1188 lines
43 KiB
Rust
1188 lines
43 KiB
Rust
use crate::AllLanguageModelSettings;
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use crate::ui::InstructionListItem;
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use anthropic::{
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AnthropicError, AnthropicModelMode, ContentDelta, Event, ResponseContent, ToolResultContent,
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ToolResultPart, Usage,
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};
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use anyhow::{Context as _, Result, anyhow};
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use collections::{BTreeMap, HashMap};
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use credentials_provider::CredentialsProvider;
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use editor::{Editor, EditorElement, EditorStyle};
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use futures::Stream;
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use futures::{FutureExt, StreamExt, future::BoxFuture, stream::BoxStream};
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use gpui::{
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AnyView, App, AsyncApp, Context, Entity, FontStyle, Subscription, Task, TextStyle, WhiteSpace,
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};
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use http_client::HttpClient;
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use language_model::{
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AuthenticateError, ConfigurationViewTargetAgent, LanguageModel,
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LanguageModelCacheConfiguration, LanguageModelCompletionError, LanguageModelId,
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LanguageModelName, LanguageModelProvider, LanguageModelProviderId, LanguageModelProviderName,
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LanguageModelProviderState, LanguageModelRequest, LanguageModelToolChoice,
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LanguageModelToolResultContent, MessageContent, RateLimiter, Role,
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};
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use language_model::{LanguageModelCompletionEvent, LanguageModelToolUse, StopReason};
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use schemars::JsonSchema;
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use serde::{Deserialize, Serialize};
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use settings::{Settings, SettingsStore};
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use std::pin::Pin;
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use std::str::FromStr;
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use std::sync::Arc;
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use strum::IntoEnumIterator;
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use theme::ThemeSettings;
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use ui::{Icon, IconName, List, Tooltip, prelude::*};
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use util::ResultExt;
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const PROVIDER_ID: LanguageModelProviderId = language_model::ANTHROPIC_PROVIDER_ID;
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const PROVIDER_NAME: LanguageModelProviderName = language_model::ANTHROPIC_PROVIDER_NAME;
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#[derive(Default, Clone, Debug, PartialEq)]
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pub struct AnthropicSettings {
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pub api_url: String,
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/// Extend Zed's list of Anthropic models.
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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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pub struct AvailableModel {
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/// The model's name in the Anthropic API. e.g. claude-3-5-sonnet-latest, claude-3-opus-20240229, etc
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pub name: String,
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/// The model's name in Zed's UI, such as in the model selector dropdown menu in the assistant panel.
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pub display_name: Option<String>,
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/// The model's context window size.
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pub max_tokens: u64,
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/// A model `name` to substitute when calling tools, in case the primary model doesn't support tool calling.
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pub tool_override: Option<String>,
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/// Configuration of Anthropic's caching API.
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pub cache_configuration: Option<LanguageModelCacheConfiguration>,
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pub max_output_tokens: Option<u64>,
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pub default_temperature: Option<f32>,
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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(Clone, Debug, Default, 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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impl From<AnthropicModelMode> for ModelMode {
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fn from(value: AnthropicModelMode) -> Self {
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match value {
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AnthropicModelMode::Default => ModelMode::Default,
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AnthropicModelMode::Thinking { budget_tokens } => ModelMode::Thinking { budget_tokens },
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}
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}
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}
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pub struct AnthropicLanguageModelProvider {
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http_client: Arc<dyn HttpClient>,
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state: gpui::Entity<State>,
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}
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const ANTHROPIC_API_KEY_VAR: &str = "ANTHROPIC_API_KEY";
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pub struct State {
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api_key: Option<String>,
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api_key_from_env: bool,
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_subscription: Subscription,
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}
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impl State {
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fn reset_api_key(&self, cx: &mut Context<Self>) -> Task<Result<()>> {
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let credentials_provider = <dyn CredentialsProvider>::global(cx);
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let api_url = AllLanguageModelSettings::get_global(cx)
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.anthropic
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.api_url
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.clone();
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cx.spawn(async move |this, cx| {
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credentials_provider
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.delete_credentials(&api_url, &cx)
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.await
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.ok();
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this.update(cx, |this, cx| {
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this.api_key = None;
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this.api_key_from_env = false;
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cx.notify();
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})
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})
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}
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fn set_api_key(&mut self, api_key: String, cx: &mut Context<Self>) -> Task<Result<()>> {
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let credentials_provider = <dyn CredentialsProvider>::global(cx);
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let api_url = AllLanguageModelSettings::get_global(cx)
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.anthropic
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.api_url
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.clone();
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cx.spawn(async move |this, cx| {
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credentials_provider
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.write_credentials(&api_url, "Bearer", api_key.as_bytes(), &cx)
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.await
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.ok();
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this.update(cx, |this, cx| {
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this.api_key = Some(api_key);
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cx.notify();
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})
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})
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}
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fn is_authenticated(&self) -> bool {
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self.api_key.is_some()
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}
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fn authenticate(&self, cx: &mut Context<Self>) -> Task<Result<(), AuthenticateError>> {
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if self.is_authenticated() {
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return Task::ready(Ok(()));
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}
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let key = AnthropicLanguageModelProvider::api_key(cx);
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cx.spawn(async move |this, cx| {
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let key = key.await?;
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this.update(cx, |this, cx| {
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this.api_key = Some(key.key);
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this.api_key_from_env = key.from_env;
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cx.notify();
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})?;
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Ok(())
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})
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}
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}
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pub struct ApiKey {
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pub key: String,
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pub from_env: bool,
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}
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impl AnthropicLanguageModelProvider {
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pub fn new(http_client: Arc<dyn HttpClient>, cx: &mut App) -> Self {
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let state = cx.new(|cx| State {
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api_key: None,
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api_key_from_env: false,
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_subscription: cx.observe_global::<SettingsStore>(|_, cx| {
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cx.notify();
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}),
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});
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Self { http_client, state }
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}
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fn create_language_model(&self, model: anthropic::Model) -> Arc<dyn LanguageModel> {
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Arc::new(AnthropicModel {
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id: LanguageModelId::from(model.id().to_string()),
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model,
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state: self.state.clone(),
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http_client: self.http_client.clone(),
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request_limiter: RateLimiter::new(4),
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})
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}
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pub fn api_key(cx: &mut App) -> Task<Result<ApiKey>> {
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let credentials_provider = <dyn CredentialsProvider>::global(cx);
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let api_url = AllLanguageModelSettings::get_global(cx)
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.anthropic
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.api_url
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.clone();
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if let Ok(key) = std::env::var(ANTHROPIC_API_KEY_VAR) {
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Task::ready(Ok(ApiKey {
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key,
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from_env: true,
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}))
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} else {
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cx.spawn(async move |cx| {
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let (_, api_key) = credentials_provider
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.read_credentials(&api_url, &cx)
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.await?
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.ok_or(AuthenticateError::CredentialsNotFound)?;
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Ok(ApiKey {
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key: String::from_utf8(api_key).context("invalid {PROVIDER_NAME} API key")?,
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from_env: false,
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})
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})
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}
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}
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}
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impl LanguageModelProviderState for AnthropicLanguageModelProvider {
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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 AnthropicLanguageModelProvider {
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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::AiAnthropic
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}
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fn default_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {
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Some(self.create_language_model(anthropic::Model::default()))
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}
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fn default_fast_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {
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Some(self.create_language_model(anthropic::Model::default_fast()))
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}
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fn recommended_models(&self, _cx: &App) -> Vec<Arc<dyn LanguageModel>> {
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[
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anthropic::Model::ClaudeSonnet4,
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anthropic::Model::ClaudeSonnet4Thinking,
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]
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.into_iter()
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.map(|model| self.create_language_model(model))
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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 mut models = BTreeMap::default();
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// Add base models from anthropic::Model::iter()
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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(), model);
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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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.anthropic
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.available_models
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.iter()
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{
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models.insert(
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model.name.clone(),
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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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max_output_tokens: model.max_output_tokens,
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default_temperature: model.default_temperature,
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extra_beta_headers: model.extra_beta_headers.clone(),
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mode: model.mode.clone().unwrap_or_default().into(),
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},
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);
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}
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models
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.into_values()
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.map(|model| self.create_language_model(model))
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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_authenticated()
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}
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fn authenticate(&self, cx: &mut App) -> Task<Result<(), AuthenticateError>> {
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self.state.update(cx, |state, cx| state.authenticate(cx))
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}
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fn configuration_view(
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&self,
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target_agent: ConfigurationViewTargetAgent,
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window: &mut Window,
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cx: &mut App,
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) -> AnyView {
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cx.new(|cx| ConfigurationView::new(self.state.clone(), target_agent, window, cx))
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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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self.state.update(cx, |state, cx| state.reset_api_key(cx))
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}
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}
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pub struct AnthropicModel {
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id: LanguageModelId,
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model: anthropic::Model,
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state: gpui::Entity<State>,
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http_client: Arc<dyn HttpClient>,
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request_limiter: RateLimiter,
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}
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pub fn count_anthropic_tokens(
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request: LanguageModelRequest,
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cx: &App,
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) -> BoxFuture<'static, Result<u64>> {
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cx.background_spawn(async move {
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let messages = request.messages;
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let mut tokens_from_images = 0;
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let mut string_messages = Vec::with_capacity(messages.len());
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for message in messages {
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use language_model::MessageContent;
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let mut string_contents = String::new();
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for content in message.content {
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match content {
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MessageContent::Text(text) => {
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string_contents.push_str(&text);
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}
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MessageContent::Thinking { .. } => {
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// Thinking blocks are not included in the input token count.
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}
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MessageContent::RedactedThinking(_) => {
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// Thinking blocks are not included in the input token count.
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}
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MessageContent::Image(image) => {
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tokens_from_images += image.estimate_tokens();
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}
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MessageContent::ToolUse(_tool_use) => {
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// TODO: Estimate token usage from tool uses.
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}
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MessageContent::ToolResult(tool_result) => match &tool_result.content {
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LanguageModelToolResultContent::Text(text) => {
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string_contents.push_str(text);
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}
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LanguageModelToolResultContent::Image(image) => {
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tokens_from_images += image.estimate_tokens();
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}
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},
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}
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}
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if !string_contents.is_empty() {
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string_messages.push(tiktoken_rs::ChatCompletionRequestMessage {
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role: match message.role {
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Role::User => "user".into(),
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Role::Assistant => "assistant".into(),
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Role::System => "system".into(),
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},
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content: Some(string_contents),
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name: None,
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function_call: None,
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});
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}
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}
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// Tiktoken doesn't yet support these models, so we manually use the
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// same tokenizer as GPT-4.
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tiktoken_rs::num_tokens_from_messages("gpt-4", &string_messages)
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.map(|tokens| (tokens + tokens_from_images) as u64)
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})
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.boxed()
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}
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|
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impl AnthropicModel {
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fn stream_completion(
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&self,
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request: anthropic::Request,
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cx: &AsyncApp,
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) -> BoxFuture<
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'static,
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Result<
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BoxStream<'static, Result<anthropic::Event, AnthropicError>>,
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LanguageModelCompletionError,
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>,
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> {
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let http_client = self.http_client.clone();
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let Ok((api_key, api_url)) = cx.read_entity(&self.state, |state, cx| {
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let settings = &AllLanguageModelSettings::get_global(cx).anthropic;
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(state.api_key.clone(), settings.api_url.clone())
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}) else {
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return futures::future::ready(Err(anyhow!("App state dropped").into())).boxed();
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};
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async move {
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let Some(api_key) = api_key else {
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return Err(LanguageModelCompletionError::NoApiKey {
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provider: PROVIDER_NAME,
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});
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};
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let request =
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anthropic::stream_completion(http_client.as_ref(), &api_url, &api_key, request);
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request.await.map_err(Into::into)
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}
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.boxed()
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}
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}
|
|
|
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impl LanguageModel for AnthropicModel {
|
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fn id(&self) -> LanguageModelId {
|
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self.id.clone()
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}
|
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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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fn provider_id(&self) -> LanguageModelProviderId {
|
|
PROVIDER_ID
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|
}
|
|
|
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fn provider_name(&self) -> LanguageModelProviderName {
|
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PROVIDER_NAME
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|
}
|
|
|
|
fn supports_tools(&self) -> bool {
|
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true
|
|
}
|
|
|
|
fn supports_images(&self) -> bool {
|
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true
|
|
}
|
|
|
|
fn supports_tool_choice(&self, choice: LanguageModelToolChoice) -> bool {
|
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match choice {
|
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LanguageModelToolChoice::Auto
|
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| LanguageModelToolChoice::Any
|
|
| LanguageModelToolChoice::None => true,
|
|
}
|
|
}
|
|
|
|
fn telemetry_id(&self) -> String {
|
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format!("anthropic/{}", self.model.id())
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|
}
|
|
|
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fn api_key(&self, cx: &App) -> Option<String> {
|
|
self.state.read(cx).api_key.clone()
|
|
}
|
|
|
|
fn max_token_count(&self) -> u64 {
|
|
self.model.max_token_count()
|
|
}
|
|
|
|
fn max_output_tokens(&self) -> Option<u64> {
|
|
Some(self.model.max_output_tokens())
|
|
}
|
|
|
|
fn count_tokens(
|
|
&self,
|
|
request: LanguageModelRequest,
|
|
cx: &App,
|
|
) -> BoxFuture<'static, Result<u64>> {
|
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count_anthropic_tokens(request, cx)
|
|
}
|
|
|
|
fn stream_completion(
|
|
&self,
|
|
request: LanguageModelRequest,
|
|
cx: &AsyncApp,
|
|
) -> BoxFuture<
|
|
'static,
|
|
Result<
|
|
BoxStream<'static, Result<LanguageModelCompletionEvent, LanguageModelCompletionError>>,
|
|
LanguageModelCompletionError,
|
|
>,
|
|
> {
|
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let request = into_anthropic(
|
|
request,
|
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self.model.request_id().into(),
|
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self.model.default_temperature(),
|
|
self.model.max_output_tokens(),
|
|
self.model.mode(),
|
|
);
|
|
let request = self.stream_completion(request, cx);
|
|
let future = self.request_limiter.stream(async move {
|
|
let response = request.await?;
|
|
Ok(AnthropicEventMapper::new().map_stream(response))
|
|
});
|
|
async move { Ok(future.await?.boxed()) }.boxed()
|
|
}
|
|
|
|
fn cache_configuration(&self) -> Option<LanguageModelCacheConfiguration> {
|
|
self.model
|
|
.cache_configuration()
|
|
.map(|config| LanguageModelCacheConfiguration {
|
|
max_cache_anchors: config.max_cache_anchors,
|
|
should_speculate: config.should_speculate,
|
|
min_total_token: config.min_total_token,
|
|
})
|
|
}
|
|
}
|
|
|
|
pub fn into_anthropic(
|
|
request: LanguageModelRequest,
|
|
model: String,
|
|
default_temperature: f32,
|
|
max_output_tokens: u64,
|
|
mode: AnthropicModelMode,
|
|
) -> anthropic::Request {
|
|
let mut new_messages: Vec<anthropic::Message> = Vec::new();
|
|
let mut system_message = String::new();
|
|
|
|
for message in request.messages {
|
|
if message.contents_empty() {
|
|
continue;
|
|
}
|
|
|
|
match message.role {
|
|
Role::User | Role::Assistant => {
|
|
let mut anthropic_message_content: Vec<anthropic::RequestContent> = message
|
|
.content
|
|
.into_iter()
|
|
.filter_map(|content| match content {
|
|
MessageContent::Text(text) => {
|
|
let text = if text.chars().last().map_or(false, |c| c.is_whitespace()) {
|
|
text.trim_end().to_string()
|
|
} else {
|
|
text
|
|
};
|
|
if !text.is_empty() {
|
|
Some(anthropic::RequestContent::Text {
|
|
text,
|
|
cache_control: None,
|
|
})
|
|
} else {
|
|
None
|
|
}
|
|
}
|
|
MessageContent::Thinking {
|
|
text: thinking,
|
|
signature,
|
|
} => {
|
|
if !thinking.is_empty() {
|
|
Some(anthropic::RequestContent::Thinking {
|
|
thinking,
|
|
signature: signature.unwrap_or_default(),
|
|
cache_control: None,
|
|
})
|
|
} else {
|
|
None
|
|
}
|
|
}
|
|
MessageContent::RedactedThinking(data) => {
|
|
if !data.is_empty() {
|
|
Some(anthropic::RequestContent::RedactedThinking { data })
|
|
} else {
|
|
None
|
|
}
|
|
}
|
|
MessageContent::Image(image) => Some(anthropic::RequestContent::Image {
|
|
source: anthropic::ImageSource {
|
|
source_type: "base64".to_string(),
|
|
media_type: "image/png".to_string(),
|
|
data: image.source.to_string(),
|
|
},
|
|
cache_control: None,
|
|
}),
|
|
MessageContent::ToolUse(tool_use) => {
|
|
Some(anthropic::RequestContent::ToolUse {
|
|
id: tool_use.id.to_string(),
|
|
name: tool_use.name.to_string(),
|
|
input: tool_use.input,
|
|
cache_control: None,
|
|
})
|
|
}
|
|
MessageContent::ToolResult(tool_result) => {
|
|
Some(anthropic::RequestContent::ToolResult {
|
|
tool_use_id: tool_result.tool_use_id.to_string(),
|
|
is_error: tool_result.is_error,
|
|
content: match tool_result.content {
|
|
LanguageModelToolResultContent::Text(text) => {
|
|
ToolResultContent::Plain(text.to_string())
|
|
}
|
|
LanguageModelToolResultContent::Image(image) => {
|
|
ToolResultContent::Multipart(vec![ToolResultPart::Image {
|
|
source: anthropic::ImageSource {
|
|
source_type: "base64".to_string(),
|
|
media_type: "image/png".to_string(),
|
|
data: image.source.to_string(),
|
|
},
|
|
}])
|
|
}
|
|
},
|
|
cache_control: None,
|
|
})
|
|
}
|
|
})
|
|
.collect();
|
|
let anthropic_role = match message.role {
|
|
Role::User => anthropic::Role::User,
|
|
Role::Assistant => anthropic::Role::Assistant,
|
|
Role::System => unreachable!("System role should never occur here"),
|
|
};
|
|
if let Some(last_message) = new_messages.last_mut() {
|
|
if last_message.role == anthropic_role {
|
|
last_message.content.extend(anthropic_message_content);
|
|
continue;
|
|
}
|
|
}
|
|
|
|
// Mark the last segment of the message as cached
|
|
if message.cache {
|
|
let cache_control_value = Some(anthropic::CacheControl {
|
|
cache_type: anthropic::CacheControlType::Ephemeral,
|
|
});
|
|
for message_content in anthropic_message_content.iter_mut().rev() {
|
|
match message_content {
|
|
anthropic::RequestContent::RedactedThinking { .. } => {
|
|
// Caching is not possible, fallback to next message
|
|
}
|
|
anthropic::RequestContent::Text { cache_control, .. }
|
|
| anthropic::RequestContent::Thinking { cache_control, .. }
|
|
| anthropic::RequestContent::Image { cache_control, .. }
|
|
| anthropic::RequestContent::ToolUse { cache_control, .. }
|
|
| anthropic::RequestContent::ToolResult { cache_control, .. } => {
|
|
*cache_control = cache_control_value;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
new_messages.push(anthropic::Message {
|
|
role: anthropic_role,
|
|
content: anthropic_message_content,
|
|
});
|
|
}
|
|
Role::System => {
|
|
if !system_message.is_empty() {
|
|
system_message.push_str("\n\n");
|
|
}
|
|
system_message.push_str(&message.string_contents());
|
|
}
|
|
}
|
|
}
|
|
|
|
anthropic::Request {
|
|
model,
|
|
messages: new_messages,
|
|
max_tokens: max_output_tokens,
|
|
system: if system_message.is_empty() {
|
|
None
|
|
} else {
|
|
Some(anthropic::StringOrContents::String(system_message))
|
|
},
|
|
thinking: if request.thinking_allowed
|
|
&& let AnthropicModelMode::Thinking { budget_tokens } = mode
|
|
{
|
|
Some(anthropic::Thinking::Enabled { budget_tokens })
|
|
} else {
|
|
None
|
|
},
|
|
tools: request
|
|
.tools
|
|
.into_iter()
|
|
.map(|tool| anthropic::Tool {
|
|
name: tool.name,
|
|
description: tool.description,
|
|
input_schema: tool.input_schema,
|
|
})
|
|
.collect(),
|
|
tool_choice: request.tool_choice.map(|choice| match choice {
|
|
LanguageModelToolChoice::Auto => anthropic::ToolChoice::Auto,
|
|
LanguageModelToolChoice::Any => anthropic::ToolChoice::Any,
|
|
LanguageModelToolChoice::None => anthropic::ToolChoice::None,
|
|
}),
|
|
metadata: None,
|
|
stop_sequences: Vec::new(),
|
|
temperature: request.temperature.or(Some(default_temperature)),
|
|
top_k: None,
|
|
top_p: None,
|
|
}
|
|
}
|
|
|
|
pub struct AnthropicEventMapper {
|
|
tool_uses_by_index: HashMap<usize, RawToolUse>,
|
|
usage: Usage,
|
|
stop_reason: StopReason,
|
|
}
|
|
|
|
impl AnthropicEventMapper {
|
|
pub fn new() -> Self {
|
|
Self {
|
|
tool_uses_by_index: HashMap::default(),
|
|
usage: Usage::default(),
|
|
stop_reason: StopReason::EndTurn,
|
|
}
|
|
}
|
|
|
|
pub fn map_stream(
|
|
mut self,
|
|
events: Pin<Box<dyn Send + Stream<Item = Result<Event, AnthropicError>>>>,
|
|
) -> impl Stream<Item = Result<LanguageModelCompletionEvent, LanguageModelCompletionError>>
|
|
{
|
|
events.flat_map(move |event| {
|
|
futures::stream::iter(match event {
|
|
Ok(event) => self.map_event(event),
|
|
Err(error) => vec![Err(error.into())],
|
|
})
|
|
})
|
|
}
|
|
|
|
pub fn map_event(
|
|
&mut self,
|
|
event: Event,
|
|
) -> Vec<Result<LanguageModelCompletionEvent, LanguageModelCompletionError>> {
|
|
match event {
|
|
Event::ContentBlockStart {
|
|
index,
|
|
content_block,
|
|
} => match content_block {
|
|
ResponseContent::Text { text } => {
|
|
vec![Ok(LanguageModelCompletionEvent::Text(text))]
|
|
}
|
|
ResponseContent::Thinking { thinking } => {
|
|
vec![Ok(LanguageModelCompletionEvent::Thinking {
|
|
text: thinking,
|
|
signature: None,
|
|
})]
|
|
}
|
|
ResponseContent::RedactedThinking { data } => {
|
|
vec![Ok(LanguageModelCompletionEvent::RedactedThinking { data })]
|
|
}
|
|
ResponseContent::ToolUse { id, name, .. } => {
|
|
self.tool_uses_by_index.insert(
|
|
index,
|
|
RawToolUse {
|
|
id,
|
|
name,
|
|
input_json: String::new(),
|
|
},
|
|
);
|
|
Vec::new()
|
|
}
|
|
},
|
|
Event::ContentBlockDelta { index, delta } => match delta {
|
|
ContentDelta::TextDelta { text } => {
|
|
vec![Ok(LanguageModelCompletionEvent::Text(text))]
|
|
}
|
|
ContentDelta::ThinkingDelta { thinking } => {
|
|
vec![Ok(LanguageModelCompletionEvent::Thinking {
|
|
text: thinking,
|
|
signature: None,
|
|
})]
|
|
}
|
|
ContentDelta::SignatureDelta { signature } => {
|
|
vec![Ok(LanguageModelCompletionEvent::Thinking {
|
|
text: "".to_string(),
|
|
signature: Some(signature),
|
|
})]
|
|
}
|
|
ContentDelta::InputJsonDelta { partial_json } => {
|
|
if let Some(tool_use) = self.tool_uses_by_index.get_mut(&index) {
|
|
tool_use.input_json.push_str(&partial_json);
|
|
|
|
// Try to convert invalid (incomplete) JSON into
|
|
// valid JSON that serde can accept, e.g. by closing
|
|
// unclosed delimiters. This way, we can update the
|
|
// UI with whatever has been streamed back so far.
|
|
if let Ok(input) = serde_json::Value::from_str(
|
|
&partial_json_fixer::fix_json(&tool_use.input_json),
|
|
) {
|
|
return vec![Ok(LanguageModelCompletionEvent::ToolUse(
|
|
LanguageModelToolUse {
|
|
id: tool_use.id.clone().into(),
|
|
name: tool_use.name.clone().into(),
|
|
is_input_complete: false,
|
|
raw_input: tool_use.input_json.clone(),
|
|
input,
|
|
},
|
|
))];
|
|
}
|
|
}
|
|
return vec![];
|
|
}
|
|
},
|
|
Event::ContentBlockStop { index } => {
|
|
if let Some(tool_use) = self.tool_uses_by_index.remove(&index) {
|
|
let input_json = tool_use.input_json.trim();
|
|
let input_value = if input_json.is_empty() {
|
|
Ok(serde_json::Value::Object(serde_json::Map::default()))
|
|
} else {
|
|
serde_json::Value::from_str(input_json)
|
|
};
|
|
let event_result = match input_value {
|
|
Ok(input) => Ok(LanguageModelCompletionEvent::ToolUse(
|
|
LanguageModelToolUse {
|
|
id: tool_use.id.into(),
|
|
name: tool_use.name.into(),
|
|
is_input_complete: true,
|
|
input,
|
|
raw_input: tool_use.input_json.clone(),
|
|
},
|
|
)),
|
|
Err(json_parse_err) => {
|
|
Ok(LanguageModelCompletionEvent::ToolUseJsonParseError {
|
|
id: tool_use.id.into(),
|
|
tool_name: tool_use.name.into(),
|
|
raw_input: input_json.into(),
|
|
json_parse_error: json_parse_err.to_string(),
|
|
})
|
|
}
|
|
};
|
|
|
|
vec![event_result]
|
|
} else {
|
|
Vec::new()
|
|
}
|
|
}
|
|
Event::MessageStart { message } => {
|
|
update_usage(&mut self.usage, &message.usage);
|
|
vec![
|
|
Ok(LanguageModelCompletionEvent::UsageUpdate(convert_usage(
|
|
&self.usage,
|
|
))),
|
|
Ok(LanguageModelCompletionEvent::StartMessage {
|
|
message_id: message.id,
|
|
}),
|
|
]
|
|
}
|
|
Event::MessageDelta { delta, usage } => {
|
|
update_usage(&mut self.usage, &usage);
|
|
if let Some(stop_reason) = delta.stop_reason.as_deref() {
|
|
self.stop_reason = match stop_reason {
|
|
"end_turn" => StopReason::EndTurn,
|
|
"max_tokens" => StopReason::MaxTokens,
|
|
"tool_use" => StopReason::ToolUse,
|
|
"refusal" => StopReason::Refusal,
|
|
_ => {
|
|
log::error!("Unexpected anthropic stop_reason: {stop_reason}");
|
|
StopReason::EndTurn
|
|
}
|
|
};
|
|
}
|
|
vec![Ok(LanguageModelCompletionEvent::UsageUpdate(
|
|
convert_usage(&self.usage),
|
|
))]
|
|
}
|
|
Event::MessageStop => {
|
|
vec![Ok(LanguageModelCompletionEvent::Stop(self.stop_reason))]
|
|
}
|
|
Event::Error { error } => {
|
|
vec![Err(error.into())]
|
|
}
|
|
_ => Vec::new(),
|
|
}
|
|
}
|
|
}
|
|
|
|
struct RawToolUse {
|
|
id: String,
|
|
name: String,
|
|
input_json: String,
|
|
}
|
|
|
|
/// Updates usage data by preferring counts from `new`.
|
|
fn update_usage(usage: &mut Usage, new: &Usage) {
|
|
if let Some(input_tokens) = new.input_tokens {
|
|
usage.input_tokens = Some(input_tokens);
|
|
}
|
|
if let Some(output_tokens) = new.output_tokens {
|
|
usage.output_tokens = Some(output_tokens);
|
|
}
|
|
if let Some(cache_creation_input_tokens) = new.cache_creation_input_tokens {
|
|
usage.cache_creation_input_tokens = Some(cache_creation_input_tokens);
|
|
}
|
|
if let Some(cache_read_input_tokens) = new.cache_read_input_tokens {
|
|
usage.cache_read_input_tokens = Some(cache_read_input_tokens);
|
|
}
|
|
}
|
|
|
|
fn convert_usage(usage: &Usage) -> language_model::TokenUsage {
|
|
language_model::TokenUsage {
|
|
input_tokens: usage.input_tokens.unwrap_or(0),
|
|
output_tokens: usage.output_tokens.unwrap_or(0),
|
|
cache_creation_input_tokens: usage.cache_creation_input_tokens.unwrap_or(0),
|
|
cache_read_input_tokens: usage.cache_read_input_tokens.unwrap_or(0),
|
|
}
|
|
}
|
|
|
|
struct ConfigurationView {
|
|
api_key_editor: Entity<Editor>,
|
|
state: gpui::Entity<State>,
|
|
load_credentials_task: Option<Task<()>>,
|
|
target_agent: ConfigurationViewTargetAgent,
|
|
}
|
|
|
|
impl ConfigurationView {
|
|
const PLACEHOLDER_TEXT: &'static str = "sk-ant-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx";
|
|
|
|
fn new(
|
|
state: gpui::Entity<State>,
|
|
target_agent: ConfigurationViewTargetAgent,
|
|
window: &mut Window,
|
|
cx: &mut Context<Self>,
|
|
) -> Self {
|
|
cx.observe(&state, |_, _, cx| {
|
|
cx.notify();
|
|
})
|
|
.detach();
|
|
|
|
let load_credentials_task = Some(cx.spawn({
|
|
let state = state.clone();
|
|
async move |this, cx| {
|
|
if let Some(task) = state
|
|
.update(cx, |state, cx| state.authenticate(cx))
|
|
.log_err()
|
|
{
|
|
// We don't log an error, because "not signed in" is also an error.
|
|
let _ = task.await;
|
|
}
|
|
this.update(cx, |this, cx| {
|
|
this.load_credentials_task = None;
|
|
cx.notify();
|
|
})
|
|
.log_err();
|
|
}
|
|
}));
|
|
|
|
Self {
|
|
api_key_editor: cx.new(|cx| {
|
|
let mut editor = Editor::single_line(window, cx);
|
|
editor.set_placeholder_text(Self::PLACEHOLDER_TEXT, cx);
|
|
editor
|
|
}),
|
|
state,
|
|
load_credentials_task,
|
|
target_agent,
|
|
}
|
|
}
|
|
|
|
fn save_api_key(&mut self, _: &menu::Confirm, window: &mut Window, cx: &mut Context<Self>) {
|
|
let api_key = self.api_key_editor.read(cx).text(cx);
|
|
if api_key.is_empty() {
|
|
return;
|
|
}
|
|
|
|
let state = self.state.clone();
|
|
cx.spawn_in(window, async move |_, cx| {
|
|
state
|
|
.update(cx, |state, cx| state.set_api_key(api_key, cx))?
|
|
.await
|
|
})
|
|
.detach_and_log_err(cx);
|
|
|
|
cx.notify();
|
|
}
|
|
|
|
fn reset_api_key(&mut self, window: &mut Window, cx: &mut Context<Self>) {
|
|
self.api_key_editor
|
|
.update(cx, |editor, cx| editor.set_text("", window, cx));
|
|
|
|
let state = self.state.clone();
|
|
cx.spawn_in(window, async move |_, cx| {
|
|
state.update(cx, |state, cx| state.reset_api_key(cx))?.await
|
|
})
|
|
.detach_and_log_err(cx);
|
|
|
|
cx.notify();
|
|
}
|
|
|
|
fn render_api_key_editor(&self, cx: &mut Context<Self>) -> impl IntoElement {
|
|
let settings = ThemeSettings::get_global(cx);
|
|
let text_style = TextStyle {
|
|
color: cx.theme().colors().text,
|
|
font_family: settings.ui_font.family.clone(),
|
|
font_features: settings.ui_font.features.clone(),
|
|
font_fallbacks: settings.ui_font.fallbacks.clone(),
|
|
font_size: rems(0.875).into(),
|
|
font_weight: settings.ui_font.weight,
|
|
font_style: FontStyle::Normal,
|
|
line_height: relative(1.3),
|
|
white_space: WhiteSpace::Normal,
|
|
..Default::default()
|
|
};
|
|
EditorElement::new(
|
|
&self.api_key_editor,
|
|
EditorStyle {
|
|
background: cx.theme().colors().editor_background,
|
|
local_player: cx.theme().players().local(),
|
|
text: text_style,
|
|
..Default::default()
|
|
},
|
|
)
|
|
}
|
|
|
|
fn should_render_editor(&self, cx: &mut Context<Self>) -> bool {
|
|
!self.state.read(cx).is_authenticated()
|
|
}
|
|
}
|
|
|
|
impl Render for ConfigurationView {
|
|
fn render(&mut self, _: &mut Window, cx: &mut Context<Self>) -> impl IntoElement {
|
|
let env_var_set = self.state.read(cx).api_key_from_env;
|
|
|
|
if self.load_credentials_task.is_some() {
|
|
div().child(Label::new("Loading credentials...")).into_any()
|
|
} else if self.should_render_editor(cx) {
|
|
v_flex()
|
|
.size_full()
|
|
.on_action(cx.listener(Self::save_api_key))
|
|
.child(Label::new(format!("To use {}, you need to add an API key. Follow these steps:", match self.target_agent {
|
|
ConfigurationViewTargetAgent::ZedAgent => "Zed's agent with Anthropic",
|
|
ConfigurationViewTargetAgent::Other(agent) => agent,
|
|
})))
|
|
.child(
|
|
List::new()
|
|
.child(
|
|
InstructionListItem::new(
|
|
"Create one by visiting",
|
|
Some("Anthropic's settings"),
|
|
Some("https://console.anthropic.com/settings/keys")
|
|
)
|
|
)
|
|
.child(
|
|
InstructionListItem::text_only("Paste your API key below and hit enter to start using the agent")
|
|
)
|
|
)
|
|
.child(
|
|
h_flex()
|
|
.w_full()
|
|
.my_2()
|
|
.px_2()
|
|
.py_1()
|
|
.bg(cx.theme().colors().editor_background)
|
|
.border_1()
|
|
.border_color(cx.theme().colors().border)
|
|
.rounded_sm()
|
|
.child(self.render_api_key_editor(cx)),
|
|
)
|
|
.child(
|
|
Label::new(
|
|
format!("You can also assign the {ANTHROPIC_API_KEY_VAR} environment variable and restart Zed."),
|
|
)
|
|
.size(LabelSize::Small)
|
|
.color(Color::Muted),
|
|
)
|
|
.into_any()
|
|
} else {
|
|
h_flex()
|
|
.mt_1()
|
|
.p_1()
|
|
.justify_between()
|
|
.rounded_md()
|
|
.border_1()
|
|
.border_color(cx.theme().colors().border)
|
|
.bg(cx.theme().colors().background)
|
|
.child(
|
|
h_flex()
|
|
.gap_1()
|
|
.child(Icon::new(IconName::Check).color(Color::Success))
|
|
.child(Label::new(if env_var_set {
|
|
format!("API key set in {ANTHROPIC_API_KEY_VAR} environment variable.")
|
|
} else {
|
|
"API key configured.".to_string()
|
|
})),
|
|
)
|
|
.child(
|
|
Button::new("reset-key", "Reset Key")
|
|
.label_size(LabelSize::Small)
|
|
.icon(Some(IconName::Trash))
|
|
.icon_size(IconSize::Small)
|
|
.icon_position(IconPosition::Start)
|
|
.disabled(env_var_set)
|
|
.when(env_var_set, |this| {
|
|
this.tooltip(Tooltip::text(format!("To reset your API key, unset the {ANTHROPIC_API_KEY_VAR} environment variable.")))
|
|
})
|
|
.on_click(cx.listener(|this, _, window, cx| this.reset_api_key(window, cx))),
|
|
)
|
|
.into_any()
|
|
}
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
use anthropic::AnthropicModelMode;
|
|
use language_model::{LanguageModelRequestMessage, MessageContent};
|
|
|
|
#[test]
|
|
fn test_cache_control_only_on_last_segment() {
|
|
let request = LanguageModelRequest {
|
|
messages: vec![LanguageModelRequestMessage {
|
|
role: Role::User,
|
|
content: vec![
|
|
MessageContent::Text("Some prompt".to_string()),
|
|
MessageContent::Image(language_model::LanguageModelImage::empty()),
|
|
MessageContent::Image(language_model::LanguageModelImage::empty()),
|
|
MessageContent::Image(language_model::LanguageModelImage::empty()),
|
|
MessageContent::Image(language_model::LanguageModelImage::empty()),
|
|
],
|
|
cache: true,
|
|
}],
|
|
thread_id: None,
|
|
prompt_id: None,
|
|
intent: None,
|
|
mode: None,
|
|
stop: vec![],
|
|
temperature: None,
|
|
tools: vec![],
|
|
tool_choice: None,
|
|
thinking_allowed: true,
|
|
};
|
|
|
|
let anthropic_request = into_anthropic(
|
|
request,
|
|
"claude-3-5-sonnet".to_string(),
|
|
0.7,
|
|
4096,
|
|
AnthropicModelMode::Default,
|
|
);
|
|
|
|
assert_eq!(anthropic_request.messages.len(), 1);
|
|
|
|
let message = &anthropic_request.messages[0];
|
|
assert_eq!(message.content.len(), 5);
|
|
|
|
assert!(matches!(
|
|
message.content[0],
|
|
anthropic::RequestContent::Text {
|
|
cache_control: None,
|
|
..
|
|
}
|
|
));
|
|
for i in 1..3 {
|
|
assert!(matches!(
|
|
message.content[i],
|
|
anthropic::RequestContent::Image {
|
|
cache_control: None,
|
|
..
|
|
}
|
|
));
|
|
}
|
|
|
|
assert!(matches!(
|
|
message.content[4],
|
|
anthropic::RequestContent::Image {
|
|
cache_control: Some(anthropic::CacheControl {
|
|
cache_type: anthropic::CacheControlType::Ephemeral,
|
|
}),
|
|
..
|
|
}
|
|
));
|
|
}
|
|
}
|