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fix(openai): map content types for Responses API (v1/responses)
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This commit is contained in:
Dustin Newkirk
2026-03-17 18:18:23 +00:00
parent cc5eba1957
commit 5a9086b883
1 changed files with 62 additions and 93 deletions
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155
src/providers/openai.rs
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@@ -92,97 +92,7 @@ impl super::Provider for OpenAIProvider {
// Read error body to diagnose. If the model requires the Responses // Read error body to diagnose. If the model requires the Responses
// API (v1/responses), retry against that endpoint. // 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") { if error_text.to_lowercase().contains("v1/responses") || error_text.to_lowercase().contains("only supported in v1/responses") {
// Build a simple `input` string by concatenating message parts. return self.chat_responses(request).await;
let messages_json = helpers::messages_to_openai_json(&request.messages).await?;
let mut inputs: Vec<String> = Vec::new();
for m in &messages_json {
let role = m["role"].as_str().unwrap_or("");
let parts = m.get("content").and_then(|c| c.as_array()).cloned().unwrap_or_default();
let mut text_parts = Vec::new();
for p in parts {
if let Some(t) = p.get("text").and_then(|v| v.as_str()) {
text_parts.push(t.to_string());
}
}
inputs.push(format!("{}: {}", role, text_parts.join("")));
}
let input_text = inputs.join("\n");
let resp = self
.client
.post(format!("{}/responses", self.config.base_url))
.header("Authorization", format!("Bearer {}", self.api_key))
.json(&serde_json::json!({ "model": request.model, "input": input_text }))
.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);
}
// Responses API: try to extract text from `output` or `candidates`
let mut content_text = String::new();
if let Some(output) = resp_json.get("output").and_then(|o| o.as_array()) {
if let Some(first) = output.get(0) {
if let Some(contents) = first.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);
} else if let Some(parts) = item.get("parts").and_then(|p| p.as_array()) {
for p in parts {
if let Some(t) = p.as_str() {
if !content_text.is_empty() { content_text.push_str("\n"); }
content_text.push_str(t);
}
}
}
}
}
}
}
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;
return Ok(ProviderResponse {
content: content_text,
reasoning_content: None,
tool_calls: None,
prompt_tokens,
completion_tokens,
reasoning_tokens: 0,
total_tokens,
cache_read_tokens: 0,
cache_write_tokens: 0,
model: request.model,
});
} }
tracing::error!("OpenAI API error ({}): {}", status, error_text); tracing::error!("OpenAI API error ({}): {}", status, error_text);
@@ -203,8 +113,32 @@ impl super::Provider for OpenAIProvider {
let mut input_parts = Vec::new(); let mut input_parts = Vec::new();
for m in &messages_json { for m in &messages_json {
let role = m["role"].as_str().unwrap_or("user"); let role = m["role"].as_str().unwrap_or("user");
let content = m.get("content").cloned().unwrap_or(serde_json::json!("")); let mut content = m.get("content").cloned().unwrap_or(serde_json::json!([]));
// Map "text" -> "input_text" and "image_url" -> "input_image" 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" => {
part_obj.insert("type".to_string(), serde_json::json!("input_text"));
}
"image_url" => {
part_obj.insert("type".to_string(), serde_json::json!("input_image"));
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() {
content = serde_json::json!([{ "type": "input_text", "text": text }]);
}
input_parts.push(serde_json::json!({ input_parts.push(serde_json::json!({
"role": role, "role": role,
"content": content "content": content
@@ -227,6 +161,11 @@ impl super::Provider for OpenAIProvider {
let resp_json: serde_json::Value = resp.json().await.map_err(|e| AppError::ProviderError(e.to_string()))?; 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 // Normalize Responses API output into ProviderResponse
let mut content_text = String::new(); let mut content_text = String::new();
if let Some(output) = resp_json.get("output").and_then(|o| o.as_array()) { if let Some(output) = resp_json.get("output").and_then(|o| o.as_array()) {
@@ -314,6 +253,12 @@ impl super::Provider for OpenAIProvider {
&self, &self,
request: UnifiedRequest, request: UnifiedRequest,
) -> Result<BoxStream<'static, Result<ProviderStreamChunk, AppError>>, AppError> { ) -> 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 messages_json = helpers::messages_to_openai_json(&request.messages).await?;
let mut body = helpers::build_openai_body(&request, messages_json, true); let mut body = helpers::build_openai_body(&request, messages_json, true);
@@ -403,8 +348,32 @@ impl super::Provider for OpenAIProvider {
let mut input_parts = Vec::new(); let mut input_parts = Vec::new();
for m in &messages_json { for m in &messages_json {
let role = m["role"].as_str().unwrap_or("user"); let role = m["role"].as_str().unwrap_or("user");
let content = m.get("content").cloned().unwrap_or(serde_json::json!("")); let mut content = m.get("content").cloned().unwrap_or(serde_json::json!([]));
// Map "text" -> "input_text" and "image_url" -> "input_image" 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" => {
part_obj.insert("type".to_string(), serde_json::json!("input_text"));
}
"image_url" => {
part_obj.insert("type".to_string(), serde_json::json!("input_image"));
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() {
content = serde_json::json!([{ "type": "input_text", "text": text }]);
}
input_parts.push(serde_json::json!({ input_parts.push(serde_json::json!({
"role": role, "role": role,
"content": content "content": content