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Groq Chat Model node#

Use the Groq Chat Model node to access Groq's large language models for conversational AI and text generation tasks.

On this page, you'll find the node parameters for the Groq 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#

  • Model: Select the model which will generate the completion. n8n dynaymically loads available models from the Groq API. Learn more in the Groq model documentation.

Node options#

  • 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.

Templates and examples#

Browse Groq Chat Model integration templates, or search all templates

Refer to Groq's API documentation for more information about the service.

View n8n's Advanced AI documentation.

AI glossary#

  • 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.