Explain return values from vector functions #1104
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JPryce-Aklundh
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| @@ -35,6 +35,8 @@ For more details, see the {link-vector-indexes}#similarity-functions[vector inde | |||
| | Both vectors must be of the same dimension. | |||
| | 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. | |||
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| 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. |
| @@ -63,6 +65,8 @@ For more details, see the {link-vector-indexes}#similarity-functions[vector inde | |||
| | 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 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. | |||
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| 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. |
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JPryce-Aklundh
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