Struct GptCompletion 

pub struct GptCompletion {
    pub model: String,
    pub tools: Option<Vec<FunctionCall>>,
    pub messages: Vec<GptMessage>,
    pub response_format: ResponseFormat,
    pub temperature: f32,
}

Fields

model: String

tools: Option<Vec<FunctionCall>>

messages: Vec<GptMessage>

response_format: ResponseFormat

temperature: f32

Implementations

impl GptCompletion

pub fn new(messages: Vec<GptMessage>, temperature: f32, is_json: bool) -> Self

Create chat completion

pub fn set_model(&mut self, model: &str)

pub fn set_tools(&mut self, tools: Option<Vec<FunctionCall>>)

pub fn set_response_format(&mut self, response_format: &ResponseFormat)

pub fn add_message(&mut self, message: &GptMessage)

Add a single new message

pub fn add_messages(&mut self, messages: &[GptMessage])

Add many new messages

Trait Implementations

impl Clone for GptCompletion

fn clone(&self) -> GptCompletion

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for GptCompletion

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

Formats the value using the given formatter.

impl Default for GptCompletion

fn default() -> Self

Create default chat completion

impl LlmCompletion for GptCompletion

fn set_temperature(&mut self, temperature: f32)

Set temperature

fn set_json(&mut self, is_json: bool)

Set output to be json. Hint in prompt still necessary.

fn add_text(&mut self, role: &str, text: &str)

Add single role and single part text

fn add_many_text(&mut self, role: &str, texts: &[String])

Add single role with multiple strings for parts as single large content

fn add_system(&mut self, system_prompt: &str)

Supply simple, ‘system’ content

fn add_multi_part_system(&mut self, system_prompts: &[String])

Supply multi-parts and single ‘system’ content

fn add_systems(&mut self, system_prompts: &[String])

Supply multi-context ‘system’ content

fn dialogue(&mut self, prompts: &[String], has_system: bool)

Supply multi-String content with user and llm alternating

fn truncate_messages(&mut self, len: usize)

Truncate messages

fn debug(&self) -> String

where Self: Debug,

Return String of Object

async fn call( system: &str, user: &[String], temperature: f32, is_json: bool, is_chat: bool, ) -> Result<LlmReturn, Box<dyn Error + Send>>

Create and call llm by supplying data and common parameters

async fn call_model( model: &str, system: &str, user: &[String], temperature: f32, is_json: bool, is_chat: bool, ) -> Result<LlmReturn, Box<dyn Error + Send>>

Create and call llm with model by supplying data and common parameters

async fn call_model_function( model: &str, system: &str, user: &[String], temperature: f32, is_json: bool, is_chat: bool, function: Option<Vec<Function>>, ) -> Result<LlmReturn, Box<dyn Error + Send>>

Create and call llm with model/function by supplying data and common parameters

impl Serialize for GptCompletion

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

where __S: Serializer,

Serialize this value into the given Serde serializer.