jarulraj

Would love to learn how the community using LLMs for data wrangling and exchange prompts [1]. For example, we iterated several times on the following "data structuring" prompt for our Github Stargazers app [2,3]:

--- Prompt to GPT-3.5

You are given a block of disorganized text extracted from the GitHub user profile of a user using an automated web scraper. The goal is to get structured results from this data. Extract the following fields from the text: name, country, city, email, occupation, programming_languages, topics_of_interest, social_media. If some field is not found, just output fieldname: N/A. Always return all the 8 field names. DO NOT add any additional text to your output. The topic_of_interest field must list a broad range of technical topics that are mentioned in any portion of the text. This field is the most important, so add as much information as you can. Do not add non-technical interests. The programming_languages field can contain one or more programming languages out of only the following 4 programming languages - Python, C++, JavaScript, Java. Do not include any other language outside these 4 languages in the output. If the user is not interested in any of these 4 programming languages, output N/A. If the country is not available, use the city field to fill the country. For example, if the city is New York, fill the country as United States. If there are social media links, including personal websites, add them to the social media section. Do NOT add social media links that are not present. Here is an example (use it only for the output format, not for the content):

  name: Pramod Chundhuri
  country: United States
  city: Atlanta
  email: pramodc@gatech.edu
  occupation: PhD student at Georgia Tech
  programming_languages: Python, C++
  topics_of_interest: PyTorch, Carla, Deep Reinforcement Learning, Query Optimization
  social_media: https://pchunduri6.github.io

----

  [1] https://en.wikipedia.org/wiki/Data_wrangling
  [2] https://github.com/pchunduri6/stargazers-reloaded
  [3] https://medium.com/evadb-blog/stargazers-reloaded-llm-powered-analyses-of-your-github-community-aef9288eb8a5

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!