feat: adding OpenAI support
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Tony Chen committed
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@@ -6,103 +6,172 @@ created on: 2024 Oct 07
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Last updated: 2024 Oct 07
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*/
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import { WorkerEntrypoint } from "cloudflare:workers";
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import {RPCEmailMessage} from './rpcEmail';
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import { RPCEmailMessage } from "./rpcEmail";
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import indexHtml from './index.html';
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import indexHtml from "./index.html";
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interface Env {
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// If you set another name in wrangler.toml as the value for 'binding',
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// replace "DB" with the variable name you defined.
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DB: D1Database;
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FrontEndAdminID: string;
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FrontEndAdminPassword: string;
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barkTokens: string;
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barkUrl: string;
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GoogleAPIKey: string;
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UseBark: string;
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// If you set another name in wrangler.toml as the value for 'binding',
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// replace "DB" with the variable name you defined.
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DB: D1Database;
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FrontEndAdminID: string;
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FrontEndAdminPassword: string;
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barkTokens: string;
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barkUrl: string;
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GoogleAPIKey: string;
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UseBark: string;
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GeminiModel?: string;
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OpenAIAPIKey?: string;
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OpenAIBaseUrl?: string;
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OpenAIModel?: string;
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}
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interface ModelResponse {
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ok: boolean;
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status: number;
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statusText: string;
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payload: unknown;
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}
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const DEFAULT_GEMINI_MODEL = "gemini-2.0-flash";
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const DEFAULT_OPENAI_BASE_URL = "https://api.openai.com";
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const DEFAULT_OPENAI_MODEL = "gpt-4o-mini";
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// Normalize model output into JSON text // 将模型输出规范化为可解析的 JSON 文本
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function extractJsonFromText(rawText: string): Record<string, unknown> | null {
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let candidate = rawText.trim();
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const jsonMatch = candidate.match(/```json\s*([\s\S]*?)\s*```/);
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if (jsonMatch && jsonMatch[1]) {
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candidate = jsonMatch[1].trim();
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console.log(`Extracted JSON Text: "${candidate}"`);
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} else {
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console.log(`Assuming entire text is JSON: "${candidate}"`);
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}
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try {
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return JSON.parse(candidate);
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} catch (parseError) {
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console.error("JSON parsing error:", parseError);
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console.log(`Problematic JSON Text: "${candidate}"`);
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return null;
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}
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}
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// Read structured content from Gemini response // 读取 Gemini 响应中的结构化文本
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function extractGeminiText(payload: unknown): string | null {
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const candidate = (payload as any)?.candidates?.[0]?.content?.parts?.[0];
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if (!candidate || typeof candidate.text !== "string") {
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console.error("Gemini response is missing expected data structure");
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return null;
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}
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return candidate.text;
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}
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// Read structured content from OpenAI response // 读取 OpenAI 响应中的结构化文本
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function extractOpenAIText(payload: unknown): string | null {
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const message = (payload as any)?.choices?.[0]?.message;
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if (!message) {
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console.error("OpenAI response is missing choices");
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return null;
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}
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const content = message.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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const textPart = content.find((part: any) => part?.type === "text" && typeof part.text === "string");
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if (textPart) {
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return textPart.text;
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}
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}
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if (typeof message?.content?.[0]?.text === "string") {
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return message.content[0].text;
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}
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console.error("OpenAI response is missing text content");
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return null;
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}
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// Remove trailing slashes to avoid double separators // 移除结尾斜杠防止重复拼接
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function normaliseBaseUrl(baseUrl: string): string {
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return baseUrl.replace(/\/+$/, "");
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}
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export default class extends WorkerEntrypoint<Env> {
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async fetch(request: Request): Promise<Response> {
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const env: Env = this.env;
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const ctx: ExecutionContext = this.ctx;
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// 将依赖 env 的常量移到函数内部
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const FrontEndAdminID = env.FrontEndAdminID;
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const FrontEndAdminPassword = env.FrontEndAdminPassword;
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async fetch(request: Request): Promise<Response> {
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const env: Env = this.env;
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const FrontEndAdminID = env.FrontEndAdminID;
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const FrontEndAdminPassword = env.FrontEndAdminPassword;
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// 提取 Authorization 头
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const authHeader = request.headers.get('Authorization');
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// Basic-auth gate for the admin console // 使用 Basic Auth 保护管理界面
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const authHeader = request.headers.get("Authorization");
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// 如果没有 Authorization 头，提示进行身份验证
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if (!authHeader) {
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return new Response('Unauthorized', {
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status: 401,
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headers: {
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'WWW-Authenticate': 'Basic realm="User Visible Realm"',
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},
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});
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}
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if (!authHeader) {
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return new Response("Unauthorized", {
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status: 401,
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headers: {
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"WWW-Authenticate": "Basic realm=\"User Visible Realm\"",
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},
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});
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}
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// 检查 Authorization 头是否使用 Basic 认证
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if (!authHeader.startsWith('Basic ')) {
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return new Response('Unauthorized', {
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status: 401,
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headers: {
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'WWW-Authenticate': 'Basic realm="User Visible Realm"',
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},
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});
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}
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if (!authHeader.startsWith("Basic ")) {
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return new Response("Unauthorized", {
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status: 401,
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headers: {
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"WWW-Authenticate": "Basic realm=\"User Visible Realm\"",
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},
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});
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}
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// 解码 base64 编码的凭据
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const base64Credentials = authHeader.substring('Basic '.length);
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const decodedCredentials = atob(base64Credentials);
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const base64Credentials = authHeader.substring("Basic ".length);
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const decodedCredentials = atob(base64Credentials);
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const [username, password] = decodedCredentials.split(":");
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// 将凭据分割为用户名和密码
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const [username, password] = decodedCredentials.split(':');
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if (username !== FrontEndAdminID || password !== FrontEndAdminPassword) {
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return new Response("Unauthorized", {
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status: 401,
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headers: {
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"WWW-Authenticate": "Basic realm=\"User Visible Realm\"",
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},
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});
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}
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// 验证凭据
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if (
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username !== FrontEndAdminID ||
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password !== FrontEndAdminPassword
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) {
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return new Response('Unauthorized', {
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status: 401,
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headers: {
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'WWW-Authenticate': 'Basic realm="User Visible Realm"',
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},
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});
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}
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try {
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const { results } = await env.DB.prepare(
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"SELECT from_org, to_addr, topic, code, created_at FROM code_mails ORDER BY created_at DESC"
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).all();
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try {
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const { results } = await env.DB.prepare(
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'SELECT from_org, to_addr, topic, code, created_at FROM code_mails ORDER BY created_at DESC'
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).all();
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let dataHtml = "";
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for (const row of results) {
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const codeLinkParts = row.code.split(",");
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let codeLinkContent;
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let dataHtml = '';
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for (const row of results) {
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const codeLinkParts = row.code.split(',');
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let codeLinkContent;
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if (codeLinkParts.length > 1) {
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const [code, link] = codeLinkParts;
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codeLinkContent = `${code}<br><a href="${link}" target="_blank">${row.topic}</a>`;
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} else if (row.code.startsWith("http")) {
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codeLinkContent = `<a href="${row.code}" target="_blank">${row.topic}</a>`;
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} else {
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codeLinkContent = row.code;
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}
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if (codeLinkParts.length > 1) {
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const [code, link] = codeLinkParts;
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codeLinkContent = `${code}<br><a href="${link}" target="_blank">${row.topic}</a>`;
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} else if (row.code.startsWith('http')) {
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codeLinkContent = `<a href="${row.code}" target="_blank">${row.topic}</a>`;
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} else {
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codeLinkContent = row.code;
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}
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dataHtml += `<tr>
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dataHtml += `<tr>
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<td>${row.from_org}</td>
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<td>${row.to_addr}</td>
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<td>${row.topic}</td>
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<td>${codeLinkContent}</td>
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<td>${row.created_at}</td>
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</tr>`;
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}
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}
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let responseHtml = indexHtml
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.replace('{{TABLE_HEADERS}}', `
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const responseHtml = indexHtml
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.replace(
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"{{TABLE_HEADERS}}",
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`
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<tr>
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<th>From</th>
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<th>To</th>
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@@ -110,48 +179,150 @@ export default class extends WorkerEntrypoint<Env> {
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<th>Code/Link</th>
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<th>Receive Time (GMT)</th>
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</tr>
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`)
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.replace('{{DATA}}', dataHtml);
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`
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)
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.replace("{{DATA}}", dataHtml);
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return new Response(responseHtml, {
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headers: {
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'Content-Type': 'text/html',
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},
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});
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} catch (error) {
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console.error('Error querying database:', error);
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return new Response('Internal Server Error', { status: 500 });
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}
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}
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return new Response(responseHtml, {
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headers: {
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"Content-Type": "text/html",
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},
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});
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} catch (error) {
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console.error("Error querying database:", error);
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return new Response("Internal Server Error", { status: 500 });
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}
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}
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// 主要功能
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async email(message: ForwardableEmailMessage): Promise<void> {
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// Call Gemini for primary extraction // 调用 Gemini 执行主解析
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private async callGemini(prompt: string, apiKey: string, model: string): Promise<ModelResponse> {
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const endpoint = `https://generativelanguage.googleapis.com/v1beta/models/${model}:generateContent?key=${apiKey}`;
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const init: RequestInit = {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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},
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body: JSON.stringify({
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contents: [
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{
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parts: [{ text: prompt }],
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},
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],
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}),
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};
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const env: Env = this.env;
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const useBark = env.UseBark.toLowerCase() === 'true'; // true or false
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const GoogleAPIKey = env.GoogleAPIKey; // "xxxxxxxxxxxxxxxxxxxxxxxx"
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try {
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const response = await fetch(endpoint, init);
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let payload: unknown = null;
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try {
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payload = await response.json();
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} catch (parseError) {
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console.error("Gemini JSON parse error:", parseError);
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}
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return {
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ok: response.ok,
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status: response.status,
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statusText: response.statusText,
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payload,
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};
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} catch (error) {
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console.error("Gemini request threw:", error);
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return {
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ok: false,
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status: 0,
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statusText: "FETCH_ERROR",
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payload: null,
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};
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}
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}
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const rawEmail = message instanceof RPCEmailMessage ? (<RPCEmailMessage>message).rawEmail : await new Response(message.raw).text();
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const message_id = message.headers.get("Message-ID");
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// Call OpenAI as the fallback extractor // 调用 OpenAI 作为兜底解析
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private async callOpenAI(
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prompt: string,
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baseUrl: string,
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apiKey: string,
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model: string
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): Promise<ModelResponse> {
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const endpoint = `${normaliseBaseUrl(baseUrl)}/v1/chat/completions`;
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const init: RequestInit = {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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Authorization: `Bearer ${apiKey}`,
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},
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body: JSON.stringify({
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model,
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temperature: 0,
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response_format: { type: "json_object" },
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messages: [
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{
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role: "system",
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content: "You return only valid JSON that matches the requested schema.",
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},
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{
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role: "user",
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content: prompt,
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},
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],
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}),
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};
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// 将电子邮件保存到数据库
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const {success} = await env.DB.prepare(
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`INSERT INTO raw_mails (from_addr, to_addr, raw, message_id) VALUES (?, ?, ?, ?)`
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).bind(
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message.from, message.to, rawEmail, message_id // 将电子邮件详细信息绑定到 SQL 语句
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).run();
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try {
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const response = await fetch(endpoint, init);
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let payload: unknown = null;
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try {
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payload = await response.json();
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} catch (parseError) {
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console.error("OpenAI JSON parse error:", parseError);
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}
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return {
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ok: response.ok,
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status: response.status,
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statusText: response.statusText,
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payload,
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};
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} catch (error) {
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console.error("OpenAI request threw:", error);
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return {
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ok: false,
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status: 0,
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statusText: "FETCH_ERROR",
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payload: null,
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};
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}
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}
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// 检查电子邮件是否成功保存
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if (!success) {
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message.setReject(`Failed to save message from ${message.from} to ${message.to}`); // 如果保存失败，则拒绝消息
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console.log(`Failed to save message from ${message.from} to ${message.to}`); // 记录保存失败
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}
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// Primary email handler // 主要邮件处理入口
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async email(message: ForwardableEmailMessage): Promise<void> {
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const env: Env = this.env;
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const useBark = env.UseBark.toLowerCase() === "true";
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const googleApiKey = env.GoogleAPIKey;
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const geminiModel = env.GeminiModel || DEFAULT_GEMINI_MODEL;
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const openAIKey = env.OpenAIAPIKey;
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const openAIBaseUrl = env.OpenAIBaseUrl || DEFAULT_OPENAI_BASE_URL;
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const openAIModel = env.OpenAIModel || DEFAULT_OPENAI_MODEL;
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// 调用AI，让AI抓取验证码，让AI返回`title`和`code`
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// title: 邮件是哪个公司/组织发来的验证码, 比如'Netflix'
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// code: 验证码/链接/密码，比如'123456'or'https://example.com/verify?code=123456',如都有则返回'code, link'
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// topic: 邮件主题，比如'line register verification'
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const aiPrompt = `
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// Pull raw email content // 获取原始邮件内容
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const rawEmail =
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message instanceof RPCEmailMessage
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? (message as RPCEmailMessage).rawEmail
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: await new Response(message.raw).text();
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const messageId = message.headers.get("Message-ID");
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// Persist raw mail payload for auditing // 将原始邮件持久化以便审计
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const { success } = await env.DB.prepare(
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"INSERT INTO raw_mails (from_addr, to_addr, raw, message_id) VALUES (?, ?, ?, ?)"
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)
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.bind(message.from, message.to, rawEmail, messageId)
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.run();
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if (!success) {
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message.setReject(`Failed to save message from ${message.from} to ${message.to}`);
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console.log(`Failed to save message from ${message.from} to ${message.to}`);
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}
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// Prompt instructs model how to format extraction // 提示词说明提取格式和字段要求
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const aiPrompt = `
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Email content: ${rawEmail}.
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Please read the email and extract the following information:
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@@ -177,146 +348,144 @@ export default class extends WorkerEntrypoint<Env> {
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}
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`;
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try {
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// 添加重试机制
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const maxRetries = 3;
|
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let retryCount = 0;
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let extractedData = null;
|
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try {
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const maxRetries = 3;
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let retryCount = 0;
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let extractedData: Record<string, unknown> | null = null;
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let geminiStatusError = false;
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while (retryCount < maxRetries && !extractedData) {
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// 调用 Google AI API 来获取 title, code, topic
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const aiResponse = await fetch(`https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-latest:generateContent?key=${GoogleAPIKey}`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json'
|
||||
},
|
||||
body: JSON.stringify({
|
||||
"contents": [
|
||||
{
|
||||
"parts": [
|
||||
{"text": aiPrompt}
|
||||
]
|
||||
}
|
||||
]
|
||||
})
|
||||
});
|
||||
// First attempt Gemini with simple retries // 先使用 Gemini 并进行简单重试
|
||||
while (retryCount < maxRetries && !extractedData && !geminiStatusError) {
|
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const geminiResult = await this.callGemini(aiPrompt, googleApiKey, geminiModel);
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console.log(`Gemini response attempt ${retryCount + 1}:`, geminiResult.payload);
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|
||||
const aiData = await aiResponse.json();
|
||||
console.log(`AI response attempt ${retryCount + 1}:`, aiData);
|
||||
// 检测ai返回格式是否正确
|
||||
if (
|
||||
aiData &&
|
||||
aiData.candidates &&
|
||||
aiData.candidates[0] &&
|
||||
aiData.candidates[0].content &&
|
||||
aiData.candidates[0].content.parts &&
|
||||
aiData.candidates[0].content.parts[0]
|
||||
) {
|
||||
let extractedText = aiData.candidates[0].content.parts[0].text;
|
||||
console.log(`Extracted Text before parsing: "${extractedText}"`);
|
||||
if (!geminiResult.ok) {
|
||||
geminiStatusError = true;
|
||||
console.error(
|
||||
`Gemini request failed with status ${geminiResult.status} ${geminiResult.statusText}`
|
||||
);
|
||||
break;
|
||||
}
|
||||
|
||||
// Use regex to extract JSON content from code blocks
|
||||
const jsonMatch = extractedText.match(/```json\s*([\s\S]*?)\s*```/);
|
||||
if (jsonMatch && jsonMatch[1]) {
|
||||
extractedText = jsonMatch[1].trim();
|
||||
console.log(`Extracted JSON Text: "${extractedText}"`);
|
||||
} else {
|
||||
// If no code block, assume the entire text is JSON
|
||||
extractedText = extractedText.trim();
|
||||
console.log(`Assuming entire text is JSON: "${extractedText}"`);
|
||||
}
|
||||
const extractedText = extractGeminiText(geminiResult.payload);
|
||||
if (extractedText) {
|
||||
console.log(`Extracted Text before parsing: "${extractedText}"`);
|
||||
const parsed = extractJsonFromText(extractedText);
|
||||
if (parsed) {
|
||||
extractedData = parsed;
|
||||
console.log("Parsed Extracted Data:", extractedData);
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// Parse
|
||||
try {
|
||||
extractedData = JSON.parse(extractedText);
|
||||
console.log(`Parsed Extracted Data:`, extractedData);
|
||||
} catch (parseError) {
|
||||
console.error("JSON parsing error:", parseError);
|
||||
console.log(`Problematic JSON Text: "${extractedText}"`);
|
||||
}
|
||||
retryCount += 1;
|
||||
if (retryCount < maxRetries) {
|
||||
console.log("Retrying AI request...");
|
||||
} else {
|
||||
console.error("Max retries reached. Unable to get valid AI response.");
|
||||
}
|
||||
}
|
||||
|
||||
} else {
|
||||
console.error("AI response is missing expected data structure");
|
||||
}
|
||||
if (!extractedData && geminiStatusError && openAIKey) {
|
||||
// Gemini failed with non-200, switch to OpenAI fallback // 当 Gemini 返回非 200 时切换到 OpenAI 兜底
|
||||
const openAIResult = await this.callOpenAI(aiPrompt, openAIBaseUrl, openAIKey, openAIModel);
|
||||
console.log("OpenAI fallback response:", openAIResult.payload);
|
||||
if (!openAIResult.ok) {
|
||||
console.error(
|
||||
`OpenAI fallback failed with status ${openAIResult.status} ${openAIResult.statusText}`
|
||||
);
|
||||
} else {
|
||||
const openAIText = extractOpenAIText(openAIResult.payload);
|
||||
if (openAIText) {
|
||||
console.log(`OpenAI text before parsing: "${openAIText}"`);
|
||||
const parsed = extractJsonFromText(openAIText);
|
||||
if (parsed) {
|
||||
extractedData = parsed;
|
||||
console.log("Parsed Extracted Data from OpenAI:", extractedData);
|
||||
}
|
||||
}
|
||||
}
|
||||
} else if (!extractedData && geminiStatusError) {
|
||||
console.error("Gemini request failed and OpenAI fallback is not configured.");
|
||||
}
|
||||
|
||||
if (!extractedData) {
|
||||
retryCount++;
|
||||
if (retryCount < maxRetries) {
|
||||
console.log("Retrying AI request...");
|
||||
} else {
|
||||
console.error("Max retries reached. Unable to get valid AI response.");
|
||||
}
|
||||
}
|
||||
}
|
||||
if (extractedData) {
|
||||
// Only persist when a code exists // 仅在存在验证码时写入数据库
|
||||
if ((extractedData as any).codeExist === 1) {
|
||||
const title = (extractedData as any).title || "Unknown Organization";
|
||||
const code = (extractedData as any).code || "No Code Found";
|
||||
const topic = (extractedData as any).topic || "No Topic Found";
|
||||
|
||||
// extract formatted data
|
||||
if (extractedData) {
|
||||
if (extractedData.codeExist === 1) {
|
||||
const title = extractedData.title || "Unknown Organization";
|
||||
const code = extractedData.code || "No Code Found";
|
||||
const topic = extractedData.topic || "No Topic Found";
|
||||
// Store parsed metadata for UI display // 保存解析结果供前端展示
|
||||
const { success: codeMailSuccess } = await env.DB.prepare(
|
||||
"INSERT INTO code_mails (from_addr, from_org, to_addr, code, topic, message_id) VALUES (?, ?, ?, ?, ?, ?)"
|
||||
)
|
||||
.bind(message.from, title, message.to, code, topic, messageId)
|
||||
.run();
|
||||
|
||||
// save extracted data to the database
|
||||
const { success: codeMailSuccess } = await env.DB.prepare(
|
||||
`INSERT INTO code_mails (from_addr, from_org, to_addr, code, topic, message_id) VALUES (?, ?, ?, ?, ?, ?)`
|
||||
).bind(
|
||||
message.from, title, message.to, code, topic, message_id
|
||||
).run();
|
||||
if (!codeMailSuccess) {
|
||||
message.setReject(
|
||||
`Failed to save extracted code for message from ${message.from} to ${message.to}`
|
||||
);
|
||||
console.log(
|
||||
`Failed to save extracted code for message from ${message.from} to ${message.to}`
|
||||
);
|
||||
}
|
||||
|
||||
if (!codeMailSuccess) {
|
||||
message.setReject(`Failed to save extracted code for message from ${message.from} to ${message.to}`);
|
||||
console.log(`Failed to save extracted code for message from ${message.from} to ${message.to}`);
|
||||
}
|
||||
if (useBark) {
|
||||
// Fan-out Bark notifications for each token // 为每个 token 发送 Bark 推送
|
||||
const barkUrl = env.barkUrl;
|
||||
const barkTokens = env.barkTokens
|
||||
.replace(/^\[|\]$/g, "")
|
||||
.split(",")
|
||||
.map((token) => token.trim());
|
||||
|
||||
// Send title and code to Bark using GET request for each token
|
||||
if (useBark) {
|
||||
const barkUrl = env.barkUrl; // "https://api.day.app"
|
||||
// [token1, token2]
|
||||
const barkTokens = env.barkTokens
|
||||
.replace(/^\[|\]$/g, '')
|
||||
.split(',')
|
||||
.map(token => token.trim());
|
||||
const barkUrlEncodedTitle = encodeURIComponent(title);
|
||||
const barkUrlEncodedCode = encodeURIComponent(code);
|
||||
|
||||
const barkUrlEncodedTitle = encodeURIComponent(title);
|
||||
const barkUrlEncodedCode = encodeURIComponent(code);
|
||||
for (const token of barkTokens) {
|
||||
const barkRequestUrl = `${barkUrl}/${token}/${barkUrlEncodedTitle}/${barkUrlEncodedCode}`;
|
||||
|
||||
for (const token of barkTokens) {
|
||||
const barkRequestUrl = `${barkUrl}/${token}/${barkUrlEncodedTitle}/${barkUrlEncodedCode}`;
|
||||
const barkResponse = await fetch(barkRequestUrl, {
|
||||
method: "GET",
|
||||
});
|
||||
|
||||
const barkResponse = await fetch(barkRequestUrl, {
|
||||
method: "GET"
|
||||
});
|
||||
if (barkResponse.ok) {
|
||||
console.log(
|
||||
`Successfully sent notification to Bark for token ${token} for message from ${message.from} to ${message.to}`
|
||||
);
|
||||
const responseData = await barkResponse.json();
|
||||
console.log("Bark response:", responseData);
|
||||
} else {
|
||||
console.error(
|
||||
`Failed to send notification to Bark for token ${token}: ${barkResponse.status} ${barkResponse.statusText}`
|
||||
);
|
||||
}
|
||||
}
|
||||
}
|
||||
} else {
|
||||
console.log("No code found in this email, skipping Bark notification.");
|
||||
}
|
||||
} else {
|
||||
console.error("Failed to extract data from AI response after retries.");
|
||||
}
|
||||
} catch (e) {
|
||||
console.error("Error calling AI or saving to database:", e);
|
||||
}
|
||||
}
|
||||
|
||||
if (barkResponse.ok) {
|
||||
console.log(`Successfully sent notification to Bark for token ${token} for message from ${message.from} to ${message.to}`);
|
||||
const responseData = await barkResponse.json();
|
||||
console.log("Bark response:", responseData);
|
||||
} else {
|
||||
console.error(`Failed to send notification to Bark for token ${token}: ${barkResponse.status} ${barkResponse.statusText}`);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
} else {
|
||||
console.log("No code found in this email, skipping Bark notification.");
|
||||
}
|
||||
} else {
|
||||
console.error("Failed to extract data from AI response after retries.");
|
||||
}
|
||||
} catch (e) {
|
||||
console.error("Error calling AI or saving to database:", e);
|
||||
}
|
||||
}
|
||||
|
||||
// 暴露rpc接口，处理来自其他worker的邮件请求
|
||||
async rpcEmail(requestBody: string): Promise<void> {
|
||||
console.log(`Received RPC email , request body: ${requestBody}`);
|
||||
const bodyObject =JSON.parse(requestBody);
|
||||
const headersObject = bodyObject.headers;
|
||||
const headers = new Headers(headersObject);
|
||||
const rpcEmailMessage: RPCEmailMessage = new RPCEmailMessage(bodyObject.from, bodyObject.to, bodyObject.rawEmail, headers);
|
||||
await this.email(rpcEmailMessage);
|
||||
}
|
||||
|
||||
}
|
||||
// Expose RPC helper for other workers // 暴露 RPC 接口供其他 Worker 调用
|
||||
async rpcEmail(requestBody: string): Promise<void> {
|
||||
console.log(`Received RPC email , request body: ${requestBody}`);
|
||||
const bodyObject = JSON.parse(requestBody);
|
||||
const headersObject = bodyObject.headers;
|
||||
const headers = new Headers(headersObject);
|
||||
const rpcEmailMessage: RPCEmailMessage = new RPCEmailMessage(
|
||||
bodyObject.from,
|
||||
bodyObject.to,
|
||||
bodyObject.rawEmail,
|
||||
headers
|
||||
);
|
||||
await this.email(rpcEmailMessage);
|
||||
}
|
||||
}
|
||||
@@ -58,6 +58,25 @@ GoogleAPIKey = "xxxxxxxxxxx"
|
||||
# your google AI API, you can have one from https://aistudio.google.com
|
||||
# 你的谷歌AI API，你可以从 https://aistudio.google.com 获取
|
||||
|
||||
GeminiModel = "gemini-2.0-flash"
|
||||
# GeminiModel: String
|
||||
# optional override for the Gemini model, defaults to "gemini-2.0-flash"
|
||||
# 可选配置，覆盖默认的 Gemini 模型名称，默认值为 "gemini-2.0-flash"
|
||||
|
||||
OpenAIAPIKey = ""
|
||||
# OpenAIAPIKey: String
|
||||
# optional key for OpenAI fallback when Gemini fails with a non-200 response
|
||||
# 可选配置，当 Gemini 返回非 200 时使用 OpenAI 作为兜底
|
||||
|
||||
OpenAIBaseUrl = "https://api.openai.com"
|
||||
# OpenAIBaseUrl: String
|
||||
# optional, change only if you proxy the OpenAI API
|
||||
# 可选配置，仅在自建代理时更改
|
||||
|
||||
OpenAIModel = "gpt-4o-mini"
|
||||
# OpenAIModel: String
|
||||
# optional override for the OpenAI model, defaults to "gpt-4o-mini"
|
||||
# 可选配置，覆盖默认的 OpenAI 模型
|
||||
|
||||
# Bind a D1 database. D1 is Cloudflare’s native serverless SQL database.
|
||||
# Docs: https://developers.cloudflare.com/workers/wrangler/configuration/#d1-databases
|
||||
|
||||
@@ -13,6 +13,10 @@ barkTokens = "[token1, token2]"
|
||||
FrontEndAdminID = "admin"
|
||||
FrontEndAdminPassword = "password"
|
||||
GoogleAPIKey = "xxxxxxxxxxx"
|
||||
GeminiModel = "gemini-2.0-flash"
|
||||
OpenAIAPIKey = ""
|
||||
OpenAIBaseUrl = "https://api.openai.com"
|
||||
OpenAIModel = "gpt-4o-mini"
|
||||
|
||||
[[d1_databases]]
|
||||
binding = "DB"
|
||||
|
||||
Reference in new issue
Block a user