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On this page

Google PaLM
===========

note

This integration does not support `gemini-*` models. Check [Google AI](/v0.2/docs/integrations/chat/google_generativeai) or [VertexAI](/v0.2/docs/integrations/llms/google_vertex_ai).

The [Google PaLM API](https://developers.generativeai.google/products/palm) can be integrated by first installing the required packages:

tip

See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install google-auth-library @google-ai/generativelanguage @langchain/community

    yarn add google-auth-library @google-ai/generativelanguage @langchain/community

    pnpm add google-auth-library @google-ai/generativelanguage @langchain/community

Create an **API key** from [Google MakerSuite](https://makersuite.google.com/app/apikey). You can then set the key as `GOOGLE_PALM_API_KEY` environment variable or pass it as `apiKey` parameter while instantiating the model.

    import { GooglePaLM } from "@langchain/community/llms/googlepalm";export const run = async () => {  const model = new GooglePaLM({    apiKey: "<YOUR API KEY>", // or set it in environment variable as `GOOGLE_PALM_API_KEY`    // other params    temperature: 1, // OPTIONAL    model: "models/text-bison-001", // OPTIONAL    maxOutputTokens: 1024, // OPTIONAL    topK: 40, // OPTIONAL    topP: 3, // OPTIONAL    safetySettings: [      // OPTIONAL      {        category: "HARM_CATEGORY_DANGEROUS",        threshold: "BLOCK_MEDIUM_AND_ABOVE",      },    ],    stopSequences: ["stop"], // OPTIONAL  });  const res = await model.invoke(    "What would be a good company name for a company that makes colorful socks?"  );  console.log({ res });};

#### API Reference:

*   [GooglePaLM](https://v02.api.js.langchain.com/classes/langchain_community_llms_googlepalm.GooglePaLM.html) from `@langchain/community/llms/googlepalm`

GooglePaLM
==========

Langchain.js supports two different authentication methods based on whether you're running in a Node.js environment or a web environment.

Setup[​](#setup "Direct link to Setup")
---------------------------------------

### Node.js[​](#nodejs "Direct link to Node.js")

To call Vertex AI models in Node, you'll need to install [Google's official auth client](https://www.npmjs.com/package/google-auth-library) as a peer dependency.

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
*   Yarn
*   pnpm

    npm install google-auth-library

    yarn add google-auth-library

    pnpm add google-auth-library

### Web[​](#web "Direct link to Web")

To call Vertex AI models in web environments (like Edge functions), you'll need to install the [`web-auth-library`](https://github.com/kriasoft/web-auth-library) pacakge as a peer dependency:

*   npm
*   Yarn
*   pnpm

    npm install web-auth-library

    yarn add web-auth-library

    pnpm add web-auth-library

Then, you'll need to add your service account credentials directly as a `GOOGLE_VERTEX_AI_WEB_CREDENTIALS` environment variable:

    GOOGLE_VERTEX_AI_WEB_CREDENTIALS={"type":"service_account","project_id":"YOUR_PROJECT-12345",...}

You can also pass your credentials directly in code like this:

tip

See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install @langchain/community

    yarn add @langchain/community

    pnpm add @langchain/community

    import { GoogleVertexAI } from "@langchain/community/llms/googlevertexai";const model = new GoogleVertexAI({  authOptions: {    credentials: {"type":"service_account","project_id":"YOUR_PROJECT-12345",...},  },});

Usage[​](#usage "Direct link to Usage")
---------------------------------------

Several models are available and can be specified by the `model` attribute in the constructor. These include:

*   text-bison (default)
*   text-bison-32k
*   code-gecko
*   code-bison

    import { GoogleVertexAI } from "@langchain/community/llms/googlevertexai";// Or, if using the web entrypoint:// import { GoogleVertexAI } from "@langchain/community/llms/googlevertexai/web";/* * Before running this, you should make sure you have created a * Google Cloud Project that is permitted to the Vertex AI API. * * You will also need permission to access this project / API. * Typically, this is done in one of three ways: * - You are logged into an account permitted to that project. * - You are running this on a machine using a service account permitted to *   the project. * - The `GOOGLE_APPLICATION_CREDENTIALS` environment variable is set to the *   path of a credentials file for a service account permitted to the project. */const model = new GoogleVertexAI({  temperature: 0.7,});const res = await model.invoke(  "What would be a good company name for a company that makes colorful socks?");console.log({ res });

#### API Reference:

*   [GoogleVertexAI](https://v02.api.js.langchain.com/classes/langchain_community_llms_googlevertexai.GoogleVertexAI.html) from `@langchain/community/llms/googlevertexai`

Google also has separate models for their "Codey" code generation models.

The "code-gecko" model is useful for code completion:

    import { GoogleVertexAI } from "@langchain/community/llms/googlevertexai";/* * Before running this, you should make sure you have created a * Google Cloud Project that is permitted to the Vertex AI API. * * You will also need permission to access this project / API. * Typically, this is done in one of three ways: * - You are logged into an account permitted to that project. * - You are running this on a machine using a service account permitted to *   the project. * - The `GOOGLE_APPLICATION_CREDENTIALS` environment variable is set to the *   path of a credentials file for a service account permitted to the project. */const model = new GoogleVertexAI({  model: "code-gecko",});const res = await model.invoke("for (let co=0;");console.log({ res });

#### API Reference:

*   [GoogleVertexAI](https://v02.api.js.langchain.com/classes/langchain_community_llms_googlevertexai.GoogleVertexAI.html) from `@langchain/community/llms/googlevertexai`

While the "code-bison" model is better at larger code generation based on a text prompt:

    import { GoogleVertexAI } from "@langchain/community/llms/googlevertexai";/* * Before running this, you should make sure you have created a * Google Cloud Project that is permitted to the Vertex AI API. * * You will also need permission to access this project / API. * Typically, this is done in one of three ways: * - You are logged into an account permitted to that project. * - You are running this on a machine using a service account permitted to *   the project. * - The `GOOGLE_APPLICATION_CREDENTIALS` environment variable is set to the *   path of a credentials file for a service account permitted to the project. */const model = new GoogleVertexAI({  model: "code-bison",  maxOutputTokens: 2048,});const res = await model.invoke(  "A Javascript function that counts from 1 to 10.");console.log({ res });

#### API Reference:

*   [GoogleVertexAI](https://v02.api.js.langchain.com/classes/langchain_community_llms_googlevertexai.GoogleVertexAI.html) from `@langchain/community/llms/googlevertexai`

### Streaming[​](#streaming "Direct link to Streaming")

Streaming in multiple chunks is supported for faster responses:

    import { GoogleVertexAI } from "@langchain/community/llms/googlevertexai";const model = new GoogleVertexAI({  temperature: 0.7,});const stream = await model.stream(  "What would be a good company name for a company that makes colorful socks?");for await (const chunk of stream) {  console.log("\n---------\nChunk:\n---------\n", chunk);}/*  ---------  Chunk:  ---------    1. Toe-tally Awesome Socks  2. The Sock Drawer  3. Happy Feet  4.   ---------  Chunk:  ---------  Sock It to Me  5. Crazy Color Socks  6. Wild and Wacky Socks  7. Fu  ---------  Chunk:  ---------  nky Feet  8. Mismatched Socks  9. Rainbow Socks  10. Sole Mates  ---------  Chunk:  ---------  */

#### API Reference:

*   [GoogleVertexAI](https://v02.api.js.langchain.com/classes/langchain_community_llms_googlevertexai.GoogleVertexAI.html) from `@langchain/community/llms/googlevertexai`

* * *

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