Struct ChatCompletionRequest 

pub struct ChatCompletionRequest {

    pub model: String,
    pub messages: Vec<ChatCompletionRequestMessage>,
    pub frequency_penalty: Option<f32>,
    pub logit_bias: Option<Map<String, Value>>,
    pub logprobs: Option<bool>,
    pub top_logprobs: Option<i32>,
    pub max_tokens: Option<i32>,
    pub n: Option<i32>,
    pub presence_penalty: Option<f32>,
    pub response_format: Option<ResponseFormat>,
    pub seed: Option<i64>,
    pub stop: Option<Stop>,
    pub stream: Option<bool>,
    pub stream_options: Option<StreamOptions>,
    pub thinking: Option<ThinkingConfig>,
    pub temperature: Option<f32>,
    pub top_p: Option<f32>,
    pub tools: Option<Vec<Tool>>,
    pub tool_choice: Option<ToolChoice>,
    pub parallel_tool_calls: Option<bool>,
    pub user: Option<String>,
}

Chat completion request

Fields

model: String

ID of the model to use

messages: Vec<ChatCompletionRequestMessage>

A list of messages comprising the conversation

frequency_penalty: Option<f32>

Number between -2.0 and 2.0 for frequency penalty

logit_bias: Option<Map<String, Value>>

Modify the likelihood of specified tokens

logprobs: Option<bool>

Whether to return log probabilities

top_logprobs: Option<i32>

Number of most likely tokens to return at each position

max_tokens: Option<i32>

Maximum number of tokens to generate

n: Option<i32>

How many chat completion choices to generate

presence_penalty: Option<f32>

Number between -2.0 and 2.0 for presence penalty

response_format: Option<ResponseFormat>

Response format configuration

seed: Option<i64>

Seed for deterministic sampling

stop: Option<Stop>

Stop sequences

stream: Option<bool>

Whether to stream the response

stream_options: Option<StreamOptions>

Stream options

thinking: Option<ThinkingConfig>

Thinking mode configuration (Claude models)

temperature: Option<f32>

Sampling temperature (0-2)

top_p: Option<f32>

Nucleus sampling parameter (0-1)

tools: Option<Vec<Tool>>

List of tools the model may call

tool_choice: Option<ToolChoice>

Controls which tool is called

parallel_tool_calls: Option<bool>

Whether to enable parallel tool calls

user: Option<String>

A unique identifier for the end-user

Implementations

impl ChatCompletionRequest

pub fn new( model: impl Into<String>, messages: Vec<ChatCompletionRequestMessage>, ) -> Self

Creates a new chat completion request with the minimum required fields.

Arguments

• model - The model ID to use (e.g., “gpt-4o”, “claude-3-5-sonnet-20241022”)
• messages - The conversation messages

Example

use portkey_sdk::model::{ChatCompletionRequest, ChatCompletionRequestMessage};

let request = ChatCompletionRequest::new(
    "gpt-4o",
    vec![
        ChatCompletionRequestMessage::user("Hello!"),
    ],
);

Trait Implementations

impl Clone for ChatCompletionRequest

fn clone(&self) -> ChatCompletionRequest

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for ChatCompletionRequest

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for ChatCompletionRequest

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for ChatCompletionRequest

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

where __S: Serializer,

Serialize this value into the given Serde serializer.