snowflake.snowpark.functions.ai_similarity¶

snowflake.snowpark.functions.ai_similarity(input1: Union[Column, str], input2: Union[Column, str], **kwargs) → Column[source]¶

Computes a similarity score based on the vector cosine similarity value of the inputs’ embedding vectors. Currently supports both text and image similarity computation.

Parameters:

  • input1 – The first input for comparison. Can be a string with text, an image (FILE data type), or a SQL object from prompt().

  • input2 – The second input for comparison. Can be a string with text, an image (FILE data type), or a SQL object from prompt(). Must be the same type as input1 (both text or both images).

  • **kwargs –

    Configuration settings specified as key/value pairs. Supported keys:

    • model: The embedding model used for embedding. For STRING input, defaults to ‘snowflake-arctic-embed-l-v2’.

      For IMAGE input, defaults to ‘voyage-multimodal-3’. Supported values include: ‘snowflake-arctic-embed-l-v2’, ‘nv-embed-qa-4’, ‘multilingual-e5-large’, ‘voyage-multilingual-2’, ‘snowflake-arctic-embed-m-v1.5’, ‘snowflake-arctic-embed-m’, ‘e5-base-v2’, ‘voyage-multimodal-3’ (for images).

Returns:

A float value of range -1 to 1 that represents the similarity score computed using vector similarity between two embedding vectors for the inputs.

Note

AI_SIMILARITY does not support computing the similarity between text and image inputs. Both inputs must be of the same type.

Examples:

>>> # Text similarity
>>> df = session.range(1).select(ai_similarity('I like this dish', 'This dish is very good').alias("similarity"))
>>> df.collect()[0][0] > 0.8
True

>>> # Text similarity with custom model
>>> df = session.range(1).select(
...     ai_similarity(
...         'I love programming',
...         '我喜欢编程',
...         model='multilingual-e5-large'
...     ).alias("similarity")
... )
>>> df.collect()[0][0] > 0.8
True

>>> # Using columns
>>> df = session.create_dataframe([
...     ['Hello world', 'Hi there'],
...     ['Good morning', 'Good evening'],
... ], schema=["text1", "text2"])
>>> df = df.select(ai_similarity(col("text1"), col("text2")).alias("similarity"))
>>> result = df.collect()
>>> result[0][0] < 0.6
True
>>> result[1][0] > 0.7
True

>>> # Image similarity
>>> _ = session.sql("CREATE OR REPLACE TEMP STAGE mystage ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE')").collect()
>>> _ = session.file.put("tests/resources/dog.jpg", "@mystage", auto_compress=False)
>>> _ = session.file.put("tests/resources/cat.jpeg", "@mystage", auto_compress=False)
>>> df = session.range(1).select(
...     ai_similarity(
...         to_file("@mystage/dog.jpg"),
...         to_file("@mystage/cat.jpeg")
...     ).alias("similarity")
... )
>>> df.collect()[0][0] < 0.5
True
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