Create Text Completion with OpenAI
Perform a text completion with OpenAI. Can be used for a variety of tasks
by @pixies
How to Use
Use this brick to create text completions using the OpenAI Text Completion API.
Inputs
| Name | Required | Type | Description |
|---|---|---|---|
stop |
array
|
Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence. | |
topP |
number
|
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. We generally recommend altering this or temperature but not both. | |
model |
string
|
ID of the model to use. You can use the List models API to see all of your available models, or see the Model overview for descriptions of them: https://beta.openai.com/docs/models/overview | |
prompt |
string
|
The prompt to generate completions for, encoded as a string | |
suffix |
string
|
The suffix that comes after a completion of inserted text. | |
maxTokens |
integer
|
The maximum number of tokens to generate in the completion. The token count of your prompt plus maxTokens cannot exceed the model's context length. Most models have a context length of 2048 tokens (except for the newest models, which support 4096). | |
openaiapi |
@pixies/openai/openaiapi integration
|
||
temperature |
number
|
What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. We generally recommend altering this or top_p but not both. | |
numCompletions |
integer
|
How many completions to generate for each prompt. Note: Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for max_tokens and stop | |
presencePenalty |
number
|
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. | |
frequencyPenalty |
number
|
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. |
Outputs
| Name | Required | Type | Description |
|---|---|---|---|
completions |
array
|
The generated completions |