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