ea0e49f568
- 将logPrompt重命名为logConversation以支持输入输出日志 - 添加manageSystemPrompt函数来管理系统提示文本文件 - 在流式和非流式请求中记录完整响应文本 - 改进提示文本提取逻辑以获取最新用户输入
551 lines
22 KiB
JavaScript
551 lines
22 KiB
JavaScript
/**
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* @license
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* Copyright 2025 Google LLC
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* SPDX-License-Identifier: Apache-2.0
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*
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* 功能：
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* 该脚本创建一个独立的 Node.js HTTP 服务器，作为 Google Cloud Code Assist API 的本地代理，
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* 但它暴露了与 OpenAI API 兼容的接口，使其可以被任何支持 OpenAI API 的客户端直接使用。
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*
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* 主要特性：
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* - **OpenAI API 兼容性**: 实现了 `/v1/models` 和 `/v1/chat/completions` 端点。
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* - **格式转换**: 自动将 OpenAI 格式的请求/响应与内部 Gemini 格式进行转换。
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* - **流式传输支持**: 完全支持 OpenAI 的流式响应 (`"stream": true`)。
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* - **灵活的认证**: 支持通过 `Authorization: Bearer <key>` 请求头、URL 查询参数 (`?key=...`) 或 `x-goog-api-key` 请求头进行 API 密钥校验。
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* - **全面且可控的日志系统**: 包括令牌剩余有效期、可输出到控制台或文件的带时间戳的提示词日志等。
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* - **可配置性**: 可以通过命令行参数配置监听地址、端口、API 密钥和提示词日志模式。
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* - **重用核心逻辑**: 底层依然使用 `gemini-core.js` 与 Google 服务通信。
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*
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* -----------------------------------------------------------------------------
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* 使用说明 & 命令行示例
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* -----------------------------------------------------------------------------
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*
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* 1. 环境设置:
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* 在项目根目录创建一个 `package.json` 文件，内容为: `{"type": "module"}`，以避免模块类型警告。
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* (此项目已提供 `package.json` 文件，无需手动创建)
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*
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* 2. 安装依赖:
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* ```bash
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* npm install
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* ```
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* 这将安装 `google-auth-library` 和 `uuid`。
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*
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* 3. 启动服务 (根据需要组合使用以下参数):
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*
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* - **默认启动**: 监听 `localhost:8000`
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* ```bash
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* node openai-api-server.js
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* ```
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* - **指定监听 IP** (位置参数):
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* ```bash
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* node openai-api-server.js 0.0.0.0
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* ```
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* - **使用命名参数指定端口**:
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* ```bash
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* node openai-api-server.js --port 8081
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* ```
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* - **使用命名参数指定 API Key**:
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* ```bash
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* node openai-api-server.js --api-key your_secret_key
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* ```
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* - **打印提示词到控制台**: 监听 `localhost`，并在控制台输出提示词详情
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* ```bash
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* node openai-api-server.js --log-prompts console
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* ```
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* - **打印提示词到文件**: 监听 `localhost`，并将提示词详情保存到一个带启动时间戳的新文件中 (例如: `prompts-20231027-153055.log`)
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* ```bash
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* node openai-api-server.js --log-prompts file
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* ```
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* - **组合使用参数** (参数顺序无关):
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* ```bash
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* node openai-api-server.js --port 8088 --api-key your_secret_key 0.0.0.0
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* ```
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*
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* - **通过 base64 编码的凭证启动** (例如，用于 Docker 或 CI/CD 环境)
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* ```bash
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* node openai-api-server.js --oauth-creds-base64 "YOUR_BASE64_ENCODED_OAUTH_CREDS_JSON"
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* ```
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*
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* - **通过指定凭证文件路径启动** (例如，用于自定义凭证位置)
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* ```bash
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* node openai-api-server.js --oauth-creds-file "/path/to/your/oauth_creds.json"
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* ```
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*
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* - **通过指定项目ID启动** (例如，用于多项目环境)
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* ```bash
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* node openai-api-server.js --project-id your-gcp-project-id
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* ```
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*
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* 4. 调用 API 接口 (假设 API Key: `your_secret_key`, 服务运行在 `localhost:8000`):
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*
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* - **a) 列出可用模型**
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* ```bash
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* curl http://localhost:8000/v1/models \
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* -H "Authorization: Bearer your_secret_key"
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* ```
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* - **b) 生成内容 - 带系统提示词 (非流式)**
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* ```bash
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* curl http://localhost:8000/v1/chat/completions \
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* -H "Content-Type: application/json" \
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* -H "Authorization: Bearer your_secret_key" \
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* -d '{
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* "model": "gemini-2.5-pro",
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* "messages": [
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* {"role": "system", "content": "你是一只名叫 Neko 的猫。"},
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* {"role": "user", "content": "你好，你叫什么名字？"}
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* ]
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* }'
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* ```
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* - **c) 生成内容 - 流式**
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* ```bash
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* curl http://localhost:8000/v1/chat/completions \
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* -H "Content-Type: application/json" \
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* -H "Authorization: Bearer your_secret_key" \
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* -d '{
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* "model": "gemini-2.5-flash",
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* "messages": [
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* {"role": "user", "content": "写一首关于宇宙的五行短诗"}
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* ],
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* "stream": true
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* }'
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*
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*/
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import * as http from 'http';
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import { v4 as uuidv4 } from 'uuid';
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import {
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GeminiApiService,
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API_ACTIONS,
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formatExpiryTime,
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logConversation, // Changed from logPrompt
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extractPromptText,
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getRequestBody,
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extractResponseText,
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manageSystemPrompt, // New import
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} from './gemini-core.js';
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// --- Configuration Parsing ---
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let HOST = 'localhost';
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let PROMPT_LOG_MODE = 'none'; // 'none', 'console', 'file'
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const PROMPT_LOG_BASE_NAME = 'prompts';
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let PROMPT_LOG_FILENAME = '';
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let REQUIRED_API_KEY = '123456'; // Default API Key
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let SERVER_PORT = 8000; // Default Port
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let OAUTH_CREDS_BASE64 = null; // New variable for base64 encoded OAuth credentials
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let OAUTH_CREDS_FILE_PATH = null; // New variable for OAuth credentials file path
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let PROJECT_ID = null; // New variable for project ID
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const args = process.argv.slice(2);
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const remainingArgs = [];
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for (let i = 0; i < args.length; i++) {
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if (args[i] === '--api-key') {
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if (i + 1 < args.length) {
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REQUIRED_API_KEY = args[i + 1];
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i++; // Skip the value
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} else {
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console.warn(`[Config Warning] --api-key flag requires a value.`);
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}
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} else if (args[i] === '--port') {
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if (i + 1 < args.length) {
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SERVER_PORT = parseInt(args[i + 1], 10);
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i++; // Skip the value
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} else {
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console.warn(`[Config Warning] --port flag requires a value.`);
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}
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} else if (args[i] === '--log-prompts') {
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if (i + 1 < args.length) {
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const mode = args[i + 1];
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if (mode === 'console' || mode === 'file') {
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PROMPT_LOG_MODE = mode;
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} else {
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console.warn(`[Config Warning] Invalid mode for --log-prompts. Expected 'console' or 'file'. Prompt logging is disabled.`);
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}
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i++; // Skip the value
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} else {
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console.warn(`[Config Warning] --log-prompts flag requires a value.`);
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}
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} else if (args[i] === '--oauth-creds-base64') {
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if (i + 1 < args.length) {
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OAUTH_CREDS_BASE64 = args[i + 1];
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i++; // Skip the value
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} else {
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console.warn(`[Config Warning] --oauth-creds-base64 flag requires a value.`);
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}
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} else if (args[i] === '--oauth-creds-file') {
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if (i + 1 < args.length) {
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OAUTH_CREDS_FILE_PATH = args[i + 1];
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i++; // Skip the value
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} else {
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console.warn(`[Config Warning] --oauth-creds-file flag requires a value.`);
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}
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} else if (args[i] === '--project-id') { // New argument for project ID
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if (i + 1 < args.length) {
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PROJECT_ID = args[i + 1];
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i++; // Skip the value
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} else {
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console.warn(`[Config Warning] --project-id flag requires a value.`);
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}
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} else {
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remainingArgs.push(args[i]);
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}
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}
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if (remainingArgs.length > 0) HOST = remainingArgs[0];
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if (PROMPT_LOG_MODE === 'file') {
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const now = new Date();
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const pad = (num) => num.toString().padStart(2, '0');
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const timestamp = `${now.getFullYear()}${pad(now.getMonth() + 1)}${pad(now.getDate())}-${pad(now.getHours())}${pad(now.getMinutes())}${pad(now.getSeconds())}`;
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PROMPT_LOG_FILENAME = `${PROMPT_LOG_BASE_NAME}-${timestamp}.log`;
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}
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// --- Constants ---
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// SERVER_PORT is now a configurable variable
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// --- Format Conversion Functions ---
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/**
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* Extracts text from the 'content' field of an OpenAI message,
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* which can be a string or an array of content parts (for multimodal input).
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* @param {string|Array<Object>} content The content field from a message.
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* @returns {string} The extracted text content.
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*/
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function extractTextFromMessageContent(content) {
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if (typeof content === 'string') {
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return content;
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}
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if (Array.isArray(content)) {
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// Filter for text parts and join them. This gracefully handles multimodal inputs
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// by only extracting the text, which is what the Gemini text models expect.
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return content
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.filter(part => part.type === 'text' && typeof part.text === 'string')
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.map(part => part.text)
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.join('\n');
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}
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// Return an empty string if content is not in a recognized format.
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return '';
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}
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function toGeminiRequest(openaiRequest) {
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const geminiRequest = {
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contents: []
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};
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let systemContent = [];
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const messages = openaiRequest.messages || [];
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// 1. Extract and combine all system messages
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const otherMessages = messages.filter(m => {
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if (m.role === 'system') {
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// Use the helper function to safely extract text from system messages
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systemContent.push(extractTextFromMessageContent(m.content));
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return false;
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}
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return true;
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});
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if (systemContent.length > 0) {
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console.log('[Debug] systemContent before join:', systemContent);
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geminiRequest.systemInstruction = {
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parts: [{
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// Now systemContent is an array of strings, so join is safe
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text: systemContent.join('\n')
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}]
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};
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}
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// 2. Process the remaining messages, merging consecutive messages of the same role.
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if (otherMessages.length > 0) {
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let currentRole = null;
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let currentContentParts = [];
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for (const message of otherMessages) {
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const role = message.role === 'assistant' ? 'model' : message.role;
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if (role !== 'user' && role !== 'model') continue; // Ignore other roles
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const messageText = extractTextFromMessageContent(message.content);
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if (role === currentRole) {
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// If the role is the same, append the content.
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currentContentParts.push(messageText);
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} else {
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// If the role changes, push the previously accumulated content.
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if (currentRole) {
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console.log('[Debug] currentContentParts before join (in loop):', currentContentParts);
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geminiRequest.contents.push({
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role: currentRole,
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parts: [{
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text: currentContentParts.join('\n')
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}]
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});
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}
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// Start a new content block for the new role.
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currentRole = role;
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currentContentParts = [messageText];
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}
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}
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// Push the last accumulated content block.
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if (currentRole) {
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console.log('[Debug] currentContentParts before join (at end):', currentContentParts);
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geminiRequest.contents.push({
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role: currentRole,
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parts: [{
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text: currentContentParts.join('\n')
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}]
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});
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}
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}
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// 3. Basic validation and logging (the API will do the final validation)
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if (geminiRequest.contents.length > 0) {
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if (geminiRequest.contents[0].role !== 'user') {
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console.warn("[Request Conversion] Warning: Conversation doesn't start with a 'user' role. The API will likely reject this request.");
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}
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if (geminiRequest.contents.length > 0 && geminiRequest.contents[geminiRequest.contents.length - 1].role !== 'user') {
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console.warn("[Request Conversion] Warning: The last message in the conversation is not from the 'user'. The API may reject this request.");
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}
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}
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console.log('[Server] Converted Gemini Request (before core processing):', JSON.stringify(geminiRequest, null, 2));
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return geminiRequest;
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}
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function toOpenAIModelList(geminiModels) {
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return {
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object: "list",
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data: geminiModels.map(modelId => ({
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id: modelId,
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object: "model",
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created: Math.floor(Date.now() / 1000),
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owned_by: "google",
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})),
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};
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}
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function toOpenAIChatCompletion(geminiResponse, model) {
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const text = extractResponseText(geminiResponse);
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return {
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id: `chatcmpl-${uuidv4()}`,
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object: "chat.completion",
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created: Math.floor(Date.now() / 1000),
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model: model,
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choices: [{
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index: 0,
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message: {
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role: "assistant",
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content: text,
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},
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finish_reason: "stop",
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}],
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usage: geminiResponse.usageMetadata ? {
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prompt_tokens: geminiResponse.usageMetadata.promptTokenCount || 0,
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completion_tokens: geminiResponse.usageMetadata.candidatesTokenCount || 0,
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total_tokens: geminiResponse.usageMetadata.totalTokenCount || 0,
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} : {
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prompt_tokens: 0,
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completion_tokens: 0,
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total_tokens: 0,
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},
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};
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}
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function toOpenAIStreamChunk(geminiChunk, model) {
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const text = extractResponseText(geminiChunk);
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return {
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id: `chatcmpl-${uuidv4()}`,
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object: "chat.completion.chunk",
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created: Math.floor(Date.now() / 1000),
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model: model,
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choices: [{
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index: 0,
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delta: { content: text },
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finish_reason: null,
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}],
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usage: geminiChunk.usageMetadata ? {
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prompt_tokens: geminiChunk.usageMetadata.promptTokenCount || 0,
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completion_tokens: geminiChunk.usageMetadata.candidatesTokenCount || 0,
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total_tokens: geminiChunk.usageMetadata.totalTokenCount || 0,
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} : {
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prompt_tokens: 0,
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completion_tokens: 0,
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total_tokens: 0,
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},
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};
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}
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// --- Authorization ---
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function isAuthorized(req, requestUrl) {
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const authHeader = req.headers['authorization'];
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const queryKey = requestUrl.searchParams.get('key');
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const headerKey = req.headers['x-goog-api-key'];
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// Check for Bearer token in Authorization header (OpenAI style)
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if (authHeader && authHeader.startsWith('Bearer ')) {
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const token = authHeader.substring(7);
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if (token === REQUIRED_API_KEY) {
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return true;
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}
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}
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// Check for API key in URL query parameter (Gemini style)
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if (queryKey === REQUIRED_API_KEY) {
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return true;
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}
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// Check for API key in x-goog-api-key header (Gemini style)
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if (headerKey === REQUIRED_API_KEY) {
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return true;
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}
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console.log(`[Auth] Unauthorized request denied. Bearer token: "${authHeader ? authHeader.substring(7) : 'N/A'}", Query key: "${queryKey}", Header key: "${headerKey}"`);
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return false;
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}
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// --- Singleton Instance & HTTP Server Handlers ---
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let apiServiceInstance = null;
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async function getApiService() {
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if (!apiServiceInstance) {
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apiServiceInstance = new GeminiApiService(HOST, OAUTH_CREDS_BASE64, OAUTH_CREDS_FILE_PATH, PROJECT_ID);
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await apiServiceInstance.initialize();
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} else if (!apiServiceInstance.isInitialized) { // Ensure re-initialization if not already initialized
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await apiServiceInstance.initialize();
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}
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return apiServiceInstance;
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}
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async function handleStreamRequest(res, service, model, requestBody) {
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res.writeHead(200, { "Content-Type": "text/event-stream", "Cache-Control": "no-cache", "Connection": "keep-alive" });
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const stream = service.generateContentStream(model, requestBody);
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console.log('[Server Response Stream]');
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process.stdout.write('> ');
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let fullResponseText = ''; // Declare fullResponseText here
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try {
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for await (const chunk of stream) {
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const openAIChunk = toOpenAIStreamChunk(chunk, model);
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const chunkText = openAIChunk.choices[0].delta.content || "";
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if (chunkText) {
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process.stdout.write(chunkText);
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fullResponseText += chunkText; // Accumulate text here
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}
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res.write(`data: ${JSON.stringify(openAIChunk)}\n\n`);
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}
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// Send the final [DONE] message according to OpenAI spec
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res.write('data: [DONE]\n\n');
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} catch (error) {
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console.error('\n[Server] Error during stream processing:', error.stack);
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if (!res.writableEnded) {
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// We may not be able to write headers, but we can try to send an error payload.
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const errorPayload = { error: { message: "An error occurred during streaming.", details: error.message } };
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res.end(JSON.stringify(errorPayload)); // End the response with an error
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}
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} finally {
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process.stdout.write('\n');
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if (!res.writableEnded) {
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res.end();
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}
|
|
// Log the full conversation here
|
|
await logConversation('output', fullResponseText, PROMPT_LOG_MODE, PROMPT_LOG_FILENAME);
|
|
}
|
|
const expiryDate = service.authClient.credentials.expiry_date;
|
|
console.log(`[Auth Token] Time until expiry: ${formatExpiryTime(expiryDate)}`);
|
|
}
|
|
|
|
async function handleUnaryRequest(res, service, model, requestBody) {
|
|
const geminiResponse = await service.generateContent(model, requestBody);
|
|
console.log('[Server] Raw Gemini Unary Response:', JSON.stringify(geminiResponse, null, 2)); // Add this line
|
|
const openAIResponse = toOpenAIChatCompletion(geminiResponse, model);
|
|
console.log('[Server Response Unary]');
|
|
process.stdout.write('> ');
|
|
const responseText = extractResponseText(geminiResponse);
|
|
process.stdout.write(responseText);
|
|
process.stdout.write('\n');
|
|
res.writeHead(200, { 'Content-Type': 'application/json' });
|
|
res.end(JSON.stringify(openAIResponse));
|
|
const expiryDate = service.authClient.credentials.expiry_date;
|
|
console.log(`[Auth Token] Time until expiry: ${formatExpiryTime(expiryDate)}`);
|
|
|
|
await logConversation('output', responseText, PROMPT_LOG_MODE, PROMPT_LOG_FILENAME);
|
|
}
|
|
|
|
function handleError(res, error) {
|
|
console.error('\n[Server] Request failed:', error.stack);
|
|
if (!res.headersSent) {
|
|
const statusCode = error.response?.status || 500;
|
|
res.writeHead(statusCode, { 'Content-Type': 'application/json' });
|
|
}
|
|
const errorPayload = { error: { message: error.message, details: error.response?.data } };
|
|
res.end(JSON.stringify(errorPayload));
|
|
}
|
|
|
|
async function requestHandler(req, res) {
|
|
console.log(`\n[Server] Received request: ${req.method} http://${req.headers.host}${req.url}`);
|
|
|
|
const requestUrl = new URL(req.url, `http://${req.headers.host}`);
|
|
|
|
if (!isAuthorized(req, requestUrl)) {
|
|
res.writeHead(401, { 'Content-Type': 'application/json' });
|
|
return res.end(JSON.stringify({ error: { message: 'Unauthorized: API key is invalid or missing. Provide it in the `Authorization: Bearer <key>` header, as a `key` query parameter, or in the `x-goog-api-key` header.' } }));
|
|
}
|
|
|
|
try {
|
|
const service = await getApiService();
|
|
|
|
if (req.method === 'GET' && requestUrl.pathname === '/v1/models') {
|
|
const models = await service.listModels();
|
|
const openAIModels = toOpenAIModelList(models.models.map(m => m.name.replace('models/', '')));
|
|
res.writeHead(200, { 'Content-Type': 'application/json' });
|
|
const expiryDate = service.authClient.credentials.expiry_date;
|
|
console.log(`[Auth Token] Time until expiry: ${formatExpiryTime(expiryDate)}`);
|
|
return res.end(JSON.stringify(openAIModels));
|
|
}
|
|
|
|
if (req.method === 'POST' && requestUrl.pathname === '/v1/chat/completions') {
|
|
const openaiRequest = await getRequestBody(req);
|
|
const model = openaiRequest.model;
|
|
const geminiRequest = toGeminiRequest(openaiRequest);
|
|
|
|
await manageSystemPrompt(geminiRequest); // Call the new function here
|
|
const promptText = extractPromptText(geminiRequest); // Use geminiRequest for logging
|
|
await logConversation('input', promptText, PROMPT_LOG_MODE, PROMPT_LOG_FILENAME);
|
|
|
|
if (openaiRequest.stream) {
|
|
await handleStreamRequest(res, service, model, geminiRequest);
|
|
} else {
|
|
await handleUnaryRequest(res, service, model, geminiRequest);
|
|
}
|
|
return;
|
|
}
|
|
|
|
res.writeHead(404, { 'Content-Type': 'application/json' });
|
|
return res.end(JSON.stringify({ error: { message: 'Not Found' } }));
|
|
|
|
} catch (error) {
|
|
handleError(res, error);
|
|
}
|
|
}
|
|
|
|
// --- Server Initialization ---
|
|
const server = http.createServer(requestHandler);
|
|
|
|
server.listen(SERVER_PORT, HOST, () => {
|
|
console.log(`--- OpenAI-Compatible Server Configuration ---`);
|
|
console.log(` Host: ${HOST}`);
|
|
console.log(` Port: ${SERVER_PORT}`);
|
|
console.log(` Required API Key: ${REQUIRED_API_KEY}`);
|
|
console.log(` Prompt Logging: ${PROMPT_LOG_MODE}${PROMPT_LOG_MODE === 'file' ? ` (to ${PROMPT_LOG_FILENAME})` : ''}`);
|
|
console.log(` OAuth Creds File Path: ${OAUTH_CREDS_FILE_PATH || 'Default'}`);
|
|
console.log(` Project ID: ${PROJECT_ID || 'Auto-discovered'}`); // Log the project ID
|
|
console.log(`---------------------------------------------`);
|
|
console.log(`\nServer running on http://${HOST}:${SERVER_PORT}`);
|
|
console.log('Initializing backend service... This may take a moment.');
|
|
getApiService().catch(err => {
|
|
console.error("[Server] Pre-warming failed.", err.message);
|
|
});
|
|
});
|