This package contains the LangChain.js integrations for Gemini through their generative-ai SDK.
npm install @langchain/google-genai @langchain/core
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/core": "^0.3.0",
"@langchain/google-genai": "^0.0.0"
},
"resolutions": {
"@langchain/core": "^0.3.0"
},
"overrides": {
"@langchain/core": "^0.3.0"
},
"pnpm": {
"overrides": {
"@langchain/core": "^0.3.0"
}
}
}
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.
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 model.invoke(new HumanMessage("Hello world!"));
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
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");
To develop the Google GenAI package, you'll need to follow these instructions:
yarn install
yarn build
Or from the repo root:
yarn build --filter=@langchain/google-genai
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
Run the linter & formatter to ensure your code is up to standard:
yarn lint && yarn format
If you add a new file to be exported, either import & re-export from src/index.ts
, or add it to the entrypoints
field in the config
variable located inside langchain.config.js
and run yarn build
to generate the new entrypoint.