Add transcribe-start/complete logs for both providers, error response body logging, and ASR provider in startup log. Filter yue (ISO 639-3) language code from OpenRouter STT requests.
Ultraworked with Sisyphus
Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
Add _ws_proxy_openrouter() handler with pcm_to_wav() converter, 3s chunk accumulation, flush_lock concurrency guard, and endpoint dispatch on ASR_PROVIDER. Language code yue filtered for OpenRouter (ISO 639-3 not supported).
Ultraworked with Sisyphus
Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
google/gemini-3.1-flash-lite is not an STT model; chirp-3 is one of the 8 supported OpenRouter STT models.
Ultraworked with Sisyphus
Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
Update TestTranscribeFull to use async/await and patch the moved OpenAI import (now in asr_providers.py). Set ASR_PROVIDER=dashscope in test fixtures to ensure tests don't pick up the real .env ASR_PROVIDER value. All 19 Phase 2 + 7 integration tests pass.
Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
test_phase5_config.py: 6 tests for ASR_PROVIDER validation and default values. test_phase5_openrouter_provider.py: 14 tests covering OpenRouterSTT transcription, retry logic, error handling, URL construction, cleanup, and factory dispatch. test_phase5_integration.py: 4 tests for full video-to-transcribe flow with both providers (mocked) and per-provider API key validation.
Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
Refactor ASRClient to delegate to provider (DashScopeASRProvider or OpenRouterASRProvider) via create_asr_provider() factory. transcribe_full() now async. Move _to_traditional to asr_providers.py (re-exported from asr_client.py for backward compat). Update video.py router to await transcribe_full() and validate API key per provider (DASHSCOPE_API_KEY for dashscope, OPENROUTER_API_KEY for openrouter).
Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
Three bugs caused 'Chunk by Question' to silently produce token chunks:
1. QuestionChunkingStrategy.chunk_pages() had a broken event-loop check
that always skipped LLM structure detection in FastAPI's async context.
Fixed by making chunk_pages() async and removing the is_running() guard.
2. get_chunking_strategy() factory never passed an LLMClient to
QuestionChunkingStrategy. Fixed by creating LLMClient in the factory
with graceful fallback to regex-only when config is incomplete.
3. rag.list_documents() and list_chunks() didn't extract strategy_type
or Q&A fields from ChromaDB metadata, so the frontend always showed
chunking_strategy='token' and null Q&A fields. Fixed by reading
these fields from ChromaDB and routing them through the API.
Also: TokenChunkingStrategy.chunk_pages() made async for consistency
with the question strategy; ingest router updated to await it.
Tests updated (asyncio.run() for sync tests, async mock chunk_pages).
8 acceptance tests with real LegCo PDFs (all @pytest.mark.acceptance + @slow).
Tests are skip()'d — run manually when real LLM is available:
pytest app/test/acceptance/test_acceptance_phase8_qa_chunking.py -v -m acceptance
Sub-Phase 8.6 (polish/edge cases) deferred — remaining items are
O1-O4 format handling, [如被追問] nested Q&A, vision loading state.
Core algorithm (8.1-8.4) is test-passing and production-ready.
Adds two new live audio sources alongside file Upload:
- System Audio: getDisplayMedia() captures system/tab audio output,
pipes through WebSocket → DashScope realtime ASR → RAG.
- Listen Mic: getUserMedia() captures microphone input via the same
audio pipeline (shared useMediaStreamASR hook).
Backend: feature toggles (system_audio_enabled, mic_enabled) in
config.py, source query param gating in ws_asr.py, 10 config tests.
Bug fix: getDisplayMedia() rejected video:false per W3C spec —
changed to video:true then stop video tracks to allow audio-only
capture on Windows/macOS Chrome.
- Backend: add stop_after_decompose flag to QueryRequest, early-return
after decomposition in SSE stream with half_question:true event
- Frontend: add decomposeOnly method to useQueryDocumentStream hook
- QueryInput: remove grey italic from ASR partial text, rename Submit
to Final Submit, add gray Half Question button that decomposes
without clearing querybox text
- LTTPage: wire handleHalfQuestion to decomposeOnly
Reverts commits 284028b through b4096d6. Phase 4 (System Audio Capture)
will replace the YouTube use case with a more versatile getDisplayMedia approach.
Removed: YouTube router, HLS proxy, YouTubeService, YouTubeInput,
YouTubeVideoPlayer, useYouTubeASR hook, all Phase 3 tests, hls.js dep,
YouTube config fields, YouTube README/plan sections.
Modified files restored to pre-Phase-3 state: LTTPage (no source toggle),
api.ts (no YouTube extract), types (no YouTube types), config.py (no
youtube fields), main.py (no YouTube router), requirements.txt (no yt-dlp),
.env.example (no YouTube vars), package.json (no hls.js).
Relevant Phase 2 code preserved: ws_asr.py (unchanged), useVideoASR,
VideoPlayer, VideoUpload, QueryInput, Full Transcript.
Replace full_text responses with character-level deltas computed from
DashScope's monotonically-growing 'text' field. Stash-only events (empty
text) are skipped; trailing stash chars sent alongside deltas and
appended on pause to complete final sentences.
Backend:
- Delta = text[len(prev_text):] — simple suffix diff, no merge logic
- Track item_id for utterance boundaries, prepend space separator
- Send stash alongside delta for frontend pause handler
Frontend:
- Accumulate deltas locally (transcriptRef += msg.delta)
- Store lastStashRef from each message
- On pause: append stash to text, fire onFinalTranscript
Plan: .plans/phase2_enhancement_delta_sse.md updated to Complete
DashScope stashes are ~7-char rolling windows, not cumulative. Each partial
event replaces the previous. Completed events rarely sent. This caused text to
jump/replace during streaming and disappear on pause.
Backend:
- Add _merge_stash() — finds overlapping suffix between successive stashes
and appends only new characters, reconstructing full utterance from partials
- format_transcription_event returns raw stash for read_events to merge
- read_events maintains partial_buffer via _merge_stash, clears on completed
- Guard against empty/whitespace-only stashes
Frontend:
- transcriptRef + onFinalTranscriptRef avoid stale closures in pause handler
- stopStreaming fires onFinalTranscript(currentText) before clearing partial
- Removed blind setPartialTranscript('') that erased text on pause
Tests: 16/16 ws_protocol tests pass, frontend tests unchanged
Plan: Updated phase2_implementation_plan.md to Complete with 11-bug log
- Add DashScope ASR and video upload config fields to Settings
- Create Pydantic models (video.py, asr.py)
- Create VideoService with validation, save, serve, delete
- Create ASR client stub with float32_to_s16le utility
- Implement POST /api/v1/video/upload with streaming validation
- Implement GET /api/v1/video/{video_id} with FileResponse
- Create WebSocket ASR endpoint stub
- Register new routers in main.py
- Update .env.example and requirements.txt
- Add reference examples for DashScope integration
- 8 tests passing (3 config + 5 video upload)
Follows Deepseek JSON Output guide: the prompt now includes the word 'json' and a format example derived from the Pydantic model schema. Added _pydantic_to_json_instruction() helper that builds a human-readable schema description with EXAMPLE JSON OUTPUT.
Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent)
Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
Added complete_structured() to mock classes, split response lists between LLMClientDP (decompose) and LLMClient (filter+generate), and patched both clients in all integration tests.
Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent)
Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
QueryDecomposer now uses LLMClientDP (Deepseek) while RelevanceFilter and RAGService continue using LLMClient (OpenRouter/vLLM).
Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent)
Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
Uses Deepseek's json_object response_format (not json_schema, which Deepseek does not support). Always disables thinking mode. Includes unit tests (12) and acceptance tests (5).
Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent)
Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
Log the complete prompt, schema, extra_body content, full API response,
token counts, and full parsed JSON output. Add exc_info=True tracebacks
on all failure paths.
vLLM servers support JSON schema enforcement via extra_body (guided_json
or structured_outputs), not OpenAI's response_format protocol. LangChain's
with_structured_output(method='json_schema') sends response_format which
vLLM ignores, causing NoneType not iterable parsing errors.
- vLLM path: direct OpenAI SDK call with extra_body={guided_json|structured_outputs}
- OpenRouter path: unchanged with_structured_output(method='json_schema')
- Try new 'structured_outputs' format first, fall back to legacy 'guided_json'
- Update _SEED_DECOMPOSE with explicit JSON array instruction
- Add diagnostic logging: exc_info=True, schema preview, prompt template preview
- Add logging in _parse_legacy_json for fallback failure debugging
vLLM's chat_template_kwargs leaked into LangChain's AsyncCompletions.parse()
via _get_langchain_model's model_kwargs, causing structured decomposition
to fail on vLLM backends. Skip vLLM-specific params when building the
LangChain model — only provider-agnostic params (OpenAI reasoning) pass through.
- POST /api/v1/v2/highlights/batch: compute and cache highlights for cited chunks
- GET /api/v1/v2/highlights: serve cached highlighted HTML pages
- chunks.py router registered in main.py
- Dynamic DB path computation (prompts.db -> highlights.db), no Settings changes
- 7 endpoint tests: POST 200/422, GET 200/404, mock service verification
- ChunkHighlightService.compute_highlights_batch(): single LLM call across
all cited chunks, grouped by sub-question, with structured output
- render_highlight_html(): self-contained HTML page with yellow-highlighted
relevant sentences, LLM reason annotations, and View Original PDF footer
- Per-target error isolation, ChromaDB miss handling, graceful degradation
- 14 tests: 7 batch service + 7 HTML rendering
Woody