Alternate Method to Import ONNX Models
Use the DBMS_DATA_MINING.IMPORT_ONNX_MODEL procedure to
import the model and declare the input name. The following procedure uses a PL/SQL helper block
that facilitates the process of importing ONNX format model into the Oracle Database. The
function reads the model file from the server's file system and imports it into the Database.
DBMS_DATA_MINING PL/SQL package.
-
Connect as
dmuser.CONN dmuser/<password>@<pdbname>; -
Run the following helper PL/SQL block:
DECLARE m_blob BLOB default empty_blob(); m_src_loc BFILE ; BEGIN DBMS_LOB.createtemporary (m_blob, FALSE); m_src_loc := BFILENAME('DM_DUMP', 'my_embedding_model.onnx'); DBMS_LOB.fileopen (m_src_loc, DBMS_LOB.file_readonly); DBMS_LOB.loadfromfile (m_blob, m_src_loc, DBMS_LOB.getlength (m_src_loc)); DBMS_LOB.CLOSE(m_src_loc); DBMS_DATA_MINING.import_onnx_model ('doc_model', m_blob, JSON('{"function" : "embedding", "embeddingOutput" : "embedding", "input": {"input": ["DATA"]}}')); DBMS_LOB.freetemporary (m_blob); END; /The code sets up a
BLOBobject and aBFILElocator, creates a temporaryBLOBfor storing themy_embedding_model.onnxfile from theDM_DUMPdirectory, and reads its contents into theBLOB. It then closes the file and uses the content to import an ONNX model into the database with specified metadata, before releasing the temporaryBLOBresources.
IMPORT_ONNX_MODEL procedure is as
follows: DBMS_DATA_MINING.IMPORT_ONNX_MODEL(model_data, model_name,
metadata). This procedure loads IMPORT_ONNX_MODEL from the
DBMS_DATA_MINING package to import the ONNX model into the Database using
the name provided in model_name, the BLOB content in m_blob,
and the associated metadata.
-
doc_model: This parameter is a user-specified name under which the imported model is stored in the Oracle Database. -
m_blob: This is a model data inBLOBthat holds the ONNX representation of the model. -
"function" : "embedding": Indicates the function name for text embedding model. -
"embeddingOutput" : "embedding": Specifies the output variable which contains the embedding results. -
"input": {"input": ["DATA"]}: Specifies a JSON object ("input") that describes the input expected by the model. It specifies that there is an input named"input", and its value should be an array with one element,"DATA". This indicates that the model expects a single string input to generate embeddings.
Alternately, the
DBMS_DATA_MINING.IMPORT_ONNX_MODEL procedure can also accept a
BLOB argument representing an ONNX file stored and loaded from OCI Object
Storage. The following is an example to load an ONNX model stored in an OCI Object
Storage.
DECLARE
model_source BLOB := NULL;
BEGIN
-- get BLOB holding onnx model
model_source := DBMS_CLOUD.GET_OBJECT(
credential_name => 'myCredential',
object_uri => 'https://objectstorage.us-phoenix -1.oraclecloud.com/' ||
'n/namespace -string/b/bucketname/o/myONNXmodel.onnx');
DBMS_DATA_MINING.IMPORT_ONNX_MODEL(
"myonnxmodel",
model_source,
JSON('{ function : "embedding" })
);
END;
/This PL/SQL block starts by initializing a model_source
variable as a BLOB type, initially set to NULL. It then retrieves an ONNX
model from Oracle Cloud Object Storage using the DBMS_CLOUD.GET_OBJECT
procedure, specifying the credentials (OBJ_STORE_CRED) and the URI of the
model. The ONNX model resides in a specific bucket named bucketname in this
case, and is accessible through the provided URL. Then, the script loads the ONNX model into
the model_source BLOB. The
DBMS_DATA_MINING.IMPORT_ONNX_MODEL procedure then imports this model into
the Oracle Database as myonnxmodel. During the import, a JSON metadata
specifies the model's function as embedding, for embedding
operations.
See IMPORT_ONNX_MODEL Procedure and GET_OBJECT Procedure and Function to learn about the PL/SQL procedure.
Example: Importing a Pretrained ONNX Model to Oracle Database
The following presents a comprehensive step-by-step example of importing ONNX embedding and generating vector embeddings.
conn sys/<password>@pdb as sysdba
grant db_developer_role to dmuser identified by dmuser;
grant create mining model to dmuser;
create or replace directory DM_DUMP as '<work directory path>';
grant read on directory dm_dump to dmuser;
grant write on directory dm_dump to dmuser;
>conn dmuser/<password>@<pdbname>;
–- Drop the model if it exits
exec DBMS_VECTOR.DROP_ONNX_MODEL(model_name => 'doc_model', force => true);
-- Load Model
EXECUTE DBMS_VECTOR.LOAD_ONNX_MODEL(
'DM_DUMP',
'my_embedding_model.onnx',
'doc_model',
JSON('{"function" : "embedding", "embeddingOutput" : "embedding"}'));
/
--Alternately, load the model
EXECUTE DBMS_DATA_MINING.IMPORT_ONNX_MODEL(
'my_embedding_model.onnx',
'doc_model',
JSON('{"function" : "embedding",
"embeddingOutput" : "embedding",
"input": {"input": ["DATA"]}}')
);
--check the attributes view
set linesize 120
col model_name format a20
col algorithm_name format a20
col algorithm format a20
col attribute_name format a20
col attribute_type format a20
col data_type format a20
SQL> SELECT model_name, attribute_name, attribute_type, data_type, vector_info
FROM user_mining_model_attributes
WHERE model_name = 'DOC_MODEL'
ORDER BY ATTRIBUTE_NAME;
OUTPUT:
MODEL_NAME ATTRIBUTE_NAME ATTRIBUTE_TYPE DATA_TYPE VECTOR_INFO
-------------------- -------------------- -------------------- ---------- ---------------
DOC_MODEL INPUT_STRING TEXT VARCHAR2
DOC_MODEL ORA$ONNXTARGET VECTOR VECTOR VECTOR(128,FLOA
T32)
SQL> SELECT MODEL_NAME, MINING_FUNCTION, ALGORITHM,
ALGORITHM_TYPE, MODEL_SIZE
FROM user_mining_models
WHERE model_name = 'DOC_MODEL'
ORDER BY MODEL_NAME;
OUTPUT:
MODEL_NAME MINING_FUNCTION ALGORITHM ALGORITHM_ MODEL_SIZE
-------------------- ------------------------------ -------------------- ---------- ----------
DOC_MODEL EMBEDDING ONNX NATIVE 17762137
SQL> select * from DM$VMDOC_MODEL ORDER BY NAME;
OUTPUT:
NAME VALUE
---------------------------------------- ----------------------------------------
Graph Description Graph combining g_8_torch_jit and torch_
jit
g_8_torch_jit
torch_jit
Graph Name g_8_torch_jit_torch_jit
Input[0] input:string[1]
Output[0] embedding:float32[?,128]
Producer Name onnx.compose.merge_models
Version 1
6 rows selected.
SQL> select * from DM$VPDOC_MODEL ORDER BY NAME;
OUTPUT:
NAME VALUE
---------------------------------------- ----------------------------------------
batching False
embeddingOutput embedding
SQL> select * from DM$VJDOC_MODEL;
OUTPUT:
METADATA
--------------------------------------------------------------------------------
{"function":"embedding","embeddingOutput":"embedding","input":{"input":["DATA"]}}
--apply the model
SQL> SELECT TO_VECTOR(VECTOR_EMBEDDING(doc_model USING 'hello' as data)) AS embedding;
--------------------------------------------------------------------------------
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-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