feat(docs): add llms.txt

cspell.config.yaml

@@ -19,6 +19,7 @@ words:
   - Hunyuan
   - importantimport
   - koemotion
+  - llms
   - masknet
   - ministral
   - mistralai

docs/app/llms.txt/route.ts

@@ -0,0 +1,45 @@
+import fg from 'fast-glob'
+import { remarkInstall } from 'fumadocs-docgen'
+import { remarkInclude } from 'fumadocs-mdx/config'
+import matter from 'gray-matter'
+import { readFile } from 'node:fs/promises'
+import { remark } from 'remark'
+import remarkGfm from 'remark-gfm'
+import remarkMdx from 'remark-mdx'
+import remarkStringify from 'remark-stringify'
+
+export const revalidate = false
+
+const processContent = async (content: string) => String(await remark()
+  .use(remarkMdx)
+  // https://fumadocs.vercel.app/docs/mdx/include
+  .use(remarkInclude)
+  // gfm styles
+  .use(remarkGfm)
+  // your remark plugins
+  .use(remarkInstall, { persist: { id: 'package-manager' } })
+  // to string
+  .use(remarkStringify)
+  .process(content))
+
+/** @see {@link https://fumadocs.vercel.app/docs/ui/llms} */
+export const GET = async () => {
+  // all scanned content
+  const files = await fg(['./content/docs/**/*.mdx'])
+
+  const scan = files.map(async (file) => {
+    const fileContent = await readFile(file)
+    const { content, data } = matter(fileContent.toString())
+    const processed = await processContent(content)
+    return [
+      `file: ${file}`,
+      `meta: ${JSON.stringify(data, null, 2)}`,
+      '',
+      processed,
+    ].join('\n')
+  })
+
+  const scanned = await Promise.all(scan)
+
+  return new Response(scanned.join('\n\n'))
+}

docs/content/docs/index.mdx

@@ -10,3 +10,26 @@ description: extra-small AI SDK for Browser, Node.js, Deno, Bun or Edge Runtime.
 xsAI has a similar interface to the Vercel AI SDK, but is significantly smaller.
 
 This makes it ideal for a variety of scenarios that require smaller bundle size, such as web applications, cli running through npx, and so on.
+
+## `llms.txt`
+
+You can access the entire xsAI documentation in Markdown format at https://xsai.js.org/llms.txt.
+
+This can be used to ask any LLM (assuming it has a big enough context window)
+
+questions about the xsAI based on the most up-to-date documentation.
+
+### Example Usage
+
+For instance, to prompt an LLM with questions about the xsAI:
+
+1. Copy the documentation contents from https://xsai.js.org/llms.txt
+2. Use the following prompt format:
+
+```
+Documentation:
+{paste documentation here}
+---
+Based on the above documentation, answer the following:
+{your question}
+```

docs/package.json

@@ -42,7 +42,13 @@
     "@xsai/stream-text": "workspace:^",
     "@xsai/tool": "workspace:^",
     "autoprefixer": "^10.4.20",
+    "fast-glob": "^3.3.3",
+    "gray-matter": "^4.0.3",
     "postcss": "^8.5.3",
+    "remark": "^15.0.1",
+    "remark-gfm": "^4.0.1",
+    "remark-mdx": "^3.1.0",
+    "remark-stringify": "^11.0.0",
     "tailwindcss": "^4.0.7",
     "valibot": "catalog:",
     "xsai": "workspace:^"