Struct CreateClassificationRequest

pub struct CreateClassificationRequest {

    pub model: String,
    pub query: String,
    pub examples: Option<Option<Vec<Vec<String>>>>,
    pub file: Option<Option<String>>,
    pub labels: Option<Option<Vec<String>>>,
    pub search_model: Option<Option<String>>,
    pub temperature: Option<Option<f32>>,
    pub logprobs: Option<Option<i32>>,
    pub max_examples: Option<Option<i32>>,
    pub logit_bias: Option<Option<Value>>,
    pub return_prompt: Option<Option<bool>>,
    pub return_metadata: Option<Option<bool>>,
    pub expand: Option<Option<Vec<Value>>>,
    pub user: Option<String>,
}

Fields

model: String

ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.

query: String

Query to be classified.

examples: Option<Option<Vec<Vec<String>>>>

A list of examples with labels, in the following format: [[\"The movie is so interesting.\", \"Positive\"], [\"It is quite boring.\", \"Negative\"], ...] All the label strings will be normalized to be capitalized. You should specify either examples or file, but not both.

file: Option<Option<String>>

The ID of the uploaded file that contains training examples. See upload file for how to upload a file of the desired format and purpose. You should specify either examples or file, but not both.

labels: Option<Option<Vec<String>>>

The set of categories being classified. If not specified, candidate labels will be automatically collected from the examples you provide. All the label strings will be normalized to be capitalized.

search_model: Option<Option<String>>

ID of the model to use for Search. You can select one of ada, babbage, curie, or davinci.

temperature: Option<Option<f32>>

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.

logprobs: Option<Option<i32>>

Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. For example, if logprobs is 5, the API will return a list of the 5 most likely tokens. The API will always return the logprob of the sampled token, so there may be up to logprobs+1 elements in the response. The maximum value for logprobs is 5. If you need more than this, please contact us through our Help center and describe your use case. When logprobs is set, completion will be automatically added into expand to get the logprobs.

max_examples: Option<Option<i32>>

The maximum number of examples to be ranked by Search when using file. Setting it to a higher value leads to improved accuracy but with increased latency and cost.

logit_bias: Option<Option<Value>>

Modify the likelihood of specified tokens appearing in the completion. Accepts a json object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100. You can use this tokenizer tool (which works for both GPT-2 and GPT-3) to convert text to token IDs. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. As an example, you can pass {\"50256\": -100} to prevent the <|endoftext|> token from being generated.

return_prompt: Option<Option<bool>>

If set to true, the returned JSON will include a "prompt" field containing the final prompt that was used to request a completion. This is mainly useful for debugging purposes.

return_metadata: Option<Option<bool>>

A special boolean flag for showing metadata. If set to true, each document entry in the returned JSON will contain a "metadata" field. This flag only takes effect when file is set.

expand: Option<Option<Vec<Value>>>

If an object name is in the list, we provide the full information of the object; otherwise, we only provide the object ID. Currently we support completion and file objects for expansion.

user: Option<String>

A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.

Implementations

impl CreateClassificationRequest

pub fn new(model: String, query: String) -> CreateClassificationRequest

Trait Implementations

impl Clone for CreateClassificationRequest

fn clone(&self) -> CreateClassificationRequest

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for CreateClassificationRequest

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for CreateClassificationRequest

fn default() -> CreateClassificationRequest

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for CreateClassificationRequest

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for CreateClassificationRequest

fn eq(&self, other: &CreateClassificationRequest) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for CreateClassificationRequest

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for CreateClassificationRequest