Google components

import Icon from "@site/src/components/icon";

<Icon name="Blocks" aria-hidden="true" /> Bundles contain custom components that support specific third-party integrations with Langflow.

This page describes the components that are available in the Google bundle.

BigQuery

Langflow integrates with Google BigQuery through the BigQuery component in the Google bundle, allowing you to execute SQL queries and retrieve data from your BigQuery datasets.

Use the BigQuery component in a flow

To use the BigQuery component in a flow, you need the following:

• A Google Cloud project with the BigQuery API enabled
• A service account with the BigQuery Job User role
• A BigQuery dataset and table
• A running Langflow server

Create a service account with BigQuery access

1. Select and enable your Google Cloud project. For more information, see Create a Google Cloud project.
2. Create a service account in your Google Cloud project. For more information, see Create a service account.
3. Assign the BigQuery Job User role to your new account. This role allows Langflow to access BigQuery resources with the service account. You may also need to allow access to your BigQuery dataset. For more information, see BigQuery access control with IAM.
4. To generate a new JSON key for the service account, navigate to your service account.
5. Click Add Key, and then click Create new key.
6. Under Key type, select JSON, and then click Create. A JSON private key file is downloaded to your machine. Now that you have a service account and a JSON private key, you need to configure the credentials in the Langflow BigQuery component.

Configure credentials in the Langflow component

With your service account configured and your credentials JSON file created, follow these steps to authenticate the Langflow application.

1. Create a new flow in Langflow.
2. In <Icon name="Blocks" aria-hidden="true" /> Bundles, find the Google BigQuery component, and then add it to your flow.
3. In the BigQuery component's Upload Service Account JSON field, click Select file.
4. In the My Files pane, select Click or drag files here. Your file browser opens.
5. In your file browser, select the service account JSON file, and then click Open.
6. In the My Files pane, select your service account JSON file, and then click Select files. The BigQuery component can now query your datasets and tables using your service account JSON file.

Query a BigQuery dataset

With your component credentials configured, query your BigQuery datasets and tables to confirm connectivity.

1. Connect Chat Input and Chat Output components to the BigQuery component.

2. Open the Playground, and then submit a valid SQL query.

   This example queries a table of Oscar winners stored within a BigQuery dataset called the_oscar_award:

   sqlCopy

   SELECT film, category, year_film
   FROM `big-query-langflow-project.the_oscar_award.oscar_winners`
   WHERE winner = TRUE
   LIMIT 10
   

   <details> <summary>Result</summary>
   textCopy

   film	category	year_film
   The Last Command	ACTOR	1927
   7th Heaven	ACTRESS	1927
   The Dove;	ART DIRECTION	1927
   Sunrise	CINEMATOGRAPHY	1927
   Sunrise	CINEMATOGRAPHY	1927
   Two Arabian Knights	DIRECTING (Comedy Picture)	1927
   7th Heaven	DIRECTING (Dramatic Picture)	1927
   Wings	ENGINEERING EFFECTS	1927
   Wings	OUTSTANDING PICTURE	1927
   Sunrise	UNIQUE AND ARTISTIC PICTURE	1927
   

   </details>

   A successful chat confirms the component can access the BigQuery table.

Google Generative AI

This component generates text using Google Generative AI models.

Supported models include the Gemini 1.5, 2.0, 2.5, and 3.0 series. The latest Gemini 3.0 models (gemini-3-pro-preview, gemini-3-flash-preview, and gemini-3-pro-image-preview) offer advanced reasoning and multimodal capabilities.

Google Generative AI parameters

Name	Type	Description
Google API Key	SecretString	Input parameter. Your Google API key to use for the Google Generative AI.
Model	String	Input parameter. The name of the model to use, such as "gemini-2.5-flash" or "gemini-3-pro-preview".
Max Output Tokens	Integer	Input parameter. The maximum number of tokens to generate.
Temperature	Float	Input parameter. Run inference with this temperature.
Top K	Integer	Input parameter. Consider the set of top K most probable tokens.
Top P	Float	Input parameter. The maximum cumulative probability of tokens to consider when sampling.
N	Integer	Input parameter. Number of chat completions to generate for each prompt.
model	LanguageModel	Output parameter. An instance of ChatGoogleGenerativeAI configured with the specified parameters.

Google Generative AI Embeddings

The Google Generative AI Embeddings component connects to Google's generative AI embedding service using the GoogleGenerativeAIEmbeddings class from the langchain-google-genai package.

For more information about using embedding model components in flows, see Embedding model components.

Google Generative AI Embeddings parameters

Name	Display Name	Info
api_key	API Key	Input parameter. The secret API key for accessing Google's generative AI service. Required.
model_name	Model Name	Input parameter. The name of the embedding model to use. Default: "models/text-embedding-004".
embeddings	Embeddings	Output parameter. The built GoogleGenerativeAIEmbeddings object.

Google Search API

This component allows you to call the Google Search API.

Google Search API parameters

Name	Type	Description
google_api_key	SecretString	Input parameter. A Google API key for authentication.
google_cse_id	SecretString	Input parameter. A Google Custom Search Engine ID.
input_value	String	Input parameter. The search query input.
k	Integer	Input parameter. The number of search results to return.
results	List[Data]	Output parameter. A list of search results.
tool	Tool	Output parameter. A Google Search tool for use in LangChain.

Other Google Search components

Langflow includes multiple components that support Google Search, such as the following:

• Apify Actors component
• SearchApi component
• Serper Google Search API component
• Web Search component

Google Vertex AI

For information about Vertex AI components, see the Vertex AI bundle.

Legacy Google components

import PartialLegacy from '@site/docs/_partial-legacy.mdx';

<PartialLegacy />

The following Google components are in legacy status:

<details> <summary>Google OAuth Token</summary>

The Google OAuth Token component was deprecated in Langflow 1.4.0.

To connect your flows to Google OAuth services, use Composio components.

</details> <details> <summary>Gmail Loader</summary>

This component loads emails from Gmail using Service Account JSON credentials and label ID filters.

As an alternative, you can use Composio components to connect your flows to Google services.

</details> <details> <summary>Google Drive Loader</summary>

This component loads documents from Google Drive using Service Account JSON credentials and document ID filters.

While there is no direct replacement, consider using the API Request component to call the Google Drive API.

</details> <details> <summary>Google Drive Search</summary>

This component searches Google Drive using Service Account JSON credentials and various query strings and filters.

While there is no direct replacement, consider using the API Request component to call the Google Drive API.

</details>

See also

• Composio bundle
• Vertex AI bundle