From 1852ed5d9dc84ba1b7d944b92e98c9bd9aad56e4 Mon Sep 17 00:00:00 2001 From: cunninghamcard-bit Date: Tue, 20 Oct 2026 08:12:47 +0000 Subject: [PATCH 1/7] fix(xai): a streaming transcription connect failure errors the stream @ai-sdk/xai 5.0.12 opens the STT WebSocket inside the returned stream (xai-transcription-model.ts:276-358), so doStream resolves and a connect failure errors the stream, cancelling the caller's audio. aimux connected before returning and failed do_stream itself. The connect now runs first inside the stream; on failure the stream yields the error and ends, which drops the audio. Co-Authored-By: Claude Opus 5.5 (1M context) Claude-Session: https://claude.ai/code/session_01RoxcKU4zBD64Lri3e4Lrq5 --- CHANGELOG.md | 4 ++++ aimux-providers/src/xai/transcription.rs | 14 ++++++++++--- .../tests/xai_transcription_test.rs | 21 +++++++++++++++++++ 3 files changed, 36 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index f8a79db29..ff7c1e1ee 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1180,6 +1180,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Fixed +- xAI streaming transcription opens its WebSocket inside the stream, as + `@ai-sdk/xai` 5.0.12 does: a connect failure no longer fails `do_stream` + but arrives as the stream's error, and the caller's audio stream is + dropped. - Google Vertex checks that a location going into a host name is one DNS label (letters, digits and hyphens), as the AI SDK does: the Gemini and Claude models, MaaS and the Speech-to-Text models (whose `region` provider diff --git a/aimux-providers/src/xai/transcription.rs b/aimux-providers/src/xai/transcription.rs index dfa0cdc13..3fd14c656 100644 --- a/aimux-providers/src/xai/transcription.rs +++ b/aimux-providers/src/xai/transcription.rs @@ -335,13 +335,12 @@ mod stream { &xai, )?; let ws_url = url.to_string(); - let mut ws = ws_connect(&WebSocketRequest::for_transcription( + let request = WebSocketRequest::for_transcription( ws_url.clone(), exchange.headers(), &options, model.web_socket.clone(), - )) - .await?; + ); let expected_done = if xai.multichannel == Some(true) { xai.channels.unwrap_or(1) as usize @@ -353,6 +352,15 @@ mod stream { let mut audio = options.audio; let stream = async_stream::stream! { + // Upstream connects inside the stream: a connect failure errors + // the stream and drops the caller's audio. + let mut ws = match ws_connect(&request).await { + Ok(ws) => ws, + Err(e) => { + yield Err(e); + return; + } + }; let mut created = false; let mut audio_done = false; let mut done_texts: BTreeMap = BTreeMap::new(); diff --git a/aimux-providers/tests/xai_transcription_test.rs b/aimux-providers/tests/xai_transcription_test.rs index 74b27979b..b1d99814e 100644 --- a/aimux-providers/tests/xai_transcription_test.rs +++ b/aimux-providers/tests/xai_transcription_test.rs @@ -760,3 +760,24 @@ async fn url_audio_is_refused_before_any_request() { assert_eq!(error.to_string(), AudioInput::url_error().to_string()); assert!(server.received_requests().await.unwrap().is_empty()); } + +/// A refused connection errors the stream: upstream connects inside the +/// stream, so `doStream` resolves and the stream carries the failure. +#[tokio::test] +async fn a_connect_failure_errors_the_stream() { + let listener = TcpListener::bind("127.0.0.1:0").await.unwrap(); + let base_url = format!("http://127.0.0.1:{}", listener.local_addr().unwrap().port()); + drop(listener); + + let result = model(&base_url, None) + .do_stream(stream_options(None, "audio/pcm", Some(16000))) + .expect("the model has do_stream") + .await + .expect("do_stream resolves; the connect failure arrives on the stream"); + let parts = tokio::time::timeout(Duration::from_secs(10), result.stream.collect::>()) + .await + .expect("stream timed out"); + + assert_eq!(parts.len(), 1, "{parts:?}"); + assert!(parts[0].is_err(), "{parts:?}"); +} From c2c702717fe6451302f73697d67267db7e684a09 Mon Sep 17 00:00:00 2001 From: cunninghamcard-bit Date: Tue, 20 Oct 2026 08:16:11 +0000 Subject: [PATCH 2/7] fix(xai): dropping a streaming transcription closes the socket In @ai-sdk/xai 5.0.12, cancelling the transcription stream runs cleanup, which calls connection.close() (xai-transcription-model.ts:290-298, :459-462). aimux only sent a close frame after transcript.done or an error event; a stream dropped earlier released the transport without one. The socket now lives in a guard that, when dropped before it was closed, sends the close frame on the runtime. Co-Authored-By: Claude Opus 5.5 (1M context) Claude-Session: https://claude.ai/code/session_01RoxcKU4zBD64Lri3e4Lrq5 --- CHANGELOG.md | 3 ++ aimux-providers/src/xai/transcription.rs | 40 ++++++++++++++- .../tests/xai_transcription_test.rs | 49 +++++++++++++++++++ 3 files changed, 90 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index ff7c1e1ee..3912ed030 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1184,6 +1184,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 `@ai-sdk/xai` 5.0.12 does: a connect failure no longer fails `do_stream` but arrives as the stream's error, and the caller's audio stream is dropped. +- Dropping an xAI streaming transcription before it finishes sends the + WebSocket close frame, as cancelling the stream does in `@ai-sdk/xai` + 5.0.12; before, only the transport was released. - Google Vertex checks that a location going into a host name is one DNS label (letters, digits and hyphens), as the AI SDK does: the Gemini and Claude models, MaaS and the Speech-to-Text models (whose `region` provider diff --git a/aimux-providers/src/xai/transcription.rs b/aimux-providers/src/xai/transcription.rs index 3fd14c656..50a13f623 100644 --- a/aimux-providers/src/xai/transcription.rs +++ b/aimux-providers/src/xai/transcription.rs @@ -229,11 +229,47 @@ mod stream { AudioChunk, TranscriptionRequest, TranscriptionStreamOptions, TranscriptionStreamPart, TranscriptionStreamResult, }; - use aimux_provider_utils::ws::{WebSocketRequest, WsMessage, ws_connect}; + use aimux_provider_utils::ws::{WebSocketRequest, WsConnection, WsMessage, ws_connect}; use futures::StreamExt; use super::*; + /// The open socket. Dropping it before [`Socket::close`] sends the close + /// frame on the runtime, as upstream's `cancel` closes the connection. + struct Socket(Option); + + impl Socket { + async fn close(&mut self) { + if let Some(mut ws) = self.0.take() { + ws.close().await; + } + } + } + + impl std::ops::Deref for Socket { + type Target = WsConnection; + + fn deref(&self) -> &WsConnection { + self.0.as_ref().expect("the socket is open until closed") + } + } + + impl std::ops::DerefMut for Socket { + fn deref_mut(&mut self) -> &mut WsConnection { + self.0.as_mut().expect("the socket is open until closed") + } + } + + impl Drop for Socket { + fn drop(&mut self) { + if let (Some(mut ws), Ok(runtime)) = + (self.0.take(), tokio::runtime::Handle::try_current()) + { + runtime.spawn(async move { ws.close().await }); + } + } + } + fn append( query: &mut url::form_urlencoded::Serializer<'_, T>, key: &str, @@ -355,7 +391,7 @@ mod stream { // Upstream connects inside the stream: a connect failure errors // the stream and drops the caller's audio. let mut ws = match ws_connect(&request).await { - Ok(ws) => ws, + Ok(ws) => Socket(Some(ws)), Err(e) => { yield Err(e); return; diff --git a/aimux-providers/tests/xai_transcription_test.rs b/aimux-providers/tests/xai_transcription_test.rs index b1d99814e..ccdf61424 100644 --- a/aimux-providers/tests/xai_transcription_test.rs +++ b/aimux-providers/tests/xai_transcription_test.rs @@ -781,3 +781,52 @@ async fn a_connect_failure_errors_the_stream() { assert_eq!(parts.len(), 1, "{parts:?}"); assert!(parts[0].is_err(), "{parts:?}"); } + +/// Dropping the stream closes the socket: upstream's `cancel` calls +/// `connection.close()`. +#[tokio::test] +async fn dropping_the_stream_closes_the_socket() { + let listener = TcpListener::bind("127.0.0.1:0").await.unwrap(); + let base_url = format!("http://127.0.0.1:{}", listener.local_addr().unwrap().port()); + let (closed_tx, closed_rx) = tokio::sync::oneshot::channel(); + tokio::spawn(async move { + let (stream, _) = listener.accept().await.unwrap(); + let mut ws = tokio_tungstenite::accept_async(stream).await.unwrap(); + ws.send(Message::Text( + json!({"type": "transcript.created"}).to_string(), + )) + .await + .unwrap(); + let mut closed = false; + while let Some(Ok(message)) = ws.next().await { + if message.is_close() { + closed = true; + break; + } + } + let _ = closed_tx.send(closed); + }); + + let mut options = stream_options(None, "audio/pcm", Some(16000)); + options.audio = Box::pin(futures::stream::pending()); + let mut result = model(&base_url, None) + .do_stream(options) + .expect("the model has do_stream") + .await + .expect("do_stream"); + let first = result.stream.next().await; + assert!( + matches!(first, Some(Ok(TranscriptionStreamPart::StreamStart { .. }))), + "{first:?}" + ); + drop(result); + + let closed = tokio::time::timeout(Duration::from_secs(5), closed_rx) + .await + .expect("the server saw the socket end") + .unwrap(); + assert!( + closed, + "no close frame was sent when the stream was dropped" + ); +} From e6084f08458c03d0616e126ed638d5938fd61f36 Mon Sep 17 00:00:00 2001 From: cunninghamcard-bit Date: Tue, 20 Oct 2026 08:24:59 +0000 Subject: [PATCH 3/7] fix(xai): connect while building the stream, as upstream does Upstream's ReadableStream start runs when doStream builds the stream, so the WebSocket is opened before doStream resolves even if the stream is never read. Connecting on the stream's first poll would skip the connection for a stream dropped unread; connect in do_stream instead and hand the outcome to the stream, which still yields a failure as its error. Co-Authored-By: Claude Opus 5.5 (1M context) Claude-Session: https://claude.ai/code/session_01RoxcKU4zBD64Lri3e4Lrq5 --- aimux-providers/src/xai/transcription.rs | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/aimux-providers/src/xai/transcription.rs b/aimux-providers/src/xai/transcription.rs index 3fd14c656..c7f342ed0 100644 --- a/aimux-providers/src/xai/transcription.rs +++ b/aimux-providers/src/xai/transcription.rs @@ -335,12 +335,15 @@ mod stream { &xai, )?; let ws_url = url.to_string(); - let request = WebSocketRequest::for_transcription( + // Upstream opens the socket while it builds the stream and reports a + // connect failure on the stream, not from doStream. + let connected = ws_connect(&WebSocketRequest::for_transcription( ws_url.clone(), exchange.headers(), &options, model.web_socket.clone(), - ); + )) + .await; let expected_done = if xai.multichannel == Some(true) { xai.channels.unwrap_or(1) as usize @@ -352,9 +355,7 @@ mod stream { let mut audio = options.audio; let stream = async_stream::stream! { - // Upstream connects inside the stream: a connect failure errors - // the stream and drops the caller's audio. - let mut ws = match ws_connect(&request).await { + let mut ws = match connected { Ok(ws) => ws, Err(e) => { yield Err(e); From 1b7c65386a92180aa9e04720d20f13800457a483 Mon Sep 17 00:00:00 2001 From: cunninghamcard-bit Date: Tue, 20 Oct 2026 08:29:28 +0000 Subject: [PATCH 4/7] test(xai): mark the existing xAI tests with their upstream cases Tests that match an @ai-sdk/xai 5.0.12 case one to one get its marker and sit in describe-level mods; tests that cover a case only in part keep a partial: doc line naming the full port in src/xai/tests/. Bodies are unchanged. Co-Authored-By: Claude Opus 5.5 (1M context) Claude-Session: https://claude.ai/code/session_01RoxcKU4zBD64Lri3e4Lrq5 --- aimux-providers/tests/xai_speech_test.rs | 541 +++++++++--------- .../tests/xai_transcription_test.rs | 442 +++++++------- .../tests/xai_upstream_e2e_test.rs | 14 +- aimux-providers/tests/xai_video_test.rs | 508 ++++++++-------- 4 files changed, 734 insertions(+), 771 deletions(-) diff --git a/aimux-providers/tests/xai_speech_test.rs b/aimux-providers/tests/xai_speech_test.rs index b9fe2e820..dc1b18a55 100644 --- a/aimux-providers/tests/xai_speech_test.rs +++ b/aimux-providers/tests/xai_speech_test.rs @@ -6,9 +6,8 @@ //! the `with_timestamps` JSON envelope) on `POST /tts`, calls `do_generate` //! and asserts on the request the server saw and on the result. //! -//! Not ported: the `specificationVersion` check (a TS type tag with no Rust -//! counterpart) and the `_internal.currentDate` injection case (the Rust model -//! has no clock seam; the real-date case covers the timestamp). +//! The upstream cases this file does not cover, or covers only in part, are +//! ported in full in `src/xai/tests/speech_model.rs`. use std::collections::HashMap; @@ -103,270 +102,313 @@ fn is_unsupported(warnings: &[Warning], expected: &str) -> bool { .any(|w| matches!(w, Warning::Unsupported { feature, .. } if feature == expected)) } -/// TS: "should expose correct provider and model information" -#[tokio::test] -async fn should_expose_correct_provider_and_model_information() { - let model = speech_model(&MockServer::start().await, None); - assert_eq!(model.provider(), "xai.speech"); - assert_eq!(model.model_id(), ""); -} +mod do_generate { + use super::*; -/// TS: "should send text with xAI defaults" -#[tokio::test] -async fn should_send_text_with_xai_defaults() { - let server = server_with(audio_response("audio/mpeg", &[])).await; - let result = speech_model(&server, None) - .do_generate(&options("Hello from the AI SDK!")) - .await - .unwrap(); + /// upstream: @ai-sdk/xai src/xai-speech-model.test.ts › doGenerate › should send text with xAI defaults + #[tokio::test] + async fn should_send_text_with_xai_defaults() { + let server = server_with(audio_response("audio/mpeg", &[])).await; + let result = speech_model(&server, None) + .do_generate(&options("Hello from the AI SDK!")) + .await + .unwrap(); - let expected = json!({ - "text": "Hello from the AI SDK!", - "voice_id": "eve", - "language": "auto", - "output_format": { "codec": "mp3" }, - }); - assert_eq!(request_body(&server).await, expected); - assert_eq!(result.request.unwrap().body, Some(expected)); - let requests = server.received_requests().await.unwrap(); - assert_eq!(requests[0].method.as_str(), "POST"); - assert_eq!(requests[0].url.path(), "/tts"); -} + let expected = json!({ + "text": "Hello from the AI SDK!", + "voice_id": "eve", + "language": "auto", + "output_format": { "codec": "mp3" }, + }); + assert_eq!(request_body(&server).await, expected); + assert_eq!(result.request.unwrap().body, Some(expected)); + let requests = server.received_requests().await.unwrap(); + assert_eq!(requests[0].method.as_str(), "POST"); + assert_eq!(requests[0].url.path(), "/tts"); + } -/// TS: "should pass standard speech options" -#[tokio::test] -async fn should_pass_standard_speech_options() { - let server = server_with(audio_response("audio/wav", &[])).await; - let mut opts = options("Hello from the AI SDK!"); - opts.voice = Some("ara".into()); - opts.language = Some("en".into()); - opts.output_format = Some("wav".into()); - opts.speed = Some(1.2); - speech_model(&server, None) - .do_generate(&opts) - .await - .unwrap(); + /// upstream: @ai-sdk/xai src/xai-speech-model.test.ts › doGenerate › should pass standard speech options + #[tokio::test] + async fn should_pass_standard_speech_options() { + let server = server_with(audio_response("audio/wav", &[])).await; + let mut opts = options("Hello from the AI SDK!"); + opts.voice = Some("ara".into()); + opts.language = Some("en".into()); + opts.output_format = Some("wav".into()); + opts.speed = Some(1.2); + speech_model(&server, None) + .do_generate(&opts) + .await + .unwrap(); - assert_eq!( - request_body(&server).await, - json!({ - "text": "Hello from the AI SDK!", - "voice_id": "ara", - "language": "en", - "output_format": { "codec": "wav" }, - "speed": 1.2, - }) - ); -} + assert_eq!( + request_body(&server).await, + json!({ + "text": "Hello from the AI SDK!", + "voice_id": "ara", + "language": "en", + "output_format": { "codec": "wav" }, + "speed": 1.2, + }) + ); + } -/// TS: "should accept the %s output format" -#[tokio::test] -async fn should_accept_each_output_format() { - for format in ["mp3", "wav", "pcm", "mulaw", "alaw"] { + /// upstream: @ai-sdk/xai src/xai-speech-model.test.ts › doGenerate › should accept the %s output format + #[tokio::test] + async fn should_accept_each_output_format() { + for format in ["mp3", "wav", "pcm", "mulaw", "alaw"] { + let server = server_with(audio_response("audio/mpeg", &[])).await; + let mut opts = options("Hello from the AI SDK!"); + opts.output_format = Some(format.into()); + let result = speech_model(&server, None) + .do_generate(&opts) + .await + .unwrap(); + + assert_eq!( + request_body(&server).await["output_format"]["codec"], + format + ); + assert!(result.warnings.is_empty(), "{format}"); + } + } + + /// upstream: @ai-sdk/xai src/xai-speech-model.test.ts › doGenerate › should map provider options onto xAI request fields + #[tokio::test] + async fn should_map_provider_options_onto_xai_request_fields() { let server = server_with(audio_response("audio/mpeg", &[])).await; - let mut opts = options("Hello from the AI SDK!"); - opts.output_format = Some(format.into()); + let opts = with_xai( + options("Hello from the AI SDK!"), + json!({ + "sampleRate": 44100, + "bitRate": 192000, + "optimizeStreamingLatency": 1, + "textNormalization": true, + }), + ); + speech_model(&server, None) + .do_generate(&opts) + .await + .unwrap(); + + let body = request_body(&server).await; + assert_eq!( + body["output_format"], + json!({ "codec": "mp3", "sample_rate": 44100, "bit_rate": 192000 }) + ); + assert_eq!(body["optimize_streaming_latency"], 1); + assert_eq!(body["text_normalization"], true); + } + + /// upstream: @ai-sdk/xai src/xai-speech-model.test.ts › doGenerate › should pass withTimestamps and replace provider options + #[tokio::test] + async fn should_pass_with_timestamps_and_replace_provider_options() { + let server = server_with(timestamps_response(&[])).await; + let opts = with_xai( + options("Hello from the AI SDK!"), + json!({ "withTimestamps": true, "replace": { "nginx": "/ˈɛndʒɪn ˈɛks/" } }), + ); + speech_model(&server, None) + .do_generate(&opts) + .await + .unwrap(); + + let body = request_body(&server).await; + assert_eq!(body["with_timestamps"], true); + assert_eq!(body["replace"], json!({ "nginx": "/ˈɛndʒɪn ˈɛks/" })); + } + + /// upstream: @ai-sdk/xai src/xai-speech-model.test.ts › doGenerate › should decode the with_timestamps envelope and extract provider metadata + #[tokio::test] + async fn should_decode_the_with_timestamps_envelope_and_extract_provider_metadata() { + let trace = "993675dc-8ea6-4f54-b4ad-a59ac2615026"; + let server = server_with(timestamps_response(&[("x-trace-id", trace)])).await; + let opts = with_xai(options("Hi"), json!({ "withTimestamps": true })); let result = speech_model(&server, None) .do_generate(&opts) .await .unwrap(); + assert!(matches!(&result.audio, AudioData::Binary(audio) if audio == &AUDIO)); assert_eq!( - request_body(&server).await["output_format"]["codec"], - format + xai_metadata(&result), + json!({ + "traceId": trace, + "duration": 1.19, + "contentType": "audio/mpeg", + "audioTimestamps": { + "graphChars": ["H", "i"], + "graphTimes": [[0.04, 0.06], [0.06, 0.1]], + }, + }) ); - assert!(result.warnings.is_empty(), "{format}"); + assert_eq!(result.response.body.unwrap()["audio"], "AQIDBA=="); } -} -/// TS: "should map provider options onto xAI request fields" -#[tokio::test] -async fn should_map_provider_options_onto_xai_request_fields() { - let server = server_with(audio_response("audio/mpeg", &[])).await; - let opts = with_xai( - options("Hello from the AI SDK!"), - json!({ - "sampleRate": 44100, - "bitRate": 192000, - "optimizeStreamingLatency": 1, - "textNormalization": true, - }), - ); - speech_model(&server, None) - .do_generate(&opts) - .await - .unwrap(); + /// upstream: @ai-sdk/xai src/xai-speech-model.test.ts › doGenerate › should extract the trace id from binary responses + #[tokio::test] + async fn should_extract_the_trace_id_from_binary_responses() { + let trace = "06e3dab5-e3ba-4c6b-83a6-1e9ea11d78af"; + let server = server_with(audio_response("audio/mpeg", &[("x-trace-id", trace)])).await; + let result = speech_model(&server, None) + .do_generate(&options("Hello from the AI SDK!")) + .await + .unwrap(); - let body = request_body(&server).await; - assert_eq!( - body["output_format"], - json!({ "codec": "mp3", "sample_rate": 44100, "bit_rate": 192000 }) - ); - assert_eq!(body["optimize_streaming_latency"], 1); - assert_eq!(body["text_normalization"], true); -} + assert_eq!(xai_metadata(&result), json!({ "traceId": trace })); + } -/// TS: "should pass withTimestamps and replace provider options" -#[tokio::test] -async fn should_pass_with_timestamps_and_replace_provider_options() { - let server = server_with(timestamps_response(&[])).await; - let opts = with_xai( - options("Hello from the AI SDK!"), - json!({ "withTimestamps": true, "replace": { "nginx": "/ˈɛndʒɪn ˈɛks/" } }), - ); - speech_model(&server, None) - .do_generate(&opts) - .await - .unwrap(); + /// upstream: @ai-sdk/xai src/xai-speech-model.test.ts › doGenerate › should return empty provider metadata when xAI headers are absent + #[tokio::test] + async fn should_return_empty_provider_metadata_when_xai_headers_are_absent() { + let server = server_with(audio_response("audio/mpeg", &[])).await; + let result = speech_model(&server, None) + .do_generate(&options("Hello from the AI SDK!")) + .await + .unwrap(); - let body = request_body(&server).await; - assert_eq!(body["with_timestamps"], true); - assert_eq!(body["replace"], json!({ "nginx": "/ˈɛndʒɪn ˈɛks/" })); -} + assert_eq!(xai_metadata(&result), json!({})); + } -/// TS: "should decode the with_timestamps envelope and extract provider metadata" -#[tokio::test] -async fn should_decode_the_with_timestamps_envelope_and_extract_provider_metadata() { - let trace = "993675dc-8ea6-4f54-b4ad-a59ac2615026"; - let server = server_with(timestamps_response(&[("x-trace-id", trace)])).await; - let opts = with_xai(options("Hi"), json!({ "withTimestamps": true })); - let result = speech_model(&server, None) - .do_generate(&opts) - .await - .unwrap(); + /// upstream: @ai-sdk/xai src/xai-speech-model.test.ts › doGenerate › should warn and use mp3 for unsupported output formats + #[tokio::test] + async fn should_warn_and_use_mp3_for_unsupported_output_formats() { + let server = server_with(audio_response("audio/mpeg", &[])).await; + let mut opts = options("Hello from the AI SDK!"); + opts.output_format = Some("flac".into()); + let result = speech_model(&server, None) + .do_generate(&opts) + .await + .unwrap(); - assert!(matches!(&result.audio, AudioData::Binary(audio) if audio == &AUDIO)); - assert_eq!( - xai_metadata(&result), - json!({ - "traceId": trace, - "duration": 1.19, - "contentType": "audio/mpeg", - "audioTimestamps": { - "graphChars": ["H", "i"], - "graphTimes": [[0.04, 0.06], [0.06, 0.1]], - }, - }) - ); - assert_eq!(result.response.body.unwrap()["audio"], "AQIDBA=="); -} + assert_eq!(request_body(&server).await["output_format"]["codec"], "mp3"); + assert!(is_unsupported(&result.warnings, "outputFormat")); + } -/// TS: "should extract the trace id from binary responses" -#[tokio::test] -async fn should_extract_the_trace_id_from_binary_responses() { - let trace = "06e3dab5-e3ba-4c6b-83a6-1e9ea11d78af"; - let server = server_with(audio_response("audio/mpeg", &[("x-trace-id", trace)])).await; - let result = speech_model(&server, None) - .do_generate(&options("Hello from the AI SDK!")) - .await - .unwrap(); + /// upstream: @ai-sdk/xai src/xai-speech-model.test.ts › doGenerate › should warn and ignore bitRate for non-mp3 output + #[tokio::test] + async fn should_warn_and_ignore_bit_rate_for_non_mp3_output() { + let server = server_with(audio_response("audio/wav", &[])).await; + let mut opts = with_xai( + options("Hello from the AI SDK!"), + json!({ "bitRate": 192000 }), + ); + opts.output_format = Some("wav".into()); + let result = speech_model(&server, None) + .do_generate(&opts) + .await + .unwrap(); - assert_eq!(xai_metadata(&result), json!({ "traceId": trace })); -} + assert_eq!( + request_body(&server).await["output_format"], + json!({ "codec": "wav" }) + ); + assert!(is_unsupported(&result.warnings, "providerOptions")); + } -/// TS: "should return empty provider metadata when xAI headers are absent" -#[tokio::test] -async fn should_return_empty_provider_metadata_when_xai_headers_are_absent() { - let server = server_with(audio_response("audio/mpeg", &[])).await; - let result = speech_model(&server, None) - .do_generate(&options("Hello from the AI SDK!")) - .await - .unwrap(); + /// upstream: @ai-sdk/xai src/xai-speech-model.test.ts › doGenerate › should warn when instructions are provided + #[tokio::test] + async fn should_warn_when_instructions_are_provided() { + let server = server_with(audio_response("audio/mpeg", &[])).await; + let mut opts = options("Hello from the AI SDK!"); + opts.instructions = Some("Speak cheerfully".into()); + let result = speech_model(&server, None) + .do_generate(&opts) + .await + .unwrap(); - assert_eq!(xai_metadata(&result), json!({})); -} + assert!(is_unsupported(&result.warnings, "instructions")); + } -/// TS: "should warn and use mp3 for unsupported output formats" -#[tokio::test] -async fn should_warn_and_use_mp3_for_unsupported_output_formats() { - let server = server_with(audio_response("audio/mpeg", &[])).await; - let mut opts = options("Hello from the AI SDK!"); - opts.output_format = Some("flac".into()); - let result = speech_model(&server, None) - .do_generate(&opts) - .await - .unwrap(); + /// upstream: @ai-sdk/xai src/xai-speech-model.test.ts › doGenerate › should pass headers and the xAI user agent + #[tokio::test] + async fn should_pass_headers_and_the_xai_user_agent() { + let server = server_with(audio_response("audio/mpeg", &[])).await; + let provider_headers = HashMap::from([( + "Custom-Provider-Header".to_string(), + Some("provider-header-value".to_string()), + )]); + let mut opts = options("Hello from the AI SDK!"); + opts.headers = Some(HashMap::from([( + "Custom-Request-Header".to_string(), + "request-header-value".to_string(), + )])); + speech_model(&server, Some(provider_headers)) + .do_generate(&opts) + .await + .unwrap(); - assert_eq!(request_body(&server).await["output_format"]["codec"], "mp3"); - assert!(is_unsupported(&result.warnings, "outputFormat")); -} + let requests = server.received_requests().await.unwrap(); + let header = |name: &str| requests[0].headers.get(name).unwrap().to_str().unwrap(); + assert_eq!(header("authorization"), "Bearer test-key"); + assert_eq!(header("content-type"), "application/json"); + assert_eq!(header("custom-provider-header"), "provider-header-value"); + assert_eq!(header("custom-request-header"), "request-header-value"); + assert!(header("user-agent").contains("ai-sdk-xai/5.0.12")); + } -/// TS: "should warn and ignore bitRate for non-mp3 output" -#[tokio::test] -async fn should_warn_and_ignore_bit_rate_for_non_mp3_output() { - let server = server_with(audio_response("audio/wav", &[])).await; - let mut opts = with_xai( - options("Hello from the AI SDK!"), - json!({ "bitRate": 192000 }), - ); - opts.output_format = Some("wav".into()); - let result = speech_model(&server, None) - .do_generate(&opts) - .await - .unwrap(); + /// upstream: @ai-sdk/xai src/xai-speech-model.test.ts › doGenerate › should return binary audio data + #[tokio::test] + async fn should_return_binary_audio_data() { + let server = server_with(audio_response("audio/mpeg", &[])).await; + let result = speech_model(&server, None) + .do_generate(&options("Hello from the AI SDK!")) + .await + .unwrap(); - assert_eq!( - request_body(&server).await["output_format"], - json!({ "codec": "wav" }) - ); - assert!(is_unsupported(&result.warnings, "providerOptions")); -} + assert!(matches!(&result.audio, AudioData::Binary(audio) if audio == &AUDIO)); + assert!(result.warnings.is_empty()); + } -/// TS: "should warn when instructions are provided" -#[tokio::test] -async fn should_warn_when_instructions_are_provided() { - let server = server_with(audio_response("audio/mpeg", &[])).await; - let mut opts = options("Hello from the AI SDK!"); - opts.instructions = Some("Speak cheerfully".into()); - let result = speech_model(&server, None) - .do_generate(&opts) - .await - .unwrap(); + /// upstream: @ai-sdk/xai src/xai-speech-model.test.ts › doGenerate › should handle API errors + #[tokio::test] + async fn should_handle_api_errors() { + let server = server_with( + ResponseTemplate::new(400).set_body_string( + json!({ "error": { "message": "Invalid text", "type": "invalid_request_error" } }) + .to_string(), + ), + ) + .await; + let error = speech_model(&server, None) + .do_generate(&options("Hello from the AI SDK!")) + .await + .unwrap_err(); - assert!(is_unsupported(&result.warnings, "instructions")); -} + let AiMuxError::ApiCall(error) = error else { + panic!("expected an ApiCall error, got {error:?}"); + }; + assert_eq!(error.message, "Invalid text"); + assert_eq!(error.status_code, Some(400)); + } -/// TS: "should pass headers and the xAI user agent" -#[tokio::test] -async fn should_pass_headers_and_the_xai_user_agent() { - let server = server_with(audio_response("audio/mpeg", &[])).await; - let provider_headers = HashMap::from([( - "Custom-Provider-Header".to_string(), - Some("provider-header-value".to_string()), - )]); - let mut opts = options("Hello from the AI SDK!"); - opts.headers = Some(HashMap::from([( - "Custom-Request-Header".to_string(), - "request-header-value".to_string(), - )])); - speech_model(&server, Some(provider_headers)) - .do_generate(&opts) - .await - .unwrap(); + /// upstream: @ai-sdk/xai src/xai-speech-model.test.ts › doGenerate › should use the real date when no custom date provider is specified + #[tokio::test] + async fn should_use_the_real_date_when_no_custom_date_provider_is_specified() { + let server = server_with(audio_response("audio/mpeg", &[])).await; + let before = chrono::Utc::now().timestamp_millis(); + let result = speech_model(&server, None) + .do_generate(&options("Hello from the AI SDK!")) + .await + .unwrap(); + let after = chrono::Utc::now().timestamp_millis(); - let requests = server.received_requests().await.unwrap(); - let header = |name: &str| requests[0].headers.get(name).unwrap().to_str().unwrap(); - assert_eq!(header("authorization"), "Bearer test-key"); - assert_eq!(header("content-type"), "application/json"); - assert_eq!(header("custom-provider-header"), "provider-header-value"); - assert_eq!(header("custom-request-header"), "request-header-value"); - assert!(header("user-agent").contains("ai-sdk-xai/5.0.12")); + // A `Date`: millisecond precision. + let timestamp = result.response.timestamp.unwrap().unix_millis(); + assert!(before <= timestamp && timestamp <= after); + assert_eq!(result.response.model_id.as_deref(), Some("")); + } } -/// TS: "should return binary audio data" +/// Checks the provider id and the empty model id; specificationVersion is not checked. partial: should expose correct provider and model information; full port in src/xai/tests/ #[tokio::test] -async fn should_return_binary_audio_data() { - let server = server_with(audio_response("audio/mpeg", &[])).await; - let result = speech_model(&server, None) - .do_generate(&options("Hello from the AI SDK!")) - .await - .unwrap(); - - assert!(matches!(&result.audio, AudioData::Binary(audio) if audio == &AUDIO)); - assert!(result.warnings.is_empty()); +async fn should_expose_correct_provider_and_model_information() { + let model = speech_model(&MockServer::start().await, None); + assert_eq!(model.provider(), "xai.speech"); + assert_eq!(model.model_id(), ""); } -/// TS: "should include response timestamp, model id, and headers" +/// Checks the response model id and the x-request-id header; the timestamp is only checked as present. partial: should include response timestamp, model id, and headers; full port in src/xai/tests/ #[tokio::test] async fn should_include_response_timestamp_model_id_and_headers() { let server = server_with(audio_response( @@ -391,42 +433,3 @@ async fn should_include_response_timestamp_model_id_and_headers() { ); assert!(result.response.timestamp.is_some()); } - -/// TS: "should handle API errors" -#[tokio::test] -async fn should_handle_api_errors() { - let server = server_with( - ResponseTemplate::new(400).set_body_string( - json!({ "error": { "message": "Invalid text", "type": "invalid_request_error" } }) - .to_string(), - ), - ) - .await; - let error = speech_model(&server, None) - .do_generate(&options("Hello from the AI SDK!")) - .await - .unwrap_err(); - - let AiMuxError::ApiCall(error) = error else { - panic!("expected an ApiCall error, got {error:?}"); - }; - assert_eq!(error.message, "Invalid text"); - assert_eq!(error.status_code, Some(400)); -} - -/// TS: "should use the real date when no custom date provider is specified" -#[tokio::test] -async fn should_use_the_real_date_when_no_custom_date_provider_is_specified() { - let server = server_with(audio_response("audio/mpeg", &[])).await; - let before = chrono::Utc::now().timestamp_millis(); - let result = speech_model(&server, None) - .do_generate(&options("Hello from the AI SDK!")) - .await - .unwrap(); - let after = chrono::Utc::now().timestamp_millis(); - - // A `Date`: millisecond precision. - let timestamp = result.response.timestamp.unwrap().unix_millis(); - assert!(before <= timestamp && timestamp <= after); - assert_eq!(result.response.model_id.as_deref(), Some("")); -} diff --git a/aimux-providers/tests/xai_transcription_test.rs b/aimux-providers/tests/xai_transcription_test.rs index ccdf61424..9e9ef2934 100644 --- a/aimux-providers/tests/xai_transcription_test.rs +++ b/aimux-providers/tests/xai_transcription_test.rs @@ -7,12 +7,8 @@ //! records the binary audio frames and the final `audio.done` text frame, then //! sends the scripted events. //! -//! Not ported (JS-only mechanics, no Rust analogue): -//! - `specificationVersion` check (part of the first provider-info case) -//! - "should cancel the audio stream when the WebSocket constructor throws" -//! - "should cancel the audio stream when an audio send throws mid-stream" -//! - "should close the WebSocket and stop reading audio when the stream is -//! cancelled" +//! The upstream cases this file does not cover, or covers only in part, are +//! ported in full in `src/xai/tests/transcription_model.rs`. use std::collections::HashMap; use std::sync::{Arc, Mutex}; @@ -330,99 +326,233 @@ fn finish(text: &str, language: Value, duration: Option) -> Value { part } -// ── tests: model information ──────────────────────────────────────────────── +mod do_generate { + use super::*; -/// TS: should expose correct provider and model information -#[test] -fn should_expose_correct_provider_and_model_information() { - let model = model("https://api.x.ai/v1", None); - assert_eq!(model.provider(), "xai.transcription"); - assert_eq!(model.model_id(), ""); -} + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › doGenerate › should send a multipart request with the audio file + #[tokio::test] + async fn should_send_a_multipart_request_with_the_audio_file() { + let server = MockServer::start().await; + mock_stt(&server, json_response(default_body(), &[])).await; -// ── tests: doGenerate ─────────────────────────────────────────────────────── + generate(&server, &call(None, "audio/wav"), None).await; -/// TS: doGenerate > should send a multipart request with the audio file -#[tokio::test] -async fn should_send_a_multipart_request_with_the_audio_file() { - let server = MockServer::start().await; - mock_stt(&server, json_response(default_body(), &[])).await; + let requests = server.received_requests().await.unwrap(); + assert_eq!(requests[0].method.as_str(), "POST"); + assert_eq!(requests[0].url.path(), "/stt"); + let parts = parse_multipart(&requests[0]); + let file = parts.iter().find(|p| p.name == "file").expect("file field"); + assert_eq!(file.filename.as_deref(), Some("audio.wav")); + assert_eq!(file.content_type.as_deref(), Some("audio/wav")); + } - generate(&server, &call(None, "audio/wav"), None).await; + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › doGenerate › should map provider options onto xAI request fields + #[tokio::test] + async fn should_map_provider_options_onto_xai_request_fields() { + let server = MockServer::start().await; + mock_stt(&server, json_response(default_body(), &[])).await; + + generate(&server, &call(Some(full_options()), "audio/pcm"), None).await; + + let parts = parse_multipart(&server.received_requests().await.unwrap()[0]); + let values = |name: &str| -> Vec<&str> { + parts + .iter() + .filter(|p| p.name == name) + .map(|p| p.value.as_str()) + .collect() + }; + for (name, expected) in [ + ("audio_format", "pcm"), + ("sample_rate", "16000"), + ("language", "en"), + ("format", "true"), + ("multichannel", "true"), + ("channels", "2"), + ("diarize", "true"), + ("filler_words", "true"), + ] { + assert_eq!(values(name), vec![expected], "field {name}"); + } + assert_eq!(values("keyterm"), vec!["AI SDK", "Grok"]); + } - let requests = server.received_requests().await.unwrap(); - assert_eq!(requests[0].method.as_str(), "POST"); - assert_eq!(requests[0].url.path(), "/stt"); - let parts = parse_multipart(&requests[0]); - let file = parts.iter().find(|p| p.name == "file").expect("file field"); - assert_eq!(file.filename.as_deref(), Some("audio.wav")); - assert_eq!(file.content_type.as_deref(), Some("audio/wav")); -} + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › doGenerate › should append file after all other multipart fields + #[tokio::test] + async fn should_append_file_after_all_other_multipart_fields() { + let server = MockServer::start().await; + mock_stt( + &server, + json_response(json!({"text": "Hello from the AI SDK!"}), &[]), + ) + .await; -/// TS: doGenerate > should map provider options onto xAI request fields -#[tokio::test] -async fn should_map_provider_options_onto_xai_request_fields() { - let server = MockServer::start().await; - mock_stt(&server, json_response(default_body(), &[])).await; + generate(&server, &call(Some(full_options()), "audio/pcm"), None).await; + + let parts = parse_multipart(&server.received_requests().await.unwrap()[0]); + let names: Vec<&str> = parts.iter().map(|p| p.name.as_str()).collect(); + assert_eq!( + names, + [ + "audio_format", + "sample_rate", + "language", + "format", + "multichannel", + "channels", + "diarize", + "filler_words", + "keyterm", + "keyterm", + "file" + ] + ); + } + + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › doGenerate › should extract text, segments, language, and duration + #[tokio::test] + async fn should_extract_text_segments_language_and_duration() { + let server = MockServer::start().await; + mock_stt(&server, json_response(default_body(), &[])).await; - generate(&server, &call(Some(full_options()), "audio/pcm"), None).await; + let result = generate(&server, &call(None, "audio/wav"), None).await; - let parts = parse_multipart(&server.received_requests().await.unwrap()[0]); - let values = |name: &str| -> Vec<&str> { - parts + assert_eq!(result.text, "Hello from the AI SDK!"); + assert_eq!(result.language.as_deref(), Some("en")); + assert_eq!(result.duration_in_seconds, Some(2.5)); + let segments: Vec<_> = result + .segments .iter() - .filter(|p| p.name == name) - .map(|p| p.value.as_str()) - .collect() - }; - for (name, expected) in [ - ("audio_format", "pcm"), - ("sample_rate", "16000"), - ("language", "en"), - ("format", "true"), - ("multichannel", "true"), - ("channels", "2"), - ("diarize", "true"), - ("filler_words", "true"), - ] { - assert_eq!(values(name), vec![expected], "field {name}"); + .map(|s| (s.text.as_str(), s.start_second, s.end_second)) + .collect(); + assert_eq!( + segments, + [("Hello", 0.0, 1.0), ("from the AI SDK!", 1.0, 2.5)] + ); + assert!(result.warnings.is_empty()); + } + + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › doGenerate › should handle missing words, duration, and empty language + #[tokio::test] + async fn should_handle_missing_words_duration_and_empty_language() { + let server = MockServer::start().await; + mock_stt( + &server, + json_response( + json!({"text": "Hello from the AI SDK!", "language": ""}), + &[], + ), + ) + .await; + + let result = generate(&server, &call(None, "audio/wav"), None).await; + + assert_eq!(result.text, "Hello from the AI SDK!"); + assert_eq!(result.language, None); + assert_eq!(result.duration_in_seconds, None); + assert!(result.segments.is_empty()); + assert!(result.warnings.is_empty()); } - assert_eq!(values("keyterm"), vec!["AI SDK", "Grok"]); } -/// TS: doGenerate > should append file after all other multipart fields -#[tokio::test] -async fn should_append_file_after_all_other_multipart_fields() { - let server = MockServer::start().await; - mock_stt( - &server, - json_response(json!({"text": "Hello from the AI SDK!"}), &[]), - ) - .await; +mod do_stream { + use super::*; + + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › doStream › should emit one transcript-final per utterance and reconstruct the finish text when transcript.done is empty + #[tokio::test] + async fn should_emit_one_final_per_utterance_and_reconstruct_finish_text() { + let events = vec![ + partial("Hello wor", false, false, Some((0.0, 0.8))), + partial("Hello world.", true, false, Some((0.0, 1.0))), + partial("Hello world.", true, true, Some((0.0, 1.0))), + done("", 1.0), + ]; + let parts = run(None, events).await.values(); + + assert_eq!( + of_kind(&parts, "transcript-final"), + vec![json!({ + "type": "transcript-final", "text": "Hello world.", "startSecond": 0.0, + "endSecond": 1.0 + })] + ); + // the speech_final:false re-send surfaces as a partial + assert_eq!(of_kind(&parts, "transcript-partial").len(), 2); + assert_eq!( + parts.last().unwrap(), + &finish("Hello world.", Value::Null, Some(1.0)) + ); + } - generate(&server, &call(Some(full_options()), "audio/pcm"), None).await; + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › doStream › should treat is_final fragments as partials and use the speech_final text for finish + #[tokio::test] + async fn should_treat_is_final_fragments_as_partials_and_use_speech_final_text() { + let events = vec![ + partial("No", true, false, Some((0.0, 0.5))), + partial(", I'm not", true, false, Some((0.5, 0.7))), + partial("No, I'm not.", true, true, Some((0.0, 1.2))), + done("", 1.2), + ]; + let parts = run(None, events).await.values(); + + assert_eq!( + kinds(&parts), + [ + "stream-start", + "transcript-partial", + "transcript-partial", + "transcript-final", + "finish" + ] + ); + assert_eq!(parts.last().unwrap()["text"], "No, I'm not."); + } - let parts = parse_multipart(&server.received_requests().await.unwrap()[0]); - let names: Vec<&str> = parts.iter().map(|p| p.name.as_str()).collect(); - assert_eq!( - names, - [ - "audio_format", - "sample_rate", - "language", - "format", - "multichannel", - "channels", - "diarize", - "filler_words", - "keyterm", - "keyterm", - "file" - ] - ); + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › doStream › should fall back to the latest pending text when no speech_final arrived before transcript.done + #[tokio::test] + async fn should_fall_back_to_latest_pending_text_without_speech_final() { + let events = vec![ + partial("Hello wor", false, false, None), + partial("Hello world", true, false, None), + done("", 1.0), + ]; + let parts = run(None, events).await.values(); + + let last = parts.last().unwrap(); + assert_eq!(kinds(&parts).last(), Some(&"finish")); + assert_eq!(last["text"], "Hello world"); + } + + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › doStream › should warn on unrecognized inputAudioFormat types + #[tokio::test] + async fn should_warn_on_unrecognized_input_audio_format_types() { + let options = stream_options(None, "audio/wav", None); + let run = run_with(options, None, vec![done("Hello", 1.0)]).await; + + match &run.parts[0] { + Ok(TranscriptionStreamPart::StreamStart { warnings }) => match warnings.as_slice() { + [Warning::Other { message }] => { + assert!( + message.contains("Unrecognized inputAudioFormat.type \"audio/wav\""), + "{message}" + ); + } + other => panic!("expected one Other warning, got {other:?}"), + }, + other => panic!("expected StreamStart, got {other:?}"), + } + } } -/// TS: doGenerate > should pass headers and the xAI user agent +/// Checks the provider id and the empty model id; specificationVersion is not checked. partial: should expose correct provider and model information; full port in src/xai/tests/ +#[test] +fn should_expose_correct_provider_and_model_information() { + let model = model("https://api.x.ai/v1", None); + assert_eq!(model.provider(), "xai.transcription"); + assert_eq!(model.model_id(), ""); +} + +/// Checks authorization, custom headers and the multipart content type; the user agent is checked without its version and the boundary only by prefix. partial: should pass headers and the xAI user agent; full port in src/xai/tests/ #[tokio::test] async fn should_pass_headers_and_the_xai_user_agent() { let server = MockServer::start().await; @@ -456,30 +586,7 @@ async fn should_pass_headers_and_the_xai_user_agent() { assert!(header("user-agent").contains("ai-sdk-xai/")); } -/// TS: doGenerate > should extract text, segments, language, and duration -#[tokio::test] -async fn should_extract_text_segments_language_and_duration() { - let server = MockServer::start().await; - mock_stt(&server, json_response(default_body(), &[])).await; - - let result = generate(&server, &call(None, "audio/wav"), None).await; - - assert_eq!(result.text, "Hello from the AI SDK!"); - assert_eq!(result.language.as_deref(), Some("en")); - assert_eq!(result.duration_in_seconds, Some(2.5)); - let segments: Vec<_> = result - .segments - .iter() - .map(|s| (s.text.as_str(), s.start_second, s.end_second)) - .collect(); - assert_eq!( - segments, - [("Hello", 0.0, 1.0), ("from the AI SDK!", 1.0, 2.5)] - ); - assert!(result.warnings.is_empty()); -} - -/// TS: doGenerate > should include response timestamp, model id, and headers +/// Checks the response model id, headers and body; the timestamp is only checked as present. partial: should include response timestamp, model id, and headers; full port in src/xai/tests/ #[tokio::test] async fn should_include_response_timestamp_model_id_and_headers() { let server = MockServer::start().await; @@ -500,31 +607,7 @@ async fn should_include_response_timestamp_model_id_and_headers() { assert_eq!(result.response.body, Some(default_body())); } -/// TS: doGenerate > should handle missing words, duration, and empty language -#[tokio::test] -async fn should_handle_missing_words_duration_and_empty_language() { - let server = MockServer::start().await; - mock_stt( - &server, - json_response( - json!({"text": "Hello from the AI SDK!", "language": ""}), - &[], - ), - ) - .await; - - let result = generate(&server, &call(None, "audio/wav"), None).await; - - assert_eq!(result.text, "Hello from the AI SDK!"); - assert_eq!(result.language, None); - assert_eq!(result.duration_in_seconds, None); - assert!(result.segments.is_empty()); - assert!(result.warnings.is_empty()); -} - -// ── tests: doStream ───────────────────────────────────────────────────────── - -/// TS: doStream > should require channels when streaming multichannel audio +/// Checks the multichannel-without-channels error and that no socket is opened; the error's argument field is not checked. partial: should require channels when streaming multichannel audio; full port in src/xai/tests/ #[tokio::test] async fn should_require_channels_when_streaming_multichannel_audio() { let listener = TcpListener::bind("127.0.0.1:0").await.unwrap(); @@ -555,7 +638,7 @@ async fn should_require_channels_when_streaming_multichannel_audio() { ); } -/// TS: doStream > should stream xAI STT over WebSocket +/// Checks the streaming URL, handshake, audio frames and parts; the response timestamp is only checked as present. partial: should stream xAI STT over WebSocket; full port in src/xai/tests/ #[tokio::test] async fn should_stream_xai_stt_over_websocket() { let options = json!({ @@ -606,9 +689,7 @@ async fn should_stream_xai_stt_over_websocket() { assert!(response.timestamp.is_some()); } -/// TS: doStream > should strip undefined header values before the WebSocket constructor -/// -/// Rust analogue: a provider header set to `None` is not sent on the handshake. +/// Checks that a provider header set to None is left off the WebSocket handshake (analogue: per-call headers cannot hold None). partial: should strip undefined header values before the WebSocket constructor; full port in src/xai/tests/ #[tokio::test] async fn should_not_send_removed_header_values_on_the_handshake() { let headers = HashMap::from([ @@ -626,72 +707,7 @@ async fn should_not_send_removed_header_values_on_the_handshake() { assert!(!run.cap.headers.contains_key("x-unset")); } -/// TS: doStream > should emit one transcript-final per utterance and reconstruct the finish text when transcript.done is empty -#[tokio::test] -async fn should_emit_one_final_per_utterance_and_reconstruct_finish_text() { - let events = vec![ - partial("Hello wor", false, false, Some((0.0, 0.8))), - partial("Hello world.", true, false, Some((0.0, 1.0))), - partial("Hello world.", true, true, Some((0.0, 1.0))), - done("", 1.0), - ]; - let parts = run(None, events).await.values(); - - assert_eq!( - of_kind(&parts, "transcript-final"), - vec![json!({ - "type": "transcript-final", "text": "Hello world.", "startSecond": 0.0, - "endSecond": 1.0 - })] - ); - // the speech_final:false re-send surfaces as a partial - assert_eq!(of_kind(&parts, "transcript-partial").len(), 2); - assert_eq!( - parts.last().unwrap(), - &finish("Hello world.", Value::Null, Some(1.0)) - ); -} - -/// TS: doStream > should treat is_final fragments as partials and use the speech_final text for finish -#[tokio::test] -async fn should_treat_is_final_fragments_as_partials_and_use_speech_final_text() { - let events = vec![ - partial("No", true, false, Some((0.0, 0.5))), - partial(", I'm not", true, false, Some((0.5, 0.7))), - partial("No, I'm not.", true, true, Some((0.0, 1.2))), - done("", 1.2), - ]; - let parts = run(None, events).await.values(); - - assert_eq!( - kinds(&parts), - [ - "stream-start", - "transcript-partial", - "transcript-partial", - "transcript-final", - "finish" - ] - ); - assert_eq!(parts.last().unwrap()["text"], "No, I'm not."); -} - -/// TS: doStream > should fall back to the latest pending text when no speech_final arrived before transcript.done -#[tokio::test] -async fn should_fall_back_to_latest_pending_text_without_speech_final() { - let events = vec![ - partial("Hello wor", false, false, None), - partial("Hello world", true, false, None), - done("", 1.0), - ]; - let parts = run(None, events).await.values(); - - let last = parts.last().unwrap(); - assert_eq!(kinds(&parts).last(), Some(&"finish")); - assert_eq!(last["text"], "Hello world"); -} - -/// TS: doStream > should join finalized utterances per channel when transcript.done is empty +/// Checks that finalized utterances are joined into the finish text; the finish part type is not checked. partial: should join finalized utterances per channel when transcript.done is empty; full port in src/xai/tests/ #[tokio::test] async fn should_join_finalized_utterances_when_transcript_done_is_empty() { let events = vec![ @@ -707,7 +723,7 @@ async fn should_join_finalized_utterances_when_transcript_done_is_empty() { ); } -/// TS: doStream > should error the stream with the server message on error events +/// Checks that an error event fails the stream with the server message; the WebSocket close is not checked. partial: should error the stream with the server message on error events; full port in src/xai/tests/ #[tokio::test] async fn should_error_the_stream_with_the_server_message_on_error_events() { let events = vec![json!({"type": "error", "message": "invalid sample_rate"})]; @@ -725,26 +741,6 @@ async fn should_error_the_stream_with_the_server_message_on_error_events() { ); } -/// TS: doStream > should warn on unrecognized inputAudioFormat types -#[tokio::test] -async fn should_warn_on_unrecognized_input_audio_format_types() { - let options = stream_options(None, "audio/wav", None); - let run = run_with(options, None, vec![done("Hello", 1.0)]).await; - - match &run.parts[0] { - Ok(TranscriptionStreamPart::StreamStart { warnings }) => match warnings.as_slice() { - [Warning::Other { message }] => { - assert!( - message.contains("Unrecognized inputAudioFormat.type \"audio/wav\""), - "{message}" - ); - } - other => panic!("expected one Other warning, got {other:?}"), - }, - other => panic!("expected StreamStart, got {other:?}"), - } -} - /// Only `transcribe` downloads an audio URL (layer 5's `AudioInput::Url`); /// the model itself refuses one before any request, as every transcription /// model does. diff --git a/aimux-providers/tests/xai_upstream_e2e_test.rs b/aimux-providers/tests/xai_upstream_e2e_test.rs index 2a0298d83..637ee4cbd 100644 --- a/aimux-providers/tests/xai_upstream_e2e_test.rs +++ b/aimux-providers/tests/xai_upstream_e2e_test.rs @@ -68,7 +68,8 @@ fn sse(events: &[Value]) -> Canned { } } -/// TS: "should extract usage correctly" (xai/src/responses/xai-responses-language-model.test.ts) +/// partial: `should extract usage correctly`; full port in +/// src/xai/tests/responses/responses_language_model.rs. #[tokio::test] async fn generate_converts_usage() { // The port used an unrelated search fixture for this case; restore the TS response. @@ -98,7 +99,8 @@ async fn generate_converts_usage() { assert_eq!(result.response.unwrap().id.as_deref(), Some("resp_123")); } -/// TS: "should expose cost_in_usd_ticks in finish providerMetadata" (xai/src/responses/xai-responses-language-model.test.ts) +/// partial: `should expose cost_in_usd_ticks in finish providerMetadata`; full port in +/// src/xai/tests/responses/responses_language_model.rs. #[tokio::test] async fn stream_reports_cost_and_text() { let events = vec![ @@ -152,7 +154,8 @@ async fn stream_reports_cost_and_text() { ); } -/// TS: "should forward web_search_call action and sources to tool-result" (xai/src/responses/xai-responses-language-model.test.ts) +/// partial: `should forward web_search_call action and sources to tool-result`; full port in +/// src/xai/tests/responses/responses_language_model.rs. #[tokio::test] async fn tool_results_preserve_actions_and_sources() { let body = json!({ @@ -214,7 +217,8 @@ async fn tool_results_preserve_actions_and_sources() { assert_eq!(result.finish_reason.unified, FinishReasonUnified::Stop); } -/// TS: "should set finish reason to error" (xai/src/responses/xai-responses-language-model.test.ts) +/// partial: `should set finish reason to error`; full port in +/// src/xai/tests/responses/responses_language_model.rs. #[tokio::test] async fn failed_response_emits_error_and_finish() { let events = vec![ @@ -266,7 +270,7 @@ async fn failed_response_emits_error_and_finish() { assert_eq!(usage.output_tokens.total, Some(0)); } -/// TS: `xaiResponsesSupportedUrls` (xai-responses-language-model.ts:142-149): +/// `xaiResponsesSupportedUrls` (xai-responses-language-model.ts:142-149): /// image, PDF and text URLs go to the API as they are. #[tokio::test] async fn responses_support_http_image_pdf_and_text_urls() { diff --git a/aimux-providers/tests/xai_video_test.rs b/aimux-providers/tests/xai_video_test.rs index b1320caa6..4c5ec5e25 100644 --- a/aimux-providers/tests/xai_video_test.rs +++ b/aimux-providers/tests/xai_video_test.rs @@ -188,9 +188,220 @@ fn assert_terminal(result: Result, message: &s } } -// ------------------------------------------------------------ constructor +mod xai_video_model { + use super::*; -/// TS: constructor > should expose correct provider and model information + mod constructor { + use super::*; + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › constructor › should send the grok-imagine-video-1.5 model id in the request body + #[tokio::test] + async fn sends_the_1_5_model_id_in_the_body() { + let s = start(V15, base()).await; + assert_eq!(s.body["model"], V15); + } + } + + mod do_start { + use super::*; + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should map current xAI generation options + #[tokio::test] + async fn maps_current_generation_options() { + let mut o = xai( + base(), + json!({ + "storageOptions": { + "filename": "result.mp4", + "expiresAfter": 86_400, + "publicUrl": {"expiresAfter": 3_600} + }, + "keyframes": [{"imageUrl": "https://example.com/middle.png", "timestampSeconds": 2.5}] + }), + ); + o.generate_audio = Some(false); + o.frame_images = last_frame(url_file("https://example.com/end.png", None)); + let s = start(V15, o).await; + assert_eq!(s.body["generate_audio"], false); + assert_eq!( + s.body["last_frame"], + json!({"url": "https://example.com/end.png"}) + ); + assert_eq!( + s.body["storage_options"], + json!({ + "filename": "result.mp4", + "expires_after": 86_400, + "public_url": {"expires_after": 3_600} + }) + ); + assert_eq!( + s.body["keyframes"], + json!([{"image": {"url": "https://example.com/middle.png"}, "timestamp_s": 2.5}]) + ); + assert_eq!(s.start.operation, json!({"requestId": "req-123"})); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should warn and omit last_frame for grok-imagine-video + #[tokio::test] + async fn warns_and_omits_last_frame_for_grok_imagine_video() { + let mut o = base(); + o.frame_images = last_frame(url_file("https://example.com/end.png", None)); + let s = start(V1, o).await; + assert!(s.body.get("last_frame").is_none()); + assert_eq!( + details(&s.start.warnings, "frameImages"), + [ + "xAI only supports last_frame with \"grok-imagine-video-1.5\". \ + The last frame was ignored." + ] + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should pass headers + #[tokio::test] + async fn do_start_passes_headers() { + let server = server_with( + json!({"request_id": "req-123"}), + json_response(done_status()), + ) + .await; + let model = model_with(&server, V1, &[("X-Custom", "value")]); + let mut o = base(); + o.headers = Some(HashMap::from([( + "X-Request-Header".to_string(), + "request-value".to_string(), + )])); + model.do_start(&o).await.unwrap(); + let request = server.received_requests().await.unwrap().remove(0); + assert_eq!(header(&request, "authorization"), "Bearer test-key"); + assert_eq!(header(&request, "x-custom"), "value"); + assert_eq!(header(&request, "x-request-header"), "request-value"); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should throw when no request_id returned + #[tokio::test] + async fn do_start_fails_without_request_id() { + let server = server_with(json!({}), json_response(done_status())).await; + let err = model_with(&server, V1, &[]) + .do_start(&base()) + .await + .unwrap_err(); + match err { + AiMuxError::Other(message) => { + assert_eq!(message, "No request_id returned from xAI API.") + } + other => panic!("unexpected error: {other:?}"), + } + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should return warnings for unsupported features + #[tokio::test] + async fn warns_for_fps_and_seed() { + let mut o = base(); + o.fps = Some(30); + o.seed = Some(42); + let s = start(V1, o).await; + assert!(warned(&s.start.warnings, "fps")); + assert!(warned(&s.start.warnings, "seed")); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should warn when explicit R2V has no references at all + #[tokio::test] + async fn warns_when_reference_to_video_has_no_references() { + let s = start(V15, xai(base(), json!({"mode": "reference-to-video"}))).await; + assert!(s.body.get("reference_images").is_none()); + assert!( + details(&s.start.warnings, "referenceImages")[0] + .contains("without reference images") + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should allow duration and aspectRatio with reference images + #[tokio::test] + async fn allows_duration_and_aspect_ratio_with_reference_images() { + let mut o = xai( + base(), + json!({"referenceImageUrls": ["https://example.com/ref.jpg"]}), + ); + o.duration = Some(8); + o.aspect_ratio = ar("16:9"); + let s = start(V1, o).await; + assert_eq!(s.body["duration"], 8); + assert_eq!(s.body["aspect_ratio"], "16:9"); + assert!(!warned(&s.start.warnings, "duration")); + assert!(!warned(&s.start.warnings, "aspectRatio")); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should omit reference_audios for an empty referenceVoiceIds array + #[tokio::test] + async fn omits_reference_audios_for_empty_voice_ids() { + let o = xai( + base(), + json!({ + "mode": "reference-to-video", + "referenceImageUrls": ["https://example.com/ref1.jpg"], + "referenceVoiceIds": [] + }), + ); + let s = start(V15, o).await; + assert!(s.body.get("reference_audios").is_none()); + assert!(!warned(&s.start.warnings, "referenceVoiceIds")); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should warn and omit referenceVoiceIds outside reference-to-video + #[tokio::test] + async fn warns_and_omits_voice_ids_outside_reference_to_video() { + let s = start(V15, xai(base(), json!({"referenceVoiceIds": ["eve"]}))).await; + assert!(s.body.get("reference_audios").is_none()); + assert!(warned(&s.start.warnings, "referenceVoiceIds")); + } + } + + mod do_status { + use super::*; + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStatus › should return pending when status is pending + #[tokio::test] + async fn do_status_returns_pending() { + for template in [ + json_response(json!({"status": "pending"})), + // xAI answers 202 while running, sometimes with an empty body. + ResponseTemplate::new(202), + ResponseTemplate::new(202) + .set_body_json(json!({"status": "pending", "progress": 40})), + ] { + let (result, _) = poll_once(template, "req-123").await; + assert!( + matches!(result, Ok(VideoOperationStatus::Pending)), + "{result:?}" + ); + } + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStatus › should pass headers to request + #[tokio::test] + async fn do_status_passes_headers() { + let server = server_with(json!({}), json_response(done_status())).await; + let model = model_with(&server, V1, &[("X-Custom", "value")]); + let mut o = base(); + o.headers = Some(HashMap::from([( + "X-Request-Header".to_string(), + "request-value".to_string(), + )])); + model + .do_status(&json!({"requestId": "req-123"}), &o) + .await + .unwrap(); + let request = server.received_requests().await.unwrap().remove(0); + assert_eq!(header(&request, "authorization"), "Bearer test-key"); + assert_eq!(header(&request, "x-custom"), "value"); + assert_eq!(header(&request, "x-request-header"), "request-value"); + } + } +} + +/// Checks the provider id, model id and maxVideosPerCall; specificationVersion is not checked. partial: should expose correct provider and model information; full port in src/xai/tests/ #[tokio::test] async fn constructor_exposes_provider_and_model_information() { let server = MockServer::start().await; @@ -200,17 +411,7 @@ async fn constructor_exposes_provider_and_model_information() { assert_eq!(model.max_videos_per_call(), Some(1)); } -/// TS: constructor > should send the grok-imagine-video-1.5 model id in the request body -#[tokio::test] -async fn sends_the_1_5_model_id_in_the_body() { - let s = start(V15, base()).await; - assert_eq!(s.body["model"], V15); -} - -// --------------------------------------------------------------- doStart - -/// TS: doStart > should return operation with requestId -/// TS: doStart > should include response metadata +/// Checks the requestId operation and the response model id, timestamp and headers. partial: should return operation with requestId; should include response metadata; full port in src/xai/tests/ #[tokio::test] async fn do_start_returns_request_id_and_response_metadata() { let s = start(V1, base()).await; @@ -221,8 +422,7 @@ async fn do_start_returns_request_id_and_response_metadata() { assert!(response.headers.is_some()); } -/// TS: doStart > should pass correct request body -/// TS: doStart > should return empty warnings for supported features +/// Checks the POST to /videos/generations with the minimal body and no warnings. partial: should pass correct request body; should return empty warnings for supported features; full port in src/xai/tests/ #[tokio::test] async fn do_start_posts_minimal_generation_body() { let s = start(V1, base()).await; @@ -232,99 +432,7 @@ async fn do_start_posts_minimal_generation_body() { assert!(s.start.warnings.is_empty()); } -/// TS: doStart > should map current xAI generation options -#[tokio::test] -async fn maps_current_generation_options() { - let mut o = xai( - base(), - json!({ - "storageOptions": { - "filename": "result.mp4", - "expiresAfter": 86_400, - "publicUrl": {"expiresAfter": 3_600} - }, - "keyframes": [{"imageUrl": "https://example.com/middle.png", "timestampSeconds": 2.5}] - }), - ); - o.generate_audio = Some(false); - o.frame_images = last_frame(url_file("https://example.com/end.png", None)); - let s = start(V15, o).await; - assert_eq!(s.body["generate_audio"], false); - assert_eq!( - s.body["last_frame"], - json!({"url": "https://example.com/end.png"}) - ); - assert_eq!( - s.body["storage_options"], - json!({ - "filename": "result.mp4", - "expires_after": 86_400, - "public_url": {"expires_after": 3_600} - }) - ); - assert_eq!( - s.body["keyframes"], - json!([{"image": {"url": "https://example.com/middle.png"}, "timestamp_s": 2.5}]) - ); - assert_eq!(s.start.operation, json!({"requestId": "req-123"})); -} - -/// TS: doStart > should warn and omit last_frame for grok-imagine-video -#[tokio::test] -async fn warns_and_omits_last_frame_for_grok_imagine_video() { - let mut o = base(); - o.frame_images = last_frame(url_file("https://example.com/end.png", None)); - let s = start(V1, o).await; - assert!(s.body.get("last_frame").is_none()); - assert_eq!( - details(&s.start.warnings, "frameImages"), - [ - "xAI only supports last_frame with \"grok-imagine-video-1.5\". \ - The last frame was ignored." - ] - ); -} - -/// TS: doStart > should pass headers -#[tokio::test] -async fn do_start_passes_headers() { - let server = server_with( - json!({"request_id": "req-123"}), - json_response(done_status()), - ) - .await; - let model = model_with(&server, V1, &[("X-Custom", "value")]); - let mut o = base(); - o.headers = Some(HashMap::from([( - "X-Request-Header".to_string(), - "request-value".to_string(), - )])); - model.do_start(&o).await.unwrap(); - let request = server.received_requests().await.unwrap().remove(0); - assert_eq!(header(&request, "authorization"), "Bearer test-key"); - assert_eq!(header(&request, "x-custom"), "value"); - assert_eq!(header(&request, "x-request-header"), "request-value"); -} - -/// TS: doStart > should throw when no request_id returned -#[tokio::test] -async fn do_start_fails_without_request_id() { - let server = server_with(json!({}), json_response(done_status())).await; - let err = model_with(&server, V1, &[]) - .do_start(&base()) - .await - .unwrap_err(); - match err { - AiMuxError::Other(message) => assert_eq!(message, "No request_id returned from xAI API."), - other => panic!("unexpected error: {other:?}"), - } -} - -// ------------------------------------------------- mode / endpoint selection - -/// TS: doStart > should use edits endpoint for video editing (legacy videoUrl without mode) -/// TS: doStart > should use edits endpoint for video editing with explicit mode -/// TS: doStart > should use extensions endpoint for extend-video mode +/// Checks the POST path and video field for legacy, edit and extend modes. partial: should use edits endpoint for video editing (legacy videoUrl without mode); should use edits endpoint for video editing with explicit mode; should use extensions endpoint for extend-video mode; full port in src/xai/tests/ #[tokio::test] async fn selects_endpoint_by_mode() { let cases = [ @@ -346,21 +454,7 @@ async fn selects_endpoint_by_mode() { } } -// ---------------------------------------------------------- warnings & fields - -/// TS: doStart > should return warnings for unsupported features -#[tokio::test] -async fn warns_for_fps_and_seed() { - let mut o = base(); - o.fps = Some(30); - o.seed = Some(42); - let s = start(V1, o).await; - assert!(warned(&s.start.warnings, "fps")); - assert!(warned(&s.start.warnings, "seed")); -} - -/// TS: doStart > should warn when n > 1 -/// TS: doStart > should not warn when n is 1 +/// Checks the n warning for n=3 and its absence for n=1. partial: should warn when n > 1; should not warn when n is 1; full port in src/xai/tests/ #[tokio::test] async fn warns_only_when_n_exceeds_one() { for (n, expected) in [(3, true), (1, false)] { @@ -371,8 +465,7 @@ async fn warns_only_when_n_exceeds_one() { } } -/// TS: doStart > should send duration in request body -/// TS: doStart > should send aspect_ratio in request body +/// Checks that duration and aspect_ratio are sent in the body. partial: should send duration in request body; should send aspect_ratio in request body; full port in src/xai/tests/ #[tokio::test] async fn sends_duration_and_aspect_ratio() { let mut o = base(); @@ -383,12 +476,7 @@ async fn sends_duration_and_aspect_ratio() { assert_eq!(s.body["aspect_ratio"], "9:16"); } -// ---------------------------------------------------------------- resolution - -/// TS: doStart > should map SDK resolution 1280x720 to 720p -/// TS: doStart > should map SDK resolution 854x480 to 480p -/// TS: doStart > should map SDK resolution 640x480 to 480p -/// TS: doStart > should map SDK resolution 1920x1080 to 1080p +/// Checks that SDK sizes map to 720p, 480p and 1080p. partial: should map SDK resolution 1280x720 to 720p; should map SDK resolution 854x480 to 480p; should map SDK resolution 640x480 to 480p; should map SDK resolution 1920x1080 to 1080p; full port in src/xai/tests/ #[tokio::test] async fn maps_sdk_resolution_to_xai_resolution() { let cases = [ @@ -405,8 +493,7 @@ async fn maps_sdk_resolution_to_xai_resolution() { } } -/// TS: doStart > should pass through provider option resolution 1080p -/// TS: doStart > should prefer provider option resolution over SDK resolution +/// Checks that a provider resolution is sent and wins over the SDK size. partial: should pass through provider option resolution 1080p; should prefer provider option resolution over SDK resolution; full port in src/xai/tests/ #[tokio::test] async fn provider_option_resolution_wins() { let s = start(V15, xai(base(), json!({"resolution": "1080p"}))).await; @@ -418,9 +505,7 @@ async fn provider_option_resolution_wins() { assert_eq!(s.body["resolution"], "480p"); } -/// TS: doStart > should warn when SDK resolution 1920x1080 is used with grok-imagine-video -/// TS: doStart > should warn when provider resolution 1080p is used with grok-imagine-video -/// TS: doStart > should not warn about 1080p with grok-imagine-video-1.5 +/// Checks the 1080p warning on grok-imagine-video (SDK and provider sources) and none on 1.5. partial: should warn when SDK resolution 1920x1080 is used with grok-imagine-video; should warn when provider resolution 1080p is used with grok-imagine-video; should not warn about 1080p with grok-imagine-video-1.5; full port in src/xai/tests/ #[tokio::test] async fn warns_about_1080p_only_on_grok_imagine_video() { let mut sdk = base(); @@ -440,8 +525,7 @@ async fn warns_about_1080p_only_on_grok_imagine_video() { assert!(start(V15, o).await.start.warnings.is_empty()); } -/// TS: doStart > should warn for unrecognized resolution format -/// TS: doStart > should warn and omit body resolution for completely unknown format +/// Checks that unrecognized resolutions are left out of the body and warned about. partial: should warn for unrecognized resolution format; should warn and omit body resolution for completely unknown format; full port in src/xai/tests/ #[tokio::test] async fn warns_and_omits_unrecognized_resolution() { for resolution in ["2560x1440", "3840x2160"] { @@ -453,11 +537,7 @@ async fn warns_and_omits_unrecognized_resolution() { } } -// -------------------------------------------------------------- image input - -/// TS: doStart > should send image object from URL-based image input -/// TS: doStart > should send image object with data URI from file data -/// TS: doStart > should send image object with data URI from base64 string +/// Checks that URL, binary and base64 image inputs are sent as image.url. partial: should send image object from URL-based image input; should send image object with data URI from file data; should send image object with data URI from base64 string; full port in src/xai/tests/ #[tokio::test] async fn sends_image_object_from_url_and_file_inputs() { let cases = [ @@ -488,17 +568,7 @@ async fn sends_image_object_from_url_and_file_inputs() { } } -// ---------------------------------------------------- edit / extension modes - -/// TS: doStart > should warn about duration in edit mode -/// TS: doStart > should warn about aspectRatio in edit mode -/// TS: doStart > should warn about resolution in edit mode -/// TS: doStart > should not warn about duration outside edit mode -/// TS: doStart > should not warn about aspectRatio outside edit mode -/// TS: doStart > should not warn about resolution outside edit mode -/// TS: doStart > should warn about aspectRatio in extension mode -/// TS: doStart > should warn about resolution in extension mode -/// TS: doStart > should not warn about duration in extension mode +/// Checks which options warn in default, edit and extend modes. partial: should warn about duration in edit mode; should warn about aspectRatio in edit mode; should warn about resolution in edit mode; should not warn about duration outside edit mode; should not warn about aspectRatio outside edit mode; should not warn about resolution outside edit mode; should warn about aspectRatio in extension mode; should warn about resolution in extension mode; should not warn about duration in extension mode; full port in src/xai/tests/ #[tokio::test] async fn warns_about_unsupported_options_per_mode() { type Setter = fn(&mut VideoCallOptions); @@ -528,10 +598,7 @@ async fn warns_about_unsupported_options_per_mode() { } } -/// TS: doStart > should omit duration, aspect_ratio, and resolution from body in edit mode -/// TS: doStart > should allow duration in extension mode -/// TS: doStart > should omit aspect_ratio and resolution from body in extension mode -/// TS: doStart > should warn about provider-level resolution in extension mode +/// Checks that unsupported fields are left out of edit and extend bodies. partial: should omit duration, aspect_ratio, and resolution from body in edit mode; should allow duration in extension mode; should omit aspect_ratio and resolution from body in extension mode; should warn about provider-level resolution in extension mode; full port in src/xai/tests/ #[tokio::test] async fn omits_unsupported_fields_from_edit_and_extension_bodies() { let mut edit = xai(base(), json!({"mode": "edit-video", "videoUrl": SRC})); @@ -561,10 +628,7 @@ async fn omits_unsupported_fields_from_edit_and_extension_bodies() { assert!(warned(&s.start.warnings, "resolution")); } -// ------------------------------------------------------- reference-to-video - -/// TS: doStart > should send reference_images array to /videos/generations with explicit mode -/// TS: doStart > should fallback to reference-to-video mode when referenceImageUrls is set without mode +/// Checks reference_images for explicit and implicit reference-to-video. partial: should send reference_images array to /videos/generations with explicit mode; should fallback to reference-to-video mode when referenceImageUrls is set without mode; full port in src/xai/tests/ #[tokio::test] async fn sends_reference_images_for_reference_to_video() { let urls = [ @@ -587,8 +651,7 @@ async fn sends_reference_images_for_reference_to_video() { } } -/// TS: doStart > should downgrade R2V 1080p to 720p with a warning -/// TS: doStart > should downgrade R2V 1920x1080 from the SDK resolution to 720p +/// Checks that 1080p is downgraded to 720p in reference-to-video with a warning. partial: should downgrade R2V 1080p to 720p with a warning; should downgrade R2V 1920x1080 from the SDK resolution to 720p; full port in src/xai/tests/ #[tokio::test] async fn downgrades_reference_to_video_1080p() { let refs = json!(["https://example.com/ref1.jpg"]); @@ -608,11 +671,7 @@ async fn downgrades_reference_to_video_1080p() { } } -/// TS: doStart > should separate image and audio inputReferences -/// TS: doStart > should support audio-only reference-to-video -/// TS: doStart > should not send an empty reference_images array for video-only inputReferences -/// TS: doStart > should combine a pinned first frame with an audio reference -/// TS: doStart > should support explicit audio-only R2V without reference_images +/// Checks that inputReferences split into reference_images and reference_audios by media type. partial: should separate image and audio inputReferences; should support audio-only reference-to-video; should not send an empty reference_images array for video-only inputReferences; should combine a pinned first frame with an audio reference; should support explicit audio-only R2V without reference_images; full port in src/xai/tests/ #[tokio::test] async fn splits_input_references_by_media_type() { let image = url_file("https://example.com/ref1.jpg", None); @@ -663,35 +722,7 @@ async fn splits_input_references_by_media_type() { assert!(s.start.warnings.is_empty()); } -/// TS: doStart > should warn when explicit R2V has no references at all -#[tokio::test] -async fn warns_when_reference_to_video_has_no_references() { - let s = start(V15, xai(base(), json!({"mode": "reference-to-video"}))).await; - assert!(s.body.get("reference_images").is_none()); - assert!(details(&s.start.warnings, "referenceImages")[0].contains("without reference images")); -} - -/// TS: doStart > should allow duration and aspectRatio with reference images -#[tokio::test] -async fn allows_duration_and_aspect_ratio_with_reference_images() { - let mut o = xai( - base(), - json!({"referenceImageUrls": ["https://example.com/ref.jpg"]}), - ); - o.duration = Some(8); - o.aspect_ratio = ar("16:9"); - let s = start(V1, o).await; - assert_eq!(s.body["duration"], 8); - assert_eq!(s.body["aspect_ratio"], "16:9"); - assert!(!warned(&s.start.warnings, "duration")); - assert!(!warned(&s.start.warnings, "aspectRatio")); -} - -// ------------------------------------------------------- reference voices - -/// TS: doStart > should send reference_audios for preset reference voices -/// TS: doStart > should send up to 3 preset reference voices in order -/// TS: doStart > should never forward referenceVoiceIds as a raw body key +/// Checks that referenceVoiceIds become reference_audios in order and are never sent raw. partial: should send reference_audios for preset reference voices; should send up to 3 preset reference voices in order; should never forward referenceVoiceIds as a raw body key; full port in src/xai/tests/ #[tokio::test] async fn sends_reference_voice_ids_as_reference_audios() { let cases = [ @@ -721,8 +752,7 @@ async fn sends_reference_voice_ids_as_reference_audios() { } } -/// TS: doStart > should reject more than 3 preset reference voices -/// TS: doStart > should reject empty-string preset reference voice ids +/// Checks that more than 3 voices, or an empty voice id, gives InvalidArgument. partial: should reject more than 3 preset reference voices; should reject empty-string preset reference voice ids; full port in src/xai/tests/ #[tokio::test] async fn rejects_invalid_reference_voice_ids() { for ids in [json!(["ara", "eve", "leo", "rex"]), json!([""])] { @@ -742,34 +772,7 @@ async fn rejects_invalid_reference_voice_ids() { } } -/// TS: doStart > should omit reference_audios for an empty referenceVoiceIds array -#[tokio::test] -async fn omits_reference_audios_for_empty_voice_ids() { - let o = xai( - base(), - json!({ - "mode": "reference-to-video", - "referenceImageUrls": ["https://example.com/ref1.jpg"], - "referenceVoiceIds": [] - }), - ); - let s = start(V15, o).await; - assert!(s.body.get("reference_audios").is_none()); - assert!(!warned(&s.start.warnings, "referenceVoiceIds")); -} - -/// TS: doStart > should warn and omit referenceVoiceIds outside reference-to-video -#[tokio::test] -async fn warns_and_omits_voice_ids_outside_reference_to_video() { - let s = start(V15, xai(base(), json!({"referenceVoiceIds": ["eve"]}))).await; - assert!(s.body.get("reference_audios").is_none()); - assert!(warned(&s.start.warnings, "referenceVoiceIds")); -} - -// -------------------------------------------------------------- doStatus - -/// TS: doStatus > should encode the request ID as a single URL path segment -/// TS: doStatus > should preserve the $requestId request ID as a URL path segment +/// Checks that the requestId is encoded as one URL path segment, dot segments included. partial: should encode the request ID as a single URL path segment; should preserve the $requestId request ID as a URL path segment; full port in src/xai/tests/ #[tokio::test] async fn do_status_encodes_request_id_as_one_path_segment() { let cases = [ @@ -784,8 +787,7 @@ async fn do_status_encodes_request_id_as_one_path_segment() { } } -/// TS: doStatus > should return completed with video data when done -/// TS: doStatus > should include response metadata +/// Checks the completed video, its xai metadata and the response. partial: should return completed with video data when done; should include response metadata; full port in src/xai/tests/ #[tokio::test] async fn do_status_returns_completed_video_and_metadata() { let (result, _) = poll_once(json_response(done_status()), "req-123").await; @@ -809,27 +811,7 @@ async fn do_status_returns_completed_video_and_metadata() { assert!(done.response.headers.is_some()); } -/// TS: doStatus > should return pending when status is pending -#[tokio::test] -async fn do_status_returns_pending() { - for template in [ - json_response(json!({"status": "pending"})), - // xAI answers 202 while running, sometimes with an empty body. - ResponseTemplate::new(202), - ResponseTemplate::new(202).set_body_json(json!({"status": "pending", "progress": 40})), - ] { - let (result, _) = poll_once(template, "req-123").await; - assert!( - matches!(result, Ok(VideoOperationStatus::Pending)), - "{result:?}" - ); - } -} - -/// TS: doStatus > should return error status on expired -/// TS: doStatus > should return error status on failed -/// TS: doStatus > should report an error status when video URL missing on done -/// TS: doStatus > should report an error status when respect_moderation is false +/// Checks that expired, failed, missing-URL and blocked statuses give non-retryable errors. partial: should return error status on expired; should return error status on failed; should report an error status when video URL missing on done; should report an error status when respect_moderation is false; full port in src/xai/tests/ #[tokio::test] async fn do_status_reports_terminal_failures_as_non_retryable_errors() { let cases = [ @@ -857,29 +839,7 @@ async fn do_status_reports_terminal_failures_as_non_retryable_errors() { } } -/// TS: doStatus > should pass headers to request -#[tokio::test] -async fn do_status_passes_headers() { - let server = server_with(json!({}), json_response(done_status())).await; - let model = model_with(&server, V1, &[("X-Custom", "value")]); - let mut o = base(); - o.headers = Some(HashMap::from([( - "X-Request-Header".to_string(), - "request-value".to_string(), - )])); - model - .do_status(&json!({"requestId": "req-123"}), &o) - .await - .unwrap(); - let request = server.received_requests().await.unwrap().remove(0); - assert_eq!(header(&request, "authorization"), "Bearer test-key"); - assert_eq!(header(&request, "x-custom"), "value"); - assert_eq!(header(&request, "x-request-header"), "request-value"); -} - -/// TS: doStatus > should include costInUsdTicks when returned in usage -/// TS: doStatus > should omit duration from metadata when absent in response -/// TS: doStatus > should omit progress from metadata when absent in response +/// Checks that costInUsdTicks is added and that duration and progress are omitted when absent. partial: should include costInUsdTicks when returned in usage; should omit duration from metadata when absent in response; should omit progress from metadata when absent in response; full port in src/xai/tests/ #[tokio::test] async fn do_status_metadata_includes_optional_fields_only_when_present() { let mut with_usage = done_status(); From 3f132a0b54f8e996d330f8b4cdf304e6fd17aee3 Mon Sep 17 00:00:00 2001 From: cunninghamcard-bit Date: Tue, 20 Oct 2026 08:29:29 +0000 Subject: [PATCH 5/7] test(xai): port the remaining upstream xAI tests One Rust file per upstream test file under src/xai/tests/, with the upstream fixtures and snapshots beside them. The error part of a stream error event is compared through a view that renames providerCode to code and takes type from the event: aimux has no AI_StreamProviderError, so the part is an AI_APICallError. Co-Authored-By: Claude Opus 5.5 (1M context) Claude-Session: https://claude.ai/code/session_01RoxcKU4zBD64Lri3e4Lrq5 --- aimux-providers/src/lib.rs | 3 + aimux-providers/src/test_support/mod.rs | 4 + aimux-providers/src/xai/mod.rs | 3 + aimux-providers/src/xai/tests/error.rs | 98 + aimux-providers/src/xai/tests/mod.rs | 9 + aimux-providers/src/xai/tests/provider.rs | 433 + .../responses/convert_responses_usage.rs | 174 + .../responses/convert_to_responses_input.rs | 844 ++ .../fixtures/xai-code-execution-tool.1.json | 68 + .../fixtures/xai-file-search-tool.1.json | 81 + .../xai-image-generation-tool.1.chunks.txt | 29 + .../fixtures/xai-image-generation-tool.1.json | 95 + .../fixtures/xai-text-streaming.1.chunks.txt | 698 ++ ...asoning-streaming-store-false.1.chunks.txt | 655 ++ ...text-with-reasoning-streaming.1.chunks.txt | 679 ++ .../fixtures/xai-web-search-tool.1.chunks.txt | 274 + .../fixtures/xai-web-search-tool.1.json | 90 + .../fixtures/xai-x-search-tool.1.json | 149 + .../fixtures/xai-x-search-tool.chunks.txt | 1757 ++++ .../src/xai/tests/responses/mod.rs | 5 + .../responses/responses_language_model.rs | 5200 ++++++++++ .../responses/responses_prepare_tools.rs | 934 ++ .../tests/responses/responses_raw_usage.rs | 248 + ...erpreter_tool_call_with_real_response.json | 13 + ...tool_call_with_provider_executed_true.json | 54 + ...x_search_tool_call_with_real_response.json | 153 + .../snapshots/should_stream_text_deltas.json | 3790 +++++++ ..._text_deltas_with_encrypted_reasoning.json | 3601 +++++++ ...uld_stream_text_deltas_with_reasoning.json | 3730 +++++++ ..._stream_web_search_with_real_response.json | 1444 +++ .../should_stream_x_search_tool_call.json | 9027 +++++++++++++++++ aimux-providers/src/xai/tests/speech_model.rs | 102 + .../xai/tests/supports_reasoning_effort.rs | 44 + .../src/xai/tests/transcription_model.rs | 858 ++ aimux-providers/src/xai/tests/video_model.rs | 1173 +++ scripts/check_provider_boundaries.sh | 4 +- 36 files changed, 36521 insertions(+), 2 deletions(-) create mode 100644 aimux-providers/src/test_support/mod.rs create mode 100644 aimux-providers/src/xai/tests/error.rs create mode 100644 aimux-providers/src/xai/tests/mod.rs create mode 100644 aimux-providers/src/xai/tests/provider.rs create mode 100644 aimux-providers/src/xai/tests/responses/convert_responses_usage.rs create mode 100644 aimux-providers/src/xai/tests/responses/convert_to_responses_input.rs create mode 100644 aimux-providers/src/xai/tests/responses/fixtures/xai-code-execution-tool.1.json create mode 100644 aimux-providers/src/xai/tests/responses/fixtures/xai-file-search-tool.1.json create mode 100644 aimux-providers/src/xai/tests/responses/fixtures/xai-image-generation-tool.1.chunks.txt create mode 100644 aimux-providers/src/xai/tests/responses/fixtures/xai-image-generation-tool.1.json create mode 100644 aimux-providers/src/xai/tests/responses/fixtures/xai-text-streaming.1.chunks.txt create mode 100644 aimux-providers/src/xai/tests/responses/fixtures/xai-text-with-reasoning-streaming-store-false.1.chunks.txt create mode 100644 aimux-providers/src/xai/tests/responses/fixtures/xai-text-with-reasoning-streaming.1.chunks.txt create mode 100644 aimux-providers/src/xai/tests/responses/fixtures/xai-web-search-tool.1.chunks.txt create mode 100644 aimux-providers/src/xai/tests/responses/fixtures/xai-web-search-tool.1.json create mode 100644 aimux-providers/src/xai/tests/responses/fixtures/xai-x-search-tool.1.json create mode 100644 aimux-providers/src/xai/tests/responses/fixtures/xai-x-search-tool.chunks.txt create mode 100644 aimux-providers/src/xai/tests/responses/mod.rs create mode 100644 aimux-providers/src/xai/tests/responses/responses_language_model.rs create mode 100644 aimux-providers/src/xai/tests/responses/responses_prepare_tools.rs create mode 100644 aimux-providers/src/xai/tests/responses/responses_raw_usage.rs create mode 100644 aimux-providers/src/xai/tests/responses/snapshots/should_include_code_interpreter_tool_call_with_real_response.json create mode 100644 aimux-providers/src/xai/tests/responses/snapshots/should_include_web_search_tool_call_with_provider_executed_true.json create mode 100644 aimux-providers/src/xai/tests/responses/snapshots/should_include_x_search_tool_call_with_real_response.json create mode 100644 aimux-providers/src/xai/tests/responses/snapshots/should_stream_text_deltas.json create mode 100644 aimux-providers/src/xai/tests/responses/snapshots/should_stream_text_deltas_with_encrypted_reasoning.json create mode 100644 aimux-providers/src/xai/tests/responses/snapshots/should_stream_text_deltas_with_reasoning.json create mode 100644 aimux-providers/src/xai/tests/responses/snapshots/should_stream_web_search_with_real_response.json create mode 100644 aimux-providers/src/xai/tests/responses/snapshots/should_stream_x_search_tool_call.json create mode 100644 aimux-providers/src/xai/tests/speech_model.rs create mode 100644 aimux-providers/src/xai/tests/supports_reasoning_effort.rs create mode 100644 aimux-providers/src/xai/tests/transcription_model.rs create mode 100644 aimux-providers/src/xai/tests/video_model.rs diff --git a/aimux-providers/src/lib.rs b/aimux-providers/src/lib.rs index 970691328..e8eb8d125 100644 --- a/aimux-providers/src/lib.rs +++ b/aimux-providers/src/lib.rs @@ -403,3 +403,6 @@ mod decision_support; pub use decision_support::DecisionProbabilitySource; mod runtime_decision; mod systemone; + +#[cfg(test)] +mod test_support; diff --git a/aimux-providers/src/test_support/mod.rs b/aimux-providers/src/test_support/mod.rs new file mode 100644 index 000000000..9bbf0e551 --- /dev/null +++ b/aimux-providers/src/test_support/mod.rs @@ -0,0 +1,4 @@ +//! Shared test helpers for the in-crate vendor tests. + +#[path = "../../tests/common/mock_fetch.rs"] +pub(crate) mod mock_fetch; diff --git a/aimux-providers/src/xai/mod.rs b/aimux-providers/src/xai/mod.rs index 29f81edaf..65a291313 100644 --- a/aimux-providers/src/xai/mod.rs +++ b/aimux-providers/src/xai/mod.rs @@ -465,3 +465,6 @@ impl ProviderDiscovery for XAIProvider { }) } } + +#[cfg(test)] +mod tests; diff --git a/aimux-providers/src/xai/tests/error.rs b/aimux-providers/src/xai/tests/error.rs new file mode 100644 index 000000000..1af3d8306 --- /dev/null +++ b/aimux-providers/src/xai/tests/error.rs @@ -0,0 +1,98 @@ +//! Upstream `xai-error.test.ts`. + +use aimux_core::ApiCallError; +use aimux_core::error::AiMuxError; +use aimux_provider_utils::{FetchResponse, ResponseHandlerInput}; +use reqwest::header::{CONTENT_TYPE, HeaderMap, HeaderValue}; +use reqwest::{StatusCode, Url}; +use serde_json::{Value, json}; + +use crate::xai::xai_failed_response_handler; + +/// The upstream `makeResponse` helper: a 400 with a JSON body. +fn make_response(url: &str, body: &Value) -> FetchResponse { + let mut headers = HeaderMap::new(); + headers.insert(CONTENT_TYPE, HeaderValue::from_static("application/json")); + FetchResponse::from_bytes( + StatusCode::BAD_REQUEST, + headers, + Url::parse(url).expect("valid URL"), + serde_json::to_vec(body).expect("body serializes").into(), + ) +} + +/// `xaiFailedResponseHandler({ url, requestBodyValues: {}, response })`, the +/// error value it returns. +async fn failed_response_error(url: &str, body: &Value) -> ApiCallError { + let output = xai_failed_response_handler() + .handle(ResponseHandlerInput { + url: url.to_string(), + request_body_values: json!({}), + response: make_response(url, body), + abort_signal: None, + max_json_response_bytes: None, + }) + .await + .expect("the failed-response handler builds an error"); + match output.value { + AiMuxError::ApiCall(error) => *error, + other => panic!("expected an APICallError, got {other:?}"), + } +} + +mod xai_failed_response_handler { + use super::*; + + /// upstream: @ai-sdk/xai src/xai-error.test.ts › xaiFailedResponseHandler › extracts message from the nested API error shape + #[tokio::test] + async fn extracts_message_from_the_nested_api_error_shape() { + let error = failed_response_error( + "https://api.x.ai/v1/images/generations", + &json!({ + "error": { + "message": "Invalid value: temperature must be between 0 and 2", + "type": "invalid_request_error", + "code": "invalid_value", + }, + }), + ) + .await; + + assert_eq!( + error.message, + "Invalid value: temperature must be between 0 and 2" + ); + } + + /// upstream: @ai-sdk/xai src/xai-error.test.ts › xaiFailedResponseHandler › extracts message and code from responses api error shape + #[tokio::test] + async fn extracts_message_and_code_from_responses_api_error_shape() { + let error = failed_response_error( + "https://api.x.ai/v1/responses", + &json!({ + "code": "Client specified an invalid argument", + "error": "Invalid request content: Each message must have at least one content element.", + }), + ) + .await; + + assert_eq!( + error.message, + "Client specified an invalid argument: Invalid request content: Each message must have at least one content element." + ); + } + + /// upstream: @ai-sdk/xai src/xai-error.test.ts › xaiFailedResponseHandler › extracts message from text to speech error shape + #[tokio::test] + async fn extracts_message_from_text_to_speech_error_shape() { + let error = failed_response_error( + "https://api.x.ai/v1/tts", + &json!({ + "error": "speed must be between 0.7 and 1.5", + }), + ) + .await; + + assert_eq!(error.message, "speed must be between 0.7 and 1.5"); + } +} diff --git a/aimux-providers/src/xai/tests/mod.rs b/aimux-providers/src/xai/tests/mod.rs new file mode 100644 index 000000000..eef440dcc --- /dev/null +++ b/aimux-providers/src/xai/tests/mod.rs @@ -0,0 +1,9 @@ +//! Ports of the `@ai-sdk/xai@5.0.12` test files. + +mod error; +mod provider; +mod responses; +mod speech_model; +mod supports_reasoning_effort; +mod transcription_model; +mod video_model; diff --git a/aimux-providers/src/xai/tests/provider.rs b/aimux-providers/src/xai/tests/provider.rs new file mode 100644 index 000000000..038d66904 --- /dev/null +++ b/aimux-providers/src/xai/tests/provider.rs @@ -0,0 +1,433 @@ +//! Upstream `xai-provider.test.ts`. +//! +//! The upstream file mocks the model classes and checks their constructor +//! arguments. Here each fact is checked through the model the provider builds: +//! its `provider()` and `model_id()`, and the URL and headers of the request +//! it sends, recorded by a [`MockFetch`]. +//! +//! The upstream `vi.mock` of `loadApiKey` returns `mock-api-key`; here the key +//! comes from `XAI_API_KEY`, which the tests set with [`EnvVar`]. The upstream +//! `VERSION` mock (`0.0.0-test`) has no counterpart: the user agent carries the +//! pinned `ai-sdk-xai/` token (see [`assert_headers`]). +//! +//! The `imageModel` cases are not ported: aimux has no xAI image model. + +use std::collections::HashMap; +use std::sync::Arc; + +use aimux_core::error::AiMuxError; +use aimux_core::options::CallOptions; +use aimux_core::provider::Provider; +use aimux_core::speech_model::{SpeechCallOptions, SpeechModel}; +use aimux_core::transcription_model::{AudioInput, TranscriptionCallOptions, TranscriptionModel}; +use aimux_core::video_model::{VideoCallOptions, VideoModel}; +use serde_json::{Value, json}; + +use crate::shared::aisdk_pins; +use crate::test_support::mock_fetch::{Canned, EnvVar, MockFetch, Seen}; +use crate::xai::{API_KEY_ENV_VAR, XAIProvider}; +use crate::{XAIProviderSettings, create_xai}; + +/// The key the upstream `loadApiKey` mock returns. +const MOCK_API_KEY: &str = "mock-api-key"; + +/// The upstream `loadApiKey` mock: the key every request uses, set through +/// `XAI_API_KEY`. Tests that hold it are `#[serial]`. +fn mock_api_key() -> EnvVar { + EnvVar::set(API_KEY_ENV_VAR, Some(MOCK_API_KEY)) +} + +/// The `ai-sdk-xai/` token of the pinned package: what the provider's +/// own headers carry. +fn user_agent() -> String { + format!("ai-sdk-xai/{}", aisdk_pins::XAI.version) +} + +/// A transport that answers every request with a 400, which fails the call +/// without a retry. The tests look at the request, not at the answer. +fn rejecting_fetch() -> Arc { + MockFetch::new(vec![Canned { + status: 400, + headers: vec![("content-type".into(), "application/json".into())], + body: br#"{"error":{"message":"rejected by the test transport"}}"#.to_vec(), + }]) +} + +/// The one request `fetch` recorded. +fn only_request(fetch: &MockFetch) -> Seen { + let mut seen = fetch.seen(); + assert_eq!(seen.len(), 1, "the call sends exactly one request"); + seen.remove(0) +} + +/// `toMatchObject` on the recorded headers: each expected header is present +/// with its value. +/// +/// The `user-agent` is matched by token. Upstream checks the provider's own +/// headers, which carry `ai-sdk-xai/` alone; the request also carries +/// the transport's `ai-sdk-provider-utils/` token, which that check +/// never sees. +fn assert_headers(seen: &Seen, expected: Value) { + for (name, value) in expected.as_object().expect("an object of headers") { + let actual = seen.headers.get(name).map(String::as_str); + let expected = value.as_str().expect("a string header value"); + if name == "user-agent" { + assert!( + actual.is_some_and(|agent| agent.split(' ').any(|token| token == expected)), + "header {name}: {actual:?} does not carry {expected}" + ); + } else { + assert_eq!(actual, Some(expected), "header {name}"); + } + } +} + +/// The error of a call that the rejecting transport answered. +fn rejected(result: Result) -> AiMuxError { + match result { + Ok(_) => panic!("the 400 answer of the test transport is an error"), + Err(error) => error, + } +} + +/// `provider.videoModel(id)`: the trait factory. +fn video_model_of(provider: &XAIProvider, model_id: &str) -> Arc { + provider + .video_model(model_id) + .expect("xAI has a video model") + .expect("the video model builds") +} + +/// `provider.speechModel()`: xAI's speech model takes no model id, so the +/// trait is given the empty id. +fn speech_model_of(provider: &XAIProvider) -> Arc { + provider + .speech_model("") + .expect("xAI has a speech model") + .expect("the speech model builds") +} + +/// `provider.transcriptionModel()`: no model id, as for speech. +fn transcription_model_of(provider: &XAIProvider) -> Arc { + provider + .transcription_model("") + .expect("xAI has a transcription model") + .expect("the transcription model builds") +} + +/// The call options of a transcription request. The audio is arbitrary: the +/// tests only look at the request the model sends. +fn transcription_call() -> TranscriptionCallOptions { + TranscriptionCallOptions::new(AudioInput::Binary(vec![1, 2, 3]), "audio/wav") +} + +mod x_ai_provider { + use super::*; + + mod create_xai { + use super::*; + + /// upstream: @ai-sdk/xai src/xai-provider.test.ts › xAIProvider › createXAI › should create an XAIProvider instance with default options + #[tokio::test] + #[serial_test::serial] + async fn should_create_an_xai_provider_instance_with_default_options() { + // The upstream `loadApiKey` is called with no key, `XAI_API_KEY` + // and "xAI API key": the key is unset here and comes from the + // environment. + let _key = EnvVar::set(API_KEY_ENV_VAR, None); + let fetch = MockFetch::new(Vec::new()); + let provider = create_xai(XAIProviderSettings { + fetch: Some(fetch.transport()), + ..Default::default() + }) + .expect("creating an xAI provider does not fail"); + let model = provider.call("model-id"); + + let error = model + .do_generate(&CallOptions::default()) + .await + .expect_err("no key is set"); + + assert!( + matches!( + &error, + AiMuxError::LoadApiKey { env_var, description } + if env_var == "XAI_API_KEY" && description == "xAI API key" + ), + "{error:?}" + ); + assert!( + fetch.seen().is_empty(), + "the key is loaded before any request is sent" + ); + } + + /// upstream: @ai-sdk/xai src/xai-provider.test.ts › xAIProvider › createXAI › should create an XAIProvider instance with custom options + #[tokio::test] + async fn should_create_an_xai_provider_instance_with_custom_options() { + let fetch = rejecting_fetch(); + let provider = create_xai(XAIProviderSettings { + api_key: Some("custom-key".to_string()), + base_url: Some("https://custom.url".to_string()), + headers: Some(HashMap::from([( + "Custom-Header".to_string(), + Some("value".to_string()), + )])), + fetch: Some(fetch.transport()), + ..Default::default() + }) + .expect("creating an xAI provider does not fail"); + let model = provider.call("model-id"); + + rejected(model.do_generate(&CallOptions::default()).await); + + let seen = only_request(&fetch); + assert_eq!(seen.url, "https://custom.url/responses"); + assert_headers( + &seen, + json!({ + "authorization": "Bearer custom-key", + "custom-header": "value", + }), + ); + } + + /// upstream: @ai-sdk/xai src/xai-provider.test.ts › xAIProvider › createXAI › should return a responses model when called as a function + #[test] + fn should_return_a_responses_model_when_called_as_a_function() { + let provider = create_xai(XAIProviderSettings::default()) + .expect("creating an xAI provider does not fail"); + let model = provider.call("foo-model-id"); + + assert_eq!(model.provider(), "xai.responses"); + assert_eq!(model.model_id(), "foo-model-id"); + } + } + + // describe('imageModel'): not ported. aimux has no xAI image model; + // `Provider::image_model` answers `NoSuchModel`. + + mod video_model { + use super::*; + + /// upstream: @ai-sdk/xai src/xai-provider.test.ts › xAIProvider › videoModel › should construct a video model with correct configuration + #[tokio::test] + #[serial_test::serial] + async fn should_construct_a_video_model_with_correct_configuration() { + let _key = mock_api_key(); + let fetch = rejecting_fetch(); + let provider = create_xai(XAIProviderSettings { + fetch: Some(fetch.transport()), + ..Default::default() + }) + .expect("creating an xAI provider does not fail"); + + let model = video_model_of(&provider, "grok-imagine-video"); + + assert_eq!(model.provider(), "xai.video"); + assert_eq!(model.model_id(), "grok-imagine-video"); + rejected(model.do_start(&VideoCallOptions::new("a prompt")).await); + assert_eq!( + only_request(&fetch).url, + "https://api.x.ai/v1/videos/generations" + ); + } + + /// upstream: @ai-sdk/xai src/xai-provider.test.ts › xAIProvider › videoModel › should use custom baseURL for video model + #[tokio::test] + #[serial_test::serial] + async fn should_use_custom_base_url_for_video_model() { + let _key = mock_api_key(); + let fetch = rejecting_fetch(); + let provider = create_xai(XAIProviderSettings { + base_url: Some("https://custom.xai.api".to_string()), + fetch: Some(fetch.transport()), + ..Default::default() + }) + .expect("creating an xAI provider does not fail"); + + let model = video_model_of(&provider, "grok-imagine-video"); + + rejected(model.do_start(&VideoCallOptions::new("a prompt")).await); + assert_eq!( + only_request(&fetch).url, + "https://custom.xai.api/videos/generations" + ); + } + + /// upstream: @ai-sdk/xai src/xai-provider.test.ts › xAIProvider › videoModel › should pass custom headers to video model + #[tokio::test] + #[serial_test::serial] + async fn should_pass_custom_headers_to_video_model() { + let _key = mock_api_key(); + let fetch = rejecting_fetch(); + let provider = create_xai(XAIProviderSettings { + headers: Some(HashMap::from([( + "Custom-Header".to_string(), + Some("test-value".to_string()), + )])), + fetch: Some(fetch.transport()), + ..Default::default() + }) + .expect("creating an xAI provider does not fail"); + + let model = video_model_of(&provider, "grok-imagine-video"); + + rejected(model.do_start(&VideoCallOptions::new("a prompt")).await); + assert_headers( + &only_request(&fetch), + json!({ + "authorization": "Bearer mock-api-key", + "custom-header": "test-value", + "user-agent": user_agent(), + }), + ); + } + + /// upstream: @ai-sdk/xai src/xai-provider.test.ts › xAIProvider › videoModel › should create a video model via .video() alias + #[test] + fn should_create_a_video_model_via_video_alias() { + let provider = create_xai(XAIProviderSettings::default()) + .expect("creating an xAI provider does not fail"); + + let model = provider.video("grok-imagine-video"); + + assert_eq!(model.provider(), "xai.video"); + assert_eq!(model.model_id(), "grok-imagine-video"); + } + } + + mod speech_model { + use super::*; + + /// upstream: @ai-sdk/xai src/xai-provider.test.ts › xAIProvider › speechModel › should construct a speech model with correct configuration + #[tokio::test] + #[serial_test::serial] + async fn should_construct_a_speech_model_with_correct_configuration() { + let _key = mock_api_key(); + let fetch = rejecting_fetch(); + let provider = create_xai(XAIProviderSettings { + fetch: Some(fetch.transport()), + ..Default::default() + }) + .expect("creating an xAI provider does not fail"); + + let model = speech_model_of(&provider); + + assert_eq!(model.provider(), "xai.speech"); + assert_eq!(model.model_id(), ""); + rejected(model.do_generate(&SpeechCallOptions::new("hello")).await); + assert_eq!(only_request(&fetch).url, "https://api.x.ai/v1/tts"); + } + + /// upstream: @ai-sdk/xai src/xai-provider.test.ts › xAIProvider › speechModel › should use custom baseURL and headers for speech models + #[tokio::test] + #[serial_test::serial] + async fn should_use_custom_base_url_and_headers_for_speech_models() { + let _key = mock_api_key(); + let fetch = rejecting_fetch(); + let provider = create_xai(XAIProviderSettings { + base_url: Some("https://custom.xai.api".to_string()), + headers: Some(HashMap::from([( + "Custom-Header".to_string(), + Some("test-value".to_string()), + )])), + fetch: Some(fetch.transport()), + ..Default::default() + }) + .expect("creating an xAI provider does not fail"); + + let model = provider.speech(); + + rejected(model.do_generate(&SpeechCallOptions::new("hello")).await); + let seen = only_request(&fetch); + assert_eq!(seen.url, "https://custom.xai.api/tts"); + assert_headers( + &seen, + json!({ + "authorization": "Bearer mock-api-key", + "custom-header": "test-value", + "user-agent": user_agent(), + }), + ); + } + + /// upstream: @ai-sdk/xai src/xai-provider.test.ts › xAIProvider › speechModel › should create a speech model via .speech() alias + #[test] + fn should_create_a_speech_model_via_speech_alias() { + let provider = create_xai(XAIProviderSettings::default()) + .expect("creating an xAI provider does not fail"); + + let model = provider.speech(); + + assert_eq!(model.provider(), "xai.speech"); + } + } + + mod transcription_model { + use super::*; + + /// upstream: @ai-sdk/xai src/xai-provider.test.ts › xAIProvider › transcriptionModel › should construct a transcription model with correct configuration + #[tokio::test] + #[serial_test::serial] + async fn should_construct_a_transcription_model_with_correct_configuration() { + let _key = mock_api_key(); + let fetch = rejecting_fetch(); + let provider = create_xai(XAIProviderSettings { + fetch: Some(fetch.transport()), + ..Default::default() + }) + .expect("creating an xAI provider does not fail"); + + let model = transcription_model_of(&provider); + + assert_eq!(model.provider(), "xai.transcription"); + assert_eq!(model.model_id(), ""); + rejected(model.do_generate(&transcription_call()).await); + assert_eq!(only_request(&fetch).url, "https://api.x.ai/v1/stt"); + } + + /// upstream: @ai-sdk/xai src/xai-provider.test.ts › xAIProvider › transcriptionModel › should use custom baseURL and headers for transcription models + #[tokio::test] + #[serial_test::serial] + async fn should_use_custom_base_url_and_headers_for_transcription_models() { + let _key = mock_api_key(); + let fetch = rejecting_fetch(); + let provider = create_xai(XAIProviderSettings { + base_url: Some("https://custom.xai.api".to_string()), + headers: Some(HashMap::from([( + "Custom-Header".to_string(), + Some("test-value".to_string()), + )])), + fetch: Some(fetch.transport()), + ..Default::default() + }) + .expect("creating an xAI provider does not fail"); + + let model = provider.transcription(); + + rejected(model.do_generate(&transcription_call()).await); + let seen = only_request(&fetch); + assert_eq!(seen.url, "https://custom.xai.api/stt"); + assert_headers( + &seen, + json!({ + "authorization": "Bearer mock-api-key", + "custom-header": "test-value", + "user-agent": user_agent(), + }), + ); + } + + /// upstream: @ai-sdk/xai src/xai-provider.test.ts › xAIProvider › transcriptionModel › should create a transcription model via .transcription() alias + #[test] + fn should_create_a_transcription_model_via_transcription_alias() { + let provider = create_xai(XAIProviderSettings::default()) + .expect("creating an xAI provider does not fail"); + + let model = provider.transcription(); + + assert_eq!(model.provider(), "xai.transcription"); + } + } +} diff --git a/aimux-providers/src/xai/tests/responses/convert_responses_usage.rs b/aimux-providers/src/xai/tests/responses/convert_responses_usage.rs new file mode 100644 index 000000000..6697c3606 --- /dev/null +++ b/aimux-providers/src/xai/tests/responses/convert_responses_usage.rs @@ -0,0 +1,174 @@ +//! Upstream `responses/convert-xai-responses-usage.test.ts`. +//! +//! `undefined` in the upstream expectations is an absent key in the serialized +//! `Usage` (serde skips `None`), so the expected JSON omits it. + +use aimux_core::types::Usage; +use serde_json::{Value, json}; + +use crate::xai::responses::convert::convert_xai_responses_usage; +use crate::xai::responses::types::XaiResponsesUsage; + +/// `convertXaiResponsesUsage(usage)` for a usage object as the API returns it. +fn convert(usage: Value) -> Usage { + let usage: XaiResponsesUsage = serde_json::from_value(usage).expect("valid xAI usage"); + convert_xai_responses_usage(&usage) +} + +mod convert_xai_responses_usage { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/convert-xai-responses-usage.test.ts › convertXaiResponsesUsage › should convert basic usage without caching or reasoning + #[test] + fn should_convert_basic_usage_without_caching_or_reasoning() { + let result = convert(json!({ + "input_tokens": 100, + "output_tokens": 50, + })); + + assert_eq!( + serde_json::to_value(&result).expect("usage serializes"), + json!({ + "inputTokens": { + "cacheRead": 0, + "noCache": 100, + "total": 100, + }, + "outputTokens": { + "reasoning": 0, + "text": 50, + "total": 50, + }, + "raw": { + "input_tokens": 100, + "output_tokens": 50, + }, + }) + ); + } + + /// upstream: @ai-sdk/xai src/responses/convert-xai-responses-usage.test.ts › convertXaiResponsesUsage › should convert usage with reasoning tokens + #[test] + fn should_convert_usage_with_reasoning_tokens() { + let result = convert(json!({ + "input_tokens": 1941, + "output_tokens": 583, + "total_tokens": 2524, + "output_tokens_details": { + "reasoning_tokens": 380, + }, + })); + + assert_eq!( + serde_json::to_value(&result.output_tokens).expect("usage serializes"), + json!({ + "reasoning": 380, + "text": 203, + "total": 583, + }) + ); + } + + /// upstream: @ai-sdk/xai src/responses/convert-xai-responses-usage.test.ts › convertXaiResponsesUsage › should convert usage with cached input tokens + #[test] + fn should_convert_usage_with_cached_input_tokens() { + let result = convert(json!({ + "input_tokens": 200, + "output_tokens": 50, + "input_tokens_details": { + "cached_tokens": 150, + }, + })); + + assert_eq!( + serde_json::to_value(&result.input_tokens).expect("usage serializes"), + json!({ + "cacheRead": 150, + "noCache": 50, + "total": 200, + }) + ); + } + + /// upstream: @ai-sdk/xai src/responses/convert-xai-responses-usage.test.ts › convertXaiResponsesUsage › should handle cached_tokens exceeding input_tokens (non-inclusive reporting) + #[test] + fn should_handle_cached_tokens_exceeding_input_tokens_non_inclusive_reporting() { + let result = convert(json!({ + "input_tokens": 4142, + "output_tokens": 254, + "input_tokens_details": { + "cached_tokens": 4328, + }, + })); + + assert_eq!( + serde_json::to_value(&result.input_tokens).expect("usage serializes"), + json!({ + "cacheRead": 4328, + "noCache": 4142, + "total": 8470, + }) + ); + } + + /// upstream: @ai-sdk/xai src/responses/convert-xai-responses-usage.test.ts › convertXaiResponsesUsage › should convert usage with both cached input and reasoning + #[test] + fn should_convert_usage_with_both_cached_input_and_reasoning() { + let result = convert(json!({ + "input_tokens": 200, + "output_tokens": 583, + "input_tokens_details": { + "cached_tokens": 150, + }, + "output_tokens_details": { + "reasoning_tokens": 380, + }, + })); + + assert_eq!( + serde_json::to_value(&result).expect("usage serializes"), + json!({ + "inputTokens": { + "cacheRead": 150, + "noCache": 50, + "total": 200, + }, + "outputTokens": { + "reasoning": 380, + "text": 203, + "total": 583, + }, + "raw": { + "input_tokens": 200, + "input_tokens_details": { + "cached_tokens": 150, + }, + "output_tokens": 583, + "output_tokens_details": { + "reasoning_tokens": 380, + }, + }, + }) + ); + } + + /// upstream: @ai-sdk/xai src/responses/convert-xai-responses-usage.test.ts › convertXaiResponsesUsage › should preserve raw usage data + #[test] + fn should_preserve_raw_usage_data() { + let raw_usage = json!({ + "input_tokens": 12, + "output_tokens": 319, + "total_tokens": 331, + "input_tokens_details": { + "cached_tokens": 2, + }, + "output_tokens_details": { + "reasoning_tokens": 317, + }, + }); + + let result = convert(raw_usage.clone()); + + assert_eq!(result.raw, raw_usage.as_object().cloned()); + } +} diff --git a/aimux-providers/src/xai/tests/responses/convert_to_responses_input.rs b/aimux-providers/src/xai/tests/responses/convert_to_responses_input.rs new file mode 100644 index 000000000..85d628940 --- /dev/null +++ b/aimux-providers/src/xai/tests/responses/convert_to_responses_input.rs @@ -0,0 +1,844 @@ +//! Upstream `responses/convert-to-xai-responses-input.test.ts`. +//! +//! The upstream converter is `async` and returns `{ input, inputWarnings }`; +//! the Rust `convert_to_xai_responses_input` is synchronous and returns the +//! pair `(input, warnings)`, so the `await`s have no counterpart here. The +//! upstream `store: true` option has none either: upstream accepts it and +//! never reads it. +//! +//! Prompts are written as the JSON the Rust prompt types deserialize from. +//! The one exception is `new Uint8Array(...)`: JSON carries bytes as base64, +//! so those parts are built as `FileBytes::Binary` in Rust. + +use aimux_core::error::AiMuxError; +use aimux_core::language_model_message::{ + FilePart, LanguageModelMessage, LanguageModelPrompt, TextPart, UserPart, +}; +use aimux_core::shared::{FileBytes, FileData}; +use aimux_core::types::Warning; +use serde_json::{Value, json}; + +use crate::xai::options::NAMESPACE; +use crate::xai::responses::convert::convert_to_xai_responses_input; + +/// The prompt in its JSON form, as the Rust prompt types deserialize it. +fn parse_prompt(prompt: Value) -> LanguageModelPrompt { + serde_json::from_value(prompt).expect("the prompt has the Rust prompt shape") +} + +/// `convertToXaiResponsesInput({ prompt })`: the `input` array and the +/// `inputWarnings`. +fn convert(prompt: &LanguageModelPrompt) -> (Value, Vec) { + let (input, warnings) = convert_to_xai_responses_input(prompt) + .expect("upstream converter does not reject this prompt"); + (Value::Array(input), warnings) +} + +/// The error `convertToXaiResponsesInput` rejects the prompt with. +fn convert_error(prompt: &LanguageModelPrompt) -> AiMuxError { + convert_to_xai_responses_input(prompt).expect_err("upstream converter rejects this prompt") +} + +/// `[{ role: 'user', content: [{ type: 'text', text }, { type: 'file', +/// mediaType, data: { type: 'data', data: new Uint8Array(bytes) } }] }]`. +fn user_text_and_binary_file(text: &str, media_type: &str, bytes: &[u8]) -> LanguageModelPrompt { + vec![LanguageModelMessage::User { + content: vec![ + UserPart::Text(TextPart { + text: text.to_string(), + provider_options: None, + }), + UserPart::File(FilePart { + data: FileData::Data { + data: FileBytes::Binary(bytes.to_vec()), + }, + media_type: media_type.to_string(), + filename: None, + provider_options: None, + }), + ], + provider_options: None, + }] +} + +mod convert_to_xai_responses_input { + use super::*; + + mod system_messages { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › system messages › should convert system messages + #[test] + fn should_convert_system_messages() { + let (input, warnings) = convert(&parse_prompt(json!([ + { "role": "system", "content": "you are a helpful assistant" }, + ]))); + + assert_eq!( + input, + json!([ + { + "content": "you are a helpful assistant", + "role": "system", + }, + ]) + ); + assert_eq!(warnings, Vec::::new()); + } + } + + mod user_messages { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › user messages › should convert single text part + #[test] + fn should_convert_single_text_part() { + let (input, _) = convert(&parse_prompt(json!([ + { "role": "user", "content": [{ "type": "text", "text": "hello" }] }, + ]))); + + assert_eq!( + input, + json!([ + { + "content": [ + { + "text": "hello", + "type": "input_text", + }, + ], + "role": "user", + }, + ]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › user messages › should concatenate multiple text parts + #[test] + fn should_concatenate_multiple_text_parts() { + let (input, _) = convert(&parse_prompt(json!([ + { + "role": "user", + "content": [ + { "type": "text", "text": "hello " }, + { "type": "text", "text": "world" }, + ], + }, + ]))); + + assert_eq!( + input, + json!([ + { + "content": [ + { + "text": "hello ", + "type": "input_text", + }, + { + "text": "world", + "type": "input_text", + }, + ], + "role": "user", + }, + ]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › user messages › should convert image file parts with URL + #[test] + fn should_convert_image_file_parts_with_url() { + let (input, warnings) = convert(&parse_prompt(json!([ + { + "role": "user", + "content": [ + { "type": "text", "text": "what is in this image" }, + { + "type": "file", + "mediaType": "image/jpeg", + "data": { "type": "url", "url": "https://example.com/image.jpg" }, + }, + ], + }, + ]))); + + assert_eq!( + input, + json!([ + { + "content": [ + { + "text": "what is in this image", + "type": "input_text", + }, + { + "image_url": "https://example.com/image.jpg", + "type": "input_image", + }, + ], + "role": "user", + }, + ]) + ); + assert_eq!(warnings, Vec::::new()); + } + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › user messages › should convert image file parts with base64 data + #[test] + fn should_convert_image_file_parts_with_base64_data() { + let (input, _) = convert(&user_text_and_binary_file( + "describe this", + "image/png", + &[1, 2, 3], + )); + + assert_eq!( + input, + json!([ + { + "content": [ + { + "text": "describe this", + "type": "input_text", + }, + { + "image_url": "data:image/png;base64,AQID", + "type": "input_image", + }, + ], + "role": "user", + }, + ]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › user messages › should convert non-image file parts with URL to input_file with file_url + #[test] + fn should_convert_non_image_file_parts_with_url_to_input_file_with_file_url() { + let (input, warnings) = convert(&parse_prompt(json!([ + { + "role": "user", + "content": [ + { "type": "text", "text": "summarize this PDF" }, + { + "type": "file", + "mediaType": "application/pdf", + "data": { "type": "url", "url": "https://example.com/document.pdf" }, + }, + ], + }, + ]))); + + assert_eq!( + input, + json!([ + { + "content": [ + { + "text": "summarize this PDF", + "type": "input_text", + }, + { + "file_url": "https://example.com/document.pdf", + "type": "input_file", + }, + ], + "role": "user", + }, + ]) + ); + assert_eq!(warnings, Vec::::new()); + } + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › user messages › should convert text/* file parts with URL to input_file with file_url + #[test] + fn should_convert_text_file_parts_with_url_to_input_file_with_file_url() { + let (input, _) = convert(&parse_prompt(json!([ + { + "role": "user", + "content": [ + { "type": "text", "text": "analyze this CSV" }, + { + "type": "file", + "mediaType": "text/csv", + "data": { "type": "url", "url": "https://example.com/data.csv" }, + }, + ], + }, + ]))); + + assert_eq!( + input, + json!([ + { + "content": [ + { + "text": "analyze this CSV", + "type": "input_text", + }, + { + "file_url": "https://example.com/data.csv", + "type": "input_file", + }, + ], + "role": "user", + }, + ]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › user messages › should throw for non-image file parts provided as inline bytes + #[test] + fn should_throw_for_non_image_file_parts_provided_as_inline_bytes() { + let error = convert_error(&user_text_and_binary_file( + "check this file", + "application/pdf", + &[1, 2, 3], + )); + + assert!( + matches!(error, AiMuxError::UnsupportedFunctionality(_)), + "{error:?}" + ); + assert!( + error + .to_string() + .contains("file part media type application/pdf"), + "{error}" + ); + } + } + + mod assistant_messages { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › assistant messages › should convert text content + #[test] + fn should_convert_text_content() { + let (input, _) = convert(&parse_prompt(json!([ + { + "role": "assistant", + "content": [{ "type": "text", "text": "hi there" }], + }, + ]))); + + assert_eq!( + input, + json!([ + { + "content": "hi there", + "role": "assistant", + }, + ]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › assistant messages › should handle client-side tool-call parts + #[test] + fn should_handle_client_side_tool_call_parts() { + let (input, _) = convert(&parse_prompt(json!([ + { + "role": "assistant", + "content": [ + { + "type": "tool-call", + "toolCallId": "call_123", + "toolName": "weather", + "input": { "location": "sf" }, + }, + ], + }, + ]))); + + assert_eq!( + input, + json!([ + { + "arguments": "{\"location\":\"sf\"}", + "call_id": "call_123", + "id": "call_123", + "name": "weather", + "status": "completed", + "type": "function_call", + }, + ]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › assistant messages › should handle client-side tool-call parts named like server-side tools + #[test] + fn should_handle_client_side_tool_call_parts_named_like_server_side_tools() { + let (input, _) = convert(&parse_prompt(json!([ + { + "role": "assistant", + "content": [ + { + "type": "tool-call", + "toolCallId": "call_ws", + "toolName": "web_search", + "input": { "query": "latest news" }, + }, + ], + }, + ]))); + + assert_eq!( + input, + json!([ + { + "arguments": "{\"query\":\"latest news\"}", + "call_id": "call_ws", + "id": "call_ws", + "name": "web_search", + "status": "completed", + "type": "function_call", + }, + ]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › assistant messages › should skip server-side tool-call parts + #[test] + fn should_skip_server_side_tool_call_parts() { + let (input, _) = convert(&parse_prompt(json!([ + { + "role": "assistant", + "content": [ + { + "type": "tool-call", + "toolCallId": "ws_123", + "toolName": "web_search", + "input": {}, + "providerExecuted": true, + }, + ], + }, + ]))); + + assert_eq!(input, json!([])); + } + } + + mod tool_messages { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › tool messages › should convert tool-result to function_call_output with json + #[test] + fn should_convert_tool_result_to_function_call_output_with_json() { + let (input, _) = convert(&parse_prompt(json!([ + { + "role": "tool", + "content": [ + { + "type": "tool-result", + "toolCallId": "call_123", + "toolName": "weather", + "output": { "type": "json", "value": { "temp": 72 } }, + }, + ], + }, + ]))); + + assert_eq!( + input, + json!([ + { + "call_id": "call_123", + "output": "{\"temp\":72}", + "type": "function_call_output", + }, + ]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › tool messages › should handle text output + #[test] + fn should_handle_text_output() { + let (input, _) = convert(&parse_prompt(json!([ + { + "role": "tool", + "content": [ + { + "type": "tool-result", + "toolCallId": "call_123", + "toolName": "weather", + "output": { "type": "text", "value": "sunny, 72 degrees" }, + }, + ], + }, + ]))); + + assert_eq!( + input, + json!([ + { + "call_id": "call_123", + "output": "sunny, 72 degrees", + "type": "function_call_output", + }, + ]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › tool messages › should preserve image URLs in content output + #[test] + fn should_preserve_image_urls_in_content_output() { + let (input, _) = convert(&parse_prompt(json!([ + { + "role": "tool", + "content": [ + { + "type": "tool-result", + "toolCallId": "call_123", + "toolName": "inspectImage", + "output": { + "type": "content", + "value": [ + { + "type": "text", + "text": "The requested image is attached.", + }, + { + "type": "file", + "mediaType": "image/png", + "data": { + "type": "url", + "url": "https://example.com/image.png", + }, + }, + ], + }, + }, + ], + }, + ]))); + + assert_eq!( + input, + json!([ + { + "call_id": "call_123", + "output": [ + { + "text": "The requested image is attached.", + "type": "input_text", + }, + { + "image_url": "https://example.com/image.png", + "type": "input_image", + }, + ], + "type": "function_call_output", + }, + ]) + ); + } + } + + mod multi_turn_conversations { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › multi-turn conversations › should handle full conversation with client-side tool calls + #[test] + fn should_handle_full_conversation_with_client_side_tool_calls() { + let (input, _) = convert(&parse_prompt(json!([ + { + "role": "user", + "content": [{ "type": "text", "text": "whats the weather" }], + }, + { + "role": "assistant", + "content": [ + { + "type": "tool-call", + "toolCallId": "call_123", + "toolName": "weather", + "input": { "location": "sf" }, + }, + ], + }, + { + "role": "tool", + "content": [ + { + "type": "tool-result", + "toolCallId": "call_123", + "toolName": "weather", + "output": { "type": "json", "value": { "temp": 72 } }, + }, + ], + }, + { + "role": "assistant", + "content": [{ "type": "text", "text": "its 72 degrees" }], + }, + ]))); + + assert_eq!( + input, + json!([ + { + "content": [ + { + "text": "whats the weather", + "type": "input_text", + }, + ], + "role": "user", + }, + { + "arguments": "{\"location\":\"sf\"}", + "call_id": "call_123", + "id": "call_123", + "name": "weather", + "status": "completed", + "type": "function_call", + }, + { + "call_id": "call_123", + "output": "{\"temp\":72}", + "type": "function_call_output", + }, + { + "content": "its 72 degrees", + "role": "assistant", + }, + ]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › multi-turn conversations › should handle conversation with server-side tool calls and item references + #[test] + fn should_handle_conversation_with_server_side_tool_calls_and_item_references() { + let (input, _) = convert(&parse_prompt(json!([ + { + "role": "user", + "content": [{ "type": "text", "text": "search for ai news" }], + }, + { + "role": "assistant", + "content": [ + { + "type": "tool-call", + "toolCallId": "ws_123", + "toolName": "web_search", + "input": {}, + "providerExecuted": true, + }, + { + "type": "tool-result", + "toolCallId": "ws_123", + "toolName": "web_search", + "output": { "type": "json", "value": {} }, + }, + { "type": "text", "text": "here are the results" }, + ], + }, + ]))); + + assert_eq!( + input, + json!([ + { + "content": [ + { + "text": "search for ai news", + "type": "input_text", + }, + ], + "role": "user", + }, + { + "content": "here are the results", + "role": "assistant", + }, + ]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › multi-turn conversations › should round-trip reasoning with encrypted content in multi-turn + #[test] + fn should_round_trip_reasoning_with_encrypted_content_in_multi_turn() { + let (input, warnings) = convert(&parse_prompt(json!([ + { + "role": "user", + "content": [{ "type": "text", "text": "What is the capital of France?" }], + }, + { + "role": "assistant", + "content": [ + { + "type": "reasoning", + "text": "The user is asking about geography.", + "providerOptions": { + (NAMESPACE): { + "itemId": "rs_789", + "reasoningEncryptedContent": "encrypted_xyz", + }, + }, + }, + { "type": "text", "text": "The capital of France is Paris." }, + ], + }, + { + "role": "user", + "content": [{ "type": "text", "text": "And what about Germany?" }], + }, + ]))); + + assert_eq!( + input, + json!([ + { + "content": [ + { + "text": "What is the capital of France?", + "type": "input_text", + }, + ], + "role": "user", + }, + { + "encrypted_content": "encrypted_xyz", + "id": "rs_789", + "status": "completed", + "summary": [ + { + "text": "The user is asking about geography.", + "type": "summary_text", + }, + ], + "type": "reasoning", + }, + { + "content": "The capital of France is Paris.", + "role": "assistant", + }, + { + "content": [ + { + "text": "And what about Germany?", + "type": "input_text", + }, + ], + "role": "user", + }, + ]) + ); + assert_eq!(warnings, Vec::::new()); + } + } + + mod top_level_only_media_type_resolution { + use super::*; + + const PNG_BASE64: &str = "iVBORw0KGgo="; + + /// The `content[0]` of the single user message the prompt converts to. + fn first_content_part(prompt: Value) -> Value { + let (input, _) = convert(&parse_prompt(prompt)); + input[0]["content"][0].clone() + } + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › top-level-only media type resolution › passes full image/png through unchanged for inline data + #[test] + fn passes_full_image_png_through_unchanged_for_inline_data() { + let part = first_content_part(json!([ + { + "role": "user", + "content": [ + { + "type": "file", + "mediaType": "image/png", + "data": { "type": "data", "data": PNG_BASE64 }, + }, + ], + }, + ])); + + assert_eq!( + part, + json!({ + "type": "input_image", + "image_url": format!("data:image/png;base64,{PNG_BASE64}"), + }) + ); + } + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › top-level-only media type resolution › detects image subtype from inline bytes for top-level "image" + #[test] + fn detects_image_subtype_from_inline_bytes_for_top_level_image() { + let part = first_content_part(json!([ + { + "role": "user", + "content": [ + { + "type": "file", + "mediaType": "image", + "data": { "type": "data", "data": PNG_BASE64 }, + }, + ], + }, + ])); + + assert_eq!( + part, + json!({ + "type": "input_image", + "image_url": format!("data:image/png;base64,{PNG_BASE64}"), + }) + ); + } + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › top-level-only media type resolution › passes through URL source for top-level-only image + #[test] + fn passes_through_url_source_for_top_level_only_image() { + let part = first_content_part(json!([ + { + "role": "user", + "content": [ + { + "type": "file", + "mediaType": "image", + "data": { "type": "url", "url": "https://example.com/x.png" }, + }, + ], + }, + ])); + + assert_eq!( + part, + json!({ + "type": "input_image", + "image_url": "https://example.com/x.png", + }) + ); + } + + /// upstream: @ai-sdk/xai src/responses/convert-to-xai-responses-input.test.ts › convertToXaiResponsesInput › top-level-only media type resolution › normalizes image/* wildcard via detection + #[test] + fn normalizes_image_wildcard_via_detection() { + let part = first_content_part(json!([ + { + "role": "user", + "content": [ + { + "type": "file", + "mediaType": "image/*", + "data": { "type": "data", "data": PNG_BASE64 }, + }, + ], + }, + ])); + + assert_eq!( + part, + json!({ + "type": "input_image", + "image_url": format!("data:image/png;base64,{PNG_BASE64}"), + }) + ); + } + } +} diff --git a/aimux-providers/src/xai/tests/responses/fixtures/xai-code-execution-tool.1.json b/aimux-providers/src/xai/tests/responses/fixtures/xai-code-execution-tool.1.json new file mode 100644 index 000000000..be990cc5a --- /dev/null +++ b/aimux-providers/src/xai/tests/responses/fixtures/xai-code-execution-tool.1.json @@ -0,0 +1,68 @@ +{ + "created_at": 1761768921, + "id": "5138abcf-4c4e-b0ab-7e0b-f4c81b98f455_us-east-1", + "max_output_tokens": null, + "model": "grok-4-fast-reasoning", + "object": "response", + "output": [ + { + "arguments": "{\"code\":\"def fibonacci(n):\\n if n <= 0:\\n return 0\\n elif n == 1:\\n return 1\\n else:\\n a, b = 0, 1\\n for _ in range(2, n + 1):\\n a, b = b, a + b\\n return b\\n\\nprint(fibonacci(10))\"}", + "call_id": "", + "name": "code_execution", + "type": "code_interpreter_call", + "id": "fc_5138abcf-4c4e-b0ab-7e0b-f4c81b98f455_us-east-1_0", + "status": "completed" + }, + { + "content": [ + { + "type": "output_text", + "text": "55", + "logprobs": [], + "annotations": [] + } + ], + "id": "msg_5138abcf-4c4e-b0ab-7e0b-f4c81b98f455_us-east-1", + "role": "assistant", + "type": "message", + "status": "completed" + } + ], + "parallel_tool_calls": true, + "previous_response_id": null, + "reasoning": { + "effort": null, + "summary": null + }, + "temperature": null, + "text": { + "format": { + "type": "text" + } + }, + "tool_choice": "auto", + "tools": [ + { + "type": "code_interpreter" + } + ], + "top_p": null, + "usage": { + "input_tokens": 1606, + "input_tokens_details": { + "cached_tokens": 1235 + }, + "output_tokens": 292, + "output_tokens_details": { + "reasoning_tokens": 190 + }, + "total_tokens": 1898, + "num_sources_used": 0, + "num_server_side_tools_used": 1 + }, + "user": null, + "incomplete_details": null, + "status": "completed", + "store": true, + "metadata": {} +} \ No newline at end of file diff --git a/aimux-providers/src/xai/tests/responses/fixtures/xai-file-search-tool.1.json b/aimux-providers/src/xai/tests/responses/fixtures/xai-file-search-tool.1.json new file mode 100644 index 000000000..2b0e3fe50 --- /dev/null +++ b/aimux-providers/src/xai/tests/responses/fixtures/xai-file-search-tool.1.json @@ -0,0 +1,81 @@ +{ + "created_at": 1762189566, + "id": "resp_file_search_123", + "max_output_tokens": null, + "model": "grok-4-fast-non-reasoning", + "object": "response", + "output": [ + { + "type": "file_search_call", + "id": "fs_123", + "status": "completed", + "queries": ["AI safety research"], + "results": [ + { + "file_id": "file_abc123", + "filename": "ai-safety-paper.pdf", + "score": 0.95, + "text": "Recent advances in AI safety research have focused on alignment techniques..." + }, + { + "file_id": "file_def456", + "filename": "research-notes.md", + "score": 0.82, + "text": "Key findings from the AI safety workshop include recommendations for..." + } + ] + }, + { + "content": [ + { + "type": "output_text", + "text": "Based on the documents in your collection, I found relevant information about AI safety research. 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+{"sequence_number":691,"type":"response.output_text.delta","content_index":0,"delta":" authentic","item_id":"msg_769f3302-64f9-4c72-2b48-860c87fd9b2a","output_index":1,"logprobs":[]} +{"sequence_number":692,"type":"response.output_text.delta","content_index":0,"delta":" flavors","item_id":"msg_769f3302-64f9-4c72-2b48-860c87fd9b2a","output_index":1,"logprobs":[]} +{"sequence_number":693,"type":"response.output_text.delta","content_index":0,"delta":".","item_id":"msg_769f3302-64f9-4c72-2b48-860c87fd9b2a","output_index":1,"logprobs":[]} +{"sequence_number":694,"type":"response.output_text.done","content_index":0,"item_id":"msg_769f3302-64f9-4c72-2b48-860c87fd9b2a","output_index":1,"text":"### Overview of Sonoran Cuisine\nSonoran cuisine originates from the state of Sonora in northwest Mexico, blending indigenous, Spanish, and American influences due to its proximity to the U.S. border and arid desert environment. It's notably rustic, flavorful, and fusion-oriented, often featuring grilled meats, fresh produce, and bold spices adapted to the region's hot, dry climate. Unlike coastal Mexican cuisines, it emphasizes hearty, protein-heavy dishes with influences from the American Southwest, such as flour tortillas and cheese.\n\n### Key Ingredients and Techniques\n- **Meats and Proteins**: Grilled or roasted meats like beef (carne asada), pork, and chicken are central, often seasoned with simple marinades of lime, garlic, and chili. Machaca (dried, shredded beef) is a hallmark preservation technique for the desert.\n- **Chiles and Spices**: Local chiles (e.g., Sonora's chiltepin, a tiny, potent pepper) add heat, while cumin, oregano, and cilantro provide earthy flavors. Beans (frijoles) and corn are staples, often prepared simply to highlight natural tastes.\n- **Tortillas and Breads**: Flour tortillas dominate, reflecting American influence, unlike corn tortillas in many other Mexican regions. Baking and grilling are common, with an emphasis on fresh, unprocessed ingredients.\n- **Regional Staples**: Desert elements like nopales (cactus pads), nopalitos, and wild herbs are incorporated for texture and nutrition, showcasing indigenous roots.\n\n### Signature Dishes\n- **Carne Asada Tacos**: Thinly sliced, grilled skirt steak served on flour tortillas with salsa, onions, and cilantro—often eaten with beans and rice.\n- **Sonoran Hot Dogs**: A fusion dish where a hot dog is wrapped in bacon, grilled, and topped with pinto beans, onions, tomatoes, mayo, mustard, and cheese in a sliced bolillo roll. This originated in border towns and highlights Mexican-American crossover.\n- **Chimichangas**: Deep-fried burritos filled with meat, cheese, and beans, served with guacamole and sour cream—a nod to Tex-Mex influences.\n- **Ceviche and Seafood**: Coastal Sonoran areas feature fresh ceviche (citrus-marinated fish) and shrimp dishes, contrasting the land-based inland cuisine.\n- **Other Notables**: Guaymas-style tamales (with meat and sauce) and caldillo (a beef stew with potatoes) emphasize slow-cooked comfort foods.\n\n### Cultural and Historical Influences\nSonora's style emerged from indigenous Yaqui and Mayo tribes, who used desert foraging, combined with Spanish colonial methods like cattle ranching. Post-Mexican-American War migration boosted American elements, creating dishes like flour-based tacos. Today, it's celebrated in cities like Hermosillo and Tucson (Arizona), influencing Southwestern U.S. cuisine. Festivals like the Sonora International Gastronomic Festival highlight its evolution, and it's known for its focus on quality ingredients over elaborate preparations, making it accessible and flavorful for everyday meals. If you're exploring, local markets (tianguis) are the best places to experience authentic flavors."} +{"sequence_number":695,"type":"response.content_part.done","content_index":0,"item_id":"msg_769f3302-64f9-4c72-2b48-860c87fd9b2a","output_index":1,"part":{"type":"output_text","text":"### Overview of Sonoran Cuisine\nSonoran cuisine originates from the state of Sonora in northwest Mexico, blending indigenous, Spanish, and American influences due to its proximity to the U.S. border and arid desert environment. It's notably rustic, flavorful, and fusion-oriented, often featuring grilled meats, fresh produce, and bold spices adapted to the region's hot, dry climate. Unlike coastal Mexican cuisines, it emphasizes hearty, protein-heavy dishes with influences from the American Southwest, such as flour tortillas and cheese.\n\n### Key Ingredients and Techniques\n- **Meats and Proteins**: Grilled or roasted meats like beef (carne asada), pork, and chicken are central, often seasoned with simple marinades of lime, garlic, and chili. Machaca (dried, shredded beef) is a hallmark preservation technique for the desert.\n- **Chiles and Spices**: Local chiles (e.g., Sonora's chiltepin, a tiny, potent pepper) add heat, while cumin, oregano, and cilantro provide earthy flavors. Beans (frijoles) and corn are staples, often prepared simply to highlight natural tastes.\n- **Tortillas and Breads**: Flour tortillas dominate, reflecting American influence, unlike corn tortillas in many other Mexican regions. Baking and grilling are common, with an emphasis on fresh, unprocessed ingredients.\n- **Regional Staples**: Desert elements like nopales (cactus pads), nopalitos, and wild herbs are incorporated for texture and nutrition, showcasing indigenous roots.\n\n### Signature Dishes\n- **Carne Asada Tacos**: Thinly sliced, grilled skirt steak served on flour tortillas with salsa, onions, and cilantro—often eaten with beans and rice.\n- **Sonoran Hot Dogs**: A fusion dish where a hot dog is wrapped in bacon, grilled, and topped with pinto beans, onions, tomatoes, mayo, mustard, and cheese in a sliced bolillo roll. This originated in border towns and highlights Mexican-American crossover.\n- **Chimichangas**: Deep-fried burritos filled with meat, cheese, and beans, served with guacamole and sour cream—a nod to Tex-Mex influences.\n- **Ceviche and Seafood**: Coastal Sonoran areas feature fresh ceviche (citrus-marinated fish) and shrimp dishes, contrasting the land-based inland cuisine.\n- **Other Notables**: Guaymas-style tamales (with meat and sauce) and caldillo (a beef stew with potatoes) emphasize slow-cooked comfort foods.\n\n### Cultural and Historical Influences\nSonora's style emerged from indigenous Yaqui and Mayo tribes, who used desert foraging, combined with Spanish colonial methods like cattle ranching. Post-Mexican-American War migration boosted American elements, creating dishes like flour-based tacos. Today, it's celebrated in cities like Hermosillo and Tucson (Arizona), influencing Southwestern U.S. cuisine. Festivals like the Sonora International Gastronomic Festival highlight its evolution, and it's known for its focus on quality ingredients over elaborate preparations, making it accessible and flavorful for everyday meals. If you're exploring, local markets (tianguis) are the best places to experience authentic flavors.","logprobs":[],"annotations":[]}} +{"sequence_number":696,"type":"response.output_item.done","item":{"content":[{"type":"output_text","text":"### Overview of Sonoran Cuisine\nSonoran cuisine originates from the state of Sonora in northwest Mexico, blending indigenous, Spanish, and American influences due to its proximity to the U.S. border and arid desert environment. It's notably rustic, flavorful, and fusion-oriented, often featuring grilled meats, fresh produce, and bold spices adapted to the region's hot, dry climate. Unlike coastal Mexican cuisines, it emphasizes hearty, protein-heavy dishes with influences from the American Southwest, such as flour tortillas and cheese.\n\n### Key Ingredients and Techniques\n- **Meats and Proteins**: Grilled or roasted meats like beef (carne asada), pork, and chicken are central, often seasoned with simple marinades of lime, garlic, and chili. Machaca (dried, shredded beef) is a hallmark preservation technique for the desert.\n- **Chiles and Spices**: Local chiles (e.g., Sonora's chiltepin, a tiny, potent pepper) add heat, while cumin, oregano, and cilantro provide earthy flavors. Beans (frijoles) and corn are staples, often prepared simply to highlight natural tastes.\n- **Tortillas and Breads**: Flour tortillas dominate, reflecting American influence, unlike corn tortillas in many other Mexican regions. Baking and grilling are common, with an emphasis on fresh, unprocessed ingredients.\n- **Regional Staples**: Desert elements like nopales (cactus pads), nopalitos, and wild herbs are incorporated for texture and nutrition, showcasing indigenous roots.\n\n### Signature Dishes\n- **Carne Asada Tacos**: Thinly sliced, grilled skirt steak served on flour tortillas with salsa, onions, and cilantro—often eaten with beans and rice.\n- **Sonoran Hot Dogs**: A fusion dish where a hot dog is wrapped in bacon, grilled, and topped with pinto beans, onions, tomatoes, mayo, mustard, and cheese in a sliced bolillo roll. This originated in border towns and highlights Mexican-American crossover.\n- **Chimichangas**: Deep-fried burritos filled with meat, cheese, and beans, served with guacamole and sour cream—a nod to Tex-Mex influences.\n- **Ceviche and Seafood**: Coastal Sonoran areas feature fresh ceviche (citrus-marinated fish) and shrimp dishes, contrasting the land-based inland cuisine.\n- **Other Notables**: Guaymas-style tamales (with meat and sauce) and caldillo (a beef stew with potatoes) emphasize slow-cooked comfort foods.\n\n### Cultural and Historical Influences\nSonora's style emerged from indigenous Yaqui and Mayo tribes, who used desert foraging, combined with Spanish colonial methods like cattle ranching. Post-Mexican-American War migration boosted American elements, creating dishes like flour-based tacos. Today, it's celebrated in cities like Hermosillo and Tucson (Arizona), influencing Southwestern U.S. cuisine. Festivals like the Sonora International Gastronomic Festival highlight its evolution, and it's known for its focus on quality ingredients over elaborate preparations, making it accessible and flavorful for everyday meals. If you're exploring, local markets (tianguis) are the best places to experience authentic flavors.","logprobs":[],"annotations":[]}],"id":"msg_769f3302-64f9-4c72-2b48-860c87fd9b2a","role":"assistant","type":"message","status":"completed"},"output_index":1} +{"sequence_number":697,"type":"response.completed","response":{"created_at":1763853500,"id":"769f3302-64f9-4c72-2b48-860c87fd9b2a","max_output_tokens":null,"model":"grok-code-fast-1","object":"response","output":[{"id":"rs_769f3302-64f9-4c72-2b48-860c87fd9b2a","summary":[{"text":"First, the question is: \"What is specifically notable about the style of Sonoran food?\" This seems like an open-ended question about cuisine. It's not closed-ended science/math/search, so I should make it maximally informative, structured, and easy to parse. Use sections if appropriate.\n\n\n## Investigating Sonoran Cuisine \n- The question focuses on what makes Sonoran food's style notable, suggesting an exploration of its unique characteristics. \n- This is an open-ended question about cuisine, requiring a structured, informative response to highlight key aspects.","type":"summary_text"}],"type":"reasoning","status":"completed"},{"content":[{"type":"output_text","text":"### Overview of Sonoran Cuisine\nSonoran cuisine originates from the state of Sonora in northwest Mexico, blending indigenous, Spanish, and American influences due to its proximity to the U.S. border and arid desert environment. It's notably rustic, flavorful, and fusion-oriented, often featuring grilled meats, fresh produce, and bold spices adapted to the region's hot, dry climate. 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Baking and grilling are common, with an emphasis on fresh, unprocessed ingredients.\n- **Regional Staples**: Desert elements like nopales (cactus pads), nopalitos, and wild herbs are incorporated for texture and nutrition, showcasing indigenous roots.\n\n### Signature Dishes\n- **Carne Asada Tacos**: Thinly sliced, grilled skirt steak served on flour tortillas with salsa, onions, and cilantro—often eaten with beans and rice.\n- **Sonoran Hot Dogs**: A fusion dish where a hot dog is wrapped in bacon, grilled, and topped with pinto beans, onions, tomatoes, mayo, mustard, and cheese in a sliced bolillo roll. 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+{"sequence_number":643,"type":"response.output_text.delta","content_index":0,"delta":"'s","item_id":"msg_0b824fe9-3250-2588-0bbf-0810402fc822","output_index":1,"logprobs":[]} +{"sequence_number":644,"type":"response.output_text.delta","content_index":0,"delta":" bounty","item_id":"msg_0b824fe9-3250-2588-0bbf-0810402fc822","output_index":1,"logprobs":[]} +{"sequence_number":645,"type":"response.output_text.delta","content_index":0,"delta":" for","item_id":"msg_0b824fe9-3250-2588-0bbf-0810402fc822","output_index":1,"logprobs":[]} +{"sequence_number":646,"type":"response.output_text.delta","content_index":0,"delta":" authentic","item_id":"msg_0b824fe9-3250-2588-0bbf-0810402fc822","output_index":1,"logprobs":[]} +{"sequence_number":647,"type":"response.output_text.delta","content_index":0,"delta":",","item_id":"msg_0b824fe9-3250-2588-0bbf-0810402fc822","output_index":1,"logprobs":[]} +{"sequence_number":648,"type":"response.output_text.delta","content_index":0,"delta":" approachable","item_id":"msg_0b824fe9-3250-2588-0bbf-0810402fc822","output_index":1,"logprobs":[]} +{"sequence_number":649,"type":"response.output_text.delta","content_index":0,"delta":" meals","item_id":"msg_0b824fe9-3250-2588-0bbf-0810402fc822","output_index":1,"logprobs":[]} +{"sequence_number":650,"type":"response.output_text.delta","content_index":0,"delta":".","item_id":"msg_0b824fe9-3250-2588-0bbf-0810402fc822","output_index":1,"logprobs":[]} +{"sequence_number":651,"type":"response.output_text.done","content_index":0,"item_id":"msg_0b824fe9-3250-2588-0bbf-0810402fc822","output_index":1,"text":"### Overview of Sonoran Cuisine\nSonoran cuisine originates from the Sonora region in northwestern Mexico, characterized by its simplicity, fresh ingredients, and emphasis on grilling and bold flavors. Unlike spicier Mexican cuisines from other regions, it's notably milder, relying on the region's abundant desert produce and livestock. This style blends indigenous influences (e.g., from Yaqui and Mayo peoples) with Spanish colonial elements, resulting in hearty, straightforward dishes that highlight natural tastes over complex preparations.\n\n### Key Ingredients and Flavors\n- **Mild Chilies and Heat**: The signature is the Mexican Sonora chile (a mild, fruity pepper), often used fresh or roasted. Unlike hotter varieties in Yucatán or Oaxaca, Sonoran food avoids excessive spice, focusing on subtle sweetness and smokiness.\n- **Desert and Local Produce**: Staples include nopales (cactus pads), beans, corn, tomatoes, and onions. The arid climate favors grilled or fresh preparations, with minimal sauces.\n- **Proteins**: Beef, especially in carne asada (grilled skirt steak), is dominant, reflecting Sonora's cattle ranching history. Other meats like pork or chicken are common, often seasoned simply with garlic, lime, and herbs.\n- **Influence of the Sea and Land**: Coastal areas incorporate seafood (e.g., shrimp or fish), while inland focuses on ranch-raised meats. Dairy is rare, emphasizing Mexican crema or cheese sparingly.\n\n### Notable Dishes and Preparation Styles\n- **Grilling as a Core Technique**: Many dishes are cooked over open flames or on a comal, imparting a charred, smoky flavor. This is seen in carne asada, served with tortillas, guacamole, and pico de gallo.\n- **Iconic Foods**: \n - **Machaca**: Shredded, dried beef slow-cooked with eggs, onions, and chilies— a breakfast staple that's simple yet flavorful.\n - **Tamales**: Steamed corn masa filled with meat or cheese, often wrapped in corn husks; Sonoran versions are less sauced than those in central Mexico.\n - **Chimichangas**: Fried burritos, a Sonora-influenced Tex-Mex creation, highlighting the region's border proximity to Arizona.\n- **Beverages**: Agua fresca (fruit waters) from local fruits like watermelon or hibiscus, and horchata, a rice-based drink, complement meals.\n\n### Cultural and Regional Context\nSonoran's style is practical for its hot, dry climate—dishes are quick to prepare and preserve well. It's less ornate than Yucatecan or Jaliscan cuisines, prioritizing sustenance over elaboration. In the U.S., especially Arizona, Sonoran influences appear in fusion foods like Mexican hot dogs (bacon-wrapped with beans and salsa). This cuisine's notable restraint in spice and focus on grilling make it distinct, emphasizing the desert's bounty for authentic, approachable meals."} +{"sequence_number":652,"type":"response.content_part.done","content_index":0,"item_id":"msg_0b824fe9-3250-2588-0bbf-0810402fc822","output_index":1,"part":{"type":"output_text","text":"### Overview of Sonoran Cuisine\nSonoran cuisine originates from the Sonora region in northwestern Mexico, characterized by its simplicity, fresh ingredients, and emphasis on grilling and bold flavors. Unlike spicier Mexican cuisines from other regions, it's notably milder, relying on the region's abundant desert produce and livestock. This style blends indigenous influences (e.g., from Yaqui and Mayo peoples) with Spanish colonial elements, resulting in hearty, straightforward dishes that highlight natural tastes over complex preparations.\n\n### Key Ingredients and Flavors\n- **Mild Chilies and Heat**: The signature is the Mexican Sonora chile (a mild, fruity pepper), often used fresh or roasted. Unlike hotter varieties in Yucatán or Oaxaca, Sonoran food avoids excessive spice, focusing on subtle sweetness and smokiness.\n- **Desert and Local Produce**: Staples include nopales (cactus pads), beans, corn, tomatoes, and onions. The arid climate favors grilled or fresh preparations, with minimal sauces.\n- **Proteins**: Beef, especially in carne asada (grilled skirt steak), is dominant, reflecting Sonora's cattle ranching history. Other meats like pork or chicken are common, often seasoned simply with garlic, lime, and herbs.\n- **Influence of the Sea and Land**: Coastal areas incorporate seafood (e.g., shrimp or fish), while inland focuses on ranch-raised meats. Dairy is rare, emphasizing Mexican crema or cheese sparingly.\n\n### Notable Dishes and Preparation Styles\n- **Grilling as a Core Technique**: Many dishes are cooked over open flames or on a comal, imparting a charred, smoky flavor. This is seen in carne asada, served with tortillas, guacamole, and pico de gallo.\n- **Iconic Foods**: \n - **Machaca**: Shredded, dried beef slow-cooked with eggs, onions, and chilies— a breakfast staple that's simple yet flavorful.\n - **Tamales**: Steamed corn masa filled with meat or cheese, often wrapped in corn husks; Sonoran versions are less sauced than those in central Mexico.\n - **Chimichangas**: Fried burritos, a Sonora-influenced Tex-Mex creation, highlighting the region's border proximity to Arizona.\n- **Beverages**: Agua fresca (fruit waters) from local fruits like watermelon or hibiscus, and horchata, a rice-based drink, complement meals.\n\n### Cultural and Regional Context\nSonoran's style is practical for its hot, dry climate—dishes are quick to prepare and preserve well. It's less ornate than Yucatecan or Jaliscan cuisines, prioritizing sustenance over elaboration. In the U.S., especially Arizona, Sonoran influences appear in fusion foods like Mexican hot dogs (bacon-wrapped with beans and salsa). This cuisine's notable restraint in spice and focus on grilling make it distinct, emphasizing the desert's bounty for authentic, approachable meals.","logprobs":[],"annotations":[]}} +{"sequence_number":653,"type":"response.output_item.done","item":{"content":[{"type":"output_text","text":"### Overview of Sonoran Cuisine\nSonoran cuisine originates from the Sonora region in northwestern Mexico, characterized by its simplicity, fresh ingredients, and emphasis on grilling and bold flavors. Unlike spicier Mexican cuisines from other regions, it's notably milder, relying on the region's abundant desert produce and livestock. This style blends indigenous influences (e.g., from Yaqui and Mayo peoples) with Spanish colonial elements, resulting in hearty, straightforward dishes that highlight natural tastes over complex preparations.\n\n### Key Ingredients and Flavors\n- **Mild Chilies and Heat**: The signature is the Mexican Sonora chile (a mild, fruity pepper), often used fresh or roasted. Unlike hotter varieties in Yucatán or Oaxaca, Sonoran food avoids excessive spice, focusing on subtle sweetness and smokiness.\n- **Desert and Local Produce**: Staples include nopales (cactus pads), beans, corn, tomatoes, and onions. The arid climate favors grilled or fresh preparations, with minimal sauces.\n- **Proteins**: Beef, especially in carne asada (grilled skirt steak), is dominant, reflecting Sonora's cattle ranching history. Other meats like pork or chicken are common, often seasoned simply with garlic, lime, and herbs.\n- **Influence of the Sea and Land**: Coastal areas incorporate seafood (e.g., shrimp or fish), while inland focuses on ranch-raised meats. Dairy is rare, emphasizing Mexican crema or cheese sparingly.\n\n### Notable Dishes and Preparation Styles\n- **Grilling as a Core Technique**: Many dishes are cooked over open flames or on a comal, imparting a charred, smoky flavor. This is seen in carne asada, served with tortillas, guacamole, and pico de gallo.\n- **Iconic Foods**: \n - **Machaca**: Shredded, dried beef slow-cooked with eggs, onions, and chilies— a breakfast staple that's simple yet flavorful.\n - **Tamales**: Steamed corn masa filled with meat or cheese, often wrapped in corn husks; Sonoran versions are less sauced than those in central Mexico.\n - **Chimichangas**: Fried burritos, a Sonora-influenced Tex-Mex creation, highlighting the region's border proximity to Arizona.\n- **Beverages**: Agua fresca (fruit waters) from local fruits like watermelon or hibiscus, and horchata, a rice-based drink, complement meals.\n\n### Cultural and Regional Context\nSonoran's style is practical for its hot, dry climate—dishes are quick to prepare and preserve well. It's less ornate than Yucatecan or Jaliscan cuisines, prioritizing sustenance over elaboration. In the U.S., especially Arizona, Sonoran influences appear in fusion foods like Mexican hot dogs (bacon-wrapped with beans and salsa). This cuisine's notable restraint in spice and focus on grilling make it distinct, emphasizing the desert's bounty for authentic, approachable meals.","logprobs":[],"annotations":[]}],"id":"msg_0b824fe9-3250-2588-0bbf-0810402fc822","role":"assistant","type":"message","status":"completed"},"output_index":1} +{"sequence_number":654,"type":"response.completed","response":{"created_at":1763860285,"id":"0b824fe9-3250-2588-0bbf-0810402fc822","max_output_tokens":null,"model":"grok-code-fast-1","object":"response","output":[{"id":"rs_0b824fe9-3250-2588-0bbf-0810402fc822","summary":[{"text":"First, the question is: \"What is specifically notable about the style of Sonoran food?\" This is an open-ended question about food culture, specifically Sonoran cuisine. It's not closed-ended math/science, so I need to be maximally informative, structure it easily parsable, perhaps with sections if needed.\n\n\n## Investigating Sonoran Cuisine \n- The question focuses on what makes Sonoran food style notable, indicating an open-ended exploration of its cultural and culinary significance. \n- Sonoran cuisine is part of food culture, suggesting a need for a structured, informative response to highlight its unique traits. \n- The task isn't math or science, so the approach will prioritize breadth and accessibility, possibly using sections for clarity.","type":"summary_text"}],"type":"reasoning","status":"completed","encrypted_content":"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"},{"content":[{"type":"output_text","text":"### Overview of Sonoran Cuisine\nSonoran cuisine originates from the Sonora region in northwestern Mexico, characterized by its simplicity, fresh ingredients, and emphasis on grilling and bold flavors. Unlike spicier Mexican cuisines from other regions, it's notably milder, relying on the region's abundant desert produce and livestock. This style blends indigenous influences (e.g., from Yaqui and Mayo peoples) with Spanish colonial elements, resulting in hearty, straightforward dishes that highlight natural tastes over complex preparations.\n\n### Key Ingredients and Flavors\n- **Mild Chilies and Heat**: The signature is the Mexican Sonora chile (a mild, fruity pepper), often used fresh or roasted. Unlike hotter varieties in Yucatán or Oaxaca, Sonoran food avoids excessive spice, focusing on subtle sweetness and smokiness.\n- **Desert and Local Produce**: Staples include nopales (cactus pads), beans, corn, tomatoes, and onions. The arid climate favors grilled or fresh preparations, with minimal sauces.\n- **Proteins**: Beef, especially in carne asada (grilled skirt steak), is dominant, reflecting Sonora's cattle ranching history. Other meats like pork or chicken are common, often seasoned simply with garlic, lime, and herbs.\n- **Influence of the Sea and Land**: Coastal areas incorporate seafood (e.g., shrimp or fish), while inland focuses on ranch-raised meats. Dairy is rare, emphasizing Mexican crema or cheese sparingly.\n\n### Notable Dishes and Preparation Styles\n- **Grilling as a Core Technique**: Many dishes are cooked over open flames or on a comal, imparting a charred, smoky flavor. This is seen in carne asada, served with tortillas, guacamole, and pico de gallo.\n- **Iconic Foods**: \n - **Machaca**: Shredded, dried beef slow-cooked with eggs, onions, and chilies— a breakfast staple that's simple yet flavorful.\n - **Tamales**: Steamed corn masa filled with meat or cheese, often wrapped in corn husks; Sonoran versions are less sauced than those in central Mexico.\n - **Chimichangas**: Fried burritos, a Sonora-influenced Tex-Mex creation, highlighting the region's border proximity to Arizona.\n- **Beverages**: Agua fresca (fruit waters) from local fruits like watermelon or hibiscus, and horchata, a rice-based drink, complement meals.\n\n### Cultural and Regional Context\nSonoran's style is practical for its hot, dry climate—dishes are quick to prepare and preserve well. It's less ornate than Yucatecan or Jaliscan cuisines, prioritizing sustenance over elaboration. In the U.S., especially Arizona, Sonoran influences appear in fusion foods like Mexican hot dogs (bacon-wrapped with beans and salsa). This cuisine's notable restraint in spice and focus on grilling make it distinct, emphasizing the desert's bounty for authentic, approachable meals.","logprobs":[],"annotations":[]}],"id":"msg_0b824fe9-3250-2588-0bbf-0810402fc822","role":"assistant","type":"message","status":"completed"}],"parallel_tool_calls":true,"previous_response_id":null,"reasoning":{"effort":null,"summary":null},"temperature":null,"text":{"format":{"type":"text"}},"tool_choice":"auto","tools":[],"top_p":null,"usage":{"input_tokens":216,"input_tokens_details":{"cached_tokens":192},"output_tokens":831,"output_tokens_details":{"reasoning_tokens":253},"total_tokens":1047,"num_sources_used":0,"num_server_side_tools_used":0},"user":null,"incomplete_details":null,"status":"completed","store":false,"metadata":{}}} \ No newline at end of file diff --git a/aimux-providers/src/xai/tests/responses/fixtures/xai-text-with-reasoning-streaming.1.chunks.txt 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+{"sequence_number":673,"type":"response.output_text.delta","content_index":0,"delta":"illo","item_id":"msg_bf3b2b34-79d4-a45c-7be8-d1e5f96386c2","output_index":1,"logprobs":[]} +{"sequence_number":674,"type":"response.output_text.delta","content_index":0,"delta":".","item_id":"msg_bf3b2b34-79d4-a45c-7be8-d1e5f96386c2","output_index":1,"logprobs":[]} +{"sequence_number":675,"type":"response.output_text.done","content_index":0,"item_id":"msg_bf3b2b34-79d4-a45c-7be8-d1e5f96386c2","output_index":1,"text":"### Overview of Sonoran Cuisine\nSonoran food originates from the Sonoran Desert region, spanning parts of northern Mexico (especially Sonora state) and southern Arizona. It's a rustic, resourceful style shaped by the arid environment, blending Indigenous, Spanish, and Mexican influences. Unlike the more complex, mole-heavy cuisines of central Mexico, Sonoran fare emphasizes simplicity, fresh local ingredients, and bold, smoky flavors from grilling and chiles. It's practical for desert living, focusing on sustenance with minimal processing.\n\n### Key Ingredients and Characteristics\n- **Local Produce and Staples**: Heavily reliant on desert-sourced items like nopales (prickly pear cactus pads), which are grilled or sautéed for their tangy, crisp texture. Beans (pinto or black), corn, and chiles (e.g., chili pequin or jalapeños) are ubiquitous, often used fresh or dried. Tomatoes, onions, and herbs like cilantro add freshness.\n- **Proteins**: Beef, pork, and chicken dominate, with grilling (asado) as a core technique, infusing meats with smoky char. Seafood appears in coastal Sonoran areas, like shrimp or fish tacos.\n- **Bold Flavors and Simplicity**: Dishes avoid heavy sauces; instead, they rely on spices, lime, and chiles for heat and acidity. Tortillas (corn or flour) serve as the base, with minimal elaboration—think of it as \"desert pragmatism\" where food is hearty yet straightforward to prepare in hot climates.\n\n### Signature Dishes\n- **Carne Asada**: Grilled skirt or flank steak marinated in lime, garlic, and cumin, served with tortillas, salsa, and beans. It's the region's barbecue staple, often enjoyed at family gatherings.\n- **Sonoran Hot Dogs**: A fusion delight where a bacon-wrapped hot dog is topped with pinto beans, salsa, onions, and mustard in a bun. Originating in Hermosillo, Mexico, it's a street food icon, blending American and Mexican influences.\n- **Tacos and Tamales**: Tacos are filled with grilled meats, nopales, or cheese, while tamales use corn masa wrapped in corn husks and steamed, often stuffed with pork or chicken.\n- **Chimichangas**: Deep-fried burritos, more associated with Arizona's Mexican-American adaptations, filled with meat, cheese, and beans.\n\n### Cultural and Regional Notes\nSonoran cuisine reflects Indigenous roots from tribes like the Tohono O'odham and Yaqui, who taught Europeans to use desert plants for survival. Spanish colonialists added wheat flour and livestock. In Mexico's Sonora, it's more traditional and seafood-influenced near the Gulf of California, while in Arizona, it incorporates Tex-Mex elements like flour tortillas and cheese. This style stands out for its resilience and adaptability, making it distinct from coastal Mexican seafood or Yucatecan spice profiles. For authentic experiences, try spots like food trucks in Tucson or markets in Hermosillo."} +{"sequence_number":676,"type":"response.content_part.done","content_index":0,"item_id":"msg_bf3b2b34-79d4-a45c-7be8-d1e5f96386c2","output_index":1,"part":{"type":"output_text","text":"### Overview of Sonoran Cuisine\nSonoran food originates from the Sonoran Desert region, spanning parts of northern Mexico (especially Sonora state) and southern Arizona. It's a rustic, resourceful style shaped by the arid environment, blending Indigenous, Spanish, and Mexican influences. Unlike the more complex, mole-heavy cuisines of central Mexico, Sonoran fare emphasizes simplicity, fresh local ingredients, and bold, smoky flavors from grilling and chiles. It's practical for desert living, focusing on sustenance with minimal processing.\n\n### Key Ingredients and Characteristics\n- **Local Produce and Staples**: Heavily reliant on desert-sourced items like nopales (prickly pear cactus pads), which are grilled or sautéed for their tangy, crisp texture. Beans (pinto or black), corn, and chiles (e.g., chili pequin or jalapeños) are ubiquitous, often used fresh or dried. Tomatoes, onions, and herbs like cilantro add freshness.\n- **Proteins**: Beef, pork, and chicken dominate, with grilling (asado) as a core technique, infusing meats with smoky char. Seafood appears in coastal Sonoran areas, like shrimp or fish tacos.\n- **Bold Flavors and Simplicity**: Dishes avoid heavy sauces; instead, they rely on spices, lime, and chiles for heat and acidity. Tortillas (corn or flour) serve as the base, with minimal elaboration—think of it as \"desert pragmatism\" where food is hearty yet straightforward to prepare in hot climates.\n\n### Signature Dishes\n- **Carne Asada**: Grilled skirt or flank steak marinated in lime, garlic, and cumin, served with tortillas, salsa, and beans. It's the region's barbecue staple, often enjoyed at family gatherings.\n- **Sonoran Hot Dogs**: A fusion delight where a bacon-wrapped hot dog is topped with pinto beans, salsa, onions, and mustard in a bun. Originating in Hermosillo, Mexico, it's a street food icon, blending American and Mexican influences.\n- **Tacos and Tamales**: Tacos are filled with grilled meats, nopales, or cheese, while tamales use corn masa wrapped in corn husks and steamed, often stuffed with pork or chicken.\n- **Chimichangas**: Deep-fried burritos, more associated with Arizona's Mexican-American adaptations, filled with meat, cheese, and beans.\n\n### Cultural and Regional Notes\nSonoran cuisine reflects Indigenous roots from tribes like the Tohono O'odham and Yaqui, who taught Europeans to use desert plants for survival. Spanish colonialists added wheat flour and livestock. In Mexico's Sonora, it's more traditional and seafood-influenced near the Gulf of California, while in Arizona, it incorporates Tex-Mex elements like flour tortillas and cheese. This style stands out for its resilience and adaptability, making it distinct from coastal Mexican seafood or Yucatecan spice profiles. For authentic experiences, try spots like food trucks in Tucson or markets in Hermosillo.","logprobs":[],"annotations":[]}} +{"sequence_number":677,"type":"response.output_item.done","item":{"content":[{"type":"output_text","text":"### Overview of Sonoran Cuisine\nSonoran food originates from the Sonoran Desert region, spanning parts of northern Mexico (especially Sonora state) and southern Arizona. It's a rustic, resourceful style shaped by the arid environment, blending Indigenous, Spanish, and Mexican influences. Unlike the more complex, mole-heavy cuisines of central Mexico, Sonoran fare emphasizes simplicity, fresh local ingredients, and bold, smoky flavors from grilling and chiles. It's practical for desert living, focusing on sustenance with minimal processing.\n\n### Key Ingredients and Characteristics\n- **Local Produce and Staples**: Heavily reliant on desert-sourced items like nopales (prickly pear cactus pads), which are grilled or sautéed for their tangy, crisp texture. Beans (pinto or black), corn, and chiles (e.g., chili pequin or jalapeños) are ubiquitous, often used fresh or dried. Tomatoes, onions, and herbs like cilantro add freshness.\n- **Proteins**: Beef, pork, and chicken dominate, with grilling (asado) as a core technique, infusing meats with smoky char. Seafood appears in coastal Sonoran areas, like shrimp or fish tacos.\n- **Bold Flavors and Simplicity**: Dishes avoid heavy sauces; instead, they rely on spices, lime, and chiles for heat and acidity. Tortillas (corn or flour) serve as the base, with minimal elaboration—think of it as \"desert pragmatism\" where food is hearty yet straightforward to prepare in hot climates.\n\n### Signature Dishes\n- **Carne Asada**: Grilled skirt or flank steak marinated in lime, garlic, and cumin, served with tortillas, salsa, and beans. It's the region's barbecue staple, often enjoyed at family gatherings.\n- **Sonoran Hot Dogs**: A fusion delight where a bacon-wrapped hot dog is topped with pinto beans, salsa, onions, and mustard in a bun. Originating in Hermosillo, Mexico, it's a street food icon, blending American and Mexican influences.\n- **Tacos and Tamales**: Tacos are filled with grilled meats, nopales, or cheese, while tamales use corn masa wrapped in corn husks and steamed, often stuffed with pork or chicken.\n- **Chimichangas**: Deep-fried burritos, more associated with Arizona's Mexican-American adaptations, filled with meat, cheese, and beans.\n\n### Cultural and Regional Notes\nSonoran cuisine reflects Indigenous roots from tribes like the Tohono O'odham and Yaqui, who taught Europeans to use desert plants for survival. Spanish colonialists added wheat flour and livestock. In Mexico's Sonora, it's more traditional and seafood-influenced near the Gulf of California, while in Arizona, it incorporates Tex-Mex elements like flour tortillas and cheese. This style stands out for its resilience and adaptability, making it distinct from coastal Mexican seafood or Yucatecan spice profiles. For authentic experiences, try spots like food trucks in Tucson or markets in Hermosillo.","logprobs":[],"annotations":[]}],"id":"msg_bf3b2b34-79d4-a45c-7be8-d1e5f96386c2","role":"assistant","type":"message","status":"completed"},"output_index":1} +{"sequence_number":678,"type":"response.completed","response":{"created_at":1763855668,"id":"bf3b2b34-79d4-a45c-7be8-d1e5f96386c2","max_output_tokens":null,"model":"grok-code-fast-1","object":"response","output":[{"id":"rs_bf3b2b34-79d4-a45c-7be8-d1e5f96386c2","summary":[{"text":"First, the question is: \"What is specifically notable about the style of Sonoran food?\" This seems like an open-ended question about cuisine, not closed-ended science/math/search. So, I need to make my response maximally informative, structured, and easily parsable. Use sections if appropriate, with descriptive headers.\n\n\n## Identifying the Question \n- The user's query asks, \"What is specifically notable about the style of Sonoran food?\" \n- This is an open-ended question about cuisine, requiring a structured, informative response. \n\n## Planning the Approach \n- The response should focus on key characteristics of Sonoran food, ensuring it’s easy to read and parse. \n- Consider using sections with clear headers to organize notable features systematically.","type":"summary_text"}],"type":"reasoning","status":"completed"},{"content":[{"type":"output_text","text":"### Overview of Sonoran Cuisine\nSonoran food originates from the Sonoran Desert region, spanning parts of northern Mexico (especially Sonora state) and southern Arizona. It's a rustic, resourceful style shaped by the arid environment, blending Indigenous, Spanish, and Mexican influences. Unlike the more complex, mole-heavy cuisines of central Mexico, Sonoran fare emphasizes simplicity, fresh local ingredients, and bold, smoky flavors from grilling and chiles. It's practical for desert living, focusing on sustenance with minimal processing.\n\n### Key Ingredients and Characteristics\n- **Local Produce and Staples**: Heavily reliant on desert-sourced items like nopales (prickly pear cactus pads), which are grilled or sautéed for their tangy, crisp texture. Beans (pinto or black), corn, and chiles (e.g., chili pequin or jalapeños) are ubiquitous, often used fresh or dried. Tomatoes, onions, and herbs like cilantro add freshness.\n- **Proteins**: Beef, pork, and chicken dominate, with grilling (asado) as a core technique, infusing meats with smoky char. Seafood appears in coastal Sonoran areas, like shrimp or fish tacos.\n- **Bold Flavors and Simplicity**: Dishes avoid heavy sauces; instead, they rely on spices, lime, and chiles for heat and acidity. Tortillas (corn or flour) serve as the base, with minimal elaboration—think of it as \"desert pragmatism\" where food is hearty yet straightforward to prepare in hot climates.\n\n### Signature Dishes\n- **Carne Asada**: Grilled skirt or flank steak marinated in lime, garlic, and cumin, served with tortillas, salsa, and beans. It's the region's barbecue staple, often enjoyed at family gatherings.\n- **Sonoran Hot Dogs**: A fusion delight where a bacon-wrapped hot dog is topped with pinto beans, salsa, onions, and mustard in a bun. 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For authentic experiences, try spots like food trucks in Tucson or markets in Hermosillo.","logprobs":[],"annotations":[]}],"id":"msg_bf3b2b34-79d4-a45c-7be8-d1e5f96386c2","role":"assistant","type":"message","status":"completed"}],"parallel_tool_calls":true,"previous_response_id":null,"reasoning":{"effort":null,"summary":null},"temperature":null,"text":{"format":{"type":"text"}},"tool_choice":"auto","tools":[],"top_p":null,"usage":{"input_tokens":216,"input_tokens_details":{"cached_tokens":192},"output_tokens":923,"output_tokens_details":{"reasoning_tokens":323},"total_tokens":1139,"num_sources_used":0,"num_server_side_tools_used":0},"user":null,"incomplete_details":null,"status":"completed","store":true,"metadata":{}}} \ No newline at end of file diff --git a/aimux-providers/src/xai/tests/responses/fixtures/xai-web-search-tool.1.chunks.txt b/aimux-providers/src/xai/tests/responses/fixtures/xai-web-search-tool.1.chunks.txt new file mode 100644 index 000000000..10df7e3e1 --- /dev/null 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Notable team members include former researchers from OpenAI, DeepMind, and other AI labs.\n\nxAI has raised significant funding, including a $6 billion Series B round in 2024, and is competing with major players like OpenAI and Google in the AI space.\n\nIf you meant \"XAI\" as in Explainable AI (a field of AI research aimed at making machine learning models more transparent and interpretable), let me know for more details on that!", + "logprobs": [], + "annotations": [ + { + "type": "url_citation", + "url": "https://www.ibm.com/think/topics/explainable-ai" + }, + { + "type": "url_citation", + "url": "https://x.ai/company" + }, + { + "type": "url_citation", + "url": "https://en.wikipedia.org/wiki/XAI_%28company%29" + }, + { + "type": "url_citation", + "url": "https://x.ai/" + }, + { + "type": "url_citation", + "url": "https://www.owkin.com/a-z-of-ai-for-healthcare/xai" + } + ] + } + ], + "id": "msg_25de2f84-163c-6e9e-e42e-cd1dbd6f9ed0", + "role": "assistant", + "type": "message", + "status": "completed" + } + ], + "parallel_tool_calls": true, + "previous_response_id": null, + "reasoning": { + "effort": null, + "summary": null + }, + "temperature": null, + "text": { + "format": { + "type": "text" + } + }, + "tool_choice": "auto", + "tools": [ + { + "type": "web_search", + "search_context_size": "medium" + } + ], + "top_p": null, + "usage": { + "input_tokens": 1941, + "input_tokens_details": { + "cached_tokens": 947 + }, + "output_tokens": 583, + "output_tokens_details": { + "reasoning_tokens": 380 + }, + "total_tokens": 2524, + "num_sources_used": 0, + "num_server_side_tools_used": 1 + }, + "user": null, + "incomplete_details": null, + "status": "completed", + "store": true, + "metadata": {} +} diff --git a/aimux-providers/src/xai/tests/responses/fixtures/xai-x-search-tool.1.json b/aimux-providers/src/xai/tests/responses/fixtures/xai-x-search-tool.1.json new file mode 100644 index 000000000..b915e5279 --- /dev/null +++ b/aimux-providers/src/xai/tests/responses/fixtures/xai-x-search-tool.1.json @@ -0,0 +1,149 @@ +{ + "created_at": 1761768904, + "id": "84c1a8ea-1f29-4a33-1049-0ed3561b0f64_us-east-1", + "max_output_tokens": null, + "model": "grok-4-fast-reasoning", + "object": "response", + "output": [ + { + "arguments": "{\"query\":\"AI artificial intelligence\",\"limit\":20,\"from_date\":\"2025-10-01\",\"to_date\":\"2025-10-29\",\"min_score_threshold\":0.2}", + "call_id": "", + "name": "x_semantic_search", + "type": "x_search_call", + "id": "fc_84c1a8ea-1f29-4a33-1049-0ed3561b0f64_us-east-1_0", + "status": "completed" + }, + { + "content": [ + { + "type": "output_text", + "text": "### What People Are Saying About AI on X (as of Late October 2025)\n\nBased on recent posts on X (formerly Twitter), discussions about AI are buzzing with a mix of excitement, technical deep dives, and cautious optimism. The conversation heavily revolves around **Artificial General Intelligence (AGI)** as the holy grail of AI development, with frequent references to a new paper from AI luminaries like Yoshua Bengio, Eric Schmidt, Gary Marcus, and Max Tegmark. People are debating definitions, progress, limitations, and real-world applications. Here's a breakdown of the main themes from the top relevant posts (focusing on the last month):\n\n#### 1. **Defining AGI and Its Distance from Current AI**\n - A huge focus is on clarifying what AGI really means. Multiple users cite the aforementioned paper, which defines AGI as an AI system that \"matches or exceeds the cognitive versatility and proficiency of a well-educated adult\" across 10 core cognitive areas (e.g., reasoning, memory, math, language, perception).\n - Example: @jiqizhixin shared the paper, noting that models like GPT-4 and GPT-5 score poorly (e.g., 0% on long-term memory), emphasizing AGI isn't here yet.\n - @alex_prompter broke it down simply: GPT-4 is at 27% toward AGI, GPT-5 at 58%, but gaps in continuous learning persist. They called it a \"clear benchmark\" without hype.\n - @AskPerplexity and @flowersslop echoed this, describing AGI as broadly adaptable like humans, not just narrow tools like today's chatbots.\n - ASI (Artificial Superintelligence) gets mentioned as the next step beyond AGI—AI surpassing humans in *all* domains, including creativity and strategy. @SciTechera tied it to the \"Singularity,\" where AI self-improves uncontrollably.\n - Skepticism: @Newaxeman called AI an \"advanced search engine\" aggregating conventional wisdom, questioning deeper intelligence claims.\n\n#### 2. **Progress and Timelines Toward AGI**\n - Optimism is high but grounded. @bindureddy (CEO of Abacus.AI) boldly predicted AGI (AI replacing humans in digital tasks) is \"just a couple of years away,\" citing missing pieces like context, tools, and self-awareness that labs are tackling.\n - @slow_developer referenced OpenAI and DeepMind's 5-level AGI framework: Current models are past Level 2 (emerging intelligence) and into early Level 3 (competent in complex tasks with supervision), but not fully autonomous yet.\n - @timmur_ stressed AGI requires efficient learning across open-ended tasks with minimal data—something no model achieves today, but \"we will solve it eventually.\"\n - Broader view: @FrnkNlsn recommended the book *Artificial Intelligence: A Modern Approach* by Russell and Norvig for a comprehensive overview, highlighting topics like knowledge representation and multi-agent systems.\n\n#### 3. **Types of AI and Distinctions**\n - Users are educating on AI's varieties to cut through confusion:\n - @y00ngipurrs differentiated analytical AI (e.g., for medicine, fraud detection) from generative AI (e.g., ChatGPT for text/art).\n - @techyoutbe contrasted AI agents (task-specific automators) with \"agentic AI\" (autonomous systems for long-term planning and multi-domain reasoning).\n - @SukhSandhu listed what AI *is* (data processor, pattern recognizer, task automator) vs. what it's *not* (conscious, emotional, intuitively moral). They warned of biases in training data.\n - @AITECHio hyped proactive \"AI agents\" that set goals and execute workflows autonomously, predicting one for every task from finance to on-chain automation.\n\n#### 4. **Applications, Projects, and Broader Impacts**\n - AI's practical side shines in Web3 and business contexts. @O_H_JAY highlighted crypto projects advancing AGI/ASI, like @SentientAGI (decentralized AGI networks), @AGI_XT (AI automation on Solana), and @StoryProtocol (on-chain IP for AI models). They urged getting in early during market dips.\n - @Voa_De_Voa discussed combining AI with compute and robotics for \"autonomous intelligence,\" but flagged centralized control as a risk.\n - @DeviprasadSD positioned AGI as a future horizon for reasoning across any task, still in research.\n - @zolayolanetwork and @davilagrau amplified the AGI definition paper, linking it to blockchain and cognitive tools.\n\n#### Overall Sentiment\n- **Positive/Visionary**: Excitement about AI augmenting human capabilities (e.g., @hailstorm_9 called AGI \"augmentation, not automation\"). Many see it transforming industries like healthcare, finance, and creative work.\n- **Cautious/Realistic**: Emphasis on limitations—current AI is \"narrow\" and lacks true understanding or adaptability. No one's claiming AGI is imminent without caveats.\n- **Volume and Engagement**: These posts garnered thousands of likes/views, with high engagement on definitions and project lists. Discussions are technical but accessible, often with images, threads, or links to papers/arXiv.\n\nThis is a snapshot from ~20 recent, highly relevant posts. AI chatter on X is evolving fast—trends like AGI benchmarks and agentic systems are dominating. If you want deeper dives into specific posts, users, or subtopics (e.g., AI ethics or tools), let me know!", + "logprobs": [], + "annotations": [ + { + "type": "url_citation", + "url": "https://x.com/i/status/1982033415697514642" + }, + { + "type": "url_citation", + "url": "https://x.com/i/status/1983126826739228851" + }, + { + "type": "url_citation", + "url": "https://x.com/i/status/1978543466042040366" + }, + { + "type": "url_citation", + "url": "https://x.com/i/status/1982845451188720069" + }, + { + "type": "url_citation", + "url": "https://x.com/i/status/1981716983168040972" + }, + { + "type": "url_citation", + "url": "https://x.com/i/status/1977864424770990183" + }, + { + "type": "url_citation", + "url": "https://x.com/i/status/1979019210870395155" + }, + { + "type": "url_citation", + "url": "https://x.com/i/status/1981725084059668849" + }, + { + "type": "url_citation", + "url": "https://x.com/i/status/1982159004857217324" + }, + { + "type": "url_citation", + "url": 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Based on the most recent posts with media (fetched in \"Latest\" order), here are the top latest videos and images focusing on product announcements and demos. These are primarily from late November 2025, centered around the launch of **Grok 4.1 Fast** (a new tool-calling AI model with a 2M-token context window) and the **Agent Tools API** (enabling autonomous web browsing, X searches, code execution, and more). I've included descriptions based on direct analysis of the media, timestamps, and context from the posts.\n\n#### Latest Video (November 19, 2025)\n- **Post Details**: From the announcement thread introducing Grok 4.1 Fast and Agent Tools API (Post ID: 1991284813727474073, ~2.3M views). This is the most recent video post.\n- **Video Description**: A 14-second demo clip (low-resolution preview at 468x270) showcasing Grok 4.1 Fast in action with the new Agent Tools API. It demonstrates real-time web browsing and research capabilities:\n - The interface is a dark-themed chat window (similar to Grok's UI) where a user queries about Tesla (e.g., \"How many Tesla factories are there?\" and follow-ups on production).\n - Grok autonomously browses websites: Frames show loading web pages (e.g., Tesla's official site), extracting data like factory locations and output gauges, and summarizing results in real-time.\n - Key features highlighted: Multi-step agentic workflow (search → browse → read documents → respond), with UI elements like loading spinners, document previews, and action buttons (e.g., \"Browse web\", \"Read document\").\n - Visual style: Clean, animated transitions between search results, web tabs, and chat responses. No subtitles, but text overlays show queries and summaries (e.g., \"Reading factory output gauges\", \"Building summary\").\n - **Relevance**: This illustrates the product's enterprise use cases like deep research and customer support. Link to full announcement: [x.ai/news/grok-4-1-fast](https://x.ai/news/grok-4-1-fast).\n- **Direct Video URL** (for viewing on X): https://video.twimg.com/amplify_video/1991284765027364866/vid/avc1/468x270/kRkbodV96jk4PmbG.mp4\n\n#### Latest Images (November 19, 2025)\nThese accompany the same Grok 4.1 Fast announcement thread. They are benchmark charts emphasizing the model's performance advantages in tool-calling, agentic tasks, and cost-efficiency compared to competitors (e.g., GPT-5, Claude Sonnet 4.5, Gemini 3 Pro).\n\n1. **Image 1: Multi-Turn Accuracy Benchmarks** (Post ID: 1991284818928366015, ~55K views)\n - **Description**: A bar chart comparing Grok models on \"Multi-Turn Acc\" (accuracy in extended conversations) and \"Multi-Turn Long Context\" (performance over long inputs). Orange bars (Grok 4.1 Fast) lead at 57.2% and 67%, vs. brown (Grok 4 Fast) at 41.6%/52.5%, and gray (Grok 4) at 20.5%/22%. Dark background with xAI branding. Highlights training improvements via long-horizon RL for consistent 2M-token performance.\n - **URL**: https://pbs.twimg.com/media/G6J2_UxacAQf8u4.jpg\n\n2. **Image 2: Enterprise Use Case Benchmarks (e.g., Customer Support)** (Post ID: 1991284822820720732, ~12K views)\n - **Description**: A scatter plot titled \"τ²-Bench Telecom\" (a telecom/customer support workflow benchmark). Points plot score (%) vs. total cost ($). Grok 4.1 Fast (orange dot) tops at ~100% score for ~$105 cost, outperforming GPT-5 high (~80%/$150+), Gemini 3 Pro (~70%/$120), Claude 4.5 Sonnet (~60%/$130), and Grok 4 Fast (~50%/$100). Verified by independent evaluator Artificial Analysis. Emphasizes real-world SOTA (state-of-the-art) for agentic workflows.\n - **URL**: https://pbs.twimg.com/media/G6J2_mcacAMtO5w.jpg\n\n3. **Image 3: Function Calling Benchmark** (Post ID: 1991284824594870293, ~8K views)\n - **Description**: A line graph on \"Berkeley Function Calling v4 Benchmark\" showing overall accuracy (%) vs. total cost ($ up to $400). Grok 4.1 Fast Reasoning (orange line) reaches 72% accuracy at low cost, leading Claude Sonnet 4.5 (gray), GPT-5 (gray dot), Gemini 3 Pro (gray, estimated), and Grok 4 (gray). Dark theme with striped borders; notes pending official results for some models.\n - **URL**: https://pbs.twimg.com/media/G6J23pzacAApgdm.jpg\n\n4. **Image 4: Agent Tools API Benchmarks** (Post ID: 1991284830420758687, ~8K views)\n - **Description**: A table comparing Grok 4.1 Fast + Agent Tools API on research/agentic benchmarks (Research-Eval, FRAMES, X Browse*). Grok leads with scores like 63.9/87.6/56.3 at low costs ($0.046–$0.091 per query), vs. GPT-5 (45.5/86.0/24.2 at $0.107–$0.198), Claude Sonnet 4.5 (41.2/85.0/14.6), and Gemini 3 Pro (55.9/90.9/26.5). Focuses on web/X integration for autonomous tasks. *X Browse is an internal xAI benchmark for multi-hop search on X.\n - **URL**: https://pbs.twimg.com/media/G6J26O_a8AAqhZJ.jpg\n\n#### Other Recent Media (November 17–19, 2025)\n- **Partnership Announcement Image (November 19, 2025)**: Post ID 1991224224313487696 (~115K views). A graphic showing xAI's partnership with Saudi Arabia's HUMAIN AI for deploying Grok nationwide and building GPU data centers. Features flags, AI icons, and text: \"Grok goes Global with KSA.\" (URL: https://pbs.twimg.com/media/G6I_4O8acAAMIWh.jpg)\n- **Grok 4.1 Announcement Images (November 17, 2025)**: Thread (Post ID: 1990530499752980638, ~33M views total) with 5+ charts:\n - Arena leaderboard (#1 at 1483 Elo).\n - EQ-Bench (emotional intelligence at 1586).\n - Creative Writing v3 (1722 Elo, +600 points).\n - Hallucination reduction (3x less error-prone).\n - User preference (65% over prior models).\n These are similar benchmark visuals, emphasizing conversational AI improvements. Available at: https://x.ai/news/grok-4-1\n\n#### Notes on Older Media\n- The next videos are from October 2025, like demos of **Grok Imagine v0.9** (AI video generation with motion, singing, dancing, and audio sync—e.g., a dragon video or Ani character clips). These are product demos but not as recent.\n- xAI posts media sparingly; the November announcements dominate the latest activity. For real-time updates, check @xai directly. If you want details on specific older items or more views, let me know!"} +{"sequence_number":1754,"type":"response.content_part.done","content_index":0,"item_id":"msg_b7b464ea-cc85-d44a-0f2f-1f7320e703c3","output_index":6,"part":{"type":"output_text","text":"### Latest Videos and Images from xAI on X (as of December 1, 2025)\n\nxAI's official X account (@xai) frequently shares media related to their AI products like Grok models, APIs, and partnerships. Based on the most recent posts with media (fetched in \"Latest\" order), here are the top latest videos and images focusing on product announcements and demos. These are primarily from late November 2025, centered around the launch of **Grok 4.1 Fast** (a new tool-calling AI model with a 2M-token context window) and the **Agent Tools API** (enabling autonomous web browsing, X searches, code execution, and more). I've included descriptions based on direct analysis of the media, timestamps, and context from the posts.\n\n#### Latest Video (November 19, 2025)\n- **Post Details**: From the announcement thread introducing Grok 4.1 Fast and Agent Tools API (Post ID: 1991284813727474073, ~2.3M views). This is the most recent video post.\n- **Video Description**: A 14-second demo clip (low-resolution preview at 468x270) showcasing Grok 4.1 Fast in action with the new Agent Tools API. It demonstrates real-time web browsing and research capabilities:\n - The interface is a dark-themed chat window (similar to Grok's UI) where a user queries about Tesla (e.g., \"How many Tesla factories are there?\" and follow-ups on production).\n - Grok autonomously browses websites: Frames show loading web pages (e.g., Tesla's official site), extracting data like factory locations and output gauges, and summarizing results in real-time.\n - Key features highlighted: Multi-step agentic workflow (search → browse → read documents → respond), with UI elements like loading spinners, document previews, and action buttons (e.g., \"Browse web\", \"Read document\").\n - Visual style: Clean, animated transitions between search results, web tabs, and chat responses. No subtitles, but text overlays show queries and summaries (e.g., \"Reading factory output gauges\", \"Building summary\").\n - **Relevance**: This illustrates the product's enterprise use cases like deep research and customer support. Link to full announcement: [x.ai/news/grok-4-1-fast](https://x.ai/news/grok-4-1-fast).\n- **Direct Video URL** (for viewing on X): https://video.twimg.com/amplify_video/1991284765027364866/vid/avc1/468x270/kRkbodV96jk4PmbG.mp4\n\n#### Latest Images (November 19, 2025)\nThese accompany the same Grok 4.1 Fast announcement thread. They are benchmark charts emphasizing the model's performance advantages in tool-calling, agentic tasks, and cost-efficiency compared to competitors (e.g., GPT-5, Claude Sonnet 4.5, Gemini 3 Pro).\n\n1. **Image 1: Multi-Turn Accuracy Benchmarks** (Post ID: 1991284818928366015, ~55K views)\n - **Description**: A bar chart comparing Grok models on \"Multi-Turn Acc\" (accuracy in extended conversations) and \"Multi-Turn Long Context\" (performance over long inputs). Orange bars (Grok 4.1 Fast) lead at 57.2% and 67%, vs. brown (Grok 4 Fast) at 41.6%/52.5%, and gray (Grok 4) at 20.5%/22%. Dark background with xAI branding. Highlights training improvements via long-horizon RL for consistent 2M-token performance.\n - **URL**: https://pbs.twimg.com/media/G6J2_UxacAQf8u4.jpg\n\n2. **Image 2: Enterprise Use Case Benchmarks (e.g., Customer Support)** (Post ID: 1991284822820720732, ~12K views)\n - **Description**: A scatter plot titled \"τ²-Bench Telecom\" (a telecom/customer support workflow benchmark). Points plot score (%) vs. total cost ($). Grok 4.1 Fast (orange dot) tops at ~100% score for ~$105 cost, outperforming GPT-5 high (~80%/$150+), Gemini 3 Pro (~70%/$120), Claude 4.5 Sonnet (~60%/$130), and Grok 4 Fast (~50%/$100). Verified by independent evaluator Artificial Analysis. Emphasizes real-world SOTA (state-of-the-art) for agentic workflows.\n - **URL**: https://pbs.twimg.com/media/G6J2_mcacAMtO5w.jpg\n\n3. **Image 3: Function Calling Benchmark** (Post ID: 1991284824594870293, ~8K views)\n - **Description**: A line graph on \"Berkeley Function Calling v4 Benchmark\" showing overall accuracy (%) vs. total cost ($ up to $400). Grok 4.1 Fast Reasoning (orange line) reaches 72% accuracy at low cost, leading Claude Sonnet 4.5 (gray), GPT-5 (gray dot), Gemini 3 Pro (gray, estimated), and Grok 4 (gray). Dark theme with striped borders; notes pending official results for some models.\n - **URL**: https://pbs.twimg.com/media/G6J23pzacAApgdm.jpg\n\n4. **Image 4: Agent Tools API Benchmarks** (Post ID: 1991284830420758687, ~8K views)\n - **Description**: A table comparing Grok 4.1 Fast + Agent Tools API on research/agentic benchmarks (Research-Eval, FRAMES, X Browse*). Grok leads with scores like 63.9/87.6/56.3 at low costs ($0.046–$0.091 per query), vs. GPT-5 (45.5/86.0/24.2 at $0.107–$0.198), Claude Sonnet 4.5 (41.2/85.0/14.6), and Gemini 3 Pro (55.9/90.9/26.5). Focuses on web/X integration for autonomous tasks. *X Browse is an internal xAI benchmark for multi-hop search on X.\n - **URL**: https://pbs.twimg.com/media/G6J26O_a8AAqhZJ.jpg\n\n#### Other Recent Media (November 17–19, 2025)\n- **Partnership Announcement Image (November 19, 2025)**: Post ID 1991224224313487696 (~115K views). A graphic showing xAI's partnership with Saudi Arabia's HUMAIN AI for deploying Grok nationwide and building GPU data centers. Features flags, AI icons, and text: \"Grok goes Global with KSA.\" (URL: https://pbs.twimg.com/media/G6I_4O8acAAMIWh.jpg)\n- **Grok 4.1 Announcement Images (November 17, 2025)**: Thread (Post ID: 1990530499752980638, ~33M views total) with 5+ charts:\n - Arena leaderboard (#1 at 1483 Elo).\n - EQ-Bench (emotional intelligence at 1586).\n - Creative Writing v3 (1722 Elo, +600 points).\n - Hallucination reduction (3x less error-prone).\n - User preference (65% over prior models).\n These are similar benchmark visuals, emphasizing conversational AI improvements. Available at: https://x.ai/news/grok-4-1\n\n#### Notes on Older Media\n- The next videos are from October 2025, like demos of **Grok Imagine v0.9** (AI video generation with motion, singing, dancing, and audio sync—e.g., a dragon video or Ani character clips). These are product demos but not as recent.\n- xAI posts media sparingly; the November announcements dominate the latest activity. For real-time updates, check @xai directly. If you want details on specific older items or more views, let me know!","logprobs":[],"annotations":[{"type":"url_citation","url":"https://x.com/i/status/1990530503129391571"},{"type":"url_citation","url":"https://x.com/i/status/1990530506879074316"},{"type":"url_citation","url":"https://x.com/i/status/1991284822820720732"},{"type":"url_citation","url":"https://x.com/i/status/1990530501237715372"},{"type":"url_citation","url":"https://x.com/i/status/1975608007250829383"},{"type":"url_citation","url":"https://x.com/i/status/1977121515587223679"},{"type":"url_citation","url":"https://x.com/i/status/1975608070245175592"},{"type":"url_citation","url":"https://x.com/i/status/1975608184711880816"},{"type":"url_citation","url":"https://x.com/i/status/1990530508770718154"},{"type":"url_citation","url":"https://x.com/i/status/1990530505209754056"},{"type":"url_citation","url":"https://x.com/i/status/1991224224313487696"},{"type":"url_citation","url":"https://x.com/i/status/1987696575314104339"},{"type":"url_citation","url":"https://x.com/i/status/1991284824594870293"},{"type":"url_citation","url":"https://x.com/i/status/1991284828667527322"},{"type":"url_citation","url":"https://x.com/i/status/1975608017396867282"},{"type":"url_citation","url":"https://x.com/i/status/1975608122845896765"},{"type":"url_citation","url":"https://x.com/i/status/1991284813727474073"},{"type":"url_citation","url":"https://x.com/i/status/1991284830420758687"},{"type":"url_citation","url":"https://x.com/i/status/1975608132530544900"},{"type":"url_citation","url":"https://x.com/i/status/1991284818928366015"}]}} +{"sequence_number":1755,"type":"response.output_item.done","item":{"content":[{"type":"output_text","text":"### Latest Videos and Images from xAI on X (as of December 1, 2025)\n\nxAI's official X account (@xai) frequently shares media related to their AI products like Grok models, APIs, and partnerships. Based on the most recent posts with media (fetched in \"Latest\" order), here are the top latest videos and images focusing on product announcements and demos. These are primarily from late November 2025, centered around the launch of **Grok 4.1 Fast** (a new tool-calling AI model with a 2M-token context window) and the **Agent Tools API** (enabling autonomous web browsing, X searches, code execution, and more). I've included descriptions based on direct analysis of the media, timestamps, and context from the posts.\n\n#### Latest Video (November 19, 2025)\n- **Post Details**: From the announcement thread introducing Grok 4.1 Fast and Agent Tools API (Post ID: 1991284813727474073, ~2.3M views). This is the most recent video post.\n- **Video Description**: A 14-second demo clip (low-resolution preview at 468x270) showcasing Grok 4.1 Fast in action with the new Agent Tools API. It demonstrates real-time web browsing and research capabilities:\n - The interface is a dark-themed chat window (similar to Grok's UI) where a user queries about Tesla (e.g., \"How many Tesla factories are there?\" and follow-ups on production).\n - Grok autonomously browses websites: Frames show loading web pages (e.g., Tesla's official site), extracting data like factory locations and output gauges, and summarizing results in real-time.\n - Key features highlighted: Multi-step agentic workflow (search → browse → read documents → respond), with UI elements like loading spinners, document previews, and action buttons (e.g., \"Browse web\", \"Read document\").\n - Visual style: Clean, animated transitions between search results, web tabs, and chat responses. No subtitles, but text overlays show queries and summaries (e.g., \"Reading factory output gauges\", \"Building summary\").\n - **Relevance**: This illustrates the product's enterprise use cases like deep research and customer support. Link to full announcement: [x.ai/news/grok-4-1-fast](https://x.ai/news/grok-4-1-fast).\n- **Direct Video URL** (for viewing on X): https://video.twimg.com/amplify_video/1991284765027364866/vid/avc1/468x270/kRkbodV96jk4PmbG.mp4\n\n#### Latest Images (November 19, 2025)\nThese accompany the same Grok 4.1 Fast announcement thread. They are benchmark charts emphasizing the model's performance advantages in tool-calling, agentic tasks, and cost-efficiency compared to competitors (e.g., GPT-5, Claude Sonnet 4.5, Gemini 3 Pro).\n\n1. **Image 1: Multi-Turn Accuracy Benchmarks** (Post ID: 1991284818928366015, ~55K views)\n - **Description**: A bar chart comparing Grok models on \"Multi-Turn Acc\" (accuracy in extended conversations) and \"Multi-Turn Long Context\" (performance over long inputs). Orange bars (Grok 4.1 Fast) lead at 57.2% and 67%, vs. brown (Grok 4 Fast) at 41.6%/52.5%, and gray (Grok 4) at 20.5%/22%. Dark background with xAI branding. Highlights training improvements via long-horizon RL for consistent 2M-token performance.\n - **URL**: https://pbs.twimg.com/media/G6J2_UxacAQf8u4.jpg\n\n2. **Image 2: Enterprise Use Case Benchmarks (e.g., Customer Support)** (Post ID: 1991284822820720732, ~12K views)\n - **Description**: A scatter plot titled \"τ²-Bench Telecom\" (a telecom/customer support workflow benchmark). Points plot score (%) vs. total cost ($). Grok 4.1 Fast (orange dot) tops at ~100% score for ~$105 cost, outperforming GPT-5 high (~80%/$150+), Gemini 3 Pro (~70%/$120), Claude 4.5 Sonnet (~60%/$130), and Grok 4 Fast (~50%/$100). Verified by independent evaluator Artificial Analysis. Emphasizes real-world SOTA (state-of-the-art) for agentic workflows.\n - **URL**: https://pbs.twimg.com/media/G6J2_mcacAMtO5w.jpg\n\n3. **Image 3: Function Calling Benchmark** (Post ID: 1991284824594870293, ~8K views)\n - **Description**: A line graph on \"Berkeley Function Calling v4 Benchmark\" showing overall accuracy (%) vs. total cost ($ up to $400). Grok 4.1 Fast Reasoning (orange line) reaches 72% accuracy at low cost, leading Claude Sonnet 4.5 (gray), GPT-5 (gray dot), Gemini 3 Pro (gray, estimated), and Grok 4 (gray). Dark theme with striped borders; notes pending official results for some models.\n - **URL**: https://pbs.twimg.com/media/G6J23pzacAApgdm.jpg\n\n4. **Image 4: Agent Tools API Benchmarks** (Post ID: 1991284830420758687, ~8K views)\n - **Description**: A table comparing Grok 4.1 Fast + Agent Tools API on research/agentic benchmarks (Research-Eval, FRAMES, X Browse*). Grok leads with scores like 63.9/87.6/56.3 at low costs ($0.046–$0.091 per query), vs. GPT-5 (45.5/86.0/24.2 at $0.107–$0.198), Claude Sonnet 4.5 (41.2/85.0/14.6), and Gemini 3 Pro (55.9/90.9/26.5). Focuses on web/X integration for autonomous tasks. *X Browse is an internal xAI benchmark for multi-hop search on X.\n - **URL**: https://pbs.twimg.com/media/G6J26O_a8AAqhZJ.jpg\n\n#### Other Recent Media (November 17–19, 2025)\n- **Partnership Announcement Image (November 19, 2025)**: Post ID 1991224224313487696 (~115K views). A graphic showing xAI's partnership with Saudi Arabia's HUMAIN AI for deploying Grok nationwide and building GPU data centers. Features flags, AI icons, and text: \"Grok goes Global with KSA.\" (URL: https://pbs.twimg.com/media/G6I_4O8acAAMIWh.jpg)\n- **Grok 4.1 Announcement Images (November 17, 2025)**: Thread (Post ID: 1990530499752980638, ~33M views total) with 5+ charts:\n - Arena leaderboard (#1 at 1483 Elo).\n - EQ-Bench (emotional intelligence at 1586).\n - Creative Writing v3 (1722 Elo, +600 points).\n - Hallucination reduction (3x less error-prone).\n - User preference (65% over prior models).\n These are similar benchmark visuals, emphasizing conversational AI improvements. Available at: https://x.ai/news/grok-4-1\n\n#### Notes on Older Media\n- The next videos are from October 2025, like demos of **Grok Imagine v0.9** (AI video generation with motion, singing, dancing, and audio sync—e.g., a dragon video or Ani character clips). These are product demos but not as recent.\n- xAI posts media sparingly; the November announcements dominate the latest activity. For real-time updates, check @xai directly. If you want details on specific older items or more views, let me know!","logprobs":[],"annotations":[{"type":"url_citation","url":"https://x.com/i/status/1990530503129391571"},{"type":"url_citation","url":"https://x.com/i/status/1990530506879074316"},{"type":"url_citation","url":"https://x.com/i/status/1991284822820720732"},{"type":"url_citation","url":"https://x.com/i/status/1990530501237715372"},{"type":"url_citation","url":"https://x.com/i/status/1975608007250829383"},{"type":"url_citation","url":"https://x.com/i/status/1977121515587223679"},{"type":"url_citation","url":"https://x.com/i/status/1975608070245175592"},{"type":"url_citation","url":"https://x.com/i/status/1975608184711880816"},{"type":"url_citation","url":"https://x.com/i/status/1990530508770718154"},{"type":"url_citation","url":"https://x.com/i/status/1990530505209754056"},{"type":"url_citation","url":"https://x.com/i/status/1991224224313487696"},{"type":"url_citation","url":"https://x.com/i/status/1987696575314104339"},{"type":"url_citation","url":"https://x.com/i/status/1991284824594870293"},{"type":"url_citation","url":"https://x.com/i/status/1991284828667527322"},{"type":"url_citation","url":"https://x.com/i/status/1975608017396867282"},{"type":"url_citation","url":"https://x.com/i/status/1975608122845896765"},{"type":"url_citation","url":"https://x.com/i/status/1991284813727474073"},{"type":"url_citation","url":"https://x.com/i/status/1991284830420758687"},{"type":"url_citation","url":"https://x.com/i/status/1975608132530544900"},{"type":"url_citation","url":"https://x.com/i/status/1991284818928366015"}]}],"id":"msg_b7b464ea-cc85-d44a-0f2f-1f7320e703c3","role":"assistant","type":"message","status":"completed"},"output_index":6} +{"sequence_number":1756,"type":"response.completed","response":{"created_at":1764611504,"id":"b7b464ea-cc85-d44a-0f2f-1f7320e703c3","max_output_tokens":null,"model":"grok-4-fast-reasoning","object":"response","output":[{"call_id":"xs_call_24148162","input":"{\"query\":\"from:xai filter:media\",\"limit\":20,\"mode\":\"Latest\"}","name":"x_keyword_search","type":"custom_tool_call","id":"ctc_b7b464ea-cc85-d44a-0f2f-1f7320e703c3_xs_call_24148162","status":"completed"},{"call_id":"xs_call_14963218","input":"{\"video_url\":\"https://video.twimg.com/amplify_video/1991284765027364866/vid/avc1/468x270/kRkbodV96jk4PmbG.mp4\"}","name":"view_x_video","type":"custom_tool_call","id":"ctc_b7b464ea-cc85-d44a-0f2f-1f7320e703c3_xs_call_14963218","status":"completed"},{"id":"ws_b7b464ea-cc85-d44a-0f2f-1f7320e703c3_call_55360208","type":"web_search_call","status":"completed","action":{"type":"search","query":"","sources":[]}},{"id":"ws_b7b464ea-cc85-d44a-0f2f-1f7320e703c3_call_63718660","type":"web_search_call","status":"completed","action":{"type":"search","query":"","sources":[]}},{"id":"ws_b7b464ea-cc85-d44a-0f2f-1f7320e703c3_call_09546004","type":"web_search_call","status":"completed","action":{"type":"search","query":"","sources":[]}},{"id":"ws_b7b464ea-cc85-d44a-0f2f-1f7320e703c3_call_45339693","type":"web_search_call","status":"completed","action":{"type":"search","query":"","sources":[]}},{"content":[{"type":"output_text","text":"### Latest Videos and Images from xAI on X (as of December 1, 2025)\n\nxAI's official X account (@xai) frequently shares media related to their AI products like Grok models, APIs, and partnerships. Based on the most recent posts with media (fetched in \"Latest\" order), here are the top latest videos and images focusing on product announcements and demos. These are primarily from late November 2025, centered around the launch of **Grok 4.1 Fast** (a new tool-calling AI model with a 2M-token context window) and the **Agent Tools API** (enabling autonomous web browsing, X searches, code execution, and more). I've included descriptions based on direct analysis of the media, timestamps, and context from the posts.\n\n#### Latest Video (November 19, 2025)\n- **Post Details**: From the announcement thread introducing Grok 4.1 Fast and Agent Tools API (Post ID: 1991284813727474073, ~2.3M views). This is the most recent video post.\n- **Video Description**: A 14-second demo clip (low-resolution preview at 468x270) showcasing Grok 4.1 Fast in action with the new Agent Tools API. It demonstrates real-time web browsing and research capabilities:\n - The interface is a dark-themed chat window (similar to Grok's UI) where a user queries about Tesla (e.g., \"How many Tesla factories are there?\" and follow-ups on production).\n - Grok autonomously browses websites: Frames show loading web pages (e.g., Tesla's official site), extracting data like factory locations and output gauges, and summarizing results in real-time.\n - Key features highlighted: Multi-step agentic workflow (search → browse → read documents → respond), with UI elements like loading spinners, document previews, and action buttons (e.g., \"Browse web\", \"Read document\").\n - Visual style: Clean, animated transitions between search results, web tabs, and chat responses. No subtitles, but text overlays show queries and summaries (e.g., \"Reading factory output gauges\", \"Building summary\").\n - **Relevance**: This illustrates the product's enterprise use cases like deep research and customer support. Link to full announcement: [x.ai/news/grok-4-1-fast](https://x.ai/news/grok-4-1-fast).\n- **Direct Video URL** (for viewing on X): https://video.twimg.com/amplify_video/1991284765027364866/vid/avc1/468x270/kRkbodV96jk4PmbG.mp4\n\n#### Latest Images (November 19, 2025)\nThese accompany the same Grok 4.1 Fast announcement thread. They are benchmark charts emphasizing the model's performance advantages in tool-calling, agentic tasks, and cost-efficiency compared to competitors (e.g., GPT-5, Claude Sonnet 4.5, Gemini 3 Pro).\n\n1. **Image 1: Multi-Turn Accuracy Benchmarks** (Post ID: 1991284818928366015, ~55K views)\n - **Description**: A bar chart comparing Grok models on \"Multi-Turn Acc\" (accuracy in extended conversations) and \"Multi-Turn Long Context\" (performance over long inputs). Orange bars (Grok 4.1 Fast) lead at 57.2% and 67%, vs. brown (Grok 4 Fast) at 41.6%/52.5%, and gray (Grok 4) at 20.5%/22%. Dark background with xAI branding. Highlights training improvements via long-horizon RL for consistent 2M-token performance.\n - **URL**: https://pbs.twimg.com/media/G6J2_UxacAQf8u4.jpg\n\n2. **Image 2: Enterprise Use Case Benchmarks (e.g., Customer Support)** (Post ID: 1991284822820720732, ~12K views)\n - **Description**: A scatter plot titled \"τ²-Bench Telecom\" (a telecom/customer support workflow benchmark). Points plot score (%) vs. total cost ($). Grok 4.1 Fast (orange dot) tops at ~100% score for ~$105 cost, outperforming GPT-5 high (~80%/$150+), Gemini 3 Pro (~70%/$120), Claude 4.5 Sonnet (~60%/$130), and Grok 4 Fast (~50%/$100). Verified by independent evaluator Artificial Analysis. Emphasizes real-world SOTA (state-of-the-art) for agentic workflows.\n - **URL**: https://pbs.twimg.com/media/G6J2_mcacAMtO5w.jpg\n\n3. **Image 3: Function Calling Benchmark** (Post ID: 1991284824594870293, ~8K views)\n - **Description**: A line graph on \"Berkeley Function Calling v4 Benchmark\" showing overall accuracy (%) vs. total cost ($ up to $400). Grok 4.1 Fast Reasoning (orange line) reaches 72% accuracy at low cost, leading Claude Sonnet 4.5 (gray), GPT-5 (gray dot), Gemini 3 Pro (gray, estimated), and Grok 4 (gray). Dark theme with striped borders; notes pending official results for some models.\n - **URL**: https://pbs.twimg.com/media/G6J23pzacAApgdm.jpg\n\n4. **Image 4: Agent Tools API Benchmarks** (Post ID: 1991284830420758687, ~8K views)\n - **Description**: A table comparing Grok 4.1 Fast + Agent Tools API on research/agentic benchmarks (Research-Eval, FRAMES, X Browse*). Grok leads with scores like 63.9/87.6/56.3 at low costs ($0.046–$0.091 per query), vs. GPT-5 (45.5/86.0/24.2 at $0.107–$0.198), Claude Sonnet 4.5 (41.2/85.0/14.6), and Gemini 3 Pro (55.9/90.9/26.5). Focuses on web/X integration for autonomous tasks. *X Browse is an internal xAI benchmark for multi-hop search on X.\n - **URL**: https://pbs.twimg.com/media/G6J26O_a8AAqhZJ.jpg\n\n#### Other Recent Media (November 17–19, 2025)\n- **Partnership Announcement Image (November 19, 2025)**: Post ID 1991224224313487696 (~115K views). A graphic showing xAI's partnership with Saudi Arabia's HUMAIN AI for deploying Grok nationwide and building GPU data centers. Features flags, AI icons, and text: \"Grok goes Global with KSA.\" (URL: https://pbs.twimg.com/media/G6I_4O8acAAMIWh.jpg)\n- **Grok 4.1 Announcement Images (November 17, 2025)**: Thread (Post ID: 1990530499752980638, ~33M views total) with 5+ charts:\n - Arena leaderboard (#1 at 1483 Elo).\n - EQ-Bench (emotional intelligence at 1586).\n - Creative Writing v3 (1722 Elo, +600 points).\n - Hallucination reduction (3x less error-prone).\n - User preference (65% over prior models).\n These are similar benchmark visuals, emphasizing conversational AI improvements. Available at: https://x.ai/news/grok-4-1\n\n#### Notes on Older Media\n- The next videos are from October 2025, like demos of **Grok Imagine v0.9** (AI video generation with motion, singing, dancing, and audio sync—e.g., a dragon video or Ani character clips). These are product demos but not as recent.\n- xAI posts media sparingly; the November announcements dominate the latest activity. For real-time updates, check @xai directly. If you want details on specific older items or more views, let me know!","logprobs":[],"annotations":[{"type":"url_citation","url":"https://x.com/i/status/1990530503129391571"},{"type":"url_citation","url":"https://x.com/i/status/1990530506879074316"},{"type":"url_citation","url":"https://x.com/i/status/1991284822820720732"},{"type":"url_citation","url":"https://x.com/i/status/1990530501237715372"},{"type":"url_citation","url":"https://x.com/i/status/1975608007250829383"},{"type":"url_citation","url":"https://x.com/i/status/1977121515587223679"},{"type":"url_citation","url":"https://x.com/i/status/1975608070245175592"},{"type":"url_citation","url":"https://x.com/i/status/1975608184711880816"},{"type":"url_citation","url":"https://x.com/i/status/1990530508770718154"},{"type":"url_citation","url":"https://x.com/i/status/1990530505209754056"},{"type":"url_citation","url":"https://x.com/i/status/1991224224313487696"},{"type":"url_citation","url":"https://x.com/i/status/1987696575314104339"},{"type":"url_citation","url":"https://x.com/i/status/1991284824594870293"},{"type":"url_citation","url":"https://x.com/i/status/1991284828667527322"},{"type":"url_citation","url":"https://x.com/i/status/1975608017396867282"},{"type":"url_citation","url":"https://x.com/i/status/1975608122845896765"},{"type":"url_citation","url":"https://x.com/i/status/1991284813727474073"},{"type":"url_citation","url":"https://x.com/i/status/1991284830420758687"},{"type":"url_citation","url":"https://x.com/i/status/1975608132530544900"},{"type":"url_citation","url":"https://x.com/i/status/1991284818928366015"}]}],"id":"msg_b7b464ea-cc85-d44a-0f2f-1f7320e703c3","role":"assistant","type":"message","status":"completed"}],"parallel_tool_calls":true,"previous_response_id":null,"reasoning":{"effort":null,"summary":null},"temperature":null,"text":{"format":{"type":"text"}},"tool_choice":"auto","tools":[{"type":"x_search","allowed_x_handles":["xai","elonmusk"],"enable_image_understanding":true,"enable_video_understanding":true}],"top_p":null,"usage":{"input_tokens":27236,"input_tokens_details":{"cached_tokens":4585},"output_tokens":3077,"output_tokens_details":{"reasoning_tokens":1091},"total_tokens":30313,"num_sources_used":0,"num_server_side_tools_used":6,"server_side_tool_usage_details":{"web_search_calls":0,"x_search_calls":1,"code_interpreter_calls":0,"file_search_calls":0,"mcp_calls":0,"document_search_calls":0}},"user":null,"incomplete_details":null,"status":"completed","store":true,"metadata":{}}} \ No newline at end of file diff --git a/aimux-providers/src/xai/tests/responses/mod.rs b/aimux-providers/src/xai/tests/responses/mod.rs new file mode 100644 index 000000000..72d90ae0d --- /dev/null +++ b/aimux-providers/src/xai/tests/responses/mod.rs @@ -0,0 +1,5 @@ +mod convert_responses_usage; +mod convert_to_responses_input; +mod responses_language_model; +mod responses_prepare_tools; +mod responses_raw_usage; diff --git a/aimux-providers/src/xai/tests/responses/responses_language_model.rs b/aimux-providers/src/xai/tests/responses/responses_language_model.rs new file mode 100644 index 000000000..558f427b9 --- /dev/null +++ b/aimux-providers/src/xai/tests/responses/responses_language_model.rs @@ -0,0 +1,5200 @@ +//! Port of `src/responses/xai-responses-language-model.test.ts` from +//! `@ai-sdk/xai@5.0.12`: the `doGenerate`, `doStream`, schema validation, +//! `response.incomplete` / `response.failed` and error event cases. +//! +//! Harness differences from the TypeScript (expectations are unchanged): +//! - `createTestServer` is the [`MockFetch`] transport: `server.calls[n]` is +//! `fetch.seen()[n]`, and the canned responses are queued in call order. +//! - `mockId()`: the TS model numbers its source ids itself, the Rust model +//! draws them from a process-wide counter. [`content_json`] renumbers them by +//! first appearance, so each result reads `id-0`, `id-1`, ... as upstream. +//! - JavaScript has one number type: [`assert_js_eq`] compares numbers as +//! `f64`, so `40` and `40.0` are equal. +//! - `undefined` properties are absent from the expected JSON. +//! - `toMatchSnapshot()` values are `snapshots/.json`, converted +//! from the vitest `.snap` file (their text matches the fixtures). + +use std::sync::Arc; + +use aimux_core::language_model::LanguageModel; +use aimux_core::language_model_message::{ + AssistantPart, FilePart, LanguageModelMessage, LanguageModelPrompt, TextPart, ToolCallPart, + ToolPart, ToolResultContent, ToolResultOutput, ToolResultPart, UserPart, +}; +use aimux_core::options::{CallOptions, ResponseFormat}; +use aimux_core::result::{GenerateResult, StreamResult}; +use aimux_core::shared::{FileBytes, FileData, SharedProviderOptions, provider_namespace}; +use aimux_core::tool::{FunctionTool, ProviderTool, Tool}; +use aimux_core::types::ReasoningEffort; +use futures::StreamExt; +use serde::Serialize; +use serde_json::{Value, json}; + +use crate::test_support::mock_fetch::{Canned, MockFetch}; +use crate::xai::options::NAMESPACE; +use crate::xai::{XAIProviderSettings, XaiResponsesModel, create_xai}; + +/// The TS `createModel()` default model id. +const DEFAULT_MODEL_ID: &str = "grok-4-fast-non-reasoning"; + +// ── helpers ───────────────────────────────────────────────────────────────── + +/// The TS `TEST_PROMPT`: one user text message "hello". +fn test_prompt() -> LanguageModelPrompt { + vec![LanguageModelMessage::user_text("hello")] +} + +/// The TS `createModel(modelId)`: the xAI provider at its default base URL, +/// `Authorization: Bearer test-key` and the mock transport. +fn create_model(fetch: &Arc, model_id: &str) -> XaiResponsesModel { + create_xai(XAIProviderSettings { + api_key: Some("test-key".to_string()), + fetch: Some(fetch.transport()), + ..Default::default() + }) + .expect("valid settings") + .responses(model_id) +} + +/// `prepareJsonResponse(body)`: one JSON response, for the first request. +fn json_fetch(body: &Value) -> Arc { + MockFetch::new(vec![Canned::json(body)]) +} + +/// `prepareJsonFixtureResponse(name)`: the parsed `__fixtures__/.json`, +/// given as `include_str!` of it. +fn json_fixture(text: &str) -> Value { + serde_json::from_str(text).expect("fixture is JSON") +} + +/// `prepareStreamChunks(chunks)`: each chunk as `data: `, then +/// `data: [DONE]`. +fn sse_fetch(frames: &[String]) -> Arc { + let mut body = String::new(); + for frame in frames { + body.push_str(&format!("data: {frame}\n\n")); + } + body.push_str("data: [DONE]\n\n"); + MockFetch::new(vec![Canned { + status: 200, + headers: vec![("content-type".into(), "text/event-stream".into())], + body: body.into_bytes(), + }]) +} + +/// The `requests[call]` body as JSON (the TS `server.calls[call].requestBodyJson`). +fn request_body(fetch: &MockFetch, call: usize) -> Value { + fetch.seen()[call].json_body() +} + +/// `toMatchSnapshot()` value: the parsed `snapshots/.json`. +fn snapshot(text: &str) -> Value { + serde_json::from_str(text).expect("snapshot is JSON") +} + +fn to_json(value: &T) -> Value { + serde_json::to_value(value).expect("value serializes") +} + +/// `result.content` as JSON, with the source ids renumbered (see the module docs). +fn content_json(result: &GenerateResult) -> Value { + renumber_source_ids(to_json(&result.content)) +} + +/// Whether a string is a source id the Rust model generates (`id-`). +fn is_source_id(text: &str) -> bool { + text.strip_prefix("id-") + .is_some_and(|digits| !digits.is_empty() && digits.bytes().all(|b| b.is_ascii_digit())) +} + +/// Renumber the `id-N` strings by first appearance: `id-0`, `id-1`, ... +fn renumber_source_ids(value: Value) -> Value { + fn walk(value: Value, seen: &mut Vec) -> Value { + match value { + Value::String(text) if is_source_id(&text) => { + let index = match seen.iter().position(|known| *known == text) { + Some(index) => index, + None => { + seen.push(text); + seen.len() - 1 + } + }; + json!(format!("id-{index}")) + } + Value::Array(items) => { + Value::Array(items.into_iter().map(|item| walk(item, seen)).collect()) + } + Value::Object(fields) => Value::Object( + fields + .into_iter() + .map(|(key, field)| (key, walk(field, seen))) + .collect(), + ), + other => other, + } + } + walk(value, &mut Vec::new()) +} + +/// JavaScript has one number type: `40` and `40.0` are the same number. +fn js_numbers(value: Value) -> Value { + match value { + Value::Number(number) => number.as_f64().map_or(Value::Number(number), |n| json!(n)), + Value::Array(items) => Value::Array(items.into_iter().map(js_numbers).collect()), + Value::Object(fields) => Value::Object( + fields + .into_iter() + .map(|(key, field)| (key, js_numbers(field))) + .collect(), + ), + other => other, + } +} + +/// `expect(actual).toEqual(expected)` on JSON. +fn assert_js_eq(actual: &Value, expected: Value) { + assert_eq!(js_numbers(actual.clone()), js_numbers(expected)); +} + +/// `expect(list).toContainEqual(item)`. +fn assert_contains(list: &Value, item: Value) { + let item = js_numbers(item); + let items = list.as_array().expect("an array"); + assert!( + items + .iter() + .any(|candidate| js_numbers(candidate.clone()) == item), + "{list:#}\ndoes not contain\n{item:#}" + ); +} + +/// `expect(actual).toMatchObject(expected)`, and `expect.objectContaining` +/// for nested objects: every key of `expected` matches, recursively. A failure +/// names the path of the key. +fn assert_matches_object(actual: &Value, expected: Value) { + assert_matches_at(actual, &expected, "$"); +} + +fn assert_matches_at(actual: &Value, expected: &Value, path: &str) { + match expected { + Value::Object(fields) => { + for (key, value) in fields { + let here = format!("{path}.{key}"); + match actual.get(key) { + Some(found) => assert_matches_at(found, value, &here), + None => panic!("{here}: missing from {actual}; expected {value}"), + } + } + } + _ => { + let (actual_js, expected_js) = + (js_numbers(actual.clone()), js_numbers(expected.clone())); + assert!( + actual_js == expected_js, + "{path}: expected {expected}, got {actual}" + ); + } + } +} + +/// `prepareStreamChunks([...])` with `JSON.stringify`-ed events: one event per chunk. +fn stream_fetch(events: Vec) -> Arc { + let chunks: Vec = events.iter().map(Value::to_string).collect(); + sse_fetch(&chunks) +} + +/// `prepareChunksFixtureResponse(name)`: each line of a `.chunks.txt` fixture +/// (given as `include_str!` of it) is one chunk, then `data: [DONE]`. +fn chunks_fixture_fetch(text: &str) -> Arc { + let chunks: Vec = text.split('\n').map(str::to_string).collect(); + sse_fetch(&chunks) +} + +/// `convertReadableStreamToArray(stream)` as JSON. An `Err` item (a failure +/// the stream cannot carry on from) is kept as `{ type: 'error', error }`. +/// Source ids are renumbered as in [`content_json`]. +async fn stream_parts(result: StreamResult) -> Value { + let mut stream = result.stream; + let mut parts = Vec::new(); + while let Some(item) = stream.next().await { + parts.push(match item { + Ok(part) => to_json(&part), + Err(error) => json!({ "type": "error", "error": to_json(&error) }), + }); + } + renumber_source_ids(Value::Array(parts)) +} + +/// `doStream(options)` on a model of `model_id` answering from `fetch`, and the +/// parts of the stream. +async fn run_stream(fetch: &Arc, model_id: &str, options: &CallOptions) -> Value { + let result = create_model(fetch, model_id) + .do_stream(options) + .await + .expect("should succeed"); + stream_parts(result).await +} + +/// `parts.filter(part => part.type === kind)`. +fn parts_of_type(parts: &Value, kind: &str) -> Vec { + parts + .as_array() + .expect("an array") + .iter() + .filter(|part| part["type"] == kind) + .cloned() + .collect() +} + +/// `parts.find(part => part.type === kind)`, or `null`. +fn first_part(parts: &Value, kind: &str) -> Value { + parts_of_type(parts, kind) + .into_iter() + .next() + .unwrap_or(Value::Null) +} + +/// The error part as upstream's `createProviderStreamError` shapes it. +/// +/// aimux has no `AI_StreamProviderError`: an error event inside the stream +/// becomes an `AI_APICallError`, whose `providerCode` holds upstream's +/// `code`. Upstream's `type` is the event's own `type`, which aimux keeps in +/// `data`. This view renames those two fields and keeps every other value. +fn stream_provider_error_view(part: &Value) -> Value { + let error = &part["error"]; + json!({ + "type": part["type"], + "error": { + "message": error["message"], + "type": error["data"]["type"], + "code": error["providerCode"], + "statusCode": error["statusCode"], + "isRetryable": error["isRetryable"], + "data": error["data"] + } + }) +} + +/// `parts.findIndex(part => part.type === kind)`: `-1` when none. +fn index_of(parts: &Value, kind: &str) -> i64 { + parts + .as_array() + .expect("an array") + .iter() + .position(|part| part["type"] == kind) + .map_or(-1, |index| index as i64) +} + +/// `providerOptions: { xai: options }` (the key is the xAI namespace). +fn xai_options(options: Value) -> SharedProviderOptions { + provider_namespace(NAMESPACE, options).expect("options are an object") +} + +/// A function tool, as the TS `{ type: 'function', name, description, inputSchema }`. +fn function_tool(name: &str, description: &str, input_schema: Value) -> Tool { + Tool::Function(FunctionTool::new(name, input_schema).with_description(description)) +} + +/// A provider-defined tool, as the TS `{ type: 'provider', id, name, args }`. +fn provider_tool(id: &str, name: &str, args: Value) -> Tool { + Tool::Provider(ProviderTool { + id: id.parse().expect("namespaced tool id"), + name: name.to_string(), + args: args.as_object().cloned().expect("args are an object"), + supports_deferred_results: None, + }) +} + +fn system_message(content: &str) -> LanguageModelMessage { + LanguageModelMessage::System { + content: content.to_string(), + provider_options: None, + } +} + +fn user_message(content: Vec) -> LanguageModelMessage { + LanguageModelMessage::User { + content, + provider_options: None, + } +} + +fn text_part(text: &str) -> UserPart { + UserPart::Text(TextPart { + text: text.to_string(), + provider_options: None, + }) +} + +/// `{ type: 'file', mediaType: 'image/png', data: Buffer.from([0, 1, 2, 3]) }`. +fn png_file(provider_options: Option) -> FilePart { + FilePart { + data: FileData::Data { + data: FileBytes::Binary(vec![0, 1, 2, 3]), + }, + media_type: "image/png".to_string(), + filename: None, + provider_options, + } +} + +/// The `/responses` body with the given output and usage that most cases use. +fn empty_response(usage: Value) -> Value { + json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [], + "usage": usage + }) +} + +// ───────────────────────────────────────────────────────────────────────────── + +mod xai_responses_language_model { + use super::*; + + mod do_generate { + use super::*; + + mod basic_text_generation { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › basic text generation › should generate text content + #[tokio::test] + async fn should_generate_text_content() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "created_at": 1700000000, + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [ + { + "type": "message", + "id": "msg_123", + "status": "completed", + "role": "assistant", + "content": [ + { + "type": "output_text", + "text": "hello world", + "annotations": [] + } + ] + } + ], + "usage": { + "input_tokens": 10, + "output_tokens": 5, + "total_tokens": 15 + } + })); + + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&CallOptions::new(test_prompt())) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + json!([{ "text": "hello world", "type": "text" }]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › basic text generation › should extract usage correctly + #[tokio::test] + async fn should_extract_usage_correctly() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [], + "usage": { + "input_tokens": 345, + "output_tokens": 538, + "total_tokens": 883, + "output_tokens_details": { + "reasoning_tokens": 123 + } + } + })); + + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&CallOptions::new(test_prompt())) + .await + .expect("should succeed"); + + assert_js_eq( + &to_json(&result.usage), + json!({ + "inputTokens": { + "cacheRead": 0, + "noCache": 345, + "total": 345 + }, + "outputTokens": { + "reasoning": 123, + "text": 415, + "total": 538 + }, + "raw": { + "input_tokens": 345, + "output_tokens": 538, + "output_tokens_details": { + "reasoning_tokens": 123 + }, + "total_tokens": 883 + } + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › basic text generation › should expose cost_in_usd_ticks in providerMetadata + #[tokio::test] + async fn should_expose_cost_in_usd_ticks_in_provider_metadata() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [], + "usage": { + "input_tokens": 10, + "output_tokens": 5, + "cost_in_usd_ticks": 113500 + } + })); + + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&CallOptions::new(test_prompt())) + .await + .expect("should succeed"); + + assert_js_eq( + &to_json(&result.provider_metadata), + json!({ "xai": { "costInUsdTicks": 113500 } }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › basic text generation › should not include providerMetadata when cost_in_usd_ticks is missing + #[tokio::test] + async fn should_not_include_provider_metadata_when_cost_in_usd_ticks_is_missing() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 5 } + })); + + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&CallOptions::new(test_prompt())) + .await + .expect("should succeed"); + + assert!(result.provider_metadata.is_none()); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › basic text generation › should expose echoed request identifiers in providerMetadata + #[tokio::test] + async fn should_expose_echoed_request_identifiers_in_provider_metadata() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 5 }, + "prompt_cache_key": "conversation-123", + "safety_identifier": "hashed-user-123" + })); + + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&CallOptions::new(test_prompt())) + .await + .expect("should succeed"); + + assert_js_eq( + &to_json(&result.provider_metadata), + json!({ + "xai": { + "promptCacheKey": "conversation-123", + "safetyIdentifier": "hashed-user-123" + } + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › basic text generation › should extract finish reason from status + #[tokio::test] + async fn should_extract_finish_reason_from_status() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 5 } + })); + + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&CallOptions::new(test_prompt())) + .await + .expect("should succeed"); + + assert_js_eq( + &to_json(&result.finish_reason), + json!({ "raw": "completed", "unified": "stop" }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › basic text generation › should return tool-calls finish reason when function_call is present + #[tokio::test] + async fn should_return_tool_calls_finish_reason_when_function_call_is_present() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [ + { + "type": "function_call", + "id": "fc_123", + "name": "weather", + "arguments": "{\"location\":\"sf\"}", + "call_id": "call_123" + } + ], + "usage": { "input_tokens": 10, "output_tokens": 5 } + })); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![function_tool( + "weather", + "get weather", + json!({ + "type": "object", + "properties": { "location": { "type": "string" } } + }), + )]); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &to_json(&result.finish_reason), + json!({ "raw": "completed", "unified": "tool-calls" }), + ); + } + } + + mod reasoning_content { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › reasoning content › should extract reasoning with encrypted content when store=false + #[tokio::test] + async fn should_extract_reasoning_with_encrypted_content_when_store_false() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [ + { + "type": "reasoning", + "id": "rs_456", + "status": "completed", + "summary": [ + { + "type": "summary_text", + "text": "First, analyze the question carefully." + } + ], + "encrypted_content": "abc123encryptedcontent" + }, + { + "type": "message", + "id": "msg_123", + "status": "completed", + "role": "assistant", + "content": [ + { + "type": "output_text", + "text": "The answer is 42.", + "annotations": [] + } + ] + } + ], + "usage": { + "input_tokens": 10, + "output_tokens": 20, + "output_tokens_details": { "reasoning_tokens": 15 } + } + })); + + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&CallOptions::new(test_prompt())) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + json!([ + { + "providerMetadata": { + "xai": { + "itemId": "rs_456", + "reasoningEncryptedContent": "abc123encryptedcontent" + } + }, + "text": "First, analyze the question carefully.", + "type": "reasoning" + }, + { + "text": "The answer is 42.", + "type": "text" + } + ]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › reasoning content › should handle reasoning without encrypted content + #[tokio::test] + async fn should_handle_reasoning_without_encrypted_content() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [ + { + "type": "reasoning", + "id": "rs_456", + "status": "completed", + "summary": [ + { + "type": "summary_text", + "text": "Thinking through the problem." + } + ] + }, + { + "type": "message", + "id": "msg_123", + "status": "completed", + "role": "assistant", + "content": [ + { + "type": "output_text", + "text": "Solution found.", + "annotations": [] + } + ] + } + ], + "usage": { "input_tokens": 10, "output_tokens": 15 } + })); + + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&CallOptions::new(test_prompt())) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + json!([ + { + "providerMetadata": { "xai": { "itemId": "rs_456" } }, + "text": "Thinking through the problem.", + "type": "reasoning" + }, + { + "text": "Solution found.", + "type": "text" + } + ]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › reasoning content › should extract reasoning from content when summary is empty + #[tokio::test] + async fn should_extract_reasoning_from_content_when_summary_is_empty() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-3-mini", + "output": [ + { + "type": "reasoning", + "id": "rs_456", + "status": "completed", + "summary": [], + "content": [ + { + "type": "reasoning_text", + "text": "Let me think step by step." + } + ] + }, + { + "type": "message", + "id": "msg_123", + "status": "completed", + "role": "assistant", + "content": [ + { + "type": "output_text", + "text": "The answer is 444.", + "annotations": [] + } + ] + } + ], + "usage": { "input_tokens": 10, "output_tokens": 15 } + })); + + let result = create_model(&fetch, "grok-3-mini") + .do_generate(&CallOptions::new(test_prompt())) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + json!([ + { + "providerMetadata": { "xai": { "itemId": "rs_456" } }, + "text": "Let me think step by step.", + "type": "reasoning" + }, + { + "text": "The answer is 444.", + "type": "text" + } + ]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › reasoning content › should extract reasoning with encrypted content but empty summary text + #[tokio::test] + async fn should_extract_reasoning_with_encrypted_content_but_empty_summary_text() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [ + { + "type": "reasoning", + "id": "rs_789", + "status": "completed", + "summary": [], + "encrypted_content": "encrypted_zdr_content_xyz" + }, + { + "type": "message", + "id": "msg_123", + "status": "completed", + "role": "assistant", + "content": [ + { + "type": "output_text", + "text": "Here is my response.", + "annotations": [] + } + ] + } + ], + "usage": { + "input_tokens": 10, + "output_tokens": 20, + "output_tokens_details": { "reasoning_tokens": 15 } + } + })); + + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&CallOptions::new(test_prompt())) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + json!([ + { + "providerMetadata": { + "xai": { + "itemId": "rs_789", + "reasoningEncryptedContent": "encrypted_zdr_content_xyz" + } + }, + "text": "", + "type": "reasoning" + }, + { + "text": "Here is my response.", + "type": "text" + } + ]), + ); + } + } + + mod settings_and_options { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › should send model id and settings + #[tokio::test] + async fn should_send_model_id_and_settings() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(vec![ + system_message("you are helpful"), + user_message(vec![text_part("hello")]), + ]); + options.temperature = Some(0.5); + options.top_p = Some(0.9); + options.max_output_tokens = Some(100); + create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &request_body(&fetch, 0), + json!({ + "input": [ + { "content": "you are helpful", "role": "system" }, + { + "content": [{ "text": "hello", "type": "input_text" }], + "role": "user" + } + ], + "max_output_tokens": 100, + "model": "grok-4-fast-non-reasoning", + "temperature": 0.5, + "top_p": 0.9 + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › should send image data in function call output + #[tokio::test] + async fn should_send_image_data_in_function_call_output() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let prompt = vec![ + user_message(vec![text_part("Read the tool image.")]), + LanguageModelMessage::Assistant { + content: vec![AssistantPart::ToolCall(ToolCallPart { + tool_call_id: "call_123".to_string(), + tool_name: "inspectImage".to_string(), + input: json!({}), + provider_executed: None, + provider_options: None, + })], + provider_options: None, + }, + LanguageModelMessage::Tool { + content: vec![ToolPart::ToolResult(ToolResultPart { + tool_call_id: "call_123".to_string(), + tool_name: "inspectImage".to_string(), + output: ToolResultOutput::Content { + value: vec![ + ToolResultContent::Text(TextPart { + text: "The requested image is attached.".to_string(), + provider_options: None, + }), + ToolResultContent::File(png_file(None)), + ], + }, + provider_options: None, + })], + provider_options: None, + }, + ]; + create_model(&fetch, "grok-4.5") + .do_generate(&CallOptions::new(prompt)) + .await + .expect("should succeed"); + + assert_js_eq( + &request_body(&fetch, 0)["input"], + json!([ + { + "content": [{ "text": "Read the tool image.", "type": "input_text" }], + "role": "user" + }, + { + "arguments": "{}", + "call_id": "call_123", + "id": "call_123", + "name": "inspectImage", + "status": "completed", + "type": "function_call" + }, + { + "call_id": "call_123", + "output": [ + { "text": "The requested image is attached.", "type": "input_text" }, + { + "image_url": "data:image/png;base64,AAECAw==", + "type": "input_image" + } + ], + "type": "function_call_output" + } + ]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › should warn about unsupported stopSequences + #[tokio::test] + async fn should_warn_about_unsupported_stop_sequences() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.stop_sequences = Some(vec!["\n\n".to_string(), "STOP".to_string()]); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &to_json(&result.warnings), + json!([{ "feature": "stopSequences", "type": "unsupported" }]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › should warn about unsupported sampling settings + #[tokio::test] + async fn should_warn_about_unsupported_sampling_settings() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.top_k = Some(10.0); + options.frequency_penalty = Some(0.5); + options.presence_penalty = Some(0.5); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &to_json(&result.warnings), + json!([ + { "feature": "frequencyPenalty", "type": "unsupported" }, + { "feature": "presencePenalty", "type": "unsupported" } + ]), + ); + } + + mod provider_options { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › provider options › reasoningEffort + #[tokio::test] + async fn reasoning_effort() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.provider_options = + Some(xai_options(json!({ "reasoningEffort": "high" }))); + create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_eq!(request_body(&fetch, 0)["reasoning"]["effort"], "high"); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › provider options › reasoningEffort: "xhigh" for grok-4.6 + #[tokio::test] + async fn reasoning_effort_xhigh_for_grok_4_6() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.provider_options = + Some(xai_options(json!({ "reasoningEffort": "xhigh" }))); + create_model(&fetch, "grok-4.6") + .do_generate(&options) + .await + .expect("should succeed"); + + assert_eq!(request_body(&fetch, 0)["reasoning"]["effort"], "xhigh"); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › provider options › reasoningEffort: "none" + #[tokio::test] + async fn reasoning_effort_none() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.provider_options = + Some(xai_options(json!({ "reasoningEffort": "none" }))); + create_model(&fetch, "grok-4.3") + .do_generate(&options) + .await + .expect("should succeed"); + + assert_eq!(request_body(&fetch, 0)["reasoning"]["effort"], "none"); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › provider options › accepts deprecated reasoningSummary without sending it + #[tokio::test] + async fn accepts_deprecated_reasoning_summary_without_sending_it() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.provider_options = + Some(xai_options(json!({ "reasoningSummary": "concise" }))); + create_model(&fetch, "grok-4.7") + .do_generate(&options) + .await + .expect("should succeed"); + + assert!(request_body(&fetch, 0).get("reasoning").is_none()); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › provider options › logprobs and topLogprobs + #[tokio::test] + async fn logprobs_and_top_logprobs() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.provider_options = Some(xai_options(json!({ + "logprobs": true, + "topLogprobs": 6 + }))); + create_model(&fetch, "grok-4-fast") + .do_generate(&options) + .await + .expect("should succeed"); + + let body = request_body(&fetch, 0); + assert_eq!(body["logprobs"], true); + assert_js_eq(&body["top_logprobs"], json!(6)); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › provider options › topLogprobs enables logprobs + #[tokio::test] + async fn top_logprobs_enables_logprobs() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.provider_options = Some(xai_options(json!({ "topLogprobs": 2 }))); + create_model(&fetch, "grok-4-fast") + .do_generate(&options) + .await + .expect("should succeed"); + + let body = request_body(&fetch, 0); + assert_eq!(body["logprobs"], true); + assert_js_eq(&body["top_logprobs"], json!(2)); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › provider options › store:true + #[tokio::test] + async fn store_true() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.provider_options = Some(xai_options(json!({ "store": true }))); + create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + let body = request_body(&fetch, 0); + assert!(body.get("store").is_none()); + assert!(body.get("include").is_none()); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › provider options › store:false + #[tokio::test] + async fn store_false() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.provider_options = Some(xai_options(json!({ "store": false }))); + create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + let body = request_body(&fetch, 0); + assert_eq!(body["store"], false); + assert_js_eq(&body["include"], json!(["reasoning.encrypted_content"])); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › provider options › previousResponseId + #[tokio::test] + async fn previous_response_id() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.provider_options = + Some(xai_options(json!({ "previousResponseId": "resp_456" }))); + create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_eq!(request_body(&fetch, 0)["previous_response_id"], "resp_456"); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › provider options › additional request options + #[tokio::test] + async fn additional_request_options() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.top_k = Some(40.0); + options.provider_options = Some(xai_options(json!({ + "minP": 0.1, + "maxTurns": 5, + "parallelToolCalls": false, + "promptCacheKey": "conversation-123", + "safetyIdentifier": "hashed-user-123", + "serviceTier": "priority", + "user": "user-123" + }))); + create_model(&fetch, "grok-4.7") + .do_generate(&options) + .await + .expect("should succeed"); + + assert_matches_object( + &request_body(&fetch, 0), + json!({ + "top_k": 40, + "min_p": 0.1, + "max_turns": 5, + "parallel_tool_calls": false, + "prompt_cache_key": "conversation-123", + "safety_identifier": "hashed-user-123", + "service_tier": "priority", + "user": "user-123" + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › provider options › serviceTier + #[tokio::test] + async fn service_tier() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4.6", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 5 }, + "service_tier": "priority" + })); + + let mut options = CallOptions::new(test_prompt()); + options.provider_options = + Some(xai_options(json!({ "serviceTier": "priority" }))); + let result = create_model(&fetch, "grok-4.6") + .do_generate(&options) + .await + .expect("should succeed"); + + assert_eq!(request_body(&fetch, 0)["service_tier"], "priority"); + assert_eq!( + to_json(&result.provider_metadata)["xai"]["serviceTier"], + "priority" + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › provider options › serviceTier reports a downgrade to default + #[tokio::test] + async fn service_tier_reports_a_downgrade_to_default() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4.6", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 5 }, + "service_tier": "default" + })); + + let mut options = CallOptions::new(test_prompt()); + options.provider_options = + Some(xai_options(json!({ "serviceTier": "priority" }))); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_eq!( + to_json(&result.provider_metadata)["xai"]["serviceTier"], + "default" + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › provider options › include with file_search_call.results + #[tokio::test] + async fn include_with_file_search_call_results() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.provider_options = Some(xai_options(json!({ + "include": ["file_search_call.results"] + }))); + create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &request_body(&fetch, 0)["include"], + json!(["file_search_call.results"]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › provider options › include with no_inline_citations + #[tokio::test] + async fn include_with_no_inline_citations() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.provider_options = Some(xai_options(json!({ + "include": ["no_inline_citations"] + }))); + create_model(&fetch, "grok-4.7") + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &request_body(&fetch, 0)["include"], + json!(["no_inline_citations"]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › provider options › include with server-side tool outputs + #[tokio::test] + async fn include_with_server_side_tool_outputs() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.provider_options = Some(xai_options(json!({ + "include": [ + "web_search_call.action.sources", + "code_interpreter_call.outputs" + ] + }))); + create_model(&fetch, "grok-4.7") + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &request_body(&fetch, 0)["include"], + json!([ + "web_search_call.action.sources", + "code_interpreter_call.outputs" + ]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › provider options › include with file_search_call.results and store:false + #[tokio::test] + async fn include_with_file_search_call_results_and_store_false() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.provider_options = Some(xai_options(json!({ + "include": ["file_search_call.results"], + "store": false + }))); + create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + let body = request_body(&fetch, 0); + assert_eq!(body["store"], false); + assert_js_eq( + &body["include"], + json!(["file_search_call.results", "reasoning.encrypted_content"]), + ); + } + } + + mod top_level_reasoning { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › top-level reasoning › should map top-level reasoning to reasoning effort + #[tokio::test] + async fn should_map_top_level_reasoning_to_reasoning_effort() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.reasoning = Some(ReasoningEffort::High); + create_model(&fetch, "grok-4.3") + .do_generate(&options) + .await + .expect("should succeed"); + + assert_eq!(request_body(&fetch, 0)["reasoning"]["effort"], "high"); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › top-level reasoning › should map top-level reasoning none to reasoning effort "none" + #[tokio::test] + async fn should_map_top_level_reasoning_none_to_reasoning_effort_none() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.reasoning = Some(ReasoningEffort::None); + create_model(&fetch, "grok-4.3") + .do_generate(&options) + .await + .expect("should succeed"); + + assert_eq!(request_body(&fetch, 0)["reasoning"]["effort"], "none"); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › top-level reasoning › should coerce top-level reasoning xhigh to reasoning effort "high" + #[tokio::test] + async fn should_coerce_top_level_reasoning_xhigh_to_reasoning_effort_high() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.reasoning = Some(ReasoningEffort::Xhigh); + create_model(&fetch, "grok-4.3") + .do_generate(&options) + .await + .expect("should succeed"); + + assert_eq!(request_body(&fetch, 0)["reasoning"]["effort"], "high"); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › top-level reasoning › should map top-level reasoning xhigh to reasoning effort "xhigh" for grok-4.6 + #[tokio::test] + async fn should_map_top_level_reasoning_xhigh_to_reasoning_effort_xhigh_for_grok_4_6() + { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.reasoning = Some(ReasoningEffort::Xhigh); + create_model(&fetch, "grok-4.6") + .do_generate(&options) + .await + .expect("should succeed"); + + assert_eq!(request_body(&fetch, 0)["reasoning"]["effort"], "xhigh"); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › top-level reasoning › should prefer providerOptions reasoningEffort over top-level reasoning + #[tokio::test] + async fn should_prefer_provider_options_reasoning_effort_over_top_level_reasoning() + { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.reasoning = Some(ReasoningEffort::None); + options.provider_options = + Some(xai_options(json!({ "reasoningEffort": "high" }))); + create_model(&fetch, "grok-4.3") + .do_generate(&options) + .await + .expect("should succeed"); + + assert_eq!(request_body(&fetch, 0)["reasoning"]["effort"], "high"); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › top-level reasoning › should omit reasoning effort and warn for models that do not support it + #[tokio::test] + async fn should_omit_reasoning_effort_and_warn_for_models_that_do_not_support_it() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.reasoning = Some(ReasoningEffort::None); + let result = create_model(&fetch, "grok-4.20-reasoning") + .do_generate(&options) + .await + .expect("should succeed"); + + assert!(request_body(&fetch, 0).get("reasoning").is_none()); + assert_contains( + &to_json(&result.warnings), + json!({ + "type": "unsupported", + "feature": "reasoning", + "details": "reasoning \"none\" is not supported by this model." + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › top-level reasoning › should still pass providerOptions reasoningEffort for models that do not support top-level reasoning + #[tokio::test] + async fn should_still_pass_provider_options_reasoning_effort_without_top_level_reasoning() + { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.provider_options = + Some(xai_options(json!({ "reasoningEffort": "none" }))); + create_model(&fetch, "grok-4.20-reasoning") + .do_generate(&options) + .await + .expect("should succeed"); + + assert_eq!(request_body(&fetch, 0)["reasoning"]["effort"], "none"); + } + } + + mod image_detail { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › image detail › should pass detail from the imageDetail provider option on image parts + #[tokio::test] + async fn should_pass_detail_from_the_image_detail_provider_option_on_image_parts() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + create_model(&fetch, "grok-4.3") + .do_generate(&CallOptions::new(vec![user_message(vec![ + text_part("What is in this image?"), + UserPart::File(png_file(Some(xai_options( + json!({ "imageDetail": "high" }), + )))), + ])])) + .await + .expect("should succeed"); + + assert_js_eq( + &request_body(&fetch, 0)["input"], + json!([ + { + "role": "user", + "content": [ + { "type": "input_text", "text": "What is in this image?" }, + { + "type": "input_image", + "image_url": "data:image/png;base64,AAECAw==", + "detail": "high" + } + ] + } + ]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › image detail › should not set detail when the imageDetail provider option is not set + #[tokio::test] + async fn should_not_set_detail_when_the_image_detail_provider_option_is_not_set() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + create_model(&fetch, "grok-4.3") + .do_generate(&CallOptions::new(vec![user_message(vec![ + text_part("What is in this image?"), + UserPart::File(png_file(None)), + ])])) + .await + .expect("should succeed"); + + assert_js_eq( + &request_body(&fetch, 0)["input"], + json!([ + { + "role": "user", + "content": [ + { "type": "input_text", "text": "What is in this image?" }, + { + "type": "input_image", + "image_url": "data:image/png;base64,AAECAw==" + } + ] + } + ]), + ); + } + } + + mod response_format { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › responseFormat › should send response format json schema + #[tokio::test] + async fn should_send_response_format_json_schema() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.response_format = Some(ResponseFormat::Json { + schema: Some(json!({ + "type": "object", + "properties": { + "name": { "type": "string" }, + "ingredients": { "type": "array", "items": { "type": "string" } } + }, + "required": ["name", "ingredients"], + "additionalProperties": false + })), + name: Some("recipe".to_string()), + description: Some("A recipe object".to_string()), + }); + create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &request_body(&fetch, 0), + json!({ + "input": [ + { + "content": [{ "text": "hello", "type": "input_text" }], + "role": "user" + } + ], + "model": "grok-4-fast-non-reasoning", + "text": { + "format": { + "description": "A recipe object", + "name": "recipe", + "schema": { + "additionalProperties": false, + "properties": { + "ingredients": { + "items": { "type": "string" }, + "type": "array" + }, + "name": { "type": "string" } + }, + "required": ["name", "ingredients"], + "type": "object" + }, + "strict": true, + "type": "json_schema" + } + } + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › responseFormat › should send response format json object when no schema provided + #[tokio::test] + async fn should_send_response_format_json_object_when_no_schema_provided() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.response_format = Some(ResponseFormat::Json { + schema: None, + name: None, + description: None, + }); + create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &request_body(&fetch, 0), + json!({ + "input": [ + { + "content": [{ "text": "hello", "type": "input_text" }], + "role": "user" + } + ], + "model": "grok-4-fast-non-reasoning", + "text": { "format": { "type": "json_object" } } + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › settings and options › responseFormat › should use default name when responseFormat.name is not provided + #[tokio::test] + async fn should_use_default_name_when_response_format_name_is_not_provided() { + let fetch = json_fetch(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )); + + let mut options = CallOptions::new(test_prompt()); + options.response_format = Some(ResponseFormat::Json { + schema: Some(json!({ + "type": "object", + "properties": { "value": { "type": "string" } } + })), + name: None, + description: None, + }); + create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_eq!( + request_body(&fetch, 0)["text"]["format"]["name"], + "response" + ); + } + } + } + + mod web_search_tool { + use super::*; + + /// The `beforeEach` of the describe: the fixture's response to a + /// `xai.web_search` request, made with the model. + async fn web_search_before_each(fetch: &Arc) -> GenerateResult { + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.web_search", + "web_search", + json!({}), + )]); + create_model(fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed") + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › web_search tool › should include web_search tool call with providerExecuted true + #[tokio::test] + async fn should_include_web_search_tool_call_with_provider_executed_true() { + let fetch = json_fetch(&json_fixture(include_str!( + "fixtures/xai-web-search-tool.1.json" + ))); + + let result = web_search_before_each(&fetch).await; + + assert_js_eq( + &content_json(&result), + snapshot(include_str!( + "snapshots/should_include_web_search_tool_call_with_provider_executed_true.json" + )), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › web_search tool › should send web_search tool with args in request + #[tokio::test] + async fn should_send_web_search_tool_with_args_in_request() { + // The `beforeEach` request is the first call; this test's is the second. + let fetch = MockFetch::new(vec![ + Canned::json(&json_fixture(include_str!( + "fixtures/xai-web-search-tool.1.json" + ))), + Canned::json(&empty_response( + json!({ "input_tokens": 10, "output_tokens": 5 }), + )), + ]); + web_search_before_each(&fetch).await; + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.web_search", + "web_search", + json!({ + "allowedDomains": ["wikipedia.org"], + "enableImageSearch": true, + "enableImageUnderstanding": true + }), + )]); + create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &request_body(&fetch, 1), + json!({ + "input": [ + { + "content": [{ "text": "hello", "type": "input_text" }], + "role": "user" + } + ], + "model": "grok-4-fast-non-reasoning", + "tools": [ + { + "allowed_domains": ["wikipedia.org"], + "enable_image_search": true, + "enable_image_understanding": true, + "type": "web_search" + } + ] + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › web_search tool › should forward web_search_call action and sources to tool-result + #[tokio::test] + async fn should_forward_web_search_call_action_and_sources_to_tool_result() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [ + { + "type": "web_search_call", + "id": "ws_action_1", + "name": "web_search", + "call_id": "", + "status": "completed", + "action": { + "type": "search", + "query": "latest AI news", + "sources": [ + { "type": "url", "url": "https://example.com/a" }, + { "type": "url", "url": "https://example.com/b" } + ] + } + }, + { + "type": "web_search_call", + "id": "ws_action_2", + "name": "web_search", + "call_id": "", + "status": "completed", + "action": { "type": "open_page", "url": "https://example.com/a" } + }, + { + "type": "web_search_call", + "id": "ws_action_3", + "name": "web_search", + "call_id": "", + "status": "completed", + "action": { + "type": "find_in_page", + "url": "https://example.com/a", + "pattern": "climate" + } + }, + { + "type": "web_search_call", + "id": "ws_action_4", + "name": "web_search", + "call_id": "", + "status": "completed", + "action": { "type": "search", "query": null, "sources": null } + }, + { + "type": "web_search_call", + "id": "ws_action_5", + "name": "web_search", + "call_id": "", + "status": "completed", + "action": { "type": "open_page", "url": null } + } + ], + "usage": { "input_tokens": 10, "output_tokens": 5 } + })); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.web_search", + "web_search", + json!({}), + )]); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + let content = content_json(&result); + assert_contains( + &content, + json!({ + "type": "tool-result", + "toolCallId": "ws_action_1", + "toolName": "web_search", + "result": { + "action": { "type": "search", "query": "latest AI news" }, + "sources": [ + { "type": "url", "url": "https://example.com/a" }, + { "type": "url", "url": "https://example.com/b" } + ] + } + }), + ); + assert_contains( + &content, + json!({ + "type": "tool-result", + "toolCallId": "ws_action_2", + "toolName": "web_search", + "result": { + "action": { "type": "openPage", "url": "https://example.com/a" } + } + }), + ); + assert_contains( + &content, + json!({ + "type": "tool-result", + "toolCallId": "ws_action_3", + "toolName": "web_search", + "result": { + "action": { + "type": "findInPage", + "url": "https://example.com/a", + "pattern": "climate" + } + } + }), + ); + assert_contains( + &content, + json!({ + "type": "tool-result", + "toolCallId": "ws_action_4", + "toolName": "web_search", + "result": { "action": { "type": "search" } } + }), + ); + assert_contains( + &content, + json!({ + "type": "tool-result", + "toolCallId": "ws_action_5", + "toolName": "web_search", + "result": { "action": { "type": "openPage", "url": null } } + }), + ); + } + } + + mod x_search_tool { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › x_search tool › should include x_search tool call with real response + #[tokio::test] + async fn should_include_x_search_tool_call_with_real_response() { + let fetch = json_fetch(&json_fixture(include_str!( + "fixtures/xai-x-search-tool.1.json" + ))); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool("xai.x_search", "x_search", json!({}))]); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + snapshot(include_str!( + "snapshots/should_include_x_search_tool_call_with_real_response.json" + )), + ); + } + } + + mod code_interpreter_tool { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › code_interpreter tool › should include code_interpreter tool call with real response + #[tokio::test] + async fn should_include_code_interpreter_tool_call_with_real_response() { + let fetch = json_fetch(&json_fixture(include_str!( + "fixtures/xai-code-execution-tool.1.json" + ))); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.code_execution", + "code_execution", + json!({}), + )]); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + snapshot(include_str!( + "snapshots/should_include_code_interpreter_tool_call_with_real_response.json" + )), + ); + } + } + + mod file_search_tool { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › file_search tool › should include file_search tool call and result with providerExecuted true + #[tokio::test] + async fn should_include_file_search_tool_call_and_result_with_provider_executed_true() { + let fetch = json_fetch(&json_fixture(include_str!( + "fixtures/xai-file-search-tool.1.json" + ))); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.file_search", + "file_search", + json!({ "vectorStoreIds": ["collection_test123"] }), + )]); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + json!([ + { + "input": "", + "providerExecuted": true, + "toolCallId": "fs_123", + "toolName": "file_search", + "type": "tool-call" + }, + { + "result": { + "queries": ["AI safety research"], + "results": [ + { + "fileId": "file_abc123", + "filename": "ai-safety-paper.pdf", + "score": 0.95, + "text": "Recent advances in AI safety research have focused on alignment techniques..." + }, + { + "fileId": "file_def456", + "filename": "research-notes.md", + "score": 0.82, + "text": "Key findings from the AI safety workshop include recommendations for..." + } + ] + }, + "toolCallId": "fs_123", + "toolName": "file_search", + "type": "tool-result" + }, + { + "text": "Based on the documents in your collection, I found relevant information about AI safety research. The main findings indicate that recent advances have focused on alignment techniques and the workshop recommendations emphasize the importance of interpretability.", + "type": "text" + } + ]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › file_search tool › should handle file_search with null results + #[tokio::test] + async fn should_handle_file_search_with_null_results() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [ + { + "type": "file_search_call", + "id": "fs_456", + "status": "completed", + "queries": ["nonexistent topic"], + "results": null + }, + { + "type": "message", + "id": "msg_456", + "status": "completed", + "role": "assistant", + "content": [ + { + "type": "output_text", + "text": "No relevant documents found.", + "annotations": [] + } + ] + } + ], + "usage": { "input_tokens": 100, "output_tokens": 20 } + })); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.file_search", + "file_search", + json!({ "vectorStoreIds": ["collection_test123"] }), + )]); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + json!([ + { + "input": "", + "providerExecuted": true, + "toolCallId": "fs_456", + "toolName": "file_search", + "type": "tool-call" + }, + { + "result": { + "queries": ["nonexistent topic"], + "results": null + }, + "toolCallId": "fs_456", + "toolName": "file_search", + "type": "tool-result" + }, + { + "text": "No relevant documents found.", + "type": "text" + } + ]), + ); + } + } + + mod image_generation_tool { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › image_generation tool › should handle image_generation tool call from response fixture + #[tokio::test] + async fn should_handle_image_generation_tool_call_from_response_fixture() { + let fetch = json_fetch(&json_fixture(include_str!( + "fixtures/xai-image-generation-tool.1.json" + ))); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.image_generation", + "image_generation", + json!({}), + )]); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + json!([ + { + "input": "{}", + "providerExecuted": true, + "toolCallId": "ig_00000000-0000-4000-8000-000000000001_call-00000000-0000-4000-8000-00000000000a-0", + "toolName": "image_generation", + "type": "tool-call" + }, + { + "result": { + "prompt": "A cute robot holding a flower, adorable design with big friendly eyes, shiny metallic body with soft rounded edges, holding a colorful flower in its hand, cheerful expression, simple clean background, high quality digital art", + "result": "ZmFrZUltYWdlQmFzZTY0RGF0YQ==" + }, + "toolCallId": "ig_00000000-0000-4000-8000-000000000001_call-00000000-0000-4000-8000-00000000000a-0", + "toolName": "image_generation", + "type": "tool-result" + }, + { + "text": "Here's a cute robot holding a flower! 🌸🤖", + "type": "text" + } + ]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › image_generation tool › should include image_generation tool call and result with providerExecuted true + #[tokio::test] + async fn should_include_image_generation_tool_call_and_result_with_provider_executed_true() + { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [ + { + "type": "image_generation_call", + "id": "ig_123", + "status": "completed", + "prompt": "A corgi surfing a big wave, Japanese woodblock print style", + "result": "base64imagedata" + }, + { + "type": "message", + "id": "msg_123", + "status": "completed", + "role": "assistant", + "content": [ + { + "type": "output_text", + "text": "Here is your image of a corgi surfing.", + "annotations": [] + } + ] + } + ], + "usage": { "input_tokens": 100, "output_tokens": 20 } + })); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.image_generation", + "image_generation", + json!({}), + )]); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + json!([ + { + "input": "{}", + "providerExecuted": true, + "toolCallId": "ig_123", + "toolName": "image_generation", + "type": "tool-call" + }, + { + "result": { + "prompt": "A corgi surfing a big wave, Japanese woodblock print style", + "result": "base64imagedata" + }, + "toolCallId": "ig_123", + "toolName": "image_generation", + "type": "tool-result" + }, + { + "text": "Here is your image of a corgi surfing.", + "type": "text" + } + ]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › image_generation tool › should emit an error tool result for image_generation calls without result + #[tokio::test] + async fn should_emit_an_error_tool_result_for_image_generation_calls_without_result() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [ + { + "type": "image_generation_call", + "id": "ig_456", + "status": "failed" + } + ], + "usage": { "input_tokens": 100, "output_tokens": 20 } + })); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.image_generation", + "image_generation", + json!({}), + )]); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + json!([ + { + "input": "{}", + "providerExecuted": true, + "toolCallId": "ig_456", + "toolName": "image_generation", + "type": "tool-call" + }, + { + "isError": true, + "result": "Image generation failed (status: failed).", + "toolCallId": "ig_456", + "toolName": "image_generation", + "type": "tool-result" + } + ]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › image_generation tool › should use the custom tool name for image_generation calls + #[tokio::test] + async fn should_use_the_custom_tool_name_for_image_generation_calls() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [ + { + "type": "image_generation_call", + "id": "ig_123", + "status": "completed", + "prompt": "A hot air balloon over the desert", + "result": "base64imagedata" + } + ], + "usage": { "input_tokens": 100, "output_tokens": 20 } + })); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.image_generation", + "generateImage", + json!({}), + )]); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + json!([ + { + "input": "{}", + "providerExecuted": true, + "toolCallId": "ig_123", + "toolName": "generateImage", + "type": "tool-call" + }, + { + "result": { + "prompt": "A hot air balloon over the desert", + "result": "base64imagedata" + }, + "toolCallId": "ig_123", + "toolName": "generateImage", + "type": "tool-result" + } + ]), + ); + } + } + + mod function_tools { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › function tools › should include function tool calls without providerExecuted + #[tokio::test] + async fn should_include_function_tool_calls_without_provider_executed() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [ + { + "type": "function_call", + "id": "fc_123", + "name": "weather", + "arguments": "{\"location\":\"sf\"}", + "call_id": "call_123" + } + ], + "usage": { "input_tokens": 10, "output_tokens": 5 } + })); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![function_tool( + "weather", + "get weather", + json!({ + "type": "object", + "properties": { "location": { "type": "string" } } + }), + )]); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + json!([ + { + "input": "{\"location\":\"sf\"}", + "toolCallId": "call_123", + "toolName": "weather", + "type": "tool-call" + } + ]), + ); + } + } + + mod citations { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › citations › should extract citations from annotations + #[tokio::test] + async fn should_extract_citations_from_annotations() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [ + { + "type": "message", + "id": "msg_123", + "status": "completed", + "role": "assistant", + "content": [ + { + "type": "output_text", + "text": "based on research", + "annotations": [ + { + "type": "url_citation", + "url": "https://example.com", + "title": "example title" + }, + { + "type": "url_citation", + "url": "https://test.com" + } + ] + } + ] + } + ], + "usage": { "input_tokens": 10, "output_tokens": 5 } + })); + + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&CallOptions::new(test_prompt())) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + json!([ + { "text": "based on research", "type": "text" }, + { + "id": "id-0", + "sourceType": "url", + "title": "example title", + "type": "source", + "url": "https://example.com" + }, + { + "id": "id-1", + "sourceType": "url", + "title": "https://test.com", + "type": "source", + "url": "https://test.com" + } + ]), + ); + } + } + + mod multiple_tools { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › multiple tools › should handle multiple server-side tools + #[tokio::test] + async fn should_handle_multiple_server_side_tools() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [ + { + "type": "web_search_call", + "id": "ws_123", + "name": "web_search", + "arguments": "{}", + "call_id": "", + "status": "completed" + }, + { + "type": "code_interpreter_call", + "id": "code_123", + "name": "code_execution", + "arguments": "{}", + "call_id": "", + "status": "completed" + } + ], + "usage": { "input_tokens": 10, "output_tokens": 5 } + })); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![ + provider_tool("xai.web_search", "web_search", json!({})), + provider_tool("xai.code_execution", "code_execution", json!({})), + ]); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + let content = content_json(&result); + assert_eq!(content.as_array().expect("an array").len(), 3); + assert_eq!(content[0]["type"], "tool-call"); + assert_eq!(content[1]["type"], "tool-result"); + assert_eq!(content[2]["type"], "tool-call"); + } + } + + mod tool_name_mapping_by_type { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › tool name mapping by type › should map web_search_call type to web_search tool name when name is empty + #[tokio::test] + async fn should_map_web_search_call_type_to_web_search_tool_name_when_name_is_empty() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [ + { + "type": "web_search_call", + "id": "ws_123", + "name": "", + "arguments": "{\"query\":\"test\"}", + "call_id": "", + "status": "completed" + } + ], + "usage": { "input_tokens": 10, "output_tokens": 5 } + })); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.web_search", + "web_search", + json!({}), + )]); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + json!([ + { + "type": "tool-call", + "toolCallId": "ws_123", + "toolName": "web_search", + "input": "{\"query\":\"test\"}", + "providerExecuted": true + }, + { + "type": "tool-result", + "toolCallId": "ws_123", + "toolName": "web_search", + "result": {} + } + ]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › tool name mapping by type › should map x_search_call type to x_search tool name when name is empty + #[tokio::test] + async fn should_map_x_search_call_type_to_x_search_tool_name_when_name_is_empty() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [ + { + "type": "x_search_call", + "id": "xs_123", + "name": "", + "arguments": "{\"query\":\"test\"}", + "call_id": "", + "status": "completed" + } + ], + "usage": { "input_tokens": 10, "output_tokens": 5 } + })); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool("xai.x_search", "x_search", json!({}))]); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + json!([ + { + "type": "tool-call", + "toolCallId": "xs_123", + "toolName": "x_search", + "input": "{\"query\":\"test\"}", + "providerExecuted": true + } + ]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › tool name mapping by type › should map code_interpreter_call type to code_execution tool name when name is empty + #[tokio::test] + async fn should_map_code_interpreter_call_type_to_code_execution_when_name_empty() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [ + { + "type": "code_interpreter_call", + "id": "ci_123", + "name": "", + "arguments": "{}", + "call_id": "", + "status": "completed" + } + ], + "usage": { "input_tokens": 10, "output_tokens": 5 } + })); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.code_execution", + "code_execution", + json!({}), + )]); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + json!([ + { + "type": "tool-call", + "toolCallId": "ci_123", + "toolName": "code_execution", + "input": "{}", + "providerExecuted": true + } + ]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › tool name mapping by type › should map code_execution_call type to code_execution tool name when name is empty + #[tokio::test] + async fn should_map_code_execution_call_type_to_code_execution_when_name_empty() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [ + { + "type": "code_execution_call", + "id": "ce_123", + "name": "", + "arguments": "{}", + "call_id": "", + "status": "completed" + } + ], + "usage": { "input_tokens": 10, "output_tokens": 5 } + })); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.code_execution", + "code_execution", + json!({}), + )]); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + json!([ + { + "type": "tool-call", + "toolCallId": "ce_123", + "toolName": "code_execution", + "input": "{}", + "providerExecuted": true + } + ]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doGenerate › tool name mapping by type › should use custom tool name from provider tool when type matches + #[tokio::test] + async fn should_use_custom_tool_name_from_provider_tool_when_type_matches() { + let fetch = json_fetch(&json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [ + { + "type": "web_search_call", + "id": "ws_123", + "name": "", + "arguments": "{}", + "call_id": "", + "status": "completed" + } + ], + "usage": { "input_tokens": 10, "output_tokens": 5 } + })); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.web_search", + "my_custom_search", + json!({}), + )]); + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&options) + .await + .expect("should succeed"); + + assert_js_eq( + &content_json(&result), + json!([ + { + "type": "tool-call", + "toolCallId": "ws_123", + "toolName": "my_custom_search", + "input": "{}", + "providerExecuted": true + }, + { + "type": "tool-result", + "toolCallId": "ws_123", + "toolName": "my_custom_search", + "result": {} + } + ]), + ); + } + } + } + + mod do_stream { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › should warn about unsupported sampling settings + #[tokio::test] + async fn should_warn_about_unsupported_sampling_settings() { + let fetch = + chunks_fixture_fetch(include_str!("fixtures/xai-text-streaming.1.chunks.txt")); + + let mut options = CallOptions::new(test_prompt()); + options.top_k = Some(10.0); + options.frequency_penalty = Some(0.5); + options.presence_penalty = Some(0.5); + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &options).await; + + assert_js_eq( + &first_part(&parts, "stream-start")["warnings"], + json!([ + { "feature": "frequencyPenalty", "type": "unsupported" }, + { "feature": "presencePenalty", "type": "unsupported" } + ]), + ); + } + + mod text_streaming { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › text streaming › should stream web search with real response + #[tokio::test] + async fn should_stream_web_search_with_real_response() { + let fetch = + chunks_fixture_fetch(include_str!("fixtures/xai-web-search-tool.1.chunks.txt")); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.web_search", + "web_search", + json!({}), + )]); + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &options).await; + + assert_js_eq( + &parts, + snapshot(include_str!( + "snapshots/should_stream_web_search_with_real_response.json" + )), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › text streaming › should stream text deltas + #[tokio::test] + async fn should_stream_text_deltas() { + let fetch = + chunks_fixture_fetch(include_str!("fixtures/xai-text-streaming.1.chunks.txt")); + + let parts = + run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + assert_js_eq( + &parts, + snapshot(include_str!("snapshots/should_stream_text_deltas.json")), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › text streaming › should stream text deltas with reasoning + #[tokio::test] + async fn should_stream_text_deltas_with_reasoning() { + let fetch = chunks_fixture_fetch(include_str!( + "fixtures/xai-text-with-reasoning-streaming.1.chunks.txt" + )); + + let parts = + run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + assert_js_eq( + &parts, + snapshot(include_str!( + "snapshots/should_stream_text_deltas_with_reasoning.json" + )), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › text streaming › should stream text deltas with encrypted reasoning + #[tokio::test] + async fn should_stream_text_deltas_with_encrypted_reasoning() { + let fetch = chunks_fixture_fetch(include_str!( + "fixtures/xai-text-with-reasoning-streaming-store-false.1.chunks.txt" + )); + + let parts = + run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + assert_js_eq( + &parts, + snapshot(include_str!( + "snapshots/should_stream_text_deltas_with_encrypted_reasoning.json" + )), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › text streaming › should include encrypted content in reasoning-end providerMetadata + #[tokio::test] + async fn should_include_encrypted_content_in_reasoning_end_provider_metadata() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "output": [] + } + }), + json!({ + "type": "response.output_item.added", + "item": { + "type": "reasoning", + "id": "rs_456", + "status": "in_progress", + "summary": [] + }, + "output_index": 0 + }), + json!({ + "type": "response.reasoning_summary_part.added", + "item_id": "rs_456", + "output_index": 0, + "summary_index": 0, + "part": { "type": "summary_text", "text": "" } + }), + json!({ + "type": "response.reasoning_summary_text.delta", + "item_id": "rs_456", + "output_index": 0, + "summary_index": 0, + "delta": "Analyzing..." + }), + json!({ + "type": "response.reasoning_summary_text.done", + "item_id": "rs_456", + "output_index": 0, + "summary_index": 0, + "text": "Analyzing..." + }), + json!({ + "type": "response.output_item.done", + "item": { + "type": "reasoning", + "id": "rs_456", + "status": "completed", + "summary": [{ "type": "summary_text", "text": "Analyzing..." }], + "encrypted_content": "encrypted_data_abc123" + }, + "output_index": 0 + }), + json!({ + "type": "response.output_item.added", + "item": { + "type": "message", + "id": "msg_789", + "role": "assistant", + "status": "in_progress", + "content": [] + }, + "output_index": 1 + }), + json!({ + "type": "response.output_text.delta", + "item_id": "msg_789", + "output_index": 1, + "content_index": 0, + "delta": "Result." + }), + json!({ + "type": "response.done", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "status": "completed", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 20 } + } + }), + ]); + + let parts = + run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + assert_js_eq( + &first_part(&parts, "reasoning-end"), + json!({ + "id": "reasoning-rs_456", + "providerMetadata": { + "xai": { + "itemId": "rs_456", + "reasoningEncryptedContent": "encrypted_data_abc123" + } + }, + "type": "reasoning-end" + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › text streaming › should emit reasoning-start before reasoning-end when reasoning_summary_part.added is not sent + #[tokio::test] + async fn should_emit_reasoning_start_before_end_without_summary_part_added() { + // Encrypted reasoning without any reasoning summary text: no + // `reasoning_summary_part.added` event. + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "output": [] + } + }), + json!({ + "type": "response.output_item.added", + "item": { + "type": "reasoning", + "id": "rs_456", + "status": "in_progress", + "summary": [] + }, + "output_index": 0 + }), + json!({ + "type": "response.output_item.done", + "item": { + "type": "reasoning", + "id": "rs_456", + "status": "completed", + "summary": [], + "encrypted_content": "encrypted_reasoning_content_xyz" + }, + "output_index": 0 + }), + json!({ + "type": "response.output_item.added", + "item": { + "type": "message", + "id": "msg_789", + "role": "assistant", + "status": "in_progress", + "content": [] + }, + "output_index": 1 + }), + json!({ + "type": "response.output_text.delta", + "item_id": "msg_789", + "output_index": 1, + "content_index": 0, + "delta": "The answer is 42." + }), + json!({ + "type": "response.done", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "status": "completed", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 20 } + } + }), + ]); + + let parts = + run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + assert_js_eq( + &first_part(&parts, "reasoning-start"), + json!({ + "id": "reasoning-rs_456", + "providerMetadata": { "xai": { "itemId": "rs_456" } }, + "type": "reasoning-start" + }), + ); + assert_js_eq( + &first_part(&parts, "reasoning-end"), + json!({ + "id": "reasoning-rs_456", + "providerMetadata": { + "xai": { + "itemId": "rs_456", + "reasoningEncryptedContent": "encrypted_reasoning_content_xyz" + } + }, + "type": "reasoning-end" + }), + ); + assert!(index_of(&parts, "reasoning-start") < index_of(&parts, "reasoning-end")); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › text streaming › should stream reasoning text deltas (response.reasoning_text.delta) + #[tokio::test] + async fn should_stream_reasoning_text_deltas_response_reasoning_text_delta() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-code-fast-1", + "output": [] + } + }), + json!({ + "type": "response.output_item.added", + "item": { + "type": "reasoning", + "id": "rs_456", + "status": "in_progress", + "summary": [] + }, + "output_index": 0 + }), + json!({ + "type": "response.reasoning_text.delta", + "item_id": "rs_456", + "output_index": 0, + "content_index": 0, + "delta": "First" + }), + json!({ + "type": "response.reasoning_text.delta", + "item_id": "rs_456", + "output_index": 0, + "content_index": 0, + "delta": ", analyze the question." + }), + json!({ + "type": "response.reasoning_text.done", + "item_id": "rs_456", + "output_index": 0, + "content_index": 0, + "text": "First, analyze the question." + }), + json!({ + "type": "response.output_item.done", + "item": { + "type": "reasoning", + "id": "rs_456", + "status": "completed", + "summary": [ + { "type": "summary_text", "text": "First, analyze the question." } + ] + }, + "output_index": 0 + }), + json!({ + "type": "response.output_item.added", + "item": { + "type": "message", + "id": "msg_789", + "role": "assistant", + "status": "in_progress", + "content": [] + }, + "output_index": 1 + }), + json!({ + "type": "response.output_text.delta", + "item_id": "msg_789", + "output_index": 1, + "content_index": 0, + "delta": "The answer." + }), + json!({ + "type": "response.done", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-code-fast-1", + "status": "completed", + "output": [], + "usage": { + "input_tokens": 10, + "output_tokens": 20, + "output_tokens_details": { "reasoning_tokens": 15 } + } + } + }), + ]); + + let parts = + run_stream(&fetch, "grok-code-fast-1", &CallOptions::new(test_prompt())).await; + + assert_js_eq( + &first_part(&parts, "reasoning-start"), + json!({ + "id": "reasoning-rs_456", + "providerMetadata": { "xai": { "itemId": "rs_456" } }, + "type": "reasoning-start" + }), + ); + let deltas = parts_of_type(&parts, "reasoning-delta"); + assert_eq!(deltas.len(), 2); + assert_eq!(deltas[0]["delta"], "First"); + assert_eq!(deltas[1]["delta"], ", analyze the question."); + assert_js_eq( + &first_part(&parts, "reasoning-end"), + json!({ + "id": "reasoning-rs_456", + "providerMetadata": { "xai": { "itemId": "rs_456" } }, + "type": "reasoning-end" + }), + ); + + // reasoning-start < reasoning-deltas < reasoning-end < text + let start = index_of(&parts, "reasoning-start"); + let first_delta = index_of(&parts, "reasoning-delta"); + let end = index_of(&parts, "reasoning-end"); + let text = index_of(&parts, "text-delta"); + assert!(start < first_delta); + assert!(first_delta < end); + assert!(end < text); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › text streaming › should emit only one reasoning-start when multiple reasoning_summary_part.added events share an item_id + #[tokio::test] + async fn should_emit_one_reasoning_start_for_shared_item_id_summary_parts() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4.3", + "output": [] + } + }), + json!({ + "type": "response.output_item.added", + "item": { + "type": "reasoning", + "id": "rs_456", + "status": "in_progress", + "summary": [] + }, + "output_index": 0 + }), + json!({ + "type": "response.reasoning_summary_part.added", + "item_id": "rs_456", + "output_index": 0, + "summary_index": 0, + "part": { "type": "summary_text", "text": "" } + }), + json!({ + "type": "response.reasoning_summary_text.delta", + "item_id": "rs_456", + "output_index": 0, + "summary_index": 0, + "delta": "First summary part." + }), + json!({ + "type": "response.reasoning_summary_part.added", + "item_id": "rs_456", + "output_index": 0, + "summary_index": 1, + "part": { "type": "summary_text", "text": "" } + }), + json!({ + "type": "response.reasoning_summary_text.delta", + "item_id": "rs_456", + "output_index": 0, + "summary_index": 1, + "delta": " Second summary part." + }), + json!({ + "type": "response.output_item.done", + "item": { + "type": "reasoning", + "id": "rs_456", + "status": "completed", + "summary": [ + { "type": "summary_text", "text": "First summary part." }, + { "type": "summary_text", "text": " Second summary part." } + ] + }, + "output_index": 0 + }), + json!({ + "type": "response.done", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4.3", + "status": "completed", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 20 } + } + }), + ]); + + let parts = + run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + let starts = parts_of_type(&parts, "reasoning-start"); + let ends = parts_of_type(&parts, "reasoning-end"); + assert_eq!(starts.len(), 1); + assert_eq!(ends.len(), 1); + assert_matches_object( + &starts[0], + json!({ + "type": "reasoning-start", + "id": "reasoning-rs_456", + "providerMetadata": { "xai": { "itemId": "rs_456" } } + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › text streaming › should stream x_search tool call + #[tokio::test] + async fn should_stream_x_search_tool_call() { + let fetch = + chunks_fixture_fetch(include_str!("fixtures/xai-x-search-tool.chunks.txt")); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool("xai.x_search", "x_search", json!({}))]); + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &options).await; + + assert_js_eq( + &parts, + snapshot(include_str!( + "snapshots/should_stream_x_search_tool_call.json" + )), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › text streaming › should not emit duplicate text-delta from response.output_item.done after streaming + #[tokio::test] + async fn should_not_emit_duplicate_text_delta_after_output_item_done() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "created_at": 1700000000, + "status": "in_progress", + "output": [] + } + }), + json!({ + "type": "response.output_item.added", + "item": { + "type": "message", + "id": "msg_123", + "status": "in_progress", + "role": "assistant", + "content": [] + }, + "output_index": 0 + }), + json!({ + "type": "response.output_text.delta", + "item_id": "msg_123", + "output_index": 0, + "content_index": 0, + "delta": "Hello" + }), + json!({ + "type": "response.output_text.delta", + "item_id": "msg_123", + "output_index": 0, + "content_index": 0, + "delta": " " + }), + json!({ + "type": "response.output_text.delta", + "item_id": "msg_123", + "output_index": 0, + "content_index": 0, + "delta": "world" + }), + json!({ + "type": "response.output_item.done", + "item": { + "type": "message", + "id": "msg_123", + "status": "completed", + "role": "assistant", + "content": [ + { + "type": "output_text", + "text": "Hello world", + "annotations": [] + } + ] + }, + "output_index": 0 + }), + json!({ + "type": "response.done", + "response": { + "id": "resp_123", + "object": "response", + "status": "completed", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 5 } + } + }), + ]); + + let parts = + run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + // Only the three streamed deltas, not the full text again. + let deltas = parts_of_type(&parts, "text-delta"); + assert_eq!(deltas.len(), 3); + let texts: Vec<&Value> = deltas.iter().map(|delta| &delta["delta"]).collect(); + assert_js_eq(&json!(texts), json!(["Hello", " ", "world"])); + assert!(deltas.iter().all(|delta| delta["delta"] != "Hello world")); + } + } + + mod tool_call_streaming { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › tool call streaming › should stream web_search tool calls + #[tokio::test] + async fn should_stream_web_search_tool_calls() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "status": "in_progress", + "output": [] + } + }), + json!({ + "type": "response.output_item.added", + "item": { + "type": "web_search_call", + "id": "ws_123", + "name": "web_search", + "arguments": "{\"query\":\"test\"}", + "call_id": "", + "status": "completed" + }, + "output_index": 0 + }), + json!({ + "type": "response.done", + "response": { + "id": "resp_123", + "object": "response", + "status": "completed", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 5 } + } + }), + ]); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.web_search", + "web_search", + json!({}), + )]); + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &options).await; + + assert_contains( + &parts, + json!({ + "type": "tool-call", + "toolCallId": "ws_123", + "toolName": "web_search", + "input": "{\"query\":\"test\"}", + "providerExecuted": true + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › tool call streaming › should emit a tool result when provider-executed tool calls finish + #[tokio::test] + async fn should_emit_a_tool_result_when_provider_executed_tool_calls_finish() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "status": "in_progress", + "output": [] + } + }), + json!({ + "type": "response.output_item.added", + "item": { + "type": "web_search_call", + "id": "ws_123", + "name": "web_search", + "arguments": "{\"query\":\"test\"}", + "call_id": "", + "status": "in_progress" + }, + "output_index": 0 + }), + json!({ + "type": "response.output_item.done", + "item": { + "type": "web_search_call", + "id": "ws_123", + "name": "web_search", + "arguments": "{\"query\":\"test\"}", + "call_id": "", + "status": "completed" + }, + "output_index": 0 + }), + json!({ + "type": "response.done", + "response": { + "id": "resp_123", + "object": "response", + "status": "completed", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 5 } + } + }), + ]); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.web_search", + "web_search", + json!({}), + )]); + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &options).await; + + assert_contains( + &parts, + json!({ + "type": "tool-call", + "toolCallId": "ws_123", + "toolName": "web_search", + "input": "{\"query\":\"test\"}", + "providerExecuted": true + }), + ); + assert_contains( + &parts, + json!({ + "type": "tool-result", + "toolCallId": "ws_123", + "toolName": "web_search", + "result": {} + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › tool call streaming › should forward web_search_call action and sources to tool-result + #[tokio::test] + async fn should_forward_web_search_call_action_and_sources_to_tool_result() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "status": "in_progress", + "output": [] + } + }), + json!({ + "type": "response.output_item.added", + "item": { + "type": "web_search_call", + "id": "ws_action", + "name": "web_search", + "call_id": "", + "status": "in_progress" + }, + "output_index": 0 + }), + json!({ + "type": "response.output_item.done", + "item": { + "type": "web_search_call", + "id": "ws_action", + "name": "web_search", + "call_id": "", + "status": "completed", + "action": { + "type": "search", + "query": "latest AI news", + "sources": [{ "type": "url", "url": "https://example.com/a" }] + } + }, + "output_index": 0 + }), + json!({ + "type": "response.done", + "response": { + "id": "resp_123", + "object": "response", + "status": "completed", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 5 } + } + }), + ]); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.web_search", + "web_search", + json!({}), + )]); + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &options).await; + + assert_contains( + &parts, + json!({ + "type": "tool-result", + "toolCallId": "ws_action", + "toolName": "web_search", + "result": { + "action": { "type": "search", "query": "latest AI news" }, + "sources": [{ "type": "url", "url": "https://example.com/a" }] + } + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › tool call streaming › should map open_page and find_in_page actions in stream tool-results + #[tokio::test] + async fn should_map_open_page_and_find_in_page_actions_in_stream_tool_results() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "status": "in_progress", + "output": [] + } + }), + json!({ + "type": "response.output_item.done", + "item": { + "type": "web_search_call", + "id": "ws_open", + "name": "web_search", + "call_id": "", + "status": "completed", + "action": { "type": "open_page", "url": "https://example.com/a" } + }, + "output_index": 0 + }), + json!({ + "type": "response.output_item.done", + "item": { + "type": "web_search_call", + "id": "ws_find", + "name": "web_search", + "call_id": "", + "status": "completed", + "action": { + "type": "find_in_page", + "url": "https://example.com/a", + "pattern": "climate" + } + }, + "output_index": 1 + }), + json!({ + "type": "response.done", + "response": { + "id": "resp_123", + "object": "response", + "status": "completed", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 5 } + } + }), + ]); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.web_search", + "web_search", + json!({}), + )]); + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &options).await; + + assert_contains( + &parts, + json!({ + "type": "tool-result", + "toolCallId": "ws_open", + "toolName": "web_search", + "result": { + "action": { "type": "openPage", "url": "https://example.com/a" } + } + }), + ); + assert_contains( + &parts, + json!({ + "type": "tool-result", + "toolCallId": "ws_find", + "toolName": "web_search", + "result": { + "action": { + "type": "findInPage", + "url": "https://example.com/a", + "pattern": "climate" + } + } + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › tool call streaming › should stream image_generation tool calls from chunks fixture + #[tokio::test] + async fn should_stream_image_generation_tool_calls_from_chunks_fixture() { + let fetch = chunks_fixture_fetch(include_str!( + "fixtures/xai-image-generation-tool.1.chunks.txt" + )); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.image_generation", + "image_generation", + json!({}), + )]); + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &options).await; + + assert_js_eq( + &parts, + Value::Array(vec![ + json!({ "type": "stream-start", "warnings": [] }), + json!({ + "id": "00000000-0000-4000-8000-000000000002", + "modelId": "grok-4.5", + "timestamp": "1970-01-01T00:00:00.000Z", + "type": "response-metadata" + }), + json!({ + "id": "ig_00000000-0000-4000-8000-000000000002_call-00000000-0000-4000-8000-00000000000b-0", + "toolName": "image_generation", + "type": "tool-input-start" + }), + json!({ + "delta": "{}", + "id": "ig_00000000-0000-4000-8000-000000000002_call-00000000-0000-4000-8000-00000000000b-0", + "type": "tool-input-delta" + }), + json!({ + "id": "ig_00000000-0000-4000-8000-000000000002_call-00000000-0000-4000-8000-00000000000b-0", + "type": "tool-input-end" + }), + json!({ + "input": "{}", + "providerExecuted": true, + "toolCallId": "ig_00000000-0000-4000-8000-000000000002_call-00000000-0000-4000-8000-00000000000b-0", + "toolName": "image_generation", + "type": "tool-call" + }), + json!({ + "result": { + "prompt": "A delicate origami paper boat sailing gently on a tranquil pond covered with floating green lily pads and autumn leaves, soft sunlight filtering through trees, peaceful nature scene, highly detailed, realistic style", + "result": "ZmFrZUltYWdlQmFzZTY0RGF0YQ==" + }, + "toolCallId": "ig_00000000-0000-4000-8000-000000000002_call-00000000-0000-4000-8000-00000000000b-0", + "toolName": "image_generation", + "type": "tool-result" + }), + json!({ "id": "text-msg_00000000-0000-4000-8000-000000000002", "type": "text-start" }), + json!({ "delta": "Here's", "id": "text-msg_00000000-0000-4000-8000-000000000002", "type": "text-delta" }), + json!({ "delta": " a", "id": "text-msg_00000000-0000-4000-8000-000000000002", "type": "text-delta" }), + json!({ "delta": " serene", "id": "text-msg_00000000-0000-4000-8000-000000000002", "type": "text-delta" }), + json!({ "delta": " image", "id": "text-msg_00000000-0000-4000-8000-000000000002", "type": "text-delta" }), + json!({ "delta": " of", "id": "text-msg_00000000-0000-4000-8000-000000000002", "type": "text-delta" }), + json!({ "delta": " a", "id": "text-msg_00000000-0000-4000-8000-000000000002", "type": "text-delta" }), + json!({ "delta": " paper", "id": "text-msg_00000000-0000-4000-8000-000000000002", "type": "text-delta" }), + json!({ "delta": " boat", "id": "text-msg_00000000-0000-4000-8000-000000000002", "type": "text-delta" }), + json!({ "delta": " sailing", "id": "text-msg_00000000-0000-4000-8000-000000000002", "type": "text-delta" }), + json!({ "delta": " on", "id": "text-msg_00000000-0000-4000-8000-000000000002", "type": "text-delta" }), + json!({ "delta": " a", "id": "text-msg_00000000-0000-4000-8000-000000000002", "type": "text-delta" }), + json!({ "delta": " leaf", "id": "text-msg_00000000-0000-4000-8000-000000000002", "type": "text-delta" }), + json!({ "delta": "-", "id": "text-msg_00000000-0000-4000-8000-000000000002", "type": "text-delta" }), + json!({ "delta": "covered", "id": "text-msg_00000000-0000-4000-8000-000000000002", "type": "text-delta" }), + json!({ "delta": " pond", "id": "text-msg_00000000-0000-4000-8000-000000000002", "type": "text-delta" }), + json!({ "delta": ":", "id": "text-msg_00000000-0000-4000-8000-000000000002", "type": "text-delta" }), + json!({ "id": "text-msg_00000000-0000-4000-8000-000000000002", "type": "text-end" }), + json!({ + "finishReason": { "raw": "completed", "unified": "stop" }, + "providerMetadata": { + "xai": { "costInUsdTicks": 123456, "serviceTier": "default" } + }, + "type": "finish", + "usage": { + "inputTokens": { "cacheRead": 20, "noCache": 80, "total": 100 }, + "outputTokens": { "reasoning": 30, "text": 20, "total": 50 }, + "raw": { + "context_details": { "input_tokens": 80, "output_tokens": 20 }, + "cost_in_usd_ticks": 123456, + "input_tokens": 100, + "input_tokens_details": { "cached_tokens": 20 }, + "num_server_side_tools_used": 1, + "num_sources_used": 0, + "output_tokens": 50, + "output_tokens_details": { "reasoning_tokens": 30 }, + "server_side_tool_usage_details": { + "code_interpreter_calls": 0, + "document_search_calls": 0, + "file_search_calls": 0, + "image_generation_calls": 1, + "mcp_calls": 0, + "web_search_calls": 0, + "x_search_calls": 0 + }, + "total_tokens": 150 + } + } + }), + ]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › tool call streaming › should stream image_generation tool calls with progress events + #[tokio::test] + async fn should_stream_image_generation_tool_calls_with_progress_events() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "status": "in_progress", + "output": [] + } + }), + json!({ + "type": "response.image_generation_call.in_progress", + "item_id": "ig_123", + "output_index": 0 + }), + json!({ + "type": "response.image_generation_call.generating", + "item_id": "ig_123", + "output_index": 0 + }), + json!({ + "type": "response.image_generation_call.completed", + "item_id": "ig_123", + "output_index": 0 + }), + json!({ + "type": "response.output_item.done", + "item": { + "type": "image_generation_call", + "id": "ig_123", + "status": "completed", + "prompt": "An origami fox in a paper forest", + "result": "base64imagedata" + }, + "output_index": 0 + }), + json!({ + "type": "response.output_text.delta", + "item_id": "msg_123", + "output_index": 1, + "content_index": 0, + "delta": "Here is your origami fox." + }), + json!({ + "type": "response.done", + "response": { + "id": "resp_123", + "object": "response", + "status": "completed", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 5 } + } + }), + ]); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.image_generation", + "image_generation", + json!({}), + )]); + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &options).await; + + assert_js_eq( + &parts, + Value::Array(vec![ + json!({ "type": "stream-start", "warnings": [] }), + json!({ + "id": "resp_123", + "modelId": "grok-4-fast-non-reasoning", + "type": "response-metadata" + }), + json!({ "id": "ig_123", "toolName": "image_generation", "type": "tool-input-start" }), + json!({ "delta": "{}", "id": "ig_123", "type": "tool-input-delta" }), + json!({ "id": "ig_123", "type": "tool-input-end" }), + json!({ + "input": "{}", + "providerExecuted": true, + "toolCallId": "ig_123", + "toolName": "image_generation", + "type": "tool-call" + }), + json!({ + "result": { + "prompt": "An origami fox in a paper forest", + "result": "base64imagedata" + }, + "toolCallId": "ig_123", + "toolName": "image_generation", + "type": "tool-result" + }), + json!({ "id": "text-msg_123", "type": "text-start" }), + json!({ + "delta": "Here is your origami fox.", + "id": "text-msg_123", + "type": "text-delta" + }), + json!({ "id": "text-msg_123", "type": "text-end" }), + json!({ + "finishReason": { "raw": "completed", "unified": "stop" }, + "type": "finish", + "usage": { + "inputTokens": { "cacheRead": 0, "noCache": 10, "total": 10 }, + "outputTokens": { "reasoning": 0, "text": 5, "total": 5 }, + "raw": { "input_tokens": 10, "output_tokens": 5 } + } + }), + ]), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › tool call streaming › should stream image_generation tool calls when only output items are emitted + #[tokio::test] + async fn should_stream_image_generation_tool_calls_when_only_output_items_are_emitted() + { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "status": "in_progress", + "output": [] + } + }), + json!({ + "type": "response.output_item.added", + "item": { + "type": "image_generation_call", + "id": "ig_123", + "status": "in_progress" + }, + "output_index": 0 + }), + json!({ + "type": "response.output_item.done", + "item": { + "type": "image_generation_call", + "id": "ig_123", + "status": "completed", + "prompt": "An origami fox in a paper forest", + "result": "base64imagedata" + }, + "output_index": 0 + }), + json!({ + "type": "response.done", + "response": { + "id": "resp_123", + "object": "response", + "status": "completed", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 5 } + } + }), + ]); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.image_generation", + "image_generation", + json!({}), + )]); + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &options).await; + + assert_contains( + &parts, + json!({ "type": "tool-input-start", "id": "ig_123", "toolName": "image_generation" }), + ); + assert_contains( + &parts, + json!({ + "type": "tool-call", + "toolCallId": "ig_123", + "toolName": "image_generation", + "input": "{}", + "providerExecuted": true + }), + ); + assert_contains( + &parts, + json!({ + "type": "tool-result", + "toolCallId": "ig_123", + "toolName": "image_generation", + "result": { + "result": "base64imagedata", + "prompt": "An origami fox in a paper forest" + } + }), + ); + assert_eq!(parts_of_type(&parts, "tool-input-start").len(), 1); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › tool call streaming › should stream an error tool result for failed image_generation calls + #[tokio::test] + async fn should_stream_an_error_tool_result_for_failed_image_generation_calls() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "status": "in_progress", + "output": [] + } + }), + json!({ + "type": "response.image_generation_call.in_progress", + "item_id": "ig_123", + "output_index": 0 + }), + json!({ + "type": "response.output_item.done", + "item": { + "type": "image_generation_call", + "id": "ig_123", + "status": "failed" + }, + "output_index": 0 + }), + json!({ + "type": "response.done", + "response": { + "id": "resp_123", + "object": "response", + "status": "completed", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 5 } + } + }), + ]); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.image_generation", + "image_generation", + json!({}), + )]); + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &options).await; + + assert_contains( + &parts, + json!({ + "type": "tool-call", + "toolCallId": "ig_123", + "toolName": "image_generation", + "input": "{}", + "providerExecuted": true + }), + ); + assert_contains( + &parts, + json!({ + "type": "tool-result", + "toolCallId": "ig_123", + "toolName": "image_generation", + "isError": true, + "result": "Image generation failed (status: failed)." + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › tool call streaming › should stream function tool call arguments + #[tokio::test] + async fn should_stream_function_tool_call_arguments() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "status": "in_progress", + "output": [] + } + }), + json!({ + "type": "response.output_item.added", + "output_index": 0, + "item": { + "type": "function_call", + "id": "fc_123", + "call_id": "call_123", + "name": "weather", + "arguments": "", + "status": "in_progress" + } + }), + json!({ + "type": "response.function_call_arguments.delta", + "item_id": "fc_123", + "output_index": 0, + "delta": "{\"location\"" + }), + json!({ + "type": "response.function_call_arguments.delta", + "item_id": "fc_123", + "output_index": 0, + "delta": ":\"sf\"}" + }), + json!({ + "type": "response.function_call_arguments.done", + "item_id": "fc_123", + "output_index": 0, + "arguments": "{\"location\":\"sf\"}" + }), + json!({ + "type": "response.output_item.done", + "output_index": 0, + "item": { + "type": "function_call", + "id": "fc_123", + "call_id": "call_123", + "name": "weather", + "arguments": "{\"location\":\"sf\"}", + "status": "completed" + } + }), + json!({ + "type": "response.done", + "response": { + "id": "resp_123", + "object": "response", + "status": "completed", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 5 } + } + }), + ]); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![function_tool( + "weather", + "get weather", + json!({ + "type": "object", + "properties": { "location": { "type": "string" } } + }), + )]); + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &options).await; + + assert_contains( + &parts, + json!({ "type": "tool-input-start", "id": "call_123", "toolName": "weather" }), + ); + assert_contains( + &parts, + json!({ "type": "tool-input-delta", "id": "call_123", "delta": "{\"location\"" }), + ); + assert_contains( + &parts, + json!({ "type": "tool-input-delta", "id": "call_123", "delta": ":\"sf\"}" }), + ); + assert_contains( + &parts, + json!({ "type": "tool-input-end", "id": "call_123" }), + ); + assert_contains( + &parts, + json!({ + "type": "tool-call", + "toolCallId": "call_123", + "toolName": "weather", + "input": "{\"location\":\"sf\"}" + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › tool call streaming › should return tool-calls finish reason when function_call is streamed + #[tokio::test] + async fn should_return_tool_calls_finish_reason_when_function_call_is_streamed() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "status": "in_progress", + "output": [] + } + }), + json!({ + "type": "response.output_item.added", + "output_index": 0, + "item": { + "type": "function_call", + "id": "fc_123", + "call_id": "call_123", + "name": "weather", + "arguments": "", + "status": "in_progress" + } + }), + json!({ + "type": "response.function_call_arguments.done", + "item_id": "fc_123", + "output_index": 0, + "arguments": "{\"location\":\"sf\"}" + }), + json!({ + "type": "response.output_item.done", + "output_index": 0, + "item": { + "type": "function_call", + "id": "fc_123", + "call_id": "call_123", + "name": "weather", + "arguments": "{\"location\":\"sf\"}", + "status": "completed" + } + }), + json!({ + "type": "response.done", + "response": { + "id": "resp_123", + "object": "response", + "status": "completed", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 5 } + } + }), + ]); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![function_tool( + "weather", + "get weather", + json!({ + "type": "object", + "properties": { "location": { "type": "string" } } + }), + )]); + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &options).await; + + assert_matches_object( + &first_part(&parts, "finish"), + json!({ + "type": "finish", + "finishReason": { "unified": "tool-calls", "raw": "completed" } + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › tool call streaming › should stream file_search tool call and result + #[tokio::test] + async fn should_stream_file_search_tool_call_and_result() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "status": "in_progress", + "output": [] + } + }), + json!({ + "type": "response.output_item.added", + "item": { + "type": "file_search_call", + "id": "fs_stream_123", + "status": "in_progress", + "queries": ["search query"], + "results": null + }, + "output_index": 0 + }), + json!({ + "type": "response.output_item.done", + "item": { + "type": "file_search_call", + "id": "fs_stream_123", + "status": "completed", + "queries": ["search query"], + "results": [ + { + "file_id": "file_abc", + "filename": "doc.txt", + "score": 0.9, + "text": "Found text content" + } + ] + }, + "output_index": 0 + }), + json!({ + "type": "response.done", + "response": { + "id": "resp_123", + "object": "response", + "status": "completed", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 5 } + } + }), + ]); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.file_search", + "file_search", + json!({ "vectorStoreIds": ["collection_123"] }), + )]); + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &options).await; + + assert_contains( + &parts, + json!({ "type": "tool-input-start", "id": "fs_stream_123", "toolName": "file_search" }), + ); + assert_contains( + &parts, + json!({ "type": "tool-input-delta", "id": "fs_stream_123", "delta": "" }), + ); + assert_contains( + &parts, + json!({ "type": "tool-input-end", "id": "fs_stream_123" }), + ); + assert_contains( + &parts, + json!({ + "type": "tool-call", + "toolCallId": "fs_stream_123", + "toolName": "file_search", + "input": "", + "providerExecuted": true + }), + ); + assert_contains( + &parts, + json!({ + "type": "tool-result", + "toolCallId": "fs_stream_123", + "toolName": "file_search", + "result": { + "queries": ["search query"], + "results": [ + { + "fileId": "file_abc", + "filename": "doc.txt", + "score": 0.9, + "text": "Found text content" + } + ] + } + }), + ); + } + } + + mod tool_name_mapping_by_type_in_streaming { + use super::*; + + /// Fixture of the streamed `output_item.added` for one server-side + /// item with an empty `name`: `response.created`, the item, `response.done`. + fn single_item_stream(item: Value) -> Arc { + stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "status": "in_progress", + "output": [] + } + }), + json!({ + "type": "response.output_item.added", + "item": item, + "output_index": 0 + }), + json!({ + "type": "response.done", + "response": { + "id": "resp_123", + "object": "response", + "status": "completed", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 5 } + } + }), + ]) + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › tool name mapping by type in streaming › should map web_search_call type to web_search tool name when name is empty + #[tokio::test] + async fn should_map_web_search_call_type_to_web_search_tool_name_when_name_is_empty() { + let fetch = single_item_stream(json!({ + "type": "web_search_call", + "id": "ws_123", + "name": "", + "arguments": "{\"query\":\"test\"}", + "call_id": "", + "status": "completed" + })); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.web_search", + "web_search", + json!({}), + )]); + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &options).await; + + assert_contains( + &parts, + json!({ + "type": "tool-call", + "toolCallId": "ws_123", + "toolName": "web_search", + "input": "{\"query\":\"test\"}", + "providerExecuted": true + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › tool name mapping by type in streaming › should map x_search_call type to x_search tool name when name is empty + #[tokio::test] + async fn should_map_x_search_call_type_to_x_search_tool_name_when_name_is_empty() { + let fetch = single_item_stream(json!({ + "type": "x_search_call", + "id": "xs_123", + "name": "", + "arguments": "{\"query\":\"test\"}", + "call_id": "", + "status": "completed" + })); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool("xai.x_search", "x_search", json!({}))]); + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &options).await; + + assert_contains( + &parts, + json!({ + "type": "tool-call", + "toolCallId": "xs_123", + "toolName": "x_search", + "input": "{\"query\":\"test\"}", + "providerExecuted": true + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › tool name mapping by type in streaming › should map code_interpreter_call type to code_execution tool name when name is empty + #[tokio::test] + async fn should_map_code_interpreter_call_type_to_code_execution_when_name_empty() { + let fetch = single_item_stream(json!({ + "type": "code_interpreter_call", + "id": "ci_123", + "name": "", + "arguments": "{}", + "call_id": "", + "status": "completed" + })); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.code_execution", + "code_execution", + json!({}), + )]); + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &options).await; + + assert_contains( + &parts, + json!({ + "type": "tool-call", + "toolCallId": "ci_123", + "toolName": "code_execution", + "input": "{}", + "providerExecuted": true + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › tool name mapping by type in streaming › should map code_execution_call type to code_execution tool name when name is empty + #[tokio::test] + async fn should_map_code_execution_call_type_to_code_execution_when_name_empty() { + let fetch = single_item_stream(json!({ + "type": "code_execution_call", + "id": "ce_123", + "name": "", + "arguments": "{}", + "call_id": "", + "status": "completed" + })); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.code_execution", + "code_execution", + json!({}), + )]); + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &options).await; + + assert_contains( + &parts, + json!({ + "type": "tool-call", + "toolCallId": "ce_123", + "toolName": "code_execution", + "input": "{}", + "providerExecuted": true + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › tool name mapping by type in streaming › should use custom tool name from provider tool when type matches + #[tokio::test] + async fn should_use_custom_tool_name_from_provider_tool_when_type_matches() { + let fetch = single_item_stream(json!({ + "type": "web_search_call", + "id": "ws_123", + "name": "", + "arguments": "{}", + "call_id": "", + "status": "completed" + })); + + let mut options = CallOptions::new(test_prompt()); + options.tools = Some(vec![provider_tool( + "xai.web_search", + "my_custom_search", + json!({}), + )]); + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &options).await; + + assert_contains( + &parts, + json!({ + "type": "tool-call", + "toolCallId": "ws_123", + "toolName": "my_custom_search", + "input": "{}", + "providerExecuted": true + }), + ); + } + } + + mod citation_streaming { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › citation streaming › should stream citations as sources + #[tokio::test] + async fn should_stream_citations_as_sources() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "status": "in_progress", + "output": [] + } + }), + json!({ + "type": "response.output_text.annotation.added", + "item_id": "msg_123", + "output_index": 0, + "content_index": 0, + "annotation_index": 0, + "annotation": { + "type": "url_citation", + "url": "https://example.com", + "title": "example" + } + }), + json!({ + "type": "response.done", + "response": { + "id": "resp_123", + "object": "response", + "status": "completed", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 5 } + } + }), + ]); + + let parts = + run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + assert_contains( + &parts, + json!({ + "type": "source", + "sourceType": "url", + "id": "id-0", + "url": "https://example.com", + "title": "example" + }), + ); + } + } + + mod cost_in_usd_ticks_streaming { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › cost_in_usd_ticks streaming › should expose cost_in_usd_ticks in finish providerMetadata + #[tokio::test] + async fn should_expose_cost_in_usd_ticks_in_finish_provider_metadata() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "output": [] + } + }), + json!({ + "type": "response.output_text.delta", + "output_index": 0, + "content_index": 0, + "delta": "Hello" + }), + json!({ + "type": "response.completed", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "status": "completed", + "output": [ + { + "type": "message", + "id": "msg_001", + "role": "assistant", + "status": "completed", + "content": [{ "type": "output_text", "text": "Hello" }] + } + ], + "usage": { + "input_tokens": 10, + "output_tokens": 5, + "cost_in_usd_ticks": 113500 + } + } + }), + ]); + + let parts = + run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + assert_matches_object( + &first_part(&parts, "finish"), + json!({ + "type": "finish", + "providerMetadata": { "xai": { "costInUsdTicks": 113500 } } + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › cost_in_usd_ticks streaming › should expose echoed request identifiers in finish providerMetadata + #[tokio::test] + async fn should_expose_echoed_request_identifiers_in_finish_provider_metadata() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "output": [] + } + }), + json!({ + "type": "response.completed", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "status": "completed", + "output": [], + "usage": { "input_tokens": 10, "output_tokens": 5 }, + "prompt_cache_key": "conversation-123", + "safety_identifier": "hashed-user-123" + } + }), + ]); + + let parts = + run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + assert_matches_object( + &first_part(&parts, "finish"), + json!({ + "type": "finish", + "providerMetadata": { + "xai": { + "promptCacheKey": "conversation-123", + "safetyIdentifier": "hashed-user-123" + } + } + }), + ); + } + } + + mod missing_usage_handling { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › doStream › missing usage handling › should handle missing usage in streaming response + #[tokio::test] + async fn should_handle_missing_usage_in_streaming_response() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "created_at": 1700000000, + "status": "in_progress", + "output": [] + } + }), + json!({ + "type": "response.output_text.delta", + "output_index": 0, + "content_index": 0, + "delta": "Hello" + }), + // The response has no `usage` field. + json!({ + "type": "response.completed", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "created_at": 1700000000, + "status": "completed", + "output": [ + { + "type": "message", + "id": "msg_001", + "role": "assistant", + "status": "completed", + "content": [{ "type": "output_text", "text": "Hello" }] + } + ] + } + }), + ]); + + let parts = + run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + assert_matches_object( + &first_part(&parts, "finish"), + json!({ + "type": "finish", + "finishReason": { "unified": "stop", "raw": "completed" }, + "usage": { + "inputTokens": { "total": 0, "noCache": 0, "cacheRead": 0, "cacheWrite": 0 }, + "outputTokens": { "total": 0, "text": 0, "reasoning": 0 } + } + }), + ); + } + } + } + + mod schema_validation { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › schema validation › should accept response.created with usage: null + #[tokio::test] + async fn should_accept_response_created_with_usage_null() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "created_at": 1700000000, + "status": "in_progress", + "output": [], + "usage": null + } + }), + json!({ + "type": "response.output_item.added", + "item": { + "id": "msg_001", + "type": "message", + "role": "assistant", + "content": [], + "status": "in_progress" + }, + "output_index": 0 + }), + json!({ + "type": "response.content_part.added", + "item_id": "msg_001", + "output_index": 0, + "content_index": 0, + "part": { "type": "output_text", "text": "" } + }), + json!({ + "type": "response.output_text.delta", + "item_id": "msg_001", + "output_index": 0, + "content_index": 0, + "delta": "Hello" + }), + json!({ + "type": "response.completed", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "created_at": 1700000000, + "status": "completed", + "output": [ + { + "id": "msg_001", + "type": "message", + "role": "assistant", + "content": [{ "type": "output_text", "text": "Hello" }], + "status": "completed" + } + ], + "usage": { + "input_tokens": 10, + "output_tokens": 5, + "total_tokens": 15 + } + } + }), + ]); + + let parts = + run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + assert_matches_object( + &first_part(&parts, "text-delta"), + json!({ "type": "text-delta", "delta": "Hello" }), + ); + assert_matches_object(&first_part(&parts, "finish"), json!({ "type": "finish" })); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › schema validation › should accept response.in_progress with usage: null + #[tokio::test] + async fn should_accept_response_in_progress_with_usage_null() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "created_at": 1700000000, + "status": "in_progress", + "output": [], + "usage": null + } + }), + json!({ + "type": "response.in_progress", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "created_at": 1700000000, + "status": "in_progress", + "output": [], + "usage": null + } + }), + json!({ + "type": "response.output_item.added", + "item": { + "id": "msg_001", + "type": "message", + "role": "assistant", + "content": [], + "status": "in_progress" + }, + "output_index": 0 + }), + json!({ + "type": "response.content_part.added", + "item_id": "msg_001", + "output_index": 0, + "content_index": 0, + "part": { "type": "output_text", "text": "" } + }), + json!({ + "type": "response.output_text.delta", + "item_id": "msg_001", + "output_index": 0, + "content_index": 0, + "delta": "Hi" + }), + json!({ + "type": "response.completed", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "created_at": 1700000000, + "status": "completed", + "output": [ + { + "id": "msg_001", + "type": "message", + "role": "assistant", + "content": [{ "type": "output_text", "text": "Hi" }], + "status": "completed" + } + ], + "usage": { + "input_tokens": 5, + "output_tokens": 1, + "total_tokens": 6 + } + } + }), + ]); + + let parts = + run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + assert_matches_object( + &first_part(&parts, "text-delta"), + json!({ "type": "text-delta", "delta": "Hi" }), + ); + } + } + + mod response_incomplete_handling { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › response.incomplete handling › should set finish reason to length for max_output_tokens + #[tokio::test] + async fn should_set_finish_reason_to_length_for_max_output_tokens() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "output": [] + } + }), + json!({ + "type": "response.output_text.delta", + "item_id": "msg_456", + "output_index": 0, + "content_index": 0, + "delta": "partial output" + }), + json!({ + "type": "response.incomplete", + "response": { + "incomplete_details": { "reason": "max_output_tokens" }, + "usage": { "input_tokens": 100, "output_tokens": 4096 } + } + }), + ]); + + let parts = + run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + assert_matches_object( + &first_part(&parts, "finish"), + json!({ + "type": "finish", + "finishReason": { "unified": "length", "raw": "max_output_tokens" }, + "usage": { + "inputTokens": { "total": 100 }, + "outputTokens": { "total": 4096 } + } + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › response.incomplete handling › should default to length when incomplete_details is missing + #[tokio::test] + async fn should_default_to_length_when_incomplete_details_is_missing() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "output": [] + } + }), + json!({ + "type": "response.incomplete", + "response": { + "usage": { "input_tokens": 50, "output_tokens": 200 } + } + }), + ]); + + let parts = + run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + assert_matches_object( + &first_part(&parts, "finish"), + json!({ + "type": "finish", + "finishReason": { "unified": "other", "raw": "incomplete" } + }), + ); + } + } + + mod response_failed_handling { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › response.failed handling › should set finish reason to error + #[tokio::test] + async fn should_set_finish_reason_to_error() { + let response_failed_event = json!({ + "type": "response.failed", + "response": { + "error": { + "code": "server_error", + "message": "Internal server error" + }, + "usage": { "input_tokens": 50, "output_tokens": 0 } + } + }); + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "output": [] + } + }), + response_failed_event.clone(), + ]); + + let parts = + run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + assert_matches_object( + &first_part(&parts, "finish"), + json!({ + "type": "finish", + "finishReason": { "unified": "error", "raw": "error" }, + "usage": { + "inputTokens": { "total": 50 }, + "outputTokens": { "total": 0 } + } + }), + ); + assert_matches_object( + &stream_provider_error_view(&first_part(&parts, "error")), + json!({ + "type": "error", + "error": { + "message": response_failed_event["response"]["error"]["message"], + "type": response_failed_event["type"], + "code": response_failed_event["response"]["error"]["code"], + "statusCode": 500, + "isRetryable": true, + "data": response_failed_event + } + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › response.failed handling › should classify insufficient quota as non-retryable + #[tokio::test] + async fn should_classify_insufficient_quota_as_non_retryable() { + let response_failed_event = json!({ + "type": "response.failed", + "response": { + "error": { + "code": "insufficient_quota", + "message": "You exceeded your current quota." + }, + "usage": { "input_tokens": 50, "output_tokens": 0 } + } + }); + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "output": [] + } + }), + response_failed_event.clone(), + ]); + + let parts = + run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + assert_matches_object( + &stream_provider_error_view(&first_part(&parts, "error")), + json!({ + "type": "error", + "error": { + "message": response_failed_event["response"]["error"]["message"], + "type": response_failed_event["type"], + "code": response_failed_event["response"]["error"]["code"], + "statusCode": 429, + "isRetryable": false, + "data": response_failed_event + } + }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › response.failed handling › should map incomplete_details reason on failed response + #[tokio::test] + async fn should_map_incomplete_details_reason_on_failed_response() { + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "output": [] + } + }), + json!({ + "type": "response.failed", + "response": { + "error": { + "code": "server_error", + "message": "response failed" + }, + "incomplete_details": { "reason": "max_output_tokens" }, + "usage": null + } + }), + ]); + + let parts = + run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + assert_matches_object( + &first_part(&parts, "finish"), + json!({ + "type": "finish", + "finishReason": { "unified": "length", "raw": "max_output_tokens" } + }), + ); + } + } + + mod error_event_handling { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-language-model.test.ts › XaiResponsesLanguageModel › error event handling › should emit error chunk for server error events + #[tokio::test] + async fn should_emit_error_chunk_for_server_error_events() { + let error_event = json!({ + "type": "error", + "code": "server_error", + "message": "Internal server error", + "param": null + }); + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "model": "grok-4-fast-non-reasoning", + "output": [] + } + }), + error_event.clone(), + ]); + + let parts = + run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + assert_matches_object( + &stream_provider_error_view(&first_part(&parts, "error")), + json!({ + "type": "error", + "error": { + "message": error_event["message"], + "type": error_event["type"], + "code": error_event["code"], + "statusCode": 500, + "isRetryable": true, + "data": error_event + } + }), + ); + } + } +} diff --git a/aimux-providers/src/xai/tests/responses/responses_prepare_tools.rs b/aimux-providers/src/xai/tests/responses/responses_prepare_tools.rs new file mode 100644 index 000000000..b9f2666bb --- /dev/null +++ b/aimux-providers/src/xai/tests/responses/responses_prepare_tools.rs @@ -0,0 +1,934 @@ +//! Upstream `responses/xai-responses-prepare-tools.test.ts`. +//! +//! The Rust `prepare_responses_tools` is synchronous, so the upstream `await` +//! has no counterpart here. + +use aimux_core::options::LanguageModelV4ToolChoice; +use aimux_core::tool::{FunctionTool, ProviderTool, Tool}; +use aimux_core::types::Warning; +use serde_json::{Value, json}; + +use crate::xai::responses::convert::{PreparedResponsesTools, prepare_responses_tools}; + +/// A provider-defined tool as the caller passes it: `{ type: 'provider', id, name, args }`. +fn provider_tool(id: &str, name: &str, args: Value) -> Tool { + Tool::Provider( + serde_json::from_value::(json!({ + "id": id, + "name": name, + "args": args, + })) + .expect("valid provider tool"), + ) +} + +/// A function tool with a description and an input schema. +fn function_tool(name: &str, description: &str, input_schema: Value) -> Tool { + Tool::Function(FunctionTool { + description: Some(description.to_string()), + ..FunctionTool::new(name, input_schema) + }) +} + +/// `prepareResponsesTools({ tools, toolChoice })`. +fn prepare( + tools: Option>, + tool_choice: Option, +) -> PreparedResponsesTools { + prepare_responses_tools(&tools, tool_choice.as_ref()) + .expect("upstream prepareResponsesTools does not reject these tools") +} + +/// The upstream whole-result snapshot `{ toolChoice, toolWarnings, tools }`. +/// A key that is `undefined` upstream is absent here. +fn snapshot(result: &PreparedResponsesTools) -> Value { + let mut object = serde_json::Map::new(); + if let Some(tool_choice) = &result.tool_choice { + object.insert("toolChoice".to_string(), tool_choice.clone()); + } + object.insert( + "toolWarnings".to_string(), + serde_json::to_value(&result.tool_warnings).expect("warnings serialize"), + ); + if let Some(tools) = &result.tools { + object.insert("tools".to_string(), Value::Array(tools.clone())); + } + Value::Object(object) +} + +mod prepare_responses_tools { + use super::*; + + mod web_search { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › web_search › should prepare web_search tool with no args + #[test] + fn should_prepare_web_search_tool_with_no_args() { + let result = prepare( + Some(vec![provider_tool( + "xai.web_search", + "web_search", + json!({}), + )]), + None, + ); + + assert_eq!( + snapshot(&result), + json!({ + "toolWarnings": [], + "tools": [ + { + "type": "web_search", + }, + ], + }) + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › web_search › should prepare web_search tool with allowed domains + #[test] + fn should_prepare_web_search_tool_with_allowed_domains() { + let result = prepare( + Some(vec![provider_tool( + "xai.web_search", + "web_search", + json!({ "allowedDomains": ["wikipedia.org", "example.com"] }), + )]), + None, + ); + + assert_eq!( + result.tools, + Some(vec![json!({ + "allowed_domains": ["wikipedia.org", "example.com"], + "type": "web_search", + })]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › web_search › should prepare web_search tool with excluded domains + #[test] + fn should_prepare_web_search_tool_with_excluded_domains() { + let result = prepare( + Some(vec![provider_tool( + "xai.web_search", + "web_search", + json!({ "excludedDomains": ["spam.com"] }), + )]), + None, + ); + + assert_eq!( + result.tools, + Some(vec![json!({ + "excluded_domains": ["spam.com"], + "type": "web_search", + })]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › web_search › should prepare web_search tool with image search + #[test] + fn should_prepare_web_search_tool_with_image_search() { + let result = prepare( + Some(vec![provider_tool( + "xai.web_search", + "web_search", + json!({ "enableImageSearch": true }), + )]), + None, + ); + + assert_eq!( + result.tools, + Some(vec![json!({ + "enable_image_search": true, + "type": "web_search", + })]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › web_search › should prepare web_search tool with image understanding + #[test] + fn should_prepare_web_search_tool_with_image_understanding() { + let result = prepare( + Some(vec![provider_tool( + "xai.web_search", + "web_search", + json!({ "enableImageUnderstanding": true }), + )]), + None, + ); + + assert_eq!( + result.tools, + Some(vec![json!({ + "enable_image_understanding": true, + "type": "web_search", + })]) + ); + } + } + + mod x_search { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › x_search › should prepare x_search tool with no args + #[test] + fn should_prepare_x_search_tool_with_no_args() { + let result = prepare( + Some(vec![provider_tool("xai.x_search", "x_search", json!({}))]), + None, + ); + + assert_eq!( + result.tools, + Some(vec![json!({ + "type": "x_search", + })]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › x_search › should prepare x_search tool with allowed handles + #[test] + fn should_prepare_x_search_tool_with_allowed_handles() { + let result = prepare( + Some(vec![provider_tool( + "xai.x_search", + "x_search", + json!({ "allowedXHandles": ["elonmusk", "xai"] }), + )]), + None, + ); + + assert_eq!( + result.tools, + Some(vec![json!({ + "allowed_x_handles": ["elonmusk", "xai"], + "type": "x_search", + })]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › x_search › should prepare x_search tool with date range + #[test] + fn should_prepare_x_search_tool_with_date_range() { + let result = prepare( + Some(vec![provider_tool( + "xai.x_search", + "x_search", + json!({ "fromDate": "2025-01-01", "toDate": "2025-12-31" }), + )]), + None, + ); + + assert_eq!( + result.tools, + Some(vec![json!({ + "from_date": "2025-01-01", + "to_date": "2025-12-31", + "type": "x_search", + })]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › x_search › should prepare x_search tool with video understanding + #[test] + fn should_prepare_x_search_tool_with_video_understanding() { + let result = prepare( + Some(vec![provider_tool( + "xai.x_search", + "x_search", + json!({ + "enableVideoUnderstanding": true, + "enableImageUnderstanding": true, + }), + )]), + None, + ); + + assert_eq!( + result.tools, + Some(vec![json!({ + "enable_image_understanding": true, + "enable_video_understanding": true, + "type": "x_search", + })]) + ); + } + } + + mod code_execution { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › code_execution › should prepare code_execution tool as code_interpreter + #[test] + fn should_prepare_code_execution_tool_as_code_interpreter() { + let result = prepare( + Some(vec![provider_tool( + "xai.code_execution", + "code_execution", + json!({}), + )]), + None, + ); + + assert_eq!( + result.tools, + Some(vec![json!({ + "type": "code_interpreter", + })]) + ); + } + } + + mod view_image { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › view_image › should prepare view_image tool + #[test] + fn should_prepare_view_image_tool() { + let result = prepare( + Some(vec![provider_tool( + "xai.view_image", + "view_image", + json!({}), + )]), + None, + ); + + assert_eq!(result.tools, Some(vec![json!({ "type": "view_image" })])); + } + } + + mod view_x_video { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › view_x_video › should prepare view_x_video tool + #[test] + fn should_prepare_view_x_video_tool() { + let result = prepare( + Some(vec![provider_tool( + "xai.view_x_video", + "view_x_video", + json!({}), + )]), + None, + ); + + assert_eq!(result.tools, Some(vec![json!({ "type": "view_x_video" })])); + } + } + + mod image_generation { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › image_generation › should prepare image_generation tool with no args + #[test] + fn should_prepare_image_generation_tool_with_no_args() { + let result = prepare( + Some(vec![provider_tool( + "xai.image_generation", + "image_generation", + json!({}), + )]), + None, + ); + + assert_eq!( + result.tools, + Some(vec![json!({ + "type": "image_generation", + })]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › image_generation › should prepare image_generation tool with action + #[test] + fn should_prepare_image_generation_tool_with_action() { + let result = prepare( + Some(vec![provider_tool( + "xai.image_generation", + "image_generation", + json!({ "action": "generate" }), + )]), + None, + ); + + assert_eq!( + result.tools, + Some(vec![json!({ + "action": "generate", + "type": "image_generation", + })]) + ); + } + } + + mod file_search { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › file_search › should prepare file_search tool with vector store IDs + #[test] + fn should_prepare_file_search_tool_with_vector_store_ids() { + let result = prepare( + Some(vec![provider_tool( + "xai.file_search", + "file_search", + json!({ "vectorStoreIds": ["collection_1", "collection_2"] }), + )]), + None, + ); + + assert_eq!( + result.tools, + Some(vec![json!({ + "type": "file_search", + "vector_store_ids": ["collection_1", "collection_2"], + })]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › file_search › should prepare file_search tool with max num results + #[test] + fn should_prepare_file_search_tool_with_max_num_results() { + let result = prepare( + Some(vec![provider_tool( + "xai.file_search", + "file_search", + json!({ + "vectorStoreIds": ["collection_1"], + "maxNumResults": 10, + }), + )]), + None, + ); + + assert_eq!( + result.tools, + Some(vec![json!({ + "max_num_results": 10, + "type": "file_search", + "vector_store_ids": ["collection_1"], + })]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › file_search › should handle multiple tools including file_search + #[test] + fn should_handle_multiple_tools_including_file_search() { + let result = prepare( + Some(vec![ + provider_tool("xai.web_search", "web_search", json!({})), + provider_tool( + "xai.file_search", + "file_search", + json!({ "vectorStoreIds": ["collection_1"] }), + ), + function_tool( + "calculator", + "calculate numbers", + json!({ "type": "object", "properties": {} }), + ), + ]), + None, + ); + + let tools = result.tools.expect("tools are prepared"); + assert_eq!(tools.len(), 3); + assert_eq!(tools[0]["type"], "web_search"); + assert_eq!(tools[1]["type"], "file_search"); + assert_eq!(tools[2]["type"], "function"); + } + } + + mod function_tools { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › function tools › should prepare function tools + #[test] + fn should_prepare_function_tools() { + let result = prepare( + Some(vec![Tool::Function(FunctionTool { + description: Some("get weather information".to_string()), + ..FunctionTool::new( + "weather", + json!({ + "type": "object", + "properties": { + "location": { "type": "string" }, + }, + "required": ["location"], + "additionalProperties": false, + }), + ) + })]), + None, + ); + + assert_eq!( + result.tools, + Some(vec![json!({ + "description": "get weather information", + "name": "weather", + "parameters": { + "additionalProperties": false, + "properties": { + "location": { + "type": "string", + }, + }, + "required": ["location"], + "type": "object", + }, + "type": "function", + })]) + ); + } + } + + mod function_tools_strict_mode { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › function tools strict mode › should pass through strict mode when strict is true + #[test] + fn should_pass_through_strict_mode_when_strict_is_true() { + let result = prepare( + Some(vec![Tool::Function(FunctionTool { + description: Some("A test function".to_string()), + strict: Some(true), + ..FunctionTool::new( + "testFunction", + json!({ "type": "object", "properties": {} }), + ) + })]), + None, + ); + + assert_eq!( + snapshot(&result), + json!({ + "toolWarnings": [], + "tools": [ + { + "description": "A test function", + "name": "testFunction", + "parameters": { + "properties": {}, + "type": "object", + }, + "strict": true, + "type": "function", + }, + ], + }) + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › function tools strict mode › should pass through strict mode when strict is false + #[test] + fn should_pass_through_strict_mode_when_strict_is_false() { + let result = prepare( + Some(vec![Tool::Function(FunctionTool { + description: Some("A test function".to_string()), + strict: Some(false), + ..FunctionTool::new( + "testFunction", + json!({ "type": "object", "properties": {} }), + ) + })]), + None, + ); + + assert_eq!( + snapshot(&result), + json!({ + "toolWarnings": [], + "tools": [ + { + "description": "A test function", + "name": "testFunction", + "parameters": { + "properties": {}, + "type": "object", + }, + "strict": false, + "type": "function", + }, + ], + }) + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › function tools strict mode › should not include strict when strict is undefined + #[test] + fn should_not_include_strict_when_strict_is_undefined() { + let result = prepare( + Some(vec![function_tool( + "testFunction", + "A test function", + json!({ "type": "object", "properties": {} }), + )]), + None, + ); + + let tools = result.tools.expect("tools are prepared"); + assert!(tools[0].get("strict").is_none()); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › function tools strict mode › should pass through strict mode for multiple tools with different strict settings + #[test] + fn should_pass_through_strict_mode_for_multiple_tools_with_different_strict_settings() { + let result = prepare( + Some(vec![ + Tool::Function(FunctionTool { + description: Some("A strict tool".to_string()), + strict: Some(true), + ..FunctionTool::new( + "strictTool", + json!({ "type": "object", "properties": {} }), + ) + }), + Tool::Function(FunctionTool { + description: Some("A non-strict tool".to_string()), + strict: Some(false), + ..FunctionTool::new( + "nonStrictTool", + json!({ "type": "object", "properties": {} }), + ) + }), + function_tool( + "defaultTool", + "A tool without strict setting", + json!({ "type": "object", "properties": {} }), + ), + ]), + None, + ); + + assert_eq!( + snapshot(&result), + json!({ + "toolWarnings": [], + "tools": [ + { + "description": "A strict tool", + "name": "strictTool", + "parameters": { + "properties": {}, + "type": "object", + }, + "strict": true, + "type": "function", + }, + { + "description": "A non-strict tool", + "name": "nonStrictTool", + "parameters": { + "properties": {}, + "type": "object", + }, + "strict": false, + "type": "function", + }, + { + "description": "A tool without strict setting", + "name": "defaultTool", + "parameters": { + "properties": {}, + "type": "object", + }, + "type": "function", + }, + ], + }) + ); + } + } + + mod tool_choice { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › tool choice › should handle tool choice auto + #[test] + fn should_handle_tool_choice_auto() { + let result = prepare( + Some(vec![provider_tool( + "xai.web_search", + "web_search", + json!({}), + )]), + Some(LanguageModelV4ToolChoice::Auto), + ); + + assert_eq!(result.tool_choice, Some(json!("auto"))); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › tool choice › should handle tool choice required + #[test] + fn should_handle_tool_choice_required() { + let result = prepare( + Some(vec![provider_tool( + "xai.web_search", + "web_search", + json!({}), + )]), + Some(LanguageModelV4ToolChoice::Required), + ); + + assert_eq!(result.tool_choice, Some(json!("required"))); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › tool choice › should handle tool choice none + #[test] + fn should_handle_tool_choice_none() { + let result = prepare( + Some(vec![provider_tool( + "xai.web_search", + "web_search", + json!({}), + )]), + Some(LanguageModelV4ToolChoice::None), + ); + + assert_eq!(result.tool_choice, Some(json!("none"))); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › tool choice › should handle specific tool choice + #[test] + fn should_handle_specific_tool_choice() { + let result = prepare( + Some(vec![function_tool( + "weather", + "get weather", + json!({ "type": "object", "properties": {} }), + )]), + Some(LanguageModelV4ToolChoice::Tool { + tool_name: "weather".to_string(), + }), + ); + + assert_eq!( + result.tool_choice, + Some(json!({ + "type": "function", + "name": "weather", + })) + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › tool choice › should warn when trying to force server-side tool via toolChoice + #[test] + fn should_warn_when_trying_to_force_server_side_tool_via_tool_choice() { + let result = prepare( + Some(vec![provider_tool( + "xai.web_search", + "web_search", + json!({}), + )]), + Some(LanguageModelV4ToolChoice::Tool { + tool_name: "web_search".to_string(), + }), + ); + + assert_eq!(result.tool_choice, None); + assert!(result.tool_warnings.contains(&Warning::Unsupported { + feature: "toolChoice for server-side tool \"web_search\"".to_string(), + details: None, + })); + } + } + + mod multiple_tools { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › multiple tools › should handle multiple tools including provider-defined and functions + #[test] + fn should_handle_multiple_tools_including_provider_defined_and_functions() { + let result = prepare( + Some(vec![ + function_tool( + "calculator", + "calculate numbers", + json!({ "type": "object", "properties": {} }), + ), + provider_tool("xai.web_search", "web_search", json!({})), + provider_tool("xai.x_search", "x_search", json!({})), + ]), + None, + ); + + let tools = result.tools.expect("tools are prepared"); + assert_eq!(tools.len(), 3); + assert_eq!(tools[0]["type"], "function"); + assert_eq!(tools[1]["type"], "web_search"); + assert_eq!(tools[2]["type"], "x_search"); + } + } + + mod empty_tools { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › empty tools › should return undefined for empty tools array + #[test] + fn should_return_undefined_for_empty_tools_array() { + let result = prepare(Some(Vec::new()), None); + + assert_eq!(result.tools, None); + assert_eq!(result.tool_choice, None); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › empty tools › should return undefined for undefined tools + #[test] + fn should_return_undefined_for_undefined_tools() { + let result = prepare(None, None); + + assert_eq!(result.tools, None); + } + } + + mod unsupported_tools { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › unsupported tools › should warn about unsupported provider-defined tools + #[test] + fn should_warn_about_unsupported_provider_defined_tools() { + let result = prepare( + Some(vec![provider_tool( + "unsupported.tool", + "unsupported", + json!({}), + )]), + None, + ); + + assert_eq!( + serde_json::to_value(&result.tool_warnings).expect("warnings serialize"), + json!([ + { + "feature": "provider-defined tool unsupported", + "type": "unsupported", + }, + ]) + ); + } + } + + mod mcp { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › mcp › should prepare mcp tool with required args only + #[test] + fn should_prepare_mcp_tool_with_required_args_only() { + let result = prepare( + Some(vec![provider_tool( + "xai.mcp", + "mcp", + json!({ + "serverUrl": "https://example.com/mcp", + "serverLabel": "test-server", + }), + )]), + None, + ); + + assert_eq!( + result.tools, + Some(vec![json!({ + "server_label": "test-server", + "server_url": "https://example.com/mcp", + "type": "mcp", + })]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › mcp › should prepare mcp tool with all optional args + #[test] + fn should_prepare_mcp_tool_with_all_optional_args() { + let result = prepare( + Some(vec![provider_tool( + "xai.mcp", + "mcp", + json!({ + "serverUrl": "https://example.com/mcp", + "serverLabel": "test-server", + "serverDescription": "A test MCP server", + "allowedTools": ["tool1", "tool2"], + "headers": { "X-Custom": "value" }, + "authorization": "Bearer token123", + }), + )]), + None, + ); + + assert_eq!( + result.tools, + Some(vec![json!({ + "allowed_tools": ["tool1", "tool2"], + "authorization": "Bearer token123", + "headers": { "X-Custom": "value" }, + "server_description": "A test MCP server", + "server_label": "test-server", + "server_url": "https://example.com/mcp", + "type": "mcp", + })]) + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › mcp › should warn when trying to force mcp tool via toolChoice + #[test] + fn should_warn_when_trying_to_force_mcp_tool_via_tool_choice() { + let result = prepare( + Some(vec![provider_tool( + "xai.mcp", + "mcp", + json!({ + "serverUrl": "https://example.com/mcp", + "serverLabel": "test-server", + }), + )]), + Some(LanguageModelV4ToolChoice::Tool { + tool_name: "mcp".to_string(), + }), + ); + + assert_eq!(result.tool_choice, None); + assert!(result.tool_warnings.contains(&Warning::Unsupported { + feature: "toolChoice for server-side tool \"mcp\"".to_string(), + details: None, + })); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-prepare-tools.test.ts › prepareResponsesTools › mcp › should handle multiple tools including mcp + #[test] + fn should_handle_multiple_tools_including_mcp() { + let result = prepare( + Some(vec![ + provider_tool("xai.web_search", "web_search", json!({})), + provider_tool( + "xai.mcp", + "mcp", + json!({ + "serverUrl": "https://example.com/mcp", + "serverLabel": "test-server", + }), + ), + function_tool( + "calculator", + "calculate numbers", + json!({ "type": "object", "properties": {} }), + ), + ]), + None, + ); + + let tools = result.tools.expect("tools are prepared"); + assert_eq!(tools.len(), 3); + assert_eq!(tools[0]["type"], "web_search"); + assert_eq!(tools[1]["type"], "mcp"); + assert_eq!(tools[2]["type"], "function"); + } + } +} diff --git a/aimux-providers/src/xai/tests/responses/responses_raw_usage.rs b/aimux-providers/src/xai/tests/responses/responses_raw_usage.rs new file mode 100644 index 000000000..29d3e01ed --- /dev/null +++ b/aimux-providers/src/xai/tests/responses/responses_raw_usage.rs @@ -0,0 +1,248 @@ +//! Port of `src/responses/xai-responses-raw-usage.test.ts` from +//! `@ai-sdk/xai@5.0.12`: the raw usage cases. +//! +//! The helpers are copied from `responses_language_model.rs` (the same +//! harness: [`MockFetch`] for `createTestServer`, `undefined` keys absent). + +use std::sync::Arc; + +use aimux_core::language_model::LanguageModel; +use aimux_core::language_model_message::{LanguageModelMessage, LanguageModelPrompt}; +use aimux_core::options::CallOptions; +use aimux_core::result::StreamResult; +use futures::StreamExt; +use serde::Serialize; +use serde_json::{Value, json}; + +use crate::test_support::mock_fetch::{Canned, MockFetch}; +use crate::xai::{XAIProviderSettings, XaiResponsesModel, create_xai}; + +/// The TS `createModel()` default model id. +const DEFAULT_MODEL_ID: &str = "grok-4-fast-non-reasoning"; + +// ── helpers ───────────────────────────────────────────────────────────────── + +/// The TS `TEST_PROMPT`: one user text message "hello". +fn test_prompt() -> LanguageModelPrompt { + vec![LanguageModelMessage::user_text("hello")] +} + +/// The TS `createModel(modelId)`: the xAI provider at its default base URL, +/// `Authorization: Bearer test-key` and the mock transport. +fn create_model(fetch: &Arc, model_id: &str) -> XaiResponsesModel { + create_xai(XAIProviderSettings { + api_key: Some("test-key".to_string()), + fetch: Some(fetch.transport()), + ..Default::default() + }) + .expect("valid settings") + .responses(model_id) +} + +/// `prepareJsonResponse(body)`: one JSON response, for the first request. +fn json_fetch(body: &Value) -> Arc { + MockFetch::new(vec![Canned::json(body)]) +} + +/// `prepareStreamChunks(chunks)`: each chunk as `data: `, then +/// `data: [DONE]`. +fn sse_fetch(frames: &[String]) -> Arc { + let mut body = String::new(); + for frame in frames { + body.push_str(&format!("data: {frame}\n\n")); + } + body.push_str("data: [DONE]\n\n"); + MockFetch::new(vec![Canned { + status: 200, + headers: vec![("content-type".into(), "text/event-stream".into())], + body: body.into_bytes(), + }]) +} + +/// `prepareStreamChunks([...])` with `JSON.stringify`-ed events: one event per chunk. +fn stream_fetch(events: Vec) -> Arc { + let chunks: Vec = events.iter().map(Value::to_string).collect(); + sse_fetch(&chunks) +} + +fn to_json(value: &T) -> Value { + serde_json::to_value(value).expect("value serializes") +} + +/// `convertReadableStreamToArray(stream)` as JSON. +async fn stream_parts(result: StreamResult) -> Value { + let mut stream = result.stream; + let mut parts = Vec::new(); + while let Some(item) = stream.next().await { + parts.push(match item { + Ok(part) => to_json(&part), + Err(error) => json!({ "type": "error", "error": to_json(&error) }), + }); + } + Value::Array(parts) +} + +/// `doStream(options)` on a model of `model_id` answering from `fetch`, and the +/// parts of the stream. +async fn run_stream(fetch: &Arc, model_id: &str, options: &CallOptions) -> Value { + let result = create_model(fetch, model_id) + .do_stream(options) + .await + .expect("should succeed"); + stream_parts(result).await +} + +/// `parts.find(part => part.type === kind)`, or `null`. +fn first_part(parts: &Value, kind: &str) -> Value { + parts + .as_array() + .expect("an array") + .iter() + .find(|part| part["type"] == kind) + .cloned() + .unwrap_or(Value::Null) +} + +fn js_numbers(value: Value) -> Value { + match value { + Value::Number(number) => number.as_f64().map_or(Value::Number(number), |n| json!(n)), + Value::Array(items) => Value::Array(items.into_iter().map(js_numbers).collect()), + Value::Object(fields) => Value::Object( + fields + .into_iter() + .map(|(key, field)| (key, js_numbers(field))) + .collect(), + ), + other => other, + } +} + +/// `expect(actual).toEqual(expected)` on JSON. +fn assert_js_eq(actual: &Value, expected: Value) { + assert_eq!(js_numbers(actual.clone()), js_numbers(expected)); +} + +/// The TS `completeUsage`: usage with every provider-specific field kept. +fn complete_usage() -> Value { + json!({ + "input_tokens": 100, + "output_tokens": 50, + "total_tokens": 150, + "input_tokens_details": { + "cached_tokens": 20, + "provider_input_metadata": { "preserved": true } + }, + "output_tokens_details": { + "reasoning_tokens": 30, + "provider_output_metadata": ["preserved", 1] + }, + "context_details": { + "input_tokens": 90, + "output_tokens": 40 + }, + "server_side_tool_usage_details": { + "web_search_calls": 1 + }, + "provider_usage_metadata": { + "nested": { "preserved": true } + } + }) +} + +/// The TS `createResponse(usage)`: a completed response with no output. +fn create_response(usage: Value) -> Value { + json!({ + "id": "resp_123", + "object": "response", + "status": "completed", + "model": "grok-4-fast-non-reasoning", + "output": [], + "usage": usage + }) +} + +// ───────────────────────────────────────────────────────────────────────────── + +mod xai_responses_language_model_raw_usage { + use super::*; + + /// upstream: @ai-sdk/xai src/responses/xai-responses-raw-usage.test.ts › XaiResponsesLanguageModel raw usage › preserves complete raw usage through doGenerate parsing + #[tokio::test] + async fn preserves_complete_raw_usage_through_do_generate_parsing() { + let fetch = json_fetch(&create_response(complete_usage())); + + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&CallOptions::new(test_prompt())) + .await + .expect("should succeed"); + + let usage = to_json(&result.usage); + assert_js_eq(&usage["raw"], complete_usage()); + assert_js_eq( + &usage["inputTokens"], + json!({ "total": 100, "noCache": 80, "cacheRead": 20 }), + ); + assert_js_eq( + &usage["outputTokens"], + json!({ "total": 50, "text": 20, "reasoning": 30 }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-raw-usage.test.ts › XaiResponsesLanguageModel raw usage › preserves complete raw usage from the final doStream usage event + #[tokio::test] + async fn preserves_complete_raw_usage_from_the_final_do_stream_usage_event() { + let initial_usage = json!({ + "input_tokens": 0, + "output_tokens": 0, + "provider_usage_metadata": { "stage": "initial" } + }); + let fetch = stream_fetch(vec![ + json!({ + "type": "response.created", + "response": { + "id": "resp_123", + "object": "response", + "status": "in_progress", + "model": "grok-4-fast-non-reasoning", + "output": [], + "usage": initial_usage + } + }), + json!({ + "type": "response.completed", + "response": create_response(complete_usage()) + }), + ]); + + let parts = run_stream(&fetch, DEFAULT_MODEL_ID, &CallOptions::new(test_prompt())).await; + + let finish = first_part(&parts, "finish"); + assert!(!finish.is_null(), "finish part missing"); + assert_js_eq(&finish["usage"]["raw"], complete_usage()); + assert_js_eq( + &finish["usage"]["inputTokens"], + json!({ "total": 100, "noCache": 80, "cacheRead": 20 }), + ); + assert_js_eq( + &finish["usage"]["outputTokens"], + json!({ "total": 50, "text": 20, "reasoning": 30 }), + ); + } + + /// upstream: @ai-sdk/xai src/responses/xai-responses-raw-usage.test.ts › XaiResponsesLanguageModel raw usage › continues validating known usage field types + #[tokio::test] + async fn continues_validating_known_usage_field_types() { + let mut usage = complete_usage(); + usage["input_tokens"] = json!("100"); + let fetch = json_fetch(&create_response(usage)); + + let result = create_model(&fetch, DEFAULT_MODEL_ID) + .do_generate(&CallOptions::new(test_prompt())) + .await; + + assert!( + result.is_err(), + "doGenerate should reject a string input_tokens" + ); + } +} diff --git a/aimux-providers/src/xai/tests/responses/snapshots/should_include_code_interpreter_tool_call_with_real_response.json b/aimux-providers/src/xai/tests/responses/snapshots/should_include_code_interpreter_tool_call_with_real_response.json new file mode 100644 index 000000000..e42e2120b --- /dev/null +++ b/aimux-providers/src/xai/tests/responses/snapshots/should_include_code_interpreter_tool_call_with_real_response.json @@ -0,0 +1,13 @@ +[ + { + "input": "{\"code\":\"def fibonacci(n):\\n if n <= 0:\\n return 0\\n elif n == 1:\\n return 1\\n else:\\n a, b = 0, 1\\n for _ in range(2, n + 1):\\n a, b = b, a + b\\n return b\\n\\nprint(fibonacci(10))\"}", + "providerExecuted": true, + "toolCallId": "fc_5138abcf-4c4e-b0ab-7e0b-f4c81b98f455_us-east-1_0", + "toolName": "code_execution", + "type": "tool-call" + }, + { + "text": "55", + "type": "text" + } +] diff --git a/aimux-providers/src/xai/tests/responses/snapshots/should_include_web_search_tool_call_with_provider_executed_true.json b/aimux-providers/src/xai/tests/responses/snapshots/should_include_web_search_tool_call_with_provider_executed_true.json new file mode 100644 index 000000000..72df67ca1 --- /dev/null +++ b/aimux-providers/src/xai/tests/responses/snapshots/should_include_web_search_tool_call_with_provider_executed_true.json @@ -0,0 +1,54 @@ +[ + { + "input": "{\"query\":\"what is xAI\",\"num_results\":5}", + "providerExecuted": true, + "toolCallId": "fc_25de2f84-163c-6e9e-e42e-cd1dbd6f9ed0_0", + "toolName": "web_search", + "type": "tool-call" + }, + { + "result": {}, + "toolCallId": "fc_25de2f84-163c-6e9e-e42e-cd1dbd6f9ed0_0", + "toolName": "web_search", + "type": "tool-result" + }, + { + "text": "xAI is an American artificial intelligence company founded by Elon Musk in July 2023. Its mission is to \"understand the true nature of the universe\" by advancing scientific discovery through AI. The company is based in the San Francisco Bay Area and focuses on building advanced AI systems, including the Grok chatbot (integrated with the X platform, formerly Twitter). Notable team members include former researchers from OpenAI, DeepMind, and other AI labs.\n\nxAI has raised significant funding, including a $6 billion Series B round in 2024, and is competing with major players like OpenAI and Google in the AI space.\n\nIf you meant \"XAI\" as in Explainable AI (a field of AI research aimed at making machine learning models more transparent and interpretable), let me know for more details on that!", + "type": "text" + }, + { + "id": "id-0", + "sourceType": "url", + "title": "https://www.ibm.com/think/topics/explainable-ai", + "type": "source", + "url": "https://www.ibm.com/think/topics/explainable-ai" + }, + { + "id": "id-1", + "sourceType": "url", + "title": "https://x.ai/company", + "type": "source", + "url": "https://x.ai/company" + }, + { + "id": "id-2", + "sourceType": "url", + "title": "https://en.wikipedia.org/wiki/XAI_%28company%29", + "type": "source", + "url": "https://en.wikipedia.org/wiki/XAI_%28company%29" + }, + { + "id": "id-3", + "sourceType": "url", + "title": "https://x.ai/", + "type": "source", + "url": "https://x.ai/" + }, + { + "id": "id-4", + "sourceType": "url", + "title": "https://www.owkin.com/a-z-of-ai-for-healthcare/xai", + "type": "source", + "url": "https://www.owkin.com/a-z-of-ai-for-healthcare/xai" + } +] diff --git a/aimux-providers/src/xai/tests/responses/snapshots/should_include_x_search_tool_call_with_real_response.json b/aimux-providers/src/xai/tests/responses/snapshots/should_include_x_search_tool_call_with_real_response.json new file mode 100644 index 000000000..cd262a2e3 --- /dev/null +++ b/aimux-providers/src/xai/tests/responses/snapshots/should_include_x_search_tool_call_with_real_response.json @@ -0,0 +1,153 @@ +[ + { + "input": "{\"query\":\"AI artificial intelligence\",\"limit\":20,\"from_date\":\"2025-10-01\",\"to_date\":\"2025-10-29\",\"min_score_threshold\":0.2}", + "providerExecuted": true, + "toolCallId": "fc_84c1a8ea-1f29-4a33-1049-0ed3561b0f64_us-east-1_0", + "toolName": "x_search", + "type": "tool-call" + }, + { + "text": "### What People Are Saying About AI on X (as of Late October 2025)\n\nBased on recent posts on X (formerly Twitter), discussions about AI are buzzing with a mix of excitement, technical deep dives, and cautious optimism. The conversation heavily revolves around **Artificial General Intelligence (AGI)** as the holy grail of AI development, with frequent references to a new paper from AI luminaries like Yoshua Bengio, Eric Schmidt, Gary Marcus, and Max Tegmark. People are debating definitions, progress, limitations, and real-world applications. Here's a breakdown of the main themes from the top relevant posts (focusing on the last month):\n\n#### 1. **Defining AGI and Its Distance from Current AI**\n - A huge focus is on clarifying what AGI really means. Multiple users cite the aforementioned paper, which defines AGI as an AI system that \"matches or exceeds the cognitive versatility and proficiency of a well-educated adult\" across 10 core cognitive areas (e.g., reasoning, memory, math, language, perception).\n - Example: @jiqizhixin shared the paper, noting that models like GPT-4 and GPT-5 score poorly (e.g., 0% on long-term memory), emphasizing AGI isn't here yet.\n - @alex_prompter broke it down simply: GPT-4 is at 27% toward AGI, GPT-5 at 58%, but gaps in continuous learning persist. They called it a \"clear benchmark\" without hype.\n - @AskPerplexity and @flowersslop echoed this, describing AGI as broadly adaptable like humans, not just narrow tools like today's chatbots.\n - ASI (Artificial Superintelligence) gets mentioned as the next step beyond AGI—AI surpassing humans in *all* domains, including creativity and strategy. @SciTechera tied it to the \"Singularity,\" where AI self-improves uncontrollably.\n - Skepticism: @Newaxeman called AI an \"advanced search engine\" aggregating conventional wisdom, questioning deeper intelligence claims.\n\n#### 2. **Progress and Timelines Toward AGI**\n - Optimism is high but grounded. @bindureddy (CEO of Abacus.AI) boldly predicted AGI (AI replacing humans in digital tasks) is \"just a couple of years away,\" citing missing pieces like context, tools, and self-awareness that labs are tackling.\n - @slow_developer referenced OpenAI and DeepMind's 5-level AGI framework: Current models are past Level 2 (emerging intelligence) and into early Level 3 (competent in complex tasks with supervision), but not fully autonomous yet.\n - @timmur_ stressed AGI requires efficient learning across open-ended tasks with minimal data—something no model achieves today, but \"we will solve it eventually.\"\n - Broader view: @FrnkNlsn recommended the book *Artificial Intelligence: A Modern Approach* by Russell and Norvig for a comprehensive overview, highlighting topics like knowledge representation and multi-agent systems.\n\n#### 3. **Types of AI and Distinctions**\n - Users are educating on AI's varieties to cut through confusion:\n - @y00ngipurrs differentiated analytical AI (e.g., for medicine, fraud detection) from generative AI (e.g., ChatGPT for text/art).\n - @techyoutbe contrasted AI agents (task-specific automators) with \"agentic AI\" (autonomous systems for long-term planning and multi-domain reasoning).\n - @SukhSandhu listed what AI *is* (data processor, pattern recognizer, task automator) vs. what it's *not* (conscious, emotional, intuitively moral). They warned of biases in training data.\n - @AITECHio hyped proactive \"AI agents\" that set goals and execute workflows autonomously, predicting one for every task from finance to on-chain automation.\n\n#### 4. **Applications, Projects, and Broader Impacts**\n - AI's practical side shines in Web3 and business contexts. @O_H_JAY highlighted crypto projects advancing AGI/ASI, like @SentientAGI (decentralized AGI networks), @AGI_XT (AI automation on Solana), and @StoryProtocol (on-chain IP for AI models). They urged getting in early during market dips.\n - @Voa_De_Voa discussed combining AI with compute and robotics for \"autonomous intelligence,\" but flagged centralized control as a risk.\n - @DeviprasadSD positioned AGI as a future horizon for reasoning across any task, still in research.\n - @zolayolanetwork and @davilagrau amplified the AGI definition paper, linking it to blockchain and cognitive tools.\n\n#### Overall Sentiment\n- **Positive/Visionary**: Excitement about AI augmenting human capabilities (e.g., @hailstorm_9 called AGI \"augmentation, not automation\"). Many see it transforming industries like healthcare, finance, and creative work.\n- **Cautious/Realistic**: Emphasis on limitations—current AI is \"narrow\" and lacks true understanding or adaptability. No one's claiming AGI is imminent without caveats.\n- **Volume and Engagement**: These posts garnered thousands of likes/views, with high engagement on definitions and project lists. Discussions are technical but accessible, often with images, threads, or links to papers/arXiv.\n\nThis is a snapshot from ~20 recent, highly relevant posts. AI chatter on X is evolving fast—trends like AGI benchmarks and agentic systems are dominating. If you want deeper dives into specific posts, users, or subtopics (e.g., AI ethics or tools), let me know!", + "type": "text" + }, + { + "id": "id-0", + "sourceType": "url", + "title": "https://x.com/i/status/1982033415697514642", + "type": "source", + "url": "https://x.com/i/status/1982033415697514642" + }, + { + "id": "id-1", + "sourceType": "url", + "title": "https://x.com/i/status/1983126826739228851", + "type": "source", + "url": "https://x.com/i/status/1983126826739228851" + }, + { + "id": "id-2", + "sourceType": "url", + "title": "https://x.com/i/status/1978543466042040366", + "type": "source", + "url": "https://x.com/i/status/1978543466042040366" + }, + { + "id": "id-3", + "sourceType": "url", + "title": "https://x.com/i/status/1982845451188720069", + "type": "source", + "url": "https://x.com/i/status/1982845451188720069" + }, + { + "id": "id-4", + "sourceType": "url", + "title": "https://x.com/i/status/1981716983168040972", + "type": "source", + "url": "https://x.com/i/status/1981716983168040972" + }, + { + "id": "id-5", + "sourceType": "url", + "title": "https://x.com/i/status/1977864424770990183", + "type": "source", + "url": "https://x.com/i/status/1977864424770990183" + }, + { + "id": "id-6", + "sourceType": "url", + "title": "https://x.com/i/status/1979019210870395155", + "type": "source", + "url": "https://x.com/i/status/1979019210870395155" + }, + { + "id": "id-7", + "sourceType": "url", + "title": "https://x.com/i/status/1981725084059668849", + "type": "source", + "url": "https://x.com/i/status/1981725084059668849" + }, + { + "id": "id-8", + "sourceType": "url", + "title": "https://x.com/i/status/1982159004857217324", + "type": "source", + "url": "https://x.com/i/status/1982159004857217324" + }, + { + "id": "id-9", + "sourceType": "url", + "title": "https://x.com/i/status/1982984027209630067", + "type": "source", + "url": "https://x.com/i/status/1982984027209630067" + }, + { + "id": "id-10", + "sourceType": "url", + "title": "https://x.com/i/status/1979140575476764920", + "type": "source", + "url": "https://x.com/i/status/1979140575476764920" + }, + { + "id": "id-11", + "sourceType": "url", + "title": "https://x.com/i/status/1978340270413377544", + "type": "source", + "url": "https://x.com/i/status/1978340270413377544" + }, + { + "id": "id-12", + "sourceType": "url", + "title": "https://x.com/i/status/1975597769089032357", + "type": "source", + "url": "https://x.com/i/status/1975597769089032357" + }, + { + "id": "id-13", + "sourceType": "url", + "title": "https://x.com/i/status/1979729023413670239", + "type": "source", + "url": "https://x.com/i/status/1979729023413670239" + }, + { + "id": "id-14", + "sourceType": "url", + "title": "https://x.com/i/status/1979892126969209017", + "type": "source", + "url": "https://x.com/i/status/1979892126969209017" + }, + { + "id": "id-15", + "sourceType": "url", + "title": "https://x.com/i/status/1974520747503792531", + "type": "source", + "url": "https://x.com/i/status/1974520747503792531" + }, + { + "id": "id-16", + "sourceType": "url", + "title": "https://x.com/i/status/1977071347584135287", + "type": "source", + "url": "https://x.com/i/status/1977071347584135287" + }, + { + "id": "id-17", + "sourceType": "url", + "title": "https://x.com/i/status/1983115564005626367", + "type": "source", + "url": "https://x.com/i/status/1983115564005626367" + }, + { + "id": "id-18", + "sourceType": "url", + "title": "https://x.com/i/status/1981679355345686727", + "type": "source", + "url": "https://x.com/i/status/1981679355345686727" + }, + { + "id": "id-19", + "sourceType": "url", + "title": "https://x.com/i/status/1982892944261582868", + "type": "source", + "url": "https://x.com/i/status/1982892944261582868" + } +] diff --git a/aimux-providers/src/xai/tests/responses/snapshots/should_stream_text_deltas.json b/aimux-providers/src/xai/tests/responses/snapshots/should_stream_text_deltas.json new file mode 100644 index 000000000..8beb227a4 --- 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Upstream `xai-speech-model.test.ts`: the `XaiSpeechModel` describe and the +//! `doGenerate` case that stamps the response. +//! +//! The request goes through a [`MockFetch`], as in the provider tests. +//! +//! Mapping notes: +//! - `specificationVersion`: `SpeechModel::specification_version()` is the +//! trait's `"v4"`, which xAI keeps. +//! - The injected `_internal.currentDate` has no counterpart: aimux stamps the +//! response with `Timestamp::now()` and has no clock seam. The stamp is +//! checked to fall within the call instead of equalling the injected date. + +use std::sync::Arc; + +use aimux_core::speech_model::{SpeechCallOptions, SpeechModel}; + +use crate::test_support::mock_fetch::{Canned, MockFetch}; +use crate::xai::XaiSpeechModel; +use crate::{XAIProviderSettings, create_xai}; + +/// The audio the upstream server answers with (`new Uint8Array([1, 2, 3, 4])`). +const AUDIO: [u8; 4] = [1, 2, 3, 4]; + +/// `createXai({ apiKey: 'test-api-key' }).speech()`, sending through `fetch`. +fn speech_model(fetch: &Arc) -> XaiSpeechModel { + create_xai(XAIProviderSettings { + api_key: Some("test-api-key".to_string()), + fetch: Some(fetch.transport()), + ..Default::default() + }) + .expect("creating an xAI provider does not fail") + .speech() +} + +/// The `audio/mpeg` answer of the upstream `prepareAudioResponse`, with extra +/// response headers. +fn audio_answer(headers: &[(&str, &str)]) -> Canned { + let mut all = vec![("content-type".to_string(), "audio/mpeg".to_string())]; + all.extend( + headers + .iter() + .map(|(name, value)| (name.to_string(), value.to_string())), + ); + Canned { + status: 200, + headers: all, + body: AUDIO.to_vec(), + } +} + +mod xai_speech_model { + use super::*; + + /// upstream: @ai-sdk/xai src/xai-speech-model.test.ts › XaiSpeechModel › should expose correct provider and model information + #[test] + fn should_expose_correct_provider_and_model_information() { + let model = create_xai(XAIProviderSettings::default()) + .expect("creating an xAI provider does not fail") + .speech(); + + assert_eq!(model.provider(), "xai.speech"); + assert_eq!(model.model_id(), ""); + assert_eq!(model.specification_version(), "v4"); + } +} + +mod do_generate { + use super::*; + + /// upstream: @ai-sdk/xai src/xai-speech-model.test.ts › doGenerate › should include response timestamp, model id, and headers + #[tokio::test] + async fn should_include_response_timestamp_model_id_and_headers() { + let fetch = MockFetch::new(vec![audio_answer(&[("x-request-id", "test-request-id")])]); + + let before = chrono::Utc::now().timestamp_millis(); + let result = speech_model(&fetch) + .do_generate(&SpeechCallOptions::new("Hello from the AI SDK!")) + .await + .expect("the call succeeds"); + let after = chrono::Utc::now().timestamp_millis(); + + let timestamp = result + .response + .timestamp + .expect("the response has a timestamp") + .unix_millis(); + assert!( + before <= timestamp && timestamp <= after, + "timestamp {timestamp} is outside the call ({before}..={after})" + ); + assert_eq!(result.response.model_id.as_deref(), Some("")); + assert_eq!( + result + .response + .headers + .expect("the response has headers") + .get("x-request-id") + .map(String::as_str), + Some("test-request-id") + ); + } +} diff --git a/aimux-providers/src/xai/tests/supports_reasoning_effort.rs b/aimux-providers/src/xai/tests/supports_reasoning_effort.rs new file mode 100644 index 000000000..e7123aa70 --- /dev/null +++ b/aimux-providers/src/xai/tests/supports_reasoning_effort.rs @@ -0,0 +1,44 @@ +//! Upstream `supports-reasoning-effort.test.ts`. + +use crate::xai::convert::supports_reasoning_effort; + +#[allow( + clippy::module_inception, + reason = "the upstream describe level keeps its name" +)] +mod supports_reasoning_effort { + use super::*; + + /// upstream: @ai-sdk/xai src/supports-reasoning-effort.test.ts › supportsReasoningEffort › should return true for %s + #[test] + fn should_return_true_for() { + for model_id in [ + "grok-4.3", + "grok-latest", + "grok-4.20-multi-agent", + "grok-4.20-multi-agent-0309", + "grok-3-mini", + ] { + assert!( + supports_reasoning_effort(model_id), + "expected {model_id} to support reasoning effort" + ); + } + } + + /// upstream: @ai-sdk/xai src/supports-reasoning-effort.test.ts › supportsReasoningEffort › should return false for %s + #[test] + fn should_return_false_for() { + for model_id in [ + "grok-4.20-reasoning", + "grok-4.20-non-reasoning", + "grok-4.20-0309-reasoning", + "grok-4.20-0309-non-reasoning", + ] { + assert!( + !supports_reasoning_effort(model_id), + "expected {model_id} not to support reasoning effort" + ); + } + } +} diff --git a/aimux-providers/src/xai/tests/transcription_model.rs b/aimux-providers/src/xai/tests/transcription_model.rs new file mode 100644 index 000000000..246e65642 --- /dev/null +++ b/aimux-providers/src/xai/tests/transcription_model.rs @@ -0,0 +1,858 @@ +//! Upstream `xai-transcription-model.test.ts`: the provider-information case, +//! the `doGenerate` cases and the `doStream` cases. +//! +//! `doGenerate` runs through a [`MockFetch`]. `doStream` runs through an +//! in-crate WebSocket double injected with the `web_socket` setting (the +//! `realtime` feature, on by default). It stands in for the upstream +//! `MockWebSocket`: it records the handshake (URL and headers) and every frame +//! the model sends, and the test feeds the server's events into it. +//! +//! Mapping notes: +//! - `specificationVersion`: `TranscriptionModel::specification_version()` is +//! the trait's `"v4"`, which xAI keeps. +//! - The injected `_internal.currentDate` has no counterpart (aimux stamps +//! `Timestamp::now()` and has no clock seam): each stamp is checked to fall +//! within its call. +//! - The multipart boundary: undici's `----formdata-undici-N` is a JS-only +//! format. Only the `multipart/form-data; boundary=` prefix is checked. +//! - The user agent carries the pinned `ai-sdk-xai/` as a token. The +//! upstream `VERSION` mock (`0.0.0-test`) has no counterpart, and the wire +//! request also carries `ai-sdk-provider-utils/`, which upstream's +//! provider-level header check never sees. +//! - Per-call headers are `HashMap`, so upstream's per-call +//! `'X-Unset': undefined` is given at provider level, where `None` removes a +//! header. +//! - `argument: 'providerOptions'`: aimux's `InvalidArgument` carries only a +//! message, so the message is checked. + +use std::collections::HashMap; +use std::sync::Arc; + +use aimux_core::shared::SharedProviderOptions; +use aimux_core::transcription_model::{AudioInput, TranscriptionCallOptions, TranscriptionModel}; +use serde_json::{Value, json}; + +use crate::test_support::mock_fetch::{Canned, MockFetch, Seen}; +use crate::xai::XaiTranscriptionModel; +use crate::xai::options::NAMESPACE; +use crate::{XAIProviderSettings, create_xai}; + +/// `createXai({ apiKey: 'test-api-key', headers }).transcription()`, sending +/// through `fetch`. +fn transcription_model( + fetch: &Arc, + headers: Option>>, +) -> XaiTranscriptionModel { + create_xai(XAIProviderSettings { + api_key: Some("test-api-key".to_string()), + headers, + fetch: Some(fetch.transport()), + ..Default::default() + }) + .expect("creating an xAI provider does not fail") + .transcription() +} + +/// `{ audio: new Uint8Array([1, 2, 3, 4]), mediaType }`. +fn audio_call(media_type: &str) -> TranscriptionCallOptions { + TranscriptionCallOptions::new(AudioInput::Binary(vec![1, 2, 3, 4]), media_type) +} + +/// `providerOptions: { xai: {...} }`. +fn xai_options(value: Value) -> SharedProviderOptions { + SharedProviderOptions::from([( + NAMESPACE.to_string(), + value.as_object().expect("an object").clone(), + )]) +} + +/// The upstream `prepareJsonResponse` answer: a JSON body with extra headers. +fn json_answer(body: Value, headers: &[(&str, &str)]) -> Canned { + let mut all = vec![("content-type".to_string(), "application/json".to_string())]; + all.extend( + headers + .iter() + .map(|(name, value)| (name.to_string(), value.to_string())), + ); + Canned { + status: 200, + headers: all, + body: serde_json::to_vec(&body).expect("the body serializes"), + } +} + +/// The body of the upstream `prepareJsonResponse`. +fn default_body() -> Value { + json!({ + "text": "Hello from the AI SDK!", + "language": "en", + "duration": 2.5, + "words": [ + { "text": "Hello", "start": 0, "end": 1 }, + { "text": "from the AI SDK!", "start": 1, "end": 2.5 }, + ], + }) +} + +/// The one request `fetch` recorded. +fn only_request(fetch: &MockFetch) -> Seen { + let mut seen = fetch.seen(); + assert_eq!(seen.len(), 1, "the call sends exactly one request"); + seen.remove(0) +} + +/// The user agent carries the pinned `ai-sdk-xai/` token; see the +/// module notes. +fn assert_user_agent_names_the_package(seen: &Seen) { + let agent = seen + .headers + .get("user-agent") + .expect("the request has a user-agent header"); + let token = format!("ai-sdk-xai/{}", crate::shared::aisdk_pins::XAI.version); + assert!( + agent.split(' ').any(|part| part == token), + "user-agent {agent:?} does not carry {token}" + ); +} + +mod xai_transcription_model { + use super::*; + + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › XaiTranscriptionModel › should expose correct provider and model information + #[test] + fn should_expose_correct_provider_and_model_information() { + let model = create_xai(XAIProviderSettings::default()) + .expect("creating an xAI provider does not fail") + .transcription(); + + assert_eq!(model.provider(), "xai.transcription"); + assert_eq!(model.model_id(), ""); + assert_eq!(model.specification_version(), "v4"); + } +} + +mod do_generate { + use super::*; + + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › doGenerate › should pass headers and the xAI user agent + #[tokio::test] + async fn should_pass_headers_and_the_xai_user_agent() { + let fetch = MockFetch::new(vec![json_answer(default_body(), &[])]); + let provider_headers = HashMap::from([( + "Custom-Provider-Header".to_string(), + Some("provider-header-value".to_string()), + )]); + let mut options = audio_call("audio/wav"); + options.headers = Some(HashMap::from([( + "Custom-Request-Header".to_string(), + "request-header-value".to_string(), + )])); + + transcription_model(&fetch, Some(provider_headers)) + .do_generate(&options) + .await + .expect("the call succeeds"); + + let seen = only_request(&fetch); + assert_eq!(seen.headers["authorization"], "Bearer test-api-key"); + assert!( + seen.headers["content-type"].starts_with("multipart/form-data; boundary="), + "{}", + seen.headers["content-type"] + ); + assert_eq!( + seen.headers["custom-provider-header"], + "provider-header-value" + ); + assert_eq!( + seen.headers["custom-request-header"], + "request-header-value" + ); + assert_user_agent_names_the_package(&seen); + } + + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › doGenerate › should include response timestamp, model id, and headers + #[tokio::test] + async fn should_include_response_timestamp_model_id_and_headers() { + let fetch = MockFetch::new(vec![json_answer( + default_body(), + &[ + ("x-request-id", "test-request-id"), + ("x-ratelimit-remaining", "123"), + ], + )]); + + let before = chrono::Utc::now().timestamp_millis(); + let result = transcription_model(&fetch, None) + .do_generate(&audio_call("audio/wav")) + .await + .expect("the call succeeds"); + let after = chrono::Utc::now().timestamp_millis(); + + let timestamp = result + .response + .timestamp + .expect("the response has a timestamp") + .unix_millis(); + assert!( + before <= timestamp && timestamp <= after, + "timestamp {timestamp} is outside the call ({before}..={after})" + ); + assert_eq!(result.response.model_id.as_deref(), Some("")); + let headers = result.response.headers.expect("the response has headers"); + assert_eq!(headers["content-type"], "application/json"); + assert_eq!(headers["x-request-id"], "test-request-id"); + assert_eq!(headers["x-ratelimit-remaining"], "123"); + } +} + +#[cfg(feature = "realtime")] +mod socket { + //! The WebSocket double: the upstream `MockWebSocket`, as a + //! [`WsTransport`](aimux_provider_utils::ws::WsTransport) that a + //! [`WsConnector`] hands out. + + use std::collections::VecDeque; + use std::io; + use std::pin::Pin; + use std::sync::atomic::{AtomicBool, Ordering}; + use std::sync::{Arc, Mutex}; + use std::task::{Context, Poll}; + + use aimux_core::error::AiMuxError; + use aimux_core::transcription_model::AudioChunk; + use aimux_provider_utils::ws::{WebSocketRequest, WsConnection, WsConnector}; + use async_trait::async_trait; + use futures::{Sink, Stream}; + use serde_json::Value; + use tokio::sync::mpsc; + use tokio_tungstenite::tungstenite::{Error as WsError, Message}; + + /// A frame the model sent, as the upstream `ws.send` mock records it. + #[derive(Debug, Clone, PartialEq)] + pub(super) enum Frame { + Binary(Vec), + Text(String), + Close, + } + + #[derive(Default)] + struct SocketState { + frames: Vec, + /// Upstream's `send` throwing for binary audio. + fail_binary: bool, + } + + /// The test's side of one socket: the server events it sends, and what + /// the model sent. + pub(super) struct SocketHandle { + state: Arc>, + events: mpsc::UnboundedSender, + } + + impl SocketHandle { + /// Every frame the model has sent so far. + pub(super) fn frames(&self) -> Vec { + self.state.lock().unwrap().frames.clone() + } + + /// Upstream `ws.message(value)`: a server event. + pub(super) fn message(&self, value: Value) { + self.events + .send(Message::Text(value.to_string())) + .expect("the socket is open"); + } + + /// Upstream's `send` throwing for binary audio from now on. + pub(super) fn fail_binary_sends(&self) { + self.state.lock().unwrap().fail_binary = true; + } + } + + /// The socket a connector hands out: server events in, frames out. + pub(super) struct MockSocket { + state: Arc>, + events: mpsc::UnboundedReceiver, + } + + /// A connected socket and the handle the test drives it with. + pub(super) fn new_socket() -> (SocketHandle, MockSocket) { + let state = Arc::new(Mutex::new(SocketState::default())); + let (events_tx, events_rx) = mpsc::unbounded_channel(); + ( + SocketHandle { + state: Arc::clone(&state), + events: events_tx, + }, + MockSocket { + state, + events: events_rx, + }, + ) + } + + impl Stream for MockSocket { + type Item = Result; + + fn poll_next(self: Pin<&mut Self>, cx: &mut Context<'_>) -> Poll> { + self.get_mut() + .events + .poll_recv(cx) + .map(|event| event.map(Ok)) + } + } + + impl Sink for MockSocket { + type Error = WsError; + + fn poll_ready(self: Pin<&mut Self>, _: &mut Context<'_>) -> Poll> { + Poll::Ready(Ok(())) + } + + fn start_send(self: Pin<&mut Self>, item: Message) -> Result<(), WsError> { + let mut state = self.state.lock().unwrap(); + let frame = match item { + Message::Binary(bytes) => { + if state.fail_binary { + return Err(WsError::Io(io::Error::other("send failed"))); + } + Frame::Binary(bytes) + } + Message::Text(text) => Frame::Text(text), + Message::Close(_) => Frame::Close, + _ => return Ok(()), + }; + state.frames.push(frame); + Ok(()) + } + + fn poll_flush(self: Pin<&mut Self>, _: &mut Context<'_>) -> Poll> { + Poll::Ready(Ok(())) + } + + fn poll_close(self: Pin<&mut Self>, _: &mut Context<'_>) -> Poll> { + Poll::Ready(Ok(())) + } + } + + /// One connect: the handshake URL and its headers. + pub(super) type Connect = (String, Vec<(String, String)>); + + /// The connector the test injects. It records each connect (URL and + /// headers) and hands out its socket, or fails with its message. + pub(super) struct MockConnector { + connects: Mutex>, + socket: Mutex>, + fail: Option, + } + + impl MockConnector { + /// A connector that hands `socket` out on its connect. + pub(super) fn with_socket(socket: MockSocket) -> Arc { + Arc::new(Self { + connects: Mutex::default(), + socket: Mutex::new(Some(socket)), + fail: None, + }) + } + + /// A connector whose every connect fails with `message` (upstream's + /// `new WebSocket()` throwing). + pub(super) fn failing(message: &str) -> Arc { + Arc::new(Self { + connects: Mutex::default(), + socket: Mutex::new(None), + fail: Some(message.to_string()), + }) + } + + /// Every connect so far: the URL and the headers. + pub(super) fn connects(&self) -> Vec { + self.connects.lock().unwrap().clone() + } + } + + #[async_trait] + impl WsConnector for MockConnector { + async fn connect(&self, request: &WebSocketRequest) -> Result { + self.connects + .lock() + .unwrap() + .push((request.url.clone(), request.headers.clone())); + if let Some(message) = &self.fail { + return Err(AiMuxError::Other(message.clone())); + } + let socket = self + .socket + .lock() + .unwrap() + .take() + .expect("one connection per test"); + Ok(WsConnection::from_transport(Box::new(socket), request)) + } + } + + /// The audio the model reads: `chunks`, then the end when `ends`, else an + /// open stream that yields nothing more. Dropping it is the cancellation + /// the upstream `cancel()` callback observes; the flag records it. + pub(super) struct Audio { + chunks: VecDeque, + ends: bool, + cancelled: Arc, + } + + impl Audio { + /// The audio, and the flag that is set once it is dropped. + pub(super) fn new(chunks: Vec>, ends: bool) -> (Self, Arc) { + let cancelled = Arc::new(AtomicBool::new(false)); + let audio = Self { + chunks: chunks.into_iter().map(AudioChunk::Binary).collect(), + ends, + cancelled: Arc::clone(&cancelled), + }; + (audio, cancelled) + } + } + + impl Stream for Audio { + type Item = AudioChunk; + + fn poll_next(self: Pin<&mut Self>, _: &mut Context<'_>) -> Poll> { + let this = self.get_mut(); + if let Some(chunk) = this.chunks.pop_front() { + Poll::Ready(Some(chunk)) + } else if this.ends { + Poll::Ready(None) + } else { + Poll::Pending + } + } + } + + impl Drop for Audio { + fn drop(&mut self) { + self.cancelled.store(true, Ordering::SeqCst); + } + } +} + +#[cfg(feature = "realtime")] +mod do_stream { + use std::sync::atomic::Ordering; + use std::time::Duration; + + use aimux_core::error::AiMuxError; + use aimux_core::transcription_model::{ + InputAudioFormat, TranscriptionStreamOptions, TranscriptionStreamPart, + }; + use futures::StreamExt; + use tokio::task::JoinHandle; + + use super::socket::{Audio, Frame, MockConnector, new_socket}; + use super::*; + + /// `createXai({ apiKey, headers, webSocket }).transcription()`. + fn transcription_model_with( + connector: Arc, + headers: Option>>, + ) -> XaiTranscriptionModel { + create_xai(XAIProviderSettings { + api_key: Some("test-api-key".to_string()), + headers, + web_socket: Some(connector), + ..Default::default() + }) + .expect("creating an xAI provider does not fail") + .transcription() + } + + /// `{ audio, inputAudioFormat: { type: 'audio/pcm', rate: 16000 }, providerOptions }`. + fn stream_options(audio: Audio, provider_options: Option) -> TranscriptionStreamOptions { + TranscriptionStreamOptions { + audio: Box::pin(audio), + input_audio_format: InputAudioFormat { + format_type: "audio/pcm".to_string(), + rate: Some(16000), + }, + provider_options: provider_options.map(xai_options), + abort_signal: None, + headers: None, + include_raw_chunks: false, + timeout: None, + } + } + + /// The parts as JSON; a stream error fails the test. + fn part_values(parts: &[Result]) -> Vec { + parts + .iter() + .map(|part| serde_json::to_value(part.as_ref().expect("a stream part")).unwrap()) + .collect() + } + + /// The first stream error of `parts`. + fn first_error(parts: &[Result]) -> &AiMuxError { + parts + .iter() + .find_map(|part| part.as_ref().err()) + .expect("the stream fails") + } + + /// The value of a header, by name, whatever its case. + fn header<'a>(headers: &'a [(String, String)], name: &str) -> Option<&'a str> { + headers + .iter() + .find(|(key, _)| key.eq_ignore_ascii_case(name)) + .map(|(_, value)| value.as_str()) + } + + /// Waits for the model, which reads the socket on its own task, to reach + /// `ready`. + async fn settle(mut ready: impl FnMut() -> bool) { + for _ in 0..2000 { + if ready() { + return; + } + tokio::time::sleep(Duration::from_millis(1)).await; + } + panic!("the model did not reach the expected state"); + } + + /// The task's result, bounded so a hung stream fails the test. + async fn finished(task: JoinHandle) -> T { + tokio::time::timeout(Duration::from_secs(5), task) + .await + .expect("the stream finishes") + .expect("the stream task does not panic") + } + + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › doStream › should require channels when streaming multichannel audio + #[tokio::test] + async fn should_require_channels_when_streaming_multichannel_audio() { + let (_handle, socket) = new_socket(); + let connector = MockConnector::with_socket(socket); + let (audio, _) = Audio::new(vec![vec![1, 2, 3]], true); + + // Upstream's `argument: 'providerOptions'` is not in aimux's error. + let error = transcription_model_with(Arc::clone(&connector), None) + .do_stream(stream_options(audio, Some(json!({ "multichannel": true })))) + .expect("the model has do_stream") + .await + .expect_err("multichannel audio needs channels"); + + assert!( + matches!( + &error, + AiMuxError::InvalidArgument(message) + if message == "providerOptions.xai.channels is required when providerOptions.xai.multichannel is true" + ), + "{error:?}" + ); + assert!(connector.connects().is_empty(), "no socket is opened"); + } + + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › doStream › should stream xAI STT over WebSocket + #[tokio::test] + async fn should_stream_xai_stt_over_websocket() { + let (handle, socket) = new_socket(); + let connector = MockConnector::with_socket(socket); + let (audio, _) = Audio::new(vec![vec![1, 2, 3]], true); + + let before = chrono::Utc::now().timestamp_millis(); + let result = transcription_model_with(Arc::clone(&connector), None) + .do_stream(stream_options( + audio, + Some(json!({ + "language": "en", + "diarize": true, + "keyterm": ["AI SDK", "Grok"], + "streaming": { + "interimResults": true, + "endpointing": 500, + "smartTurn": 0.7, + "smartTurnTimeout": 3000, + }, + })), + )) + .expect("the model has do_stream") + .await + .expect("do_stream succeeds"); + + let connects = connector.connects(); + assert_eq!(connects.len(), 1); + assert_eq!( + connects[0].0, + "wss://api.x.ai/v1/stt?sample_rate=16000&encoding=pcm&language=en&diarize=true&interim_results=true&endpointing=500&smart_turn=0.7&smart_turn_timeout=3000&keyterm=AI+SDK&keyterm=Grok" + ); + assert_eq!( + header(&connects[0].1, "authorization"), + Some("Bearer test-api-key") + ); + assert!( + handle.frames().is_empty(), + "nothing is sent before transcript.created" + ); + + let response = result.response.expect("the stream result has a response"); + let collecting = tokio::spawn(result.stream.collect::>()); + handle.message(json!({ "type": "transcript.created" })); + settle(|| handle.frames().len() == 2).await; + + let frames = handle.frames(); + assert_eq!(frames[0], Frame::Binary(vec![1, 2, 3])); + let Frame::Text(done) = &frames[1] else { + panic!("expected the audio.done text frame, got {:?}", frames[1]); + }; + assert_eq!( + serde_json::from_str::(done).unwrap(), + json!({ "type": "audio.done" }) + ); + + handle.message(json!({ + "type": "transcript.partial", + "text": "Hel", + "is_final": false, + "speech_final": false, + "start": 0, + "duration": 0.5, + })); + handle.message(json!({ + "type": "transcript.partial", + "text": "Hello", + "is_final": true, + "speech_final": true, + "start": 0, + "duration": 1, + })); + handle.message(json!({ + "type": "transcript.done", + "text": "Hello", + "duration": 1, + })); + + let parts = finished(collecting).await; + let after = chrono::Utc::now().timestamp_millis(); + assert_eq!( + part_values(&parts), + vec![ + json!({ "type": "stream-start", "warnings": [] }), + json!({ + "type": "transcript-partial", + "text": "Hel", + "startSecond": 0.0, + "durationInSeconds": 0.5, + }), + json!({ + "type": "transcript-final", + "text": "Hello", + "startSecond": 0.0, + "endSecond": 1.0, + }), + json!({ + "type": "finish", + "text": "Hello", + "segments": [], + "language": "en", + "durationInSeconds": 1.0, + }), + ] + ); + + // Upstream's `{ timestamp: testDate, modelId: '' }`, with the stamp + // checked to fall within the call. + let timestamp = response + .timestamp + .expect("the response has a timestamp") + .unix_millis(); + assert!( + before <= timestamp && timestamp <= after, + "timestamp {timestamp} is outside the call ({before}..={after})" + ); + assert_eq!(response.model_id.as_deref(), Some("")); + assert!(response.headers.is_none() && response.body.is_none()); + } + + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › doStream › should strip undefined header values before the WebSocket constructor + #[tokio::test] + async fn should_strip_undefined_header_values_before_the_web_socket_constructor() { + let (_handle, socket) = new_socket(); + let connector = MockConnector::with_socket(socket); + // `'X-Unset': undefined` is given at provider level: a per-call header + // cannot hold an undefined value. + let model = transcription_model_with( + Arc::clone(&connector), + Some(HashMap::from([("X-Unset".to_string(), None)])), + ); + let (audio, _) = Audio::new(vec![vec![1, 2, 3]], true); + let mut options = stream_options(audio, None); + options.headers = Some(HashMap::from([( + "Custom-Header".to_string(), + "custom-value".to_string(), + )])); + + let result = model + .do_stream(options) + .expect("the model has do_stream") + .await + .expect("do_stream succeeds"); + drop(result.stream); + + let connects = connector.connects(); + let headers = &connects[0].1; + assert!(header(headers, "x-unset").is_none(), "{headers:?}"); + assert_eq!( + header(headers, "authorization"), + Some("Bearer test-api-key") + ); + assert_eq!(header(headers, "custom-header"), Some("custom-value")); + } + + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › doStream › should cancel the audio stream when the WebSocket constructor throws + #[tokio::test] + async fn should_cancel_the_audio_stream_when_the_web_socket_constructor_throws() { + let connector = MockConnector::failing("constructor failed"); + let (audio, cancelled) = Audio::new(Vec::new(), false); + + let result = transcription_model_with(connector, None) + .do_stream(stream_options(audio, None)) + .expect("the model has do_stream") + .await + .expect("doStream resolves, and the connect failure is read from its stream"); + + let parts = finished(tokio::spawn(result.stream.collect::>())).await; + let error = first_error(&parts); + assert!(error.to_string().contains("constructor failed"), "{error}"); + assert!( + cancelled.load(Ordering::SeqCst), + "the audio stream is cancelled" + ); + } + + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › doStream › should cancel the audio stream when an audio send throws mid-stream + #[tokio::test] + async fn should_cancel_the_audio_stream_when_an_audio_send_throws_mid_stream() { + let (handle, socket) = new_socket(); + handle.fail_binary_sends(); + let connector = MockConnector::with_socket(socket); + let (audio, cancelled) = Audio::new(vec![vec![1, 2, 3]], false); + + let result = transcription_model_with(connector, None) + .do_stream(stream_options(audio, None)) + .expect("the model has do_stream") + .await + .expect("do_stream succeeds"); + + let collecting = tokio::spawn(result.stream.collect::>()); + handle.message(json!({ "type": "transcript.created" })); + let parts = finished(collecting).await; + + let error = first_error(&parts); + assert!(error.to_string().contains("send failed"), "{error}"); + assert!( + cancelled.load(Ordering::SeqCst), + "the audio stream is cancelled" + ); + } + + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › doStream › should join finalized utterances per channel when transcript.done is empty + #[tokio::test] + async fn should_join_finalized_utterances_per_channel_when_transcript_done_is_empty() { + let (handle, socket) = new_socket(); + let connector = MockConnector::with_socket(socket); + let (audio, _) = Audio::new(vec![vec![1, 2, 3]], true); + + let result = transcription_model_with(connector, None) + .do_stream(stream_options(audio, None)) + .expect("the model has do_stream") + .await + .expect("do_stream succeeds"); + + let collecting = tokio::spawn(result.stream.collect::>()); + handle.message(json!({ "type": "transcript.created" })); + handle.message(json!({ + "type": "transcript.partial", + "text": "First utterance.", + "is_final": true, + "speech_final": true, + "start": 0, + "duration": 1, + })); + handle.message(json!({ + "type": "transcript.partial", + "text": "Second utterance.", + "is_final": true, + "speech_final": true, + "start": 1, + "duration": 1, + })); + handle.message(json!({ "type": "transcript.done", "text": "", "duration": 2 })); + + let parts = part_values(&finished(collecting).await); + let finish = parts.last().expect("the stream has parts"); + assert_eq!(finish["type"], "finish"); + assert_eq!(finish["text"], "First utterance. Second utterance."); + } + + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › doStream › should error the stream with the server message on error events + #[tokio::test] + async fn should_error_the_stream_with_the_server_message_on_error_events() { + let (handle, socket) = new_socket(); + let connector = MockConnector::with_socket(socket); + let (audio, _) = Audio::new(vec![vec![1, 2, 3]], true); + + let result = transcription_model_with(connector, None) + .do_stream(stream_options(audio, None)) + .expect("the model has do_stream") + .await + .expect("do_stream succeeds"); + + let collecting = tokio::spawn(result.stream.collect::>()); + handle.message(json!({ "type": "transcript.created" })); + settle(|| handle.frames().len() == 2).await; + handle.message(json!({ "type": "error", "message": "invalid sample_rate" })); + let parts = finished(collecting).await; + + let error = first_error(&parts); + assert!(error.to_string().contains("invalid sample_rate"), "{error}"); + assert!( + handle.frames().contains(&Frame::Close), + "the socket is closed after the error" + ); + } + + /// upstream: @ai-sdk/xai src/xai-transcription-model.test.ts › doStream › should close the WebSocket and stop reading audio when the stream is cancelled + #[tokio::test] + async fn should_close_the_web_socket_and_stop_reading_audio_when_the_stream_is_cancelled() { + let (handle, socket) = new_socket(); + let connector = MockConnector::with_socket(socket); + let (audio, cancelled) = Audio::new(Vec::new(), false); + + let result = transcription_model_with(connector, None) + .do_stream(stream_options(audio, None)) + .expect("the model has do_stream") + .await + .expect("do_stream succeeds"); + + let mut stream = result.stream; + handle.message(json!({ "type": "transcript.created" })); + let first = stream.next().await; + assert!( + matches!(first, Some(Ok(TranscriptionStreamPart::StreamStart { .. }))), + "{first:?}" + ); + + // Upstream `result.stream.cancel()`: the stream is dropped; its + // `await flush()`: the close frame goes out on its own task. + drop(stream); + settle(|| handle.frames().contains(&Frame::Close)).await; + + assert!( + handle.frames().contains(&Frame::Close), + "no Close frame was sent when the stream was cancelled" + ); + assert!( + cancelled.load(Ordering::SeqCst), + "the audio stream is cancelled" + ); + } +} diff --git a/aimux-providers/src/xai/tests/video_model.rs b/aimux-providers/src/xai/tests/video_model.rs new file mode 100644 index 000000000..38a7f8ee3 --- /dev/null +++ b/aimux-providers/src/xai/tests/video_model.rs @@ -0,0 +1,1173 @@ +//! Ports of `src/xai-video-model.test.ts` from `@ai-sdk/xai@5.0.12`: the +//! `doStart` and `doStatus` cases that `tests/xai_video_test.rs` does not cover. +//! +//! Harness differences from the TypeScript (expectations are unchanged): +//! - `createTestServer` is the in-crate [`MockFetch`]: it answers requests in +//! order and records each one. `server.calls[0]` is the first recorded +//! request, `requestBodyJson` is [`Seen::json_body`]. +//! - `toStrictEqual` / `toEqual` compare JSON with [`assert_strict_eq`], and +//! `toMatchObject` is [`assert_matches`]. Both compare every number as an +//! `f64`, because JavaScript has one number type (`5` and `5.0` are equal). +//! - `_internal.currentDate` cannot be injected: `XaiVideoModel` reads the clock +//! itself. The two response-metadata cases check that the timestamp lies +//! between two clock reads, instead of `2024-01-01T00:00:00Z`. +//! - A terminal status (`status: 'error'` upstream) is `Err(AiMuxError::ApiCall)` +//! here, non-retryable. Its message is compared. The error arm carries no +//! `VideoResponse`, so the upstream `result.response` check of the expired +//! case is not ported. +//! - Upstream's `if (result.status === 'completed')` guards in the metadata +//! cases are asserted directly, so a result that is not completed fails. +//! - `createModel`'s default `api-key` header is not used: no case here checks a +//! header, and the Rust provider sends `Authorization: Bearer`. + +use std::sync::Arc; + +use serde_json::{Value, json}; + +use aimux_core::Timestamp; +use aimux_core::error::AiMuxError; +use aimux_core::shared::{AspectRatio, Size, Warning, provider_namespace}; +use aimux_core::video_model::{ + VideoCallOptions, VideoFile, VideoFileData, VideoModel, VideoOperationStart, + VideoOperationStatus, VideoResult, +}; + +use crate::test_support::mock_fetch::{Canned, MockFetch, Seen}; +use crate::xai::options::NAMESPACE; +use crate::xai::{XAIProviderSettings, XaiVideoModel, create_xai}; + +// ---------------------------------------------------------------- helpers + +const BASE_URL: &str = "https://api.example.com"; +const PROMPT: &str = "A chicken flying into the sunset"; +const SRC: &str = "https://example.com/source-video.mp4"; +const VIDEO_URL: &str = "https://vidgen.x.ai/output/video-001.mp4"; +const V1: &str = "grok-imagine-video"; +const V15: &str = "grok-imagine-video-1.5"; + +/// `createVideoResponse`. +fn create_video_response() -> Value { + json!({ "request_id": "req-123" }) +} + +/// `doneStatusResponse`. +fn done_status_response() -> Value { + json!({ + "status": "done", + "video": { "url": VIDEO_URL, "duration": 5, "respect_moderation": true }, + "model": V1, + "progress": 100 + }) +} + +/// `createModel({ modelId })`, on the mock transport. +fn model(fetch: &Arc, model_id: &str) -> XaiVideoModel { + create_xai(XAIProviderSettings { + api_key: Some("test-key".into()), + base_url: Some(BASE_URL.into()), + fetch: Some(fetch.transport()), + ..Default::default() + }) + .expect("xai provider should build") + .video(model_id) +} + +/// `defaultOptions`: the prompt and `n: 1`, nothing else. +fn default_options() -> VideoCallOptions { + VideoCallOptions::new(PROMPT) +} + +/// `providerOptions: { xai: value }`. +fn with_xai(mut options: VideoCallOptions, value: Value) -> VideoCallOptions { + options.provider_options = + provider_namespace(NAMESPACE, value).expect("xai options are an object"); + options +} + +/// `doStatus` takes no prompt upstream. +fn status_options() -> VideoCallOptions { + VideoCallOptions { + prompt: None, + ..VideoCallOptions::new(PROMPT) + } +} + +fn url_file(url: &str, media_type: Option<&str>) -> VideoFile { + VideoFile::Url { + url: url.into(), + media_type: media_type.map(str::to_owned), + } +} + +fn ratio(s: &str) -> Option { + Some(AspectRatio::parse(s).unwrap()) +} + +fn size(s: &str) -> Option { + Some(Size::parse(s).unwrap()) +} + +/// `doStart` against a mock that answers the generation request with `answer`. +/// Returns the result and every request the mock saw. +async fn do_start_answered( + model_id: &str, + options: &VideoCallOptions, + answer: Canned, +) -> (Result, Vec) { + let fetch = MockFetch::new(vec![answer]); + let result = model(&fetch, model_id).do_start(options).await; + (result, fetch.seen()) +} + +/// `doStart` against a server that returns `request_id: "req-123"`, with the +/// one request it sent. +async fn start(model_id: &str, options: &VideoCallOptions) -> (VideoOperationStart, Seen) { + let (result, mut seen) = + do_start_answered(model_id, options, Canned::json(&create_video_response())).await; + (result.expect("doStart should succeed"), seen.remove(0)) +} + +/// `doStatus` against a mock that answers with `answer`. Returns the result and +/// every request the mock saw. +async fn do_status_answered( + request_id: &str, + answer: Canned, +) -> (Result, Vec) { + let fetch = MockFetch::new(vec![answer]); + let result = model(&fetch, V1) + .do_status(&json!({ "requestId": request_id }), &status_options()) + .await; + (result, fetch.seen()) +} + +/// The `completed` arm of a status. +fn completed(result: Result) -> VideoResult { + match result { + Ok(VideoOperationStatus::Completed(done)) => done, + other => panic!("expected a completed status, got {other:?}"), + } +} + +/// The message of an error status. Upstream `status: 'error'` is `Err` here. +fn error_message(result: Result) -> String { + match result { + Err(AiMuxError::ApiCall(error)) => error.message, + other => panic!("expected an error status, got {other:?}"), + } +} + +/// `providerMetadata`, as JSON. +fn metadata_json(done: &VideoResult) -> Value { + serde_json::to_value(&done.provider_metadata).expect("metadata serializes") +} + +/// Every number as an `f64`: JavaScript has one number type. +fn js(value: &Value) -> Value { + match value { + Value::Number(n) => n.as_f64().map_or(Value::Null, |f| json!(f)), + Value::Array(items) => Value::Array(items.iter().map(js).collect()), + Value::Object(map) => Value::Object(map.iter().map(|(k, v)| (k.clone(), js(v))).collect()), + other => other.clone(), + } +} + +/// `toStrictEqual` / `toEqual` on JSON values. +fn assert_strict_eq(actual: &Value, expected: &Value) { + assert_eq!(js(actual), js(expected), "actual: {actual}"); +} + +/// `toMatchObject`: every key of `expected` is in `actual` and matches. Arrays +/// have equal length and match element by element. +fn assert_matches(actual: &Value, expected: &Value) { + match (actual, expected) { + (Value::Object(actual_map), Value::Object(expected_map)) => { + for (key, want) in expected_map { + let got = actual_map + .get(key) + .unwrap_or_else(|| panic!("missing key `{key}` in {actual}")); + assert_matches(got, want); + } + } + (Value::Array(actual_items), Value::Array(expected_items)) => { + assert_eq!(actual_items.len(), expected_items.len(), "in {actual}"); + for (got, want) in actual_items.iter().zip(expected_items) { + assert_matches(got, want); + } + } + _ => assert_eq!(js(actual), js(expected)), + } +} + +/// The `details` of each `unsupported` warning on `feature`, `""` when absent. +fn unsupported_details<'a>(warnings: &'a [Warning], feature: &str) -> Vec<&'a str> { + warnings + .iter() + .filter_map(|w| match w { + Warning::Unsupported { + feature: f, + details, + } if f == feature => Some(details.as_deref().unwrap_or("")), + _ => None, + }) + .collect() +} + +/// `toContainEqual(objectContaining({ type: 'unsupported', feature }))`. +fn has_unsupported(warnings: &[Warning], feature: &str) -> bool { + !unsupported_details(warnings, feature).is_empty() +} + +/// `toContainEqual(objectContaining({ type: 'unsupported', feature, details: +/// stringContaining(needle) }))`. +fn has_unsupported_containing(warnings: &[Warning], feature: &str, needle: &str) -> bool { + unsupported_details(warnings, feature) + .iter() + .any(|details| details.contains(needle)) +} + +/// `not.toContainEqual(objectContaining({ feature }))`: no warning of any type +/// names `feature`. +fn names_feature(warnings: &[Warning], feature: &str) -> bool { + warnings.iter().any(|w| { + matches!( + w, + Warning::Unsupported { feature: f, .. } | Warning::Compatibility { feature: f, .. } + if f == feature + ) + }) +} + +mod xai_video_model { + use super::*; + + mod constructor { + use super::*; + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › constructor › should expose correct provider and model information + #[tokio::test] + async fn should_expose_correct_provider_and_model_information() { + let fetch = MockFetch::new(vec![]); + let video = model(&fetch, V1); + assert_eq!(video.provider(), "xai.video"); + assert_eq!(video.model_id(), V1); + assert_eq!(video.specification_version(), "v4"); + assert_eq!(video.max_videos_per_call(), Some(1)); + } + } + + mod do_start { + use super::*; + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should return operation with requestId + #[tokio::test] + async fn should_return_operation_with_request_id() { + let (result, _) = start(V1, &default_options()).await; + assert_strict_eq(&result.operation, &json!({ "requestId": "req-123" })); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should pass correct request body + #[tokio::test] + async fn should_pass_correct_request_body() { + let (_, request) = start(V1, &default_options()).await; + assert_eq!(request.method, "POST"); + assert_eq!(request.url, format!("{BASE_URL}/videos/generations")); + assert_strict_eq( + &request.json_body(), + &json!({ "model": V1, "prompt": PROMPT }), + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should use edits endpoint for video editing (legacy videoUrl without mode) + #[tokio::test] + async fn should_use_edits_endpoint_for_video_editing_legacy_video_url_without_mode() { + let options = with_xai(default_options(), json!({ "videoUrl": SRC })); + let (_, request) = start(V1, &options).await; + assert_eq!(request.method, "POST"); + assert_eq!(request.url, format!("{BASE_URL}/videos/edits")); + assert_matches(&request.json_body(), &json!({ "video": { "url": SRC } })); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should use edits endpoint for video editing with explicit mode + #[tokio::test] + async fn should_use_edits_endpoint_for_video_editing_with_explicit_mode() { + let options = with_xai( + default_options(), + json!({ "mode": "edit-video", "videoUrl": SRC }), + ); + let (_, request) = start(V1, &options).await; + assert_eq!(request.method, "POST"); + assert_eq!(request.url, format!("{BASE_URL}/videos/edits")); + assert_matches(&request.json_body(), &json!({ "video": { "url": SRC } })); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should use extensions endpoint for extend-video mode + #[tokio::test] + async fn should_use_extensions_endpoint_for_extend_video_mode() { + let options = with_xai( + default_options(), + json!({ "mode": "extend-video", "videoUrl": SRC }), + ); + let (_, request) = start(V1, &options).await; + assert_eq!(request.method, "POST"); + assert_eq!(request.url, format!("{BASE_URL}/videos/extensions")); + assert_matches(&request.json_body(), &json!({ "video": { "url": SRC } })); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should include response metadata + #[tokio::test] + async fn should_include_response_metadata() { + // Upstream injects `currentDate` and expects `timestamp: testDate`. + // The clock is read inside the model here, so the timestamp is + // checked to lie between two reads. + let before = Timestamp::now(); + let (result, _) = start(V1, &default_options()).await; + let after = Timestamp::now(); + let timestamp = result.response.timestamp.expect("timestamp is set"); + assert!(before <= timestamp && timestamp <= after); + assert_eq!(result.response.model_id.as_deref(), Some(V1)); + assert!(result.response.headers.is_some()); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should send duration in request body + #[tokio::test] + async fn should_send_duration_in_request_body() { + let mut options = default_options(); + options.duration = Some(10); + let (_, request) = start(V1, &options).await; + assert_matches(&request.json_body(), &json!({ "duration": 10 })); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should send aspect_ratio in request body + #[tokio::test] + async fn should_send_aspect_ratio_in_request_body() { + let mut options = default_options(); + options.aspect_ratio = ratio("9:16"); + let (_, request) = start(V1, &options).await; + assert_matches(&request.json_body(), &json!({ "aspect_ratio": "9:16" })); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should map SDK resolution 1280x720 to 720p + #[tokio::test] + async fn should_map_sdk_resolution_1280x720_to_720p() { + let mut options = default_options(); + options.resolution = size("1280x720"); + let (_, request) = start(V1, &options).await; + assert_matches(&request.json_body(), &json!({ "resolution": "720p" })); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should map SDK resolution 854x480 to 480p + #[tokio::test] + async fn should_map_sdk_resolution_854x480_to_480p() { + let mut options = default_options(); + options.resolution = size("854x480"); + let (_, request) = start(V1, &options).await; + assert_matches(&request.json_body(), &json!({ "resolution": "480p" })); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should map SDK resolution 640x480 to 480p + #[tokio::test] + async fn should_map_sdk_resolution_640x480_to_480p() { + let mut options = default_options(); + options.resolution = size("640x480"); + let (_, request) = start(V1, &options).await; + assert_matches(&request.json_body(), &json!({ "resolution": "480p" })); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should map SDK resolution 1920x1080 to 1080p + #[tokio::test] + async fn should_map_sdk_resolution_1920x1080_to_1080p() { + let mut options = default_options(); + options.resolution = size("1920x1080"); + let (_, request) = start(V15, &options).await; + assert_matches(&request.json_body(), &json!({ "resolution": "1080p" })); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should pass through provider option resolution 1080p + #[tokio::test] + async fn should_pass_through_provider_option_resolution_1080p() { + let options = with_xai(default_options(), json!({ "resolution": "1080p" })); + let (_, request) = start(V15, &options).await; + assert_matches(&request.json_body(), &json!({ "resolution": "1080p" })); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should warn when SDK resolution 1920x1080 is used with grok-imagine-video + #[tokio::test] + async fn should_warn_when_sdk_resolution_1920x1080_is_used_with_grok_imagine_video() { + let mut options = default_options(); + options.resolution = size("1920x1080"); + let (result, request) = start(V1, &options).await; + assert_matches(&request.json_body(), &json!({ "resolution": "1080p" })); + assert!( + has_unsupported_containing( + &result.warnings, + "resolution", + "does not support 1080p" + ), + "{:?}", + result.warnings + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should warn when provider resolution 1080p is used with grok-imagine-video + #[tokio::test] + async fn should_warn_when_provider_resolution_1080p_is_used_with_grok_imagine_video() { + let options = with_xai(default_options(), json!({ "resolution": "1080p" })); + let (result, request) = start(V1, &options).await; + assert_matches(&request.json_body(), &json!({ "resolution": "1080p" })); + assert!( + has_unsupported_containing( + &result.warnings, + "resolution", + "does not support 1080p" + ), + "{:?}", + result.warnings + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should not warn about 1080p with grok-imagine-video-1.5 + #[tokio::test] + async fn should_not_warn_about_1080p_with_grok_imagine_video_1_5() { + let mut options = default_options(); + options.resolution = size("1920x1080"); + let (result, _) = start(V15, &options).await; + assert!(result.warnings.is_empty(), "{:?}", result.warnings); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should prefer provider option resolution over SDK resolution + #[tokio::test] + async fn should_prefer_provider_option_resolution_over_sdk_resolution() { + let mut options = with_xai(default_options(), json!({ "resolution": "480p" })); + options.resolution = size("1280x720"); + let (_, request) = start(V1, &options).await; + assert_matches(&request.json_body(), &json!({ "resolution": "480p" })); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should warn for unrecognized resolution format + #[tokio::test] + async fn should_warn_for_unrecognized_resolution_format() { + let mut options = default_options(); + options.resolution = size("2560x1440"); + let (result, _) = start(V1, &options).await; + assert!( + has_unsupported(&result.warnings, "resolution"), + "{:?}", + result.warnings + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should warn and omit body resolution for completely unknown format + #[tokio::test] + async fn should_warn_and_omit_body_resolution_for_completely_unknown_format() { + let mut options = default_options(); + options.resolution = size("3840x2160"); + let (result, request) = start(V1, &options).await; + assert!(request.json_body().get("resolution").is_none()); + assert!( + has_unsupported(&result.warnings, "resolution"), + "{:?}", + result.warnings + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should send image object from URL-based image input + #[tokio::test] + async fn should_send_image_object_from_url_based_image_input() { + let mut options = default_options(); + options.image = Some(url_file("https://example.com/image.png", None)); + let (_, request) = start(V1, &options).await; + assert_matches( + &request.json_body(), + &json!({ "image": { "url": "https://example.com/image.png" } }), + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should send image object with data URI from file data + #[tokio::test] + async fn should_send_image_object_with_data_uri_from_file_data() { + let mut options = default_options(); + options.image = Some(VideoFile::File { + media_type: "image/png".into(), + // PNG magic bytes. + data: VideoFileData::Binary(vec![137, 80, 78, 71]), + }); + let (_, request) = start(V1, &options).await; + assert_matches( + &request.json_body(), + &json!({ "image": { "url": "data:image/png;base64,iVBORw==" } }), + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should send image object with data URI from base64 string + #[tokio::test] + async fn should_send_image_object_with_data_uri_from_base64_string() { + let mut options = default_options(); + options.image = Some(VideoFile::File { + media_type: "image/jpeg".into(), + data: VideoFileData::Base64("aGVsbG8=".into()), + }); + let (_, request) = start(V1, &options).await; + assert_matches( + &request.json_body(), + &json!({ "image": { "url": "data:image/jpeg;base64,aGVsbG8=" } }), + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should warn about duration in edit mode + #[tokio::test] + async fn should_warn_about_duration_in_edit_mode() { + let mut options = with_xai( + default_options(), + json!({ "mode": "edit-video", "videoUrl": SRC }), + ); + options.duration = Some(10); + let (result, _) = start(V1, &options).await; + assert!( + has_unsupported(&result.warnings, "duration"), + "{:?}", + result.warnings + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should warn about aspectRatio in edit mode + #[tokio::test] + async fn should_warn_about_aspect_ratio_in_edit_mode() { + let mut options = with_xai( + default_options(), + json!({ "mode": "edit-video", "videoUrl": SRC }), + ); + options.aspect_ratio = ratio("16:9"); + let (result, _) = start(V1, &options).await; + assert!( + has_unsupported(&result.warnings, "aspectRatio"), + "{:?}", + result.warnings + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should warn about resolution in edit mode + #[tokio::test] + async fn should_warn_about_resolution_in_edit_mode() { + let mut options = with_xai( + default_options(), + json!({ "mode": "edit-video", "videoUrl": SRC }), + ); + options.resolution = size("1280x720"); + let (result, _) = start(V1, &options).await; + assert!( + has_unsupported(&result.warnings, "resolution"), + "{:?}", + result.warnings + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should not warn about duration outside edit mode + #[tokio::test] + async fn should_not_warn_about_duration_outside_edit_mode() { + let mut options = default_options(); + options.duration = Some(10); + let (result, _) = start(V1, &options).await; + assert!(!names_feature(&result.warnings, "duration")); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should not warn about aspectRatio outside edit mode + #[tokio::test] + async fn should_not_warn_about_aspect_ratio_outside_edit_mode() { + let mut options = default_options(); + options.aspect_ratio = ratio("16:9"); + let (result, _) = start(V1, &options).await; + assert!(!names_feature(&result.warnings, "aspectRatio")); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should not warn about resolution outside edit mode + #[tokio::test] + async fn should_not_warn_about_resolution_outside_edit_mode() { + let mut options = default_options(); + options.resolution = size("1280x720"); + let (result, _) = start(V1, &options).await; + assert!(!names_feature(&result.warnings, "resolution")); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should omit duration, aspect_ratio, and resolution from body in edit mode + #[tokio::test] + async fn should_omit_duration_aspect_ratio_and_resolution_from_body_in_edit_mode() { + let mut options = with_xai( + default_options(), + json!({ "mode": "edit-video", "videoUrl": SRC }), + ); + options.duration = Some(10); + options.aspect_ratio = ratio("16:9"); + options.resolution = size("1280x720"); + let (_, request) = start(V1, &options).await; + let body = request.json_body(); + assert!(body.get("duration").is_none()); + assert!(body.get("aspect_ratio").is_none()); + assert!(body.get("resolution").is_none()); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should return empty warnings for supported features + #[tokio::test] + async fn should_return_empty_warnings_for_supported_features() { + let (result, _) = start(V1, &default_options()).await; + assert!(result.warnings.is_empty(), "{:?}", result.warnings); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should warn when n > 1 + #[tokio::test] + async fn should_warn_when_n_greater_than_1() { + let mut options = default_options(); + options.n = 3; + let (result, _) = start(V1, &options).await; + assert!( + has_unsupported(&result.warnings, "n"), + "{:?}", + result.warnings + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should not warn when n is 1 + #[tokio::test] + async fn should_not_warn_when_n_is_1() { + let mut options = default_options(); + options.n = 1; + let (result, _) = start(V1, &options).await; + assert!(!names_feature(&result.warnings, "n")); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should allow duration in extension mode + #[tokio::test] + async fn should_allow_duration_in_extension_mode() { + let mut options = with_xai( + default_options(), + json!({ "mode": "extend-video", "videoUrl": SRC }), + ); + options.duration = Some(6); + let (_, request) = start(V1, &options).await; + assert_matches(&request.json_body(), &json!({ "duration": 6 })); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should warn about aspectRatio in extension mode + #[tokio::test] + async fn should_warn_about_aspect_ratio_in_extension_mode() { + let mut options = with_xai( + default_options(), + json!({ "mode": "extend-video", "videoUrl": SRC }), + ); + options.aspect_ratio = ratio("16:9"); + let (result, _) = start(V1, &options).await; + assert!( + has_unsupported(&result.warnings, "aspectRatio"), + "{:?}", + result.warnings + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should warn about resolution in extension mode + #[tokio::test] + async fn should_warn_about_resolution_in_extension_mode() { + let mut options = with_xai( + default_options(), + json!({ "mode": "extend-video", "videoUrl": SRC }), + ); + options.resolution = size("1280x720"); + let (result, _) = start(V1, &options).await; + assert!( + has_unsupported(&result.warnings, "resolution"), + "{:?}", + result.warnings + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should omit aspect_ratio and resolution from body in extension mode + #[tokio::test] + async fn should_omit_aspect_ratio_and_resolution_from_body_in_extension_mode() { + let mut options = with_xai( + default_options(), + json!({ "mode": "extend-video", "videoUrl": SRC }), + ); + options.aspect_ratio = ratio("16:9"); + options.resolution = size("1280x720"); + let (_, request) = start(V1, &options).await; + let body = request.json_body(); + assert!(body.get("aspect_ratio").is_none()); + assert!(body.get("resolution").is_none()); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should not warn about duration in extension mode + #[tokio::test] + async fn should_not_warn_about_duration_in_extension_mode() { + let mut options = with_xai( + default_options(), + json!({ "mode": "extend-video", "videoUrl": SRC }), + ); + options.duration = Some(6); + let (result, _) = start(V1, &options).await; + assert!(!names_feature(&result.warnings, "duration")); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should warn about provider-level resolution in extension mode + #[tokio::test] + async fn should_warn_about_provider_level_resolution_in_extension_mode() { + let options = with_xai( + default_options(), + json!({ "mode": "extend-video", "videoUrl": SRC, "resolution": "720p" }), + ); + let (result, request) = start(V1, &options).await; + assert!(request.json_body().get("resolution").is_none()); + assert!( + has_unsupported(&result.warnings, "resolution"), + "{:?}", + result.warnings + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should send reference_images array to /videos/generations with explicit mode + #[tokio::test] + async fn should_send_reference_images_array_to_videos_generations_with_explicit_mode() { + let options = with_xai( + default_options(), + json!({ + "mode": "reference-to-video", + "referenceImageUrls": [ + "https://example.com/ref1.jpg", + "https://example.com/ref2.jpg" + ] + }), + ); + let (_, request) = start(V1, &options).await; + assert_eq!(request.method, "POST"); + assert_eq!(request.url, format!("{BASE_URL}/videos/generations")); + assert_matches( + &request.json_body(), + &json!({ + "reference_images": [ + { "url": "https://example.com/ref1.jpg" }, + { "url": "https://example.com/ref2.jpg" } + ] + }), + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should downgrade R2V 1080p to 720p with a warning + #[tokio::test] + async fn should_downgrade_r2v_1080p_to_720p_with_a_warning() { + let options = with_xai( + default_options(), + json!({ + "mode": "reference-to-video", + "referenceImageUrls": ["https://example.com/ref1.jpg"], + "resolution": "1080p" + }), + ); + let (result, request) = start(V15, &options).await; + assert_matches(&request.json_body(), &json!({ "resolution": "720p" })); + assert!( + has_unsupported(&result.warnings, "resolution"), + "{:?}", + result.warnings + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should downgrade R2V 1920x1080 from the SDK resolution to 720p + #[tokio::test] + async fn should_downgrade_r2v_1920x1080_from_the_sdk_resolution_to_720p() { + let mut options = with_xai( + default_options(), + json!({ + "mode": "reference-to-video", + "referenceImageUrls": ["https://example.com/ref1.jpg"] + }), + ); + options.resolution = size("1920x1080"); + let (result, request) = start(V15, &options).await; + assert_matches(&request.json_body(), &json!({ "resolution": "720p" })); + assert!( + has_unsupported(&result.warnings, "resolution"), + "{:?}", + result.warnings + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should separate image and audio inputReferences + #[tokio::test] + async fn should_separate_image_and_audio_input_references() { + let mut options = default_options(); + options.input_references = Some(vec![ + url_file("https://example.com/ref1.jpg", None), + url_file("https://example.com/voice.mp3", Some("audio/mpeg")), + ]); + let (result, request) = start(V15, &options).await; + let body = request.json_body(); + assert_matches( + &body, + &json!({ + "reference_images": [{ "url": "https://example.com/ref1.jpg" }], + "reference_audios": [{ "url": "https://example.com/voice.mp3" }] + }), + ); + assert_eq!(body["reference_images"].as_array().map(Vec::len), Some(1)); + assert!(result.warnings.is_empty(), "{:?}", result.warnings); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should support audio-only reference-to-video + #[tokio::test] + async fn should_support_audio_only_reference_to_video() { + let mut options = default_options(); + options.input_references = Some(vec![url_file( + "https://example.com/voice.mp3", + Some("audio/mpeg"), + )]); + let (result, request) = start(V15, &options).await; + let body = request.json_body(); + assert_matches( + &body, + &json!({ "reference_audios": [{ "url": "https://example.com/voice.mp3" }] }), + ); + assert!(body.get("reference_images").is_none()); + assert!(result.warnings.is_empty(), "{:?}", result.warnings); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should not send an empty reference_images array for video-only inputReferences + #[tokio::test] + async fn should_not_send_an_empty_reference_images_array_for_video_only_input_references() { + let mut options = default_options(); + options.input_references = Some(vec![url_file( + "https://example.com/clip.mp4", + Some("video/mp4"), + )]); + let (_, request) = start(V15, &options).await; + assert!(request.json_body().get("reference_images").is_none()); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should combine a pinned first frame with an audio reference + #[tokio::test] + async fn should_combine_a_pinned_first_frame_with_an_audio_reference() { + let mut options = default_options(); + options.image = Some(url_file( + "https://example.com/start.jpg", + Some("image/jpeg"), + )); + options.input_references = Some(vec![url_file( + "https://example.com/voice.mp3", + Some("audio/mpeg"), + )]); + let (result, request) = start(V15, &options).await; + let body = request.json_body(); + assert_matches( + &body, + &json!({ + "image": { "url": "https://example.com/start.jpg" }, + "reference_audios": [{ "url": "https://example.com/voice.mp3" }] + }), + ); + assert!(body.get("reference_images").is_none()); + assert!(result.warnings.is_empty(), "{:?}", result.warnings); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should support explicit audio-only R2V without reference_images + #[tokio::test] + async fn should_support_explicit_audio_only_r2v_without_reference_images() { + let mut options = with_xai(default_options(), json!({ "mode": "reference-to-video" })); + options.input_references = Some(vec![url_file( + "https://example.com/voice.mp3", + Some("audio/mpeg"), + )]); + let (result, request) = start(V15, &options).await; + let body = request.json_body(); + assert!(body.get("reference_images").is_none()); + assert_matches( + &body, + &json!({ "reference_audios": [{ "url": "https://example.com/voice.mp3" }] }), + ); + assert!(result.warnings.is_empty(), "{:?}", result.warnings); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should fallback to reference-to-video mode when referenceImageUrls is set without mode + #[tokio::test] + async fn should_fallback_to_r2v_when_reference_image_urls_set_without_mode() { + let options = with_xai( + default_options(), + json!({ "referenceImageUrls": ["https://example.com/ref1.jpg"] }), + ); + let (_, request) = start(V1, &options).await; + assert_eq!(request.url, format!("{BASE_URL}/videos/generations")); + assert_matches( + &request.json_body(), + &json!({ "reference_images": [{ "url": "https://example.com/ref1.jpg" }] }), + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should send reference_audios for preset reference voices + #[tokio::test] + async fn should_send_reference_audios_for_preset_reference_voices() { + let options = with_xai( + default_options(), + json!({ + "mode": "reference-to-video", + "referenceImageUrls": ["https://example.com/ref1.jpg"], + "referenceVoiceIds": ["eve"] + }), + ); + let (_, request) = start(V15, &options).await; + assert_eq!(request.url, format!("{BASE_URL}/videos/generations")); + assert_matches( + &request.json_body(), + &json!({ + "reference_images": [{ "url": "https://example.com/ref1.jpg" }], + "reference_audios": [{ "voice_id": "eve" }] + }), + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should send up to 3 preset reference voices in order + #[tokio::test] + async fn should_send_up_to_3_preset_reference_voices_in_order() { + let options = with_xai( + default_options(), + json!({ + "mode": "reference-to-video", + "referenceImageUrls": ["https://example.com/ref1.jpg"], + "referenceVoiceIds": ["eve", "leo", "rex"] + }), + ); + let (_, request) = start(V15, &options).await; + assert_matches( + &request.json_body(), + &json!({ + "reference_audios": [ + { "voice_id": "eve" }, + { "voice_id": "leo" }, + { "voice_id": "rex" } + ] + }), + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should reject more than 3 preset reference voices + #[tokio::test] + async fn should_reject_more_than_3_preset_reference_voices() { + let options = with_xai( + default_options(), + json!({ + "mode": "reference-to-video", + "referenceImageUrls": ["https://example.com/ref1.jpg"], + "referenceVoiceIds": ["ara", "eve", "leo", "rex"] + }), + ); + let (result, _) = + do_start_answered(V15, &options, Canned::json(&create_video_response())).await; + assert!( + matches!(result, Err(AiMuxError::InvalidArgument(_))), + "{result:?}" + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should reject empty-string preset reference voice ids + #[tokio::test] + async fn should_reject_empty_string_preset_reference_voice_ids() { + let options = with_xai( + default_options(), + json!({ + "mode": "reference-to-video", + "referenceImageUrls": ["https://example.com/ref1.jpg"], + "referenceVoiceIds": [""] + }), + ); + let (result, _) = + do_start_answered(V15, &options, Canned::json(&create_video_response())).await; + assert!( + matches!(result, Err(AiMuxError::InvalidArgument(_))), + "{result:?}" + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStart › should never forward referenceVoiceIds as a raw body key + #[tokio::test] + async fn should_never_forward_reference_voice_ids_as_a_raw_body_key() { + let options = with_xai( + default_options(), + json!({ + "mode": "reference-to-video", + "referenceImageUrls": ["https://example.com/ref1.jpg"], + "referenceVoiceIds": ["eve"] + }), + ); + let (_, request) = start(V15, &options).await; + assert!(request.json_body().get("referenceVoiceIds").is_none()); + } + } + + mod do_status { + use super::*; + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStatus › should encode the request ID as a single URL path segment + #[tokio::test] + async fn should_encode_the_request_id_as_a_single_url_path_segment() { + // `encodeURIComponent('abc/../../internal')`. + let (_, requests) = + do_status_answered("abc/../../internal", Canned::json(&done_status_response())) + .await; + assert_eq!( + requests[0].url, + format!("{BASE_URL}/videos/abc%2F..%2F..%2Finternal") + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStatus › should preserve the $requestId request ID as a URL path segment + #[tokio::test] + async fn should_preserve_the_request_id_as_a_url_path_segment() { + // The dot segments are sent encoded twice, so URL parsing keeps them. + for (request_id, encoded_request_id) in [(".", "%252E"), ("..", "%252E%252E")] { + let (_, requests) = + do_status_answered(request_id, Canned::json(&done_status_response())).await; + assert_eq!( + requests[0].url, + format!("{BASE_URL}/videos/{encoded_request_id}"), + "requestId {request_id:?}" + ); + } + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStatus › should return completed with video data when done + #[tokio::test] + async fn should_return_completed_with_video_data_when_done() { + let (result, _) = + do_status_answered("req-123", Canned::json(&done_status_response())).await; + let done = completed(result); + assert_eq!(done.videos.len(), 1); + assert_strict_eq( + &serde_json::to_value(&done.videos[0]).expect("video serializes"), + &json!({ "type": "url", "url": VIDEO_URL, "mediaType": "video/mp4" }), + ); + assert_strict_eq( + &metadata_json(&done), + &json!({ + "xai": { + "requestId": "req-123", + "videoUrl": VIDEO_URL, + "duration": 5, + "progress": 100 + } + }), + ); + } + + /// partial: `doStatus › should return error status on expired`. The + /// upstream `result.response` check (`timestamp`, `modelId`, + /// `headers`) is not expressible: an error status is `Err(AiMuxError)` + /// here, which carries no `VideoResponse` (sub-feature in + /// `layout.toml`). + #[tokio::test] + async fn should_return_error_status_on_expired() { + let body = json!({ "status": "expired", "model": V1 }); + let (result, _) = do_status_answered("req-123", Canned::json(&body)).await; + assert_eq!(error_message(result), "Video generation request expired."); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStatus › should return error status on failed + #[tokio::test] + async fn should_return_error_status_on_failed() { + let body = json!({ + "status": "failed", + "model": V1, + "progress": 0, + "error": { "message": "Content policy violation" } + }); + let (result, _) = do_status_answered("req-123", Canned::json(&body)).await; + assert_eq!( + error_message(result), + "Video generation failed: Content policy violation" + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStatus › should report an error status when video URL missing on done + #[tokio::test] + async fn should_report_an_error_status_when_video_url_missing_on_done() { + let body = json!({ "status": "done", "video": null, "model": V1 }); + let (result, _) = do_status_answered("req-123", Canned::json(&body)).await; + assert_eq!( + error_message(result), + "Video generation completed but no video URL was returned." + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStatus › should report an error status when respect_moderation is false + #[tokio::test] + async fn should_report_an_error_status_when_respect_moderation_is_false() { + let body = json!({ + "status": "done", + "video": { "url": "", "respect_moderation": false }, + "model": V1 + }); + let (result, _) = do_status_answered("req-123", Canned::json(&body)).await; + assert_eq!( + error_message(result), + "Video generation was blocked due to a content policy violation." + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStatus › should include response metadata + #[tokio::test] + async fn should_include_response_metadata() { + // Upstream injects `currentDate` and expects `timestamp: testDate`. + // The clock is read inside the model here, so the timestamp is + // checked to lie between two reads. + let before = Timestamp::now(); + let (result, _) = + do_status_answered("req-123", Canned::json(&done_status_response())).await; + let after = Timestamp::now(); + let done = completed(result); + let timestamp = done.response.timestamp.expect("timestamp is set"); + assert!(before <= timestamp && timestamp <= after); + assert_eq!(done.response.model_id.as_deref(), Some(V1)); + assert!(done.response.headers.is_some()); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStatus › should include costInUsdTicks when returned in usage + #[tokio::test] + async fn should_include_cost_in_usd_ticks_when_returned_in_usage() { + let mut body = done_status_response(); + body["usage"] = json!({ "cost_in_usd_ticks": 4_000_000_000_u64 }); + let (result, _) = do_status_answered("req-123", Canned::json(&body)).await; + let done = completed(result); + assert_strict_eq( + &metadata_json(&done), + &json!({ + "xai": { + "requestId": "req-123", + "videoUrl": VIDEO_URL, + "duration": 5, + "progress": 100, + "costInUsdTicks": 4_000_000_000_u64 + } + }), + ); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStatus › should omit duration from metadata when absent in response + #[tokio::test] + async fn should_omit_duration_from_metadata_when_absent_in_response() { + let body = json!({ + "status": "done", + "video": { "url": VIDEO_URL, "respect_moderation": true }, + "model": V1 + }); + let (result, _) = do_status_answered("req-123", Canned::json(&body)).await; + let done = completed(result); + assert!(metadata_json(&done)["xai"].get("duration").is_none()); + } + + /// upstream: @ai-sdk/xai src/xai-video-model.test.ts › XaiVideoModel › doStatus › should omit progress from metadata when absent in response + #[tokio::test] + async fn should_omit_progress_from_metadata_when_absent_in_response() { + let body = json!({ + "status": "done", + "video": { "url": VIDEO_URL, "duration": 5, "respect_moderation": true }, + "model": V1 + }); + let (result, _) = do_status_answered("req-123", Canned::json(&body)).await; + let done = completed(result); + assert!(metadata_json(&done)["xai"].get("progress").is_none()); + } + } +} diff --git a/scripts/check_provider_boundaries.sh b/scripts/check_provider_boundaries.sh index df1578cd2..54d3fb7fc 100755 --- a/scripts/check_provider_boundaries.sh +++ b/scripts/check_provider_boundaries.sh @@ -214,7 +214,7 @@ rule7b_hits="$( violation 'bedrock::sigv4 is back (use SigV4Fetch)' "$rule7b_hits" rule7c_hits="$( # Non-test, non-comment lines of every file but the two helpers. - find $gvb_family -name '*.rs' ! -name options.rs -print0 | + find $gvb_family -name '*.rs' ! -name options.rs ! -path '*/tests/*' -print0 | xargs -0 awk ' FNR == 1 { in_tests = 0 } /^#\[cfg\(test\)\]/ { in_tests = 1 } @@ -245,7 +245,7 @@ rule8_hits="$( violation 'Azure / xAI / Mistral / Cohere / Hugging Face / Codex / Open Responses / Voyage / ElevenLabs reads builder-era config state' "$rule8_hits" rule8b_hits="$( # Non-test, non-comment lines of every file but the options helpers. - find $vendor_family -name '*.rs' ! -name options.rs -print0 | + find $vendor_family -name '*.rs' ! -name options.rs ! -path '*/tests/*' -print0 | xargs -0 awk ' FNR == 1 { in_tests = 0 } /^#\[cfg\(test\)\]/ { in_tests = 1 } From 8c30faea05f2bac456602da08489c29fa05e6d7b Mon Sep 17 00:00:00 2001 From: cunninghamcard-bit Date: Tue, 20 Oct 2026 08:29:29 +0000 Subject: [PATCH 6/7] test(xai): map the xAI sources in layout.toml Co-Authored-By: Claude Opus 5.5 (1M context) Claude-Session: https://claude.ai/code/session_01RoxcKU4zBD64Lri3e4Lrq5 --- aimux-providers/layout.toml | 262 ++++++++++++++++++++++++++++++++++++ 1 file changed, 262 insertions(+) create mode 100644 aimux-providers/layout.toml diff --git a/aimux-providers/layout.toml b/aimux-providers/layout.toml new file mode 100644 index 000000000..4cc6e0207 --- /dev/null +++ b/aimux-providers/layout.toml @@ -0,0 +1,262 @@ +# Upstream file map for aimux-providers (RFC-0046 §4.4, RFC-0045 §3). +# One table per vendor with an upstream package; versions come from +# fixtures/aisdk/VERSIONS.json. `rust` paths are relative to src/. + +[xai] +package = "@ai-sdk/xai" + +[[xai.files]] +upstream = "src/files/xai-files-api.ts" +status = "missing" +issue = 275 + +[[xai.files]] +upstream = "src/files/xai-files-options.ts" +status = "missing" +issue = 275 + +[[xai.files]] +upstream = "src/files/xai-files.ts" +status = "missing" +issue = 275 + +[[xai.files]] +upstream = "src/get-response-metadata.ts" +rust = "xai/responses/mod.rs" +status = "mapped" + +[xai.files.tests] + +[[xai.files]] +upstream = "src/index.ts" +rust = "xai/mod.rs" +status = "mapped" + +[xai.files.tests] + +[[xai.files]] +upstream = "src/map-xai-finish-reason.ts" +status = "not-applicable" +reason = "used only by xai-batch.ts; Batch is out of scope (docs/aisdk-architecture-alignment.md, 本期范围外)" + +[[xai.files]] +upstream = "src/realtime/index.ts" +status = "not-applicable" +reason = "Realtime is out of scope (docs/aisdk-architecture-alignment.md, 本期范围外)" + +[[xai.files]] +upstream = "src/realtime/xai-realtime-event-mapper.ts" +status = "not-applicable" +reason = "Realtime is out of scope (docs/aisdk-architecture-alignment.md, 本期范围外)" + +[[xai.files]] +upstream = "src/realtime/xai-realtime-model-options.ts" +status = "not-applicable" +reason = "Realtime is out of scope (docs/aisdk-architecture-alignment.md, 本期范围外)" + +[[xai.files]] +upstream = "src/realtime/xai-realtime-model.ts" +status = "not-applicable" +reason = "Realtime is out of scope (docs/aisdk-architecture-alignment.md, 本期范围外)" + +[[xai.files]] +upstream = "src/responses/convert-to-xai-responses-input.ts" +rust = "xai/responses/convert.rs" +status = "mapped" + +[xai.files.tests] + +[[xai.files]] +upstream = "src/responses/convert-xai-responses-usage.ts" +rust = "xai/responses/convert.rs" +status = "mapped" + +[xai.files.tests] + +[[xai.files]] +upstream = "src/responses/map-xai-responses-finish-reason.ts" +rust = "xai/responses/convert.rs" +status = "mapped" + +[xai.files.tests] + +[[xai.files]] +upstream = "src/responses/xai-responses-api.ts" +rust = "xai/responses/types.rs" +status = "mapped" + +[xai.files.tests] + +[[xai.files]] +upstream = "src/responses/xai-responses-language-model-options.ts" +rust = "xai/responses/convert.rs" +status = "mapped" + +[xai.files.tests] + +[[xai.files]] +upstream = "src/responses/xai-responses-language-model.ts" +rust = "xai/responses/mod.rs" +status = "mapped" + +[xai.files.tests] + +[[xai.files]] +upstream = "src/responses/xai-responses-prepare-tools.ts" +rust = "xai/responses/convert.rs" +status = "mapped" + +[xai.files.tests] + +[[xai.files]] +upstream = "src/supports-reasoning-effort.ts" +rust = "xai/convert.rs" +status = "mapped" + +[xai.files.tests] + +[[xai.files]] +upstream = "src/tool/code-execution.ts" +status = "missing" +issue = 275 + +[[xai.files]] +upstream = "src/tool/file-search.ts" +status = "missing" +issue = 275 + +[[xai.files]] +upstream = "src/tool/image-generation.ts" +status = "missing" +issue = 275 + +[[xai.files]] +upstream = "src/tool/index.ts" +status = "missing" +issue = 275 + +[[xai.files]] +upstream = "src/tool/mcp-server.ts" +status = "missing" +issue = 275 + +[[xai.files]] +upstream = "src/tool/view-image.ts" +status = "missing" +issue = 275 + +[[xai.files]] +upstream = "src/tool/view-x-video.ts" +status = "missing" +issue = 275 + +[[xai.files]] +upstream = "src/tool/web-search.ts" +status = "missing" +issue = 275 + +[[xai.files]] +upstream = "src/tool/x-search.ts" +status = "missing" +issue = 275 + +[[xai.files]] +upstream = "src/version.ts" +status = "not-applicable" +reason = "the crate version is used instead (RFC-0046 §4.2)" + +[[xai.files]] +upstream = "src/xai-batch.ts" +status = "not-applicable" +reason = "Batch is out of scope (docs/aisdk-architecture-alignment.md, 本期范围外)" + +[[xai.files]] +upstream = "src/xai-error.ts" +rust = "xai/mod.rs" +status = "mapped" + +[xai.files.tests] + +[[xai.files]] +upstream = "src/xai-file-part-options.ts" +rust = "xai/responses/convert.rs" +status = "mapped" + +[xai.files.tests] + +[[xai.files]] +upstream = "src/xai-image-model-options.ts" +status = "missing" +issue = 275 + +[[xai.files]] +upstream = "src/xai-image-model.ts" +status = "missing" +issue = 275 + +[[xai.files]] +upstream = "src/xai-image-settings.ts" +status = "missing" +issue = 275 + +[[xai.files]] +upstream = "src/xai-provider.ts" +rust = "xai/mod.rs" +status = "mapped" + +[xai.files.tests] +sub-feature = [ + { case = "xAIProvider › imageModel › should construct an image model with correct configuration", issue = 275, reason = "aimux has no xAI image model; Provider::image_model answers NoSuchModel" }, + { case = "xAIProvider › imageModel › should pass custom headers to image model", issue = 275, reason = "aimux has no xAI image model; Provider::image_model answers NoSuchModel" }, + { case = "xAIProvider › imageModel › should use custom baseURL for image model", issue = 275, reason = "aimux has no xAI image model; Provider::image_model answers NoSuchModel" }, +] + +[[xai.files]] +upstream = "src/xai-speech-model-options.ts" +rust = "xai/speech.rs" +status = "mapped" + +[xai.files.tests] + +[[xai.files]] +upstream = "src/xai-speech-model.ts" +rust = "xai/speech.rs" +status = "mapped" + +[xai.files.tests] + +[[xai.files]] +upstream = "src/xai-transcription-model-options.ts" +rust = "xai/transcription.rs" +status = "mapped" + +[xai.files.tests] + +[[xai.files]] +upstream = "src/xai-transcription-model.ts" +rust = "xai/transcription.rs" +status = "mapped" + +[xai.files.tests] + +[[xai.files]] +upstream = "src/xai-video-model-options.ts" +rust = "xai/video.rs" +status = "mapped" + +[xai.files.tests] + +[[xai.files]] +upstream = "src/xai-video-model.ts" +rust = "xai/video.rs" +status = "mapped" + +[xai.files.tests] +sub-feature = [ + { case = "XaiVideoModel › doStatus › should return error status on expired", issue = 276, reason = "aimux-core VideoOperationStatus has only Pending and Completed; an error status is Err(AiMuxError), which carries no response metadata" }, +] + +[[xai.files]] +upstream = "src/xai-video-settings.ts" +status = "not-applicable" +reason = "only the XaiVideoModelId string union; Rust takes the model id as &str" From 470453793ab7e26ccf880242090c278a60aa6b0c Mon Sep 17 00:00:00 2001 From: cunninghamcard-bit Date: Sun, 25 Oct 2026 08:28:52 +0000 Subject: [PATCH 7/7] fix(xai): an unpolled transcription stream still closes the socket when dropped The socket guard was built in the first statement of the async_stream::stream! body, which runs only on the first poll. A stream that do_stream returned with an open socket but that was dropped before next() released the transport without a close frame. The guard is now built in do_stream, right after the connect, and moved into the stream. The CHANGELOG said the socket is opened inside the stream; do_stream still awaits ws_connect. The entry now says what changed: a connect failure arrives as the stream's error. Test: dropping_an_unpolled_stream_closes_the_socket (tests/xai_transcription_test.rs) fails without the change. Co-Authored-By: Claude Opus 5.5 (1M context) Claude-Session: https://claude.ai/code/session_01RoxcKU4zBD64Lri3e4Lrq5 --- CHANGELOG.md | 11 +++--- aimux-providers/src/xai/transcription.rs | 7 +++- .../tests/xai_transcription_test.rs | 39 +++++++++++++++++++ 3 files changed, 50 insertions(+), 7 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 4586484de..26ff193c4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1222,13 +1222,14 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Fixed -- xAI streaming transcription opens its WebSocket inside the stream, as - `@ai-sdk/xai` 5.0.12 does: a connect failure no longer fails `do_stream` - but arrives as the stream's error, and the caller's audio stream is - dropped. +- A failed WebSocket connect in xAI streaming transcription arrives as the + stream's error, as `@ai-sdk/xai` 5.0.12 reports it: `do_stream` still + awaits the connect but no longer fails on it, and the caller's audio + stream is dropped. - Dropping an xAI streaming transcription before it finishes sends the WebSocket close frame, as cancelling the stream does in `@ai-sdk/xai` - 5.0.12; before, only the transport was released. + 5.0.12, also when the stream is dropped before it is first polled; before, + only the transport was released. - A streamed SSE event that is not JSON yields `JsonParse` reading `JSON parsing failed: Text: .\nError message: `, the AI SDK's `JSONParseError` message, as `@ai-sdk/openai-compatible` diff --git a/aimux-providers/src/xai/transcription.rs b/aimux-providers/src/xai/transcription.rs index 53ddf19b3..fd6efa015 100644 --- a/aimux-providers/src/xai/transcription.rs +++ b/aimux-providers/src/xai/transcription.rs @@ -386,7 +386,10 @@ mod stream { &options, model.web_socket.clone(), )) - .await; + .await + // The guard is built here, outside the stream body, so a stream + // dropped before its first poll still closes the socket. + .map(|ws| Socket(Some(ws))); let expected_done = if xai.multichannel == Some(true) { xai.channels.unwrap_or(1) as usize @@ -399,7 +402,7 @@ mod stream { let stream = async_stream::stream! { let mut ws = match connected { - Ok(ws) => Socket(Some(ws)), + Ok(ws) => ws, Err(e) => { yield Err(e); return; diff --git a/aimux-providers/tests/xai_transcription_test.rs b/aimux-providers/tests/xai_transcription_test.rs index 39cc80dbe..9baee0acb 100644 --- a/aimux-providers/tests/xai_transcription_test.rs +++ b/aimux-providers/tests/xai_transcription_test.rs @@ -830,3 +830,42 @@ async fn dropping_the_stream_closes_the_socket() { "no close frame was sent when the stream was dropped" ); } + +/// A stream dropped before its first poll still closes the socket: the +/// connect is awaited in `do_stream`, so the socket is open by then. +#[tokio::test] +async fn dropping_an_unpolled_stream_closes_the_socket() { + let listener = TcpListener::bind("127.0.0.1:0").await.unwrap(); + let base_url = format!("http://127.0.0.1:{}", listener.local_addr().unwrap().port()); + let (closed_tx, closed_rx) = tokio::sync::oneshot::channel(); + tokio::spawn(async move { + let (stream, _) = listener.accept().await.unwrap(); + let mut ws = tokio_tungstenite::accept_async(stream).await.unwrap(); + let mut closed = false; + while let Some(Ok(message)) = ws.next().await { + if message.is_close() { + closed = true; + break; + } + } + let _ = closed_tx.send(closed); + }); + + let mut options = stream_options(None, "audio/pcm", Some(16000)); + options.audio = Box::pin(futures::stream::pending()); + let result = model(&base_url, None) + .do_stream(options) + .expect("the model has do_stream") + .await + .expect("do_stream"); + drop(result); + + let closed = tokio::time::timeout(Duration::from_secs(5), closed_rx) + .await + .expect("the server saw the socket end") + .unwrap(); + assert!( + closed, + "no close frame was sent when the unpolled stream was dropped" + ); +}