awesome-test-automation VS gorilla

https://github.com/atinfo/awesome-test-automation/blob/maste...

vcr.py, playback, and rr do [HTTP,] test recording and playback. httprunner can replay HAR. DevTools can save http requests and responses to HAR files.

awesome-web-archiving lists a number of tools that work with WARC; but only har2warc:

I don't have personal experience, but I have this link bookmarked, might help you: https://github.com/atinfo/awesome-test-automation/blob/master/python-test-automation.md

For large projects, simple assert statements aren't enough to adequately write and manage tests. You'll require built-in module unittest or popular third-party modules like pytest. See python test automation frameworks for more resources.

Do you leverage https://gorilla.cs.berkeley.edu/ at all? If not, perhaps consider if it would solve some pain for you.

* Gorilla [https://github.com/ShishirPatil/gorilla]

Could be interesting to try some of these exercises with these models.

gorilla

Nice this made me go back and check up on the Gorilla LLM project [1] to see whats they are doing with API and if they have applied their fine tuning to any of the newer foundation models but looks like things have slowed down since they launched (?) or maybe development is happening elsewhere on some invisible discord channel but I hope the intersection of API calling and LLM as a logic processing function keep getting focus it's an important direction for interop across the web.

[1] https://github.com/ShishirPatil/gorilla

"Gorilla: Large Language Model Connected with Massive APIs" (2023) https://gorilla.cs.berkeley.edu/ :

> Gorilla enables LLMs to use tools by invoking APIs. Given a natural language query, Gorilla comes up with the semantically- and syntactically- correct API to invoke. With Gorilla, we are the first to demonstrate how to use LLMs to invoke 1,600+ (and growing) API calls accurately while reducing hallucination. We also release APIBench, the largest collection of APIs, curated and easy to be trained on! Join us, as we try to expand the largest API store and teach LLMs how to write them!

eval/:

Humans of AI Podcast #7

An amazing conversation with Shishir Patil the creator of the Gorilla LLM, a large language model specifically trained to use APIs!

Shishir is currently a 5th year PhD student at the University of California, Berkeley whose work broadly covers ML-Systems, LLMs, Edge-ML, and Sky computing.

Definitely give the episode a listen to hear Shishir's story.

And to read more about #GorillaLLM, check out the project page!

https://gorilla.cs.berkeley.edu