Files
oak-gpui/crates/language_models/src/settings.rs
T
Anthony EidandBen Kunkle f2c3f3b168 settings ui: Start work on creating the initial structure (#36904)
## Goal 

This PR creates the initial settings ui structure with the primary goal
of making a settings UI that is
- Comprehensive: All settings are available through the UI
- Correct: Easy to understand the underlying JSON file from the UI
- Intuitive
- Easy to implement per setting so that UI is not a hindrance to future
settings changes

### Structure

The overall structure is settings layer -> data layer -> ui layer.

The settings layer is the pre-existing settings definitions, that
implement the `Settings` trait. The data layer is constructed from
settings primarily through the `SettingsUi` trait, and it's associated
derive macro. The data layer tracks the grouping of the settings, the
json path of the settings, and a data representation of how to render
the controls for the setting in the UI, that is either a marker value
for the component to use (avoiding a dependency on the `ui` crate) or a
custom render function.

Abstracting the data layer from the ui layer allows crates depending on
`settings` to implement their own UI without having to add additional UI
dependencies, thus avoiding circular dependencies. In cases where custom
UI is desired, and a creating a custom render function in the same crate
is infeasible due to circular dependencies, the current solution is to
implement a marker for the component in the `settings` crate, and then
handle the rendering of that component in `settings_ui`.

### Foundation 

This PR creates a macro and a trait both called `SettingsUi`. The
`SettingsUi` trait is added as a new trait bound on the `Settings`
trait, this allows the type system to guarantee that all settings
implement UI functionality. The macro is used to derived the trait for
most types, and can be modified through attributes for unique cases as
well.

A derive-macro is used to generate the settings UI trait impl, allowing
it the UI generation to be generated from the static information in our
code base (`default.json`, Struct/Enum names, field names, `serde`
attributes, etc). This allows the UI to be auto-generated for the most
part, and ensures consistency across the UI.


#### Immediate Follow ups

- Add a new `SettingsPath` trait that will be a trait bound on
`SettingsUi` and `Settings`
- This trait will replace the `Settings::key` value to enable
`SettingsUi` to infer the json path of it's derived type
- Figure out how to render `Option<T> where T: SettingsUi` correctly
- Handle `serde` attributes in the `SettingsUi` proc macro to correctly
get json path from a type's field and identity

Release Notes:

- N/A

---------

Co-authored-by: Ben Kunkle <ben@zed.dev>
2025-08-29 16:56:10 -04:00

326 lines
11 KiB
Rust

use std::sync::Arc;
use anyhow::Result;
use collections::HashMap;
use gpui::App;
use schemars::JsonSchema;
use serde::{Deserialize, Serialize};
use settings::{Settings, SettingsSources, SettingsUi};
use crate::provider::{
self,
anthropic::AnthropicSettings,
bedrock::AmazonBedrockSettings,
cloud::{self, ZedDotDevSettings},
deepseek::DeepSeekSettings,
google::GoogleSettings,
lmstudio::LmStudioSettings,
mistral::MistralSettings,
ollama::OllamaSettings,
open_ai::OpenAiSettings,
open_ai_compatible::OpenAiCompatibleSettings,
open_router::OpenRouterSettings,
vercel::VercelSettings,
x_ai::XAiSettings,
};
/// Initializes the language model settings.
pub fn init_settings(cx: &mut App) {
AllLanguageModelSettings::register(cx);
}
#[derive(Default, SettingsUi)]
pub struct AllLanguageModelSettings {
pub anthropic: AnthropicSettings,
pub bedrock: AmazonBedrockSettings,
pub deepseek: DeepSeekSettings,
pub google: GoogleSettings,
pub lmstudio: LmStudioSettings,
pub mistral: MistralSettings,
pub ollama: OllamaSettings,
pub open_router: OpenRouterSettings,
pub openai: OpenAiSettings,
pub openai_compatible: HashMap<Arc<str>, OpenAiCompatibleSettings>,
pub vercel: VercelSettings,
pub x_ai: XAiSettings,
pub zed_dot_dev: ZedDotDevSettings,
}
#[derive(Default, Clone, Debug, Serialize, Deserialize, PartialEq, JsonSchema)]
pub struct AllLanguageModelSettingsContent {
pub anthropic: Option<AnthropicSettingsContent>,
pub bedrock: Option<AmazonBedrockSettingsContent>,
pub deepseek: Option<DeepseekSettingsContent>,
pub google: Option<GoogleSettingsContent>,
pub lmstudio: Option<LmStudioSettingsContent>,
pub mistral: Option<MistralSettingsContent>,
pub ollama: Option<OllamaSettingsContent>,
pub open_router: Option<OpenRouterSettingsContent>,
pub openai: Option<OpenAiSettingsContent>,
pub openai_compatible: Option<HashMap<Arc<str>, OpenAiCompatibleSettingsContent>>,
pub vercel: Option<VercelSettingsContent>,
pub x_ai: Option<XAiSettingsContent>,
#[serde(rename = "zed.dev")]
pub zed_dot_dev: Option<ZedDotDevSettingsContent>,
}
#[derive(Clone, Debug, Serialize, Deserialize, PartialEq, JsonSchema)]
pub struct AnthropicSettingsContent {
pub api_url: Option<String>,
pub available_models: Option<Vec<provider::anthropic::AvailableModel>>,
}
#[derive(Default, Clone, Debug, Serialize, Deserialize, PartialEq, JsonSchema)]
pub struct AmazonBedrockSettingsContent {
available_models: Option<Vec<provider::bedrock::AvailableModel>>,
endpoint_url: Option<String>,
region: Option<String>,
profile: Option<String>,
authentication_method: Option<provider::bedrock::BedrockAuthMethod>,
}
#[derive(Default, Clone, Debug, Serialize, Deserialize, PartialEq, JsonSchema)]
pub struct OllamaSettingsContent {
pub api_url: Option<String>,
pub available_models: Option<Vec<provider::ollama::AvailableModel>>,
}
#[derive(Default, Clone, Debug, Serialize, Deserialize, PartialEq, JsonSchema)]
pub struct LmStudioSettingsContent {
pub api_url: Option<String>,
pub available_models: Option<Vec<provider::lmstudio::AvailableModel>>,
}
#[derive(Default, Clone, Debug, Serialize, Deserialize, PartialEq, JsonSchema)]
pub struct DeepseekSettingsContent {
pub api_url: Option<String>,
pub available_models: Option<Vec<provider::deepseek::AvailableModel>>,
}
#[derive(Default, Clone, Debug, Serialize, Deserialize, PartialEq, JsonSchema)]
pub struct MistralSettingsContent {
pub api_url: Option<String>,
pub available_models: Option<Vec<provider::mistral::AvailableModel>>,
}
#[derive(Clone, Debug, Serialize, Deserialize, PartialEq, JsonSchema)]
pub struct OpenAiSettingsContent {
pub api_url: Option<String>,
pub available_models: Option<Vec<provider::open_ai::AvailableModel>>,
}
#[derive(Clone, Debug, Serialize, Deserialize, PartialEq, JsonSchema)]
pub struct OpenAiCompatibleSettingsContent {
pub api_url: String,
pub available_models: Vec<provider::open_ai_compatible::AvailableModel>,
}
#[derive(Default, Clone, Debug, Serialize, Deserialize, PartialEq, JsonSchema)]
pub struct VercelSettingsContent {
pub api_url: Option<String>,
pub available_models: Option<Vec<provider::vercel::AvailableModel>>,
}
#[derive(Default, Clone, Debug, Serialize, Deserialize, PartialEq, JsonSchema)]
pub struct GoogleSettingsContent {
pub api_url: Option<String>,
pub available_models: Option<Vec<provider::google::AvailableModel>>,
}
#[derive(Default, Clone, Debug, Serialize, Deserialize, PartialEq, JsonSchema)]
pub struct XAiSettingsContent {
pub api_url: Option<String>,
pub available_models: Option<Vec<provider::x_ai::AvailableModel>>,
}
#[derive(Default, Clone, Debug, Serialize, Deserialize, PartialEq, JsonSchema)]
pub struct ZedDotDevSettingsContent {
available_models: Option<Vec<cloud::AvailableModel>>,
}
#[derive(Default, Clone, Debug, Serialize, Deserialize, PartialEq, JsonSchema)]
pub struct OpenRouterSettingsContent {
pub api_url: Option<String>,
pub available_models: Option<Vec<provider::open_router::AvailableModel>>,
}
impl settings::Settings for AllLanguageModelSettings {
const KEY: Option<&'static str> = Some("language_models");
const PRESERVED_KEYS: Option<&'static [&'static str]> = Some(&["version"]);
type FileContent = AllLanguageModelSettingsContent;
fn load(sources: SettingsSources<Self::FileContent>, _: &mut App) -> Result<Self> {
fn merge<T>(target: &mut T, value: Option<T>) {
if let Some(value) = value {
*target = value;
}
}
let mut settings = AllLanguageModelSettings::default();
for value in sources.defaults_and_customizations() {
// Anthropic
let anthropic = value.anthropic.clone();
merge(
&mut settings.anthropic.api_url,
anthropic.as_ref().and_then(|s| s.api_url.clone()),
);
merge(
&mut settings.anthropic.available_models,
anthropic.as_ref().and_then(|s| s.available_models.clone()),
);
// Bedrock
let bedrock = value.bedrock.clone();
merge(
&mut settings.bedrock.profile_name,
bedrock.as_ref().map(|s| s.profile.clone()),
);
merge(
&mut settings.bedrock.authentication_method,
bedrock.as_ref().map(|s| s.authentication_method.clone()),
);
merge(
&mut settings.bedrock.region,
bedrock.as_ref().map(|s| s.region.clone()),
);
merge(
&mut settings.bedrock.endpoint,
bedrock.as_ref().map(|s| s.endpoint_url.clone()),
);
// Ollama
let ollama = value.ollama.clone();
merge(
&mut settings.ollama.api_url,
value.ollama.as_ref().and_then(|s| s.api_url.clone()),
);
merge(
&mut settings.ollama.available_models,
ollama.as_ref().and_then(|s| s.available_models.clone()),
);
// LM Studio
let lmstudio = value.lmstudio.clone();
merge(
&mut settings.lmstudio.api_url,
value.lmstudio.as_ref().and_then(|s| s.api_url.clone()),
);
merge(
&mut settings.lmstudio.available_models,
lmstudio.as_ref().and_then(|s| s.available_models.clone()),
);
// DeepSeek
let deepseek = value.deepseek.clone();
merge(
&mut settings.deepseek.api_url,
value.deepseek.as_ref().and_then(|s| s.api_url.clone()),
);
merge(
&mut settings.deepseek.available_models,
deepseek.as_ref().and_then(|s| s.available_models.clone()),
);
// OpenAI
let openai = value.openai.clone();
merge(
&mut settings.openai.api_url,
openai.as_ref().and_then(|s| s.api_url.clone()),
);
merge(
&mut settings.openai.available_models,
openai.as_ref().and_then(|s| s.available_models.clone()),
);
// OpenAI Compatible
if let Some(openai_compatible) = value.openai_compatible.clone() {
for (id, openai_compatible_settings) in openai_compatible {
settings.openai_compatible.insert(
id,
OpenAiCompatibleSettings {
api_url: openai_compatible_settings.api_url,
available_models: openai_compatible_settings.available_models,
},
);
}
}
// Vercel
let vercel = value.vercel.clone();
merge(
&mut settings.vercel.api_url,
vercel.as_ref().and_then(|s| s.api_url.clone()),
);
merge(
&mut settings.vercel.available_models,
vercel.as_ref().and_then(|s| s.available_models.clone()),
);
// XAI
let x_ai = value.x_ai.clone();
merge(
&mut settings.x_ai.api_url,
x_ai.as_ref().and_then(|s| s.api_url.clone()),
);
merge(
&mut settings.x_ai.available_models,
x_ai.as_ref().and_then(|s| s.available_models.clone()),
);
// ZedDotDev
merge(
&mut settings.zed_dot_dev.available_models,
value
.zed_dot_dev
.as_ref()
.and_then(|s| s.available_models.clone()),
);
merge(
&mut settings.google.api_url,
value.google.as_ref().and_then(|s| s.api_url.clone()),
);
merge(
&mut settings.google.available_models,
value
.google
.as_ref()
.and_then(|s| s.available_models.clone()),
);
// Mistral
let mistral = value.mistral.clone();
merge(
&mut settings.mistral.api_url,
mistral.as_ref().and_then(|s| s.api_url.clone()),
);
merge(
&mut settings.mistral.available_models,
mistral.as_ref().and_then(|s| s.available_models.clone()),
);
// OpenRouter
let open_router = value.open_router.clone();
merge(
&mut settings.open_router.api_url,
open_router.as_ref().and_then(|s| s.api_url.clone()),
);
merge(
&mut settings.open_router.available_models,
open_router
.as_ref()
.and_then(|s| s.available_models.clone()),
);
}
Ok(settings)
}
fn import_from_vscode(_vscode: &settings::VsCodeSettings, _current: &mut Self::FileContent) {}
}