Retrieve agent configuration

<p>Determines the level of randomness in the generated output. Can take values between 0 and 1.0, with higher values like 0.8 resulting in more randomness, and lower values like 0.2 making the output more predictable. See original <a href="https://console.anthropic.com/docs/api/reference">definition</a>.</p>

<p>Only sample from the top K options for each subsequent token. Used to remove "long tail" low probability responses. Defaults to -1, which disables it. See original <a href="https://console.anthropic.com/docs/api/reference">definition</a>. See <a href="https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277">technical details</a>.</p>

<p>An alternative to the <code>temperature</code> option. Uses a method called nucleus sampling, which involves considering only the tokens with the highest probability mass. It specifies the proportion of the probability mass to consider, with a value of 0.1 indicating that only the tokens comprising the top 10% probability mass will be taken into account. In essence, <code>top_p</code> determines the size of the set of tokens to choose from, based on their probabilities. Defaults to -1, which disables it. See original <a href="https://console.anthropic.com/docs/api/reference">definition</a>.</p>

<p>Can take values between -2.0 and 2.0, with positive values penalizing the model for generating new tokens that have already appeared frequently in the generated text so far. This reduces the likelihood of the model repeating the same line or phrase verbatim. In essence, a higher positive value for <code>frequency_penalty</code> encourages the model to use less frequently used tokens in the generated text. See original <a href="https://platform.openai.com/docs/api-reference/completions/create#completions/create-frequency_penalty">definition</a>.</p>

<p>Can take values between -2.0 and 2.0, with positive values penalizing the model for generating new tokens that have already appeared in the generated text. This encourages the model to generate text about new topics and increases the likelihood of generating more diverse content. In essence, a higher positive value for <code>presence_penalty</code> will lead to more avoidance of repeating previously used tokens in the generated text. See original <a href="https://platform.openai.com/docs/api-reference/completions/create#completions/create-presence_penalty">definition</a>.</p>

<p>Determines the level of randomness in the generated output. Can take values between 0 and 2.0, with higher values like 0.8 resulting in more randomness, and lower values like 0.2 making the output more predictable. See original <a href="https://platform.openai.com/docs/api-reference/completions/create#completions/create-temperature">definition</a>.</p>

<p>An alternative to the <code>temperature</code> option. Uses a method called nucleus sampling, which involves considering only the tokens with the highest probability mass. It specifies the proportion of the probability mass to consider, with a value of 0.1 indicating that only the tokens comprising the top 10% probability mass will be taken into account. In essence, <code>top_p</code> determines the size of the set of tokens to choose from, based on their probabilities. See original <a href="https://platform.openai.com/docs/api-reference/completions/create#completions/create-top_p">definition</a>.</p>