VECTOR_EMBEDDING

Use VECTOR_EMBEDDING to generate a single vector embedding for different data types using embedding or feature extraction machine learning models.

Syntax

Description of vector_embedding.eps follows

Description of the illustration vector_embedding.eps

mining_attribute_clause::=

Description of the illustration mining_attribute_clause.eps

Purpose

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 is 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 of thousand tokens. An attribute with a type that has no conversion to VARCHAR2 results in a SQL compilation error.

For feature extraction models Oracle Machine Learning for SQL supports standard Oracle data types except DATE, TIMESTAMP, RAW, and LONG. Oracle Machine Learning supports date type (datetime, date, timestamp) for case_id, CLOB/BLOB/FILE that are interpreted as text columns, and the following collection types as well:

DM_NESTED_CATEGORICALS
DM_NESTED_NUMERICALS
DM_NESTED_BINARY_DOUBLES
DM_NESTED_BINARY_FLOATS

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.

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