- 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.
- Add highlight_prompt, highlight_response, highlight_time_ms to QueryHistoryDetail type
- Add 'Highlights' bar segment with pink color in TimingBar component
- Pass highlightTimeMs to TimingBar in HistoryCard expanded view
- Add collapsible sections for highlight prompt and response in HistoryCard detail
- Mark Phase 5.4 complete with actual commit log
- Add Phase 5.4 completion checklist (15 items all checked)
- Add production notes (Vite proxy, port conflicts, cache location)
- Update test counts to current (108 backend, 45 frontend, 153 total)
- Update Decision #12 to reflect inline citation link upgrade
- Vite dev server doesn't proxy /api/v1/v2/ paths to backend
- Changed fetch URL and getHighlightUrl to use http://localhost:8000
- Fixed inline citation highlight URLs in buildCitationUrl
- Cleaned up debug code
- Added sub_question_text to frontend SourceMetadata type
- SubQuestionSection enriches sources with parent sub-question text
- buildCitationUrl routes to highlight page when sub_question_text present
- processCitations threads highlightReadyKeys through inline citations
- citationParser.ts: extractCitedSources() parses answer text for [citations],
resolves against SourceMetadata, returns deduplicated cited sources
- ResponsePanel.tsx: useEffect fires POST /api/v1/v2/highlights/batch after
answer renders; View PDF link upgrades in-place to highlighted HTML when
batch completes; stays as raw PDF on failure
- Updated plan: LLM-based relevance detection, eager background computation,
single batched LLM call, sqlite cache, regex sentence splitter
- 45 frontend tests: 28 citationParser + 17 ResponsePanel (including 4 new
sub-question highlight tests)
- 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>
Woody