use anyhow::Result;use collections::BTreeMap;use credentials_provider::CredentialsProvider;use futures::{FutureExt, StreamExt, future::BoxFuture};use gpui::{App, AppContext, AsyncApp, Context, Entity, SharedString, Task};use http_client::{CustomHeaders, HttpClient};use language_model::{    ApiKeyConfiguration, ApiKeyState, AuthenticateError, CompactionResult, EnvVar,    FastModeConfirmation, IconOrSvg, LanguageModel, LanguageModelCompletionError,    LanguageModelCompletionEvent, LanguageModelEffortLevel, LanguageModelId, LanguageModelName,    LanguageModelProvider, LanguageModelProviderId, LanguageModelProviderName,    LanguageModelProviderState, LanguageModelRequest, LanguageModelToolChoice, OPEN_AI_PROVIDER_ID,    OPEN_AI_PROVIDER_NAME, ProviderSettingsView, RateLimiter, env_var,};use open_ai::{    ResponseStreamEvent,    responses::{        CompactRequest, CompactedResponse, Request as ResponseRequest,        StreamEvent as ResponsesStreamEvent, compact_response, stream_response,    },    stream_completion,};use settings::{OpenAiAvailableModel as AvailableModel, Settings, SettingsStore};use std::sync::{Arc, LazyLock};use strum::IntoEnumIterator;use ui::IconName; use open_ai::completion::token_usage_from_response_usage;pub use open_ai::completion::{    ChatCompletionMaxTokensParameter, OpenAiEventMapper, OpenAiResponseEventMapper, into_open_ai,    into_open_ai_response,}; const PROVIDER_ID: LanguageModelProviderId = OPEN_AI_PROVIDER_ID;const PROVIDER_NAME: LanguageModelProviderName = OPEN_AI_PROVIDER_NAME; const API_KEY_ENV_VAR_NAME: &str = "OPENAI_API_KEY";static API_KEY_ENV_VAR: LazyLock<EnvVar> = env_var!(API_KEY_ENV_VAR_NAME); #[derive(Default, Clone, Debug, PartialEq)]pub struct OpenAiSettings {    pub api_url: String,    pub available_models: Vec<AvailableModel>,    pub custom_headers: CustomHeaders,} pub struct OpenAiLanguageModelProvider {    http_client: Arc<dyn HttpClient>,    state: Entity<State>,} pub struct State {    api_key_state: ApiKeyState,    credentials_provider: Arc<dyn CredentialsProvider>,} impl State {    fn is_authenticated(&self) -> bool {        self.api_key_state.has_key()    }     fn set_api_key(&mut self, api_key: Option<String>, cx: &mut Context<Self>) -> Task<Result<()>> {        let credentials_provider = self.credentials_provider.clone();        let api_url = OpenAiLanguageModelProvider::api_url(cx);        self.api_key_state.store(            api_url,            api_key,            |this| &mut this.api_key_state,            credentials_provider,            cx,        )    }     fn authenticate(&mut self, cx: &mut Context<Self>) -> Task<Result<(), AuthenticateError>> {        let credentials_provider = self.credentials_provider.clone();        let api_url = OpenAiLanguageModelProvider::api_url(cx);        self.api_key_state.load_if_needed(            api_url,            |this| &mut this.api_key_state,            credentials_provider,            cx,        )    }} impl OpenAiLanguageModelProvider {    pub fn new(        http_client: Arc<dyn HttpClient>,        credentials_provider: Arc<dyn CredentialsProvider>,        cx: &mut App,    ) -> Self {        let state = cx.new(|cx| {            cx.observe_global::<SettingsStore>(|this: &mut State, cx| {                let credentials_provider = this.credentials_provider.clone();                let api_url = Self::api_url(cx);                this.api_key_state.handle_url_change(                    api_url,                    |this| &mut this.api_key_state,                    credentials_provider,                    cx,                );                cx.notify();            })            .detach();            State {                api_key_state: ApiKeyState::new(Self::api_url(cx), (*API_KEY_ENV_VAR).clone()),                credentials_provider,            }        });         Self { http_client, state }    }     fn create_language_model(&self, model: open_ai::Model) -> Arc<dyn LanguageModel> {        Arc::new(OpenAiLanguageModel {            id: LanguageModelId::from(model.id().to_string()),            model,            state: self.state.clone(),            http_client: self.http_client.clone(),            request_limiter: RateLimiter::new(4),        })    }     fn settings(cx: &App) -> &OpenAiSettings {        &crate::AllLanguageModelSettings::get_global(cx).openai    }     fn api_url(cx: &App) -> SharedString {        let api_url = &Self::settings(cx).api_url;        if api_url.is_empty() {            open_ai::OPEN_AI_API_URL.into()        } else {            SharedString::new(api_url.as_str())        }    }} impl LanguageModelProviderState for OpenAiLanguageModelProvider {    type ObservableEntity = State;     fn observable_entity(&self) -> Option<Entity<Self::ObservableEntity>> {        Some(self.state.clone())    }} impl LanguageModelProvider for OpenAiLanguageModelProvider {    fn id(&self) -> LanguageModelProviderId {        PROVIDER_ID    }     fn name(&self) -> LanguageModelProviderName {        PROVIDER_NAME    }     fn icon(&self) -> IconOrSvg {        IconOrSvg::Icon(IconName::AiOpenAi)    }     fn default_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {        Some(self.create_language_model(open_ai::Model::default()))    }     fn default_fast_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {        Some(self.create_language_model(open_ai::Model::default_fast()))    }     fn provided_models(&self, cx: &App) -> Vec<Arc<dyn LanguageModel>> {        let mut models = BTreeMap::default();         // Add base models from open_ai::Model::iter()        for model in open_ai::Model::iter() {            if !matches!(model, open_ai::Model::Custom { .. }) {                models.insert(model.id().to_string(), model);            }        }         // Override with available models from settings        for model in &OpenAiLanguageModelProvider::settings(cx).available_models {            models.insert(                model.name.clone(),                open_ai::Model::Custom {                    name: model.name.clone(),                    display_name: model.display_name.clone(),                    max_tokens: model.max_tokens,                    max_output_tokens: model.max_output_tokens,                    max_completion_tokens: model.max_completion_tokens,                    reasoning_effort: model.reasoning_effort,                    supports_chat_completions: model.capabilities.chat_completions,                    supports_images: model.capabilities.images,                },            );        }         models            .into_values()            .map(|model| self.create_language_model(model))            .collect()    }     fn is_authenticated(&self, cx: &App) -> bool {        self.state.read(cx).is_authenticated()    }     fn authenticate(&self, cx: &mut App) -> Task<Result<(), AuthenticateError>> {        self.state.update(cx, |state, cx| state.authenticate(cx))    }     fn settings_view(&self, cx: &mut App) -> Option<ProviderSettingsView> {        let state = self.state.read(cx);        Some(ProviderSettingsView::ApiKey(ApiKeyConfiguration::new(            state.api_key_state.has_key(),            state.api_key_state.is_from_env_var(),            state.api_key_state.env_var_name().clone(),            "https://platform.openai.com/api-keys".into(),        )))    }     fn set_api_key(&self, api_key: Option<String>, cx: &mut App) -> Task<Result<()>> {        self.state            .update(cx, |state, cx| state.set_api_key(api_key, cx))    }     fn fast_mode_confirmation(&self, _cx: &App) -> Option<FastModeConfirmation> {        Some(FastModeConfirmation {            title: "Enable Fast Mode for OpenAI?".into(),            message: "Fast mode sends requests using OpenAI's Priority processing tier, which \                targets significantly lower latency than the standard tier and is billed at a \                premium per-token rate."                .into(),        })    }} fn default_thinking_reasoning_effort(model: &open_ai::Model) -> Option<open_ai::ReasoningEffort> {    use open_ai::ReasoningEffort;     model        .reasoning_effort()        .filter(|effort| open_ai_reasoning_effort_is_supported(*effort))        .or_else(|| {            let supported_efforts = model.supported_reasoning_efforts();            if supported_efforts.contains(&ReasoningEffort::Medium) {                Some(ReasoningEffort::Medium)            } else {                supported_efforts                    .iter()                    .copied()                    .find(|effort| open_ai_reasoning_effort_is_supported(*effort))            }        })} fn open_ai_reasoning_effort_is_supported(effort: open_ai::ReasoningEffort) -> bool {    effort != open_ai::ReasoningEffort::None} fn normalize_open_ai_response_thinking_effort(    request: &mut LanguageModelRequest,    model: &open_ai::Model,) {    let selected_effort_is_supported = request        .thinking_effort        .as_deref()        .and_then(|effort| effort.parse::<open_ai::ReasoningEffort>().ok())        .is_some_and(|effort| {            open_ai_reasoning_effort_is_supported(effort)                && model.supported_reasoning_efforts().contains(&effort)        });     if !selected_effort_is_supported {        request.thinking_effort = None;    }} fn supports_selectable_thinking_effort(model: &open_ai::Model) -> bool {    model.uses_responses_api()        && model            .supported_reasoning_efforts()            .iter()            .any(|effort| open_ai_reasoning_effort_is_supported(*effort))} fn supported_thinking_effort_levels(model: &open_ai::Model) -> Vec<LanguageModelEffortLevel> {    if !supports_selectable_thinking_effort(model) {        return Vec::new();    }     let default_effort = default_thinking_reasoning_effort(model);    model        .supported_reasoning_efforts()        .iter()        .copied()        .filter_map(|effort| {            if !open_ai_reasoning_effort_is_supported(effort) {                return None;            }             Some(LanguageModelEffortLevel {                name: effort.label().into(),                value: effort.value().into(),                is_default: Some(effort) == default_effort,            })        })        .collect()} #[cfg(test)]mod tests {    use super::*;     #[test]    fn supported_thinking_effort_levels_hide_none() {        let effort_levels = supported_thinking_effort_levels(&open_ai::Model::FivePointTwo);        let values = effort_levels            .iter()            .map(|level| level.value.as_ref())            .collect::<Vec<_>>();         assert_eq!(values, ["low", "medium", "high", "xhigh"]);        assert_eq!(            effort_levels                .iter()                .find(|level| level.is_default)                .map(|level| level.value.as_ref()),            Some("medium")        );    }     #[test]    fn models_supporting_only_none_have_no_selectable_thinking_effort() {        let model = open_ai::Model::Custom {            name: "custom-model".to_string(),            display_name: None,            max_tokens: 128_000,            max_output_tokens: None,            max_completion_tokens: None,            reasoning_effort: Some(open_ai::ReasoningEffort::None),            supports_chat_completions: false,            supports_images: true,        };         assert!(!supports_selectable_thinking_effort(&model));        assert!(supported_thinking_effort_levels(&model).is_empty());        assert!(            model                .supported_reasoning_efforts()                .contains(&open_ai::ReasoningEffort::None)        );    }} pub struct OpenAiLanguageModel {    id: LanguageModelId,    model: open_ai::Model,    state: Entity<State>,    http_client: Arc<dyn HttpClient>,    request_limiter: RateLimiter,} impl OpenAiLanguageModel {    fn stream_completion(        &self,        request: open_ai::Request,        cx: &AsyncApp,    ) -> BoxFuture<'static, Result<futures::stream::BoxStream<'static, Result<ResponseStreamEvent>>>>    {        let http_client = self.http_client.clone();         let (api_key, api_url, extra_headers) = self.state.read_with(cx, |state, cx| {            let api_url = OpenAiLanguageModelProvider::api_url(cx);            let extra_headers = OpenAiLanguageModelProvider::settings(cx)                .custom_headers                .clone();            (state.api_key_state.key(&api_url), api_url, extra_headers)        });         let future = self.request_limiter.stream(async move {            let provider = PROVIDER_NAME;            let Some(api_key) = api_key else {                return Err(LanguageModelCompletionError::NoApiKey { provider });            };            let request = stream_completion(                http_client.as_ref(),                provider.0.as_str(),                &api_url,                &api_key,                request,                &extra_headers,            );            let response = request.await?;            Ok(response)        });         async move { Ok(future.await?.boxed()) }.boxed()    }     fn stream_response(        &self,        request: ResponseRequest,        cx: &AsyncApp,    ) -> BoxFuture<'static, Result<futures::stream::BoxStream<'static, Result<ResponsesStreamEvent>>>>    {        let http_client = self.http_client.clone();         let (api_key, api_url, extra_headers) = self.state.read_with(cx, |state, cx| {            let api_url = OpenAiLanguageModelProvider::api_url(cx);            let extra_headers = OpenAiLanguageModelProvider::settings(cx)                .custom_headers                .clone();            (state.api_key_state.key(&api_url), api_url, extra_headers)        });         let provider = PROVIDER_NAME;        let future = self.request_limiter.stream(async move {            let Some(api_key) = api_key else {                return Err(LanguageModelCompletionError::NoApiKey { provider });            };            let request = stream_response(                http_client.as_ref(),                provider.0.as_str(),                &api_url,                &api_key,                request,                &extra_headers,            );            let response = request.await?;            Ok(response)        });         async move { Ok(future.await?.boxed()) }.boxed()    }     fn compact_response(        &self,        request: CompactRequest,        cx: &AsyncApp,    ) -> BoxFuture<'static, Result<CompactedResponse, LanguageModelCompletionError>> {        let http_client = self.http_client.clone();         let (api_key, api_url, extra_headers) = self.state.read_with(cx, |state, cx| {            let api_url = OpenAiLanguageModelProvider::api_url(cx);            let extra_headers = OpenAiLanguageModelProvider::settings(cx)                .custom_headers                .clone();            (state.api_key_state.key(&api_url), api_url, extra_headers)        });         let provider = PROVIDER_NAME;        let future = self.request_limiter.run(async move {            let Some(api_key) = api_key else {                return Err(LanguageModelCompletionError::NoApiKey { provider });            };            Ok(compact_response(                http_client.as_ref(),                provider.0.as_str(),                &api_url,                &api_key,                request,                &extra_headers,            )            .await?)        });         future.boxed()    }} impl LanguageModel for OpenAiLanguageModel {    fn id(&self) -> LanguageModelId {        self.id.clone()    }     fn name(&self) -> LanguageModelName {        LanguageModelName::from(self.model.display_name().to_string())    }     fn provider_id(&self) -> LanguageModelProviderId {        PROVIDER_ID    }     fn provider_name(&self) -> LanguageModelProviderName {        PROVIDER_NAME    }     fn supports_tools(&self) -> bool {        true    }     fn supports_images(&self) -> bool {        use open_ai::Model;        match &self.model {            Model::FourOmniMini            | Model::Five            | Model::FiveMini            | Model::FiveNano            | Model::FivePointOne            | Model::FivePointTwo            | Model::FivePointThreeCodex            | Model::FivePointFour            | Model::FivePointFourMini

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