Document vector similarity functions #913
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| | `vector.similarity.euclidean(a, NULL)` returns `NULL`. | ||
| | Both vectors must be of the same dimension. | ||
| | Both vectors must be {link-vector-indexes}#indexes-vector-similarity-euclidean[*valid*] with respect to Euclidean similarity. | ||
| | The implementation is exactly identical to that of the `latest` available vector index provider (currently `vector-2.0`). |
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I think we avoid saying words like "currently" in the docs :)
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Thanks @nilsceberg, looks good overall! I have added some release version information, and also have a few editorial suggestions. Let me know what you think :)
| These vector similarity functions are identical to those used by Neo4j's {link-vector-indexes}[vector search indexes]. | ||
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| Consider for example _k_-nearest neighbor queries, which return the _k_ entities with highest similarity scores based on comparing their associated vectors with a query vector. | ||
| Such queries can be run against vector indexes to quickly get an approximate result. |
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I'm not sure it is clear what "approximate result" means here?
| Such queries can be run against vector indexes to quickly get an approximate result. | |
| Such queries can be run against vector indexes to return an approximate similarity score. |
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That's fair. I wanted to avoid restating too much from the vector index documentation, but I'll see if I can clarify this bit.
It's not the score that's approximate; it's whether or not the true nearest neighbours are included in the result set.
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I think we should not assume that people have read that page, so a bit more might be necessary here (maybe something like "return an approximate result indicating whether or not the true nearest neighbors are included in the results".
| Consider for example _k_-nearest neighbor queries, which return the _k_ entities with highest similarity scores based on comparing their associated vectors with a query vector. | ||
| Such queries can be run against vector indexes to quickly get an approximate result. | ||
| However, they can also be expressed as an exhaustive search using similarity functions directly. | ||
| While this is typically significantly slower than using an index, it is exact and does not require an existing index. |
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| While this is typically significantly slower than using an index, it is exact and does not require an existing index. | |
| While vector functions are typically slower than using an index, they are exact and do not require an existing index. |
Co-authored-by: Jens Pryce-Åklundh <[email protected]>
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Thanks @JPryce-Aklundh, great suggestions - I'll revisit the part you had questions on. |
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@JPryce-Aklundh What do you think about something like this? |
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Great @nilsceberg - thanks! We just need to add the page-role indicating 5.18 on the full vector functions page. Are you OK with me fixing that? |
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@JPryce-Aklundh Please do, thanks! :) |
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You had already added it @nilsceberg :) so I will merge this 👍 |
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Thanks for the documentation updates. The preview documentation has now been torn down - reopening this PR will republish it. |
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