1use anyhow::{Result, anyhow};
2use collections::{BTreeMap, HashMap};
3use futures::Stream;
4use futures::{FutureExt, StreamExt, future, future::BoxFuture};
5use gpui::{AnyView, App, AsyncApp, Context, Entity, SharedString, Task, Window};
6use http_client::HttpClient;
7use language_model::{
8 ApiKeyState, AuthenticateError, EnvVar, LanguageModel, LanguageModelCompletionError,
9 LanguageModelCompletionEvent, LanguageModelId, LanguageModelName, LanguageModelProvider,
10 LanguageModelProviderId, LanguageModelProviderName, LanguageModelProviderState,
11 LanguageModelRequest, LanguageModelToolChoice, LanguageModelToolResultContent,
12 LanguageModelToolUse, MessageContent, RateLimiter, Role, StopReason, TokenUsage, env_var,
13};
14use menu;
15use open_ai::{
16 ImageUrl, Model, OPEN_AI_API_URL, ReasoningEffort, ResponseStreamEvent, stream_completion,
17};
18use settings::{OpenAiAvailableModel as AvailableModel, Settings, SettingsStore};
19use std::pin::Pin;
20use std::str::FromStr as _;
21use std::sync::{Arc, LazyLock};
22use strum::IntoEnumIterator;
23use ui::{ButtonLink, ConfiguredApiCard, List, ListBulletItem, prelude::*};
24use ui_input::InputField;
25use util::ResultExt;
26
27const PROVIDER_ID: LanguageModelProviderId = language_model::OPEN_AI_PROVIDER_ID;
28const PROVIDER_NAME: LanguageModelProviderName = language_model::OPEN_AI_PROVIDER_NAME;
29
30const API_KEY_ENV_VAR_NAME: &str = "OPENAI_API_KEY";
31static API_KEY_ENV_VAR: LazyLock<EnvVar> = env_var!(API_KEY_ENV_VAR_NAME);
32
33#[derive(Default, Clone, Debug, PartialEq)]
34pub struct OpenAiSettings {
35 pub api_url: String,
36 pub available_models: Vec<AvailableModel>,
37}
38
39pub struct OpenAiLanguageModelProvider {
40 http_client: Arc<dyn HttpClient>,
41 state: Entity<State>,
42}
43
44pub struct State {
45 api_key_state: ApiKeyState,
46}
47
48impl State {
49 fn is_authenticated(&self) -> bool {
50 self.api_key_state.has_key()
51 }
52
53 fn set_api_key(&mut self, api_key: Option<String>, cx: &mut Context<Self>) -> Task<Result<()>> {
54 let api_url = OpenAiLanguageModelProvider::api_url(cx);
55 self.api_key_state
56 .store(api_url, api_key, |this| &mut this.api_key_state, cx)
57 }
58
59 fn authenticate(&mut self, cx: &mut Context<Self>) -> Task<Result<(), AuthenticateError>> {
60 let api_url = OpenAiLanguageModelProvider::api_url(cx);
61 self.api_key_state
62 .load_if_needed(api_url, |this| &mut this.api_key_state, cx)
63 }
64}
65
66impl OpenAiLanguageModelProvider {
67 pub fn new(http_client: Arc<dyn HttpClient>, cx: &mut App) -> Self {
68 let state = cx.new(|cx| {
69 cx.observe_global::<SettingsStore>(|this: &mut State, cx| {
70 let api_url = Self::api_url(cx);
71 this.api_key_state
72 .handle_url_change(api_url, |this| &mut this.api_key_state, cx);
73 cx.notify();
74 })
75 .detach();
76 State {
77 api_key_state: ApiKeyState::new(Self::api_url(cx), (*API_KEY_ENV_VAR).clone()),
78 }
79 });
80
81 Self { http_client, state }
82 }
83
84 fn create_language_model(&self, model: open_ai::Model) -> Arc<dyn LanguageModel> {
85 Arc::new(OpenAiLanguageModel {
86 id: LanguageModelId::from(model.id().to_string()),
87 model,
88 state: self.state.clone(),
89 http_client: self.http_client.clone(),
90 request_limiter: RateLimiter::new(4),
91 })
92 }
93
94 fn settings(cx: &App) -> &OpenAiSettings {
95 &crate::AllLanguageModelSettings::get_global(cx).openai
96 }
97
98 fn api_url(cx: &App) -> SharedString {
99 let api_url = &Self::settings(cx).api_url;
100 if api_url.is_empty() {
101 open_ai::OPEN_AI_API_URL.into()
102 } else {
103 SharedString::new(api_url.as_str())
104 }
105 }
106}
107
108impl LanguageModelProviderState for OpenAiLanguageModelProvider {
109 type ObservableEntity = State;
110
111 fn observable_entity(&self) -> Option<Entity<Self::ObservableEntity>> {
112 Some(self.state.clone())
113 }
114}
115
116impl LanguageModelProvider for OpenAiLanguageModelProvider {
117 fn id(&self) -> LanguageModelProviderId {
118 PROVIDER_ID
119 }
120
121 fn name(&self) -> LanguageModelProviderName {
122 PROVIDER_NAME
123 }
124
125 fn icon(&self) -> IconName {
126 IconName::AiOpenAi
127 }
128
129 fn default_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {
130 Some(self.create_language_model(open_ai::Model::default()))
131 }
132
133 fn default_fast_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {
134 Some(self.create_language_model(open_ai::Model::default_fast()))
135 }
136
137 fn provided_models(&self, cx: &App) -> Vec<Arc<dyn LanguageModel>> {
138 let mut models = BTreeMap::default();
139
140 // Add base models from open_ai::Model::iter()
141 for model in open_ai::Model::iter() {
142 if !matches!(model, open_ai::Model::Custom { .. }) {
143 models.insert(model.id().to_string(), model);
144 }
145 }
146
147 // Override with available models from settings
148 for model in &OpenAiLanguageModelProvider::settings(cx).available_models {
149 models.insert(
150 model.name.clone(),
151 open_ai::Model::Custom {
152 name: model.name.clone(),
153 display_name: model.display_name.clone(),
154 max_tokens: model.max_tokens,
155 max_output_tokens: model.max_output_tokens,
156 max_completion_tokens: model.max_completion_tokens,
157 reasoning_effort: model.reasoning_effort.clone(),
158 },
159 );
160 }
161
162 models
163 .into_values()
164 .map(|model| self.create_language_model(model))
165 .collect()
166 }
167
168 fn is_authenticated(&self, cx: &App) -> bool {
169 self.state.read(cx).is_authenticated()
170 }
171
172 fn authenticate(&self, cx: &mut App) -> Task<Result<(), AuthenticateError>> {
173 self.state.update(cx, |state, cx| state.authenticate(cx))
174 }
175
176 fn configuration_view(
177 &self,
178 _target_agent: language_model::ConfigurationViewTargetAgent,
179 window: &mut Window,
180 cx: &mut App,
181 ) -> AnyView {
182 cx.new(|cx| ConfigurationView::new(self.state.clone(), window, cx))
183 .into()
184 }
185
186 fn reset_credentials(&self, cx: &mut App) -> Task<Result<()>> {
187 self.state
188 .update(cx, |state, cx| state.set_api_key(None, cx))
189 }
190}
191
192pub struct OpenAiLanguageModel {
193 id: LanguageModelId,
194 model: open_ai::Model,
195 state: Entity<State>,
196 http_client: Arc<dyn HttpClient>,
197 request_limiter: RateLimiter,
198}
199
200impl OpenAiLanguageModel {
201 fn stream_completion(
202 &self,
203 request: open_ai::Request,
204 cx: &AsyncApp,
205 ) -> BoxFuture<'static, Result<futures::stream::BoxStream<'static, Result<ResponseStreamEvent>>>>
206 {
207 let http_client = self.http_client.clone();
208
209 let Ok((api_key, api_url)) = self.state.read_with(cx, |state, cx| {
210 let api_url = OpenAiLanguageModelProvider::api_url(cx);
211 (state.api_key_state.key(&api_url), api_url)
212 }) else {
213 return future::ready(Err(anyhow!("App state dropped"))).boxed();
214 };
215
216 let future = self.request_limiter.stream(async move {
217 let provider = PROVIDER_NAME;
218 let Some(api_key) = api_key else {
219 return Err(LanguageModelCompletionError::NoApiKey { provider });
220 };
221 let request = stream_completion(
222 http_client.as_ref(),
223 provider.0.as_str(),
224 &api_url,
225 &api_key,
226 request,
227 );
228 let response = request.await?;
229 Ok(response)
230 });
231
232 async move { Ok(future.await?.boxed()) }.boxed()
233 }
234}
235
236impl LanguageModel for OpenAiLanguageModel {
237 fn id(&self) -> LanguageModelId {
238 self.id.clone()
239 }
240
241 fn name(&self) -> LanguageModelName {
242 LanguageModelName::from(self.model.display_name().to_string())
243 }
244
245 fn provider_id(&self) -> LanguageModelProviderId {
246 PROVIDER_ID
247 }
248
249 fn provider_name(&self) -> LanguageModelProviderName {
250 PROVIDER_NAME
251 }
252
253 fn supports_tools(&self) -> bool {
254 true
255 }
256
257 fn supports_images(&self) -> bool {
258 use open_ai::Model;
259 match &self.model {
260 Model::FourOmni
261 | Model::FourOmniMini
262 | Model::FourPointOne
263 | Model::FourPointOneMini
264 | Model::FourPointOneNano
265 | Model::Five
266 | Model::FiveMini
267 | Model::FiveNano
268 | Model::FivePointOne
269 | Model::FivePointTwo
270 | Model::O1
271 | Model::O3
272 | Model::O4Mini => true,
273 Model::ThreePointFiveTurbo
274 | Model::Four
275 | Model::FourTurbo
276 | Model::O3Mini
277 | Model::Custom { .. } => false,
278 }
279 }
280
281 fn supports_tool_choice(&self, choice: LanguageModelToolChoice) -> bool {
282 match choice {
283 LanguageModelToolChoice::Auto => true,
284 LanguageModelToolChoice::Any => true,
285 LanguageModelToolChoice::None => true,
286 }
287 }
288
289 fn telemetry_id(&self) -> String {
290 format!("openai/{}", self.model.id())
291 }
292
293 fn max_token_count(&self) -> u64 {
294 self.model.max_token_count()
295 }
296
297 fn max_output_tokens(&self) -> Option<u64> {
298 self.model.max_output_tokens()
299 }
300
301 fn count_tokens(
302 &self,
303 request: LanguageModelRequest,
304 cx: &App,
305 ) -> BoxFuture<'static, Result<u64>> {
306 count_open_ai_tokens(request, self.model.clone(), cx)
307 }
308
309 fn stream_completion(
310 &self,
311 request: LanguageModelRequest,
312 cx: &AsyncApp,
313 ) -> BoxFuture<
314 'static,
315 Result<
316 futures::stream::BoxStream<
317 'static,
318 Result<LanguageModelCompletionEvent, LanguageModelCompletionError>,
319 >,
320 LanguageModelCompletionError,
321 >,
322 > {
323 let request = into_open_ai(
324 request,
325 self.model.id(),
326 self.model.supports_parallel_tool_calls(),
327 self.model.supports_prompt_cache_key(),
328 self.max_output_tokens(),
329 self.model.reasoning_effort(),
330 );
331 let completions = self.stream_completion(request, cx);
332 async move {
333 let mapper = OpenAiEventMapper::new();
334 Ok(mapper.map_stream(completions.await?).boxed())
335 }
336 .boxed()
337 }
338}
339
340pub fn into_open_ai(
341 request: LanguageModelRequest,
342 model_id: &str,
343 supports_parallel_tool_calls: bool,
344 supports_prompt_cache_key: bool,
345 max_output_tokens: Option<u64>,
346 reasoning_effort: Option<ReasoningEffort>,
347) -> open_ai::Request {
348 let stream = !model_id.starts_with("o1-");
349
350 let mut messages = Vec::new();
351 for message in request.messages {
352 for content in message.content {
353 match content {
354 MessageContent::Text(text) | MessageContent::Thinking { text, .. } => {
355 if !text.trim().is_empty() {
356 add_message_content_part(
357 open_ai::MessagePart::Text { text },
358 message.role,
359 &mut messages,
360 );
361 }
362 }
363 MessageContent::RedactedThinking(_) => {}
364 MessageContent::Image(image) => {
365 add_message_content_part(
366 open_ai::MessagePart::Image {
367 image_url: ImageUrl {
368 url: image.to_base64_url(),
369 detail: None,
370 },
371 },
372 message.role,
373 &mut messages,
374 );
375 }
376 MessageContent::ToolUse(tool_use) => {
377 let tool_call = open_ai::ToolCall {
378 id: tool_use.id.to_string(),
379 content: open_ai::ToolCallContent::Function {
380 function: open_ai::FunctionContent {
381 name: tool_use.name.to_string(),
382 arguments: serde_json::to_string(&tool_use.input)
383 .unwrap_or_default(),
384 },
385 },
386 };
387
388 if let Some(open_ai::RequestMessage::Assistant { tool_calls, .. }) =
389 messages.last_mut()
390 {
391 tool_calls.push(tool_call);
392 } else {
393 messages.push(open_ai::RequestMessage::Assistant {
394 content: None,
395 tool_calls: vec![tool_call],
396 });
397 }
398 }
399 MessageContent::ToolResult(tool_result) => {
400 let content = match &tool_result.content {
401 LanguageModelToolResultContent::Text(text) => {
402 vec![open_ai::MessagePart::Text {
403 text: text.to_string(),
404 }]
405 }
406 LanguageModelToolResultContent::Image(image) => {
407 vec![open_ai::MessagePart::Image {
408 image_url: ImageUrl {
409 url: image.to_base64_url(),
410 detail: None,
411 },
412 }]
413 }
414 };
415
416 messages.push(open_ai::RequestMessage::Tool {
417 content: content.into(),
418 tool_call_id: tool_result.tool_use_id.to_string(),
419 });
420 }
421 }
422 }
423 }
424
425 open_ai::Request {
426 model: model_id.into(),
427 messages,
428 stream,
429 stop: request.stop,
430 temperature: request.temperature.or(Some(1.0)),
431 max_completion_tokens: max_output_tokens,
432 parallel_tool_calls: if supports_parallel_tool_calls && !request.tools.is_empty() {
433 // Disable parallel tool calls, as the Agent currently expects a maximum of one per turn.
434 Some(false)
435 } else {
436 None
437 },
438 prompt_cache_key: if supports_prompt_cache_key {
439 request.thread_id
440 } else {
441 None
442 },
443 tools: request
444 .tools
445 .into_iter()
446 .map(|tool| open_ai::ToolDefinition::Function {
447 function: open_ai::FunctionDefinition {
448 name: tool.name,
449 description: Some(tool.description),
450 parameters: Some(tool.input_schema),
451 },
452 })
453 .collect(),
454 tool_choice: request.tool_choice.map(|choice| match choice {
455 LanguageModelToolChoice::Auto => open_ai::ToolChoice::Auto,
456 LanguageModelToolChoice::Any => open_ai::ToolChoice::Required,
457 LanguageModelToolChoice::None => open_ai::ToolChoice::None,
458 }),
459 reasoning_effort,
460 }
461}
462
463fn add_message_content_part(
464 new_part: open_ai::MessagePart,
465 role: Role,
466 messages: &mut Vec<open_ai::RequestMessage>,
467) {
468 match (role, messages.last_mut()) {
469 (Role::User, Some(open_ai::RequestMessage::User { content }))
470 | (
471 Role::Assistant,
472 Some(open_ai::RequestMessage::Assistant {
473 content: Some(content),
474 ..
475 }),
476 )
477 | (Role::System, Some(open_ai::RequestMessage::System { content, .. })) => {
478 content.push_part(new_part);
479 }
480 _ => {
481 messages.push(match role {
482 Role::User => open_ai::RequestMessage::User {
483 content: open_ai::MessageContent::from(vec![new_part]),
484 },
485 Role::Assistant => open_ai::RequestMessage::Assistant {
486 content: Some(open_ai::MessageContent::from(vec![new_part])),
487 tool_calls: Vec::new(),
488 },
489 Role::System => open_ai::RequestMessage::System {
490 content: open_ai::MessageContent::from(vec![new_part]),
491 },
492 });
493 }
494 }
495}
496
497pub struct OpenAiEventMapper {
498 tool_calls_by_index: HashMap<usize, RawToolCall>,
499}
500
501impl OpenAiEventMapper {
502 pub fn new() -> Self {
503 Self {
504 tool_calls_by_index: HashMap::default(),
505 }
506 }
507
508 pub fn map_stream(
509 mut self,
510 events: Pin<Box<dyn Send + Stream<Item = Result<ResponseStreamEvent>>>>,
511 ) -> impl Stream<Item = Result<LanguageModelCompletionEvent, LanguageModelCompletionError>>
512 {
513 events.flat_map(move |event| {
514 futures::stream::iter(match event {
515 Ok(event) => self.map_event(event),
516 Err(error) => vec![Err(LanguageModelCompletionError::from(anyhow!(error)))],
517 })
518 })
519 }
520
521 pub fn map_event(
522 &mut self,
523 event: ResponseStreamEvent,
524 ) -> Vec<Result<LanguageModelCompletionEvent, LanguageModelCompletionError>> {
525 let mut events = Vec::new();
526 if let Some(usage) = event.usage {
527 events.push(Ok(LanguageModelCompletionEvent::UsageUpdate(TokenUsage {
528 input_tokens: usage.prompt_tokens,
529 output_tokens: usage.completion_tokens,
530 cache_creation_input_tokens: 0,
531 cache_read_input_tokens: 0,
532 })));
533 }
534
535 let Some(choice) = event.choices.first() else {
536 return events;
537 };
538
539 if let Some(delta) = choice.delta.as_ref() {
540 if let Some(content) = delta.content.clone() {
541 events.push(Ok(LanguageModelCompletionEvent::Text(content)));
542 }
543
544 if let Some(tool_calls) = delta.tool_calls.as_ref() {
545 for tool_call in tool_calls {
546 let entry = self.tool_calls_by_index.entry(tool_call.index).or_default();
547
548 if let Some(tool_id) = tool_call.id.clone() {
549 entry.id = tool_id;
550 }
551
552 if let Some(function) = tool_call.function.as_ref() {
553 if let Some(name) = function.name.clone() {
554 entry.name = name;
555 }
556
557 if let Some(arguments) = function.arguments.clone() {
558 entry.arguments.push_str(&arguments);
559 }
560 }
561 }
562 }
563 }
564
565 match choice.finish_reason.as_deref() {
566 Some("stop") => {
567 events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::EndTurn)));
568 }
569 Some("tool_calls") => {
570 events.extend(self.tool_calls_by_index.drain().map(|(_, tool_call)| {
571 match serde_json::Value::from_str(&tool_call.arguments) {
572 Ok(input) => Ok(LanguageModelCompletionEvent::ToolUse(
573 LanguageModelToolUse {
574 id: tool_call.id.clone().into(),
575 name: tool_call.name.as_str().into(),
576 is_input_complete: true,
577 input,
578 raw_input: tool_call.arguments.clone(),
579 thought_signature: None,
580 },
581 )),
582 Err(error) => Ok(LanguageModelCompletionEvent::ToolUseJsonParseError {
583 id: tool_call.id.into(),
584 tool_name: tool_call.name.into(),
585 raw_input: tool_call.arguments.clone().into(),
586 json_parse_error: error.to_string(),
587 }),
588 }
589 }));
590
591 events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::ToolUse)));
592 }
593 Some(stop_reason) => {
594 log::error!("Unexpected OpenAI stop_reason: {stop_reason:?}",);
595 events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::EndTurn)));
596 }
597 None => {}
598 }
599
600 events
601 }
602}
603
604#[derive(Default)]
605struct RawToolCall {
606 id: String,
607 name: String,
608 arguments: String,
609}
610
611pub(crate) fn collect_tiktoken_messages(
612 request: LanguageModelRequest,
613) -> Vec<tiktoken_rs::ChatCompletionRequestMessage> {
614 request
615 .messages
616 .into_iter()
617 .map(|message| tiktoken_rs::ChatCompletionRequestMessage {
618 role: match message.role {
619 Role::User => "user".into(),
620 Role::Assistant => "assistant".into(),
621 Role::System => "system".into(),
622 },
623 content: Some(message.string_contents()),
624 name: None,
625 function_call: None,
626 })
627 .collect::<Vec<_>>()
628}
629
630pub fn count_open_ai_tokens(
631 request: LanguageModelRequest,
632 model: Model,
633 cx: &App,
634) -> BoxFuture<'static, Result<u64>> {
635 cx.background_spawn(async move {
636 let messages = collect_tiktoken_messages(request);
637 match model {
638 Model::Custom { max_tokens, .. } => {
639 let model = if max_tokens >= 100_000 {
640 // If the max tokens is 100k or more, it is likely the o200k_base tokenizer from gpt4o
641 "gpt-4o"
642 } else {
643 // Otherwise fallback to gpt-4, since only cl100k_base and o200k_base are
644 // supported with this tiktoken method
645 "gpt-4"
646 };
647 tiktoken_rs::num_tokens_from_messages(model, &messages)
648 }
649 // Currently supported by tiktoken_rs
650 // Sometimes tiktoken-rs is behind on model support. If that is the case, make a new branch
651 // arm with an override. We enumerate all supported models here so that we can check if new
652 // models are supported yet or not.
653 Model::ThreePointFiveTurbo
654 | Model::Four
655 | Model::FourTurbo
656 | Model::FourOmni
657 | Model::FourOmniMini
658 | Model::FourPointOne
659 | Model::FourPointOneMini
660 | Model::FourPointOneNano
661 | Model::O1
662 | Model::O3
663 | Model::O3Mini
664 | Model::O4Mini
665 | Model::Five
666 | Model::FiveMini
667 | Model::FiveNano => tiktoken_rs::num_tokens_from_messages(model.id(), &messages),
668 // GPT-5.1 and 5.2 don't have dedicated tiktoken support; use gpt-5 tokenizer
669 Model::FivePointOne | Model::FivePointTwo => {
670 tiktoken_rs::num_tokens_from_messages("gpt-5", &messages)
671 }
672 }
673 .map(|tokens| tokens as u64)
674 })
675 .boxed()
676}
677
678struct ConfigurationView {
679 api_key_editor: Entity<InputField>,
680 state: Entity<State>,
681 load_credentials_task: Option<Task<()>>,
682}
683
684impl ConfigurationView {
685 fn new(state: Entity<State>, window: &mut Window, cx: &mut Context<Self>) -> Self {
686 let api_key_editor = cx.new(|cx| {
687 InputField::new(
688 window,
689 cx,
690 "sk-000000000000000000000000000000000000000000000000",
691 )
692 });
693
694 cx.observe(&state, |_, _, cx| {
695 cx.notify();
696 })
697 .detach();
698
699 let load_credentials_task = Some(cx.spawn_in(window, {
700 let state = state.clone();
701 async move |this, cx| {
702 if let Some(task) = state
703 .update(cx, |state, cx| state.authenticate(cx))
704 .log_err()
705 {
706 // We don't log an error, because "not signed in" is also an error.
707 let _ = task.await;
708 }
709 this.update(cx, |this, cx| {
710 this.load_credentials_task = None;
711 cx.notify();
712 })
713 .log_err();
714 }
715 }));
716
717 Self {
718 api_key_editor,
719 state,
720 load_credentials_task,
721 }
722 }
723
724 fn save_api_key(&mut self, _: &menu::Confirm, window: &mut Window, cx: &mut Context<Self>) {
725 let api_key = self.api_key_editor.read(cx).text(cx).trim().to_string();
726 if api_key.is_empty() {
727 return;
728 }
729
730 // url changes can cause the editor to be displayed again
731 self.api_key_editor
732 .update(cx, |editor, cx| editor.set_text("", window, cx));
733
734 let state = self.state.clone();
735 cx.spawn_in(window, async move |_, cx| {
736 state
737 .update(cx, |state, cx| state.set_api_key(Some(api_key), cx))?
738 .await
739 })
740 .detach_and_log_err(cx);
741 }
742
743 fn reset_api_key(&mut self, window: &mut Window, cx: &mut Context<Self>) {
744 self.api_key_editor
745 .update(cx, |input, cx| input.set_text("", window, cx));
746
747 let state = self.state.clone();
748 cx.spawn_in(window, async move |_, cx| {
749 state
750 .update(cx, |state, cx| state.set_api_key(None, cx))?
751 .await
752 })
753 .detach_and_log_err(cx);
754 }
755
756 fn should_render_editor(&self, cx: &mut Context<Self>) -> bool {
757 !self.state.read(cx).is_authenticated()
758 }
759}
760
761impl Render for ConfigurationView {
762 fn render(&mut self, _: &mut Window, cx: &mut Context<Self>) -> impl IntoElement {
763 let env_var_set = self.state.read(cx).api_key_state.is_from_env_var();
764 let configured_card_label = if env_var_set {
765 format!("API key set in {API_KEY_ENV_VAR_NAME} environment variable")
766 } else {
767 let api_url = OpenAiLanguageModelProvider::api_url(cx);
768 if api_url == OPEN_AI_API_URL {
769 "API key configured".to_string()
770 } else {
771 format!("API key configured for {}", api_url)
772 }
773 };
774
775 let api_key_section = if self.should_render_editor(cx) {
776 v_flex()
777 .on_action(cx.listener(Self::save_api_key))
778 .child(Label::new("To use Zed's agent with OpenAI, you need to add an API key. Follow these steps:"))
779 .child(
780 List::new()
781 .child(
782 ListBulletItem::new("")
783 .child(Label::new("Create one by visiting"))
784 .child(ButtonLink::new("OpenAI's console", "https://platform.openai.com/api-keys"))
785 )
786 .child(
787 ListBulletItem::new("Ensure your OpenAI account has credits")
788 )
789 .child(
790 ListBulletItem::new("Paste your API key below and hit enter to start using the agent")
791 ),
792 )
793 .child(self.api_key_editor.clone())
794 .child(
795 Label::new(format!(
796 "You can also assign the {API_KEY_ENV_VAR_NAME} environment variable and restart Zed."
797 ))
798 .size(LabelSize::Small)
799 .color(Color::Muted),
800 )
801 .child(
802 Label::new(
803 "Note that having a subscription for another service like GitHub Copilot won't work.",
804 )
805 .size(LabelSize::Small).color(Color::Muted),
806 )
807 .into_any_element()
808 } else {
809 ConfiguredApiCard::new(configured_card_label)
810 .disabled(env_var_set)
811 .on_click(cx.listener(|this, _, window, cx| this.reset_api_key(window, cx)))
812 .when(env_var_set, |this| {
813 this.tooltip_label(format!("To reset your API key, unset the {API_KEY_ENV_VAR_NAME} environment variable."))
814 })
815 .into_any_element()
816 };
817
818 let compatible_api_section = h_flex()
819 .mt_1p5()
820 .gap_0p5()
821 .flex_wrap()
822 .when(self.should_render_editor(cx), |this| {
823 this.pt_1p5()
824 .border_t_1()
825 .border_color(cx.theme().colors().border_variant)
826 })
827 .child(
828 h_flex()
829 .gap_2()
830 .child(
831 Icon::new(IconName::Info)
832 .size(IconSize::XSmall)
833 .color(Color::Muted),
834 )
835 .child(Label::new("Zed also supports OpenAI-compatible models.")),
836 )
837 .child(
838 Button::new("docs", "Learn More")
839 .icon(IconName::ArrowUpRight)
840 .icon_size(IconSize::Small)
841 .icon_color(Color::Muted)
842 .on_click(move |_, _window, cx| {
843 cx.open_url("https://zed.dev/docs/ai/llm-providers#openai-api-compatible")
844 }),
845 );
846
847 if self.load_credentials_task.is_some() {
848 div().child(Label::new("Loading credentials…")).into_any()
849 } else {
850 v_flex()
851 .size_full()
852 .child(api_key_section)
853 .child(compatible_api_section)
854 .into_any()
855 }
856 }
857}
858
859#[cfg(test)]
860mod tests {
861 use gpui::TestAppContext;
862 use language_model::LanguageModelRequestMessage;
863
864 use super::*;
865
866 #[gpui::test]
867 fn tiktoken_rs_support(cx: &TestAppContext) {
868 let request = LanguageModelRequest {
869 thread_id: None,
870 prompt_id: None,
871 intent: None,
872 mode: None,
873 messages: vec![LanguageModelRequestMessage {
874 role: Role::User,
875 content: vec![MessageContent::Text("message".into())],
876 cache: false,
877 reasoning_details: None,
878 }],
879 tools: vec![],
880 tool_choice: None,
881 stop: vec![],
882 temperature: None,
883 thinking_allowed: true,
884 };
885
886 // Validate that all models are supported by tiktoken-rs
887 for model in Model::iter() {
888 let count = cx
889 .executor()
890 .block(count_open_ai_tokens(
891 request.clone(),
892 model,
893 &cx.app.borrow(),
894 ))
895 .unwrap();
896 assert!(count > 0);
897 }
898 }
899}