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Please use the package [`@langchain/openai`](https://www.npmjs.com/package/@langchain/openai) instead.\n> You can find the migration guide [here](https://js.langchain.com/v0.2/docs/integrations/llms/azure#migration-from-azure-openai-sdk).\n\n# @langchain/azure-openai\n\nThis package contains the Azure SDK for OpenAI LangChain.js integrations.\n\nIt provides Azure OpenAI support through the [Azure SDK for OpenAI](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai) library. \n\n## Installation\n\n```bash npm2yarn\nnpm install @langchain/azure-openai\n```\n\nThis package, along with the main LangChain package, depends on [`@langchain/core`](https://npmjs.com/package/@langchain/core/).\nIf you are using this package with other LangChain packages, you should make sure that all of the packages depend on the same instance of @langchain/core.\nYou can do so by adding appropriate fields to your project's `package.json` like this:\n\n```json\n{\n  \"name\": \"your-project\",\n  \"version\": \"0.0.0\",\n  \"dependencies\": {\n    \"@langchain/azure-openai\": \"^0.0.4\",\n    \"langchain\": \"0.0.207\"\n  },\n  \"resolutions\": {\n    \"@langchain/core\": \"0.1.5\"\n  },\n  \"overrides\": {\n    \"@langchain/core\": \"0.1.5\"\n  },\n  \"pnpm\": {\n    \"overrides\": {\n      \"@langchain/core\": \"0.1.5\"\n    }\n  }\n}\n```\n\nThe field you need depends on the package manager you're using, but we recommend adding a field for the common `yarn`, `npm`, and `pnpm` to maximize compatibility.\n\n## Chat Models\n\nThis package contains the `AzureChatOpenAI` class, which is the recommended way to interface with deployed models on Azure OpenAI.\n\nTo use, install the requirements, and configure your environment.\n\n```bash\nexport AZURE_OPENAI_API_ENDPOINT=<your_endpoint>\nexport AZURE_OPENAI_API_KEY=<your_key>\nexport AZURE_OPENAI_API_DEPLOYMENT_NAME=<your_deployment_name>\n```\n\nThen initialize the model and make the calls:\n\n```typescript\nimport { AzureChatOpenAI } from \"@langchain/azure-openai\";\n\nconst model = new AzureChatOpenAI({\n  // Note that the following are optional, and will default to the values below\n  // if not provided.\n  azureOpenAIEndpoint: process.env.AZURE_OPENAI_API_ENDPOINT,\n  azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY,\n  azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME,\n});\nconst response = await model.invoke(new HumanMessage(\"Hello world!\"));\n```\n\n### Streaming\n\n```typescript\nimport { AzureChatOpenAI } from \"@langchain/azure-openai\";\n\nconst model = new AzureChatOpenAI({\n  // Note that the following are optional, and will default to the values below\n  // if not provided.\n  azureOpenAIEndpoint: process.env.AZURE_OPENAI_API_ENDPOINT,\n  azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY,\n  azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME,\n});\nconst response = await model.stream(new HumanMessage(\"Hello world!\"));\n```\n\n## Embeddings\n\nThis package also supports embeddings with Azure OpenAI.\n\n```typescript\nimport { AzureOpenAIEmbeddings } from \"@langchain/azure-openai\";\n\nconst embeddings = new AzureOpenAIEmbeddings({\n  // Note that the following are optional, and will default to the values below\n  // if not provided.\n  azureOpenAIEndpoint: process.env.AZURE_OPENAI_API_ENDPOINT,\n  azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY,\n  azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME,\n});\nconst res = await embeddings.embedQuery(\"Hello world\");\n```\n\n## Using Azure managed identity\n\nIf you're using [Azure Managed Identity](https://learn.microsoft.com/azure/ai-services/openai/how-to/managed-identity), you can also pass the credentials directly to the constructor:\n\n```typescript\nimport { DefaultAzureCredential } from \"@azure/identity\";\nimport { AzureOpenAI } from \"@langchain/azure-openai\";\n\nconst credentials = new DefaultAzureCredential();\n\nconst model = new AzureOpenAI({\n  credentials,\n  azureOpenAIEndpoint: process.env.AZURE_OPENAI_API_ENDPOINT,\n  azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME,\n});\n```\n\n## Compatibility with OpenAI API\n\nThis library is provides compatibility with the OpenAI API. You can use an API key from OpenAI's developer portal like in the example below:\n\n```typescript\nimport { AzureOpenAI, OpenAIKeyCredential } from \"@langchain/azure-openai\";\n\nconst model = new AzureOpenAI({\n  modelName: \"gpt-3.5-turbo\",\n  credentials: new OpenAIKeyCredential(\"<your_openai_api_key>\"),\n});\n```\n\n## Development\n\nTo develop the Azure OpenAI package, you'll need to follow these instructions:\n\n### Install dependencies\n\n```bash\nyarn install\n```\n\n### Build the package\n\n```bash\nyarn build\n```\n\nOr from the repo root:\n\n```bash\nyarn build --filter=@langchain/azure-openai\n```\n\n### Run tests\n\nTest files should live within a `tests/` file in the `src/` folder. Unit tests should end in `.test.ts` and integration tests should\nend in `.int.test.ts`:\n\n```bash\n$ yarn test\n$ yarn test:int\n```\n\n### Lint & Format\n\nRun the linter & formatter to ensure your code is up to standard:\n\n```bash\nyarn lint && yarn format\n```\n\n### Adding new entrypoints\n\nIf you add a new file to be exported, either import & re-export from `src/index.ts`, or add it to `scripts/create-entrypoints.js` and run `yarn build` to generate the new entrypoint.\n","readmeFilename":"README.md"}