evadb

Yes, this is an excellent RAG use-case! The vector index that I use in the repository uses EvaDB [1] to retrieve the top-K matches to the user queries from the available data sources. So, you can manually inspect the best matches to your query from the research article and verify the correctness of the LLM responses. [1] https://github.com/georgia-tech-db/evadb

Cool project! Can you elaborate on "scalable" AI deployment? We are exploring the data + AI space in EvaDB and would love to exchange notes [1]. [1] https://github.com/georgia-tech-db/evadb

Very cool project, @MatthausK! What are your thoughts on reducing LLM cost? We are also exploring LLM-based data wrangling using EvaDB and cost is an important concern [1, 2, 3]. [1] https://github.com/georgia-tech-db/evadb [2] https://medium.com/evadb-blog/stargazers-reloaded-llm-powere... [3] https://github.com/pchunduri6/stargazers-reloaded

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 JavaSc

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) FROM ObjectDetectionVideos WHERE id < 20 LIMIT 5; SELECT id FROM ObjectDetectionVideos WHERE ['pedestrian', 'car'] <@ Yolo(data).label; Would love to hear your thoughts on ChatCraft and a more constrained function interface.

Nice writeup! Here is another example of using LLMs is to augmenting existing software that we are exploring -- specifically SQL database systems. We are using LLMs inside a SQL query to power a "soft join" between SQL tables for when a correspondence is only implied (e.g. different address formats, etc.). --- Create a reference table that maps neighborhoods to zipcodes using ChatGPT CREATE TABLE reference_table AS SELECT parkname, parktype, ChatGPT( "Return the San Francisco neighborhood name when provided with a zipcode. The possible neighborhoods are: {neighbourhoods_str}. The response should an item from the provided list. Do not add any more words.", zipcode) FROM postgres_db.recreational_park_dataset; --- Map Airbnb listings to park SELECT airbnb_listing.neighbourhood FROM

Thanks for sharing that observation on customer chatbots. 1. Will that query look like this: SELECT LLM("{user_question}", order_info) FROM postgres_data.order_table WHERE user_id = “101”; 2. How will a feature store, like Hopsworks, help in this app? Shameless self-plug: We are building EvaDB [1], a query engine for shipping fast AI-powered apps with SQL. Would love to exchange notes on such apps if you're up for it! [1] https://github.com/georgia-tech-db/evadb

Congrats on the launch, @ukuina! Neat idea. Would love to learn more about how you are querying the summaries/comments database and how you are planning to extend the queries in this app. Shameless self-plug: We are building EvaDB [1], a query engine for shipping fast AI-powered apps with SQL. Here is an illustrative query for analyzing food reviews stored in Postgres and generating responses for negative reviews: SELECT ChatGPT("Respond to the review with a solution to address the reviewer's concern", review) FROM postgres_data.review_table WHERE ChatGPT("Is the review positive or negative? Only reply 'positive' or 'negative'.", review) = "negative" AND location = “waffle house”; It would be interesting to learn about the queries needed for supporting HackYourNews application. Would love to exchange notes on this if you'

Highly recommend Lark for Python projects -- it's easy to use :) Their IDE was super useful for debugging the grammar: https://www.lark-parser.org/ide/ We use Lark for a SQL-like language tailored for using AI models in EvaDB: https://github.com/georgia-tech-db/evadb/blob/master/evadb/p... https://github.com/georgia-tech-db/evadb/ If you like Lark, please consider sponsoring them: https://github.com/sponsors/lark-parser

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

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

We also love SQL, @potatoman22 :) BTW, EVA itself is designed for analyzing sports games -- like touchdowns in a football game. This notebook shows how an action recognition model can be run on an American Sign Language video to identify the correct word [1]. Similarly, a model for identifying touchdowns and players can be used to run interesting queries over a football game. SELECT FIRST(id), ASLActionRecognition(SEGMENT(data)) FROM ASL_ACTIONS SAMPLE 5 GROUP BY '16f'; [1] https://github.com/georgia-tech-db/eva/blob/master/tutorials...

Thank you for reporting the typo. We are working on fixing it. Please find the page here https://github.com/georgia-tech-db/eva/blob/master/docs/sour...

Great question! We have a built-in integration test for 1 million images [1] and have benchmarked EVA on large unstructured datasets for our ACM SIGMOD paper [2]. Coming up with an open benchmark for AI-Relational database systems is essential [3]. Please share your thoughts on any ideas for benchmarking :) [1] https://github.com/georgia-tech-db/eva/blob/e123820c79b902d5... [2] https://dl.acm.org/doi/abs/10.1145/3514221.3526142 [3] https://dl.acm.org/doi/10.1145/3299869.3324955