docs: add rag example (vercel#2456)

content/examples/03-node/01-generating-text/06-rag.mdx

@@ -0,0 +1,70 @@
+---
+title: RAG
+description: Learn how to build a Retrieval Augmented Generation application with Node.js.
+---
+
+# RAG
+
+Retrieval Augmented Generation (RAG) is a technique that enhances the capabilities of language models by providing them with relevant information from external sources during the generation process.
+This approach allows the model to access and incorporate up-to-date or specific knowledge that may not be present in its original training data.
+
+This example uses [the following essay](https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt) as an input (`essay.txt`). This example uses a simple in-memory vector database to store and retrieve relevant information. For a more in-depth guide, check out the [RAG Chatbot Guide](/docs/guides/rag-chatbot) which will show you how to build a RAG chatbot with [Next.js](https://nextjs.org), [Drizzle ORM](https://orm.drizzle.team/) and [Postgres](https://postgresql.org).
+
+```ts
+import fs from 'fs';
+import path from 'path';
+import dotenv from 'dotenv';
+import { openai } from '@ai-sdk/openai';
+import { cosineSimilarity, embed, embedMany, generateText } from 'ai';
+
+dotenv.config();
+
+async function main() {
+  const db: { embedding: number[]; value: string }[] = [];
+
+  const essay = fs.readFileSync(path.join(__dirname, 'essay.txt'), 'utf8');
+  const chunks = essay
+    .split('.')
+    .map(chunk => chunk.trim())
+    .filter(chunk => chunk.length > 0 && chunk !== '\n');
+
+  const { embeddings } = await embedMany({
+    model: openai.embedding('text-embedding-3-small'),
+    values: chunks,
+  });
+  embeddings.forEach((e, i) => {
+    db.push({
+      embedding: e,
+      value: chunks[i],
+    });
+  });
+
+  const input =
+    'What were the two main things the author worked on before college?';
+
+  const { embedding } = await embed({
+    model: openai.embedding('text-embedding-3-small'),
+    value: input,
+  });
+  const context = db
+    .map(item => ({
+      document: item,
+      similarity: cosineSimilarity(embedding, item.embedding),
+    }))
+    .sort((a, b) => b.similarity - a.similarity)
+    .slice(0, 3)
+    .map(r => r.document.value)
+    .join('\n');
+
+  const { text } = await generateText({
+    model: openai('gpt-4o'),
+    prompt: `Answer the following question based only on the provided context:
+             ${context}
+
+             Question: ${input}`,
+  });
+  console.log(text);
+}
+
+main().catch(console.error);
+```