Get Deployed Prompt

The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.

A description of what the function does, used by the model to choose when and how to call the function.

The parameters the functions accepts, described as a JSON Schema object.

Raw list of messages comprising the llm conversation. Prompts variables will not be filled in.

A list of messages comprising the llm conversation. Prompt template variables will be filled in. Same fields as raw_messages.

Key value pairs for prompt template. The keys should match the names of the deployed prompt template’s variables.

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.

The maximum number of tokens to generate in the chat completion. The total length of input tokens and generated tokens is limited by the model’s context length max_length will be mapped to max_tokens or max_tokens_to_sample for openai and anthropic, respectively.

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. Providers generally recommend altering this or temp but not both.

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.

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.

An object specifying the format that the model must output. Compatible with GPT-4 Turbo and all GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106.