feat(openai): implement tool support for gpt-5.4 via Responses API
- Implement polymorphic 'input' structure for /responses endpoint - Map 'tool' role to 'function_call_output' items - Handle assistant 'tool_calls' as separate 'function_call' items - Add synchronous and streaming parsers for function_call items - Fix 400 Bad Request 'Invalid value: tool' error
This commit is contained in:
@@ -112,10 +112,57 @@ impl super::Provider for OpenAIProvider {
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let messages_json = helpers::messages_to_openai_json(&request.messages).await?;
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let mut input_parts = Vec::new();
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for m in &messages_json {
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let mut role = m["role"].as_str().unwrap_or("user").to_string();
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// Newer models (gpt-5, o1) prefer "developer" over "system"
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if role == "system" {
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role = "developer".to_string();
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let role = m["role"].as_str().unwrap_or("user");
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if role == "tool" {
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input_parts.push(serde_json::json!({
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"type": "function_call_output",
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"call_id": m.get("tool_call_id").and_then(|v| v.as_str()).unwrap_or(""),
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"output": m.get("content").and_then(|v| v.as_str()).unwrap_or("")
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}));
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continue;
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}
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if role == "assistant" && m.get("tool_calls").is_some() {
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// Push message part if it exists
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let content_val = m.get("content").cloned().unwrap_or(serde_json::json!(""));
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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()) {
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let mut content = content_val.clone();
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if let Some(text) = content.as_str() {
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content = serde_json::json!([{ "type": "output_text", "text": text }]);
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} else if let Some(arr) = content.as_array_mut() {
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for part in arr {
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if let Some(obj) = part.as_object_mut() {
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if obj.get("type").and_then(|v| v.as_str()) == Some("text") {
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obj.insert("type".to_string(), serde_json::json!("output_text"));
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}
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}
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}
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}
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input_parts.push(serde_json::json!({
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"type": "message",
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"role": "assistant",
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"content": content
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}));
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}
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// Push tool calls as separate items
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if let Some(tcs) = m.get("tool_calls").and_then(|v| v.as_array()) {
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for tc in tcs {
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input_parts.push(serde_json::json!({
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"type": "function_call",
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"call_id": tc["id"],
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"name": tc["function"]["name"],
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"arguments": tc["function"]["arguments"]
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}));
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}
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}
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continue;
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}
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let mut mapped_role = role.to_string();
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if mapped_role == "system" {
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mapped_role = "developer".to_string();
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}
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let mut content = m.get("content").cloned().unwrap_or(serde_json::json!([]));
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@@ -127,12 +174,11 @@ impl super::Provider for OpenAIProvider {
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if let Some(t) = part_obj.get("type").and_then(|v| v.as_str()) {
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match t {
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"text" => {
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let new_type = if role == "assistant" { "output_text" } else { "input_text" };
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let new_type = if mapped_role == "assistant" { "output_text" } else { "input_text" };
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part_obj.insert("type".to_string(), serde_json::json!(new_type));
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}
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"image_url" => {
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// Assistant typically doesn't have image_url in history this way, but for safety:
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let new_type = if role == "assistant" { "output_image" } else { "input_image" };
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let new_type = if mapped_role == "assistant" { "output_image" } else { "input_image" };
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part_obj.insert("type".to_string(), serde_json::json!(new_type));
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if let Some(img_url) = part_obj.remove("image_url") {
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part_obj.insert("image".to_string(), img_url);
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@@ -144,12 +190,13 @@ impl super::Provider for OpenAIProvider {
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}
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}
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} else if let Some(text) = content.as_str() {
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let new_type = if role == "assistant" { "output_text" } else { "input_text" };
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let new_type = if mapped_role == "assistant" { "output_text" } else { "input_text" };
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content = serde_json::json!([{ "type": new_type, "text": text }]);
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}
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input_parts.push(serde_json::json!({
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"role": role,
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"type": "message",
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"role": mapped_role,
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"content": content
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}));
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}
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@@ -200,18 +247,43 @@ impl super::Provider for OpenAIProvider {
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// Normalize Responses API output into ProviderResponse
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let mut content_text = String::new();
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let mut tool_calls = Vec::new();
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if let Some(output) = resp_json.get("output").and_then(|o| o.as_array()) {
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for out in output {
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if let Some(contents) = out.get("content").and_then(|c| c.as_array()) {
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for item in contents {
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if let Some(text) = item.get("text").and_then(|t| t.as_str()) {
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if !content_text.is_empty() { content_text.push_str("\n"); }
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content_text.push_str(text);
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} else if let Some(parts) = item.get("parts").and_then(|p| p.as_array()) {
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for p in parts {
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if let Some(t) = p.as_str() {
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let item_type = out.get("type").and_then(|v| v.as_str()).unwrap_or("");
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match item_type {
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"message" => {
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if let Some(contents) = out.get("content").and_then(|c| c.as_array()) {
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for item in contents {
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if let Some(text) = item.get("text").and_then(|t| t.as_str()) {
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if !content_text.is_empty() { content_text.push_str("\n"); }
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content_text.push_str(t);
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content_text.push_str(text);
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}
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}
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}
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}
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"function_call" => {
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let id = out.get("call_id")
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.or_else(|| out.get("item_id"))
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.or_else(|| out.get("id"))
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.and_then(|v| v.as_str())
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.unwrap_or("")
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.to_string();
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let name = out.get("name").and_then(|v| v.as_str()).unwrap_or("").to_string();
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let arguments = out.get("arguments").and_then(|v| v.as_str()).unwrap_or("").to_string();
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tool_calls.push(crate::models::ToolCall {
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id,
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call_type: "function".to_string(),
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function: crate::models::FunctionCall { name, arguments },
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});
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}
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_ => {
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// Fallback for older/nested structure
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if let Some(contents) = out.get("content").and_then(|c| c.as_array()) {
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for item in contents {
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if let Some(text) = item.get("text").and_then(|t| t.as_str()) {
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if !content_text.is_empty() { content_text.push_str("\n"); }
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content_text.push_str(text);
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}
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}
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}
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@@ -244,7 +316,7 @@ impl super::Provider for OpenAIProvider {
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Ok(ProviderResponse {
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content: content_text,
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reasoning_content: None,
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tool_calls: None,
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tool_calls: if tool_calls.is_empty() { None } else { Some(tool_calls) },
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prompt_tokens,
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completion_tokens,
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reasoning_tokens: 0,
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@@ -379,10 +451,57 @@ impl super::Provider for OpenAIProvider {
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let messages_json = helpers::messages_to_openai_json(&request.messages).await?;
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let mut input_parts = Vec::new();
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for m in &messages_json {
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let mut role = m["role"].as_str().unwrap_or("user").to_string();
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// Newer models (gpt-5, o1) prefer "developer" over "system"
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if role == "system" {
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role = "developer".to_string();
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let role = m["role"].as_str().unwrap_or("user");
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if role == "tool" {
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input_parts.push(serde_json::json!({
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"type": "function_call_output",
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"call_id": m.get("tool_call_id").and_then(|v| v.as_str()).unwrap_or(""),
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"output": m.get("content").and_then(|v| v.as_str()).unwrap_or("")
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}));
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continue;
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}
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if role == "assistant" && m.get("tool_calls").is_some() {
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// Push message part if it exists
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let content_val = m.get("content").cloned().unwrap_or(serde_json::json!(""));
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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()) {
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let mut content = content_val.clone();
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if let Some(text) = content.as_str() {
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content = serde_json::json!([{ "type": "output_text", "text": text }]);
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} else if let Some(arr) = content.as_array_mut() {
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for part in arr {
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if let Some(obj) = part.as_object_mut() {
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if obj.get("type").and_then(|v| v.as_str()) == Some("text") {
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obj.insert("type".to_string(), serde_json::json!("output_text"));
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}
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}
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}
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}
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input_parts.push(serde_json::json!({
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"type": "message",
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"role": "assistant",
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"content": content
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}));
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}
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// Push tool calls as separate items
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if let Some(tcs) = m.get("tool_calls").and_then(|v| v.as_array()) {
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for tc in tcs {
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input_parts.push(serde_json::json!({
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"type": "function_call",
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"call_id": tc["id"],
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"name": tc["function"]["name"],
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"arguments": tc["function"]["arguments"]
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}));
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}
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}
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continue;
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}
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let mut mapped_role = role.to_string();
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if mapped_role == "system" {
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mapped_role = "developer".to_string();
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}
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let mut content = m.get("content").cloned().unwrap_or(serde_json::json!([]));
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@@ -394,12 +513,11 @@ impl super::Provider for OpenAIProvider {
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if let Some(t) = part_obj.get("type").and_then(|v| v.as_str()) {
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match t {
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"text" => {
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let new_type = if role == "assistant" { "output_text" } else { "input_text" };
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let new_type = if mapped_role == "assistant" { "output_text" } else { "input_text" };
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part_obj.insert("type".to_string(), serde_json::json!(new_type));
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}
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"image_url" => {
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// Assistant typically doesn't have image_url in history this way, but for safety:
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let new_type = if role == "assistant" { "output_image" } else { "input_image" };
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let new_type = if mapped_role == "assistant" { "output_image" } else { "input_image" };
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part_obj.insert("type".to_string(), serde_json::json!(new_type));
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if let Some(img_url) = part_obj.remove("image_url") {
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part_obj.insert("image".to_string(), img_url);
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@@ -411,12 +529,13 @@ impl super::Provider for OpenAIProvider {
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}
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}
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} else if let Some(text) = content.as_str() {
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let new_type = if role == "assistant" { "output_text" } else { "input_text" };
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let new_type = if mapped_role == "assistant" { "output_text" } else { "input_text" };
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content = serde_json::json!([{ "type": new_type, "text": text }]);
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}
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input_parts.push(serde_json::json!({
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"role": role,
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"type": "message",
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"role": mapped_role,
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"content": content
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}));
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}
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@@ -475,6 +594,7 @@ impl super::Provider for OpenAIProvider {
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// Responses API specific parsing for streaming
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let mut content = String::new();
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let mut finish_reason = None;
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let mut tool_calls = None;
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let event_type = chunk.get("type").and_then(|v| v.as_str()).unwrap_or("");
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@@ -484,15 +604,35 @@ impl super::Provider for OpenAIProvider {
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content.push_str(delta);
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}
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}
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"response.output_text.done" => {
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if let Some(text) = chunk.get("text").and_then(|v| v.as_str()) {
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// Some implementations send the full text at the end
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// We usually prefer deltas, but if we haven't seen them, this is the fallback.
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// However, if we're already yielding deltas, we might not want this.
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// For now, let's just use it as a signal that we're done.
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finish_reason = Some("stop".to_string());
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"response.item.delta" => {
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if let Some(delta) = chunk.get("delta") {
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let t = delta.get("type").and_then(|v| v.as_str()).unwrap_or("");
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if t == "function_call" {
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let call_id = delta.get("call_id")
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.or_else(|| chunk.get("item_id"))
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.and_then(|v| v.as_str());
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let name = delta.get("name").and_then(|v| v.as_str());
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let arguments = delta.get("arguments").and_then(|v| v.as_str());
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tool_calls = Some(vec![crate::models::ToolCallDelta {
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index: chunk.get("output_index").and_then(|v| v.as_u64()).unwrap_or(0) as u32,
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id: call_id.map(|s| s.to_string()),
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call_type: Some("function".to_string()),
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function: Some(crate::models::FunctionCallDelta {
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name: name.map(|s| s.to_string()),
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arguments: arguments.map(|s| s.to_string()),
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}),
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}]);
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} else if t == "message" {
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if let Some(text) = delta.get("text").and_then(|v| v.as_str()) {
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content.push_str(text);
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}
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}
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}
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}
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"response.output_text.done" | "response.item.done" => {
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finish_reason = Some("stop".to_string());
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}
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"response.done" => {
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finish_reason = Some("stop".to_string());
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}
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@@ -514,12 +654,12 @@ impl super::Provider for OpenAIProvider {
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}
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}
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if !content.is_empty() || finish_reason.is_some() {
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if !content.is_empty() || finish_reason.is_some() || tool_calls.is_some() {
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yield ProviderStreamChunk {
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content,
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reasoning_content: None,
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finish_reason,
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tool_calls: None,
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tool_calls,
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model: model.clone(),
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usage: None,
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};
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