Ollama on HTTPS for SQL Server
Here is a quick procedure to deploy an Ubuntu container with Ollama and expose its API over HTTPS. The goal is to allow a fast deployment, even for those unfamiliar with Docker or Language Models, making it easy to set up an offline platform for generating embeddings and using Small Language Models This is particularly useful when testing SQL Server 2025 for fully on-premises environment use cases, since SQL Server only allows access to HTTPS endpoints. However, HTTP remains open for testing purposes. Please note that this example is CPU-based, as deploying with (integrated) GPU support involves additional, less straightforward steps. This example is provided solely to illustrate the concept, is not intended for production use, and comes without any guarantee of performance or security. Prerequisites To continue, you need to have Docker Desktop, WSL and SQL Server 2025 (currently Release Candidate 1) Docker Desktop Install WSL | Microsoft Learn SQL Server 2025 Preview | Microsoft Evaluation Center Create a Dockerfile First, create a working directory. In this example, C:\Docker\Ollama will be used. Simply create a file named Dockerfile (without an extension) and paste the following content into it. FROM ubuntu:25.10 RUN apt update && apt install -y curl gnupg2 ca-certificates lsb-release apt-transport-https software-properties-common unzip nano openssl net-tools RUN curl -fsSL https://ollama.com/install.sh | bash RUN curl -1sLf 'https://dl.cloudsmith.io/public/caddy/stable/gpg.key' | gpg --dearmor -o /usr/share/keyrings/caddy-stable-archive-keyring.gpg RUN curl -1sLf 'https://dl.cloudsmith.io/public/caddy/stable/debian.deb.txt' | tee /etc/apt/sources.list.d/caddy-stable.list RUN apt update && apt install -y caddy RUN mkdir -p /etc/caddy/certs RUN cat > /etc/caddy/certs/san.cnf <<EOF [req] default_bits = 2048 prompt = no default_md = sha256 req_extensions = req_ext distinguished_name = dn [dn] CN = 127.0.0.1 [req_ext] subjectAltName = @alt_names [alt_names] IP.1 = 127.0.0.1 DNS.1 = localhost EOF RUN openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout /etc/caddy/certs/localhost.key -out /etc/caddy/certs/localhost.crt -config /etc/caddy/certs/san.cnf -extensions req_ext RUN echo "https://:443 {\n tls /etc/caddy/certs/localhost.crt /etc/caddy/certs/localhost.key\n reverse_proxy localhost:11434\n}" >> /etc/caddy/Caddyfile RUN echo "#!/bin/bash" > /usr/local/bin/entrypoint.sh && \ echo "set -e" >> /usr/local/bin/entrypoint.sh && \ echo "OLLAMA_HOST=0.0.0.0 ollama serve >> /var/log/ollama.log 2>&1 &" >> /usr/local/bin/entrypoint.sh && \ echo "caddy run --config /etc/caddy/Caddyfile --adapter caddyfile >> /var/log/caddy.log 2>&1 &" >> /usr/local/bin/entrypoint.sh && \ echo "tail -f /var/log/ollama.log /var/log/caddy.log" >> /usr/local/bin/entrypoint.sh && \ chmod 755 /usr/local/bin/entrypoint.sh ENTRYPOINT ["/usr/local/bin/entrypoint.sh"] For your information, this file allows the creation of an image based on Ubuntu 25.10 and includes: Ollama, for running the models Caddy, for the reverse proxy Creation of a certificate for the HTTPS endpoint on localhost Create the container After opening a Powershell terminal, execute the following commands: cd C:\Docker\Ollama #Build the image from the Dockerfile. docker build -t ollama-https . #Create a container based on the image ollama-https docker run --name ollama-https -d -it -p 443:443 -p 11434:11434 ollama-https #Copy the certificate created into the current Windows directory docker cp ollama-https:/etc/caddy/certs/localhost.crt . # Install the certificate in Trusted Root Certification Authorities Import-Certificate -FilePath "localhost.crt" -CertStoreLocation "Cert:\LocalMachine\Root" #Check Https (wget https://localhost).Content #Check Http (wget http://localhost:11434).Content Ollama is now running With a browser, connect to https://localhost Retrieve Models No model is retrieved when the image is created, as this depends on each use case, and for some models, the size can be substantial. Here’s a quick example for pulling an embedding model, Nomic, and a small language model, Phi3. Ollama Search docker exec ollama-https ollama pull nomic-embed-text docker exec ollama-https ollama pull phi3:mini A quick example with SQL Server 2025 A quick demonstration using the WideWorldImporters database (Wide World Importers sample database) use [master] GO ALTER DATABASE WideWorldImporters SET COMPATIBILITY_LEVEL = 170 WITH ROLLBACK IMMEDIATE GO DBCC TRACEON(466, 474, 13981, -1) GO Note: With RC1, you can use the PREVIEW_FEATURES database-scoped configuration T-SQL Declare an external model for embeddings. use [WideWorldImporters] GO CREATE EXTERNAL MODEL NomicLocal AUTHORIZATION dbo WITH ( LOCATION = 'https://localhost/api/embed', API_FORMAT = 'ollama', MODEL_TYPE = EMBEDDINGS, MODEL = 'nomic-embed-text' ) to enable semantic search capabilities on StockItems, we will create a dedicated table to store embeddings (no chunking in this example) along with a vector index optimized for cosine similarity use [WideWorldImporters] GO CREATE TABLE [Warehouse].[StockItemsEmbedding](StockItemEmbeddingID int identity (1,1) PRIMARY KEY, StockItemId int, SearchDetails nvarchar(max), Embedding vector(768)) GO INSERT INTO [Warehouse].[StockItemsEmbedding] SELECT si.StockItemID, si.SearchDetails, AI_GENERATE_EMBEDDINGS(si.SearchDetails USE MODEL NomicLocal) /* Generate embeddings from declared external model */ FROM [Warehouse].[StockItems] si GO /* Check */ SELECT * FROM [Warehouse].[StockItemsEmbedding] GO CREATE VECTOR INDEX IXV_1 ON [Warehouse].[StockItemsEmbedding] (Embedding) WITH (METRIC = 'cosine', TYPE = 'DiskANN') GO /* User Input */ DECLARE @UserInput varchar(max) = 'Which product is best suited for shipping small items?' /* and Generate embeddings for user input */ DECLARE @UserInputV vector(768) = AI_GENERATE_EMBEDDINGS(@UserInput USE MODEL NomicLocal) DECLARE @ModelInput nvarchar(max) DECLARE Payload nvarchar(max) DECLARE Response nvarchar(max) /* Similarity Search on StockItems and Model Input creation*/ SELECT @ModelInput = STRING_AGG('ProductDetails: ' + sie.SearchDetails + 'UnitPrice: ' + CAST(si.UnitPrice AS nvarchar(max)), ' \n\n') FROM VECTOR_SEARCH( TABLE = [Warehouse].[StockItemsEmbedding] as sie, COLUMN = Embedding, SIMILAR_TO = @UserInputV, METRIC = 'cosine', TOP_N = 10 ) JOIN [Warehouse].[StockItems] si ON si.StockItemId = sie.StockItemId /* Generate payload for response generation */ SELECT = '{"model": "phi3:mini", "stream": false, "prompt":"You are acting as a customer advisor responsible for recommending the most suitable products based on customer needs, providing clear and personalized suggestions. Question : ' + @UserInput + '\n\nList of Items : ' + @ModelInput + '"}'; EXECUTE sp_invoke_external_rest_endpoint @url = 'https://localhost/api/generate', @method = 'POST', = , @timeout = 230, = OUTPUT; PRINT JSON_VALUE(@response, '$.result.response') LangChain You can also have a try with LangChain. Same demo with a small difference, there is no vector index created on the vector store table. The table has been modified, but only for demonstration purposes. Reference: SQLServer | 🦜️🔗 LangChain # PREREQ #sudo apt-get update && sudo apt-get install -y unixodbc # sudo apt-get update # sudo apt-get install -y curl gnupg2 # curl https://packages.microsoft.com/keys/microsoft.asc | sudo apt-key add - # curl https://packages.microsoft.com/config/debian/11/prod.list | sudo tee /etc/apt/sources.list.d/mssql-release.list # sudo apt-get update # sudo ACCEPT_EULA=Y apt-get install -y msodbcsql18 # pip3 install langchain langchain-sqlserver langchain-ollama langchain-community import pyodbc from langchain_sqlserver import SQLServer_VectorStore from langchain_ollama import OllamaEmbeddings from langchain_ollama import ChatOllama from langchain.schema import Document from langchain_community.vectorstores.utils import DistanceStrategy #Prompt for testing _USER_INPUT = 'Which product is best suited for shipping small items?' ############### Params ########################################## print("\033[93mSetting up variables...\033[0m") _SQL_DRIVER = "ODBC Driver 18 for SQL Server" _SQL_SERVER = "localhost\\SQL2K25" _SQL_DATABASE = "WideWorldImporters" _SQL_USERNAME = "lc" _SQL_PASSWORD = "lc" _SQL_TRUST_CERT = "yes" _SQL_VECTOR_STORE_TABLE = "StockItem_VectorStore" # Table name for vector storage _MODIFY_TABLE_TO_USE_SQL_VECTOR_INDEX = True #As vector index not considered currently in langchain and structure does not match vector index requirements _CONNECTION_STRING = f"Driver={{{_SQL_DRIVER}}};Server={_SQL_SERVER};Database={_SQL_DATABASE};UID={_SQL_USERNAME};PWD={_SQL_PASSWORD};TrustServerCertificate={_SQL_TRUST_CERT}" _OLLAMA_API_URL = "https://localhost" _OLLAMA_EMBEDDING_MODEL = "nomic-embed-text:latest" _OLLAMA_EMBEDDING_VECTOR_SIZE = 768 _OLLAMA_SLM_MODEL = "phi3:mini" # Model for SLM queries ################################################################### #Define Ollama embeddings embeddings = OllamaEmbeddings( model=_OLLAMA_EMBEDDING_MODEL, base_url=_OLLAMA_API_URL ) conn = pyodbc.connect(_CONNECTION_STRING) cursor = conn.cursor() #Drop embeddings table if it exists print("\033[93mDropping existing vector store table if it exists...\033[0m") cursor.execute(f"DROP TABLE IF EXISTS Warehouse.{_SQL_VECTOR_STORE_TABLE};") print("\033[93mConnecting to SQL Server and fetching data...\033[0m") cursor.execute("SELECT StockItemId, SearchDetails, UnitPrice FROM Warehouse.StockItems;") rows = cursor.fetchall() print(f"\033[93mFound {len(rows)} records to process\033[0m") # Create documents from the fetched data documents = [ Document( page_content=row.SearchDetails, metadata={ "StockItemId": row.StockItemId, "UnitPrice": float(row.UnitPrice) # Convert Decimal to float } ) for row in rows ] conn.commit() #Creating vector store print("\033[93mCreating vector store...\033[0m") vector_store = SQLServer_VectorStore( connection_string=_CONNECTION_STRING, distance_strategy=DistanceStrategy.COSINE, # If not provided, defaults to COSINE embedding_function=embeddings, embedding_length=_OLLAMA_EMBEDDING_VECTOR_SIZE, db_schema = "Warehouse", table_name=_SQL_VECTOR_STORE_TABLE ) print("\033[93mAdding to vector store...\033[0m") try: vector_store.add_documents(documents) print("\033[93mSuccessfully added to vector store!\033[0m") except Exception as e: print(f"\033[91mError adding documents: {e}\033[0m") #Vector index not yet integrated in SQL Server VectorStore (drop auto-created nonclustered PK and generating int clustered PK if (_MODIFY_TABLE_TO_USE_SQL_VECTOR_INDEX): print("\033[93mModifying structure to create vector index...\033[0m") cursor.execute("DECLARE @AutoCreatedPK sysname, @SQL nvarchar(max);" f"SELECT @AutoCreatedPK = name FROM sys.key_constraints WHERE type = 'PK' AND parent_object_id = object_id('Warehouse.{_SQL_VECTOR_STORE_TABLE}');" f"SELECT @SQL = 'ALTER TABLE Warehouse.{_SQL_VECTOR_STORE_TABLE} DROP CONSTRAINT ' + @AutoCreatedPK + ';'" "EXEC sp_executesql @SQL;" f"ALTER TABLE Warehouse.{_SQL_VECTOR_STORE_TABLE} ADD Alt_Id int identity(1,1);" f"ALTER TABLE Warehouse.{_SQL_VECTOR_STORE_TABLE} ADD CONSTRAINT PK_{_SQL_VECTOR_STORE_TABLE} PRIMARY KEY (Alt_Id);") conn.commit() print("\033[93mCreating vector index...\033[0m") cursor.execute(f"CREATE VECTOR INDEX IV_{_SQL_VECTOR_STORE_TABLE} ON [Warehouse].[{_SQL_VECTOR_STORE_TABLE}] (embeddings) WITH (METRIC = 'cosine', TYPE = 'DiskANN');") conn.commit() #Generate prompt then answer print(f"\033[92mUser Input: {_USER_INPUT}\033[0m") context = [ { "Item": doc.page_content, "UnitPrice": doc.metadata.get("UnitPrice", None) } for doc in vector_store.similarity_search(_USER_INPUT, k=3) ] llm = ChatOllama(model=_OLLAMA_SLM_MODEL,base_url=_OLLAMA_API_URL) prompt = ( f"You are acting as a customer advisor responsible for recommending the most suitable products based on customer needs, providing clear and personalized suggestions" f"Context: {context}\n\nQuestion: {_USER_INPUT}\n\n") response = llm.invoke(prompt) print(f"\033[36m{response.content}\033[0m") Note : If using devcontainer with VSCode add "runArgs": [ "--network=host" ] to devcontainer.json to allow connections to “localhost”. Import and install the previously created certificat docker cp C:\Docker\Ollama\localhost.crt <devcontainer name>:/usr/local/share/ca-certificates/localhost.crt docker exec <devcontainer name> "update-ca-certificates" Disclaimer The sample scripts are not supported under any Microsoft standard support program or service. The sample scripts are provided AS IS without warranty of any kind. Microsoft further disclaims all implied warranties including, without limitation, any implied warranties of merchantability or of fitness for a particular purpose. The entire risk arising out of the use or performance of the sample scripts and documentation remains with you. In no event shall Microsoft, its authors, or anyone else involved in the creation, production, or delivery of the scripts be liable for any damages whatsoever (including, without limitation, damages for loss of business profits, business interruption, loss of business information, or other pecuniary loss) arising out of the use of or inability to use the sample scripts or documentation, even if Microsoft has been advised of the possibility of such damages.Using Entra ID Authentication with Arc-Enabled SQL Server in a .NET Windows Forms Application
Introduction: This guide demonstrates how to securely connect a .NET Framework Windows Forms application to an Arc-enabled SQL Server 2022 instance using Entra ID (Azure AD) authentication. It covers user authentication, token management, and secure connection practices, with code samples and screenshots. In many modern applications, it is common practice to use an application web service to mediate access to SQL Server. This approach can offer several advantages, such as improved security, scalability, and centralized management of database connections. However, there are scenarios where directly connecting to SQL Server is more appropriate. This guide focuses on such scenarios, providing a solution for applications that need direct access to SQL Server. This model is particularly useful for applications like SQL Server Management Studio (SSMS), which require direct database connections to perform their functions. By using Entra ID authentication, we can ensure that these direct connections are secure and that user credentials are managed efficiently. By following the steps outlined in this guide, developers can ensure secure and efficient connections between their .NET Windows Forms applications and Arc-enabled SQL Server instances using Entra ID authentication. This approach not only enhances security but also simplifies the management of user credentials and access tokens, providing a robust solution for modern application development. SAMPLE CODE: GitHub Repository Prerequisites Arc-enabled SQL Server 2022/2025 configured for Entra ID authentication Entra ID (Azure AD) tenant and app registration .NET Framework 4.6.2 Windows Forms application (Not required .NET version, only what the solution is based on) Microsoft.Identity.Client, Microsoft.Data.SqlClient NuGet packages Application Overview User authenticates with Entra ID Token is acquired and used to connect to SQL Server Option to persist token cache or keep it in memory Data is retrieved and displayed in a DataGridView Similar setup to use SSMS with Entra ID in articles below. Windows Form Sample Check User Button shows the current user The Connect to Entra ID at Login button will verify if you are logged in and try to connect to SQL Server. If the user is not logged in, an Entra ID authentication window will be displayed or ask you to log in. Once logged in it shows a Connection successful message box stating the connection to the database was completed. The Load Data button queries the Adventure Works database Person table and loads the names into the datagridview. The Cache Token to Disk checkbox option either caches to memory when unchecked and would require reauthentication after the application closes, or the option to cache to disk the token to be read on future application usage. If the file is cached to disk, the location of the cached file is (C:\Users\[useraccount]\AppData\Local). This sample does not encrypt the file which is something that would be recommended for production use. This code uses MSAL (Microsoft Authentication Library) to authenticate users in a .NET application using their Microsoft Entra ID (Azure AD) credentials. It configures the app with its client ID, tenant ID, redirect URI, and logging settings to enable secure token-based authentication. //Application registration ClientID, and TenantID are required for MSAL authentication private static IPublicClientApplication app = PublicClientApplicationBuilder.Create("YourApplicationClientID") .WithAuthority(AzureCloudInstance.AzurePublic, "YourTenantID") .WithRedirectUri("http://localhost") .WithLogging((level, message, containsPii) => Debug.WriteLine($"MSAL: {message}"), LogLevel.Verbose, true, true) .Build(); This method handles user login by either enabling persistent token caching or setting up temporary in-memory caching, depending on the input. It then attempts to silently acquire an access token for Azure SQL Database using cached credentials, falling back to interactive login if no account is found. private async Task<AuthenticationResult> LoginAsync(bool persistCache) { if (persistCache) TokenCacheHelper.EnablePersistence(app.UserTokenCache); else { app.UserTokenCache.SetBeforeAccess(args => { }); app.UserTokenCache.SetAfterAccess(args => { }); } string[] scopes = new[] { "https://database.windows.net//.default" }; var accounts = await app.GetAccountsAsync(); if (accounts == null || !accounts.Any()) return await app.AcquireTokenInteractive(scopes).ExecuteAsync(); var account = accounts.FirstOrDefault(); return await app.AcquireTokenSilent(scopes, account).ExecuteAsync(); } Connecting to SQL Server with Access Token This code connects to an Azure SQL Database using a connection string and an access token obtained through MSAL authentication. It securely opens the database connection by assigning the token to the SqlConnection object, enabling authenticated access without storing credentials in the connection string. This sample uses a self-signed certificate, in production always configure SQL Server protocols with a certificate issued by a trusted Certificate Authority (CA). TrustServerCertificate=True bypasses certificate validation and can allow MITM attacks. For production, use a trusted Certificate Authority and change TrustServerCertificate=True to TrustServerCertificate=False. Configure Client Computer and Application for Encryption - SQL Server | Microsoft Learn string connectionString = $"Server={txtSqlServer.Text};Database=AdventureWorks2019;Encrypt=True;TrustServerCertificate=True;"; var result = await LoginAsync(checkBox1.Checked); using (var conn = new SqlConnection(connectionString)) { conn.AccessToken = result.AccessToken; conn.Open(); // ... use connection ... } Fetching Data into DataGridView This method authenticates the user and connects to an Azure SQL Database using an access token, and runs a SQL query to retrieve the top 1,000 names from the Person table. It loads the results into a DataTable, which can then be used for display or further processing in the application. private async Task<DataTable> FetchDataAsync() { var dataTable = new DataTable(); var result = await LoginAsync(checkBox1.Checked); using (var conn = new SqlConnection(connectionString)) { conn.AccessToken = result.AccessToken; await conn.OpenAsync(); using (var cmd = new SqlCommand("SELECT TOP (1000) [FirstName], [MiddleName], [LastName] FROM [AdventureWorks2019].[Person].[Person]", conn)) using (var reader = await cmd.ExecuteReaderAsync()) { dataTable.Load(reader); } } return dataTable; } Configure Azure Arc SQL Server to use Entra ID authentication Using SQL Server 2022 follow the instructions here to setup the key vault and certificate when configuring. This article can also be used to configure SSMS to use Entra ID authentication. Detailed steps located here: Set up Microsoft Entra authentication for SQL Server - SQL Server | Microsoft Learn Using SQL Server 2025 the setup is much easier as you do not need to configure a Key Vault, or certificates as it is relying on using the managed identity for the authentication. Entra ID App Registration Steps Register a new app in Azure AD Add a redirect URI (http://localhost) Add API permissions for https://database.windows.net/.default On the Entra ID app registration, click on API Permissions. Add the API’s for Microsoft Graph: User.Read.All Application.Read.All Group.Read.All Add a permission for Azure SQL Database. If Azure SQL database is not shown in the list ensure that the Resource Provider is registered for Microsoft.Sql. Choose Delegated permissions and select user_impersonation, Click Add permission for the Azure SQL Database. NOTE: Once the permissions are added ensure that you grant admin consent on the items. Security Considerations Never store client secrets in client apps Use in-memory token cache for higher security, or encrypted disk cache for convenience Use user tokens for auditing and least privilege References Microsoft Docs: Azure AD Authentication for SQL Server MSAL.NET Documentation Arc-enabled SQL Server Documentation Conclusion: By following the steps outlined in this guide, developers can ensure secure and efficient connections between their .NET Windows Forms applications and Arc-enabled SQL Server instances using Entra ID authentication. This approach not only enhances security but also simplifies the management of user credentials and access tokens, providing a robust solution for modern application development. *** Disclaimer *** The sample scripts are not supported under any Microsoft standard support program or service. The sample scripts are provided AS IS without warranty of any kind. Microsoft further disclaims all implied warranties including, without limitation, any implied warranties of merchantability or of fitness for a particular purpose. The entire risk arising out of the use or performance of the sample scripts and documentation remains with you. In no event shall Microsoft, its authors, or anyone else involved in the creation, production, or delivery of the scripts be liable for any damages whatsoever (including, without limitation, damages for loss of business profits, business interruption, loss of business information, or other pecuniary loss) arising out of the use of or inability to use the sample scripts or documentation, even if Microsoft has been advised of the possibility of such damages.
