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https://github.com/openai/codex.git
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cb9a189857
- Make Config.model optional and centralize default-selection logic in ModelsManager, including a default_model helper (with codex-auto-balanced when available) so sessions now carry an explicit chosen model separate from the base config. - Resolve `model` once in `core` and `tui` from config. Then store the state of it on other structs. - Move refreshing models to be before resolving the default model
75 lines
2.9 KiB
Rust
75 lines
2.9 KiB
Rust
use chrono::DateTime;
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use chrono::Utc;
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use codex_core::openai_models::model_presets::all_model_presets;
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use codex_protocol::openai_models::ClientVersion;
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use codex_protocol::openai_models::ConfigShellToolType;
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use codex_protocol::openai_models::ModelInfo;
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use codex_protocol::openai_models::ModelPreset;
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use codex_protocol::openai_models::ModelVisibility;
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use serde_json::json;
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use std::path::Path;
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/// Convert a ModelPreset to ModelInfo for cache storage.
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fn preset_to_info(preset: &ModelPreset, priority: i32) -> ModelInfo {
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ModelInfo {
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slug: preset.id.clone(),
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display_name: preset.display_name.clone(),
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description: Some(preset.description.clone()),
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default_reasoning_level: preset.default_reasoning_effort,
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supported_reasoning_levels: preset.supported_reasoning_efforts.clone(),
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shell_type: ConfigShellToolType::ShellCommand,
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visibility: if preset.show_in_picker {
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ModelVisibility::List
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} else {
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ModelVisibility::Hide
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},
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minimal_client_version: ClientVersion(0, 1, 0),
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supported_in_api: true,
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priority,
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upgrade: preset.upgrade.as_ref().map(|u| u.id.clone()),
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base_instructions: None,
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}
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}
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/// Write a models_cache.json file to the codex home directory.
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/// This prevents ModelsManager from making network requests to refresh models.
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/// The cache will be treated as fresh (within TTL) and used instead of fetching from the network.
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/// Uses the built-in model presets from ModelsManager, converted to ModelInfo format.
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pub fn write_models_cache(codex_home: &Path) -> std::io::Result<()> {
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// Get all presets and filter for show_in_picker (same as builtin_model_presets does)
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let presets: Vec<&ModelPreset> = all_model_presets()
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.iter()
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.filter(|preset| preset.show_in_picker)
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.collect();
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// Convert presets to ModelInfo, assigning priorities (higher = earlier in list)
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// Priority is used for sorting, so first model gets highest priority
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let models: Vec<ModelInfo> = presets
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.iter()
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.enumerate()
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.map(|(idx, preset)| {
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// Higher priority = earlier in list, so reverse the index
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let priority = (presets.len() - idx) as i32;
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preset_to_info(preset, priority)
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})
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.collect();
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write_models_cache_with_models(codex_home, models)
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}
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/// Write a models_cache.json file with specific models.
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/// Useful when tests need specific models to be available.
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pub fn write_models_cache_with_models(
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codex_home: &Path,
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models: Vec<ModelInfo>,
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) -> std::io::Result<()> {
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let cache_path = codex_home.join("models_cache.json");
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// DateTime<Utc> serializes to RFC3339 format by default with serde
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let fetched_at: DateTime<Utc> = Utc::now();
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let cache = json!({
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"fetched_at": fetched_at,
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"etag": null,
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"models": models
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});
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std::fs::write(cache_path, serde_json::to_string_pretty(&cache)?)
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}
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