series_cosine_similarity()
Applies to: ✅ Microsoft Fabric ✅ Azure Data Explorer ✅ Azure Monitor ✅ Microsoft Sentinel
Calculate the cosine similarity of two numerical vectors.
The function series_cosine_similarity() takes two numeric series as input, and calculates their cosine similarity.
Syntax
series_cosine_similarity(series1, series2)
Learn more about syntax conventions.
Parameters
| Name | Type | Required | Description |
|---|---|---|---|
| series1, series2 | dynamic |
✔️ | Input arrays with numeric data. |
Returns
Returns a value of type real whose value is the cosine similarity of series1 with series2.
In case both series length isn't equal, the longer series will be truncated to the length of the shorter one.
Any non-numeric element of the input series will be ignored.
Note
If one or both input arrays are empty, the result will be null.
Optimizing performance
For enhanced performance and reduced storage requirements when using this function, consider using the Vector16 encoding policy for storing floating-point vectors that don't require 64 bits precision, such as ML vector embeddings. The Vector16 profile, which utilizes the Bfloat16 floating-point representation, can significantly optimize the operation and reduce storage size by a factor of 4. For more details on the Vector16 encoding policy, refer to the Encoding Policy Types.
Example
datatable(s1:dynamic, s2:dynamic)
[
dynamic([0.1,0.2,0.1,0.2]), dynamic([0.11,0.2,0.11,0.21]),
dynamic([0.1,0.2,0.1,0.2]), dynamic([1,2,3,4]),
]
| extend cosine_similarity=series_cosine_similarity(s1, s2)
| s1 | s2 | cosine_similarity |
|---|---|---|
| [0.1,0.2,0.1,0.2] | [0.11,0.2,0.11,0.21] | 0.99935343825504 |
| [0.1,0.2,0.1,0.2] | [1,2,3,4] | 0.923760430703401 |