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fix: chat completions API now also passes tools along

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This commit is contained in:
Michael Bolin
2025-05-30 13:13:11 -07:00
parent e207f20f64
commit ca2c197a85
5 changed files with 411 additions and 158 deletions
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231
codex-rs/core/src/chat_completions.rs
View File

@@ -25,10 +25,10 @@ use crate::flags::OPENAI_REQUEST_MAX_RETRIES;
use crate::flags::OPENAI_STREAM_IDLE_TIMEOUT_MS;
use crate::models::ContentItem;
use crate::models::ResponseItem;
use crate::openai_tools::create_tools_json;
use crate::util::backoff;
/// Implementation for the classic Chat Completions API. This is intentionally
/// minimal: we only stream back plain assistant text.
/// Implementation for the classic Chat Completions API.
pub(crate) async fn stream_chat_completions(
prompt: &Prompt,
model: &str,
@@ -42,31 +42,111 @@ pub(crate) async fn stream_chat_completions(
messages.push(json!({"role": "system", "content": full_instructions}));
for item in &prompt.input {
if let ResponseItem::Message { role, content } = item {
let mut text = String::new();
for c in content {
match c {
ContentItem::InputText { text: t } | ContentItem::OutputText { text: t } => {
text.push_str(t);
match item {
ResponseItem::Message { role, content } => {
let mut text = String::new();
for c in content {
match c {
ContentItem::InputText { text: t }
| ContentItem::OutputText { text: t } => {
text.push_str(t);
}
_ => {}
}
_ => {}
}
messages.push(json!({"role": role, "content": text}));
}
ResponseItem::FunctionCall {
name,
arguments,
call_id,
} => {
messages.push(json!({
"role": "assistant",
"content": null,
"tool_calls": [{
"id": call_id,
"type": "function",
"function": {
"name": name,
"arguments": arguments,
}
}]
}));
}
ResponseItem::LocalShellCall {
id,
call_id: _,
status,
action,
} => {
// Confirm with API team.
messages.push(json!({
"role": "assistant",
"content": null,
"tool_calls": [{
"id": id.clone().unwrap_or_else(|| "".to_string()),
"type": "local_shell_call",
"status": status,
"action": action,
}]
}));
}
ResponseItem::FunctionCallOutput { call_id, output } => {
messages.push(json!({
"role": "tool",
"tool_call_id": call_id,
"content": output.content,
}));
}
ResponseItem::Reasoning { .. } | ResponseItem::Other => {
// Omit these items from the conversation history.
continue;
}
messages.push(json!({"role": role, "content": text}));
}
}
let tools_json = create_tools_json(prompt, model)?;
// create_tools_json() returns JSON values that are compatible with
// Function Calling in the Responses API:
// https://platform.openai.com/docs/guides/function-calling?api-mode=responses
// So we must rewrite "tools" to match the chat completions tool call format:
// https://platform.openai.com/docs/guides/function-calling?api-mode=chat
let tools_json = tools_json
.into_iter()
.filter_map(|mut tool| {
if tool.get("type") != Some(&serde_json::Value::String("function".to_string())) {
return None;
}
if let Some(map) = tool.as_object_mut() {
// Remove "type" field as it is not needed in chat completions.
map.remove("type");
Some(json!({
"type": "function",
"function": map,
}))
} else {
None
}
})
.collect::<Vec<serde_json::Value>>();
let payload = json!({
"model": model,
"messages": messages,
"stream": true
"stream": true,
"tools": tools_json,
});
let base_url = provider.base_url.trim_end_matches('/');
let url = format!("{}/chat/completions", base_url);
debug!(url, "POST (chat)");
trace!("request payload: {}", payload);
trace!(
"request payload: {}",
serde_json::to_string_pretty(&payload).unwrap_or_default()
);
let api_key = provider.api_key()?;
let mut attempt = 0;
@@ -134,6 +214,21 @@ where
let idle_timeout = *OPENAI_STREAM_IDLE_TIMEOUT_MS;
// State to accumulate a function call across streaming chunks.
// OpenAI may split the `arguments` string over multiple `delta` events
// until the chunk whose `finish_reason` is `tool_calls` is emitted. We
// keep collecting the pieces here and forward a single
// `ResponseItem::FunctionCall` once the call is complete.
#[derive(Default)]
struct FunctionCallState {
name: Option<String>,
arguments: String,
call_id: Option<String>,
active: bool,
}
let mut fn_call_state = FunctionCallState::default();
loop {
let sse = match timeout(idle_timeout, stream.next()).await {
Ok(Some(Ok(ev))) => ev,
@@ -173,23 +268,89 @@ where
Ok(v) => v,
Err(_) => continue,
};
trace!("chat_completions received SSE chunk: {chunk:?}");
let content_opt = chunk
.get("choices")
.and_then(|c| c.get(0))
.and_then(|c| c.get("delta"))
.and_then(|d| d.get("content"))
.and_then(|c| c.as_str());
let choice_opt = chunk.get("choices").and_then(|c| c.get(0));
if let Some(content) = content_opt {
let item = ResponseItem::Message {
role: "assistant".to_string(),
content: vec![ContentItem::OutputText {
text: content.to_string(),
}],
};
if let Some(choice) = choice_opt {
// Handle assistant content tokens.
if let Some(content) = choice
.get("delta")
.and_then(|d| d.get("content"))
.and_then(|c| c.as_str())
{
let item = ResponseItem::Message {
role: "assistant".to_string(),
content: vec![ContentItem::OutputText {
text: content.to_string(),
}],
};
let _ = tx_event.send(Ok(ResponseEvent::OutputItemDone(item))).await;
let _ = tx_event.send(Ok(ResponseEvent::OutputItemDone(item))).await;
}
// Handle streaming function / tool calls.
if let Some(tool_calls) = choice
.get("delta")
.and_then(|d| d.get("tool_calls"))
.and_then(|tc| tc.as_array())
{
if let Some(tool_call) = tool_calls.first() {
// Mark that we have an active function call in progress.
fn_call_state.active = true;
// Extract call_id if present.
if let Some(id) = tool_call.get("id").and_then(|v| v.as_str()) {
fn_call_state.call_id.get_or_insert_with(|| id.to_string());
}
// Extract function details if present.
if let Some(function) = tool_call.get("function") {
if let Some(name) = function.get("name").and_then(|n| n.as_str()) {
fn_call_state.name.get_or_insert_with(|| name.to_string());
}
if let Some(args_fragment) =
function.get("arguments").and_then(|a| a.as_str())
{
fn_call_state.arguments.push_str(args_fragment);
}
}
}
}
// Emit end-of-turn when finish_reason signals completion.
if let Some(finish_reason) = choice.get("finish_reason").and_then(|v| v.as_str()) {
match finish_reason {
"tool_calls" if fn_call_state.active => {
// Build the FunctionCall response item.
let item = ResponseItem::FunctionCall {
name: fn_call_state.name.clone().unwrap_or_else(|| "".to_string()),
arguments: fn_call_state.arguments.clone(),
call_id: fn_call_state.call_id.clone().unwrap_or_else(String::new),
};
// Emit it downstream.
let _ = tx_event.send(Ok(ResponseEvent::OutputItemDone(item))).await;
}
"stop" => {
// Regular turn without tool-call.
}
_ => {}
}
// Emit Completed regardless of reason so the agent can advance.
let _ = tx_event
.send(Ok(ResponseEvent::Completed {
response_id: String::new(),
}))
.await;
// Prepare for potential next turn (should not happen in same stream).
// fn_call_state = FunctionCallState::default();
return; // End processing for this SSE stream.
}
}
}
}
@@ -236,9 +397,14 @@ where
Poll::Ready(None) => return Poll::Ready(None),
Poll::Ready(Some(Err(e))) => return Poll::Ready(Some(Err(e))),
Poll::Ready(Some(Ok(ResponseEvent::OutputItemDone(item)))) => {
// Accumulate *assistant* text but do not emit yet.
if let crate::models::ResponseItem::Message { role, content } = &item {
if role == "assistant" {
// If this is an incremental assistant message chunk, accumulate but
// do NOT emit yet. Forward any other item (e.g. FunctionCall) right
// away so downstream consumers see it.
let is_assistant_delta = matches!(&item, crate::models::ResponseItem::Message { role, .. } if role == "assistant");
if is_assistant_delta {
if let crate::models::ResponseItem::Message { content, .. } = &item {
if let Some(text) = content.iter().find_map(|c| match c {
crate::models::ContentItem::OutputText { text } => Some(text),
_ => None,
@@ -246,10 +412,13 @@ where
this.cumulative.push_str(text);
}
}
// Swallow partial assistant chunk; keep polling.
continue;
}
// Swallow partial event; keep polling.
continue;
// Not an assistant message – forward immediately.
return Poll::Ready(Some(Ok(ResponseEvent::OutputItemDone(item))));
}
Poll::Ready(Some(Ok(ResponseEvent::Completed { response_id }))) => {
if !this.cumulative.is_empty() {

116
codex-rs/core/src/client.rs
View File

@@ -1,7 +1,5 @@
use std::collections::BTreeMap;
use std::io::BufRead;
use std::path::Path;
use std::sync::LazyLock;
use std::time::Duration;
use bytes::Bytes;
@@ -11,7 +9,6 @@ use reqwest::StatusCode;
use serde::Deserialize;
use serde::Serialize;
use serde_json::Value;
use serde_json::json;
use tokio::sync::mpsc;
use tokio::time::timeout;
use tokio_util::io::ReaderStream;
@@ -36,71 +33,9 @@ use crate::flags::OPENAI_STREAM_IDLE_TIMEOUT_MS;
use crate::model_provider_info::ModelProviderInfo;
use crate::model_provider_info::WireApi;
use crate::models::ResponseItem;
use crate::openai_tools::create_tools_json;
use crate::util::backoff;
/// When serialized as JSON, this produces a valid "Tool" in the OpenAI
/// Responses API.
#[derive(Debug, Clone, Serialize)]
#[serde(tag = "type")]
enum OpenAiTool {
#[serde(rename = "function")]
Function(ResponsesApiTool),
#[serde(rename = "local_shell")]
LocalShell {},
}
#[derive(Debug, Clone, Serialize)]
struct ResponsesApiTool {
name: &'static str,
description: &'static str,
strict: bool,
parameters: JsonSchema,
}
/// Generic JSON‑Schema subset needed for our tool definitions
#[derive(Debug, Clone, Serialize)]
#[serde(tag = "type", rename_all = "lowercase")]
enum JsonSchema {
String,
Number,
Array {
items: Box<JsonSchema>,
},
Object {
properties: BTreeMap<String, JsonSchema>,
required: &'static [&'static str],
#[serde(rename = "additionalProperties")]
additional_properties: bool,
},
}
/// Tool usage specification
static DEFAULT_TOOLS: LazyLock<Vec<OpenAiTool>> = LazyLock::new(|| {
let mut properties = BTreeMap::new();
properties.insert(
"command".to_string(),
JsonSchema::Array {
items: Box::new(JsonSchema::String),
},
);
properties.insert("workdir".to_string(), JsonSchema::String);
properties.insert("timeout".to_string(), JsonSchema::Number);
vec![OpenAiTool::Function(ResponsesApiTool {
name: "shell",
description: "Runs a shell command, and returns its output.",
strict: false,
parameters: JsonSchema::Object {
properties,
required: &["command"],
additional_properties: false,
},
})]
});
static DEFAULT_CODEX_MODEL_TOOLS: LazyLock<Vec<OpenAiTool>> =
LazyLock::new(|| vec![OpenAiTool::LocalShell {}]);
#[derive(Clone)]
pub struct ModelClient {
model: String,
@@ -161,27 +96,8 @@ impl ModelClient {
return stream_from_fixture(path).await;
}
// Assemble tool list: built-in tools + any extra tools from the prompt.
let default_tools = if self.model.starts_with("codex") {
&DEFAULT_CODEX_MODEL_TOOLS
} else {
&DEFAULT_TOOLS
};
let mut tools_json = Vec::with_capacity(default_tools.len() + prompt.extra_tools.len());
for t in default_tools.iter() {
tools_json.push(serde_json::to_value(t)?);
}
tools_json.extend(
prompt
.extra_tools
.clone()
.into_iter()
.map(|(name, tool)| mcp_tool_to_openai_tool(name, tool)),
);
debug!("tools_json: {}", serde_json::to_string_pretty(&tools_json)?);
let full_instructions = prompt.get_full_instructions();
let tools_json = create_tools_json(prompt, &self.model)?;
let payload = Payload {
model: &self.model,
instructions: &full_instructions,
@@ -276,34 +192,6 @@ impl ModelClient {
}
}
fn mcp_tool_to_openai_tool(
fully_qualified_name: String,
tool: mcp_types::Tool,
) -> serde_json::Value {
let mcp_types::Tool {
description,
mut input_schema,
..
} = tool;
// OpenAI models mandate the "properties" field in the schema. The Agents
// SDK fixed this by inserting an empty object for "properties" if it is not
// already present https://github.com/openai/openai-agents-python/issues/449
// so here we do the same.
if input_schema.properties.is_none() {
input_schema.properties = Some(serde_json::Value::Object(serde_json::Map::new()));
}
// TODO(mbolin): Change the contract of this function to return
// ResponsesApiTool.
json!({
"name": fully_qualified_name,
"description": description,
"parameters": input_schema,
"type": "function",
})
}
#[derive(Debug, Deserialize, Serialize)]
struct SseEvent {
#[serde(rename = "type")]

100
codex-rs/core/src/codex.rs
View File

@@ -20,6 +20,7 @@ use codex_apply_patch::MaybeApplyPatchVerified;
use codex_apply_patch::maybe_parse_apply_patch_verified;
use codex_apply_patch::print_summary;
use futures::prelude::*;
use mcp_types::CallToolResult;
use serde::Serialize;
use serde_json;
use tokio::sync::Notify;
@@ -388,7 +389,7 @@ impl Session {
tool: &str,
arguments: Option<serde_json::Value>,
timeout: Option<Duration>,
) -> anyhow::Result<mcp_types::CallToolResult> {
) -> anyhow::Result<CallToolResult> {
self.mcp_connection_manager
.call_tool(server, tool, arguments, timeout)
.await
@@ -775,6 +776,7 @@ async fn run_task(sess: Arc<Session>, sub_id: String, input: Vec<InputItem>) {
let mut pending_response_input: Vec<ResponseInputItem> = vec![ResponseInputItem::from(input)];
let last_agent_message: Option<String>;
loop {
debug!("pending_response_input: {pending_response_input:?}");
let mut net_new_turn_input = pending_response_input
.drain(..)
.map(ResponseItem::from)
@@ -828,31 +830,102 @@ async fn run_task(sess: Arc<Session>, sub_id: String, input: Vec<InputItem>) {
})
})
.collect();
debug!("Turn input: {turn_input:?}");
match run_turn(&sess, sub_id.clone(), turn_input).await {
Ok(turn_output) => {
let (items, responses): (Vec<_>, Vec<_>) = turn_output
.into_iter()
.map(|p| (p.item, p.response))
.unzip();
let responses = responses
.into_iter()
.flatten()
.collect::<Vec<ResponseInputItem>>();
let mut items_to_record_to_conversation_history = Vec::<ResponseItem>::new();
let mut responses = Vec::<ResponseInputItem>::new();
for processed_response_item in turn_output {
let ProcessedResponseItem { item, response } = processed_response_item;
match (&item, &response) {
(ResponseItem::Message { role, content, .. }, None)
if role == "assistant" =>
{
// If the model returned a message, we need to record it.
items_to_record_to_conversation_history.push(ResponseItem::Message {
content: content.clone(),
role: "assistant".to_string(),
});
}
(
ResponseItem::LocalShellCall { .. },
Some(ResponseInputItem::FunctionCallOutput { call_id, output }),
) => {
items_to_record_to_conversation_history.push(item);
items_to_record_to_conversation_history.push(
ResponseItem::FunctionCallOutput {
call_id: call_id.clone(),
output: output.clone(),
},
);
}
(
ResponseItem::FunctionCall { .. },
Some(ResponseInputItem::FunctionCallOutput { call_id, output }),
) => {
items_to_record_to_conversation_history.push(item);
items_to_record_to_conversation_history.push(
ResponseItem::FunctionCallOutput {
call_id: call_id.clone(),
output: output.clone(),
},
);
}
(
ResponseItem::FunctionCall { .. },
Some(ResponseInputItem::McpToolCallOutput { call_id, result }),
) => {
items_to_record_to_conversation_history.push(item);
// let (content, success): (String, Option<bool>) = match result {
// Ok(CallToolResult { content, is_error }) => {
// (content, is_error.or_else(false))
// }
// Err(e) => (e.clone(), Some(true)),
// };
// items_to_record_to_conversation_history.push(
// ResponseItem::FunctionCallOutput {
// call_id: call_id.clone(),
// output: FunctionCallOutputPayload { content, success },
// },
// );
warn!(
"Skipping MCP tool call output: {call_id:?} with response: {result:?}"
);
}
_ => {
warn!("Unexpected response item: {item:?} with response: {response:?}");
}
};
if let Some(response) = response {
responses.push(response);
}
}
// Only attempt to take the lock if there is something to record.
if !items.is_empty() {
if !items_to_record_to_conversation_history.is_empty() {
// First persist model-generated output to the rollout file – this only borrows.
sess.record_rollout_items(&items).await;
sess.record_rollout_items(&items_to_record_to_conversation_history)
.await;
debug!(
"has transcript? {}",
sess.state.lock().unwrap().zdr_transcript.is_some()
);
// For ZDR we also need to keep a transcript clone.
if let Some(transcript) = sess.state.lock().unwrap().zdr_transcript.as_mut() {
transcript.record_items(&items);
debug!(
"Recording items to transcript: {items_to_record_to_conversation_history:?}"
);
transcript.record_items(&items_to_record_to_conversation_history);
}
}
if responses.is_empty() {
debug!("Turn completed");
last_agent_message = get_last_assistant_message_from_turn(&items);
last_agent_message = get_last_assistant_message_from_turn(
&items_to_record_to_conversation_history,
);
sess.maybe_notify(UserNotification::AgentTurnComplete {
turn_id: sub_id.clone(),
input_messages: turn_input_messages,
@@ -959,6 +1032,7 @@ async fn run_turn(
/// events map to a `ResponseItem`. A `ResponseItem` may need to be
/// "handled" such that it produces a `ResponseInputItem` that needs to be
/// sent back to the model on the next turn.
#[derive(Debug)]
struct ProcessedResponseItem {
item: ResponseItem,
response: Option<ResponseInputItem>,

1
codex-rs/core/src/lib.rs
View File

@@ -27,6 +27,7 @@ mod model_provider_info;
pub use model_provider_info::ModelProviderInfo;
pub use model_provider_info::WireApi;
mod models;
mod openai_tools;
mod project_doc;
pub mod protocol;
mod rollout;

121
codex-rs/core/src/openai_tools.rs Normal file
View File

@@ -0,0 +1,121 @@
use serde::Serialize;
use serde_json::json;
use std::collections::BTreeMap;
use std::sync::LazyLock;
#[derive(Debug, Clone, Serialize)]
pub(crate) struct ResponsesApiTool {
name: &'static str,
description: &'static str,
strict: bool,
parameters: JsonSchema,
}
/// When serialized as JSON, this produces a valid "Tool" in the OpenAI
/// Responses API.
#[derive(Debug, Clone, Serialize)]
#[serde(tag = "type")]
pub(crate) enum OpenAiTool {
#[serde(rename = "function")]
Function(ResponsesApiTool),
#[serde(rename = "local_shell")]
LocalShell {},
}
/// Generic JSON‑Schema subset needed for our tool definitions
#[derive(Debug, Clone, Serialize)]
#[serde(tag = "type", rename_all = "lowercase")]
pub(crate) enum JsonSchema {
String,
Number,
Array {
items: Box<JsonSchema>,
},
Object {
properties: BTreeMap<String, JsonSchema>,
required: &'static [&'static str],
#[serde(rename = "additionalProperties")]
additional_properties: bool,
},
}
/// Tool usage specification
static DEFAULT_TOOLS: LazyLock<Vec<OpenAiTool>> = LazyLock::new(|| {
let mut properties = BTreeMap::new();
properties.insert(
"command".to_string(),
JsonSchema::Array {
items: Box::new(JsonSchema::String),
},
);
properties.insert("workdir".to_string(), JsonSchema::String);
properties.insert("timeout".to_string(), JsonSchema::Number);
vec![OpenAiTool::Function(ResponsesApiTool {
name: "shell",
description: "Runs a shell command, and returns its output.",
strict: false,
parameters: JsonSchema::Object {
properties,
required: &["command"],
additional_properties: false,
},
})]
});
static DEFAULT_CODEX_MODEL_TOOLS: LazyLock<Vec<OpenAiTool>> =
LazyLock::new(|| vec![OpenAiTool::LocalShell {}]);
pub(crate) fn create_tools_json(
prompt: &crate::client_common::Prompt,
model: &str,
) -> crate::error::Result<Vec<serde_json::Value>> {
// Assemble tool list: built-in tools + any extra tools from the prompt.
let default_tools = if model.starts_with("codex") {
&DEFAULT_CODEX_MODEL_TOOLS
} else {
&DEFAULT_TOOLS
};
let mut tools_json = Vec::with_capacity(default_tools.len() + prompt.extra_tools.len());
for t in default_tools.iter() {
tools_json.push(serde_json::to_value(t)?);
}
tools_json.extend(
prompt
.extra_tools
.clone()
.into_iter()
.map(|(name, tool)| mcp_tool_to_openai_tool(name, tool)),
);
tracing::debug!("tools_json: {}", serde_json::to_string_pretty(&tools_json)?);
Ok(tools_json)
}
fn mcp_tool_to_openai_tool(
fully_qualified_name: String,
tool: mcp_types::Tool,
) -> serde_json::Value {
let mcp_types::Tool {
description,
mut input_schema,
..
} = tool;
// OpenAI models mandate the "properties" field in the schema. The Agents
// SDK fixed this by inserting an empty object for "properties" if it is not
// already present https://github.com/openai/openai-agents-python/issues/449
// so here we do the same.
if input_schema.properties.is_none() {
input_schema.properties = Some(serde_json::Value::Object(serde_json::Map::new()));
}
// TODO(mbolin): Change the contract of this function to return
// ResponsesApiTool.
json!({
"name": fully_qualified_name,
"description": description,
"parameters": input_schema,
"type": "function",
})
}