Struct AzureOpenAI 

pub struct AzureOpenAI {

    pub api_key: String,
    pub api_version: String,
    pub base_url: Url,
    pub model: String,
    pub max_tokens: Option<u32>,
    pub temperature: Option<f32>,
    pub system: Option<String>,
    pub timeout_seconds: Option<u64>,
    pub top_p: Option<f32>,
    pub top_k: Option<u32>,
    pub tools: Option<Vec<Tool>>,
    pub tool_choice: Option<ToolChoice>,
    pub embedding_encoding_format: Option<String>,
    pub embedding_dimensions: Option<u32>,
    pub reasoning_effort: Option<String>,
    pub json_schema: Option<StructuredOutputFormat>,
    /* private fields */
}

Client for interacting with Azure OpenAI’s API.

Provides methods for chat and completion requests using Azure OpenAI’s models.

Fields

api_key: String

api_version: String

base_url: Url

model: String

max_tokens: Option<u32>

temperature: Option<f32>

system: Option<String>

timeout_seconds: Option<u64>

top_p: Option<f32>

top_k: Option<u32>

tools: Option<Vec<Tool>>

tool_choice: Option<ToolChoice>

embedding_encoding_format: Option<String>

Embedding parameters

embedding_dimensions: Option<u32>

reasoning_effort: Option<String>

json_schema: Option<StructuredOutputFormat>

JSON schema for structured output

Implementations

impl AzureOpenAI

pub fn new( api_key: impl Into<String>, api_version: impl Into<String>, deployment_id: impl Into<String>, endpoint: impl Into<String>, model: Option<String>, max_tokens: Option<u32>, temperature: Option<f32>, timeout_seconds: Option<u64>, system: Option<String>, top_p: Option<f32>, top_k: Option<u32>, embedding_encoding_format: Option<String>, embedding_dimensions: Option<u32>, tools: Option<Vec<Tool>>, tool_choice: Option<ToolChoice>, reasoning_effort: Option<String>, json_schema: Option<StructuredOutputFormat>, ) -> Self

Creates a new OpenAI client with the specified configuration.

Arguments

• api_key - OpenAI API key
• model - Model to use (defaults to “gpt-3.5-turbo”)
• max_tokens - Maximum tokens to generate
• temperature - Sampling temperature
• timeout_seconds - Request timeout in seconds
• system - System prompt
• stream - Whether to stream responses
• top_p - Top-p sampling parameter
• top_k - Top-k sampling parameter
• embedding_encoding_format - Format for embedding outputs
• embedding_dimensions - Dimensions for embedding vectors
• tools - Function tools that the model can use
• tool_choice - Determines how the model uses tools
• reasoning_effort - Reasoning effort level
• json_schema - JSON schema for structured output

Trait Implementations

impl ChatProvider for AzureOpenAI

fn chat_with_tools<'life0, 'life1, 'life2, 'async_trait>( &'life0 self, messages: &'life1 [ChatMessage], tools: Option<&'life2 [Tool]>, ) -> Pin<Box<dyn Future<Output = Result<Box<dyn ChatResponse>, LLMError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait, 'life2: 'async_trait,

Sends a chat request to OpenAI’s API.

Arguments

• messages - Slice of chat messages representing the conversation
• tools - Optional slice of tools to use in the chat

Returns

The model’s response text or an error

fn chat<'life0, 'life1, 'async_trait>( &'life0 self, messages: &'life1 [ChatMessage], ) -> Pin<Box<dyn Future<Output = Result<Box<dyn ChatResponse>, LLMError>> + Send + 'async_trait>>

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

Sends a chat request to the provider with a sequence of messages.

fn chat_with_web_search<'life0, 'async_trait>( &'life0 self, _input: String, ) -> Pin<Box<dyn Future<Output = Result<Box<dyn ChatResponse>, LLMError>> + Send + 'async_trait>>

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

Sends a chat with web search request to the provider

fn chat_stream<'life0, 'life1, 'async_trait>( &'life0 self, _messages: &'life1 [ChatMessage], ) -> Pin<Box<dyn Future<Output = Result<Pin<Box<dyn Stream<Item = Result<String, LLMError>> + Send>>, LLMError>> + Send + 'async_trait>>

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

Sends a streaming chat request to the provider with a sequence of messages.

fn chat_stream_struct<'life0, 'life1, 'async_trait>( &'life0 self, _messages: &'life1 [ChatMessage], ) -> Pin<Box<dyn Future<Output = Result<Pin<Box<dyn Stream<Item = Result<StreamResponse, LLMError>> + Send>>, LLMError>> + Send + 'async_trait>>

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

Sends a streaming chat request that returns structured response chunks.

fn memory_contents<'life0, 'async_trait>( &'life0 self, ) -> Pin<Box<dyn Future<Output = Option<Vec<ChatMessage>>> + Send + 'async_trait>>

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

Get current memory contents if provider supports memory

fn summarize_history<'life0, 'life1, 'async_trait>( &'life0 self, msgs: &'life1 [ChatMessage], ) -> Pin<Box<dyn Future<Output = Result<String, LLMError>> + Send + 'async_trait>>

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

Summarizes a conversation history into a concise 2-3 sentence summary

impl CompletionProvider for AzureOpenAI

fn complete<'life0, 'life1, 'async_trait>( &'life0 self, _req: &'life1 CompletionRequest, ) -> Pin<Box<dyn Future<Output = Result<CompletionResponse, LLMError>> + Send + 'async_trait>>

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

Sends a completion request to OpenAI’s API.

Currently not implemented.

impl EmbeddingProvider for AzureOpenAI

fn embed<'life0, 'async_trait>( &'life0 self, input: Vec<String>, ) -> Pin<Box<dyn Future<Output = Result<Vec<Vec<f32>>, LLMError>> + Send + 'async_trait>>

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

impl LLMProvider for AzureOpenAI

fn tools(&self) -> Option<&[Tool]>

impl ModelsProvider for AzureOpenAI

fn list_models<'life0, 'life1, 'async_trait>( &'life0 self, _request: Option<&'life1 ModelListRequest>, ) -> Pin<Box<dyn Future<Output = Result<Box<dyn ModelListResponse>, LLMError>> + Send + 'async_trait>>

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

Asynchronously retrieves the list of available models ID’s from the provider.

impl SpeechToTextProvider for AzureOpenAI

fn transcribe<'life0, 'async_trait>( &'life0 self, _audio: Vec<u8>, ) -> Pin<Box<dyn Future<Output = Result<String, LLMError>> + Send + 'async_trait>>

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

Transcribe the given audio bytes into text

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

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

impl TextToSpeechProvider for AzureOpenAI

fn speech<'life0, 'life1, 'async_trait>( &'life0 self, _text: &'life1 str, ) -> Pin<Box<dyn Future<Output = Result<Vec<u8>, LLMError>> + Send + 'async_trait>>

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

Convert the given text into speech audio