diff --git a/TypeScript/README.md b/TypeScript/README.md new file mode 100644 index 0000000..b786481 --- /dev/null +++ b/TypeScript/README.md @@ -0,0 +1,205 @@ +# Quickstart: Vector search with TypeScript in Azure SQL Database + +This sample demonstrates how to perform **native vector search** in Azure SQL Database using TypeScript and Node.js. + +It uses: + +- **[tedious](https://www.npmjs.com/package/tedious)** — Microsoft's Node.js driver for SQL Server, with Azure AD authentication +- **[@azure/openai](https://www.npmjs.com/package/@azure/openai)** + **[openai](https://www.npmjs.com/package/openai)** — Azure OpenAI SDK for generating embeddings +- **[@azure/identity](https://www.npmjs.com/package/@azure/identity)** — `DefaultAzureCredential` for passwordless authentication to both Azure SQL and Azure OpenAI + +## What the sample does + +1. Connects to Azure SQL Database using `DefaultAzureCredential` (no passwords or API keys) +2. Creates a table with a `VECTOR(1536)` column +3. Generates text embeddings using Azure OpenAI `text-embedding-3-small` +4. Inserts sample hotel data with vector embeddings +5. Performs a vector similarity search using `VECTOR_DISTANCE()` +6. Displays the top matching results with similarity scores + +## Prerequisites + +- **Azure subscription** — [Create one free](https://azure.microsoft.com/free/) +- **Azure SQL Database** with native vector support — [Quickstart: Create a single database](https://learn.microsoft.com/azure/azure-sql/database/single-database-create-quickstart) +- **Azure OpenAI resource** with a `text-embedding-3-small` deployment — [Create and deploy an Azure OpenAI Service resource](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource) +- **Node.js 20+** — [Download Node.js](https://nodejs.org/) +- **Azure CLI** — [Install the Azure CLI](https://learn.microsoft.com/cli/azure/install-azure-cli), signed in with `az login` + +> [!IMPORTANT] +> Your Azure identity must have access to both the Azure SQL Database and the Azure OpenAI resource. Assign yourself the **SQL DB Contributor** role on the SQL server and the **Cognitive Services OpenAI User** role on the OpenAI resource, or equivalent permissions. + +## Get started + +### 1. Clone the repository + +```bash +git clone https://github.com/Azure-Samples/azure-sql-db-vector-search.git +cd azure-sql-db-vector-search/TypeScript +``` + +### 2. Install dependencies + +```bash +npm install +``` + +### 3. Configure environment variables + +Copy the sample environment file and fill in your values: + +```bash +cp sample.env .env +``` + +Edit `.env` with your Azure resource details: + +```env +AZURE_SQL_SERVER=.database.windows.net +AZURE_SQL_DATABASE= +AZURE_OPENAI_ENDPOINT=https://.openai.azure.com +AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-3-small +``` + +> [!NOTE] +> No API keys are needed. The sample uses `DefaultAzureCredential`, which automatically uses your Azure CLI login, managed identity, or other credential sources. + +### 4. Run the sample + +```bash +npm start +``` + +This runs the TypeScript code directly using `tsx` with Node.js 20+ native env-file loading. + +## Expected output + +``` +=== Azure SQL Vector Search — TypeScript Quickstart === + +Server: .database.windows.net +Database: +OpenAI: https://.openai.azure.com +Deployment: text-embedding-3-small + +Connecting to Azure SQL Database... +Connected. + +Creating hotels table (if not exists)... +Table ready. + +Generating embeddings with Azure OpenAI... +Generated 5 embeddings (dimension: 1536). + +Inserting hotel data with embeddings... + Inserted: Oceanview Resort & Spa + Inserted: Mountain Lodge Retreat + Inserted: Downtown City Hotel + Inserted: Desert Oasis Inn + Inserted: Lakeside Cabin Village + +Searching for: "luxury beachfront hotel with ocean views and spa" + +--- Search Results (Top 3 by Cosine Distance) --- + + Hotel: Oceanview Resort & Spa + Description: A luxurious beachfront resort offering stunning ocean views, a full-service spa, infin... + Distance: 0.1234 + Similarity: 0.8766 + + Hotel: Lakeside Cabin Village + Description: Rustic yet comfortable lakeside cabins surrounded by forest. Enjoy kayaking, fishing, ... + Distance: 0.3456 + Similarity: 0.6544 + + Hotel: Desert Oasis Inn + Description: A unique desert retreat with adobe-style architecture, stargazing terrace, and cactus ... + Distance: 0.3789 + Similarity: 0.6211 + +Done. Connection closed. +``` + +> [!NOTE] +> Actual distance and similarity values will vary based on the embedding model's output. + +## Understanding the code + +### Connection with DefaultAzureCredential + +The sample uses `tedious` with Azure AD token-based authentication. A token is acquired from `DefaultAzureCredential` for the `https://database.windows.net/.default` scope: + +```typescript +const credential = new DefaultAzureCredential(); +const token = await credential.getToken("https://database.windows.net/.default"); +``` + +### Table with VECTOR column + +Azure SQL Database supports the native `VECTOR` type. The table is created with a `VECTOR(1536)` column to store embeddings: + +```sql +CREATE TABLE dbo.hotels_typescript ( + id INT IDENTITY(1,1) PRIMARY KEY, + name NVARCHAR(200) NOT NULL, + description NVARCHAR(MAX) NOT NULL, + embedding VECTOR(1536) NULL +); +``` + +### Generating embeddings + +Embeddings are generated using Azure OpenAI's `text-embedding-3-small` model through the `openai` SDK with `@azure/identity` for authentication: + +```typescript +import { getBearerTokenProvider } from "@azure/identity"; +import { AzureOpenAI } from "openai"; + +const azureADTokenProvider = getBearerTokenProvider( + credential, + "https://cognitiveservices.azure.com/.default" +); +const openaiClient = new AzureOpenAI({ + endpoint: config.azureOpenAiEndpoint, + azureADTokenProvider, + apiVersion: "2024-10-21", +}); + +const response = await openaiClient.embeddings.create({ + model: deployment, + input: texts, +}); +``` + +### Vector similarity search + +The `VECTOR_DISTANCE()` T-SQL function computes cosine distance between the query vector and stored embeddings: + +```sql +SELECT TOP 3 + name, description, + VECTOR_DISTANCE('cosine', embedding, CAST(@queryVector AS VECTOR(1536))) AS distance +FROM dbo.hotels_typescript +ORDER BY distance; +``` + +A lower distance means higher similarity. + +## Clean up resources + +To remove the sample table from your database: + +```sql +DROP TABLE IF EXISTS dbo.hotels_typescript; +``` + +To avoid ongoing charges, delete the Azure resources you created if they were only for this quickstart: + +- [Delete the Azure SQL Database](https://learn.microsoft.com/azure/azure-sql/database/single-database-manage#delete-a-single-database) +- [Delete the Azure OpenAI resource](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource#delete-a-resource) + +## Related content + +- [Vectors in Azure SQL and SQL Server](https://learn.microsoft.com/sql/sql-server/ai/vectors) +- [VECTOR_DISTANCE (Transact-SQL)](https://learn.microsoft.com/sql/t-sql/functions/vector-distance-transact-sql) +- [Azure OpenAI text embeddings](https://learn.microsoft.com/azure/ai-services/openai/concepts/models#embeddings) +- [DefaultAzureCredential overview](https://learn.microsoft.com/javascript/api/overview/azure/identity-readme#defaultazurecredential) diff --git a/TypeScript/package-lock.json b/TypeScript/package-lock.json new file mode 100644 index 0000000..683aea4 --- /dev/null +++ b/TypeScript/package-lock.json @@ -0,0 +1,1493 @@ +{ + "name": "azure-sql-vector-search-typescript", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "azure-sql-vector-search-typescript", + "version": "1.0.0", + "dependencies": { + "@azure/identity": "^4.9.1", + "@azure/openai": "2.0.0", + "dotenv": "^16.5.0", + "openai": "^6.34.0", + "tedious": "^19.0.0" + }, + "devDependencies": { + "@types/node": "^22.15.0", + "tsx": "^4.19.0", + "typescript": "^5.8.0" + }, + 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"openai": "^6.34.0", + "tedious": "^19.0.0" + }, + "devDependencies": { + "@types/node": "^22.15.0", + "tsx": "^4.19.0", + "typescript": "^5.8.0" + }, + "engines": { + "node": ">=20.0.0" + } +} diff --git a/TypeScript/sample.env b/TypeScript/sample.env new file mode 100644 index 0000000..c749e11 --- /dev/null +++ b/TypeScript/sample.env @@ -0,0 +1,4 @@ +AZURE_SQL_SERVER=.database.windows.net +AZURE_SQL_DATABASE= +AZURE_OPENAI_ENDPOINT=https://.openai.azure.com +AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-3-small diff --git a/TypeScript/src/config.ts b/TypeScript/src/config.ts new file mode 100644 index 0000000..2eb6894 --- /dev/null +++ b/TypeScript/src/config.ts @@ -0,0 +1,28 @@ +export interface AppConfig { + azureSqlServer: string; + azureSqlDatabase: string; + azureOpenAiEndpoint: string; + azureOpenAiEmbeddingDeployment: string; +} + +export function loadConfig(): AppConfig { + const required = (key: string): string => { + const value = process.env[key]; + if (!value) { + throw new Error( + `Missing required environment variable: ${key}. ` + + "Copy sample.env to .env and fill in your values." + ); + } + return value; + }; + + return { + azureSqlServer: required("AZURE_SQL_SERVER"), + azureSqlDatabase: required("AZURE_SQL_DATABASE"), + azureOpenAiEndpoint: required("AZURE_OPENAI_ENDPOINT"), + azureOpenAiEmbeddingDeployment: required( + "AZURE_OPENAI_EMBEDDING_DEPLOYMENT" + ), + }; +} diff --git a/TypeScript/src/index.ts b/TypeScript/src/index.ts new file mode 100644 index 0000000..4bfa667 --- /dev/null +++ b/TypeScript/src/index.ts @@ -0,0 +1,288 @@ +import { Connection, Request, TYPES } from "tedious"; +import { + DefaultAzureCredential, + getBearerTokenProvider, + type AccessToken, +} from "@azure/identity"; +import { AzureOpenAI } from "openai"; +import { loadConfig } from "./config.js"; + +// --------------------------------------------------------------------------- +// Sample hotel data +// --------------------------------------------------------------------------- +const hotels = [ + { + name: "Oceanview Resort & Spa", + description: + "A luxurious beachfront resort offering stunning ocean views, a full-service spa, " + + "infinity pool, and fine dining. Perfect for romantic getaways and family vacations " + + "with direct beach access and water sports.", + }, + { + name: "Mountain Lodge Retreat", + description: + "A cozy mountain lodge nestled in the pine forests with hiking trails, a stone " + + "fireplace lounge, and farm-to-table restaurant. Ideal for nature lovers seeking " + + "a peaceful escape with skiing and snowboarding nearby.", + }, + { + name: "Downtown City Hotel", + description: + "A modern boutique hotel in the heart of the city, walking distance to museums, " + + "theaters, and shopping districts. Features a rooftop bar, business center, and " + + "contemporary rooms with skyline views.", + }, + { + name: "Desert Oasis Inn", + description: + "A unique desert retreat with adobe-style architecture, stargazing terrace, and " + + "cactus gardens. Offers guided desert tours, a refreshing pool, and southwestern " + + "cuisine in a tranquil setting.", + }, + { + name: "Lakeside Cabin Village", + description: + "Rustic yet comfortable lakeside cabins surrounded by forest. Enjoy kayaking, " + + "fishing, campfire evenings, and a general store. A family-friendly destination " + + "for outdoor adventures and relaxation by the water.", + }, +]; + +// --------------------------------------------------------------------------- +// Azure SQL helpers (tedious) +// --------------------------------------------------------------------------- +function connectToSql( + server: string, + database: string, + credential: DefaultAzureCredential +): Promise { + return new Promise((resolve, reject) => { + const config = { + server, + authentication: { + type: "azure-active-directory-access-token" as const, + options: { + token: "", // set dynamically below + }, + }, + options: { + database, + encrypt: true, + port: 1433, + rowCollectionOnDone: true, + rowCollectionOnRequestCompletion: true, + }, + }; + + // Acquire a token for Azure SQL + credential + .getToken("https://database.windows.net/.default") + .then((tokenResponse: AccessToken) => { + config.authentication.options.token = tokenResponse.token; + const connection = new Connection(config); + + connection.on("connect", (err) => { + if (err) { + reject(err); + } else { + resolve(connection); + } + }); + + connection.on("error", reject); + connection.connect(); + }) + .catch(reject); + }); +} + +function executeSql( + connection: Connection, + sql: string, + parameters?: Array<{ name: string; type: unknown; value: unknown }> +): Promise[]> { + return new Promise((resolve, reject) => { + const rows: Record[] = []; + const request = new Request(sql, (err, _rowCount, resultRows) => { + if (err) { + reject(err); + return; + } + // Build rows from column metadata + if (resultRows) { + for (const row of resultRows) { + const obj: Record = {}; + for (const col of row) { + obj[col.metadata.colName] = col.value; + } + rows.push(obj); + } + } + resolve(rows); + }); + + if (parameters) { + for (const p of parameters) { + request.addParameter(p.name, p.type as any, p.value); + } + } + + connection.execSql(request); + }); +} + +// --------------------------------------------------------------------------- +// Azure OpenAI helper +// --------------------------------------------------------------------------- +async function generateEmbeddings( + client: AzureOpenAI, + deployment: string, + texts: string[] +): Promise { + const response = await client.embeddings.create({ + model: deployment, + input: texts, + }); + return response.data.map((item) => item.embedding); +} + +// Converts a number[] embedding to the JSON-array string format that +// Azure SQL's VECTOR type accepts, e.g. "[0.123,0.456,...]" +function vectorToString(embedding: number[]): string { + return "[" + embedding.join(",") + "]"; +} + +// --------------------------------------------------------------------------- +// Main +// --------------------------------------------------------------------------- +async function main(): Promise { + console.log("=== Azure SQL Vector Search — TypeScript Quickstart ===\n"); + + // 1. Load configuration + const config = loadConfig(); + console.log(`Server: ${config.azureSqlServer}`); + console.log(`Database: ${config.azureSqlDatabase}`); + console.log(`OpenAI: ${config.azureOpenAiEndpoint}`); + console.log(`Deployment: ${config.azureOpenAiEmbeddingDeployment}\n`); + + // 2. Authenticate with DefaultAzureCredential (used for both SQL and OpenAI) + const credential = new DefaultAzureCredential(); + + // 3. Connect to Azure SQL + console.log("Connecting to Azure SQL Database..."); + const connection = connectToSql( + config.azureSqlServer, + config.azureSqlDatabase, + credential + ); + const conn = await connection; + console.log("Connected.\n"); + + // 4. Create the hotels table with a VECTOR(1536) column + console.log("Creating hotels table (if not exists)..."); + await executeSql( + conn, + `IF NOT EXISTS (SELECT * FROM sys.tables WHERE name = 'hotels_typescript') + BEGIN + CREATE TABLE dbo.hotels_typescript ( + id INT IDENTITY(1,1) PRIMARY KEY, + name NVARCHAR(200) NOT NULL, + description NVARCHAR(MAX) NOT NULL, + embedding VECTOR(1536) NULL + ); + END` + ); + console.log("Table ready.\n"); + + // 5. Generate embeddings with Azure OpenAI + console.log("Generating embeddings with Azure OpenAI..."); + const azureADTokenProvider = getBearerTokenProvider( + credential, + "https://cognitiveservices.azure.com/.default" + ); + const openaiClient = new AzureOpenAI({ + endpoint: config.azureOpenAiEndpoint, + azureADTokenProvider, + apiVersion: "2024-10-21", + }); + + const descriptions = hotels.map((h) => h.description); + const embeddings = await generateEmbeddings( + openaiClient, + config.azureOpenAiEmbeddingDeployment, + descriptions + ); + console.log(`Generated ${embeddings.length} embeddings (dimension: ${embeddings[0].length}).\n`); + + // 6. Insert sample data + console.log("Inserting hotel data with embeddings..."); + + // Clear previous run data + await executeSql(conn, "DELETE FROM dbo.hotels_typescript"); + + for (let i = 0; i < hotels.length; i++) { + const hotel = hotels[i]; + const embeddingStr = vectorToString(embeddings[i]); + await executeSql( + conn, + `INSERT INTO dbo.hotels_typescript (name, description, embedding) + VALUES (@name, @description, CAST(@embedding AS VECTOR(1536)))`, + [ + { name: "name", type: TYPES.NVarChar, value: hotel.name }, + { name: "description", type: TYPES.NVarChar, value: hotel.description }, + { name: "embedding", type: TYPES.NVarChar, value: embeddingStr }, + ] + ); + console.log(` Inserted: ${hotel.name}`); + } + console.log(); + + // 7. Perform vector similarity search + const searchQuery = "luxury beachfront hotel with ocean views and spa"; + console.log(`Searching for: "${searchQuery}"\n`); + + const queryEmbeddings = await generateEmbeddings( + openaiClient, + config.azureOpenAiEmbeddingDeployment, + [searchQuery] + ); + const queryVectorStr = vectorToString(queryEmbeddings[0]); + + const results = await executeSql( + conn, + `SELECT TOP 3 + name, + description, + VECTOR_DISTANCE('cosine', embedding, CAST(@queryVector AS VECTOR(1536))) AS distance + FROM dbo.hotels_typescript + ORDER BY distance`, + [ + { + name: "queryVector", + type: TYPES.NVarChar, + value: queryVectorStr, + }, + ] + ); + + // 8. Display results + console.log("--- Search Results (Top 3 by Cosine Distance) ---\n"); + for (const row of results) { + const distance = Number(row["distance"]); + const similarity = (1 - distance).toFixed(4); + console.log(` Hotel: ${row["name"]}`); + console.log(` Description: ${(row["description"] as string).substring(0, 100)}...`); + console.log(` Distance: ${distance.toFixed(4)}`); + console.log(` Similarity: ${similarity}`); + console.log(); + } + + // Cleanup: close connection + conn.close(); + console.log("Done. Connection closed."); +} + +main().catch((err) => { + console.error("Error:", err); + process.exit(1); +}); diff --git a/TypeScript/tsconfig.json b/TypeScript/tsconfig.json new file mode 100644 index 0000000..1d3a9f0 --- /dev/null +++ b/TypeScript/tsconfig.json @@ -0,0 +1,17 @@ +{ + "compilerOptions": { + "target": "ES2022", + "module": "Node16", + "moduleResolution": "Node16", + "strict": true, + "esModuleInterop": true, + "skipLibCheck": true, + "forceConsistentCasingInFileNames": true, + "outDir": "./dist", + "rootDir": "./src", + "declaration": true, + "resolveJsonModule": true + }, + "include": ["src/**/*"], + "exclude": ["node_modules", "dist"] +} diff --git a/azure.yaml b/azure.yaml new file mode 100644 index 0000000..1420fa6 --- /dev/null +++ b/azure.yaml @@ -0,0 +1,6 @@ +# yaml-language-server: $schema=https://raw.githubusercontent.com/Azure/azure-dev/main/schemas/v1.0/azure.yaml.json + +name: azure-sql-vector-search-quickstart +metadata: + template: azure-sql-vector-search-quickstart +services: diff --git a/infra/abbreviations.json b/infra/abbreviations.json new file mode 100644 index 0000000..d37c9b6 --- /dev/null +++ b/infra/abbreviations.json @@ -0,0 +1,8 @@ +{ + "managedIdentityUserAssignedIdentities": "mi-", + "resourcesResourceGroups": "rg-", + "sqlServers": "sql-", + "sqlServersDatabases": "sqldb-", + "cognitiveServicesAccounts": "cog-", + "cognitiveServicesOpenAI": "openai-" +} diff --git a/infra/main.bicep b/infra/main.bicep new file mode 100644 index 0000000..aa58826 --- /dev/null +++ b/infra/main.bicep @@ -0,0 +1,143 @@ +targetScope = 'subscription' + +@minLength(1) +@maxLength(64) +@description('Name of the environment which is used to generate a short unique hash used in all resources.') +param environmentName string + +@minLength(1) +@description('Location for all resources') +// https://learn.microsoft.com/azure/ai-services/openai/concepts/models?tabs=python-secure%2Cglobal-standard%2Cstandard-chat-completions#models-by-deployment-type +@allowed([ + 'eastus2' + 'swedencentral' +]) +@metadata({ + azd: { + type: 'location' + } +}) +param location string + +@description('Id of the principal to assign database and application roles.') +param deploymentUserPrincipalId string = '' + +var resourceToken = toLower(uniqueString(subscription().id, environmentName, location)) +var tags = { 'azd-env-name': environmentName } +var prefix = '${environmentName}${resourceToken}' + +// Azure OpenAI model configuration +// +// QUOTA REQUIREMENT: The embedding model below requires available quota in the +// target region (eastus2 or swedencentral). If deployment fails with +// "InsufficientQuota" or "The specified capacity ... is not available", try: +// 1. A different allowed region (change the @allowed list above) +// 2. A different SKU (e.g., swap 'Standard' ↔ 'GlobalStandard') +// 3. Requesting a quota increase in the Azure Portal under +// Subscriptions > Resource providers > Microsoft.CognitiveServices > Quotas + +// Embedding model: text-embedding-3-small, version 1, deployed as Standard +// This is the model used by all language samples to generate 1536-dimension vectors. +var embeddingModelName = 'text-embedding-3-small' +var embeddingModelVersion = '1' +var embeddingModelSkuName = 'Standard' +var embeddingModelCapacity = 10 + +// Note: No chat model is needed for the vector search quickstart (embedding only). +// If a chat model is added later, use gpt-4.1-mini (version 2025-04-14, Standard). +// gpt-4o-mini Standard was deprecated 2026-03-31; use gpt-4.1-mini instead. + +// SQL Database configuration +var sqlDatabaseName = 'vectordb' + +// Organize resources in a resource group +resource resourceGroup 'Microsoft.Resources/resourceGroups@2021-04-01' = { + name: '${environmentName}-${resourceToken}-rg' + location: location + tags: tags +} + +module managedIdentity 'br/public:avm/res/managed-identity/user-assigned-identity:0.4.0' = { + name: 'user-assigned-identity' + scope: resourceGroup + params: { + name: 'managed-identity-${prefix}' + location: location + tags: tags + } +} + +var openAiServiceName = 'openai-${prefix}' +module openAi 'br/public:avm/res/cognitive-services/account:0.7.1' = { + name: 'openai' + scope: resourceGroup + params: { + name: openAiServiceName + location: location + tags: tags + kind: 'OpenAI' + sku: 'S0' + customSubDomainName: openAiServiceName + networkAcls: { + defaultAction: 'Allow' + bypass: 'AzureServices' + } + deployments: [ + { + name: embeddingModelName + model: { + format: 'OpenAI' + name: embeddingModelName + version: embeddingModelVersion + } + sku: { + name: embeddingModelSkuName + capacity: embeddingModelCapacity + } + } + ] + roleAssignments: concat( + [ + { + principalId: managedIdentity.outputs.principalId + roleDefinitionIdOrName: 'Cognitive Services OpenAI User' + } + ], + !empty(deploymentUserPrincipalId) + ? [ + { + principalId: deploymentUserPrincipalId + roleDefinitionIdOrName: 'Cognitive Services OpenAI User' + } + ] + : [] + ) + } +} + +module sqlDatabase './sql-database.bicep' = { + name: 'sql-database' + scope: resourceGroup + params: { + serverName: 'sql-${prefix}' + databaseName: sqlDatabaseName + location: location + tags: tags + aadAdminObjectId: deploymentUserPrincipalId + managedIdentityPrincipalId: managedIdentity.outputs.principalId + } +} + +// General outputs +output AZURE_LOCATION string = location +output AZURE_TENANT_ID string = tenant().tenantId +output AZURE_RESOURCE_GROUP string = resourceGroup.name + +// Azure OpenAI outputs +output AZURE_OPENAI_ENDPOINT string = openAi.outputs.endpoint +output AZURE_OPENAI_EMBEDDING_MODEL string = embeddingModelName +output AZURE_OPENAI_EMBEDDING_DEPLOYMENT string = embeddingModelName + +// Azure SQL outputs +output AZURE_SQL_SERVER string = sqlDatabase.outputs.fullyQualifiedDomainName +output AZURE_SQL_DATABASE string = sqlDatabaseName diff --git a/infra/main.bicepparam b/infra/main.bicepparam new file mode 100644 index 0000000..a536325 --- /dev/null +++ b/infra/main.bicepparam @@ -0,0 +1,5 @@ +using './main.bicep' + +param environmentName = readEnvironmentVariable('AZURE_ENV_NAME', 'development') +param location = readEnvironmentVariable('AZURE_LOCATION', 'eastus2') +param deploymentUserPrincipalId = readEnvironmentVariable('AZURE_PRINCIPAL_ID', '') diff --git a/infra/sql-database.bicep b/infra/sql-database.bicep new file mode 100644 index 0000000..b5bad4f --- /dev/null +++ b/infra/sql-database.bicep @@ -0,0 +1,72 @@ +metadata description = 'Create Azure SQL Server and Database with Azure AD-only authentication.' + +param serverName string +param databaseName string +param location string = resourceGroup().location +param tags object = {} + +@description('Object ID of the Azure AD user to set as SQL Server administrator.') +param aadAdminObjectId string + +@description('Principal ID of the managed identity for role assignments.') +param managedIdentityPrincipalId string = '' + +resource sqlServer 'Microsoft.Sql/servers@2023-08-01-preview' = { + name: serverName + location: location + tags: tags + properties: { + // Azure AD-only authentication — no SQL auth passwords + administrators: { + administratorType: 'ActiveDirectory' + principalType: 'User' + login: 'aad-admin' + sid: aadAdminObjectId + tenantId: tenant().tenantId + azureADOnlyAuthentication: true + } + minimalTlsVersion: '1.2' + } +} + +// Allow Azure services (including Azure OpenAI, managed identities) to connect +resource firewallAllowAzure 'Microsoft.Sql/servers/firewallRules@2023-08-01-preview' = { + parent: sqlServer + name: 'AllowAllWindowsAzureIps' + properties: { + startIpAddress: '0.0.0.0' + endIpAddress: '0.0.0.0' + } +} + +resource sqlDatabase 'Microsoft.Sql/servers/databases@2023-08-01-preview' = { + parent: sqlServer + name: databaseName + location: location + tags: tags + sku: { + name: 'S0' + tier: 'Standard' + } +} + +// SQL Server Contributor role for managed identity (management plane access) +// Data plane access (query/insert) requires T-SQL GRANT after deployment. +var sqlServerContributorRole = subscriptionResourceId( + 'Microsoft.Authorization/roleDefinitions', + '6d8ee4ec-f05a-4a1d-8b00-a9b17e38b437' +) + +resource managedIdentityRoleAssignment 'Microsoft.Authorization/roleAssignments@2022-04-01' = if (!empty(managedIdentityPrincipalId)) { + name: guid(sqlServer.id, managedIdentityPrincipalId, sqlServerContributorRole) + scope: sqlServer + properties: { + roleDefinitionId: sqlServerContributorRole + principalId: managedIdentityPrincipalId + principalType: 'ServicePrincipal' + } +} + +output fullyQualifiedDomainName string = sqlServer.properties.fullyQualifiedDomainName +output serverName string = sqlServer.name +output databaseName string = sqlDatabase.name