5c400dac8d
Going for a different, arguably simpler design for the Assistant 2 empty state here. Also took the opportunity to adjust other elements like the toolbar, message editor, and some items in the configuration page. <img src="https://github.com/user-attachments/assets/03fd1d48-a675-4eac-b694-bbe4eeaf06e9" width="700px"/> Release Notes: - N/A
861 lines
32 KiB
Rust
861 lines
32 KiB
Rust
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::{AnyElement, AnyView, App, AsyncApp, Context, Entity, Subscription, Task};
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use http_client::{AsyncBody, HttpClient, Method, Response, StatusCode};
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use language_model::{
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AuthenticateError, CloudModel, LanguageModel, LanguageModelCacheConfiguration, LanguageModelId,
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LanguageModelName, LanguageModelProviderId, LanguageModelProviderName,
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LanguageModelProviderState, LanguageModelProviderTosView, LanguageModelRequest, RateLimiter,
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ZED_CLOUD_PROVIDER_ID,
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};
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use language_model::{
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LanguageModelAvailability, LanguageModelCompletionEvent, LanguageModelProvider, LlmApiToken,
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MaxMonthlySpendReachedError, PaymentRequiredError, RefreshLlmTokenListener,
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};
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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::io::{AsyncReadExt, BufReader};
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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 ui::{prelude::*, TintColor};
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use crate::provider::anthropic::{
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count_anthropic_tokens, into_anthropic, map_to_language_model_completion_events,
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};
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use crate::provider::google::into_google;
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use crate::provider::open_ai::{count_open_ai_tokens, into_open_ai};
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use crate::AllLanguageModelSettings;
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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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/// Any extra beta headers to provide when using the model.
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#[serde(default)]
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pub extra_beta_headers: Vec<String>,
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}
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pub struct CloudLanguageModelProvider {
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client: Arc<Client>,
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state: gpui::Entity<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: Entity<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: Entity<UserStore>,
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status: client::Status,
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cx: &mut Context<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 Context<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: &App) -> 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 Context<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: Entity<UserStore>, client: Arc<Client>, cx: &mut App) -> 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(|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::Entity<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(ZED_CLOUD_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 default_model(&self, cx: &App) -> Option<Arc<dyn LanguageModel>> {
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let llm_api_token = self.state.read(cx).llm_api_token.clone();
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let model = CloudModel::Anthropic(anthropic::Model::default());
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Some(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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}))
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}
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fn provided_models(&self, cx: &App) -> 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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models.insert(
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anthropic::Model::Claude3_7Sonnet.id().to_string(),
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CloudModel::Anthropic(anthropic::Model::Claude3_7Sonnet),
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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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extra_beta_headers: model.extra_beta_headers.clone(),
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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: &App) -> 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 App) -> Task<Result<(), AuthenticateError>> {
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Task::ready(Ok(()))
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}
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fn configuration_view(&self, _: &mut Window, cx: &mut App) -> AnyView {
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cx.new(|_| 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: &App) -> 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(
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&self,
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view: LanguageModelProviderTosView,
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cx: &mut App,
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) -> Option<AnyElement> {
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render_accept_terms(self.state.clone(), view, cx)
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}
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fn reset_credentials(&self, _cx: &mut App) -> Task<Result<()>> {
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Task::ready(Ok(()))
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}
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}
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fn render_accept_terms(
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state: Entity<State>,
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view_kind: LanguageModelProviderTosView,
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cx: &mut App,
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) -> Option<AnyElement> {
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if state.read(cx).has_accepted_terms_of_service(cx) {
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return None;
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}
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let accept_terms_disabled = state.read(cx).accept_terms.is_some();
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let terms_button = Button::new("terms_of_service", "Terms of Service")
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.style(ButtonStyle::Subtle)
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.icon(IconName::ArrowUpRight)
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.icon_color(Color::Muted)
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.icon_size(IconSize::XSmall)
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.on_click(move |_, _window, cx| cx.open_url("https://zed.dev/terms-of-service"));
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let text = "To start using Zed AI, please read and accept the";
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let form = v_flex()
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.w_full()
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.gap_2()
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.child(
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h_flex()
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.flex_wrap()
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.items_start()
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.child(Label::new(text))
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.child(terms_button),
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)
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.child({
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let button_container = h_flex().w_full().child(
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Button::new("accept_terms", "I accept the Terms of Service")
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.style(ButtonStyle::Tinted(TintColor::Accent))
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.disabled(accept_terms_disabled)
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.on_click({
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let state = state.downgrade();
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move |_, _window, cx| {
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state
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.update(cx, |state, cx| state.accept_terms_of_service(cx))
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.ok();
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}
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}),
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);
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match view_kind {
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LanguageModelProviderTosView::PromptEditorPopup => button_container.justify_end(),
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LanguageModelProviderTosView::Configuration
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| LanguageModelProviderTosView::ThreadEmptyState => {
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button_container.justify_start()
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}
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}
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});
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Some(form.into_any())
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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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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,
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body: PerformCompletionParams,
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) -> 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?;
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let mut did_retry = false;
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let response = loop {
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let request_builder = http_client::Request::builder();
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let request = request_builder
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.method(Method::POST)
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.uri(http_client.build_zed_llm_url("/completion", &[])?.as_ref())
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.header("Content-Type", "application/json")
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.header("Authorization", format!("Bearer {token}"))
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.body(serde_json::to_string(&body)?.into())?;
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let mut response = http_client.send(request).await?;
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|
if response.status().is_success() {
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break response;
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} else if !did_retry
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&& response
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.headers()
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.get(EXPIRED_LLM_TOKEN_HEADER_NAME)
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|
.is_some()
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{
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did_retry = true;
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token = llm_api_token.refresh(&client).await?;
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} else if response.status() == StatusCode::FORBIDDEN
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&& response
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.headers()
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.get(MAX_LLM_MONTHLY_SPEND_REACHED_HEADER_NAME)
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.is_some()
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{
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break Err(anyhow!(MaxMonthlySpendReachedError))?;
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} else if response.status() == StatusCode::PAYMENT_REQUIRED {
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break Err(anyhow!(PaymentRequiredError))?;
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} else {
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let mut body = String::new();
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response.body_mut().read_to_string(&mut body).await?;
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break Err(anyhow!(
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"cloud language model completion failed with status {}: {body}",
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response.status()
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))?;
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}
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};
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Ok(response)
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}
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}
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|
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impl LanguageModel for CloudLanguageModel {
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fn id(&self) -> LanguageModelId {
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self.id.clone()
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}
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fn name(&self) -> LanguageModelName {
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LanguageModelName::from(self.model.display_name().to_string())
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}
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|
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fn icon(&self) -> Option<IconName> {
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|
self.model.icon()
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}
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|
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fn provider_id(&self) -> LanguageModelProviderId {
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|
LanguageModelProviderId(ZED_CLOUD_PROVIDER_ID.into())
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}
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|
|
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: &App,
|
|
) -> 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 = into_google(request, 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: &AsyncApp,
|
|
) -> BoxFuture<'static, Result<BoxStream<'static, Result<LanguageModelCompletionEvent>>>> {
|
|
match &self.model {
|
|
CloudModel::Anthropic(model) => {
|
|
let request = into_anthropic(
|
|
request,
|
|
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 = into_open_ai(request, 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 = into_google(request, 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: &AsyncApp,
|
|
) -> 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 = into_anthropic(
|
|
request,
|
|
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 =
|
|
into_open_ai(request, 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()),
|
|
}
|
|
},
|
|
)
|
|
}
|
|
|
|
struct ConfigurationView {
|
|
state: gpui::Entity<State>,
|
|
}
|
|
|
|
impl ConfigurationView {
|
|
fn authenticate(&mut self, cx: &mut Context<Self>) {
|
|
self.state.update(cx, |state, cx| {
|
|
state.authenticate(cx).detach_and_log_err(cx);
|
|
});
|
|
cx.notify();
|
|
}
|
|
}
|
|
|
|
impl Render for ConfigurationView {
|
|
fn render(&mut self, _: &mut Window, cx: &mut Context<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()
|
|
.w_full()
|
|
.children(render_accept_terms(
|
|
self.state.clone(),
|
|
LanguageModelProviderTosView::Configuration,
|
|
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))),
|
|
)
|
|
}
|
|
}
|
|
}
|