docs: mdx, light/dark mode, ask (#400)
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import { readFile } from "fs/promises";
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import { join } from "path";
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import { convertToModelMessages, stepCountIs, streamText } from "ai";
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import type { ModelMessage, UIMessage } from "ai";
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import { createBashTool } from "bash-tool";
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import { headers } from "next/headers";
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import { allDocsPages } from "@/lib/docs-navigation";
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import { mdxToCleanMarkdown } from "@/lib/mdx-to-markdown";
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import { minuteRateLimit, dailyRateLimit } from "@/lib/rate-limit";
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export const maxDuration = 60;
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const DEFAULT_MODEL = "anthropic/claude-haiku-4.5";
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const SYSTEM_PROMPT = `You are a helpful documentation assistant for agent-browser, a headless browser automation CLI designed for AI agents.
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GitHub repository: https://github.com/vercel-labs/agent-browser
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Documentation: https://agent-browser.dev
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npm package: agent-browser
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You have access to the full agent-browser documentation via the bash and readFile tools. The docs are available as markdown files in the /workspace/ directory.
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When answering questions:
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- Use the bash tool to list files (ls /workspace/) or search for content (grep -r "keyword" /workspace/)
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- Use the readFile tool to read specific documentation pages (e.g. readFile with path "/workspace/index.md")
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- Always base your answers on the actual documentation content
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- Be concise and accurate
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- If the docs don't cover a topic, say so honestly
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- Do NOT include source references or file paths in your response`;
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async function loadDocsFiles(): Promise<Record<string, string>> {
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const files: Record<string, string> = {};
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const results = await Promise.allSettled(
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allDocsPages.map(async (page) => {
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const slug = page.href === "/" ? "" : page.href.replace(/^\//, "");
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const filePath = slug
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? join(process.cwd(), "src", "app", slug, "page.mdx")
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: join(process.cwd(), "src", "app", "page.mdx");
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const raw = await readFile(filePath, "utf-8");
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const md = mdxToCleanMarkdown(raw);
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const fileName = slug ? `/${slug}.md` : "/index.md";
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return { fileName, md };
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}),
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);
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for (const result of results) {
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if (result.status === "fulfilled") {
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files[result.value.fileName] = result.value.md;
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}
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}
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return files;
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}
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function addCacheControl(messages: ModelMessage[]): ModelMessage[] {
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if (messages.length === 0) return messages;
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return messages.map((message, index) => {
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if (index === messages.length - 1) {
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return {
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...message,
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providerOptions: {
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...message.providerOptions,
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anthropic: { cacheControl: { type: "ephemeral" } },
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},
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};
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}
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return message;
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});
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}
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export async function POST(req: Request) {
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const headersList = await headers();
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const ip = headersList.get("x-forwarded-for")?.split(",")[0] ?? "anonymous";
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const [minuteResult, dailyResult] = await Promise.all([
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minuteRateLimit.limit(ip),
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dailyRateLimit.limit(ip),
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]);
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if (!minuteResult.success || !dailyResult.success) {
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const isMinuteLimit = !minuteResult.success;
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return new Response(
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JSON.stringify({
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error: "Rate limit exceeded",
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message: isMinuteLimit
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? "Too many requests. Please wait a moment before trying again."
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: "Daily limit reached. Please try again tomorrow.",
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}),
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{
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status: 429,
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headers: { "Content-Type": "application/json" },
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},
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);
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}
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const { messages }: { messages: UIMessage[] } = await req.json();
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const docsFiles = await loadDocsFiles();
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const { tools } = await createBashTool({ files: docsFiles });
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const result = streamText({
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model: DEFAULT_MODEL,
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system: SYSTEM_PROMPT,
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messages: await convertToModelMessages(messages),
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stopWhen: stepCountIs(5),
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tools,
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prepareStep: ({ messages: stepMessages }) => ({
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messages: addCacheControl(stepMessages),
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}),
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});
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return result.toUIMessageStreamResponse();
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}
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