oasdiff VS LAVIS

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:

For multimodal, there's China (*many), then Salesforce.

Been trying for all day to get a working inference for this example: https://github.com/salesforce/LAVIS/tree/main/projects/instructblip

There's also been big breakthroughs in computer vision. Not that long ago it was hard to recognize if a photo contained a bird; that's solved now by models like CLIP, Yolo, or Segment Anything. Now research has moved on to generating 3D scenes from images or interactively answering questions about images.

The new hotness is multimodal vision-language models like InstructBLIP that can interactively answer questions about images. Check out the examples in the github repo, I would not have thought this was possible a few years ago.

Salesforce’s BLIP family has a new member– InstructBLIP, a vision-language instruction-tuning framework using BLIP-2 models. It has achieved state-of-the-art zero-shot generalization performance on a wide range of vision-language tasks, substantially outperforming BLIP-2 and Flamingo. (Source)

Github

Most of my workflows are self-made. For captioning I used Blip-2 in a custom script I made that automates the process by going into directories and their sub-directories and creates a .txt file beside each image. This way I can keep my images organized in their proper directories, without having to put dump them all in a single place.