1use anyhow::{Context as _, Result};
2use collections::BTreeMap;
3use credentials_provider::CredentialsProvider;
4use futures::{FutureExt, Stream, StreamExt, future::BoxFuture};
5use google_ai::{
6 FunctionDeclaration, GenerateContentResponse, GoogleModelMode, Part, SystemInstruction,
7 ThinkingConfig, UsageMetadata,
8};
9use gpui::{AnyView, App, AsyncApp, Context, Entity, SharedString, Task, Window};
10use http_client::HttpClient;
11use language_model::{
12 AuthenticateError, ConfigurationViewTargetAgent, EnvVar, LanguageModelCompletionError,
13 LanguageModelCompletionEvent, LanguageModelToolChoice, LanguageModelToolSchemaFormat,
14 LanguageModelToolUse, LanguageModelToolUseId, MessageContent, StopReason,
15};
16use language_model::{
17 IconOrSvg, LanguageModel, LanguageModelId, LanguageModelName, LanguageModelProvider,
18 LanguageModelProviderId, LanguageModelProviderName, LanguageModelProviderState,
19 LanguageModelRequest, RateLimiter, Role,
20};
21use schemars::JsonSchema;
22use serde::{Deserialize, Serialize};
23pub use settings::GoogleAvailableModel as AvailableModel;
24use settings::{Settings, SettingsStore};
25use std::pin::Pin;
26use std::sync::{
27 Arc, LazyLock,
28 atomic::{self, AtomicU64},
29};
30use strum::IntoEnumIterator;
31use ui::{ButtonLink, ConfiguredApiCard, List, ListBulletItem, prelude::*};
32use ui_input::InputField;
33use util::ResultExt;
34
35use language_model::{ApiKey, ApiKeyState};
36
37const PROVIDER_ID: LanguageModelProviderId = language_model::GOOGLE_PROVIDER_ID;
38const PROVIDER_NAME: LanguageModelProviderName = language_model::GOOGLE_PROVIDER_NAME;
39
40#[derive(Default, Clone, Debug, PartialEq)]
41pub struct GoogleSettings {
42 pub api_url: String,
43 pub available_models: Vec<AvailableModel>,
44}
45
46#[derive(Clone, Copy, Debug, Default, PartialEq, Serialize, Deserialize, JsonSchema)]
47#[serde(tag = "type", rename_all = "lowercase")]
48pub enum ModelMode {
49 #[default]
50 Default,
51 Thinking {
52 /// The maximum number of tokens to use for reasoning. Must be lower than the model's `max_output_tokens`.
53 budget_tokens: Option<u32>,
54 },
55}
56
57pub struct GoogleLanguageModelProvider {
58 http_client: Arc<dyn HttpClient>,
59 state: Entity<State>,
60}
61
62pub struct State {
63 api_key_state: ApiKeyState,
64}
65
66const GEMINI_API_KEY_VAR_NAME: &str = "GEMINI_API_KEY";
67const GOOGLE_AI_API_KEY_VAR_NAME: &str = "GOOGLE_AI_API_KEY";
68
69static API_KEY_ENV_VAR: LazyLock<EnvVar> = LazyLock::new(|| {
70 // Try GEMINI_API_KEY first as primary, fallback to GOOGLE_AI_API_KEY
71 EnvVar::new(GEMINI_API_KEY_VAR_NAME.into()).or(EnvVar::new(GOOGLE_AI_API_KEY_VAR_NAME.into()))
72});
73
74impl State {
75 fn is_authenticated(&self) -> bool {
76 self.api_key_state.has_key()
77 }
78
79 fn set_api_key(&mut self, api_key: Option<String>, cx: &mut Context<Self>) -> Task<Result<()>> {
80 let api_url = GoogleLanguageModelProvider::api_url(cx);
81 self.api_key_state
82 .store(api_url, api_key, |this| &mut this.api_key_state, cx)
83 }
84
85 fn authenticate(&mut self, cx: &mut Context<Self>) -> Task<Result<(), AuthenticateError>> {
86 let api_url = GoogleLanguageModelProvider::api_url(cx);
87 self.api_key_state
88 .load_if_needed(api_url, |this| &mut this.api_key_state, cx)
89 }
90}
91
92impl GoogleLanguageModelProvider {
93 pub fn new(http_client: Arc<dyn HttpClient>, cx: &mut App) -> Self {
94 let state = cx.new(|cx| {
95 cx.observe_global::<SettingsStore>(|this: &mut State, cx| {
96 let api_url = Self::api_url(cx);
97 this.api_key_state
98 .handle_url_change(api_url, |this| &mut this.api_key_state, cx);
99 cx.notify();
100 })
101 .detach();
102 State {
103 api_key_state: ApiKeyState::new(Self::api_url(cx), (*API_KEY_ENV_VAR).clone()),
104 }
105 });
106
107 Self { http_client, state }
108 }
109
110 fn create_language_model(&self, model: google_ai::Model) -> Arc<dyn LanguageModel> {
111 Arc::new(GoogleLanguageModel {
112 id: LanguageModelId::from(model.id().to_string()),
113 model,
114 state: self.state.clone(),
115 http_client: self.http_client.clone(),
116 request_limiter: RateLimiter::new(4),
117 })
118 }
119
120 pub fn api_key_for_gemini_cli(cx: &mut App) -> Task<Result<String>> {
121 if let Some(key) = API_KEY_ENV_VAR.value.clone() {
122 return Task::ready(Ok(key));
123 }
124 let credentials_provider = <dyn CredentialsProvider>::global(cx);
125 let api_url = Self::api_url(cx).to_string();
126 cx.spawn(async move |cx| {
127 Ok(
128 ApiKey::load_from_system_keychain(&api_url, credentials_provider.as_ref(), cx)
129 .await?
130 .key()
131 .to_string(),
132 )
133 })
134 }
135
136 fn settings(cx: &App) -> &GoogleSettings {
137 &crate::AllLanguageModelSettings::get_global(cx).google
138 }
139
140 fn api_url(cx: &App) -> SharedString {
141 let api_url = &Self::settings(cx).api_url;
142 if api_url.is_empty() {
143 google_ai::API_URL.into()
144 } else {
145 SharedString::new(api_url.as_str())
146 }
147 }
148}
149
150impl LanguageModelProviderState for GoogleLanguageModelProvider {
151 type ObservableEntity = State;
152
153 fn observable_entity(&self) -> Option<Entity<Self::ObservableEntity>> {
154 Some(self.state.clone())
155 }
156}
157
158impl LanguageModelProvider for GoogleLanguageModelProvider {
159 fn id(&self) -> LanguageModelProviderId {
160 PROVIDER_ID
161 }
162
163 fn name(&self) -> LanguageModelProviderName {
164 PROVIDER_NAME
165 }
166
167 fn icon(&self) -> IconOrSvg {
168 IconOrSvg::Icon(IconName::AiGoogle)
169 }
170
171 fn default_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {
172 Some(self.create_language_model(google_ai::Model::default()))
173 }
174
175 fn default_fast_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {
176 Some(self.create_language_model(google_ai::Model::default_fast()))
177 }
178
179 fn provided_models(&self, cx: &App) -> Vec<Arc<dyn LanguageModel>> {
180 let mut models = BTreeMap::default();
181
182 // Add base models from google_ai::Model::iter()
183 for model in google_ai::Model::iter() {
184 if !matches!(model, google_ai::Model::Custom { .. }) {
185 models.insert(model.id().to_string(), model);
186 }
187 }
188
189 // Override with available models from settings
190 for model in &GoogleLanguageModelProvider::settings(cx).available_models {
191 models.insert(
192 model.name.clone(),
193 google_ai::Model::Custom {
194 name: model.name.clone(),
195 display_name: model.display_name.clone(),
196 max_tokens: model.max_tokens,
197 mode: model.mode.unwrap_or_default(),
198 },
199 );
200 }
201
202 models
203 .into_values()
204 .map(|model| {
205 Arc::new(GoogleLanguageModel {
206 id: LanguageModelId::from(model.id().to_string()),
207 model,
208 state: self.state.clone(),
209 http_client: self.http_client.clone(),
210 request_limiter: RateLimiter::new(4),
211 }) as Arc<dyn LanguageModel>
212 })
213 .collect()
214 }
215
216 fn is_authenticated(&self, cx: &App) -> bool {
217 self.state.read(cx).is_authenticated()
218 }
219
220 fn authenticate(&self, cx: &mut App) -> Task<Result<(), AuthenticateError>> {
221 self.state.update(cx, |state, cx| state.authenticate(cx))
222 }
223
224 fn configuration_view(
225 &self,
226 target_agent: language_model::ConfigurationViewTargetAgent,
227 window: &mut Window,
228 cx: &mut App,
229 ) -> AnyView {
230 cx.new(|cx| ConfigurationView::new(self.state.clone(), target_agent, window, cx))
231 .into()
232 }
233
234 fn reset_credentials(&self, cx: &mut App) -> Task<Result<()>> {
235 self.state
236 .update(cx, |state, cx| state.set_api_key(None, cx))
237 }
238}
239
240pub struct GoogleLanguageModel {
241 id: LanguageModelId,
242 model: google_ai::Model,
243 state: Entity<State>,
244 http_client: Arc<dyn HttpClient>,
245 request_limiter: RateLimiter,
246}
247
248impl GoogleLanguageModel {
249 fn stream_completion(
250 &self,
251 request: google_ai::GenerateContentRequest,
252 cx: &AsyncApp,
253 ) -> BoxFuture<
254 'static,
255 Result<futures::stream::BoxStream<'static, Result<GenerateContentResponse>>>,
256 > {
257 let http_client = self.http_client.clone();
258
259 let (api_key, api_url) = self.state.read_with(cx, |state, cx| {
260 let api_url = GoogleLanguageModelProvider::api_url(cx);
261 (state.api_key_state.key(&api_url), api_url)
262 });
263
264 async move {
265 let api_key = api_key.context("Missing Google API key")?;
266 let request = google_ai::stream_generate_content(
267 http_client.as_ref(),
268 &api_url,
269 &api_key,
270 request,
271 );
272 request.await.context("failed to stream completion")
273 }
274 .boxed()
275 }
276}
277
278impl LanguageModel for GoogleLanguageModel {
279 fn id(&self) -> LanguageModelId {
280 self.id.clone()
281 }
282
283 fn name(&self) -> LanguageModelName {
284 LanguageModelName::from(self.model.display_name().to_string())
285 }
286
287 fn provider_id(&self) -> LanguageModelProviderId {
288 PROVIDER_ID
289 }
290
291 fn provider_name(&self) -> LanguageModelProviderName {
292 PROVIDER_NAME
293 }
294
295 fn supports_tools(&self) -> bool {
296 self.model.supports_tools()
297 }
298
299 fn supports_images(&self) -> bool {
300 self.model.supports_images()
301 }
302
303 fn supports_tool_choice(&self, choice: LanguageModelToolChoice) -> bool {
304 match choice {
305 LanguageModelToolChoice::Auto
306 | LanguageModelToolChoice::Any
307 | LanguageModelToolChoice::None => true,
308 }
309 }
310
311 fn tool_input_format(&self) -> LanguageModelToolSchemaFormat {
312 LanguageModelToolSchemaFormat::JsonSchemaSubset
313 }
314
315 fn telemetry_id(&self) -> String {
316 format!("google/{}", self.model.request_id())
317 }
318
319 fn max_token_count(&self) -> u64 {
320 self.model.max_token_count()
321 }
322
323 fn max_output_tokens(&self) -> Option<u64> {
324 self.model.max_output_tokens()
325 }
326
327 fn count_tokens(
328 &self,
329 request: LanguageModelRequest,
330 cx: &App,
331 ) -> BoxFuture<'static, Result<u64>> {
332 let model_id = self.model.request_id().to_string();
333 let request = into_google(request, model_id, self.model.mode());
334 let http_client = self.http_client.clone();
335 let api_url = GoogleLanguageModelProvider::api_url(cx);
336 let api_key = self.state.read(cx).api_key_state.key(&api_url);
337
338 async move {
339 let Some(api_key) = api_key else {
340 return Err(LanguageModelCompletionError::NoApiKey {
341 provider: PROVIDER_NAME,
342 }
343 .into());
344 };
345 let response = google_ai::count_tokens(
346 http_client.as_ref(),
347 &api_url,
348 &api_key,
349 google_ai::CountTokensRequest {
350 generate_content_request: request,
351 },
352 )
353 .await?;
354 Ok(response.total_tokens)
355 }
356 .boxed()
357 }
358
359 fn stream_completion(
360 &self,
361 request: LanguageModelRequest,
362 cx: &AsyncApp,
363 ) -> BoxFuture<
364 'static,
365 Result<
366 futures::stream::BoxStream<
367 'static,
368 Result<LanguageModelCompletionEvent, LanguageModelCompletionError>,
369 >,
370 LanguageModelCompletionError,
371 >,
372 > {
373 let request = into_google(
374 request,
375 self.model.request_id().to_string(),
376 self.model.mode(),
377 );
378 let request = self.stream_completion(request, cx);
379 let future = self.request_limiter.stream(async move {
380 let response = request.await.map_err(LanguageModelCompletionError::from)?;
381 Ok(GoogleEventMapper::new().map_stream(response))
382 });
383 async move { Ok(future.await?.boxed()) }.boxed()
384 }
385}
386
387pub fn into_google(
388 mut request: LanguageModelRequest,
389 model_id: String,
390 mode: GoogleModelMode,
391) -> google_ai::GenerateContentRequest {
392 fn map_content(content: Vec<MessageContent>) -> Vec<Part> {
393 content
394 .into_iter()
395 .flat_map(|content| match content {
396 language_model::MessageContent::Text(text) => {
397 if !text.is_empty() {
398 vec![Part::TextPart(google_ai::TextPart { text })]
399 } else {
400 vec![]
401 }
402 }
403 language_model::MessageContent::Thinking {
404 text: _,
405 signature: Some(signature),
406 } => {
407 if !signature.is_empty() {
408 vec![Part::ThoughtPart(google_ai::ThoughtPart {
409 thought: true,
410 thought_signature: signature,
411 })]
412 } else {
413 vec![]
414 }
415 }
416 language_model::MessageContent::Thinking { .. } => {
417 vec![]
418 }
419 language_model::MessageContent::RedactedThinking(_) => vec![],
420 language_model::MessageContent::Image(image) => {
421 vec![Part::InlineDataPart(google_ai::InlineDataPart {
422 inline_data: google_ai::GenerativeContentBlob {
423 mime_type: "image/png".to_string(),
424 data: image.source.to_string(),
425 },
426 })]
427 }
428 language_model::MessageContent::ToolUse(tool_use) => {
429 // Normalize empty string signatures to None
430 let thought_signature = tool_use.thought_signature.filter(|s| !s.is_empty());
431
432 vec![Part::FunctionCallPart(google_ai::FunctionCallPart {
433 function_call: google_ai::FunctionCall {
434 name: tool_use.name.to_string(),
435 args: tool_use.input,
436 },
437 thought_signature,
438 })]
439 }
440 language_model::MessageContent::ToolResult(tool_result) => {
441 match tool_result.content {
442 language_model::LanguageModelToolResultContent::Text(text) => {
443 vec![Part::FunctionResponsePart(
444 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": text
450 }),
451 },
452 },
453 )]
454 }
455 language_model::LanguageModelToolResultContent::Image(image) => {
456 vec![
457 Part::FunctionResponsePart(google_ai::FunctionResponsePart {
458 function_response: google_ai::FunctionResponse {
459 name: tool_result.tool_name.to_string(),
460 // The API expects a valid JSON object
461 response: serde_json::json!({
462 "output": "Tool responded with an image"
463 }),
464 },
465 }),
466 Part::InlineDataPart(google_ai::InlineDataPart {
467 inline_data: google_ai::GenerativeContentBlob {
468 mime_type: "image/png".to_string(),
469 data: image.source.to_string(),
470 },
471 }),
472 ]
473 }
474 }
475 }
476 })
477 .collect()
478 }
479
480 let system_instructions = if request
481 .messages
482 .first()
483 .is_some_and(|msg| matches!(msg.role, Role::System))
484 {
485 let message = request.messages.remove(0);
486 Some(SystemInstruction {
487 parts: map_content(message.content),
488 })
489 } else {
490 None
491 };
492
493 google_ai::GenerateContentRequest {
494 model: google_ai::ModelName { model_id },
495 system_instruction: system_instructions,
496 contents: request
497 .messages
498 .into_iter()
499 .filter_map(|message| {
500 let parts = map_content(message.content);
501 if parts.is_empty() {
502 None
503 } else {
504 Some(google_ai::Content {
505 parts,
506 role: match message.role {
507 Role::User => google_ai::Role::User,
508 Role::Assistant => google_ai::Role::Model,
509 Role::System => google_ai::Role::User, // Google AI doesn't have a system role
510 },
511 })
512 }
513 })
514 .collect(),
515 generation_config: Some(google_ai::GenerationConfig {
516 candidate_count: Some(1),
517 stop_sequences: Some(request.stop),
518 max_output_tokens: None,
519 temperature: request.temperature.map(|t| t as f64).or(Some(1.0)),
520 thinking_config: match (request.thinking_allowed, mode) {
521 (true, GoogleModelMode::Thinking { budget_tokens }) => {
522 budget_tokens.map(|thinking_budget| ThinkingConfig { thinking_budget })
523 }
524 _ => None,
525 },
526 top_p: None,
527 top_k: None,
528 }),
529 safety_settings: None,
530 tools: (!request.tools.is_empty()).then(|| {
531 vec![google_ai::Tool {
532 function_declarations: request
533 .tools
534 .into_iter()
535 .map(|tool| FunctionDeclaration {
536 name: tool.name,
537 description: tool.description,
538 parameters: tool.input_schema,
539 })
540 .collect(),
541 }]
542 }),
543 tool_config: request.tool_choice.map(|choice| google_ai::ToolConfig {
544 function_calling_config: google_ai::FunctionCallingConfig {
545 mode: match choice {
546 LanguageModelToolChoice::Auto => google_ai::FunctionCallingMode::Auto,
547 LanguageModelToolChoice::Any => google_ai::FunctionCallingMode::Any,
548 LanguageModelToolChoice::None => google_ai::FunctionCallingMode::None,
549 },
550 allowed_function_names: None,
551 },
552 }),
553 }
554}
555
556pub struct GoogleEventMapper {
557 usage: UsageMetadata,
558 stop_reason: StopReason,
559}
560
561impl GoogleEventMapper {
562 pub fn new() -> Self {
563 Self {
564 usage: UsageMetadata::default(),
565 stop_reason: StopReason::EndTurn,
566 }
567 }
568
569 pub fn map_stream(
570 mut self,
571 events: Pin<Box<dyn Send + Stream<Item = Result<GenerateContentResponse>>>>,
572 ) -> impl Stream<Item = Result<LanguageModelCompletionEvent, LanguageModelCompletionError>>
573 {
574 events
575 .map(Some)
576 .chain(futures::stream::once(async { None }))
577 .flat_map(move |event| {
578 futures::stream::iter(match event {
579 Some(Ok(event)) => self.map_event(event),
580 Some(Err(error)) => {
581 vec![Err(LanguageModelCompletionError::from(error))]
582 }
583 None => vec![Ok(LanguageModelCompletionEvent::Stop(self.stop_reason))],
584 })
585 })
586 }
587
588 pub fn map_event(
589 &mut self,
590 event: GenerateContentResponse,
591 ) -> Vec<Result<LanguageModelCompletionEvent, LanguageModelCompletionError>> {
592 static TOOL_CALL_COUNTER: AtomicU64 = AtomicU64::new(0);
593
594 let mut events: Vec<_> = Vec::new();
595 let mut wants_to_use_tool = false;
596 if let Some(usage_metadata) = event.usage_metadata {
597 update_usage(&mut self.usage, &usage_metadata);
598 events.push(Ok(LanguageModelCompletionEvent::UsageUpdate(
599 convert_usage(&self.usage),
600 )))
601 }
602
603 if let Some(prompt_feedback) = event.prompt_feedback
604 && let Some(block_reason) = prompt_feedback.block_reason.as_deref()
605 {
606 self.stop_reason = match block_reason {
607 "SAFETY" | "OTHER" | "BLOCKLIST" | "PROHIBITED_CONTENT" | "IMAGE_SAFETY" => {
608 StopReason::Refusal
609 }
610 _ => {
611 log::error!("Unexpected Google block_reason: {block_reason}");
612 StopReason::Refusal
613 }
614 };
615 events.push(Ok(LanguageModelCompletionEvent::Stop(self.stop_reason)));
616
617 return events;
618 }
619
620 if let Some(candidates) = event.candidates {
621 for candidate in candidates {
622 if let Some(finish_reason) = candidate.finish_reason.as_deref() {
623 self.stop_reason = match finish_reason {
624 "STOP" => StopReason::EndTurn,
625 "MAX_TOKENS" => StopReason::MaxTokens,
626 _ => {
627 log::error!("Unexpected google finish_reason: {finish_reason}");
628 StopReason::EndTurn
629 }
630 };
631 }
632 candidate
633 .content
634 .parts
635 .into_iter()
636 .for_each(|part| match part {
637 Part::TextPart(text_part) => {
638 events.push(Ok(LanguageModelCompletionEvent::Text(text_part.text)))
639 }
640 Part::InlineDataPart(_) => {}
641 Part::FunctionCallPart(function_call_part) => {
642 wants_to_use_tool = true;
643 let name: Arc<str> = function_call_part.function_call.name.into();
644 let next_tool_id =
645 TOOL_CALL_COUNTER.fetch_add(1, atomic::Ordering::SeqCst);
646 let id: LanguageModelToolUseId =
647 format!("{}-{}", name, next_tool_id).into();
648
649 // Normalize empty string signatures to None
650 let thought_signature = function_call_part
651 .thought_signature
652 .filter(|s| !s.is_empty());
653
654 events.push(Ok(LanguageModelCompletionEvent::ToolUse(
655 LanguageModelToolUse {
656 id,
657 name,
658 is_input_complete: true,
659 raw_input: function_call_part.function_call.args.to_string(),
660 input: function_call_part.function_call.args,
661 thought_signature,
662 },
663 )));
664 }
665 Part::FunctionResponsePart(_) => {}
666 Part::ThoughtPart(part) => {
667 events.push(Ok(LanguageModelCompletionEvent::Thinking {
668 text: "(Encrypted thought)".to_string(), // TODO: Can we populate this from thought summaries?
669 signature: Some(part.thought_signature),
670 }));
671 }
672 });
673 }
674 }
675
676 // Even when Gemini wants to use a Tool, the API
677 // responds with `finish_reason: STOP`
678 if wants_to_use_tool {
679 self.stop_reason = StopReason::ToolUse;
680 events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::ToolUse)));
681 }
682 events
683 }
684}
685
686pub fn count_google_tokens(
687 request: LanguageModelRequest,
688 cx: &App,
689) -> BoxFuture<'static, Result<u64>> {
690 // We couldn't use the GoogleLanguageModelProvider to count tokens because the github copilot doesn't have the access to google_ai directly.
691 // So we have to use tokenizer from tiktoken_rs to count tokens.
692 cx.background_spawn(async move {
693 let messages = request
694 .messages
695 .into_iter()
696 .map(|message| tiktoken_rs::ChatCompletionRequestMessage {
697 role: match message.role {
698 Role::User => "user".into(),
699 Role::Assistant => "assistant".into(),
700 Role::System => "system".into(),
701 },
702 content: Some(message.string_contents()),
703 name: None,
704 function_call: None,
705 })
706 .collect::<Vec<_>>();
707
708 // Tiktoken doesn't yet support these models, so we manually use the
709 // same tokenizer as GPT-4.
710 tiktoken_rs::num_tokens_from_messages("gpt-4", &messages).map(|tokens| tokens as u64)
711 })
712 .boxed()
713}
714
715fn update_usage(usage: &mut UsageMetadata, new: &UsageMetadata) {
716 if let Some(prompt_token_count) = new.prompt_token_count {
717 usage.prompt_token_count = Some(prompt_token_count);
718 }
719 if let Some(cached_content_token_count) = new.cached_content_token_count {
720 usage.cached_content_token_count = Some(cached_content_token_count);
721 }
722 if let Some(candidates_token_count) = new.candidates_token_count {
723 usage.candidates_token_count = Some(candidates_token_count);
724 }
725 if let Some(tool_use_prompt_token_count) = new.tool_use_prompt_token_count {
726 usage.tool_use_prompt_token_count = Some(tool_use_prompt_token_count);
727 }
728 if let Some(thoughts_token_count) = new.thoughts_token_count {
729 usage.thoughts_token_count = Some(thoughts_token_count);
730 }
731 if let Some(total_token_count) = new.total_token_count {
732 usage.total_token_count = Some(total_token_count);
733 }
734}
735
736fn convert_usage(usage: &UsageMetadata) -> language_model::TokenUsage {
737 let prompt_tokens = usage.prompt_token_count.unwrap_or(0);
738 let cached_tokens = usage.cached_content_token_count.unwrap_or(0);
739 let input_tokens = prompt_tokens - cached_tokens;
740 let output_tokens = usage.candidates_token_count.unwrap_or(0);
741
742 language_model::TokenUsage {
743 input_tokens,
744 output_tokens,
745 cache_read_input_tokens: cached_tokens,
746 cache_creation_input_tokens: 0,
747 }
748}
749
750struct ConfigurationView {
751 api_key_editor: Entity<InputField>,
752 state: Entity<State>,
753 target_agent: language_model::ConfigurationViewTargetAgent,
754 load_credentials_task: Option<Task<()>>,
755}
756
757impl ConfigurationView {
758 fn new(
759 state: Entity<State>,
760 target_agent: language_model::ConfigurationViewTargetAgent,
761 window: &mut Window,
762 cx: &mut Context<Self>,
763 ) -> Self {
764 cx.observe(&state, |_, _, cx| {
765 cx.notify();
766 })
767 .detach();
768
769 let load_credentials_task = Some(cx.spawn_in(window, {
770 let state = state.clone();
771 async move |this, cx| {
772 if let Some(task) = Some(state.update(cx, |state, cx| state.authenticate(cx))) {
773 // We don't log an error, because "not signed in" is also an error.
774 let _ = task.await;
775 }
776 this.update(cx, |this, cx| {
777 this.load_credentials_task = None;
778 cx.notify();
779 })
780 .log_err();
781 }
782 }));
783
784 Self {
785 api_key_editor: cx.new(|cx| InputField::new(window, cx, "AIzaSy...")),
786 target_agent,
787 state,
788 load_credentials_task,
789 }
790 }
791
792 fn save_api_key(&mut self, _: &menu::Confirm, window: &mut Window, cx: &mut Context<Self>) {
793 let api_key = self.api_key_editor.read(cx).text(cx).trim().to_string();
794 if api_key.is_empty() {
795 return;
796 }
797
798 // url changes can cause the editor to be displayed again
799 self.api_key_editor
800 .update(cx, |editor, cx| editor.set_text("", window, cx));
801
802 let state = self.state.clone();
803 cx.spawn_in(window, async move |_, cx| {
804 state
805 .update(cx, |state, cx| state.set_api_key(Some(api_key), cx))
806 .await
807 })
808 .detach_and_log_err(cx);
809 }
810
811 fn reset_api_key(&mut self, window: &mut Window, cx: &mut Context<Self>) {
812 self.api_key_editor
813 .update(cx, |editor, cx| editor.set_text("", window, cx));
814
815 let state = self.state.clone();
816 cx.spawn_in(window, async move |_, cx| {
817 state
818 .update(cx, |state, cx| state.set_api_key(None, cx))
819 .await
820 })
821 .detach_and_log_err(cx);
822 }
823
824 fn should_render_editor(&self, cx: &mut Context<Self>) -> bool {
825 !self.state.read(cx).is_authenticated()
826 }
827}
828
829impl Render for ConfigurationView {
830 fn render(&mut self, _: &mut Window, cx: &mut Context<Self>) -> impl IntoElement {
831 let env_var_set = self.state.read(cx).api_key_state.is_from_env_var();
832 let configured_card_label = if env_var_set {
833 format!(
834 "API key set in {} environment variable",
835 API_KEY_ENV_VAR.name
836 )
837 } else {
838 let api_url = GoogleLanguageModelProvider::api_url(cx);
839 if api_url == google_ai::API_URL {
840 "API key configured".to_string()
841 } else {
842 format!("API key configured for {}", api_url)
843 }
844 };
845
846 if self.load_credentials_task.is_some() {
847 div()
848 .child(Label::new("Loading credentials..."))
849 .into_any_element()
850 } else if self.should_render_editor(cx) {
851 v_flex()
852 .size_full()
853 .on_action(cx.listener(Self::save_api_key))
854 .child(Label::new(format!("To use {}, you need to add an API key. Follow these steps:", match &self.target_agent {
855 ConfigurationViewTargetAgent::ZedAgent => "Zed's agent with Google AI".into(),
856 ConfigurationViewTargetAgent::Other(agent) => agent.clone(),
857 })))
858 .child(
859 List::new()
860 .child(
861 ListBulletItem::new("")
862 .child(Label::new("Create one by visiting"))
863 .child(ButtonLink::new("Google AI's console", "https://aistudio.google.com/app/apikey"))
864 )
865 .child(
866 ListBulletItem::new("Paste your API key below and hit enter to start using the agent")
867 )
868 )
869 .child(self.api_key_editor.clone())
870 .child(
871 Label::new(
872 format!("You can also set the {GEMINI_API_KEY_VAR_NAME} environment variable and restart Zed."),
873 )
874 .size(LabelSize::Small).color(Color::Muted),
875 )
876 .into_any_element()
877 } else {
878 ConfiguredApiCard::new(configured_card_label)
879 .disabled(env_var_set)
880 .on_click(cx.listener(|this, _, window, cx| this.reset_api_key(window, cx)))
881 .when(env_var_set, |this| {
882 this.tooltip_label(format!("To reset your API key, make sure {GEMINI_API_KEY_VAR_NAME} and {GOOGLE_AI_API_KEY_VAR_NAME} environment variables are unset."))
883 })
884 .into_any_element()
885 }
886 }
887}
888
889#[cfg(test)]
890mod tests {
891 use super::*;
892 use google_ai::{
893 Content, FunctionCall, FunctionCallPart, GenerateContentCandidate, GenerateContentResponse,
894 Part, Role as GoogleRole, TextPart,
895 };
896 use language_model::{LanguageModelToolUseId, MessageContent, Role};
897 use serde_json::json;
898
899 #[test]
900 fn test_function_call_with_signature_creates_tool_use_with_signature() {
901 let mut mapper = GoogleEventMapper::new();
902
903 let response = GenerateContentResponse {
904 candidates: Some(vec![GenerateContentCandidate {
905 index: Some(0),
906 content: Content {
907 parts: vec![Part::FunctionCallPart(FunctionCallPart {
908 function_call: FunctionCall {
909 name: "test_function".to_string(),
910 args: json!({"arg": "value"}),
911 },
912 thought_signature: Some("test_signature_123".to_string()),
913 })],
914 role: GoogleRole::Model,
915 },
916 finish_reason: None,
917 finish_message: None,
918 safety_ratings: None,
919 citation_metadata: None,
920 }]),
921 prompt_feedback: None,
922 usage_metadata: None,
923 };
924
925 let events = mapper.map_event(response);
926
927 assert_eq!(events.len(), 2); // ToolUse event + Stop event
928
929 if let Ok(LanguageModelCompletionEvent::ToolUse(tool_use)) = &events[0] {
930 assert_eq!(tool_use.name.as_ref(), "test_function");
931 assert_eq!(
932 tool_use.thought_signature.as_deref(),
933 Some("test_signature_123")
934 );
935 } else {
936 panic!("Expected ToolUse event");
937 }
938 }
939
940 #[test]
941 fn test_function_call_without_signature_has_none() {
942 let mut mapper = GoogleEventMapper::new();
943
944 let response = GenerateContentResponse {
945 candidates: Some(vec![GenerateContentCandidate {
946 index: Some(0),
947 content: Content {
948 parts: vec![Part::FunctionCallPart(FunctionCallPart {
949 function_call: FunctionCall {
950 name: "test_function".to_string(),
951 args: json!({"arg": "value"}),
952 },
953 thought_signature: None,
954 })],
955 role: GoogleRole::Model,
956 },
957 finish_reason: None,
958 finish_message: None,
959 safety_ratings: None,
960 citation_metadata: None,
961 }]),
962 prompt_feedback: None,
963 usage_metadata: None,
964 };
965
966 let events = mapper.map_event(response);
967
968 if let Ok(LanguageModelCompletionEvent::ToolUse(tool_use)) = &events[0] {
969 assert_eq!(tool_use.thought_signature, None);
970 } else {
971 panic!("Expected ToolUse event");
972 }
973 }
974
975 #[test]
976 fn test_empty_string_signature_normalized_to_none() {
977 let mut mapper = GoogleEventMapper::new();
978
979 let response = GenerateContentResponse {
980 candidates: Some(vec![GenerateContentCandidate {
981 index: Some(0),
982 content: Content {
983 parts: vec![Part::FunctionCallPart(FunctionCallPart {
984 function_call: FunctionCall {
985 name: "test_function".to_string(),
986 args: json!({"arg": "value"}),
987 },
988 thought_signature: Some("".to_string()),
989 })],
990 role: GoogleRole::Model,
991 },
992 finish_reason: None,
993 finish_message: None,
994 safety_ratings: None,
995 citation_metadata: None,
996 }]),
997 prompt_feedback: None,
998 usage_metadata: None,
999 };
1000
1001 let events = mapper.map_event(response);
1002
1003 if let Ok(LanguageModelCompletionEvent::ToolUse(tool_use)) = &events[0] {
1004 assert_eq!(tool_use.thought_signature, None);
1005 } else {
1006 panic!("Expected ToolUse event");
1007 }
1008 }
1009
1010 #[test]
1011 fn test_parallel_function_calls_preserve_signatures() {
1012 let mut mapper = GoogleEventMapper::new();
1013
1014 let response = GenerateContentResponse {
1015 candidates: Some(vec![GenerateContentCandidate {
1016 index: Some(0),
1017 content: Content {
1018 parts: vec![
1019 Part::FunctionCallPart(FunctionCallPart {
1020 function_call: FunctionCall {
1021 name: "function_1".to_string(),
1022 args: json!({"arg": "value1"}),
1023 },
1024 thought_signature: Some("signature_1".to_string()),
1025 }),
1026 Part::FunctionCallPart(FunctionCallPart {
1027 function_call: FunctionCall {
1028 name: "function_2".to_string(),
1029 args: json!({"arg": "value2"}),
1030 },
1031 thought_signature: None,
1032 }),
1033 ],
1034 role: GoogleRole::Model,
1035 },
1036 finish_reason: None,
1037 finish_message: None,
1038 safety_ratings: None,
1039 citation_metadata: None,
1040 }]),
1041 prompt_feedback: None,
1042 usage_metadata: None,
1043 };
1044
1045 let events = mapper.map_event(response);
1046
1047 assert_eq!(events.len(), 3); // 2 ToolUse events + Stop event
1048
1049 if let Ok(LanguageModelCompletionEvent::ToolUse(tool_use)) = &events[0] {
1050 assert_eq!(tool_use.name.as_ref(), "function_1");
1051 assert_eq!(tool_use.thought_signature.as_deref(), Some("signature_1"));
1052 } else {
1053 panic!("Expected ToolUse event for function_1");
1054 }
1055
1056 if let Ok(LanguageModelCompletionEvent::ToolUse(tool_use)) = &events[1] {
1057 assert_eq!(tool_use.name.as_ref(), "function_2");
1058 assert_eq!(tool_use.thought_signature, None);
1059 } else {
1060 panic!("Expected ToolUse event for function_2");
1061 }
1062 }
1063
1064 #[test]
1065 fn test_tool_use_with_signature_converts_to_function_call_part() {
1066 let tool_use = language_model::LanguageModelToolUse {
1067 id: LanguageModelToolUseId::from("test_id"),
1068 name: "test_function".into(),
1069 raw_input: json!({"arg": "value"}).to_string(),
1070 input: json!({"arg": "value"}),
1071 is_input_complete: true,
1072 thought_signature: Some("test_signature_456".to_string()),
1073 };
1074
1075 let request = super::into_google(
1076 LanguageModelRequest {
1077 messages: vec![language_model::LanguageModelRequestMessage {
1078 role: Role::Assistant,
1079 content: vec![MessageContent::ToolUse(tool_use)],
1080 cache: false,
1081 reasoning_details: None,
1082 }],
1083 ..Default::default()
1084 },
1085 "gemini-2.5-flash".to_string(),
1086 GoogleModelMode::Default,
1087 );
1088
1089 assert_eq!(request.contents[0].parts.len(), 1);
1090 if let Part::FunctionCallPart(fc_part) = &request.contents[0].parts[0] {
1091 assert_eq!(fc_part.function_call.name, "test_function");
1092 assert_eq!(
1093 fc_part.thought_signature.as_deref(),
1094 Some("test_signature_456")
1095 );
1096 } else {
1097 panic!("Expected FunctionCallPart");
1098 }
1099 }
1100
1101 #[test]
1102 fn test_tool_use_without_signature_omits_field() {
1103 let tool_use = language_model::LanguageModelToolUse {
1104 id: LanguageModelToolUseId::from("test_id"),
1105 name: "test_function".into(),
1106 raw_input: json!({"arg": "value"}).to_string(),
1107 input: json!({"arg": "value"}),
1108 is_input_complete: true,
1109 thought_signature: None,
1110 };
1111
1112 let request = super::into_google(
1113 LanguageModelRequest {
1114 messages: vec![language_model::LanguageModelRequestMessage {
1115 role: Role::Assistant,
1116 content: vec![MessageContent::ToolUse(tool_use)],
1117 cache: false,
1118 reasoning_details: None,
1119 }],
1120 ..Default::default()
1121 },
1122 "gemini-2.5-flash".to_string(),
1123 GoogleModelMode::Default,
1124 );
1125
1126 assert_eq!(request.contents[0].parts.len(), 1);
1127 if let Part::FunctionCallPart(fc_part) = &request.contents[0].parts[0] {
1128 assert_eq!(fc_part.thought_signature, None);
1129 } else {
1130 panic!("Expected FunctionCallPart");
1131 }
1132 }
1133
1134 #[test]
1135 fn test_empty_signature_in_tool_use_normalized_to_none() {
1136 let tool_use = language_model::LanguageModelToolUse {
1137 id: LanguageModelToolUseId::from("test_id"),
1138 name: "test_function".into(),
1139 raw_input: json!({"arg": "value"}).to_string(),
1140 input: json!({"arg": "value"}),
1141 is_input_complete: true,
1142 thought_signature: Some("".to_string()),
1143 };
1144
1145 let request = super::into_google(
1146 LanguageModelRequest {
1147 messages: vec![language_model::LanguageModelRequestMessage {
1148 role: Role::Assistant,
1149 content: vec![MessageContent::ToolUse(tool_use)],
1150 cache: false,
1151 reasoning_details: None,
1152 }],
1153 ..Default::default()
1154 },
1155 "gemini-2.5-flash".to_string(),
1156 GoogleModelMode::Default,
1157 );
1158
1159 if let Part::FunctionCallPart(fc_part) = &request.contents[0].parts[0] {
1160 assert_eq!(fc_part.thought_signature, None);
1161 } else {
1162 panic!("Expected FunctionCallPart");
1163 }
1164 }
1165
1166 #[test]
1167 fn test_round_trip_preserves_signature() {
1168 let mut mapper = GoogleEventMapper::new();
1169
1170 // Simulate receiving a response from Google with a signature
1171 let response = GenerateContentResponse {
1172 candidates: Some(vec![GenerateContentCandidate {
1173 index: Some(0),
1174 content: Content {
1175 parts: vec![Part::FunctionCallPart(FunctionCallPart {
1176 function_call: FunctionCall {
1177 name: "test_function".to_string(),
1178 args: json!({"arg": "value"}),
1179 },
1180 thought_signature: Some("round_trip_sig".to_string()),
1181 })],
1182 role: GoogleRole::Model,
1183 },
1184 finish_reason: None,
1185 finish_message: None,
1186 safety_ratings: None,
1187 citation_metadata: None,
1188 }]),
1189 prompt_feedback: None,
1190 usage_metadata: None,
1191 };
1192
1193 let events = mapper.map_event(response);
1194
1195 let tool_use = if let Ok(LanguageModelCompletionEvent::ToolUse(tool_use)) = &events[0] {
1196 tool_use.clone()
1197 } else {
1198 panic!("Expected ToolUse event");
1199 };
1200
1201 // Convert back to Google format
1202 let request = super::into_google(
1203 LanguageModelRequest {
1204 messages: vec![language_model::LanguageModelRequestMessage {
1205 role: Role::Assistant,
1206 content: vec![MessageContent::ToolUse(tool_use)],
1207 cache: false,
1208 reasoning_details: None,
1209 }],
1210 ..Default::default()
1211 },
1212 "gemini-2.5-flash".to_string(),
1213 GoogleModelMode::Default,
1214 );
1215
1216 // Verify signature is preserved
1217 if let Part::FunctionCallPart(fc_part) = &request.contents[0].parts[0] {
1218 assert_eq!(fc_part.thought_signature.as_deref(), Some("round_trip_sig"));
1219 } else {
1220 panic!("Expected FunctionCallPart");
1221 }
1222 }
1223
1224 #[test]
1225 fn test_mixed_text_and_function_call_with_signature() {
1226 let mut mapper = GoogleEventMapper::new();
1227
1228 let response = GenerateContentResponse {
1229 candidates: Some(vec![GenerateContentCandidate {
1230 index: Some(0),
1231 content: Content {
1232 parts: vec![
1233 Part::TextPart(TextPart {
1234 text: "I'll help with that.".to_string(),
1235 }),
1236 Part::FunctionCallPart(FunctionCallPart {
1237 function_call: FunctionCall {
1238 name: "helper_function".to_string(),
1239 args: json!({"query": "help"}),
1240 },
1241 thought_signature: Some("mixed_sig".to_string()),
1242 }),
1243 ],
1244 role: GoogleRole::Model,
1245 },
1246 finish_reason: None,
1247 finish_message: None,
1248 safety_ratings: None,
1249 citation_metadata: None,
1250 }]),
1251 prompt_feedback: None,
1252 usage_metadata: None,
1253 };
1254
1255 let events = mapper.map_event(response);
1256
1257 assert_eq!(events.len(), 3); // Text event + ToolUse event + Stop event
1258
1259 if let Ok(LanguageModelCompletionEvent::Text(text)) = &events[0] {
1260 assert_eq!(text, "I'll help with that.");
1261 } else {
1262 panic!("Expected Text event");
1263 }
1264
1265 if let Ok(LanguageModelCompletionEvent::ToolUse(tool_use)) = &events[1] {
1266 assert_eq!(tool_use.name.as_ref(), "helper_function");
1267 assert_eq!(tool_use.thought_signature.as_deref(), Some("mixed_sig"));
1268 } else {
1269 panic!("Expected ToolUse event");
1270 }
1271 }
1272
1273 #[test]
1274 fn test_special_characters_in_signature_preserved() {
1275 let mut mapper = GoogleEventMapper::new();
1276
1277 let signature_with_special_chars = "sig<>\"'&%$#@!{}[]".to_string();
1278
1279 let response = GenerateContentResponse {
1280 candidates: Some(vec![GenerateContentCandidate {
1281 index: Some(0),
1282 content: Content {
1283 parts: vec![Part::FunctionCallPart(FunctionCallPart {
1284 function_call: FunctionCall {
1285 name: "test_function".to_string(),
1286 args: json!({"arg": "value"}),
1287 },
1288 thought_signature: Some(signature_with_special_chars.clone()),
1289 })],
1290 role: GoogleRole::Model,
1291 },
1292 finish_reason: None,
1293 finish_message: None,
1294 safety_ratings: None,
1295 citation_metadata: None,
1296 }]),
1297 prompt_feedback: None,
1298 usage_metadata: None,
1299 };
1300
1301 let events = mapper.map_event(response);
1302
1303 if let Ok(LanguageModelCompletionEvent::ToolUse(tool_use)) = &events[0] {
1304 assert_eq!(
1305 tool_use.thought_signature.as_deref(),
1306 Some(signature_with_special_chars.as_str())
1307 );
1308 } else {
1309 panic!("Expected ToolUse event");
1310 }
1311 }
1312}