oasdiff VS clip-retrieval

We will use an open-source GitHub action, oasdiff-action, based on the tool ‘oasdiff.’

Nice, Have you come across this tool oasdiff from the article? It may help with detect API breaking changes in swagger

While you might wish that pull request reviewers would spot any breaking changes, relying solely on this method is not certain and might lead to failure eventually. If you have OpenAPI/Swagger documentation for your APIs, these can be version-controlled and included in a CI pipeline. APISIX doesn't natively support direct integration with version control systems like Git for API specification changes. However, you can set up a process outside APISIX. Tools like Oasdiff or Bump can identify changes in API specs, and trigger a CI pipeline (add GitHub Action) that runs tests against the route endpoints in APISIX to ensure no breaking changes are introduced.

Now that we have a way to lookup our API’s behavior with Git, we can start testing for breaking changes between versions of our API. We’ll be using Optic (an open source tool I created) to do just that. If you are looking for other options I recommend https://github.com/OpenAPITools/openapi-diff or https://github.com/Tufin/oasdiff.

2. Delete the API at the sunset date or later

People seem to want such a process in order to prevent breaking-changes.

I wrote a diff tool for OpenAPI spec which supports detection of breaking-changes and I recently extended it to support this process and a bit more.

Now I'm looking for feedback.

Proposed Solution (currently in Beta): https://github.com/Tufin/oasdiff#non-breaking-removal-of-deprecated-resources

Related requests:

The tool clip-retreival lets you filter those 400 million images to whatever subsets you're interested in --- for example, 10,000 images of (mostly) handwriting.

Hehe, well you know, PR welcome, the front end is 500 lines https://github.com/rom1504/clip-retrieval/blob/main/front/sr...

Other people have done a few alternate front ends already

This one is meant to be functional, but could sure be made prettier

This might work: https://github.com/rom1504/clip-retrieval .

HaveIBeenTrained uses clip retrieval to search the Laion-5B and Laion-400M image datasets. These are currently the largest public text-to-image datsets, and they are used to train models like Stable Diffusion, Imagen, among many others.

Done https://github.com/rom1504/clip-retrieval/commit/53e3383f58b...

Using clip for searching is better than direct text indexing for a variety of reasons but here for example because it matches better what stable diffusion sees

Based on OpenAI's CLIP and the clip-retrieval library (https://github.com/rom1504/clip-retrieval), I've built an end-to-end demo for a semantic and similarity image search engine. It's incredibly powerful for finding similar images amongst large image datasets, or just submitting text/natural language queries and finding the most relevant images in your dataset. Really useful tool for introspection into large datasets before annotation or ML work begins. This could potentially be used to filter or downsize your datasets by several orders of magnitude and make annotation and ML work easier and less costly.

Checkout the demo here:

http://ec2-52-39-251-116.us-west-2.compute.amazonaws.com/

And you can checkout our website or email me for updates and email list, etc.:

https://machineperception.co

Assuming you have an NVIDIA GPU, you can build a semantic search engine by indexing CLIP embeds (image or text).

https://github.com/rom1504/clip-retrieval