In order to use IBM watsonx.ai models, you'll need to create an IBM watsonx.ai account and obtain an API key. After successfully authenticating using the IBM watsonx.ai Authenticator node, you can select a Large Language Model (LLM) from a predefined list.
Refer to the IBM watsonx.ai documentation for more information on available chat models. At the moment, only the chat models from foundation models are supported. Refer to Choosing a model page for more information on chat models that support tool calls.
Note: If you want to use a space, make sure that the space has a valid runtime service instance. You can check this at IBM watsonx.ai Studio under Manage tab in your space.
The model to use for the chat completion.
Sampling temperature to use, between 0.0 and 2.0. Higher values will make the output more random, while lower values will make it more focused and deterministic.
It is generally recommended altering this or top_p but not both.
The maximum number of tokens to generate.
This value, plus the token count of your prompt, cannot exceed the model's context length.
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.
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.
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