[!IMPORTANT]
This package is now deprecated in favor of the new Azure integration in the OpenAI SDK. Please use the package @langchain/openai instead.
You can find the migration guide here.
@langchain/azure-openai
This package contains the Azure SDK for OpenAI LangChain.js integrations.
It provides Azure OpenAI support through the Azure SDK for OpenAI library.
Installation
npm install @langchain/azure-openai
This package, along with the main LangChain package, depends on @langchain/core.
If 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.
You can do so by adding appropriate fields to your project's package.json like this:
{
"name": "your-project",
"version": "0.0.0",
"dependencies": {
"@langchain/azure-openai": "^0.0.4",
"langchain": "0.0.207"
},
"resolutions": {
"@langchain/core": "0.1.5"
},
"overrides": {
"@langchain/core": "0.1.5"
},
"pnpm": {
"overrides": {
"@langchain/core": "0.1.5"
}
}
}
The 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.
Chat Models
This package contains the AzureChatOpenAI class, which is the recommended way to interface with deployed models on Azure OpenAI.
To use, install the requirements, and configure your environment.
export AZURE_OPENAI_API_ENDPOINT=<your_endpoint>
export AZURE_OPENAI_API_KEY=<your_key>
export AZURE_OPENAI_API_DEPLOYMENT_NAME=<your_deployment_name>
Then initialize the model and make the calls:
import { AzureChatOpenAI } from "@langchain/azure-openai";
const model = new AzureChatOpenAI({
// Note that the following are optional, and will default to the values below
// if not provided.
azureOpenAIEndpoint: process.env.AZURE_OPENAI_API_ENDPOINT,
azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY,
azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME,
});
const response = await model.invoke(new HumanMessage("Hello world!"));
Streaming
import { AzureChatOpenAI } from "@langchain/azure-openai";
const model = new AzureChatOpenAI({
// Note that the following are optional, and will default to the values below
// if not provided.
azureOpenAIEndpoint: process.env.AZURE_OPENAI_API_ENDPOINT,
azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY,
azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME,
});
const response = await model.stream(new HumanMessage("Hello world!"));
Embeddings
This package also supports embeddings with Azure OpenAI.
import { AzureOpenAIEmbeddings } from "@langchain/azure-openai";
const embeddings = new AzureOpenAIEmbeddings({
// Note that the following are optional, and will default to the values below
// if not provided.
azureOpenAIEndpoint: process.env.AZURE_OPENAI_API_ENDPOINT,
azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY,
azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME,
});
const res = await embeddings.embedQuery("Hello world");
Using Azure managed identity
If you're using Azure Managed Identity, you can also pass the credentials directly to the constructor:
import { DefaultAzureCredential } from "@azure/identity";
import { AzureOpenAI } from "@langchain/azure-openai";
const credentials = new DefaultAzureCredential();
const model = new AzureOpenAI({
credentials,
azureOpenAIEndpoint: process.env.AZURE_OPENAI_API_ENDPOINT,
azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME,
});
Compatibility with OpenAI API
This library is provides compatibility with the OpenAI API. You can use an API key from OpenAI's developer portal like in the example below:
import { AzureOpenAI, OpenAIKeyCredential } from "@langchain/azure-openai";
const model = new AzureOpenAI({
modelName: "gpt-3.5-turbo",
credentials: new OpenAIKeyCredential("<your_openai_api_key>"),
});
Development
To develop the Azure OpenAI package, you'll need to follow these instructions:
Install dependencies
yarn install
Build the package
yarn build
Or from the repo root:
yarn build --filter=@langchain/azure-openai
Run tests
Test files should live within a tests/ file in the src/ folder. Unit tests should end in .test.ts and integration tests should
end in .int.test.ts:
$ yarn test
$ yarn test:int
Lint & Format
Run the linter & formatter to ensure your code is up to standard:
yarn lint && yarn format
Adding new entrypoints
If 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.
