@basproul/google-genai

@langchain/google-genai

This package contains the LangChain.js integrations for Gemini through their generative-ai SDK.

Installation

npm install @langchain/google-genai

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 field to your project's package.json like this:

{
  "name": "your-project",
  "version": "0.0.0",
  "dependencies": {
    "@langchain/google-genai": "^0.0.0",
    "langchain": "0.0.207"
  },
  "resolutions": {
    "@langchain/core": "0.1.2"
  },
  "overrides": {
    "@langchain/core": "0.1.2"
  },
  "pnpm": {
    "overrides": {
      "@langchain/core": "0.1.2"
    }
  }
}

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 ChatGoogleGenerativeAI class, which is the recommended way to interface with the Google Gemini series of models.

To use, install the requirements, and configure your environment.

export GOOGLE_API_KEY=your-api-key

Then initialize

import { ChatGoogleGenerativeAI } from "@langchain/google-genai";

const model = new ChatGoogleGenerativeAI({
  modelName: "gemini-pro",
  maxOutputTokens: 2048,
});
const response = await mode.invoke(new HumanMessage("Hello world!"));

Multimodal inputs

Gemini vision model supports image inputs when providing a single chat message. Example:

npm install @langchain/core

import fs from "fs";
import { ChatGoogleGenerativeAI } from "@langchain/google-genai";
import { HumanMessage } from "@langchain/core/messages";

const vision = new ChatGoogleGenerativeAI({
  modelName: "gemini-pro-vision",
  maxOutputTokens: 2048,
});
const image = fs.readFileSync("./hotdog.jpg").toString("base64");
const input = [
  new HumanMessage({
    content: [
      {
        type: "text",
        text: "Describe the following image.",
      },
      {
        type: "image_url",
        image_url: `data:image/png;base64,${image}`,
      },
    ],
  }),
];

const res = await vision.invoke(input);

The value of image_url can be any of the following:

• A public image URL
• An accessible gcs file (e.g., "gcs://path/to/file.png")
• A base64 encoded image (e.g., data:image/png;base64,abcd124)
• A PIL image

Embeddings

This package also adds support for google's embeddings models.

import { GoogleGenerativeAIEmbeddings } from "@langchain/google-genai";
import { TaskType } from "@google/generative-ai";

const embeddings = new GoogleGenerativeAIEmbeddings({
  modelName: "embedding-001", // 768 dimensions
  taskType: TaskType.RETRIEVAL_DOCUMENT,
  title: "Document title",
});

const res = await embeddings.embedQuery("OK Google");

Development

To develop the Google GenAI 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/google-genai

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.