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Expected: Bearer \" };\n }\n \n const token = parts[1];\n if (!AUTH_KEYS.includes(token)) {\n return { success: false, error: \"Invalid API key\" };\n }\n \n return { success: true };\n}\n\nfunction createErrorResponse(status: number, message: string, type: string = \"invalid_request_error\"): Response {\n return new Response(JSON.stringify({\n error: {\n message,\n type,\n param: null,\n code: null\n }\n }), {\n status,\n headers: {\n \"Content-Type\": \"application/json\",\n \"Access-Control-Allow-Origin\": \"*\",\n }\n });\n}\n\n// ==================== 模型列表处理 ====================\n\nfunction handleModelsRequest(): Response {\n const timestamp = Math.floor(Date.now() / 1000);\n \n const models: Array<{ id: string; object: string; created: number; owned_by: string }> = [];\n \n for (const model of MODELS) {\n models.push({\n id: model.name,\n object: \"model\",\n created: timestamp,\n owned_by: \"stockai\"\n });\n }\n \n return new Response(JSON.stringify({\n object: \"list\",\n data: models\n }), {\n status: 200,\n headers: {\n \"Content-Type\": \"application/json\",\n \"Access-Control-Allow-Origin\": \"*\",\n }\n });\n}\n\n// ==================== 模型名称映射 ====================\n\nfunction getModelValue(modelName: string): string | null {\n const model = MODELS.find(m => m.name === modelName);\n return model ? model.value : null;\n}\n\nfunction isSearchModel(modelValue: string): boolean {\n return modelValue.includes(\"-search\");\n}\n\n// ==================== 消息格式化 ====================\n\ninterface Message {\n role: string;\n content: string | Array<{ type: string; text?: string; image_url?: { url: string } }>;\n}\n\nfunction formatMessagesToSingleText(messages: Message[]): string {\n return messages.map(msg => {\n let content: string;\n if (typeof msg.content === \"string\") {\n content = msg.content;\n } else if (Array.isArray(msg.content)) {\n content = msg.content\n .filter(item => item.type === \"text\" && item.text)\n .map(item => item.text)\n .join(\" \");\n } else {\n content = \"\";\n }\n return `${msg.role}: ${content}`;\n }).join(\"\\n\");\n}\n\n// ==================== SSE 解析器 ====================\n\ninterface SSEEvent {\n type: string;\n id?: string;\n delta?: string;\n data?: string;\n messageId?: string;\n toolCallId?: string;\n toolName?: string;\n input?: unknown;\n output?: unknown;\n inputTextDelta?: string;\n}\n\nfunction parseSSELine(line: string): SSEEvent | null {\n if (!line.startsWith(\"data: \")) {\n return null;\n }\n \n const data = line.slice(6);\n if (data === \"[DONE]\") {\n return { type: \"done\" };\n }\n \n try {\n return JSON.parse(data) as SSEEvent;\n } catch {\n return null;\n }\n}\n\n// ==================== 工具调用格式化 ====================\n\nfunction formatToolCall(toolName: string, input: unknown): string {\n let markdown = `\\n\\n---\\n**🔧 工具调用: ${toolName}**\\n\\n`;\n \n if (input && typeof input === \"object\") {\n const inputObj = input as Record;\n markdown += \"**参数:**\\n\";\n for (const [key, value] of Object.entries(inputObj)) {\n markdown += `- \\`${key}\\`: ${JSON.stringify(value)}\\n`;\n }\n }\n \n return markdown;\n}\n\nfunction formatToolOutput(toolName: string, output: unknown): string {\n let markdown = `\\n**📋 ${toolName} 结果:**\\n\\n`;\n \n if (output && typeof output === \"object\") {\n const outputObj = output as { content?: Array<{ type: string; text?: string }>, isError?: boolean };\n \n if (outputObj.isError) {\n markdown += \"⚠️ 工具执行出错\\n\";\n }\n \n if (outputObj.content && Array.isArray(outputObj.content)) {\n for (const item of outputObj.content) {\n if (item.type === \"text\" && item.text) {\n // 提取关键信息，格式化输出\n const text = item.text;\n // 尝试解析标题和URL\n const titleMatch = text.match(/Title: (.+?)(?:\\n|$)/);\n const urlMatch = text.match(/URL: (.+?)(?:\\n|$)/);\n \n if (titleMatch && urlMatch) {\n markdown += `- [${titleMatch[1]}](${urlMatch[1]})\\n`;\n } else {\n // 截取前200字符避免过长\n markdown += text.substring(0, 200) + (text.length > 200 ? \"...\" : \"\") + \"\\n\\n\";\n }\n }\n }\n }\n }\n \n markdown += \"\\n---\\n\\n\";\n return markdown;\n}\n\n// ==================== 聊天请求处理 ====================\n\ninterface ChatRequest {\n model: string;\n messages: Message[];\n stream?: boolean;\n temperature?: number;\n max_tokens?: number;\n}\n\nasync function handleChatCompletions(request: Request): Promise {\n let requestBody: ChatRequest;\n \n try {\n requestBody = await request.json();\n } catch {\n return createErrorResponse(400, \"Invalid JSON in request body\");\n }\n \n const { model, messages, stream = false } = requestBody;\n \n if (!model) {\n return createErrorResponse(400, \"Missing 'model' field\");\n }\n \n if (!messages || !Array.isArray(messages) || messages.length === 0) {\n return createErrorResponse(400, \"Missing or invalid 'messages' field\");\n }\n \n // 获取模型对应的 value\n const modelValue = getModelValue(model);\n if (!modelValue) {\n return createErrorResponse(400, `Unknown model: ${model}. Please use model name from /v1/models`);\n }\n \n const webSearch = isSearchModel(modelValue);\n \n log(`Model: ${model}, Value: ${modelValue}, WebSearch: ${webSearch}, Stream: ${stream}`);\n \n // 格式化消息为单条文本\n const formattedMessage = formatMessagesToSingleText(messages);\n log(\"Formatted message:\", formattedMessage.substring(0, 100) + \"...\");\n \n // 获取随机 UA\n const userAgent = getRandomUserAgent();\n log(\"Using User-Agent:\", userAgent);\n \n try {\n // 构造请求体\n const requestId = generateRandomId(16);\n const messageId = generateRandomId(16);\n \n const stockaiRequest = {\n model: modelValue,\n webSearch: webSearch,\n id: requestId,\n messages: [{\n parts: [{\n type: \"text\",\n text: formattedMessage\n }],\n id: messageId,\n role: \"user\"\n }],\n trigger: \"submit-message\"\n };\n \n log(\"Sending request to StockAI...\");\n log(\"Request body:\", JSON.stringify(stockaiRequest));\n \n // 发送请求\n const chatResponse = await fetch(`${BASE_URL}/api/chat`, {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n \"user-agent\": AI_SDK_UA,\n \"User-Agent\": userAgent,\n \"Referer\": `${BASE_URL}/`,\n },\n body: JSON.stringify(stockaiRequest)\n });\n \n if (!chatResponse.ok) {\n const errorText = await chatResponse.text();\n logError(\"StockAI API error:\", chatResponse.status, errorText);\n return createErrorResponse(502, `Upstream API error: ${chatResponse.status}`);\n }\n \n // 处理 SSE 响应\n if (stream) {\n return await handleStreamResponse(chatResponse, model);\n } else {\n return await handleNonStreamResponse(chatResponse, model);\n }\n \n } catch (error) {\n logError(\"Error processing chat request:\", error);\n return createErrorResponse(500, `Internal server error: ${error instanceof Error ? error.message : \"Unknown error\"}`);\n }\n}\n\n// ==================== 流式响应处理 ====================\n\nasync function handleStreamResponse(response: Response, model: string): Promise {\n const responseId = `chatcmpl-${generateUUID()}`;\n const timestamp = Math.floor(Date.now() / 1000);\n \n const encoder = new TextEncoder();\n const decoder = new TextDecoder();\n \n const reader = response.body?.getReader();\n if (!reader) {\n return createErrorResponse(500, \"Failed to read response stream\");\n }\n \n let buffer = \"\";\n let reasoningContent = \"\";\n let textContent = \"\";\n let toolCallInfo = \"\";\n let isReasoningPhase = false;\n let isTextPhase = false;\n let sentRoleChunk = false;\n let currentToolName = \"\";\n let currentToolInput = \"\";\n \n const stream = new ReadableStream({\n async start(controller) {\n try {\n // 发送首个块\n const firstChunk = {\n id: responseId,\n object: \"chat.completion.chunk\",\n created: timestamp,\n model: model,\n choices: [{\n index: 0,\n delta: {\n role: \"assistant\",\n content: \"\"\n },\n logprobs: null,\n finish_reason: null\n }]\n };\n controller.enqueue(encoder.encode(`data: ${JSON.stringify(firstChunk)}\\n\\n`));\n sentRoleChunk = true;\n \n while (true) {\n const { done, value } = await reader.read();\n if (done) break;\n \n buffer += decoder.decode(value, { stream: true });\n const lines = buffer.split(\"\\n\");\n buffer = lines.pop() || \"\";\n \n for (const line of lines) {\n if (!line.trim()) continue;\n \n const event = parseSSELine(line);\n if (!event) continue;\n \n log(\"SSE Event:\", event.type, event);\n \n switch (event.type) {\n case \"reasoning-start\":\n isReasoningPhase = true;\n break;\n \n case \"reasoning-delta\":\n if (event.delta) {\n reasoningContent += event.delta;\n // 发送思考内容\n const reasoningChunk = {\n id: responseId,\n object: \"chat.completion.chunk\",\n created: timestamp,\n model: model,\n choices: [{\n index: 0,\n delta: {\n content: null,\n reasoning_content: event.delta\n },\n logprobs: null,\n finish_reason: null\n }]\n };\n controller.enqueue(encoder.encode(`data: ${JSON.stringify(reasoningChunk)}\\n\\n`));\n }\n break;\n \n case \"reasoning-end\":\n isReasoningPhase = false;\n break;\n \n case \"text-start\":\n isTextPhase = true;\n // 如果有工具调用信息，先发送\n if (toolCallInfo) {\n const toolChunk = {\n id: responseId,\n object: \"chat.completion.chunk\",\n created: timestamp,\n model: model,\n choices: [{\n index: 0,\n delta: {\n content: toolCallInfo\n },\n logprobs: null,\n finish_reason: null\n }]\n };\n controller.enqueue(encoder.encode(`data: ${JSON.stringify(toolChunk)}\\n\\n`));\n toolCallInfo = \"\";\n }\n break;\n \n case \"text-delta\":\n if (event.delta) {\n textContent += event.delta;\n const textChunk = {\n id: responseId,\n object: \"chat.completion.chunk\",\n created: timestamp,\n model: model,\n choices: [{\n index: 0,\n delta: {\n content: event.delta\n },\n logprobs: null,\n finish_reason: null\n }]\n };\n controller.enqueue(encoder.encode(`data: ${JSON.stringify(textChunk)}\\n\\n`));\n }\n break;\n \n case \"text-end\":\n isTextPhase = false;\n break;\n \n case \"tool-input-start\":\n currentToolName = event.toolName || \"\";\n currentToolInput = \"\";\n break;\n \n case \"tool-input-delta\":\n if (event.inputTextDelta) {\n currentToolInput += event.inputTextDelta;\n }\n break;\n \n case \"tool-input-available\":\n if (event.toolName && event.input) {\n toolCallInfo += formatToolCall(event.toolName, event.input);\n }\n break;\n \n case \"tool-output-available\":\n if (event.toolName && event.output) {\n toolCallInfo += formatToolOutput(event.toolName, event.output);\n }\n break;\n \n case \"data-text\":\n // 处理 stockai/news 等模型的特殊响应格式\n if (event.data && event.data !== \"Loading...\") {\n const dataChunk = {\n id: responseId,\n object: \"chat.completion.chunk\",\n created: timestamp,\n model: model,\n choices: [{\n index: 0,\n delta: {\n content: event.data\n },\n logprobs: null,\n finish_reason: null\n }]\n };\n controller.enqueue(encoder.encode(`data: ${JSON.stringify(dataChunk)}\\n\\n`));\n }\n break;\n \n case \"done\":\n case \"finish\":\n // 发送结束块\n const finishChunk = {\n id: responseId,\n object: \"chat.completion.chunk\",\n created: timestamp,\n model: model,\n choices: [{\n index: 0,\n delta: {},\n logprobs: null,\n finish_reason: \"stop\"\n }],\n usage: {\n prompt_tokens: 0,\n completion_tokens: textContent.length + reasoningContent.length,\n total_tokens: textContent.length + reasoningContent.length,\n completion_tokens_details: {\n reasoning_tokens: reasoningContent.length\n }\n }\n };\n controller.enqueue(encoder.encode(`data: ${JSON.stringify(finishChunk)}\\n\\n`));\n controller.enqueue(encoder.encode(\"data: [DONE]\\n\\n\"));\n break;\n }\n }\n }\n \n controller.close();\n } catch (error) {\n logError(\"Stream error:\", error);\n controller.error(error);\n }\n }\n });\n \n return new Response(stream, {\n status: 200,\n headers: {\n \"Content-Type\": \"text/event-stream\",\n \"Cache-Control\": \"no-cache\",\n \"Connection\": \"keep-alive\",\n \"Access-Control-Allow-Origin\": \"*\",\n }\n });\n}\n\n// ==================== 非流式响应处理 ====================\n\nasync function handleNonStreamResponse(response: Response, model: string): Promise {\n const responseId = `chatcmpl-${generateUUID()}`;\n const timestamp = Math.floor(Date.now() / 1000);\n \n const decoder = new TextDecoder();\n const reader = response.body?.getReader();\n \n if (!reader) {\n return createErrorResponse(500, \"Failed to read response stream\");\n }\n \n let buffer = \"\";\n let reasoningContent = \"\";\n let textContent = \"\";\n let toolCallInfo = \"\";\n let currentToolName = \"\";\n let currentToolInput = \"\";\n \n // 读取所有 SSE 事件\n while (true) {\n const { done, value } = await reader.read();\n if (done) break;\n \n buffer += decoder.decode(value, { stream: true });\n const lines = buffer.split(\"\\n\");\n buffer = lines.pop() || \"\";\n \n for (const line of lines) {\n if (!line.trim()) continue;\n \n const event = parseSSELine(line);\n if (!event) continue;\n \n log(\"SSE Event (non-stream):\", event.type);\n \n switch (event.type) {\n case \"reasoning-delta\":\n if (event.delta) {\n reasoningContent += event.delta;\n }\n break;\n \n case \"text-delta\":\n if (event.delta) {\n textContent += event.delta;\n }\n break;\n \n case \"tool-input-available\":\n if (event.toolName && event.input) {\n toolCallInfo += formatToolCall(event.toolName, event.input);\n }\n break;\n \n case \"tool-output-available\":\n if (event.toolName && event.output) {\n toolCallInfo += formatToolOutput(event.toolName, event.output);\n }\n break;\n \n case \"data-text\":\n // 处理 stockai/news 等模型的特殊响应格式\n if (event.data && event.data !== \"Loading...\") {\n textContent += event.data;\n }\n break;\n }\n }\n }\n \n // 组合最终内容：工具调用信息 + 正式回复\n const finalContent = toolCallInfo + textContent;\n \n const responseBody = {\n id: responseId,\n object: \"chat.completion\",\n created: timestamp,\n model: model,\n choices: [{\n index: 0,\n message: {\n role: \"assistant\",\n content: finalContent,\n ...(reasoningContent ? { reasoning_content: reasoningContent } : {})\n },\n logprobs: null,\n finish_reason: \"stop\"\n }],\n usage: {\n prompt_tokens: 0,\n completion_tokens: finalContent.length + reasoningContent.length,\n total_tokens: finalContent.length + reasoningContent.length,\n ...(reasoningContent ? {\n completion_tokens_details: {\n reasoning_tokens: reasoningContent.length\n }\n } : {})\n }\n };\n \n return new Response(JSON.stringify(responseBody), {\n status: 200,\n headers: {\n \"Content-Type\": \"application/json\",\n \"Access-Control-Allow-Origin\": \"*\",\n }\n });\n}\n\n// ==================== CORS 预检处理 ====================\n\nfunction handleCORS(): Response {\n return new Response(null, {\n status: 204,\n headers: {\n \"Access-Control-Allow-Origin\": \"*\",\n \"Access-Control-Allow-Methods\": \"GET, POST, OPTIONS\",\n \"Access-Control-Allow-Headers\": \"Content-Type, Authorization, X-Requested-With\",\n \"Access-Control-Max-Age\": \"86400\",\n }\n });\n}\n\n// ==================== 主路由处理 ====================\nexport default async function (request: Request): Promise {\n const url = new URL(request.url);\n const path = url.pathname;\n const method = request.method;\n \n log(`${method} ${path}`);\n \n // 处理 CORS 预检请求\n if (method === \"OPTIONS\") {\n return handleCORS();\n }\n \n // 根路径 - 返回服务信息\n if (path === \"/\" || path === \"\") {\n return new Response(JSON.stringify({\n service: \"StockAI OpenAI Compatible API Proxy\",\n version: \"1.0.0\",\n endpoints: {\n models: \"/v1/models\",\n chat: \"/v1/chat/completions\"\n },\n debug: DEBUG,\n availableModels: MODELS.map(m => m.name)\n }), {\n status: 200,\n headers: {\n \"Content-Type\": \"application/json\",\n \"Access-Control-Allow-Origin\": \"*\",\n }\n });\n }\n \n // 健康检查\n if (path === \"/health\") {\n return new Response(JSON.stringify({ status: \"ok\" }), {\n status: 200,\n headers: {\n \"Content-Type\": \"application/json\",\n \"Access-Control-Allow-Origin\": \"*\",\n }\n });\n }\n \n // API 路由 - 需要鉴权\n if (path === \"/v1/models\" && method === \"GET\") {\n const auth = authenticateRequest(request);\n if (!auth.success) {\n return createErrorResponse(401, auth.error!, \"authentication_error\");\n }\n return handleModelsRequest();\n }\n \n if (path === \"/v1/chat/completions\" && method === \"POST\") {\n const auth = authenticateRequest(request);\n if (!auth.success) {\n return createErrorResponse(401, auth.error!, \"authentication_error\");\n }\n return await handleChatCompletions(request);\n }\n \n // 404 - 路由不存在\n return createErrorResponse(404, `Endpoint not found: ${method} ${path}`, 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Diff · v10