Struct langchain_rust::tools::Text2SpeechOpenAI

pub struct Text2SpeechOpenAI<C: Config> { /* private fields */ }

Implementations

impl<C: Config> Text2SpeechOpenAI<C>

pub fn new(config: C) -> Self

pub fn with_model(self, model: SpeechModel) -> Self

pub fn with_voice(self, voice: Voice) -> Self

pub fn with_storage<SS: SpeechStorage + 'static>(self, storage: SS) -> Self

pub fn with_response_format(self, response_format: SpeechResponseFormat) -> Self

pub fn with_path<S: Into<String>>(self, path: S) -> Self

pub fn with_config(self, config: C) -> Self

Trait Implementations

impl<C: Clone + Config> Clone for Text2SpeechOpenAI<C>

fn clone(&self) -> Text2SpeechOpenAI<C>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Default for Text2SpeechOpenAI<OpenAIConfig>

fn default() -> Self

Returns the “default value” for a type.

impl<C: Config + Send + Sync> Tool for Text2SpeechOpenAI<C>

fn name(&self) -> String

Returns the name of the tool.

fn description(&self) -> String

Provides a description of what the tool does and when to use it.

fn run<'life0, 'async_trait>( &'life0 self, input: Value ) -> Pin<Box<dyn Future<Output = Result<String, Box<dyn Error>>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait,

Executes the core functionality of the tool.

fn parameters(&self) -> Value

This are the parametters for OpenAi-like function call. You should return a jsnon like this one

fn call<'life0, 'life1, 'async_trait>( &'life0 self, input: &'life1 str ) -> Pin<Box<dyn Future<Output = Result<String, Box<dyn Error>>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Processes an input string and executes the tool’s functionality, returning a Result.

fn parse_input<'life0, 'life1, 'async_trait>( &'life0 self, input: &'life1 str ) -> Pin<Box<dyn Future<Output = Value> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Parses the input string, which could be a JSON value or a raw string, depending on the LLM model.