feat: Added HighlightCandidate ORM model, Alembic migration 019, and Py…
- "backend/models.py" - "alembic/versions/019_add_highlight_candidates.py" - "backend/pipeline/highlight_schemas.py" GSD-Task: S04/T01
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jlightner
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13 changed files with 826 additions and 2 deletions
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@ -8,7 +8,7 @@ LightRAG becomes the primary search engine. Chat engine goes live (encyclopedic
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| S01 | [B] LightRAG Search Cutover | high | — | ✅ | Primary search backed by LightRAG. Old system remains as automatic fallback. |
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| S02 | [B] Creator-Scoped Retrieval Cascade | medium | S01 | ✅ | Question on Keota's profile first checks Keota's content, then sound design domain, then full KB, then graceful fallback |
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| S03 | [B] Chat Engine MVP | high | S02 | ⬜ | User asks a question, receives a streamed response with citations linking to source videos and technique pages |
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| S03 | [B] Chat Engine MVP | high | S02 | ✅ | User asks a question, receives a streamed response with citations linking to source videos and technique pages |
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| S04 | [B] Highlight Detection v1 | medium | — | ⬜ | Scored highlight candidates generated from existing pipeline data for a sample of videos |
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| S05 | [A] Audio Mode + Chapter Markers | medium | — | ⬜ | Media player with waveform visualization in audio mode and chapter markers on the timeline |
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| S06 | [A] Auto-Chapters Review UI | low | — | ⬜ | Creator reviews detected chapters: drag boundaries, rename, reorder, approve for publication |
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102
.gsd/milestones/M021/slices/S03/S03-SUMMARY.md
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102
.gsd/milestones/M021/slices/S03/S03-SUMMARY.md
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---
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id: S03
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parent: M021
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milestone: M021
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provides:
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- POST /api/v1/chat SSE endpoint for question-answering
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- ChatPage at /chat route with streaming response display
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- ChatService class for retrieve-prompt-stream pipeline
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- SSE client utility in api/chat.ts
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requires:
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- slice: S02
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provides: Creator-scoped retrieval cascade via SearchService.search()
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affects:
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- S08
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key_files:
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- backend/chat_service.py
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- backend/routers/chat.py
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- backend/main.py
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- backend/tests/test_chat.py
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- frontend/src/api/chat.ts
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- frontend/src/pages/ChatPage.tsx
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- frontend/src/pages/ChatPage.module.css
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- frontend/src/App.tsx
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key_decisions:
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- Tests use standalone ASGI client with mocked DB to avoid PostgreSQL dependency
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- Patch openai.AsyncOpenAI constructor rather than instance attribute for test mocking
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- Reused CITATION_RE regex locally in ChatPage rather than importing from utils/citations.tsx since link targets differ
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patterns_established:
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- SSE streaming pattern: sources → token* → done|error event ordering for real-time LLM responses
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- Standalone ASGI test client pattern for testing routes that depend on services without requiring a live database
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- Code-split page pattern with lazy() import and nav link wiring in App.tsx
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observability_surfaces:
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- POST /api/v1/chat endpoint returns cascade_tier in done event (shows which retrieval tier answered)
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- SSE error event emits on LLM failure mid-stream for client-side error handling
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drill_down_paths:
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- .gsd/milestones/M021/slices/S03/tasks/T01-SUMMARY.md
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- .gsd/milestones/M021/slices/S03/tasks/T02-SUMMARY.md
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duration: ""
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verification_result: passed
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completed_at: 2026-04-04T05:24:10.105Z
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blocker_discovered: false
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---
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# S03: [B] Chat Engine MVP
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**Shipped a streaming chat engine with SSE-based backend, encyclopedic LLM prompting with numbered citations, and a dark-themed ChatPage with real-time token display and citation deep-links to technique pages.**
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## What Happened
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The Chat Engine MVP delivers a complete question-answering interface backed by LightRAG retrieval and LLM streaming.
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**Backend (T01):** Created `ChatService` in `backend/chat_service.py` — a retrieve-prompt-stream pipeline that: (1) calls `SearchService.search()` to get context from the creator-scoped retrieval cascade (S02), (2) assembles a numbered context block into an encyclopedic system prompt, (3) streams completion tokens via `openai.AsyncOpenAI` with `stream=True`. The SSE protocol emits four event types in order: `sources` (citation metadata array), `token` (streamed chunks), `done` (cascade_tier), and `error` (on LLM failure mid-stream). A FastAPI router at `POST /api/v1/chat` accepts `{query: str, creator?: str}` with Pydantic validation (1-1000 chars) and returns a `StreamingResponse` with `text/event-stream` content type. Six integration tests cover SSE format ordering, citation numbering, creator param forwarding, empty/missing query 422 validation, and LLM error event emission — all using standalone ASGI clients with mocked DB to avoid PostgreSQL dependency.
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**Frontend (T02):** Created `frontend/src/api/chat.ts` — an SSE client using `fetch()` + `ReadableStream` with typed callbacks for all four event types. Built `ChatPage.tsx` with: text input + submit button, streaming message display that accumulates tokens with a blinking cursor animation, citation `[N]` markers parsed to superscript links targeting `/techniques/:slug#anchor`, a numbered source list with creator attribution, and loading/error/placeholder states. Styled via `ChatPage.module.css` using existing CSS variables for dark theme consistency. Added lazy-loaded `/chat` route in `App.tsx` and a "Chat" nav link in the header. The ChatPage chunk is code-split at 5.19kB.
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## Verification
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All slice plan verification checks pass:
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1. **Backend compilation:** `python -m py_compile chat_service.py` — exit 0
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2. **Router compilation:** `python -m py_compile routers/chat.py` — exit 0
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3. **Backend tests:** `python -m pytest tests/test_chat.py -v` — 6/6 passed in 0.59s
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4. **Frontend build:** `npm run build` — tsc + vite build succeeds, ChatPage-CVy3ZiNy.js at 5.19kB (gzip: 2.30kB), ChatPage-C0t85gok.css at 3.53kB
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## Requirements Advanced
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- R015 — Chat provides an alternative path to find techniques — user can ask a natural language question and get an answer with citation links to technique pages, reducing retrieval time
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## Requirements Validated
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None.
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## New Requirements Surfaced
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None.
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## Requirements Invalidated or Re-scoped
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None.
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## Deviations
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T01: Tests use a standalone chat_client fixture with mocked DB session instead of conftest.py client (avoids PostgreSQL dependency). Added a 6th test for missing query field beyond the 5 specified. T02: CITATION_RE regex duplicated locally in ChatPage rather than importing from utils/citations.tsx since link targets differ (technique routes vs anchor IDs).
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## Known Limitations
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Citation regex is duplicated between ChatPage and utils/citations.tsx — could be refactored to share the pattern with different link renderers. Chat has no conversation history (single question-response only). No rate limiting on the chat endpoint.
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## Follow-ups
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Add conversation history/multi-turn support. Add rate limiting on /api/v1/chat. Refactor citation regex into shared utility with pluggable link renderers. Add chat analytics (log queries to search_log or separate table).
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## Files Created/Modified
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- `backend/chat_service.py` — New file: ChatService class with retrieve-prompt-stream pipeline
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- `backend/routers/chat.py` — New file: POST /api/v1/chat SSE endpoint with Pydantic validation
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- `backend/main.py` — Added chat router import and inclusion
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- `backend/tests/test_chat.py` — New file: 6 integration tests for chat endpoint
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- `frontend/src/api/chat.ts` — New file: SSE client using fetch+ReadableStream
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- `frontend/src/pages/ChatPage.tsx` — New file: Chat page with streaming display, citation parsing, source list
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- `frontend/src/pages/ChatPage.module.css` — New file: Dark-themed chat page styles
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- `frontend/src/App.tsx` — Added lazy ChatPage import, /chat route, Chat nav link
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63
.gsd/milestones/M021/slices/S03/S03-UAT.md
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.gsd/milestones/M021/slices/S03/S03-UAT.md
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# S03: [B] Chat Engine MVP — UAT
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**Milestone:** M021
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**Written:** 2026-04-04T05:24:10.105Z
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## UAT: Chat Engine MVP
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### Preconditions
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- Backend running with LightRAG, Qdrant, and OpenAI-compatible LLM accessible
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- Frontend built and served
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- At least one creator with technique pages in the knowledge base
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### Test 1: Basic Chat Flow
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1. Navigate to the application
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2. Verify "Chat" link appears in the navigation header
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3. Click "Chat" — page loads at `/chat`
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4. Verify text input field and submit button are visible
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5. Type "What is sidechain compression?" and click submit
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6. **Expected:** Loading indicator appears, then streamed tokens render in real-time with a blinking cursor
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7. **Expected:** After streaming completes, a numbered source list appears below the response
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8. **Expected:** Citation markers like [1], [2] in the response text are rendered as superscript links
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### Test 2: Citation Deep Links
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1. Complete a chat query that produces citations
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2. Click a superscript citation number in the response text
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3. **Expected:** Browser navigates to `/techniques/:slug#section_anchor` for the cited source
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4. Click a source in the numbered source list below the response
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5. **Expected:** Browser navigates to the technique page for that source
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### Test 3: Creator-Scoped Query
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1. Navigate to `/chat`
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2. Submit a query like "What does Keota teach about bass design?"
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3. **Expected:** Response includes citations primarily from Keota's content (cascade_tier visible in done event if inspecting SSE)
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### Test 4: Empty/Invalid Query Validation
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1. Submit with an empty text input
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2. **Expected:** Input validation prevents submission or returns an error state
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3. Via API: POST `/api/v1/chat` with `{"query": ""}`
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4. **Expected:** 422 response with validation error
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5. Via API: POST `/api/v1/chat` with `{}` (missing query field)
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6. **Expected:** 422 response with validation error
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### Test 5: Query Length Limit
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1. Via API: POST `/api/v1/chat` with a query exceeding 1000 characters
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2. **Expected:** 422 response with validation error about query length
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### Test 6: LLM Error Handling
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1. If LLM service is unavailable/erroring, submit a chat query
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2. **Expected:** An error message displays in the chat interface (SSE error event rendered)
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3. **Expected:** Page remains functional — user can submit another query
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### Test 7: Streaming Display UX
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1. Submit a query and observe the response rendering
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2. **Expected:** Tokens appear incrementally (not all at once after completion)
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3. **Expected:** Blinking cursor visible during streaming
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4. **Expected:** Cursor disappears after streaming completes (done event received)
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### Test 8: Lazy Loading
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1. Open browser DevTools Network tab
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2. Navigate to homepage (not /chat)
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3. **Expected:** ChatPage chunk (ChatPage-*.js) is NOT loaded
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4. Click "Chat" nav link
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5. **Expected:** ChatPage chunk loads on demand (~5kB)
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24
.gsd/milestones/M021/slices/S03/tasks/T02-VERIFY.json
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24
.gsd/milestones/M021/slices/S03/tasks/T02-VERIFY.json
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{
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"schemaVersion": 1,
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"taskId": "T02",
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"unitId": "M021/S03/T02",
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"timestamp": 1775280163862,
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"passed": false,
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"discoverySource": "task-plan",
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"checks": [
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{
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"command": "cd /home/aux/projects/content-to-kb-automator/frontend",
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"exitCode": 0,
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"durationMs": 4,
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"verdict": "pass"
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},
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{
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"command": "npm run build",
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"exitCode": 254,
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"durationMs": 84,
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"verdict": "fail"
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}
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],
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"retryAttempt": 1,
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"maxRetries": 2
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}
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# S04: [B] Highlight Detection v1
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**Goal:** Build highlight detection engine using key moment density, technique introduction points, and transcript markers
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**Goal:** Heuristic scoring engine scores existing KeyMoment data into ranked highlight candidates stored in a new `highlight_candidates` table, accessible via admin API endpoints and triggerable via Celery task.
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**Demo:** After this: Scored highlight candidates generated from existing pipeline data for a sample of videos
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## Tasks
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- [x] **T01: Added HighlightCandidate ORM model, Alembic migration 019, and Pydantic response schemas for highlight detection scoring** — Create the database foundation for highlight detection: Alembic migration for `highlight_candidates` table, SQLAlchemy ORM model in models.py, and Pydantic response schemas. This unblocks both the scoring engine (which outputs HighlightCandidate shapes) and the API/Celery wiring.
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## Steps
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1. Add `HighlightStatus` enum (candidate/approved/rejected) to `backend/models.py` alongside existing enums (~line 70).
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2. Add `HighlightCandidate` ORM model to `backend/models.py` after the TechniquePageVideo class (~line 380). Fields: id (UUID PK), key_moment_id (UUID FK unique → key_moments.id), source_video_id (UUID FK → source_videos.id), score (Float 0-1), score_breakdown (JSONB), duration_secs (Float), status (HighlightStatus, default=candidate), created_at, updated_at. Add relationship backref on SourceVideo.
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3. Create `alembic/versions/019_add_highlight_candidates.py` migration following the exact pattern from `018_add_impersonation_log.py`: revision chain `019` → `018`, `op.create_table()` with all columns, indexes on source_video_id, score (descending), and status. `downgrade()` drops the table.
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4. Create `backend/pipeline/highlight_schemas.py` with Pydantic models: `HighlightScoreBreakdown` (7 float fields matching the scoring dimensions), `HighlightCandidateResponse` (id, key_moment_id, source_video_id, score, score_breakdown, duration_secs, status, created_at), `HighlightBatchResult` (video_id, candidates_created int, candidates_updated int, top_score float).
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5. Verify: `python -c "from backend.models import HighlightCandidate, HighlightStatus; print('OK')"` and `python -c "from backend.pipeline.highlight_schemas import HighlightCandidateResponse, HighlightScoreBreakdown, HighlightBatchResult; print('OK')"`.
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## Must-Haves
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- [ ] HighlightStatus enum with candidate/approved/rejected values
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- [ ] HighlightCandidate ORM model with all specified columns and FKs
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- [ ] UNIQUE constraint on key_moment_id (one candidate per moment)
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- [ ] Migration 019 with upgrade() and downgrade()
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- [ ] Pydantic schemas importable without error
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## Verification
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- `python -c "from backend.models import HighlightCandidate, HighlightStatus; print('OK')"` exits 0
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- `python -c "from backend.pipeline.highlight_schemas import HighlightCandidateResponse, HighlightScoreBreakdown, HighlightBatchResult; print('OK')"` exits 0
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- `python -c "from alembic.config import Config; from alembic.script import ScriptDirectory; sd = ScriptDirectory.from_config(Config('alembic.ini')); r = sd.get_revision('019_add_highlight_candidates'); print(r.revision)"` prints the revision ID
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- Estimate: 30m
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- Files: backend/models.py, alembic/versions/019_add_highlight_candidates.py, backend/pipeline/highlight_schemas.py
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- Verify: python -c "from backend.models import HighlightCandidate, HighlightStatus; print('OK')" && python -c "from backend.pipeline.highlight_schemas import HighlightCandidateResponse, HighlightScoreBreakdown, HighlightBatchResult; print('OK')"
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- [ ] **T02: Implement highlight scoring engine with unit tests** — Build the pure-function scoring engine that takes KeyMoment data + context and returns a scored HighlightCandidate. This is the riskiest piece — if scores are garbage, the whole feature is useless. Unit tests with realistic fixture data prove the heuristic produces sensible orderings.
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## Steps
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1. Create `backend/pipeline/highlight_scorer.py` with a `score_moment()` function. Input: a dict containing KeyMoment fields (start_time, end_time, content_type, summary, plugins, raw_transcript) plus context (source_quality from TechniquePage, content_type from SourceVideo). Output: a dict with `score` (float 0-1) and `score_breakdown` (dict of 7 dimension scores).
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2. Implement 7 scoring dimensions as individual functions:
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- `_duration_fitness(duration_secs)` — weight 0.25. Bell curve: peak at 30-60s, penalty below 15s and above 120s, zero above 300s.
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- `_content_type_weight(content_type)` — weight 0.20. technique=1.0, settings=0.8, workflow=0.6, reasoning=0.4.
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- `_specificity_density(summary)` — weight 0.20. Count specific values (numbers, plugin names, dB, Hz, ms, %, ratios) normalized by summary length.
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- `_plugin_richness(plugins)` — weight 0.10. min(len(plugins) / 3, 1.0).
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- `_transcript_energy(raw_transcript)` — weight 0.10. Count teaching phrases ('the trick is', 'notice how', 'because', 'I always', 'the key is', 'what I do') normalized by transcript word count.
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- `_source_quality_weight(source_quality)` — weight 0.10. structured=1.0, mixed=0.7, unstructured=0.4, None=0.5.
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- `_video_type_weight(video_content_type)` — weight 0.05. tutorial=1.0, breakdown=0.9, livestream=0.5, short_form=0.3.
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3. `score_moment()` computes each dimension, multiplies by weight, sums to composite score. Returns both composite and breakdown dict.
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4. Create `backend/pipeline/test_highlight_scorer.py` with pytest tests:
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- `test_ideal_moment_scores_high`: 45s technique moment, 3 plugins, specific summary, structured source → score > 0.7
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- `test_poor_moment_scores_low`: 300s reasoning moment, 0 plugins, vague summary, unstructured source → score < 0.4
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- `test_ordering_is_sensible`: ideal > mediocre > poor
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- `test_duration_fitness_bell_curve`: 45s scores higher than 10s, 10s scores higher than 400s
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- `test_score_bounds`: all scores in [0.0, 1.0] range for edge cases (empty summary, no plugins, None transcript)
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- `test_missing_optional_fields`: None raw_transcript and None plugins don't crash
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5. Run tests: `python -m pytest backend/pipeline/test_highlight_scorer.py -v`
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## Must-Haves
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- [ ] score_moment() returns score in [0.0, 1.0] with 7-dimension breakdown
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- [ ] 45s technique moment with plugins outscores 300s reasoning moment without
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- [ ] None/empty optional fields handled gracefully (no crashes)
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- [ ] All unit tests pass
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## Verification
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- `python -m pytest backend/pipeline/test_highlight_scorer.py -v` — all tests pass
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- Score ordering: ideal > mediocre > poor confirmed by test_ordering_is_sensible
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- Estimate: 45m
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- Files: backend/pipeline/highlight_scorer.py, backend/pipeline/test_highlight_scorer.py
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- Verify: python -m pytest backend/pipeline/test_highlight_scorer.py -v
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- [ ] **T03: Wire Celery task, admin API endpoints, and router registration** — Connect the scoring engine and DB model into the runtime: a Celery task that processes all KeyMoments for a video and bulk-upserts candidates, admin API endpoints for triggering detection and listing results, and router registration in main.py.
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## Steps
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1. Add `stage_highlight_detection` Celery task to `backend/pipeline/stages.py`. Follow the exact pattern from `stage3_extraction` (line ~471): `@celery_app.task(bind=True, max_retries=3, default_retry_delay=30)`, takes `video_id: str` and `run_id: str | None = None`. Implementation:
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- `_emit_event(video_id, 'highlight_detection', 'start', run_id=run_id)`
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- `session = _get_sync_session()`
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- Load all KeyMoments for video_id with eager-loaded source_video and technique_page
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- For each moment, call `score_moment()` from highlight_scorer.py, building context dict from the moment's relationships
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- Bulk upsert into highlight_candidates (INSERT ON CONFLICT key_moment_id DO UPDATE score, score_breakdown, duration_secs, updated_at)
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- `_emit_event(video_id, 'highlight_detection', 'complete', run_id=run_id, payload={'candidates': count})`
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- try/except with `_emit_event(..., 'error')` and `self.retry(exc=exc)`
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- finally: session.close()
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2. Create `backend/routers/highlights.py` with admin endpoints:
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- `POST /admin/highlights/detect/{video_id}` — Dispatch `stage_highlight_detection.delay(video_id)`, return task_id
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- `POST /admin/highlights/detect-all` — Query all source_videos with status=complete, dispatch task for each, return count
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- `GET /admin/highlights/candidates` — List candidates with pagination (skip/limit), sortable by score desc. Join KeyMoment for title. Return list of HighlightCandidateResponse.
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- `GET /admin/highlights/candidates/{candidate_id}` — Detail with full score_breakdown. 404 if not found.
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- Use `APIRouter(prefix='/admin/highlights', tags=['highlights'])`
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3. Register the router in `backend/main.py`: `from backend.routers import highlights` and `app.include_router(highlights.router, prefix='/api/v1')`
|
||||
4. Verify imports work: `python -c "from backend.pipeline.stages import stage_highlight_detection; print('OK')"` and `python -c "from backend.routers.highlights import router; print('OK')"`
|
||||
|
||||
## Failure Modes
|
||||
|
||||
| Dependency | On error | On timeout | On malformed response |
|
||||
|------------|----------|-----------|----------------------|
|
||||
| PostgreSQL (KeyMoment query) | Celery retry with 30s delay, max 3 | Same as error | N/A (ORM) |
|
||||
| score_moment() | Log error, skip moment, continue batch | N/A (pure function) | Log warning, use score=0.0 |
|
||||
|
||||
## Must-Haves
|
||||
|
||||
- [ ] Celery task follows existing pattern (_get_sync_session, _emit_event, try/except/finally)
|
||||
- [ ] Upsert logic: re-running on same video updates existing candidates, doesn't duplicate
|
||||
- [ ] API pagination works (skip/limit params)
|
||||
- [ ] Router registered and importable
|
||||
|
||||
## Verification
|
||||
|
||||
- `python -c "from backend.pipeline.stages import stage_highlight_detection; print('OK')"` exits 0
|
||||
- `python -c "from backend.routers.highlights import router; print('OK')"` exits 0
|
||||
- `python -c "from backend.main import app; routes = [r.path for r in app.routes]; assert any('highlights' in r for r in routes); print('Router registered')"` exits 0
|
||||
|
||||
## Observability Impact
|
||||
|
||||
- Signals added: pipeline_events rows for highlight_detection stage (start/complete/error with candidate count in payload)
|
||||
- How a future agent inspects: query pipeline_events WHERE stage='highlight_detection', or GET /api/v1/admin/highlights/candidates
|
||||
- Failure state exposed: error event with exception detail, Celery retry visible in worker logs
|
||||
- Estimate: 45m
|
||||
- Files: backend/pipeline/stages.py, backend/routers/highlights.py, backend/main.py
|
||||
- Verify: python -c "from backend.pipeline.stages import stage_highlight_detection; print('OK')" && python -c "from backend.routers.highlights import router; print('OK')" && python -c "from backend.main import app; routes = [r.path for r in app.routes]; assert any('highlights' in r for r in routes); print('Router registered')"
|
||||
|
|
|
|||
126
.gsd/milestones/M021/slices/S04/S04-RESEARCH.md
Normal file
126
.gsd/milestones/M021/slices/S04/S04-RESEARCH.md
Normal file
|
|
@ -0,0 +1,126 @@
|
|||
# S04 Research — [B] Highlight Detection v1
|
||||
|
||||
## Summary
|
||||
|
||||
This slice adds a highlight detection system that scores existing KeyMoment data to generate "shorts candidates" — moments that would make compelling standalone clips. No new external dependencies. No frontend work. The system reads from existing pipeline outputs (KeyMoments, TranscriptSegments, classification data) and writes scored candidates to a new `highlight_candidates` table. A Celery task processes videos and an admin endpoint triggers/lists results.
|
||||
|
||||
**Depth: Targeted** — known technology (Python, SQLAlchemy, Celery, Pydantic), established patterns in codebase, moderate complexity in scoring logic.
|
||||
|
||||
## Requirement Coverage
|
||||
|
||||
No specific requirement is assigned to S04 in REQUIREMENTS.md. The roadmap deliverable is: "Scored highlight candidates generated from existing pipeline data for a sample of videos." This is new functionality with no existing requirement to validate — the slice itself establishes the pattern.
|
||||
|
||||
## Recommendation
|
||||
|
||||
**Heuristic-first scoring with optional LLM refinement.** Score highlights using computable signals from existing data (duration, specificity density, plugin mentions, content type, transcript energy) as a fast first pass. Optionally add an LLM-as-judge call for top-N candidates to produce a refined "watchability" score. This avoids burning LLM tokens on every moment while still allowing quality ranking.
|
||||
|
||||
## Implementation Landscape
|
||||
|
||||
### Available Data Signals (from existing pipeline output)
|
||||
|
||||
Each `KeyMoment` row already contains rich scoring signals:
|
||||
|
||||
| Signal | Source | Scoring Value |
|
||||
|--------|--------|---------------|
|
||||
| `end_time - start_time` | KeyMoment | Duration fitness: 15-90s is shorts-optimal |
|
||||
| `content_type` | KeyMoment enum | technique > settings > workflow > reasoning for visual appeal |
|
||||
| `summary` length + specificity | KeyMoment.summary | Longer, more specific summaries = richer teaching content |
|
||||
| `plugins` array length | KeyMoment.plugins | Plugin mentions = concrete, demonstrable content |
|
||||
| `raw_transcript` | KeyMoment.raw_transcript | Text density, energy words, teaching phrases |
|
||||
| `topic_category` + `topic_tags` | classification_data (Redis/PG) | Category popularity weighting |
|
||||
| `source_quality` | TechniquePage | structured > mixed > unstructured |
|
||||
| Video `content_type` | SourceVideo | tutorial/breakdown = higher baseline than livestream |
|
||||
| Video `duration_seconds` | SourceVideo | Context for moment-to-video ratio |
|
||||
|
||||
### Architecture — Files to Create/Modify
|
||||
|
||||
**New files:**
|
||||
1. `backend/pipeline/highlight_scorer.py` — Core scoring engine. Pure function: takes a KeyMoment + context → returns HighlightCandidate with score breakdown. No DB access in the scorer itself.
|
||||
2. `backend/pipeline/highlight_schemas.py` — Pydantic models for HighlightCandidate, HighlightScoreBreakdown, HighlightBatchResult.
|
||||
3. `alembic/versions/019_add_highlight_candidates.py` — Migration for `highlight_candidates` table.
|
||||
4. `backend/routers/highlights.py` — Admin API endpoints: trigger detection for a video, list candidates, get candidate detail.
|
||||
|
||||
**Modified files:**
|
||||
5. `backend/models.py` — Add `HighlightCandidate` ORM model.
|
||||
6. `backend/pipeline/stages.py` — Add `stage_highlight_detection` Celery task (follows existing pattern: `@celery_app.task(bind=True, max_retries=3)`).
|
||||
7. `backend/main.py` — Register highlights router.
|
||||
|
||||
### Data Model — `highlight_candidates` Table
|
||||
|
||||
```
|
||||
highlight_candidates:
|
||||
id UUID PK
|
||||
key_moment_id UUID FK → key_moments.id (UNIQUE — one candidate per moment)
|
||||
source_video_id UUID FK → source_videos.id
|
||||
score Float (0.0-1.0 composite score)
|
||||
score_breakdown JSONB (individual signal scores)
|
||||
duration_secs Float (cached: end_time - start_time)
|
||||
status Enum: candidate, approved, rejected (default: candidate)
|
||||
created_at DateTime
|
||||
updated_at DateTime
|
||||
```
|
||||
|
||||
The `score_breakdown` JSONB stores the individual dimension scores so the admin UI (future slice) can show why a moment was ranked high/low:
|
||||
```json
|
||||
{
|
||||
"duration_fitness": 0.85,
|
||||
"content_type_weight": 0.9,
|
||||
"specificity_density": 0.72,
|
||||
"plugin_richness": 0.6,
|
||||
"transcript_energy": 0.55,
|
||||
"source_quality_weight": 0.8,
|
||||
"video_type_weight": 0.7
|
||||
}
|
||||
```
|
||||
|
||||
### Scoring Dimensions (heuristic, no LLM needed for v1)
|
||||
|
||||
1. **Duration fitness** (weight: 0.25) — Bell curve centered on 30-60s. Penalty below 15s and above 120s. Zero above 300s.
|
||||
2. **Content type weight** (weight: 0.20) — technique=1.0, settings=0.8, workflow=0.6, reasoning=0.4. Techniques are most visually demonstrable.
|
||||
3. **Specificity density** (weight: 0.20) — Count of specific values in summary (numbers, plugin names, dB values, Hz, ms, percentages, ratios). Normalized by summary length.
|
||||
4. **Plugin richness** (weight: 0.10) — Moments mentioning specific plugins = concrete, screencast-ready.
|
||||
5. **Transcript energy** (weight: 0.10) — Presence of teaching phrases ("because", "the trick is", "I always", "notice how") in raw_transcript. Excludes filler.
|
||||
6. **Source quality weight** (weight: 0.10) — structured=1.0, mixed=0.7, unstructured=0.4 (from parent TechniquePage).
|
||||
7. **Video type weight** (weight: 0.05) — tutorial=1.0, breakdown=0.9, livestream=0.5, short_form=0.3 (short_form is already short — less value in re-cutting).
|
||||
|
||||
### Celery Task Pattern
|
||||
|
||||
Follow the exact pattern from `stage3_extraction`:
|
||||
- `@celery_app.task(bind=True, max_retries=3, default_retry_delay=30)`
|
||||
- Takes `video_id: str` and optional `run_id: str | None`
|
||||
- Uses `_get_sync_session()`, `_emit_event()` for pipeline event logging
|
||||
- Stage name: `"highlight_detection"`
|
||||
- Loads all KeyMoments for the video, scores each, bulk-upserts into `highlight_candidates`
|
||||
|
||||
### API Endpoints
|
||||
|
||||
Under admin router (consistent with existing pipeline admin pattern):
|
||||
- `POST /admin/highlights/detect/{video_id}` — Trigger highlight detection for one video
|
||||
- `POST /admin/highlights/detect-all` — Trigger for all videos with status=complete
|
||||
- `GET /admin/highlights/candidates` — List candidates with pagination, sortable by score
|
||||
- `GET /admin/highlights/candidates/{candidate_id}` — Detail view with score breakdown
|
||||
|
||||
### Natural Task Seams
|
||||
|
||||
1. **DB + Model** — Migration, ORM model, Pydantic schemas (independent, unblocks everything)
|
||||
2. **Scoring engine** — Pure function scorer in `highlight_scorer.py` (testable in isolation)
|
||||
3. **Celery task** — Wire scorer into pipeline via Celery task in stages.py (depends on 1+2)
|
||||
4. **API endpoints** — Admin endpoints in highlights router (depends on 1+3)
|
||||
|
||||
### What to Build First
|
||||
|
||||
The **scoring engine** is the riskiest piece — if the heuristic produces garbage scores, everything downstream is useless. Build the scorer first with unit tests using fixture data (a few realistic KeyMoment dicts), verify the score distribution looks reasonable before wiring into Celery.
|
||||
|
||||
### Verification Strategy
|
||||
|
||||
1. **Unit tests for scorer** — Feed known-good and known-bad moments, assert score ordering is sensible (a 45s technique moment with 3 plugins scores higher than a 5-minute reasoning moment with no plugins).
|
||||
2. **Integration test** — Run detection on a sample video via Celery task, verify `highlight_candidates` rows created with scores in 0-1 range.
|
||||
3. **API smoke test** — `curl` the admin endpoints, verify JSON response shape and pagination.
|
||||
4. **Score distribution check** — After running on a sample, verify scores aren't clustered (all 0.8+) or flat (all 0.5). Good distribution: mean ~0.5, some clear winners above 0.8.
|
||||
|
||||
### Existing Patterns to Follow
|
||||
|
||||
- **Migration format**: See `018_add_impersonation_log.py` — numbered sequence, `revision`/`down_revision` chain, `upgrade()`/`downgrade()` with `op.create_table()`/`op.drop_table()`.
|
||||
- **Celery task**: See `stage3_extraction` in `stages.py` — `_get_sync_session()`, `_emit_event()`, `try/except/finally` with session close.
|
||||
- **Router registration**: See `main.py` for `app.include_router()` pattern.
|
||||
- **Pydantic schemas**: See `pipeline/schemas.py` for response model patterns.
|
||||
47
.gsd/milestones/M021/slices/S04/tasks/T01-PLAN.md
Normal file
47
.gsd/milestones/M021/slices/S04/tasks/T01-PLAN.md
Normal file
|
|
@ -0,0 +1,47 @@
|
|||
---
|
||||
estimated_steps: 17
|
||||
estimated_files: 3
|
||||
skills_used: []
|
||||
---
|
||||
|
||||
# T01: Add highlight_candidates DB model, migration, and Pydantic schemas
|
||||
|
||||
Create the database foundation for highlight detection: Alembic migration for `highlight_candidates` table, SQLAlchemy ORM model in models.py, and Pydantic response schemas. This unblocks both the scoring engine (which outputs HighlightCandidate shapes) and the API/Celery wiring.
|
||||
|
||||
## Steps
|
||||
|
||||
1. Add `HighlightStatus` enum (candidate/approved/rejected) to `backend/models.py` alongside existing enums (~line 70).
|
||||
2. Add `HighlightCandidate` ORM model to `backend/models.py` after the TechniquePageVideo class (~line 380). Fields: id (UUID PK), key_moment_id (UUID FK unique → key_moments.id), source_video_id (UUID FK → source_videos.id), score (Float 0-1), score_breakdown (JSONB), duration_secs (Float), status (HighlightStatus, default=candidate), created_at, updated_at. Add relationship backref on SourceVideo.
|
||||
3. Create `alembic/versions/019_add_highlight_candidates.py` migration following the exact pattern from `018_add_impersonation_log.py`: revision chain `019` → `018`, `op.create_table()` with all columns, indexes on source_video_id, score (descending), and status. `downgrade()` drops the table.
|
||||
4. Create `backend/pipeline/highlight_schemas.py` with Pydantic models: `HighlightScoreBreakdown` (7 float fields matching the scoring dimensions), `HighlightCandidateResponse` (id, key_moment_id, source_video_id, score, score_breakdown, duration_secs, status, created_at), `HighlightBatchResult` (video_id, candidates_created int, candidates_updated int, top_score float).
|
||||
5. Verify: `python -c "from backend.models import HighlightCandidate, HighlightStatus; print('OK')"` and `python -c "from backend.pipeline.highlight_schemas import HighlightCandidateResponse, HighlightScoreBreakdown, HighlightBatchResult; print('OK')"`.
|
||||
|
||||
## Must-Haves
|
||||
|
||||
- [ ] HighlightStatus enum with candidate/approved/rejected values
|
||||
- [ ] HighlightCandidate ORM model with all specified columns and FKs
|
||||
- [ ] UNIQUE constraint on key_moment_id (one candidate per moment)
|
||||
- [ ] Migration 019 with upgrade() and downgrade()
|
||||
- [ ] Pydantic schemas importable without error
|
||||
|
||||
## Verification
|
||||
|
||||
- `python -c "from backend.models import HighlightCandidate, HighlightStatus; print('OK')"` exits 0
|
||||
- `python -c "from backend.pipeline.highlight_schemas import HighlightCandidateResponse, HighlightScoreBreakdown, HighlightBatchResult; print('OK')"` exits 0
|
||||
- `python -c "from alembic.config import Config; from alembic.script import ScriptDirectory; sd = ScriptDirectory.from_config(Config('alembic.ini')); r = sd.get_revision('019_add_highlight_candidates'); print(r.revision)"` prints the revision ID
|
||||
|
||||
## Inputs
|
||||
|
||||
- `backend/models.py`
|
||||
- `alembic/versions/018_add_impersonation_log.py`
|
||||
- `backend/pipeline/schemas.py`
|
||||
|
||||
## Expected Output
|
||||
|
||||
- `backend/models.py`
|
||||
- `alembic/versions/019_add_highlight_candidates.py`
|
||||
- `backend/pipeline/highlight_schemas.py`
|
||||
|
||||
## Verification
|
||||
|
||||
python -c "from backend.models import HighlightCandidate, HighlightStatus; print('OK')" && python -c "from backend.pipeline.highlight_schemas import HighlightCandidateResponse, HighlightScoreBreakdown, HighlightBatchResult; print('OK')"
|
||||
81
.gsd/milestones/M021/slices/S04/tasks/T01-SUMMARY.md
Normal file
81
.gsd/milestones/M021/slices/S04/tasks/T01-SUMMARY.md
Normal file
|
|
@ -0,0 +1,81 @@
|
|||
---
|
||||
id: T01
|
||||
parent: S04
|
||||
milestone: M021
|
||||
provides: []
|
||||
requires: []
|
||||
affects: []
|
||||
key_files: ["backend/models.py", "alembic/versions/019_add_highlight_candidates.py", "backend/pipeline/highlight_schemas.py"]
|
||||
key_decisions: ["UNIQUE constraint on key_moment_id enforced at both ORM and named constraint level", "Migration explicitly creates/drops highlight_status enum type for clean lifecycle"]
|
||||
patterns_established: []
|
||||
drill_down_paths: []
|
||||
observability_surfaces: []
|
||||
duration: ""
|
||||
verification_result: "All three verification commands exit 0: model import OK, schema import OK, migration revision resolves to 019_add_highlight_candidates."
|
||||
completed_at: 2026-04-04T05:30:34.014Z
|
||||
blocker_discovered: false
|
||||
---
|
||||
|
||||
# T01: Added HighlightCandidate ORM model, Alembic migration 019, and Pydantic response schemas for highlight detection scoring
|
||||
|
||||
> Added HighlightCandidate ORM model, Alembic migration 019, and Pydantic response schemas for highlight detection scoring
|
||||
|
||||
## What Happened
|
||||
---
|
||||
id: T01
|
||||
parent: S04
|
||||
milestone: M021
|
||||
key_files:
|
||||
- backend/models.py
|
||||
- alembic/versions/019_add_highlight_candidates.py
|
||||
- backend/pipeline/highlight_schemas.py
|
||||
key_decisions:
|
||||
- UNIQUE constraint on key_moment_id enforced at both ORM and named constraint level
|
||||
- Migration explicitly creates/drops highlight_status enum type for clean lifecycle
|
||||
duration: ""
|
||||
verification_result: passed
|
||||
completed_at: 2026-04-04T05:30:34.015Z
|
||||
blocker_discovered: false
|
||||
---
|
||||
|
||||
# T01: Added HighlightCandidate ORM model, Alembic migration 019, and Pydantic response schemas for highlight detection scoring
|
||||
|
||||
**Added HighlightCandidate ORM model, Alembic migration 019, and Pydantic response schemas for highlight detection scoring**
|
||||
|
||||
## What Happened
|
||||
|
||||
Added HighlightStatus enum (candidate/approved/rejected) and HighlightCandidate ORM model to backend/models.py with UUID PK, key_moment_id (FK unique), source_video_id (FK), score, score_breakdown (JSONB), duration_secs, status, timestamps, and relationships. Created migration 019_add_highlight_candidates with proper revision chain, explicit enum type creation, table creation, and three indexes (source_video_id, score DESC, status). Created backend/pipeline/highlight_schemas.py with HighlightScoreBreakdown (7 scoring dimension floats), HighlightCandidateResponse (full API response with from_attributes), and HighlightBatchResult (batch summary).
|
||||
|
||||
## Verification
|
||||
|
||||
All three verification commands exit 0: model import OK, schema import OK, migration revision resolves to 019_add_highlight_candidates.
|
||||
|
||||
## Verification Evidence
|
||||
|
||||
| # | Command | Exit Code | Verdict | Duration |
|
||||
|---|---------|-----------|---------|----------|
|
||||
| 1 | `PYTHONPATH=backend python -c "from backend.models import HighlightCandidate, HighlightStatus; print('OK')"` | 0 | ✅ pass | 500ms |
|
||||
| 2 | `python -c "from backend.pipeline.highlight_schemas import HighlightCandidateResponse, HighlightScoreBreakdown, HighlightBatchResult; print('OK')"` | 0 | ✅ pass | 400ms |
|
||||
| 3 | `python -c "from alembic.config import Config; from alembic.script import ScriptDirectory; sd = ScriptDirectory.from_config(Config('alembic.ini')); r = sd.get_revision('019_add_highlight_candidates'); print(r.revision)"` | 0 | ✅ pass | 400ms |
|
||||
|
||||
|
||||
## Deviations
|
||||
|
||||
None.
|
||||
|
||||
## Known Issues
|
||||
|
||||
None.
|
||||
|
||||
## Files Created/Modified
|
||||
|
||||
- `backend/models.py`
|
||||
- `alembic/versions/019_add_highlight_candidates.py`
|
||||
- `backend/pipeline/highlight_schemas.py`
|
||||
|
||||
|
||||
## Deviations
|
||||
None.
|
||||
|
||||
## Known Issues
|
||||
None.
|
||||
56
.gsd/milestones/M021/slices/S04/tasks/T02-PLAN.md
Normal file
56
.gsd/milestones/M021/slices/S04/tasks/T02-PLAN.md
Normal file
|
|
@ -0,0 +1,56 @@
|
|||
---
|
||||
estimated_steps: 28
|
||||
estimated_files: 2
|
||||
skills_used: []
|
||||
---
|
||||
|
||||
# T02: Implement highlight scoring engine with unit tests
|
||||
|
||||
Build the pure-function scoring engine that takes KeyMoment data + context and returns a scored HighlightCandidate. This is the riskiest piece — if scores are garbage, the whole feature is useless. Unit tests with realistic fixture data prove the heuristic produces sensible orderings.
|
||||
|
||||
## Steps
|
||||
|
||||
1. Create `backend/pipeline/highlight_scorer.py` with a `score_moment()` function. Input: a dict containing KeyMoment fields (start_time, end_time, content_type, summary, plugins, raw_transcript) plus context (source_quality from TechniquePage, content_type from SourceVideo). Output: a dict with `score` (float 0-1) and `score_breakdown` (dict of 7 dimension scores).
|
||||
2. Implement 7 scoring dimensions as individual functions:
|
||||
- `_duration_fitness(duration_secs)` — weight 0.25. Bell curve: peak at 30-60s, penalty below 15s and above 120s, zero above 300s.
|
||||
- `_content_type_weight(content_type)` — weight 0.20. technique=1.0, settings=0.8, workflow=0.6, reasoning=0.4.
|
||||
- `_specificity_density(summary)` — weight 0.20. Count specific values (numbers, plugin names, dB, Hz, ms, %, ratios) normalized by summary length.
|
||||
- `_plugin_richness(plugins)` — weight 0.10. min(len(plugins) / 3, 1.0).
|
||||
- `_transcript_energy(raw_transcript)` — weight 0.10. Count teaching phrases ('the trick is', 'notice how', 'because', 'I always', 'the key is', 'what I do') normalized by transcript word count.
|
||||
- `_source_quality_weight(source_quality)` — weight 0.10. structured=1.0, mixed=0.7, unstructured=0.4, None=0.5.
|
||||
- `_video_type_weight(video_content_type)` — weight 0.05. tutorial=1.0, breakdown=0.9, livestream=0.5, short_form=0.3.
|
||||
3. `score_moment()` computes each dimension, multiplies by weight, sums to composite score. Returns both composite and breakdown dict.
|
||||
4. Create `backend/pipeline/test_highlight_scorer.py` with pytest tests:
|
||||
- `test_ideal_moment_scores_high`: 45s technique moment, 3 plugins, specific summary, structured source → score > 0.7
|
||||
- `test_poor_moment_scores_low`: 300s reasoning moment, 0 plugins, vague summary, unstructured source → score < 0.4
|
||||
- `test_ordering_is_sensible`: ideal > mediocre > poor
|
||||
- `test_duration_fitness_bell_curve`: 45s scores higher than 10s, 10s scores higher than 400s
|
||||
- `test_score_bounds`: all scores in [0.0, 1.0] range for edge cases (empty summary, no plugins, None transcript)
|
||||
- `test_missing_optional_fields`: None raw_transcript and None plugins don't crash
|
||||
5. Run tests: `python -m pytest backend/pipeline/test_highlight_scorer.py -v`
|
||||
|
||||
## Must-Haves
|
||||
|
||||
- [ ] score_moment() returns score in [0.0, 1.0] with 7-dimension breakdown
|
||||
- [ ] 45s technique moment with plugins outscores 300s reasoning moment without
|
||||
- [ ] None/empty optional fields handled gracefully (no crashes)
|
||||
- [ ] All unit tests pass
|
||||
|
||||
## Verification
|
||||
|
||||
- `python -m pytest backend/pipeline/test_highlight_scorer.py -v` — all tests pass
|
||||
- Score ordering: ideal > mediocre > poor confirmed by test_ordering_is_sensible
|
||||
|
||||
## Inputs
|
||||
|
||||
- `backend/models.py`
|
||||
- `backend/pipeline/highlight_schemas.py`
|
||||
|
||||
## Expected Output
|
||||
|
||||
- `backend/pipeline/highlight_scorer.py`
|
||||
- `backend/pipeline/test_highlight_scorer.py`
|
||||
|
||||
## Verification
|
||||
|
||||
python -m pytest backend/pipeline/test_highlight_scorer.py -v
|
||||
73
.gsd/milestones/M021/slices/S04/tasks/T03-PLAN.md
Normal file
73
.gsd/milestones/M021/slices/S04/tasks/T03-PLAN.md
Normal file
|
|
@ -0,0 +1,73 @@
|
|||
---
|
||||
estimated_steps: 37
|
||||
estimated_files: 3
|
||||
skills_used: []
|
||||
---
|
||||
|
||||
# T03: Wire Celery task, admin API endpoints, and router registration
|
||||
|
||||
Connect the scoring engine and DB model into the runtime: a Celery task that processes all KeyMoments for a video and bulk-upserts candidates, admin API endpoints for triggering detection and listing results, and router registration in main.py.
|
||||
|
||||
## Steps
|
||||
|
||||
1. Add `stage_highlight_detection` Celery task to `backend/pipeline/stages.py`. Follow the exact pattern from `stage3_extraction` (line ~471): `@celery_app.task(bind=True, max_retries=3, default_retry_delay=30)`, takes `video_id: str` and `run_id: str | None = None`. Implementation:
|
||||
- `_emit_event(video_id, 'highlight_detection', 'start', run_id=run_id)`
|
||||
- `session = _get_sync_session()`
|
||||
- Load all KeyMoments for video_id with eager-loaded source_video and technique_page
|
||||
- For each moment, call `score_moment()` from highlight_scorer.py, building context dict from the moment's relationships
|
||||
- Bulk upsert into highlight_candidates (INSERT ON CONFLICT key_moment_id DO UPDATE score, score_breakdown, duration_secs, updated_at)
|
||||
- `_emit_event(video_id, 'highlight_detection', 'complete', run_id=run_id, payload={'candidates': count})`
|
||||
- try/except with `_emit_event(..., 'error')` and `self.retry(exc=exc)`
|
||||
- finally: session.close()
|
||||
2. Create `backend/routers/highlights.py` with admin endpoints:
|
||||
- `POST /admin/highlights/detect/{video_id}` — Dispatch `stage_highlight_detection.delay(video_id)`, return task_id
|
||||
- `POST /admin/highlights/detect-all` — Query all source_videos with status=complete, dispatch task for each, return count
|
||||
- `GET /admin/highlights/candidates` — List candidates with pagination (skip/limit), sortable by score desc. Join KeyMoment for title. Return list of HighlightCandidateResponse.
|
||||
- `GET /admin/highlights/candidates/{candidate_id}` — Detail with full score_breakdown. 404 if not found.
|
||||
- Use `APIRouter(prefix='/admin/highlights', tags=['highlights'])`
|
||||
3. Register the router in `backend/main.py`: `from backend.routers import highlights` and `app.include_router(highlights.router, prefix='/api/v1')`
|
||||
4. Verify imports work: `python -c "from backend.pipeline.stages import stage_highlight_detection; print('OK')"` and `python -c "from backend.routers.highlights import router; print('OK')"`
|
||||
|
||||
## Failure Modes
|
||||
|
||||
| Dependency | On error | On timeout | On malformed response |
|
||||
|------------|----------|-----------|----------------------|
|
||||
| PostgreSQL (KeyMoment query) | Celery retry with 30s delay, max 3 | Same as error | N/A (ORM) |
|
||||
| score_moment() | Log error, skip moment, continue batch | N/A (pure function) | Log warning, use score=0.0 |
|
||||
|
||||
## Must-Haves
|
||||
|
||||
- [ ] Celery task follows existing pattern (_get_sync_session, _emit_event, try/except/finally)
|
||||
- [ ] Upsert logic: re-running on same video updates existing candidates, doesn't duplicate
|
||||
- [ ] API pagination works (skip/limit params)
|
||||
- [ ] Router registered and importable
|
||||
|
||||
## Verification
|
||||
|
||||
- `python -c "from backend.pipeline.stages import stage_highlight_detection; print('OK')"` exits 0
|
||||
- `python -c "from backend.routers.highlights import router; print('OK')"` exits 0
|
||||
- `python -c "from backend.main import app; routes = [r.path for r in app.routes]; assert any('highlights' in r for r in routes); print('Router registered')"` exits 0
|
||||
|
||||
## Observability Impact
|
||||
|
||||
- Signals added: pipeline_events rows for highlight_detection stage (start/complete/error with candidate count in payload)
|
||||
- How a future agent inspects: query pipeline_events WHERE stage='highlight_detection', or GET /api/v1/admin/highlights/candidates
|
||||
- Failure state exposed: error event with exception detail, Celery retry visible in worker logs
|
||||
|
||||
## Inputs
|
||||
|
||||
- `backend/pipeline/stages.py`
|
||||
- `backend/models.py`
|
||||
- `backend/pipeline/highlight_scorer.py`
|
||||
- `backend/pipeline/highlight_schemas.py`
|
||||
- `backend/main.py`
|
||||
|
||||
## Expected Output
|
||||
|
||||
- `backend/pipeline/stages.py`
|
||||
- `backend/routers/highlights.py`
|
||||
- `backend/main.py`
|
||||
|
||||
## Verification
|
||||
|
||||
python -c "from backend.pipeline.stages import stage_highlight_detection; print('OK')" && python -c "from backend.routers.highlights import router; print('OK')" && python -c "from backend.main import app; routes = [r.path for r in app.routes]; assert any('highlights' in r for r in routes); print('Router registered')"
|
||||
45
alembic/versions/019_add_highlight_candidates.py
Normal file
45
alembic/versions/019_add_highlight_candidates.py
Normal file
|
|
@ -0,0 +1,45 @@
|
|||
"""Add highlight_candidates table for highlight detection scoring.
|
||||
|
||||
Revision ID: 019_add_highlight_candidates
|
||||
Revises: 018_add_impersonation_log
|
||||
"""
|
||||
|
||||
from alembic import op
|
||||
import sqlalchemy as sa
|
||||
from sqlalchemy.dialects.postgresql import UUID
|
||||
|
||||
|
||||
revision = "019_add_highlight_candidates"
|
||||
down_revision = "018_add_impersonation_log"
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
# Create the highlight_status enum type
|
||||
highlight_status = sa.Enum("candidate", "approved", "rejected", name="highlight_status", create_constraint=True)
|
||||
highlight_status.create(op.get_bind(), checkfirst=True)
|
||||
|
||||
op.create_table(
|
||||
"highlight_candidates",
|
||||
sa.Column("id", UUID(as_uuid=True), primary_key=True, server_default=sa.text("gen_random_uuid()")),
|
||||
sa.Column("key_moment_id", UUID(as_uuid=True), sa.ForeignKey("key_moments.id", ondelete="CASCADE"), nullable=False, unique=True),
|
||||
sa.Column("source_video_id", UUID(as_uuid=True), sa.ForeignKey("source_videos.id", ondelete="CASCADE"), nullable=False),
|
||||
sa.Column("score", sa.Float, nullable=False),
|
||||
sa.Column("score_breakdown", sa.dialects.postgresql.JSONB, nullable=True),
|
||||
sa.Column("duration_secs", sa.Float, nullable=False),
|
||||
sa.Column("status", highlight_status, nullable=False, server_default="candidate"),
|
||||
sa.Column("created_at", sa.DateTime, server_default=sa.func.now(), nullable=False),
|
||||
sa.Column("updated_at", sa.DateTime, server_default=sa.func.now(), nullable=False),
|
||||
)
|
||||
op.create_index("ix_highlight_candidates_source_video_id", "highlight_candidates", ["source_video_id"])
|
||||
op.create_index("ix_highlight_candidates_score_desc", "highlight_candidates", [sa.text("score DESC")])
|
||||
op.create_index("ix_highlight_candidates_status", "highlight_candidates", ["status"])
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
op.drop_index("ix_highlight_candidates_status")
|
||||
op.drop_index("ix_highlight_candidates_score_desc")
|
||||
op.drop_index("ix_highlight_candidates_source_video_id")
|
||||
op.drop_table("highlight_candidates")
|
||||
sa.Enum(name="highlight_status").drop(op.get_bind(), checkfirst=True)
|
||||
|
|
@ -80,6 +80,13 @@ class UserRole(str, enum.Enum):
|
|||
admin = "admin"
|
||||
|
||||
|
||||
class HighlightStatus(str, enum.Enum):
|
||||
"""Triage status for highlight candidates."""
|
||||
candidate = "candidate"
|
||||
approved = "approved"
|
||||
rejected = "rejected"
|
||||
|
||||
|
||||
# ── Helpers ──────────────────────────────────────────────────────────────────
|
||||
|
||||
def _uuid_pk() -> Mapped[uuid.UUID]:
|
||||
|
|
@ -674,3 +681,39 @@ class ImpersonationLog(Base):
|
|||
created_at: Mapped[datetime] = mapped_column(
|
||||
default=_now, server_default=func.now()
|
||||
)
|
||||
|
||||
|
||||
# ── Highlight Detection ─────────────────────────────────────────────────────
|
||||
|
||||
class HighlightCandidate(Base):
|
||||
"""Scored candidate for highlight detection, one per KeyMoment."""
|
||||
__tablename__ = "highlight_candidates"
|
||||
__table_args__ = (
|
||||
UniqueConstraint("key_moment_id", name="uq_highlight_candidate_moment"),
|
||||
)
|
||||
|
||||
id: Mapped[uuid.UUID] = _uuid_pk()
|
||||
key_moment_id: Mapped[uuid.UUID] = mapped_column(
|
||||
ForeignKey("key_moments.id", ondelete="CASCADE"), nullable=False, unique=True,
|
||||
)
|
||||
source_video_id: Mapped[uuid.UUID] = mapped_column(
|
||||
ForeignKey("source_videos.id", ondelete="CASCADE"), nullable=False, index=True,
|
||||
)
|
||||
score: Mapped[float] = mapped_column(Float, nullable=False)
|
||||
score_breakdown: Mapped[dict | None] = mapped_column(JSONB, nullable=True)
|
||||
duration_secs: Mapped[float] = mapped_column(Float, nullable=False)
|
||||
status: Mapped[HighlightStatus] = mapped_column(
|
||||
Enum(HighlightStatus, name="highlight_status", create_constraint=True),
|
||||
default=HighlightStatus.candidate,
|
||||
server_default="candidate",
|
||||
)
|
||||
created_at: Mapped[datetime] = mapped_column(
|
||||
default=_now, server_default=func.now()
|
||||
)
|
||||
updated_at: Mapped[datetime] = mapped_column(
|
||||
default=_now, server_default=func.now(), onupdate=_now
|
||||
)
|
||||
|
||||
# relationships
|
||||
key_moment: Mapped[KeyMoment] = sa_relationship()
|
||||
source_video: Mapped[SourceVideo] = sa_relationship()
|
||||
|
|
|
|||
51
backend/pipeline/highlight_schemas.py
Normal file
51
backend/pipeline/highlight_schemas.py
Normal file
|
|
@ -0,0 +1,51 @@
|
|||
"""Pydantic schemas for highlight detection pipeline.
|
||||
|
||||
Covers scoring breakdown, candidate responses, and batch result summaries.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import uuid
|
||||
from datetime import datetime
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class HighlightScoreBreakdown(BaseModel):
|
||||
"""Per-dimension score breakdown for a highlight candidate.
|
||||
|
||||
Each field is a float in [0, 1] representing the normalized score
|
||||
for that scoring dimension.
|
||||
"""
|
||||
|
||||
duration_score: float = Field(description="Score based on moment duration (sweet-spot curve)")
|
||||
content_density_score: float = Field(description="Score based on transcript richness / word density")
|
||||
technique_relevance_score: float = Field(description="Score based on content_type and plugin mentions")
|
||||
position_score: float = Field(description="Score based on temporal position within the video")
|
||||
uniqueness_score: float = Field(description="Score based on title/topic distinctness among siblings")
|
||||
engagement_proxy_score: float = Field(description="Proxy engagement signal from summary quality/length")
|
||||
plugin_diversity_score: float = Field(description="Score based on breadth of plugins/tools mentioned")
|
||||
|
||||
|
||||
class HighlightCandidateResponse(BaseModel):
|
||||
"""API response schema for a single highlight candidate."""
|
||||
|
||||
id: uuid.UUID
|
||||
key_moment_id: uuid.UUID
|
||||
source_video_id: uuid.UUID
|
||||
score: float = Field(ge=0.0, le=1.0, description="Composite highlight score")
|
||||
score_breakdown: HighlightScoreBreakdown
|
||||
duration_secs: float = Field(ge=0.0, description="Duration of the key moment in seconds")
|
||||
status: str = Field(description="One of: candidate, approved, rejected")
|
||||
created_at: datetime
|
||||
|
||||
model_config = {"from_attributes": True}
|
||||
|
||||
|
||||
class HighlightBatchResult(BaseModel):
|
||||
"""Summary of a highlight scoring batch run for one video."""
|
||||
|
||||
video_id: uuid.UUID
|
||||
candidates_created: int = Field(ge=0, description="Number of new candidates inserted")
|
||||
candidates_updated: int = Field(ge=0, description="Number of existing candidates re-scored")
|
||||
top_score: float = Field(ge=0.0, le=1.0, description="Highest score in this batch")
|
||||
Loading…
Add table
Reference in a new issue