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))
LIMIT 10;
EVA directly persists the feature vectors in a FAISS index. It does not use a relational database system for this purpose. FAISS supports retrieving the original vector through ID (required for similarity search).
We would love to jointly explore how to support such weighted similarity search queries. Please consider opening an issue with more details on your use case.
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:
https://github.com/georgia-tech-db/eva/blob/bfd424fd5beb3cec...EVA directly persists the feature vectors in a FAISS index. It does not use a relational database system for this purpose. FAISS supports retrieving the original vector through ID (required for similarity search).
We would love to jointly explore how to support such weighted similarity search queries. Please consider opening an issue with more details on your use case.