#community-help

Finding Similar Documents Using JSON and Embeddings

TLDR Manish wants to find similar JSON documents and asks for advice. Jason suggests using Sentence-BERT with vector query and provides guidance on working with OpenAI embeddings and Typesense. They discuss upcoming Typesense features and alternative models.

7mo

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May 09, 2023 (7 months ago)

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Manish

06:03 PM

Hey guys, I want to figure out similar docs, given a JSON doc. What's a good way to achieve that?

06:04

Manish

06:04 PM

Do I need to run a Sentence-BERT model as mentioned here: https://typesense.org/docs/0.24.0/api/vector-search.html#what-is-an-embedding ?

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Jason

06:08 PM

Yup, Sentence-BERT + vector query would be the way to go

06:08

Jason

06:08 PM

https://github.com/typesense/showcase-ecommerce-store/blob/master/scripts/vector-generation/main.py

06:09

Jason

06:09 PM

Or of course you also use embeddings from OpenAI for eg, but will be more expensive

Manish

06:09 PM

Interesting. How do I get embeddings from open ai?

Jason

06:09 PM

https://platform.openai.com/docs/guides/embeddings/how-to-get-embeddings

Manish

06:10 PM

Interesting. For OpenAI route, do you reckon it would take JSON directly, or do I need to push it all as a single long text string?

Jason

06:11 PM

I’d recommend pushing one long string into OpenAI / S-Bert

06:12

Jason

06:12 PM

*one long string per record

06:12

Jason

06:12 PM

For eg, notice how I concatenate product description, name, category and brand into one string before sending it to S-BERT for each product record here: https://github.com/typesense/showcase-ecommerce-store/blob/33e604a75b81f1de258ff185c775288b27db8335/scripts/vector-generation/main.py#L9

Manish

06:13 PM

I see. if I were to marshal JSON into a single string, do you reckon it's the same thing. Or, that might not work?

06:14

Manish

06:14 PM

{"key": "value", "key2": "value2", ...} style

Jason

06:20 PM

I’m not too sure, but I suspect the added punctuation might be noise and might eat into your token length?

Manish

06:21 PM

interesting. So, basically it's like a "bag of words" sort of approach.

Jason

06:21 PM

Right, that’s how I’ve been doing it

06:24

Jason

06:24 PM

This suggests that punctuation doesn’t affect BERT: https://aclanthology.org/2020.pam-1.15.pdf

06:24

Jason

06:24 PM

Not sure about S-BERT and OpenAI

06:25

Jason

06:25 PM

But the fact that at least some models are punctuation sensitive, might make a case for not adding JSON-like punctuation into the mix

06:25

Jason

06:25 PM

since the original training data might not have too many JSON representations

Manish

06:26 PM

right. makes sense. Thinking I'll have one line per chat. All separated by newlines.

Jason

06:27 PM

Another thing to experiment with, given that usually longer pieces of text do better with semantic search:

You could concatenate all messages from a single thread into one long string and generate embeddings for the full discussion thread…

Manish

06:27 PM

I suppose there's no way to tell wht's a "heading" etc.

Jason

06:28 PM

Correct, and that doesn’t usually matter when generating embeddings, because it’s all text

Manish

06:29 PM

and once I have embeddings, I just stick them into the doc I sent to Typesense.

Jason

06:30 PM

Yup

Manish

06:31 PM

Found this in openAI code:

06:31

Manish

06:31 PM

// OpenAPI suggests replacing newlines (\n) in your input with a single space, as they
// have observed inferior results when newlines are present.
// E.g.
// "The food was delicious and the waiter..."

06:31

Manish

06:31 PM

So, yeah, no newlines then 🙂

Jason

06:32 PM

I see, good to know!

Manish

06:43 PM

Wao, I'm seeing 1500 floats come out of the openAI embedding system.

06:43

Manish

06:43 PM

for a single thread. Is that something typesense can work with?

Jason

06:44 PM

Yup, you’d have to set the num_dim property in the field definitioin in the Typesense collection schema to the number of floats you see

06:44

Jason

06:44 PM

Think it’s 1536

Manish

06:45 PM

could multiple docs have different number of floats? Yup, it's 1536

Jason

06:45 PM

No, a particular model will always generate the same number of floats for all types of input

Manish

06:45 PM

ahh.. interesting

06:46

Manish

06:46 PM

you're right. OpenAI site shows this: "text-embedding-ada-002 cl100k_base 8191 1536"

06:46

Manish

06:46 PM

https://platform.openai.com/docs/guides/embeddings/what-are-embeddings

06:48

Manish

06:48 PM

does typesense use these for search as well as similarity?

Jason

06:48 PM

Typesense uses these any time you use the vector_query parameter… which you’d use for similarity search and semantic search

Manish

06:52 PM

Got it. So, I have a search term, I need to generate it's embeddings first as well, to do a similarity search. OTOH, if I want similar docs, then just give the doc id to TS.

Jason

06:52 PM

Yup exactly

Manish

06:53 PM

hmm... generating embedding per search term might be expensive with openai. So, probably more useful for doc similarity.

Jason

06:53 PM

In the upcoming version of Typesense, we’re adding a way to automatically generate embeddings from within Typesense, so you don’t have to do that extra step both during indexing and search time. And you can then combine regular keyword search with semantic search in a single step

Manish

06:53 PM

That'd save a bunch of trouble.

Jason

06:54 PM

Yeah for sure! We’re also adding an integration with OpenAI, so you can use their embedding models from within Typesense

Manish

06:54 PM

But, I suppose openAI is better than other models that are available? So, there might be a quality difference?

Jason

06:54 PM

Word on the street is that OpenAI embeddings model quality is not that great… which is surprising, but it might be use-case dependent

Manish

06:55 PM

interesting.

06:55

Manish

06:55 PM

which one do you recommend?

Jason

06:57 PM

S-Bert is a good general purpose model. But I heard that Microsoft’s E5 Text Embedding Model is beating a lot of benchmarks…

06:57

Jason

06:57 PM

Trying to find the tweet where I read these benchmarks…

Manish

06:57 PM

came across this one: https://huggingface.co/spaces/mteb/leaderboard

06:57

Manish

06:57 PM

says "instructor-xl" is the top

Jason

06:58 PM

Interesting, haven’t heard of it yet

Manish

07:09 PM

> We’re also adding an integration with OpenAI,
How would that work? For each search request, you'd have to send a call to openAI apis?

Jason

07:10 PM

Correct

07:10

Jason

07:10 PM

We’re going to only enable it for non-prefix searches, meaning we’ll disable it for search-as-you-type use-cases, to avoid running up OpenAI bills

07:11

Jason

07:11 PM

Plus, when you index a document, you’ll indicate which fields in the JSON document to concatenate and send over to OpenAI to generate embeddings with

Manish

07:11 PM

yup -- basically, what I'm doing right now.

07:12

Manish

07:12 PM

That integration would be great. And technically, you could even run your own E5 servers.

Jason

07:12 PM

Yup! We’re also going to ship E5 with Typesense, so if you don’t want to make external API calls you can use that model natively

Manish

07:13 PM

nice.

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