{"repo": "bzcasper/dev-projects", "files": {"prompt-optimizer/packages/core/tests/integration/llm/openai.test.js": {"0b4c8ef1f163270c9981bfb72e2fea72a7b74e59": {"t": 1785746673.0333135, "n": 14973, "h": "6f0969ebb344fcec", "c": "import { createLLMService, ModelManager, LocalStorageProvider } from '../../../src/index.js';\nimport { expect, describe, it, beforeEach, beforeAll } from 'vitest';\nimport dotenv from 'dotenv';\nimport path from 'path';\n\n// 加载环境变量\nbeforeAll(() => {\n  dotenv.config({ path: path.resolve(process.cwd(), '.env.local') });\n});\n\ndescribe('OpenAI API 真实连接测试', () => {\n  // 检查OpenAI兼容的环境变量（任何一个存在就可以运行测试）\n  const openaiCompatibleKeys = [\n    'OPENAI_API_KEY', 'VITE_OPENAI_API_KEY',\n    'DEEPSEEK_API_KEY', 'VITE_DEEPSEEK_API_KEY', \n    'SILICONFLOW_API_KEY', 'VITE_SILICONFLOW_API_KEY',\n    'ZHIPU_API_KEY', 'VITE_ZHIPU_API_KEY',\n    'CUSTOM_API_KEY', 'VITE_CUSTOM_API_KEY'\n  ];\n\n  const availableKeys = openaiCompatibleKeys.filter(key => \n    process.env[key] && process.env[key].trim()\n  );\n\n  if (availableKeys.length === 0) {\n    console.log('跳过 OpenAI 真实API测试：未设置任何 OpenAI 兼容的 API 密钥');\n    it.skip('应该能正确调用 OpenAI 兼容的 API', () => {});\n    it.skip('应该能正确处理多轮对话', () => {});\n    it.skip('应该能正确使用高级参数', () => {});\n    return;\n  }\n\n  // 选择第一个可用的密钥和对应的配置\n  const getModelConfig = () => {\n    if (process.env.SILICONFLOW_API_KEY || process.env.VITE_SILICONFLOW_API_KEY) {\n      return {\n        key: 'siliconflow',\n        apiKey: process.env.SILICONFLOW_API_KEY || process.env.VITE_SILICONFLOW_API_KEY,\n        baseURL: 'https://api.siliconflow.cn/v1',\n        defaultModel: 'Qwen/Qwen3-8B'\n      };\n    }\n    if (process.env.OPENAI_API_KEY || process.env.VITE_OPENAI_API_KEY) {\n      return {\n        key: 'openai',\n        apiKey: process.env.OPENAI_API_KEY || process.env.VITE_OPENAI_API_KEY,\n        baseURL: 'https://api.openai.com/v1',\n        defaultModel: 'gpt-3.5-turbo'\n      };\n    }\n    if (process.env.DEEPSEEK_API_KEY || process.env.VITE_DEEPSEEK_API_KEY) {\n      return {\n        key: 'deepseek',\n        apiKey: process.env.DEEPSEEK_API_KEY || process.env.VITE_DEEPSEEK_API_KEY,\n        baseURL: 'https://api.deepseek.com/v1',\n        defaultModel: 'deepseek-chat'\n      };\n    }\n    if (process.env.ZHIPU_API_KEY || process.env.VITE_ZHIPU_API_KEY) {\n      return {\n        key: 'zhipu',\n        apiKey: process.env.ZHIPU_API_KEY || process.env.VITE_ZHIPU_API_KEY,\n        baseURL: 'https://open.bigmodel.cn/api/paas/v4',\n        defaultModel: 'glm-4-flash'\n      };\n    }\n    if (process.env.CUSTOM_API_KEY || process.env.VITE_CUSTOM_API_KEY) {\n      const baseURL = process.env.CUSTOM_API_BASE_URL || process.env.VITE_CUSTOM_API_BASE_URL;\n      const model = process.env.CUSTOM_API_MODEL || process.env.VITE_CUSTOM_API_MODEL;\n      \n      // 只有当baseURL和model都有值时才返回custom配置\n      if (baseURL && model) {\n        return {\n          key: 'custom',\n          apiKey: process.env.CUSTOM_API_KEY || process.env.VITE_CUSTOM_API_KEY,\n          baseURL: baseURL,\n          defaultModel: model\n        };\n      }\n    }\n    return null;\n  };\n\n  const modelConfig = getModelConfig();\n  \n  if (!modelConfig) {\n    console.log('跳过 OpenAI 真实API测试：无有效的模型配置');\n    it.skip('应该能正确调用 OpenAI 兼容的 API', () => {});\n    it.skip('应该能正确处理多轮对话', () => {});\n    it.skip('应该能正确使用高级参数', () => {});\n    return;\n  }\n\n  console.log(`使用 ${modelConfig.key} 进行 OpenAI 兼容 API 测试，模型: ${modelConfig.defaultModel}`);\n\n  it('应该能正确调用 OpenAI 兼容的 API', async () => {\n    const storage = new LocalStorageProvider();\n    const modelManager = new ModelManager(storage);\n    const llmService = createLLMService(modelManager);\n\n    try {\n      // 更新模型配置\n      await modelManager.updateModel(modelConfig.key, {\n        apiKey: modelConfig.apiKey,\n        baseURL: modelConfig.baseURL,\n        defaultModel: modelConfig.defaultModel,\n        enabled: true,\n        provider: 'openai' // 所有兼容提供商都使用openai提供商类型\n      });\n\n      const messages = [\n        { role: 'user', content: '你好，请用一句话介绍你自己' }\n      ];\n\n      const response = await llmService.sendMessage(messages, modelConfig.key);\n      expect(response).toBeDefined();\n      expect(typeof response).toBe('string');\n      expect(response.length).toBeGreaterThan(0);\n    } catch (error) {\n      console.error(`API调用失败 (${modelConfig.key}):`, error.message);\n      // 如果是400错误，可能是配置问题，跳过测试\n      if (error.message.includes('400')) {\n        console.log(`跳过测试：${modelConfig.key} API配置可能有问题`);\n        return;\n      }\n      throw error;\n    }\n  }, 300000);\n\n  it('应该能正确处理多轮对话', async () => {\n    const storage = new LocalStorageProvider();\n    const modelManager = new ModelManager(storage);\n    const llmService = createLLMService(modelManager);\n\n    try {\n      // 更新模型配置\n      await modelManager.updateModel(modelConfig.key, {\n        apiKey: modelConfig.apiKey,\n        baseURL: modelConfig.baseURL,\n        defaultModel: modelConfig.defaultModel,\n        enabled: true,\n        provider: 'openai'\n      });\n\n      const messages = [\n        { role: 'user', content: '你好，我们来玩个游戏' },\n        { role: 'assistant', content: '好啊，你想玩什么游戏？' },\n        { role: 'user', content: '我们来玩猜数字游戏，1到100之间' }\n      ];\n\n      const response = await llmService.sendMessage(messages, modelConfig.key);\n      expect(response).toBeDefined();\n      expect(typeof response).toBe('string');\n      expect(response.length).toBeGreaterThan(0);\n    } catch (error) {\n      console.error(`多轮对话测试失败 (${modelConfig.key}):`, error.message);\n      if (error.message.includes('400')) {\n        console.log(`跳过测试：${modelConfig.key} API配置可能有问题`);\n        return;\n      }\n      throw error;\n    }\n  }, 300000);\n\n  it('应该能正确使用高级参数', async () => {\n    const storage = new LocalStorageProvider();\n    const modelManager = new ModelManager(storage);\n    const llmService = createLLMService(modelManager);\n\n    try {\n      // 更新模型配置，包含高级参数\n      await modelManager.updateModel(modelConfig.key, {\n        apiKey: modelConfig.apiKey,\n        baseURL: modelConfig.baseURL,\n        defaultModel: modelConfig.defaultModel,\n        enabled: true,\n        provider: 'openai',\n        llmParams: {\n          temperature: 0.3,\n          max_tokens: 100\n        }\n      });\n\n      const messages = [\n        { role: 'user', content: '请用一句话回答：什么是人工智能？' }\n      ];\n\n      const response = await llmService.sendMessage(messages, modelConfig.key);\n      expect(response).toBeDefined();\n      expect(typeof response).toBe('string');\n      expect(response.length).toBeGreaterThan(0);\n      // 由于设置了max_tokens=100，响应应该相对较短\n      expect(response.length).toBeLessThan(200);\n    } catch (error) {\n      console.error(`高级参数测试失败 (${modelConfig.key}):`, error.message);\n      if (error.message.includes('400')) {\n        console.log(`跳过测试：${modelConfig.key} API配置可能有问题`);\n        return;\n      }\n      throw error;\n    }\n  }, 300000);\n\n  it('应该能兼容处理所有模型的响应格式（reasoning_content + think标签 + 普通文本）', async () => {\n    const storage = new LocalStorageProvider();\n    const modelManager = new ModelManager(storage);\n    const llmService = createLLMService(modelManager);\n\n    try {\n      // 测试通用兼容性处理\n      await modelManager.updateModel(modelConfig.key, {\n        apiKey: modelConfig.apiKey,\n        baseURL: modelConfig.baseURL,\n        defaultModel: modelConfig.defaultModel,\n        enabled: true,\n        provider: 'openai',\n        llmParams: {\n          temperature: 0.1,\n          max_tokens: 100\n        }\n      });\n\n      const testMessages = [\n        {\n          role: 'user',\n          content: '请简单回答：什么是AI？'\n        }\n      ];\n\n      // 测试非流式处理\n      const result = await llmService.sendMessage(testMessages, modelConfig.key);\n      \n      expect(result).toBeTruthy();\n      expect(typeof result).toBe('string');\n      expect(result.length).toBeGreaterThan(0);\n      \n      console.log('兼容性测试结果:', {\n        hasThinkTags: result.includes('<think>'),\n        hasContent: result.length > 0,\n        result: result\n      });\n\n      // 测试流式处理\n      let streamResult = '';\n      let tokenCount = 0;\n      let isCompleted = false;\n      let hasError = false;\n\n      await llmService.sendMessageStream(testMessages, modelConfig.key, {\n        onToken: (token) => {\n          streamResult += token;\n          tokenCount++;\n        },\n        onComplete: (response) => {\n          isCompleted = true;\n        },\n        onError: (error) => {\n          hasError = true;\n          console.error('流式测试错误:', error);\n        }\n      });\n\n      expect(hasError).toBe(false);\n      expect(isCompleted).toBe(true);\n      expect(streamResult.length).toBeGreaterThan(0);\n      expect(tokenCount).toBeGreaterThan(0);\n\n      console.log('流式兼容性测试结果:', {\n        tokenCount,\n        hasThinkTags: streamResult.includes('<think>'),\n        streamLength: streamResult.length,\n        isCompleted\n      });\n\n    } catch (error) {\n      console.error('兼容性测试失败:', error);\n      throw error;\n    }\n  },300000);\n\n  it('应该能正确处理reasoning_content的流式输出', async () => {\n    const storage = new LocalStorageProvider();\n    const modelManager = new ModelManager(storage);\n    const llmService = createLLMService(modelManager);\n\n    try {\n      // 配置模型\n      await modelManager.updateModel(modelConfig.key, {\n        apiKey: modelConfig.apiKey,\n        baseURL: modelConfig.baseURL,\n        defaultModel: modelConfig.defaultModel,\n        enabled: true,\n        provider: 'openai',\n        llmParams: {\n          temperature: 0.1,\n          max_tokens: 2000\n        }\n      });\n\n      const testMessages = [\n        {\n          role: 'user',\n          content: '你是谁'\n        }\n      ];\n\n      // 模拟包含reasoning_content的流式响应\n      let fullResult = '';\n      let tokenCount = 0;\n      let hasThinkTags = false;\n      let thinkTagsClosed = false;\n      let isCompleted = false;\n      let hasError = false;\n\n      await llmService.sendMessageStream(testMessages, modelConfig.key, {\n        onToken: (token) => {\n          fullResult += token;\n          tokenCount++;\n          \n          // 检查think标签的完整性\n          if (token.includes('<think>')) {\n            hasThinkTags = true;\n          }\n          if (token.includes('</think>')) {\n            thinkTagsClosed = true;\n          }\n        },\n        onComplete: (response) => {\n          isCompleted = true;\n        },\n        onError: (error) => {\n          hasError = true;\n          console.error('流式测试错误:', error);\n        }\n      });\n\n      // 等待流式完成\n      await new Promise(resolve => setTimeout(resolve, 1000));\n\n      console.log('reasoning_content流式测试结果:', {\n        tokenCount,\n        hasThinkTags,\n        thinkTagsClosed,\n        isCompleted,\n        hasError,\n        resultLength: fullResult.length,\n        fullResult: fullResult\n      });\n\n      expect(isCompleted).toBe(true);\n      expect(hasError).toBe(false);\n      expect(tokenCount).toBeGreaterThan(0);\n      expect(fullResult.length).toBeGreaterThan(0);\n      \n      // 如果有think标签，检查它们是否正确闭合\n      const thinkOpenCount = (fullResult.match(/<think>/g) || []).length;\n      const thinkCloseCount = (fullResult.match(/<\\/think>/g) || []).length;\n      \n      if (thinkOpenCount > 0) {\n        expect(thinkOpenCount).toBe(thinkCloseCount);\n        console.log(`✅ Think标签匹配: ${thinkOpenCount} 个开始标签, ${thinkCloseCount} 个结束标签`);\n      }\n\n    } catch (error) {\n      console.error('reasoning_content流式测试失败:', error);\n      throw error;\n    }\n  },300000);\n\n  it('应该能使用结构化API发送消息', async () => {\n    const storage = new LocalStorageProvider();\n    const modelManager = new ModelManager(storage);\n    const llmService = createLLMService(modelManager);\n\n    try {\n      // 配置模型\n      await modelManager.updateModel(modelConfig.key, {\n        apiKey: modelConfig.apiKey,\n        baseURL: modelConfig.baseURL,\n        defaultModel: modelConfig.defaultModel,\n        enabled: true,\n        provider: 'openai',\n        llmParams: {\n          temperature: 0.3,\n          max_tokens: 100\n        }\n      });\n\n      const testMessages = [\n        {\n          role: 'user',\n          content: '请简单回答：什么是AI？'\n        }\n      ];\n\n      // 测试结构化API\n      const response = await llmService.sendMessageStructured(testMessages, modelConfig.key);\n      \n      expect(response).toBeDefined();\n      expect(typeof response).toBe('object');\n      expect(response.content).toBeDefined();\n      expect(typeof response.content).toBe('string');\n      expect(response.content.length).toBeGreaterThan(0);\n      \n      // 检查元数据\n      expect(response.metadata).toBeDefined();\n      expect(response.metadata.model).toBe(modelConfig.defaultModel);\n      \n      console.log('结构化API测试结果:', {\n        hasContent: response.content.length > 0,\n        hasReasoning: !!response.reasoning,\n        content: response.content,\n        reasoning: response.reasoning,\n        model: response.metadata?.model\n      });\n\n    } catch (error) {\n      console.error('结构化API测试失败:', error);\n      throw error;\n    }\n  }, 300000);\n\n  it('应该能使用结构化回调进行流式处理', async () => {\n    const storage = new LocalStorageProvider();\n    const modelManager = new ModelManager(storage);\n    const llmService = createLLMService(modelManager);\n\n    try {\n      // 配置模型\n      await modelManager.updateModel(modelConfig.key, {\n        apiKey: modelConfig.apiKey,\n        baseURL: modelConfig.baseURL,\n        defaultModel: modelConfig.defaultModel,\n        enabled: true,\n        provider: 'openai',\n        llmParams: {\n          temperature: 0.1,\n          max_tokens: 1500\n        }\n      });\n\n      const testMessages = [\n        {\n          role: 'user',\n          content: '请简单回答：什么是AI？'\n        }\n      ];\n\n      let contentTokens = '';\n      let reasoningTokens = '';\n      let finalResponse = null;\n      let contentTokenCount = 0;\n      let reasoningTokenCount = 0;\n      let isCompleted = false;\n      let hasError = false;\n\n      await llmService.sendMessageStream(testMessages, modelConfig.key, {\n        onToken: (token) => {\n          contentTokens += token;\n          contentTokenCount++;\n        },\n        onReasoningToken: (token) => {\n          reasoningTokens += token;\n          reasoningTokenCount++;\n        },\n        onComplete: (response) => {\n          finalResponse = response;\n          isCompleted = true;\n        },\n        onError: (error) => {\n          hasError = true;\n          console.error('结构化流式测试错误:', error);\n        }\n      });\n\n      // 等待流式完成\n      await new Promise(resolve => setTimeout(resolve, 1000));\n\n      console.log('结构化流式测试结果:', {\n        contentTokenCount,\n        reasoningTokenCount,\n        isCompleted,\n        hasError,\n        content: contentTokens,\n        reasoning: reasoningTokens,\n        finalResponse: finalResponse\n      });\n\n      expect(isCompleted).toBe(true);\n      expect(hasError).toBe(false);\n      expect(finalResponse).toBeDefined();\n      expect(finalResponse.content).toBeDefined();\n      expect(contentTokenCount).toBeGreaterThan(0);\n      expect(contentTokens.length).toBeGreaterThan(0);\n      \n      // 验证内容一致性\n      expect(contentTokens).toBe(finalResponse.content);\n      \n      // 如果有推理内容，验证一致性\n      if (reasoningTokenCount > 0) {\n        expect(reasoningTokens).toBe(finalResponse.reasoning || '');\n      }\n\n    } catch (error) {\n      console.error('结构化流式测试失败:', error);\n      throw error;\n    }\n  }, 300000);\n}); "}, "efe8e75ac7c39bb3679157d41d7949388eefe5ee": {"t": 1785766739.266065, "n": 14973, "h": "6f0969ebb344fcec", "c": "import { createLLMService, ModelManager, LocalStorageProvider } from '../../../src/index.js';\nimport { expect, describe, it, beforeEach, beforeAll } from 'vitest';\nimport dotenv from 'dotenv';\nimport path from 'path';\n\n// 加载环境变量\nbeforeAll(() => {\n  dotenv.config({ path: path.resolve(process.cwd(), '.env.local') });\n});\n\ndescribe('OpenAI API 真实连接测试', () => {\n  // 检查OpenAI兼容的环境变量（任何一个存在就可以运行测试）\n  const openaiCompatibleKeys = [\n    'OPENAI_API_KEY', 'VITE_OPENAI_API_KEY',\n    'DEEPSEEK_API_KEY', 'VITE_DEEPSEEK_API_KEY', \n    'SILICONFLOW_API_KEY', 'VITE_SILICONFLOW_API_KEY',\n    'ZHIPU_API_KEY', 'VITE_ZHIPU_API_KEY',\n    'CUSTOM_API_KEY', 'VITE_CUSTOM_API_KEY'\n  ];\n\n  const availableKeys = openaiCompatibleKeys.filter(key => \n    process.env[key] && process.env[key].trim()\n  );\n\n  if (availableKeys.length === 0) {\n    console.log('跳过 OpenAI 真实API测试：未设置任何 OpenAI 兼容的 API 密钥');\n    it.skip('应该能正确调用 OpenAI 兼容的 API', () => {});\n    it.skip('应该能正确处理多轮对话', () => {});\n    it.skip('应该能正确使用高级参数', () => {});\n    return;\n  }\n\n  // 选择第一个可用的密钥和对应的配置\n  const getModelConfig = () => {\n    if (process.env.SILICONFLOW_API_KEY || process.env.VITE_SILICONFLOW_API_KEY) {\n      return {\n        key: 'siliconflow',\n        apiKey: process.env.SILICONFLOW_API_KEY || process.env.VITE_SILICONFLOW_API_KEY,\n        baseURL: 'https://api.siliconflow.cn/v1',\n        defaultModel: 'Qwen/Qwen3-8B'\n      };\n    }\n    if (process.env.OPENAI_API_KEY || process.env.VITE_OPENAI_API_KEY) {\n      return {\n        key: 'openai',\n        apiKey: process.env.OPENAI_API_KEY || process.env.VITE_OPENAI_API_KEY,\n        baseURL: 'https://api.openai.com/v1',\n        defaultModel: 'gpt-3.5-turbo'\n      };\n    }\n    if (process.env.DEEPSEEK_API_KEY || process.env.VITE_DEEPSEEK_API_KEY) {\n      return {\n        key: 'deepseek',\n        apiKey: process.env.DEEPSEEK_API_KEY || process.env.VITE_DEEPSEEK_API_KEY,\n        baseURL: 'https://api.deepseek.com/v1',\n        defaultModel: 'deepseek-chat'\n      };\n    }\n    if (process.env.ZHIPU_API_KEY || process.env.VITE_ZHIPU_API_KEY) {\n      return {\n        key: 'zhipu',\n        apiKey: process.env.ZHIPU_API_KEY || process.env.VITE_ZHIPU_API_KEY,\n        baseURL: 'https://open.bigmodel.cn/api/paas/v4',\n        defaultModel: 'glm-4-flash'\n      };\n    }\n    if (process.env.CUSTOM_API_KEY || process.env.VITE_CUSTOM_API_KEY) {\n      const baseURL = process.env.CUSTOM_API_BASE_URL || process.env.VITE_CUSTOM_API_BASE_URL;\n      const model = process.env.CUSTOM_API_MODEL || process.env.VITE_CUSTOM_API_MODEL;\n      \n      // 只有当baseURL和model都有值时才返回custom配置\n      if (baseURL && model) {\n        return {\n          key: 'custom',\n          apiKey: process.env.CUSTOM_API_KEY || process.env.VITE_CUSTOM_API_KEY,\n          baseURL: baseURL,\n          defaultModel: model\n        };\n      }\n    }\n    return null;\n  };\n\n  const modelConfig = getModelConfig();\n  \n  if (!modelConfig) {\n    console.log('跳过 OpenAI 真实API测试：无有效的模型配置');\n    it.skip('应该能正确调用 OpenAI 兼容的 API', () => {});\n    it.skip('应该能正确处理多轮对话', () => {});\n    it.skip('应该能正确使用高级参数', () => {});\n    return;\n  }\n\n  console.log(`使用 ${modelConfig.key} 进行 OpenAI 兼容 API 测试，模型: ${modelConfig.defaultModel}`);\n\n  it('应该能正确调用 OpenAI 兼容的 API', async () => {\n    const storage = new LocalStorageProvider();\n    const modelManager = new ModelManager(storage);\n    const llmService = createLLMService(modelManager);\n\n    try {\n      // 更新模型配置\n      await modelManager.updateModel(modelConfig.key, {\n        apiKey: modelConfig.apiKey,\n        baseURL: modelConfig.baseURL,\n        defaultModel: modelConfig.defaultModel,\n        enabled: true,\n        provider: 'openai' // 所有兼容提供商都使用openai提供商类型\n      });\n\n      const messages = [\n        { role: 'user', content: '你好，请用一句话介绍你自己' }\n      ];\n\n      const response = await llmService.sendMessage(messages, modelConfig.key);\n      expect(response).toBeDefined();\n      expect(typeof response).toBe('string');\n      expect(response.length).toBeGreaterThan(0);\n    } catch (error) {\n      console.error(`API调用失败 (${modelConfig.key}):`, error.message);\n      // 如果是400错误，可能是配置问题，跳过测试\n      if (error.message.includes('400')) {\n        console.log(`跳过测试：${modelConfig.key} API配置可能有问题`);\n        return;\n      }\n      throw error;\n    }\n  }, 300000);\n\n  it('应该能正确处理多轮对话', async () => {\n    const storage = new LocalStorageProvider();\n    const modelManager = new ModelManager(storage);\n    const llmService = createLLMService(modelManager);\n\n    try {\n      // 更新模型配置\n      await modelManager.updateModel(modelConfig.key, {\n        apiKey: modelConfig.apiKey,\n        baseURL: modelConfig.baseURL,\n        defaultModel: modelConfig.defaultModel,\n        enabled: true,\n        provider: 'openai'\n      });\n\n      const messages = [\n        { role: 'user', content: '你好，我们来玩个游戏' },\n        { role: 'assistant', content: '好啊，你想玩什么游戏？' },\n        { role: 'user', content: '我们来玩猜数字游戏，1到100之间' }\n      ];\n\n      const response = await llmService.sendMessage(messages, modelConfig.key);\n      expect(response).toBeDefined();\n      expect(typeof response).toBe('string');\n      expect(response.length).toBeGreaterThan(0);\n    } catch (error) {\n      console.error(`多轮对话测试失败 (${modelConfig.key}):`, error.message);\n      if (error.message.includes('400')) {\n        console.log(`跳过测试：${modelConfig.key} API配置可能有问题`);\n        return;\n      }\n      throw error;\n    }\n  }, 300000);\n\n  it('应该能正确使用高级参数', async () => {\n    const storage = new LocalStorageProvider();\n    const modelManager = new ModelManager(storage);\n    const llmService = createLLMService(modelManager);\n\n    try {\n      // 更新模型配置，包含高级参数\n      await modelManager.updateModel(modelConfig.key, {\n        apiKey: modelConfig.apiKey,\n        baseURL: modelConfig.baseURL,\n        defaultModel: modelConfig.defaultModel,\n        enabled: true,\n        provider: 'openai',\n        llmParams: {\n          temperature: 0.3,\n          max_tokens: 100\n        }\n      });\n\n      const messages = [\n        { role: 'user', content: '请用一句话回答：什么是人工智能？' }\n      ];\n\n      const response = await llmService.sendMessage(messages, modelConfig.key);\n      expect(response).toBeDefined();\n      expect(typeof response).toBe('string');\n      expect(response.length).toBeGreaterThan(0);\n      // 由于设置了max_tokens=100，响应应该相对较短\n      expect(response.length).toBeLessThan(200);\n    } catch (error) {\n      console.error(`高级参数测试失败 (${modelConfig.key}):`, error.message);\n      if (error.message.includes('400')) {\n        console.log(`跳过测试：${modelConfig.key} API配置可能有问题`);\n        return;\n      }\n      throw error;\n    }\n  }, 300000);\n\n  it('应该能兼容处理所有模型的响应格式（reasoning_content + think标签 + 普通文本）', async () => {\n    const storage = new LocalStorageProvider();\n    const modelManager = new ModelManager(storage);\n    const llmService = createLLMService(modelManager);\n\n    try {\n      // 测试通用兼容性处理\n      await modelManager.updateModel(modelConfig.key, {\n        apiKey: modelConfig.apiKey,\n        baseURL: modelConfig.baseURL,\n        defaultModel: modelConfig.defaultModel,\n        enabled: true,\n        provider: 'openai',\n        llmParams: {\n          temperature: 0.1,\n          max_tokens: 100\n        }\n      });\n\n      const testMessages = [\n        {\n          role: 'user',\n          content: '请简单回答：什么是AI？'\n        }\n      ];\n\n      // 测试非流式处理\n      const result = await llmService.sendMessage(testMessages, modelConfig.key);\n      \n      expect(result).toBeTruthy();\n      expect(typeof result).toBe('string');\n      expect(result.length).toBeGreaterThan(0);\n      \n      console.log('兼容性测试结果:', {\n        hasThinkTags: result.includes('<think>'),\n        hasContent: result.length > 0,\n        result: result\n      });\n\n      // 测试流式处理\n      let streamResult = '';\n      let tokenCount = 0;\n      let isCompleted = false;\n      let hasError = false;\n\n      await llmService.sendMessageStream(testMessages, modelConfig.key, {\n        onToken: (token) => {\n          streamResult += token;\n          tokenCount++;\n        },\n        onComplete: (response) => {\n          isCompleted = true;\n        },\n        onError: (error) => {\n          hasError = true;\n          console.error('流式测试错误:', error);\n        }\n      });\n\n      expect(hasError).toBe(false);\n      expect(isCompleted).toBe(true);\n      expect(streamResult.length).toBeGreaterThan(0);\n      expect(tokenCount).toBeGreaterThan(0);\n\n      console.log('流式兼容性测试结果:', {\n        tokenCount,\n        hasThinkTags: streamResult.includes('<think>'),\n        streamLength: streamResult.length,\n        isCompleted\n      });\n\n    } catch (error) {\n      console.error('兼容性测试失败:', error);\n      throw error;\n    }\n  },300000);\n\n  it('应该能正确处理reasoning_content的流式输出', async () => {\n    const storage = new LocalStorageProvider();\n    const modelManager = new ModelManager(storage);\n    const llmService = createLLMService(modelManager);\n\n    try {\n      // 配置模型\n      await modelManager.updateModel(modelConfig.key, {\n        apiKey: modelConfig.apiKey,\n        baseURL: modelConfig.baseURL,\n        defaultModel: modelConfig.defaultModel,\n        enabled: true,\n        provider: 'openai',\n        llmParams: {\n          temperature: 0.1,\n          max_tokens: 2000\n        }\n      });\n\n      const testMessages = [\n        {\n          role: 'user',\n          content: '你是谁'\n        }\n      ];\n\n      // 模拟包含reasoning_content的流式响应\n      let fullResult = '';\n      let tokenCount = 0;\n      let hasThinkTags = false;\n      let thinkTagsClosed = false;\n      let isCompleted = false;\n      let hasError = false;\n\n      await llmService.sendMessageStream(testMessages, modelConfig.key, {\n        onToken: (token) => {\n          fullResult += token;\n          tokenCount++;\n          \n          // 检查think标签的完整性\n          if (token.includes('<think>')) {\n            hasThinkTags = true;\n          }\n          if (token.includes('</think>')) {\n            thinkTagsClosed = true;\n          }\n        },\n        onComplete: (response) => {\n          isCompleted = true;\n        },\n        onError: (error) => {\n          hasError = true;\n          console.error('流式测试错误:', error);\n        }\n      });\n\n      // 等待流式完成\n      await new Promise(resolve => setTimeout(resolve, 1000));\n\n      console.log('reasoning_content流式测试结果:', {\n        tokenCount,\n        hasThinkTags,\n        thinkTagsClosed,\n        isCompleted,\n        hasError,\n        resultLength: fullResult.length,\n        fullResult: fullResult\n      });\n\n      expect(isCompleted).toBe(true);\n      expect(hasError).toBe(false);\n      expect(tokenCount).toBeGreaterThan(0);\n      expect(fullResult.length).toBeGreaterThan(0);\n      \n      // 如果有think标签，检查它们是否正确闭合\n      const thinkOpenCount = (fullResult.match(/<think>/g) || []).length;\n      const thinkCloseCount = (fullResult.match(/<\\/think>/g) || []).length;\n      \n      if (thinkOpenCount > 0) {\n        expect(thinkOpenCount).toBe(thinkCloseCount);\n        console.log(`✅ Think标签匹配: ${thinkOpenCount} 个开始标签, ${thinkCloseCount} 个结束标签`);\n      }\n\n    } catch (error) {\n      console.error('reasoning_content流式测试失败:', error);\n      throw error;\n    }\n  },300000);\n\n  it('应该能使用结构化API发送消息', async () => {\n    const storage = new LocalStorageProvider();\n    const modelManager = new ModelManager(storage);\n    const llmService = createLLMService(modelManager);\n\n    try {\n      // 配置模型\n      await modelManager.updateModel(modelConfig.key, {\n        apiKey: modelConfig.apiKey,\n        baseURL: modelConfig.baseURL,\n        defaultModel: modelConfig.defaultModel,\n        enabled: true,\n        provider: 'openai',\n        llmParams: {\n          temperature: 0.3,\n          max_tokens: 100\n        }\n      });\n\n      const testMessages = [\n        {\n          role: 'user',\n          content: '请简单回答：什么是AI？'\n        }\n      ];\n\n      // 测试结构化API\n      const response = await llmService.sendMessageStructured(testMessages, modelConfig.key);\n      \n      expect(response).toBeDefined();\n      expect(typeof response).toBe('object');\n      expect(response.content).toBeDefined();\n      expect(typeof response.content).toBe('string');\n      expect(response.content.length).toBeGreaterThan(0);\n      \n      // 检查元数据\n      expect(response.metadata).toBeDefined();\n      expect(response.metadata.model).toBe(modelConfig.defaultModel);\n      \n      console.log('结构化API测试结果:', {\n        hasContent: response.content.length > 0,\n        hasReasoning: !!response.reasoning,\n        content: response.content,\n        reasoning: response.reasoning,\n        model: response.metadata?.model\n      });\n\n    } catch (error) {\n      console.error('结构化API测试失败:', error);\n      throw error;\n    }\n  }, 300000);\n\n  it('应该能使用结构化回调进行流式处理', async () => {\n    const storage = new LocalStorageProvider();\n    const modelManager = new ModelManager(storage);\n    const llmService = createLLMService(modelManager);\n\n    try {\n      // 配置模型\n      await modelManager.updateModel(modelConfig.key, {\n        apiKey: modelConfig.apiKey,\n        baseURL: modelConfig.baseURL,\n        defaultModel: modelConfig.defaultModel,\n        enabled: true,\n        provider: 'openai',\n        llmParams: {\n          temperature: 0.1,\n          max_tokens: 1500\n        }\n      });\n\n      const testMessages = [\n        {\n          role: 'user',\n          content: '请简单回答：什么是AI？'\n        }\n      ];\n\n      let contentTokens = '';\n      let reasoningTokens = '';\n      let finalResponse = null;\n      let contentTokenCount = 0;\n      let reasoningTokenCount = 0;\n      let isCompleted = false;\n      let hasError = false;\n\n      await llmService.sendMessageStream(testMessages, modelConfig.key, {\n        onToken: (token) => {\n          contentTokens += token;\n          contentTokenCount++;\n        },\n        onReasoningToken: (token) => {\n          reasoningTokens += token;\n          reasoningTokenCount++;\n        },\n        onComplete: (response) => {\n          finalResponse = response;\n          isCompleted = true;\n        },\n        onError: (error) => {\n          hasError = true;\n          console.error('结构化流式测试错误:', error);\n        }\n      });\n\n      // 等待流式完成\n      await new Promise(resolve => setTimeout(resolve, 1000));\n\n      console.log('结构化流式测试结果:', {\n        contentTokenCount,\n        reasoningTokenCount,\n        isCompleted,\n        hasError,\n        content: contentTokens,\n        reasoning: reasoningTokens,\n        finalResponse: finalResponse\n      });\n\n      expect(isCompleted).toBe(true);\n      expect(hasError).toBe(false);\n      expect(finalResponse).toBeDefined();\n      expect(finalResponse.content).toBeDefined();\n      expect(contentTokenCount).toBeGreaterThan(0);\n      expect(contentTokens.length).toBeGreaterThan(0);\n      \n      // 验证内容一致性\n      expect(contentTokens).toBe(finalResponse.content);\n      \n      // 如果有推理内容，验证一致性\n      if (reasoningTokenCount > 0) {\n        expect(reasoningTokens).toBe(finalResponse.reasoning || '');\n      }\n\n    } catch (error) {\n      console.error('结构化流式测试失败:', error);\n      throw error;\n    }\n  }, 300000);\n}); "}}, "prompt-optimizer/docker/generate-config.sh": {"0b4c8ef1f163270c9981bfb72e2fea72a7b74e59": {"t": 1786068350.498124, "n": 1782, "h": "592e0728a5f543aa", "c": "#!/bin/sh\n\n# 配置文件路径\nCONFIG_FILE=\"/usr/share/nginx/html/config.js\"\n\necho \"开始生成运行时配置文件...\"\n\n# 扫描动态自定义模型环境变量\necho \"扫描动态自定义模型环境变量...\"\nDYNAMIC_CONFIG=\"\"\nDYNAMIC_COUNT=0\n\n# 扫描所有VITE_CUSTOM_API_*_suffix模式的环境变量\nfor var in $(env | grep '^VITE_CUSTOM_API_.*_[^=]*=' | cut -d= -f1); do\n  # 验证环境变量名格式\n  if echo \"$var\" | grep -qE '^VITE_CUSTOM_API_(KEY|BASE_URL|MODEL)_[a-zA-Z0-9_-]+$'; then\n    # 提取配置类型和后缀\n    config_type=$(echo \"$var\" | sed 's/^VITE_CUSTOM_API_\\([^_]*\\)_.*$/\\1/')\n    suffix=$(echo \"$var\" | sed 's/^VITE_CUSTOM_API_[^_]*_\\(.*\\)$/\\1/')\n\n    # 获取环境变量值（安全方式）\n    value=$(printenv \"$var\" 2>/dev/null)\n\n    if [ -n \"$value\" ]; then\n      # 移除VITE_前缀生成运行时配置键名\n      runtime_key=$(echo \"$var\" | sed 's/^VITE_//')\n\n      # 简单有效的转义：只转义必要的字符\n      # - 反斜杠、双引号\n      escaped_value=$(printf '%s' \"$value\" | sed 's/\\\\/\\\\\\\\/g; s/\"/\\\\\"/g')\n\n      # 添加到动态配置中\n      if [ -n \"$DYNAMIC_CONFIG\" ]; then\n        DYNAMIC_CONFIG=\"$DYNAMIC_CONFIG,\"\n      fi\n      DYNAMIC_CONFIG=\"$DYNAMIC_CONFIG\n  $runtime_key: \\\"$escaped_value\\\"\"\n\n      DYNAMIC_COUNT=$((DYNAMIC_COUNT + 1))\n      echo \"Found: $runtime_key\"\n    fi\n  fi\ndone\n\necho \"Found $DYNAMIC_COUNT dynamic custom model configurations\"\n\n# 生成配置文件\ncat > $CONFIG_FILE << EOF\nwindow.runtime_config = {\n  OPENAI_API_KEY: \"${VITE_OPENAI_API_KEY:-}\",\n  GEMINI_API_KEY: \"${VITE_GEMINI_API_KEY:-}\",\n  DEEPSEEK_API_KEY: \"${VITE_DEEPSEEK_API_KEY:-}\",\n  ZHIPU_API_KEY: \"${VITE_ZHIPU_API_KEY:-}\",\n  SILICONFLOW_API_KEY: \"${VITE_SILICONFLOW_API_KEY:-}\",\n  CUSTOM_API_KEY: \"${VITE_CUSTOM_API_KEY:-}\",\n  CUSTOM_API_BASE_URL: \"${VITE_CUSTOM_API_BASE_URL:-}\",\n  CUSTOM_API_MODEL: \"${VITE_CUSTOM_API_MODEL:-}\"${DYNAMIC_CONFIG:+,$DYNAMIC_CONFIG}\n};\nconsole.log(\"运行时配置已加载，包含 $DYNAMIC_COUNT 个动态自定义模型\");\nEOF\n\necho \"配置文件已生成: $CONFIG_FILE\"\necho \"动态配置: $DYNAMIC_COUNT 项\""}}}}