Making sure nothing's broken

Author: Oxyjun

src/content/changelog/ai-search/2025-04-07-autorag-open-beta.mdx

@@ -1,23 +1,23 @@
 ---
-title: Create fully-managed RAG pipelines for your AI applications with AI Search
-description: AI Search lets you create fully-managed, retrieval-augmented generation (RAG) pipelines that continuously updates and scales on Cloudflare.
+title: Create fully-managed RAG pipelines for your AI applications with AutoRAG
+description: AutoRAG lets you create fully-managed, retrieval-augmented generation (RAG) pipelines that continuously updates and scales on Cloudflare.
 products:
   - ai-search
   - vectorize
 date: 2025-04-07
 ---
 
-[AI Search](/ai-search/) is now in open beta, making it easy for you to build fully-managed retrieval-augmented generation (RAG) pipelines without managing infrastructure. Just upload your docs to [R2](/r2/get-started/), and AI Search handles the rest: embeddings, indexing, retrieval, and response generation via API.
+[AutoRAG](/ai-search/) is now in open beta, making it easy for you to build fully-managed retrieval-augmented generation (RAG) pipelines without managing infrastructure. Just upload your docs to [R2](/r2/get-started/), and AutoRAG handles the rest: embeddings, indexing, retrieval, and response generation via API.
 
-![AI Search open beta demo](~/assets/images/changelog/ai-search/autorag-open-beta.gif)
+![AutoRAG open beta demo](~/assets/images/changelog/ai-search/autorag-open-beta.gif)
 
-With AI Search, you can:
+With AutoRAG, you can:
 
 - **Customize your pipeline:** Choose from [Workers AI](/workers-ai) models, configure chunking strategies, edit system prompts, and more.
-- **Instant setup:** AI Search provisions everything you need from [Vectorize](/vectorize), [AI gateway](/ai-gateway), to pipeline logic for you, so you can go from zero to a working RAG pipeline in seconds.
-- **Keep your index fresh:** AI Search continuously syncs your index with your data source to ensure responses stay accurate and up to date.
+- **Instant setup:** AutoRAG provisions everything you need from [Vectorize](/vectorize), [AI gateway](/ai-gateway), to pipeline logic for you, so you can go from zero to a working RAG pipeline in seconds.
+- **Keep your index fresh:** AutoRAG continuously syncs your index with your data source to ensure responses stay accurate and up to date.
 - **Ask questions:** Query your data and receive grounded responses via a [Workers binding](/ai-search/usage/workers-binding/) or [API](/ai-search/usage/rest-api/).
 
-Whether you're building internal tools, AI-powered search, or a support assistant, AI Search gets you from idea to deployment in minutes.
+Whether you're building internal tools, AI-powered search, or a support assistant, AutoRAG gets you from idea to deployment in minutes.
 
 Get started in the [Cloudflare dashboard](https://dash.cloudflare.com/?to=/:account/ai/autorag) or check out the [guide](/ai-search/get-started/) for instructions on how to build your RAG pipeline today.

src/content/changelog/ai-search/2025-04-23-autorag-metadata-filtering.mdx

@@ -1,12 +1,12 @@
 ---
-title: Metadata filtering and multitenancy support in AI Search
-description: Add metadata filters to AI Search queries to enable multitenancy and control the scope of retrieved results.
+title: Metadata filtering and multitenancy support in AutoRAG
+description: Add metadata filters to AutoRAG queries to enable multitenancy and control the scope of retrieved results.
 products:
   - ai-search
 date: 2025-04-23
 ---
 
-You can now filter [AI Search](/ai-search/) search results by `folder` and `timestamp` using [metadata filtering](/ai-search/configuration/metadata) to narrow down the scope of your query.
+You can now filter [AutoRAG](/ai-search/) search results by `folder` and `timestamp` using [metadata filtering](/ai-search/configuration/metadata) to narrow down the scope of your query.
 
 This makes it easy to build [multitenant experiences](/ai-search/how-to/multitenancy/) where each user can only access their own data. By organizing your content into per-tenant folders and applying a `folder` filter at query time, you ensure that each tenant retrieves only their own documents.
 
@@ -31,6 +31,6 @@ const response = await env.AI.autorag("my-autorag").search({
 });
 ```
 
-You can use metadata filtering by creating a new AI Search or reindexing existing data. To reindex all content in an existing AI Search, update any chunking setting and select **Sync index**. Metadata filtering is available for all data indexed on or after **April 21, 2025**.
+You can use metadata filtering by creating a new AutoRAG or reindexing existing data. To reindex all content in an existing AutoRAG, update any chunking setting and select **Sync index**. Metadata filtering is available for all data indexed on or after **April 21, 2025**.
 
-If you are new to AI Search, get started with the [Get started AI Search guide](/ai-search/get-started/).
+If you are new to AutoRAG, get started with the [Get started AutoRAG guide](/ai-search/get-started/).

src/content/changelog/ai-search/2025-06-19-autorag-custom-metadata-and-context.mdx

@@ -1,18 +1,18 @@
 ---
-title: View custom metadata in responses and guide AI-search with context in AI Search
-description: You can now view custom metadata in AI Search search responses and use a context field to provide additional guidance to AI-generated answers.
+title: View custom metadata in responses and guide AI-search with context in AutoRAG
+description: You can now view custom metadata in AutoRAG search responses and use a context field to provide additional guidance to AI-generated answers.
 products:
   - ai-search
 date: 2025-06-19
 ---
 
-In [AI Search](/ai-search/), you can now view your object's custom metadata in the response from [`/search`](/ai-search/usage/workers-binding/) and [`/ai-search`](/ai-search/usage/workers-binding/), and optionally add a `context` field in the custom metadata of an object to provide additional guidance for AI-generated answers.
+In [AutoRAG](/ai-search/), you can now view your object's custom metadata in the response from [`/search`](/ai-search/usage/workers-binding/) and [`/ai-search`](/ai-search/usage/workers-binding/), and optionally add a `context` field in the custom metadata of an object to provide additional guidance for AI-generated answers.
 
 You can add [custom metadata](/r2/api/workers/workers-api-reference/#r2putoptions) to an object when uploading it to your R2 bucket.
 
 # Object's custom metadata in search responses
 
-When you run a search, AI Search now returns any custom metadata associated with the object. This metadata appears in the response inside `attributes` then `file` , and can be used for downstream processing.
+When you run a search, AutoRAG now returns any custom metadata associated with the object. This metadata appears in the response inside `attributes` then `file` , and can be used for downstream processing.
 
 For example, the `attributes` section of your search response may look like:
 
@@ -32,7 +32,7 @@ For example, the `attributes` section of your search response may look like:
 
 # Add a `context` field to guide LLM answers
 
-When you include a custom metadata field named `context`, AI Search attaches that value to each chunk of the file. When you run an `/ai-search` query, this `context` is passed to the LLM and can be used as additional input when generating an answer.
+When you include a custom metadata field named `context`, AutoRAG attaches that value to each chunk of the file. When you run an `/ai-search` query, this `context` is passed to the LLM and can be used as additional input when generating an answer.
 
 We recommend using the `context` field to describe supplemental information you want the LLM to consider, such as a summary of the document or a source URL. If you have several different metadata attributes, you can join them together however you choose within the `context` string.
 
@@ -46,4 +46,4 @@ For example:
 
 This gives you more control over how your content is interpreted, without requiring you to modify the original contents of the file.
 
-Learn more in AI Search's [metadata filtering documentation](/ai-search/configuration/metadata).
+Learn more in AutoRAG's [metadata filtering documentation](/ai-search/configuration/metadata).

src/content/changelog/ai-search/2025-06-19-autorag-filename-filter.mdx

@@ -1,14 +1,14 @@
 ---
-title: Filter your AI Search search by file name
-description: You can now filter AI Search search queries by file name, allowing you to control which files can be retrieved for a given query.
+title: Filter your AutoRAG search by file name
+description: You can now filter AutoRAG search queries by file name, allowing you to control which files can be retrieved for a given query.
 products:
   - ai-search
 date: 2025-06-19
 ---
 
-In [AI Search](/ai-search/), you can now [filter](/ai-search/configuration/metadata/) by an object's file name using the `filename` attribute, giving you more control over which files are searched for a given query.
+In [AutoRAG](/ai-search/), you can now [filter](/ai-search/configuration/metadata/) by an object's file name using the `filename` attribute, giving you more control over which files are searched for a given query.
 
-This is useful when your application has already determined which files should be searched. For example, you might query a PostgreSQL database to get a list of files a user has access to based on their permissions, and then use that list to limit what AI Search retrieves.
+This is useful when your application has already determined which files should be searched. For example, you might query a PostgreSQL database to get a list of files a user has access to based on their permissions, and then use that list to limit what AutoRAG retrieves.
 
 For example, your search query may look like:
 
@@ -23,6 +23,6 @@ const response = await env.AI.autorag("my-autorag").search({
 });
 ```
 
-This allows you to connect your application logic with AI Search's retrieval process, making it easy to control what gets searched without needing to reindex or modify your data.
+This allows you to connect your application logic with AutoRAG's retrieval process, making it easy to control what gets searched without needing to reindex or modify your data.
 
-Learn more in AI Search's [metadata filtering documentation](/ai-search/configuration/metadata/).
+Learn more in AutoRAG's [metadata filtering documentation](/ai-search/configuration/metadata/).

src/content/changelog/ai-search/2025-07-08-autorag-jobs-view.mdx

@@ -1,25 +1,25 @@
 ---
-title: Faster indexing and new Jobs view in AI Search
+title: Faster indexing and new Jobs view in AutoRAG
 description: Track your indexing pipeline in real time with 3–5× faster indexing and a new Jobs dashboard.
 products:
   - ai-search
 date: 2025-07-08
 ---
 
-You can now expect **3-5× faster indexing** in AI Search, and with it, a brand new **Jobs view** to help you monitor indexing progress.
+You can now expect **3-5× faster indexing** in AutoRAG, and with it, a brand new **Jobs view** to help you monitor indexing progress.
 
-With each AI Search, indexing jobs are automatically triggered to sync your data source (i.e. R2 bucket) with your Vectorize index, ensuring new or updated files are reflected in your query results. You can also trigger jobs manually via the [Sync API](/api/resources/ai-search/subresources/rags/) or by clicking “Sync index” in the dashboard.
+With each AutoRAG, indexing jobs are automatically triggered to sync your data source (i.e. R2 bucket) with your Vectorize index, ensuring new or updated files are reflected in your query results. You can also trigger jobs manually via the [Sync API](/api/resources/ai-search/subresources/rags/) or by clicking “Sync index” in the dashboard.
 
 With the new jobs observability, you can now:
 
 - View the status, job ID, source, start time, duration and last sync time for each indexing job
 - Inspect real-time logs of job events (e.g. `Starting indexing data source...`)
-- See a history of past indexing jobs under the Jobs tab of your AI Search
+- See a history of past indexing jobs under the Jobs tab of your AutoRAG
 
-![AI Search jobs](~/assets/images/changelog/ai-search/autorag-jobs-view.gif)
+![AutoRAG jobs](~/assets/images/changelog/ai-search/autorag-jobs-view.gif)
 
 This makes it easier to understand what’s happening behind the scenes.
 
-**Coming soon:** We’re adding APIs to programmatically check indexing status, making it even easier to integrate AI Search into your workflows.
+**Coming soon:** We’re adding APIs to programmatically check indexing status, making it even easier to integrate AutoRAG into your workflows.
 
 Try it out today on the [Cloudflare dashboard](https://dash.cloudflare.com/?to=/:account/ai/autorag).

src/content/docs/ai-search/platform/release-note.mdx

@@ -13,6 +13,6 @@ import { ProductReleaseNotes } from "~/components";
 
 This release notes section covers regular updates and minor fixes. For major feature releases or significant updates, see the [changelog](/changelog).
 
-{/* <!-- Actual content lives in /src/content/release-notes/autorag.yaml. Update the file there for new entries to appear here. For more details, refer to https://developers.cloudflare.com/style-guide/documentation-content-strategy/content-types/changelog/#yaml-file --> */}
+{/* <!-- Actual content lives in /src/content/release-notes/ai-search.yaml. Update the file there for new entries to appear here. For more details, refer to https://developers.cloudflare.com/style-guide/documentation-content-strategy/content-types/changelog/#yaml-file --> */}
 
 <ProductReleaseNotes />

src/content/docs/reference-architecture/diagrams/ai/ai-rag.mdx

@@ -4,7 +4,7 @@ pcx_content_type: reference-architecture-diagram
 tags:
   - AI
 products:
-  - ai-search
+  - AI Search
   - Workers AI
   - Workers
   - Queues