1. SQL Language Reference
  2. Functions
  3. VECTOR_EMBEDDING

VECTOR_EMBEDDING

Syntax


Description of vector_embedding.eps follows
Description of the illustration vector_embedding.eps

mining_attribute_clause::=


Description of mining_attribute_clause.eps follows
Description of the illustration mining_attribute_clause.eps

Purpose

Use VECTOR_EMBEDDING if you want to generate a single vector embedding for different data types. To get embedding, this function uses pretrained ONNX embedding machine learning models.

The function accepts the following types as input:

VARCHAR2 for text embedding models. Oracle automatically converts any other type to VARCHAR2 except for NCLOB, which will be automatically converted to NVARCHAR2. Oracle does not expect values whose textual representation exceeds the maximum size of a VARCHAR2, since embedding models support only text that translates to a couple thousand tokens. An attribute with a type that has no conversion to VARCHAR2 results in a SQL compilation error.

The function always returns a VECTOR type, whose dimension is dictated by the model itself. The model stores the dimension information in metadata within the data dictionary.

You can use VECTOR_EMBEDDING in SELECT clauses, in predicates, and as an operand for SQL operations accepting a VECTOR type.

See Also:

Vector Functions

Parameters

model_name refers to the name of the imported embedding model that implements the embedding machine learning function.

mining_attribute_clause

  • The mining_attribute_clause argument identifies the column attributes to use as predictors for scoring. This is used as a convenience, as the embedding operator only accepts single input value.

  • USING * : all the relevant attributes present in the input (supplied in JSON metadata) are used. This is used as a convenience. For an embedding model, the operator only takes one input value as embedding models have only one column.

  • USING expr [AS alias] [, expr [AS alias] ] : all the relevant attributes present in the comma-separated list of column expressions are used. This syntax is consistent with the syntax of other machine learning operators. You may specify more than one attribute, however, the embedding model only takes one relevant input. Therefore, you must specify a single mining attribute.

Example

The following example generates vector embeddings with "hello" as the input, utilizing the pretrained ONNX format model my_embedding_model.onnx imported into the Database. For complete example, see Import ONNX Models and Generate Embeddings

SELECT TO_VECTOR(VECTOR_EMBEDDING(model USING 'hello' as data)) AS embedding;
--------------------------------------------------------------------------------
[-9.76553112E-002,-9.89954844E-002,7.69771636E-003,-4.16760892E-003,-9.69305634E-002,
-3.01141385E-002,-2.63396613E-002,-2.98553891E-002,5.96499592E-002,4.13885899E-002,
5.32859489E-002,6.57707453E-002,-1.47056757E-002,-4.18472625E-002,4.1588001E-002,
-2.86354572E-002,-7.56499246E-002,-4.16395674E-003,-1.52879998E-001,6.60010576E-002,
-3.9013084E-002,3.15719917E-002,1.2428958E-002,-2.47651711E-002,-1.16851285E-001,
-7.82847106E-002,3.34323719E-002,8.03267583E-002,1.70483496E-002,-5.42407483E-002,
6.54291287E-002,-4.81935125E-003,6.11041225E-002,6.64106477E-003,-5.47