use serde::Deserialize;
use serde_json::json;
use crate::model::{ContentBlock, Message, ModelError, ModelResponse};
use crate::tool::{ToolCall, ToolSpec};
use super::types::{
ChatResponse, OpenAiFunctionCall, OpenAiMessage, OpenAiTool, OpenAiToolCall, OpenAiToolFunction,
};
pub(crate) fn convert_openai_response(response: ChatResponse) -> Result<ModelResponse, ModelError> {
let message = response
.choices
.into_iter()
.next()
.ok_or_else(|| ModelError::InvalidResponse("missing choices".into()))?
.message;
let mut blocks = Vec::new();
if let Some(content) = message.content.filter(|s| !s.is_empty()) {
blocks.push(ContentBlock::Text(content));
}
for call in message.tool_calls.unwrap_or_default() {
let arguments = serde_json::from_str(&call.function.arguments).map_err(|e| {
ModelError::InvalidResponse(format!(
"invalid tool call arguments for {}: {e}",
call.function.name
))
})?;
blocks.push(ContentBlock::ToolCall(ToolCall {
id: call.id,
name: call.function.name,
arguments,
}));
}
if blocks.is_empty() {
Err(ModelError::InvalidResponse(
"assistant message had no content or tool calls".into(),
))
} else {
Ok(ModelResponse::Assistant(blocks))
}
}
pub(crate) fn codex_reasoning_param(
effort: Option<&str>,
summary: Option<&str>,
) -> Option<serde_json::Value> {
let effort = effort.filter(|value| !value.eq_ignore_ascii_case("none"));
let summary = summary.filter(|value| !value.eq_ignore_ascii_case("none"));
if effort.is_none() && summary.is_none() {
return None;
}
let mut reasoning = serde_json::Map::new();
if let Some(effort) = effort {
reasoning.insert("effort".into(), json!(effort));
}
if let Some(summary) = summary {
reasoning.insert("summary".into(), json!(summary));
}
Some(serde_json::Value::Object(reasoning))
}
pub(crate) fn to_openai_tool(tool: ToolSpec) -> OpenAiTool {
OpenAiTool {
kind: "function",
function: OpenAiToolFunction {
name: tool.name,
description: tool.description,
parameters: tool.input_schema,
strict: false,
},
}
}
pub(crate) fn to_responses_tool(tool: ToolSpec) -> serde_json::Value {
if tool.name == "web_search" {
return json!({
"type": "web_search",
"external_web_access": true,
});
}
json!({
"type": "function",
"name": tool.name,
"description": tool.description,
"parameters": tool.input_schema,
"strict": false,
})
}
pub(crate) fn codex_input_items(
messages: Vec<Message>,
instructions: &mut Vec<String>,
) -> Result<Vec<serde_json::Value>, ModelError> {
let mut input = Vec::new();
for message in messages {
match message {
Message::System(content) => instructions.push(content),
Message::User(blocks) => input.push(json!({
"role": "user",
"content": codex_content_blocks(&blocks),
})),
Message::Assistant(blocks) => {
let text = blocks
.iter()
.filter_map(|block| match block {
ContentBlock::Text(text) => Some(text.as_str()),
ContentBlock::ToolCall(_) | ContentBlock::Image(_) => None,
})
.collect::<Vec<_>>()
.join("\n");
if !text.is_empty() {
input.push(json!({ "role": "assistant", "content": text }));
}
for block in blocks {
if let ContentBlock::ToolCall(call) = block {
input.push(json!({
"type": "function_call",
"call_id": call.id,
"name": call.name,
"arguments": serde_json::to_string(&call.arguments).map_err(|e| ModelError::InvalidResponse(format!("invalid tool call arguments: {e}")))?,
}));
}
}
}
Message::ToolResult(result) => input.push(json!({
"type": "function_call_output",
"call_id": result.id,
"output": result.content,
})),
}
}
Ok(input)
}
pub(crate) fn to_openai_message(message: Message) -> Result<OpenAiMessage, ModelError> {
match message {
Message::System(content) => Ok(openai_text_message("system", content)),
Message::User(blocks) => Ok(OpenAiMessage {
role: "user".into(),
content: Some(chat_content_blocks(&blocks)),
tool_calls: None,
tool_call_id: None,
}),
Message::Assistant(blocks) => {
let content = blocks
.iter()
.filter_map(|b| match b {
ContentBlock::Text(text) => Some(text.as_str()),
ContentBlock::ToolCall(_) | ContentBlock::Image(_) => None,
})
.collect::<Vec<_>>()
.join("\n");
let tool_calls = blocks
.into_iter()
.filter_map(|b| match b {
ContentBlock::ToolCall(call) => Some(tool_call_to_openai(call)),
ContentBlock::Text(_) | ContentBlock::Image(_) => None,
})
.collect::<Result<Vec<_>, _>>()?;
Ok(OpenAiMessage {
role: "assistant".into(),
content: if content.is_empty() {
None
} else {
Some(json!(content))
},
tool_calls: if tool_calls.is_empty() {
None
} else {
Some(tool_calls)
},
tool_call_id: None,
})
}
Message::ToolResult(result) => Ok(OpenAiMessage {
role: "tool".into(),
content: Some(json!(result.content)),
tool_calls: None,
tool_call_id: Some(result.id),
}),
}
}
fn openai_text_message(role: &str, content: String) -> OpenAiMessage {
OpenAiMessage {
role: role.into(),
content: Some(json!(content)),
tool_calls: None,
tool_call_id: None,
}
}
fn tool_call_to_openai(call: ToolCall) -> Result<OpenAiToolCall, ModelError> {
let arguments = serde_json::to_string(&call.arguments)
.map_err(|e| ModelError::InvalidResponse(format!("invalid tool call arguments: {e}")))?;
Ok(OpenAiToolCall {
id: call.id,
kind: "function".into(),
function: OpenAiFunctionCall {
name: call.name,
arguments,
},
})
}
fn chat_content_blocks(blocks: &[ContentBlock]) -> serde_json::Value {
let content = blocks
.iter()
.map(|block| match block {
ContentBlock::Text(text) => json!({ "type": "text", "text": text }),
ContentBlock::Image(image) => json!({
"type": "image_url",
"image_url": { "url": format!("data:{};base64,{}", image.mime_type, image.data) },
}),
ContentBlock::ToolCall(call) => {
json!({ "type": "text", "text": render_tool_call(call) })
}
})
.collect::<Vec<_>>();
json!(content)
}
fn codex_content_blocks(blocks: &[ContentBlock]) -> serde_json::Value {
let content = blocks
.iter()
.map(|block| match block {
ContentBlock::Text(text) => json!({ "type": "input_text", "text": text }),
ContentBlock::Image(image) => json!({
"type": "input_image",
"image_url": format!("data:{};base64,{}", image.mime_type, image.data),
}),
ContentBlock::ToolCall(call) => {
json!({ "type": "input_text", "text": render_tool_call(call) })
}
})
.collect::<Vec<_>>();
json!(content)
}
fn render_tool_call(call: &ToolCall) -> String {
let arguments = serde_json::to_string_pretty(&call.arguments)
.unwrap_or_else(|_| call.arguments.to_string());
format!("Tool call: {}\n{}", call.name, arguments)
}
#[derive(Deserialize)]
pub(crate) struct ResponsesResponse {
output_text: Option<String>,
output: Option<Vec<ResponseOutput>>,
}
#[derive(Deserialize)]
struct ResponseOutput {
content: Option<Vec<ResponseContent>>,
}
#[derive(Deserialize)]
struct ResponseContent {
text: Option<String>,
annotations: Option<Vec<ResponseAnnotation>>,
}
#[derive(Deserialize)]
struct ResponseAnnotation {
#[serde(rename = "type")]
kind: Option<String>,
title: Option<String>,
url: Option<String>,
}
pub(crate) fn extract_response_text(response: ResponsesResponse) -> Result<String, ModelError> {
let mut content_texts = Vec::new();
let mut citations = Vec::new();
for content in response
.output
.unwrap_or_default()
.into_iter()
.flat_map(|o| o.content.unwrap_or_default())
{
if let Some(text) = content.text.filter(|text| !text.is_empty()) {
content_texts.push(text);
}
for annotation in content.annotations.unwrap_or_default() {
if annotation.kind.as_deref() == Some("url_citation") {
if let Some(url) = annotation.url.filter(|url| !url.trim().is_empty()) {
citations.push((annotation.title, url));
}
}
}
}
let mut text = response
.output_text
.filter(|text| !text.is_empty())
.unwrap_or_else(|| content_texts.join("\n"));
if text.is_empty() {
return Err(ModelError::InvalidResponse("missing response text".into()));
}
append_response_citations(&mut text, citations);
Ok(text)
}
fn append_response_citations(text: &mut String, citations: Vec<(Option<String>, String)>) {
let mut seen = std::collections::HashSet::new();
let citations = citations
.into_iter()
.filter(|(_, url)| seen.insert(url.clone()))
.collect::<Vec<_>>();
if citations.is_empty() {
return;
}
text.push_str("\n\nSources:");
for (title, url) in citations {
let title = title
.filter(|title| !title.trim().is_empty())
.unwrap_or_else(|| url.clone());
text.push_str(&format!("\n- {title}: {url}"));
}
}