--- title: "CREATE EXTERNAL MODEL (Transact-SQL)" description: CREATE EXTERNAL MODEL (Transact-SQL) for creating an external model object that contains the location, authentication method, and purpose of an AI model inference endpoint. author: jettermctedder ms.author: bspendolini ms.reviewer: randolphwest ms.date: 08/15/2025 ms.service: sql ms.subservice: t-sql ms.topic: "reference" ms.custom: - sql-ai - build-2025 f1_keywords: - "CREATE_EXTERNAL_MODEL" - "EXTERNAL MODEL" - "ai_generate_embeddings" helpviewer_keywords: - "External" - "External model" - "ai_generate_embeddings, external model" dev_langs: - "TSQL" monikerRange: "=azuresqldb-current || >=sql-server-ver17 || >=sql-server-linux-ver17" --- # CREATE EXTERNAL MODEL (Transact-SQL) [!INCLUDE [sqlserver2025](../../includes/applies-to-version/sqlserver2025.md)] Creates an external model object that contains the location, authentication method, and purpose of an AI model inference endpoint. ## Syntax :::image type="icon" source="../../includes/media/topic-link-icon.svg" border="false"::: [Transact-SQL syntax conventions](../../t-sql/language-elements/transact-sql-syntax-conventions-transact-sql.md) ```syntaxsql CREATE EXTERNAL MODEL external_model_object_name [ AUTHORIZATION owner_name ] WITH ( LOCATION = '://[:]' , API_FORMAT = '' , MODEL_TYPE = EMBEDDINGS , MODEL = 'text-embedding-model-name' [ , CREDENTIAL = ] [ , 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 not specified, ownership is given to the current user. Depending on permissions and roles, explicit permission needs to be granted 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 being 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 Indicate which `DATABASE SCOPED CREDENTIAL` object is used with the AI model inference endpoint. More on accepted credential types and naming rules can be found in [sp_invoke_external_rest_endpoint](../../relational-databases/system-stored-procedures/sp-invoke-external-rest-endpoint-transact-sql.md), or in the [Remarks](#remarks) section of this article. ### PARAMETERS A valid JSON string that contains runtime parameters to be appended to the AI model inference endpoint request message. For example: ```text '{ "dimensions": 1536 }' ``` ### LOCAL_RUNTIME_PATH `LOCAL_RUNTIME_PATH` specifies the directory on the local SQL Server 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: ```sql GRANT CREATE EXTERNAL MODEL TO []; ``` or ```sql GRANT ALTER ANY EXTERNAL MODEL TO []; ``` ### External model grants To use an external model in an AI function, a principal must be granted the ability to `EXECUTE` it. For example: ```sql GRANT EXECUTE ON EXTERNAL MODEL::MODEL_NAME TO []; 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 `` should be a whole number between zero (`0`) and ten (`10`), inclusive, and can't be `NULL` or negative. ```text { "sql_rest_options": { "retry_count": } } ``` For example, to set the `retry_count` to 3, you would write the following JSON string: ```json { "sql_rest_options": { "retry_count": 3 } } ``` ### Retry count with other parameters Retry count can also be combined with other parameters as long as it's a valid JSON string. ```json { "dimensions": 725, "sql_rest_options": { "retry_count": 5 } } ``` ## Remarks ### HTTPS and TLS Only AI model inference endpoints that are configured to use HTTPS with TLS encryption protocol are supported for the `LOCATION` parameter. ### 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](/azure/ai-services/openai/reference#embeddings) | https://{endpoint}/openai/deployments/{deployment-id}/embeddings?api-version={date} | | [OpenAI](https://platform.openai.com/docs/api-reference/embeddings/create) | https://{server_name}/v1/embeddings | | [Ollama](https://github.com/ollama/ollama/blob/main/docs/api.md#generate-embeddings) | 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](/azure/ai-services/openai/how-to/create-resource) - [OpenAI](https://platform.openai.com/docs/guides/embeddings) - [Ollama](https://github.com/ollama/ollama/blob/main/docs/api.md#generate-embeddings) ### Credential name rules for EXTERNAL MODEL The created `DATABASE SCOPED CREDENTIAL` used by an `EXTERNAL MODEL` must adhere to specific following rules: - Must be a valid URL - The URL domain must be one of those domains included in the allowlist - 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 match completely with the respective part of URL path in the credential name - The credential must point to a path that is 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](https://www.rfc-editor.org/rfc/rfc3986#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," and [RFC 7230 Section 2.7.3](https://www.rfc-editor.org/rfc/rfc7230#section-2.7.3) mentions that "all other are compared in a case-sensitive manner." As there's a collation rule set at the database level, the following logic is applied, to be coherent with the database collation rule, and the RFC mentioned previously. (The described rule could potentially be more restrictive than the RFC rules, for example if 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`) 1. Check that the URL and credential match using the database collation rules (and without doing any URL encoding). ### Managed Identity To use [Managed Identity](/entra/identity/managed-identities-azure-resources/overview) for authentication on SQL Server 2025, you must enable the option by using `sp_configure` with a user that is [granted the ALTER SETTINGS server-level permission](../../relational-databases/system-stored-procedures/sp-configure-transact-sql.md#permissions). ```sql EXECUTE sp_configure 'allow server scoped db credentials', 1; RECONFIGURE WITH OVERRIDE; ``` ### SCHEMABINDING Dropping views created with `SCHEMABINDING` and referencing an `EXTERNAL MODEL` (such as a `SELECT` statement using `AI_GENERATE_EMBEDDINGS`) is prevented with the Database Engine raising an error. The view definition itself must first be modified or dropped to remove dependencies referencing an `EXTERNAL MODEL`. ## Catalog View External Model metadata can be viewed with querying the `sys.external_models` catalog view. Note, you must have access to a model to be able to view the metadata. ```sql 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](/entra/identity/managed-identities-azure-resources/overview) for authentication. > [!IMPORTANT] > If using Managed Identity with Azure OpenAI and SQL Server 2025, the [Cognitive Services OpenAI Contributor](/azure/role-based-access-control/built-in-roles#ai--machine-learning) role must be granted to [SQL Server's system-assigned managed identity enabled by Azure Arc](../../sql-server/azure-arc/managed-identity.md). For more information, see [Role-based access control for Azure OpenAI in Azure AI Foundry Models](/azure/ai-services/openai/how-to/role-based-access-control). ```sql -- Create access credentials to Azure OpenAI using a managed identity: CREATE DATABASE SCOPED CREDENTIAL [https://my-azure-openai-endpoint.openai.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.openai.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.openai.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. ```sql -- Create access credentials to Azure OpenAI using a key: CREATE DATABASE SCOPED CREDENTIAL [https://my-azure-openai-endpoint.openai.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.openai.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.openai.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. ```sql 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. ```sql -- 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 a local ONNX runtime This example guides you through setting up SQL Server 2025 with an ONNX runtime to enable local AI-powered text embedding generation. It only applies on Windows. [ONNX Runtime](https://onnxruntime.ai/) 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. > [!IMPORTANT] > This feature requires that [SQL Server Machine Learning Services](../../machine-learning/install/sql-machine-learning-services-windows-install.md) is installed. ### Step 1: Enable developer preview features on SQL Server 2025 Run the following SQL command to enable SQL Server 2025 preview features in the database you would like use for this example: ```sql 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 SQL: ```sql EXEC 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 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: ```powershell cd C:\ mkdir onnx_runtime ``` Next, [Download](https://github.com/microsoft/onnxruntime/releases) the ONNX Runtime (version ≥ 1.19) that is 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](https://github.com/mlc-ai/tokenizers-cpp/tree/main) 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\`. ```powershell cd C:\onnx_runtime mkdir model ``` This example uses the `all-MiniLM-L6-v2-onnx` model, which can be downloaded from [Hugging Face](https://huggingface.co/nsense/all-MiniLM-L6-v2-onnx). Clone the repository into the `C:\onnx_runtime\model` directory with the following [git](https://git-scm.com/downloads) command: *If not installed, you can download git from the following [download link](https://git-scm.com/downloads) or via winget (winget install Microsoft.Git)* ```cmd 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: ```powershell $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 SQL to register your ONNX model as an external model object: ***The 'PARAMETERS' value used here is a placeholder needed for SQL Server 2025 RC 0.*** ```sql 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\' ); ``` > [!IMPORTANT] > `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 SQL: ```sql 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 SQL statement 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 telemetry Run the following SQL to enable telemetry for troubleshooting. ```sql 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 SQL query see the captured telemetry: ```sql 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 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 SQL: ```sql DROP EXTERNAL MODEL myLocalOnnxModel; ``` To remove the directory permissions, run the following PowerShell commands: ```powershell $Acl.RemoveAccessRule($AccessRule) Set-Acl -Path $AIExtPath -AclObject $Acl ``` Finally, delete the `C:/onnx_runtime` directory. ## Related content - [ALTER EXTERNAL MODEL (Transact-SQL)](alter-external-model-transact-sql.md) - [DROP EXTERNAL MODEL (Transact-SQL)](drop-external-model-transact-sql.md) - [AI_GENERATE_EMBEDDINGS (Transact-SQL)](../functions/ai-generate-embeddings-transact-sql.md) - [AI_GENERATE_CHUNKS (Transact-SQL) (Preview)](../functions/ai-generate-chunks-transact-sql.md) - [sys.external_models](../../relational-databases/system-catalog-views/sys-external-models-transact-sql.md) - [Create and deploy an Azure OpenAI in Azure AI Foundry Models resource](/azure/ai-services/openai/how-to/create-resource) - [Server configuration options](../../database-engine/configure-windows/server-configuration-options-sql-server.md) - [Role-based access control for Azure OpenAI in Azure AI Foundry Models](/azure/ai-services/openai/how-to/role-based-access-control)