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Google
======

Functionality related to [Google Cloud Platform](https://cloud.google.com/)

Chat models[​](#chat-models "Direct link to Chat models")
---------------------------------------------------------

### Gemini Models[​](#gemini-models "Direct link to Gemini Models")

Access Gemini models such as `gemini-pro` and `gemini-pro-vision` through the [`ChatGoogleGenerativeAI`](/v0.2/docs/integrations/chat/google_generativeai), or if using VertexAI, via the [`ChatVertexAI`](/v0.2/docs/integrations/chat/google_vertex_ai) class.

*   GenAI
*   VertexAI

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/google-genai

    yarn add @langchain/google-genai

    pnpm add @langchain/google-genai

Configure your API key.

    export GOOGLE_API_KEY=your-api-key

    import { ChatGoogleGenerativeAI } from "@langchain/google-genai";const model = new ChatGoogleGenerativeAI({  model: "gemini-pro",  maxOutputTokens: 2048,});// Batch and stream are also supportedconst res = await model.invoke([  [    "human",    "What would be a good company name for a company that makes colorful socks?",  ],]);

Gemini vision models support image inputs when providing a single human message. For example:

    const visionModel = new ChatGoogleGenerativeAI({  model: "gemini-pro-vision",  maxOutputTokens: 2048,});const image = fs.readFileSync("./hotdog.jpg").toString("base64");const input2 = [  new HumanMessage({    content: [      {        type: "text",        text: "Describe the following image.",      },      {        type: "image_url",        image_url: `data:image/png;base64,${image}`,      },    ],  }),];const res = await visionModel.invoke(input2);

tip

Click [here](/v0.2/docs/integrations/chat/google_generativeai) for the `@langchain/google-genai` specific integration docs

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/google-vertexai

    yarn add @langchain/google-vertexai

    pnpm add @langchain/google-vertexai

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

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

or as a file path:

    GOOGLE_VERTEX_AI_WEB_CREDENTIALS_FILE=/path/to/your/credentials.json

    import { ChatVertexAI } from "@langchain/google-vertexai";// Or, if using the web entrypoint:// import { ChatVertexAI } from "@langchain/google-vertexai-web";const model = new ChatVertexAI({  model: "gemini-1.0-pro",  maxOutputTokens: 2048,});// Batch and stream are also supportedconst res = await model.invoke([  [    "human",    "What would be a good company name for a company that makes colorful socks?",  ],]);

Gemini vision models support image inputs when providing a single human message. For example:

    const visionModel = new ChatVertexAI({  model: "gemini-pro-vision",  maxOutputTokens: 2048,});const image = fs.readFileSync("./hotdog.png").toString("base64");const input2 = [  new HumanMessage({    content: [      {        type: "text",        text: "Describe the following image.",      },      {        type: "image_url",        image_url: `data:image/png;base64,${image}`,      },    ],  }),];const res = await visionModel.invoke(input2);

tip

Click [here](/v0.2/docs/integrations/chat/google_vertex_ai) for the `@langchain/google-vertexai` specific integration docs

The value of `image_url` must be a base64 encoded image (e.g., `data:image/png;base64,abcd124`).

### Vertex AI (Legacy)[​](#vertex-ai-legacy "Direct link to Vertex AI (Legacy)")

tip

See the legacy Google PaLM and VertexAI documentation [here](/v0.2/docs/integrations/chat/google_palm) for chat, and [here](/v0.2/docs/integrations/llms/google_palm) for LLMs.

Vector Store[​](#vector-store "Direct link to Vector Store")
------------------------------------------------------------

### Vertex AI Vector Search[​](#vertex-ai-vector-search "Direct link to Vertex AI Vector Search")

> [Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview), formerly known as Vertex AI Matching Engine, provides the industry's leading high-scale low latency vector database. These vector databases are commonly referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service.

    import { MatchingEngine } from "langchain/vectorstores/googlevertexai";

Tools[​](#tools "Direct link to Tools")
---------------------------------------

### Google Search[​](#google-search "Direct link to Google Search")

*   Set up a Custom Search Engine, following [these instructions](https://stackoverflow.com/questions/37083058/programmatically-searching-google-in-python-using-custom-search)
*   Get an API Key and Custom Search Engine ID from the previous step, and set them as environment variables `GOOGLE_API_KEY` and `GOOGLE_CSE_ID` respectively

There exists a `GoogleCustomSearch` utility which wraps this API. To import this utility:

    import { GoogleCustomSearch } from "langchain/tools";

We can easily load this wrapper as a Tool (to use with an Agent). We can do this with:

    const tools = [new GoogleCustomSearch({})];// Pass this variable into your agent.

* * *

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*   [Chat models](#chat-models)
    *   [Gemini Models](#gemini-models)
    *   [Vertex AI (Legacy)](#vertex-ai-legacy)
*   [Vector Store](#vector-store)
    *   [Vertex AI Vector Search](#vertex-ai-vector-search)
*   [Tools](#tools)
    *   [Google Search](#google-search)

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