gorilla
evadb
gorilla | evadb | |
---|---|---|
51 | 27 | |
10,118 | 2,578 | |
- | 0.9% | |
8.9 | 9.5 | |
4 days ago | 13 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
gorilla
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Launch HN: Nango (YC W23) – Open-Source Unified API
Do you leverage https://gorilla.cs.berkeley.edu/ at all? If not, perhaps consider if it would solve some pain for you.
- Autonomous LLM agents with human-out-of-loop
- Show HN: I made a script to scrape your Facebook group
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Pushing ChatGPT's Structured Data Support to Its Limits
* Gorilla [https://github.com/ShishirPatil/gorilla]
Could be interesting to try some of these exercises with these models.
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Guidance for selecting a function-calling library?
gorilla
- Gorilla: An API Store for LLMs
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Show HN: OpenAPI DevTools – Chrome ext. that generates an API spec as you browse
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
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RestGPT
"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/:
- Calling APIs with Natural Language
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Shishir Patil: Teaching AI to Use APIs with Gorilla LLM – Humans of AI Podcast
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
evadb
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Show HN: Stargazers Reloaded – LLM-Powered Analyses of Your GitHub Community
Hey friends!
We have built an app for getting insights about your favorite GitHub community using large language models.
The app uses LLMs to analyze the GitHub profiles of users who have starred the repository, capturing key details like the topics they are interested in. It takes screenshots of the stargazer's GitHub webpage, extracts text using an OCR model, and extracts insights embedded in the extracted text using LLMs.
This app is inspired by the “original” Stargazers app written by Spencer Kimball (CEO of CockroachDB). While the original app exclusively used the GitHub API, this LLM-powered app built using EvaDB additionally extracts insights from unstructured data obtained from the stargazers’ webpages.
Our analysis of the fast-growing GPT4All community showed that the majority of the stargazers are proficient in Python and JavaScript, and 43% of them are interested in Web Development. Web developers love open-source LLMs!
We found that directly using GPT-4 to generate the “golden” table is super expensive — costing $60 to process the information of 1000 stargazers. To maintain accuracy while also reducing cost, we set up an LLM model cascade in a SQL query, running GPT-3.5 before GPT-4, that lowers the cost to $5.5 for analyzing 1000 GitHub stargazers.
We’ve been working on this app for a month now and are excited to open source it today :)
Some useful links:
* Blog Post - https://medium.com/evadb-blog/stargazers-reloaded-llm-powere...
* GitHub Repository - https://github.com/pchunduri6/stargazers-reloaded/
* EvaDB - https://github.com/georgia-tech-db/evadb
Please let us know what you think!
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Language Model UXes in 2027
The discord link seems to be not working. Just a heads up.
The YOLO example on your Github page is super interesting. We are finding it easier to get LLMs to write functions with a more constrained function interface in EvaDB. Here is an example of an YOLO function in EvaDB: https://github.com/georgia-tech-db/evadb/blob/staging/evadb/....
Once the function is loaded, it can be used in queries in this way:
SELECT id, Yolo(data)
- EvaDB: Bring AI to your Database System
- Show HN: I wrote a RDBMS (SQLite clone) from scratch in pure Python
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Gorilla: Large Language Model Connected with APIs
Neat idea, @shishirpatil! We are developing EvaDB [1] for shipping simpler, faster, and cost-effective AI apps. Can you share your thoughts on transforming the output of the Gorilla LLM to functions in EvaDB apps -- like this function that uses the HuggingFace API -- https://evadb.readthedocs.io/en/stable/source/tutorials/07-o...?
[1] https://github.com/georgia-tech-db/eva
- PrivateGPT in SQL
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Eva AI-Relational Database System
Thanks for checking! Currently, we have a Docker image for deploying EVA [1]. We plan to release a Terraform config soon that will make it easier to deploy EVA DB on an AWS/Azure server with GPUs.
[1] https://github.com/georgia-tech-db/eva/tree/master/docker
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This week's top indie A.I projects, launches and resources
EVA AI-Relational Database System; build simpler and faster AI-powered apps
- Show HN: EVA – AI-Relational Database System
What are some alternatives?
DB-GPT - AI Native Data App Development framework with AWEL(Agentic Workflow Expression Language) and Agents
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
Voyager - An Open-Ended Embodied Agent with Large Language Models
emdash - 📚🧙♂️ Wisdom indexer — use AI to organize text snippets so you can actually remember & learn from what you read
gorilla-cli - LLMs for your CLI
jsonformer - A Bulletproof Way to Generate Structured JSON from Language Models
Gin - Gin is a HTTP web framework written in Go (Golang). It features a Martini-like API with much better performance -- up to 40 times faster. If you need smashing performance, get yourself some Gin.
MindsDB - The platform for customizing AI from enterprise data
GirlfriendGPT - Girlfriend GPT is a Python project to build your own AI girlfriend using ChatGPT4.0
gpt-json - Structured and typehinted GPT responses in Python
SuperAGI - <⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.