evadb

Great question! Yes, EVA supports NLP pipelines thanks to our recent integration of Hugging Face pipelines last month. Here is an illustrative text classification application: -- Text classification application in EVA CREATE TABLE IF NOT EXISTS MyCSV (id INTEGER UNIQUE, comment TEXT(30)); LOAD CSV 'csv_file_path' INTO MyCSV; CREATE UDF HFTextClassifier TYPE HuggingFace 'task' 'text-classification'; SELECT HFTextClassifier(comment) FROM MyCSV; EVA supports many other NLP pipelines, including summarization and text2text generation. [2] is an illustrative notebook that presents an HF-based object segmentation pipeline (not NLP-based though). We would love to jointly explore how to best support your NLP pipeline. Please consider opening an issue with more details on your use case. [1] https://github.com/georgia-tech-db/eva/b

Hey Simon! Thanks for sharing this. I have long admired your work on Datasette :) We will check out your posts for ideas on coping with prompt injection. I just came across your recent post on the ChatGPT SQL function in SQLite [1]. We just added a ChatGPT-based UDF in EVA [2]. I would love to hear your thoughts on the difference between these two approaches. Another coincidence is that EVA uses SQLite for managing structured data by default. Can EVA's SQLite database be an interesting use case for Datasette? [1] https://simonwillison.net/2023/Apr/29/enriching-data/ [2] https://github.com/georgia-tech-db/eva/pull/655

Thanks for the clarification. EVA currently does not support remote GPUs. Our ongoing integration [1] of the Ray distributed compute framework [2] into EVA will soon allow us to support remote GPUs. We would love to jointly explore how to best support remote GPUs. Please consider opening an issue with more details on your use case. [1] https://github.com/georgia-tech-db/eva/blob/master/eva/exper... [2] https://docs.ray.io/en/latest/cluster/vms/getting-started.ht...

Great questions! EVA does not currently support a weighted similarity search. We are working on creating a notebook to illustrate similarity queries. But, EVA already supports the queries of this form: -- Step 1: Extract objects in Reddit images using the YOLO object detector CREATE TABLE reddit_dataset_object (name, data, bboxes) AS SELECT name, data, labels FROM reddit_dataset JOIN LATERAL UNNEST(YoloV5(data)) AS Obj(labels, bboxes, scores); -- Step 2: Build index over features extracted using SIFT CREATE INDEX reddit_sift_object_index ON reddit_dataset_object (SiftFeatureExtractor(Crop(data, bboxes))) USING HNSW; -- Step 3: Retrieve the top 10 most similar images SELECT id FROM reddit_sift_object_index ORDER BY Similarity(SiftFeatureExtractor(Open(”“input_img_path.jpg”)), SiftFeatureExtractor(data))