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Show HN: Building a web search engine from scratch with 3B neural embeddings

blog.wilsonl.in

71–80 of 124 posts

Re: Show HN: Building a web search engine from scratch with 3B neural embeddings

#71
post #69
post #57

Earlier quoted context omitted.

I don’t think OpenAI train on data processed via the API, unless there’s an exception specifically for this.

Can you truly trust them though?

Yes, it would be disastrous for OpenAI if it got out they are training on B2B data despite saying they don’t.

Re: Show HN: Building a web search engine from scratch with 3B neural embeddings

#74
post #71
post #69

Earlier quoted context omitted.

Can you truly trust them though?

Yes, it would be disastrous for OpenAI if it got out they are training on B2B data despite saying they don’t.

Yeah, so many companies have been completely ruined after similar PR disasters /s

Re: Show HN: Building a web search engine from scratch with 3B neural embeddings

#75
post #51

"There was one surprise when I revisited costs: OpenAI charges an unusually low $0.0001 / 1M tokens for batch inference on their latest embedding model. Even conservatively assuming I had 1 billion crawled pages, each with 1K tokens (abnormally long), it would only cost $100 to generate embeddings for all of them. By comparison, running my own inference, even with cheap Runpod spot GPUs, would cost on the order of 10…

It'd be a way to put crap or poisoned data into their training data if that is the case. I wouldn't.

Re: Show HN: Building a web search engine from scratch with 3B neural embeddings

#76
post #49

A vector-only search engine will fail for a lot of common use cases where the keywords do matter. I tried searching for `garbanzo bean stew` and got totally irrelevant bean recipes.

Yes, indeed. I just tried search "Apple", and apple.com is not on the first page.

Re: Show HN: Building a web search engine from scratch with 3B neural embeddings

#77
post #51

"There was one surprise when I revisited costs: OpenAI charges an unusually low $0.0001 / 1M tokens for batch inference on their latest embedding model. Even conservatively assuming I had 1 billion crawled pages, each with 1K tokens (abnormally long), it would only cost $100 to generate embeddings for all of them. By comparison, running my own inference, even with cheap Runpod spot GPUs, would cost on the order of 10…

> OpenAI charges an unusually low $0.0001 / 1M tokens for batch inference on their latest embedding model.

Is this the drug dealer scheme? Get you hooked later jack up prices? After all, the alternative would be regenerating all your embeddings no?

Re: Show HN: Building a web search engine from scratch with 3B neural embeddings

#78
post #73

The title should be “10x engineer creates Google in their spare time” But seriously what an amazing write up, plus animations, analysis etc etc. Bravo. It was also ironic to see AWS failing quite a few use cases here. Stuff to think about.

Also looking into the AWS limits;

> SQS had very low concurrent rate limits that could not keep up with the throughput of thousands of workers across the pipeline.

I could not find this perhaps the author meant Lambda limits?

> services like S3 have quite low rate limits — there are hard limits, but also dynamic per-account/bucket quotas

You have virtually unlimited throughput with prefix partitions

Re: Show HN: Building a web search engine from scratch with 3B neural embeddings

#79
post #71
post #69

Earlier quoted context omitted.

Can you truly trust them though?

Yes, it would be disastrous for OpenAI if it got out they are training on B2B data despite saying they don’t.

Have they said they don't? (actually curious)
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