8204ef1e51
Users now accept ToS from Zed's website when they sign in to Zed the first time. So it's no longer possible that a signed in account could not have accepted the ToS. Release Notes: - N/A --------- Co-authored-by: Mikayla Maki <mikayla.c.maki@gmail.com>
1330 lines
49 KiB
Rust
1330 lines
49 KiB
Rust
use ai_onboarding::YoungAccountBanner;
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use anthropic::AnthropicModelMode;
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use anyhow::{Context as _, Result, anyhow};
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use chrono::{DateTime, Utc};
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use client::{Client, ModelRequestUsage, UserStore, zed_urls};
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use cloud_llm_client::{
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CLIENT_SUPPORTS_STATUS_MESSAGES_HEADER_NAME, CURRENT_PLAN_HEADER_NAME, CompletionBody,
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CompletionEvent, CompletionRequestStatus, CountTokensBody, CountTokensResponse,
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EXPIRED_LLM_TOKEN_HEADER_NAME, ListModelsResponse, MODEL_REQUESTS_RESOURCE_HEADER_VALUE, Plan,
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SERVER_SUPPORTS_STATUS_MESSAGES_HEADER_NAME, SUBSCRIPTION_LIMIT_RESOURCE_HEADER_NAME,
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TOOL_USE_LIMIT_REACHED_HEADER_NAME, ZED_VERSION_HEADER_NAME,
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};
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use futures::{
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AsyncBufReadExt, FutureExt, Stream, StreamExt, future::BoxFuture, stream::BoxStream,
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};
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use google_ai::GoogleModelMode;
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use gpui::{
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AnyElement, AnyView, App, AsyncApp, Context, Entity, SemanticVersion, Subscription, Task,
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};
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use http_client::http::{HeaderMap, HeaderValue};
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use http_client::{AsyncBody, HttpClient, Method, Response, StatusCode};
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use language_model::{
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AuthenticateError, LanguageModel, LanguageModelCacheConfiguration,
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LanguageModelCompletionError, LanguageModelCompletionEvent, LanguageModelId, LanguageModelName,
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LanguageModelProvider, LanguageModelProviderId, LanguageModelProviderName,
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LanguageModelProviderState, LanguageModelRequest, LanguageModelToolChoice,
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LanguageModelToolSchemaFormat, LlmApiToken, ModelRequestLimitReachedError,
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PaymentRequiredError, RateLimiter, RefreshLlmTokenListener,
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};
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use release_channel::AppVersion;
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use schemars::JsonSchema;
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use serde::{Deserialize, Serialize, de::DeserializeOwned};
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use settings::SettingsStore;
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use smol::io::{AsyncReadExt, BufReader};
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use std::pin::Pin;
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use std::str::FromStr as _;
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use std::sync::Arc;
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use std::time::Duration;
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use thiserror::Error;
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use ui::{TintColor, prelude::*};
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use util::{ResultExt as _, maybe};
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use crate::provider::anthropic::{AnthropicEventMapper, count_anthropic_tokens, into_anthropic};
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use crate::provider::google::{GoogleEventMapper, into_google};
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use crate::provider::open_ai::{OpenAiEventMapper, count_open_ai_tokens, into_open_ai};
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pub const PROVIDER_ID: LanguageModelProviderId = language_model::ZED_CLOUD_PROVIDER_ID;
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pub const PROVIDER_NAME: LanguageModelProviderName = language_model::ZED_CLOUD_PROVIDER_NAME;
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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<u64>,
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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<u64>,
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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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/// The model's mode (e.g. thinking)
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pub mode: Option<ModelMode>,
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}
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#[derive(Default, Clone, Debug, PartialEq, Serialize, Deserialize, JsonSchema)]
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#[serde(tag = "type", rename_all = "lowercase")]
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pub enum ModelMode {
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#[default]
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Default,
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Thinking {
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/// The maximum number of tokens to use for reasoning. Must be lower than the model's `max_output_tokens`.
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budget_tokens: Option<u32>,
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},
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}
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impl From<ModelMode> for AnthropicModelMode {
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fn from(value: ModelMode) -> Self {
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match value {
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ModelMode::Default => AnthropicModelMode::Default,
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ModelMode::Thinking { budget_tokens } => AnthropicModelMode::Thinking { budget_tokens },
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}
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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::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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models: Vec<Arc<cloud_llm_client::LanguageModel>>,
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default_model: Option<Arc<cloud_llm_client::LanguageModel>>,
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default_fast_model: Option<Arc<cloud_llm_client::LanguageModel>>,
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recommended_models: Vec<Arc<cloud_llm_client::LanguageModel>>,
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_fetch_models_task: Task<()>,
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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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let mut current_user = user_store.read(cx).watch_current_user();
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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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models: Vec::new(),
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default_model: None,
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default_fast_model: None,
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recommended_models: Vec::new(),
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_fetch_models_task: cx.spawn(async move |this, cx| {
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maybe!(async {
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let (client, llm_api_token) = this
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.read_with(cx, |this, _cx| (client.clone(), this.llm_api_token.clone()))?;
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while current_user.borrow().is_none() {
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current_user.next().await;
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}
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let response =
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Self::fetch_models(client.clone(), llm_api_token.clone()).await?;
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this.update(cx, |this, cx| this.update_models(response, cx))?;
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anyhow::Ok(())
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})
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.await
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.context("failed to fetch Zed models")
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.log_err();
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}),
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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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move |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(async move |this, cx| {
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llm_api_token.refresh(&client).await?;
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let response = Self::fetch_models(client, llm_api_token).await?;
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this.update(cx, |this, cx| {
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this.update_models(response, cx);
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})
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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, cx: &App) -> bool {
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self.user_store.read(cx).current_user().is_none()
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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(async move |state, cx| {
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client.sign_in_with_optional_connect(true, cx).await?;
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state.update(cx, |_, cx| cx.notify())
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})
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}
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fn update_models(&mut self, response: ListModelsResponse, cx: &mut Context<Self>) {
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let mut models = Vec::new();
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for model in response.models {
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models.push(Arc::new(model.clone()));
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// Right now we represent thinking variants of models as separate models on the client,
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// so we need to insert variants for any model that supports thinking.
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if model.supports_thinking {
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models.push(Arc::new(cloud_llm_client::LanguageModel {
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id: cloud_llm_client::LanguageModelId(format!("{}-thinking", model.id).into()),
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display_name: format!("{} Thinking", model.display_name),
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..model
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}));
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}
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}
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self.default_model = models
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.iter()
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.find(|model| model.id == response.default_model)
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.cloned();
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self.default_fast_model = models
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.iter()
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.find(|model| model.id == response.default_fast_model)
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.cloned();
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self.recommended_models = response
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.recommended_models
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.iter()
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.filter_map(|id| models.iter().find(|model| &model.id == id))
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.cloned()
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.collect();
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self.models = models;
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cx.notify();
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}
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async fn fetch_models(
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client: Arc<Client>,
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llm_api_token: LlmApiToken,
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) -> Result<ListModelsResponse> {
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let http_client = &client.http_client();
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let token = llm_api_token.acquire(&client).await?;
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let request = http_client::Request::builder()
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.method(Method::GET)
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.uri(http_client.build_zed_llm_url("/models", &[])?.as_ref())
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.header("Authorization", format!("Bearer {token}"))
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.body(AsyncBody::empty())?;
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let mut response = http_client
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.send(request)
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.await
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.context("failed to send list models request")?;
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if response.status().is_success() {
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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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Ok(serde_json::from_str(&body)?)
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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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anyhow::bail!(
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"error listing models.\nStatus: {:?}\nBody: {body}",
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response.status(),
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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(async move |cx| {
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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(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,
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_maintain_client_status: maintain_client_status,
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}
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}
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fn create_language_model(
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&self,
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model: Arc<cloud_llm_client::LanguageModel>,
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llm_api_token: LlmApiToken,
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) -> Arc<dyn LanguageModel> {
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Arc::new(CloudLanguageModel {
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id: LanguageModelId(SharedString::from(model.id.0.clone())),
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model,
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llm_api_token,
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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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}
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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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PROVIDER_ID
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}
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fn name(&self) -> LanguageModelProviderName {
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PROVIDER_NAME
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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 default_model = self.state.read(cx).default_model.clone()?;
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let llm_api_token = self.state.read(cx).llm_api_token.clone();
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Some(self.create_language_model(default_model, llm_api_token))
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}
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fn default_fast_model(&self, cx: &App) -> Option<Arc<dyn LanguageModel>> {
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let default_fast_model = self.state.read(cx).default_fast_model.clone()?;
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let llm_api_token = self.state.read(cx).llm_api_token.clone();
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Some(self.create_language_model(default_fast_model, llm_api_token))
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}
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fn recommended_models(&self, cx: &App) -> Vec<Arc<dyn LanguageModel>> {
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let llm_api_token = self.state.read(cx).llm_api_token.clone();
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self.state
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.read(cx)
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.recommended_models
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.iter()
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.cloned()
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.map(|model| self.create_language_model(model, llm_api_token.clone()))
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.collect()
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}
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fn provided_models(&self, cx: &App) -> Vec<Arc<dyn LanguageModel>> {
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let llm_api_token = self.state.read(cx).llm_api_token.clone();
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self.state
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.read(cx)
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.models
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.iter()
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.cloned()
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.map(|model| self.create_language_model(model, llm_api_token.clone()))
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.collect()
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}
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fn is_authenticated(&self, cx: &App) -> bool {
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let state = self.state.read(cx);
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!state.is_signed_out(cx)
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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(
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&self,
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_target_agent: language_model::ConfigurationViewTargetAgent,
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_: &mut Window,
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cx: &mut App,
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) -> AnyView {
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cx.new(|_| ConfigurationView::new(self.state.clone()))
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.into()
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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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|
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pub struct CloudLanguageModel {
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id: LanguageModelId,
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model: Arc<cloud_llm_client::LanguageModel>,
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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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struct PerformLlmCompletionResponse {
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response: Response<AsyncBody>,
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usage: Option<ModelRequestUsage>,
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tool_use_limit_reached: bool,
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includes_status_messages: bool,
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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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app_version: Option<SemanticVersion>,
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body: CompletionBody,
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) -> Result<PerformLlmCompletionResponse> {
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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 refreshed_token = false;
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|
|
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loop {
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let request_builder = http_client::Request::builder()
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.method(Method::POST)
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|
.uri(http_client.build_zed_llm_url("/completions", &[])?.as_ref());
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|
let request_builder = if let Some(app_version) = app_version {
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request_builder.header(ZED_VERSION_HEADER_NAME, app_version.to_string())
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} else {
|
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request_builder
|
|
};
|
|
|
|
let request = request_builder
|
|
.header("Content-Type", "application/json")
|
|
.header("Authorization", format!("Bearer {token}"))
|
|
.header(CLIENT_SUPPORTS_STATUS_MESSAGES_HEADER_NAME, "true")
|
|
.body(serde_json::to_string(&body)?.into())?;
|
|
let mut response = http_client.send(request).await?;
|
|
let status = response.status();
|
|
if status.is_success() {
|
|
let includes_status_messages = response
|
|
.headers()
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|
.get(SERVER_SUPPORTS_STATUS_MESSAGES_HEADER_NAME)
|
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.is_some();
|
|
|
|
let tool_use_limit_reached = response
|
|
.headers()
|
|
.get(TOOL_USE_LIMIT_REACHED_HEADER_NAME)
|
|
.is_some();
|
|
|
|
let usage = if includes_status_messages {
|
|
None
|
|
} else {
|
|
ModelRequestUsage::from_headers(response.headers()).ok()
|
|
};
|
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|
|
return Ok(PerformLlmCompletionResponse {
|
|
response,
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|
usage,
|
|
includes_status_messages,
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|
tool_use_limit_reached,
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});
|
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}
|
|
|
|
if !refreshed_token
|
|
&& response
|
|
.headers()
|
|
.get(EXPIRED_LLM_TOKEN_HEADER_NAME)
|
|
.is_some()
|
|
{
|
|
token = llm_api_token.refresh(&client).await?;
|
|
refreshed_token = true;
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continue;
|
|
}
|
|
|
|
if status == StatusCode::FORBIDDEN
|
|
&& response
|
|
.headers()
|
|
.get(SUBSCRIPTION_LIMIT_RESOURCE_HEADER_NAME)
|
|
.is_some()
|
|
{
|
|
if let Some(MODEL_REQUESTS_RESOURCE_HEADER_VALUE) = response
|
|
.headers()
|
|
.get(SUBSCRIPTION_LIMIT_RESOURCE_HEADER_NAME)
|
|
.and_then(|resource| resource.to_str().ok())
|
|
&& let Some(plan) = response
|
|
.headers()
|
|
.get(CURRENT_PLAN_HEADER_NAME)
|
|
.and_then(|plan| plan.to_str().ok())
|
|
.and_then(|plan| cloud_llm_client::Plan::from_str(plan).ok())
|
|
{
|
|
return Err(anyhow!(ModelRequestLimitReachedError { plan }));
|
|
}
|
|
} else if status == StatusCode::PAYMENT_REQUIRED {
|
|
return Err(anyhow!(PaymentRequiredError));
|
|
}
|
|
|
|
let mut body = String::new();
|
|
let headers = response.headers().clone();
|
|
response.body_mut().read_to_string(&mut body).await?;
|
|
return Err(anyhow!(ApiError {
|
|
status,
|
|
body,
|
|
headers
|
|
}));
|
|
}
|
|
}
|
|
}
|
|
|
|
#[derive(Debug, Error)]
|
|
#[error("cloud language model request failed with status {status}: {body}")]
|
|
struct ApiError {
|
|
status: StatusCode,
|
|
body: String,
|
|
headers: HeaderMap<HeaderValue>,
|
|
}
|
|
|
|
/// Represents error responses from Zed's cloud API.
|
|
///
|
|
/// Example JSON for an upstream HTTP error:
|
|
/// ```json
|
|
/// {
|
|
/// "code": "upstream_http_error",
|
|
/// "message": "Received an error from the Anthropic API: upstream connect error or disconnect/reset before headers, reset reason: connection timeout",
|
|
/// "upstream_status": 503
|
|
/// }
|
|
/// ```
|
|
#[derive(Debug, serde::Deserialize)]
|
|
struct CloudApiError {
|
|
code: String,
|
|
message: String,
|
|
#[serde(default)]
|
|
#[serde(deserialize_with = "deserialize_optional_status_code")]
|
|
upstream_status: Option<StatusCode>,
|
|
#[serde(default)]
|
|
retry_after: Option<f64>,
|
|
}
|
|
|
|
fn deserialize_optional_status_code<'de, D>(deserializer: D) -> Result<Option<StatusCode>, D::Error>
|
|
where
|
|
D: serde::Deserializer<'de>,
|
|
{
|
|
let opt: Option<u16> = Option::deserialize(deserializer)?;
|
|
Ok(opt.and_then(|code| StatusCode::from_u16(code).ok()))
|
|
}
|
|
|
|
impl From<ApiError> for LanguageModelCompletionError {
|
|
fn from(error: ApiError) -> Self {
|
|
if let Ok(cloud_error) = serde_json::from_str::<CloudApiError>(&error.body)
|
|
&& cloud_error.code.starts_with("upstream_http_")
|
|
{
|
|
let status = if let Some(status) = cloud_error.upstream_status {
|
|
status
|
|
} else if cloud_error.code.ends_with("_error") {
|
|
error.status
|
|
} else {
|
|
// If there's a status code in the code string (e.g. "upstream_http_429")
|
|
// then use that; otherwise, see if the JSON contains a status code.
|
|
cloud_error
|
|
.code
|
|
.strip_prefix("upstream_http_")
|
|
.and_then(|code_str| code_str.parse::<u16>().ok())
|
|
.and_then(|code| StatusCode::from_u16(code).ok())
|
|
.unwrap_or(error.status)
|
|
};
|
|
|
|
return LanguageModelCompletionError::UpstreamProviderError {
|
|
message: cloud_error.message,
|
|
status,
|
|
retry_after: cloud_error.retry_after.map(Duration::from_secs_f64),
|
|
};
|
|
}
|
|
|
|
let retry_after = None;
|
|
LanguageModelCompletionError::from_http_status(
|
|
PROVIDER_NAME,
|
|
error.status,
|
|
error.body,
|
|
retry_after,
|
|
)
|
|
}
|
|
}
|
|
|
|
impl LanguageModel for CloudLanguageModel {
|
|
fn id(&self) -> LanguageModelId {
|
|
self.id.clone()
|
|
}
|
|
|
|
fn name(&self) -> LanguageModelName {
|
|
LanguageModelName::from(self.model.display_name.clone())
|
|
}
|
|
|
|
fn provider_id(&self) -> LanguageModelProviderId {
|
|
PROVIDER_ID
|
|
}
|
|
|
|
fn provider_name(&self) -> LanguageModelProviderName {
|
|
PROVIDER_NAME
|
|
}
|
|
|
|
fn upstream_provider_id(&self) -> LanguageModelProviderId {
|
|
use cloud_llm_client::LanguageModelProvider::*;
|
|
match self.model.provider {
|
|
Anthropic => language_model::ANTHROPIC_PROVIDER_ID,
|
|
OpenAi => language_model::OPEN_AI_PROVIDER_ID,
|
|
Google => language_model::GOOGLE_PROVIDER_ID,
|
|
}
|
|
}
|
|
|
|
fn upstream_provider_name(&self) -> LanguageModelProviderName {
|
|
use cloud_llm_client::LanguageModelProvider::*;
|
|
match self.model.provider {
|
|
Anthropic => language_model::ANTHROPIC_PROVIDER_NAME,
|
|
OpenAi => language_model::OPEN_AI_PROVIDER_NAME,
|
|
Google => language_model::GOOGLE_PROVIDER_NAME,
|
|
}
|
|
}
|
|
|
|
fn supports_tools(&self) -> bool {
|
|
self.model.supports_tools
|
|
}
|
|
|
|
fn supports_images(&self) -> bool {
|
|
self.model.supports_images
|
|
}
|
|
|
|
fn supports_tool_choice(&self, choice: LanguageModelToolChoice) -> bool {
|
|
match choice {
|
|
LanguageModelToolChoice::Auto
|
|
| LanguageModelToolChoice::Any
|
|
| LanguageModelToolChoice::None => true,
|
|
}
|
|
}
|
|
|
|
fn supports_burn_mode(&self) -> bool {
|
|
self.model.supports_max_mode
|
|
}
|
|
|
|
fn telemetry_id(&self) -> String {
|
|
format!("zed.dev/{}", self.model.id)
|
|
}
|
|
|
|
fn tool_input_format(&self) -> LanguageModelToolSchemaFormat {
|
|
match self.model.provider {
|
|
cloud_llm_client::LanguageModelProvider::Anthropic
|
|
| cloud_llm_client::LanguageModelProvider::OpenAi => {
|
|
LanguageModelToolSchemaFormat::JsonSchema
|
|
}
|
|
cloud_llm_client::LanguageModelProvider::Google => {
|
|
LanguageModelToolSchemaFormat::JsonSchemaSubset
|
|
}
|
|
}
|
|
}
|
|
|
|
fn max_token_count(&self) -> u64 {
|
|
self.model.max_token_count as u64
|
|
}
|
|
|
|
fn max_token_count_in_burn_mode(&self) -> Option<u64> {
|
|
self.model
|
|
.max_token_count_in_max_mode
|
|
.filter(|_| self.model.supports_max_mode)
|
|
.map(|max_token_count| max_token_count as u64)
|
|
}
|
|
|
|
fn cache_configuration(&self) -> Option<LanguageModelCacheConfiguration> {
|
|
match &self.model.provider {
|
|
cloud_llm_client::LanguageModelProvider::Anthropic => {
|
|
Some(LanguageModelCacheConfiguration {
|
|
min_total_token: 2_048,
|
|
should_speculate: true,
|
|
max_cache_anchors: 4,
|
|
})
|
|
}
|
|
cloud_llm_client::LanguageModelProvider::OpenAi
|
|
| cloud_llm_client::LanguageModelProvider::Google => None,
|
|
}
|
|
}
|
|
|
|
fn count_tokens(
|
|
&self,
|
|
request: LanguageModelRequest,
|
|
cx: &App,
|
|
) -> BoxFuture<'static, Result<u64>> {
|
|
match self.model.provider {
|
|
cloud_llm_client::LanguageModelProvider::Anthropic => {
|
|
count_anthropic_tokens(request, cx)
|
|
}
|
|
cloud_llm_client::LanguageModelProvider::OpenAi => {
|
|
let model = match open_ai::Model::from_id(&self.model.id.0) {
|
|
Ok(model) => model,
|
|
Err(err) => return async move { Err(anyhow!(err)) }.boxed(),
|
|
};
|
|
count_open_ai_tokens(request, model, cx)
|
|
}
|
|
cloud_llm_client::LanguageModelProvider::Google => {
|
|
let client = self.client.clone();
|
|
let llm_api_token = self.llm_api_token.clone();
|
|
let model_id = self.model.id.to_string();
|
|
let generate_content_request =
|
|
into_google(request, model_id.clone(), GoogleModelMode::Default);
|
|
async move {
|
|
let http_client = &client.http_client();
|
|
let token = llm_api_token.acquire(&client).await?;
|
|
|
|
let request_body = CountTokensBody {
|
|
provider: cloud_llm_client::LanguageModelProvider::Google,
|
|
model: model_id,
|
|
provider_request: serde_json::to_value(&google_ai::CountTokensRequest {
|
|
generate_content_request,
|
|
})?,
|
|
};
|
|
let request = http_client::Request::builder()
|
|
.method(Method::POST)
|
|
.uri(
|
|
http_client
|
|
.build_zed_llm_url("/count_tokens", &[])?
|
|
.as_ref(),
|
|
)
|
|
.header("Content-Type", "application/json")
|
|
.header("Authorization", format!("Bearer {token}"))
|
|
.body(serde_json::to_string(&request_body)?.into())?;
|
|
let mut response = http_client.send(request).await?;
|
|
let status = response.status();
|
|
let headers = response.headers().clone();
|
|
let mut response_body = String::new();
|
|
response
|
|
.body_mut()
|
|
.read_to_string(&mut response_body)
|
|
.await?;
|
|
|
|
if status.is_success() {
|
|
let response_body: CountTokensResponse =
|
|
serde_json::from_str(&response_body)?;
|
|
|
|
Ok(response_body.tokens as u64)
|
|
} else {
|
|
Err(anyhow!(ApiError {
|
|
status,
|
|
body: response_body,
|
|
headers
|
|
}))
|
|
}
|
|
}
|
|
.boxed()
|
|
}
|
|
}
|
|
}
|
|
|
|
fn stream_completion(
|
|
&self,
|
|
request: LanguageModelRequest,
|
|
cx: &AsyncApp,
|
|
) -> BoxFuture<
|
|
'static,
|
|
Result<
|
|
BoxStream<'static, Result<LanguageModelCompletionEvent, LanguageModelCompletionError>>,
|
|
LanguageModelCompletionError,
|
|
>,
|
|
> {
|
|
let thread_id = request.thread_id.clone();
|
|
let prompt_id = request.prompt_id.clone();
|
|
let intent = request.intent;
|
|
let mode = request.mode;
|
|
let app_version = cx.update(|cx| AppVersion::global(cx)).ok();
|
|
let thinking_allowed = request.thinking_allowed;
|
|
match self.model.provider {
|
|
cloud_llm_client::LanguageModelProvider::Anthropic => {
|
|
let request = into_anthropic(
|
|
request,
|
|
self.model.id.to_string(),
|
|
1.0,
|
|
self.model.max_output_tokens as u64,
|
|
if thinking_allowed && self.model.id.0.ends_with("-thinking") {
|
|
AnthropicModelMode::Thinking {
|
|
budget_tokens: Some(4_096),
|
|
}
|
|
} else {
|
|
AnthropicModelMode::Default
|
|
},
|
|
);
|
|
let client = self.client.clone();
|
|
let llm_api_token = self.llm_api_token.clone();
|
|
let future = self.request_limiter.stream(async move {
|
|
let PerformLlmCompletionResponse {
|
|
response,
|
|
usage,
|
|
includes_status_messages,
|
|
tool_use_limit_reached,
|
|
} = Self::perform_llm_completion(
|
|
client.clone(),
|
|
llm_api_token,
|
|
app_version,
|
|
CompletionBody {
|
|
thread_id,
|
|
prompt_id,
|
|
intent,
|
|
mode,
|
|
provider: cloud_llm_client::LanguageModelProvider::Anthropic,
|
|
model: request.model.clone(),
|
|
provider_request: serde_json::to_value(&request)
|
|
.map_err(|e| anyhow!(e))?,
|
|
},
|
|
)
|
|
.await
|
|
.map_err(|err| match err.downcast::<ApiError>() {
|
|
Ok(api_err) => anyhow!(LanguageModelCompletionError::from(api_err)),
|
|
Err(err) => anyhow!(err),
|
|
})?;
|
|
|
|
let mut mapper = AnthropicEventMapper::new();
|
|
Ok(map_cloud_completion_events(
|
|
Box::pin(
|
|
response_lines(response, includes_status_messages)
|
|
.chain(usage_updated_event(usage))
|
|
.chain(tool_use_limit_reached_event(tool_use_limit_reached)),
|
|
),
|
|
move |event| mapper.map_event(event),
|
|
))
|
|
});
|
|
async move { Ok(future.await?.boxed()) }.boxed()
|
|
}
|
|
cloud_llm_client::LanguageModelProvider::OpenAi => {
|
|
let client = self.client.clone();
|
|
let model = match open_ai::Model::from_id(&self.model.id.0) {
|
|
Ok(model) => model,
|
|
Err(err) => return async move { Err(anyhow!(err).into()) }.boxed(),
|
|
};
|
|
let request = into_open_ai(
|
|
request,
|
|
model.id(),
|
|
model.supports_parallel_tool_calls(),
|
|
model.supports_prompt_cache_key(),
|
|
None,
|
|
None,
|
|
);
|
|
let llm_api_token = self.llm_api_token.clone();
|
|
let future = self.request_limiter.stream(async move {
|
|
let PerformLlmCompletionResponse {
|
|
response,
|
|
usage,
|
|
includes_status_messages,
|
|
tool_use_limit_reached,
|
|
} = Self::perform_llm_completion(
|
|
client.clone(),
|
|
llm_api_token,
|
|
app_version,
|
|
CompletionBody {
|
|
thread_id,
|
|
prompt_id,
|
|
intent,
|
|
mode,
|
|
provider: cloud_llm_client::LanguageModelProvider::OpenAi,
|
|
model: request.model.clone(),
|
|
provider_request: serde_json::to_value(&request)
|
|
.map_err(|e| anyhow!(e))?,
|
|
},
|
|
)
|
|
.await?;
|
|
|
|
let mut mapper = OpenAiEventMapper::new();
|
|
Ok(map_cloud_completion_events(
|
|
Box::pin(
|
|
response_lines(response, includes_status_messages)
|
|
.chain(usage_updated_event(usage))
|
|
.chain(tool_use_limit_reached_event(tool_use_limit_reached)),
|
|
),
|
|
move |event| mapper.map_event(event),
|
|
))
|
|
});
|
|
async move { Ok(future.await?.boxed()) }.boxed()
|
|
}
|
|
cloud_llm_client::LanguageModelProvider::Google => {
|
|
let client = self.client.clone();
|
|
let request =
|
|
into_google(request, self.model.id.to_string(), GoogleModelMode::Default);
|
|
let llm_api_token = self.llm_api_token.clone();
|
|
let future = self.request_limiter.stream(async move {
|
|
let PerformLlmCompletionResponse {
|
|
response,
|
|
usage,
|
|
includes_status_messages,
|
|
tool_use_limit_reached,
|
|
} = Self::perform_llm_completion(
|
|
client.clone(),
|
|
llm_api_token,
|
|
app_version,
|
|
CompletionBody {
|
|
thread_id,
|
|
prompt_id,
|
|
intent,
|
|
mode,
|
|
provider: cloud_llm_client::LanguageModelProvider::Google,
|
|
model: request.model.model_id.clone(),
|
|
provider_request: serde_json::to_value(&request)
|
|
.map_err(|e| anyhow!(e))?,
|
|
},
|
|
)
|
|
.await?;
|
|
|
|
let mut mapper = GoogleEventMapper::new();
|
|
Ok(map_cloud_completion_events(
|
|
Box::pin(
|
|
response_lines(response, includes_status_messages)
|
|
.chain(usage_updated_event(usage))
|
|
.chain(tool_use_limit_reached_event(tool_use_limit_reached)),
|
|
),
|
|
move |event| mapper.map_event(event),
|
|
))
|
|
});
|
|
async move { Ok(future.await?.boxed()) }.boxed()
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
fn map_cloud_completion_events<T, F>(
|
|
stream: Pin<Box<dyn Stream<Item = Result<CompletionEvent<T>>> + Send>>,
|
|
mut map_callback: F,
|
|
) -> BoxStream<'static, Result<LanguageModelCompletionEvent, LanguageModelCompletionError>>
|
|
where
|
|
T: DeserializeOwned + 'static,
|
|
F: FnMut(T) -> Vec<Result<LanguageModelCompletionEvent, LanguageModelCompletionError>>
|
|
+ Send
|
|
+ 'static,
|
|
{
|
|
stream
|
|
.flat_map(move |event| {
|
|
futures::stream::iter(match event {
|
|
Err(error) => {
|
|
vec![Err(LanguageModelCompletionError::from(error))]
|
|
}
|
|
Ok(CompletionEvent::Status(event)) => {
|
|
vec![Ok(LanguageModelCompletionEvent::StatusUpdate(event))]
|
|
}
|
|
Ok(CompletionEvent::Event(event)) => map_callback(event),
|
|
})
|
|
})
|
|
.boxed()
|
|
}
|
|
|
|
fn usage_updated_event<T>(
|
|
usage: Option<ModelRequestUsage>,
|
|
) -> impl Stream<Item = Result<CompletionEvent<T>>> {
|
|
futures::stream::iter(usage.map(|usage| {
|
|
Ok(CompletionEvent::Status(
|
|
CompletionRequestStatus::UsageUpdated {
|
|
amount: usage.amount as usize,
|
|
limit: usage.limit,
|
|
},
|
|
))
|
|
}))
|
|
}
|
|
|
|
fn tool_use_limit_reached_event<T>(
|
|
tool_use_limit_reached: bool,
|
|
) -> impl Stream<Item = Result<CompletionEvent<T>>> {
|
|
futures::stream::iter(tool_use_limit_reached.then(|| {
|
|
Ok(CompletionEvent::Status(
|
|
CompletionRequestStatus::ToolUseLimitReached,
|
|
))
|
|
}))
|
|
}
|
|
|
|
fn response_lines<T: DeserializeOwned>(
|
|
response: Response<AsyncBody>,
|
|
includes_status_messages: bool,
|
|
) -> impl Stream<Item = Result<CompletionEvent<T>>> {
|
|
futures::stream::try_unfold(
|
|
(String::new(), BufReader::new(response.into_body())),
|
|
move |(mut line, mut body)| async move {
|
|
match body.read_line(&mut line).await {
|
|
Ok(0) => Ok(None),
|
|
Ok(_) => {
|
|
let event = if includes_status_messages {
|
|
serde_json::from_str::<CompletionEvent<T>>(&line)?
|
|
} else {
|
|
CompletionEvent::Event(serde_json::from_str::<T>(&line)?)
|
|
};
|
|
|
|
line.clear();
|
|
Ok(Some((event, (line, body))))
|
|
}
|
|
Err(e) => Err(e.into()),
|
|
}
|
|
},
|
|
)
|
|
}
|
|
|
|
#[derive(IntoElement, RegisterComponent)]
|
|
struct ZedAiConfiguration {
|
|
is_connected: bool,
|
|
plan: Option<Plan>,
|
|
subscription_period: Option<(DateTime<Utc>, DateTime<Utc>)>,
|
|
eligible_for_trial: bool,
|
|
account_too_young: bool,
|
|
sign_in_callback: Arc<dyn Fn(&mut Window, &mut App) + Send + Sync>,
|
|
}
|
|
|
|
impl RenderOnce for ZedAiConfiguration {
|
|
fn render(self, _window: &mut Window, _cx: &mut App) -> impl IntoElement {
|
|
let young_account_banner = YoungAccountBanner;
|
|
|
|
let is_pro = self.plan == Some(Plan::ZedPro);
|
|
let subscription_text = match (self.plan, self.subscription_period) {
|
|
(Some(Plan::ZedPro), Some(_)) => {
|
|
"You have access to Zed's hosted models through your Pro subscription."
|
|
}
|
|
(Some(Plan::ZedProTrial), Some(_)) => {
|
|
"You have access to Zed's hosted models through your Pro trial."
|
|
}
|
|
(Some(Plan::ZedFree), Some(_)) => {
|
|
"You have basic access to Zed's hosted models through the Free plan."
|
|
}
|
|
_ => {
|
|
if self.eligible_for_trial {
|
|
"Subscribe for access to Zed's hosted models. Start with a 14 day free trial."
|
|
} else {
|
|
"Subscribe for access to Zed's hosted models."
|
|
}
|
|
}
|
|
};
|
|
|
|
let manage_subscription_buttons = if is_pro {
|
|
Button::new("manage_settings", "Manage Subscription")
|
|
.full_width()
|
|
.style(ButtonStyle::Tinted(TintColor::Accent))
|
|
.on_click(|_, _, cx| cx.open_url(&zed_urls::account_url(cx)))
|
|
.into_any_element()
|
|
} else if self.plan.is_none() || self.eligible_for_trial {
|
|
Button::new("start_trial", "Start 14-day Free Pro Trial")
|
|
.full_width()
|
|
.style(ui::ButtonStyle::Tinted(ui::TintColor::Accent))
|
|
.on_click(|_, _, cx| cx.open_url(&zed_urls::start_trial_url(cx)))
|
|
.into_any_element()
|
|
} else {
|
|
Button::new("upgrade", "Upgrade to Pro")
|
|
.full_width()
|
|
.style(ui::ButtonStyle::Tinted(ui::TintColor::Accent))
|
|
.on_click(|_, _, cx| cx.open_url(&zed_urls::upgrade_to_zed_pro_url(cx)))
|
|
.into_any_element()
|
|
};
|
|
|
|
if !self.is_connected {
|
|
return v_flex()
|
|
.gap_2()
|
|
.child(Label::new("Sign in to have access to Zed's complete agentic experience with hosted models."))
|
|
.child(
|
|
Button::new("sign_in", "Sign In to use Zed AI")
|
|
.icon_color(Color::Muted)
|
|
.icon(IconName::Github)
|
|
.icon_size(IconSize::Small)
|
|
.icon_position(IconPosition::Start)
|
|
.full_width()
|
|
.on_click({
|
|
let callback = self.sign_in_callback.clone();
|
|
move |_, window, cx| (callback)(window, cx)
|
|
}),
|
|
);
|
|
}
|
|
|
|
v_flex().gap_2().w_full().map(|this| {
|
|
if self.account_too_young {
|
|
this.child(young_account_banner).child(
|
|
Button::new("upgrade", "Upgrade to Pro")
|
|
.style(ui::ButtonStyle::Tinted(ui::TintColor::Accent))
|
|
.full_width()
|
|
.on_click(|_, _, cx| cx.open_url(&zed_urls::upgrade_to_zed_pro_url(cx))),
|
|
)
|
|
} else {
|
|
this.text_sm()
|
|
.child(subscription_text)
|
|
.child(manage_subscription_buttons)
|
|
}
|
|
})
|
|
}
|
|
}
|
|
|
|
struct ConfigurationView {
|
|
state: Entity<State>,
|
|
sign_in_callback: Arc<dyn Fn(&mut Window, &mut App) + Send + Sync>,
|
|
}
|
|
|
|
impl ConfigurationView {
|
|
fn new(state: Entity<State>) -> Self {
|
|
let sign_in_callback = Arc::new({
|
|
let state = state.clone();
|
|
move |_window: &mut Window, cx: &mut App| {
|
|
state.update(cx, |state, cx| {
|
|
state.authenticate(cx).detach_and_log_err(cx);
|
|
});
|
|
}
|
|
});
|
|
|
|
Self {
|
|
state,
|
|
sign_in_callback,
|
|
}
|
|
}
|
|
}
|
|
|
|
impl Render for ConfigurationView {
|
|
fn render(&mut self, _: &mut Window, cx: &mut Context<Self>) -> impl IntoElement {
|
|
let state = self.state.read(cx);
|
|
let user_store = state.user_store.read(cx);
|
|
|
|
ZedAiConfiguration {
|
|
is_connected: !state.is_signed_out(cx),
|
|
plan: user_store.plan(),
|
|
subscription_period: user_store.subscription_period(),
|
|
eligible_for_trial: user_store.trial_started_at().is_none(),
|
|
account_too_young: user_store.account_too_young(),
|
|
sign_in_callback: self.sign_in_callback.clone(),
|
|
}
|
|
}
|
|
}
|
|
|
|
impl Component for ZedAiConfiguration {
|
|
fn name() -> &'static str {
|
|
"AI Configuration Content"
|
|
}
|
|
|
|
fn sort_name() -> &'static str {
|
|
"AI Configuration Content"
|
|
}
|
|
|
|
fn scope() -> ComponentScope {
|
|
ComponentScope::Onboarding
|
|
}
|
|
|
|
fn preview(_window: &mut Window, _cx: &mut App) -> Option<AnyElement> {
|
|
fn configuration(
|
|
is_connected: bool,
|
|
plan: Option<Plan>,
|
|
eligible_for_trial: bool,
|
|
account_too_young: bool,
|
|
) -> AnyElement {
|
|
ZedAiConfiguration {
|
|
is_connected,
|
|
plan,
|
|
subscription_period: plan
|
|
.is_some()
|
|
.then(|| (Utc::now(), Utc::now() + chrono::Duration::days(7))),
|
|
eligible_for_trial,
|
|
account_too_young,
|
|
sign_in_callback: Arc::new(|_, _| {}),
|
|
}
|
|
.into_any_element()
|
|
}
|
|
|
|
Some(
|
|
v_flex()
|
|
.p_4()
|
|
.gap_4()
|
|
.children(vec![
|
|
single_example("Not connected", configuration(false, None, false, false)),
|
|
single_example(
|
|
"Accept Terms of Service",
|
|
configuration(true, None, true, false),
|
|
),
|
|
single_example(
|
|
"No Plan - Not eligible for trial",
|
|
configuration(true, None, false, false),
|
|
),
|
|
single_example(
|
|
"No Plan - Eligible for trial",
|
|
configuration(true, None, true, false),
|
|
),
|
|
single_example(
|
|
"Free Plan",
|
|
configuration(true, Some(Plan::ZedFree), true, false),
|
|
),
|
|
single_example(
|
|
"Zed Pro Trial Plan",
|
|
configuration(true, Some(Plan::ZedProTrial), true, false),
|
|
),
|
|
single_example(
|
|
"Zed Pro Plan",
|
|
configuration(true, Some(Plan::ZedPro), true, false),
|
|
),
|
|
])
|
|
.into_any_element(),
|
|
)
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
use http_client::http::{HeaderMap, StatusCode};
|
|
use language_model::LanguageModelCompletionError;
|
|
|
|
#[test]
|
|
fn test_api_error_conversion_with_upstream_http_error() {
|
|
// upstream_http_error with 503 status should become ServerOverloaded
|
|
let error_body = r#"{"code":"upstream_http_error","message":"Received an error from the Anthropic API: upstream connect error or disconnect/reset before headers, reset reason: connection timeout","upstream_status":503}"#;
|
|
|
|
let api_error = ApiError {
|
|
status: StatusCode::INTERNAL_SERVER_ERROR,
|
|
body: error_body.to_string(),
|
|
headers: HeaderMap::new(),
|
|
};
|
|
|
|
let completion_error: LanguageModelCompletionError = api_error.into();
|
|
|
|
match completion_error {
|
|
LanguageModelCompletionError::UpstreamProviderError { message, .. } => {
|
|
assert_eq!(
|
|
message,
|
|
"Received an error from the Anthropic API: upstream connect error or disconnect/reset before headers, reset reason: connection timeout"
|
|
);
|
|
}
|
|
_ => panic!(
|
|
"Expected UpstreamProviderError for upstream 503, got: {:?}",
|
|
completion_error
|
|
),
|
|
}
|
|
|
|
// upstream_http_error with 500 status should become ApiInternalServerError
|
|
let error_body = r#"{"code":"upstream_http_error","message":"Received an error from the OpenAI API: internal server error","upstream_status":500}"#;
|
|
|
|
let api_error = ApiError {
|
|
status: StatusCode::INTERNAL_SERVER_ERROR,
|
|
body: error_body.to_string(),
|
|
headers: HeaderMap::new(),
|
|
};
|
|
|
|
let completion_error: LanguageModelCompletionError = api_error.into();
|
|
|
|
match completion_error {
|
|
LanguageModelCompletionError::UpstreamProviderError { message, .. } => {
|
|
assert_eq!(
|
|
message,
|
|
"Received an error from the OpenAI API: internal server error"
|
|
);
|
|
}
|
|
_ => panic!(
|
|
"Expected UpstreamProviderError for upstream 500, got: {:?}",
|
|
completion_error
|
|
),
|
|
}
|
|
|
|
// upstream_http_error with 429 status should become RateLimitExceeded
|
|
let error_body = r#"{"code":"upstream_http_error","message":"Received an error from the Google API: rate limit exceeded","upstream_status":429}"#;
|
|
|
|
let api_error = ApiError {
|
|
status: StatusCode::INTERNAL_SERVER_ERROR,
|
|
body: error_body.to_string(),
|
|
headers: HeaderMap::new(),
|
|
};
|
|
|
|
let completion_error: LanguageModelCompletionError = api_error.into();
|
|
|
|
match completion_error {
|
|
LanguageModelCompletionError::UpstreamProviderError { message, .. } => {
|
|
assert_eq!(
|
|
message,
|
|
"Received an error from the Google API: rate limit exceeded"
|
|
);
|
|
}
|
|
_ => panic!(
|
|
"Expected UpstreamProviderError for upstream 429, got: {:?}",
|
|
completion_error
|
|
),
|
|
}
|
|
|
|
// Regular 500 error without upstream_http_error should remain ApiInternalServerError for Zed
|
|
let error_body = "Regular internal server error";
|
|
|
|
let api_error = ApiError {
|
|
status: StatusCode::INTERNAL_SERVER_ERROR,
|
|
body: error_body.to_string(),
|
|
headers: HeaderMap::new(),
|
|
};
|
|
|
|
let completion_error: LanguageModelCompletionError = api_error.into();
|
|
|
|
match completion_error {
|
|
LanguageModelCompletionError::ApiInternalServerError { provider, message } => {
|
|
assert_eq!(provider, PROVIDER_NAME);
|
|
assert_eq!(message, "Regular internal server error");
|
|
}
|
|
_ => panic!(
|
|
"Expected ApiInternalServerError for regular 500, got: {:?}",
|
|
completion_error
|
|
),
|
|
}
|
|
|
|
// upstream_http_429 format should be converted to UpstreamProviderError
|
|
let error_body = r#"{"code":"upstream_http_429","message":"Upstream Anthropic rate limit exceeded.","retry_after":30.5}"#;
|
|
|
|
let api_error = ApiError {
|
|
status: StatusCode::INTERNAL_SERVER_ERROR,
|
|
body: error_body.to_string(),
|
|
headers: HeaderMap::new(),
|
|
};
|
|
|
|
let completion_error: LanguageModelCompletionError = api_error.into();
|
|
|
|
match completion_error {
|
|
LanguageModelCompletionError::UpstreamProviderError {
|
|
message,
|
|
status,
|
|
retry_after,
|
|
} => {
|
|
assert_eq!(message, "Upstream Anthropic rate limit exceeded.");
|
|
assert_eq!(status, StatusCode::TOO_MANY_REQUESTS);
|
|
assert_eq!(retry_after, Some(Duration::from_secs_f64(30.5)));
|
|
}
|
|
_ => panic!(
|
|
"Expected UpstreamProviderError for upstream_http_429, got: {:?}",
|
|
completion_error
|
|
),
|
|
}
|
|
|
|
// Invalid JSON in error body should fall back to regular error handling
|
|
let error_body = "Not JSON at all";
|
|
|
|
let api_error = ApiError {
|
|
status: StatusCode::INTERNAL_SERVER_ERROR,
|
|
body: error_body.to_string(),
|
|
headers: HeaderMap::new(),
|
|
};
|
|
|
|
let completion_error: LanguageModelCompletionError = api_error.into();
|
|
|
|
match completion_error {
|
|
LanguageModelCompletionError::ApiInternalServerError { provider, .. } => {
|
|
assert_eq!(provider, PROVIDER_NAME);
|
|
}
|
|
_ => panic!(
|
|
"Expected ApiInternalServerError for invalid JSON, got: {:?}",
|
|
completion_error
|
|
),
|
|
}
|
|
}
|
|
}
|