Name
gpt3TurboPrediction
Description
Dedicated node to call GPT3.5-Turbo (ChatGPT model) and returns the result as a string.
The base gpt3Prediction
node also supports the gpt-3.5-turbo
model but only accepts a prompt
string as input. If you want to send multiple messages to the model, you can use this node.
API Key
Uses the openai
API key:
Example
Input
Property | Type | Required | Description | Default Value |
---|---|---|---|---|
messages | Array<{role: 'user' | 'system' | 'assistant', content: string}> |
Yes | The prompt to pass GPT3.5-Turbo | |
model | enum('gpt-3.5-turbo') | No | ID of the model to use | gpt-3.5-turbo |
max_tokens | number | No | The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length. Most models have a context length of 2048 tokens (except for the newest models, which support 4096). | 200 |
temperature | number | No | What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
We generally recommend altering this or top_p but not both. |
0.7 |
top_p | number | No | An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. It is generally recommend altering this or temperature but not both. |
1 |
frequency_penalty | number | No | Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. | 0 |
presence_penalty | number | No | Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. | 0 |
Output
Property | Type |
---|---|
text | string |