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 AuthenticateError, LanguageModel, LanguageModelCompletionError, LanguageModelCompletionEvent,
9 LanguageModelId, LanguageModelName, LanguageModelProvider, LanguageModelProviderId,
10 LanguageModelProviderName, LanguageModelProviderState, LanguageModelRequest,
11 LanguageModelToolChoice, LanguageModelToolResultContent, LanguageModelToolUse, MessageContent,
12 RateLimiter, Role, StopReason, TokenUsage,
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::{List, prelude::*};
24use ui_input::InputField;
25use util::ResultExt;
26use zed_env_vars::{EnvVar, env_var};
27
28use crate::ui::ConfiguredApiCard;
29use crate::{api_key::ApiKeyState, ui::InstructionListItem};
30
31const PROVIDER_ID: LanguageModelProviderId = language_model::OPEN_AI_PROVIDER_ID;
32const PROVIDER_NAME: LanguageModelProviderName = language_model::OPEN_AI_PROVIDER_NAME;
33
34const API_KEY_ENV_VAR_NAME: &str = "OPENAI_API_KEY";
35static API_KEY_ENV_VAR: LazyLock<EnvVar> = env_var!(API_KEY_ENV_VAR_NAME);
36
37#[derive(Default, Clone, Debug, PartialEq)]
38pub struct OpenAiSettings {
39 pub api_url: String,
40 pub available_models: Vec<AvailableModel>,
41}
42
43pub struct OpenAiLanguageModelProvider {
44 http_client: Arc<dyn HttpClient>,
45 state: Entity<State>,
46}
47
48pub struct State {
49 api_key_state: ApiKeyState,
50}
51
52impl State {
53 fn is_authenticated(&self) -> bool {
54 self.api_key_state.has_key()
55 }
56
57 fn set_api_key(&mut self, api_key: Option<String>, cx: &mut Context<Self>) -> Task<Result<()>> {
58 let api_url = OpenAiLanguageModelProvider::api_url(cx);
59 self.api_key_state
60 .store(api_url, api_key, |this| &mut this.api_key_state, cx)
61 }
62
63 fn authenticate(&mut self, cx: &mut Context<Self>) -> Task<Result<(), AuthenticateError>> {
64 let api_url = OpenAiLanguageModelProvider::api_url(cx);
65 self.api_key_state.load_if_needed(
66 api_url,
67 &API_KEY_ENV_VAR,
68 |this| &mut this.api_key_state,
69 cx,
70 )
71 }
72}
73
74impl OpenAiLanguageModelProvider {
75 pub fn new(http_client: Arc<dyn HttpClient>, cx: &mut App) -> Self {
76 let state = cx.new(|cx| {
77 cx.observe_global::<SettingsStore>(|this: &mut State, cx| {
78 let api_url = Self::api_url(cx);
79 this.api_key_state.handle_url_change(
80 api_url,
81 &API_KEY_ENV_VAR,
82 |this| &mut this.api_key_state,
83 cx,
84 );
85 cx.notify();
86 })
87 .detach();
88 State {
89 api_key_state: ApiKeyState::new(Self::api_url(cx)),
90 }
91 });
92
93 Self { http_client, state }
94 }
95
96 fn create_language_model(&self, model: open_ai::Model) -> Arc<dyn LanguageModel> {
97 Arc::new(OpenAiLanguageModel {
98 id: LanguageModelId::from(model.id().to_string()),
99 model,
100 state: self.state.clone(),
101 http_client: self.http_client.clone(),
102 request_limiter: RateLimiter::new(4),
103 })
104 }
105
106 fn settings(cx: &App) -> &OpenAiSettings {
107 &crate::AllLanguageModelSettings::get_global(cx).openai
108 }
109
110 fn api_url(cx: &App) -> SharedString {
111 let api_url = &Self::settings(cx).api_url;
112 if api_url.is_empty() {
113 open_ai::OPEN_AI_API_URL.into()
114 } else {
115 SharedString::new(api_url.as_str())
116 }
117 }
118}
119
120impl LanguageModelProviderState for OpenAiLanguageModelProvider {
121 type ObservableEntity = State;
122
123 fn observable_entity(&self) -> Option<Entity<Self::ObservableEntity>> {
124 Some(self.state.clone())
125 }
126}
127
128impl LanguageModelProvider for OpenAiLanguageModelProvider {
129 fn id(&self) -> LanguageModelProviderId {
130 PROVIDER_ID
131 }
132
133 fn name(&self) -> LanguageModelProviderName {
134 PROVIDER_NAME
135 }
136
137 fn icon(&self) -> IconName {
138 IconName::AiOpenAi
139 }
140
141 fn default_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {
142 Some(self.create_language_model(open_ai::Model::default()))
143 }
144
145 fn default_fast_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {
146 Some(self.create_language_model(open_ai::Model::default_fast()))
147 }
148
149 fn provided_models(&self, cx: &App) -> Vec<Arc<dyn LanguageModel>> {
150 let mut models = BTreeMap::default();
151
152 // Add base models from open_ai::Model::iter()
153 for model in open_ai::Model::iter() {
154 if !matches!(model, open_ai::Model::Custom { .. }) {
155 models.insert(model.id().to_string(), model);
156 }
157 }
158
159 // Override with available models from settings
160 for model in &OpenAiLanguageModelProvider::settings(cx).available_models {
161 models.insert(
162 model.name.clone(),
163 open_ai::Model::Custom {
164 name: model.name.clone(),
165 display_name: model.display_name.clone(),
166 max_tokens: model.max_tokens,
167 max_output_tokens: model.max_output_tokens,
168 max_completion_tokens: model.max_completion_tokens,
169 reasoning_effort: model.reasoning_effort.clone(),
170 },
171 );
172 }
173
174 models
175 .into_values()
176 .map(|model| self.create_language_model(model))
177 .collect()
178 }
179
180 fn is_authenticated(&self, cx: &App) -> bool {
181 self.state.read(cx).is_authenticated()
182 }
183
184 fn authenticate(&self, cx: &mut App) -> Task<Result<(), AuthenticateError>> {
185 self.state.update(cx, |state, cx| state.authenticate(cx))
186 }
187
188 fn configuration_view(
189 &self,
190 _target_agent: language_model::ConfigurationViewTargetAgent,
191 window: &mut Window,
192 cx: &mut App,
193 ) -> AnyView {
194 cx.new(|cx| ConfigurationView::new(self.state.clone(), window, cx))
195 .into()
196 }
197
198 fn reset_credentials(&self, cx: &mut App) -> Task<Result<()>> {
199 self.state
200 .update(cx, |state, cx| state.set_api_key(None, cx))
201 }
202}
203
204pub struct OpenAiLanguageModel {
205 id: LanguageModelId,
206 model: open_ai::Model,
207 state: Entity<State>,
208 http_client: Arc<dyn HttpClient>,
209 request_limiter: RateLimiter,
210}
211
212impl OpenAiLanguageModel {
213 fn stream_completion(
214 &self,
215 request: open_ai::Request,
216 cx: &AsyncApp,
217 ) -> BoxFuture<'static, Result<futures::stream::BoxStream<'static, Result<ResponseStreamEvent>>>>
218 {
219 let http_client = self.http_client.clone();
220
221 let Ok((api_key, api_url)) = self.state.read_with(cx, |state, cx| {
222 let api_url = OpenAiLanguageModelProvider::api_url(cx);
223 (state.api_key_state.key(&api_url), api_url)
224 }) else {
225 return future::ready(Err(anyhow!("App state dropped"))).boxed();
226 };
227
228 let future = self.request_limiter.stream(async move {
229 let provider = PROVIDER_NAME;
230 let Some(api_key) = api_key else {
231 return Err(LanguageModelCompletionError::NoApiKey { provider });
232 };
233 let request = stream_completion(
234 http_client.as_ref(),
235 provider.0.as_str(),
236 &api_url,
237 &api_key,
238 request,
239 );
240 let response = request.await?;
241 Ok(response)
242 });
243
244 async move { Ok(future.await?.boxed()) }.boxed()
245 }
246}
247
248impl LanguageModel for OpenAiLanguageModel {
249 fn id(&self) -> LanguageModelId {
250 self.id.clone()
251 }
252
253 fn name(&self) -> LanguageModelName {
254 LanguageModelName::from(self.model.display_name().to_string())
255 }
256
257 fn provider_id(&self) -> LanguageModelProviderId {
258 PROVIDER_ID
259 }
260
261 fn provider_name(&self) -> LanguageModelProviderName {
262 PROVIDER_NAME
263 }
264
265 fn supports_tools(&self) -> bool {
266 true
267 }
268
269 fn supports_images(&self) -> bool {
270 use open_ai::Model;
271 match &self.model {
272 Model::FourOmni
273 | Model::FourOmniMini
274 | Model::FourPointOne
275 | Model::FourPointOneMini
276 | Model::FourPointOneNano
277 | Model::Five
278 | Model::FiveMini
279 | Model::FiveNano
280 | Model::O1
281 | Model::O3
282 | Model::O4Mini => true,
283 Model::ThreePointFiveTurbo
284 | Model::Four
285 | Model::FourTurbo
286 | Model::O3Mini
287 | Model::Custom { .. } => false,
288 }
289 }
290
291 fn supports_tool_choice(&self, choice: LanguageModelToolChoice) -> bool {
292 match choice {
293 LanguageModelToolChoice::Auto => true,
294 LanguageModelToolChoice::Any => true,
295 LanguageModelToolChoice::None => true,
296 }
297 }
298
299 fn telemetry_id(&self) -> String {
300 format!("openai/{}", self.model.id())
301 }
302
303 fn max_token_count(&self) -> u64 {
304 self.model.max_token_count()
305 }
306
307 fn max_output_tokens(&self) -> Option<u64> {
308 self.model.max_output_tokens()
309 }
310
311 fn count_tokens(
312 &self,
313 request: LanguageModelRequest,
314 cx: &App,
315 ) -> BoxFuture<'static, Result<u64>> {
316 count_open_ai_tokens(request, self.model.clone(), cx)
317 }
318
319 fn stream_completion(
320 &self,
321 request: LanguageModelRequest,
322 cx: &AsyncApp,
323 ) -> BoxFuture<
324 'static,
325 Result<
326 futures::stream::BoxStream<
327 'static,
328 Result<LanguageModelCompletionEvent, LanguageModelCompletionError>,
329 >,
330 LanguageModelCompletionError,
331 >,
332 > {
333 let request = into_open_ai(
334 request,
335 self.model.id(),
336 self.model.supports_parallel_tool_calls(),
337 self.model.supports_prompt_cache_key(),
338 self.max_output_tokens(),
339 self.model.reasoning_effort(),
340 );
341 let completions = self.stream_completion(request, cx);
342 async move {
343 let mapper = OpenAiEventMapper::new();
344 Ok(mapper.map_stream(completions.await?).boxed())
345 }
346 .boxed()
347 }
348}
349
350pub fn into_open_ai(
351 request: LanguageModelRequest,
352 model_id: &str,
353 supports_parallel_tool_calls: bool,
354 supports_prompt_cache_key: bool,
355 max_output_tokens: Option<u64>,
356 reasoning_effort: Option<ReasoningEffort>,
357) -> open_ai::Request {
358 let stream = !model_id.starts_with("o1-");
359
360 let mut messages = Vec::new();
361 for message in request.messages {
362 for content in message.content {
363 match content {
364 MessageContent::Text(text) | MessageContent::Thinking { text, .. } => {
365 if !text.trim().is_empty() {
366 add_message_content_part(
367 open_ai::MessagePart::Text { text },
368 message.role,
369 &mut messages,
370 );
371 }
372 }
373 MessageContent::RedactedThinking(_) => {}
374 MessageContent::Image(image) => {
375 add_message_content_part(
376 open_ai::MessagePart::Image {
377 image_url: ImageUrl {
378 url: image.to_base64_url(),
379 detail: None,
380 },
381 },
382 message.role,
383 &mut messages,
384 );
385 }
386 MessageContent::ToolUse(tool_use) => {
387 let tool_call = open_ai::ToolCall {
388 id: tool_use.id.to_string(),
389 content: open_ai::ToolCallContent::Function {
390 function: open_ai::FunctionContent {
391 name: tool_use.name.to_string(),
392 arguments: serde_json::to_string(&tool_use.input)
393 .unwrap_or_default(),
394 },
395 },
396 };
397
398 if let Some(open_ai::RequestMessage::Assistant { tool_calls, .. }) =
399 messages.last_mut()
400 {
401 tool_calls.push(tool_call);
402 } else {
403 messages.push(open_ai::RequestMessage::Assistant {
404 content: None,
405 tool_calls: vec![tool_call],
406 });
407 }
408 }
409 MessageContent::ToolResult(tool_result) => {
410 let content = match &tool_result.content {
411 LanguageModelToolResultContent::Text(text) => {
412 vec![open_ai::MessagePart::Text {
413 text: text.to_string(),
414 }]
415 }
416 LanguageModelToolResultContent::Image(image) => {
417 vec![open_ai::MessagePart::Image {
418 image_url: ImageUrl {
419 url: image.to_base64_url(),
420 detail: None,
421 },
422 }]
423 }
424 };
425
426 messages.push(open_ai::RequestMessage::Tool {
427 content: content.into(),
428 tool_call_id: tool_result.tool_use_id.to_string(),
429 });
430 }
431 }
432 }
433 }
434
435 open_ai::Request {
436 model: model_id.into(),
437 messages,
438 stream,
439 stop: request.stop,
440 temperature: request.temperature.unwrap_or(1.0),
441 max_completion_tokens: max_output_tokens,
442 parallel_tool_calls: if supports_parallel_tool_calls && !request.tools.is_empty() {
443 // Disable parallel tool calls, as the Agent currently expects a maximum of one per turn.
444 Some(false)
445 } else {
446 None
447 },
448 prompt_cache_key: if supports_prompt_cache_key {
449 request.thread_id
450 } else {
451 None
452 },
453 tools: request
454 .tools
455 .into_iter()
456 .map(|tool| open_ai::ToolDefinition::Function {
457 function: open_ai::FunctionDefinition {
458 name: tool.name,
459 description: Some(tool.description),
460 parameters: Some(tool.input_schema),
461 },
462 })
463 .collect(),
464 tool_choice: request.tool_choice.map(|choice| match choice {
465 LanguageModelToolChoice::Auto => open_ai::ToolChoice::Auto,
466 LanguageModelToolChoice::Any => open_ai::ToolChoice::Required,
467 LanguageModelToolChoice::None => open_ai::ToolChoice::None,
468 }),
469 reasoning_effort,
470 }
471}
472
473fn add_message_content_part(
474 new_part: open_ai::MessagePart,
475 role: Role,
476 messages: &mut Vec<open_ai::RequestMessage>,
477) {
478 match (role, messages.last_mut()) {
479 (Role::User, Some(open_ai::RequestMessage::User { content }))
480 | (
481 Role::Assistant,
482 Some(open_ai::RequestMessage::Assistant {
483 content: Some(content),
484 ..
485 }),
486 )
487 | (Role::System, Some(open_ai::RequestMessage::System { content, .. })) => {
488 content.push_part(new_part);
489 }
490 _ => {
491 messages.push(match role {
492 Role::User => open_ai::RequestMessage::User {
493 content: open_ai::MessageContent::from(vec![new_part]),
494 },
495 Role::Assistant => open_ai::RequestMessage::Assistant {
496 content: Some(open_ai::MessageContent::from(vec![new_part])),
497 tool_calls: Vec::new(),
498 },
499 Role::System => open_ai::RequestMessage::System {
500 content: open_ai::MessageContent::from(vec![new_part]),
501 },
502 });
503 }
504 }
505}
506
507pub struct OpenAiEventMapper {
508 tool_calls_by_index: HashMap<usize, RawToolCall>,
509}
510
511impl OpenAiEventMapper {
512 pub fn new() -> Self {
513 Self {
514 tool_calls_by_index: HashMap::default(),
515 }
516 }
517
518 pub fn map_stream(
519 mut self,
520 events: Pin<Box<dyn Send + Stream<Item = Result<ResponseStreamEvent>>>>,
521 ) -> impl Stream<Item = Result<LanguageModelCompletionEvent, LanguageModelCompletionError>>
522 {
523 events.flat_map(move |event| {
524 futures::stream::iter(match event {
525 Ok(event) => self.map_event(event),
526 Err(error) => vec![Err(LanguageModelCompletionError::from(anyhow!(error)))],
527 })
528 })
529 }
530
531 pub fn map_event(
532 &mut self,
533 event: ResponseStreamEvent,
534 ) -> Vec<Result<LanguageModelCompletionEvent, LanguageModelCompletionError>> {
535 let mut events = Vec::new();
536 if let Some(usage) = event.usage {
537 events.push(Ok(LanguageModelCompletionEvent::UsageUpdate(TokenUsage {
538 input_tokens: usage.prompt_tokens,
539 output_tokens: usage.completion_tokens,
540 cache_creation_input_tokens: 0,
541 cache_read_input_tokens: 0,
542 })));
543 }
544
545 let Some(choice) = event.choices.first() else {
546 return events;
547 };
548
549 if let Some(delta) = choice.delta.as_ref() {
550 if let Some(content) = delta.content.clone() {
551 events.push(Ok(LanguageModelCompletionEvent::Text(content)));
552 }
553
554 if let Some(tool_calls) = delta.tool_calls.as_ref() {
555 for tool_call in tool_calls {
556 let entry = self.tool_calls_by_index.entry(tool_call.index).or_default();
557
558 if let Some(tool_id) = tool_call.id.clone() {
559 entry.id = tool_id;
560 }
561
562 if let Some(function) = tool_call.function.as_ref() {
563 if let Some(name) = function.name.clone() {
564 entry.name = name;
565 }
566
567 if let Some(arguments) = function.arguments.clone() {
568 entry.arguments.push_str(&arguments);
569 }
570 }
571 }
572 }
573 }
574
575 match choice.finish_reason.as_deref() {
576 Some("stop") => {
577 events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::EndTurn)));
578 }
579 Some("tool_calls") => {
580 events.extend(self.tool_calls_by_index.drain().map(|(_, tool_call)| {
581 match serde_json::Value::from_str(&tool_call.arguments) {
582 Ok(input) => Ok(LanguageModelCompletionEvent::ToolUse(
583 LanguageModelToolUse {
584 id: tool_call.id.clone().into(),
585 name: tool_call.name.as_str().into(),
586 is_input_complete: true,
587 input,
588 raw_input: tool_call.arguments.clone(),
589 },
590 )),
591 Err(error) => Ok(LanguageModelCompletionEvent::ToolUseJsonParseError {
592 id: tool_call.id.into(),
593 tool_name: tool_call.name.into(),
594 raw_input: tool_call.arguments.clone().into(),
595 json_parse_error: error.to_string(),
596 }),
597 }
598 }));
599
600 events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::ToolUse)));
601 }
602 Some(stop_reason) => {
603 log::error!("Unexpected OpenAI stop_reason: {stop_reason:?}",);
604 events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::EndTurn)));
605 }
606 None => {}
607 }
608
609 events
610 }
611}
612
613#[derive(Default)]
614struct RawToolCall {
615 id: String,
616 name: String,
617 arguments: String,
618}
619
620pub(crate) fn collect_tiktoken_messages(
621 request: LanguageModelRequest,
622) -> Vec<tiktoken_rs::ChatCompletionRequestMessage> {
623 request
624 .messages
625 .into_iter()
626 .map(|message| tiktoken_rs::ChatCompletionRequestMessage {
627 role: match message.role {
628 Role::User => "user".into(),
629 Role::Assistant => "assistant".into(),
630 Role::System => "system".into(),
631 },
632 content: Some(message.string_contents()),
633 name: None,
634 function_call: None,
635 })
636 .collect::<Vec<_>>()
637}
638
639pub fn count_open_ai_tokens(
640 request: LanguageModelRequest,
641 model: Model,
642 cx: &App,
643) -> BoxFuture<'static, Result<u64>> {
644 cx.background_spawn(async move {
645 let messages = collect_tiktoken_messages(request);
646
647 match model {
648 Model::Custom { max_tokens, .. } => {
649 let model = if max_tokens >= 100_000 {
650 // If the max tokens is 100k or more, it is likely the o200k_base tokenizer from gpt4o
651 "gpt-4o"
652 } else {
653 // Otherwise fallback to gpt-4, since only cl100k_base and o200k_base are
654 // supported with this tiktoken method
655 "gpt-4"
656 };
657 tiktoken_rs::num_tokens_from_messages(model, &messages)
658 }
659 // Currently supported by tiktoken_rs
660 // Sometimes tiktoken-rs is behind on model support. If that is the case, make a new branch
661 // arm with an override. We enumerate all supported models here so that we can check if new
662 // models are supported yet or not.
663 Model::ThreePointFiveTurbo
664 | Model::Four
665 | Model::FourTurbo
666 | Model::FourOmni
667 | Model::FourOmniMini
668 | Model::FourPointOne
669 | Model::FourPointOneMini
670 | Model::FourPointOneNano
671 | Model::O1
672 | Model::O3
673 | Model::O3Mini
674 | Model::O4Mini => tiktoken_rs::num_tokens_from_messages(model.id(), &messages),
675 // GPT-5 models don't have tiktoken support yet; fall back on gpt-4o tokenizer
676 Model::Five | Model::FiveMini | Model::FiveNano => {
677 tiktoken_rs::num_tokens_from_messages("gpt-4o", &messages)
678 }
679 }
680 .map(|tokens| tokens as u64)
681 })
682 .boxed()
683}
684
685struct ConfigurationView {
686 api_key_editor: Entity<InputField>,
687 state: Entity<State>,
688 load_credentials_task: Option<Task<()>>,
689}
690
691impl ConfigurationView {
692 fn new(state: Entity<State>, window: &mut Window, cx: &mut Context<Self>) -> Self {
693 let api_key_editor = cx.new(|cx| {
694 InputField::new(
695 window,
696 cx,
697 "sk-000000000000000000000000000000000000000000000000",
698 )
699 });
700
701 cx.observe(&state, |_, _, cx| {
702 cx.notify();
703 })
704 .detach();
705
706 let load_credentials_task = Some(cx.spawn_in(window, {
707 let state = state.clone();
708 async move |this, cx| {
709 if let Some(task) = state
710 .update(cx, |state, cx| state.authenticate(cx))
711 .log_err()
712 {
713 // We don't log an error, because "not signed in" is also an error.
714 let _ = task.await;
715 }
716 this.update(cx, |this, cx| {
717 this.load_credentials_task = None;
718 cx.notify();
719 })
720 .log_err();
721 }
722 }));
723
724 Self {
725 api_key_editor,
726 state,
727 load_credentials_task,
728 }
729 }
730
731 fn save_api_key(&mut self, _: &menu::Confirm, window: &mut Window, cx: &mut Context<Self>) {
732 let api_key = self.api_key_editor.read(cx).text(cx).trim().to_string();
733 if api_key.is_empty() {
734 return;
735 }
736
737 // url changes can cause the editor to be displayed again
738 self.api_key_editor
739 .update(cx, |editor, cx| editor.set_text("", window, cx));
740
741 let state = self.state.clone();
742 cx.spawn_in(window, async move |_, cx| {
743 state
744 .update(cx, |state, cx| state.set_api_key(Some(api_key), cx))?
745 .await
746 })
747 .detach_and_log_err(cx);
748 }
749
750 fn reset_api_key(&mut self, window: &mut Window, cx: &mut Context<Self>) {
751 self.api_key_editor
752 .update(cx, |input, cx| input.set_text("", window, cx));
753
754 let state = self.state.clone();
755 cx.spawn_in(window, async move |_, cx| {
756 state
757 .update(cx, |state, cx| state.set_api_key(None, cx))?
758 .await
759 })
760 .detach_and_log_err(cx);
761 }
762
763 fn should_render_editor(&self, cx: &mut Context<Self>) -> bool {
764 !self.state.read(cx).is_authenticated()
765 }
766}
767
768impl Render for ConfigurationView {
769 fn render(&mut self, _: &mut Window, cx: &mut Context<Self>) -> impl IntoElement {
770 let env_var_set = self.state.read(cx).api_key_state.is_from_env_var();
771 let configured_card_label = if env_var_set {
772 format!("API key set in {API_KEY_ENV_VAR_NAME} environment variable")
773 } else {
774 let api_url = OpenAiLanguageModelProvider::api_url(cx);
775 if api_url == OPEN_AI_API_URL {
776 "API key configured".to_string()
777 } else {
778 format!("API key configured for {}", api_url)
779 }
780 };
781
782 let api_key_section = if self.should_render_editor(cx) {
783 v_flex()
784 .on_action(cx.listener(Self::save_api_key))
785 .child(Label::new("To use Zed's agent with OpenAI, you need to add an API key. Follow these steps:"))
786 .child(
787 List::new()
788 .child(InstructionListItem::new(
789 "Create one by visiting",
790 Some("OpenAI's console"),
791 Some("https://platform.openai.com/api-keys"),
792 ))
793 .child(InstructionListItem::text_only(
794 "Ensure your OpenAI account has credits",
795 ))
796 .child(InstructionListItem::text_only(
797 "Paste your API key below and hit enter to start using the assistant",
798 )),
799 )
800 .child(self.api_key_editor.clone())
801 .child(
802 Label::new(format!(
803 "You can also assign the {API_KEY_ENV_VAR_NAME} environment variable and restart Zed."
804 ))
805 .size(LabelSize::Small)
806 .color(Color::Muted),
807 )
808 .child(
809 Label::new(
810 "Note that having a subscription for another service like GitHub Copilot won't work.",
811 )
812 .size(LabelSize::Small).color(Color::Muted),
813 )
814 .into_any_element()
815 } else {
816 ConfiguredApiCard::new(configured_card_label)
817 .disabled(env_var_set)
818 .on_click(cx.listener(|this, _, window, cx| this.reset_api_key(window, cx)))
819 .when(env_var_set, |this| {
820 this.tooltip_label(format!("To reset your API key, unset the {API_KEY_ENV_VAR_NAME} environment variable."))
821 })
822 .into_any_element()
823 };
824
825 let compatible_api_section = h_flex()
826 .mt_1p5()
827 .gap_0p5()
828 .flex_wrap()
829 .when(self.should_render_editor(cx), |this| {
830 this.pt_1p5()
831 .border_t_1()
832 .border_color(cx.theme().colors().border_variant)
833 })
834 .child(
835 h_flex()
836 .gap_2()
837 .child(
838 Icon::new(IconName::Info)
839 .size(IconSize::XSmall)
840 .color(Color::Muted),
841 )
842 .child(Label::new("Zed also supports OpenAI-compatible models.")),
843 )
844 .child(
845 Button::new("docs", "Learn More")
846 .icon(IconName::ArrowUpRight)
847 .icon_size(IconSize::Small)
848 .icon_color(Color::Muted)
849 .on_click(move |_, _window, cx| {
850 cx.open_url("https://zed.dev/docs/ai/llm-providers#openai-api-compatible")
851 }),
852 );
853
854 if self.load_credentials_task.is_some() {
855 div().child(Label::new("Loading credentials…")).into_any()
856 } else {
857 v_flex()
858 .size_full()
859 .child(api_key_section)
860 .child(compatible_api_section)
861 .into_any()
862 }
863 }
864}
865
866#[cfg(test)]
867mod tests {
868 use gpui::TestAppContext;
869 use language_model::LanguageModelRequestMessage;
870
871 use super::*;
872
873 #[gpui::test]
874 fn tiktoken_rs_support(cx: &TestAppContext) {
875 let request = LanguageModelRequest {
876 thread_id: None,
877 prompt_id: None,
878 intent: None,
879 mode: None,
880 messages: vec![LanguageModelRequestMessage {
881 role: Role::User,
882 content: vec![MessageContent::Text("message".into())],
883 cache: false,
884 }],
885 tools: vec![],
886 tool_choice: None,
887 stop: vec![],
888 temperature: None,
889 thinking_allowed: true,
890 };
891
892 // Validate that all models are supported by tiktoken-rs
893 for model in Model::iter() {
894 let count = cx
895 .executor()
896 .block(count_open_ai_tokens(
897 request.clone(),
898 model,
899 &cx.app.borrow(),
900 ))
901 .unwrap();
902 assert!(count > 0);
903 }
904 }
905}