evadb

Got it! EVA is designed for the local use case. You can define a Python function that wraps around the LLM model and use it anywhere in the query (we refer to such functions as user-defined functions or UDFs). This notebook illustrates a UDF that wraps around a custom PyTorch vision model: https://evadb.readthedocs.io/en/stable/source/tutorials/04-c... These functions can be written quickly (~50 lines of Python code). Here is the built-in Resnet50 UDF in EVA: https://github.com/georgia-tech-db/eva/blob/master/eva/udfs/... This page describes the steps involved in writing a UDF in EVA: https://evadb.readthedocs.io/en/stable/source/reference/udf.... Please open an issue on the Github repo; we will gladly support your use case :)

Great question! We will be adding a ChatGPT-based user-defined function this week (https://github.com/georgia-tech-db/eva/pull/655/). With LLM-based functions, EVA will support more interesting queries like this: SELECT ChatGPT(TextSummarizer(SpeechRecognizer(audio)), "Is this video related to the Russia-Ukraine war?") FROM VIDEO_CLIPS; Here, EVA sends the audio of each video clip to a speech recognition model on Hugging Face. It then sends the recognized text to a text summarizer model. EVA executes both models on local GPUs. Lastly, EVA sends the text summary to ChatGPT as a part of the prompt. The ChatGPT UDF is executed remotely. The critical feature of EVA is that the query optimizer factors the dollar cost of running models for a given AI task (like a question-answering LLM). It picks the appropriate model pipeline wit

Hi friends, We are building EVA, an AI-Relational database system with first-class support for deep learning models. Our goal with EVA is to create a platform that supports AI-powered multi-modal database applications operating on structured (tables, feature vectors, etc.) and unstructured data (videos, podcasts, pdf, etc.) with deep learning models. EVA comes with a wide range of models for analyzing unstructured data, including models for object detection, OCR, text summarization, audio speech recognition, and more. The key feature of EVA is its AI-centric query optimizer. This optimizer is designed to speed up AI-powered applications using a collection of optimizations inspired by relational database systems. Two of the most important optimizations are: + Caching: EVA automatically reuses previous query results (e.g., inference results