Added Integrated SDKs section to AI Search docs

src/content/docs/ai-search/get-started/api.mdx

@@ -2,7 +2,7 @@
 title: REST API
 pcx_content_type: get-started
 sidebar:
-  order: 2
+  order: 5
 description: Create AI Search instances programmatically using the REST API.
 products:
   - ai-search

src/content/docs/ai-search/get-started/dashboard.mdx

@@ -2,7 +2,7 @@
 title: Dashboard
 pcx_content_type: get-started
 sidebar:
-  order: 1
+  order: 4
 description: Create and configure AI Search using the Cloudflare dashboard.
 products:
   - ai-search

src/content/docs/ai-search/get-started/index.mdx

@@ -13,30 +13,36 @@ products:
   - ai-search
 ---
 
-import { DashButton, CardGrid, LinkCard } from "~/components";
+import { CardGrid, LinkCard } from "~/components";
 
 AI Search is a managed search service. Connect a website, an R2 bucket, or upload your own documents, and AI Search indexes your content for natural language queries.
 
-## Prerequisites
-
-If you plan to use an R2 bucket as your data source, you must have an active [R2 subscription](/r2/get-started/) before creating your AI Search instance. If you plan to use a website or upload files directly, no additional setup is required.
-
 ## Choose your setup method
 
 <CardGrid>
+	<LinkCard
+		title="Workers API"
+		description="Create, populate, and query an AI Search instance from a Cloudflare Worker."
+		href="/ai-search/get-started/workers/"
+	/>
+	<LinkCard
+		title="Python SDK"
+		description="Create, populate, and query an AI Search instance from Python."
+		href="/ai-search/get-started/python/"
+	/>
+	<LinkCard
+		title="CLI"
+		description="Create and manage AI Search instances from the command line."
+		href="/ai-search/get-started/wrangler/"
+	/>
 	<LinkCard
 		title="Dashboard"
 		description="Create and configure AI Search using the Cloudflare dashboard."
 		href="/ai-search/get-started/dashboard/"
 	/>
 	<LinkCard
-		title="API"
+		title="REST API"
 		description="Create AI Search instances programmatically using the REST API."
 		href="/ai-search/get-started/api/"
 	/>
-	<LinkCard
-		title="Wrangler commands"
-		description="Create and manage AI Search instances from the command line."
-		href="/ai-search/get-started/wrangler/"
-	/>
 </CardGrid>

src/content/docs/ai-search/get-started/python.mdx

@@ -0,0 +1,152 @@
+---
+title: Python SDK
+pcx_content_type: get-started
+sidebar:
+  order: 2
+description: Create, populate, and query an AI Search instance from Python.
+products:
+  - ai-search
+---
+
+import { LinkCard, DashButton } from "~/components";
+
+This guide walks you through creating an AI Search instance, uploading content, and querying it from a Python application using the [Cloudflare Python SDK](https://github.com/cloudflare/cloudflare-python).
+
+## Prerequisites
+
+- [Python](https://www.python.org/downloads/) 3.8 or later.
+- Your [account ID](/fundamentals/account/find-account-and-zone-ids/).
+
+This guide uses the `default` [namespace](/ai-search/concepts/namespaces/), which exists automatically on every account. To group instances into your own namespace, create one with `client.aisearch.namespaces.create()`.
+
+## 1. Create an API token
+
+You need an API token with **AI Search:Edit** and **AI Search:Run** permissions.
+
+1. In the Cloudflare dashboard, go to **My Profile** > **API Tokens**.
+
+   <DashButton url="/profile/api-tokens" />
+
+2. Select **Create Token**.
+3. Select **Create Custom Token**.
+4. Enter a **Token name**, for example `AI Search Python`.
+5. Under **Permissions**, add two permissions:
+   - **Account** > **AI Search:Edit**
+   - **Account** > **AI Search:Run**
+6. Select **Continue to summary**, then select **Create Token**.
+7. Copy and save the token value. This is your `API_TOKEN`.
+
+## 2. Set up your Python environment
+
+Create a project directory and a virtual environment to isolate your dependencies.
+
+```sh
+mkdir ai-search-python && cd ai-search-python
+python3 -m venv .venv
+source .venv/bin/activate
+```
+
+On Windows, activate the virtual environment with `.venv\Scripts\activate` instead.
+
+## 3. Install the Cloudflare Python SDK
+
+Install the official `cloudflare` package:
+
+```sh
+pip install cloudflare
+```
+
+## 4. Set your credentials
+
+Export your account ID and API token as environment variables.
+
+```sh
+export CLOUDFLARE_ACCOUNT_ID="<ACCOUNT_ID>"
+export CLOUDFLARE_API_TOKEN="<API_TOKEN>"
+```
+
+## 5. Create an AI Search instance
+
+Create a file named `quickstart.py`. The following code sets up a client and creates an instance named `my-instance` in the `default` namespace. Because no data source is specified, the instance uses [built-in storage](/ai-search/configuration/data-source/built-in-storage/), so you can upload files to it directly.
+
+```python title="quickstart.py"
+import os
+
+from cloudflare import Cloudflare
+
+client = Cloudflare(api_token=os.environ["CLOUDFLARE_API_TOKEN"])
+account_id = os.environ["CLOUDFLARE_ACCOUNT_ID"]
+
+instance = client.aisearch.namespaces.instances.create(
+    name="default",
+    account_id=account_id,
+    id="my-instance",
+)
+
+print(f"Created instance: {instance.id}")
+```
+
+:::note
+Creating an instance is a one-time action. If you run the script again, remove this step, because an instance name must be unique within a namespace.
+:::
+
+## 6. Upload content
+
+Add the following to `quickstart.py` to upload a document. Setting `wait_for_completion` to `True` waits for indexing before returning so the file is ready to search. If indexing is still finishing, `item.status` may be `running`; the file continues indexing in the background and becomes searchable shortly after.
+
+```python title="quickstart.py"
+item = client.aisearch.namespaces.instances.items.upload(
+    id="my-instance",
+    account_id=account_id,
+    name="default",
+    file={
+        "file": (
+            "getting-started.md",
+            b"AI Search indexes uploaded content for retrieval.",
+            "text/markdown",
+        ),
+        "wait_for_completion": True,
+    },
+)
+
+print(f"Uploaded item status: {item.status}")
+```
+
+## 7. Search your instance
+
+Add the following to `quickstart.py` to run a query against your indexed content.
+
+```python title="quickstart.py"
+results = client.aisearch.namespaces.instances.search(
+    id="my-instance",
+    account_id=account_id,
+    name="default",
+    query="How does AI Search handle uploaded content?",
+)
+
+if results.chunks:
+    print(results.chunks[0].text)
+else:
+    print("No results yet — your content may still be indexing. Try again in a moment.")
+```
+
+Run the script:
+
+```sh
+python quickstart.py
+```
+
+If the search returns no results, the content may still be indexing. Wait a moment, then run the search again.
+
+## Next steps
+
+<LinkCard
+	title="REST API"
+	description="Query AI Search using HTTP requests."
+	href="/ai-search/api/search/rest-api/"
+/>
+<LinkCard
+	title="Workers API"
+	description="Query AI Search from within a Cloudflare Worker."
+	href="/ai-search/get-started/workers/"
+/>

src/content/docs/ai-search/get-started/workers.mdx

@@ -0,0 +1,147 @@
+---
+title: Workers binding
+pcx_content_type: get-started
+sidebar:
+  order: 1
+description: Create, populate, and query an AI Search instance from a Cloudflare Worker.
+products:
+  - ai-search
+---
+
+import {
+	Render,
+	PackageManagers,
+	WranglerConfig,
+	TypeScriptExample,
+	LinkCard,
+} from "~/components";
+
+This guide walks you through creating and querying an AI Search instance from a [Cloudflare Worker](/workers/) using the Workers Binding. The Workers Binding uses a runtime [API](/ai-search/api/search/workers-binding/) that runs inside a Worker and calls AI Search without managing API tokens.
+
+<Render file="prereqs" product="workers" />
+
+## 1. Create a Worker project
+
+Create a new Worker project using the `create-cloudflare` CLI (C3). [C3](https://github.com/cloudflare/workers-sdk/tree/main/packages/create-cloudflare) is a command-line tool designed to help you set up and deploy new applications to Cloudflare.
+
+Create a new project named `ai-search-tutorial` by running:
+
+<PackageManagers type="create" pkg="cloudflare@latest" args={"ai-search-tutorial"} />
+
+<Render
+	file="c3-post-run-steps"
+	product="workers"
+	params={{
+		category: "hello-world",
+		type: "Worker only",
+		lang: "TypeScript",
+	}}
+/>
+
+Go to your application directory:
+
+```sh
+cd ai-search-tutorial
+```
+
+## 2. Connect your Worker to AI Search
+
+Create a binding between your Worker and your AI Search instance. [Bindings](/workers/runtime-apis/bindings/) allow your Worker to interact with resources on the Cloudflare Developer Platform.
+
+Add the following to your [Wrangler configuration file](/workers/wrangler/configuration/):
+
+<WranglerConfig>
+
+```toml
+[[ai_search_namespaces]]
+binding = "AI_SEARCH"
+namespace = "default"
+remote = true
+```
+
+</WranglerConfig>
+
+This binds the `default` namespace to `env.AI_SEARCH`. Instances that you create without specifying a namespace belong to the `default` namespace. The `remote` option lets `wrangler dev` proxy requests to your deployed instance, since AI Search does not run locally. For all binding options, refer to the [Workers binding reference](/ai-search/api/search/workers-binding/).
+
+## 3. Create and query AI Search from your Worker
+
+Update the `src/index.ts` file in your `ai-search-tutorial` directory with the following code. It exposes two routes: `/setup` creates an instance named `my-instance` and indexes a sample document, and the default route queries it.
+
+<TypeScriptExample filename="src/index.ts">
+
+```ts
+export interface Env {
+	AI_SEARCH: AiSearchNamespace;
+}
+
+export default {
+	async fetch(request, env): Promise<Response> {
+		const url = new URL(request.url);
+
+		// Visit /setup once to create an instance and index a sample document.
+		if (url.pathname === "/setup") {
+			const instance = await env.AI_SEARCH.create({ id: "my-instance" });
+			const item = await instance.items.uploadAndPoll(
+				"getting-started.md",
+				"AI Search indexes uploaded content for retrieval.",
+			);
+			return Response.json({ created: "my-instance", status: item.status });
+		}
+
+		// Query the instance.
+		const query = url.searchParams.get("q") ?? "What does AI Search do?";
+
+		const results = await env.AI_SEARCH.get("my-instance").search({
+			messages: [{ role: "user", content: query }],
+			ai_search_options: {
+				retrieval: { max_num_results: 3 },
+			},
+		});
+
+		return Response.json(results.chunks);
+	},
+} satisfies ExportedHandler<Env>;
+```
+
+</TypeScriptExample>
+
+## 4. Develop locally
+
+Start a local development server:
+
+```sh
+npx wrangler dev
+```
+
+Wrangler gives you a URL (usually `localhost:8787`). Visit `/setup` once to create your instance and index the sample document, then query it at `/?q=your+search+terms`.
+
+## 5. Deploy your Worker
+
+Log in with your Cloudflare account:
+
+```sh
+npx wrangler login
+```
+
+Deploy your Worker to make it accessible on the Internet:
+
+```sh
+npx wrangler deploy
+```
+
+```txt
+https://ai-search-tutorial.<YOUR_SUBDOMAIN>.workers.dev
+```
+
+## Next steps
+
+<LinkCard
+	title="Search Workers binding"
+	description="Full reference for searching and chatting from a Worker."
+	href="/ai-search/api/search/workers-binding/"
+/>
+<LinkCard
+	title="Items Workers binding"
+	description="Upload, list, and manage documents from a Worker."
+	href="/ai-search/api/items/workers-binding/"
+/>

src/content/docs/ai-search/get-started/wrangler.mdx

@@ -1,5 +1,5 @@
 ---
-title: Wrangler commands
+title: CLI
 pcx_content_type: get-started
 sidebar:
   order: 3