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//! Azure OpenAI provider.
//!
//! Uses the OpenAI Responses API format but with Azure-specific authentication
//! and URL patterns.
//!
//! Base URL format: `https://{resource}.openai.azure.com/openai/deployments/{deployment}`
//! Auth: `api-key` header or Azure AD Bearer token.
use super::traits::*;
use crate::types::*;
use async_trait::async_trait;
use futures::StreamExt;
use reqwest_eventsource::EventSource;
use serde::Deserialize;
use tokio::sync::mpsc;
use tracing::{debug, warn};
pub struct AzureOpenAiProvider;
#[async_trait]
impl StreamProvider for AzureOpenAiProvider {
async fn stream(
&self,
config: StreamConfig,
tx: mpsc::UnboundedSender<StreamEvent>,
cancel: tokio_util::sync::CancellationToken,
) -> Result<Message, ProviderError> {
let model_config = config
.model_config
.as_ref()
.ok_or_else(|| ProviderError::Other("ModelConfig required".into()))?;
// Azure uses the Responses API format
let url = format!(
"{}/responses?api-version=2025-01-01-preview",
model_config.base_url
);
let body = build_azure_request_body(&config);
debug!("Azure OpenAI request: model={} url={}", config.model, url);
let client = reqwest::Client::new();
let mut request = client
.post(&url)
.header("content-type", "application/json")
.header("api-key", &config.api_key);
for (k, v) in &model_config.headers {
request = request.header(k, v);
}
let request = request.json(&body);
let mut es =
EventSource::new(request).map_err(|e| ProviderError::Network(e.to_string()))?;
let mut content: Vec<Content> = Vec::new();
let mut usage = Usage::default();
let mut stop_reason = StopReason::Stop;
let mut tool_call_buffers: Vec<ToolCallBuffer> = Vec::new();
let _ = tx.send(StreamEvent::Start);
loop {
tokio::select! {
_ = cancel.cancelled() => {
es.close();
return Err(ProviderError::Cancelled);
}
event = es.next() => {
match event {
None => break,
Some(Ok(reqwest_eventsource::Event::Open)) => {}
Some(Ok(reqwest_eventsource::Event::Message(msg))) => {
match msg.event.as_str() {
"response.output_text.delta" => {
if let Ok(data) = serde_json::from_str::<DeltaEvent>(&msg.data) {
let idx = content.iter().position(|c| matches!(c, Content::Text { .. }));
let idx = match idx {
Some(i) => i,
None => {
content.push(Content::Text { text: String::new() });
content.len() - 1
}
};
if let Some(Content::Text { text }) = content.get_mut(idx) {
text.push_str(&data.delta);
}
let _ = tx.send(StreamEvent::TextDelta {
content_index: idx,
delta: data.delta,
});
}
}
"response.function_call_arguments.start" => {
if let Ok(data) = serde_json::from_str::<FnCallStartEvent>(&msg.data) {
tool_call_buffers.push(ToolCallBuffer {
id: data.call_id.unwrap_or_default(),
name: data.name.unwrap_or_default(),
arguments: String::new(),
});
let buf = tool_call_buffers.last().unwrap();
let _ = tx.send(StreamEvent::ToolCallStart {
content_index: content.len() + tool_call_buffers.len() - 1,
id: buf.id.clone(),
name: buf.name.clone(),
});
}
}
"response.function_call_arguments.delta" => {
if let Ok(data) = serde_json::from_str::<DeltaEvent>(&msg.data) {
if let Some(buf) = tool_call_buffers.last_mut() {
buf.arguments.push_str(&data.delta);
let _ = tx.send(StreamEvent::ToolCallDelta {
content_index: content.len() + tool_call_buffers.len() - 1,
delta: data.delta,
});
}
}
}
"response.completed" => {
if let Ok(data) = serde_json::from_str::<CompletedEvent>(&msg.data) {
if let Some(resp) = data.response {
if let Some(u) = resp.usage {
usage.input = u.input_tokens;
usage.output = u.output_tokens;
usage.total_tokens = u.total_tokens;
}
}
}
break;
}
"error" => {
let provider_err = classify_sse_error_event(&msg.data);
warn!("Azure OpenAI error: {}", provider_err);
return Err(provider_err);
}
_ => {}
}
}
Some(Err(e)) => {
let provider_err = classify_eventsource_error(e).await;
warn!("Azure SSE error: {}", provider_err);
return Err(provider_err);
}
}
}
}
}
for buf in &tool_call_buffers {
let args = serde_json::from_str(&buf.arguments)
.unwrap_or(serde_json::Value::Object(Default::default()));
content.push(Content::ToolCall {
provider_metadata: None,
id: buf.id.clone(),
name: buf.name.clone(),
arguments: args,
});
let _ = tx.send(StreamEvent::ToolCallEnd {
content_index: content.len() - 1,
});
}
if content
.iter()
.any(|c| matches!(c, Content::ToolCall { .. }))
{
stop_reason = StopReason::ToolUse;
}
let message = Message::Assistant {
content,
stop_reason,
model: config.model.clone(),
provider: model_config.provider.clone(),
usage,
timestamp: now_ms(),
error_message: None,
};
let _ = tx.send(StreamEvent::Done {
message: message.clone(),
});
Ok(message)
}
}
struct ToolCallBuffer {
id: String,
name: String,
arguments: String,
}
fn build_azure_request_body(config: &StreamConfig) -> serde_json::Value {
// Same format as OpenAI Responses API
let mut input: Vec<serde_json::Value> = Vec::new();
for msg in &config.messages {
match msg {
Message::User { content, .. } => {
// Build content array for user message (supports text + images)
let user_content: Vec<serde_json::Value> = content
.iter()
.filter(|c| !matches!(c, Content::Text { text } if text.is_empty()))
.filter_map(|c| match c {
Content::Text { text } => Some(serde_json::json!({
"type": "input_text",
"text": text,
})),
Content::Image { data, mime_type } => Some(serde_json::json!({
"type": "input_image",
"image_url": format!("data:{};base64,{}", mime_type, data),
})),
_ => None,
})
.collect();
if user_content.len() == 1 && user_content[0]["type"] == "input_text" {
// Simple text-only message can use shorthand format
input.push(serde_json::json!({
"role": "user",
"content": user_content[0]["text"].as_str().unwrap_or(""),
}));
} else {
// Multi-modal content uses array format
input.push(serde_json::json!({
"role": "user",
"content": user_content,
}));
}
}
Message::Assistant { content, .. } => {
for c in content {
match c {
Content::Text { text } if text.is_empty() => {}
Content::Text { text } => {
input.push(serde_json::json!({
"type": "message",
"role": "assistant",
"content": [{"type": "output_text", "text": text}],
}));
}
Content::ToolCall {
id,
name,
arguments,
..
} => {
input.push(serde_json::json!({
"type": "function_call",
"call_id": id,
"name": name,
"arguments": arguments.to_string(),
}));
}
_ => {}
}
}
}
Message::ToolResult {
tool_call_id,
content,
..
} => {
let output_val = if content.iter().any(|c| matches!(c, Content::Image { .. })) {
let parts: Vec<serde_json::Value> = content
.iter()
.filter(|c| !matches!(c, Content::Text { text } if text.is_empty()))
.filter_map(|c| match c {
Content::Text { text } => Some(serde_json::json!({
"type": "input_text",
"text": text,
})),
Content::Image { data, mime_type } => Some(serde_json::json!({
"type": "input_image",
"image_url": format!("data:{};base64,{}", mime_type, data),
})),
_ => None,
})
.collect();
serde_json::json!(parts)
} else {
let text = content
.iter()
.find_map(|c| match c {
Content::Text { text } => Some(text.clone()),
_ => None,
})
.unwrap_or_default();
serde_json::json!(text)
};
input.push(serde_json::json!({
"type": "function_call_output",
"call_id": tool_call_id,
"output": output_val,
}));
}
}
}
let mut body = serde_json::json!({
"model": config.model,
"stream": true,
"input": input,
});
if !config.system_prompt.is_empty() {
body["instructions"] = serde_json::json!(config.system_prompt);
}
if let Some(max) = config.max_tokens {
body["max_output_tokens"] = serde_json::json!(max);
}
if !config.tools.is_empty() {
let tools: Vec<serde_json::Value> = config
.tools
.iter()
.map(|t| {
serde_json::json!({
"type": "function",
"name": t.name,
"description": t.description,
"parameters": t.parameters,
})
})
.collect();
body["tools"] = serde_json::json!(tools);
}
if let Some(temp) = config.temperature {
body["temperature"] = serde_json::json!(temp);
}
body
}
// Event types
#[derive(Deserialize)]
struct DeltaEvent {
delta: String,
}
#[derive(Deserialize)]
struct FnCallStartEvent {
#[serde(default)]
call_id: Option<String>,
#[serde(default)]
name: Option<String>,
}
#[derive(Deserialize)]
struct CompletedEvent {
#[serde(default)]
response: Option<ResponseData>,
}
#[derive(Deserialize)]
struct ResponseData {
#[serde(default)]
usage: Option<AzureUsage>,
}
#[derive(Deserialize)]
struct AzureUsage {
#[serde(default)]
input_tokens: u64,
#[serde(default)]
output_tokens: u64,
#[serde(default)]
total_tokens: u64,
}