Create main.ts

main.ts

@@ -0,0 +1,646 @@
+import { serve } from "https://deno.land/std@0.208.0/http/server.ts";
+
+// --- 常量定义 ---
+const MAX_DOCUMENT_SIZE_MB = 20; // 设置最大文档大小限制（单位：MB）
+const MAX_DOCUMENT_SIZE_BYTES = MAX_DOCUMENT_SIZE_MB * 1024 * 1024;
+const MODELS_CACHE_DURATION = 60000; // 1分钟模型缓存
+
+interface OpenAIMessage {
+  role: "system" | "user" | "assistant";
+  content: string | Array<{
+    type: string;
+    text?: string;
+    image_url?: { url: string };
+    document?: { url: string; type: string }; // 支持多种文档类型
+  }>;
+}
+
+interface OpenAIRequest {
+  model: string;
+  messages: OpenAIMessage[];
+  max_tokens?: number;
+  temperature?: number;
+  stream?: boolean;
+}
+
+class GoogleAIService {
+  public apiKeys: string[];
+  public currentKeyIndex = 0;
+  public cachedModels: any[] = [];
+  public modelsLastFetch = 0;
+
+  constructor() {
+    this.apiKeys = [];
+    let i = 1;
+    while (true) {
+      const key = Deno.env.get(`GOOGLE_AI_KEY_${i}`) ||
+                  (i === 1 ? Deno.env.get("GOOGLE_AI_KEY") : null);
+      if (!key) break;
+      this.apiKeys.push(key);
+      i++;
+    }
+
+    if (this.apiKeys.length === 0) {
+      throw new Error("No Google AI API keys found in environment variables (e.g., GOOGLE_AI_KEY_1, GOOGLE_AI_KEY)");
+    }
+  }
+
+  private getNextApiKey(): string {
+    const key = this.apiKeys[this.currentKeyIndex];
+    this.currentKeyIndex = (this.currentKeyIndex + 1) % this.apiKeys.length;
+    return key;
+  }
+
+  async fetchOfficialModels(): Promise<any[]> {
+    const now = Date.now();
+    if (this.cachedModels.length > 0 && (now - this.modelsLastFetch) < MODELS_CACHE_DURATION) {
+      return this.cachedModels;
+    }
+
+    const apiKey = this.getNextApiKey();
+    try {
+      const response = await fetch(
+        `https://generativelanguage.googleapis.com/v1beta/models?key=${apiKey}`,
+        { method: "GET", headers: { "Content-Type": "application/json" } }
+      );
+
+      if (!response.ok) {
+        console.warn(`Failed to fetch models from Google AI: ${response.status}. Using fallback models.`);
+        return this.getFallbackModels();
+      }
+
+      const data = await response.json();
+      if (data.models && Array.isArray(data.models)) {
+        this.cachedModels = data.models.filter((model: any) =>
+          model.supportedGenerationMethods?.includes('generateContent')
+        );
+        this.modelsLastFetch = now;
+        console.log(`Fetched ${this.cachedModels.length} models from Google AI`);
+        return this.cachedModels;
+      }
+      return this.getFallbackModels();
+    } catch (error) {
+      console.warn("Error fetching models from Google AI:", error.message, ". Using fallback models.");
+      return this.getFallbackModels();
+    }
+  }
+
+  private getFallbackModels(): any[] {
+    return [
+      { name: "models/gemini-1.5-pro", displayName: "Gemini 1.5 Pro", description: "Mid-size multimodal model that supports up to 1 million tokens, images, and documents (PDF, TXT, MD)", supportedGenerationMethods: ["generateContent"], maxTokens: 1000000, supportsDocuments: true },
+      { name: "models/gemini-1.5-flash", displayName: "Gemini 1.5 Flash", description: "Fast and versatile multimodal model for diverse tasks, supports images and documents (PDF, TXT, MD)", supportedGenerationMethods: ["generateContent"], maxTokens: 1000000, supportsDocuments: true },
+      { name: "models/gemini-2.0-flash-preview-image-generation", displayName: "Gemini 2.0 Flash Image Generation", description: "Advanced model for generating and editing high-quality images with text and image outputs", supportedGenerationMethods: ["generateContent"], maxTokens: 100000, capabilities: ["text", "image_generation", "image_editing"] }
+    ];
+  }
+
+  public isVisionModel = (modelName: string): boolean => modelName.toLowerCase().includes('vision') || modelName.toLowerCase().includes('pro');
+  public isImageGenerationModel = (modelName: string): boolean => modelName.includes('image-generation') || modelName === 'gemini-2.0-flash-preview-image-generation';
+  public isImageEditingModel = (modelName: string): boolean => modelName.includes('image-generation') || modelName === 'gemini-2.0-flash-preview-image-generation';
+  public isDocumentModel = (modelName: string): boolean => modelName.toLowerCase().includes('gemini-1.5') || modelName.toLowerCase().includes('pro') || modelName.toLowerCase().includes('flash');
+
+  private getDocumentType(url: string): string {
+    const lowerUrl = url.toLowerCase();
+    if (lowerUrl.startsWith('data:application/pdf') || lowerUrl.includes('.pdf')) return 'pdf';
+    if (lowerUrl.startsWith('data:text/plain') || lowerUrl.includes('.txt')) return 'txt';
+    if (lowerUrl.startsWith('data:text/markdown') || lowerUrl.includes('.md')) return 'md';
+    if (lowerUrl.startsWith('data:application/msword') || lowerUrl.includes('.doc')) return 'doc';
+    if (lowerUrl.startsWith('data:application/vnd.openxmlformats-officedocument.wordprocessingml.document') || lowerUrl.includes('.docx')) return 'docx';
+    return 'unknown';
+  }
+
+  /**
+   * [关键改进] 提取并验证文档数据，增加大小检查和更稳健的解析
+   */
+  private extractDocumentData(documentUrl: string): { mimeType: string; data: string; text?: string; docType: string } {
+    const docType = this.getDocumentType(documentUrl);
+
+    if (!documentUrl.startsWith("data:")) {
+      if (documentUrl.startsWith("http")) {
+        throw new Error("Document URL downloads are not supported. Please provide base64 encoded data URLs.");
+      }
+      // 如果不是data url或http url，则假定为纯base64数据，但这是一种不推荐的格式
+      // 为了健壮性，我们强制要求使用标准的 data URL
+      throw new Error("Document must be provided as a standard base64 data URL (e.g., 'data:application/pdf;base64,...').");
+    }
+
+    const parts = documentUrl.split(",");
+    if (parts.length !== 2) {
+        throw new Error("Invalid data URL format for document. Expected 'data:[mime];base64,[data]'.");
+    }
+    const [mimeInfo, base64Data] = parts;
+
+    // **改进1: 检查文件大小**
+    // Base64 字符串的长度约是原始数据的 4/3。
+    const approxSizeInBytes = base64Data.length * 0.75;
+    if (approxSizeInBytes > MAX_DOCUMENT_SIZE_BYTES) {
+        throw new Error(`Document size (${(approxSizeInBytes / 1024 / 1024).toFixed(2)}MB) exceeds the ${MAX_DOCUMENT_SIZE_MB}MB limit.`);
+    }
+
+    const mimeType = mimeInfo.split(":")[1]?.split(";")[0] || 'application/octet-stream';
+
+    if (docType === 'txt' || docType === 'md') {
+      try {
+        const textContent = atob(base64Data);
+        return { mimeType, data: base64Data, text: textContent, docType };
+      } catch (error) {
+        console.error(`Failed to decode base64 content for ${docType}:`, error);
+        throw new Error(`Invalid base64 encoding for ${docType} document.`);
+      }
+    }
+    
+    // 自动识别PDF的MIME类型
+    const finalMimeType = docType === 'pdf' ? 'application/pdf' : mimeType;
+    return { mimeType: finalMimeType, data: base64Data, docType };
+  }
+  
+  private extractImageData(imageUrl: string): { mimeType: string; data: string } {
+    if (imageUrl.startsWith("data:image/")) {
+      const [mimeInfo, base64Data] = imageUrl.split(",");
+      const mimeType = mimeInfo.split(":")[1].split(";")[0];
+      return { mimeType, data: base64Data };
+    } else if (imageUrl.startsWith("http")) {
+      throw new Error("URL images are not supported yet. Please provide base64 encoded images.");
+    } else {
+      return { mimeType: "image/jpeg", data: imageUrl };
+    }
+  }
+
+  async generateContentWithDocument(messages: OpenAIMessage[], modelName: string): Promise<string> {
+    const apiKey = this.getNextApiKey();
+    const fullModelName = modelName.startsWith('models/') ? modelName : `models/${modelName}`;
+    const documentModel = this.isDocumentModel(fullModelName) ? fullModelName : 'models/gemini-1.5-pro-latest';
+
+    console.log(`Processing document with model: ${documentModel}`);
+
+    let contents;
+    try {
+      contents = messages.map(msg => {
+        if (typeof msg.content === "string") {
+          return { role: msg.role === "assistant" ? "model" : "user", parts: [{ text: msg.content }] };
+        }
+
+        const messageParts = msg.content.map(part => {
+          if (part.type === "text") return { text: part.text };
+
+          if (part.type === "image_url" && part.image_url) {
+            const { mimeType, data } = this.extractImageData(part.image_url.url);
+            return { inlineData: { mimeType, data } };
+          }
+
+          if (part.type === "document" && part.document) {
+            const docData = this.extractDocumentData(part.document.url);
+            console.log(`Processing document: ${docData.docType}, mime: ${docData.mimeType}, size: ${(docData.data.length * 0.75 / 1024).toFixed(2)} KB`);
+
+            if (docData.docType === 'txt' || docData.docType === 'md') {
+              const prefix = docData.docType === 'md' ? 'Markdown document content:\n' : 'Text document content:\n';
+              return { text: `${prefix}${docData.text}` };
+            }
+            if (docData.docType === 'pdf') {
+              return { inlineData: { mimeType: docData.mimeType, data: docData.data } };
+            }
+            return { text: `[Document type '${docData.docType}' is not supported for direct processing. Please convert to PDF, TXT, or MD.]` };
+          }
+          return { text: "" };
+        });
+        return { role: msg.role === "assistant" ? "model" : "user", parts: messageParts.filter(p => p.text || p.inlineData) };
+      });
+    } catch (error) {
+      throw error;
+    }
+
+    const requestBody = {
+      contents,
+      generationConfig: { temperature: 0.7, maxOutputTokens: 8192 }
+    };
+
+    const response = await fetch(
+      `https://generativelanguage.googleapis.com/v1beta/${documentModel}:generateContent?key=${apiKey}`,
+      {
+        method: "POST",
+        headers: { "Content-Type": "application/json" },
+        body: JSON.stringify(requestBody),
+      }
+    );
+
+    if (!response.ok) {
+      const errorBody = await response.json().catch(() => response.text());
+      const errorMessage = errorBody?.error?.message || JSON.stringify(errorBody);
+      console.error(`Google API Error: ${response.status} - ${errorMessage}`);
+      throw new Error(`Google API request failed with status ${response.status}: ${errorMessage}`);
+    }
+
+    const data = await response.json();
+    const promptFeedback = data.promptFeedback;
+    if (promptFeedback && promptFeedback.blockReason) {
+      const reason = promptFeedback.blockReason;
+      const safetyRatings = promptFeedback.safetyRatings?.map((r: any) => `${r.category}: ${r.probability}`).join(', ') || 'N/A';
+      throw new Error(`Request blocked by Google API. Reason: ${reason}. Safety Ratings: [${safetyRatings}]`);
+    }
+
+    if (!data.candidates || data.candidates.length === 0) {
+      throw new Error("No response generated for document content. The content might be empty or unreadable.");
+    }
+
+    const candidate = data.candidates[0];
+    if (candidate.finishReason === "SAFETY") {
+        throw new Error("Response blocked due to safety filters. Check content for sensitive topics.");
+    }
+    if (candidate.finishReason === "RECITATION") {
+        throw new Error("Response blocked due to recitation policy. The model's output was too similar to a copyrighted source.");
+    }
+
+    return candidate.content?.parts[0]?.text || "Document processed, but no text response was generated.";
+  }
+  
+  // The rest of the original methods from the user's code
+  async generateContent(messages: OpenAIMessage[], modelName: string, enableSearch: boolean = false): Promise<string> {
+    const hasDocument = messages.some(msg => Array.isArray(msg.content) && msg.content.some(part => part.type === "document"));
+    if (hasDocument) {
+      return await this.generateContentWithDocument(messages, modelName);
+    }
+
+    const apiKey = this.getNextApiKey();
+    const fullModelName = modelName.startsWith('models/') ? modelName : `models/${modelName}`;
+
+    const contents = messages.map(msg => {
+      if (typeof msg.content === "string") {
+        return { role: msg.role === "assistant" ? "model" : "user", parts: [{ text: msg.content }] };
+      } else {
+        const messageParts = msg.content.map(part => {
+          if (part.type === "text") {
+            return { text: part.text };
+          } else if (part.type === "image_url" && part.image_url) {
+            const imageData = part.image_url.url;
+            if (imageData.startsWith("data:image/")) {
+              const { mimeType, data } = this.extractImageData(imageData);
+              return { inlineData: { mimeType, data } };
+            } else {
+              return { fileData: { mimeType: "image/jpeg", fileUri: imageData } };
+            }
+          }
+          return { text: "" };
+        });
+        return { role: msg.role === "assistant" ? "model" : "user", parts: messageParts };
+      }
+    });
+
+    const requestBody: any = {
+      contents,
+      generationConfig: { temperature: 0.7, maxOutputTokens: 4096 }
+    };
+    if (enableSearch) {
+      requestBody.tools = [{ googleSearchRetrieval: {} }];
+    }
+
+    const response = await fetch(
+      `https://generativelanguage.googleapis.com/v1beta/${fullModelName}:generateContent?key=${apiKey}`,
+      { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify(requestBody) }
+    );
+
+    if (!response.ok) {
+      const errorText = await response.text();
+      throw new Error(`Google AI API error: ${response.status} - ${errorText}`);
+    }
+    const data = await response.json();
+    if (!data.candidates || data.candidates.length === 0) {
+      throw new Error("No response generated from Google AI");
+    }
+    const candidate = data.candidates[0];
+    if (candidate.finishReason === "SAFETY") {
+      throw new Error("Response blocked due to safety filters");
+    }
+    return candidate.content?.parts[0]?.text || "No response generated";
+  }
+
+  async generateOrEditImageWithGemini(prompt: string, modelName: string = "gemini-2.0-flash-preview-image-generation", inputImage?: { mimeType: string; data: string }): Promise<{ text?: string; imageBase64?: string; imageUrl?: string }> {
+    const apiKey = this.getNextApiKey();
+    const fullModelName = modelName.startsWith('models/') ? modelName : `models/${modelName}`;
+    const requestParts: any[] = [{ text: prompt }];
+
+    if (inputImage) {
+      requestParts.push({ inline_data: { mime_type: inputImage.mimeType, data: inputImage.data } });
+      console.log(`Editing image with model: ${fullModelName}`);
+    } else {
+      console.log(`Generating image with model: ${fullModelName}`);
+    }
+
+    const requestBody = {
+      contents: [{ parts: requestParts }],
+      generationConfig: { responseModalities: ["TEXT", "IMAGE"], temperature: 0.7 }
+    };
+
+    const response = await fetch(
+      `https://generativelanguage.googleapis.com/v1beta/${fullModelName}:generateContent?key=${apiKey}`,
+      { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify(requestBody) }
+    );
+
+    if (!response.ok) {
+      const errorText = await response.text();
+      throw new Error(`Image ${inputImage ? 'editing' : 'generation'} failed: ${response.status} - ${errorText}`);
+    }
+    const data = await response.json();
+    if (!data.candidates || data.candidates.length === 0) {
+      throw new Error(`No ${inputImage ? 'edited' : 'generated'} image returned`);
+    }
+
+    const candidate = data.candidates[0];
+    if (candidate.finishReason === "SAFETY") {
+      throw new Error(`Image ${inputImage ? 'editing' : 'generation'} blocked due to safety filters`);
+    }
+
+    const responseParts = candidate.content?.parts || [];
+    let textResponse = "";
+    let imageBase64 = "";
+
+    for (const part of responseParts) {
+      if (part.text) textResponse += part.text;
+      if (part.inlineData?.data) imageBase64 = part.inlineData.data;
+      if (part.inline_data?.data) imageBase64 = part.inline_data.data;
+    }
+
+    const result: { text?: string; imageBase64?: string; imageUrl?: string } = {};
+    if (textResponse) result.text = textResponse;
+    if (imageBase64) {
+      result.imageBase64 = imageBase64;
+      result.imageUrl = `data:image/png;base64,${imageBase64}`;
+    }
+    return result;
+  }
+    
+  async generateContentWithGrounding(messages: OpenAIMessage[], modelName: string): Promise<string> {
+    const apiKey = this.getNextApiKey();
+    const fullModelName = modelName.startsWith('models/') ? modelName : `models/${modelName}`;
+    const contents = messages.map(msg => ({ role: msg.role === 'assistant' ? 'model' : 'user', parts: [{ text: typeof msg.content === 'string' ? msg.content : '' }] }));
+
+    const requestBody = {
+      contents,
+      tools: [{ googleSearch: {} }],
+      generationConfig: { temperature: 0.7, maxOutputTokens: 4096 }
+    };
+
+    const response = await fetch(
+      `https://generativelanguage.googleapis.com/v1beta/${fullModelName}:generateContent?key=${apiKey}`,
+      { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify(requestBody) }
+    );
+
+    if (!response.ok) {
+      console.warn(`Google Search API failed: ${response.status}, trying alternative.`);
+      return await this.generateContentWithSearchPrompt(messages, modelName);
+    }
+
+    const data = await response.json();
+    if (!data.candidates || data.candidates.length === 0) {
+      return await this.generateContentWithSearchPrompt(messages, modelName);
+    }
+
+    const candidate = data.candidates[0];
+    if (candidate.finishReason === "SAFETY") {
+      throw new Error("Response blocked due to safety filters");
+    }
+    return candidate.content?.parts[0]?.text || "No response generated";
+  }
+
+  async generateContentWithSearchPrompt(messages: OpenAIMessage[], modelName: string): Promise<string> {
+    const enhancedMessages = [...messages];
+    const lastMessage = enhancedMessages[enhancedMessages.length - 1];
+    if (typeof lastMessage.content === "string") {
+      lastMessage.content = `Please provide the most current and accurate information available about: ${lastMessage.content}.`;
+    }
+    return await this.generateContent(enhancedMessages, modelName, false);
+  }
+
+  async generateOrEditImage(prompt: string, modelName: string, inputImages?: any[]): Promise<string> {
+    if (this.isImageGenerationModel(modelName)) {
+      try {
+        let inputImage: { mimeType: string; data: string } | undefined;
+        if (inputImages && inputImages.length > 0) {
+          inputImage = this.extractImageData(inputImages[0].url);
+        }
+        const result = await this.generateOrEditImageWithGemini(prompt, modelName, inputImage);
+        let response = "";
+        if (result.text) response += result.text + "\\\\n\\\\n";
+        if (result.imageUrl) response += `${inputImage ? 'Edited' : 'Generated'} image:\\\\n${result.imageUrl}`;
+        return response || `Image processing complete.`;
+      } catch (error) {
+        return `Image processing failed: ${error.message}`;
+      }
+    }
+    return `Model ${modelName} does not support image generation. Use a model like gemini-2.0-flash-preview-image-generation.`;
+  }
+}
+
+class OpenAICompatibleServer {
+  private googleAI: GoogleAIService;
+  private authKey: string;
+
+  constructor() {
+    this.googleAI = new GoogleAIService();
+    this.authKey = Deno.env.get("AUTH_KEY") || "";
+  }
+
+  private authenticate(request: Request): boolean {
+    if (!this.authKey) return true;
+    const authHeader = request.headers.get("Authorization");
+    return authHeader ? authHeader.replace("Bearer ", "") === this.authKey : false;
+  }
+
+  private isDocumentContent(url?: string): boolean {
+    if (!url) return false;
+    const lowerUrl = url.toLowerCase();
+    return lowerUrl.includes('.pdf') || lowerUrl.startsWith('data:application/pdf') ||
+           lowerUrl.includes('.txt') || lowerUrl.startsWith('data:text/plain') ||
+           lowerUrl.includes('.md') || lowerUrl.startsWith('data:text/markdown');
+  }
+
+  private async handleChatCompletions(request: Request): Promise<Response> {
+    try {
+      const body: OpenAIRequest = await request.json();
+      const requestedModel = body.model || "gemini-1.5-pro";
+      const stream = body.stream || false;
+      console.log(`Request for model: ${requestedModel}, stream: ${stream}`);
+
+      const lastMessage = body.messages[body.messages.length - 1];
+      const content = typeof lastMessage.content === "string"
+        ? lastMessage.content
+        : (Array.isArray(lastMessage.content) ? lastMessage.content.map(p => p.text || "").join(" ") : "");
+
+      const hasDocument = body.messages.some(msg =>
+        Array.isArray(msg.content) &&
+        msg.content.some(part => part.type === "document" || this.isDocumentContent(part.document?.url))
+      );
+
+      const hasImages = body.messages.some(msg => Array.isArray(msg.content) && msg.content.some(part => part.type === "image_url"));
+      
+      let inputImages: any[] = [];
+      if (hasImages) {
+        body.messages.forEach(msg => {
+          if (Array.isArray(msg.content)) {
+            msg.content.forEach(part => {
+              if (part.type === "image_url" && part.image_url) inputImages.push({ url: part.image_url.url });
+            });
+          }
+        });
+      }
+      
+      let responseText: string;
+
+      // Routing logic based on keywords and content types
+      if (hasDocument) {
+        responseText = await this.googleAI.generateContentWithDocument(body.messages, requestedModel);
+      } else if (this.googleAI.isImageEditingModel(requestedModel) && hasImages) {
+        responseText = await this.googleAI.generateOrEditImage(content, requestedModel, inputImages);
+      } else if (this.googleAI.isImageGenerationModel(requestedModel)) {
+        responseText = await this.googleAI.generateOrEditImage(content, requestedModel);
+      } else if (content.toLowerCase().startsWith("/search:")) {
+        const query = content.substring(8).trim();
+        const searchMessages = [{ ...lastMessage, content: query }];
+        responseText = await this.googleAI.generateContentWithGrounding(searchMessages, requestedModel);
+      } else {
+        responseText = await this.googleAI.generateContent(body.messages, requestedModel, false);
+      }
+
+      if (stream) {
+        const streamResponse = await this.streamStringAsOpenAIResponse(responseText, requestedModel);
+        return new Response(streamResponse, {
+          headers: { "Content-Type": "text/event-stream", "Cache-Control": "no-cache", "Connection": "keep-alive", "Access-Control-Allow-Origin": "*" }
+        });
+      } else {
+        const responsePayload = {
+          id: `chatcmpl-${Date.now()}`, object: "chat.completion", created: Math.floor(Date.now() / 1000), model: requestedModel,
+          choices: [{ index: 0, message: { role: "assistant", content: responseText }, finish_reason: "stop" }],
+          usage: { prompt_tokens: 0, completion_tokens: 0, total_tokens: 0 }
+        };
+        return new Response(JSON.stringify(responsePayload), { headers: { "Content-Type": "application/json" } });
+      }
+    } catch (error) {
+      console.error("Error in chat completions:", error.message);
+      const status = error.message.includes("exceeds the limit") || error.message.includes("Invalid") ? 400 : 500;
+      return new Response(
+        JSON.stringify({
+          error: {
+            message: error.message,
+            type: status === 400 ? "invalid_request_error" : "api_error",
+            code: null
+          }
+        }),
+        { status, headers: { "Content-Type": "application/json" } }
+      );
+    }
+  }
+  
+  private async streamStringAsOpenAIResponse(content: string, modelName: string): Promise<ReadableStream<Uint8Array>> {
+    const encoder = new TextEncoder();
+    const streamId = `chatcmpl-${Date.now()}`;
+    const creationTime = Math.floor(Date.now() / 1000);
+    let contentQueue = content.split('');
+
+    return new ReadableStream({
+        start(controller) {
+            const initialChunk = { id: streamId, object: 'chat.completion.chunk', created: creationTime, model: modelName, choices: [{ index: 0, delta: { role: 'assistant', content: '' }, finish_reason: null }] };
+            controller.enqueue(encoder.encode(`data: ${JSON.stringify(initialChunk)}\n\n`));
+        },
+        pull(controller) {
+            if (contentQueue.length === 0) {
+                const finalChunk = { id: streamId, object: 'chat.completion.chunk', created: creationTime, model: modelName, choices: [{ index: 0, delta: {}, finish_reason: 'stop' }] };
+                controller.enqueue(encoder.encode(`data: ${JSON.stringify(finalChunk)}\n\n`));
+                controller.enqueue(encoder.encode('data: [DONE]\n\n'));
+                controller.close();
+                return;
+            }
+            const char = contentQueue.shift();
+            const chunk = { id: streamId, object: 'chat.completion.chunk', created: creationTime, model: modelName, choices: [{ index: 0, delta: { content: char }, finish_reason: null }] };
+            controller.enqueue(encoder.encode(`data: ${JSON.stringify(chunk)}\n\n`));
+        }
+    });
+  }
+  
+  private async handleModels(): Promise<Response> {
+    try {
+      const googleModels = await this.googleAI.fetchOfficialModels();
+      const models = {
+        object: "list",
+        data: googleModels.map(model => {
+          const modelId = model.name.replace('models/', '');
+          return {
+            id: modelId, object: "model", created: Math.floor(Date.now() / 1000), owned_by: "google",
+            description: model.description || model.displayName, maxTokens: model.inputTokenLimit || model.maxTokens
+          };
+        })
+      };
+      return new Response(JSON.stringify(models), { headers: { "Content-Type": "application/json" } });
+    } catch (error) {
+      console.error("Error fetching models:", error);
+      return new Response(JSON.stringify({ error: { message: "Failed to fetch models." } }), { status: 500 });
+    }
+  }
+  
+  private async handleStatus(): Promise<Response> {
+      const status = {
+          status: "healthy", timestamp: new Date().toISOString(), version: "2.5.0",
+          api_keys_loaded: this.googleAI.apiKeys.length,
+          models_in_cache: this.googleAI.cachedModels.length,
+          models_last_fetched: this.googleAI.modelsLastFetch > 0 ? new Date(this.googleAI.modelsLastFetch).toISOString() : "never"
+      };
+      return new Response(JSON.stringify(status), { headers: { "Content-Type": "application/json" } });
+  }
+
+  async handleRequest(request: Request): Promise<Response> {
+    const corsHeaders = {
+      "Access-Control-Allow-Origin": "*",
+      "Access-Control-Allow-Methods": "GET, POST, OPTIONS",
+      "Access-Control-Allow-Headers": "Content-Type, Authorization",
+    };
+
+    if (request.method === "OPTIONS") {
+      return new Response(null, { headers: corsHeaders });
+    }
+
+    const url = new URL(request.url);
+    let response: Response;
+
+    // Handle routes
+    if (url.pathname === "/health" || url.pathname === "/status") {
+      response = await this.handleStatus();
+    } else if (!this.authenticate(request)) {
+      response = new Response(JSON.stringify({ error: { message: "Unauthorized" } }), { status: 401 });
+    } else if (url.pathname === "/v1/chat/completions" && request.method === "POST") {
+      response = await this.handleChatCompletions(request);
+    } else if (url.pathname === "/v1/models" && request.method === "GET") {
+      response = await this.handleModels();
+    } else {
+      response = new Response("Not Found", { status: 404 });
+    }
+
+    // Add CORS headers to all responses
+    const finalHeaders = new Headers(response.headers);
+    for (const [key, value] of Object.entries(corsHeaders)) {
+      finalHeaders.set(key, value);
+    }
+
+    return new Response(response.body, { status: response.status, headers: finalHeaders });
+  }
+}
+
+// --- 服务器启动 ---
+const server = new OpenAICompatibleServer();
+
+console.log("🚀 OpenAI Compatible Server with Google AI starting on port 8000...");
+console.log(`✅ Loaded ${server.googleAI.apiKeys.length} API key(s).`);
+console.log(`📄 Max document size set to ${MAX_DOCUMENT_SIZE_MB}MB.`);
+
+// Pre-fetch models at startup
+server.googleAI.fetchOfficialModels().then(models => {
+  console.log(`✅ Successfully fetched ${models.length} models from Google AI.`);
+}).catch(error => {
+  console.warn(`⚠️ Could not pre-fetch models: ${error.message}. Will use fallbacks or fetch on first request.`);
+});
+
+console.log("\n🔗 Endpoints:");
+console.log("   POST /v1/chat/completions");
+console.log("   GET  /v1/models");
+console.log("   GET  /status");
+
+await serve(
+  (request: Request) => server.handleRequest(request),
+  { port: 8000 }
+);