[Workers AI]moved markdown to features (#21716)

* moved markdown to features

* links

* redirect

Author: daisyfaithauma

public/__redirects

@@ -1607,10 +1607,14 @@
 # Guides section
 /workers-ai/demos/ /workers-ai/guides/demos-architectures/ 301
 
+# Features section
+/workers-ai/markdown-conversion/ /workers-ai/features/markdown-conversion/ 301
+
 # workflows
 
 /workflows/reference/storage-options/ /workers/platform/storage-options/ 301
 
+
 # workers KV
 /kv/platform/environments/ /kv/reference/environments/ 301
 /kv/platform/kv-commands/ /kv/reference/kv-commands/ 301

src/content/changelog/workers-ai/2025-03-20-markdown-conversion.mdx

@@ -14,27 +14,30 @@ In this example, we fetch a PDF document and an image from R2 and feed them both
 import { Env } from "./env";
 
 export default {
-  async fetch(request: Request, env: Env, ctx: ExecutionContext) {
-
-    // https://pub-979cb28270cc461d94bc8a169d8f389d.r2.dev/somatosensory.pdf
-    const pdf = await env.R2.get('somatosensory.pdf');
-
-    // https://pub-979cb28270cc461d94bc8a169d8f389d.r2.dev/cat.jpeg
-    const cat = await env.R2.get('cat.jpeg');
-
-    return Response.json(
-      await env.AI.toMarkdown([
-        {
-          name: "somatosensory.pdf",
-          blob: new Blob([await pdf.arrayBuffer()], { type: "application/octet-stream" }),
-        },
-        {
-          name: "cat.jpeg",
-          blob: new Blob([await cat.arrayBuffer()], { type: "application/octet-stream" }),
-        },
-      ]),
-    );
-  },
+	async fetch(request: Request, env: Env, ctx: ExecutionContext) {
+		// https://pub-979cb28270cc461d94bc8a169d8f389d.r2.dev/somatosensory.pdf
+		const pdf = await env.R2.get("somatosensory.pdf");
+
+		// https://pub-979cb28270cc461d94bc8a169d8f389d.r2.dev/cat.jpeg
+		const cat = await env.R2.get("cat.jpeg");
+
+		return Response.json(
+			await env.AI.toMarkdown([
+				{
+					name: "somatosensory.pdf",
+					blob: new Blob([await pdf.arrayBuffer()], {
+						type: "application/octet-stream",
+					}),
+				},
+				{
+					name: "cat.jpeg",
+					blob: new Blob([await cat.arrayBuffer()], {
+						type: "application/octet-stream",
+					}),
+				},
+			]),
+		);
+	},
 };
 ```
 
@@ -59,4 +62,4 @@ This is the result:
 ]
 ```
 
-See [Markdown Conversion](/workers-ai/markdown-conversion/) for more information on supported formats, REST API and pricing.
+See [Markdown Conversion](/workers-ai/features/markdown-conversion/) for more information on supported formats, REST API and pricing.

src/content/docs/autorag/concepts/how-autorag-works.mdx

@@ -19,7 +19,7 @@ Indexing begins automatically when you create an AutoRAG instance and connect a
 Here is what happens during indexing:
 
 1. **Data ingestion:** AutoRAG reads from your connected data source.
-2. **Markdown conversion:** AutoRAG uses [Workers AI’s Markdown Conversion](/workers-ai/markdown-conversion/) to convert [supported data types](/autorag/configuration/data-source/) into structured Markdown. This ensures consistency across diverse file types. For images, Workers AI is used to perform object detection followed by vision-to-language transformation to convert images into Markdown text.
+2. **Markdown conversion:** AutoRAG uses [Workers AI’s Markdown Conversion](/workers-ai/features/markdown-conversion/) to convert [supported data types](/autorag/configuration/data-source/) into structured Markdown. This ensures consistency across diverse file types. For images, Workers AI is used to perform object detection followed by vision-to-language transformation to convert images into Markdown text.
 3. **Chunking:** The extracted text is [chunked](/autorag/configuration/chunking/) into smaller pieces to improve retrieval granularity.
 4. **Embedding:** Each chunk is embedded using Workers AI’s embedding model to transform the content into vectors.
 5. **Vector storage:** The resulting vectors, along with metadata like file name, are stored in a the [Vectorize](/vectorize/) database created on your Cloudflare account.

src/content/docs/autorag/configuration/data-source.mdx

@@ -55,6 +55,6 @@ AutoRAG supports the following plain text file types:
 
 ### Rich format file types
 
-AutoRAG uses [Markdown Conversion](/workers-ai/markdown-conversion/) to convert rich format files to markdown. The following table lists the supported formats that will be converted to Markdown:
+AutoRAG uses [Markdown Conversion](/workers-ai/features/markdown-conversion/) to convert rich format files to markdown. The following table lists the supported formats that will be converted to Markdown:
 
 <Render file="markdown-conversion-support" product="workers-ai" />

src/content/docs/workers-ai/markdown-conversion.mdx renamed to src/content/docs/workers-ai/features/markdown-conversion.mdx

@@ -109,4 +109,4 @@
 
 <sup>1</sup> Image conversion uses two Workers AI models for object detection
 and summarization. See [Workers AI
-pricing](/workers-ai/markdown-conversion/#pricing) for more details.
+pricing](/workers-ai/features/markdown-conversion/#pricing) for more details.