RAG Setup · Nuxt AI Ready · Nuxt SEO

-
-
-
-

[Sign In](https://nuxtseo.com/auth/github)

[Nuxt SEO on GitHub](https://github.com/harlan-zw/nuxt-seo)

AI Ready

-
-
-
-
-
-
-
-
-
-

Search…```k`` /`

v1.1.2

- [Discord Support](https://discord.com/invite/275MBUBvgP)

### Getting Started

-
-

### Core Concepts

-
-
-
-
-
-
-
-

### Advanced

-

Advanced

# RAG Setup

[Copy for LLMs](https://nuxtseo.com/docs/ai-ready/advanced/rag-example.md)

Nuxt AI Ready outputs clean markdown optimized for vectorizing. This guide shows how to build a RAG pipeline using `llms-full.txt`.

## [Fetch Markdown Content](#fetch-markdown-content)

`llms-full.txt` contains all pages as markdown, separated by `---` dividers with frontmatter:

```
const RE_PAGE_SPLIT = /^---$/m

const response = await fetch('https://yoursite.com/llms-full.txt')
const content = await response.text()

// Split into pages
const pages = content.split(RE_PAGE_SPLIT).filter(Boolean).map((block) => {
  const [, frontmatter, ...rest] = block.split(RE_PAGE_SPLIT)
  const markdown = rest.join('---').trim()

  // Parse frontmatter
  const meta: Record<string, string> = {}
  frontmatter?.split('\n').forEach((line) => {
    const [key, ...val] = line.split(':')
    if (key?.trim())
      meta[key.trim()] = val.join(':').trim()
  })

  return { ...meta, markdown }
})
```

## [Generate Embeddings](#generate-embeddings)

Use any embedding provider. Example with [OpenAI](https://openai.com):

```
import OpenAI from 'openai'

const openai = new OpenAI()

async function embed(text: string) {
  const response = await openai.embeddings.create({
    model: 'text-embedding-3-small',
    input: text,
  })
  return response.data[0].embedding
}

// Embed each page
const vectors = await Promise.all(
  pages.map(async page => ({
    id: page.route,
    embedding: await embed(page.markdown),
    metadata: { title: page.title, route: page.route }
  }))
)
```

## [Store in Vector DB](#store-in-vector-db)

### [sqlite-vec (Local)](#sqlite-vec-local)

```
import Database from 'better-sqlite3'
import * as sqliteVec from 'sqlite-vec'

const db = new Database(':memory:')
sqliteVec.load(db)

db.exec(\`
  CREATE VIRTUAL TABLE pages USING vec0(
    id TEXT PRIMARY KEY,
    embedding FLOAT[1536]
  )
\`)

const insert = db.prepare('INSERT INTO pages VALUES (?, ?)')
for (const v of vectors) {
  insert.run(v.id, new Float32Array(v.embedding))
}
```

### [Upstash Vector (Serverless)](#upstash-vector-serverless)

```
import { Index } from '@upstash/vector'

const index = new Index()

await index.upsert(vectors.map(v => ({
  id: v.id,
  vector: v.embedding,
  metadata: v.metadata
})))
```

## [Query](#query)

```
async function search(query: string, topK = 5) {
  const queryEmbedding = await embed(query)

  // sqlite-vec
  const results = db.prepare(\`
    SELECT id, distance
    FROM pages
    WHERE embedding MATCH ?
    ORDER BY distance
    LIMIT ?
  \`).all(new Float32Array(queryEmbedding), topK)

  return results
}

// Use in RAG prompt
const relevant = await search('how do I configure meta tags?')
const context = relevant.map(r => pages.find(p => p.route === r.id)?.markdown).join('\n\n')
```

## [Chunking Strategy](#chunking-strategy)

By default, each page is one chunk. For large pages, split by heading:

```
const RE_HEADING_SPLIT = /^##\s+/m

function chunkByHeading(markdown: string, route: string) {
  const sections = markdown.split(RE_HEADING_SPLIT)
  return sections.map((section, i) => ({
    id: \`${route}#${i}\`,
    content: section.trim(),
    route
  }))
}
```

| Strategy | When to use |
| --- | --- |
| Page-level | Small pages (<2k tokens), general search |
| Heading-level | Long docs, precise retrieval needed |
| Sliding window | Dense technical content, overlap matters |

## [Build Script](#build-script)

Run vectorization at build time:

```
// scripts/vectorize.ts
import { readFileSync } from 'node:fs'

const llmsFull = readFileSync('.output/public/llms-full.txt', 'utf-8')
// ... parse and vectorize as above
```

Add to your build:

```
{
  "scripts": {
    "generate": "nuxt generate && tsx scripts/vectorize.ts"
  }
}
```

[Edit this page](https://github.com/nuxt-seo-pro/nuxt-ai-ready/edit/main/docs/content/3.advanced/0.rag-example.md)

[Markdown For LLMs](https://nuxtseo.com/docs/ai-ready/advanced/rag-example.md)

Did this page help you?

### Related

On this page

- [Fetch Markdown Content](#fetch-markdown-content)
- [Generate Embeddings](#generate-embeddings)
- [Store in Vector DB](#store-in-vector-db)
- [Query](#query)
- [Chunking Strategy](#chunking-strategy)
- [Build Script](#build-script)

[GitHub](https://github.com/harlan-zw/nuxt-seo) [ Discord](https://discord.com/invite/275MBUBvgP)

###

-
-

Modules

-
-
-
-
-
-
-
-
-

###

-
-
-

###

Nuxt

-
-
-
-
-

Vue

-
-
-
-
-
-
-
-

###

-
-
-
-
-
-
-
-
-
-

Copyright © 2023-2026 Harlan Wilton - [MIT License](https://github.com/harlan-zw/nuxt-seo/blob/main/license) · [mdream](https://mdream.dev)