# Similarity search based on multiple text attributes **URL:** \ **Category:** Intermediate\ **Created:** [December 4, 2023, 8:39pm UTC](https://discuss.huggingface.co/t/similarity-search-based-on-multiple-text-attributes/64576 "2023-12-04T20:39:31Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![jssaluja](https://avatars.discourse-cdn.com/v4/letter/j/b4bc9f/32.png) [@jssaluja](https://discuss.huggingface.co/u/jssaluja)\ **Post date:** [December 4, 2023, 8:39pm UTC](https://discuss.huggingface.co/t/similarity-search-based-on-multiple-text-attributes/64576/1 "2023-12-04T20:39:31Z")
Good day experts I need to build a system to recommend related rows. If query = row1, I need results ranked by similarity which combines signal from all attributes. Each row has multiple text attributes. Each of these attribute is a independent document. Attribute1 = doc1, Attribute2 = doc2… and so on. Number of documents per row is variable Row 1 may contain Attribute1 = doc1, Attribute 2 = doc2. Row 2 may contain Attribute1 = doc1, Attribute 2 = doc2 … Attribute10 = doc10. I considered two approaches. 1. Concatenate first n characters of each attribute (doc) and get embeddings for one concatenated string 2. Get embeddings for each attribute (doc) separately Approach 1 saves us cost of fetching and storing embeddings. This requires concatenating chunks from separate documents. I have a concern. This approach may result in a concatenated string with abrupt change in content and semantic meaning at the points of concatenation. Approach 2 is costly, we need to fetch embeddings for multiple documents per row. However I think, the embeddings are more usable as each document retains its meaning. Question 1 - Which approach do experts recommend 1 or 2, and why ? Question 2 - Any guidance on doing similarity search for embeddings from multiple text attributes ? Can we simply concatenate the embeddings ? How do we handle rows with different number of attributes ie., different length of concatenated embeddings ?