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
DOCX and TXT ingestion now produces chunk_file_path + per-chunk PDF files matching the PDF ingestion flow. Uses reportlab to render chunk text as simple PDFs with automatic text wrapping.
- Add reportlab==4.2.5 to requirements.txt
- New utils/text_to_pdf.py: generate_text_pdf() renders chunk text as PDF
- Ingest router DOCX/TXT branches: generate chunk_N.pdf per chunk, store in chunk_file_paths
- Graceful degradation: chunk_file_path stays None if PDF generation fails
- Update test_phase1_ingest_page_aware.py assertions: DOCX chunks now HAVE chunk_file_path
- New test_phase5_docx_pdf_generation.py: 5 tests (DOCX PDF gen, TXT PDF gen, PDF regression, file count, graceful degradation)
- 361 backend tests pass (4 pre-existing embedding failures unrelated)
Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
Multi-stage Dockerfile: Node builds frontend, Python serves both API
and static files. docker-compose.yml with named volumes for ChromaDB,
chunks, and SQLite data. nginx.conf as reverse proxy with 350M upload
limit and 300s LLM proxy timeout. README with dev setup, deploy steps,
env vars table, and architecture diagram.
Backend main.py: add catch-all route to serve frontend/dist/static
files in production. Only activates when dist/ exists.
Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent)
Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
Woody