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Nesting, Vector Search, and Scalability in Typesense

TLDR Vishal asks about nesting, vector search, and scalability in Typesense. Kishore Nallan explains that nesting can be done to any level, vectors can be stored at any level, and the hnsw library is used for cos similarity implementation.

7mo

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May 09, 2023 (7 months ago)

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Vishal

02:16 AM

Question - lets say I have automobile manufacturers as collections (honda, toyota, etc), within each manufacturer, i want to store multiple models with attributes for those models; how does this work with typesense? Is this possible? How far does the nesting go?

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Kishore Nallan

02:35 AM

You can nest to any level. If you can post a sample document, I can comment further.

Vishal

02:46 AM

Would look like this:
• honda
◦ country of origin
◦ otherstats1
◦ otherstats2
◦ automobiles
▪︎ model_id: 01
• model_name: accord
• model_year: 2020
• model_color: blue
◦ color_attribute1:34
◦ color_attribute2:343
▪︎ model_id:02
• model_name: accord
• model_year: 2020
• model_color: blue
◦ color_attribute1:34
◦ color_attribute2:343

02:47

Vishal

02:47 AM

so would i be able to search for models with color attributes above a certain value?

02:47

Vishal

02:47 AM

(across all manufacturers honda, toyota, etc)

Kishore Nallan

02:48 AM

Yes but only a single document will store everything?

Vishal

02:49 AM

I am considering each model_id to be a separate document

02:49

Vishal

02:49 AM

is this correct?

Kishore Nallan

02:51 AM

Yes correct and you can use group_by if you want to aggregate on a brand

Vishal

02:52 AM

ok, so in sql, one would likely make two tables: one for the highest level (manufacturers) and one for car models (models) and join the two on some key; in typesense everything can just be nested infinitely?

02:53

Vishal

02:53 AM

also, for vector search, this will also work at any level of nesting?

Kishore Nallan

02:57 AM

For vector search you've to choose to vectorize a given string.

Vishal

02:58 AM

for vector search, i expect to be able to store a n-dimensional vector as an array at any arbitrary level of nesting

02:59

Vishal

02:59 AM

(eg in the example i gave, assume model_id has a model_vector and color_attribute1 has a color_attribute1_vector)

Kishore Nallan

03:01 AM

Vectors need a large context to be effective. You can combine several field values and then vectorize that.

03:01

Kishore Nallan

03:01 AM

As long as you follow the same order in all records

Vishal

03:01 AM

understood, the generation of said vectors would happen external to this and not necessarily index the text fields therein

Kishore Nallan

03:02 AM

In next version of typesense we can do this augmentation of fields automatically to generate a vector

Vishal

03:02 AM

which model would you use?

Kishore Nallan

03:02 AM

But yes it must be a separate field which you can then vector outside.

Vishal

03:02 AM

ok -- 2000 dimension vector is fine I assume?

Kishore Nallan

03:02 AM

We support a few. E.g. sentence transformers models.

03:03

Kishore Nallan

03:03 AM

We also support openAI integration

Vishal

03:03 AM

understood, will likely be using something from transformers

03:04

Vishal

03:04 AM

ok, one more q - the cos similarity implementation is scalable and on par with faiss?

Kishore Nallan

03:05 AM

We use hnsw library which is pretty robust

Vishal

03:07 AM

https://medium.com/gsi-technology/ann-benchmarks-a-data-scientists-journey-to-billion-scale-performance-db191f043a27 fyi

03:08

Vishal

03:08 AM

ok -- thanks for all of the help, big fan of typesense from what i've seen so far!

Kishore Nallan

03:20 AM

Thank you!

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