# Vector Search Filter and Cosine Similarity in Typesense

TLDR **LT** asked about vector search result filtering and cosine similarity. **Kishore Nallan** explained how cosine distance is related to similarity and shared plans to add a threshold restriction option.

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##### Mar 25, 2023 (6 months ago)

##### LT

10:09 PM**Kishore Nallan**,

**Krish**asked 2 months ago about the vector search, wether the results can be filtered with "filter_by=vector_distance:>0.25".

You answered then "Nope that's not possible but these distances don't carry any semantic absolute meaning. They are only useful as relative values."

But just for my udneerstanding: The vector distance is the cosine similarity between the requested embedding and the on in typesense right? Because if I want to store face emebddings and the cosine similarity indicates how similar they are, the distance is very meaningful.

Are there plans to add filter_by functionality for vector distance or is the "go-to-way" for the next years to postprocess the results?

##### LT

10:51 PM##### Mar 26, 2023 (6 months ago)

##### Kishore Nallan

01:49 PM*similarity*is a range between -1 to 1.

`cosine_distance = 1 - cosine_similarity`

When 2 vectors are exactly

**same**, the cosine similarity be 1, so the cosine distance will be 0.

Likewise, when 2 vectors are very

**different**then the cosine similarity will be -1 so the cosine distance will be 2.

We plan to add a way to restrict results based on a threshold. When I meant "don't carry any semantic absolute meaning" I meant generically across datasets. It's still useful to have a cutoff threshold for some datasets so we will be adding an option for that.

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