Struct CreateAnswerRequest

pub struct CreateAnswerRequest {

    pub model: String,
    pub question: String,
    pub examples: Vec<Vec<String>>,
    pub examples_context: String,
    pub documents: Option<Option<Vec<String>>>,
    pub file: Option<Option<String>>,
    pub search_model: Option<Option<String>>,
    pub max_rerank: Option<Option<i32>>,
    pub temperature: Option<Option<f32>>,
    pub logprobs: Option<Option<i32>>,
    pub max_tokens: Option<Option<i32>>,
    pub stop: Option<Option<Box<CreateAnswerRequestStop>>>,
    pub n: Option<Option<i32>>,
    pub logit_bias: Option<Option<Value>>,
    pub return_metadata: Option<Option<bool>>,
    pub return_prompt: Option<Option<bool>>,
    pub expand: Option<Option<Vec<Value>>>,
    pub user: Option<String>,
}

Fields

model: String

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

question: String

Question to get answered.

examples: Vec<Vec<String>>

List of (question, answer) pairs that will help steer the model towards the tone and answer format you’d like. We recommend adding 2 to 3 examples.

examples_context: String

A text snippet containing the contextual information used to generate the answers for the examples you provide.

documents: Option<Option<Vec<String>>>

List of documents from which the answer for the input question should be derived. If this is an empty list, the question will be answered based on the question-answer examples. You should specify either documents or a file, but not both.

file: Option<Option<String>>

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

search_model: Option<Option<String>>

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

max_rerank: Option<Option<i32>>

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

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_tokens: Option<Option<i32>>

The maximum number of tokens allowed for the generated answer

stop: Option<Option<Box<CreateAnswerRequestStop>>>

n: Option<Option<i32>>

How many answers to generate for each question.

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_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.

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.

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 CreateAnswerRequest

pub fn new( model: String, question: String, examples: Vec<Vec<String>>, examples_context: String, ) -> CreateAnswerRequest

Trait Implementations

impl Clone for CreateAnswerRequest

fn clone(&self) -> CreateAnswerRequest

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateAnswerRequest

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

Formats the value using the given formatter.

impl Default for CreateAnswerRequest

fn default() -> CreateAnswerRequest

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for CreateAnswerRequest

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for CreateAnswerRequest

fn eq(&self, other: &CreateAnswerRequest) -> 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 CreateAnswerRequest

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 CreateAnswerRequest