CREATE EXTERNAL MODEL (Transact-SQL)

Applies to: SQL Server 2025 (17.x) Azure SQL Database SQL database in Microsoft Fabric

Creates an external model object that contains the location, authentication method, and purpose of an AI model inference endpoint.

Syntax

Transact-SQL syntax conventions

CREATE EXTERNAL MODEL external_model_object_name
[ AUTHORIZATION owner_name ]
WITH
  (   LOCATION = '<prefix>://<path>[:<port>]'
    , API_FORMAT = '<OpenAI, Azure OpenAI, etc>'
    , MODEL_TYPE = EMBEDDINGS
    , MODEL = 'text-embedding-model-name'
    [ , CREDENTIAL = <credential_name> ]
    [ , PARAMETERS = '{"valid":"JSON"}' ]
    [ , LOCAL_RUNTIME_PATH = 'path to the ONNX Runtime files' ]
  );

Arguments

external_model_object_name

Specifies the user-defined name for the external model. The name must be unique within the database.

owner_name

Specifies the name of the user or role that owns the external model. If you don't specify this argument, the current user becomes the owner. Depending on permissions and roles, you might need to grant explicit permission to users to use specific external models.

LOCATION

Provides the connectivity protocol and path to the AI model inference endpoint.

API_FORMAT

The API message format for the AI model inference endpoint provider.

Accepted values are:

• Azure OpenAI
• OpenAI
• Ollama
• ONNX Runtime

MODEL_TYPE

The type of model accessed from the AI model inference endpoint location.

Accepted values are:

• EMBEDDINGS

MODEL

The specific model hosted by the AI provider. For example, text-embedding-ada-002, text-embedding-3-large, or o3-mini.

CREDENTIAL

Specifies the DATABASE SCOPED CREDENTIAL object used with the AI model inference endpoint. For more information about accepted credential types and naming rules, see sp_invoke_external_rest_endpoint or the Remarks section of this article.

PARAMETERS

A valid JSON string that contains runtime parameters to append to the AI model inference endpoint request message. For example:

'{ "dimensions": 1536 }'

LOCAL_RUNTIME_PATH

LOCAL_RUNTIME_PATH specifies the directory on the local SQL Server instance where the ONNX Runtime executables are located.

Permissions

External model creation and altering

Requires ALTER ANY EXTERNAL MODEL or CREATE EXTERNAL MODEL database permission.

For example:

GRANT CREATE EXTERNAL MODEL TO [<PRINCIPAL>];

Or:

GRANT ALTER ANY EXTERNAL MODEL TO [<PRINCIPAL>];

External model grants

To use an external model in an AI function, a principal must be granted the ability to EXECUTE it.

For example:

GRANT EXECUTE ON EXTERNAL MODEL::MODEL_NAME TO [<PRINCIPAL>];
GO

Retry count

If the embeddings call encounters HTTP status codes indicating temporary issues, you can configure the request to automatically retry. To specify the number of retries, add the following JSON to the PARAMETERS on the EXTERNAL MODEL. The <number_of_retries> should be a whole number between zero (0) and ten (10), inclusive, and can't be NULL or negative.

{ "sql_rest_options": { "retry_count": <number_of_retries> } }

For example, to set the retry_count to 3, use the following JSON string:

{ "sql_rest_options": { "retry_count": 3 } }

Retry count with other parameters

You can combine retry count with other parameters as long as the JSON string is valid.

{ "dimensions": 725, "sql_rest_options": { "retry_count": 5 } }

Remarks

HTTPS and TLS

For the LOCATION parameter, only AI model inference endpoints configured to use HTTPS with the TLS encryption protocol are supported.

Accepted API formats and model types

The following sections outline accepted API formats for each MODEL_TYPE.

API_FORMAT for EMBEDDINGS

This table outlines the API formats and URL endpoint structures for the EMBEDDINGS model type. To view specific payload structures, use the link in the API Format column.

API format	Location path format
Azure OpenAI	https://{endpoint}/openai/deployments/{deployment-id}/embeddings?api-version={date}
OpenAI	https://{server_name}/v1/embeddings
Ollama	https://localhost:{port}/api/embed

Create embedding endpoints

For more information on creating embedding endpoints, use these links for the appropriate AI model inference endpoint provider:

• Azure OpenAI in Azure AI Foundry Models
• OpenAI
• Ollama

Credential name rules for external model

The created DATABASE SCOPED CREDENTIAL used by an external model must follow these rules:

• Must be a valid URL

• The URL domain must be one of those domains included in the allow list.

• The URL must not contain a query string

• Protocol + Fully Qualified Domain Name (FQDN) of the called URL must match Protocol + FQDN of the credential name

• Each part of the called URL path must completely match the respective part of the URL path in the credential name.

• The credential must point to a path that's more generic than the request URL. For example, a credential created for path https://northwind.azurewebsite.net/customers can't be used for the URL https://northwind.azurewebsite.net.

Collation and credential name rules

RFC 3986 Section 6.2.2.1 states that "When a URI uses components of the generic syntax, the component syntax equivalence rules always apply; namely, that the scheme and host are case-insensitive." RFC 7230 Section 2.7.3 mentions that "all other are compared in a case-sensitive manner."

Because a collation rule is set at the database level, the following logic applies to keep the database collation rule and the RFC rules consistent. (The described rule could potentially be more restrictive than the RFC rules, for example if the database is set to use a case-sensitive collation.)

1. Check if the URL and credential match using the RFC, which means:

   • Check the scheme and host using a case-insensitive collation (Latin1_General_100_CI_AS_KS_WS_SC)
   • Check all other segments of the URL are compared in a case-sensitive collation (Latin1_General_100_BIN2)

2. Check that the URL and credential match using the database collation rules (and without doing any URL encoding).

Managed identity

To use the managed identity of the Arc/VM host as a database level credential in SQL Server 2025 (17.x), you must enable the option by using sp_configure with a user that is granted the ALTER SETTINGS server-level permission.

EXECUTE sp_configure 'allow server scoped db credentials', 1;
RECONFIGURE WITH OVERRIDE;

SCHEMABINDING

Views created with SCHEMABINDING that reference an external model (such as a SELECT statement using AI_GENERATE_EMBEDDINGS) can't be dropped, and the Database Engine raises an error. To remove dependencies referencing an external model, you must first modify or drop the view definition.

Catalog view

You can view external model metadata by querying the sys.external_models catalog view. You must have access to a model to view its metadata.

SELECT *
FROM sys.external_models;

Examples with remote endpoints

Create an EXTERNAL MODEL with Azure OpenAI using Managed Identity

This example creates an external model of the EMBEDDINGS type using Azure OpenAI and uses Managed Identity for authentication.

In SQL Server 2025 (17.x) and later versions, you must connect your SQL Server to Azure Arc and enable the primary managed identity.

Important

If you use Managed Identity with Azure OpenAI and SQL Server 2025 (17.x), the Cognitive Services OpenAI Contributor role must be granted to SQL Server's system-assigned managed identity enabled by Azure Arc. For more information, see Role-based access control for Azure OpenAI in Azure AI Foundry Models.

Create access credentials to Azure OpenAI using a managed identity:

CREATE DATABASE SCOPED CREDENTIAL [https://my-azure-openai-endpoint.cognitiveservices.azure.com/]
    WITH IDENTITY = 'Managed Identity', secret = '{"resourceid":"https://cognitiveservices.azure.com"}';
GO

Create the external model:

CREATE EXTERNAL MODEL MyAzureOpenAIModel
AUTHORIZATION CRM_User
WITH (
      LOCATION = 'https://my-azure-openai-endpoint.cognitiveservices.azure.com/openai/deployments/text-embedding-ada-002/embeddings?api-version=2024-02-01',
      API_FORMAT = 'Azure OpenAI',
      MODEL_TYPE = EMBEDDINGS,
      MODEL = 'text-embedding-ada-002',
      CREDENTIAL = [https://my-azure-openai-endpoint.cognitiveservices.azure.com/]
);

Create an external model with Azure OpenAI using API keys and parameters

This example creates an external model of the EMBEDDINGS type using Azure OpenAI and uses API Keys for authentication. The example also uses PARAMETERS to set the dimensions parameter at the endpoint to 725.

Create access credentials to Azure OpenAI using a key:

CREATE DATABASE SCOPED CREDENTIAL [https://my-azure-openai-endpoint.cognitiveservices.azure.com/]
    WITH IDENTITY = 'HTTPEndpointHeaders', secret = '{"api-key":"YOUR_AZURE_OPENAI_KEY"}';
GO

Create the external model:

CREATE EXTERNAL MODEL MyAzureOpenAIModel
AUTHORIZATION CRM_User
WITH (
      LOCATION = 'https://my-azure-openai-endpoint.cognitiveservices.azure.com/openai/deployments/text-embedding-3-small/embeddings?api-version=2024-02-01',
      API_FORMAT = 'Azure OpenAI',
      MODEL_TYPE = EMBEDDINGS,
      MODEL = 'text-embedding-3-small',
      CREDENTIAL = [https://my-azure-openai-endpoint.cognitiveservices.azure.com/],
      PARAMETERS = '{"dimensions":725}'
);

Create an EXTERNAL MODEL with Ollama and an explicit owner

This example creates an external model of the EMBEDDINGS type using Ollama hosted locally for development purposes.

CREATE EXTERNAL MODEL MyOllamaModel
AUTHORIZATION AI_User
WITH (
      LOCATION = 'https://localhost:11435/api/embed',
      API_FORMAT = 'Ollama',
      MODEL_TYPE = EMBEDDINGS,
      MODEL = 'all-minilm'
);

Create an EXTERNAL MODEL with OpenAI

This example creates an external model of the EMBEDDINGS type using the OpenAI API_FORMAT and HTTP header based credentials for authentication.

-- Create access credentials
CREATE DATABASE SCOPED CREDENTIAL [https://openai.com]
WITH IDENTITY = 'HTTPEndpointHeaders', secret = '{"Bearer":"YOUR_OPENAI_KEY"}';
GO

-- Create the external model
CREATE EXTERNAL MODEL MyAzureOpenAIModel
AUTHORIZATION CRM_User
WITH (
      LOCATION = 'https://api.openai.com/v1/embeddings',
      API_FORMAT = 'OpenAI',
      MODEL_TYPE = EMBEDDINGS,
      MODEL = 'text-embedding-ada-002',
      CREDENTIAL = [https://openai.com]
);

Example with ONNX Runtime running locally

ONNX Runtime is an open-source inference engine that allows you to run machine learning models locally, making it ideal for integrating AI capabilities into SQL Server environments.

This example guides you through setting up SQL Server 2025 (17.x) with ONNX Runtime to enable local AI-powered text embedding generation. It only applies on Windows.

Important

This feature requires that SQL Server Machine Learning Services is installed.

Step 1: Enable developer preview features on SQL Server 2025

Run the following Transact-SQL (T-SQL) command to enable SQL Server 2025 (17.x) preview features in the database you would like use for this example:

ALTER DATABASE SCOPED CONFIGURATION
SET PREVIEW_FEATURES = ON;

Step 2: Enable the local AI runtime on SQL Server 2025

Enable external AI runtimes by running the following T-SQL query:

EXECUTE sp_configure 'external AI runtimes enabled', 1;
RECONFIGURE WITH OVERRIDE;

Step 3: Set up the ONNX Runtime library

Create a directory on the SQL Server instance to hold the ONNX Runtime library files. In this example, C:\onnx_runtime is used.

You can use the following commands to create the directory:

cd C:\
mkdir onnx_runtime

Next, download a version of ONNX Runtime (1.19 or greater) that's appropriate for your operating system. After unzipping the download, copy the onnxruntime.dll (located in the lib directory) to the C:\onnx_runtime directory that was created.

Step 4: Set up the tokenization library

Download and build the tokenizers-cpp library from GitHub. Once the dll is created, place the tokenizer in the C:\onnx_runtime directory.

Note

Ensure the created dll is named tokenizers_cpp.dll

Step 5: Download the ONNX model

Start by creating the model directory in C:\onnx_runtime\.

cd C:\onnx_runtime
mkdir model

This example uses the all-MiniLM-L6-v2-onnx model, which can be downloaded from Hugging Face.

Clone the repository into the C:\onnx_runtime\model directory with the following git command:

If not installed, you can download git from the following download link or via winget (winget install Microsoft.Git)

cd C:\onnx_runtime\model
git clone https://huggingface.co/nsense/all-MiniLM-L6-v2-onnx

Step 6: Set directory permissions

Use the following PowerShell script to provide the MSSQLLaunchpad user access to the ONNX Runtime directory:

$AIExtPath = "C:\onnx_runtime";
$Acl = Get-Acl -Path $AIExtPath
$AccessRule = New-Object System.Security.AccessControl.FileSystemAccessRule("MSSQLLaunchpad", "FullControl", "ContainerInherit,ObjectInherit", "None","Allow")
$Acl.AddAccessRule($AccessRule)
Set-Acl -Path $AIExtPath -AclObject $Acl

Step 7: Create the external model

Run the following query to register your ONNX model as an external model object:

The 'PARAMETERS' value used here is a placeholder needed for SQL Server 2025 (17.x).

CREATE EXTERNAL MODEL myLocalOnnxModel
WITH (
    LOCATION = 'C:\onnx_runtime\model\all-MiniLM-L6-v2-onnx',
    API_FORMAT = 'ONNX Runtime',
    MODEL_TYPE = EMBEDDINGS,
    MODEL = 'allMiniLM',
    PARAMETERS = '{"valid":"JSON"}',
    LOCAL_RUNTIME_PATH = 'C:\onnx_runtime\'
);

• LOCATION should point to the directory containing model.onnx and tokenizer.json files.
• LOCAL_RUNTIME_PATH should point to directory containing onnxruntime.dll and tokenizer_cpp.dll files.

Step 8: Generate embeddings

Use the ai_generate_embeddings function to test the model by running the following query:

SELECT AI_GENERATE_EMBEDDINGS(N'Test Text' USE MODEL myLocalOnnxModel);

This command launches the AIRuntimeHost, load the required DLLs, and processes the input text.

The result from the previous query is an array of embeddings:

[0.320098,0.568766,0.154386,0.205526,-0.027379,-0.149689,-0.022946,-0.385856,-0.039183...]

Enable XEvent system logging

Run the following query to enable system logging for troubleshooting.

CREATE EVENT SESSION newevt
ON SERVER
ADD EVENT ai_generate_embeddings_airuntime_trace
(
    ACTION (sqlserver.sql_text, sqlserver.session_id)
)
ADD TARGET package0.ring_buffer
WITH (MAX_MEMORY = 4096 KB, EVENT_RETENTION_MODE = ALLOW_SINGLE_EVENT_LOSS, MAX_DISPATCH_LATENCY = 30 SECONDS, TRACK_CAUSALITY = ON, STARTUP_STATE = OFF);
GO

ALTER EVENT SESSION newevt ON SERVER STATE = START;
GO

Next, use this query see the captured system logs:

SELECT event_data.value('(@name)[1]', 'varchar(100)') AS event_name,
       event_data.value('(@timestamp)[1]', 'datetime2') AS [timestamp],
       event_data.value('(data[@name = "model_name"]/value)[1]', 'nvarchar(200)') AS model_name,
       event_data.value('(data[@name = "phase_name"]/value)[1]', 'nvarchar(100)') AS phase,
       event_data.value('(data[@name = "message"]/value)[1]', 'nvarchar(max)') AS message,
       event_data.value('(data[@name = "request_id"]/value)[1]', 'nvarchar(max)') AS session_id,
       event_data.value('(data[@name = "error_code"]/value)[1]', 'bigint') AS error_code
FROM (SELECT CAST (target_data AS XML) AS target_data
      FROM sys.dm_xe_sessions AS s
           INNER JOIN sys.dm_xe_session_targets AS t
               ON s.address = t.event_session_address
      WHERE s.name = 'newevt'
            AND t.target_name = 'ring_buffer') AS data
CROSS APPLY target_data.nodes('//RingBufferTarget/event') AS XEvent(event_data);

Clean up

To remove the external model object, run the following T-SQL statement:

DROP EXTERNAL MODEL myLocalOnnxModel;

To remove the directory permissions, run the following PowerShell commands:

$Acl.RemoveAccessRule($AccessRule)
Set-Acl -Path $AIExtPath -AclObject $Acl

Finally, delete the C:/onnx_runtime directory.

Related content

• ALTER EXTERNAL MODEL (Transact-SQL)
• DROP EXTERNAL MODEL (Transact-SQL)
• AI_GENERATE_EMBEDDINGS (Transact-SQL)
• AI_GENERATE_CHUNKS (Transact-SQL)
• sys.external_models
• Create and deploy an Azure OpenAI in Azure AI Foundry Models resource
• Server configuration options
• Role-based access control for Azure OpenAI in Azure AI Foundry Models