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, GoogleModelMode, Part, SystemInstruction,
8 ThinkingConfig, UsageMetadata,
9};
10use gpui::{
11 AnyView, App, AsyncApp, Context, Entity, FontStyle, Subscription, Task, TextStyle, WhiteSpace,
12};
13use http_client::HttpClient;
14use language_model::{
15 AuthenticateError, ConfigurationViewTargetAgent, LanguageModelCompletionError,
16 LanguageModelCompletionEvent, LanguageModelToolChoice, LanguageModelToolSchemaFormat,
17 LanguageModelToolUse, LanguageModelToolUseId, MessageContent, StopReason,
18};
19use language_model::{
20 LanguageModel, LanguageModelId, LanguageModelName, LanguageModelProvider,
21 LanguageModelProviderId, LanguageModelProviderName, LanguageModelProviderState,
22 LanguageModelRequest, RateLimiter, Role,
23};
24use schemars::JsonSchema;
25use serde::{Deserialize, Serialize};
26pub use settings::GoogleAvailableModel as AvailableModel;
27use settings::{Settings, SettingsStore};
28use std::pin::Pin;
29use std::sync::{
30 Arc,
31 atomic::{self, AtomicU64},
32};
33use strum::IntoEnumIterator;
34use theme::ThemeSettings;
35use ui::{Icon, IconName, List, Tooltip, prelude::*};
36use util::ResultExt;
37
38use crate::AllLanguageModelSettings;
39use crate::ui::InstructionListItem;
40
41use super::anthropic::ApiKey;
42
43const PROVIDER_ID: LanguageModelProviderId = language_model::GOOGLE_PROVIDER_ID;
44const PROVIDER_NAME: LanguageModelProviderName = language_model::GOOGLE_PROVIDER_NAME;
45
46#[derive(Default, Clone, Debug, PartialEq)]
47pub struct GoogleSettings {
48 pub api_url: String,
49 pub available_models: Vec<AvailableModel>,
50}
51
52#[derive(Clone, Copy, Debug, Default, PartialEq, Serialize, Deserialize, JsonSchema)]
53#[serde(tag = "type", rename_all = "lowercase")]
54pub enum ModelMode {
55 #[default]
56 Default,
57 Thinking {
58 /// The maximum number of tokens to use for reasoning. Must be lower than the model's `max_output_tokens`.
59 budget_tokens: Option<u32>,
60 },
61}
62
63pub struct GoogleLanguageModelProvider {
64 http_client: Arc<dyn HttpClient>,
65 state: gpui::Entity<State>,
66}
67
68pub struct State {
69 api_key: Option<String>,
70 api_key_from_env: bool,
71 _subscription: Subscription,
72}
73
74const GEMINI_API_KEY_VAR: &str = "GEMINI_API_KEY";
75const GOOGLE_AI_API_KEY_VAR: &str = "GOOGLE_AI_API_KEY";
76
77impl State {
78 fn is_authenticated(&self) -> bool {
79 self.api_key.is_some()
80 }
81
82 fn reset_api_key(&self, cx: &mut Context<Self>) -> Task<Result<()>> {
83 let credentials_provider = <dyn CredentialsProvider>::global(cx);
84 let api_url = AllLanguageModelSettings::get_global(cx)
85 .google
86 .api_url
87 .clone();
88 cx.spawn(async move |this, cx| {
89 credentials_provider
90 .delete_credentials(&api_url, cx)
91 .await
92 .log_err();
93 this.update(cx, |this, cx| {
94 this.api_key = None;
95 this.api_key_from_env = false;
96 cx.notify();
97 })
98 })
99 }
100
101 fn set_api_key(&mut self, api_key: String, cx: &mut Context<Self>) -> Task<Result<()>> {
102 let credentials_provider = <dyn CredentialsProvider>::global(cx);
103 let api_url = AllLanguageModelSettings::get_global(cx)
104 .google
105 .api_url
106 .clone();
107 cx.spawn(async move |this, cx| {
108 credentials_provider
109 .write_credentials(&api_url, "Bearer", api_key.as_bytes(), cx)
110 .await?;
111 this.update(cx, |this, cx| {
112 this.api_key = Some(api_key);
113 cx.notify();
114 })
115 })
116 }
117
118 fn authenticate(&self, cx: &mut Context<Self>) -> Task<Result<(), AuthenticateError>> {
119 if self.is_authenticated() {
120 return Task::ready(Ok(()));
121 }
122
123 let credentials_provider = <dyn CredentialsProvider>::global(cx);
124 let api_url = AllLanguageModelSettings::get_global(cx)
125 .google
126 .api_url
127 .clone();
128
129 cx.spawn(async move |this, cx| {
130 let (api_key, from_env) = if let Ok(api_key) = std::env::var(GOOGLE_AI_API_KEY_VAR) {
131 (api_key, true)
132 } else if let Ok(api_key) = std::env::var(GEMINI_API_KEY_VAR) {
133 (api_key, true)
134 } else {
135 let (_, api_key) = credentials_provider
136 .read_credentials(&api_url, cx)
137 .await?
138 .ok_or(AuthenticateError::CredentialsNotFound)?;
139 (
140 String::from_utf8(api_key).context("invalid {PROVIDER_NAME} API key")?,
141 false,
142 )
143 };
144
145 this.update(cx, |this, cx| {
146 this.api_key = Some(api_key);
147 this.api_key_from_env = from_env;
148 cx.notify();
149 })?;
150
151 Ok(())
152 })
153 }
154}
155
156impl GoogleLanguageModelProvider {
157 pub fn new(http_client: Arc<dyn HttpClient>, cx: &mut App) -> Self {
158 let state = cx.new(|cx| State {
159 api_key: None,
160 api_key_from_env: false,
161 _subscription: cx.observe_global::<SettingsStore>(|_, cx| {
162 cx.notify();
163 }),
164 });
165
166 Self { http_client, state }
167 }
168
169 fn create_language_model(&self, model: google_ai::Model) -> Arc<dyn LanguageModel> {
170 Arc::new(GoogleLanguageModel {
171 id: LanguageModelId::from(model.id().to_string()),
172 model,
173 state: self.state.clone(),
174 http_client: self.http_client.clone(),
175 request_limiter: RateLimiter::new(4),
176 })
177 }
178
179 pub fn api_key(cx: &mut App) -> Task<Result<ApiKey>> {
180 let credentials_provider = <dyn CredentialsProvider>::global(cx);
181 let api_url = AllLanguageModelSettings::get_global(cx)
182 .google
183 .api_url
184 .clone();
185
186 if let Ok(key) = std::env::var(GEMINI_API_KEY_VAR) {
187 Task::ready(Ok(ApiKey {
188 key,
189 from_env: true,
190 }))
191 } else {
192 cx.spawn(async move |cx| {
193 let (_, api_key) = credentials_provider
194 .read_credentials(&api_url, cx)
195 .await?
196 .ok_or(AuthenticateError::CredentialsNotFound)?;
197
198 Ok(ApiKey {
199 key: String::from_utf8(api_key).context("invalid {PROVIDER_NAME} API key")?,
200 from_env: false,
201 })
202 })
203 }
204 }
205}
206
207impl LanguageModelProviderState for GoogleLanguageModelProvider {
208 type ObservableEntity = State;
209
210 fn observable_entity(&self) -> Option<gpui::Entity<Self::ObservableEntity>> {
211 Some(self.state.clone())
212 }
213}
214
215impl LanguageModelProvider for GoogleLanguageModelProvider {
216 fn id(&self) -> LanguageModelProviderId {
217 PROVIDER_ID
218 }
219
220 fn name(&self) -> LanguageModelProviderName {
221 PROVIDER_NAME
222 }
223
224 fn icon(&self) -> IconName {
225 IconName::AiGoogle
226 }
227
228 fn default_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {
229 Some(self.create_language_model(google_ai::Model::default()))
230 }
231
232 fn default_fast_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {
233 Some(self.create_language_model(google_ai::Model::default_fast()))
234 }
235
236 fn provided_models(&self, cx: &App) -> Vec<Arc<dyn LanguageModel>> {
237 let mut models = BTreeMap::default();
238
239 // Add base models from google_ai::Model::iter()
240 for model in google_ai::Model::iter() {
241 if !matches!(model, google_ai::Model::Custom { .. }) {
242 models.insert(model.id().to_string(), model);
243 }
244 }
245
246 // Override with available models from settings
247 for model in &AllLanguageModelSettings::get_global(cx)
248 .google
249 .available_models
250 {
251 models.insert(
252 model.name.clone(),
253 google_ai::Model::Custom {
254 name: model.name.clone(),
255 display_name: model.display_name.clone(),
256 max_tokens: model.max_tokens,
257 mode: model.mode.unwrap_or_default().into(),
258 },
259 );
260 }
261
262 models
263 .into_values()
264 .map(|model| {
265 Arc::new(GoogleLanguageModel {
266 id: LanguageModelId::from(model.id().to_string()),
267 model,
268 state: self.state.clone(),
269 http_client: self.http_client.clone(),
270 request_limiter: RateLimiter::new(4),
271 }) as Arc<dyn LanguageModel>
272 })
273 .collect()
274 }
275
276 fn is_authenticated(&self, cx: &App) -> bool {
277 self.state.read(cx).is_authenticated()
278 }
279
280 fn authenticate(&self, cx: &mut App) -> Task<Result<(), AuthenticateError>> {
281 self.state.update(cx, |state, cx| state.authenticate(cx))
282 }
283
284 fn configuration_view(
285 &self,
286 target_agent: language_model::ConfigurationViewTargetAgent,
287 window: &mut Window,
288 cx: &mut App,
289 ) -> AnyView {
290 cx.new(|cx| ConfigurationView::new(self.state.clone(), target_agent, window, cx))
291 .into()
292 }
293
294 fn reset_credentials(&self, cx: &mut App) -> Task<Result<()>> {
295 self.state.update(cx, |state, cx| state.reset_api_key(cx))
296 }
297}
298
299pub struct GoogleLanguageModel {
300 id: LanguageModelId,
301 model: google_ai::Model,
302 state: gpui::Entity<State>,
303 http_client: Arc<dyn HttpClient>,
304 request_limiter: RateLimiter,
305}
306
307impl GoogleLanguageModel {
308 fn stream_completion(
309 &self,
310 request: google_ai::GenerateContentRequest,
311 cx: &AsyncApp,
312 ) -> BoxFuture<
313 'static,
314 Result<futures::stream::BoxStream<'static, Result<GenerateContentResponse>>>,
315 > {
316 let http_client = self.http_client.clone();
317
318 let Ok((api_key, api_url)) = cx.read_entity(&self.state, |state, cx| {
319 let settings = &AllLanguageModelSettings::get_global(cx).google;
320 (state.api_key.clone(), settings.api_url.clone())
321 }) else {
322 return futures::future::ready(Err(anyhow!("App state dropped"))).boxed();
323 };
324
325 async move {
326 let api_key = api_key.context("Missing Google API key")?;
327 let request = google_ai::stream_generate_content(
328 http_client.as_ref(),
329 &api_url,
330 &api_key,
331 request,
332 );
333 request.await.context("failed to stream completion")
334 }
335 .boxed()
336 }
337}
338
339impl LanguageModel for GoogleLanguageModel {
340 fn id(&self) -> LanguageModelId {
341 self.id.clone()
342 }
343
344 fn name(&self) -> LanguageModelName {
345 LanguageModelName::from(self.model.display_name().to_string())
346 }
347
348 fn provider_id(&self) -> LanguageModelProviderId {
349 PROVIDER_ID
350 }
351
352 fn provider_name(&self) -> LanguageModelProviderName {
353 PROVIDER_NAME
354 }
355
356 fn supports_tools(&self) -> bool {
357 self.model.supports_tools()
358 }
359
360 fn supports_images(&self) -> bool {
361 self.model.supports_images()
362 }
363
364 fn supports_tool_choice(&self, choice: LanguageModelToolChoice) -> bool {
365 match choice {
366 LanguageModelToolChoice::Auto
367 | LanguageModelToolChoice::Any
368 | LanguageModelToolChoice::None => true,
369 }
370 }
371
372 fn tool_input_format(&self) -> LanguageModelToolSchemaFormat {
373 LanguageModelToolSchemaFormat::JsonSchemaSubset
374 }
375
376 fn telemetry_id(&self) -> String {
377 format!("google/{}", self.model.request_id())
378 }
379
380 fn max_token_count(&self) -> u64 {
381 self.model.max_token_count()
382 }
383
384 fn max_output_tokens(&self) -> Option<u64> {
385 self.model.max_output_tokens()
386 }
387
388 fn count_tokens(
389 &self,
390 request: LanguageModelRequest,
391 cx: &App,
392 ) -> BoxFuture<'static, Result<u64>> {
393 let model_id = self.model.request_id().to_string();
394 let request = into_google(request, model_id, self.model.mode());
395 let http_client = self.http_client.clone();
396 let api_key = self.state.read(cx).api_key.clone();
397
398 let settings = &AllLanguageModelSettings::get_global(cx).google;
399 let api_url = settings.api_url.clone();
400
401 async move {
402 let api_key = api_key.context("Missing Google API key")?;
403 let response = google_ai::count_tokens(
404 http_client.as_ref(),
405 &api_url,
406 &api_key,
407 google_ai::CountTokensRequest {
408 generate_content_request: request,
409 },
410 )
411 .await?;
412 Ok(response.total_tokens)
413 }
414 .boxed()
415 }
416
417 fn stream_completion(
418 &self,
419 request: LanguageModelRequest,
420 cx: &AsyncApp,
421 ) -> BoxFuture<
422 'static,
423 Result<
424 futures::stream::BoxStream<
425 'static,
426 Result<LanguageModelCompletionEvent, LanguageModelCompletionError>,
427 >,
428 LanguageModelCompletionError,
429 >,
430 > {
431 let request = into_google(
432 request,
433 self.model.request_id().to_string(),
434 self.model.mode(),
435 );
436 let request = self.stream_completion(request, cx);
437 let future = self.request_limiter.stream(async move {
438 let response = request.await.map_err(LanguageModelCompletionError::from)?;
439 Ok(GoogleEventMapper::new().map_stream(response))
440 });
441 async move { Ok(future.await?.boxed()) }.boxed()
442 }
443}
444
445pub fn into_google(
446 mut request: LanguageModelRequest,
447 model_id: String,
448 mode: GoogleModelMode,
449) -> google_ai::GenerateContentRequest {
450 fn map_content(content: Vec<MessageContent>) -> Vec<Part> {
451 content
452 .into_iter()
453 .flat_map(|content| match content {
454 language_model::MessageContent::Text(text) => {
455 if !text.is_empty() {
456 vec![Part::TextPart(google_ai::TextPart { text })]
457 } else {
458 vec![]
459 }
460 }
461 language_model::MessageContent::Thinking {
462 text: _,
463 signature: Some(signature),
464 } => {
465 if !signature.is_empty() {
466 vec![Part::ThoughtPart(google_ai::ThoughtPart {
467 thought: true,
468 thought_signature: signature,
469 })]
470 } else {
471 vec![]
472 }
473 }
474 language_model::MessageContent::Thinking { .. } => {
475 vec![]
476 }
477 language_model::MessageContent::RedactedThinking(_) => vec![],
478 language_model::MessageContent::Image(image) => {
479 vec![Part::InlineDataPart(google_ai::InlineDataPart {
480 inline_data: google_ai::GenerativeContentBlob {
481 mime_type: "image/png".to_string(),
482 data: image.source.to_string(),
483 },
484 })]
485 }
486 language_model::MessageContent::ToolUse(tool_use) => {
487 vec![Part::FunctionCallPart(google_ai::FunctionCallPart {
488 function_call: google_ai::FunctionCall {
489 name: tool_use.name.to_string(),
490 args: tool_use.input,
491 },
492 })]
493 }
494 language_model::MessageContent::ToolResult(tool_result) => {
495 match tool_result.content {
496 language_model::LanguageModelToolResultContent::Text(text) => {
497 vec![Part::FunctionResponsePart(
498 google_ai::FunctionResponsePart {
499 function_response: google_ai::FunctionResponse {
500 name: tool_result.tool_name.to_string(),
501 // The API expects a valid JSON object
502 response: serde_json::json!({
503 "output": text
504 }),
505 },
506 },
507 )]
508 }
509 language_model::LanguageModelToolResultContent::Image(image) => {
510 vec![
511 Part::FunctionResponsePart(google_ai::FunctionResponsePart {
512 function_response: google_ai::FunctionResponse {
513 name: tool_result.tool_name.to_string(),
514 // The API expects a valid JSON object
515 response: serde_json::json!({
516 "output": "Tool responded with an image"
517 }),
518 },
519 }),
520 Part::InlineDataPart(google_ai::InlineDataPart {
521 inline_data: google_ai::GenerativeContentBlob {
522 mime_type: "image/png".to_string(),
523 data: image.source.to_string(),
524 },
525 }),
526 ]
527 }
528 }
529 }
530 })
531 .collect()
532 }
533
534 let system_instructions = if request
535 .messages
536 .first()
537 .is_some_and(|msg| matches!(msg.role, Role::System))
538 {
539 let message = request.messages.remove(0);
540 Some(SystemInstruction {
541 parts: map_content(message.content),
542 })
543 } else {
544 None
545 };
546
547 google_ai::GenerateContentRequest {
548 model: google_ai::ModelName { model_id },
549 system_instruction: system_instructions,
550 contents: request
551 .messages
552 .into_iter()
553 .filter_map(|message| {
554 let parts = map_content(message.content);
555 if parts.is_empty() {
556 None
557 } else {
558 Some(google_ai::Content {
559 parts,
560 role: match message.role {
561 Role::User => google_ai::Role::User,
562 Role::Assistant => google_ai::Role::Model,
563 Role::System => google_ai::Role::User, // Google AI doesn't have a system role
564 },
565 })
566 }
567 })
568 .collect(),
569 generation_config: Some(google_ai::GenerationConfig {
570 candidate_count: Some(1),
571 stop_sequences: Some(request.stop),
572 max_output_tokens: None,
573 temperature: request.temperature.map(|t| t as f64).or(Some(1.0)),
574 thinking_config: match (request.thinking_allowed, mode) {
575 (true, GoogleModelMode::Thinking { budget_tokens }) => {
576 budget_tokens.map(|thinking_budget| ThinkingConfig { thinking_budget })
577 }
578 _ => None,
579 },
580 top_p: None,
581 top_k: None,
582 }),
583 safety_settings: None,
584 tools: (!request.tools.is_empty()).then(|| {
585 vec![google_ai::Tool {
586 function_declarations: request
587 .tools
588 .into_iter()
589 .map(|tool| FunctionDeclaration {
590 name: tool.name,
591 description: tool.description,
592 parameters: tool.input_schema,
593 })
594 .collect(),
595 }]
596 }),
597 tool_config: request.tool_choice.map(|choice| google_ai::ToolConfig {
598 function_calling_config: google_ai::FunctionCallingConfig {
599 mode: match choice {
600 LanguageModelToolChoice::Auto => google_ai::FunctionCallingMode::Auto,
601 LanguageModelToolChoice::Any => google_ai::FunctionCallingMode::Any,
602 LanguageModelToolChoice::None => google_ai::FunctionCallingMode::None,
603 },
604 allowed_function_names: None,
605 },
606 }),
607 }
608}
609
610pub struct GoogleEventMapper {
611 usage: UsageMetadata,
612 stop_reason: StopReason,
613}
614
615impl GoogleEventMapper {
616 pub fn new() -> Self {
617 Self {
618 usage: UsageMetadata::default(),
619 stop_reason: StopReason::EndTurn,
620 }
621 }
622
623 pub fn map_stream(
624 mut self,
625 events: Pin<Box<dyn Send + Stream<Item = Result<GenerateContentResponse>>>>,
626 ) -> impl Stream<Item = Result<LanguageModelCompletionEvent, LanguageModelCompletionError>>
627 {
628 events
629 .map(Some)
630 .chain(futures::stream::once(async { None }))
631 .flat_map(move |event| {
632 futures::stream::iter(match event {
633 Some(Ok(event)) => self.map_event(event),
634 Some(Err(error)) => {
635 vec![Err(LanguageModelCompletionError::from(error))]
636 }
637 None => vec![Ok(LanguageModelCompletionEvent::Stop(self.stop_reason))],
638 })
639 })
640 }
641
642 pub fn map_event(
643 &mut self,
644 event: GenerateContentResponse,
645 ) -> Vec<Result<LanguageModelCompletionEvent, LanguageModelCompletionError>> {
646 static TOOL_CALL_COUNTER: AtomicU64 = AtomicU64::new(0);
647
648 let mut events: Vec<_> = Vec::new();
649 let mut wants_to_use_tool = false;
650 if let Some(usage_metadata) = event.usage_metadata {
651 update_usage(&mut self.usage, &usage_metadata);
652 events.push(Ok(LanguageModelCompletionEvent::UsageUpdate(
653 convert_usage(&self.usage),
654 )))
655 }
656 if let Some(candidates) = event.candidates {
657 for candidate in candidates {
658 if let Some(finish_reason) = candidate.finish_reason.as_deref() {
659 self.stop_reason = match finish_reason {
660 "STOP" => StopReason::EndTurn,
661 "MAX_TOKENS" => StopReason::MaxTokens,
662 _ => {
663 log::error!("Unexpected google finish_reason: {finish_reason}");
664 StopReason::EndTurn
665 }
666 };
667 }
668 candidate
669 .content
670 .parts
671 .into_iter()
672 .for_each(|part| match part {
673 Part::TextPart(text_part) => {
674 events.push(Ok(LanguageModelCompletionEvent::Text(text_part.text)))
675 }
676 Part::InlineDataPart(_) => {}
677 Part::FunctionCallPart(function_call_part) => {
678 wants_to_use_tool = true;
679 let name: Arc<str> = function_call_part.function_call.name.into();
680 let next_tool_id =
681 TOOL_CALL_COUNTER.fetch_add(1, atomic::Ordering::SeqCst);
682 let id: LanguageModelToolUseId =
683 format!("{}-{}", name, next_tool_id).into();
684
685 events.push(Ok(LanguageModelCompletionEvent::ToolUse(
686 LanguageModelToolUse {
687 id,
688 name,
689 is_input_complete: true,
690 raw_input: function_call_part.function_call.args.to_string(),
691 input: function_call_part.function_call.args,
692 },
693 )));
694 }
695 Part::FunctionResponsePart(_) => {}
696 Part::ThoughtPart(part) => {
697 events.push(Ok(LanguageModelCompletionEvent::Thinking {
698 text: "(Encrypted thought)".to_string(), // TODO: Can we populate this from thought summaries?
699 signature: Some(part.thought_signature),
700 }));
701 }
702 });
703 }
704 }
705
706 // Even when Gemini wants to use a Tool, the API
707 // responds with `finish_reason: STOP`
708 if wants_to_use_tool {
709 self.stop_reason = StopReason::ToolUse;
710 events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::ToolUse)));
711 }
712 events
713 }
714}
715
716pub fn count_google_tokens(
717 request: LanguageModelRequest,
718 cx: &App,
719) -> BoxFuture<'static, Result<u64>> {
720 // We couldn't use the GoogleLanguageModelProvider to count tokens because the github copilot doesn't have the access to google_ai directly.
721 // So we have to use tokenizer from tiktoken_rs to count tokens.
722 cx.background_spawn(async move {
723 let messages = request
724 .messages
725 .into_iter()
726 .map(|message| tiktoken_rs::ChatCompletionRequestMessage {
727 role: match message.role {
728 Role::User => "user".into(),
729 Role::Assistant => "assistant".into(),
730 Role::System => "system".into(),
731 },
732 content: Some(message.string_contents()),
733 name: None,
734 function_call: None,
735 })
736 .collect::<Vec<_>>();
737
738 // Tiktoken doesn't yet support these models, so we manually use the
739 // same tokenizer as GPT-4.
740 tiktoken_rs::num_tokens_from_messages("gpt-4", &messages).map(|tokens| tokens as u64)
741 })
742 .boxed()
743}
744
745fn update_usage(usage: &mut UsageMetadata, new: &UsageMetadata) {
746 if let Some(prompt_token_count) = new.prompt_token_count {
747 usage.prompt_token_count = Some(prompt_token_count);
748 }
749 if let Some(cached_content_token_count) = new.cached_content_token_count {
750 usage.cached_content_token_count = Some(cached_content_token_count);
751 }
752 if let Some(candidates_token_count) = new.candidates_token_count {
753 usage.candidates_token_count = Some(candidates_token_count);
754 }
755 if let Some(tool_use_prompt_token_count) = new.tool_use_prompt_token_count {
756 usage.tool_use_prompt_token_count = Some(tool_use_prompt_token_count);
757 }
758 if let Some(thoughts_token_count) = new.thoughts_token_count {
759 usage.thoughts_token_count = Some(thoughts_token_count);
760 }
761 if let Some(total_token_count) = new.total_token_count {
762 usage.total_token_count = Some(total_token_count);
763 }
764}
765
766fn convert_usage(usage: &UsageMetadata) -> language_model::TokenUsage {
767 let prompt_tokens = usage.prompt_token_count.unwrap_or(0);
768 let cached_tokens = usage.cached_content_token_count.unwrap_or(0);
769 let input_tokens = prompt_tokens - cached_tokens;
770 let output_tokens = usage.candidates_token_count.unwrap_or(0);
771
772 language_model::TokenUsage {
773 input_tokens,
774 output_tokens,
775 cache_read_input_tokens: cached_tokens,
776 cache_creation_input_tokens: 0,
777 }
778}
779
780struct ConfigurationView {
781 api_key_editor: Entity<Editor>,
782 state: gpui::Entity<State>,
783 target_agent: language_model::ConfigurationViewTargetAgent,
784 load_credentials_task: Option<Task<()>>,
785}
786
787impl ConfigurationView {
788 fn new(
789 state: gpui::Entity<State>,
790 target_agent: language_model::ConfigurationViewTargetAgent,
791 window: &mut Window,
792 cx: &mut Context<Self>,
793 ) -> Self {
794 cx.observe(&state, |_, _, cx| {
795 cx.notify();
796 })
797 .detach();
798
799 let load_credentials_task = Some(cx.spawn_in(window, {
800 let state = state.clone();
801 async move |this, cx| {
802 if let Some(task) = state
803 .update(cx, |state, cx| state.authenticate(cx))
804 .log_err()
805 {
806 // We don't log an error, because "not signed in" is also an error.
807 let _ = task.await;
808 }
809 this.update(cx, |this, cx| {
810 this.load_credentials_task = None;
811 cx.notify();
812 })
813 .log_err();
814 }
815 }));
816
817 Self {
818 api_key_editor: cx.new(|cx| {
819 let mut editor = Editor::single_line(window, cx);
820 editor.set_placeholder_text("AIzaSy...", window, cx);
821 editor
822 }),
823 target_agent,
824 state,
825 load_credentials_task,
826 }
827 }
828
829 fn save_api_key(&mut self, _: &menu::Confirm, window: &mut Window, cx: &mut Context<Self>) {
830 let api_key = self.api_key_editor.read(cx).text(cx);
831 if api_key.is_empty() {
832 return;
833 }
834
835 let state = self.state.clone();
836 cx.spawn_in(window, async move |_, cx| {
837 state
838 .update(cx, |state, cx| state.set_api_key(api_key, cx))?
839 .await
840 })
841 .detach_and_log_err(cx);
842
843 cx.notify();
844 }
845
846 fn reset_api_key(&mut self, window: &mut Window, cx: &mut Context<Self>) {
847 self.api_key_editor
848 .update(cx, |editor, cx| editor.set_text("", window, cx));
849
850 let state = self.state.clone();
851 cx.spawn_in(window, async move |_, cx| {
852 state.update(cx, |state, cx| state.reset_api_key(cx))?.await
853 })
854 .detach_and_log_err(cx);
855
856 cx.notify();
857 }
858
859 fn render_api_key_editor(&self, cx: &mut Context<Self>) -> impl IntoElement {
860 let settings = ThemeSettings::get_global(cx);
861 let text_style = TextStyle {
862 color: cx.theme().colors().text,
863 font_family: settings.ui_font.family.clone(),
864 font_features: settings.ui_font.features.clone(),
865 font_fallbacks: settings.ui_font.fallbacks.clone(),
866 font_size: rems(0.875).into(),
867 font_weight: settings.ui_font.weight,
868 font_style: FontStyle::Normal,
869 line_height: relative(1.3),
870 white_space: WhiteSpace::Normal,
871 ..Default::default()
872 };
873 EditorElement::new(
874 &self.api_key_editor,
875 EditorStyle {
876 background: cx.theme().colors().editor_background,
877 local_player: cx.theme().players().local(),
878 text: text_style,
879 ..Default::default()
880 },
881 )
882 }
883
884 fn should_render_editor(&self, cx: &mut Context<Self>) -> bool {
885 !self.state.read(cx).is_authenticated()
886 }
887}
888
889impl Render for ConfigurationView {
890 fn render(&mut self, _: &mut Window, cx: &mut Context<Self>) -> impl IntoElement {
891 let env_var_set = self.state.read(cx).api_key_from_env;
892
893 if self.load_credentials_task.is_some() {
894 div().child(Label::new("Loading credentials...")).into_any()
895 } else if self.should_render_editor(cx) {
896 v_flex()
897 .size_full()
898 .on_action(cx.listener(Self::save_api_key))
899 .child(Label::new(format!("To use {}, you need to add an API key. Follow these steps:", match &self.target_agent {
900 ConfigurationViewTargetAgent::ZedAgent => "Zed's agent with Google AI".into(),
901 ConfigurationViewTargetAgent::Other(agent) => agent.clone(),
902 })))
903 .child(
904 List::new()
905 .child(InstructionListItem::new(
906 "Create one by visiting",
907 Some("Google AI's console"),
908 Some("https://aistudio.google.com/app/apikey"),
909 ))
910 .child(InstructionListItem::text_only(
911 "Paste your API key below and hit enter to start using the assistant",
912 )),
913 )
914 .child(
915 h_flex()
916 .w_full()
917 .my_2()
918 .px_2()
919 .py_1()
920 .bg(cx.theme().colors().editor_background)
921 .border_1()
922 .border_color(cx.theme().colors().border)
923 .rounded_sm()
924 .child(self.render_api_key_editor(cx)),
925 )
926 .child(
927 Label::new(
928 format!("You can also assign the {GEMINI_API_KEY_VAR} environment variable and restart Zed."),
929 )
930 .size(LabelSize::Small).color(Color::Muted),
931 )
932 .into_any()
933 } else {
934 h_flex()
935 .mt_1()
936 .p_1()
937 .justify_between()
938 .rounded_md()
939 .border_1()
940 .border_color(cx.theme().colors().border)
941 .bg(cx.theme().colors().background)
942 .child(
943 h_flex()
944 .gap_1()
945 .child(Icon::new(IconName::Check).color(Color::Success))
946 .child(Label::new(if env_var_set {
947 format!("API key set in {GEMINI_API_KEY_VAR} environment variable.")
948 } else {
949 "API key configured.".to_string()
950 })),
951 )
952 .child(
953 Button::new("reset-key", "Reset Key")
954 .label_size(LabelSize::Small)
955 .icon(Some(IconName::Trash))
956 .icon_size(IconSize::Small)
957 .icon_position(IconPosition::Start)
958 .disabled(env_var_set)
959 .when(env_var_set, |this| {
960 this.tooltip(Tooltip::text(format!("To reset your API key, make sure {GEMINI_API_KEY_VAR} and {GOOGLE_AI_API_KEY_VAR} environment variables are unset.")))
961 })
962 .on_click(cx.listener(|this, _, window, cx| this.reset_api_key(window, cx))),
963 )
964 .into_any()
965 }
966 }
967}