Struct GenerationConfig 

pub struct GenerationConfig {

    pub candidate_count: Option<i32>,
    pub stop_sequences: Vec<String>,
    pub max_output_tokens: Option<i32>,
    pub temperature: Option<f32>,
    pub top_p: Option<f32>,
    pub top_k: Option<i32>,
    pub response_mime_type: String,
    pub response_schema: Option<Schema>,
    pub presence_penalty: Option<f32>,
    pub frequency_penalty: Option<f32>,
    pub response_logprobs: Option<bool>,
    pub logprobs: Option<i32>,
    pub enable_enhanced_civic_answers: Option<bool>,
    pub response_modalities: Vec<i32>,
    pub speech_config: Option<SpeechConfig>,
}

Configuration options for model generation and outputs. Not all parameters are configurable for every model.

Fields

candidate_count: Option<i32>

Optional. Number of generated responses to return.

Currently, this value can only be set to 1. If unset, this will default to 1.

stop_sequences: Vec<String>

Optional. The set of character sequences (up to 5) that will stop output generation. If specified, the API will stop at the first appearance of a stop_sequence. The stop sequence will not be included as part of the response.

max_output_tokens: Option<i32>

Optional. The maximum number of tokens to include in a response candidate.

Note: The default value varies by model, see the Model.output_token_limit attribute of the Model returned from the getModel function.

temperature: Option<f32>

Optional. Controls the randomness of the output.

Note: The default value varies by model, see the Model.temperature attribute of the Model returned from the getModel function.

Values can range from [0.0, 2.0].

top_p: Option<f32>

Optional. The maximum cumulative probability of tokens to consider when sampling.

The model uses combined Top-k and Top-p (nucleus) sampling.

Tokens are sorted based on their assigned probabilities so that only the most likely tokens are considered. Top-k sampling directly limits the maximum number of tokens to consider, while Nucleus sampling limits the number of tokens based on the cumulative probability.

Note: The default value varies by Model and is specified by theModel.top_p attribute returned from the getModel function. An empty top_k attribute indicates that the model doesn’t apply top-k sampling and doesn’t allow setting top_k on requests.

top_k: Option<i32>

Optional. The maximum number of tokens to consider when sampling.

Gemini models use Top-p (nucleus) sampling or a combination of Top-k and nucleus sampling. Top-k sampling considers the set of top_k most probable tokens. Models running with nucleus sampling don’t allow top_k setting.

Note: The default value varies by Model and is specified by theModel.top_p attribute returned from the getModel function. An empty top_k attribute indicates that the model doesn’t apply top-k sampling and doesn’t allow setting top_k on requests.

response_mime_type: String

Optional. MIME type of the generated candidate text. Supported MIME types are: text/plain: (default) Text output. application/json: JSON response in the response candidates. text/x.enum: ENUM as a string response in the response candidates. Refer to the docs for a list of all supported text MIME types.

response_schema: Option<Schema>

Optional. Output schema of the generated candidate text. Schemas must be a subset of the OpenAPI schema and can be objects, primitives or arrays.

If set, a compatible response_mime_type must also be set. Compatible MIME types: application/json: Schema for JSON response. Refer to the JSON text generation guide for more details.

presence_penalty: Option<f32>

Optional. Presence penalty applied to the next token’s logprobs if the token has already been seen in the response.

This penalty is binary on/off and not dependant on the number of times the token is used (after the first). Use [frequency_penalty][google.ai.generativelanguage.v1beta.GenerationConfig.frequency_penalty] for a penalty that increases with each use.

A positive penalty will discourage the use of tokens that have already been used in the response, increasing the vocabulary.

A negative penalty will encourage the use of tokens that have already been used in the response, decreasing the vocabulary.

frequency_penalty: Option<f32>

Optional. Frequency penalty applied to the next token’s logprobs, multiplied by the number of times each token has been seen in the respponse so far.

A positive penalty will discourage the use of tokens that have already been used, proportional to the number of times the token has been used: The more a token is used, the more dificult it is for the model to use that token again increasing the vocabulary of responses.

Caution: A negative penalty will encourage the model to reuse tokens proportional to the number of times the token has been used. Small negative values will reduce the vocabulary of a response. Larger negative values will cause the model to start repeating a common token until it hits the [max_output_tokens][google.ai.generativelanguage.v1beta.GenerationConfig.max_output_tokens] limit.

response_logprobs: Option<bool>

Optional. If true, export the logprobs results in response.

logprobs: Option<i32>

Optional. Only valid if [response_logprobs=True][google.ai.generativelanguage.v1beta.GenerationConfig.response_logprobs]. This sets the number of top logprobs to return at each decoding step in the [Candidate.logprobs_result][google.ai.generativelanguage.v1beta.Candidate.logprobs_result].

enable_enhanced_civic_answers: Option<bool>

Optional. Enables enhanced civic answers. It may not be available for all models.

response_modalities: Vec<i32>

Optional. The requested modalities of the response. Represents the set of modalities that the model can return, and should be expected in the response. This is an exact match to the modalities of the response.

A model may have multiple combinations of supported modalities. If the requested modalities do not match any of the supported combinations, an error will be returned.

An empty list is equivalent to requesting only text.

speech_config: Option<SpeechConfig>

Optional. The speech generation config.

Implementations

impl GenerationConfig

pub fn candidate_count(&self) -> i32

Returns the value of candidate_count, or the default value if candidate_count is unset.

pub fn max_output_tokens(&self) -> i32

Returns the value of max_output_tokens, or the default value if max_output_tokens is unset.

pub fn temperature(&self) -> f32

Returns the value of temperature, or the default value if temperature is unset.

pub fn top_p(&self) -> f32

Returns the value of top_p, or the default value if top_p is unset.

pub fn top_k(&self) -> i32

Returns the value of top_k, or the default value if top_k is unset.

pub fn presence_penalty(&self) -> f32

Returns the value of presence_penalty, or the default value if presence_penalty is unset.

pub fn frequency_penalty(&self) -> f32

Returns the value of frequency_penalty, or the default value if frequency_penalty is unset.

pub fn response_logprobs(&self) -> bool

Returns the value of response_logprobs, or the default value if response_logprobs is unset.

pub fn logprobs(&self) -> i32

Returns the value of logprobs, or the default value if logprobs is unset.

pub fn enable_enhanced_civic_answers(&self) -> bool

Returns the value of enable_enhanced_civic_answers, or the default value if enable_enhanced_civic_answers is unset.

pub fn response_modalities( &self, ) -> FilterMap<Cloned<Iter<'_, i32>>, fn(i32) -> Option<Modality>>

Returns an iterator which yields the valid enum values contained in response_modalities.

pub fn push_response_modalities(&mut self, value: Modality)

Appends the provided enum value to response_modalities.

Trait Implementations

impl Clone for GenerationConfig

fn clone(&self) -> GenerationConfig

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for GenerationConfig

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

Formats the value using the given formatter.

impl Default for GenerationConfig

fn default() -> Self

Returns the “default value” for a type.

impl Message for GenerationConfig

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode(&self, buf: &mut impl BufMut) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

fn encode_length_delimited( &self, buf: &mut impl BufMut, ) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message with a length-delimiter to a buffer.

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message with a length-delimiter to a newly allocated buffer.

fn decode(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes a length-delimited instance of the message from the buffer.

fn merge(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes an instance of the message from a buffer, and merges it into self.

fn merge_length_delimited(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq for GenerationConfig

fn eq(&self, other: &GenerationConfig) -> 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 StructuralPartialEq for GenerationConfig