OpenAI responses streaming (#5837)

Co-authored-by: Michael Neale <michael.neale@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
This commit is contained in:
David Katz
2025-12-12 17:25:36 -05:00
committed by GitHub
parent 5c964d2691
commit 239f4ec8aa
3 changed files with 839 additions and 55 deletions
@@ -4,4 +4,5 @@ pub mod databricks;
pub mod gcpvertexai;
pub mod google;
pub mod openai;
pub mod openai_responses;
pub mod snowflake;
@@ -0,0 +1,696 @@
use crate::conversation::message::{Message, MessageContent};
use crate::model::ModelConfig;
use crate::providers::base::{ProviderUsage, Usage};
use anyhow::{anyhow, Error};
use async_stream::try_stream;
use chrono;
use futures::Stream;
use rmcp::model::{object, CallToolRequestParam, RawContent, Role, Tool};
use serde::{Deserialize, Serialize};
use serde_json::{json, Value};
use std::ops::Deref;
#[derive(Debug, Serialize, Deserialize)]
pub struct ResponsesApiResponse {
pub id: String,
pub object: String,
pub created_at: i64,
pub status: String,
pub model: String,
pub output: Vec<ResponseOutputItem>,
#[serde(skip_serializing_if = "Option::is_none")]
pub reasoning: Option<ResponseReasoningInfo>,
#[serde(skip_serializing_if = "Option::is_none")]
pub usage: Option<ResponseUsage>,
}
#[derive(Debug, Serialize, Deserialize)]
#[serde(tag = "type")]
#[serde(rename_all = "snake_case")]
pub enum ResponseOutputItem {
Reasoning {
id: String,
#[serde(skip_serializing_if = "Option::is_none")]
summary: Option<Vec<String>>,
},
Message {
id: String,
status: String,
role: String,
content: Vec<ResponseContentBlock>,
},
FunctionCall {
id: String,
status: String,
#[serde(skip_serializing_if = "Option::is_none")]
call_id: Option<String>,
name: String,
arguments: String,
},
}
#[derive(Debug, Serialize, Deserialize)]
#[serde(tag = "type")]
#[serde(rename_all = "snake_case")]
pub enum ResponseContentBlock {
OutputText {
text: String,
#[serde(skip_serializing_if = "Option::is_none")]
annotations: Option<Vec<Value>>,
},
ToolCall {
id: String,
name: String,
input: Value,
},
}
#[derive(Debug, Serialize, Deserialize)]
pub struct ResponseReasoningInfo {
pub effort: String,
#[serde(skip_serializing_if = "Option::is_none")]
pub summary: Option<String>,
}
#[derive(Debug, Serialize, Deserialize)]
pub struct ResponseUsage {
pub input_tokens: i32,
pub output_tokens: i32,
pub total_tokens: i32,
}
#[derive(Debug, Serialize, Deserialize)]
#[serde(tag = "type")]
#[serde(rename_all = "snake_case")]
pub enum ResponsesStreamEvent {
#[serde(rename = "response.created")]
ResponseCreated {
sequence_number: i32,
response: ResponseMetadata,
},
#[serde(rename = "response.in_progress")]
ResponseInProgress {
sequence_number: i32,
response: ResponseMetadata,
},
#[serde(rename = "response.output_item.added")]
OutputItemAdded {
sequence_number: i32,
output_index: i32,
item: ResponseOutputItemInfo,
},
#[serde(rename = "response.content_part.added")]
ContentPartAdded {
sequence_number: i32,
item_id: String,
output_index: i32,
content_index: i32,
part: ContentPart,
},
#[serde(rename = "response.output_text.delta")]
OutputTextDelta {
sequence_number: i32,
item_id: String,
output_index: i32,
content_index: i32,
delta: String,
#[serde(skip_serializing_if = "Option::is_none")]
logprobs: Option<Vec<Value>>,
#[serde(skip_serializing_if = "Option::is_none")]
obfuscation: Option<String>,
},
#[serde(rename = "response.output_item.done")]
OutputItemDone {
sequence_number: i32,
output_index: i32,
item: ResponseOutputItemInfo,
},
#[serde(rename = "response.content_part.done")]
ContentPartDone {
sequence_number: i32,
item_id: String,
output_index: i32,
content_index: i32,
part: ContentPart,
},
#[serde(rename = "response.output_text.done")]
OutputTextDone {
sequence_number: i32,
item_id: String,
output_index: i32,
content_index: i32,
text: String,
#[serde(skip_serializing_if = "Option::is_none")]
logprobs: Option<Vec<Value>>,
},
#[serde(rename = "response.completed")]
ResponseCompleted {
sequence_number: i32,
response: ResponseMetadata,
},
#[serde(rename = "response.failed")]
ResponseFailed { sequence_number: i32, error: Value },
#[serde(rename = "response.function_call_arguments.delta")]
FunctionCallArgumentsDelta {
sequence_number: i32,
item_id: String,
output_index: i32,
delta: String,
#[serde(skip_serializing_if = "Option::is_none")]
obfuscation: Option<String>,
},
#[serde(rename = "response.function_call_arguments.done")]
FunctionCallArgumentsDone {
sequence_number: i32,
item_id: String,
output_index: i32,
arguments: String,
},
#[serde(rename = "error")]
Error { error: Value },
}
#[derive(Debug, Serialize, Deserialize)]
pub struct ResponseMetadata {
pub id: String,
pub object: String,
pub created_at: i64,
pub status: String,
pub model: String,
pub output: Vec<ResponseOutputItemInfo>,
#[serde(skip_serializing_if = "Option::is_none")]
pub usage: Option<ResponseUsage>,
#[serde(skip_serializing_if = "Option::is_none")]
pub reasoning: Option<ResponseReasoningInfo>,
}
#[derive(Debug, Serialize, Deserialize, Clone)]
#[serde(tag = "type")]
#[serde(rename_all = "snake_case")]
pub enum ResponseOutputItemInfo {
Reasoning {
id: String,
summary: Vec<String>,
},
Message {
id: String,
status: String,
role: String,
content: Vec<ContentPart>,
},
FunctionCall {
id: String,
status: String,
call_id: String,
name: String,
arguments: String,
},
}
#[derive(Debug, Serialize, Deserialize, Clone)]
#[serde(tag = "type")]
#[serde(rename_all = "snake_case")]
pub enum ContentPart {
OutputText {
text: String,
#[serde(skip_serializing_if = "Option::is_none")]
annotations: Option<Vec<Value>>,
#[serde(skip_serializing_if = "Option::is_none")]
logprobs: Option<Vec<Value>>,
},
ToolCall {
id: String,
name: String,
arguments: String,
},
}
fn add_conversation_history(input_items: &mut Vec<Value>, messages: &[Message]) {
for message in messages.iter().filter(|m| m.is_agent_visible()) {
let has_only_tool_content = message.content.iter().all(|c| {
matches!(
c,
MessageContent::ToolRequest(_) | MessageContent::ToolResponse(_)
)
});
if has_only_tool_content {
continue;
}
if message.role != Role::User && message.role != Role::Assistant {
continue;
}
let role = match message.role {
Role::User => "user",
Role::Assistant => "assistant",
};
let mut content_items = Vec::new();
for content in &message.content {
if let MessageContent::Text(text) = content {
if !text.text.is_empty() {
let content_type = if message.role == Role::Assistant {
"output_text"
} else {
"input_text"
};
content_items.push(json!({
"type": content_type,
"text": text.text
}));
}
}
}
if !content_items.is_empty() {
input_items.push(json!({
"role": role,
"content": content_items
}));
}
}
}
fn add_function_calls(input_items: &mut Vec<Value>, messages: &[Message]) {
for message in messages.iter().filter(|m| m.is_agent_visible()) {
if message.role == Role::Assistant {
for content in &message.content {
if let MessageContent::ToolRequest(request) = content {
if let Ok(tool_call) = &request.tool_call {
let arguments_str = tool_call
.arguments
.as_ref()
.map(|args| {
serde_json::to_string(args).unwrap_or_else(|_| "{}".to_string())
})
.unwrap_or_else(|| "{}".to_string());
tracing::debug!(
"Replaying function_call with call_id: {}, name: {}",
request.id,
tool_call.name
);
input_items.push(json!({
"type": "function_call",
"call_id": request.id,
"name": tool_call.name,
"arguments": arguments_str
}));
}
}
}
}
}
}
fn add_function_call_outputs(input_items: &mut Vec<Value>, messages: &[Message]) {
for message in messages.iter().filter(|m| m.is_agent_visible()) {
for content in &message.content {
if let MessageContent::ToolResponse(response) = content {
match &response.tool_result {
Ok(contents) => {
let text_content: Vec<String> = contents
.content
.iter()
.filter_map(|c| {
if let RawContent::Text(t) = c.deref() {
Some(t.text.clone())
} else {
None
}
})
.collect();
if !text_content.is_empty() {
tracing::debug!(
"Sending function_call_output with call_id: {}",
response.id
);
input_items.push(json!({
"type": "function_call_output",
"call_id": response.id,
"output": text_content.join("\n")
}));
}
}
Err(error_data) => {
// Handle error responses - must send them back to the API
// to avoid "No tool output found" errors
tracing::debug!(
"Sending function_call_output error with call_id: {}",
response.id
);
input_items.push(json!({
"type": "function_call_output",
"call_id": response.id,
"output": format!("Error: {}", error_data.message)
}));
}
}
}
}
}
}
pub fn create_responses_request(
model_config: &ModelConfig,
system: &str,
messages: &[Message],
tools: &[Tool],
) -> anyhow::Result<Value, Error> {
let mut input_items = Vec::new();
if !system.is_empty() {
input_items.push(json!({
"role": "system",
"content": [{
"type": "input_text",
"text": system
}]
}));
}
add_conversation_history(&mut input_items, messages);
add_function_calls(&mut input_items, messages);
add_function_call_outputs(&mut input_items, messages);
let mut payload = json!({
"model": model_config.model_name,
"input": input_items,
"store": false, // Don't store responses on server (we replay history ourselves)
});
if !tools.is_empty() {
let tools_spec: Vec<Value> = tools
.iter()
.map(|tool| {
json!({
"type": "function",
"name": tool.name,
"description": tool.description,
"parameters": tool.input_schema,
})
})
.collect();
payload
.as_object_mut()
.unwrap()
.insert("tools".to_string(), json!(tools_spec));
}
if let Some(temp) = model_config.temperature {
payload
.as_object_mut()
.unwrap()
.insert("temperature".to_string(), json!(temp));
}
if let Some(tokens) = model_config.max_tokens {
payload
.as_object_mut()
.unwrap()
.insert("max_output_tokens".to_string(), json!(tokens));
}
Ok(payload)
}
pub fn responses_api_to_message(response: &ResponsesApiResponse) -> anyhow::Result<Message> {
let mut content = Vec::new();
for item in &response.output {
match item {
ResponseOutputItem::Reasoning { .. } => {
continue;
}
ResponseOutputItem::Message {
content: msg_content,
..
} => {
for block in msg_content {
match block {
ResponseContentBlock::OutputText { text, .. } => {
if !text.is_empty() {
content.push(MessageContent::text(text));
}
}
ResponseContentBlock::ToolCall { id, name, input } => {
content.push(MessageContent::tool_request(
id.clone(),
Ok(CallToolRequestParam {
name: name.clone().into(),
arguments: Some(object(input.clone())),
}),
));
}
}
}
}
ResponseOutputItem::FunctionCall {
id,
name,
arguments,
..
} => {
tracing::debug!("Received FunctionCall with id: {}, name: {}", id, name);
let parsed_args = if arguments.is_empty() {
json!({})
} else {
serde_json::from_str(arguments).unwrap_or_else(|_| json!({}))
};
content.push(MessageContent::tool_request(
id.clone(),
Ok(CallToolRequestParam {
name: name.clone().into(),
arguments: Some(object(parsed_args)),
}),
));
}
}
}
let mut message = Message::new(Role::Assistant, chrono::Utc::now().timestamp(), content);
message = message.with_id(response.id.clone());
Ok(message)
}
pub fn get_responses_usage(response: &ResponsesApiResponse) -> Usage {
response.usage.as_ref().map_or_else(Usage::default, |u| {
Usage::new(
Some(u.input_tokens),
Some(u.output_tokens),
Some(u.total_tokens),
)
})
}
fn process_streaming_output_items(
output_items: Vec<ResponseOutputItemInfo>,
is_text_response: bool,
) -> Vec<MessageContent> {
let mut content = Vec::new();
for item in output_items {
match item {
ResponseOutputItemInfo::Reasoning { .. } => {
// Skip reasoning items
}
ResponseOutputItemInfo::Message { content: parts, .. } => {
for part in parts {
match part {
ContentPart::OutputText { text, .. } => {
if !text.is_empty() && !is_text_response {
content.push(MessageContent::text(&text));
}
}
ContentPart::ToolCall {
id,
name,
arguments,
} => {
let parsed_args = if arguments.is_empty() {
json!({})
} else {
serde_json::from_str(&arguments).unwrap_or_else(|_| json!({}))
};
content.push(MessageContent::tool_request(
id,
Ok(CallToolRequestParam {
name: name.into(),
arguments: Some(object(parsed_args)),
}),
));
}
}
}
}
ResponseOutputItemInfo::FunctionCall {
call_id,
name,
arguments,
..
} => {
let parsed_args = if arguments.is_empty() {
json!({})
} else {
serde_json::from_str(&arguments).unwrap_or_else(|_| json!({}))
};
content.push(MessageContent::tool_request(
call_id,
Ok(CallToolRequestParam {
name: name.into(),
arguments: Some(object(parsed_args)),
}),
));
}
}
}
content
}
pub fn responses_api_to_streaming_message<S>(
mut stream: S,
) -> impl Stream<Item = anyhow::Result<(Option<Message>, Option<ProviderUsage>)>> + 'static
where
S: Stream<Item = anyhow::Result<String>> + Unpin + Send + 'static,
{
try_stream! {
use futures::StreamExt;
let mut accumulated_text = String::new();
let mut response_id: Option<String> = None;
let mut model_name: Option<String> = None;
let mut final_usage: Option<ProviderUsage> = None;
let mut output_items: Vec<ResponseOutputItemInfo> = Vec::new();
let mut is_text_response = false;
'outer: while let Some(response) = stream.next().await {
let response_str = response?;
// Skip empty lines
if response_str.trim().is_empty() {
continue;
}
// Parse SSE format: "event: <type>\ndata: <json>"
// For now, we only care about the data line
let data_line = if response_str.starts_with("data: ") {
response_str.strip_prefix("data: ").unwrap()
} else if response_str.starts_with("event: ") {
// Skip event type lines
continue;
} else {
// Try to parse as-is in case there's no prefix
&response_str
};
if data_line == "[DONE]" {
break 'outer;
}
let event: ResponsesStreamEvent = serde_json::from_str(data_line)
.map_err(|e| anyhow!("Failed to parse Responses stream event: {}: {:?}", e, data_line))?;
match event {
ResponsesStreamEvent::ResponseCreated { response, .. } |
ResponsesStreamEvent::ResponseInProgress { response, .. } => {
response_id = Some(response.id);
model_name = Some(response.model);
}
ResponsesStreamEvent::OutputTextDelta { delta, .. } => {
is_text_response = true;
accumulated_text.push_str(&delta);
// Yield incremental text updates for true streaming
let mut content = Vec::new();
if !delta.is_empty() {
content.push(MessageContent::text(&delta));
}
let mut msg = Message::new(Role::Assistant, chrono::Utc::now().timestamp(), content);
// Add ID so desktop client knows these deltas are part of the same message
if let Some(id) = &response_id {
msg = msg.with_id(id.clone());
}
yield (Some(msg), None);
}
ResponsesStreamEvent::OutputItemDone { item, .. } => {
output_items.push(item);
}
ResponsesStreamEvent::OutputTextDone { .. } => {
// Text is already complete from deltas, this is just a summary event
}
ResponsesStreamEvent::ResponseCompleted { response, .. } => {
let model = model_name.as_ref().unwrap_or(&response.model);
let usage = response.usage.as_ref().map_or_else(
Usage::default,
|u| Usage::new(
Some(u.input_tokens),
Some(u.output_tokens),
Some(u.total_tokens),
),
);
final_usage = Some(ProviderUsage {
usage,
model: model.clone(),
});
// For complete output, use the response output items
if !response.output.is_empty() {
output_items = response.output;
}
break 'outer;
}
ResponsesStreamEvent::FunctionCallArgumentsDelta { .. } => {
// Function call arguments are being streamed, but we'll get the complete
// arguments in the OutputItemDone event, so we can ignore deltas for now
}
ResponsesStreamEvent::FunctionCallArgumentsDone { .. } => {
// Arguments are complete, will be in the OutputItemDone event
}
ResponsesStreamEvent::ResponseFailed { error, .. } => {
Err(anyhow!("Responses API failed: {:?}", error))?;
}
ResponsesStreamEvent::Error { error } => {
Err(anyhow!("Responses API error: {:?}", error))?;
}
_ => {
// Ignore other event types (OutputItemAdded, ContentPartAdded, ContentPartDone)
}
}
}
// Process final output items and yield usage data
let content = process_streaming_output_items(output_items, is_text_response);
if !content.is_empty() {
let mut message = Message::new(Role::Assistant, chrono::Utc::now().timestamp(), content);
if let Some(id) = response_id {
message = message.with_id(id);
}
yield (Some(message), final_usage);
} else if let Some(usage) = final_usage {
yield (None, Some(usage));
}
}
}
+142 -55
View File
@@ -15,7 +15,13 @@ use super::api_client::{ApiClient, AuthMethod};
use super::base::{ConfigKey, ModelInfo, Provider, ProviderMetadata, ProviderUsage, Usage};
use super::embedding::{EmbeddingCapable, EmbeddingRequest, EmbeddingResponse};
use super::errors::ProviderError;
use super::formats::openai::{create_request, get_usage, response_to_message};
use super::formats::openai::{
create_request, get_usage, response_to_message, response_to_streaming_message,
};
use super::formats::openai_responses::{
create_responses_request, get_responses_usage, responses_api_to_message,
responses_api_to_streaming_message, ResponsesApiResponse,
};
use super::retry::ProviderRetry;
use super::utils::{
get_model, handle_response_openai_compat, handle_status_openai_compat, ImageFormat,
@@ -25,7 +31,6 @@ use crate::conversation::message::Message;
use crate::model::ModelConfig;
use crate::providers::base::MessageStream;
use crate::providers::formats::openai::response_to_streaming_message;
use crate::providers::utils::RequestLog;
use rmcp::model::Tool;
@@ -41,6 +46,8 @@ pub const OPEN_AI_KNOWN_MODELS: &[(&str, usize)] = &[
("gpt-3.5-turbo", 16_385),
("gpt-4-turbo", 128_000),
("o4-mini", 128_000),
("gpt-5.1-codex", 400_000),
("gpt-5-codex", 400_000),
];
pub const OPEN_AI_DOC_URL: &str = "https://platform.openai.com/docs/models";
@@ -184,6 +191,10 @@ impl OpenAiProvider {
})
}
fn uses_responses_api(model_name: &str) -> bool {
model_name.starts_with("gpt-5-codex") || model_name.starts_with("gpt-5.1-codex")
}
async fn post(&self, payload: &Value) -> Result<Value, ProviderError> {
let response = self
.api_client
@@ -191,6 +202,14 @@ impl OpenAiProvider {
.await?;
handle_response_openai_compat(response).await
}
async fn post_responses(&self, payload: &Value) -> Result<Value, ProviderError> {
let response = self
.api_client
.response_post("v1/responses", payload)
.await?;
handle_response_openai_compat(response).await
}
}
#[async_trait]
@@ -238,31 +257,62 @@ impl Provider for OpenAiProvider {
messages: &[Message],
tools: &[Tool],
) -> Result<(Message, ProviderUsage), ProviderError> {
let payload = create_request(model_config, system, messages, tools, &ImageFormat::OpenAi)?;
if Self::uses_responses_api(&model_config.model_name) {
let payload = create_responses_request(model_config, system, messages, tools)?;
let mut log = RequestLog::start(&self.model, &payload)?;
let mut log = RequestLog::start(&self.model, &payload)?;
let json_response = self
.with_retry(|| async {
let payload_clone = payload.clone();
self.post(&payload_clone).await
})
.await
.inspect_err(|e| {
let _ = log.error(e);
})?;
let json_response = self
.with_retry(|| async {
let payload_clone = payload.clone();
self.post_responses(&payload_clone).await
})
.await
.inspect_err(|e| {
let _ = log.error(e);
})?;
let message = response_to_message(&json_response)?;
let usage = json_response
.get("usage")
.map(get_usage)
.unwrap_or_else(|| {
tracing::debug!("Failed to get usage data");
Usage::default()
});
let responses_api_response: ResponsesApiResponse =
serde_json::from_value(json_response.clone()).map_err(|e| {
ProviderError::ExecutionError(format!(
"Failed to parse responses API response: {}",
e
))
})?;
let model = get_model(&json_response);
log.write(&json_response, Some(&usage))?;
Ok((message, ProviderUsage::new(model, usage)))
let message = responses_api_to_message(&responses_api_response)?;
let usage = get_responses_usage(&responses_api_response);
let model = responses_api_response.model.clone();
log.write(&json_response, Some(&usage))?;
Ok((message, ProviderUsage::new(model, usage)))
} else {
let payload =
create_request(model_config, system, messages, tools, &ImageFormat::OpenAi)?;
let mut log = RequestLog::start(&self.model, &payload)?;
let json_response = self
.with_retry(|| async {
let payload_clone = payload.clone();
self.post(&payload_clone).await
})
.await
.inspect_err(|e| {
let _ = log.error(e);
})?;
let message = response_to_message(&json_response)?;
let usage = json_response
.get("usage")
.map(get_usage)
.unwrap_or_else(|| {
tracing::debug!("Failed to get usage data");
Usage::default()
});
let model = get_model(&json_response);
log.write(&json_response, Some(&usage))?;
Ok((message, ProviderUsage::new(model, usage)))
}
}
async fn fetch_supported_models(&self) -> Result<Option<Vec<String>>, ProviderError> {
@@ -319,40 +369,77 @@ impl Provider for OpenAiProvider {
messages: &[Message],
tools: &[Tool],
) -> Result<MessageStream, ProviderError> {
let mut payload =
create_request(&self.model, system, messages, tools, &ImageFormat::OpenAi)?;
payload["stream"] = serde_json::Value::Bool(true);
payload["stream_options"] = json!({
"include_usage": true,
});
let mut log = RequestLog::start(&self.model, &payload)?;
if Self::uses_responses_api(&self.model.model_name) {
let mut payload = create_responses_request(&self.model, system, messages, tools)?;
payload["stream"] = serde_json::Value::Bool(true);
let response = self
.with_retry(|| async {
let resp = self
.api_client
.response_post(&self.base_path, &payload)
.await?;
handle_status_openai_compat(resp).await
})
.await
.inspect_err(|e| {
let _ = log.error(e);
})?;
let stream = response.bytes_stream().map_err(io::Error::other);
let mut log = RequestLog::start(&self.model, &payload)?;
Ok(Box::pin(try_stream! {
let stream_reader = StreamReader::new(stream);
let framed = FramedRead::new(stream_reader, LinesCodec::new()).map_err(anyhow::Error::from);
let response = self
.with_retry(|| async {
let payload_clone = payload.clone();
let resp = self
.api_client
.response_post("v1/responses", &payload_clone)
.await?;
handle_status_openai_compat(resp).await
})
.await
.inspect_err(|e| {
let _ = log.error(e);
})?;
let message_stream = response_to_streaming_message(framed);
pin!(message_stream);
while let Some(message) = message_stream.next().await {
let (message, usage) = message.map_err(|e| ProviderError::RequestFailed(format!("Stream decode error: {}", e)))?;
log.write(&message, usage.as_ref().map(|f| f.usage).as_ref())?;
yield (message, usage);
}
}))
let stream = response.bytes_stream().map_err(io::Error::other);
Ok(Box::pin(try_stream! {
let stream_reader = StreamReader::new(stream);
let framed = FramedRead::new(stream_reader, LinesCodec::new()).map_err(anyhow::Error::from);
let message_stream = responses_api_to_streaming_message(framed);
pin!(message_stream);
while let Some(message) = message_stream.next().await {
let (message, usage) = message.map_err(|e| ProviderError::RequestFailed(format!("Stream decode error: {}", e)))?;
log.write(&message, usage.as_ref().map(|f| f.usage).as_ref())?;
yield (message, usage);
}
}))
} else {
let mut payload =
create_request(&self.model, system, messages, tools, &ImageFormat::OpenAi)?;
payload["stream"] = serde_json::Value::Bool(true);
payload["stream_options"] = json!({
"include_usage": true,
});
let mut log = RequestLog::start(&self.model, &payload)?;
let response = self
.with_retry(|| async {
let resp = self
.api_client
.response_post(&self.base_path, &payload)
.await?;
handle_status_openai_compat(resp).await
})
.await
.inspect_err(|e| {
let _ = log.error(e);
})?;
let stream = response.bytes_stream().map_err(io::Error::other);
Ok(Box::pin(try_stream! {
let stream_reader = StreamReader::new(stream);
let framed = FramedRead::new(stream_reader, LinesCodec::new()).map_err(anyhow::Error::from);
let message_stream = response_to_streaming_message(framed);
pin!(message_stream);
while let Some(message) = message_stream.next().await {
let (message, usage) = message.map_err(|e| ProviderError::RequestFailed(format!("Stream decode error: {}", e)))?;
log.write(&message, usage.as_ref().map(|f| f.usage).as_ref())?;
yield (message, usage);
}
}))
}
}
}