- Categories:
String & binary functions (AI Functions)
EMBED_TEXT_1024 (SNOWFLAKE.CORTEX)¶
Note
AI_EMBED is the latest version of this function. Use AI_EMBED for the latest functionality. You can continue to use EMBED_TEXT_1024 (SNOWFLAKE.CORTEX).
Creates a vector embedding of 1024 dimensions from text.
Syntax¶
SNOWFLAKE.CORTEX.EMBED_TEXT_1024( <model>, <text> )
Arguments¶
model
A string specifying the vector embedding model to be used to generate the embedding. This must be one of the following values.
snowflake-arctic-embed-l-v2.0
snowflake-arctic-embed-l-v2.0-8k
nv-embed-qa-4
multilingual-e5-large
voyage-multilingual-2
Supported models might have different costs.
text
The text for which an embedding should be calculated.
Returns¶
A vector embedding of type VECTOR.
Access control requirements¶
You must use a role that has been granted the SNOWFLAKE.CORTEX_USER database role or the SNOWFLAKE.CORTEX_EMBED_USER database role to call this function. See Cortex LLM privileges for more information on granting one of these privileges.
You must also have the USAGE privilege on the SNOWFLAKE.CORTEX schema to call this function.
Example¶
In this example, a vector embedding is generated for the phrase hello world
using the snowflake-arctic-embed-l-v2.0
model:
SELECT SNOWFLAKE.CORTEX.EMBED_TEXT_1024('snowflake-arctic-embed-l-v2.0', 'hello world');
In this example, a vector embedding is generated for the Spanish phrase hola mundo
using the snowflake-arctic-embed-l-v2.0
model:
SELECT SNOWFLAKE.CORTEX.EMBED_TEXT_1024('snowflake-arctic-embed-l-v2.0', 'hola mundo');
Legal notices¶
Refer to Snowflake AI and ML.
Limitations¶
Snowflake Cortex functions do not support dynamic tables.