Release note
This release notes section covers regular updates and minor fixes. For major feature releases or significant updates, see the changelog.
AI Search now supports EMACS Lisp (.el) files and the .htm extension for HTML documents.
You can now filter which paths to include or exclude from indexing for both website and R2 data sources.
API instance creation is now simpler with optional token_id and model fields.
Website instances now respect sitemap <priority> for indexing order and <changefreq> for re-crawl frequency. Added support for .gz compressed sitemaps and partial URLs in robots.txt and sitemaps.
We have improved indexing performance for all AI Search instances. Support for more and larger files is coming.
Fixed a bug where query rewrites were not visible in the AI Gateway logs.
AI Search now supports custom HTTP headers for website crawling, allowing you to index content behind authentication or access controls.
You can now enable reranking to reorder retrieved documents by semantic relevance and set system prompts directly in API requests for per-query control.
Connect your provider keys through AI Gateway to use models from OpenAI, Anthropic, and other providers for both embeddings and inference.
Our conversion utility can now convert .docx and .odt files to Markdown, making these files available to index inside your AutoRAG instance.
AI Search now includes a Metrics tab to track file indexing, search activity, and top retrievals.
AI Search now supports websites as a data source. Connect your domain to automatically crawl and index your site content with continuous re-crawling. Also includes NLWeb integration for conversational search with /ask and /mcp endpoints.
The maximum number of results returned from a query has been increased from 20 to 50. This allows you to surface more relevant matches in a single request.
When a file is deleted from your R2 bucket, its corresponding chunks are now automatically removed from the Vectorize index linked to your AI Search instance during the next sync.
Indexing is now 3-5x faster. A new Jobs view lets you monitor indexing progress, view job status, and inspect real-time logs.
The cooldown period between sync jobs has been reduced to 3 minutes, allowing you to trigger syncs more frequently.
You can now filter AI Search queries by file name using the filename attribute for more control over which files are searched.
AI Search now returns custom metadata in search responses. You can also add a context field to guide AI-generated answers.
You can now index rich format files (e.g., PDF) up to 4 MB in size, up from the previous 1 MB limit.
The dashboard now includes a new “Processing” step for the indexing pipeline that displays the files currently being processed.
You can now trigger a sync job for an AI Search using the Sync REST API. This scans your data source for changes and queues updated or previously errored files for indexing.
Files modified in your source R2 bucket will now be updated in the AI Search index during the next sync. For example, if you upload a new version of an existing file, the changes will be reflected in the index after the subsequent sync job. Please note that deleted files are not yet removed from the index. We are actively working on this functionality.
Files that failed to index will now be automatically retried in the next indexing job. For instance, if a file initially failed because it was oversized but was then corrected (e.g. replaced with a file of the same name/key within the size limit), it will be re-attempted during the next scheduled sync.
Resolved an issue where certain characters (e.g. '#') were being cut off during the recursive chunking and embedding process. This fix ensures complete character processing in the indexing process.
AI Search now supports R2 buckets configured with European Union (EU) jurisdiction restrictions. Previously, files in EU-restricted R2 buckets would not index when linked. This issue has been resolved, and all EU-restricted R2 buckets should now function as expected.
Filter search results by folder and timestamp to enable multitenancy and control the scope of retrieved results.
AI Search now supports response streaming in the AI Search method of the Workers binding, allowing you to stream results as they're retrieved by setting stream: true.
AI Search allows developers to create fully-managed retrieval-augmented generation (RAG) pipelines powered by Cloudflare allowing developers to integrate context-aware AI into their applications without managing infrastructure. Get started today on the Cloudflare Dashboard.
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