Groq Chat Model#
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: The model which will generate the completion. Learn more in the Groq model documentation. Available models are loaded dynamically from the Groq API.
Node options#
- Maximum Number of Tokens: the maximum number of tokens to generate in the completion.
- Sampling Temperature: controls randomness. Lowering the value results in less random completions. As the temperature approaches zero, the model will become deterministic and repetitive. Valid range is 0 to 1.
Templates and examples#
Related resources#
Refer to Groq's API 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.