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Portilla Edo"},"license":"Apache-2.0","homepage":"https://github.com/genkit-ai/azure-foundry-js-plugin#readme","keywords":["genkit","genkit-plugin","genkit-embedder","genkit-model","openai","gpt-4","gpt-4.1","o3","gpt","gpt-3.5","ai","genai","generative-ai"],"repository":{"type":"git","url":"git+https://github.com/genkit-ai/azure-foundry-js-plugin.git"},"description":"Genkit AI framework plugin for Azure OpenAI APIs.","maintainers":[{"name":"xavidop","email":"xavierportillaedo@gmail.com"},{"name":"thefirecompany","email":"oss@bloom.diy"}],"readme":"\n\n**`genkitx-azure-openai`** is a community plugin for using Azure OpenAI APIs with\n[Genkit](https://github.com/firebase/genkit).\n## Installation\n\nInstall the plugin in your project with your favorite package manager:\n\n- `npm install genkitx-azure-openai`\n- `yarn add genkitx-azure-openai`\n- `pnpm add genkitx-azure-openai`\n\n### Initialize\n\nYou'll also need to have an Azure OpenAI instance deployed. You can deploy a version on Azure Portal following [this guide](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource?pivots=web-portal).\n\nOnce you have your instance running, make sure you have the endpoint and key. You can find them in the Azure Portal, under the \"Keys and Endpoint\" section of your instance.\n\nYou can then define the following environment variables to use the service:\n\n```\nAZURE_OPENAI_ENDPOINT=<YOUR_ENDPOINT>\nAZURE_OPENAI_API_KEY=<YOUR_KEY>\nOPENAI_API_VERSION=<YOUR_API_VERSION>\n```\n\nAlternatively, you can pass the values directly to the `azureOpenAI` constructor:\n\n```typescript\nimport { azureOpenAI, gpt4o } from 'genkitx-azure-openai';\nimport { genkit } from 'genkit';\nconst apiVersion = '2024-10-21';\n\nconst ai = genkit({\n  plugins: [\n    azureOpenAI({\n      apiKey: '<your_key>',\n      endpoint: '<your_endpoint>',\n      deployment: '<your_embedding_deployment_name',\n      apiVersion,\n    }),\n    // other plugins\n  ],\n  model: gpt4o,\n});\n```\n\nIf you're using Azure Managed Identity, you can also pass the credentials directly to the constructor:\n\n```typescript\nimport { azureOpenAI, gpt4o } from 'genkitx-azure-openai';\nimport { genkit } from 'genkit';\nimport {\n  DefaultAzureCredential,\n  getBearerTokenProvider,\n} from '@azure/identity';\nconst apiVersion = '2024-10-21';\n\nconst credential = new DefaultAzureCredential();\nconst scope = 'https://cognitiveservices.azure.com/.default';\nconst azureADTokenProvider = getBearerTokenProvider(credential, scope);\n\nconst ai = genkit({\n  plugins: [\n    azureOpenAI({\n      azureADTokenProvider,\n      endpoint: '<your_endpoint>',\n      deployment: '<your_embedding_deployment_name',\n      apiVersion,\n    }),\n    // other plugins\n  ],\n  model: gpt4o,\n});\n```\n\n### Basic examples\n\nThe simplest way to call the text generation model is by using the helper function `generate`:\n\n```typescript\n// Basic usage of an LLM\nconst response = await ai.generate({\n  prompt: 'Tell me a joke.',\n});\n\nconsole.log(await response.text);\n```\n\nUsing the same interface, you can prompt a multimodal model:\n\n```typescript\nconst response = await ai.generate({\n  model: gpt4o,\n  prompt: [\n    { text: 'What animal is in the photo?' },\n    { media: { url: imageUrl } },\n  ],\n  config: {\n    // control of the level of visual detail when processing image embeddings\n    // Low detail level also decreases the token usage\n    visualDetailLevel: 'low',\n  },\n});\nconsole.log(await response.text);\n```\n\nFor more detailed examples and the explanation of other functionalities, refer to the example in the [official Github repo of the plugin](examples/README.md) or in the [official Genkit documentation](https://genkit.dev/docs/get-started/).\n\n### Deploy as Azure Functions\n\nYou can deploy Genkit flows as Azure Functions HTTP triggers using `onCallGenkit`. It auto-registers the function with `app.http()` using the flow name, handles CORS, supports streaming via SSE, and provides authentication via `ContextProvider`:\n\n```typescript\nimport { genkit, z } from 'genkit';\nimport { azureOpenAI, gpt4o, onCallGenkit } from 'genkitx-azure-openai';\n\nconst ai = genkit({ plugins: [azureOpenAI()], model: gpt4o });\n\nconst jokeFlow = ai.defineFlow(\n  { name: 'jokeFlow', inputSchema: z.object({ subject: z.string() }), outputSchema: z.object({ joke: z.string() }) },\n  async (input) => {\n    const { text } = await ai.generate({ prompt: `Tell me a joke about ${input.subject}` });\n    return { joke: text };\n  }\n);\n\n// Automatically registered as POST /api/jokeFlow\nexport const jokeHandler = onCallGenkit(jokeFlow);\n```\n\nSee the full [Azure Functions example](examples/azure-functions/) for streaming, authentication, and deployment instructions.\n\n## Contributing\n\nWant to contribute to the project? That's awesome! 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