> For the complete documentation index, see [llms.txt](https://docs.n8n.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.lmchatgooglevertex.md).

# Google Vertex Chat Model

Use the Google Vertex AI Chat Model node to use Google's Vertex AI chat models with conversational agents[^1].

On this page, you'll find the node parameters for the Google Vertex AI Chat Model node, and links to more resources.

{% hint style="info" %}
**Credentials**

You can find authentication information for this node [here](/integrations/builtin/credentials/google/service-account.md).
{% endhint %}

{% hint style="info" %}
**Parameter resolution in sub-nodes**

Sub-nodes behave differently to other nodes when processing multiple items using an expression.

Most nodes, including root nodes, take any number of items as input, process these items, and output the results. You can use expressions to refer to input items, and the node resolves the expression for each item in turn. For example, given an input of five `name` values, the expression `{{ $json.name }}` resolves to each name in turn.

In sub-nodes, the expression always resolves to the first item. For example, given an input of five `name` values, the expression `{{ $json.name }}` always resolves to the first name.
{% endhint %}

## Node parameters <a href="#node-parameters" id="node-parameters"></a>

* **Project ID**: Select the project ID from your Google Cloud account to use. n8n dynamically loads projects from the Google Cloud account, but you can also enter it manually.
* **Model Name**: Select the name of the model to use to generate the completion, for example `gemini-1.5-flash-001`, `gemini-1.5-pro-001`, etc. Refer to [Google models](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models) for a list of available models.

## Node options <a href="#node-options" id="node-options"></a>

* **Maximum Number of Tokens**: Enter the maximum number of tokens used, which sets the completion length.
* **Sampling Temperature**: Use this option to control the randomness of the sampling process. A higher temperature creates more diverse sampling, but increases the risk of hallucinations.
* **Thinking Budget**: Controls reasoning tokens for thinking models. Set to `0` to disable automatic thinking. Set to `-1` for dynamic thinking. Leave empty for auto mode.
* **Top K**: Enter the number of token choices the model uses to generate the next token.
* **Top P**: Use this option to set the probability the completion should use. Use a lower value to ignore less probable options.
* **Safety Settings**: Gemini supports adjustable safety settings. Refer to Google's [Gemini API safety settings](https://ai.google.dev/docs/safety_setting_gemini) for information on the available filters and levels.

## Templates and examples <a href="#templates-and-examples" id="templates-and-examples"></a>

[Browse Google Vertex Chat Model node documentation integration templates](https://n8n.io/integrations/google-vertex-chat-model) or [search all templates](https://n8n.io/workflows/)

## Related resources <a href="#related-resources" id="related-resources"></a>

Refer to [LangChain's Google Vertex AI documentation](https://js.langchain.com/docs/integrations/chat/google_vertex_ai/) for more information about the service.

View n8n's [Advanced AI](https://github.com/n8n-io/n8n-docs/blob/main/advanced-ai/index.md) documentation.

[^1]: AI agents are artificial intelligence systems capable of responding to requests, making decisions, and performing real-world tasks for users. They use large language models (LLMs) to interpret user input and make decisions about how to best process requests using the information and resources they have available.


---

# Agent Instructions
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## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.lmchatgooglevertex.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.