use anyhow::{Result, anyhow};use collections::{HashMap, IndexMap};use credentials_provider::CredentialsProvider;use deepseek::DEEPSEEK_API_URL; use futures::Stream;use futures::{FutureExt, StreamExt, future::BoxFuture, stream::BoxStream};use gpui::{App, AppContext, AsyncApp, Context, Entity, SharedString, Task};use http_client::{CustomHeaders, HttpClient};use language_model::{    ApiKeyConfiguration, ApiKeyState, AuthenticateError, EnvVar, IconOrSvg, LanguageModel,    LanguageModelCompletionError, LanguageModelCompletionEvent, LanguageModelEffortLevel,    LanguageModelId, LanguageModelName, LanguageModelProvider, LanguageModelProviderId,    LanguageModelProviderName, LanguageModelProviderState, LanguageModelRequest,    LanguageModelToolChoice, LanguageModelToolResultContent, LanguageModelToolUse, MessageContent,    ProviderSettingsView, RateLimiter, Role, StopReason, TokenUsage, env_var,};pub use settings::DeepseekAvailableModel as AvailableModel;use settings::{Settings, SettingsStore};use std::pin::Pin;use std::sync::{Arc, LazyLock}; use ui::IconName; use language_model::util::{fix_streamed_json, parse_tool_arguments}; const PROVIDER_ID: LanguageModelProviderId = LanguageModelProviderId::new("deepseek");const PROVIDER_NAME: LanguageModelProviderName = LanguageModelProviderName::new("DeepSeek"); const API_KEY_ENV_VAR_NAME: &str = "DEEPSEEK_API_KEY";static API_KEY_ENV_VAR: LazyLock<EnvVar> = env_var!(API_KEY_ENV_VAR_NAME); #[derive(Default)]struct RawToolCall {    id: String,    name: String,    arguments: String,} #[derive(Default, Clone, Debug, PartialEq)]pub struct DeepSeekSettings {    pub api_url: String,    pub available_models: Vec<AvailableModel>,    pub custom_headers: CustomHeaders,}pub struct DeepSeekLanguageModelProvider {    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 = DeepSeekLanguageModelProvider::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 = DeepSeekLanguageModelProvider::api_url(cx);        self.api_key_state.load_if_needed(            api_url,            |this| &mut this.api_key_state,            credentials_provider,            cx,        )    }} impl DeepSeekLanguageModelProvider {    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: deepseek::Model) -> Arc<dyn LanguageModel> {        Arc::new(DeepSeekLanguageModel {            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) -> &DeepSeekSettings {        &crate::AllLanguageModelSettings::get_global(cx).deepseek    }     fn api_url(cx: &App) -> SharedString {        let api_url = &Self::settings(cx).api_url;        if api_url.is_empty() {            DEEPSEEK_API_URL.into()        } else {            SharedString::new(api_url.as_str())        }    }} impl LanguageModelProviderState for DeepSeekLanguageModelProvider {    type ObservableEntity = State;     fn observable_entity(&self) -> Option<Entity<Self::ObservableEntity>> {        Some(self.state.clone())    }} impl LanguageModelProvider for DeepSeekLanguageModelProvider {    fn id(&self) -> LanguageModelProviderId {        PROVIDER_ID    }     fn name(&self) -> LanguageModelProviderName {        PROVIDER_NAME    }     fn icon(&self) -> IconOrSvg {        IconOrSvg::Icon(IconName::AiDeepSeek)    }     fn default_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {        Some(self.create_language_model(deepseek::Model::default()))    }     fn default_fast_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {        Some(self.create_language_model(deepseek::Model::default_fast()))    }     fn provided_models(&self, cx: &App) -> Vec<Arc<dyn LanguageModel>> {        let mut models = IndexMap::default();         models.insert("deepseek-v4-flash", deepseek::Model::V4Flash);        models.insert("deepseek-v4-pro", deepseek::Model::V4Pro);         for available_model in &Self::settings(cx).available_models {            models.insert(                &available_model.name,                deepseek::Model::Custom {                    name: available_model.name.clone(),                    display_name: available_model.display_name.clone(),                    max_tokens: available_model.max_tokens,                    max_output_tokens: available_model.max_output_tokens,                },            );        }         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.deepseek.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))    }} pub struct DeepSeekLanguageModel {    id: LanguageModelId,    model: deepseek::Model,    state: Entity<State>,    http_client: Arc<dyn HttpClient>,    request_limiter: RateLimiter,} impl DeepSeekLanguageModel {    fn stream_completion(        &self,        request: deepseek::Request,        cx: &AsyncApp,    ) -> BoxFuture<'static, Result<BoxStream<'static, Result<deepseek::StreamResponse>>>> {        let http_client = self.http_client.clone();         let (api_key, api_url, extra_headers) = self.state.read_with(cx, |state, cx| {            let api_url = DeepSeekLanguageModelProvider::api_url(cx);            let extra_headers = DeepSeekLanguageModelProvider::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 Some(api_key) = api_key else {                return Err(LanguageModelCompletionError::NoApiKey {                    provider: PROVIDER_NAME,                });            };            let request = deepseek::stream_completion(                http_client.as_ref(),                &api_url,                &api_key,                request,                &extra_headers,            );            let response = request.await?;            Ok(response)        });         async move { Ok(future.await?.boxed()) }.boxed()    }} impl LanguageModel for DeepSeekLanguageModel {    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_streaming_tools(&self) -> bool {        true    }     fn supports_thinking(&self) -> bool {        matches!(            self.model,            deepseek::Model::V4Flash | deepseek::Model::V4Pro        )    }     fn supported_effort_levels(&self) -> Vec<LanguageModelEffortLevel> {        if !self.supports_thinking() {            return Vec::new();        }         vec![            LanguageModelEffortLevel {                name: "High".into(),                value: "high".into(),                is_default: true,            },            LanguageModelEffortLevel {                name: "Max".into(),                value: "max".into(),                is_default: false,            },        ]    }     fn supports_tool_choice(&self, _choice: LanguageModelToolChoice) -> bool {        true    }     fn supports_images(&self) -> bool {        false    }     fn telemetry_id(&self) -> String {        format!("deepseek/{}", self.model.id())    }     fn max_token_count(&self) -> u64 {        self.model.max_token_count()    }     fn max_output_tokens(&self) -> Option<u64> {        self.model.max_output_tokens()    }     fn stream_completion(        &self,        request: LanguageModelRequest,        cx: &AsyncApp,    ) -> BoxFuture<        'static,        Result<            BoxStream<'static, Result<LanguageModelCompletionEvent, LanguageModelCompletionError>>,            LanguageModelCompletionError,        >,    > {        let request = match into_deepseek(request, &self.model, self.max_output_tokens()) {            Ok(request) => request,            Err(error) => return async move { Err(error.into()) }.boxed(),        };        let stream = self.stream_completion(request, cx);         async move {            let mapper = DeepSeekEventMapper::new();            Ok(mapper.map_stream(stream.await?).boxed())        }        .boxed()    }} pub fn into_deepseek(    request: LanguageModelRequest,    model: &deepseek::Model,    max_output_tokens: Option<u64>,) -> Result<deepseek::Request> {    if request.contains_custom_tool_input() {        anyhow::bail!("DeepSeek does not support custom tools");    }     let thinking = deepseek_thinking(model, request.thinking_allowed);    let thinking_enabled = thinking        .as_ref()        .is_some_and(|thinking| thinking.kind == deepseek::ThinkingType::Enabled);     let mut messages = Vec::new();    let mut current_reasoning: Option<String> = None;     for message in request.messages {        for content in message.content {            match content {                MessageContent::Text(text) => {                    let should_add = if message.role == Role::User {                        !text.trim().is_empty()                    } else {                        !text.is_empty()                    };                     if should_add {                        messages.push(match message.role {                            Role::User => deepseek::RequestMessage::User { content: text },                            Role::Assistant => deepseek::RequestMessage::Assistant {                                content: Some(text),                                tool_calls: Vec::new(),                                reasoning_content: current_reasoning.take(),                            },                            Role::System => deepseek::RequestMessage::System { content: text },                        });                    }                }                MessageContent::Thinking { text, .. } => {                    // Accumulate reasoning content for next assistant message                    current_reasoning.get_or_insert_default().push_str(&text);                }                MessageContent::RedactedThinking(_) => {}                MessageContent::Image(_) => {}                MessageContent::Compaction(_) => {}                MessageContent::ToolUse(tool_use) => {                    let input = tool_use                        .input                        .as_json()                        .ok_or_else(|| anyhow!("DeepSeek does not support custom tool calls"))?;                    let tool_call = deepseek::ToolCall {                        id: tool_use.id.to_string(),                        content: deepseek::ToolCallContent::Function {                            function: deepseek::FunctionContent {                                name: tool_use.name.to_string(),                                arguments: serde_json::to_string(input).unwrap_or_default(),                            },                        },                    };                     if let Some(deepseek::RequestMessage::Assistant { tool_calls, .. }) =                        messages.last_mut()                    {                        tool_calls.push(tool_call);                    } else {                        messages.push(deepseek::RequestMessage::Assistant {                            content: None,                            tool_calls: vec![tool_call],                            reasoning_content: current_reasoning.take(),                        });                    }                }                MessageContent::ToolResult(tool_result) => {                    let mut text_parts: Vec<String> = Vec::new();                    for part in &tool_result.content {                        match part {                            LanguageModelToolResultContent::Text(text) => {                                text_parts.push(text.to_string());                            }                            LanguageModelToolResultContent::Image(_) => {                                text_parts.push("[Tool responded with an image]".to_string());                            }                        }                    }                    let content = if text_parts.is_empty() {                        "<Tool returned an empty string>".to_string()                    } else {                        text_parts.join("\n")                    };                    messages.push(deepseek::RequestMessage::Tool {                        content,                        tool_call_id: tool_result.tool_use_id.to_string(),                    });                }            }        }    }     Ok(deepseek::Request {        model: model.id().to_string(),        messages,        stream: true,        max_tokens: max_output_tokens,        temperature: if thinking_enabled {            None        } else {            request.temperature        },        thinking,        reasoning_effort: if thinking_enabled {            into_deepseek_reasoning_effort(request.thinking_effort.as_deref())        } else {            None        },        response_format: None,        tool_choice: request.tool_choice.map(|choice| match choice {            LanguageModelToolChoice::Auto => deepseek::ToolChoice::Auto,            LanguageModelToolChoice::Any => deepseek::ToolChoice::Required,            LanguageModelToolChoice::None => deepseek::ToolChoice::None,        }),        tools: request            .tools            .into_iter()            .map(|tool| {                let input_schema = match tool.input {                    language_model::LanguageModelRequestToolInput::Function {                        input_schema,                        ..                    } => input_schema,                    language_model::LanguageModelRequestToolInput::Custom { .. } => {                        return Err(anyhow::anyhow!("DeepSeek does not support custom tools"));                    }                };                Ok(deepseek::ToolDefinition::Function {                    function: deepseek::FunctionDefinition {                        name: tool.name,                        description: Some(tool.description),                        parameters: Some(input_schema),                    },                })            })            .collect::<Result<_>>()?,    })} 

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