-
Notifications
You must be signed in to change notification settings - Fork 64
Explain return values from vector functions #1104
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Explain return values from vector functions #1104
Conversation
parnmatt
left a comment
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Looks reasonable, just suggesting being consistent with the numbers being in backticks
| | Both vectors must be {link-vector-indexes}#indexes-vector-similarity-cosine[*valid*] with respect to cosine similarity. | ||
| | The implementation is identical to that of the latest available vector index provider (`vector-2.0`). | ||
| | `vector.similarity.cosine()` returns the neighborhood of nodes along with their respective cosine similarity scores, sorted in descending order of similarity. | ||
| The similarity score range from `0` and 1, with scores closer to `1` indicating a higher degree of similarity between the indexed vector and the query vector. |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
| The similarity score range from `0` and 1, with scores closer to `1` indicating a higher degree of similarity between the indexed vector and the query vector. | |
| The similarity score range from `0` and `1`, with scores closer to `1` indicating a higher degree of similarity between the indexed vector and the query vector. |
| | Both vectors must be {link-vector-indexes}#indexes-vector-similarity-euclidean[*valid*] with respect to Euclidean similarity. | ||
| | The implementation is identical to that of the latest available vector index provider (`vector-2.0`). | ||
| | `vector.similarity.euclidean()` returns the neighborhood of nodes along with their respective Euclidean similarity scores, sorted in descending order of similarity. | ||
| The similarity score range from `0` and 1, with scores closer to `1` indicating a higher degree of similarity between the indexed vector and the query vector. |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
| The similarity score range from `0` and 1, with scores closer to `1` indicating a higher degree of similarity between the indexed vector and the query vector. | |
| The similarity score range from `0` and `1`, with scores closer to `1` indicating a higher degree of similarity between the indexed vector and the query vector. |
|
Thanks for the documentation updates. The preview documentation has now been torn down - reopening this PR will republish it. |
No description provided.