This node establishes a connection with an Azure OpenAI Chat Model. After successfully authenticating using the Azure OpenAI Authenticator node, enter the deployment name of the model you want to use. You can find the models on the Azure AI Studio at 'Management - Deployments'.
Note: Chat models are LLMs that have been fine-tuned for chat-based usecases. As such, these models can also be used in other applications as well.
The name of the deployed model to use. Find the deployed models on the Azure AI Studio.
The maximum number of tokens to generate.
This value, plus the token count of your prompt, cannot exceed the model's context length.
Sampling temperature to use, between 0.0 and 2.0.
Higher values will lead to less deterministic answers.
Try 0.9 for more creative applications, and 0 for ones with a well-defined answer. It is generally recommended altering this, or Top-p, but not both.
How many chat completion choices to generate for each input message.
Note: This parameter can quickly consume your token quota.
Set the seed parameter to any integer of your choice to have (mostly) deterministic outputs. The default value of 0 means that no seed is specified.
If the seed and other model parameters are the same for each request, then responses will be mostly identical. There is a chance that responses will differ, due to the inherent non-determinism of OpenAI models.
Please note that this feature is in beta and only currently supported for gpt-4-1106-preview and gpt-3.5-turbo-1106 [1].
[1] OpenAI Cookbook
Maximum number of concurrent requests to LLMs that can be made, whether through API calls or to an inference server. Exceeding this limit may result in temporary restrictions on your access.
It is important to plan your usage according to the model provider's rate limits, and keep in mind that both software and hardware constraints can impact performance.
For OpenAI, please refer to the Limits page for the rate limits available to you.
An alternative to sampling with temperature, where the model considers the results of the tokens (words) with top_p probability mass. Hence, 0.1 means only the tokens comprising the top 10% probability mass are considered.
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