Convert Text String to Embedding Within Oracle AI Database

Perform a text-to-embedding transformation by accessing a vector embedding model stored in the database.

You can download an embedding machine learning model, convert it into ONNX format (if not already in ONNX format), and load the model into Oracle AI Database. You can then access that model to vectorize your data that is used to populate a vector index. Note that you must use the same embedding model on both the data to be indexed and the user's input query. In this example, you can see how to vectorize a user's input query on the fly.

Here, you can call either the VECTOR_EMBEDDING SQL function or the UTL_TO_EMBEDDING PL/SQL function (note the singular "embedding"). Both VECTOR_EMBEDDING and UTL_TO_EMBEDDING directly return a VECTOR type (not an array).

To convert a user's input text string "hello" to a vector embedding, using an embedding model in ONNX format:

1. Connect to Oracle AI Database as a local user.
   1. Log in to SQL*Plus as the SYS user, connecting as SYSDBA:

      conn sys/password as sysdba

      CREATE TABLESPACE tbs1
      DATAFILE 'tbs5.dbf' SIZE 20G AUTOEXTEND ON
      EXTENT MANAGEMENT LOCAL
      SEGMENT SPACE MANAGEMENT AUTO;

      SET ECHO ON
      SET FEEDBACK 1
      SET NUMWIDTH 10
      SET LINESIZE 80
      SET TRIMSPOOL ON
      SET TAB OFF
      SET PAGESIZE 10000
      SET LONG 10000

   2. Create a local user (docuser) and grant necessary privileges:

      DROP USER docuser cascade;

      CREATE USER docuser identified by docuser DEFAULT TABLESPACE tbs1 quota unlimited on tbs1;

      GRANT DB_DEVELOPER_ROLE, create credential to docuser;

   3. Connect as the local user (docuser):

      CONN docuser/password

2. Call either VECTOR_EMBEDDING or UTL_TO_EMBEDDING.

   1. Load your ONNX format embedding model into Oracle AI Database.

      For detailed information on how to perform this step, see Import ONNX Models into Oracle AI Database End-to-End Example.

   2. Call VECTOR_EMBEDDING or UTL_TO_EMBEDDING. You can use UTL_TO_EMBEDDING either from the DBMS_VECTOR or the DBMS_VECTOR_CHAIN package, depending on your use case.

      • VECTOR_EMBEDDING:

        SELECT TO_VECTOR(VECTOR_EMBEDDING(doc_model USING 'hello' as data)) AS embedding;

      • DBMS_VECTOR.UTL_TO_EMBEDDING:

        var params clob;
        exec :params := '{"provider":"database", "model":"doc_model"}';
        
        select dbms_vector.utl_to_embedding('hello', json(:params)) from dual;

      Here, doc_model specifies the name under which your embedding model is stored in the database.

   The generated embedding appears as follows:

   EMBEDDING
   -------------------------------------------------------------------------------------------------------------------------------------
   [8.78423732E-003,-4.29633334E-002,-5.93001908E-003,-4.65480909E-002,2.14333013E-002,6.53376281E-002,-5.93746938E-002,2.10403297E-002,
   4.38376889E-002,5.22960871E-002,1.25104953E-002,6.49512559E-002,-9.26998071E-003,-6.97442219E-002,-3.02916039E-002,-4.74979728E-003,
   -1.08755399E-002,-4.63751052E-003,3.62781435E-002,-9.35919806E-002,-1.13934642E-002,-5.74270077E-002,-1.36667723E-002,2.42995787E-002,
   -6.96804151E-002,4.93822657E-002,1.01460628E-002,-1.56464987E-002,-2.39410568E-002,-4.27529104E-002,-5.65665103E-002,-1.74160264E-002,
   5.05326502E-002,4.31500375E-002,-2.6994409E-002,-1.72731467E-002,9.30535868E-002,6.85951149E-004,5.61876409E-003,-9.0233935E-003,
   -2.55788807E-002,-2.04174276E-002,3.74175981E-002,-1.67872179E-002,1.07479304E-001,-6.64602639E-003,-7.65537247E-002,-9.71965566E-002,
   -3.99636962E-002,-2.57076006E-002,-5.62455431E-002,-1.3583754E-001,3.45946029E-002,1.85191762E-002,3.01524661E-002,-2.62163244E-002,
   -4.05582506E-003,1.72979087E-002,-3.66434865E-002,-1.72491539E-002,3.95228416E-002,-1.05518714E-001,-1.27463877E-001,1.42578809E-002