cosine_similarity

def cosine_similarity(left: ExprLike, right: ExprLike) -> Expr

Compute the cosine similarity of left and right.

See the Wikipedia article “Cosine similarity” .

Only implemented for fixed size lists of the 16-, 32-, and 64-bit floating-point types.

Examples

{/pytest-codeblocks:skip/}

>>> import spiral.expressions as se
>>> import pyarrow as pa
>>> needle = pa.scalar([1, 0, 0], type=pa.list_(pa.float64(), 3))
>>> sp.scan({"result": se.vector.cosine_similarity(t["embedding"], needle)}).to_table()
pyarrow.Table
x: int64
result: double
----
x: ...
result: ...

Arguments:

• left - an array of fixed-size-list of length N and type T
• right - an array of fixed-size-list of length N and type T

Returns:

an array of T

dot

def dot(left: ExprLike, right: ExprLike) -> Expr

Compute the dot product of left and right.

Only implemented for fixed size lists of the 16-, 32-, and 64-bit floating-point types.

Examples

{/pytest-codeblocks:skip/}

>>> import spiral.expressions as se
>>> sp.scan({"5_34_sim": se.vector.dot(t["layer5"], t["layer34"])}).to_table()
pyarrow.Table
x: int64
5_34_sim: double
----
x: ...
4_34_sim: ...

Arguments:

• left - an array of fixed-size-list of length N and type T
• right - an array of fixed-size-list of length N and type T

Returns:

an array of T

norm

def norm(array: ExprLike) -> Expr

Compute the Euclidean (L2) norm of array.

Only implemented for fixed size lists of the 16-, 32-, and 64-bit floating-point types.

Examples

{/pytest-codeblocks:skip/}

>>> import spiral.expressions as se
>>> sp.scan({"norm": se.vector.norm(t["embedding"])}).to_table()
pyarrow.Table
x: int64
norm: double
----
x: ...
norm: ...

Arguments:

• array - an array of fixed-size-list of type T

Returns:

an array of T