openapi-devtools VS gorilla

Not specific to the CPG realm, but I've had some very good luck working with the OpenAPI-devtools Chrome Extension[1] (previous discussion here on hackernews[2]) to discover the underlying APIs of various sites that I want to scrape data from.

[1] https://github.com/AndrewWalsh/openapi-devtools

[2] https://news.ycombinator.com/item?id=38012032

As a follow up, the algorithm that powers this makes use of the chrome.devtools.network API. Specifically it passes the Request object that is in the HAR 1.2 archive format.

So if you can pass the equivalent of that in Firefox/other browsers to the insert method and switch things up a bit, it should be relatively straightforward. I will think about pulling out the core logic into its own lib.

https://developer.chrome.com/docs/extensions/reference/devto...

https://developer.chrome.com/docs/extensions/reference/devto...

https://github.com/AndrewWalsh/openapi-devtools/blob/main/sr...

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