---
title: Google components
slug: /bundles-google
---
import Icon from "@site/src/components/icon";
[**Bundles**](/components-bundle-components) 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](https://cloud.google.com/bigquery) through the **BigQuery** component in the [**Google** bundle](/bundles-google), 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](https://developers.google.com/workspace/guides/create-project) with the BigQuery API enabled
* A [service account](https://developers.google.com/workspace/guides/create-credentials#service-account) with the **BigQuery Job User** role
* A [BigQuery dataset and table](https://cloud.google.com/bigquery/docs/datasets-intro)
* A [running Langflow server](/get-started-installation)
#### Create a service account with BigQuery access
1. Select and enable your Google Cloud project.
For more information, see [Create a Google Cloud project](https://developers.google.com/workspace/guides/create-project).
2. Create a service account in your Google Cloud project.
For more information, see [Create a service account](https://developers.google.com/workspace/guides/create-credentials#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](https://cloud.google.com/bigquery/docs/access-control).
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 **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`:
```sql
SELECT film, category, year_film
FROM `big-query-langflow-project.the_oscar_award.oscar_winners`
WHERE winner = TRUE
LIMIT 10
```
Result
```text
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
```
A successful chat confirms the component can access the BigQuery table.
## Google Generative AI
This component generates text using [Google Generative AI models](https://cloud.google.com/vertex-ai/docs/).
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](/components-embedding-models).
### 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](/bundles-apify)
* [**SearchApi** component](/bundles-searchapi)
* [**Serper Google Search API** component](/bundles-serper)
* [**Web Search** component](/web-search)
## Google Vertex AI
For information about Vertex AI components, see the [**Vertex AI** bundle](/bundles-vertexai).
## Legacy Google components
import PartialLegacy from '@site/docs/_partial-legacy.mdx';
The following Google components are in legacy status:
Google OAuth Token
The **Google OAuth Token** component was deprecated in Langflow 1.4.0.
To connect your flows to Google OAuth services, use [Composio components](/bundles-composio).
Gmail Loader
This component loads emails from Gmail using [Service Account JSON](https://developers.google.com/identity/protocols/oauth2/service-account) credentials and label ID filters.
As an alternative, you can use [Composio components](/bundles-composio) to connect your flows to Google services.
Google Drive Loader
This component loads documents from Google Drive using [Service Account JSON](https://developers.google.com/identity/protocols/oauth2/service-account) credentials and document ID filters.
While there is no direct replacement, consider using the [**API Request** component](/api-request) to call the Google Drive API.
Google Drive Search
This component searches Google Drive using [Service Account JSON](https://developers.google.com/identity/protocols/oauth2/service-account) credentials and various query strings and filters.
While there is no direct replacement, consider using the [**API Request** component](/api-request) to call the Google Drive API.
## See also
- [**Composio** bundle](/bundles-composio)
- [**Vertex AI** bundle](/bundles-vertexai)