Convert Text String to Embedding Using Public REST Providers

Perform a text-to-embedding transformation, using publicly hosted third-party embedding models by Cohere, Google AI, Hugging Face, OpenAI, Vertex AI, or Generative AI.

You can use third-party embedding models 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.

API used: Here, you can call the chainable utility function UTL_TO_EMBEDDING (note the singular "embedding") from either the DBMS_VECTOR or the DBMS_VECTOR_CHAIN package, depending on your use case. This example uses the DBMS_VECTOR.UTL_TO_EMBEDDING API.

WARNING:

Certain features of the database may allow you to access services offered separately by third-parties, for example, through the use of JSON specifications that facilitate your access to REST APIs.

Your use of these features is solely at your own risk, and you are solely responsible for complying with any terms and conditions related to use of any such third-party services. Notwithstanding any other terms and conditions related to the third-party services, your use of such database features constitutes your acceptance of that risk and express exclusion of Oracle's responsibility or liability for any damages resulting from such access.

UTL_TO_EMBEDDING directly returns a VECTOR type (not an array).

To convert a user's input text string "hello" to a vector embedding, using a public third-party embedding model:

1. Connect to Oracle Database as a local user.
   1. Log in to SQL*Plus as the sys user, connecting as sysdba, to your PDB:

      conn sys/password@CDB_PDB as sysdba

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

   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 to Oracle Database as docuser and alter the environment settings for your session:

      CONN docuser/password@CDB_PDB

      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. Set the HTTP proxy server, if configured.

   EXEC UTL_HTTP.SET_PROXY('<proxy-hostname>:<proxy-port>');

3. Grant connect privilege to allow connection to the host.

   Grant connect privilege to docuser for connecting to the host, using the DBMS_NETWORK_ACL_ADMIN procedure. This example uses * to allow any host. However, you can explicitly specify the host that you want to connect to.

   BEGIN
     DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
       host => '*',
       ace => xs$ace_type(privilege_list => xs$name_list('connect'),
                          principal_name => 'docuser',
                          principal_type => xs_acl.ptype_db));
   END;
   /

4. Create credentials and call UTL_TO_EMBEDDING.

   Set up your credentials for the REST provider that you want to access (such as Cohere, Generative AI, Google AI, Hugging Face, OpenAI, or Vertex AI), and then call UTL_TO_EMBEDDING.

   • Using Cohere, Google AI, Hugging Face, OpenAI, and Vertex AI:

     1. Run DBMS_VECTOR.CREATE_CREDENTIAL to create and store a credential.

        Cohere, Google AI, Hugging Face, OpenAI, and Vertex AI require the following authentication parameter:

        { "access_token": "<access token>" }

        You will later refer to this credential name when declaring JSON parameters for the UTL_to_EMBEDDING call.

        exec dbms_vector.drop_credential('<credential name>');

        declare
          jo json_object_t;
        begin
          jo := json_object_t();
          jo.put('access_token', '<access token>');
          dbms_vector.create_credential(
            credential_name   => '<credential name>',
            params            => json(jo.to_string));
        end;
        /

        Replace the access_token and credential_name values. For example:

        declare
          jo json_object_t;
        begin
          jo := json_object_t();
          jo.put('access_token', 'AbabA1B123aBc123AbabAb123a1a2ab');
          dbms_vector.create_credential(
            credential_name   => 'HF_CRED',
            params            => json(jo.to_string));
        end;
        /

     2. Call DBMS_VECTOR.UTL_TO_EMBEDDING:

        -- select example
        
        var params clob;
        exec :params := '
        {
          "provider": "<REST provider>",
          "credential_name": "<credential name>",
          "url": "<REST endpoint URL for embedding service>",
          "model": "<embedding model name>"
        }';
        
        select dbms_vector.utl_to_embedding('hello', json(:params)) from dual;
        
        -- PL/SQL example
        
        declare
          input clob;
          params clob;
          v vector;
        begin
          input := 'hello';
        
          params := '
        {
          "provider": "<REST provider>",
          "credential_name": "<credential name>",
          "url": "<REST endpoint URL for embedding service>",
          "model": "<embedding model name>"
        }';
        
          v := dbms_vector.utl_to_embedding(input, json(params));
          dbms_output.put_line(vector_serialize(v));
        exception
          when OTHERS THEN
            DBMS_OUTPUT.PUT_LINE (SQLERRM);
            DBMS_OUTPUT.PUT_LINE (SQLCODE);
        end;
        /

        Replace the provider, credential_name, url, and model values. Optionally, you can specify additional REST provider parameters.

        Cohere example:

        {
          "provider": "cohere",
          "credential_name": "COHERE_CRED",
          "url": "https://api.cohere.example.com/embed",
          "model": "embed-model",
          "input_type": "search_query"
        }

        Google AI example:

        {
          "provider": "googleai",
          "credential_name": "GOOGLEAI_CRED",
          "url": "https://googleapis.example.com/models/",
          "model": "embed-model"
        }

        Hugging Face example:

        {
          "provider": "huggingface",
          "credential_name": "HF_CRED",
          "url": "https://api.huggingface.example.com/",
          "model": "embed-model"
        }

        OpenAI example:

        {
          "provider": "openai",
          "credential_name": "OPENAI_CRED",
          "url": "https://api.openai.example.com/embeddings",
          "model": "embed-model"
        }

        Vertex AI example:

        {
          "provider": "vertexai",
          "credential_name": "VERTEXAI_CRED",
          "url": "https://googleapis.example.com/models/",
          "model": "embed-model"
        }

   • Using Generative AI:

     1. Run DBMS_VECTOR.CREATE_CREDENTIAL to create and store an OCI credential (OCI_CRED).

        Generative AI requires the following authentication parameters:

        { 
        "user_ocid": "<user ocid>",
        "tenancy_ocid": "<tenancy ocid>",
        "compartment_ocid": "<compartment ocid>",
        "private_key": "<private key>",
        "fingerprint": "<fingerprint>" 
        }

        You will later refer to this credential name when declaring JSON parameters for the UTL_to_EMBEDDING call.

        Note:

        The generated private key may appear as:

        -----BEGIN RSA PRIVATE KEY-----
        <private key string>
        -----END RSA PRIVATE KEY-----

        You pass the <private key string> value (excluding the BEGIN and END lines), either as a single line or as multiple lines.

        exec dbms_vector.drop_credential('OCI_CRED');

        declare
          jo json_object_t;
        begin
          jo := json_object_t();
          jo.put('user_ocid','<user ocid>');
          jo.put('tenancy_ocid','<tenancy ocid>');
          jo.put('compartment_ocid','<compartment ocid>');
          jo.put('private_key','<private key>');
          jo.put('fingerprint','<fingerprint>');
          dbms_vector.create_credential(
            credential_name   => 'OCI_CRED',
            params            => json(jo.to_string));
        end;
        /

        Replace all the authentication parameter values. For example:

        declare
          jo json_object_t;
        begin
          jo := json_object_t();
          jo.put('user_ocid','ocid1.user.oc1..aabbalbbaa1112233aabbaabb1111222aa1111bb');
          jo.put('tenancy_ocid','ocid1.tenancy.oc1..aaaaalbbbb1112233aaaabbaa1111222aaa111a');
          jo.put('compartment_ocid','ocid1.compartment.oc1..ababalabab1112233abababab1111222aba11ab');
          jo.put('private_key','AAAaaaBBB11112222333...AAA111AAABBB222aaa1a/+');
          jo.put('fingerprint','01:1a:a1:aa:12:a1:12:1a:ab:12:01:ab:a1:12:ab:1a');
          dbms_vector.create_credential(
            credential_name   => 'OCI_CRED',
            parameters        => json(jo.to_string));
        end;
        /

     2. Call DBMS_VECTOR.UTL_TO_EMBEDDING:

        -- select example
        
        var params clob;
        exec :params := '
        {
          "provider": "ocigenai",
          "credential_name": "OCI_CRED",
          "url": "<REST endpoint URL for embedding service>",
          "model": "<REST provider embedding model name>"
        }';
        
        select dbms_vector.utl_to_embedding('hello', json(:params)) from dual;
        
        -- PL/SQL example
        
        declare
          input clob;
          params clob;
          v vector;
        begin
          input := 'hello;
        
          params := '
        {
          "provider": "ocigenai",
          "credential_name": "OCI_CRED",
          "url": "<REST endpoint URL for embedding service>",
          "model": "<REST provider embedding model name>"
        }';
        
          v := dbms_vector.utl_to_embedding(input, json(params));
          dbms_output.put_line(vector_serialize(v));
        exception
          when OTHERS THEN
            DBMS_OUTPUT.PUT_LINE (SQLERRM);
            DBMS_OUTPUT.PUT_LINE (SQLCODE);
        end;
        /

        Replace the url and model values. Optionally, you can specify additional REST provider-specific parameters.

        For example:

        {
          "provider": "ocigenai",
          "credential_name": "OCI_CRED",
          "url": "https://generativeai.oci.example.com/embedText",
          "model": "embed-modelname",
          "batch_size": 10
        }

   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

This example uses the default settings for each provider. For detailed information on additional parameters, refer to your third-party provider's documentation.