1use anyhow::{Result, anyhow};
2use collections::HashMap;
3use futures::Stream;
4use futures::{FutureExt, StreamExt, future::BoxFuture, stream::BoxStream};
5use gpui::{AnyView, App, AsyncApp, Context, Subscription, Task};
6use http_client::HttpClient;
7use language_model::{
8 AuthenticateError, LanguageModelCompletionError, LanguageModelCompletionEvent,
9 LanguageModelToolChoice, LanguageModelToolResultContent, LanguageModelToolUse, MessageContent,
10 StopReason,
11};
12use language_model::{
13 LanguageModel, LanguageModelId, LanguageModelName, LanguageModelProvider,
14 LanguageModelProviderId, LanguageModelProviderName, LanguageModelProviderState,
15 LanguageModelRequest, RateLimiter, Role,
16};
17use lmstudio::{ModelType, get_models};
18use schemars::JsonSchema;
19use serde::{Deserialize, Serialize};
20use settings::{Settings, SettingsStore};
21use std::pin::Pin;
22use std::str::FromStr;
23use std::{collections::BTreeMap, sync::Arc};
24use ui::{ButtonLike, Indicator, List, prelude::*};
25use util::ResultExt;
26
27use crate::AllLanguageModelSettings;
28use crate::ui::InstructionListItem;
29
30const LMSTUDIO_DOWNLOAD_URL: &str = "https://lmstudio.ai/download";
31const LMSTUDIO_CATALOG_URL: &str = "https://lmstudio.ai/models";
32const LMSTUDIO_SITE: &str = "https://lmstudio.ai/";
33
34const PROVIDER_ID: &str = "lmstudio";
35const PROVIDER_NAME: &str = "LM Studio";
36
37#[derive(Default, Debug, Clone, PartialEq)]
38pub struct LmStudioSettings {
39 pub api_url: String,
40 pub available_models: Vec<AvailableModel>,
41}
42
43#[derive(Clone, Debug, PartialEq, Serialize, Deserialize, JsonSchema)]
44pub struct AvailableModel {
45 pub name: String,
46 pub display_name: Option<String>,
47 pub max_tokens: usize,
48 pub supports_tool_calls: bool,
49 pub supports_images: bool,
50}
51
52pub struct LmStudioLanguageModelProvider {
53 http_client: Arc<dyn HttpClient>,
54 state: gpui::Entity<State>,
55}
56
57pub struct State {
58 http_client: Arc<dyn HttpClient>,
59 available_models: Vec<lmstudio::Model>,
60 fetch_model_task: Option<Task<Result<()>>>,
61 _subscription: Subscription,
62}
63
64impl State {
65 fn is_authenticated(&self) -> bool {
66 !self.available_models.is_empty()
67 }
68
69 fn fetch_models(&mut self, cx: &mut Context<Self>) -> Task<Result<()>> {
70 let settings = &AllLanguageModelSettings::get_global(cx).lmstudio;
71 let http_client = self.http_client.clone();
72 let api_url = settings.api_url.clone();
73
74 // As a proxy for the server being "authenticated", we'll check if its up by fetching the models
75 cx.spawn(async move |this, cx| {
76 let models = get_models(http_client.as_ref(), &api_url, None).await?;
77
78 let mut models: Vec<lmstudio::Model> = models
79 .into_iter()
80 .filter(|model| model.r#type != ModelType::Embeddings)
81 .map(|model| {
82 lmstudio::Model::new(
83 &model.id,
84 None,
85 model
86 .loaded_context_length
87 .or_else(|| model.max_context_length),
88 model.capabilities.supports_tool_calls(),
89 model.capabilities.supports_images() || model.r#type == ModelType::Vlm,
90 )
91 })
92 .collect();
93
94 models.sort_by(|a, b| a.name.cmp(&b.name));
95
96 this.update(cx, |this, cx| {
97 this.available_models = models;
98 cx.notify();
99 })
100 })
101 }
102
103 fn restart_fetch_models_task(&mut self, cx: &mut Context<Self>) {
104 let task = self.fetch_models(cx);
105 self.fetch_model_task.replace(task);
106 }
107
108 fn authenticate(&mut self, cx: &mut Context<Self>) -> Task<Result<(), AuthenticateError>> {
109 if self.is_authenticated() {
110 return Task::ready(Ok(()));
111 }
112
113 let fetch_models_task = self.fetch_models(cx);
114 cx.spawn(async move |_this, _cx| Ok(fetch_models_task.await?))
115 }
116}
117
118impl LmStudioLanguageModelProvider {
119 pub fn new(http_client: Arc<dyn HttpClient>, cx: &mut App) -> Self {
120 let this = Self {
121 http_client: http_client.clone(),
122 state: cx.new(|cx| {
123 let subscription = cx.observe_global::<SettingsStore>({
124 let mut settings = AllLanguageModelSettings::get_global(cx).lmstudio.clone();
125 move |this: &mut State, cx| {
126 let new_settings = &AllLanguageModelSettings::get_global(cx).lmstudio;
127 if &settings != new_settings {
128 settings = new_settings.clone();
129 this.restart_fetch_models_task(cx);
130 cx.notify();
131 }
132 }
133 });
134
135 State {
136 http_client,
137 available_models: Default::default(),
138 fetch_model_task: None,
139 _subscription: subscription,
140 }
141 }),
142 };
143 this.state
144 .update(cx, |state, cx| state.restart_fetch_models_task(cx));
145 this
146 }
147}
148
149impl LanguageModelProviderState for LmStudioLanguageModelProvider {
150 type ObservableEntity = State;
151
152 fn observable_entity(&self) -> Option<gpui::Entity<Self::ObservableEntity>> {
153 Some(self.state.clone())
154 }
155}
156
157impl LanguageModelProvider for LmStudioLanguageModelProvider {
158 fn id(&self) -> LanguageModelProviderId {
159 LanguageModelProviderId(PROVIDER_ID.into())
160 }
161
162 fn name(&self) -> LanguageModelProviderName {
163 LanguageModelProviderName(PROVIDER_NAME.into())
164 }
165
166 fn icon(&self) -> IconName {
167 IconName::AiLmStudio
168 }
169
170 fn default_model(&self, _: &App) -> Option<Arc<dyn LanguageModel>> {
171 // We shouldn't try to select default model, because it might lead to a load call for an unloaded model.
172 // In a constrained environment where user might not have enough resources it'll be a bad UX to select something
173 // to load by default.
174 None
175 }
176
177 fn default_fast_model(&self, _: &App) -> Option<Arc<dyn LanguageModel>> {
178 // See explanation for default_model.
179 None
180 }
181
182 fn provided_models(&self, cx: &App) -> Vec<Arc<dyn LanguageModel>> {
183 let mut models: BTreeMap<String, lmstudio::Model> = BTreeMap::default();
184
185 // Add models from the LM Studio API
186 for model in self.state.read(cx).available_models.iter() {
187 models.insert(model.name.clone(), model.clone());
188 }
189
190 // Override with available models from settings
191 for model in AllLanguageModelSettings::get_global(cx)
192 .lmstudio
193 .available_models
194 .iter()
195 {
196 models.insert(
197 model.name.clone(),
198 lmstudio::Model {
199 name: model.name.clone(),
200 display_name: model.display_name.clone(),
201 max_tokens: model.max_tokens,
202 supports_tool_calls: model.supports_tool_calls,
203 supports_images: model.supports_images,
204 },
205 );
206 }
207
208 models
209 .into_values()
210 .map(|model| {
211 Arc::new(LmStudioLanguageModel {
212 id: LanguageModelId::from(model.name.clone()),
213 model: model.clone(),
214 http_client: self.http_client.clone(),
215 request_limiter: RateLimiter::new(4),
216 }) as Arc<dyn LanguageModel>
217 })
218 .collect()
219 }
220
221 fn is_authenticated(&self, cx: &App) -> bool {
222 self.state.read(cx).is_authenticated()
223 }
224
225 fn authenticate(&self, cx: &mut App) -> Task<Result<(), AuthenticateError>> {
226 self.state.update(cx, |state, cx| state.authenticate(cx))
227 }
228
229 fn configuration_view(&self, _window: &mut Window, cx: &mut App) -> AnyView {
230 let state = self.state.clone();
231 cx.new(|cx| ConfigurationView::new(state, cx)).into()
232 }
233
234 fn reset_credentials(&self, cx: &mut App) -> Task<Result<()>> {
235 self.state.update(cx, |state, cx| state.fetch_models(cx))
236 }
237}
238
239pub struct LmStudioLanguageModel {
240 id: LanguageModelId,
241 model: lmstudio::Model,
242 http_client: Arc<dyn HttpClient>,
243 request_limiter: RateLimiter,
244}
245
246impl LmStudioLanguageModel {
247 fn to_lmstudio_request(
248 &self,
249 request: LanguageModelRequest,
250 ) -> lmstudio::ChatCompletionRequest {
251 let mut messages = Vec::new();
252
253 for message in request.messages {
254 for content in message.content {
255 match content {
256 MessageContent::Text(text) => add_message_content_part(
257 lmstudio::MessagePart::Text { text },
258 message.role,
259 &mut messages,
260 ),
261 MessageContent::Thinking { .. } => {}
262 MessageContent::RedactedThinking(_) => {}
263 MessageContent::Image(image) => {
264 add_message_content_part(
265 lmstudio::MessagePart::Image {
266 image_url: lmstudio::ImageUrl {
267 url: image.to_base64_url(),
268 detail: None,
269 },
270 },
271 message.role,
272 &mut messages,
273 );
274 }
275 MessageContent::ToolUse(tool_use) => {
276 let tool_call = lmstudio::ToolCall {
277 id: tool_use.id.to_string(),
278 content: lmstudio::ToolCallContent::Function {
279 function: lmstudio::FunctionContent {
280 name: tool_use.name.to_string(),
281 arguments: serde_json::to_string(&tool_use.input)
282 .unwrap_or_default(),
283 },
284 },
285 };
286
287 if let Some(lmstudio::ChatMessage::Assistant { tool_calls, .. }) =
288 messages.last_mut()
289 {
290 tool_calls.push(tool_call);
291 } else {
292 messages.push(lmstudio::ChatMessage::Assistant {
293 content: None,
294 tool_calls: vec![tool_call],
295 });
296 }
297 }
298 MessageContent::ToolResult(tool_result) => {
299 let content = match &tool_result.content {
300 LanguageModelToolResultContent::Text(text) => {
301 vec![lmstudio::MessagePart::Text {
302 text: text.to_string(),
303 }]
304 }
305 LanguageModelToolResultContent::Image(image) => {
306 vec![lmstudio::MessagePart::Image {
307 image_url: lmstudio::ImageUrl {
308 url: image.to_base64_url(),
309 detail: None,
310 },
311 }]
312 }
313 };
314
315 messages.push(lmstudio::ChatMessage::Tool {
316 content: content.into(),
317 tool_call_id: tool_result.tool_use_id.to_string(),
318 });
319 }
320 }
321 }
322 }
323
324 lmstudio::ChatCompletionRequest {
325 model: self.model.name.clone(),
326 messages,
327 stream: true,
328 max_tokens: Some(-1),
329 stop: Some(request.stop),
330 // In LM Studio you can configure specific settings you'd like to use for your model.
331 // For example Qwen3 is recommended to be used with 0.7 temperature.
332 // It would be a bad UX to silently override these settings from Zed, so we pass no temperature as a default.
333 temperature: request.temperature.or(None),
334 tools: request
335 .tools
336 .into_iter()
337 .map(|tool| lmstudio::ToolDefinition::Function {
338 function: lmstudio::FunctionDefinition {
339 name: tool.name,
340 description: Some(tool.description),
341 parameters: Some(tool.input_schema),
342 },
343 })
344 .collect(),
345 tool_choice: request.tool_choice.map(|choice| match choice {
346 LanguageModelToolChoice::Auto => lmstudio::ToolChoice::Auto,
347 LanguageModelToolChoice::Any => lmstudio::ToolChoice::Required,
348 LanguageModelToolChoice::None => lmstudio::ToolChoice::None,
349 }),
350 }
351 }
352
353 fn stream_completion(
354 &self,
355 request: lmstudio::ChatCompletionRequest,
356 cx: &AsyncApp,
357 ) -> BoxFuture<
358 'static,
359 Result<futures::stream::BoxStream<'static, Result<lmstudio::ResponseStreamEvent>>>,
360 > {
361 let http_client = self.http_client.clone();
362 let Ok(api_url) = cx.update(|cx| {
363 let settings = &AllLanguageModelSettings::get_global(cx).lmstudio;
364 settings.api_url.clone()
365 }) else {
366 return futures::future::ready(Err(anyhow!("App state dropped"))).boxed();
367 };
368
369 let future = self.request_limiter.stream(async move {
370 let request = lmstudio::stream_chat_completion(http_client.as_ref(), &api_url, request);
371 let response = request.await?;
372 Ok(response)
373 });
374
375 async move { Ok(future.await?.boxed()) }.boxed()
376 }
377}
378
379impl LanguageModel for LmStudioLanguageModel {
380 fn id(&self) -> LanguageModelId {
381 self.id.clone()
382 }
383
384 fn name(&self) -> LanguageModelName {
385 LanguageModelName::from(self.model.display_name().to_string())
386 }
387
388 fn provider_id(&self) -> LanguageModelProviderId {
389 LanguageModelProviderId(PROVIDER_ID.into())
390 }
391
392 fn provider_name(&self) -> LanguageModelProviderName {
393 LanguageModelProviderName(PROVIDER_NAME.into())
394 }
395
396 fn supports_tools(&self) -> bool {
397 self.model.supports_tool_calls()
398 }
399
400 fn supports_tool_choice(&self, choice: LanguageModelToolChoice) -> bool {
401 self.supports_tools()
402 && match choice {
403 LanguageModelToolChoice::Auto => true,
404 LanguageModelToolChoice::Any => true,
405 LanguageModelToolChoice::None => true,
406 }
407 }
408
409 fn supports_images(&self) -> bool {
410 self.model.supports_images
411 }
412
413 fn telemetry_id(&self) -> String {
414 format!("lmstudio/{}", self.model.id())
415 }
416
417 fn max_token_count(&self) -> usize {
418 self.model.max_token_count()
419 }
420
421 fn count_tokens(
422 &self,
423 request: LanguageModelRequest,
424 _cx: &App,
425 ) -> BoxFuture<'static, Result<usize>> {
426 // Endpoint for this is coming soon. In the meantime, hacky estimation
427 let token_count = request
428 .messages
429 .iter()
430 .map(|msg| msg.string_contents().split_whitespace().count())
431 .sum::<usize>();
432
433 let estimated_tokens = (token_count as f64 * 0.75) as usize;
434 async move { Ok(estimated_tokens) }.boxed()
435 }
436
437 fn stream_completion(
438 &self,
439 request: LanguageModelRequest,
440 cx: &AsyncApp,
441 ) -> BoxFuture<
442 'static,
443 Result<
444 BoxStream<'static, Result<LanguageModelCompletionEvent, LanguageModelCompletionError>>,
445 LanguageModelCompletionError,
446 >,
447 > {
448 let request = self.to_lmstudio_request(request);
449 let completions = self.stream_completion(request, cx);
450 async move {
451 let mapper = LmStudioEventMapper::new();
452 Ok(mapper.map_stream(completions.await?).boxed())
453 }
454 .boxed()
455 }
456}
457
458struct LmStudioEventMapper {
459 tool_calls_by_index: HashMap<usize, RawToolCall>,
460}
461
462impl LmStudioEventMapper {
463 fn new() -> Self {
464 Self {
465 tool_calls_by_index: HashMap::default(),
466 }
467 }
468
469 pub fn map_stream(
470 mut self,
471 events: Pin<Box<dyn Send + Stream<Item = Result<lmstudio::ResponseStreamEvent>>>>,
472 ) -> impl Stream<Item = Result<LanguageModelCompletionEvent, LanguageModelCompletionError>>
473 {
474 events.flat_map(move |event| {
475 futures::stream::iter(match event {
476 Ok(event) => self.map_event(event),
477 Err(error) => vec![Err(LanguageModelCompletionError::Other(anyhow!(error)))],
478 })
479 })
480 }
481
482 pub fn map_event(
483 &mut self,
484 event: lmstudio::ResponseStreamEvent,
485 ) -> Vec<Result<LanguageModelCompletionEvent, LanguageModelCompletionError>> {
486 let Some(choice) = event.choices.into_iter().next() else {
487 return vec![Err(LanguageModelCompletionError::Other(anyhow!(
488 "Response contained no choices"
489 )))];
490 };
491
492 let mut events = Vec::new();
493 if let Some(content) = choice.delta.content {
494 events.push(Ok(LanguageModelCompletionEvent::Text(content)));
495 }
496
497 if let Some(reasoning_content) = choice.delta.reasoning_content {
498 events.push(Ok(LanguageModelCompletionEvent::Thinking {
499 text: reasoning_content,
500 signature: None,
501 }));
502 }
503
504 if let Some(tool_calls) = choice.delta.tool_calls {
505 for tool_call in tool_calls {
506 let entry = self.tool_calls_by_index.entry(tool_call.index).or_default();
507
508 if let Some(tool_id) = tool_call.id {
509 entry.id = tool_id;
510 }
511
512 if let Some(function) = tool_call.function {
513 if let Some(name) = function.name {
514 // At the time of writing this code LM Studio (0.3.15) is incompatible with the OpenAI API:
515 // 1. It sends function name in the first chunk
516 // 2. It sends empty string in the function name field in all subsequent chunks for arguments
517 // According to https://platform.openai.com/docs/guides/function-calling?api-mode=responses#streaming
518 // function name field should be sent only inside the first chunk.
519 if !name.is_empty() {
520 entry.name = name;
521 }
522 }
523
524 if let Some(arguments) = function.arguments {
525 entry.arguments.push_str(&arguments);
526 }
527 }
528 }
529 }
530
531 match choice.finish_reason.as_deref() {
532 Some("stop") => {
533 events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::EndTurn)));
534 }
535 Some("tool_calls") => {
536 events.extend(self.tool_calls_by_index.drain().map(|(_, tool_call)| {
537 match serde_json::Value::from_str(&tool_call.arguments) {
538 Ok(input) => Ok(LanguageModelCompletionEvent::ToolUse(
539 LanguageModelToolUse {
540 id: tool_call.id.into(),
541 name: tool_call.name.into(),
542 is_input_complete: true,
543 input,
544 raw_input: tool_call.arguments,
545 },
546 )),
547 Err(error) => Err(LanguageModelCompletionError::BadInputJson {
548 id: tool_call.id.into(),
549 tool_name: tool_call.name.into(),
550 raw_input: tool_call.arguments.into(),
551 json_parse_error: error.to_string(),
552 }),
553 }
554 }));
555
556 events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::ToolUse)));
557 }
558 Some(stop_reason) => {
559 log::error!("Unexpected OpenAI stop_reason: {stop_reason:?}",);
560 events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::EndTurn)));
561 }
562 None => {}
563 }
564
565 events
566 }
567}
568
569#[derive(Default)]
570struct RawToolCall {
571 id: String,
572 name: String,
573 arguments: String,
574}
575
576fn add_message_content_part(
577 new_part: lmstudio::MessagePart,
578 role: Role,
579 messages: &mut Vec<lmstudio::ChatMessage>,
580) {
581 match (role, messages.last_mut()) {
582 (Role::User, Some(lmstudio::ChatMessage::User { content }))
583 | (
584 Role::Assistant,
585 Some(lmstudio::ChatMessage::Assistant {
586 content: Some(content),
587 ..
588 }),
589 )
590 | (Role::System, Some(lmstudio::ChatMessage::System { content })) => {
591 content.push_part(new_part);
592 }
593 _ => {
594 messages.push(match role {
595 Role::User => lmstudio::ChatMessage::User {
596 content: lmstudio::MessageContent::from(vec![new_part]),
597 },
598 Role::Assistant => lmstudio::ChatMessage::Assistant {
599 content: Some(lmstudio::MessageContent::from(vec![new_part])),
600 tool_calls: Vec::new(),
601 },
602 Role::System => lmstudio::ChatMessage::System {
603 content: lmstudio::MessageContent::from(vec![new_part]),
604 },
605 });
606 }
607 }
608}
609
610struct ConfigurationView {
611 state: gpui::Entity<State>,
612 loading_models_task: Option<Task<()>>,
613}
614
615impl ConfigurationView {
616 pub fn new(state: gpui::Entity<State>, cx: &mut Context<Self>) -> Self {
617 let loading_models_task = Some(cx.spawn({
618 let state = state.clone();
619 async move |this, cx| {
620 if let Some(task) = state
621 .update(cx, |state, cx| state.authenticate(cx))
622 .log_err()
623 {
624 task.await.log_err();
625 }
626 this.update(cx, |this, cx| {
627 this.loading_models_task = None;
628 cx.notify();
629 })
630 .log_err();
631 }
632 }));
633
634 Self {
635 state,
636 loading_models_task,
637 }
638 }
639
640 fn retry_connection(&self, cx: &mut App) {
641 self.state
642 .update(cx, |state, cx| state.fetch_models(cx))
643 .detach_and_log_err(cx);
644 }
645}
646
647impl Render for ConfigurationView {
648 fn render(&mut self, _window: &mut Window, cx: &mut Context<Self>) -> impl IntoElement {
649 let is_authenticated = self.state.read(cx).is_authenticated();
650
651 let lmstudio_intro = "Run local LLMs like Llama, Phi, and Qwen.";
652
653 if self.loading_models_task.is_some() {
654 div().child(Label::new("Loading models...")).into_any()
655 } else {
656 v_flex()
657 .gap_2()
658 .child(
659 v_flex().gap_1().child(Label::new(lmstudio_intro)).child(
660 List::new()
661 .child(InstructionListItem::text_only(
662 "LM Studio needs to be running with at least one model downloaded.",
663 ))
664 .child(InstructionListItem::text_only(
665 "To get your first model, try running `lms get qwen2.5-coder-7b`",
666 )),
667 ),
668 )
669 .child(
670 h_flex()
671 .w_full()
672 .justify_between()
673 .gap_2()
674 .child(
675 h_flex()
676 .w_full()
677 .gap_2()
678 .map(|this| {
679 if is_authenticated {
680 this.child(
681 Button::new("lmstudio-site", "LM Studio")
682 .style(ButtonStyle::Subtle)
683 .icon(IconName::ArrowUpRight)
684 .icon_size(IconSize::XSmall)
685 .icon_color(Color::Muted)
686 .on_click(move |_, _window, cx| {
687 cx.open_url(LMSTUDIO_SITE)
688 })
689 .into_any_element(),
690 )
691 } else {
692 this.child(
693 Button::new(
694 "download_lmstudio_button",
695 "Download LM Studio",
696 )
697 .style(ButtonStyle::Subtle)
698 .icon(IconName::ArrowUpRight)
699 .icon_size(IconSize::XSmall)
700 .icon_color(Color::Muted)
701 .on_click(move |_, _window, cx| {
702 cx.open_url(LMSTUDIO_DOWNLOAD_URL)
703 })
704 .into_any_element(),
705 )
706 }
707 })
708 .child(
709 Button::new("view-models", "Model Catalog")
710 .style(ButtonStyle::Subtle)
711 .icon(IconName::ArrowUpRight)
712 .icon_size(IconSize::XSmall)
713 .icon_color(Color::Muted)
714 .on_click(move |_, _window, cx| {
715 cx.open_url(LMSTUDIO_CATALOG_URL)
716 }),
717 ),
718 )
719 .map(|this| {
720 if is_authenticated {
721 this.child(
722 ButtonLike::new("connected")
723 .disabled(true)
724 .cursor_style(gpui::CursorStyle::Arrow)
725 .child(
726 h_flex()
727 .gap_2()
728 .child(Indicator::dot().color(Color::Success))
729 .child(Label::new("Connected"))
730 .into_any_element(),
731 ),
732 )
733 } else {
734 this.child(
735 Button::new("retry_lmstudio_models", "Connect")
736 .icon_position(IconPosition::Start)
737 .icon_size(IconSize::XSmall)
738 .icon(IconName::Play)
739 .on_click(cx.listener(move |this, _, _window, cx| {
740 this.retry_connection(cx)
741 })),
742 )
743 }
744 }),
745 )
746 .into_any()
747 }
748 }
749}