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Google PaLM

danger

The Google PaLM API is deprecated and will be removed in 0.3.0. Please use the Google GenAI or VertexAI integrations instead.

note

This integration does not support gemini-* models. Check Google GenAI or VertexAI.

The Google PaLM API can be integrated by first installing the required packages:

tip

See this section for general instructions on installing integration packages.

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

Create an API key from Google MakerSuite. 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 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​

Node.js​

To call Vertex AI models in Node, you'll need to install Google's official auth client 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 install google-auth-library

Web​

To call Vertex AI models in web environments (like Edge functions), you'll need to install the web-auth-library pacakge as a peer dependency:

npm install 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.

npm install @langchain/community
import { GoogleVertexAI } from "@langchain/community/llms/googlevertexai";

const model = new GoogleVertexAI({
  authOptions: {
    credentials: {"type":"service_account","project_id":"YOUR_PROJECT-12345",...},
  },
});

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:

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:

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:

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:

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