1use std::pin::Pin;
2use std::str::FromStr as _;
3use std::sync::Arc;
4
5use anthropic::AnthropicModelMode;
6use anyhow::{Result, anyhow};
7use collections::HashMap;
8use copilot::{GlobalCopilotAuth, Status};
9use copilot_chat::responses as copilot_responses;
10use copilot_chat::{
11 ChatLocation, ChatMessage, ChatMessageContent, ChatMessagePart, CopilotChat,
12 CopilotChatConfiguration, Function, FunctionContent, ImageUrl, Model as CopilotChatModel,
13 ModelVendor, Request as CopilotChatRequest, ResponseEvent, Tool, ToolCall, ToolCallContent,
14 ToolChoice,
15};
16use futures::future::BoxFuture;
17use futures::stream::BoxStream;
18use futures::{FutureExt, Stream, StreamExt};
19use gpui::{AnyView, App, AsyncApp, Entity, Subscription, Task};
20use http_client::StatusCode;
21use language::language_settings::all_language_settings;
22use language_model::{
23 AuthenticateError, CompletionIntent, IconOrSvg, LanguageModel, LanguageModelCompletionError,
24 LanguageModelCompletionEvent, LanguageModelCostInfo, LanguageModelEffortLevel, LanguageModelId,
25 LanguageModelName, LanguageModelProvider, LanguageModelProviderId, LanguageModelProviderName,
26 LanguageModelProviderState, LanguageModelRequest, LanguageModelRequestMessage,
27 LanguageModelToolChoice, LanguageModelToolResultContent, LanguageModelToolSchemaFormat,
28 LanguageModelToolUse, MessageContent, RateLimiter, Role, StopReason, TokenUsage,
29};
30use settings::SettingsStore;
31use ui::prelude::*;
32use util::debug_panic;
33
34use crate::provider::anthropic::{AnthropicEventMapper, into_anthropic};
35use crate::provider::util::{fix_streamed_json, parse_tool_arguments};
36
37const PROVIDER_ID: LanguageModelProviderId = LanguageModelProviderId::new("copilot_chat");
38const PROVIDER_NAME: LanguageModelProviderName =
39 LanguageModelProviderName::new("GitHub Copilot Chat");
40
41pub struct CopilotChatLanguageModelProvider {
42 state: Entity<State>,
43}
44
45pub struct State {
46 _copilot_chat_subscription: Option<Subscription>,
47 _settings_subscription: Subscription,
48}
49
50impl State {
51 fn is_authenticated(&self, cx: &App) -> bool {
52 CopilotChat::global(cx)
53 .map(|m| m.read(cx).is_authenticated())
54 .unwrap_or(false)
55 }
56}
57
58impl CopilotChatLanguageModelProvider {
59 pub fn new(cx: &mut App) -> Self {
60 let state = cx.new(|cx| {
61 let copilot_chat_subscription = CopilotChat::global(cx)
62 .map(|copilot_chat| cx.observe(&copilot_chat, |_, _, cx| cx.notify()));
63 State {
64 _copilot_chat_subscription: copilot_chat_subscription,
65 _settings_subscription: cx.observe_global::<SettingsStore>(|_, cx| {
66 if let Some(copilot_chat) = CopilotChat::global(cx) {
67 let language_settings = all_language_settings(None, cx);
68 let configuration = CopilotChatConfiguration {
69 enterprise_uri: language_settings
70 .edit_predictions
71 .copilot
72 .enterprise_uri
73 .clone(),
74 };
75 copilot_chat.update(cx, |chat, cx| {
76 chat.set_configuration(configuration, cx);
77 });
78 }
79 cx.notify();
80 }),
81 }
82 });
83
84 Self { state }
85 }
86
87 fn create_language_model(&self, model: CopilotChatModel) -> Arc<dyn LanguageModel> {
88 Arc::new(CopilotChatLanguageModel {
89 model,
90 request_limiter: RateLimiter::new(4),
91 })
92 }
93}
94
95impl LanguageModelProviderState for CopilotChatLanguageModelProvider {
96 type ObservableEntity = State;
97
98 fn observable_entity(&self) -> Option<Entity<Self::ObservableEntity>> {
99 Some(self.state.clone())
100 }
101}
102
103impl LanguageModelProvider for CopilotChatLanguageModelProvider {
104 fn id(&self) -> LanguageModelProviderId {
105 PROVIDER_ID
106 }
107
108 fn name(&self) -> LanguageModelProviderName {
109 PROVIDER_NAME
110 }
111
112 fn icon(&self) -> IconOrSvg {
113 IconOrSvg::Icon(IconName::Copilot)
114 }
115
116 fn default_model(&self, cx: &App) -> Option<Arc<dyn LanguageModel>> {
117 let models = CopilotChat::global(cx).and_then(|m| m.read(cx).models())?;
118 models
119 .first()
120 .map(|model| self.create_language_model(model.clone()))
121 }
122
123 fn default_fast_model(&self, cx: &App) -> Option<Arc<dyn LanguageModel>> {
124 // The default model should be Copilot Chat's 'base model', which is likely a relatively fast
125 // model (e.g. 4o) and a sensible choice when considering premium requests
126 self.default_model(cx)
127 }
128
129 fn provided_models(&self, cx: &App) -> Vec<Arc<dyn LanguageModel>> {
130 let Some(models) = CopilotChat::global(cx).and_then(|m| m.read(cx).models()) else {
131 return Vec::new();
132 };
133 models
134 .iter()
135 .map(|model| self.create_language_model(model.clone()))
136 .collect()
137 }
138
139 fn is_authenticated(&self, cx: &App) -> bool {
140 self.state.read(cx).is_authenticated(cx)
141 }
142
143 fn authenticate(&self, cx: &mut App) -> Task<Result<(), AuthenticateError>> {
144 if self.is_authenticated(cx) {
145 return Task::ready(Ok(()));
146 };
147
148 let Some(copilot) = GlobalCopilotAuth::try_global(cx).cloned() else {
149 return Task::ready(Err(anyhow!(concat!(
150 "Copilot must be enabled for Copilot Chat to work. ",
151 "Please enable Copilot and try again."
152 ))
153 .into()));
154 };
155
156 let err = match copilot.0.read(cx).status() {
157 Status::Authorized => return Task::ready(Ok(())),
158 Status::Disabled => anyhow!(
159 "Copilot must be enabled for Copilot Chat to work. Please enable Copilot and try again."
160 ),
161 Status::Error(err) => anyhow!(format!(
162 "Received the following error while signing into Copilot: {err}"
163 )),
164 Status::Starting { task: _ } => anyhow!(
165 "Copilot is still starting, please wait for Copilot to start then try again"
166 ),
167 Status::Unauthorized => anyhow!(
168 "Unable to authorize with Copilot. Please make sure that you have an active Copilot and Copilot Chat subscription."
169 ),
170 Status::SignedOut { .. } => {
171 anyhow!("You have signed out of Copilot. Please sign in to Copilot and try again.")
172 }
173 Status::SigningIn { prompt: _ } => anyhow!("Still signing into Copilot..."),
174 };
175
176 Task::ready(Err(err.into()))
177 }
178
179 fn configuration_view(
180 &self,
181 _target_agent: language_model::ConfigurationViewTargetAgent,
182 _: &mut Window,
183 cx: &mut App,
184 ) -> AnyView {
185 cx.new(|cx| {
186 copilot_ui::ConfigurationView::new(
187 |cx| {
188 CopilotChat::global(cx)
189 .map(|m| m.read(cx).is_authenticated())
190 .unwrap_or(false)
191 },
192 copilot_ui::ConfigurationMode::Chat,
193 cx,
194 )
195 })
196 .into()
197 }
198
199 fn reset_credentials(&self, _cx: &mut App) -> Task<Result<()>> {
200 Task::ready(Err(anyhow!(
201 "Signing out of GitHub Copilot Chat is currently not supported."
202 )))
203 }
204}
205
206fn collect_tiktoken_messages(
207 request: LanguageModelRequest,
208) -> Vec<tiktoken_rs::ChatCompletionRequestMessage> {
209 request
210 .messages
211 .into_iter()
212 .map(|message| tiktoken_rs::ChatCompletionRequestMessage {
213 role: match message.role {
214 Role::User => "user".into(),
215 Role::Assistant => "assistant".into(),
216 Role::System => "system".into(),
217 },
218 content: Some(message.string_contents()),
219 name: None,
220 function_call: None,
221 })
222 .collect::<Vec<_>>()
223}
224
225pub struct CopilotChatLanguageModel {
226 model: CopilotChatModel,
227 request_limiter: RateLimiter,
228}
229
230impl LanguageModel for CopilotChatLanguageModel {
231 fn id(&self) -> LanguageModelId {
232 LanguageModelId::from(self.model.id().to_string())
233 }
234
235 fn name(&self) -> LanguageModelName {
236 LanguageModelName::from(self.model.display_name().to_string())
237 }
238
239 fn provider_id(&self) -> LanguageModelProviderId {
240 PROVIDER_ID
241 }
242
243 fn provider_name(&self) -> LanguageModelProviderName {
244 PROVIDER_NAME
245 }
246
247 fn supports_tools(&self) -> bool {
248 self.model.supports_tools()
249 }
250
251 fn supports_streaming_tools(&self) -> bool {
252 true
253 }
254
255 fn supports_images(&self) -> bool {
256 self.model.supports_vision()
257 }
258
259 fn supports_thinking(&self) -> bool {
260 self.model.can_think()
261 }
262
263 fn supported_effort_levels(&self) -> Vec<LanguageModelEffortLevel> {
264 let levels = self.model.reasoning_effort_levels();
265 if levels.is_empty() {
266 return vec![];
267 }
268 levels
269 .iter()
270 .map(|level| {
271 let name: SharedString = match level.as_str() {
272 "low" => "Low".into(),
273 "medium" => "Medium".into(),
274 "high" => "High".into(),
275 _ => SharedString::from(level.clone()),
276 };
277 LanguageModelEffortLevel {
278 name,
279 value: SharedString::from(level.clone()),
280 is_default: level == "high",
281 }
282 })
283 .collect()
284 }
285
286 fn tool_input_format(&self) -> LanguageModelToolSchemaFormat {
287 match self.model.vendor() {
288 ModelVendor::OpenAI | ModelVendor::Anthropic => {
289 LanguageModelToolSchemaFormat::JsonSchema
290 }
291 ModelVendor::Google | ModelVendor::XAI | ModelVendor::Unknown => {
292 LanguageModelToolSchemaFormat::JsonSchemaSubset
293 }
294 }
295 }
296
297 fn supports_tool_choice(&self, choice: LanguageModelToolChoice) -> bool {
298 match choice {
299 LanguageModelToolChoice::Auto
300 | LanguageModelToolChoice::Any
301 | LanguageModelToolChoice::None => self.supports_tools(),
302 }
303 }
304
305 fn model_cost_info(&self) -> Option<LanguageModelCostInfo> {
306 LanguageModelCostInfo::RequestCost {
307 cost_per_request: self.model.multiplier(),
308 }
309 .into()
310 }
311
312 fn telemetry_id(&self) -> String {
313 format!("copilot_chat/{}", self.model.id())
314 }
315
316 fn max_token_count(&self) -> u64 {
317 self.model.max_token_count()
318 }
319
320 fn count_tokens(
321 &self,
322 request: LanguageModelRequest,
323 cx: &App,
324 ) -> BoxFuture<'static, Result<u64>> {
325 let model = self.model.clone();
326 cx.background_spawn(async move {
327 let messages = collect_tiktoken_messages(request);
328 // Copilot uses OpenAI tiktoken tokenizer for all it's model irrespective of the underlying provider(vendor).
329 let tokenizer_model = match model.tokenizer() {
330 Some("o200k_base") => "gpt-4o",
331 Some("cl100k_base") => "gpt-4",
332 _ => "gpt-4o",
333 };
334
335 tiktoken_rs::num_tokens_from_messages(tokenizer_model, &messages)
336 .map(|tokens| tokens as u64)
337 })
338 .boxed()
339 }
340
341 fn stream_completion(
342 &self,
343 request: LanguageModelRequest,
344 cx: &AsyncApp,
345 ) -> BoxFuture<
346 'static,
347 Result<
348 BoxStream<'static, Result<LanguageModelCompletionEvent, LanguageModelCompletionError>>,
349 LanguageModelCompletionError,
350 >,
351 > {
352 let is_user_initiated = request.intent.is_none_or(|intent| match intent {
353 CompletionIntent::UserPrompt
354 | CompletionIntent::ThreadContextSummarization
355 | CompletionIntent::InlineAssist
356 | CompletionIntent::TerminalInlineAssist
357 | CompletionIntent::GenerateGitCommitMessage => true,
358
359 CompletionIntent::Subagent
360 | CompletionIntent::ToolResults
361 | CompletionIntent::ThreadSummarization
362 | CompletionIntent::CreateFile
363 | CompletionIntent::EditFile => false,
364 });
365
366 if self.model.supports_messages() {
367 let location = intent_to_chat_location(request.intent);
368 let model = self.model.clone();
369 let request_limiter = self.request_limiter.clone();
370 let future = cx.spawn(async move |cx| {
371 let effort = request
372 .thinking_effort
373 .as_ref()
374 .and_then(|e| anthropic::Effort::from_str(e).ok());
375
376 let mut anthropic_request = into_anthropic(
377 request,
378 model.id().to_string(),
379 0.0,
380 model.max_output_tokens() as u64,
381 if model.supports_adaptive_thinking() {
382 AnthropicModelMode::Thinking {
383 budget_tokens: None,
384 }
385 } else if model.can_think() {
386 AnthropicModelMode::Thinking {
387 budget_tokens: compute_thinking_budget(
388 model.min_thinking_budget(),
389 model.max_thinking_budget(),
390 model.max_output_tokens() as u32,
391 ),
392 }
393 } else {
394 AnthropicModelMode::Default
395 },
396 );
397
398 anthropic_request.temperature = None;
399
400 // The Copilot proxy doesn't support eager_input_streaming on tools.
401 for tool in &mut anthropic_request.tools {
402 tool.eager_input_streaming = false;
403 }
404
405 if model.supports_adaptive_thinking() {
406 if anthropic_request.thinking.is_some() {
407 anthropic_request.thinking = Some(anthropic::Thinking::Adaptive);
408 anthropic_request.output_config = Some(anthropic::OutputConfig { effort });
409 }
410 }
411
412 let anthropic_beta = if !model.supports_adaptive_thinking() && model.can_think() {
413 Some("interleaved-thinking-2025-05-14".to_string())
414 } else {
415 None
416 };
417
418 let body = serde_json::to_string(&anthropic::StreamingRequest {
419 base: anthropic_request,
420 stream: true,
421 })
422 .map_err(|e| anyhow::anyhow!(e))?;
423
424 let stream = CopilotChat::stream_messages(
425 body,
426 location,
427 is_user_initiated,
428 anthropic_beta,
429 cx.clone(),
430 );
431
432 request_limiter
433 .stream(async move {
434 let events = stream.await?;
435 let mapper = AnthropicEventMapper::new();
436 Ok(mapper.map_stream(events).boxed())
437 })
438 .await
439 });
440 return async move { Ok(future.await?.boxed()) }.boxed();
441 }
442
443 if self.model.supports_response() {
444 let location = intent_to_chat_location(request.intent);
445 let responses_request = into_copilot_responses(&self.model, request);
446 let request_limiter = self.request_limiter.clone();
447 let future = cx.spawn(async move |cx| {
448 let request = CopilotChat::stream_response(
449 responses_request,
450 location,
451 is_user_initiated,
452 cx.clone(),
453 );
454 request_limiter
455 .stream(async move {
456 let stream = request.await?;
457 let mapper = CopilotResponsesEventMapper::new();
458 Ok(mapper.map_stream(stream).boxed())
459 })
460 .await
461 });
462 return async move { Ok(future.await?.boxed()) }.boxed();
463 }
464
465 let location = intent_to_chat_location(request.intent);
466 let copilot_request = match into_copilot_chat(&self.model, request) {
467 Ok(request) => request,
468 Err(err) => return futures::future::ready(Err(err.into())).boxed(),
469 };
470 let is_streaming = copilot_request.stream;
471
472 let request_limiter = self.request_limiter.clone();
473 let future = cx.spawn(async move |cx| {
474 let request = CopilotChat::stream_completion(
475 copilot_request,
476 location,
477 is_user_initiated,
478 cx.clone(),
479 );
480 request_limiter
481 .stream(async move {
482 let response = request.await?;
483 Ok(map_to_language_model_completion_events(
484 response,
485 is_streaming,
486 ))
487 })
488 .await
489 });
490 async move { Ok(future.await?.boxed()) }.boxed()
491 }
492}
493
494pub fn map_to_language_model_completion_events(
495 events: Pin<Box<dyn Send + Stream<Item = Result<ResponseEvent>>>>,
496 is_streaming: bool,
497) -> impl Stream<Item = Result<LanguageModelCompletionEvent, LanguageModelCompletionError>> {
498 #[derive(Default)]
499 struct RawToolCall {
500 id: String,
501 name: String,
502 arguments: String,
503 thought_signature: Option<String>,
504 }
505
506 struct State {
507 events: Pin<Box<dyn Send + Stream<Item = Result<ResponseEvent>>>>,
508 tool_calls_by_index: HashMap<usize, RawToolCall>,
509 reasoning_opaque: Option<String>,
510 reasoning_text: Option<String>,
511 }
512
513 futures::stream::unfold(
514 State {
515 events,
516 tool_calls_by_index: HashMap::default(),
517 reasoning_opaque: None,
518 reasoning_text: None,
519 },
520 move |mut state| async move {
521 if let Some(event) = state.events.next().await {
522 match event {
523 Ok(event) => {
524 let Some(choice) = event.choices.first() else {
525 return Some((
526 vec![Err(anyhow!("Response contained no choices").into())],
527 state,
528 ));
529 };
530
531 let delta = if is_streaming {
532 choice.delta.as_ref()
533 } else {
534 choice.message.as_ref()
535 };
536
537 let Some(delta) = delta else {
538 return Some((
539 vec![Err(anyhow!("Response contained no delta").into())],
540 state,
541 ));
542 };
543
544 let mut events = Vec::new();
545 if let Some(content) = delta.content.clone() {
546 events.push(Ok(LanguageModelCompletionEvent::Text(content)));
547 }
548
549 // Capture reasoning data from the delta (e.g. for Gemini 3)
550 if let Some(opaque) = delta.reasoning_opaque.clone() {
551 state.reasoning_opaque = Some(opaque);
552 }
553 if let Some(text) = delta.reasoning_text.clone() {
554 state.reasoning_text = Some(text);
555 }
556
557 for (index, tool_call) in delta.tool_calls.iter().enumerate() {
558 let tool_index = tool_call.index.unwrap_or(index);
559 let entry = state.tool_calls_by_index.entry(tool_index).or_default();
560
561 if let Some(tool_id) = tool_call.id.clone() {
562 entry.id = tool_id;
563 }
564
565 if let Some(function) = tool_call.function.as_ref() {
566 if let Some(name) = function.name.clone() {
567 entry.name = name;
568 }
569
570 if let Some(arguments) = function.arguments.clone() {
571 entry.arguments.push_str(&arguments);
572 }
573
574 if let Some(thought_signature) = function.thought_signature.clone()
575 {
576 entry.thought_signature = Some(thought_signature);
577 }
578 }
579
580 if !entry.id.is_empty() && !entry.name.is_empty() {
581 if let Ok(input) = serde_json::from_str::<serde_json::Value>(
582 &fix_streamed_json(&entry.arguments),
583 ) {
584 events.push(Ok(LanguageModelCompletionEvent::ToolUse(
585 LanguageModelToolUse {
586 id: entry.id.clone().into(),
587 name: entry.name.as_str().into(),
588 is_input_complete: false,
589 input,
590 raw_input: entry.arguments.clone(),
591 thought_signature: entry.thought_signature.clone(),
592 },
593 )));
594 }
595 }
596 }
597
598 if let Some(usage) = event.usage {
599 events.push(Ok(LanguageModelCompletionEvent::UsageUpdate(
600 TokenUsage {
601 input_tokens: usage.prompt_tokens,
602 output_tokens: usage.completion_tokens,
603 cache_creation_input_tokens: 0,
604 cache_read_input_tokens: 0,
605 },
606 )));
607 }
608
609 match choice.finish_reason.as_deref() {
610 Some("stop") => {
611 events.push(Ok(LanguageModelCompletionEvent::Stop(
612 StopReason::EndTurn,
613 )));
614 }
615 Some("tool_calls") => {
616 // Gemini 3 models send reasoning_opaque/reasoning_text that must
617 // be preserved and sent back in subsequent requests. Emit as
618 // ReasoningDetails so the agent stores it in the message.
619 if state.reasoning_opaque.is_some()
620 || state.reasoning_text.is_some()
621 {
622 let mut details = serde_json::Map::new();
623 if let Some(opaque) = state.reasoning_opaque.take() {
624 details.insert(
625 "reasoning_opaque".to_string(),
626 serde_json::Value::String(opaque),
627 );
628 }
629 if let Some(text) = state.reasoning_text.take() {
630 details.insert(
631 "reasoning_text".to_string(),
632 serde_json::Value::String(text),
633 );
634 }
635 events.push(Ok(
636 LanguageModelCompletionEvent::ReasoningDetails(
637 serde_json::Value::Object(details),
638 ),
639 ));
640 }
641
642 events.extend(state.tool_calls_by_index.drain().map(
643 |(_, tool_call)| match parse_tool_arguments(
644 &tool_call.arguments,
645 ) {
646 Ok(input) => Ok(LanguageModelCompletionEvent::ToolUse(
647 LanguageModelToolUse {
648 id: tool_call.id.into(),
649 name: tool_call.name.as_str().into(),
650 is_input_complete: true,
651 input,
652 raw_input: tool_call.arguments,
653 thought_signature: tool_call.thought_signature,
654 },
655 )),
656 Err(error) => Ok(
657 LanguageModelCompletionEvent::ToolUseJsonParseError {
658 id: tool_call.id.into(),
659 tool_name: tool_call.name.as_str().into(),
660 raw_input: tool_call.arguments.into(),
661 json_parse_error: error.to_string(),
662 },
663 ),
664 },
665 ));
666
667 events.push(Ok(LanguageModelCompletionEvent::Stop(
668 StopReason::ToolUse,
669 )));
670 }
671 Some(stop_reason) => {
672 log::error!("Unexpected Copilot Chat stop_reason: {stop_reason:?}");
673 events.push(Ok(LanguageModelCompletionEvent::Stop(
674 StopReason::EndTurn,
675 )));
676 }
677 None => {}
678 }
679
680 return Some((events, state));
681 }
682 Err(err) => return Some((vec![Err(anyhow!(err).into())], state)),
683 }
684 }
685
686 None
687 },
688 )
689 .flat_map(futures::stream::iter)
690}
691
692pub struct CopilotResponsesEventMapper {
693 pending_stop_reason: Option<StopReason>,
694}
695
696impl CopilotResponsesEventMapper {
697 pub fn new() -> Self {
698 Self {
699 pending_stop_reason: None,
700 }
701 }
702
703 pub fn map_stream(
704 mut self,
705 events: Pin<Box<dyn Send + Stream<Item = Result<copilot_responses::StreamEvent>>>>,
706 ) -> impl Stream<Item = Result<LanguageModelCompletionEvent, LanguageModelCompletionError>>
707 {
708 events.flat_map(move |event| {
709 futures::stream::iter(match event {
710 Ok(event) => self.map_event(event),
711 Err(error) => vec![Err(LanguageModelCompletionError::from(anyhow!(error)))],
712 })
713 })
714 }
715
716 fn map_event(
717 &mut self,
718 event: copilot_responses::StreamEvent,
719 ) -> Vec<Result<LanguageModelCompletionEvent, LanguageModelCompletionError>> {
720 match event {
721 copilot_responses::StreamEvent::OutputItemAdded { item, .. } => match item {
722 copilot_responses::ResponseOutputItem::Message { id, .. } => {
723 vec![Ok(LanguageModelCompletionEvent::StartMessage {
724 message_id: id,
725 })]
726 }
727 _ => Vec::new(),
728 },
729
730 copilot_responses::StreamEvent::OutputTextDelta { delta, .. } => {
731 if delta.is_empty() {
732 Vec::new()
733 } else {
734 vec![Ok(LanguageModelCompletionEvent::Text(delta))]
735 }
736 }
737
738 copilot_responses::StreamEvent::OutputItemDone { item, .. } => match item {
739 copilot_responses::ResponseOutputItem::Message { .. } => Vec::new(),
740 copilot_responses::ResponseOutputItem::FunctionCall {
741 call_id,
742 name,
743 arguments,
744 thought_signature,
745 ..
746 } => {
747 let mut events = Vec::new();
748 match parse_tool_arguments(&arguments) {
749 Ok(input) => events.push(Ok(LanguageModelCompletionEvent::ToolUse(
750 LanguageModelToolUse {
751 id: call_id.into(),
752 name: name.as_str().into(),
753 is_input_complete: true,
754 input,
755 raw_input: arguments.clone(),
756 thought_signature,
757 },
758 ))),
759 Err(error) => {
760 events.push(Ok(LanguageModelCompletionEvent::ToolUseJsonParseError {
761 id: call_id.into(),
762 tool_name: name.as_str().into(),
763 raw_input: arguments.clone().into(),
764 json_parse_error: error.to_string(),
765 }))
766 }
767 }
768 // Record that we already emitted a tool-use stop so we can avoid duplicating
769 // a Stop event on Completed.
770 self.pending_stop_reason = Some(StopReason::ToolUse);
771 events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::ToolUse)));
772 events
773 }
774 copilot_responses::ResponseOutputItem::Reasoning {
775 summary,
776 encrypted_content,
777 ..
778 } => {
779 let mut events = Vec::new();
780
781 if let Some(blocks) = summary {
782 let mut text = String::new();
783 for block in blocks {
784 text.push_str(&block.text);
785 }
786 if !text.is_empty() {
787 events.push(Ok(LanguageModelCompletionEvent::Thinking {
788 text,
789 signature: None,
790 }));
791 }
792 }
793
794 if let Some(data) = encrypted_content {
795 events.push(Ok(LanguageModelCompletionEvent::RedactedThinking { data }));
796 }
797
798 events
799 }
800 },
801
802 copilot_responses::StreamEvent::Completed { response } => {
803 let mut events = Vec::new();
804 if let Some(usage) = response.usage {
805 events.push(Ok(LanguageModelCompletionEvent::UsageUpdate(TokenUsage {
806 input_tokens: usage.input_tokens.unwrap_or(0),
807 output_tokens: usage.output_tokens.unwrap_or(0),
808 cache_creation_input_tokens: 0,
809 cache_read_input_tokens: 0,
810 })));
811 }
812 if self.pending_stop_reason.take() != Some(StopReason::ToolUse) {
813 events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::EndTurn)));
814 }
815 events
816 }
817
818 copilot_responses::StreamEvent::Incomplete { response } => {
819 let reason = response
820 .incomplete_details
821 .as_ref()
822 .and_then(|details| details.reason.as_ref());
823 let stop_reason = match reason {
824 Some(copilot_responses::IncompleteReason::MaxOutputTokens) => {
825 StopReason::MaxTokens
826 }
827 Some(copilot_responses::IncompleteReason::ContentFilter) => StopReason::Refusal,
828 _ => self
829 .pending_stop_reason
830 .take()
831 .unwrap_or(StopReason::EndTurn),
832 };
833
834 let mut events = Vec::new();
835 if let Some(usage) = response.usage {
836 events.push(Ok(LanguageModelCompletionEvent::UsageUpdate(TokenUsage {
837 input_tokens: usage.input_tokens.unwrap_or(0),
838 output_tokens: usage.output_tokens.unwrap_or(0),
839 cache_creation_input_tokens: 0,
840 cache_read_input_tokens: 0,
841 })));
842 }
843 events.push(Ok(LanguageModelCompletionEvent::Stop(stop_reason)));
844 events
845 }
846
847 copilot_responses::StreamEvent::Failed { response } => {
848 let provider = PROVIDER_NAME;
849 let (status_code, message) = match response.error {
850 Some(error) => {
851 let status_code = StatusCode::from_str(&error.code)
852 .unwrap_or(StatusCode::INTERNAL_SERVER_ERROR);
853 (status_code, error.message)
854 }
855 None => (
856 StatusCode::INTERNAL_SERVER_ERROR,
857 "response.failed".to_string(),
858 ),
859 };
860 vec![Err(LanguageModelCompletionError::HttpResponseError {
861 provider,
862 status_code,
863 message,
864 })]
865 }
866
867 copilot_responses::StreamEvent::GenericError { error } => vec![Err(
868 LanguageModelCompletionError::Other(anyhow!(error.message)),
869 )],
870
871 copilot_responses::StreamEvent::Created { .. }
872 | copilot_responses::StreamEvent::Unknown => Vec::new(),
873 }
874 }
875}
876
877fn into_copilot_chat(
878 model: &CopilotChatModel,
879 request: LanguageModelRequest,
880) -> Result<CopilotChatRequest> {
881 let temperature = request.temperature;
882 let tool_choice = request.tool_choice;
883
884 let mut request_messages: Vec<LanguageModelRequestMessage> = Vec::new();
885 for message in request.messages {
886 if let Some(last_message) = request_messages.last_mut() {
887 if last_message.role == message.role {
888 last_message.content.extend(message.content);
889 } else {
890 request_messages.push(message);
891 }
892 } else {
893 request_messages.push(message);
894 }
895 }
896
897 let mut messages: Vec<ChatMessage> = Vec::new();
898 for message in request_messages {
899 match message.role {
900 Role::User => {
901 for content in &message.content {
902 if let MessageContent::ToolResult(tool_result) = content {
903 let content = match &tool_result.content {
904 LanguageModelToolResultContent::Text(text) => text.to_string().into(),
905 LanguageModelToolResultContent::Image(image) => {
906 if model.supports_vision() {
907 ChatMessageContent::Multipart(vec![ChatMessagePart::Image {
908 image_url: ImageUrl {
909 url: image.to_base64_url(),
910 },
911 }])
912 } else {
913 debug_panic!(
914 "This should be caught at {} level",
915 tool_result.tool_name
916 );
917 "[Tool responded with an image, but this model does not support vision]".to_string().into()
918 }
919 }
920 };
921
922 messages.push(ChatMessage::Tool {
923 tool_call_id: tool_result.tool_use_id.to_string(),
924 content,
925 });
926 }
927 }
928
929 let mut content_parts = Vec::new();
930 for content in &message.content {
931 match content {
932 MessageContent::Text(text) | MessageContent::Thinking { text, .. }
933 if !text.is_empty() =>
934 {
935 if let Some(ChatMessagePart::Text { text: text_content }) =
936 content_parts.last_mut()
937 {
938 text_content.push_str(text);
939 } else {
940 content_parts.push(ChatMessagePart::Text {
941 text: text.to_string(),
942 });
943 }
944 }
945 MessageContent::Image(image) if model.supports_vision() => {
946 content_parts.push(ChatMessagePart::Image {
947 image_url: ImageUrl {
948 url: image.to_base64_url(),
949 },
950 });
951 }
952 _ => {}
953 }
954 }
955
956 if !content_parts.is_empty() {
957 messages.push(ChatMessage::User {
958 content: content_parts.into(),
959 });
960 }
961 }
962 Role::Assistant => {
963 let mut tool_calls = Vec::new();
964 for content in &message.content {
965 if let MessageContent::ToolUse(tool_use) = content {
966 tool_calls.push(ToolCall {
967 id: tool_use.id.to_string(),
968 content: ToolCallContent::Function {
969 function: FunctionContent {
970 name: tool_use.name.to_string(),
971 arguments: serde_json::to_string(&tool_use.input)?,
972 thought_signature: tool_use.thought_signature.clone(),
973 },
974 },
975 });
976 }
977 }
978
979 let text_content = {
980 let mut buffer = String::new();
981 for string in message.content.iter().filter_map(|content| match content {
982 MessageContent::Text(text) => Some(text.as_str()),
983 MessageContent::Thinking { .. }
984 | MessageContent::ToolUse(_)
985 | MessageContent::RedactedThinking(_)
986 | MessageContent::ToolResult(_)
987 | MessageContent::Image(_) => None,
988 }) {
989 buffer.push_str(string);
990 }
991
992 buffer
993 };
994
995 // Extract reasoning_opaque and reasoning_text from reasoning_details
996 let (reasoning_opaque, reasoning_text) =
997 if let Some(details) = &message.reasoning_details {
998 let opaque = details
999 .get("reasoning_opaque")
1000 .and_then(|v| v.as_str())
1001 .map(|s| s.to_string());
1002 let text = details
1003 .get("reasoning_text")
1004 .and_then(|v| v.as_str())
1005 .map(|s| s.to_string());
1006 (opaque, text)
1007 } else {
1008 (None, None)
1009 };
1010
1011 messages.push(ChatMessage::Assistant {
1012 content: if text_content.is_empty() {
1013 ChatMessageContent::empty()
1014 } else {
1015 text_content.into()
1016 },
1017 tool_calls,
1018 reasoning_opaque,
1019 reasoning_text,
1020 });
1021 }
1022 Role::System => messages.push(ChatMessage::System {
1023 content: message.string_contents(),
1024 }),
1025 }
1026 }
1027
1028 let tools = request
1029 .tools
1030 .iter()
1031 .map(|tool| Tool::Function {
1032 function: Function {
1033 name: tool.name.clone(),
1034 description: tool.description.clone(),
1035 parameters: tool.input_schema.clone(),
1036 },
1037 })
1038 .collect::<Vec<_>>();
1039
1040 Ok(CopilotChatRequest {
1041 n: 1,
1042 stream: model.uses_streaming(),
1043 temperature: temperature.unwrap_or(0.1),
1044 model: model.id().to_string(),
1045 messages,
1046 tools,
1047 tool_choice: tool_choice.map(|choice| match choice {
1048 LanguageModelToolChoice::Auto => ToolChoice::Auto,
1049 LanguageModelToolChoice::Any => ToolChoice::Required,
1050 LanguageModelToolChoice::None => ToolChoice::None,
1051 }),
1052 thinking_budget: None,
1053 })
1054}
1055
1056fn compute_thinking_budget(
1057 min_budget: Option<u32>,
1058 max_budget: Option<u32>,
1059 max_output_tokens: u32,
1060) -> Option<u32> {
1061 let configured_budget: u32 = 16000;
1062 let min_budget = min_budget.unwrap_or(1024);
1063 let max_budget = max_budget.unwrap_or(max_output_tokens.saturating_sub(1));
1064 let normalized = configured_budget.max(min_budget);
1065 Some(
1066 normalized
1067 .min(max_budget)
1068 .min(max_output_tokens.saturating_sub(1)),
1069 )
1070}
1071
1072fn intent_to_chat_location(intent: Option<CompletionIntent>) -> ChatLocation {
1073 match intent {
1074 Some(CompletionIntent::UserPrompt) => ChatLocation::Agent,
1075 Some(CompletionIntent::Subagent) => ChatLocation::Agent,
1076 Some(CompletionIntent::ToolResults) => ChatLocation::Agent,
1077 Some(CompletionIntent::ThreadSummarization) => ChatLocation::Panel,
1078 Some(CompletionIntent::ThreadContextSummarization) => ChatLocation::Panel,
1079 Some(CompletionIntent::CreateFile) => ChatLocation::Agent,
1080 Some(CompletionIntent::EditFile) => ChatLocation::Agent,
1081 Some(CompletionIntent::InlineAssist) => ChatLocation::Editor,
1082 Some(CompletionIntent::TerminalInlineAssist) => ChatLocation::Terminal,
1083 Some(CompletionIntent::GenerateGitCommitMessage) => ChatLocation::Other,
1084 None => ChatLocation::Panel,
1085 }
1086}
1087
1088fn into_copilot_responses(
1089 model: &CopilotChatModel,
1090 request: LanguageModelRequest,
1091) -> copilot_responses::Request {
1092 use copilot_responses as responses;
1093
1094 let LanguageModelRequest {
1095 thread_id: _,
1096 prompt_id: _,
1097 intent: _,
1098 messages,
1099 tools,
1100 tool_choice,
1101 stop: _,
1102 temperature,
1103 thinking_allowed,
1104 thinking_effort: _,
1105 speed: _,
1106 } = request;
1107
1108 let mut input_items: Vec<responses::ResponseInputItem> = Vec::new();
1109
1110 for message in messages {
1111 match message.role {
1112 Role::User => {
1113 for content in &message.content {
1114 if let MessageContent::ToolResult(tool_result) = content {
1115 let output = match &tool_result.content {
1116 LanguageModelToolResultContent::Text(text) => {
1117 responses::ResponseFunctionOutput::Text(text.to_string())
1118 }
1119 LanguageModelToolResultContent::Image(image) => {
1120 if model.supports_vision() {
1121 responses::ResponseFunctionOutput::Content(vec![
1122 responses::ResponseInputContent::InputImage {
1123 image_url: Some(image.to_base64_url()),
1124 detail: Default::default(),
1125 },
1126 ])
1127 } else {
1128 debug_panic!(
1129 "This should be caught at {} level",
1130 tool_result.tool_name
1131 );
1132 responses::ResponseFunctionOutput::Text(
1133 "[Tool responded with an image, but this model does not support vision]".into(),
1134 )
1135 }
1136 }
1137 };
1138
1139 input_items.push(responses::ResponseInputItem::FunctionCallOutput {
1140 call_id: tool_result.tool_use_id.to_string(),
1141 output,
1142 status: None,
1143 });
1144 }
1145 }
1146
1147 let mut parts: Vec<responses::ResponseInputContent> = Vec::new();
1148 for content in &message.content {
1149 match content {
1150 MessageContent::Text(text) => {
1151 parts.push(responses::ResponseInputContent::InputText {
1152 text: text.clone(),
1153 });
1154 }
1155
1156 MessageContent::Image(image) => {
1157 if model.supports_vision() {
1158 parts.push(responses::ResponseInputContent::InputImage {
1159 image_url: Some(image.to_base64_url()),
1160 detail: Default::default(),
1161 });
1162 }
1163 }
1164 _ => {}
1165 }
1166 }
1167
1168 if !parts.is_empty() {
1169 input_items.push(responses::ResponseInputItem::Message {
1170 role: "user".into(),
1171 content: Some(parts),
1172 status: None,
1173 });
1174 }
1175 }
1176
1177 Role::Assistant => {
1178 for content in &message.content {
1179 if let MessageContent::ToolUse(tool_use) = content {
1180 input_items.push(responses::ResponseInputItem::FunctionCall {
1181 call_id: tool_use.id.to_string(),
1182 name: tool_use.name.to_string(),
1183 arguments: tool_use.raw_input.clone(),
1184 status: None,
1185 thought_signature: tool_use.thought_signature.clone(),
1186 });
1187 }
1188 }
1189
1190 for content in &message.content {
1191 if let MessageContent::RedactedThinking(data) = content {
1192 input_items.push(responses::ResponseInputItem::Reasoning {
1193 id: None,
1194 summary: Vec::new(),
1195 encrypted_content: data.clone(),
1196 });
1197 }
1198 }
1199
1200 let mut parts: Vec<responses::ResponseInputContent> = Vec::new();
1201 for content in &message.content {
1202 match content {
1203 MessageContent::Text(text) => {
1204 parts.push(responses::ResponseInputContent::OutputText {
1205 text: text.clone(),
1206 });
1207 }
1208 MessageContent::Image(_) => {
1209 parts.push(responses::ResponseInputContent::OutputText {
1210 text: "[image omitted]".to_string(),
1211 });
1212 }
1213 _ => {}
1214 }
1215 }
1216
1217 if !parts.is_empty() {
1218 input_items.push(responses::ResponseInputItem::Message {
1219 role: "assistant".into(),
1220 content: Some(parts),
1221 status: Some("completed".into()),
1222 });
1223 }
1224 }
1225
1226 Role::System => {
1227 let mut parts: Vec<responses::ResponseInputContent> = Vec::new();
1228 for content in &message.content {
1229 if let MessageContent::Text(text) = content {
1230 parts.push(responses::ResponseInputContent::InputText {
1231 text: text.clone(),
1232 });
1233 }
1234 }
1235
1236 if !parts.is_empty() {
1237 input_items.push(responses::ResponseInputItem::Message {
1238 role: "system".into(),
1239 content: Some(parts),
1240 status: None,
1241 });
1242 }
1243 }
1244 }
1245 }
1246
1247 let converted_tools: Vec<responses::ToolDefinition> = tools
1248 .into_iter()
1249 .map(|tool| responses::ToolDefinition::Function {
1250 name: tool.name,
1251 description: Some(tool.description),
1252 parameters: Some(tool.input_schema),
1253 strict: None,
1254 })
1255 .collect();
1256
1257 let mapped_tool_choice = tool_choice.map(|choice| match choice {
1258 LanguageModelToolChoice::Auto => responses::ToolChoice::Auto,
1259 LanguageModelToolChoice::Any => responses::ToolChoice::Required,
1260 LanguageModelToolChoice::None => responses::ToolChoice::None,
1261 });
1262
1263 responses::Request {
1264 model: model.id().to_string(),
1265 input: input_items,
1266 stream: model.uses_streaming(),
1267 temperature,
1268 tools: converted_tools,
1269 tool_choice: mapped_tool_choice,
1270 reasoning: if thinking_allowed {
1271 Some(copilot_responses::ReasoningConfig {
1272 effort: copilot_responses::ReasoningEffort::Medium,
1273 summary: Some(copilot_responses::ReasoningSummary::Detailed),
1274 })
1275 } else {
1276 None
1277 },
1278 include: Some(vec![
1279 copilot_responses::ResponseIncludable::ReasoningEncryptedContent,
1280 ]),
1281 store: false,
1282 }
1283}
1284
1285#[cfg(test)]
1286mod tests {
1287 use super::*;
1288 use copilot_chat::responses;
1289 use futures::StreamExt;
1290
1291 fn map_events(events: Vec<responses::StreamEvent>) -> Vec<LanguageModelCompletionEvent> {
1292 futures::executor::block_on(async {
1293 CopilotResponsesEventMapper::new()
1294 .map_stream(Box::pin(futures::stream::iter(events.into_iter().map(Ok))))
1295 .collect::<Vec<_>>()
1296 .await
1297 .into_iter()
1298 .map(Result::unwrap)
1299 .collect()
1300 })
1301 }
1302
1303 #[test]
1304 fn responses_stream_maps_text_and_usage() {
1305 let events = vec![
1306 responses::StreamEvent::OutputItemAdded {
1307 output_index: 0,
1308 sequence_number: None,
1309 item: responses::ResponseOutputItem::Message {
1310 id: "msg_1".into(),
1311 role: "assistant".into(),
1312 content: Some(Vec::new()),
1313 },
1314 },
1315 responses::StreamEvent::OutputTextDelta {
1316 item_id: "msg_1".into(),
1317 output_index: 0,
1318 delta: "Hello".into(),
1319 },
1320 responses::StreamEvent::Completed {
1321 response: responses::Response {
1322 usage: Some(responses::ResponseUsage {
1323 input_tokens: Some(5),
1324 output_tokens: Some(3),
1325 total_tokens: Some(8),
1326 }),
1327 ..Default::default()
1328 },
1329 },
1330 ];
1331
1332 let mapped = map_events(events);
1333 assert!(matches!(
1334 mapped[0],
1335 LanguageModelCompletionEvent::StartMessage { ref message_id } if message_id == "msg_1"
1336 ));
1337 assert!(matches!(
1338 mapped[1],
1339 LanguageModelCompletionEvent::Text(ref text) if text == "Hello"
1340 ));
1341 assert!(matches!(
1342 mapped[2],
1343 LanguageModelCompletionEvent::UsageUpdate(TokenUsage {
1344 input_tokens: 5,
1345 output_tokens: 3,
1346 ..
1347 })
1348 ));
1349 assert!(matches!(
1350 mapped[3],
1351 LanguageModelCompletionEvent::Stop(StopReason::EndTurn)
1352 ));
1353 }
1354
1355 #[test]
1356 fn responses_stream_maps_tool_calls() {
1357 let events = vec![responses::StreamEvent::OutputItemDone {
1358 output_index: 0,
1359 sequence_number: None,
1360 item: responses::ResponseOutputItem::FunctionCall {
1361 id: Some("fn_1".into()),
1362 call_id: "call_1".into(),
1363 name: "do_it".into(),
1364 arguments: "{\"x\":1}".into(),
1365 status: None,
1366 thought_signature: None,
1367 },
1368 }];
1369
1370 let mapped = map_events(events);
1371 assert!(matches!(
1372 mapped[0],
1373 LanguageModelCompletionEvent::ToolUse(ref use_) if use_.id.to_string() == "call_1" && use_.name.as_ref() == "do_it"
1374 ));
1375 assert!(matches!(
1376 mapped[1],
1377 LanguageModelCompletionEvent::Stop(StopReason::ToolUse)
1378 ));
1379 }
1380
1381 #[test]
1382 fn responses_stream_handles_json_parse_error() {
1383 let events = vec![responses::StreamEvent::OutputItemDone {
1384 output_index: 0,
1385 sequence_number: None,
1386 item: responses::ResponseOutputItem::FunctionCall {
1387 id: Some("fn_1".into()),
1388 call_id: "call_1".into(),
1389 name: "do_it".into(),
1390 arguments: "{not json}".into(),
1391 status: None,
1392 thought_signature: None,
1393 },
1394 }];
1395
1396 let mapped = map_events(events);
1397 assert!(matches!(
1398 mapped[0],
1399 LanguageModelCompletionEvent::ToolUseJsonParseError { ref id, ref tool_name, .. }
1400 if id.to_string() == "call_1" && tool_name.as_ref() == "do_it"
1401 ));
1402 assert!(matches!(
1403 mapped[1],
1404 LanguageModelCompletionEvent::Stop(StopReason::ToolUse)
1405 ));
1406 }
1407
1408 #[test]
1409 fn responses_stream_maps_reasoning_summary_and_encrypted_content() {
1410 let events = vec![responses::StreamEvent::OutputItemDone {
1411 output_index: 0,
1412 sequence_number: None,
1413 item: responses::ResponseOutputItem::Reasoning {
1414 id: "r1".into(),
1415 summary: Some(vec![responses::ResponseReasoningItem {
1416 kind: "summary_text".into(),
1417 text: "Chain".into(),
1418 }]),
1419 encrypted_content: Some("ENC".into()),
1420 },
1421 }];
1422
1423 let mapped = map_events(events);
1424 assert!(matches!(
1425 mapped[0],
1426 LanguageModelCompletionEvent::Thinking { ref text, signature: None } if text == "Chain"
1427 ));
1428 assert!(matches!(
1429 mapped[1],
1430 LanguageModelCompletionEvent::RedactedThinking { ref data } if data == "ENC"
1431 ));
1432 }
1433
1434 #[test]
1435 fn responses_stream_handles_incomplete_max_tokens() {
1436 let events = vec![responses::StreamEvent::Incomplete {
1437 response: responses::Response {
1438 usage: Some(responses::ResponseUsage {
1439 input_tokens: Some(10),
1440 output_tokens: Some(0),
1441 total_tokens: Some(10),
1442 }),
1443 incomplete_details: Some(responses::IncompleteDetails {
1444 reason: Some(responses::IncompleteReason::MaxOutputTokens),
1445 }),
1446 ..Default::default()
1447 },
1448 }];
1449
1450 let mapped = map_events(events);
1451 assert!(matches!(
1452 mapped[0],
1453 LanguageModelCompletionEvent::UsageUpdate(TokenUsage {
1454 input_tokens: 10,
1455 output_tokens: 0,
1456 ..
1457 })
1458 ));
1459 assert!(matches!(
1460 mapped[1],
1461 LanguageModelCompletionEvent::Stop(StopReason::MaxTokens)
1462 ));
1463 }
1464
1465 #[test]
1466 fn responses_stream_handles_incomplete_content_filter() {
1467 let events = vec![responses::StreamEvent::Incomplete {
1468 response: responses::Response {
1469 usage: None,
1470 incomplete_details: Some(responses::IncompleteDetails {
1471 reason: Some(responses::IncompleteReason::ContentFilter),
1472 }),
1473 ..Default::default()
1474 },
1475 }];
1476
1477 let mapped = map_events(events);
1478 assert!(matches!(
1479 mapped.last().unwrap(),
1480 LanguageModelCompletionEvent::Stop(StopReason::Refusal)
1481 ));
1482 }
1483
1484 #[test]
1485 fn responses_stream_completed_no_duplicate_after_tool_use() {
1486 let events = vec![
1487 responses::StreamEvent::OutputItemDone {
1488 output_index: 0,
1489 sequence_number: None,
1490 item: responses::ResponseOutputItem::FunctionCall {
1491 id: Some("fn_1".into()),
1492 call_id: "call_1".into(),
1493 name: "do_it".into(),
1494 arguments: "{}".into(),
1495 status: None,
1496 thought_signature: None,
1497 },
1498 },
1499 responses::StreamEvent::Completed {
1500 response: responses::Response::default(),
1501 },
1502 ];
1503
1504 let mapped = map_events(events);
1505
1506 let mut stop_count = 0usize;
1507 let mut saw_tool_use_stop = false;
1508 for event in mapped {
1509 if let LanguageModelCompletionEvent::Stop(reason) = event {
1510 stop_count += 1;
1511 if matches!(reason, StopReason::ToolUse) {
1512 saw_tool_use_stop = true;
1513 }
1514 }
1515 }
1516 assert_eq!(stop_count, 1, "should emit exactly one Stop event");
1517 assert!(saw_tool_use_stop, "Stop reason should be ToolUse");
1518 }
1519
1520 #[test]
1521 fn responses_stream_failed_maps_http_response_error() {
1522 let events = vec![responses::StreamEvent::Failed {
1523 response: responses::Response {
1524 error: Some(responses::ResponseError {
1525 code: "429".into(),
1526 message: "too many requests".into(),
1527 }),
1528 ..Default::default()
1529 },
1530 }];
1531
1532 let mapped_results = futures::executor::block_on(async {
1533 CopilotResponsesEventMapper::new()
1534 .map_stream(Box::pin(futures::stream::iter(events.into_iter().map(Ok))))
1535 .collect::<Vec<_>>()
1536 .await
1537 });
1538
1539 assert_eq!(mapped_results.len(), 1);
1540 match &mapped_results[0] {
1541 Err(LanguageModelCompletionError::HttpResponseError {
1542 status_code,
1543 message,
1544 ..
1545 }) => {
1546 assert_eq!(*status_code, http_client::StatusCode::TOO_MANY_REQUESTS);
1547 assert_eq!(message, "too many requests");
1548 }
1549 other => panic!("expected HttpResponseError, got {:?}", other),
1550 }
1551 }
1552
1553 #[test]
1554 fn chat_completions_stream_maps_reasoning_data() {
1555 use copilot_chat::{
1556 FunctionChunk, ResponseChoice, ResponseDelta, ResponseEvent, Role, ToolCallChunk,
1557 };
1558
1559 let events = vec![
1560 ResponseEvent {
1561 choices: vec![ResponseChoice {
1562 index: Some(0),
1563 finish_reason: None,
1564 delta: Some(ResponseDelta {
1565 content: None,
1566 role: Some(Role::Assistant),
1567 tool_calls: vec![ToolCallChunk {
1568 index: Some(0),
1569 id: Some("call_abc123".to_string()),
1570 function: Some(FunctionChunk {
1571 name: Some("list_directory".to_string()),
1572 arguments: Some("{\"path\":\"test\"}".to_string()),
1573 thought_signature: None,
1574 }),
1575 }],
1576 reasoning_opaque: Some("encrypted_reasoning_token_xyz".to_string()),
1577 reasoning_text: Some("Let me check the directory".to_string()),
1578 }),
1579 message: None,
1580 }],
1581 id: "chatcmpl-123".to_string(),
1582 usage: None,
1583 },
1584 ResponseEvent {
1585 choices: vec![ResponseChoice {
1586 index: Some(0),
1587 finish_reason: Some("tool_calls".to_string()),
1588 delta: Some(ResponseDelta {
1589 content: None,
1590 role: None,
1591 tool_calls: vec![],
1592 reasoning_opaque: None,
1593 reasoning_text: None,
1594 }),
1595 message: None,
1596 }],
1597 id: "chatcmpl-123".to_string(),
1598 usage: None,
1599 },
1600 ];
1601
1602 let mapped = futures::executor::block_on(async {
1603 map_to_language_model_completion_events(
1604 Box::pin(futures::stream::iter(events.into_iter().map(Ok))),
1605 true,
1606 )
1607 .collect::<Vec<_>>()
1608 .await
1609 });
1610
1611 let mut has_reasoning_details = false;
1612 let mut has_tool_use = false;
1613 let mut reasoning_opaque_value: Option<String> = None;
1614 let mut reasoning_text_value: Option<String> = None;
1615
1616 for event_result in mapped {
1617 match event_result {
1618 Ok(LanguageModelCompletionEvent::ReasoningDetails(details)) => {
1619 has_reasoning_details = true;
1620 reasoning_opaque_value = details
1621 .get("reasoning_opaque")
1622 .and_then(|v| v.as_str())
1623 .map(|s| s.to_string());
1624 reasoning_text_value = details
1625 .get("reasoning_text")
1626 .and_then(|v| v.as_str())
1627 .map(|s| s.to_string());
1628 }
1629 Ok(LanguageModelCompletionEvent::ToolUse(tool_use)) => {
1630 has_tool_use = true;
1631 assert_eq!(tool_use.id.to_string(), "call_abc123");
1632 assert_eq!(tool_use.name.as_ref(), "list_directory");
1633 }
1634 _ => {}
1635 }
1636 }
1637
1638 assert!(
1639 has_reasoning_details,
1640 "Should emit ReasoningDetails event for Gemini 3 reasoning"
1641 );
1642 assert!(has_tool_use, "Should emit ToolUse event");
1643 assert_eq!(
1644 reasoning_opaque_value,
1645 Some("encrypted_reasoning_token_xyz".to_string()),
1646 "Should capture reasoning_opaque"
1647 );
1648 assert_eq!(
1649 reasoning_text_value,
1650 Some("Let me check the directory".to_string()),
1651 "Should capture reasoning_text"
1652 );
1653 }
1654}