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