider过滤参数定义
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const availableParams = advancedParameterDefinitions.filter(def =>
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def.appliesToProviders.includes(currentProvider) &&
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!Object.keys(currentParams).includes(def.name)
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);
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```
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### 提供商映射关系
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```typescript
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const providerMapping = {
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// OpenAI兼容类型
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'openai': ['temperature', 'top_p', 'max_tokens', 'presence_penalty', 'frequency_penalty', 'timeout'],
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'deepseek': ['temperature', 'top_p', 'max_tokens', 'presence_penalty', 'frequency_penalty', 'timeout'],
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'zhipu': ['temperature', 'top_p', 'max_tokens', 'presence_penalty', 'frequency_penalty', 'timeout'],
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'siliconflow': ['temperature', 'top_p', 'max_tokens', 'presence_penalty', 'frequency_penalty', 'timeout'],
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'custom': ['temperature', 'top_p', 'max_tokens', 'presence_penalty', 'frequency_penalty', 'timeout'],
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// Gemini类型
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'gemini': ['temperature', 'topP', 'maxOutputTokens', 'topK', 'candidateCount', 'stopSequences']
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};
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```
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### UI显示增强
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- 显示当前提供商类型
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- 显示可选参数数量
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- 彩色状态指示
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- 自动过滤已配置参数
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## 📋 配置示例
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### OpenAI模型配置
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```json
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{
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"name": "OpenAI GPT-4",
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"provider": "openai",
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"llmParams": {
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"temperature": 0.3, // 低随机性，更确定的输出
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"max_tokens": 4096, // 限制输出长度
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"top_p": 0.8, // 核心采样
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"presence_penalty": 0.1, // 鼓励新话题
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"timeout": 90000 // 90秒超时
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}
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}
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```
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### DeepSeek模型配置
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```json
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{
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"name": "DeepSeek Coder V3",
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"provider": "deepseek",
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"llmParams": {
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"temperature": 0.1, // 代码生成需要低随机性
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"max_tokens": 8192, // 较长的代码输出
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"top_p": 0.95, // 平衡多样性和质量
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"timeout": 120000 // 代码生成可能需要更长时间
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}
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}
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```
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### Gemini模型配置
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```json
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{
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"name": "Gemini Pro",
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"provider": "gemini",
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"llmParams": {
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"temperature": 0.8, // 创意任务高随机性
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"maxOutputTokens": 2048, // 适中输出长度
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"topP": 0.95, // 核心采样
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"topK": 40, // Top-K采样
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"candidateCount": 1, // 单个响应
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"stopSequences": ["END", "STOP"] // 自定义停止词
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}
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}
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```
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### 自定义模型配置
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```json
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{
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"name": "Custom LLaMA",
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"provider": "custom",
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"llmParams": {
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"temperature": 0.7,
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"max_tokens": 4096,
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// 自定义参数示例
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"repetition_penalty": 1.1,
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"do_sample": true,
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"pad_token_id": 0,
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"eos_token_id": 2
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}
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}
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```
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## 🔍 验证与调试
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### 参数验证API
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```typescript
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import { validateLLMParams } from '@prompt-optimizer/core';
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const validation = validateLLMParams(llmParams, provider);
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if (!validation.isValid) {
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console.error('参数验证失败:', validation.errors);
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validation.errors.forEach(error => {
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console.error(`- ${error.parameterName}: ${error.message}`);
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});
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}
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if (validation.warnings.length > 0) {
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console.warn('参数警告:', validation.warnings);
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validation.warnings.forEach(warning => {
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console.warn(`- ${warning.parameterName}: ${warning.message}`);
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});
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}
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```
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### 测试每个参数
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系统为每个参数提供独立的测试用例：
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```typescript
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// 测试temperature参数
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await testParameter('temperature', 0.3, provider);
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// 测试max_tokens参数
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await testParameter('max_tokens', 100, provider);
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// 测试组合参数
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await testParameters({
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temperature: 0.6,
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max_tokens: 150,
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top_p: 0.9
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}, provider);
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```
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## ⚡ 最佳实践
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### 1. 参数选择策略
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```typescript
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// 代码生成任务
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const codingParams = {
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temperature: 0.1, // 低随机性
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max_tokens: 8192, // 长输出
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top_p: 0.95 // 高质量采样
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};
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// 创意写作任务
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const creativeParams = {
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temperature: 0.8, // 高随机性
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max_tokens: 2048, // 适中输出
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top_p: 0.9, // 平衡采样
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presence_penalty: 0.3 // 鼓励新想法
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};
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// 问答任务
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const qaParams = {
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temperature: 0.3, // 中等随机性
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max_tokens: 1024, // 简洁回答
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frequency_penalty: 0.1 // 避免重复
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};
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```
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### 2. 渐进式调优
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```typescript
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// 第一步：基础配置
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let params = {
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temperature: 0.7
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};
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// 第二步：添加输出控制
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params = {
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...params,
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max_tokens: 2048,
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top_p: 0.9
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};
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// 第三步：精细调整
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params = {
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...params,
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presence_penalty: 0.1,
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frequency_penalty: 0.1
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};
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```
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### 3. 性能优化
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```typescript
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// 快速响应场景
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const fastParams = {
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max_tokens: 512, // 限制输出长度
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timeout: 30000 // 较短超时
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};
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|
||||
// 高质量场景
|
||||
const qualityParams = {
|
||||
temperature: 0.2, // 低随机性
|
||||
top_p: 0.8, // 精确采样
|
||||
timeout: 120000 // 较长超时
|
||||
};
|
||||
```
|
||||
|
||||
## 🛠️ 故障排除
|
||||
|
||||
### 常见问题诊断
|
||||
|
||||
1. **参数不生效**
|
||||
```typescript
|
||||
// 检查参数名是否正确
|
||||
console.log('支持的参数:', advancedParameterDefinitions
|
||||
.filter(def => def.appliesToProviders.includes(provider))
|
||||
.map(def => def.name));
|
||||
```
|
||||
|
||||
2. **类型错误**
|
||||
```typescript
|
||||
// 确保参数类型正确
|
||||
const temperature = parseFloat(userInput); // 确保是number
|
||||
const maxTokens = parseInt(userInput, 10); // 确保是integer
|
||||
```
|
||||
|
||||
3. **范围错误**
|
||||
```typescript
|
||||
// 检查参数范围
|
||||
if (temperature < 0 || temperature > 2) {
|
||||
throw new Error('temperature必须在0-2之间');
|
||||
}
|
||||
```
|
||||
|
||||
### 调试工具
|
||||
|
||||
1. **启用详细日志**
|
||||
```typescript
|
||||
// 在modelManager中启用调试
|
||||
const debugMode = process.env.NODE_ENV === 'development';
|
||||
if (debugMode) {
|
||||
console.log('LLM参数配置:', llmParams);
|
||||
console.log('当前provider:', provider);
|
||||
}
|
||||
```
|
||||
|
||||
2. **参数传递跟踪**
|
||||
```typescript
|
||||
// 查看实际传递的参数
|
||||
console.log('传递给SDK的参数:', {
|
||||
...completionConfig,
|
||||
provider,
|
||||
timestamp: new Date().toISOString()
|
||||
});
|
||||
```
|
||||
|
||||
## 📝 更新日志
|
||||
|
||||
### 2024.12.20 - 参数透明化更新
|
||||
- ✅ 移除所有自动设置的默认值
|
||||
- ✅ 改进参数类型自动过滤
|
||||
- ✅ 优化UI显示和标签
|
||||
- ✅ 增强测试覆盖率
|
||||
- ✅ 添加参数组合测试
|
||||
- ✅ 完善故障排除指南
|
||||
|
||||
### 核心改进
|
||||
- **透明化原则**: 只传递用户明确配置的参数
|
||||
- **智能分类**: 根据provider自动显示相关参数
|
||||
- **UI优化**: 移除标签中的冗余提供商标识
|
||||
- **测试完善**: 为每个参数添加独立测试用例
|
||||
@@ -340,10 +340,40 @@
|
||||
### 4.4 模型管理流程
|
||||
|
||||
1. **模型配置管理**
|
||||
- 模型配置更新
|
||||
- 连接测试
|
||||
- 配置验证
|
||||
- 错误处理
|
||||
- 模型配置更新: 用户可以更新模型的名称、基础URL、API密钥、可用模型列表、默认模型以及是否启用。
|
||||
- **高级LLM参数 (`llmParams`)**:
|
||||
- `ModelConfig` 接口包含一个 `llmParams?: Record<string, any>;` 字段。
|
||||
- 此字段允许用户为每个模型配置提供一个灵活的键值对映射，用于指定特定于该LLM提供商SDK的参数。
|
||||
- 用户可以添加其LLM SDK支持的任何参数。
|
||||
- **示例**:
|
||||
- **OpenAI/OpenAI兼容API (如 DeepSeek, Zhipu):**
|
||||
```json
|
||||
"llmParams": {
|
||||
"temperature": 0.7,
|
||||
"max_tokens": 4096,
|
||||
"timeout": 60000, // 用于OpenAI客户端的请求超时 (毫秒)
|
||||
"top_p": 0.9,
|
||||
"frequency_penalty": 0.5
|
||||
// ... 其他OpenAI支持的参数
|
||||
}
|
||||
```
|
||||
- **Gemini:**
|
||||
```json
|
||||
"llmParams": {
|
||||
"temperature": 0.8,
|
||||
"maxOutputTokens": 2048, // 注意: Gemini使用maxOutputTokens
|
||||
"topP": 0.95,
|
||||
"topK": 40
|
||||
// ... 其他Gemini支持的参数
|
||||
}
|
||||
```
|
||||
- **`LLMService` 如何处理 `llmParams`**:
|
||||
- 对于OpenAI兼容的API, `timeout` 值（如果提供）用于配置OpenAI JavaScript SDK客户端实例的超时设置。其余参数（如 `temperature`, `max_tokens`, `top_p` 等）会直接传递给 `chat.completions.create()` 方法。
|
||||
- 对于Gemini, `temperature`, `maxOutputTokens`, `topP`, `topK` 等参数会包含在传递给 `model.startChat()` 的 `generationConfig` 对象中。
|
||||
- 未被服务明确处理的参数（即非 `timeout` for OpenAI, 或非已知Gemini参数）通常会被安全地传递给相应SDK的请求中，如果SDK支持它们。
|
||||
- 连接测试: 验证API密钥和基础URL是否正确，以及模型是否可用。
|
||||
- 配置验证: 确保所有必填字段都已填写，并且格式正确。`llmParams` 字段（如果提供）必须是一个对象。
|
||||
- 错误处理: 在配置不正确或连接失败时提供明确的错误信息。
|
||||
|
||||
2. **API密钥管理**
|
||||
- 密钥设置与加密
|
||||
|
||||
@@ -1,13 +0,0 @@
|
||||
{
|
||||
"compilerOptions": {
|
||||
"baseUrl": ".",
|
||||
"paths": {
|
||||
"@prompt-optimizer/core": ["packages/core"],
|
||||
"@prompt-optimizer/ui": ["packages/ui"],
|
||||
"@prompt-optimizer/web": ["packages/web"],
|
||||
"@prompt-optimizer/extension": ["packages/extension"]
|
||||
}
|
||||
},
|
||||
"include": ["packages/*/src"],
|
||||
"exclude": ["node_modules", "**/node_modules", "**/dist"]
|
||||
}
|
||||
@@ -20,6 +20,16 @@ export * from './services/llm/errors'
|
||||
export { ModelManager, modelManager } from './services/model/manager'
|
||||
export * from './services/model/types'
|
||||
export * from './services/model/defaults'
|
||||
export * from './services/model/advancedParameterDefinitions'
|
||||
export {
|
||||
validateLLMParams,
|
||||
getSupportedParameters
|
||||
} from './services/model/validation'
|
||||
export type {
|
||||
ValidationResult,
|
||||
ValidationError as LLMValidationError,
|
||||
ValidationWarning
|
||||
} from './services/model/validation'
|
||||
|
||||
// 导出提示词服务相关
|
||||
export { PromptService, createPromptService } from './services/prompt/service'
|
||||
|
||||
@@ -156,7 +156,8 @@ export class DataManager {
|
||||
provider: model.provider || existingModel.provider,
|
||||
enabled: model.enabled !== undefined ? model.enabled : existingModel.enabled, // 优先使用导入的启用状态
|
||||
...(model.apiKey !== undefined && { apiKey: model.apiKey }),
|
||||
...(model.useVercelProxy !== undefined && { useVercelProxy: model.useVercelProxy })
|
||||
...(model.useVercelProxy !== undefined && { useVercelProxy: model.useVercelProxy }),
|
||||
...(model.llmParams !== undefined && { llmParams: model.llmParams })
|
||||
};
|
||||
await this.modelManagerInstance.updateModel(model.key, mergedConfig);
|
||||
console.log(`模型 ${model.key} 已存在，已更新配置（使用导入的启用状态: ${mergedConfig.enabled}）`);
|
||||
@@ -170,7 +171,8 @@ export class DataManager {
|
||||
provider: model.provider || 'custom',
|
||||
enabled: model.enabled !== undefined ? model.enabled : false, // 使用导入的启用状态，默认为false
|
||||
...(model.apiKey !== undefined && { apiKey: model.apiKey }),
|
||||
...(model.useVercelProxy !== undefined && { useVercelProxy: model.useVercelProxy })
|
||||
...(model.useVercelProxy !== undefined && { useVercelProxy: model.useVercelProxy }),
|
||||
...(model.llmParams !== undefined && { llmParams: model.llmParams })
|
||||
};
|
||||
await this.modelManagerInstance.addModel(model.key, newModelConfig);
|
||||
console.log(`已导入新模型 ${model.key}（启用状态: ${newModelConfig.enabled}）`);
|
||||
|
||||
@@ -79,20 +79,18 @@ export class LLMService implements ILLMService {
|
||||
}
|
||||
|
||||
// 创建OpenAI实例配置
|
||||
const defaultTimeout = isStream ? 90000 : 60000;
|
||||
const timeout = modelConfig.llmParams?.timeout !== undefined
|
||||
? modelConfig.llmParams.timeout
|
||||
: defaultTimeout;
|
||||
const config: any = {
|
||||
apiKey: apiKey,
|
||||
baseURL: finalBaseURL,
|
||||
dangerouslyAllowBrowser: true,
|
||||
timeout: 60000, // 设置60秒超时时间，文本生成可能需要较长时间
|
||||
maxRetries: 3 // 设置重试次数
|
||||
timeout: timeout, // Use the new timeout logic
|
||||
maxRetries: isStream ? 2 : 3
|
||||
};
|
||||
|
||||
// 为流式请求添加额外配置
|
||||
if (isStream) {
|
||||
config.timeout = 90000; // 流式请求使用更长的超时时间
|
||||
config.maxRetries = 2; // 流式请求减少重试次数
|
||||
}
|
||||
|
||||
const instance = new OpenAI(config);
|
||||
|
||||
return instance;
|
||||
@@ -142,11 +140,19 @@ export class LLMService implements ILLMService {
|
||||
content: msg.content
|
||||
}));
|
||||
|
||||
const response = await openai.chat.completions.create({
|
||||
const {
|
||||
timeout, // Handled in getOpenAIInstance
|
||||
model: llmParamsModel, // Avoid overriding main model
|
||||
messages: llmParamsMessages, // Avoid overriding main messages
|
||||
...restLlmParams
|
||||
} = modelConfig.llmParams || {};
|
||||
|
||||
const completionConfig: any = {
|
||||
model: modelConfig.defaultModel,
|
||||
messages: formattedMessages,
|
||||
temperature: 0.7
|
||||
});
|
||||
...restLlmParams // Spread other params from llmParams
|
||||
};
|
||||
const response = await openai.chat.completions.create(completionConfig);
|
||||
|
||||
return response.choices[0].message.content || '';
|
||||
}
|
||||
@@ -168,9 +174,15 @@ export class LLMService implements ILLMService {
|
||||
const conversationMessages = messages.filter(msg => msg.role !== 'system');
|
||||
|
||||
// 创建聊天会话
|
||||
const chat = model.startChat({
|
||||
const generationConfig = this.buildGeminiGenerationConfig(modelConfig.llmParams);
|
||||
|
||||
const chatOptions: any = {
|
||||
history: this.formatGeminiHistory(conversationMessages)
|
||||
});
|
||||
};
|
||||
if (Object.keys(generationConfig).length > 0) {
|
||||
chatOptions.generationConfig = generationConfig;
|
||||
}
|
||||
const chat = model.startChat(chatOptions);
|
||||
|
||||
// 获取最后一条用户消息
|
||||
const lastUserMessage = conversationMessages.length > 0 &&
|
||||
@@ -310,16 +322,28 @@ export class LLMService implements ILLMService {
|
||||
|
||||
try {
|
||||
console.log('开始创建流式请求...');
|
||||
const stream = await openai.chat.completions.create({
|
||||
const {
|
||||
timeout, // Handled in getOpenAIInstance
|
||||
model: llmParamsModel, // Avoid overriding main model
|
||||
messages: llmParamsMessages, // Avoid overriding main messages
|
||||
stream: llmParamsStream, // Avoid overriding main stream flag
|
||||
...restLlmParams
|
||||
} = modelConfig.llmParams || {};
|
||||
|
||||
const completionConfig: any = {
|
||||
model: modelConfig.defaultModel,
|
||||
messages: formattedMessages,
|
||||
temperature: 0.7,
|
||||
stream: true
|
||||
});
|
||||
stream: true, // Essential for streaming
|
||||
...restLlmParams // User-defined parameters from llmParams
|
||||
};
|
||||
|
||||
// 直接使用流式响应，无需类型转换
|
||||
const stream = await openai.chat.completions.create(completionConfig);
|
||||
|
||||
console.log('成功获取到流式响应');
|
||||
|
||||
for await (const chunk of stream) {
|
||||
// 使用类型断言来确保TypeScript知道这是流式响应
|
||||
for await (const chunk of stream as any) {
|
||||
const content = chunk.choices[0]?.delta?.content || '';
|
||||
if (content) {
|
||||
console.log('收到数据块:', {
|
||||
@@ -363,9 +387,15 @@ export class LLMService implements ILLMService {
|
||||
const conversationMessages = messages.filter(msg => msg.role !== 'system');
|
||||
|
||||
// 创建聊天会话
|
||||
const chat = model.startChat({
|
||||
const generationConfig = this.buildGeminiGenerationConfig(modelConfig.llmParams);
|
||||
|
||||
const chatOptions: any = {
|
||||
history: this.formatGeminiHistory(conversationMessages)
|
||||
});
|
||||
};
|
||||
if (Object.keys(generationConfig).length > 0) {
|
||||
chatOptions.generationConfig = generationConfig;
|
||||
}
|
||||
const chat = model.startChat(chatOptions);
|
||||
|
||||
// 获取最后一条用户消息
|
||||
const lastUserMessage = conversationMessages.length > 0 &&
|
||||
@@ -588,6 +618,56 @@ export class LLMService implements ILLMService {
|
||||
];
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 构建Gemini生成配置
|
||||
*
|
||||
* 注意：此方法假设传入的 llmParams 已经通过 ModelManager.validateConfig()
|
||||
* 中的 validateLLMParams 验证，确保安全性
|
||||
*/
|
||||
private buildGeminiGenerationConfig(llmParams: Record<string, any> = {}): any {
|
||||
const {
|
||||
temperature,
|
||||
maxOutputTokens,
|
||||
topP,
|
||||
topK,
|
||||
candidateCount,
|
||||
stopSequences,
|
||||
...otherParams
|
||||
} = llmParams;
|
||||
|
||||
const generationConfig: any = {};
|
||||
|
||||
// 添加已知参数
|
||||
if (temperature !== undefined) {
|
||||
generationConfig.temperature = temperature;
|
||||
}
|
||||
if (maxOutputTokens !== undefined) {
|
||||
generationConfig.maxOutputTokens = maxOutputTokens;
|
||||
}
|
||||
if (topP !== undefined) {
|
||||
generationConfig.topP = topP;
|
||||
}
|
||||
if (topK !== undefined) {
|
||||
generationConfig.topK = topK;
|
||||
}
|
||||
if (candidateCount !== undefined) {
|
||||
generationConfig.candidateCount = candidateCount;
|
||||
}
|
||||
if (stopSequences !== undefined && Array.isArray(stopSequences)) {
|
||||
generationConfig.stopSequences = stopSequences;
|
||||
}
|
||||
|
||||
// 添加其他参数 (已在上层验证过安全性)
|
||||
// 排除一些明显不属于 Gemini generationConfig 的参数
|
||||
for (const [key, value] of Object.entries(otherParams)) {
|
||||
if (!['timeout', 'model', 'messages', 'stream'].includes(key)) {
|
||||
generationConfig[key] = value;
|
||||
}
|
||||
}
|
||||
|
||||
return generationConfig;
|
||||
}
|
||||
}
|
||||
|
||||
// 导出工厂函数
|
||||
|
||||
@@ -0,0 +1,178 @@
|
||||
// packages/core/src/services/model/advancedParameterDefinitions.ts
|
||||
|
||||
export interface AdvancedParameterDefinition {
|
||||
id: string; // Unique ID across all definitions, e.g., "openai_temperature"
|
||||
name: string; // Actual parameter name used by the SDK, e.g., "temperature"
|
||||
|
||||
labelKey: string; // i18n key for UI label, e.g., "params.temperature.label"
|
||||
descriptionKey: string; // i18n key for UI description, e.g., "params.temperature.description"
|
||||
|
||||
type: 'number' | 'string' | 'boolean' | 'integer';
|
||||
|
||||
defaultValue?: any;
|
||||
|
||||
minValue?: number;
|
||||
maxValue?: number;
|
||||
step?: number;
|
||||
|
||||
unit?: string; // e.g., "ms"
|
||||
unitKey?: string; // i18n key for the unit, e.g., "params.tokens.unit"
|
||||
|
||||
appliesToProviders: string[]; // e.g., ["openai", "deepseek"] or ["gemini"]
|
||||
|
||||
// Optional: For string types with predefined choices
|
||||
// allowedValues?: string[];
|
||||
// allowedValuesLabelsKey?: string; // i18n key for labels of allowedValues (if they differ from values)
|
||||
}
|
||||
|
||||
export const advancedParameterDefinitions: AdvancedParameterDefinition[] = [
|
||||
// Common Parameters (can be mapped or used if name matches)
|
||||
{
|
||||
id: "common_temperature",
|
||||
name: "temperature",
|
||||
labelKey: "params.temperature.label",
|
||||
descriptionKey: "params.temperature.description",
|
||||
type: "number",
|
||||
defaultValue: 0.7,
|
||||
minValue: 0.0,
|
||||
maxValue: 2.0, // Max for OpenAI; Gemini is often 0-1. UI might need to adjust range based on provider.
|
||||
step: 0.1,
|
||||
appliesToProviders: ["openai", "gemini", "deepseek", "custom", "zhipu", "siliconflow"]
|
||||
},
|
||||
{
|
||||
id: "common_top_p",
|
||||
name: "top_p", // OpenAI, Zhipu, SiliconFlow use top_p
|
||||
labelKey: "params.top_p.label",
|
||||
descriptionKey: "params.top_p.description",
|
||||
type: "number",
|
||||
defaultValue: 1.0,
|
||||
minValue: 0.0,
|
||||
maxValue: 1.0,
|
||||
step: 0.01,
|
||||
appliesToProviders: ["openai", "deepseek", "custom", "zhipu", "siliconflow"]
|
||||
},
|
||||
// OpenAI / OpenAI-Compatible Specific
|
||||
{
|
||||
id: "openai_max_tokens",
|
||||
name: "max_tokens",
|
||||
labelKey: "params.max_tokens.label",
|
||||
descriptionKey: "params.max_tokens.description",
|
||||
type: "integer",
|
||||
defaultValue: 40000,
|
||||
minValue: 1,
|
||||
step: 1,
|
||||
unitKey: "params.tokens.unit",
|
||||
appliesToProviders: ["openai", "deepseek", "custom", "zhipu", "siliconflow"]
|
||||
},
|
||||
{
|
||||
id: "openai_presence_penalty",
|
||||
name: "presence_penalty",
|
||||
labelKey: "params.presence_penalty.label",
|
||||
descriptionKey: "params.presence_penalty.description",
|
||||
type: "number",
|
||||
defaultValue: 0,
|
||||
minValue: -2.0,
|
||||
maxValue: 2.0,
|
||||
step: 0.1,
|
||||
appliesToProviders: ["openai", "deepseek", "custom", "zhipu", "siliconflow"]
|
||||
},
|
||||
{
|
||||
id: "openai_frequency_penalty",
|
||||
name: "frequency_penalty",
|
||||
labelKey: "params.frequency_penalty.label",
|
||||
descriptionKey: "params.frequency_penalty.description",
|
||||
type: "number",
|
||||
defaultValue: 0,
|
||||
minValue: -2.0,
|
||||
maxValue: 2.0,
|
||||
step: 0.1,
|
||||
appliesToProviders: ["openai", "deepseek", "custom", "zhipu", "siliconflow"]
|
||||
},
|
||||
{
|
||||
id: "openai_timeout", // This is a client configuration for OpenAI
|
||||
name: "timeout",
|
||||
labelKey: "params.timeout.label",
|
||||
descriptionKey: "params.timeout.description_openai", // Specific description
|
||||
type: "integer",
|
||||
defaultValue: 60000,
|
||||
minValue: 1000,
|
||||
step: 1000,
|
||||
unit: "ms",
|
||||
appliesToProviders: ["openai", "deepseek", "custom", "zhipu", "siliconflow"]
|
||||
},
|
||||
// Gemini Specific
|
||||
{
|
||||
id: "gemini_maxOutputTokens",
|
||||
name: "maxOutputTokens",
|
||||
labelKey: "params.maxOutputTokens.label",
|
||||
descriptionKey: "params.maxOutputTokens.description",
|
||||
type: "integer",
|
||||
defaultValue: 40000,
|
||||
minValue: 1,
|
||||
step: 1,
|
||||
unitKey: "params.tokens.unit",
|
||||
appliesToProviders: ["gemini"]
|
||||
},
|
||||
{
|
||||
id: "gemini_topP", // Gemini uses "topP" (capital P)
|
||||
name: "topP",
|
||||
labelKey: "params.top_p.label", // Can share label if meaning is identical
|
||||
descriptionKey: "params.top_p.description", // Can share description
|
||||
type: "number",
|
||||
defaultValue: 1.0, // Or Gemini's typical default if different
|
||||
minValue: 0.0,
|
||||
maxValue: 1.0,
|
||||
step: 0.01,
|
||||
appliesToProviders: ["gemini"]
|
||||
},
|
||||
{
|
||||
id: "gemini_topK",
|
||||
name: "topK",
|
||||
labelKey: "params.top_k.label",
|
||||
descriptionKey: "params.top_k.description",
|
||||
type: "integer",
|
||||
defaultValue: 1, // Gemini default, but often user wants to adjust
|
||||
minValue: 1,
|
||||
step: 1,
|
||||
appliesToProviders: ["gemini"]
|
||||
},
|
||||
{
|
||||
id: "gemini_candidateCount",
|
||||
name: "candidateCount",
|
||||
labelKey: "params.candidateCount.label",
|
||||
descriptionKey: "params.candidateCount.description",
|
||||
type: "integer",
|
||||
defaultValue: 1,
|
||||
minValue: 1,
|
||||
maxValue: 8, // Check Gemini docs for actual max
|
||||
step: 1,
|
||||
appliesToProviders: ["gemini"]
|
||||
},
|
||||
{
|
||||
id: "gemini_stopSequences",
|
||||
name: "stopSequences", // Array of strings
|
||||
labelKey: "params.stopSequences.label",
|
||||
descriptionKey: "params.stopSequences.description",
|
||||
type: "string", // Special handling: array of strings but UI input as comma-separated string
|
||||
defaultValue: [], // Array of strings
|
||||
appliesToProviders: ["gemini"]
|
||||
},
|
||||
// Add more definitions as needed for other parameters and providers.
|
||||
// For example, Zhipu specific parameters, Groq, Anthropic etc.
|
||||
];
|
||||
|
||||
// It might be useful to also export a map for easier lookup by id or name
|
||||
// export const advancedParameterDefinitionsMapById: Record<string, AdvancedParameterDefinition> =
|
||||
// advancedParameterDefinitions.reduce((acc, def) => {
|
||||
// acc[def.id] = def;
|
||||
// return acc;
|
||||
// }, {} as Record<string, AdvancedParameterDefinition>);
|
||||
|
||||
// export const advancedParameterDefinitionsMapByName: Record<string, AdvancedParameterDefinition> =
|
||||
// advancedParameterDefinitions.reduce((acc, def) => {
|
||||
// acc[def.name] = def; // Note: 'name' might not be unique across all providers if we don't make it so
|
||||
// return acc;
|
||||
// }, {} as Record<string, AdvancedParameterDefinition>);
|
||||
|
||||
// Consider provider-specific name uniqueness if using name as a map key.
|
||||
// For UI filtering, iterating the array and filtering by appliesToProviders is often sufficient.
|
||||
@@ -51,7 +51,9 @@ export const defaultModels: Record<string, ModelConfig> = {
|
||||
defaultModel: 'gpt-3.5-turbo',
|
||||
apiKey: OPENAI_API_KEY,
|
||||
enabled: !!OPENAI_API_KEY,
|
||||
provider: 'openai'
|
||||
provider: 'openai',
|
||||
llmParams: {
|
||||
}
|
||||
},
|
||||
gemini: {
|
||||
name: 'Gemini',
|
||||
@@ -60,7 +62,9 @@ export const defaultModels: Record<string, ModelConfig> = {
|
||||
defaultModel: 'gemini-2.0-flash',
|
||||
apiKey: GEMINI_API_KEY,
|
||||
enabled: !!GEMINI_API_KEY,
|
||||
provider: 'gemini'
|
||||
provider: 'gemini',
|
||||
llmParams: {
|
||||
}
|
||||
},
|
||||
deepseek: {
|
||||
name: 'DeepSeek',
|
||||
@@ -69,7 +73,9 @@ export const defaultModels: Record<string, ModelConfig> = {
|
||||
defaultModel: 'deepseek-chat',
|
||||
apiKey: DEEPSEEK_API_KEY,
|
||||
enabled: !!DEEPSEEK_API_KEY,
|
||||
provider: 'deepseek'
|
||||
provider: 'deepseek',
|
||||
llmParams: {
|
||||
}
|
||||
},
|
||||
siliconflow: {
|
||||
name: 'SiliconFlow',
|
||||
@@ -78,7 +84,9 @@ export const defaultModels: Record<string, ModelConfig> = {
|
||||
defaultModel: 'Pro/deepseek-ai/DeepSeek-V3',
|
||||
apiKey: SILICONFLOW_API_KEY,
|
||||
enabled: !!SILICONFLOW_API_KEY,
|
||||
provider: 'siliconflow'
|
||||
provider: 'siliconflow',
|
||||
llmParams: {
|
||||
}
|
||||
},
|
||||
zhipu: {
|
||||
name: 'Zhipu',
|
||||
@@ -87,7 +95,9 @@ export const defaultModels: Record<string, ModelConfig> = {
|
||||
defaultModel: 'glm-4-flash',
|
||||
apiKey: ZHIPU_API_KEY,
|
||||
enabled: !!ZHIPU_API_KEY,
|
||||
provider: 'zhipu'
|
||||
provider: 'zhipu',
|
||||
llmParams: {
|
||||
}
|
||||
},
|
||||
custom: {
|
||||
name: 'Custom',
|
||||
@@ -96,6 +106,8 @@ export const defaultModels: Record<string, ModelConfig> = {
|
||||
defaultModel: CUSTOM_API_MODEL,
|
||||
apiKey: CUSTOM_API_KEY,
|
||||
enabled: !!CUSTOM_API_KEY,
|
||||
provider: 'custom'
|
||||
provider: 'custom',
|
||||
llmParams: {
|
||||
}
|
||||
}
|
||||
};
|
||||
@@ -4,6 +4,7 @@ import { StorageFactory } from '../storage/factory';
|
||||
import { StorageAdapter } from '../storage/adapter';
|
||||
import { defaultModels } from './defaults';
|
||||
import { ModelConfigError } from '../llm/errors';
|
||||
import { validateLLMParams } from './validation';
|
||||
|
||||
/**
|
||||
* 模型管理器实现
|
||||
@@ -36,10 +37,45 @@ export class ModelManager implements IModelManager {
|
||||
let hasChanges = false;
|
||||
Object.entries(defaultModels).forEach(([key, config]) => {
|
||||
if (!this.models[key]) {
|
||||
this.models[key] = {
|
||||
...config
|
||||
this.models[key] = {
|
||||
...config,
|
||||
// Deep copy llmParams to avoid reference sharing
|
||||
...(config.llmParams && { llmParams: { ...config.llmParams } })
|
||||
};
|
||||
hasChanges = true;
|
||||
} else { // Model exists in storage, check for llmParams updates
|
||||
let modelUpdated = false;
|
||||
if (config.llmParams) { // If default config has llmParams
|
||||
if (!this.models[key].llmParams) { // If stored model has no llmParams
|
||||
this.models[key].llmParams = { ...config.llmParams };
|
||||
modelUpdated = true;
|
||||
} else { // Stored model has llmParams, merge new default keys
|
||||
for (const paramKey in config.llmParams) {
|
||||
if (this.models[key].llmParams[paramKey] === undefined && config.llmParams.hasOwnProperty(paramKey)) {
|
||||
this.models[key].llmParams[paramKey] = config.llmParams[paramKey];
|
||||
modelUpdated = true;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
// Ensure llmParams is an object if it was created/modified
|
||||
if (this.models[key].llmParams && (typeof this.models[key].llmParams !== 'object' || this.models[key].llmParams === null)) {
|
||||
this.models[key].llmParams = {}; // Initialize to empty object if invalid
|
||||
modelUpdated = true;
|
||||
}
|
||||
|
||||
// Remove old top-level properties if they exist on stored model
|
||||
const oldParams = ['maxTokens', 'temperature', 'timeout'];
|
||||
for (const oldParam of oldParams) {
|
||||
if (this.models[key].hasOwnProperty(oldParam)) {
|
||||
delete (this.models[key] as any)[oldParam];
|
||||
modelUpdated = true;
|
||||
}
|
||||
}
|
||||
|
||||
if (modelUpdated) {
|
||||
hasChanges = true;
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
@@ -104,13 +140,21 @@ export class ModelManager implements IModelManager {
|
||||
}
|
||||
return {
|
||||
...models,
|
||||
[key]: { ...config }
|
||||
[key]: {
|
||||
...config,
|
||||
// Deep copy llmParams to avoid reference sharing
|
||||
...(config.llmParams && { llmParams: { ...config.llmParams } })
|
||||
}
|
||||
};
|
||||
}
|
||||
);
|
||||
|
||||
// 更新内存状态
|
||||
this.models[key] = { ...config };
|
||||
this.models[key] = {
|
||||
...config,
|
||||
// Deep copy llmParams to avoid reference sharing
|
||||
...(config.llmParams && { llmParams: { ...config.llmParams } })
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -130,7 +174,11 @@ export class ModelManager implements IModelManager {
|
||||
throw new ModelConfigError(`模型 ${key} 不存在`);
|
||||
}
|
||||
// 如果是内置模型但尚未配置，创建初始配置
|
||||
models[key] = { ...defaultModels[key] };
|
||||
models[key] = {
|
||||
...defaultModels[key],
|
||||
// Deep copy llmParams to avoid reference sharing
|
||||
...(defaultModels[key].llmParams && { llmParams: { ...defaultModels[key].llmParams } })
|
||||
};
|
||||
}
|
||||
|
||||
// 合并配置时保留原有 enabled 状态
|
||||
@@ -138,16 +186,19 @@ export class ModelManager implements IModelManager {
|
||||
...models[key],
|
||||
...config,
|
||||
// 确保 enabled 属性存在
|
||||
enabled: config.enabled !== undefined ? config.enabled : models[key].enabled
|
||||
enabled: config.enabled !== undefined ? config.enabled : models[key].enabled,
|
||||
// Deep copy llmParams to avoid reference sharing
|
||||
...(config.llmParams && { llmParams: { ...config.llmParams } })
|
||||
};
|
||||
|
||||
// 如果更新了关键字段或尝试启用模型，需要验证配置
|
||||
// 如果更新了关键字段, 尝试启用模型, 或者更新了llmParams，需要验证配置
|
||||
if (
|
||||
config.name !== undefined ||
|
||||
config.baseURL !== undefined ||
|
||||
config.models !== undefined ||
|
||||
config.defaultModel !== undefined ||
|
||||
config.apiKey !== undefined ||
|
||||
config.llmParams !== undefined || // Added llmParams as a trigger
|
||||
config.enabled
|
||||
) {
|
||||
this.validateConfig(updatedConfig);
|
||||
@@ -268,6 +319,24 @@ export class ModelManager implements IModelManager {
|
||||
errors.push('默认模型必须在模型列表中');
|
||||
}
|
||||
|
||||
// Validate llmParams structure
|
||||
if (config.llmParams !== undefined && (typeof config.llmParams !== 'object' || config.llmParams === null || Array.isArray(config.llmParams))) {
|
||||
errors.push('llmParams必须是一个对象');
|
||||
}
|
||||
|
||||
// Validate llmParams content for security and correctness
|
||||
if (config.llmParams && typeof config.llmParams === 'object') {
|
||||
const provider = config.provider || 'openai'; // Default to openai provider for validation
|
||||
const validation = validateLLMParams(config.llmParams, provider);
|
||||
|
||||
if (!validation.isValid) {
|
||||
const paramErrors = validation.errors.map(error =>
|
||||
`参数 ${error.parameterName}: ${error.message}`
|
||||
);
|
||||
errors.push(...paramErrors);
|
||||
}
|
||||
}
|
||||
|
||||
if (errors.length > 0) {
|
||||
throw new ModelConfigError('无效的模型配置：' + errors.join('、'));
|
||||
}
|
||||
|
||||
@@ -18,6 +18,8 @@ export interface ModelConfig {
|
||||
provider: 'deepseek' | 'gemini' | 'custom' | 'zhipu' | string;
|
||||
/** 是否使用Vercel代理 */
|
||||
useVercelProxy?: boolean;
|
||||
/** LLM特定参数 */
|
||||
llmParams?: Record<string, any>;
|
||||
}
|
||||
|
||||
/**
|
||||
|
||||
@@ -0,0 +1,194 @@
|
||||
import { advancedParameterDefinitions, AdvancedParameterDefinition } from './advancedParameterDefinitions';
|
||||
|
||||
// 危险参数黑名单
|
||||
const DANGEROUS_PARAMS = [
|
||||
'eval', 'exec', 'function', 'script', 'code',
|
||||
'apiKey', 'api_key', 'secret', 'password', 'credentials',
|
||||
'authorization', 'baseURL', 'base_url', 'endpoint', 'url',
|
||||
'__proto__', 'constructor', 'prototype', 'require', 'import'
|
||||
];
|
||||
|
||||
export interface ValidationResult {
|
||||
isValid: boolean;
|
||||
errors: ValidationError[];
|
||||
warnings: ValidationWarning[];
|
||||
}
|
||||
|
||||
export interface ValidationError {
|
||||
parameterName: string;
|
||||
parameterValue: any;
|
||||
message: string;
|
||||
expectedType?: string;
|
||||
expectedRange?: string;
|
||||
}
|
||||
|
||||
export interface ValidationWarning {
|
||||
parameterName: string;
|
||||
parameterValue: any;
|
||||
message: string;
|
||||
}
|
||||
|
||||
|
||||
|
||||
/**
|
||||
* 检查参数是否在危险黑名单中
|
||||
*/
|
||||
function isDangerousParameter(paramName: string): boolean {
|
||||
return DANGEROUS_PARAMS.some(dangerous =>
|
||||
paramName.toLowerCase().includes(dangerous.toLowerCase())
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* 验证llmParams中的已知参数（增强安全版本）
|
||||
*/
|
||||
export function validateLLMParams(
|
||||
llmParams: Record<string, any> | undefined,
|
||||
provider: string
|
||||
): ValidationResult {
|
||||
const result: ValidationResult = {
|
||||
isValid: true,
|
||||
errors: [],
|
||||
warnings: []
|
||||
};
|
||||
|
||||
if (!llmParams) {
|
||||
return result;
|
||||
}
|
||||
|
||||
// 获取该提供商支持的参数定义
|
||||
const supportedParams = advancedParameterDefinitions.filter(
|
||||
def => def.appliesToProviders.includes(provider)
|
||||
);
|
||||
|
||||
// 验证每个已知参数
|
||||
for (const [paramName, paramValue] of Object.entries(llmParams)) {
|
||||
// 首先检查危险参数
|
||||
if (isDangerousParameter(paramName)) {
|
||||
result.isValid = false;
|
||||
result.errors.push({
|
||||
parameterName: paramName,
|
||||
parameterValue: paramValue,
|
||||
message: `Parameter '${paramName}' is potentially dangerous and not allowed for security reasons.`
|
||||
});
|
||||
continue;
|
||||
}
|
||||
|
||||
const paramDef = supportedParams.find(def => def.name === paramName);
|
||||
|
||||
if (!paramDef) {
|
||||
// 未知参数警告 - 统一使用黑名单机制
|
||||
// 对于个人化前端应用，未知参数不应该导致验证失败，只产生警告
|
||||
result.warnings.push({
|
||||
parameterName: paramName,
|
||||
parameterValue: paramValue,
|
||||
message: `Parameter '${paramName}' is not defined in parameter specifications for ${provider}. It will be passed to the SDK but may not be supported or could cause issues.`
|
||||
});
|
||||
continue;
|
||||
}
|
||||
|
||||
// 验证参数类型和范围
|
||||
const validation = validateSingleParameter(paramValue, paramDef);
|
||||
if (!validation.isValid) {
|
||||
result.isValid = false;
|
||||
result.errors.push({
|
||||
parameterName: paramName,
|
||||
parameterValue: paramValue,
|
||||
message: validation.message!,
|
||||
expectedType: paramDef.type,
|
||||
expectedRange: getExpectedRange(paramDef)
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
function validateSingleParameter(
|
||||
value: any,
|
||||
def: AdvancedParameterDefinition
|
||||
): { isValid: boolean; message?: string } {
|
||||
// Special validation: stopSequences should be string array (although type is defined as string, but actually is array)
|
||||
if (def.name === 'stopSequences') {
|
||||
if (!Array.isArray(value)) {
|
||||
return {
|
||||
isValid: false,
|
||||
message: `Parameter 'stopSequences' should be a string array, but received ${typeof value}`
|
||||
};
|
||||
}
|
||||
|
||||
if (!value.every(item => typeof item === 'string')) {
|
||||
return {
|
||||
isValid: false,
|
||||
message: `Parameter 'stopSequences' array should only contain strings`
|
||||
};
|
||||
}
|
||||
|
||||
// Skip regular type check for stopSequences
|
||||
return { isValid: true };
|
||||
}
|
||||
|
||||
// Type validation
|
||||
if (!validateType(value, def.type)) {
|
||||
return {
|
||||
isValid: false,
|
||||
message: `Parameter '${def.name}' should be of type ${def.type}, but received ${typeof value}`
|
||||
};
|
||||
}
|
||||
|
||||
// Numeric range validation
|
||||
if (def.type === 'number' || def.type === 'integer') {
|
||||
if (def.minValue !== undefined && value < def.minValue) {
|
||||
return {
|
||||
isValid: false,
|
||||
message: `Parameter '${def.name}' value ${value} is less than minimum value ${def.minValue}`
|
||||
};
|
||||
}
|
||||
|
||||
if (def.maxValue !== undefined && value > def.maxValue) {
|
||||
return {
|
||||
isValid: false,
|
||||
message: `Parameter '${def.name}' value ${value} is greater than maximum value ${def.maxValue}`
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
return { isValid: true };
|
||||
}
|
||||
|
||||
function validateType(value: any, expectedType: string): boolean {
|
||||
switch (expectedType) {
|
||||
case 'number':
|
||||
return typeof value === 'number' && !isNaN(value);
|
||||
case 'integer':
|
||||
return typeof value === 'number' && Number.isInteger(value);
|
||||
case 'string':
|
||||
return typeof value === 'string';
|
||||
case 'boolean':
|
||||
return typeof value === 'boolean';
|
||||
default:
|
||||
return true;
|
||||
}
|
||||
}
|
||||
|
||||
function getExpectedRange(def: AdvancedParameterDefinition): string {
|
||||
if (def.type === 'number' || def.type === 'integer') {
|
||||
if (def.minValue !== undefined && def.maxValue !== undefined) {
|
||||
return `${def.minValue} - ${def.maxValue}`;
|
||||
} else if (def.minValue !== undefined) {
|
||||
return `>= ${def.minValue}`;
|
||||
} else if (def.maxValue !== undefined) {
|
||||
return `<= ${def.maxValue}`;
|
||||
}
|
||||
}
|
||||
return '';
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取提供商支持的参数列表
|
||||
*/
|
||||
export function getSupportedParameters(provider: string): AdvancedParameterDefinition[] {
|
||||
return advancedParameterDefinitions.filter(
|
||||
def => def.appliesToProviders.includes(provider)
|
||||
);
|
||||
}
|
||||
@@ -1,4 +1,4 @@
|
||||
import { createLLMService, ModelManager } from '../../../src/index.js';
|
||||
import { createLLMService, ModelManager, LocalStorageProvider } from '../../../src/index.js';
|
||||
import { expect, describe, it, beforeEach, beforeAll } from 'vitest';
|
||||
import dotenv from 'dotenv';
|
||||
import path from 'path';
|
||||
@@ -19,7 +19,8 @@ describe('DeepSeek API 测试', () => {
|
||||
}
|
||||
|
||||
it('应该能正确调用 DeepSeek API', async () => {
|
||||
const modelManager = new ModelManager();
|
||||
const storage = new LocalStorageProvider();
|
||||
const modelManager = new ModelManager(storage);
|
||||
const llmService = createLLMService(modelManager);
|
||||
|
||||
// 更新 DeepSeek 配置
|
||||
@@ -39,7 +40,8 @@ describe('DeepSeek API 测试', () => {
|
||||
}, 25000);
|
||||
|
||||
it('应该能正确处理多轮对话', async () => {
|
||||
const modelManager = new ModelManager();
|
||||
const storage = new LocalStorageProvider();
|
||||
const modelManager = new ModelManager(storage);
|
||||
const llmService = createLLMService(modelManager);
|
||||
|
||||
// 更新 DeepSeek 配置
|
||||
|
||||
@@ -0,0 +1,203 @@
|
||||
import { createLLMService, ModelManager, LocalStorageProvider } from '../../../src/index.js';
|
||||
import { expect, describe, it, beforeEach, beforeAll } from 'vitest';
|
||||
import dotenv from 'dotenv';
|
||||
import path from 'path';
|
||||
|
||||
// 加载环境变量
|
||||
beforeAll(() => {
|
||||
dotenv.config({ path: path.resolve(process.cwd(), '.env.local') });
|
||||
});
|
||||
|
||||
describe('OpenAI API 真实连接测试', () => {
|
||||
// 检查OpenAI兼容的环境变量（任何一个存在就可以运行测试）
|
||||
const openaiCompatibleKeys = [
|
||||
'OPENAI_API_KEY', 'VITE_OPENAI_API_KEY',
|
||||
'DEEPSEEK_API_KEY', 'VITE_DEEPSEEK_API_KEY',
|
||||
'SILICONFLOW_API_KEY', 'VITE_SILICONFLOW_API_KEY',
|
||||
'ZHIPU_API_KEY', 'VITE_ZHIPU_API_KEY',
|
||||
'CUSTOM_API_KEY', 'VITE_CUSTOM_API_KEY'
|
||||
];
|
||||
|
||||
const availableKeys = openaiCompatibleKeys.filter(key =>
|
||||
process.env[key] && process.env[key].trim()
|
||||
);
|
||||
|
||||
if (availableKeys.length === 0) {
|
||||
console.log('跳过 OpenAI 真实API测试：未设置任何 OpenAI 兼容的 API 密钥');
|
||||
it.skip('应该能正确调用 OpenAI 兼容的 API', () => {});
|
||||
it.skip('应该能正确处理多轮对话', () => {});
|
||||
it.skip('应该能正确使用高级参数', () => {});
|
||||
return;
|
||||
}
|
||||
|
||||
// 选择第一个可用的密钥和对应的配置
|
||||
const getModelConfig = () => {
|
||||
if (process.env.OPENAI_API_KEY || process.env.VITE_OPENAI_API_KEY) {
|
||||
return {
|
||||
key: 'openai',
|
||||
apiKey: process.env.OPENAI_API_KEY || process.env.VITE_OPENAI_API_KEY,
|
||||
baseURL: 'https://api.openai.com/v1',
|
||||
defaultModel: 'gpt-3.5-turbo'
|
||||
};
|
||||
}
|
||||
if (process.env.DEEPSEEK_API_KEY || process.env.VITE_DEEPSEEK_API_KEY) {
|
||||
return {
|
||||
key: 'deepseek',
|
||||
apiKey: process.env.DEEPSEEK_API_KEY || process.env.VITE_DEEPSEEK_API_KEY,
|
||||
baseURL: 'https://api.deepseek.com/v1',
|
||||
defaultModel: 'deepseek-chat'
|
||||
};
|
||||
}
|
||||
if (process.env.SILICONFLOW_API_KEY || process.env.VITE_SILICONFLOW_API_KEY) {
|
||||
return {
|
||||
key: 'siliconflow',
|
||||
apiKey: process.env.SILICONFLOW_API_KEY || process.env.VITE_SILICONFLOW_API_KEY,
|
||||
baseURL: 'https://api.siliconflow.cn/v1',
|
||||
defaultModel: 'Pro/deepseek-ai/DeepSeek-V3'
|
||||
};
|
||||
}
|
||||
if (process.env.ZHIPU_API_KEY || process.env.VITE_ZHIPU_API_KEY) {
|
||||
return {
|
||||
key: 'zhipu',
|
||||
apiKey: process.env.ZHIPU_API_KEY || process.env.VITE_ZHIPU_API_KEY,
|
||||
baseURL: 'https://open.bigmodel.cn/api/paas/v4',
|
||||
defaultModel: 'glm-4-flash'
|
||||
};
|
||||
}
|
||||
if (process.env.CUSTOM_API_KEY || process.env.VITE_CUSTOM_API_KEY) {
|
||||
const baseURL = process.env.CUSTOM_API_BASE_URL || process.env.VITE_CUSTOM_API_BASE_URL;
|
||||
const model = process.env.CUSTOM_API_MODEL || process.env.VITE_CUSTOM_API_MODEL;
|
||||
|
||||
// 只有当baseURL和model都有值时才返回custom配置
|
||||
if (baseURL && model) {
|
||||
return {
|
||||
key: 'custom',
|
||||
apiKey: process.env.CUSTOM_API_KEY || process.env.VITE_CUSTOM_API_KEY,
|
||||
baseURL: baseURL,
|
||||
defaultModel: model
|
||||
};
|
||||
}
|
||||
}
|
||||
return null;
|
||||
};
|
||||
|
||||
const modelConfig = getModelConfig();
|
||||
|
||||
if (!modelConfig) {
|
||||
console.log('跳过 OpenAI 真实API测试：无有效的模型配置');
|
||||
it.skip('应该能正确调用 OpenAI 兼容的 API', () => {});
|
||||
it.skip('应该能正确处理多轮对话', () => {});
|
||||
it.skip('应该能正确使用高级参数', () => {});
|
||||
return;
|
||||
}
|
||||
|
||||
console.log(`使用 ${modelConfig.key} 进行 OpenAI 兼容 API 测试，模型: ${modelConfig.defaultModel}`);
|
||||
|
||||
it('应该能正确调用 OpenAI 兼容的 API', async () => {
|
||||
const storage = new LocalStorageProvider();
|
||||
const modelManager = new ModelManager(storage);
|
||||
const llmService = createLLMService(modelManager);
|
||||
|
||||
try {
|
||||
// 更新模型配置
|
||||
await modelManager.updateModel(modelConfig.key, {
|
||||
apiKey: modelConfig.apiKey,
|
||||
baseURL: modelConfig.baseURL,
|
||||
defaultModel: modelConfig.defaultModel,
|
||||
enabled: true,
|
||||
provider: 'openai' // 所有兼容提供商都使用openai提供商类型
|
||||
});
|
||||
|
||||
const messages = [
|
||||
{ role: 'user', content: '你好，请用一句话介绍你自己' }
|
||||
];
|
||||
|
||||
const response = await llmService.sendMessage(messages, modelConfig.key);
|
||||
expect(response).toBeDefined();
|
||||
expect(typeof response).toBe('string');
|
||||
expect(response.length).toBeGreaterThan(0);
|
||||
} catch (error) {
|
||||
console.error(`API调用失败 (${modelConfig.key}):`, error.message);
|
||||
// 如果是400错误，可能是配置问题，跳过测试
|
||||
if (error.message.includes('400')) {
|
||||
console.log(`跳过测试：${modelConfig.key} API配置可能有问题`);
|
||||
return;
|
||||
}
|
||||
throw error;
|
||||
}
|
||||
}, 30000);
|
||||
|
||||
it('应该能正确处理多轮对话', async () => {
|
||||
const storage = new LocalStorageProvider();
|
||||
const modelManager = new ModelManager(storage);
|
||||
const llmService = createLLMService(modelManager);
|
||||
|
||||
try {
|
||||
// 更新模型配置
|
||||
await modelManager.updateModel(modelConfig.key, {
|
||||
apiKey: modelConfig.apiKey,
|
||||
baseURL: modelConfig.baseURL,
|
||||
defaultModel: modelConfig.defaultModel,
|
||||
enabled: true,
|
||||
provider: 'openai'
|
||||
});
|
||||
|
||||
const messages = [
|
||||
{ role: 'user', content: '你好，我们来玩个游戏' },
|
||||
{ role: 'assistant', content: '好啊，你想玩什么游戏？' },
|
||||
{ role: 'user', content: '我们来玩猜数字游戏，1到100之间' }
|
||||
];
|
||||
|
||||
const response = await llmService.sendMessage(messages, modelConfig.key);
|
||||
expect(response).toBeDefined();
|
||||
expect(typeof response).toBe('string');
|
||||
expect(response.length).toBeGreaterThan(0);
|
||||
} catch (error) {
|
||||
console.error(`多轮对话测试失败 (${modelConfig.key}):`, error.message);
|
||||
if (error.message.includes('400')) {
|
||||
console.log(`跳过测试：${modelConfig.key} API配置可能有问题`);
|
||||
return;
|
||||
}
|
||||
throw error;
|
||||
}
|
||||
}, 30000);
|
||||
|
||||
it('应该能正确使用高级参数', async () => {
|
||||
const storage = new LocalStorageProvider();
|
||||
const modelManager = new ModelManager(storage);
|
||||
const llmService = createLLMService(modelManager);
|
||||
|
||||
try {
|
||||
// 更新模型配置，包含高级参数
|
||||
await modelManager.updateModel(modelConfig.key, {
|
||||
apiKey: modelConfig.apiKey,
|
||||
baseURL: modelConfig.baseURL,
|
||||
defaultModel: modelConfig.defaultModel,
|
||||
enabled: true,
|
||||
provider: 'openai',
|
||||
llmParams: {
|
||||
temperature: 0.3,
|
||||
max_tokens: 100
|
||||
}
|
||||
});
|
||||
|
||||
const messages = [
|
||||
{ role: 'user', content: '请用一句话回答：什么是人工智能？' }
|
||||
];
|
||||
|
||||
const response = await llmService.sendMessage(messages, modelConfig.key);
|
||||
expect(response).toBeDefined();
|
||||
expect(typeof response).toBe('string');
|
||||
expect(response.length).toBeGreaterThan(0);
|
||||
// 由于设置了max_tokens=100，响应应该相对较短
|
||||
expect(response.length).toBeLessThan(500);
|
||||
} catch (error) {
|
||||
console.error(`高级参数测试失败 (${modelConfig.key}):`, error.message);
|
||||
if (error.message.includes('400')) {
|
||||
console.log(`跳过测试：${modelConfig.key} API配置可能有问题`);
|
||||
return;
|
||||
}
|
||||
throw error;
|
||||
}
|
||||
}, 30000);
|
||||
});
|
||||
@@ -1,90 +0,0 @@
|
||||
import { describe, it, expect, beforeEach, vi } from 'vitest';
|
||||
import { LLMService, ModelManager, RequestConfigError, APIError } from '../src';
|
||||
import { createMockStorage } from './mocks/mockStorage';
|
||||
|
||||
describe('LLMService', () => {
|
||||
let llmService;
|
||||
let modelManager;
|
||||
const testProvider = 'test-provider';
|
||||
const mockModelConfig = {
|
||||
name: 'Test Model',
|
||||
provider: 'openai',
|
||||
apiKey: 'test-key',
|
||||
baseURL: 'https://api.test.com',
|
||||
defaultModel: 'test-model',
|
||||
enabled: true,
|
||||
models: ['test-model']
|
||||
};
|
||||
|
||||
beforeEach(() => {
|
||||
const mockStorage = createMockStorage();
|
||||
// 模拟getAllModels返回空数组
|
||||
mockStorage.getItem.mockResolvedValue(null);
|
||||
|
||||
modelManager = new ModelManager(mockStorage);
|
||||
llmService = new LLMService(modelManager);
|
||||
|
||||
// 模拟getAllModels方法
|
||||
vi.spyOn(modelManager, 'getAllModels').mockResolvedValue([]);
|
||||
});
|
||||
|
||||
describe('消息验证', () => {
|
||||
it('应该正确验证消息格式', () => {
|
||||
const validMessages = [
|
||||
{ role: 'system', content: 'test' },
|
||||
{ role: 'user', content: 'test' }
|
||||
];
|
||||
expect(() => llmService['validateMessages'](validMessages)).not.toThrow();
|
||||
});
|
||||
|
||||
it('当消息格式无效时应抛出错误', () => {
|
||||
const invalidMessages = [
|
||||
{ role: 'invalid', content: 'test' }
|
||||
];
|
||||
expect(() => llmService['validateMessages'](invalidMessages))
|
||||
.toThrow('不支持的消息类型: invalid');
|
||||
});
|
||||
});
|
||||
|
||||
describe('模型配置验证', () => {
|
||||
it('应该正确验证模型配置', () => {
|
||||
expect(() => llmService['validateModelConfig'](mockModelConfig)).not.toThrow();
|
||||
});
|
||||
|
||||
it('当模型配置无效时应抛出错误', () => {
|
||||
const invalidConfig = { ...mockModelConfig, apiKey: '' };
|
||||
expect(() => llmService['validateModelConfig'](invalidConfig))
|
||||
.toThrow('API密钥不能为空');
|
||||
});
|
||||
|
||||
it('当模型未启用时应抛出错误', () => {
|
||||
const disabledConfig = { ...mockModelConfig, enabled: false };
|
||||
expect(() => llmService['validateModelConfig'](disabledConfig))
|
||||
.toThrow('模型未启用');
|
||||
});
|
||||
});
|
||||
|
||||
describe('发送消息', () => {
|
||||
it('当提供商不存在时应抛出错误', async () => {
|
||||
vi.spyOn(modelManager, 'getModel').mockResolvedValue(undefined);
|
||||
|
||||
const messages = [
|
||||
{ role: 'system', content: 'test' }
|
||||
];
|
||||
await expect(llmService.sendMessage(messages, 'non-existent-provider'))
|
||||
.rejects
|
||||
.toThrow(RequestConfigError);
|
||||
});
|
||||
|
||||
it('当消息格式无效时应抛出错误', async () => {
|
||||
vi.spyOn(modelManager, 'getModel').mockResolvedValue(mockModelConfig);
|
||||
|
||||
const invalidMessages = [
|
||||
{ role: 'invalid', content: 'test' }
|
||||
];
|
||||
await expect(llmService.sendMessage(invalidMessages, testProvider))
|
||||
.rejects
|
||||
.toThrow(RequestConfigError);
|
||||
});
|
||||
});
|
||||
});
|
||||
@@ -295,6 +295,191 @@ describe('DataManager', () => {
|
||||
expect(mockTemplateManager.deleteTemplate).toHaveBeenCalledWith(id);
|
||||
});
|
||||
});
|
||||
|
||||
it('should handle partial UI setting import failures gracefully', async () => {
|
||||
const partialFailData = {
|
||||
userSettings: {
|
||||
'theme-id': 'dark',
|
||||
'preferred-language': 'en-US',
|
||||
'app:selected-optimize-model': 'model1'
|
||||
}
|
||||
};
|
||||
|
||||
// 模拟第二个设置项失败
|
||||
mockStorage.setItem = vi.fn()
|
||||
.mockResolvedValueOnce(undefined) // 第一个成功
|
||||
.mockRejectedValueOnce(new Error('Storage quota exceeded')) // 第二个失败
|
||||
.mockResolvedValueOnce(undefined); // 第三个成功
|
||||
|
||||
// 不应该抛出异常，而是继续导入其他设置
|
||||
await expect(dataManager.importAllData(JSON.stringify(partialFailData))).resolves.not.toThrow();
|
||||
|
||||
// 验证失败的设置被记录但不影响其他设置的导入
|
||||
expect(mockStorage.setItem).toHaveBeenCalledTimes(3);
|
||||
});
|
||||
|
||||
it('should correctly import and export models with llmParams', async () => {
|
||||
// 测试包含 llmParams 的模型配置
|
||||
const modelsWithLlmParams = [
|
||||
{
|
||||
key: 'openai',
|
||||
name: 'OpenAI Model',
|
||||
provider: 'openai',
|
||||
enabled: true,
|
||||
baseURL: 'https://api.openai.com/v1',
|
||||
models: ['gpt-3.5-turbo'],
|
||||
defaultModel: 'gpt-3.5-turbo',
|
||||
llmParams: {
|
||||
temperature: 0.7,
|
||||
max_tokens: 4096,
|
||||
timeout: 30000
|
||||
}
|
||||
},
|
||||
{
|
||||
key: 'gemini',
|
||||
name: 'Gemini Model',
|
||||
provider: 'gemini',
|
||||
enabled: false,
|
||||
baseURL: 'https://generativelanguage.googleapis.com/v1beta',
|
||||
models: ['gemini-2.0-flash'],
|
||||
defaultModel: 'gemini-2.0-flash',
|
||||
llmParams: {
|
||||
temperature: 0.8,
|
||||
maxOutputTokens: 2048,
|
||||
topP: 0.95,
|
||||
topK: 40
|
||||
}
|
||||
}
|
||||
];
|
||||
|
||||
// 设置导出模拟
|
||||
mockModelManager.getAllModels.mockResolvedValue(modelsWithLlmParams);
|
||||
|
||||
// 导出数据
|
||||
const exportedData = await dataManager.exportAllData();
|
||||
const parsedData = JSON.parse(exportedData);
|
||||
|
||||
// 验证导出的模型包含 llmParams
|
||||
expect(parsedData.models).toHaveLength(2);
|
||||
expect(parsedData.models[0].llmParams).toEqual({
|
||||
temperature: 0.7,
|
||||
max_tokens: 4096,
|
||||
timeout: 30000
|
||||
});
|
||||
expect(parsedData.models[1].llmParams).toEqual({
|
||||
temperature: 0.8,
|
||||
maxOutputTokens: 2048,
|
||||
topP: 0.95,
|
||||
topK: 40
|
||||
});
|
||||
|
||||
// 重置模拟
|
||||
vi.clearAllMocks();
|
||||
|
||||
// 模拟getModel返回空（新模型）
|
||||
mockModelManager.getModel.mockResolvedValue(null);
|
||||
|
||||
// 导入数据
|
||||
await dataManager.importAllData(exportedData);
|
||||
|
||||
// 验证导入时 llmParams 被正确传递
|
||||
expect(mockModelManager.addModel).toHaveBeenCalledTimes(2);
|
||||
|
||||
const firstModelCall = mockModelManager.addModel.mock.calls[0];
|
||||
const secondModelCall = mockModelManager.addModel.mock.calls[1];
|
||||
|
||||
expect(firstModelCall[1].llmParams).toEqual({
|
||||
temperature: 0.7,
|
||||
max_tokens: 4096,
|
||||
timeout: 30000
|
||||
});
|
||||
|
||||
expect(secondModelCall[1].llmParams).toEqual({
|
||||
temperature: 0.8,
|
||||
maxOutputTokens: 2048,
|
||||
topP: 0.95,
|
||||
topK: 40
|
||||
});
|
||||
});
|
||||
|
||||
it('should handle models without llmParams during import', async () => {
|
||||
// 测试不包含 llmParams 的模型（向后兼容性）
|
||||
const modelsWithoutLlmParams = {
|
||||
models: [
|
||||
{
|
||||
key: 'legacy-model',
|
||||
name: 'Legacy Model',
|
||||
provider: 'custom',
|
||||
enabled: true,
|
||||
baseURL: 'https://api.example.com/v1',
|
||||
models: ['legacy-model'],
|
||||
defaultModel: 'legacy-model'
|
||||
// 没有 llmParams 字段
|
||||
}
|
||||
]
|
||||
};
|
||||
|
||||
// 模拟getModel返回空（新模型）
|
||||
mockModelManager.getModel.mockResolvedValue(null);
|
||||
|
||||
// 导入数据
|
||||
await dataManager.importAllData(JSON.stringify(modelsWithoutLlmParams));
|
||||
|
||||
// 验证导入成功
|
||||
expect(mockModelManager.addModel).toHaveBeenCalledTimes(1);
|
||||
|
||||
const modelCall = mockModelManager.addModel.mock.calls[0];
|
||||
|
||||
// 验证没有 llmParams 字段（undefined 不会被包含在配置中）
|
||||
expect(modelCall[1]).not.toHaveProperty('llmParams');
|
||||
});
|
||||
|
||||
it('should update existing models with llmParams', async () => {
|
||||
// 测试更新现有模型时包含 llmParams
|
||||
const existingModel = {
|
||||
name: 'Existing Model',
|
||||
provider: 'openai',
|
||||
enabled: false,
|
||||
baseURL: 'https://api.openai.com/v1',
|
||||
models: ['gpt-3.5-turbo'],
|
||||
defaultModel: 'gpt-3.5-turbo'
|
||||
// 现有模型没有 llmParams
|
||||
};
|
||||
|
||||
const updatedModelData = {
|
||||
models: [{
|
||||
key: 'existing-model',
|
||||
name: 'Updated Model',
|
||||
provider: 'openai',
|
||||
enabled: true,
|
||||
baseURL: 'https://api.openai.com/v1',
|
||||
models: ['gpt-4'],
|
||||
defaultModel: 'gpt-4',
|
||||
llmParams: {
|
||||
temperature: 0.5,
|
||||
max_tokens: 2048
|
||||
}
|
||||
}]
|
||||
};
|
||||
|
||||
// 模拟现有模型
|
||||
mockModelManager.getModel.mockResolvedValue(existingModel);
|
||||
|
||||
// 导入数据
|
||||
await dataManager.importAllData(JSON.stringify(updatedModelData));
|
||||
|
||||
// 验证调用了updateModel而不是addModel
|
||||
expect(mockModelManager.updateModel).toHaveBeenCalledTimes(1);
|
||||
expect(mockModelManager.addModel).not.toHaveBeenCalled();
|
||||
|
||||
const updateCall = mockModelManager.updateModel.mock.calls[0];
|
||||
|
||||
// 验证 llmParams 被包含在更新配置中
|
||||
expect(updateCall[1].llmParams).toEqual({
|
||||
temperature: 0.5,
|
||||
max_tokens: 2048
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
describe('UI配置导入安全性测试', () => {
|
||||
@@ -341,27 +526,5 @@ describe('DataManager', () => {
|
||||
expect(mockStorage.setItem).not.toHaveBeenCalledWith('app:selected-optimize-model', expect.any(String));
|
||||
expect(mockStorage.setItem).not.toHaveBeenCalledWith('app:selected-test-model', expect.any(String));
|
||||
});
|
||||
|
||||
it('should handle partial UI setting import failures gracefully', async () => {
|
||||
const partialFailData = {
|
||||
userSettings: {
|
||||
'theme-id': 'dark',
|
||||
'preferred-language': 'en-US',
|
||||
'app:selected-optimize-model': 'model1'
|
||||
}
|
||||
};
|
||||
|
||||
// 模拟第二个设置项失败
|
||||
mockStorage.setItem = vi.fn()
|
||||
.mockResolvedValueOnce(undefined) // 第一个成功
|
||||
.mockRejectedValueOnce(new Error('Storage quota exceeded')) // 第二个失败
|
||||
.mockResolvedValueOnce(undefined); // 第三个成功
|
||||
|
||||
// 不应该抛出异常，而是继续导入其他设置
|
||||
await expect(dataManager.importAllData(JSON.stringify(partialFailData))).resolves.not.toThrow();
|
||||
|
||||
// 验证失败的设置被记录但不影响其他设置的导入
|
||||
expect(mockStorage.setItem).toHaveBeenCalledTimes(3);
|
||||
});
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,618 @@
|
||||
import { describe, it, expect, beforeAll } from 'vitest';
|
||||
import { createLLMService, ModelManager, LocalStorageProvider } from '../../../src/index.js';
|
||||
import { validateLLMParams } from '../../../src/services/model/validation';
|
||||
import type { ModelConfig } from '../../../src/services/model/types';
|
||||
import dotenv from 'dotenv';
|
||||
import path from 'path';
|
||||
|
||||
// Load environment variables
|
||||
beforeAll(() => {
|
||||
dotenv.config({ path: path.resolve(process.cwd(), '.env.local') });
|
||||
});
|
||||
|
||||
describe('LLM Parameters (llmParams) Functionality', () => {
|
||||
// Check for available API keys
|
||||
const openaiCompatibleKeys = [
|
||||
'OPENAI_API_KEY', 'VITE_OPENAI_API_KEY',
|
||||
'DEEPSEEK_API_KEY', 'VITE_DEEPSEEK_API_KEY',
|
||||
'SILICONFLOW_API_KEY', 'VITE_SILICONFLOW_API_KEY',
|
||||
'ZHIPU_API_KEY', 'VITE_ZHIPU_API_KEY',
|
||||
'CUSTOM_API_KEY', 'VITE_CUSTOM_API_KEY'
|
||||
];
|
||||
|
||||
const geminiKeys = [
|
||||
'GEMINI_API_KEY', 'VITE_GEMINI_API_KEY'
|
||||
];
|
||||
|
||||
const hasOpenAICompatibleKey = openaiCompatibleKeys.some(key =>
|
||||
process.env[key] && process.env[key].trim()
|
||||
);
|
||||
|
||||
const hasGeminiKey = geminiKeys.some(key =>
|
||||
process.env[key] && process.env[key].trim()
|
||||
);
|
||||
|
||||
// Configuration interface
|
||||
interface ProviderConfig {
|
||||
key: string;
|
||||
apiKey: string;
|
||||
baseURL: string;
|
||||
defaultModel: string;
|
||||
provider: string;
|
||||
}
|
||||
|
||||
// Get all available OpenAI compatible configurations
|
||||
const getAvailableOpenAICompatibleConfigs = (): ProviderConfig[] => {
|
||||
const configs: ProviderConfig[] = [];
|
||||
|
||||
if (process.env.OPENAI_API_KEY || process.env.VITE_OPENAI_API_KEY) {
|
||||
configs.push({
|
||||
key: 'openai',
|
||||
apiKey: (process.env.OPENAI_API_KEY || process.env.VITE_OPENAI_API_KEY)!,
|
||||
baseURL: 'https://api.openai.com/v1',
|
||||
defaultModel: 'gpt-3.5-turbo',
|
||||
provider: 'openai'
|
||||
});
|
||||
}
|
||||
|
||||
if (process.env.DEEPSEEK_API_KEY || process.env.VITE_DEEPSEEK_API_KEY) {
|
||||
configs.push({
|
||||
key: 'deepseek',
|
||||
apiKey: (process.env.DEEPSEEK_API_KEY || process.env.VITE_DEEPSEEK_API_KEY)!,
|
||||
baseURL: 'https://api.deepseek.com/v1',
|
||||
defaultModel: 'deepseek-chat',
|
||||
provider: 'deepseek'
|
||||
});
|
||||
}
|
||||
|
||||
if (process.env.SILICONFLOW_API_KEY || process.env.VITE_SILICONFLOW_API_KEY) {
|
||||
configs.push({
|
||||
key: 'siliconflow',
|
||||
apiKey: (process.env.SILICONFLOW_API_KEY || process.env.VITE_SILICONFLOW_API_KEY)!,
|
||||
baseURL: 'https://api.siliconflow.cn/v1',
|
||||
defaultModel: 'Pro/deepseek-ai/DeepSeek-V3',
|
||||
provider: 'siliconflow'
|
||||
});
|
||||
}
|
||||
|
||||
if (process.env.ZHIPU_API_KEY || process.env.VITE_ZHIPU_API_KEY) {
|
||||
configs.push({
|
||||
key: 'zhipu',
|
||||
apiKey: (process.env.ZHIPU_API_KEY || process.env.VITE_ZHIPU_API_KEY)!,
|
||||
baseURL: 'https://open.bigmodel.cn/api/paas/v4',
|
||||
defaultModel: 'glm-4-flash',
|
||||
provider: 'zhipu'
|
||||
});
|
||||
}
|
||||
|
||||
if (process.env.CUSTOM_API_KEY || process.env.VITE_CUSTOM_API_KEY) {
|
||||
configs.push({
|
||||
key: 'custom',
|
||||
apiKey: (process.env.CUSTOM_API_KEY || process.env.VITE_CUSTOM_API_KEY)!,
|
||||
baseURL: (process.env.CUSTOM_API_BASE_URL || process.env.VITE_CUSTOM_API_BASE_URL)!,
|
||||
defaultModel: (process.env.CUSTOM_API_MODEL || process.env.VITE_CUSTOM_API_MODEL)!,
|
||||
provider: 'custom'
|
||||
});
|
||||
}
|
||||
|
||||
return configs;
|
||||
};
|
||||
|
||||
// Get OpenAI compatible configuration (for backward compatibility)
|
||||
const getOpenAICompatibleConfig = () => {
|
||||
const configs = getAvailableOpenAICompatibleConfigs();
|
||||
return configs.length > 0 ? configs[0] : null;
|
||||
};
|
||||
|
||||
// Get Gemini configuration
|
||||
const getGeminiConfig = () => {
|
||||
if (process.env.GEMINI_API_KEY || process.env.VITE_GEMINI_API_KEY) {
|
||||
return {
|
||||
key: 'gemini', // Use existing model key
|
||||
apiKey: process.env.GEMINI_API_KEY || process.env.VITE_GEMINI_API_KEY,
|
||||
baseURL: 'https://generativelanguage.googleapis.com/v1beta',
|
||||
defaultModel: 'gemini-2.0-flash',
|
||||
provider: 'gemini'
|
||||
};
|
||||
}
|
||||
return null;
|
||||
};
|
||||
|
||||
describe('OpenAI Compatible Providers', () => {
|
||||
const openaiConfig = getOpenAICompatibleConfig();
|
||||
|
||||
if (!hasOpenAICompatibleKey || !openaiConfig) {
|
||||
console.log('Skipping OpenAI Compatible tests: No API key available');
|
||||
it.skip('should handle llmParams for OpenAI compatible providers', () => {});
|
||||
return;
|
||||
}
|
||||
|
||||
it('should use custom parameters from llmParams', async () => {
|
||||
const storage = new LocalStorageProvider();
|
||||
const modelManager = new ModelManager(storage);
|
||||
const llmService = createLLMService(modelManager);
|
||||
|
||||
// Configure model with llmParams
|
||||
await modelManager.updateModel(openaiConfig.key, {
|
||||
name: 'Test OpenAI Compatible',
|
||||
apiKey: openaiConfig.apiKey,
|
||||
baseURL: openaiConfig.baseURL,
|
||||
defaultModel: openaiConfig.defaultModel,
|
||||
enabled: true,
|
||||
provider: openaiConfig.provider,
|
||||
models: [openaiConfig.defaultModel],
|
||||
llmParams: {
|
||||
temperature: 0.1, // Very low temperature for predictable output
|
||||
max_tokens: 50 // Short response
|
||||
}
|
||||
});
|
||||
|
||||
const messages = [
|
||||
{ role: 'user' as const, content: 'Say exactly: "Hello World"' }
|
||||
];
|
||||
|
||||
const response = await llmService.sendMessage(messages, openaiConfig.key);
|
||||
expect(response).toBeDefined();
|
||||
expect(typeof response).toBe('string');
|
||||
expect(response.length).toBeGreaterThan(0);
|
||||
// With low temperature and specific instruction, response should be short and focused
|
||||
expect(response.length).toBeLessThan(200);
|
||||
}, 30000);
|
||||
|
||||
it('should handle timeout parameter for OpenAI compatible providers', async () => {
|
||||
const storage = new LocalStorageProvider();
|
||||
const modelManager = new ModelManager(storage);
|
||||
const llmService = createLLMService(modelManager);
|
||||
|
||||
// Configure model with custom timeout
|
||||
await modelManager.updateModel(openaiConfig.key, {
|
||||
name: 'Test OpenAI Compatible',
|
||||
apiKey: openaiConfig.apiKey,
|
||||
baseURL: openaiConfig.baseURL,
|
||||
defaultModel: openaiConfig.defaultModel,
|
||||
enabled: true,
|
||||
provider: openaiConfig.provider,
|
||||
models: [openaiConfig.defaultModel],
|
||||
llmParams: {
|
||||
timeout: 30000, // 30 seconds timeout
|
||||
temperature: 0.5
|
||||
}
|
||||
});
|
||||
|
||||
const messages = [
|
||||
{ role: 'user' as const, content: 'Hello' }
|
||||
];
|
||||
|
||||
const response = await llmService.sendMessage(messages, openaiConfig.key);
|
||||
expect(response).toBeDefined();
|
||||
expect(typeof response).toBe('string');
|
||||
expect(response.length).toBeGreaterThan(0);
|
||||
}, 35000);
|
||||
});
|
||||
|
||||
describe('Gemini Provider', () => {
|
||||
const geminiConfig = getGeminiConfig();
|
||||
|
||||
if (!hasGeminiKey || !geminiConfig) {
|
||||
console.log('Skipping Gemini tests: No GEMINI_API_KEY available');
|
||||
it.skip('should handle llmParams for Gemini provider', () => {});
|
||||
return;
|
||||
}
|
||||
|
||||
it('should use Gemini-specific parameters from llmParams', async () => {
|
||||
const storage = new LocalStorageProvider();
|
||||
const modelManager = new ModelManager(storage);
|
||||
const llmService = createLLMService(modelManager);
|
||||
|
||||
// Configure Gemini model with llmParams
|
||||
await modelManager.updateModel(geminiConfig.key, {
|
||||
name: 'Test Gemini',
|
||||
apiKey: geminiConfig.apiKey,
|
||||
baseURL: geminiConfig.baseURL,
|
||||
defaultModel: geminiConfig.defaultModel,
|
||||
enabled: true,
|
||||
provider: geminiConfig.provider,
|
||||
models: [geminiConfig.defaultModel],
|
||||
llmParams: {
|
||||
temperature: 0.2,
|
||||
maxOutputTokens: 100,
|
||||
topP: 0.8,
|
||||
topK: 20
|
||||
}
|
||||
});
|
||||
|
||||
const messages = [
|
||||
{ role: 'user' as const, content: 'Tell me a very short fact about AI' }
|
||||
];
|
||||
|
||||
const response = await llmService.sendMessage(messages, geminiConfig.key);
|
||||
expect(response).toBeDefined();
|
||||
expect(typeof response).toBe('string');
|
||||
expect(response.length).toBeGreaterThan(0);
|
||||
// With maxOutputTokens=100, response should be relatively short
|
||||
expect(response.length).toBeLessThan(500);
|
||||
}, 30000);
|
||||
});
|
||||
|
||||
describe('Parameter Validation', () => {
|
||||
it('should validate OpenAI parameters correctly', () => {
|
||||
const validParams = {
|
||||
temperature: 0.7,
|
||||
max_tokens: 2048,
|
||||
timeout: 60000
|
||||
};
|
||||
|
||||
const result = validateLLMParams(validParams, 'openai');
|
||||
|
||||
// Debug: print validation results if test fails
|
||||
if (!result.isValid) {
|
||||
console.log('OpenAI validation failed:', JSON.stringify(result, null, 2));
|
||||
}
|
||||
|
||||
expect(result.isValid).toBe(true);
|
||||
expect(result.errors).toHaveLength(0);
|
||||
});
|
||||
|
||||
it('should detect invalid parameter types', () => {
|
||||
const invalidParams = {
|
||||
temperature: 'invalid', // should be number
|
||||
max_tokens: 2048.5 // should be integer
|
||||
};
|
||||
|
||||
const result = validateLLMParams(invalidParams, 'openai');
|
||||
|
||||
expect(result.isValid).toBe(false);
|
||||
expect(result.errors).toHaveLength(2);
|
||||
});
|
||||
|
||||
it('should detect out-of-range parameter values', () => {
|
||||
const outOfRangeParams = {
|
||||
temperature: 3.0, // exceeds maximum 2.0
|
||||
presence_penalty: -3.0 // below minimum -2.0
|
||||
};
|
||||
|
||||
const result = validateLLMParams(outOfRangeParams, 'openai');
|
||||
|
||||
expect(result.isValid).toBe(false);
|
||||
expect(result.errors).toHaveLength(2);
|
||||
});
|
||||
|
||||
it('should warn about unknown parameters', () => {
|
||||
const unknownParams = {
|
||||
temperature: 0.7,
|
||||
unknown_param: 'value'
|
||||
};
|
||||
|
||||
const result = validateLLMParams(unknownParams, 'openai');
|
||||
|
||||
expect(result.isValid).toBe(true);
|
||||
expect(result.warnings).toHaveLength(1);
|
||||
expect(result.warnings[0].parameterName).toBe('unknown_param');
|
||||
});
|
||||
|
||||
it('should validate Gemini-specific parameters', () => {
|
||||
const geminiParams = {
|
||||
temperature: 0.8,
|
||||
maxOutputTokens: 2048,
|
||||
topK: 40,
|
||||
stopSequences: ['END', 'STOP']
|
||||
};
|
||||
|
||||
const result = validateLLMParams(geminiParams, 'gemini');
|
||||
|
||||
// Debug: print validation results if test fails
|
||||
if (!result.isValid) {
|
||||
console.log('Gemini validation failed:', JSON.stringify(result, null, 2));
|
||||
}
|
||||
|
||||
expect(result.isValid).toBe(true);
|
||||
expect(result.errors).toHaveLength(0);
|
||||
});
|
||||
|
||||
it('should validate stopSequences array correctly', () => {
|
||||
const invalidStopSequences = {
|
||||
stopSequences: 'should_be_array'
|
||||
};
|
||||
|
||||
const result = validateLLMParams(invalidStopSequences, 'gemini');
|
||||
|
||||
expect(result.isValid).toBe(false);
|
||||
expect(result.errors[0].parameterName).toBe('stopSequences');
|
||||
});
|
||||
|
||||
it('should filter unsafe parameters in Gemini configuration', () => {
|
||||
// 这里我们测试的是参数验证，虽然buildGeminiGenerationConfig是私有方法
|
||||
// 但我们可以通过集成测试来验证它的行为
|
||||
const unsafeParams = {
|
||||
temperature: 0.8,
|
||||
maxOutputTokens: 2048,
|
||||
// 这些参数应该被警告或过滤
|
||||
dangerousParam: 'malicious_value',
|
||||
__proto__: 'attack',
|
||||
eval: 'dangerous_code'
|
||||
};
|
||||
|
||||
const result = validateLLMParams(unsafeParams, 'gemini');
|
||||
|
||||
// 验证不安全的参数被拒绝
|
||||
expect(result.warnings.length).toBeGreaterThan(0);
|
||||
expect(result.warnings.some(w => w.parameterName === 'dangerousParam')).toBe(true);
|
||||
});
|
||||
});
|
||||
|
||||
describe('Individual Parameter Tests', () => {
|
||||
const openaiCompatibleConfigs = getAvailableOpenAICompatibleConfigs();
|
||||
const geminiConfig = getGeminiConfig();
|
||||
|
||||
// Temperature parameter tests
|
||||
describe('Temperature Parameter', () => {
|
||||
// Test for all OpenAI compatible providers
|
||||
openaiCompatibleConfigs.forEach((config) => {
|
||||
it(`should accept valid temperature for ${config.provider} provider`, async () => {
|
||||
const storage = new LocalStorageProvider();
|
||||
const modelManager = new ModelManager(storage);
|
||||
const llmService = createLLMService(modelManager);
|
||||
|
||||
await modelManager.updateModel(config.key, {
|
||||
name: `Test ${config.provider} Temperature`,
|
||||
apiKey: config.apiKey,
|
||||
baseURL: config.baseURL,
|
||||
defaultModel: config.defaultModel,
|
||||
enabled: true,
|
||||
provider: config.provider,
|
||||
models: [config.defaultModel],
|
||||
llmParams: {
|
||||
temperature: 0.3
|
||||
}
|
||||
});
|
||||
|
||||
const messages = [{ role: 'user' as const, content: 'Hello' }];
|
||||
|
||||
const response = await llmService.sendMessage(messages, config.key);
|
||||
expect(response).toBeDefined();
|
||||
expect(typeof response).toBe('string');
|
||||
expect(response.length).toBeGreaterThan(0);
|
||||
}, 30000);
|
||||
});
|
||||
});
|
||||
|
||||
// Top P parameter tests
|
||||
describe('Top P Parameter', () => {
|
||||
// Test for all OpenAI compatible providers
|
||||
openaiCompatibleConfigs.forEach((config) => {
|
||||
it(`should accept valid top_p for ${config.provider} provider`, async () => {
|
||||
const storage = new LocalStorageProvider();
|
||||
const modelManager = new ModelManager(storage);
|
||||
const llmService = createLLMService(modelManager);
|
||||
|
||||
await modelManager.updateModel(config.key, {
|
||||
name: `Test ${config.provider} Top P`,
|
||||
apiKey: config.apiKey,
|
||||
baseURL: config.baseURL,
|
||||
defaultModel: config.defaultModel,
|
||||
enabled: true,
|
||||
provider: config.provider,
|
||||
models: [config.defaultModel],
|
||||
llmParams: {
|
||||
top_p: 0.9
|
||||
}
|
||||
});
|
||||
|
||||
const messages = [{ role: 'user' as const, content: 'Hello' }];
|
||||
|
||||
const response = await llmService.sendMessage(messages, config.key);
|
||||
expect(response).toBeDefined();
|
||||
expect(typeof response).toBe('string');
|
||||
expect(response.length).toBeGreaterThan(0);
|
||||
}, 30000);
|
||||
});
|
||||
});
|
||||
|
||||
// Max Tokens parameter tests (OpenAI compatible)
|
||||
describe('Max Tokens Parameter', () => {
|
||||
// Test for all OpenAI compatible providers
|
||||
openaiCompatibleConfigs.forEach((config) => {
|
||||
it(`should accept valid max_tokens for ${config.provider} provider`, async () => {
|
||||
const storage = new LocalStorageProvider();
|
||||
const modelManager = new ModelManager(storage);
|
||||
const llmService = createLLMService(modelManager);
|
||||
|
||||
await modelManager.updateModel(config.key, {
|
||||
name: `Test ${config.provider} Max Tokens`,
|
||||
apiKey: config.apiKey,
|
||||
baseURL: config.baseURL,
|
||||
defaultModel: config.defaultModel,
|
||||
enabled: true,
|
||||
provider: config.provider,
|
||||
models: [config.defaultModel],
|
||||
llmParams: {
|
||||
max_tokens: 100
|
||||
}
|
||||
});
|
||||
|
||||
const messages = [{ role: 'user' as const, content: 'Tell me a short fact' }];
|
||||
|
||||
const response = await llmService.sendMessage(messages, config.key);
|
||||
expect(response).toBeDefined();
|
||||
expect(typeof response).toBe('string');
|
||||
expect(response.length).toBeGreaterThan(0);
|
||||
}, 30000);
|
||||
});
|
||||
});
|
||||
|
||||
// Frequency Penalty parameter tests
|
||||
describe('Frequency Penalty Parameter', () => {
|
||||
// Test for all OpenAI compatible providers
|
||||
openaiCompatibleConfigs.forEach((config) => {
|
||||
it(`should accept valid frequency_penalty for ${config.provider} provider`, async () => {
|
||||
const storage = new LocalStorageProvider();
|
||||
const modelManager = new ModelManager(storage);
|
||||
const llmService = createLLMService(modelManager);
|
||||
|
||||
await modelManager.updateModel(config.key, {
|
||||
name: `Test ${config.provider} Frequency Penalty`,
|
||||
apiKey: config.apiKey,
|
||||
baseURL: config.baseURL,
|
||||
defaultModel: config.defaultModel,
|
||||
enabled: true,
|
||||
provider: config.provider,
|
||||
models: [config.defaultModel],
|
||||
llmParams: {
|
||||
frequency_penalty: 0.3
|
||||
}
|
||||
});
|
||||
|
||||
const messages = [{ role: 'user' as const, content: 'Hello' }];
|
||||
|
||||
const response = await llmService.sendMessage(messages, config.key);
|
||||
expect(response).toBeDefined();
|
||||
expect(typeof response).toBe('string');
|
||||
expect(response.length).toBeGreaterThan(0);
|
||||
}, 20000);
|
||||
});
|
||||
});
|
||||
|
||||
// Gemini specific parameters
|
||||
describe('Gemini Specific Parameters', () => {
|
||||
if (hasGeminiKey && geminiConfig) {
|
||||
it('should accept valid maxOutputTokens for Gemini provider', async () => {
|
||||
const storage = new LocalStorageProvider();
|
||||
const modelManager = new ModelManager(storage);
|
||||
const llmService = createLLMService(modelManager);
|
||||
|
||||
await modelManager.updateModel(geminiConfig.key, {
|
||||
name: 'Test Gemini Max Output Tokens',
|
||||
apiKey: geminiConfig.apiKey,
|
||||
baseURL: geminiConfig.baseURL,
|
||||
defaultModel: geminiConfig.defaultModel,
|
||||
enabled: true,
|
||||
provider: geminiConfig.provider,
|
||||
models: [geminiConfig.defaultModel],
|
||||
llmParams: {
|
||||
maxOutputTokens: 200
|
||||
}
|
||||
});
|
||||
|
||||
const messages = [{ role: 'user' as const, content: 'Tell me about AI' }];
|
||||
|
||||
const response = await llmService.sendMessage(messages, geminiConfig.key);
|
||||
expect(response).toBeDefined();
|
||||
expect(typeof response).toBe('string');
|
||||
expect(response.length).toBeGreaterThan(0);
|
||||
}, 30000);
|
||||
|
||||
it('should accept valid candidateCount for Gemini provider', async () => {
|
||||
const storage = new LocalStorageProvider();
|
||||
const modelManager = new ModelManager(storage);
|
||||
const llmService = createLLMService(modelManager);
|
||||
|
||||
await modelManager.updateModel(geminiConfig.key, {
|
||||
name: 'Test Gemini Candidate Count',
|
||||
apiKey: geminiConfig.apiKey,
|
||||
baseURL: geminiConfig.baseURL,
|
||||
defaultModel: geminiConfig.defaultModel,
|
||||
enabled: true,
|
||||
provider: geminiConfig.provider,
|
||||
models: [geminiConfig.defaultModel],
|
||||
llmParams: {
|
||||
candidateCount: 1
|
||||
}
|
||||
});
|
||||
|
||||
const messages = [{ role: 'user' as const, content: 'Hello' }];
|
||||
|
||||
const response = await llmService.sendMessage(messages, geminiConfig.key);
|
||||
expect(response).toBeDefined();
|
||||
expect(typeof response).toBe('string');
|
||||
expect(response.length).toBeGreaterThan(0);
|
||||
}, 30000);
|
||||
}
|
||||
});
|
||||
|
||||
// Combined parameters tests
|
||||
describe('Combined Parameters', () => {
|
||||
// Test for all OpenAI compatible providers
|
||||
openaiCompatibleConfigs.forEach((config) => {
|
||||
it(`should handle multiple parameters for ${config.provider} provider`, async () => {
|
||||
const storage = new LocalStorageProvider();
|
||||
const modelManager = new ModelManager(storage);
|
||||
const llmService = createLLMService(modelManager);
|
||||
|
||||
await modelManager.updateModel(config.key, {
|
||||
name: `Test ${config.provider} Combined`,
|
||||
apiKey: config.apiKey,
|
||||
baseURL: config.baseURL,
|
||||
defaultModel: config.defaultModel,
|
||||
enabled: true,
|
||||
provider: config.provider,
|
||||
models: [config.defaultModel],
|
||||
llmParams: {
|
||||
temperature: 0.6,
|
||||
max_tokens: 150,
|
||||
top_p: 0.9,
|
||||
presence_penalty: 0.2,
|
||||
frequency_penalty: 0.1,
|
||||
timeout: 25000
|
||||
}
|
||||
});
|
||||
|
||||
const messages = [{ role: 'user' as const, content: 'Tell me a short story' }];
|
||||
|
||||
const response = await llmService.sendMessage(messages, config.key);
|
||||
expect(response).toBeDefined();
|
||||
expect(typeof response).toBe('string');
|
||||
expect(response.length).toBeGreaterThan(0);
|
||||
}, 30000);
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
describe('Edge Cases', () => {
|
||||
const openaiCompatibleConfigs = getAvailableOpenAICompatibleConfigs();
|
||||
|
||||
it('should handle missing llmParams gracefully', () => {
|
||||
const result = validateLLMParams(undefined, 'openai');
|
||||
expect(result.isValid).toBe(true);
|
||||
expect(result.errors).toHaveLength(0);
|
||||
expect(result.warnings).toHaveLength(0);
|
||||
});
|
||||
|
||||
it('should handle empty llmParams object', () => {
|
||||
const result = validateLLMParams({}, 'openai');
|
||||
expect(result.isValid).toBe(true);
|
||||
expect(result.errors).toHaveLength(0);
|
||||
expect(result.warnings).toHaveLength(0);
|
||||
});
|
||||
|
||||
// Test that no default values are set when parameters are not provided
|
||||
describe('No Default Values', () => {
|
||||
// Test for all OpenAI compatible providers
|
||||
openaiCompatibleConfigs.forEach((config) => {
|
||||
it(`should not set default values when not provided for ${config.provider}`, async () => {
|
||||
const storage = new LocalStorageProvider();
|
||||
const modelManager = new ModelManager(storage);
|
||||
const llmService = createLLMService(modelManager);
|
||||
|
||||
await modelManager.updateModel(config.key, {
|
||||
name: `Test ${config.provider} No Defaults`,
|
||||
apiKey: config.apiKey,
|
||||
baseURL: config.baseURL,
|
||||
defaultModel: config.defaultModel,
|
||||
enabled: true,
|
||||
provider: config.provider,
|
||||
models: [config.defaultModel],
|
||||
// No llmParams provided - testing parameter transparency
|
||||
});
|
||||
|
||||
const messages = [{ role: 'user' as const, content: 'Hello' }];
|
||||
|
||||
const response = await llmService.sendMessage(messages, config.key);
|
||||
expect(response).toBeDefined();
|
||||
expect(typeof response).toBe('string');
|
||||
expect(response.length).toBeGreaterThan(0);
|
||||
// Should work fine without any default values being set
|
||||
}, 30000);
|
||||
});
|
||||
});
|
||||
});
|
||||
});
|
||||
@@ -162,14 +162,153 @@ describe('ModelManager', () => {
|
||||
it('should return only enabled models', async () => {
|
||||
const enabledModel = createModelConfig('EnabledModel', true);
|
||||
const disabledModel = createModelConfig('DisabledModel', false);
|
||||
|
||||
await modelManager.addModel('enabledKey', enabledModel);
|
||||
await modelManager.addModel('disabledKey', disabledModel);
|
||||
|
||||
|
||||
await modelManager.addModel('enabled', enabledModel);
|
||||
await modelManager.addModel('disabled', disabledModel);
|
||||
|
||||
const enabledModels = await modelManager.getEnabledModels();
|
||||
expect(enabledModels).toHaveLength(1);
|
||||
expect(enabledModels[0].key).toBe('enabled');
|
||||
});
|
||||
});
|
||||
|
||||
describe('llmParams deep copy', () => {
|
||||
it('should deep copy llmParams to avoid reference sharing when adding models', async () => {
|
||||
const originalLlmParams = {
|
||||
temperature: 0.7,
|
||||
max_tokens: 4096
|
||||
};
|
||||
|
||||
expect(enabledModels.some(m => m.key === 'enabledKey')).toBe(true);
|
||||
expect(enabledModels.some(m => m.key === 'disabledKey')).toBe(false);
|
||||
const modelConfig = createModelConfig('TestModel', true, 'test_key', ['model1'], 'model1', 'openai');
|
||||
modelConfig.llmParams = originalLlmParams;
|
||||
|
||||
await modelManager.addModel('test-model', modelConfig);
|
||||
|
||||
// Modify the original llmParams
|
||||
originalLlmParams.temperature = 0.9;
|
||||
originalLlmParams.max_tokens = 2048;
|
||||
|
||||
// Get the stored model and verify it wasn't affected
|
||||
const storedModel = await modelManager.getModel('test-model');
|
||||
expect(storedModel?.llmParams?.temperature).toBe(0.7);
|
||||
expect(storedModel?.llmParams?.max_tokens).toBe(4096);
|
||||
});
|
||||
|
||||
it('should deep copy llmParams when updating models', async () => {
|
||||
const initialModel = createModelConfig('TestModel', true);
|
||||
await modelManager.addModel('test-model', initialModel);
|
||||
|
||||
const updateLlmParams = {
|
||||
temperature: 0.5,
|
||||
top_p: 0.9
|
||||
};
|
||||
|
||||
await modelManager.updateModel('test-model', {
|
||||
llmParams: updateLlmParams
|
||||
});
|
||||
|
||||
// Modify the original update params
|
||||
updateLlmParams.temperature = 1.0;
|
||||
updateLlmParams.top_p = 0.5;
|
||||
|
||||
// Get the stored model and verify it wasn't affected
|
||||
const storedModel = await modelManager.getModel('test-model');
|
||||
expect(storedModel?.llmParams?.temperature).toBe(0.5);
|
||||
expect(storedModel?.llmParams?.top_p).toBe(0.9);
|
||||
});
|
||||
|
||||
it('should handle undefined llmParams gracefully', async () => {
|
||||
const modelConfig = createModelConfig('TestModel', true);
|
||||
// Explicitly set llmParams to undefined
|
||||
modelConfig.llmParams = undefined;
|
||||
|
||||
await modelManager.addModel('test-model', modelConfig);
|
||||
|
||||
const storedModel = await modelManager.getModel('test-model');
|
||||
expect(storedModel?.llmParams).toBeUndefined();
|
||||
});
|
||||
});
|
||||
|
||||
describe('llmParams security validation', () => {
|
||||
it('should reject dangerous parameters when adding models', async () => {
|
||||
const modelWithDangerousParams = createModelConfig('DangerousModel', true, 'test_key', ['model1'], 'model1', 'openai');
|
||||
modelWithDangerousParams.llmParams = {
|
||||
temperature: 0.7,
|
||||
__proto__: { malicious: 'code' }, // Dangerous parameter
|
||||
constructor: function() { return 'hack'; } // Another dangerous parameter
|
||||
};
|
||||
|
||||
await expect(modelManager.addModel('dangerous-model', modelWithDangerousParams))
|
||||
.rejects.toThrow(ModelConfigError);
|
||||
});
|
||||
|
||||
it('should reject invalid parameter types when adding models', async () => {
|
||||
const modelWithInvalidTypes = createModelConfig('InvalidModel', true, 'test_key', ['model1'], 'model1', 'openai');
|
||||
modelWithInvalidTypes.llmParams = {
|
||||
temperature: 'invalid_string', // Should be number
|
||||
max_tokens: 1024.5 // Should be integer
|
||||
};
|
||||
|
||||
await expect(modelManager.addModel('invalid-model', modelWithInvalidTypes))
|
||||
.rejects.toThrow(ModelConfigError);
|
||||
});
|
||||
|
||||
it('should reject out-of-range parameters when adding models', async () => {
|
||||
const modelWithOutOfRangeParams = createModelConfig('OutOfRangeModel', true, 'test_key', ['model1'], 'model1', 'openai');
|
||||
modelWithOutOfRangeParams.llmParams = {
|
||||
temperature: 5.0, // Exceeds maximum 2.0
|
||||
presence_penalty: -3.0 // Below minimum -2.0
|
||||
};
|
||||
|
||||
await expect(modelManager.addModel('out-of-range-model', modelWithOutOfRangeParams))
|
||||
.rejects.toThrow(ModelConfigError);
|
||||
});
|
||||
|
||||
it('should accept valid parameters when adding models', async () => {
|
||||
const modelWithValidParams = createModelConfig('ValidModel', true, 'test_key', ['model1'], 'model1', 'openai');
|
||||
modelWithValidParams.llmParams = {
|
||||
temperature: 0.7,
|
||||
max_tokens: 2048,
|
||||
top_p: 0.9,
|
||||
presence_penalty: 0.1,
|
||||
frequency_penalty: 0.1
|
||||
};
|
||||
|
||||
await expect(modelManager.addModel('valid-model', modelWithValidParams))
|
||||
.resolves.not.toThrow();
|
||||
|
||||
const storedModel = await modelManager.getModel('valid-model');
|
||||
expect(storedModel?.llmParams).toEqual(modelWithValidParams.llmParams);
|
||||
});
|
||||
|
||||
it('should validate llmParams when updating models', async () => {
|
||||
const initialModel = createModelConfig('TestModel', true, 'test_key', ['model1'], 'model1', 'openai');
|
||||
await modelManager.addModel('test-model', initialModel);
|
||||
|
||||
// Try to update with dangerous parameters
|
||||
await expect(modelManager.updateModel('test-model', {
|
||||
llmParams: {
|
||||
temperature: 0.5,
|
||||
eval: 'malicious_code()' // Dangerous parameter
|
||||
}
|
||||
})).rejects.toThrow(ModelConfigError);
|
||||
});
|
||||
|
||||
it('should validate provider-specific parameters', async () => {
|
||||
const geminiModel = createModelConfig('GeminiModel', true, 'test_key', ['gemini-pro'], 'gemini-pro', 'gemini');
|
||||
geminiModel.llmParams = {
|
||||
temperature: 0.8,
|
||||
maxOutputTokens: 2048,
|
||||
topK: 40,
|
||||
topP: 0.9,
|
||||
stopSequences: ['END', 'STOP']
|
||||
};
|
||||
|
||||
await expect(modelManager.addModel('gemini-model', geminiModel))
|
||||
.resolves.not.toThrow();
|
||||
|
||||
const storedModel = await modelManager.getModel('gemini-model');
|
||||
expect(storedModel?.llmParams).toEqual(geminiModel.llmParams);
|
||||
});
|
||||
});
|
||||
});
|
||||
@@ -105,10 +105,10 @@
|
||||
<Teleport to="body">
|
||||
<!-- 编辑模型弹窗 -->
|
||||
<div v-if="isEditing"
|
||||
class="fixed inset-0 z-[60] flex items-center justify-center overflow-y-auto">
|
||||
class="fixed inset-0 z-[60] flex items-center justify-center p-4">
|
||||
<div class="fixed inset-0 bg-black/60 backdrop-blur-sm"></div>
|
||||
|
||||
<div class="relative theme-manager-container w-full max-w-2xl m-4 z-10"
|
||||
<div class="relative theme-manager-container w-full max-w-2xl max-h-[90vh] overflow-y-auto z-10"
|
||||
@click.stop>
|
||||
<div class="p-6 space-y-6">
|
||||
<div class="flex items-center justify-between">
|
||||
@@ -167,6 +167,116 @@
|
||||
<span class="cursor-help ml-1" :title="t('modelManager.useVercelProxyHint')">?</span>
|
||||
</label>
|
||||
</div>
|
||||
|
||||
<!-- Advanced Parameters Section -->
|
||||
<div class="pt-4 mt-4 border-t theme-manager-border">
|
||||
<div class="flex items-center justify-between mb-4">
|
||||
<div>
|
||||
<h4 class="text-md font-semibold theme-manager-text">
|
||||
{{ t('modelManager.advancedParameters.title') }}
|
||||
</h4>
|
||||
<p class="text-xs theme-manager-text-secondary mt-1">
|
||||
{{ t('modelManager.advancedParameters.currentProvider') }}: <span class="font-medium text-purple-600">{{ currentProviderType === 'custom' ? t('modelManager.advancedParameters.customProvider') : currentProviderType.toUpperCase() }}</span>
|
||||
<span v-if="availableLLMParamDefinitions.length > 0"> ({{ availableLLMParamDefinitions.length }}{{ t('modelManager.advancedParameters.availableParams') }})</span>
|
||||
<span v-else class="text-yellow-600"> ({{ t('modelManager.advancedParameters.noAvailableParams') }})</span>
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<!-- 参数选择器 -->
|
||||
<select v-model="selectedNewLLMParamId"
|
||||
@change="handleQuickAddParam"
|
||||
class="theme-manager-input text-sm py-1.5 px-3 min-w-[180px] max-w-[220px]">
|
||||
<option disabled value="">{{ t('modelManager.advancedParameters.select') }}</option>
|
||||
<option v-for="paramDef in availableLLMParamDefinitions" :key="paramDef.id" :value="paramDef.id">
|
||||
{{ paramDef.labelKey ? t(paramDef.labelKey) : paramDef.name }}
|
||||
</option>
|
||||
<option value="custom">{{ t('modelManager.advancedParameters.custom') }}</option>
|
||||
</select>
|
||||
</div>
|
||||
|
||||
<div v-if="Object.keys(currentLLMParams || {}).length === 0" class="text-sm theme-manager-text-secondary mb-3">
|
||||
{{ t('modelManager.advancedParameters.noParamsConfigured') }}
|
||||
</div>
|
||||
|
||||
<!-- 参数显示 -->
|
||||
<div v-for="(value, key) in currentLLMParams" :key="key" class="mb-4">
|
||||
<div class="p-3 theme-manager-card rounded-lg border theme-manager-border">
|
||||
<!-- 参数标题行 -->
|
||||
<div class="flex items-center justify-between mb-2">
|
||||
<div class="flex items-center gap-2">
|
||||
<span class="text-sm font-medium theme-manager-text">
|
||||
{{ getParamMetadata(key)?.label || key }}
|
||||
</span>
|
||||
<span v-if="!getParamMetadata(key)" class="px-2 py-0.5 text-xs theme-manager-tag rounded">
|
||||
{{ t('modelManager.advancedParameters.customParam') }}
|
||||
</span>
|
||||
</div>
|
||||
<button @click="removeLLMParam(key)" type="button"
|
||||
class="w-6 h-6 text-red-500 hover:text-white hover:bg-red-500 rounded text-xs flex items-center justify-center transition-colors">
|
||||
×
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<!-- 参数描述 -->
|
||||
<p v-if="getParamMetadata(key)?.description" class="text-xs theme-manager-text-secondary mb-2">
|
||||
{{ getParamMetadata(key)?.description }}
|
||||
</p>
|
||||
|
||||
<!-- 输入字段 -->
|
||||
<div class="w-full">
|
||||
<!-- Boolean 类型 -->
|
||||
<template v-if="getParamMetadata(key)?.type === 'boolean'">
|
||||
<label class="flex items-center cursor-pointer">
|
||||
<input v-model="currentLLMParams[key]"
|
||||
type="checkbox"
|
||||
:id="`llmparam-${isEditing ? 'edit' : 'add'}-${key}`"
|
||||
class="w-4 h-4 text-purple-600 rounded focus:ring-purple-500" />
|
||||
<span class="ml-2 text-sm theme-manager-text">
|
||||
{{ currentLLMParams[key] ? t('common.enabled') : t('common.disabled') }}
|
||||
</span>
|
||||
</label>
|
||||
</template>
|
||||
|
||||
<!-- Number/Integer 类型 -->
|
||||
<template v-else-if="getParamMetadata(key)?.type === 'number' || (getParamMetadata(key)?.type === 'integer' && getParamMetadata(key)?.name !== 'stopSequences')">
|
||||
<div class="space-y-2">
|
||||
<input v-model.number="currentLLMParams[key]"
|
||||
type="number"
|
||||
:min="getParamMetadata(key)?.minValue"
|
||||
:max="getParamMetadata(key)?.maxValue"
|
||||
:step="getParamMetadata(key)?.step"
|
||||
class="theme-manager-input w-full text-sm"
|
||||
:class="{ 'border-red-500': isParamInvalid(key, currentLLMParams[key]) }"
|
||||
:placeholder="getParamMetadata(key)?.defaultValue !== undefined ? String(getParamMetadata(key)?.defaultValue) : ''" />
|
||||
|
||||
<!-- 范围提示 -->
|
||||
<div v-if="getParamMetadata(key)?.minValue !== undefined && getParamMetadata(key)?.maxValue !== undefined"
|
||||
class="text-xs theme-manager-text-secondary">
|
||||
范围: {{ getParamMetadata(key)?.minValue }} - {{ getParamMetadata(key)?.maxValue }}{{ getParamMetadata(key)?.unit || '' }}
|
||||
</div>
|
||||
|
||||
<!-- 错误提示 -->
|
||||
<div v-if="isParamInvalid(key, currentLLMParams[key])" class="text-red-500 text-xs">
|
||||
{{ getParamValidationMessage(key, currentLLMParams[key]) }}
|
||||
</div>
|
||||
</div>
|
||||
</template>
|
||||
|
||||
<!-- String 类型 -->
|
||||
<template v-else>
|
||||
<input v-model="currentLLMParams[key]"
|
||||
type="text"
|
||||
class="theme-manager-input w-full text-sm"
|
||||
:placeholder="getParamMetadata(key)?.name === 'stopSequences' ? t('modelManager.advancedParameters.stopSequencesPlaceholder') : (getParamMetadata(key)?.defaultValue !== undefined ? String(getParamMetadata(key)?.defaultValue) : '')" />
|
||||
<p v-if="getParamMetadata(key)?.name === 'stopSequences'" class="text-xs theme-manager-text-secondary mt-1">
|
||||
{{ t('modelManager.advancedParameters.stopSequencesPlaceholder') }}
|
||||
</p>
|
||||
</template>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="flex justify-end space-x-3 pt-4">
|
||||
<button
|
||||
type="button"
|
||||
@@ -188,10 +298,10 @@
|
||||
</div>
|
||||
|
||||
<!-- 添加模型弹窗 -->
|
||||
<div v-if="showAddForm" class="fixed inset-0 z-[60] flex items-center justify-center overflow-y-auto">
|
||||
<div v-if="showAddForm" class="fixed inset-0 z-[60] flex items-center justify-center p-4">
|
||||
<div class="fixed inset-0 bg-black/60 backdrop-blur-sm"></div>
|
||||
|
||||
<div class="relative theme-manager-container w-full max-w-2xl m-4 z-10" @click.stop>
|
||||
<div class="relative theme-manager-container w-full max-w-2xl max-h-[90vh] overflow-y-auto z-10" @click.stop>
|
||||
<div class="p-6 space-y-6">
|
||||
<div class="flex items-center justify-between">
|
||||
<h3 class="text-xl font-semibold theme-manager-text">{{ t('modelManager.addModel') }}</h3>
|
||||
@@ -250,6 +360,115 @@
|
||||
<span class="cursor-help ml-1" :title="t('modelManager.useVercelProxyHint')">?</span>
|
||||
</label>
|
||||
</div>
|
||||
<!-- Advanced Parameters Section FOR ADD MODEL -->
|
||||
<div class="pt-4 mt-4 border-t theme-manager-border">
|
||||
<div class="flex items-center justify-between mb-4">
|
||||
<div>
|
||||
<h4 class="text-md font-semibold theme-manager-text">
|
||||
{{ t('modelManager.advancedParameters.title') }}
|
||||
</h4>
|
||||
<p class="text-xs theme-manager-text-secondary mt-1">
|
||||
{{ t('modelManager.advancedParameters.currentProvider') }}: <span class="font-medium text-purple-600">{{ currentProviderType === 'custom' ? t('modelManager.advancedParameters.customProvider') : currentProviderType.toUpperCase() }}</span>
|
||||
<span v-if="availableLLMParamDefinitions.length > 0"> ({{ availableLLMParamDefinitions.length }}{{ t('modelManager.advancedParameters.availableParams') }})</span>
|
||||
<span v-else class="text-yellow-600"> ({{ t('modelManager.advancedParameters.noAvailableParams') }})</span>
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<!-- 参数选择器 -->
|
||||
<select v-model="selectedNewLLMParamId"
|
||||
@change="handleQuickAddParam"
|
||||
class="theme-manager-input text-sm py-1.5 px-3 min-w-[180px] max-w-[220px]">
|
||||
<option disabled value="">{{ t('modelManager.advancedParameters.select') }}</option>
|
||||
<option v-for="paramDef in availableLLMParamDefinitions" :key="paramDef.id" :value="paramDef.id">
|
||||
{{ paramDef.labelKey ? t(paramDef.labelKey) : paramDef.name }}
|
||||
</option>
|
||||
<option value="custom">{{ t('modelManager.advancedParameters.custom') }}</option>
|
||||
</select>
|
||||
</div>
|
||||
|
||||
<div v-if="Object.keys(currentLLMParams || {}).length === 0" class="text-sm theme-manager-text-secondary mb-3">
|
||||
{{ t('modelManager.advancedParameters.noParamsConfigured') }}
|
||||
</div>
|
||||
|
||||
<!-- 参数显示 -->
|
||||
<div v-for="(value, key) in currentLLMParams" :key="key" class="mb-4">
|
||||
<div class="p-3 theme-manager-card rounded-lg border theme-manager-border">
|
||||
<!-- 参数标题行 -->
|
||||
<div class="flex items-center justify-between mb-2">
|
||||
<div class="flex items-center gap-2">
|
||||
<span class="text-sm font-medium theme-manager-text">
|
||||
{{ getParamMetadata(key)?.label || key }}
|
||||
</span>
|
||||
<span v-if="!getParamMetadata(key)" class="px-2 py-0.5 text-xs theme-manager-tag rounded">
|
||||
{{ t('modelManager.advancedParameters.customParam') }}
|
||||
</span>
|
||||
</div>
|
||||
<button @click="removeLLMParam(key)" type="button"
|
||||
class="w-6 h-6 text-red-500 hover:text-white hover:bg-red-500 rounded text-xs flex items-center justify-center transition-colors">
|
||||
×
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<!-- 参数描述 -->
|
||||
<p v-if="getParamMetadata(key)?.description" class="text-xs theme-manager-text-secondary mb-2">
|
||||
{{ getParamMetadata(key)?.description }}
|
||||
</p>
|
||||
|
||||
<!-- 输入字段 -->
|
||||
<div class="w-full">
|
||||
<!-- Boolean 类型 -->
|
||||
<template v-if="getParamMetadata(key)?.type === 'boolean'">
|
||||
<label class="flex items-center cursor-pointer">
|
||||
<input v-model="currentLLMParams[key]"
|
||||
type="checkbox"
|
||||
:id="`llmparam-${isEditing ? 'edit' : 'add'}-${key}`"
|
||||
class="w-4 h-4 text-purple-600 rounded focus:ring-purple-500" />
|
||||
<span class="ml-2 text-sm theme-manager-text">
|
||||
{{ currentLLMParams[key] ? t('common.enabled') : t('common.disabled') }}
|
||||
</span>
|
||||
</label>
|
||||
</template>
|
||||
|
||||
<!-- Number/Integer 类型 -->
|
||||
<template v-else-if="getParamMetadata(key)?.type === 'number' || (getParamMetadata(key)?.type === 'integer' && getParamMetadata(key)?.name !== 'stopSequences')">
|
||||
<div class="space-y-2">
|
||||
<input v-model.number="currentLLMParams[key]"
|
||||
type="number"
|
||||
:min="getParamMetadata(key)?.minValue"
|
||||
:max="getParamMetadata(key)?.maxValue"
|
||||
:step="getParamMetadata(key)?.step"
|
||||
class="theme-manager-input w-full text-sm"
|
||||
:class="{ 'border-red-500': isParamInvalid(key, currentLLMParams[key]) }"
|
||||
:placeholder="getParamMetadata(key)?.defaultValue !== undefined ? String(getParamMetadata(key)?.defaultValue) : ''" />
|
||||
|
||||
<!-- 范围提示 -->
|
||||
<div v-if="getParamMetadata(key)?.minValue !== undefined && getParamMetadata(key)?.maxValue !== undefined"
|
||||
class="text-xs theme-manager-text-secondary">
|
||||
范围: {{ getParamMetadata(key)?.minValue }} - {{ getParamMetadata(key)?.maxValue }}{{ getParamMetadata(key)?.unit || '' }}
|
||||
</div>
|
||||
|
||||
<!-- 错误提示 -->
|
||||
<div v-if="isParamInvalid(key, currentLLMParams[key])" class="text-red-500 text-xs">
|
||||
{{ getParamValidationMessage(key, currentLLMParams[key]) }}
|
||||
</div>
|
||||
</div>
|
||||
</template>
|
||||
|
||||
<!-- String 类型 -->
|
||||
<template v-else>
|
||||
<input v-model="currentLLMParams[key]"
|
||||
type="text"
|
||||
class="theme-manager-input w-full text-sm"
|
||||
:placeholder="getParamMetadata(key)?.name === 'stopSequences' ? t('modelManager.advancedParameters.stopSequencesPlaceholder') : (getParamMetadata(key)?.defaultValue !== undefined ? String(getParamMetadata(key)?.defaultValue) : '')" />
|
||||
<p v-if="getParamMetadata(key)?.name === 'stopSequences'" class="text-xs theme-manager-text-secondary mt-1">
|
||||
{{ t('modelManager.advancedParameters.stopSequencesPlaceholder') }}
|
||||
</p>
|
||||
</template>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="flex justify-end space-x-3 pt-4">
|
||||
<button type="button" @click="showAddForm = false" class="theme-manager-button-secondary">
|
||||
{{ t('common.cancel') }}
|
||||
@@ -269,13 +488,14 @@
|
||||
</template>
|
||||
|
||||
<script setup>
|
||||
import { ref, onMounted, defineEmits, watch } from 'vue';
|
||||
import { ref, onMounted, defineEmits, watch, computed } from 'vue'; // Added computed
|
||||
import { useI18n } from 'vue-i18n';
|
||||
import {
|
||||
modelManager,
|
||||
createLLMService,
|
||||
checkVercelApiAvailability,
|
||||
resetVercelStatusCache
|
||||
resetVercelStatusCache,
|
||||
advancedParameterDefinitions // NEW IMPORT
|
||||
} from '@prompt-optimizer/core';
|
||||
import { useToast } from '../composables/useToast';
|
||||
import InputWithSelect from './InputWithSelect.vue'
|
||||
@@ -293,6 +513,9 @@ const isLoadingModels = ref(false);
|
||||
const testingConnections = ref({});
|
||||
// 是否支持Vercel代理
|
||||
const vercelProxyAvailable = ref(false);
|
||||
// For Advanced Parameters UI
|
||||
const selectedNewLLMParamId = ref(''); // Stores ID of param selected from dropdown
|
||||
const customLLMParam = ref({ key: '', value: '' });
|
||||
|
||||
// 数据状态
|
||||
const models = ref([]);
|
||||
@@ -306,7 +529,9 @@ const newModel = ref({
|
||||
baseURL: '',
|
||||
defaultModel: '',
|
||||
apiKey: '',
|
||||
useVercelProxy: false
|
||||
useVercelProxy: false,
|
||||
provider: 'custom',
|
||||
llmParams: {}
|
||||
});
|
||||
|
||||
// =============== 初始化和辅助函数 ===============
|
||||
@@ -465,10 +690,10 @@ const editModel = async (key) => {
|
||||
apiKey: maskedApiKey,
|
||||
displayMaskedKey: true,
|
||||
originalApiKey: model.apiKey,
|
||||
useVercelProxy: model.useVercelProxy,
|
||||
provider: model.provider,
|
||||
useVercelProxy: model.useVercelProxy === undefined ? false : model.useVercelProxy, // Ensure default
|
||||
provider: model.provider || 'custom', // Ensure provider is set
|
||||
enabled: model.enabled,
|
||||
// 其他需要的字段...
|
||||
llmParams: model.llmParams ? JSON.parse(JSON.stringify(model.llmParams)) : {} // Deep copy llmParams
|
||||
};
|
||||
|
||||
// 初始化模型选项
|
||||
@@ -652,7 +877,7 @@ const handleFetchNewModels = async () => {
|
||||
const customConfig = {
|
||||
baseURL: baseURL,
|
||||
apiKey: apiKey,
|
||||
provider: 'custom', // 新模型默认为自定义提供商
|
||||
provider: currentProviderType.value || 'custom',
|
||||
useVercelProxy: newModel.value.useVercelProxy
|
||||
};
|
||||
|
||||
@@ -710,7 +935,8 @@ const saveEdit = async () => {
|
||||
provider: editingModel.value.provider || 'custom',
|
||||
enabled: editingModel.value.enabled !== undefined
|
||||
? editingModel.value.enabled
|
||||
: true
|
||||
: true,
|
||||
llmParams: editingModel.value.llmParams || {}
|
||||
};
|
||||
|
||||
// 直接更新原始模型
|
||||
@@ -744,8 +970,9 @@ const addCustomModel = async () => {
|
||||
defaultModel: newModel.value.defaultModel,
|
||||
apiKey: newModel.value.apiKey,
|
||||
enabled: true,
|
||||
provider: 'custom',
|
||||
useVercelProxy: newModel.value.useVercelProxy
|
||||
provider: currentProviderType.value || 'custom',
|
||||
useVercelProxy: newModel.value.useVercelProxy,
|
||||
llmParams: newModel.value.llmParams || {}
|
||||
}
|
||||
|
||||
await modelManager.addModel(newModel.value.key, config)
|
||||
@@ -759,7 +986,9 @@ const addCustomModel = async () => {
|
||||
baseURL: '',
|
||||
defaultModel: '',
|
||||
apiKey: '',
|
||||
useVercelProxy: false
|
||||
useVercelProxy: false,
|
||||
provider: 'custom',
|
||||
llmParams: {}
|
||||
}
|
||||
toast.success('模型添加成功')
|
||||
} catch (error) {
|
||||
@@ -777,6 +1006,208 @@ watch(() => editingModel.value?.apiKey, (newValue) => {
|
||||
}
|
||||
});
|
||||
|
||||
// =============== Advanced Parameters Computed Properties ===============
|
||||
const currentLLMParams = computed(() => {
|
||||
return isEditing.value ? (editingModel.value?.llmParams || {}) : newModel.value.llmParams;
|
||||
});
|
||||
|
||||
const currentProviderType = computed(() => {
|
||||
if (isEditing.value) {
|
||||
return editingModel.value?.provider || 'custom';
|
||||
}
|
||||
// For new models, derive from key if it matches a known provider, else default to 'custom'
|
||||
// This helps in suggesting relevant advanced parameters early.
|
||||
const knownProviders = ['openai', 'gemini', 'deepseek', 'zhipu', 'siliconflow'];
|
||||
if (newModel.value.key && knownProviders.includes(newModel.value.key.toLowerCase())) {
|
||||
return newModel.value.key.toLowerCase();
|
||||
}
|
||||
return newModel.value.provider || 'custom';
|
||||
});
|
||||
|
||||
const getParamMetadata = (paramName) => {
|
||||
if (!advancedParameterDefinitions) return null;
|
||||
// Ensure currentProviderType.value is valid before using in .includes()
|
||||
const provider = currentProviderType.value || 'custom';
|
||||
const definition = advancedParameterDefinitions.find(def => def.name === paramName && def.appliesToProviders.includes(provider));
|
||||
|
||||
if (definition) {
|
||||
return {
|
||||
...definition,
|
||||
label: definition.labelKey ? t(definition.labelKey) : definition.name,
|
||||
description: definition.descriptionKey ? t(definition.descriptionKey) : `(${paramName})`, // Fallback description
|
||||
unit: definition.unitKey ? t(definition.unitKey) : (definition.unit || '')
|
||||
};
|
||||
}
|
||||
return null; // For custom params or if not found
|
||||
};
|
||||
|
||||
const availableLLMParamDefinitions = computed(() => {
|
||||
if (!advancedParameterDefinitions) return [];
|
||||
const currentParams = currentLLMParams.value || {};
|
||||
const provider = currentProviderType.value || 'custom';
|
||||
|
||||
return advancedParameterDefinitions.filter(def =>
|
||||
def.appliesToProviders.includes(provider) &&
|
||||
!Object.keys(currentParams).includes(def.name)
|
||||
);
|
||||
});
|
||||
|
||||
const removeLLMParam = (paramKey) => {
|
||||
if (currentLLMParams.value) {
|
||||
delete currentLLMParams.value[paramKey];
|
||||
// Vue 3 might need a bit more help for reactivity on nested objects if not using Vue.set or Vue.delete
|
||||
// However, direct deletion and assignment for the whole llmParams object during save should be fine.
|
||||
// For local reactivity within the form, this should work.
|
||||
}
|
||||
};
|
||||
|
||||
const quickAddLLMParam = () => {
|
||||
const paramsObject = currentLLMParams.value;
|
||||
if (!paramsObject) return; // Should not happen if initialized correctly
|
||||
|
||||
if (selectedNewLLMParamId.value === 'custom') {
|
||||
if (customLLMParam.value.key && !paramsObject[customLLMParam.value.key]) {
|
||||
// For custom params, initially store value as string.
|
||||
// Type conversion can be attempted by user or upon processing if needed.
|
||||
paramsObject[customLLMParam.value.key] = customLLMParam.value.value;
|
||||
}
|
||||
customLLMParam.value = { key: '', value: '' }; // Reset custom input
|
||||
} else {
|
||||
const definition = advancedParameterDefinitions.find(def => def.id === selectedNewLLMParamId.value);
|
||||
if (definition && !paramsObject[definition.name]) {
|
||||
let val = definition.defaultValue;
|
||||
// Basic type handling for default values
|
||||
if (definition.type === 'boolean') {
|
||||
val = (val === undefined) ? false : Boolean(val);
|
||||
} else if (definition.type === 'integer' && val !== undefined) {
|
||||
val = parseInt(String(val), 10);
|
||||
} else if (definition.type === 'number' && val !== undefined) {
|
||||
val = parseFloat(String(val));
|
||||
} else if (definition.name === 'stopSequences') { // Special handling for stopSequences
|
||||
val = Array.isArray(val) ? val : ( (typeof val === 'string' && val) ? val.split(',').map(s => s.trim()).filter(s => s) : [] );
|
||||
}
|
||||
// All other types, including string, or if val is undefined, will use val as is.
|
||||
paramsObject[definition.name] = val;
|
||||
}
|
||||
}
|
||||
selectedNewLLMParamId.value = ''; // Reset select
|
||||
};
|
||||
|
||||
// 处理快速添加参数（选择后立即添加）
|
||||
const handleQuickAddParam = () => {
|
||||
if (selectedNewLLMParamId.value !== 'custom') {
|
||||
quickAddLLMParam();
|
||||
}
|
||||
// 如果是custom，等待用户输入完成后再添加
|
||||
};
|
||||
|
||||
// 处理自定义参数添加
|
||||
const handleCustomParamAdd = () => {
|
||||
if (customLLMParam.value.key && customLLMParam.value.value) {
|
||||
quickAddLLMParam();
|
||||
}
|
||||
};
|
||||
|
||||
// 取消自定义参数输入
|
||||
const cancelCustomParam = () => {
|
||||
selectedNewLLMParamId.value = '';
|
||||
customLLMParam.value = { key: '', value: '' };
|
||||
};
|
||||
|
||||
// 验证参数是否有效
|
||||
const isParamInvalid = (paramName, value) => {
|
||||
const metadata = getParamMetadata(paramName);
|
||||
// 如果是自定义参数（没有metadata），只做基础验证
|
||||
if (!metadata) {
|
||||
// 对自定义参数进行基础验证：不能为空且不能包含危险字符
|
||||
if (value === undefined || value === null || value === '') return false;
|
||||
|
||||
// 检查是否包含潜在危险的参数名
|
||||
const dangerousNames = ['eval', 'exec', 'script', '__proto__', 'constructor'];
|
||||
if (dangerousNames.some(dangerous => paramName.toLowerCase().includes(dangerous))) {
|
||||
return true;
|
||||
}
|
||||
|
||||
return false;
|
||||
}
|
||||
|
||||
if (value === undefined || value === null || value === '') return false;
|
||||
|
||||
if (metadata.type === 'number' || metadata.type === 'integer') {
|
||||
const numValue = Number(value);
|
||||
if (isNaN(numValue)) return true;
|
||||
|
||||
if (metadata.minValue !== undefined && numValue < metadata.minValue) return true;
|
||||
if (metadata.maxValue !== undefined && numValue > metadata.maxValue) return true;
|
||||
|
||||
if (metadata.type === 'integer' && !Number.isInteger(numValue)) return true;
|
||||
}
|
||||
|
||||
return false;
|
||||
};
|
||||
|
||||
// 获取参数验证错误信息
|
||||
const getParamValidationMessage = (paramName, value) => {
|
||||
const metadata = getParamMetadata(paramName);
|
||||
// 自定义参数的错误信息
|
||||
if (!metadata) {
|
||||
const dangerousNames = ['eval', 'exec', 'script', '__proto__', 'constructor'];
|
||||
if (dangerousNames.some(dangerous => paramName.toLowerCase().includes(dangerous))) {
|
||||
return t('modelManager.advancedParameters.validation.dangerousParam');
|
||||
}
|
||||
return '';
|
||||
}
|
||||
|
||||
if (metadata.type === 'number' || metadata.type === 'integer') {
|
||||
const numValue = Number(value);
|
||||
if (isNaN(numValue)) {
|
||||
return t('modelManager.advancedParameters.validation.invalidNumber', {
|
||||
type: metadata.type === 'integer' ? t('common.integer') : t('common.number')
|
||||
});
|
||||
}
|
||||
|
||||
if (metadata.minValue !== undefined && numValue < metadata.minValue) {
|
||||
return t('modelManager.advancedParameters.validation.belowMin', {
|
||||
min: metadata.minValue
|
||||
});
|
||||
}
|
||||
if (metadata.maxValue !== undefined && numValue > metadata.maxValue) {
|
||||
return t('modelManager.advancedParameters.validation.aboveMax', {
|
||||
max: metadata.maxValue
|
||||
});
|
||||
}
|
||||
|
||||
if (metadata.type === 'integer' && !Number.isInteger(numValue)) {
|
||||
return t('modelManager.advancedParameters.validation.mustBeInteger');
|
||||
}
|
||||
}
|
||||
|
||||
return '';
|
||||
};
|
||||
|
||||
watch(() => newModel.value.key, (newKey) => {
|
||||
// If the key changes and implies a different provider type for a new model,
|
||||
// it might be good to reset llmParams or re-evaluate defaults.
|
||||
// For simplicity now, we can reset if the key change might imply a different context.
|
||||
// This is a basic reset; more sophisticated logic could merge common params if desired.
|
||||
const knownProviders = ['openai', 'gemini', 'deepseek', 'zhipu', 'siliconflow'];
|
||||
let newProvider = 'custom';
|
||||
if (newKey && knownProviders.includes(newKey.toLowerCase())) {
|
||||
newProvider = newKey.toLowerCase();
|
||||
}
|
||||
|
||||
if (newModel.value.provider !== newProvider) {
|
||||
// If provider type effectively changes, reset llmParams.
|
||||
// This helps if user types "openai", then changes mind to "gemini".
|
||||
// The UI will then show relevant default params for "gemini".
|
||||
newModel.value.llmParams = {};
|
||||
newModel.value.provider = newProvider; // Update the provider field too
|
||||
} else if (newModel.value.provider === 'custom' && newProvider === 'custom' && newModel.value.key !== '') {
|
||||
// If still custom but key has changed, it's a new custom model, reset llmParams
|
||||
newModel.value.llmParams = {};
|
||||
}
|
||||
});
|
||||
|
||||
// =============== 生命周期钩子 ===============
|
||||
// 初始化
|
||||
onMounted(() => {
|
||||
@@ -797,4 +1228,12 @@ onMounted(() => {
|
||||
opacity: 0;
|
||||
transform: scale(0.95);
|
||||
}
|
||||
|
||||
/* 文本截断样式 */
|
||||
.line-clamp-2 {
|
||||
display: -webkit-box;
|
||||
-webkit-line-clamp: 2;
|
||||
-webkit-box-orient: vertical;
|
||||
overflow: hidden;
|
||||
}
|
||||
</style>
|
||||
@@ -19,6 +19,7 @@ export default {
|
||||
enabled: 'Enabled',
|
||||
disabled: 'Disabled',
|
||||
add: 'Add',
|
||||
remove: 'Remove',
|
||||
title: 'Title',
|
||||
description: 'Description',
|
||||
lastModified: 'Last Modified',
|
||||
@@ -40,6 +41,8 @@ export default {
|
||||
selectFile: 'Select File',
|
||||
exporting: 'Exporting...',
|
||||
importing: 'Importing...',
|
||||
number: 'Number',
|
||||
integer: 'Integer',
|
||||
},
|
||||
nav: {
|
||||
home: 'Home',
|
||||
@@ -85,6 +88,32 @@ export default {
|
||||
useVercelProxyHint: 'Using Vercel proxy can solve CORS issues, but may trigger risk control from some providers. Please use with caution',
|
||||
addModel: 'Add',
|
||||
|
||||
// Advanced Parameters
|
||||
advancedParameters: {
|
||||
title: 'Advanced Parameters',
|
||||
noParamsConfigured: 'No advanced parameters configured',
|
||||
customParam: 'Custom',
|
||||
add: 'Add Parameter',
|
||||
select: 'Select a parameter',
|
||||
selectTitle: 'Add Advanced Parameter',
|
||||
custom: 'Custom Parameter',
|
||||
customKeyPlaceholder: 'Enter parameter name',
|
||||
customValuePlaceholder: 'Enter parameter value',
|
||||
stopSequencesPlaceholder: 'Enter stop sequences (comma-separated)',
|
||||
unitLabel: 'Unit',
|
||||
currentProvider: 'Current Provider',
|
||||
customProvider: 'Custom',
|
||||
availableParams: 'available parameters',
|
||||
noAvailableParams: 'no available parameters',
|
||||
validation: {
|
||||
dangerousParam: 'This parameter is considered dangerous and is not allowed',
|
||||
invalidNumber: 'Must be a valid number',
|
||||
belowMin: 'Value cannot be less than {min}',
|
||||
aboveMax: 'Value cannot be greater than {max}',
|
||||
mustBeInteger: 'Must be an integer'
|
||||
}
|
||||
},
|
||||
|
||||
// Placeholders
|
||||
modelKeyPlaceholder: 'Enter model key',
|
||||
displayNamePlaceholder: 'Enter display name',
|
||||
@@ -397,5 +426,50 @@ export default {
|
||||
successWithRefresh: 'Data imported successfully, page will refresh to update data'
|
||||
},
|
||||
warning: 'Importing data will overwrite existing history records, model configurations, custom templates and all user settings (including theme, language preferences, etc.). Please ensure you have backed up important data.'
|
||||
},
|
||||
params: {
|
||||
"temperature": {
|
||||
"label": "Temperature",
|
||||
"description": "Controls randomness: Lower values (e.g., 0.2) make the output more focused and deterministic, while higher values (e.g., 0.8) make it more random."
|
||||
},
|
||||
"top_p": {
|
||||
"label": "Top P",
|
||||
"description": "Nucleus sampling. Considers tokens with top P probability mass. E.g., 0.1 means only tokens comprising the top 10% probability mass are considered."
|
||||
},
|
||||
"max_tokens": {
|
||||
"label": "Max Tokens",
|
||||
"description": "Maximum number of tokens to generate in the completion."
|
||||
},
|
||||
"presence_penalty": {
|
||||
"label": "Presence Penalty",
|
||||
"description": "Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics."
|
||||
},
|
||||
"frequency_penalty": {
|
||||
"label": "Frequency Penalty",
|
||||
"description": "Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim."
|
||||
},
|
||||
"timeout": {
|
||||
"label": "Timeout (ms)",
|
||||
"description_openai": "Request timeout in milliseconds for the OpenAI client connection."
|
||||
},
|
||||
"maxOutputTokens": {
|
||||
"label": "Max Output Tokens",
|
||||
"description": "Maximum number of tokens the model can output in a single response."
|
||||
},
|
||||
"top_k": {
|
||||
"label": "Top K",
|
||||
"description": "Filters the next token choices to the K most likely tokens. Helps to reduce nonsensical token generation."
|
||||
},
|
||||
"candidateCount": {
|
||||
"label": "Candidate Count",
|
||||
"description": "Number of generated responses to return. Must be between 1 and 8."
|
||||
},
|
||||
"stopSequences": {
|
||||
"label": "Stop Sequences",
|
||||
"description": "Custom strings that will stop output generation if encountered. Specify multiple sequences separated by commas."
|
||||
},
|
||||
"tokens": {
|
||||
"unit": "tokens"
|
||||
}
|
||||
}
|
||||
};
|
||||
@@ -19,6 +19,7 @@ export default {
|
||||
enabled: '已启用',
|
||||
disabled: '已禁用',
|
||||
add: '添加',
|
||||
remove: '移除',
|
||||
title: '标题',
|
||||
description: '描述',
|
||||
lastModified: '最后修改',
|
||||
@@ -40,6 +41,8 @@ export default {
|
||||
selectFile: '选择文件',
|
||||
exporting: '导出中...',
|
||||
importing: '导入中...',
|
||||
number: '数字',
|
||||
integer: '整数',
|
||||
},
|
||||
nav: {
|
||||
home: '首页',
|
||||
@@ -82,9 +85,35 @@ export default {
|
||||
clickToFetchModels: '点击箭头获取模型列表',
|
||||
apiKey: 'API密钥',
|
||||
useVercelProxy: '使用Vercel代理',
|
||||
useVercelProxyHint: '使用Vercel代理可以解决跨域问题，但可能触发部分厂商风控，请酌情使用',
|
||||
useVercelProxyHint: '使用Vercel代理可以解决跨域问题，但可能触发某些提供商的风控，请谨慎使用',
|
||||
addModel: '添加',
|
||||
|
||||
// 高级参数
|
||||
advancedParameters: {
|
||||
title: '高级参数',
|
||||
noParamsConfigured: '未配置高级参数',
|
||||
customParam: '自定义',
|
||||
add: '添加参数',
|
||||
select: '选择参数',
|
||||
selectTitle: '添加高级参数',
|
||||
custom: '自定义参数',
|
||||
customKeyPlaceholder: '输入参数名称',
|
||||
customValuePlaceholder: '输入参数值',
|
||||
stopSequencesPlaceholder: '输入停止序列（逗号分隔）',
|
||||
unitLabel: '单位',
|
||||
currentProvider: '当前提供商',
|
||||
customProvider: '自定义',
|
||||
availableParams: '个可选参数',
|
||||
noAvailableParams: '无可选参数',
|
||||
validation: {
|
||||
dangerousParam: '此参数名称包含潜在危险字符，不允许使用',
|
||||
invalidNumber: '参数值必须是有效的{type}',
|
||||
belowMin: '参数值不能小于 {min}',
|
||||
aboveMax: '参数值不能大于 {max}',
|
||||
mustBeInteger: '参数值必须是整数'
|
||||
}
|
||||
},
|
||||
|
||||
// 占位符
|
||||
modelKeyPlaceholder: '请输入模型标识',
|
||||
displayNamePlaceholder: '请输入显示名称',
|
||||
@@ -397,5 +426,50 @@ export default {
|
||||
successWithRefresh: '数据导入成功，页面将刷新以更新数据'
|
||||
},
|
||||
warning: '导入数据将覆盖现有的历史记录、模型配置、自定义提示词和所有用户设置（包括主题、语言偏好等），请确保已备份重要数据。'
|
||||
},
|
||||
params: {
|
||||
"temperature": {
|
||||
"label": "温度 (Temperature)",
|
||||
"description": "控制随机性：较低的值（例如0.2）使输出更集中和确定，较高的值（例如0.8）使其更随机。"
|
||||
},
|
||||
"top_p": {
|
||||
"label": "Top P (核心采样)",
|
||||
"description": "核心采样。仅考虑累积概率达到Top P阈值的Token。例如，0.1表示仅考虑构成最高10%概率质量的Token。"
|
||||
},
|
||||
"max_tokens": {
|
||||
"label": "最大Token数",
|
||||
"description": "在补全中生成的最大Token数量。"
|
||||
},
|
||||
"presence_penalty": {
|
||||
"label": "存在惩罚 (Presence Penalty)",
|
||||
"description": "介于-2.0和2.0之间的数字。正值会根据新Token是否已在文本中出现来惩罚它们，增加模型谈论新主题的可能性。"
|
||||
},
|
||||
"frequency_penalty": {
|
||||
"label": "频率惩罚 (Frequency Penalty)",
|
||||
"description": "介于-2.0和2.0之间的数字。正值会根据新Token在文本中已出现的频率来惩罚它们，降低模型逐字重复相同行的可能性。"
|
||||
},
|
||||
"timeout": {
|
||||
"label": "超时时间 (毫秒)",
|
||||
"description_openai": "OpenAI客户端连接的请求超时时间（毫秒）。"
|
||||
},
|
||||
"maxOutputTokens": {
|
||||
"label": "最大输出Token数",
|
||||
"description": "模型在单个响应中可以输出的最大Token数。"
|
||||
},
|
||||
"top_k": {
|
||||
"label": "Top K (K选顶)",
|
||||
"description": "将下一个Token的选择范围限制为K个最可能的Token。有助于减少无意义Token的生成。"
|
||||
},
|
||||
"candidateCount": {
|
||||
"label": "候选数量",
|
||||
"description": "返回的生成响应数量。必须介于1和8之间。"
|
||||
},
|
||||
"stopSequences": {
|
||||
"label": "停止序列",
|
||||
"description": "遇到时将停止输出生成的自定义字符串。用逗号分隔多个序列。"
|
||||
},
|
||||
"tokens": {
|
||||
"unit": "令牌"
|
||||
}
|
||||
}
|
||||
};
|
||||
Reference in new issue
Block a user