Vertex AI

Bundles contain custom components that support specific third-party integrations with Langflow.

This page describes the components that are available in the Vertex AI bundle.

For more information about Vertex AI features and functionality used by Vertex AI components, see the Vertex AI documentation.

For other Google components, see the Google bundle.

Vertex AI text generation

The Vertex AI component generates text using Google Vertex AI models.

It can output either a Model Response (Message) or a Language Model (LanguageModel).

Use the Language Model output when you want to use a Vertex AI model as the LLM for another LLM-driven component, such as a Language Model or Smart Function component.

For more information, see Language Model components.

Vertex AI text generation parameters

Many Vertex AI component input parameters are hidden by default in the visual editor. You can toggle parameters through the Controls in the component's header menu.

Name	Type	Description
credentials	File	Input parameter. JSON credentials file. Leave empty to fall back to environment variables. File type: JSON. For more information, see Create a service account for Vertex AI authentication.
model_name	String	Input parameter. The name of the Vertex AI model to use. Default: "gemini-1.5-pro".
project	String	Input parameter. The project ID (advanced).
location	String	Input parameter. The location for the Vertex AI API. Default: "us-central1" (advanced).
max_output_tokens	Integer	Input parameter. The maximum number of tokens to generate (advanced).
max_retries	Integer	Input parameter. Maximum number of retries for API calls. Default: 1 (advanced).
temperature	Float	Input parameter. Controls randomness in the output. Default: 0.0.
top_k	Integer	Input parameter. The number of highest-probability vocabulary tokens to keep for top-k-filtering (advanced).
top_p	Float	Input parameter. The cumulative probability of all highest-probability vocabulary tokens that are kept for nucleus sampling. Default: 0.95 (advanced).
verbose	Boolean	Input parameter. Whether to print verbose output. Default: False (advanced).

For more information about Vertex AI text generation parameters, see the Vertex AI content generation parameters documentation.

Vertex AI Embeddings

The Vertex AI Embeddings component is a wrapper around the Google Vertex AI Embeddings API for embedding generation.

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

Vertex AI Embeddings parameters

Many Vertex AI Embeddings component input parameters are hidden by default in the visual editor. You can toggle parameters through the Controls in the component's header menu.

Name	Type	Description
credentials	Credentials	Input parameter. JSON credentials file. Leave empty to fall back to environment variables. File type: JSON. For more information, see Create a service account for Vertex AI authentication.
location	String	Input parameter. The default location to use when making API calls. Default: us-central1.
max_output_tokens	Integer	Input parameter. The token limit determines the maximum amount of text output from one prompt. Default: 128.
model_name	String	Input parameter. The name of the Vertex AI large language model. Default: text-bison.
project	String	Input parameter. The default GCP project to use when making Vertex API calls.
request_parallelism	Integer	Input parameter. The amount of parallelism allowed for requests issued to Vertex AI models. Default: 5.
temperature	Float	Input parameter. Tunes the degree of randomness in text generations. Should be a non-negative value. Default: 0.
top_k	Integer	Input parameter. How the model selects tokens for output. The next token is selected from the top k tokens. Default: 40.
top_p	Float	Input parameter. Tokens are selected from the most probable to least until the sum of their probabilities exceeds the top p value. Default: 0.95.
tuned_model_name	String	Input parameter. The name of a tuned model. If provided, model_name is ignored.
verbose	Boolean	Input parameter. This parameter controls the level of detail in the output. When set to True, it prints internal states of the chain to help debug. Default: False.
embeddings	Embeddings	Output parameter. An instance for generating embeddings using Vertex AI.

Create a service account for Vertex AI authentication

The Vertex AI Embeddings and Vertex AI components authenticate with the Google Vertex AI API using a service account JSON file.

These components require that you provide a service account JSON file in the Credentials parameter (credentials). You can either provide the file directly in your component's settings or you can use a GOOGLE_APPLICATION_CREDENTIALS environment variable to reference the path to your JSON file.

The following steps explain how to create a service account for Langflow, create a service account JSON key file, and then use the JSON key file in Langflow:

1. If you haven't done so already, enable the Vertex AI APIs in your Google Cloud project.

2. Create a service account in your Google Cloud project.

   It is recommended that you create a custom service account for Vertex AI because Langflow uses this account to access the Vertex AI APIs.

3. Assign the Vertex AI Service Agent role to the service account.

   This role allows Langflow to access Vertex AI resources. For more information, see Vertex AI access control with IAM.

4. Create a service account key in JSON format for your Langflow Vertex AI service account.

   When you click Create, the JSON key file is downloaded.

5. In Langflow, create or open a flow with the Vertex AI or Vertex AI Embeddings component.

6. In the component's Credentials field, do one of the following:

   • Select your service account JSON file directly.

   • Leave the Credentials field empty if you want to pull the key file from an environment variable. Then, you must provide the path to your JSON file in a GOOGLE_APPLICATION_CREDENTIALS environment variable that is set somewhere relative to your Langflow instance. For example, you can set the environment variable in your terminal, your Langflow .env file, or otherwise in the environment where your Langflow server or application runs.

   

7. Run your flow to verify that Langflow can use the service account credentials successfully.

   For example, in a flow with Chat Input, Chat Output, and Vertex AI components, you can open the Playground and try chatting with the LLM. If the LLM responds, then the credentials are configured correctly.