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* fix: nightly now properly gets 1.9.0 branch (#12215) before it was attempting to pull release-notes as letters are alphanumerically after numbers when we sort -V then grab tail now we only look at branch names that follow the pattern '^release-[0-9]+\.[0-9]+\.[0-9]+$' * docs: add search icon (#12216) add-back-svg * initial-content * cut-1.8-release-and-include-next-version * stage-1.8.0-and-next --------- Co-authored-by: Adam-Aghili <149833988+Adam-Aghili@users.noreply.github.com>
202 lines
8.9 KiB
Plaintext
202 lines
8.9 KiB
Plaintext
---
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title: Google components
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slug: /bundles-google
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---
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import Icon from "@site/src/components/icon";
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<Icon name="Blocks" aria-hidden="true" /> [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
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This page describes the components that are available in the **Google** bundle.
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## BigQuery
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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.
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### Use the BigQuery component in a flow
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To use the **BigQuery** component in a flow, you need the following:
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* A [Google Cloud project](https://developers.google.com/workspace/guides/create-project) with the BigQuery API enabled
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* A [service account](https://developers.google.com/workspace/guides/create-credentials#service-account) with the **BigQuery Job User** role
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* A [BigQuery dataset and table](https://cloud.google.com/bigquery/docs/datasets-intro)
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* A [running Langflow server](/get-started-installation)
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#### Create a service account with BigQuery access
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1. Select and enable your Google Cloud project.
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For more information, see [Create a Google Cloud project](https://developers.google.com/workspace/guides/create-project).
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2. Create a service account in your Google Cloud project.
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For more information, see [Create a service account](https://developers.google.com/workspace/guides/create-credentials#service-account).
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3. Assign the **BigQuery Job User** role to your new account.
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This role allows Langflow to access BigQuery resources with the service account.
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You may also need to allow access to your BigQuery dataset.
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For more information, see [BigQuery access control with IAM](https://cloud.google.com/bigquery/docs/access-control).
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4. To generate a new JSON key for the service account, navigate to your service account.
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5. Click **Add Key**, and then click **Create new key**.
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6. Under **Key type**, select **JSON**, and then click **Create**.
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A JSON private key file is downloaded to your machine.
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Now that you have a service account and a JSON private key, you need to configure the credentials in the Langflow **BigQuery** component.
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#### Configure credentials in the Langflow component
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With your service account configured and your credentials JSON file created, follow these steps to authenticate the Langflow application.
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1. Create a new flow in Langflow.
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2. In <Icon name="Blocks" aria-hidden="true" /> **Bundles**, find the Google **BigQuery** component, and then add it to your flow.
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3. In the **BigQuery** component's **Upload Service Account JSON** field, click **Select file**.
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4. In the **My Files** pane, select **Click or drag files here**.
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Your file browser opens.
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5. In your file browser, select the service account JSON file, and then click **Open**.
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6. In the **My Files** pane, select your service account JSON file, and then click **Select files**.
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The **BigQuery** component can now query your datasets and tables using your service account JSON file.
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#### Query a BigQuery dataset
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With your component credentials configured, query your BigQuery datasets and tables to confirm connectivity.
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1. Connect **Chat Input** and **Chat Output** components to the **BigQuery** component.
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2. Open the **Playground**, and then submit a valid SQL query.
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This example queries a table of Oscar winners stored within a BigQuery dataset called `the_oscar_award`:
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```sql
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SELECT film, category, year_film
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FROM `big-query-langflow-project.the_oscar_award.oscar_winners`
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WHERE winner = TRUE
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LIMIT 10
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```
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<details>
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<summary>Result</summary>
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```text
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film category year_film
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The Last Command ACTOR 1927
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7th Heaven ACTRESS 1927
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The Dove; ART DIRECTION 1927
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Sunrise CINEMATOGRAPHY 1927
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Sunrise CINEMATOGRAPHY 1927
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Two Arabian Knights DIRECTING (Comedy Picture) 1927
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7th Heaven DIRECTING (Dramatic Picture) 1927
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Wings ENGINEERING EFFECTS 1927
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Wings OUTSTANDING PICTURE 1927
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Sunrise UNIQUE AND ARTISTIC PICTURE 1927
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```
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</details>
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A successful chat confirms the component can access the BigQuery table.
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## Google Generative AI
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This component generates text using [Google Generative AI models](https://cloud.google.com/vertex-ai/docs/).
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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.
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### Google Generative AI parameters
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| Name | Type | Description |
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|------|------|-------------|
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| Google API Key | SecretString | Input parameter. Your Google API key to use for the Google Generative AI. |
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| Model | String | Input parameter. The name of the model to use, such as `"gemini-2.5-flash"` or `"gemini-3-pro-preview"`. |
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| Max Output Tokens | Integer | Input parameter. The maximum number of tokens to generate. |
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| Temperature | Float | Input parameter. Run inference with this temperature. |
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| Top K | Integer | Input parameter. Consider the set of top K most probable tokens. |
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| Top P | Float | Input parameter. The maximum cumulative probability of tokens to consider when sampling. |
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| N | Integer | Input parameter. Number of chat completions to generate for each prompt. |
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| model | LanguageModel | Output parameter. An instance of ChatGoogleGenerativeAI configured with the specified parameters. |
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## Google Generative AI Embeddings
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The **Google Generative AI Embeddings** component connects to Google's generative AI embedding service using the GoogleGenerativeAIEmbeddings class from the `langchain-google-genai` package.
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For more information about using embedding model components in flows, see [Embedding model components](/components-embedding-models).
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### Google Generative AI Embeddings parameters
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| Name | Display Name | Info |
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|------|--------------|------|
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| api_key | API Key | Input parameter. The secret API key for accessing Google's generative AI service. Required. |
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| model_name | Model Name | Input parameter. The name of the embedding model to use. Default: "models/text-embedding-004". |
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| embeddings | Embeddings | Output parameter. The built GoogleGenerativeAIEmbeddings object. |
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## Google Search API
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This component allows you to call the Google Search API.
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### Google Search API parameters
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| Name | Type | Description |
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|------|------|-------------|
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| google_api_key | SecretString | Input parameter. A Google API key for authentication. |
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| google_cse_id | SecretString | Input parameter. A Google Custom Search Engine ID. |
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| input_value | String | Input parameter. The search query input. |
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| k | Integer | Input parameter. The number of search results to return. |
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| results | List[Data] | Output parameter. A list of search results. |
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| tool | Tool | Output parameter. A Google Search tool for use in LangChain. |
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### Other Google Search components
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Langflow includes multiple components that support Google Search, such as the following:
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* [**Apify Actors** component](/bundles-apify)
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* [**SearchApi** component](/bundles-searchapi)
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* [**Serper Google Search API** component](/bundles-serper)
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* [**Web Search** component](/web-search)
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## Google Vertex AI
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For information about Vertex AI components, see the [**Vertex AI** bundle](/bundles-vertexai).
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## Legacy Google components
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import PartialLegacy from '@site/docs/_partial-legacy.mdx';
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<PartialLegacy />
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The following Google components are in legacy status:
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<details>
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<summary>Google OAuth Token</summary>
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The **Google OAuth Token** component was deprecated in Langflow 1.4.0.
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To connect your flows to Google OAuth services, use [Composio components](/bundles-composio).
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</details>
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<details>
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<summary>Gmail Loader</summary>
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This component loads emails from Gmail using [Service Account JSON](https://developers.google.com/identity/protocols/oauth2/service-account) credentials and label ID filters.
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As an alternative, you can use [Composio components](/bundles-composio) to connect your flows to Google services.
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</details>
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<details>
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<summary>Google Drive Loader</summary>
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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.
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While there is no direct replacement, consider using the [**API Request** component](/api-request) to call the Google Drive API.
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</details>
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<details>
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<summary>Google Drive Search</summary>
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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.
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While there is no direct replacement, consider using the [**API Request** component](/api-request) to call the Google Drive API.
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</details>
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## See also
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- [**Composio** bundle](/bundles-composio)
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- [**Vertex AI** bundle](/bundles-vertexai) |