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Expose embeddings API #490

Merged
merged 5 commits into from
May 28, 2024
Merged

Expose embeddings API #490

merged 5 commits into from
May 28, 2024

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fantix
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@fantix fantix commented May 1, 2024

This PR adds EdgeDBAI.generate_embeddings() function to allow users to generate embedding vectors without using the AI index in EdgeDB with custom input text.

I'll create another PR to add docs.

@fantix fantix requested review from 1st1 and elprans May 1, 2024 16:55
stream=True,
).to_httpx_request(),
) as event_source:
event_source.response.raise_for_status()
for sse in event_source.iter_sse():
yield sse.data

def generate_custom_embeddings(self, *inputs: str, model: str):

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working on the JS version of this but my instinct was to name this generateEmbeddings. Is there a significance to the "custom" here? Does it help to denote that these are not the automatically indexed ones?

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@fantix fantix May 1, 2024

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Yeah, that was my intention, but I'm not sure this "custom" makes sense (like in English).

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I wonder if custom communicates enough to pay for the cost. search would be even more specific and point at the intended use case, but maybe that's too restrictive since you might use it outside of ext::ai::search? Does generate_embeddings make it seem too much like you're triggering something rather than doing this one-off embedding generation?

The only reason I ask is that having "custom" here would make me as a developer want to know more about what "custom" means and what other non-custom methods their might be. Maybe that's a good thing and worth adding here, but it feels a little like an unnecessary mental speed bump.

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@fantix fantix May 1, 2024

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Does generate_embeddings make it seem too much like you're triggering something rather than doing this one-off embedding generation?

Yeah, I had the same struggle. I thought about retrieve_embeddings(), which is just worse. (maybe generate_oneoff_embeddings()?)

Maybe that's a good thing and worth adding here, but it feels a little like an unnecessary mental speed bump.

Right, I'll also change it to generate_embeddings() and add an explanation in the docs.

edgedb/ai/core.py Outdated Show resolved Hide resolved
):
if context is None:
context = self.context
) -> typing.Iterator[str]:
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This returned str is a JSON string like:

{"type": "content_block_delta","index":0,"delta":{"type": "text_delta", "text": " blocking"}}

@fantix fantix requested a review from elprans May 2, 2024 22:19
@fantix fantix merged commit 7386cd0 into master May 28, 2024
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@fantix fantix deleted the ai branch May 28, 2024 15:18
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3 participants