Google Vertex Chat Model#
Use the Google Vertex AI Chat Model node to use Google's Vertex AI chat models with conversational agents.
On this page, you'll find the node parameters for the Google Vertex AI Chat Model node, and links to more resources.
Credentials
You can find authentication information for this node here.
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.
Node parameters#
Project ID: the project ID from your Google Cloud account to use. n8n dynamically loads projects from the Google Cloud account, but you can also specify it manually.
Model Name: the name of the model to use to generate the completion, for example gemini-1.5-flash-001, gemini-1.5-pro-001, etc. You can find the list of available models here.
Node options#
- Maximum Number of Tokens: change the maximum possible length of the completion.
- Sampling Temperature: controls the randomness of the sampling process. A higher temperature creates more diverse sampling, but increases the risk of hallucinations.
- Top K: the number of token choices the model uses to generate the next token.
- Top P: use a lower value to ignore less probable options.
- Safety Settings: Gemini supports adjustable safety settings. Refer to Google's Gemini API safety settings for information on the available filters and levels.
Templates and examples#
Related resources#
Refer to LangChain's Google Vertex AI documentation for more information about the service.
View n8n's Advanced AI documentation.
- completion: Completions are the responses generated by a model like GPT.
- hallucinations: Hallucination in AI is when an LLM (large language model) mistakenly perceives patterns or objects that don't exist.
- vector database: A vector database stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.
- vector store: A vector store, or vector database, stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.