hobokenchicken
57aa0aa70e
- Sequential next_tool_index is now used for both Responses API 'function_call' events and the proxy's 'tool_uses' JSON extraction. - This ensures tool_calls arrays in the stream always start at index 0 and are dense, even if standard and embedded calls were somehow mixed. - Fixed 'payload_idx' logic to correctly align argument chunks with their initialization chunks.
1003 lines
48 KiB
Rust
1003 lines
48 KiB
Rust
use anyhow::Result;
|
|
use async_trait::async_trait;
|
|
use futures::stream::BoxStream;
|
|
use futures::StreamExt;
|
|
|
|
use super::helpers;
|
|
use super::{ProviderResponse, ProviderStreamChunk};
|
|
use crate::{config::AppConfig, errors::AppError, models::UnifiedRequest};
|
|
|
|
pub struct OpenAIProvider {
|
|
client: reqwest::Client,
|
|
config: crate::config::OpenAIConfig,
|
|
api_key: String,
|
|
pricing: Vec<crate::config::ModelPricing>,
|
|
}
|
|
|
|
impl OpenAIProvider {
|
|
pub fn new(config: &crate::config::OpenAIConfig, app_config: &AppConfig) -> Result<Self> {
|
|
let api_key = app_config.get_api_key("openai")?;
|
|
Self::new_with_key(config, app_config, api_key)
|
|
}
|
|
|
|
pub fn new_with_key(config: &crate::config::OpenAIConfig, app_config: &AppConfig, api_key: String) -> Result<Self> {
|
|
let client = reqwest::Client::builder()
|
|
.connect_timeout(std::time::Duration::from_secs(5))
|
|
.timeout(std::time::Duration::from_secs(300))
|
|
.pool_idle_timeout(std::time::Duration::from_secs(90))
|
|
.pool_max_idle_per_host(4)
|
|
.tcp_keepalive(std::time::Duration::from_secs(15))
|
|
.build()?;
|
|
|
|
Ok(Self {
|
|
client,
|
|
config: config.clone(),
|
|
api_key,
|
|
pricing: app_config.pricing.openai.clone(),
|
|
})
|
|
}
|
|
|
|
/// GPT-5.4 models sometimes emit parallel tool calls as a JSON block starting with
|
|
/// '{"tool_uses":' inside a text message instead of discrete function_call items.
|
|
/// This method attempts to extract and parse such tool calls.
|
|
pub fn parse_tool_uses_json(text: &str) -> Vec<crate::models::ToolCall> {
|
|
let mut calls = Vec::new();
|
|
if let Some(start) = text.find("{\"tool_uses\":") {
|
|
// ... (rest of method unchanged)
|
|
// Find the end of the JSON block by matching braces
|
|
let sub = &text[start..];
|
|
let mut brace_count = 0;
|
|
let mut end_idx = 0;
|
|
let mut found = false;
|
|
|
|
for (i, c) in sub.char_indices() {
|
|
if c == '{' { brace_count += 1; }
|
|
else if c == '}' {
|
|
brace_count -= 1;
|
|
if brace_count == 0 {
|
|
end_idx = i + 1;
|
|
found = true;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
if found {
|
|
let json_str = &sub[..end_idx];
|
|
if let Ok(val) = serde_json::from_str::<serde_json::Value>(json_str) {
|
|
if let Some(uses) = val.get("tool_uses").and_then(|u| u.as_array()) {
|
|
for (idx, u) in uses.iter().enumerate() {
|
|
let name = u.get("recipient_name")
|
|
.and_then(|v| v.as_str())
|
|
.unwrap_or("unknown")
|
|
// Strip "functions." prefix if present
|
|
.replace("functions.", "");
|
|
let arguments = u.get("parameters")
|
|
.map(|v| v.to_string())
|
|
.unwrap_or_else(|| "{}".to_string());
|
|
|
|
calls.push(crate::models::ToolCall {
|
|
id: format!("call_tu_{}_{}", uuid::Uuid::new_v4().to_string()[..8].to_string(), idx),
|
|
call_type: "function".to_string(),
|
|
function: crate::models::FunctionCall { name, arguments },
|
|
});
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
calls
|
|
}
|
|
|
|
/// Strips internal metadata prefixes like 'to=multi_tool_use.parallel' from model responses.
|
|
pub fn strip_internal_metadata(text: &str) -> String {
|
|
let mut result = text.to_string();
|
|
|
|
// Patterns to strip
|
|
let patterns = [
|
|
"to=multi_tool_use.parallel",
|
|
"to=functions.multi_tool_use",
|
|
"ส่งเงินบาทไทยjson", // User reported Thai text preamble
|
|
];
|
|
|
|
for p in patterns {
|
|
if let Some(start) = result.find(p) {
|
|
// Remove the pattern
|
|
result.replace_range(start..start + p.len(), "");
|
|
}
|
|
}
|
|
|
|
result
|
|
}
|
|
}
|
|
|
|
#[async_trait]
|
|
impl super::Provider for OpenAIProvider {
|
|
fn name(&self) -> &str {
|
|
"openai"
|
|
}
|
|
|
|
fn supports_model(&self, model: &str) -> bool {
|
|
model.starts_with("gpt-") ||
|
|
model.starts_with("o1-") ||
|
|
model.starts_with("o2-") ||
|
|
model.starts_with("o3-") ||
|
|
model.starts_with("o4-") ||
|
|
model.starts_with("o5-") ||
|
|
model.contains("gpt-5")
|
|
}
|
|
|
|
fn supports_multimodal(&self) -> bool {
|
|
true
|
|
}
|
|
|
|
async fn chat_completion(&self, request: UnifiedRequest) -> Result<ProviderResponse, AppError> {
|
|
// Allow proactive routing to Responses API based on heuristic
|
|
let model_lc = request.model.to_lowercase();
|
|
if model_lc.contains("gpt-5") || model_lc.contains("codex") {
|
|
return self.chat_responses(request).await;
|
|
}
|
|
|
|
let messages_json = helpers::messages_to_openai_json(&request.messages).await?;
|
|
let mut body = helpers::build_openai_body(&request, messages_json, false);
|
|
|
|
// Transition: Newer OpenAI models (o1, o3, gpt-5) require max_completion_tokens
|
|
// instead of the legacy max_tokens parameter.
|
|
if request.model.starts_with("o1-") || request.model.starts_with("o3-") || request.model.contains("gpt-5") {
|
|
if let Some(max_tokens) = body.as_object_mut().and_then(|obj| obj.remove("max_tokens")) {
|
|
body["max_completion_tokens"] = max_tokens;
|
|
}
|
|
}
|
|
|
|
let response = self
|
|
.client
|
|
.post(format!("{}/chat/completions", self.config.base_url))
|
|
.header("Authorization", format!("Bearer {}", self.api_key))
|
|
.json(&body)
|
|
.send()
|
|
.await
|
|
.map_err(|e| AppError::ProviderError(e.to_string()))?;
|
|
|
|
if !response.status().is_success() {
|
|
let status = response.status();
|
|
let error_text = response.text().await.unwrap_or_default();
|
|
|
|
// Read error body to diagnose. If the model requires the Responses
|
|
// API (v1/responses), retry against that endpoint.
|
|
if error_text.to_lowercase().contains("v1/responses") || error_text.to_lowercase().contains("only supported in v1/responses") {
|
|
return self.chat_responses(request).await;
|
|
}
|
|
|
|
tracing::error!("OpenAI API error ({}): {}", status, error_text);
|
|
return Err(AppError::ProviderError(format!("OpenAI API error ({}): {}", status, error_text)));
|
|
}
|
|
|
|
let resp_json: serde_json::Value = response
|
|
.json()
|
|
.await
|
|
.map_err(|e| AppError::ProviderError(e.to_string()))?;
|
|
|
|
helpers::parse_openai_response(&resp_json, request.model)
|
|
}
|
|
|
|
async fn chat_responses(&self, request: UnifiedRequest) -> Result<ProviderResponse, AppError> {
|
|
// Build a structured input for the Responses API.
|
|
let messages_json = helpers::messages_to_openai_json(&request.messages).await?;
|
|
let mut input_parts = Vec::new();
|
|
for m in &messages_json {
|
|
let role = m["role"].as_str().unwrap_or("user");
|
|
|
|
if role == "tool" {
|
|
input_parts.push(serde_json::json!({
|
|
"type": "function_call_output",
|
|
"call_id": m.get("tool_call_id").and_then(|v| v.as_str()).unwrap_or(""),
|
|
"output": m.get("content").and_then(|v| v.as_str()).unwrap_or("")
|
|
}));
|
|
continue;
|
|
}
|
|
|
|
if role == "assistant" && m.get("tool_calls").is_some() {
|
|
// Push message part if it exists
|
|
let content_val = m.get("content").cloned().unwrap_or(serde_json::json!(""));
|
|
if !content_val.is_null() && (content_val.is_array() && !content_val.as_array().unwrap().is_empty() || content_val.is_string() && !content_val.as_str().unwrap().is_empty()) {
|
|
let mut content = content_val.clone();
|
|
if let Some(text) = content.as_str() {
|
|
content = serde_json::json!([{ "type": "output_text", "text": text }]);
|
|
} else if let Some(arr) = content.as_array_mut() {
|
|
for part in arr {
|
|
if let Some(obj) = part.as_object_mut() {
|
|
if obj.get("type").and_then(|v| v.as_str()) == Some("text") {
|
|
obj.insert("type".to_string(), serde_json::json!("output_text"));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
input_parts.push(serde_json::json!({
|
|
"type": "message",
|
|
"role": "assistant",
|
|
"content": content
|
|
}));
|
|
}
|
|
|
|
// Push tool calls as separate items
|
|
if let Some(tcs) = m.get("tool_calls").and_then(|v| v.as_array()) {
|
|
for tc in tcs {
|
|
input_parts.push(serde_json::json!({
|
|
"type": "function_call",
|
|
"call_id": tc["id"],
|
|
"name": tc["function"]["name"],
|
|
"arguments": tc["function"]["arguments"]
|
|
}));
|
|
}
|
|
}
|
|
continue;
|
|
}
|
|
|
|
let mut mapped_role = role.to_string();
|
|
if mapped_role == "system" {
|
|
mapped_role = "developer".to_string();
|
|
}
|
|
|
|
let mut content = m.get("content").cloned().unwrap_or(serde_json::json!([]));
|
|
|
|
// Map content types based on role for Responses API
|
|
if let Some(content_array) = content.as_array_mut() {
|
|
for part in content_array {
|
|
if let Some(part_obj) = part.as_object_mut() {
|
|
if let Some(t) = part_obj.get("type").and_then(|v| v.as_str()) {
|
|
match t {
|
|
"text" => {
|
|
let new_type = if mapped_role == "assistant" { "output_text" } else { "input_text" };
|
|
part_obj.insert("type".to_string(), serde_json::json!(new_type));
|
|
}
|
|
"image_url" => {
|
|
let new_type = if mapped_role == "assistant" { "output_image" } else { "input_image" };
|
|
part_obj.insert("type".to_string(), serde_json::json!(new_type));
|
|
if let Some(img_url) = part_obj.remove("image_url") {
|
|
part_obj.insert("image".to_string(), img_url);
|
|
}
|
|
}
|
|
_ => {}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else if let Some(text) = content.as_str() {
|
|
// If it's just a string, send it as a string instead of an array of objects
|
|
// as it's safer for standard conversational messages.
|
|
content = serde_json::json!(text);
|
|
}
|
|
|
|
let mut msg_item = serde_json::json!({
|
|
"type": "message",
|
|
"role": mapped_role,
|
|
"content": content
|
|
});
|
|
if let Some(name) = m.get("name") {
|
|
msg_item["name"] = name.clone();
|
|
}
|
|
input_parts.push(msg_item);
|
|
}
|
|
|
|
let mut body = serde_json::json!({
|
|
"model": request.model,
|
|
"input": input_parts,
|
|
});
|
|
|
|
// Add standard parameters
|
|
if let Some(temp) = request.temperature {
|
|
body["temperature"] = serde_json::json!(temp);
|
|
}
|
|
|
|
// Newer models (gpt-5, o1) in Responses API use max_output_tokens
|
|
if let Some(max_tokens) = request.max_tokens {
|
|
if request.model.contains("gpt-5") || request.model.starts_with("o1-") || request.model.starts_with("o3-") {
|
|
body["max_output_tokens"] = serde_json::json!(max_tokens);
|
|
} else {
|
|
body["max_tokens"] = serde_json::json!(max_tokens);
|
|
}
|
|
}
|
|
|
|
if let Some(tools) = &request.tools {
|
|
let flattened: Vec<serde_json::Value> = tools.iter().map(|t| {
|
|
let mut obj = serde_json::json!({
|
|
"type": t.tool_type,
|
|
"name": t.function.name,
|
|
});
|
|
if let Some(desc) = &t.function.description {
|
|
obj["description"] = serde_json::json!(desc);
|
|
}
|
|
if let Some(params) = &t.function.parameters {
|
|
obj["parameters"] = params.clone();
|
|
}
|
|
obj
|
|
}).collect();
|
|
body["tools"] = serde_json::json!(flattened);
|
|
}
|
|
if let Some(tool_choice) = &request.tool_choice {
|
|
match tool_choice {
|
|
crate::models::ToolChoice::Mode(mode) => {
|
|
body["tool_choice"] = serde_json::json!(mode);
|
|
}
|
|
crate::models::ToolChoice::Specific(specific) => {
|
|
body["tool_choice"] = serde_json::json!({
|
|
"type": specific.choice_type,
|
|
"name": specific.function.name,
|
|
});
|
|
}
|
|
}
|
|
}
|
|
|
|
let resp = self
|
|
.client
|
|
.post(format!("{}/responses", self.config.base_url))
|
|
.header("Authorization", format!("Bearer {}", self.api_key))
|
|
.json(&body)
|
|
.send()
|
|
.await
|
|
.map_err(|e| AppError::ProviderError(e.to_string()))?;
|
|
|
|
if !resp.status().is_success() {
|
|
let err = resp.text().await.unwrap_or_default();
|
|
return Err(AppError::ProviderError(format!("OpenAI Responses API error: {}", err)));
|
|
}
|
|
|
|
let resp_json: serde_json::Value = resp.json().await.map_err(|e| AppError::ProviderError(e.to_string()))?;
|
|
|
|
// Try to normalize: if it's chat-style, use existing parser
|
|
if resp_json.get("choices").is_some() {
|
|
return helpers::parse_openai_response(&resp_json, request.model);
|
|
}
|
|
|
|
// Normalize Responses API output into ProviderResponse
|
|
let mut content_text = String::new();
|
|
let mut tool_calls = Vec::new();
|
|
if let Some(output) = resp_json.get("output").and_then(|o| o.as_array()) {
|
|
for out in output {
|
|
let item_type = out.get("type").and_then(|v| v.as_str()).unwrap_or("");
|
|
match item_type {
|
|
"message" => {
|
|
if let Some(contents) = out.get("content").and_then(|c| c.as_array()) {
|
|
for item in contents {
|
|
if let Some(text) = item.get("text").and_then(|t| t.as_str()) {
|
|
if !content_text.is_empty() { content_text.push_str("\n"); }
|
|
content_text.push_str(text);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
"function_call" => {
|
|
let id = out.get("call_id")
|
|
.or_else(|| out.get("item_id"))
|
|
.or_else(|| out.get("id"))
|
|
.and_then(|v| v.as_str())
|
|
.unwrap_or("")
|
|
.to_string();
|
|
let name = out.get("name").and_then(|v| v.as_str()).unwrap_or("").to_string();
|
|
let arguments = out.get("arguments").and_then(|v| v.as_str()).unwrap_or("").to_string();
|
|
tool_calls.push(crate::models::ToolCall {
|
|
id,
|
|
call_type: "function".to_string(),
|
|
function: crate::models::FunctionCall { name, arguments },
|
|
});
|
|
}
|
|
_ => {
|
|
// Fallback for older/nested structure
|
|
if let Some(contents) = out.get("content").and_then(|c| c.as_array()) {
|
|
for item in contents {
|
|
if let Some(text) = item.get("text").and_then(|t| t.as_str()) {
|
|
if !content_text.is_empty() { content_text.push_str("\n"); }
|
|
content_text.push_str(text);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if content_text.is_empty() {
|
|
if let Some(cands) = resp_json.get("candidates").and_then(|c| c.as_array()) {
|
|
if let Some(c0) = cands.get(0) {
|
|
if let Some(content) = c0.get("content") {
|
|
if let Some(parts) = content.get("parts").and_then(|p| p.as_array()) {
|
|
for p in parts {
|
|
if let Some(t) = p.get("text").and_then(|v| v.as_str()) {
|
|
if !content_text.is_empty() { content_text.push_str("\n"); }
|
|
content_text.push_str(t);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
let prompt_tokens = resp_json.get("usage").and_then(|u| u.get("prompt_tokens")).and_then(|v| v.as_u64()).unwrap_or(0) as u32;
|
|
let completion_tokens = resp_json.get("usage").and_then(|u| u.get("completion_tokens")).and_then(|v| v.as_u64()).unwrap_or(0) as u32;
|
|
let total_tokens = resp_json.get("usage").and_then(|u| u.get("total_tokens")).and_then(|v| v.as_u64()).unwrap_or(0) as u32;
|
|
|
|
// GPT-5.4 parallel tool calls might be embedded in content_text as a JSON block
|
|
let embedded_calls = Self::parse_tool_uses_json(&content_text);
|
|
if !embedded_calls.is_empty() {
|
|
// Strip the JSON part from content_text to keep it clean
|
|
if let Some(start) = content_text.find("{\"tool_uses\":") {
|
|
content_text = content_text[..start].to_string();
|
|
}
|
|
tool_calls.extend(embedded_calls);
|
|
}
|
|
|
|
content_text = Self::strip_internal_metadata(&content_text);
|
|
|
|
Ok(ProviderResponse {
|
|
content: content_text,
|
|
reasoning_content: None,
|
|
tool_calls: if tool_calls.is_empty() { None } else { Some(tool_calls) },
|
|
prompt_tokens,
|
|
completion_tokens,
|
|
reasoning_tokens: 0,
|
|
total_tokens,
|
|
cache_read_tokens: 0,
|
|
cache_write_tokens: 0,
|
|
model: request.model,
|
|
})
|
|
}
|
|
|
|
fn estimate_tokens(&self, request: &UnifiedRequest) -> Result<u32> {
|
|
Ok(crate::utils::tokens::estimate_request_tokens(&request.model, request))
|
|
}
|
|
|
|
fn calculate_cost(
|
|
&self,
|
|
model: &str,
|
|
prompt_tokens: u32,
|
|
completion_tokens: u32,
|
|
cache_read_tokens: u32,
|
|
cache_write_tokens: u32,
|
|
registry: &crate::models::registry::ModelRegistry,
|
|
) -> f64 {
|
|
helpers::calculate_cost_with_registry(
|
|
model,
|
|
prompt_tokens,
|
|
completion_tokens,
|
|
cache_read_tokens,
|
|
cache_write_tokens,
|
|
registry,
|
|
&self.pricing,
|
|
0.15,
|
|
0.60,
|
|
)
|
|
}
|
|
|
|
async fn chat_completion_stream(
|
|
&self,
|
|
request: UnifiedRequest,
|
|
) -> Result<BoxStream<'static, Result<ProviderStreamChunk, AppError>>, AppError> {
|
|
// Allow proactive routing to Responses API based on heuristic
|
|
let model_lc = request.model.to_lowercase();
|
|
if model_lc.contains("gpt-5") || model_lc.contains("codex") {
|
|
return self.chat_responses_stream(request).await;
|
|
}
|
|
|
|
let messages_json = helpers::messages_to_openai_json(&request.messages).await?;
|
|
let mut body = helpers::build_openai_body(&request, messages_json, true);
|
|
|
|
// Standard OpenAI cleanup
|
|
if let Some(obj) = body.as_object_mut() {
|
|
// stream_options.include_usage is supported by OpenAI for token usage in streaming
|
|
// Transition: Newer OpenAI models (o1, o3, gpt-5) require max_completion_tokens
|
|
if request.model.starts_with("o1-") || request.model.starts_with("o3-") || request.model.contains("gpt-5") {
|
|
if let Some(max_tokens) = obj.remove("max_tokens") {
|
|
obj.insert("max_completion_tokens".to_string(), max_tokens);
|
|
}
|
|
}
|
|
}
|
|
|
|
let url = format!("{}/chat/completions", self.config.base_url);
|
|
let api_key = self.api_key.clone();
|
|
let probe_client = self.client.clone();
|
|
let probe_body = body.clone();
|
|
let model = request.model.clone();
|
|
|
|
let es = reqwest_eventsource::EventSource::new(
|
|
self.client
|
|
.post(&url)
|
|
.header("Authorization", format!("Bearer {}", self.api_key))
|
|
.json(&body),
|
|
)
|
|
.map_err(|e| AppError::ProviderError(format!("Failed to create EventSource: {}", e)))?;
|
|
|
|
let stream = async_stream::try_stream! {
|
|
let mut es = es;
|
|
while let Some(event) = es.next().await {
|
|
match event {
|
|
Ok(reqwest_eventsource::Event::Message(msg)) => {
|
|
if msg.data == "[DONE]" {
|
|
break;
|
|
}
|
|
|
|
let chunk: serde_json::Value = serde_json::from_str(&msg.data)
|
|
.map_err(|e| AppError::ProviderError(format!("Failed to parse stream chunk: {}", e)))?;
|
|
|
|
if let Some(p_chunk) = helpers::parse_openai_stream_chunk(&chunk, &model, None) {
|
|
yield p_chunk?;
|
|
}
|
|
}
|
|
Ok(_) => continue,
|
|
Err(reqwest_eventsource::Error::StreamEnded) => break,
|
|
Err(e) => {
|
|
// Attempt to probe for the actual error body
|
|
let probe_resp = probe_client
|
|
.post(&url)
|
|
.header("Authorization", format!("Bearer {}", api_key))
|
|
.json(&probe_body)
|
|
.send()
|
|
.await;
|
|
|
|
match probe_resp {
|
|
Ok(r) if !r.status().is_success() => {
|
|
let status = r.status();
|
|
let error_body = r.text().await.unwrap_or_default();
|
|
tracing::error!("OpenAI Stream Error Probe ({}): {}", status, error_body);
|
|
tracing::debug!("Offending OpenAI Request Body: {}", serde_json::to_string(&probe_body).unwrap_or_default());
|
|
Err(AppError::ProviderError(format!("OpenAI API error ({}): {}", status, error_body)))?;
|
|
}
|
|
Ok(_) => {
|
|
// Probe returned success? This is unexpected if the original stream failed.
|
|
Err(AppError::ProviderError(format!("Stream error (probe returned 200): {}", e)))?;
|
|
}
|
|
Err(probe_err) => {
|
|
// Probe itself failed
|
|
tracing::error!("OpenAI Stream Error Probe failed: {}", probe_err);
|
|
Err(AppError::ProviderError(format!("Stream error (probe failed: {}): {}", probe_err, e)))?;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
Ok(Box::pin(stream))
|
|
}
|
|
|
|
async fn chat_responses_stream(
|
|
&self,
|
|
request: UnifiedRequest,
|
|
) -> Result<BoxStream<'static, Result<ProviderStreamChunk, AppError>>, AppError> {
|
|
// Build a structured input for the Responses API.
|
|
let messages_json = helpers::messages_to_openai_json(&request.messages).await?;
|
|
let mut input_parts = Vec::new();
|
|
for m in &messages_json {
|
|
let role = m["role"].as_str().unwrap_or("user");
|
|
|
|
if role == "tool" {
|
|
input_parts.push(serde_json::json!({
|
|
"type": "function_call_output",
|
|
"call_id": m.get("tool_call_id").and_then(|v| v.as_str()).unwrap_or(""),
|
|
"output": m.get("content").and_then(|v| v.as_str()).unwrap_or("")
|
|
}));
|
|
continue;
|
|
}
|
|
|
|
if role == "assistant" && m.get("tool_calls").is_some() {
|
|
// Push message part if it exists
|
|
let content_val = m.get("content").cloned().unwrap_or(serde_json::json!(""));
|
|
if !content_val.is_null() && (content_val.is_array() && !content_val.as_array().unwrap().is_empty() || content_val.is_string() && !content_val.as_str().unwrap().is_empty()) {
|
|
let mut content = content_val.clone();
|
|
if let Some(text) = content.as_str() {
|
|
content = serde_json::json!([{ "type": "output_text", "text": text }]);
|
|
} else if let Some(arr) = content.as_array_mut() {
|
|
for part in arr {
|
|
if let Some(obj) = part.as_object_mut() {
|
|
if obj.get("type").and_then(|v| v.as_str()) == Some("text") {
|
|
obj.insert("type".to_string(), serde_json::json!("output_text"));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
input_parts.push(serde_json::json!({
|
|
"type": "message",
|
|
"role": "assistant",
|
|
"content": content
|
|
}));
|
|
}
|
|
|
|
// Push tool calls as separate items
|
|
if let Some(tcs) = m.get("tool_calls").and_then(|v| v.as_array()) {
|
|
for tc in tcs {
|
|
input_parts.push(serde_json::json!({
|
|
"type": "function_call",
|
|
"call_id": tc["id"],
|
|
"name": tc["function"]["name"],
|
|
"arguments": tc["function"]["arguments"]
|
|
}));
|
|
}
|
|
}
|
|
continue;
|
|
}
|
|
|
|
let mut mapped_role = role.to_string();
|
|
if mapped_role == "system" {
|
|
mapped_role = "developer".to_string();
|
|
}
|
|
|
|
let mut content = m.get("content").cloned().unwrap_or(serde_json::json!([]));
|
|
|
|
// Map content types based on role for Responses API
|
|
if let Some(content_array) = content.as_array_mut() {
|
|
for part in content_array {
|
|
if let Some(part_obj) = part.as_object_mut() {
|
|
if let Some(t) = part_obj.get("type").and_then(|v| v.as_str()) {
|
|
match t {
|
|
"text" => {
|
|
let new_type = if mapped_role == "assistant" { "output_text" } else { "input_text" };
|
|
part_obj.insert("type".to_string(), serde_json::json!(new_type));
|
|
}
|
|
"image_url" => {
|
|
let new_type = if mapped_role == "assistant" { "output_image" } else { "input_image" };
|
|
part_obj.insert("type".to_string(), serde_json::json!(new_type));
|
|
if let Some(img_url) = part_obj.remove("image_url") {
|
|
part_obj.insert("image".to_string(), img_url);
|
|
}
|
|
}
|
|
_ => {}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else if let Some(text) = content.as_str() {
|
|
// If it's just a string, send it as a string instead of an array of objects
|
|
// as it's safer for standard conversational messages.
|
|
content = serde_json::json!(text);
|
|
}
|
|
|
|
let mut msg_item = serde_json::json!({
|
|
"type": "message",
|
|
"role": mapped_role,
|
|
"content": content
|
|
});
|
|
if let Some(name) = m.get("name") {
|
|
msg_item["name"] = name.clone();
|
|
}
|
|
input_parts.push(msg_item);
|
|
}
|
|
|
|
let mut body = serde_json::json!({
|
|
"model": request.model,
|
|
"input": input_parts,
|
|
});
|
|
|
|
// Add standard parameters
|
|
if let Some(temp) = request.temperature {
|
|
body["temperature"] = serde_json::json!(temp);
|
|
}
|
|
|
|
// Newer models (gpt-5, o1) in Responses API use max_output_tokens
|
|
if let Some(max_tokens) = request.max_tokens {
|
|
if request.model.contains("gpt-5") || request.model.starts_with("o1-") || request.model.starts_with("o3-") {
|
|
body["max_output_tokens"] = serde_json::json!(max_tokens);
|
|
} else {
|
|
body["max_tokens"] = serde_json::json!(max_tokens);
|
|
}
|
|
}
|
|
|
|
if let Some(tools) = &request.tools {
|
|
let flattened: Vec<serde_json::Value> = tools.iter().map(|t| {
|
|
let mut obj = serde_json::json!({
|
|
"type": t.tool_type,
|
|
"name": t.function.name,
|
|
});
|
|
if let Some(desc) = &t.function.description {
|
|
obj["description"] = serde_json::json!(desc);
|
|
}
|
|
if let Some(params) = &t.function.parameters {
|
|
obj["parameters"] = params.clone();
|
|
}
|
|
obj
|
|
}).collect();
|
|
body["tools"] = serde_json::json!(flattened);
|
|
}
|
|
if let Some(tool_choice) = &request.tool_choice {
|
|
match tool_choice {
|
|
crate::models::ToolChoice::Mode(mode) => {
|
|
body["tool_choice"] = serde_json::json!(mode);
|
|
}
|
|
crate::models::ToolChoice::Specific(specific) => {
|
|
body["tool_choice"] = serde_json::json!({
|
|
"type": specific.choice_type,
|
|
"name": specific.function.name,
|
|
});
|
|
}
|
|
}
|
|
}
|
|
|
|
body["stream"] = serde_json::json!(true);
|
|
let url = format!("{}/responses", self.config.base_url);
|
|
let api_key = self.api_key.clone();
|
|
let model = request.model.clone();
|
|
let probe_client = self.client.clone();
|
|
let probe_body = body.clone();
|
|
|
|
let es = reqwest_eventsource::EventSource::new(
|
|
self.client
|
|
.post(&url)
|
|
.header("Authorization", format!("Bearer {}", api_key))
|
|
.header("Accept", "text/event-stream")
|
|
.json(&body),
|
|
)
|
|
.map_err(|e| AppError::ProviderError(format!("Failed to create EventSource for Responses API: {}", e)))?;
|
|
|
|
let stream = async_stream::try_stream! {
|
|
let mut es = es;
|
|
let mut content_buffer = String::new();
|
|
let mut has_tool_calls = false;
|
|
let mut tool_index_map = std::collections::HashMap::<u32, u32>::new();
|
|
let mut next_tool_index = 0u32;
|
|
|
|
while let Some(event) = es.next().await {
|
|
match event {
|
|
Ok(reqwest_eventsource::Event::Message(msg)) => {
|
|
if msg.data == "[DONE]" {
|
|
break;
|
|
}
|
|
|
|
let chunk: serde_json::Value = serde_json::from_str(&msg.data)
|
|
.map_err(|e| AppError::ProviderError(format!("Failed to parse Responses stream chunk: {}", e)))?;
|
|
|
|
// Try standard OpenAI parsing first (choices/usage)
|
|
if let Some(p_chunk) = helpers::parse_openai_stream_chunk(&chunk, &model, None) {
|
|
yield p_chunk?;
|
|
} else {
|
|
// Responses API specific parsing for streaming
|
|
let mut finish_reason = None;
|
|
let mut tool_calls = None;
|
|
|
|
let event_type = chunk.get("type").and_then(|v| v.as_str()).unwrap_or("");
|
|
|
|
match event_type {
|
|
"response.output_text.delta" => {
|
|
if let Some(delta) = chunk.get("delta").and_then(|v| v.as_str()) {
|
|
content_buffer.push_str(delta);
|
|
}
|
|
}
|
|
"response.output_item.added" => {
|
|
if let Some(item) = chunk.get("item") {
|
|
if item.get("type").and_then(|v| v.as_str()) == Some("function_call") {
|
|
has_tool_calls = true;
|
|
let call_id = item.get("call_id").and_then(|v| v.as_str());
|
|
let name = item.get("name").and_then(|v| v.as_str());
|
|
|
|
let out_idx = chunk.get("output_index").and_then(|v| v.as_u64()).unwrap_or(0) as u32;
|
|
let tc_idx = *tool_index_map.entry(out_idx).or_insert_with(|| {
|
|
let i = next_tool_index;
|
|
next_tool_index += 1;
|
|
i
|
|
});
|
|
|
|
tool_calls = Some(vec![crate::models::ToolCallDelta {
|
|
index: tc_idx,
|
|
id: call_id.map(|s| s.to_string()),
|
|
call_type: Some("function".to_string()),
|
|
function: Some(crate::models::FunctionCallDelta {
|
|
name: name.map(|s| s.to_string()),
|
|
arguments: Some("".to_string()), // Start with empty arguments
|
|
}),
|
|
}]);
|
|
}
|
|
}
|
|
}
|
|
"response.function_call_arguments.delta" => {
|
|
if let Some(delta) = chunk.get("delta").and_then(|v| v.as_str()) {
|
|
has_tool_calls = true;
|
|
|
|
let out_idx = chunk.get("output_index").and_then(|v| v.as_u64()).unwrap_or(0) as u32;
|
|
let tc_idx = *tool_index_map.entry(out_idx).or_insert_with(|| {
|
|
let i = next_tool_index;
|
|
next_tool_index += 1;
|
|
i
|
|
});
|
|
|
|
tool_calls = Some(vec![crate::models::ToolCallDelta {
|
|
index: tc_idx,
|
|
id: None,
|
|
call_type: None,
|
|
function: Some(crate::models::FunctionCallDelta {
|
|
name: None,
|
|
arguments: Some(delta.to_string()),
|
|
}),
|
|
}]);
|
|
}
|
|
}
|
|
"response.completed" => {
|
|
finish_reason = Some(if has_tool_calls { "tool_calls".to_string() } else { "stop".to_string() });
|
|
}
|
|
_ => {}
|
|
}
|
|
|
|
// Process content_buffer to extract embedded tool calls or yield text
|
|
if !content_buffer.is_empty() {
|
|
// If we see the start of a tool call block, we wait for the full block
|
|
if content_buffer.contains("{\"tool_uses\":") {
|
|
let embedded_calls = Self::parse_tool_uses_json(&content_buffer);
|
|
if !embedded_calls.is_empty() {
|
|
has_tool_calls = true;
|
|
if let Some(start) = content_buffer.find("{\"tool_uses\":") {
|
|
// Yield text before the JSON block
|
|
let preamble = content_buffer[..start].to_string();
|
|
let stripped_preamble = Self::strip_internal_metadata(&preamble);
|
|
if !stripped_preamble.is_empty() {
|
|
yield ProviderStreamChunk {
|
|
content: stripped_preamble,
|
|
reasoning_content: None,
|
|
finish_reason: None,
|
|
tool_calls: None,
|
|
model: model.clone(),
|
|
usage: None,
|
|
};
|
|
}
|
|
|
|
// Yield the tool calls in two chunks to mimic standard streaming behavior
|
|
// Chunk 1: Initialization (id, name)
|
|
let init_deltas: Vec<crate::models::ToolCallDelta> = embedded_calls.iter().map(|tc| {
|
|
let tc_idx = next_tool_index;
|
|
next_tool_index += 1;
|
|
crate::models::ToolCallDelta {
|
|
index: tc_idx,
|
|
id: Some(tc.id.clone()),
|
|
call_type: Some("function".to_string()),
|
|
function: Some(crate::models::FunctionCallDelta {
|
|
name: Some(tc.function.name.clone()),
|
|
arguments: Some("".to_string()),
|
|
}),
|
|
}
|
|
}).collect();
|
|
|
|
yield ProviderStreamChunk {
|
|
content: String::new(),
|
|
reasoning_content: None,
|
|
finish_reason: None,
|
|
tool_calls: Some(init_deltas),
|
|
model: model.clone(),
|
|
usage: None,
|
|
};
|
|
|
|
// Chunk 2: Payload (arguments)
|
|
// Reset temp index for payload chunk
|
|
let mut payload_idx = next_tool_index - embedded_calls.len() as u32;
|
|
let arg_deltas: Vec<crate::models::ToolCallDelta> = embedded_calls.into_iter().map(|tc| {
|
|
let tc_idx = payload_idx;
|
|
payload_idx += 1;
|
|
crate::models::ToolCallDelta {
|
|
index: tc_idx,
|
|
id: None,
|
|
call_type: None,
|
|
function: Some(crate::models::FunctionCallDelta {
|
|
name: None,
|
|
arguments: Some(tc.function.arguments),
|
|
}),
|
|
}
|
|
}).collect();
|
|
|
|
yield ProviderStreamChunk {
|
|
content: String::new(),
|
|
reasoning_content: None,
|
|
finish_reason: None,
|
|
tool_calls: Some(arg_deltas),
|
|
model: model.clone(),
|
|
usage: None,
|
|
};
|
|
|
|
// Remove the processed part from buffer
|
|
// We need to find the end index correctly
|
|
let sub = &content_buffer[start..];
|
|
let mut brace_count = 0;
|
|
let mut end_idx = 0;
|
|
for (i, c) in sub.char_indices() {
|
|
if c == '{' { brace_count += 1; }
|
|
else if c == '}' {
|
|
brace_count -= 1;
|
|
if brace_count == 0 {
|
|
end_idx = start + i + 1;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
if end_idx > 0 {
|
|
content_buffer = content_buffer[end_idx..].to_string();
|
|
} else {
|
|
content_buffer.clear();
|
|
}
|
|
}
|
|
}
|
|
// If we have "{"tool_uses":" but no full block yet, we just wait (don't yield)
|
|
} else if content_buffer.contains("to=multi_tool_use.parallel") {
|
|
// Wait for the JSON block that usually follows
|
|
} else {
|
|
// Standard text, yield and clear buffer
|
|
let content = std::mem::take(&mut content_buffer);
|
|
let stripped_content = Self::strip_internal_metadata(&content);
|
|
if !stripped_content.is_empty() {
|
|
yield ProviderStreamChunk {
|
|
content: stripped_content,
|
|
reasoning_content: None,
|
|
finish_reason: None,
|
|
tool_calls: None,
|
|
model: model.clone(),
|
|
usage: None,
|
|
};
|
|
}
|
|
}
|
|
}
|
|
|
|
if finish_reason.is_some() || tool_calls.is_some() {
|
|
yield ProviderStreamChunk {
|
|
content: String::new(),
|
|
reasoning_content: None,
|
|
finish_reason,
|
|
tool_calls,
|
|
model: model.clone(),
|
|
usage: None,
|
|
};
|
|
}
|
|
}
|
|
}
|
|
Ok(_) => continue,
|
|
Err(reqwest_eventsource::Error::StreamEnded) => break,
|
|
Err(e) => {
|
|
tracing::error!("Responses stream encountered an error: {}", e);
|
|
// Attempt to probe for the actual error body
|
|
let mut probe_body_no_stream = probe_body.clone();
|
|
if let Some(obj) = probe_body_no_stream.as_object_mut() {
|
|
obj.remove("stream");
|
|
}
|
|
|
|
let probe_resp = probe_client
|
|
.post(&url)
|
|
.header("Authorization", format!("Bearer {}", api_key))
|
|
.header("Accept", "application/json")
|
|
.json(&probe_body_no_stream)
|
|
.send()
|
|
.await;
|
|
|
|
match probe_resp {
|
|
Ok(r) => {
|
|
let status = r.status();
|
|
let body = r.text().await.unwrap_or_default();
|
|
if status.is_success() {
|
|
let preview = if body.len() > 500 { format!("{}...", &body[..500]) } else { body.clone() };
|
|
tracing::warn!("Responses stream ended prematurely but probe returned 200 OK. Body: {}", preview);
|
|
Err(AppError::ProviderError(format!("Responses stream ended (server sent 200 OK with body: {})", preview)))?;
|
|
} else {
|
|
tracing::error!("OpenAI Responses Stream Error Probe ({}): {}", status, body);
|
|
Err(AppError::ProviderError(format!("OpenAI Responses API error ({}): {}", status, body)))?;
|
|
}
|
|
}
|
|
Err(probe_err) => {
|
|
tracing::error!("OpenAI Responses Stream Error Probe failed: {}", probe_err);
|
|
Err(AppError::ProviderError(format!("Responses stream error (probe failed: {}): {}", probe_err, e)))?;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Final flush of content_buffer if not empty
|
|
if !content_buffer.is_empty() {
|
|
let stripped = Self::strip_internal_metadata(&content_buffer);
|
|
if !stripped.is_empty() {
|
|
yield ProviderStreamChunk {
|
|
content: stripped,
|
|
reasoning_content: None,
|
|
finish_reason: None,
|
|
tool_calls: None,
|
|
model: model.clone(),
|
|
usage: None,
|
|
};
|
|
}
|
|
}
|
|
};
|
|
|
|
Ok(Box::pin(stream))
|
|
}
|
|
}
|