1use anyhow::{Context as _, Result, anyhow};
2use collections::BTreeMap;
3use credentials_provider::CredentialsProvider;
4use editor::{Editor, EditorElement, EditorStyle};
5use futures::{FutureExt, Stream, StreamExt, future::BoxFuture};
6use google_ai::{
7 FunctionDeclaration, GenerateContentResponse, Part, SystemInstruction, UsageMetadata,
8};
9use gpui::{
10 AnyView, App, AsyncApp, Context, Entity, FontStyle, Subscription, Task, TextStyle, WhiteSpace,
11};
12use http_client::HttpClient;
13use language_model::{
14 AuthenticateError, LanguageModelCompletionError, LanguageModelCompletionEvent,
15 LanguageModelToolChoice, LanguageModelToolSchemaFormat, LanguageModelToolUse,
16 LanguageModelToolUseId, MessageContent, StopReason,
17};
18use language_model::{
19 LanguageModel, LanguageModelId, LanguageModelName, LanguageModelProvider,
20 LanguageModelProviderId, LanguageModelProviderName, LanguageModelProviderState,
21 LanguageModelRequest, RateLimiter, Role,
22};
23use schemars::JsonSchema;
24use serde::{Deserialize, Serialize};
25use settings::{Settings, SettingsStore};
26use std::pin::Pin;
27use std::sync::{
28 Arc,
29 atomic::{self, AtomicU64},
30};
31use strum::IntoEnumIterator;
32use theme::ThemeSettings;
33use ui::{Icon, IconName, List, Tooltip, prelude::*};
34use util::ResultExt;
35
36use crate::AllLanguageModelSettings;
37use crate::ui::InstructionListItem;
38
39const PROVIDER_ID: &str = "google";
40const PROVIDER_NAME: &str = "Google AI";
41
42#[derive(Default, Clone, Debug, PartialEq)]
43pub struct GoogleSettings {
44 pub api_url: String,
45 pub available_models: Vec<AvailableModel>,
46}
47
48#[derive(Clone, Debug, PartialEq, Serialize, Deserialize, JsonSchema)]
49pub struct AvailableModel {
50 name: String,
51 display_name: Option<String>,
52 max_tokens: usize,
53}
54
55pub struct GoogleLanguageModelProvider {
56 http_client: Arc<dyn HttpClient>,
57 state: gpui::Entity<State>,
58}
59
60pub struct State {
61 api_key: Option<String>,
62 api_key_from_env: bool,
63 _subscription: Subscription,
64}
65
66const GOOGLE_AI_API_KEY_VAR: &str = "GOOGLE_AI_API_KEY";
67
68impl State {
69 fn is_authenticated(&self) -> bool {
70 self.api_key.is_some()
71 }
72
73 fn reset_api_key(&self, cx: &mut Context<Self>) -> Task<Result<()>> {
74 let credentials_provider = <dyn CredentialsProvider>::global(cx);
75 let api_url = AllLanguageModelSettings::get_global(cx)
76 .google
77 .api_url
78 .clone();
79 cx.spawn(async move |this, cx| {
80 credentials_provider
81 .delete_credentials(&api_url, &cx)
82 .await
83 .log_err();
84 this.update(cx, |this, cx| {
85 this.api_key = None;
86 this.api_key_from_env = false;
87 cx.notify();
88 })
89 })
90 }
91
92 fn set_api_key(&mut self, api_key: String, cx: &mut Context<Self>) -> Task<Result<()>> {
93 let credentials_provider = <dyn CredentialsProvider>::global(cx);
94 let api_url = AllLanguageModelSettings::get_global(cx)
95 .google
96 .api_url
97 .clone();
98 cx.spawn(async move |this, cx| {
99 credentials_provider
100 .write_credentials(&api_url, "Bearer", api_key.as_bytes(), &cx)
101 .await?;
102 this.update(cx, |this, cx| {
103 this.api_key = Some(api_key);
104 cx.notify();
105 })
106 })
107 }
108
109 fn authenticate(&self, cx: &mut Context<Self>) -> Task<Result<(), AuthenticateError>> {
110 if self.is_authenticated() {
111 return Task::ready(Ok(()));
112 }
113
114 let credentials_provider = <dyn CredentialsProvider>::global(cx);
115 let api_url = AllLanguageModelSettings::get_global(cx)
116 .google
117 .api_url
118 .clone();
119
120 cx.spawn(async move |this, cx| {
121 let (api_key, from_env) = if let Ok(api_key) = std::env::var(GOOGLE_AI_API_KEY_VAR) {
122 (api_key, true)
123 } else {
124 let (_, api_key) = credentials_provider
125 .read_credentials(&api_url, &cx)
126 .await?
127 .ok_or(AuthenticateError::CredentialsNotFound)?;
128 (
129 String::from_utf8(api_key).context("invalid {PROVIDER_NAME} API key")?,
130 false,
131 )
132 };
133
134 this.update(cx, |this, cx| {
135 this.api_key = Some(api_key);
136 this.api_key_from_env = from_env;
137 cx.notify();
138 })?;
139
140 Ok(())
141 })
142 }
143}
144
145impl GoogleLanguageModelProvider {
146 pub fn new(http_client: Arc<dyn HttpClient>, cx: &mut App) -> Self {
147 let state = cx.new(|cx| State {
148 api_key: None,
149 api_key_from_env: false,
150 _subscription: cx.observe_global::<SettingsStore>(|_, cx| {
151 cx.notify();
152 }),
153 });
154
155 Self { http_client, state }
156 }
157
158 fn create_language_model(&self, model: google_ai::Model) -> Arc<dyn LanguageModel> {
159 Arc::new(GoogleLanguageModel {
160 id: LanguageModelId::from(model.id().to_string()),
161 model,
162 state: self.state.clone(),
163 http_client: self.http_client.clone(),
164 request_limiter: RateLimiter::new(4),
165 })
166 }
167}
168
169impl LanguageModelProviderState for GoogleLanguageModelProvider {
170 type ObservableEntity = State;
171
172 fn observable_entity(&self) -> Option<gpui::Entity<Self::ObservableEntity>> {
173 Some(self.state.clone())
174 }
175}
176
177impl LanguageModelProvider for GoogleLanguageModelProvider {
178 fn id(&self) -> LanguageModelProviderId {
179 LanguageModelProviderId(PROVIDER_ID.into())
180 }
181
182 fn name(&self) -> LanguageModelProviderName {
183 LanguageModelProviderName(PROVIDER_NAME.into())
184 }
185
186 fn icon(&self) -> IconName {
187 IconName::AiGoogle
188 }
189
190 fn default_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {
191 Some(self.create_language_model(google_ai::Model::default()))
192 }
193
194 fn default_fast_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {
195 Some(self.create_language_model(google_ai::Model::default_fast()))
196 }
197
198 fn provided_models(&self, cx: &App) -> Vec<Arc<dyn LanguageModel>> {
199 let mut models = BTreeMap::default();
200
201 // Add base models from google_ai::Model::iter()
202 for model in google_ai::Model::iter() {
203 if !matches!(model, google_ai::Model::Custom { .. }) {
204 models.insert(model.id().to_string(), model);
205 }
206 }
207
208 // Override with available models from settings
209 for model in &AllLanguageModelSettings::get_global(cx)
210 .google
211 .available_models
212 {
213 models.insert(
214 model.name.clone(),
215 google_ai::Model::Custom {
216 name: model.name.clone(),
217 display_name: model.display_name.clone(),
218 max_tokens: model.max_tokens,
219 },
220 );
221 }
222
223 models
224 .into_values()
225 .map(|model| {
226 Arc::new(GoogleLanguageModel {
227 id: LanguageModelId::from(model.id().to_string()),
228 model,
229 state: self.state.clone(),
230 http_client: self.http_client.clone(),
231 request_limiter: RateLimiter::new(4),
232 }) as Arc<dyn LanguageModel>
233 })
234 .collect()
235 }
236
237 fn is_authenticated(&self, cx: &App) -> bool {
238 self.state.read(cx).is_authenticated()
239 }
240
241 fn authenticate(&self, cx: &mut App) -> Task<Result<(), AuthenticateError>> {
242 self.state.update(cx, |state, cx| state.authenticate(cx))
243 }
244
245 fn configuration_view(&self, window: &mut Window, cx: &mut App) -> AnyView {
246 cx.new(|cx| ConfigurationView::new(self.state.clone(), window, cx))
247 .into()
248 }
249
250 fn reset_credentials(&self, cx: &mut App) -> Task<Result<()>> {
251 self.state.update(cx, |state, cx| state.reset_api_key(cx))
252 }
253}
254
255pub struct GoogleLanguageModel {
256 id: LanguageModelId,
257 model: google_ai::Model,
258 state: gpui::Entity<State>,
259 http_client: Arc<dyn HttpClient>,
260 request_limiter: RateLimiter,
261}
262
263impl GoogleLanguageModel {
264 fn stream_completion(
265 &self,
266 request: google_ai::GenerateContentRequest,
267 cx: &AsyncApp,
268 ) -> BoxFuture<
269 'static,
270 Result<futures::stream::BoxStream<'static, Result<GenerateContentResponse>>>,
271 > {
272 let http_client = self.http_client.clone();
273
274 let Ok((api_key, api_url)) = cx.read_entity(&self.state, |state, cx| {
275 let settings = &AllLanguageModelSettings::get_global(cx).google;
276 (state.api_key.clone(), settings.api_url.clone())
277 }) else {
278 return futures::future::ready(Err(anyhow!("App state dropped"))).boxed();
279 };
280
281 async move {
282 let api_key = api_key.context("Missing Google API key")?;
283 let request = google_ai::stream_generate_content(
284 http_client.as_ref(),
285 &api_url,
286 &api_key,
287 request,
288 );
289 request.await.context("failed to stream completion")
290 }
291 .boxed()
292 }
293}
294
295impl LanguageModel for GoogleLanguageModel {
296 fn id(&self) -> LanguageModelId {
297 self.id.clone()
298 }
299
300 fn name(&self) -> LanguageModelName {
301 LanguageModelName::from(self.model.display_name().to_string())
302 }
303
304 fn provider_id(&self) -> LanguageModelProviderId {
305 LanguageModelProviderId(PROVIDER_ID.into())
306 }
307
308 fn provider_name(&self) -> LanguageModelProviderName {
309 LanguageModelProviderName(PROVIDER_NAME.into())
310 }
311
312 fn supports_tools(&self) -> bool {
313 true
314 }
315
316 fn supports_images(&self) -> bool {
317 true
318 }
319
320 fn supports_tool_choice(&self, choice: LanguageModelToolChoice) -> bool {
321 match choice {
322 LanguageModelToolChoice::Auto
323 | LanguageModelToolChoice::Any
324 | LanguageModelToolChoice::None => true,
325 }
326 }
327
328 fn tool_input_format(&self) -> LanguageModelToolSchemaFormat {
329 LanguageModelToolSchemaFormat::JsonSchemaSubset
330 }
331
332 fn telemetry_id(&self) -> String {
333 format!("google/{}", self.model.id())
334 }
335
336 fn max_token_count(&self) -> usize {
337 self.model.max_token_count()
338 }
339
340 fn count_tokens(
341 &self,
342 request: LanguageModelRequest,
343 cx: &App,
344 ) -> BoxFuture<'static, Result<usize>> {
345 let model_id = self.model.id().to_string();
346 let request = into_google(request, model_id.clone());
347 let http_client = self.http_client.clone();
348 let api_key = self.state.read(cx).api_key.clone();
349
350 let settings = &AllLanguageModelSettings::get_global(cx).google;
351 let api_url = settings.api_url.clone();
352
353 async move {
354 let api_key = api_key.context("Missing Google API key")?;
355 let response = google_ai::count_tokens(
356 http_client.as_ref(),
357 &api_url,
358 &api_key,
359 google_ai::CountTokensRequest {
360 generate_content_request: request,
361 },
362 )
363 .await?;
364 Ok(response.total_tokens)
365 }
366 .boxed()
367 }
368
369 fn stream_completion(
370 &self,
371 request: LanguageModelRequest,
372 cx: &AsyncApp,
373 ) -> BoxFuture<
374 'static,
375 Result<
376 futures::stream::BoxStream<
377 'static,
378 Result<LanguageModelCompletionEvent, LanguageModelCompletionError>,
379 >,
380 >,
381 > {
382 let request = into_google(request, self.model.id().to_string());
383 let request = self.stream_completion(request, cx);
384 let future = self.request_limiter.stream(async move {
385 let response = request
386 .await
387 .map_err(|err| LanguageModelCompletionError::Other(anyhow!(err)))?;
388 Ok(GoogleEventMapper::new().map_stream(response))
389 });
390 async move { Ok(future.await?.boxed()) }.boxed()
391 }
392}
393
394pub fn into_google(
395 mut request: LanguageModelRequest,
396 model_id: String,
397) -> google_ai::GenerateContentRequest {
398 fn map_content(content: Vec<MessageContent>) -> Vec<Part> {
399 content
400 .into_iter()
401 .flat_map(|content| match content {
402 language_model::MessageContent::Text(text)
403 | language_model::MessageContent::Thinking { text, .. } => {
404 if !text.is_empty() {
405 vec![Part::TextPart(google_ai::TextPart { text })]
406 } else {
407 vec![]
408 }
409 }
410 language_model::MessageContent::RedactedThinking(_) => vec![],
411 language_model::MessageContent::Image(image) => {
412 vec![Part::InlineDataPart(google_ai::InlineDataPart {
413 inline_data: google_ai::GenerativeContentBlob {
414 mime_type: "image/png".to_string(),
415 data: image.source.to_string(),
416 },
417 })]
418 }
419 language_model::MessageContent::ToolUse(tool_use) => {
420 vec![Part::FunctionCallPart(google_ai::FunctionCallPart {
421 function_call: google_ai::FunctionCall {
422 name: tool_use.name.to_string(),
423 args: tool_use.input,
424 },
425 })]
426 }
427 language_model::MessageContent::ToolResult(tool_result) => {
428 match tool_result.content {
429 language_model::LanguageModelToolResultContent::Text(text) => {
430 vec![Part::FunctionResponsePart(
431 google_ai::FunctionResponsePart {
432 function_response: google_ai::FunctionResponse {
433 name: tool_result.tool_name.to_string(),
434 // The API expects a valid JSON object
435 response: serde_json::json!({
436 "output": text
437 }),
438 },
439 },
440 )]
441 }
442 language_model::LanguageModelToolResultContent::Image(image) => {
443 vec![
444 Part::FunctionResponsePart(google_ai::FunctionResponsePart {
445 function_response: google_ai::FunctionResponse {
446 name: tool_result.tool_name.to_string(),
447 // The API expects a valid JSON object
448 response: serde_json::json!({
449 "output": "Tool responded with an image"
450 }),
451 },
452 }),
453 Part::InlineDataPart(google_ai::InlineDataPart {
454 inline_data: google_ai::GenerativeContentBlob {
455 mime_type: "image/png".to_string(),
456 data: image.source.to_string(),
457 },
458 }),
459 ]
460 }
461 }
462 }
463 })
464 .collect()
465 }
466
467 let system_instructions = if request
468 .messages
469 .first()
470 .map_or(false, |msg| matches!(msg.role, Role::System))
471 {
472 let message = request.messages.remove(0);
473 Some(SystemInstruction {
474 parts: map_content(message.content),
475 })
476 } else {
477 None
478 };
479
480 google_ai::GenerateContentRequest {
481 model: google_ai::ModelName { model_id },
482 system_instruction: system_instructions,
483 contents: request
484 .messages
485 .into_iter()
486 .filter_map(|message| {
487 let parts = map_content(message.content);
488 if parts.is_empty() {
489 None
490 } else {
491 Some(google_ai::Content {
492 parts,
493 role: match message.role {
494 Role::User => google_ai::Role::User,
495 Role::Assistant => google_ai::Role::Model,
496 Role::System => google_ai::Role::User, // Google AI doesn't have a system role
497 },
498 })
499 }
500 })
501 .collect(),
502 generation_config: Some(google_ai::GenerationConfig {
503 candidate_count: Some(1),
504 stop_sequences: Some(request.stop),
505 max_output_tokens: None,
506 temperature: request.temperature.map(|t| t as f64).or(Some(1.0)),
507 top_p: None,
508 top_k: None,
509 }),
510 safety_settings: None,
511 tools: (request.tools.len() > 0).then(|| {
512 vec![google_ai::Tool {
513 function_declarations: request
514 .tools
515 .into_iter()
516 .map(|tool| FunctionDeclaration {
517 name: tool.name,
518 description: tool.description,
519 parameters: tool.input_schema,
520 })
521 .collect(),
522 }]
523 }),
524 tool_config: request.tool_choice.map(|choice| google_ai::ToolConfig {
525 function_calling_config: google_ai::FunctionCallingConfig {
526 mode: match choice {
527 LanguageModelToolChoice::Auto => google_ai::FunctionCallingMode::Auto,
528 LanguageModelToolChoice::Any => google_ai::FunctionCallingMode::Any,
529 LanguageModelToolChoice::None => google_ai::FunctionCallingMode::None,
530 },
531 allowed_function_names: None,
532 },
533 }),
534 }
535}
536
537pub struct GoogleEventMapper {
538 usage: UsageMetadata,
539 stop_reason: StopReason,
540}
541
542impl GoogleEventMapper {
543 pub fn new() -> Self {
544 Self {
545 usage: UsageMetadata::default(),
546 stop_reason: StopReason::EndTurn,
547 }
548 }
549
550 pub fn map_stream(
551 mut self,
552 events: Pin<Box<dyn Send + Stream<Item = Result<GenerateContentResponse>>>>,
553 ) -> impl Stream<Item = Result<LanguageModelCompletionEvent, LanguageModelCompletionError>>
554 {
555 events
556 .map(Some)
557 .chain(futures::stream::once(async { None }))
558 .flat_map(move |event| {
559 futures::stream::iter(match event {
560 Some(Ok(event)) => self.map_event(event),
561 Some(Err(error)) => {
562 vec![Err(LanguageModelCompletionError::Other(anyhow!(error)))]
563 }
564 None => vec![Ok(LanguageModelCompletionEvent::Stop(self.stop_reason))],
565 })
566 })
567 }
568
569 pub fn map_event(
570 &mut self,
571 event: GenerateContentResponse,
572 ) -> Vec<Result<LanguageModelCompletionEvent, LanguageModelCompletionError>> {
573 static TOOL_CALL_COUNTER: AtomicU64 = AtomicU64::new(0);
574
575 let mut events: Vec<_> = Vec::new();
576 let mut wants_to_use_tool = false;
577 if let Some(usage_metadata) = event.usage_metadata {
578 update_usage(&mut self.usage, &usage_metadata);
579 events.push(Ok(LanguageModelCompletionEvent::UsageUpdate(
580 convert_usage(&self.usage),
581 )))
582 }
583 if let Some(candidates) = event.candidates {
584 for candidate in candidates {
585 if let Some(finish_reason) = candidate.finish_reason.as_deref() {
586 self.stop_reason = match finish_reason {
587 "STOP" => StopReason::EndTurn,
588 "MAX_TOKENS" => StopReason::MaxTokens,
589 _ => {
590 log::error!("Unexpected google finish_reason: {finish_reason}");
591 StopReason::EndTurn
592 }
593 };
594 }
595 candidate
596 .content
597 .parts
598 .into_iter()
599 .for_each(|part| match part {
600 Part::TextPart(text_part) => {
601 events.push(Ok(LanguageModelCompletionEvent::Text(text_part.text)))
602 }
603 Part::InlineDataPart(_) => {}
604 Part::FunctionCallPart(function_call_part) => {
605 wants_to_use_tool = true;
606 let name: Arc<str> = function_call_part.function_call.name.into();
607 let next_tool_id =
608 TOOL_CALL_COUNTER.fetch_add(1, atomic::Ordering::SeqCst);
609 let id: LanguageModelToolUseId =
610 format!("{}-{}", name, next_tool_id).into();
611
612 events.push(Ok(LanguageModelCompletionEvent::ToolUse(
613 LanguageModelToolUse {
614 id,
615 name,
616 is_input_complete: true,
617 raw_input: function_call_part.function_call.args.to_string(),
618 input: function_call_part.function_call.args,
619 },
620 )));
621 }
622 Part::FunctionResponsePart(_) => {}
623 });
624 }
625 }
626
627 // Even when Gemini wants to use a Tool, the API
628 // responds with `finish_reason: STOP`
629 if wants_to_use_tool {
630 self.stop_reason = StopReason::ToolUse;
631 events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::ToolUse)));
632 }
633 events
634 }
635}
636
637pub fn count_google_tokens(
638 request: LanguageModelRequest,
639 cx: &App,
640) -> BoxFuture<'static, Result<usize>> {
641 // We couldn't use the GoogleLanguageModelProvider to count tokens because the github copilot doesn't have the access to google_ai directly.
642 // So we have to use tokenizer from tiktoken_rs to count tokens.
643 cx.background_spawn(async move {
644 let messages = request
645 .messages
646 .into_iter()
647 .map(|message| tiktoken_rs::ChatCompletionRequestMessage {
648 role: match message.role {
649 Role::User => "user".into(),
650 Role::Assistant => "assistant".into(),
651 Role::System => "system".into(),
652 },
653 content: Some(message.string_contents()),
654 name: None,
655 function_call: None,
656 })
657 .collect::<Vec<_>>();
658
659 // Tiktoken doesn't yet support these models, so we manually use the
660 // same tokenizer as GPT-4.
661 tiktoken_rs::num_tokens_from_messages("gpt-4", &messages)
662 })
663 .boxed()
664}
665
666fn update_usage(usage: &mut UsageMetadata, new: &UsageMetadata) {
667 if let Some(prompt_token_count) = new.prompt_token_count {
668 usage.prompt_token_count = Some(prompt_token_count);
669 }
670 if let Some(cached_content_token_count) = new.cached_content_token_count {
671 usage.cached_content_token_count = Some(cached_content_token_count);
672 }
673 if let Some(candidates_token_count) = new.candidates_token_count {
674 usage.candidates_token_count = Some(candidates_token_count);
675 }
676 if let Some(tool_use_prompt_token_count) = new.tool_use_prompt_token_count {
677 usage.tool_use_prompt_token_count = Some(tool_use_prompt_token_count);
678 }
679 if let Some(thoughts_token_count) = new.thoughts_token_count {
680 usage.thoughts_token_count = Some(thoughts_token_count);
681 }
682 if let Some(total_token_count) = new.total_token_count {
683 usage.total_token_count = Some(total_token_count);
684 }
685}
686
687fn convert_usage(usage: &UsageMetadata) -> language_model::TokenUsage {
688 let prompt_tokens = usage.prompt_token_count.unwrap_or(0) as u32;
689 let cached_tokens = usage.cached_content_token_count.unwrap_or(0) as u32;
690 let input_tokens = prompt_tokens - cached_tokens;
691 let output_tokens = usage.candidates_token_count.unwrap_or(0) as u32;
692
693 language_model::TokenUsage {
694 input_tokens,
695 output_tokens,
696 cache_read_input_tokens: cached_tokens,
697 cache_creation_input_tokens: 0,
698 }
699}
700
701struct ConfigurationView {
702 api_key_editor: Entity<Editor>,
703 state: gpui::Entity<State>,
704 load_credentials_task: Option<Task<()>>,
705}
706
707impl ConfigurationView {
708 fn new(state: gpui::Entity<State>, window: &mut Window, cx: &mut Context<Self>) -> Self {
709 cx.observe(&state, |_, _, cx| {
710 cx.notify();
711 })
712 .detach();
713
714 let load_credentials_task = Some(cx.spawn_in(window, {
715 let state = state.clone();
716 async move |this, cx| {
717 if let Some(task) = state
718 .update(cx, |state, cx| state.authenticate(cx))
719 .log_err()
720 {
721 // We don't log an error, because "not signed in" is also an error.
722 let _ = task.await;
723 }
724 this.update(cx, |this, cx| {
725 this.load_credentials_task = None;
726 cx.notify();
727 })
728 .log_err();
729 }
730 }));
731
732 Self {
733 api_key_editor: cx.new(|cx| {
734 let mut editor = Editor::single_line(window, cx);
735 editor.set_placeholder_text("AIzaSy...", cx);
736 editor
737 }),
738 state,
739 load_credentials_task,
740 }
741 }
742
743 fn save_api_key(&mut self, _: &menu::Confirm, window: &mut Window, cx: &mut Context<Self>) {
744 let api_key = self.api_key_editor.read(cx).text(cx);
745 if api_key.is_empty() {
746 return;
747 }
748
749 let state = self.state.clone();
750 cx.spawn_in(window, async move |_, cx| {
751 state
752 .update(cx, |state, cx| state.set_api_key(api_key, cx))?
753 .await
754 })
755 .detach_and_log_err(cx);
756
757 cx.notify();
758 }
759
760 fn reset_api_key(&mut self, window: &mut Window, cx: &mut Context<Self>) {
761 self.api_key_editor
762 .update(cx, |editor, cx| editor.set_text("", window, cx));
763
764 let state = self.state.clone();
765 cx.spawn_in(window, async move |_, cx| {
766 state.update(cx, |state, cx| state.reset_api_key(cx))?.await
767 })
768 .detach_and_log_err(cx);
769
770 cx.notify();
771 }
772
773 fn render_api_key_editor(&self, cx: &mut Context<Self>) -> impl IntoElement {
774 let settings = ThemeSettings::get_global(cx);
775 let text_style = TextStyle {
776 color: cx.theme().colors().text,
777 font_family: settings.ui_font.family.clone(),
778 font_features: settings.ui_font.features.clone(),
779 font_fallbacks: settings.ui_font.fallbacks.clone(),
780 font_size: rems(0.875).into(),
781 font_weight: settings.ui_font.weight,
782 font_style: FontStyle::Normal,
783 line_height: relative(1.3),
784 white_space: WhiteSpace::Normal,
785 ..Default::default()
786 };
787 EditorElement::new(
788 &self.api_key_editor,
789 EditorStyle {
790 background: cx.theme().colors().editor_background,
791 local_player: cx.theme().players().local(),
792 text: text_style,
793 ..Default::default()
794 },
795 )
796 }
797
798 fn should_render_editor(&self, cx: &mut Context<Self>) -> bool {
799 !self.state.read(cx).is_authenticated()
800 }
801}
802
803impl Render for ConfigurationView {
804 fn render(&mut self, _: &mut Window, cx: &mut Context<Self>) -> impl IntoElement {
805 let env_var_set = self.state.read(cx).api_key_from_env;
806
807 if self.load_credentials_task.is_some() {
808 div().child(Label::new("Loading credentials...")).into_any()
809 } else if self.should_render_editor(cx) {
810 v_flex()
811 .size_full()
812 .on_action(cx.listener(Self::save_api_key))
813 .child(Label::new("To use Zed's assistant with Google AI, you need to add an API key. Follow these steps:"))
814 .child(
815 List::new()
816 .child(InstructionListItem::new(
817 "Create one by visiting",
818 Some("Google AI's console"),
819 Some("https://aistudio.google.com/app/apikey"),
820 ))
821 .child(InstructionListItem::text_only(
822 "Paste your API key below and hit enter to start using the assistant",
823 )),
824 )
825 .child(
826 h_flex()
827 .w_full()
828 .my_2()
829 .px_2()
830 .py_1()
831 .bg(cx.theme().colors().editor_background)
832 .border_1()
833 .border_color(cx.theme().colors().border)
834 .rounded_sm()
835 .child(self.render_api_key_editor(cx)),
836 )
837 .child(
838 Label::new(
839 format!("You can also assign the {GOOGLE_AI_API_KEY_VAR} environment variable and restart Zed."),
840 )
841 .size(LabelSize::Small).color(Color::Muted),
842 )
843 .into_any()
844 } else {
845 h_flex()
846 .mt_1()
847 .p_1()
848 .justify_between()
849 .rounded_md()
850 .border_1()
851 .border_color(cx.theme().colors().border)
852 .bg(cx.theme().colors().background)
853 .child(
854 h_flex()
855 .gap_1()
856 .child(Icon::new(IconName::Check).color(Color::Success))
857 .child(Label::new(if env_var_set {
858 format!("API key set in {GOOGLE_AI_API_KEY_VAR} environment variable.")
859 } else {
860 "API key configured.".to_string()
861 })),
862 )
863 .child(
864 Button::new("reset-key", "Reset Key")
865 .label_size(LabelSize::Small)
866 .icon(Some(IconName::Trash))
867 .icon_size(IconSize::Small)
868 .icon_position(IconPosition::Start)
869 .disabled(env_var_set)
870 .when(env_var_set, |this| {
871 this.tooltip(Tooltip::text(format!("To reset your API key, unset the {GOOGLE_AI_API_KEY_VAR} environment variable.")))
872 })
873 .on_click(cx.listener(|this, _, window, cx| this.reset_api_key(window, cx))),
874 )
875 .into_any()
876 }
877 }
878}