Fix OpenAI Codex contract, SSE parsing, and default xhigh reasoning
This commit is contained in:
parent
007368d586
commit
39087a446d
2 changed files with 330 additions and 11 deletions
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@ -14,7 +14,7 @@ pub const OPENAI_OAUTH_CLIENT_ID: &str = "app_EMoamEEZ73f0CkXaXp7hrann";
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pub const OPENAI_OAUTH_AUTHORIZE_URL: &str = "https://auth.openai.com/oauth/authorize";
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pub const OPENAI_OAUTH_AUTHORIZE_URL: &str = "https://auth.openai.com/oauth/authorize";
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pub const OPENAI_OAUTH_TOKEN_URL: &str = "https://auth.openai.com/oauth/token";
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pub const OPENAI_OAUTH_TOKEN_URL: &str = "https://auth.openai.com/oauth/token";
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pub const OPENAI_OAUTH_DEVICE_CODE_URL: &str = "https://auth.openai.com/oauth/device/code";
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pub const OPENAI_OAUTH_DEVICE_CODE_URL: &str = "https://auth.openai.com/oauth/device/code";
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pub const OPENAI_OAUTH_REDIRECT_URI: &str = "http://127.0.0.1:1455/auth/callback";
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pub const OPENAI_OAUTH_REDIRECT_URI: &str = "http://localhost:1455/auth/callback";
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#[derive(Debug, Clone)]
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#[derive(Debug, Clone)]
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pub struct PkceState {
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pub struct PkceState {
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@ -1,12 +1,15 @@
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use crate::auth::AuthService;
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use crate::auth::AuthService;
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use crate::auth::openai_oauth::extract_account_id_from_jwt;
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use crate::providers::traits::Provider;
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use crate::providers::traits::Provider;
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use crate::providers::ProviderRuntimeOptions;
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use crate::providers::ProviderRuntimeOptions;
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use async_trait::async_trait;
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use async_trait::async_trait;
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use reqwest::Client;
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use reqwest::Client;
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use serde::{Deserialize, Serialize};
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use serde::{Deserialize, Serialize};
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use serde_json::Value;
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use std::path::PathBuf;
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use std::path::PathBuf;
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const CODEX_RESPONSES_URL: &str = "https://chatgpt.com/backend-api/codex/responses";
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const CODEX_RESPONSES_URL: &str = "https://chatgpt.com/backend-api/codex/responses";
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const DEFAULT_CODEX_INSTRUCTIONS: &str = "You are ZeroClaw, a concise and helpful coding assistant.";
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pub struct OpenAiCodexProvider {
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pub struct OpenAiCodexProvider {
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auth: AuthService,
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auth: AuthService,
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@ -18,15 +21,38 @@ pub struct OpenAiCodexProvider {
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struct ResponsesRequest {
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struct ResponsesRequest {
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model: String,
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model: String,
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input: Vec<ResponsesInput>,
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input: Vec<ResponsesInput>,
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#[serde(skip_serializing_if = "Option::is_none")]
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instructions: String,
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instructions: Option<String>,
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store: bool,
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stream: bool,
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stream: bool,
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text: ResponsesTextOptions,
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reasoning: ResponsesReasoningOptions,
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include: Vec<String>,
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tool_choice: String,
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parallel_tool_calls: bool,
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}
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}
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#[derive(Debug, Serialize)]
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#[derive(Debug, Serialize)]
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struct ResponsesInput {
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struct ResponsesInput {
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role: String,
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role: String,
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content: String,
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content: Vec<ResponsesInputContent>,
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}
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#[derive(Debug, Serialize)]
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struct ResponsesInputContent {
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#[serde(rename = "type")]
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kind: String,
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text: String,
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}
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#[derive(Debug, Serialize)]
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struct ResponsesTextOptions {
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verbosity: String,
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}
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#[derive(Debug, Serialize)]
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struct ResponsesReasoningOptions {
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effort: String,
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summary: String,
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}
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}
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#[derive(Debug, Deserialize)]
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#[derive(Debug, Deserialize)]
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@ -88,6 +114,51 @@ fn first_nonempty(text: Option<&str>) -> Option<String> {
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})
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})
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}
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}
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fn resolve_instructions(system_prompt: Option<&str>) -> String {
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first_nonempty(system_prompt).unwrap_or_else(|| DEFAULT_CODEX_INSTRUCTIONS.to_string())
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}
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fn normalize_model_id(model: &str) -> &str {
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model.rsplit('/').next().unwrap_or(model)
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}
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fn clamp_reasoning_effort(model: &str, effort: &str) -> String {
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let id = normalize_model_id(model);
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if (id.starts_with("gpt-5.2") || id.starts_with("gpt-5.3")) && effort == "minimal" {
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return "low".to_string();
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}
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if id == "gpt-5.1" && effort == "xhigh" {
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return "high".to_string();
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}
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if id == "gpt-5.1-codex-mini" {
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return if effort == "high" || effort == "xhigh" {
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"high".to_string()
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} else {
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"medium".to_string()
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};
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}
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effort.to_string()
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}
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fn resolve_reasoning_effort(model: &str) -> String {
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let raw = std::env::var("ZEROCLAW_CODEX_REASONING_EFFORT")
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.ok()
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.and_then(|value| first_nonempty(Some(&value)))
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.unwrap_or_else(|| "xhigh".to_string())
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.to_ascii_lowercase();
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clamp_reasoning_effort(model, &raw)
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}
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fn nonempty_preserve(text: Option<&str>) -> Option<String> {
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text.and_then(|value| {
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if value.is_empty() {
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None
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} else {
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Some(value.to_string())
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}
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})
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}
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fn extract_responses_text(response: &ResponsesResponse) -> Option<String> {
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fn extract_responses_text(response: &ResponsesResponse) -> Option<String> {
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if let Some(text) = first_nonempty(response.output_text.as_deref()) {
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if let Some(text) = first_nonempty(response.output_text.as_deref()) {
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return Some(text);
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return Some(text);
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@ -114,6 +185,155 @@ fn extract_responses_text(response: &ResponsesResponse) -> Option<String> {
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None
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None
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}
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}
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fn extract_stream_event_text(event: &Value, saw_delta: bool) -> Option<String> {
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let event_type = event.get("type").and_then(Value::as_str);
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match event_type {
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Some("response.output_text.delta") => {
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nonempty_preserve(event.get("delta").and_then(Value::as_str))
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}
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Some("response.output_text.done") if !saw_delta => {
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nonempty_preserve(event.get("text").and_then(Value::as_str))
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}
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Some("response.completed") | Some("response.done") => event
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.get("response")
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.and_then(|value| serde_json::from_value::<ResponsesResponse>(value.clone()).ok())
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.and_then(|response| extract_responses_text(&response)),
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_ => None,
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}
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}
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fn parse_sse_text(body: &str) -> anyhow::Result<Option<String>> {
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let mut saw_delta = false;
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let mut delta_accumulator = String::new();
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let mut fallback_text = None;
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let mut buffer = body.to_string();
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let mut process_event = |event: Value| -> anyhow::Result<()> {
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if let Some(message) = extract_stream_error_message(&event) {
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return Err(anyhow::anyhow!("OpenAI Codex stream error: {message}"));
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}
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if let Some(text) = extract_stream_event_text(&event, saw_delta) {
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let event_type = event.get("type").and_then(Value::as_str);
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if event_type == Some("response.output_text.delta") {
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saw_delta = true;
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delta_accumulator.push_str(&text);
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} else if fallback_text.is_none() {
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fallback_text = Some(text);
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}
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}
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Ok(())
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};
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let mut process_chunk = |chunk: &str| -> anyhow::Result<()> {
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let data_lines: Vec<String> = chunk
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.lines()
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.filter_map(|line| line.strip_prefix("data:"))
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.map(|line| line.trim().to_string())
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.collect();
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if data_lines.is_empty() {
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return Ok(());
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}
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let joined = data_lines.join("\n");
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let trimmed = joined.trim();
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if trimmed.is_empty() || trimmed == "[DONE]" {
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return Ok(());
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}
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if let Ok(event) = serde_json::from_str::<Value>(trimmed) {
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return process_event(event);
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}
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for line in data_lines {
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let line = line.trim();
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if line.is_empty() || line == "[DONE]" {
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continue;
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}
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if let Ok(event) = serde_json::from_str::<Value>(line) {
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process_event(event)?;
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}
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}
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Ok(())
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};
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loop {
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let Some(idx) = buffer.find("\n\n") else {
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break;
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};
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let chunk = buffer[..idx].to_string();
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buffer = buffer[idx + 2..].to_string();
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process_chunk(&chunk)?;
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}
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if !buffer.trim().is_empty() {
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process_chunk(&buffer)?;
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}
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if saw_delta {
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return Ok(nonempty_preserve(Some(&delta_accumulator)));
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}
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Ok(fallback_text)
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}
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fn extract_stream_error_message(event: &Value) -> Option<String> {
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let event_type = event.get("type").and_then(Value::as_str);
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if event_type == Some("error") {
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return first_nonempty(
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event
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.get("message")
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.and_then(Value::as_str)
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.or_else(|| event.get("code").and_then(Value::as_str))
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.or_else(|| {
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event
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.get("error")
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.and_then(|error| error.get("message"))
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.and_then(Value::as_str)
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}),
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);
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}
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if event_type == Some("response.failed") {
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return first_nonempty(
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event
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.get("response")
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.and_then(|response| response.get("error"))
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.and_then(|error| error.get("message"))
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.and_then(Value::as_str),
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);
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}
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None
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}
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async fn decode_responses_body(response: reqwest::Response) -> anyhow::Result<String> {
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let body = response.text().await?;
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if let Some(text) = parse_sse_text(&body)? {
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return Ok(text);
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}
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let body_trimmed = body.trim_start();
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let looks_like_sse = body_trimmed.starts_with("event:") || body_trimmed.starts_with("data:");
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if looks_like_sse {
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return Err(anyhow::anyhow!(
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"No response from OpenAI Codex stream payload: {}",
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super::sanitize_api_error(&body)
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));
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}
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let parsed: ResponsesResponse = serde_json::from_str(&body).map_err(|err| {
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anyhow::anyhow!(
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"OpenAI Codex JSON parse failed: {err}. Payload: {}",
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super::sanitize_api_error(&body)
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)
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})?;
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extract_responses_text(&parsed).ok_or_else(|| anyhow::anyhow!("No response from OpenAI Codex"))
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}
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#[async_trait]
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#[async_trait]
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impl Provider for OpenAiCodexProvider {
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impl Provider for OpenAiCodexProvider {
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async fn chat_with_system(
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async fn chat_with_system(
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@ -123,6 +343,9 @@ impl Provider for OpenAiCodexProvider {
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model: &str,
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model: &str,
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_temperature: f64,
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_temperature: f64,
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) -> anyhow::Result<String> {
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) -> anyhow::Result<String> {
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let profile = self
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.auth
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.get_profile("openai-codex", self.auth_profile_override.as_deref())?;
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let access_token = self
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let access_token = self
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.auth
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.auth
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.get_valid_openai_access_token(self.auth_profile_override.as_deref())
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.get_valid_openai_access_token(self.auth_profile_override.as_deref())
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@ -132,21 +355,47 @@ impl Provider for OpenAiCodexProvider {
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"OpenAI Codex auth profile not found. Run `zeroclaw auth login --provider openai-codex`."
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"OpenAI Codex auth profile not found. Run `zeroclaw auth login --provider openai-codex`."
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)
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)
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})?;
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})?;
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let account_id = profile
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.and_then(|profile| profile.account_id)
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.or_else(|| extract_account_id_from_jwt(&access_token))
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.ok_or_else(|| {
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anyhow::anyhow!(
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"OpenAI Codex account id not found in auth profile/token. Run `zeroclaw auth login --provider openai-codex` again."
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)
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})?;
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let request = ResponsesRequest {
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let request = ResponsesRequest {
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model: model.to_string(),
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model: model.to_string(),
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input: vec![ResponsesInput {
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input: vec![ResponsesInput {
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role: "user".to_string(),
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role: "user".to_string(),
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content: message.to_string(),
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content: vec![ResponsesInputContent {
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kind: "input_text".to_string(),
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text: message.to_string(),
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}],
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}],
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}],
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instructions: system_prompt.map(str::to_string),
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instructions: resolve_instructions(system_prompt),
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stream: false,
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store: false,
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stream: true,
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text: ResponsesTextOptions {
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verbosity: "medium".to_string(),
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},
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reasoning: ResponsesReasoningOptions {
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effort: resolve_reasoning_effort(model),
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summary: "auto".to_string(),
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},
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include: vec!["reasoning.encrypted_content".to_string()],
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tool_choice: "auto".to_string(),
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parallel_tool_calls: true,
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};
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};
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let response = self
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let response = self
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.client
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.client
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.post(CODEX_RESPONSES_URL)
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.post(CODEX_RESPONSES_URL)
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.header("Authorization", format!("Bearer {access_token}"))
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.header("Authorization", format!("Bearer {access_token}"))
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.header("chatgpt-account-id", account_id)
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.header("OpenAI-Beta", "responses=experimental")
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.header("originator", "pi")
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.header("accept", "text/event-stream")
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.header("Content-Type", "application/json")
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.header("Content-Type", "application/json")
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.json(&request)
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.json(&request)
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.send()
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.send()
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@ -156,10 +405,7 @@ impl Provider for OpenAiCodexProvider {
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return Err(super::api_error("OpenAI Codex", response).await);
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return Err(super::api_error("OpenAI Codex", response).await);
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}
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}
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let parsed: ResponsesResponse = response.json().await?;
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decode_responses_body(response).await
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extract_responses_text(&parsed)
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.ok_or_else(|| anyhow::anyhow!("No response from OpenAI Codex"))
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}
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}
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}
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}
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|
|
@ -195,4 +441,77 @@ mod tests {
|
||||||
let path = default_zeroclaw_dir();
|
let path = default_zeroclaw_dir();
|
||||||
assert!(!path.as_os_str().is_empty());
|
assert!(!path.as_os_str().is_empty());
|
||||||
}
|
}
|
||||||
|
|
||||||
|
#[test]
|
||||||
|
fn resolve_instructions_uses_default_when_missing() {
|
||||||
|
assert_eq!(
|
||||||
|
resolve_instructions(None),
|
||||||
|
DEFAULT_CODEX_INSTRUCTIONS.to_string()
|
||||||
|
);
|
||||||
|
}
|
||||||
|
|
||||||
|
#[test]
|
||||||
|
fn resolve_instructions_uses_default_when_blank() {
|
||||||
|
assert_eq!(
|
||||||
|
resolve_instructions(Some(" ")),
|
||||||
|
DEFAULT_CODEX_INSTRUCTIONS.to_string()
|
||||||
|
);
|
||||||
|
}
|
||||||
|
|
||||||
|
#[test]
|
||||||
|
fn resolve_instructions_uses_system_prompt_when_present() {
|
||||||
|
assert_eq!(
|
||||||
|
resolve_instructions(Some("Be strict")),
|
||||||
|
"Be strict".to_string()
|
||||||
|
);
|
||||||
|
}
|
||||||
|
|
||||||
|
#[test]
|
||||||
|
fn clamp_reasoning_effort_adjusts_known_models() {
|
||||||
|
assert_eq!(
|
||||||
|
clamp_reasoning_effort("gpt-5.3-codex", "minimal"),
|
||||||
|
"low".to_string()
|
||||||
|
);
|
||||||
|
assert_eq!(
|
||||||
|
clamp_reasoning_effort("gpt-5.1", "xhigh"),
|
||||||
|
"high".to_string()
|
||||||
|
);
|
||||||
|
assert_eq!(
|
||||||
|
clamp_reasoning_effort("gpt-5.1-codex-mini", "low"),
|
||||||
|
"medium".to_string()
|
||||||
|
);
|
||||||
|
assert_eq!(
|
||||||
|
clamp_reasoning_effort("gpt-5.1-codex-mini", "xhigh"),
|
||||||
|
"high".to_string()
|
||||||
|
);
|
||||||
|
assert_eq!(
|
||||||
|
clamp_reasoning_effort("gpt-5.3-codex", "xhigh"),
|
||||||
|
"xhigh".to_string()
|
||||||
|
);
|
||||||
|
}
|
||||||
|
|
||||||
|
#[test]
|
||||||
|
fn parse_sse_text_reads_output_text_delta() {
|
||||||
|
let payload = r#"data: {"type":"response.created","response":{"id":"resp_123"}}
|
||||||
|
|
||||||
|
data: {"type":"response.output_text.delta","delta":"Hello"}
|
||||||
|
data: {"type":"response.output_text.delta","delta":" world"}
|
||||||
|
data: {"type":"response.completed","response":{"output_text":"Hello world"}}
|
||||||
|
data: [DONE]
|
||||||
|
"#;
|
||||||
|
|
||||||
|
assert_eq!(
|
||||||
|
parse_sse_text(payload).unwrap().as_deref(),
|
||||||
|
Some("Hello world")
|
||||||
|
);
|
||||||
|
}
|
||||||
|
|
||||||
|
#[test]
|
||||||
|
fn parse_sse_text_falls_back_to_completed_response() {
|
||||||
|
let payload = r#"data: {"type":"response.completed","response":{"output_text":"Done"}}
|
||||||
|
data: [DONE]
|
||||||
|
"#;
|
||||||
|
|
||||||
|
assert_eq!(parse_sse_text(payload).unwrap().as_deref(), Some("Done"));
|
||||||
|
}
|
||||||
}
|
}
|
||||||
|
|
|
||||||
Loading…
Add table
Add a link
Reference in a new issue