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Understanding Pgvector's HNSW Index Storage in Postgres

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Re: Understanding Pgvector's HNSW Index Storage in Postgres

#11

Post by tveita »

jadbox wrote: post #2

Vector searching had strange quirks where searching for "cat" would return mostly a lot of paragraphs unrelated to the word. I was using 3072 length for OAI text-embedding-3-large. Each entry was roughly 1-2 paragraphs. For my recent project, I found that PGroonga was more reliable for full text document lookup (with some fuzzy matching support).

General text embeddings aren't that great for keyword search. The best embedding match for a paragraph is going to be another paragraph with the same meaning. "Cats are natural carnivores." should be very similar to "Felines eat meat.", and somewhat similar to "Tigers eat meat."

"cat" is not a sentence - the embedding should be close to other contextless single words, and maybe just slightly closer to paragraphs about cats than paragraphs about non-cats.

A simple way to use embeddings for search is to generate text of the same kind you expect your users to input - e.g. if you expect users to ask questions in natural language, have a cheap LLM turn paragraphs like "Cats are natural carnivores." into questions like "What do cats eat?" "What is natural behaviour for cats?" and then index the embeddings for those.

Re: Understanding Pgvector's HNSW Index Storage in Postgres

#12

Post by ryoshu »

jadbox wrote: post #2

Vector searching had strange quirks where searching for "cat" would return mostly a lot of paragraphs unrelated to the word. I was using 3072 length for OAI text-embedding-3-large. Each entry was roughly 1-2 paragraphs. For my recent project, I found that PGroonga was more reliable for full text document lookup (with some fuzzy matching support).

bm25 + vector search works better than vector search alone.

Re: Understanding Pgvector's HNSW Index Storage in Postgres

#14

Post by codetrotter »

tveita wrote: post #11
jadbox wrote: post #2

Vector searching had strange quirks where searching for "cat" would return mostly a lot of paragraphs unrelated to the word. I was using 3072 length for OAI text-embedding-3-large. Each entry was roughly 1-2 paragraphs. For my recent project, I found that PGroonga was more reliable for full text document lookup (with some fuzzy matching support).

General text embeddings aren't that great for keyword search. The best embedding match for a paragraph is going to be another paragraph with the same meaning. "Cats are natural carnivores." should be very similar to "Felines eat meat.", and somewhat similar to "Tigers eat meat." "cat" is not a sentence - the embedding should be close to other contextless single words, and maybe just slightly closer to paragraphs abou…

I wonder what kind of sentence should be the best match when using embeddings to search for things similar to the following grammatically correct sentence:

Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo

https://en.wikipedia.org/wiki/Buffalo_buffalo_Buffalo_buffal...

Well… I guess the most relevant results would simply be text from that Wikipedia article about this sentence, or from comments on HN and Reddit about this specific sentence, and then various texts about grammar that talk about similar concepts to this.

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