npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

@langchain/azure-openai

v0.0.11

Published

Azure SDK for OpenAI integrations for LangChain.js

Downloads

36,354

Readme

[!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.