Add autorag recipes page

src/content/docs/autorag/configuration/indexing.mdx

@@ -32,6 +32,6 @@ Factors that affect performance include:
 
 To ensure smooth and reliable indexing:
 
-- Make sure your files are within the size limit (10 MB) and in a supported format to avoid being skipped.
+- Make sure your files are within the [**size limit**](/autorag/platform/limits-pricing/#limits) and in a supported format to avoid being skipped.
 - Keep your Service API token valid to prevent indexing failures.
 - Regularly clean up outdated or unnecessary content in your knowledge base to avoid hitting [Vectorize index limits](/vectorize/platform/limits/).

src/content/docs/autorag/usage/recipes.mdx

@@ -0,0 +1,99 @@
+---
+pcx_content_type: concept
+title: Recipes
+sidebar:
+  order: 5
+---
+
+import {
+	Badge,
+	Description,
+	Render,
+	TabItem,
+	Tabs,
+	WranglerConfig,
+	MetaInfo,
+	Type,
+} from "~/components";
+
+This section provides practical examples and recipes for common use cases. These examples are done using [Workers Binding](/autorag/usage/workers-binding/) but can be easely adapted to use the [REST API](/autorag/usage/rest-api/) instead.
+
+## Bring your own model
+
+You can use AutoRAG for search while leveraging a model outside of Workers AI to generate responses. Here is an example of how you can use an OpenAI model to generate your responses.
+
+```ts
+import {openai} from '@ai-sdk/openai';
+import {generateText} from "ai";
+
+export interface Env {
+  AI: Ai;
+	OPENAI_API_KEY: string;
+}
+
+export default {
+  async fetch(request, env): Promise<Response> {
+  	// Parse incoming url
+		const url = new URL(request.url)
+
+		// Get the user query or default to a predefined one
+		const userQuery = url.searchParams.get('query') ?? 'How do I train a llama to deliver coffee?'
+
+		// Search for documents in AutoRAG
+		const searchResult =  await env.AI.autorag('my-rag').search({query: userQuery})
+
+		if (searchResult.data.length === 0) {
+			// No matching documents
+			return Response.json({text: `No data found for query "${userQuery}"`})
+		}
+
+		// Join all document chunks into a single string
+		const chunks = searchResult.data.map((item) => {
+			const data = item.content.map((content) => {
+				return content.text
+			}).join('\n\n')
+
+			return `<file name="${item.filename}">${data}</file>`
+		}).join('\n\n')
+
+		// Send the user query + matched documents to openai for answer
+		const generateResult = await generateText({
+			model: openai("gpt-4o-mini"),
+			messages: [
+				{role: 'system', content: 'You are a helpful assistant and your task is to answer the user question using the provided files.'},
+				{role: 'user', content: chunks},
+				{role: 'user', content: userQuery},
+			],
+		});
+
+		// Return the generated answer
+		return Response.json({text: generateResult.text});
+  },
+} satisfies ExportedHandler<Env>;
+```
+
+## Simple search engine
+
+Using the `search` method you can implement a simple but fast search engine.
+
+To replicate this example remember to:
+- Disable `rewrite_query` as you want to match the original user query
+- Configure your AutoRAG to have small chunk sizes, usually 256 tokens is enough
+
+```ts
+export interface Env {
+  AI: Ai;
+}
+
+export default {
+  async fetch(request, env): Promise<Response> {
+		const url = new URL(request.url)
+		const userQuery = url.searchParams.get('query') ?? 'How do I train a llama to deliver coffee?'
+		const searchResult =  await env.AI.autorag('my-rag').search({query: userQuery, rewrite_query: false})
+
+		return Response.json({
+			files: searchResult.data.map((obj) => obj.filename)
+		})
+  },
+} satisfies ExportedHandler<Env>;
+```

src/content/products/autorag.yaml

@@ -10,7 +10,7 @@ product:
 meta:
   title: AutoRAG
   description: Create fully managed RAG pipelines for your AI applications.
-  author: '@cloudflare'
+  author: "@cloudflare"
 
 resources:
-    discord: https://discord.gg/cloudflaredev
+  discord: https://discord.gg/cloudflaredev