Add language_models crate to house language model providers (#20945)
This PR adds a new `language_models` crate to house the various language model providers. By extracting the provider definitions out of `language_model`, we're able to remove `language_model`'s dependency on `editor`, which improves incremental compilation when changing `editor`. Release Notes: - N/A
This commit is contained in:
Marshall Bowers
GitHub
parent
335b112abd
commit
cbba44900d
27 changed files with 265 additions and 199 deletions
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@ -1,957 +0,0 @@
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use super::open_ai::count_open_ai_tokens;
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use crate::provider::anthropic::map_to_language_model_completion_events;
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use crate::{
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settings::AllLanguageModelSettings, CloudModel, LanguageModel, LanguageModelCacheConfiguration,
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LanguageModelId, LanguageModelName, LanguageModelProviderId, LanguageModelProviderName,
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LanguageModelProviderState, LanguageModelRequest, RateLimiter,
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};
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use anthropic::AnthropicError;
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use anyhow::{anyhow, Result};
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use client::{
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zed_urls, Client, PerformCompletionParams, UserStore, EXPIRED_LLM_TOKEN_HEADER_NAME,
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MAX_LLM_MONTHLY_SPEND_REACHED_HEADER_NAME,
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};
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use collections::BTreeMap;
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use feature_flags::{FeatureFlagAppExt, LlmClosedBeta, ZedPro};
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use futures::{
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future::BoxFuture, stream::BoxStream, AsyncBufReadExt, FutureExt, Stream, StreamExt,
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TryStreamExt as _,
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};
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use gpui::{
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AnyElement, AnyView, AppContext, AsyncAppContext, EventEmitter, FontWeight, Global, Model,
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ModelContext, ReadGlobal, Subscription, Task,
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};
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use http_client::{AsyncBody, HttpClient, Method, Response, StatusCode};
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use proto::TypedEnvelope;
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use schemars::JsonSchema;
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use serde::{de::DeserializeOwned, Deserialize, Serialize};
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use serde_json::value::RawValue;
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use settings::{Settings, SettingsStore};
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use smol::{
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io::{AsyncReadExt, BufReader},
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lock::{RwLock, RwLockUpgradableReadGuard, RwLockWriteGuard},
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};
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use std::fmt;
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use std::{
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future,
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sync::{Arc, LazyLock},
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};
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use strum::IntoEnumIterator;
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use thiserror::Error;
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use ui::{prelude::*, TintColor};
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use crate::{LanguageModelAvailability, LanguageModelCompletionEvent, LanguageModelProvider};
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use super::anthropic::count_anthropic_tokens;
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pub const PROVIDER_ID: &str = "zed.dev";
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pub const PROVIDER_NAME: &str = "Zed";
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const ZED_CLOUD_PROVIDER_ADDITIONAL_MODELS_JSON: Option<&str> =
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option_env!("ZED_CLOUD_PROVIDER_ADDITIONAL_MODELS_JSON");
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fn zed_cloud_provider_additional_models() -> &'static [AvailableModel] {
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static ADDITIONAL_MODELS: LazyLock<Vec<AvailableModel>> = LazyLock::new(|| {
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ZED_CLOUD_PROVIDER_ADDITIONAL_MODELS_JSON
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.map(|json| serde_json::from_str(json).unwrap())
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.unwrap_or_default()
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});
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ADDITIONAL_MODELS.as_slice()
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}
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#[derive(Default, Clone, Debug, PartialEq)]
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pub struct ZedDotDevSettings {
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pub available_models: Vec<AvailableModel>,
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}
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#[derive(Clone, Debug, PartialEq, Serialize, Deserialize, JsonSchema)]
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#[serde(rename_all = "lowercase")]
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pub enum AvailableProvider {
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Anthropic,
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OpenAi,
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Google,
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}
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#[derive(Clone, Debug, PartialEq, Serialize, Deserialize, JsonSchema)]
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pub struct AvailableModel {
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/// The provider of the language model.
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pub provider: AvailableProvider,
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/// The model's name in the provider's API. e.g. claude-3-5-sonnet-20240620
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pub name: String,
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/// The name displayed in the UI, such as in the assistant panel model dropdown menu.
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pub display_name: Option<String>,
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/// The size of the context window, indicating the maximum number of tokens the model can process.
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pub max_tokens: usize,
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/// The maximum number of output tokens allowed by the model.
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pub max_output_tokens: Option<u32>,
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/// The maximum number of completion tokens allowed by the model (o1-* only)
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pub max_completion_tokens: Option<u32>,
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/// Override this model with a different Anthropic model for tool calls.
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pub tool_override: Option<String>,
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/// Indicates whether this custom model supports caching.
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pub cache_configuration: Option<LanguageModelCacheConfiguration>,
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/// The default temperature to use for this model.
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pub default_temperature: Option<f32>,
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}
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struct GlobalRefreshLlmTokenListener(Model<RefreshLlmTokenListener>);
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impl Global for GlobalRefreshLlmTokenListener {}
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pub struct RefreshLlmTokenEvent;
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pub struct RefreshLlmTokenListener {
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_llm_token_subscription: client::Subscription,
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}
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impl EventEmitter<RefreshLlmTokenEvent> for RefreshLlmTokenListener {}
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impl RefreshLlmTokenListener {
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pub fn register(client: Arc<Client>, cx: &mut AppContext) {
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let listener = cx.new_model(|cx| RefreshLlmTokenListener::new(client, cx));
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cx.set_global(GlobalRefreshLlmTokenListener(listener));
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}
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pub fn global(cx: &AppContext) -> Model<Self> {
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GlobalRefreshLlmTokenListener::global(cx).0.clone()
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}
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fn new(client: Arc<Client>, cx: &mut ModelContext<Self>) -> Self {
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Self {
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_llm_token_subscription: client
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.add_message_handler(cx.weak_model(), Self::handle_refresh_llm_token),
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}
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}
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async fn handle_refresh_llm_token(
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this: Model<Self>,
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_: TypedEnvelope<proto::RefreshLlmToken>,
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mut cx: AsyncAppContext,
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) -> Result<()> {
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this.update(&mut cx, |_this, cx| cx.emit(RefreshLlmTokenEvent))
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}
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}
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pub struct CloudLanguageModelProvider {
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client: Arc<Client>,
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state: gpui::Model<State>,
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_maintain_client_status: Task<()>,
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}
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pub struct State {
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client: Arc<Client>,
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llm_api_token: LlmApiToken,
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user_store: Model<UserStore>,
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status: client::Status,
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accept_terms: Option<Task<Result<()>>>,
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_settings_subscription: Subscription,
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_llm_token_subscription: Subscription,
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}
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impl State {
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fn new(
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client: Arc<Client>,
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user_store: Model<UserStore>,
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status: client::Status,
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cx: &mut ModelContext<Self>,
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) -> Self {
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let refresh_llm_token_listener = RefreshLlmTokenListener::global(cx);
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Self {
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client: client.clone(),
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llm_api_token: LlmApiToken::default(),
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user_store,
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status,
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accept_terms: None,
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_settings_subscription: cx.observe_global::<SettingsStore>(|_, cx| {
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cx.notify();
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}),
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_llm_token_subscription: cx.subscribe(
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&refresh_llm_token_listener,
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|this, _listener, _event, cx| {
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let client = this.client.clone();
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let llm_api_token = this.llm_api_token.clone();
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cx.spawn(|_this, _cx| async move {
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llm_api_token.refresh(&client).await?;
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anyhow::Ok(())
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})
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.detach_and_log_err(cx);
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},
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),
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}
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}
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fn is_signed_out(&self) -> bool {
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self.status.is_signed_out()
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}
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fn authenticate(&self, cx: &mut ModelContext<Self>) -> Task<Result<()>> {
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let client = self.client.clone();
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cx.spawn(move |this, mut cx| async move {
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client.authenticate_and_connect(true, &cx).await?;
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this.update(&mut cx, |_, cx| cx.notify())
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})
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}
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fn has_accepted_terms_of_service(&self, cx: &AppContext) -> bool {
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self.user_store
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.read(cx)
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.current_user_has_accepted_terms()
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.unwrap_or(false)
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}
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fn accept_terms_of_service(&mut self, cx: &mut ModelContext<Self>) {
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let user_store = self.user_store.clone();
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self.accept_terms = Some(cx.spawn(move |this, mut cx| async move {
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let _ = user_store
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.update(&mut cx, |store, cx| store.accept_terms_of_service(cx))?
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.await;
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this.update(&mut cx, |this, cx| {
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this.accept_terms = None;
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cx.notify()
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})
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}));
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}
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}
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impl CloudLanguageModelProvider {
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pub fn new(user_store: Model<UserStore>, client: Arc<Client>, cx: &mut AppContext) -> Self {
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let mut status_rx = client.status();
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let status = *status_rx.borrow();
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let state = cx.new_model(|cx| State::new(client.clone(), user_store.clone(), status, cx));
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let state_ref = state.downgrade();
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let maintain_client_status = cx.spawn(|mut cx| async move {
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while let Some(status) = status_rx.next().await {
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if let Some(this) = state_ref.upgrade() {
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_ = this.update(&mut cx, |this, cx| {
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if this.status != status {
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this.status = status;
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cx.notify();
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}
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});
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} else {
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break;
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}
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}
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});
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Self {
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client,
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state: state.clone(),
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_maintain_client_status: maintain_client_status,
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}
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}
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}
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impl LanguageModelProviderState for CloudLanguageModelProvider {
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type ObservableEntity = State;
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fn observable_entity(&self) -> Option<gpui::Model<Self::ObservableEntity>> {
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Some(self.state.clone())
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}
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}
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impl LanguageModelProvider for CloudLanguageModelProvider {
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fn id(&self) -> LanguageModelProviderId {
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LanguageModelProviderId(PROVIDER_ID.into())
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}
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fn name(&self) -> LanguageModelProviderName {
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LanguageModelProviderName(PROVIDER_NAME.into())
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}
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fn icon(&self) -> IconName {
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IconName::AiZed
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}
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fn provided_models(&self, cx: &AppContext) -> Vec<Arc<dyn LanguageModel>> {
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let mut models = BTreeMap::default();
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if cx.is_staff() {
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for model in anthropic::Model::iter() {
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if !matches!(model, anthropic::Model::Custom { .. }) {
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models.insert(model.id().to_string(), CloudModel::Anthropic(model));
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}
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}
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for model in open_ai::Model::iter() {
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if !matches!(model, open_ai::Model::Custom { .. }) {
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models.insert(model.id().to_string(), CloudModel::OpenAi(model));
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}
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}
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for model in google_ai::Model::iter() {
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if !matches!(model, google_ai::Model::Custom { .. }) {
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models.insert(model.id().to_string(), CloudModel::Google(model));
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}
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}
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} else {
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models.insert(
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anthropic::Model::Claude3_5Sonnet.id().to_string(),
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CloudModel::Anthropic(anthropic::Model::Claude3_5Sonnet),
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);
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}
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let llm_closed_beta_models = if cx.has_flag::<LlmClosedBeta>() {
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zed_cloud_provider_additional_models()
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} else {
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&[]
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};
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// Override with available models from settings
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for model in AllLanguageModelSettings::get_global(cx)
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.zed_dot_dev
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.available_models
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.iter()
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.chain(llm_closed_beta_models)
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.cloned()
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{
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let model = match model.provider {
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AvailableProvider::Anthropic => CloudModel::Anthropic(anthropic::Model::Custom {
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name: model.name.clone(),
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display_name: model.display_name.clone(),
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max_tokens: model.max_tokens,
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tool_override: model.tool_override.clone(),
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cache_configuration: model.cache_configuration.as_ref().map(|config| {
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anthropic::AnthropicModelCacheConfiguration {
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max_cache_anchors: config.max_cache_anchors,
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should_speculate: config.should_speculate,
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min_total_token: config.min_total_token,
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}
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}),
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default_temperature: model.default_temperature,
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max_output_tokens: model.max_output_tokens,
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}),
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AvailableProvider::OpenAi => CloudModel::OpenAi(open_ai::Model::Custom {
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name: model.name.clone(),
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display_name: model.display_name.clone(),
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max_tokens: model.max_tokens,
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max_output_tokens: model.max_output_tokens,
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max_completion_tokens: model.max_completion_tokens,
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}),
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AvailableProvider::Google => CloudModel::Google(google_ai::Model::Custom {
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name: model.name.clone(),
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display_name: model.display_name.clone(),
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max_tokens: model.max_tokens,
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}),
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};
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models.insert(model.id().to_string(), model.clone());
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}
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let llm_api_token = self.state.read(cx).llm_api_token.clone();
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models
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.into_values()
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.map(|model| {
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Arc::new(CloudLanguageModel {
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id: LanguageModelId::from(model.id().to_string()),
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model,
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llm_api_token: llm_api_token.clone(),
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client: self.client.clone(),
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request_limiter: RateLimiter::new(4),
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}) as Arc<dyn LanguageModel>
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})
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.collect()
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}
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fn is_authenticated(&self, cx: &AppContext) -> bool {
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!self.state.read(cx).is_signed_out()
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}
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fn authenticate(&self, _cx: &mut AppContext) -> Task<Result<()>> {
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Task::ready(Ok(()))
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}
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fn configuration_view(&self, cx: &mut WindowContext) -> AnyView {
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cx.new_view(|_cx| ConfigurationView {
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state: self.state.clone(),
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})
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.into()
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}
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fn must_accept_terms(&self, cx: &AppContext) -> bool {
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!self.state.read(cx).has_accepted_terms_of_service(cx)
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}
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fn render_accept_terms(&self, cx: &mut WindowContext) -> Option<AnyElement> {
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let state = self.state.read(cx);
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let terms = [(
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"terms_of_service",
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"Terms of Service",
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"https://zed.dev/terms-of-service",
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)]
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.map(|(id, label, url)| {
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Button::new(id, label)
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.style(ButtonStyle::Subtle)
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.icon(IconName::ExternalLink)
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.icon_size(IconSize::XSmall)
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.icon_color(Color::Muted)
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.on_click(move |_, cx| cx.open_url(url))
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});
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|
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if state.has_accepted_terms_of_service(cx) {
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None
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} else {
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let disabled = state.accept_terms.is_some();
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Some(
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v_flex()
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.gap_2()
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.child(
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v_flex()
|
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.child(Label::new("Terms and Conditions").weight(FontWeight::MEDIUM))
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.child(
|
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Label::new(
|
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"Please read and accept our terms and conditions to continue.",
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)
|
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.size(LabelSize::Small),
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),
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)
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.child(v_flex().gap_1().children(terms))
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.child(
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h_flex().justify_end().child(
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Button::new("accept_terms", "I've read it and accept it")
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.disabled(disabled)
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.on_click({
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let state = self.state.downgrade();
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move |_, cx| {
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state
|
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.update(cx, |state, cx| {
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state.accept_terms_of_service(cx)
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})
|
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.ok();
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}
|
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}),
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),
|
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)
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.into_any(),
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)
|
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}
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}
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|
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fn reset_credentials(&self, _cx: &mut AppContext) -> Task<Result<()>> {
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Task::ready(Ok(()))
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}
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}
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|
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pub struct CloudLanguageModel {
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id: LanguageModelId,
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model: CloudModel,
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llm_api_token: LlmApiToken,
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client: Arc<Client>,
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request_limiter: RateLimiter,
|
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}
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|
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#[derive(Clone, Default)]
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struct LlmApiToken(Arc<RwLock<Option<String>>>);
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|
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#[derive(Error, Debug)]
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pub struct PaymentRequiredError;
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|
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impl fmt::Display for PaymentRequiredError {
|
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fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
|
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write!(
|
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f,
|
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"Payment required to use this language model. Please upgrade your account."
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)
|
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}
|
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}
|
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|
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#[derive(Error, Debug)]
|
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pub struct MaxMonthlySpendReachedError;
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|
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impl fmt::Display for MaxMonthlySpendReachedError {
|
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fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
|
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write!(
|
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f,
|
||||
"Maximum spending limit reached for this month. For more usage, increase your spending limit."
|
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)
|
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}
|
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}
|
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|
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impl CloudLanguageModel {
|
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async fn perform_llm_completion(
|
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client: Arc<Client>,
|
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llm_api_token: LlmApiToken,
|
||||
body: PerformCompletionParams,
|
||||
) -> Result<Response<AsyncBody>> {
|
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let http_client = &client.http_client();
|
||||
|
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let mut token = llm_api_token.acquire(&client).await?;
|
||||
let mut did_retry = false;
|
||||
|
||||
let response = loop {
|
||||
let request_builder = http_client::Request::builder();
|
||||
let request = request_builder
|
||||
.method(Method::POST)
|
||||
.uri(http_client.build_zed_llm_url("/completion", &[])?.as_ref())
|
||||
.header("Content-Type", "application/json")
|
||||
.header("Authorization", format!("Bearer {token}"))
|
||||
.body(serde_json::to_string(&body)?.into())?;
|
||||
let mut response = http_client.send(request).await?;
|
||||
if response.status().is_success() {
|
||||
break response;
|
||||
} else if !did_retry
|
||||
&& response
|
||||
.headers()
|
||||
.get(EXPIRED_LLM_TOKEN_HEADER_NAME)
|
||||
.is_some()
|
||||
{
|
||||
did_retry = true;
|
||||
token = llm_api_token.refresh(&client).await?;
|
||||
} else if response.status() == StatusCode::FORBIDDEN
|
||||
&& response
|
||||
.headers()
|
||||
.get(MAX_LLM_MONTHLY_SPEND_REACHED_HEADER_NAME)
|
||||
.is_some()
|
||||
{
|
||||
break Err(anyhow!(MaxMonthlySpendReachedError))?;
|
||||
} else if response.status() == StatusCode::PAYMENT_REQUIRED {
|
||||
break Err(anyhow!(PaymentRequiredError))?;
|
||||
} else {
|
||||
let mut body = String::new();
|
||||
response.body_mut().read_to_string(&mut body).await?;
|
||||
break Err(anyhow!(
|
||||
"cloud language model completion failed with status {}: {body}",
|
||||
response.status()
|
||||
))?;
|
||||
}
|
||||
};
|
||||
|
||||
Ok(response)
|
||||
}
|
||||
}
|
||||
|
||||
impl LanguageModel for CloudLanguageModel {
|
||||
fn id(&self) -> LanguageModelId {
|
||||
self.id.clone()
|
||||
}
|
||||
|
||||
fn name(&self) -> LanguageModelName {
|
||||
LanguageModelName::from(self.model.display_name().to_string())
|
||||
}
|
||||
|
||||
fn icon(&self) -> Option<IconName> {
|
||||
self.model.icon()
|
||||
}
|
||||
|
||||
fn provider_id(&self) -> LanguageModelProviderId {
|
||||
LanguageModelProviderId(PROVIDER_ID.into())
|
||||
}
|
||||
|
||||
fn provider_name(&self) -> LanguageModelProviderName {
|
||||
LanguageModelProviderName(PROVIDER_NAME.into())
|
||||
}
|
||||
|
||||
fn telemetry_id(&self) -> String {
|
||||
format!("zed.dev/{}", self.model.id())
|
||||
}
|
||||
|
||||
fn availability(&self) -> LanguageModelAvailability {
|
||||
self.model.availability()
|
||||
}
|
||||
|
||||
fn max_token_count(&self) -> usize {
|
||||
self.model.max_token_count()
|
||||
}
|
||||
|
||||
fn cache_configuration(&self) -> Option<LanguageModelCacheConfiguration> {
|
||||
match &self.model {
|
||||
CloudModel::Anthropic(model) => {
|
||||
model
|
||||
.cache_configuration()
|
||||
.map(|cache| LanguageModelCacheConfiguration {
|
||||
max_cache_anchors: cache.max_cache_anchors,
|
||||
should_speculate: cache.should_speculate,
|
||||
min_total_token: cache.min_total_token,
|
||||
})
|
||||
}
|
||||
CloudModel::OpenAi(_) | CloudModel::Google(_) => None,
|
||||
}
|
||||
}
|
||||
|
||||
fn count_tokens(
|
||||
&self,
|
||||
request: LanguageModelRequest,
|
||||
cx: &AppContext,
|
||||
) -> BoxFuture<'static, Result<usize>> {
|
||||
match self.model.clone() {
|
||||
CloudModel::Anthropic(_) => count_anthropic_tokens(request, cx),
|
||||
CloudModel::OpenAi(model) => count_open_ai_tokens(request, model, cx),
|
||||
CloudModel::Google(model) => {
|
||||
let client = self.client.clone();
|
||||
let request = request.into_google(model.id().into());
|
||||
let request = google_ai::CountTokensRequest {
|
||||
contents: request.contents,
|
||||
};
|
||||
async move {
|
||||
let request = serde_json::to_string(&request)?;
|
||||
let response = client
|
||||
.request(proto::CountLanguageModelTokens {
|
||||
provider: proto::LanguageModelProvider::Google as i32,
|
||||
request,
|
||||
})
|
||||
.await?;
|
||||
Ok(response.token_count as usize)
|
||||
}
|
||||
.boxed()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fn stream_completion(
|
||||
&self,
|
||||
request: LanguageModelRequest,
|
||||
_cx: &AsyncAppContext,
|
||||
) -> BoxFuture<'static, Result<BoxStream<'static, Result<LanguageModelCompletionEvent>>>> {
|
||||
match &self.model {
|
||||
CloudModel::Anthropic(model) => {
|
||||
let request = request.into_anthropic(
|
||||
model.id().into(),
|
||||
model.default_temperature(),
|
||||
model.max_output_tokens(),
|
||||
);
|
||||
let client = self.client.clone();
|
||||
let llm_api_token = self.llm_api_token.clone();
|
||||
let future = self.request_limiter.stream(async move {
|
||||
let response = Self::perform_llm_completion(
|
||||
client.clone(),
|
||||
llm_api_token,
|
||||
PerformCompletionParams {
|
||||
provider: client::LanguageModelProvider::Anthropic,
|
||||
model: request.model.clone(),
|
||||
provider_request: RawValue::from_string(serde_json::to_string(
|
||||
&request,
|
||||
)?)?,
|
||||
},
|
||||
)
|
||||
.await?;
|
||||
Ok(map_to_language_model_completion_events(Box::pin(
|
||||
response_lines(response).map_err(AnthropicError::Other),
|
||||
)))
|
||||
});
|
||||
async move { Ok(future.await?.boxed()) }.boxed()
|
||||
}
|
||||
CloudModel::OpenAi(model) => {
|
||||
let client = self.client.clone();
|
||||
let request = request.into_open_ai(model.id().into(), model.max_output_tokens());
|
||||
let llm_api_token = self.llm_api_token.clone();
|
||||
let future = self.request_limiter.stream(async move {
|
||||
let response = Self::perform_llm_completion(
|
||||
client.clone(),
|
||||
llm_api_token,
|
||||
PerformCompletionParams {
|
||||
provider: client::LanguageModelProvider::OpenAi,
|
||||
model: request.model.clone(),
|
||||
provider_request: RawValue::from_string(serde_json::to_string(
|
||||
&request,
|
||||
)?)?,
|
||||
},
|
||||
)
|
||||
.await?;
|
||||
Ok(open_ai::extract_text_from_events(response_lines(response)))
|
||||
});
|
||||
async move {
|
||||
Ok(future
|
||||
.await?
|
||||
.map(|result| result.map(LanguageModelCompletionEvent::Text))
|
||||
.boxed())
|
||||
}
|
||||
.boxed()
|
||||
}
|
||||
CloudModel::Google(model) => {
|
||||
let client = self.client.clone();
|
||||
let request = request.into_google(model.id().into());
|
||||
let llm_api_token = self.llm_api_token.clone();
|
||||
let future = self.request_limiter.stream(async move {
|
||||
let response = Self::perform_llm_completion(
|
||||
client.clone(),
|
||||
llm_api_token,
|
||||
PerformCompletionParams {
|
||||
provider: client::LanguageModelProvider::Google,
|
||||
model: request.model.clone(),
|
||||
provider_request: RawValue::from_string(serde_json::to_string(
|
||||
&request,
|
||||
)?)?,
|
||||
},
|
||||
)
|
||||
.await?;
|
||||
Ok(google_ai::extract_text_from_events(response_lines(
|
||||
response,
|
||||
)))
|
||||
});
|
||||
async move {
|
||||
Ok(future
|
||||
.await?
|
||||
.map(|result| result.map(LanguageModelCompletionEvent::Text))
|
||||
.boxed())
|
||||
}
|
||||
.boxed()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fn use_any_tool(
|
||||
&self,
|
||||
request: LanguageModelRequest,
|
||||
tool_name: String,
|
||||
tool_description: String,
|
||||
input_schema: serde_json::Value,
|
||||
_cx: &AsyncAppContext,
|
||||
) -> BoxFuture<'static, Result<BoxStream<'static, Result<String>>>> {
|
||||
let client = self.client.clone();
|
||||
let llm_api_token = self.llm_api_token.clone();
|
||||
|
||||
match &self.model {
|
||||
CloudModel::Anthropic(model) => {
|
||||
let mut request = request.into_anthropic(
|
||||
model.tool_model_id().into(),
|
||||
model.default_temperature(),
|
||||
model.max_output_tokens(),
|
||||
);
|
||||
request.tool_choice = Some(anthropic::ToolChoice::Tool {
|
||||
name: tool_name.clone(),
|
||||
});
|
||||
request.tools = vec![anthropic::Tool {
|
||||
name: tool_name.clone(),
|
||||
description: tool_description,
|
||||
input_schema,
|
||||
}];
|
||||
|
||||
self.request_limiter
|
||||
.run(async move {
|
||||
let response = Self::perform_llm_completion(
|
||||
client.clone(),
|
||||
llm_api_token,
|
||||
PerformCompletionParams {
|
||||
provider: client::LanguageModelProvider::Anthropic,
|
||||
model: request.model.clone(),
|
||||
provider_request: RawValue::from_string(serde_json::to_string(
|
||||
&request,
|
||||
)?)?,
|
||||
},
|
||||
)
|
||||
.await?;
|
||||
|
||||
Ok(anthropic::extract_tool_args_from_events(
|
||||
tool_name,
|
||||
Box::pin(response_lines(response)),
|
||||
)
|
||||
.await?
|
||||
.boxed())
|
||||
})
|
||||
.boxed()
|
||||
}
|
||||
CloudModel::OpenAi(model) => {
|
||||
let mut request =
|
||||
request.into_open_ai(model.id().into(), model.max_output_tokens());
|
||||
request.tool_choice = Some(open_ai::ToolChoice::Other(
|
||||
open_ai::ToolDefinition::Function {
|
||||
function: open_ai::FunctionDefinition {
|
||||
name: tool_name.clone(),
|
||||
description: None,
|
||||
parameters: None,
|
||||
},
|
||||
},
|
||||
));
|
||||
request.tools = vec![open_ai::ToolDefinition::Function {
|
||||
function: open_ai::FunctionDefinition {
|
||||
name: tool_name.clone(),
|
||||
description: Some(tool_description),
|
||||
parameters: Some(input_schema),
|
||||
},
|
||||
}];
|
||||
|
||||
self.request_limiter
|
||||
.run(async move {
|
||||
let response = Self::perform_llm_completion(
|
||||
client.clone(),
|
||||
llm_api_token,
|
||||
PerformCompletionParams {
|
||||
provider: client::LanguageModelProvider::OpenAi,
|
||||
model: request.model.clone(),
|
||||
provider_request: RawValue::from_string(serde_json::to_string(
|
||||
&request,
|
||||
)?)?,
|
||||
},
|
||||
)
|
||||
.await?;
|
||||
|
||||
Ok(open_ai::extract_tool_args_from_events(
|
||||
tool_name,
|
||||
Box::pin(response_lines(response)),
|
||||
)
|
||||
.await?
|
||||
.boxed())
|
||||
})
|
||||
.boxed()
|
||||
}
|
||||
CloudModel::Google(_) => {
|
||||
future::ready(Err(anyhow!("tool use not implemented for Google AI"))).boxed()
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fn response_lines<T: DeserializeOwned>(
|
||||
response: Response<AsyncBody>,
|
||||
) -> impl Stream<Item = Result<T>> {
|
||||
futures::stream::try_unfold(
|
||||
(String::new(), BufReader::new(response.into_body())),
|
||||
move |(mut line, mut body)| async {
|
||||
match body.read_line(&mut line).await {
|
||||
Ok(0) => Ok(None),
|
||||
Ok(_) => {
|
||||
let event: T = serde_json::from_str(&line)?;
|
||||
line.clear();
|
||||
Ok(Some((event, (line, body))))
|
||||
}
|
||||
Err(e) => Err(e.into()),
|
||||
}
|
||||
},
|
||||
)
|
||||
}
|
||||
|
||||
impl LlmApiToken {
|
||||
async fn acquire(&self, client: &Arc<Client>) -> Result<String> {
|
||||
let lock = self.0.upgradable_read().await;
|
||||
if let Some(token) = lock.as_ref() {
|
||||
Ok(token.to_string())
|
||||
} else {
|
||||
Self::fetch(RwLockUpgradableReadGuard::upgrade(lock).await, client).await
|
||||
}
|
||||
}
|
||||
|
||||
async fn refresh(&self, client: &Arc<Client>) -> Result<String> {
|
||||
Self::fetch(self.0.write().await, client).await
|
||||
}
|
||||
|
||||
async fn fetch<'a>(
|
||||
mut lock: RwLockWriteGuard<'a, Option<String>>,
|
||||
client: &Arc<Client>,
|
||||
) -> Result<String> {
|
||||
let response = client.request(proto::GetLlmToken {}).await?;
|
||||
*lock = Some(response.token.clone());
|
||||
Ok(response.token.clone())
|
||||
}
|
||||
}
|
||||
|
||||
struct ConfigurationView {
|
||||
state: gpui::Model<State>,
|
||||
}
|
||||
|
||||
impl ConfigurationView {
|
||||
fn authenticate(&mut self, cx: &mut ViewContext<Self>) {
|
||||
self.state.update(cx, |state, cx| {
|
||||
state.authenticate(cx).detach_and_log_err(cx);
|
||||
});
|
||||
cx.notify();
|
||||
}
|
||||
|
||||
fn render_accept_terms(&mut self, cx: &mut ViewContext<Self>) -> Option<AnyElement> {
|
||||
if self.state.read(cx).has_accepted_terms_of_service(cx) {
|
||||
return None;
|
||||
}
|
||||
|
||||
let accept_terms_disabled = self.state.read(cx).accept_terms.is_some();
|
||||
|
||||
let terms_button = Button::new("terms_of_service", "Terms of Service")
|
||||
.style(ButtonStyle::Subtle)
|
||||
.icon(IconName::ExternalLink)
|
||||
.icon_color(Color::Muted)
|
||||
.on_click(move |_, cx| cx.open_url("https://zed.dev/terms-of-service"));
|
||||
|
||||
let text =
|
||||
"In order to use Zed AI, please read and accept our terms and conditions to continue:";
|
||||
|
||||
let form = v_flex()
|
||||
.gap_2()
|
||||
.child(Label::new("Terms and Conditions"))
|
||||
.child(Label::new(text))
|
||||
.child(h_flex().justify_center().child(terms_button))
|
||||
.child(
|
||||
h_flex().justify_center().child(
|
||||
Button::new("accept_terms", "I've read and accept the terms of service")
|
||||
.style(ButtonStyle::Tinted(TintColor::Accent))
|
||||
.disabled(accept_terms_disabled)
|
||||
.on_click({
|
||||
let state = self.state.downgrade();
|
||||
move |_, cx| {
|
||||
state
|
||||
.update(cx, |state, cx| state.accept_terms_of_service(cx))
|
||||
.ok();
|
||||
}
|
||||
}),
|
||||
),
|
||||
);
|
||||
|
||||
Some(form.into_any())
|
||||
}
|
||||
}
|
||||
|
||||
impl Render for ConfigurationView {
|
||||
fn render(&mut self, cx: &mut ViewContext<Self>) -> impl IntoElement {
|
||||
const ZED_AI_URL: &str = "https://zed.dev/ai";
|
||||
|
||||
let is_connected = !self.state.read(cx).is_signed_out();
|
||||
let plan = self.state.read(cx).user_store.read(cx).current_plan();
|
||||
let has_accepted_terms = self.state.read(cx).has_accepted_terms_of_service(cx);
|
||||
|
||||
let is_pro = plan == Some(proto::Plan::ZedPro);
|
||||
let subscription_text = Label::new(if is_pro {
|
||||
"You have full access to Zed's hosted LLMs, which include models from Anthropic, OpenAI, and Google. They come with faster speeds and higher limits through Zed Pro."
|
||||
} else {
|
||||
"You have basic access to models from Anthropic through the Zed AI Free plan."
|
||||
});
|
||||
let manage_subscription_button = if is_pro {
|
||||
Some(
|
||||
h_flex().child(
|
||||
Button::new("manage_settings", "Manage Subscription")
|
||||
.style(ButtonStyle::Tinted(TintColor::Accent))
|
||||
.on_click(cx.listener(|_, _, cx| cx.open_url(&zed_urls::account_url(cx)))),
|
||||
),
|
||||
)
|
||||
} else if cx.has_flag::<ZedPro>() {
|
||||
Some(
|
||||
h_flex()
|
||||
.gap_2()
|
||||
.child(
|
||||
Button::new("learn_more", "Learn more")
|
||||
.style(ButtonStyle::Subtle)
|
||||
.on_click(cx.listener(|_, _, cx| cx.open_url(ZED_AI_URL))),
|
||||
)
|
||||
.child(
|
||||
Button::new("upgrade", "Upgrade")
|
||||
.style(ButtonStyle::Subtle)
|
||||
.color(Color::Accent)
|
||||
.on_click(
|
||||
cx.listener(|_, _, cx| cx.open_url(&zed_urls::account_url(cx))),
|
||||
),
|
||||
),
|
||||
)
|
||||
} else {
|
||||
None
|
||||
};
|
||||
|
||||
if is_connected {
|
||||
v_flex()
|
||||
.gap_3()
|
||||
.max_w_4_5()
|
||||
.children(self.render_accept_terms(cx))
|
||||
.when(has_accepted_terms, |this| {
|
||||
this.child(subscription_text)
|
||||
.children(manage_subscription_button)
|
||||
})
|
||||
} else {
|
||||
v_flex()
|
||||
.gap_2()
|
||||
.child(Label::new("Use Zed AI to access hosted language models."))
|
||||
.child(
|
||||
Button::new("sign_in", "Sign In")
|
||||
.icon_color(Color::Muted)
|
||||
.icon(IconName::Github)
|
||||
.icon_position(IconPosition::Start)
|
||||
.on_click(cx.listener(move |this, _, cx| this.authenticate(cx))),
|
||||
)
|
||||
}
|
||||
}
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue