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Integrations: Chat Models

LangChain offers a number of Chat Models implementations that integrate with various model providers. These are:

ChatOpenAI​

import { ChatOpenAI } from "langchain/chat_models/openai";

const model = new ChatOpenAI({
  temperature: 0.9,
  openAIApiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.OPENAI_API_KEY
});

API Reference:

Azure ChatOpenAI​

import { ChatOpenAI } from "langchain/chat_models/openai";

const model = new ChatOpenAI({
  temperature: 0.9,
  azureOpenAIApiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.AZURE_OPENAI_API_KEY
  azureOpenAIApiInstanceName: "YOUR-INSTANCE-NAME", // In Node.js defaults to process.env.AZURE_OPENAI_API_INSTANCE_NAME
  azureOpenAIApiDeploymentName: "YOUR-DEPLOYMENT-NAME", // In Node.js defaults to process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME
  azureOpenAIApiVersion: "YOUR-API-VERSION", // In Node.js defaults to process.env.AZURE_OPENAI_API_VERSION
});

API Reference:

ChatAnthropic​

import { ChatAnthropic } from "langchain/chat_models/anthropic";

const model = new ChatAnthropic({
  temperature: 0.9,
  apiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.ANTHROPIC_API_KEY
});

API Reference:

Google Vertex AI​

The Vertex AI implementation is meant to be used in Node.js and not directly from a browser, since it requires a service account to use.

Before running this code, you should make sure the Vertex AI API is enabled for the relevant project and that you've authenticated to Google Cloud using one of these methods:

  • You are logged into an account (using gcloud auth application-default login) permitted to that project.
  • You are running on a machine using a service account that is permitted to the project.
  • You have downloaded the credentials for a service account that is permitted to the project and set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path of this file.
npm install google-auth-library

The ChatGoogleVertexAI class works just like other chat-based LLMs, with a few exceptions:

  1. The first SystemChatMessage passed in is mapped to the "context" parameter that the PaLM model expects. No other SystemChatMessages are allowed.
  2. After the first SystemChatMessage, there must be an odd number of messages, representing a conversation between a human and the model.
  3. Human messages must alternate with AI messages.
import { ChatGoogleVertexAI } from "langchain/chat_models/googlevertexai";

const model = new ChatGoogleVertexAI({
  temperature: 0.7,
});

API Reference:

There is also an optional examples constructor parameter that can help the model understand what an appropriate response looks like.

import { ChatGoogleVertexAI } from "langchain/chat_models/googlevertexai";
import {
  AIChatMessage,
  HumanChatMessage,
  SystemChatMessage,
} from "langchain/schema";

export const run = async () => {
  const examples = [
    {
      input: new HumanChatMessage("What is your favorite sock color?"),
      output: new AIChatMessage("My favorite sock color be arrrr-ange!"),
    },
  ];
  const model = new ChatGoogleVertexAI({
    temperature: 0.7,
    examples,
  });
  const questions = [
    new SystemChatMessage(
      "You are a funny assistant that answers in pirate language."
    ),
    new HumanChatMessage("What is your favorite food?"),
  ];
  // You can also use the model as part of a chain
  const res = await model.call(questions);
  console.log({ res });
};

API Reference: