snowflake.snowpark.functions.ai_embed

snowflake.snowpark.functions.ai_embed(model: str, input: Union[Column, str]) → Column[source]

Creates an embedding vector from text or an image.

Parameters:

  • model –

    A string specifying the vector embedding model to be used. Supported models:

    For text embeddings:

    • ’snowflake-arctic-embed-l-v2.0’: Arctic large model (default for text)

    • ’snowflake-arctic-embed-l-v2.0-8k’: Arctic large model with 8K context

    • ’nv-embed-qa-4’: NVIDIA embedding model for Q&A

    • ’multilingual-e5-large’: Multilingual embedding model

    • ’voyage-multilingual-2’: Voyage multilingual model

    For image embeddings:

    • ’voyage-multimodal-3’: Voyage multimodal model (only for images)

  • input – The string or image (as a FILE object) to generate an embedding from. Can be a string with text or a FILE column containing an image.

Returns:

A VECTOR containing the embedding representation of the input.

Examples:

>>> # Text embedding
>>> df = session.range(1).select(
...     ai_embed('snowflake-arctic-embed-l-v2.0', 'Hello, world!').alias("embedding")
... )
>>> result = df.collect()[0][0]
>>> len(result) > 0
True

>>> # Text embedding with multilingual model
>>> df = session.create_dataframe([
...     ['Hello world'],
...     ['Bonjour le monde'],
...     ['Hola mundo']
... ], schema=["text"])
>>> df = df.select(
...     col("text"),
...     ai_embed('multilingual-e5-large', col("text")).alias("embedding")
... )
>>> embeddings = df.collect()
>>> all(len(row[1]) > 0 for row in embeddings)
True

>>> # Image embedding
>>> _ = session.sql("CREATE OR REPLACE TEMP STAGE mystage ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE')").collect()
>>> _ = session.file.put("tests/resources/dog.jpg", "@mystage", auto_compress=False)
>>> df = session.range(1).select(
...     ai_embed('voyage-multimodal-3', to_file('@mystage/dog.jpg')).alias("image_embedding")
... )
>>> result = df.collect()[0][0]
>>> len(result) > 0
True