{"type":"thinking_level_change","id":"006ab7f7","parentId":null,"timestamp":"2026-03-29T23:02:09.930Z","thinkingLevel":"off"}
{"type":"model_change","id":"d45de452","parentId":"006ab7f7","timestamp":"2026-03-29T23:02:09.930Z","provider":"anthropic","modelId":"claude-opus-4-6"}
{"type":"custom_message","customType":"gsd-auto","content":"You are executing GSD auto-mode.\n\n## Working Directory\n\nYour working directory is `/home/aux/projects/content-to-kb-automator`. All file reads, writes, and shell commands MUST operate relative to this directory. Do NOT `cd` to any other directory.\n\nAll relevant context has been preloaded below — start working immediately without re-reading these files.\n\n## Executor Context Constraints\n\nThe agent that executes each task has a **200K token** context window.\n- Recommended task count for this slice: **2–6 tasks**\n- Each task gets ~320K chars of inline context (plans, code, decisions)\n- Keep individual tasks completable within a single context window — if a task needs more context than fits, split it\n\nThen:\n0. If `REQUIREMENTS.md` was preloaded above, identify which Active requirements the roadmap says this slice owns or supports. These are the requirements this plan must deliver — every owned requirement needs at least one task that directly advances it, and verification must prove the requirement is met.\n1. Read the templates:\n   - `~/.gsd/agent/extensions/gsd/templates/plan.md`\n   - `~/.gsd/agent/extensions/gsd/templates/task-plan.md`\n2.  Record the installed skills you expect executors to use in each task plan's `skills_used` frontmatter.\n3. Define slice-level verification — the objective stopping condition for this slice:\n   - For non-trivial slices: plan actual test files with real assertions. Name the files.\n   - For simple slices: executable commands or script assertions are fine.\n   - If the project is non-trivial and has no test framework, the first task should set one up.\n   - If this slice establishes a boundary contract, verification must exercise that contract.\n4. **For non-trivial slices only** — plan observability, proof level, and integration closure:\n   - Include `Observability / Diagnostics` for backend, integration, async, stateful, or UI slices where failure diagnosis matters.\n   - Fill `Proof Level` and `Integration Closure` when the slice crosses runtime boundaries or has meaningful integration concerns.\n   - **Omit these sections entirely for simple slices** where they would all be \"none\" or trivially obvious.\n5. **Quality gates** — for non-trivial slices, fill the Threat Surface (Q3) and Requirement Impact (Q4) sections in the slice plan:\n   - **Threat Surface:** Identify abuse scenarios, data exposure risks, and input trust boundaries. Required when the slice handles user input, authentication, authorization, or sensitive data. Omit entirely for internal refactoring or simple changes.\n   - **Requirement Impact:** List which existing requirements this slice touches, what must be re-verified after shipping, and which prior decisions should be reconsidered. Omit entirely if no existing requirements are affected.\n   - For each task in a non-trivial slice, fill Failure Modes (Q5), Load Profile (Q6), and Negative Tests (Q7) in the task plan when the task has external dependencies, shared resources, or non-trivial input handling. Omit for simple tasks.\n6. Decompose the slice into tasks, each fitting one context window. Each task needs:\n   - a concrete, action-oriented title\n   - the inline task entry fields defined in the plan.md template (Why / Files / Do / Verify / Done when)\n   - a matching task plan file with description, steps, must-haves, verification, inputs, and expected output\n   - **Inputs and Expected Output must list concrete backtick-wrapped file paths** (e.g. `` `src/types.ts` ``). These are machine-parsed to derive task dependencies — vague prose without paths breaks parallel execution. Every task must have at least one output file path.\n   - Observability Impact section **only if the task touches runtime boundaries, async flows, or error paths** — omit it otherwise\n7. **Persist planning state through `gsd_plan_slice`.** Call it with the full slice planning payload (goal, demo, must-haves, verification, tasks, and metadata). The tool inserts all tasks in the same transaction, writes to the DB, and renders `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S04/S04-PLAN.md` and `.gsd/milestones/M001/slices/S04/tasks/T##-PLAN.md` files automatically. Do **not** call `gsd_plan_task` separately — `gsd_plan_slice` handles task persistence. Do **not** rely on direct `PLAN.md` writes as the source of truth; the DB-backed tool is the canonical write path for slice and task planning state.\n8. **Self-audit the plan.** Walk through each check — if any fail, fix the plan files before moving on:\n    - **Completion semantics:** If every task were completed exactly as written, the slice goal/demo should actually be true.\n    - **Requirement coverage:** Every must-have in the slice maps to at least one task. No must-have is orphaned. If `REQUIREMENTS.md` exists, every Active requirement this slice owns maps to at least one task.\n    - **Task completeness:** Every task has steps, must-haves, verification, inputs, and expected output — none are blank or vague. Inputs and Expected Output list backtick-wrapped file paths, not prose descriptions.\n    - **Dependency correctness:** Task ordering is consistent. No task references work from a later task.\n    - **Key links planned:** For every pair of artifacts that must connect, there is an explicit step that wires them.\n    - **Scope sanity:** Target 2–5 steps and 3–8 files per task. 10+ steps or 12+ files — must split. Each task must be completable in a single fresh context window.\n    - **Feature completeness:** Every task produces real, user-facing progress — not just internal scaffolding.\n    - **Quality gate coverage:** For non-trivial slices, Threat Surface and Requirement Impact sections are present and specific (not placeholder text). For non-trivial tasks, Failure Modes, Load Profile, and Negative Tests are addressed in the task plan.\n10. If planning produced structural decisions, append them to `.gsd/DECISIONS.md`\n11. Do not commit — .gsd/ planning docs are managed externally and not tracked in git.\n\nThe slice directory and tasks/ subdirectory already exist. Do NOT mkdir. All work stays in your working directory: `/home/aux/projects/content-to-kb-automator`.\n\n**You MUST call `gsd_plan_slice` to persist the planning state before finishing.**\n\nWhen done, say: \"Slice S04 planned.\"\n## Requirements Advanced\n\n- R003 — Full pipeline stages 2-6 implemented and tested: segmentation, extraction, classification, synthesis, embedding. 10 integration tests verify all stages with mocked LLM and real PostgreSQL.\n- R009 — Write path implemented: EmbeddingClient generates vectors, QdrantManager upserts with metadata payloads. Read path (search query) deferred to S05.\n- R011 — Stage 4 loads canonical_tags.yaml for classification. Tag taxonomy is config-driven.\n- R012 — run_pipeline orchestrator resumes from last completed stage. Auto-dispatch from ingest handles new videos. Manual trigger supports re-processing.\n- R013 — 4 prompt template files loaded from configurable prompts_path. Manual trigger enables re-processing after prompt edits.\n\n## Requirements Validated\n\n- R003 — 10 integration tests prove full pipeline: stage2 updates topic_labels, stage3 creates KeyMoments, stage4 classifies tags, stage5 creates TechniquePages, stage6 embeds to Qdrant. Resumability and LLM fallback tested.\n- R013 — 4 prompt files in prompts/, loaded from configurable path, POST /api/v1/pipeline/trigger/{video_id} enables re-processing.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## UNIT: Plan Slice S04 (\"Review Queue Admin UI\") — Milestone M001\n\n## Inlined Context (preloaded — do not re-read these files)\n\n### Milestone Roadmap\nSource: `.gsd/milestones/M001/M001-ROADMAP.md`\n\n# M001: Chrysopedia Foundation — Infrastructure, Pipeline Core, and Skeleton UI\n\n## Vision\nStand up the complete Chrysopedia stack: Docker Compose deployment on ub01, PostgreSQL data model, FastAPI backend with transcript ingestion, Whisper transcription script for the desktop, LLM extraction pipeline (stages 2-5), review queue, Qdrant integration, and the search-first web UI with technique pages, creators, and topics browsing. By the end, a video file can be transcribed → ingested → extracted → reviewed → searched and read in the web UI.\n\n## Slice Overview\n| ID | Slice | Risk | Depends | Done | After this |\n|----|-------|------|---------|------|------------|\n| S01 | Docker Compose + Database + Whisper Script | low | — | ✅ | docker compose up -d starts all services on ub01; Whisper script transcribes a sample video to JSON |\n| S02 | Transcript Ingestion API | low | S01 | ✅ | POST a transcript JSON file to the API; Creator and Source Video records appear in PostgreSQL |\n| S03 | LLM Extraction Pipeline + Qdrant Integration | high | S02 | ✅ | A transcript JSON triggers stages 2-5: segmentation → extraction → classification → synthesis. Technique pages with key moments appear in DB. Qdrant has searchable embeddings. |\n| S04 | Review Queue Admin UI | medium | S03 | ⬜ | Admin views pending key moments, approves/edits/rejects them, toggles between review and auto mode |\n| S05 | Search-First Web UI | medium | S03 | ⬜ | User searches for a technique, gets semantic results in <500ms, clicks through to a full technique page with study guide prose, key moments, and related links |\n\n---\n\n### Slice Research\nSource: `.gsd/milestones/M001/slices/S04/S04-RESEARCH.md`\n\n# S04 — Review Queue Admin UI — Research\n\n**Date:** 2026-03-29\n\n## Summary\n\nS04 builds the admin review queue for Chrysopedia — the UI and API that let an administrator review, edit, approve, reject, split, and merge extracted key moments before they're published. The spec (§8) defines two modes: **review mode** (all moments queued for human review) and **auto mode** (moments publish directly, queue becomes an audit log). A system-level mode toggle switches between them. The `review_mode: bool = True` setting already exists in `config.py`.\n\nThe work naturally splits into two halves: **backend API endpoints** for review queue operations (list/filter moments, approve/reject/edit, split/merge, mode toggle, status counts) and a **React frontend** admin UI. The frontend is currently a bare placeholder (`package.json` with no framework) but the Docker build pipeline (Node build → nginx SPA serving with `/api/` proxy) is already wired. The backend has established patterns for async SQLAlchemy endpoints, Pydantic schemas, and test fixtures.\n\nThis is medium-complexity CRUD + admin UI work. The data model already has `KeyMoment.review_status` (pending/approved/edited/rejected) and `SourceVideo.processing_status`. The riskiest parts are: (1) split/merge operations on key moments (modifying timestamps and creating/deleting rows), and (2) bootstrapping the React app from zero. No novel technology or unfamiliar APIs involved.\n\n## Recommendation\n\nBuild the backend review API first (new `routers/review.py`), then initialize the React frontend with a minimal admin UI. Use the existing async SQLAlchemy patterns from `routers/creators.py` and `routers/videos.py`. For the frontend, use React + React Router + a lightweight fetching approach (fetch or a small library). No heavy framework needed — this is a single-user admin tool, not a high-traffic public site.\n\nThe mode toggle should be a runtime-mutable setting, not just the config file default. Since there's only one admin, store the toggle in Redis (like stage 4's classification data) or add a `system_settings` table. Redis is simpler and already used by the project.\n\n## Implementation Landscape\n\n### Key Files\n\n**Existing (read/extend):**\n- `backend/models.py` — Has `KeyMoment` (with `review_status: ReviewStatus`), `SourceVideo` (with `processing_status`), `TechniquePage`. All the DB models needed for review are already defined.\n- `backend/schemas.py` — Has `KeyMomentRead`, `KeyMomentBase`. Needs new schemas for review actions (approve, edit, split, merge responses).\n- `backend/config.py` — Has `review_mode: bool = True`. This is the default; runtime toggle needs a separate mechanism.\n- `backend/database.py` — `get_session` async dependency. Used by all routers.\n- `backend/main.py` — Mount point for new review router.\n- `backend/tests/conftest.py` — Test infrastructure with async/sync fixtures, `pre_ingested_video`.\n- `backend/pipeline/stages.py` — `stage5_synthesis` reads `settings.review_mode` to set processing_status. The mode toggle affects new pipeline runs, not existing moments.\n- `backend/routers/pipeline.py` — `POST /pipeline/trigger/{video_id}` for re-processing after prompt edits.\n\n**New files to create:**\n- `backend/routers/review.py` — Review queue API endpoints:\n  - `GET /api/v1/review/queue` — List key moments with filter (status), pagination, grouped by video\n  - `GET /api/v1/review/stats` — Counts by review_status (pending, approved, edited, rejected)\n  - `POST /api/v1/review/moments/{moment_id}/approve` — Set review_status=approved\n  - `POST /api/v1/review/moments/{moment_id}/reject` — Set review_status=rejected\n  - `PUT /api/v1/review/moments/{moment_id}` — Edit fields + set review_status=edited\n  - `POST /api/v1/review/moments/{moment_id}/split` — Split into two moments\n  - `POST /api/v1/review/moments/{moment_id}/merge` — Merge with adjacent moment\n  - `GET /api/v1/review/mode` — Get current review/auto mode\n  - `PUT /api/v1/review/mode` — Toggle review/auto mode\n- `backend/tests/test_review.py` — Integration tests for review API\n- `frontend/src/` — React app source (App, Router, pages)\n- `frontend/package.json` — Updated with React, build tooling\n- `frontend/vite.config.ts` — Vite config for React build\n- `frontend/index.html` — SPA entry point\n- `frontend/src/pages/ReviewQueue.tsx` — Queue view with filter tabs, status counts\n- `frontend/src/pages/MomentReview.tsx` — Individual moment review with actions\n- `frontend/src/components/` — Shared UI components\n\n### Build Order\n\n1. **Backend review API + tests** — Build `routers/review.py` with all endpoints, add schemas, mount in `main.py`, write integration tests. This is the foundation — the frontend is just a consumer of these endpoints. Prove the API works with tests before touching the frontend.\n\n2. **React app bootstrap** — Initialize the React app in `frontend/` with Vite + TypeScript. Get `npm run dev` and `npm run build` working. Verify the Docker build pipeline produces a working SPA.\n\n3. **Review queue UI pages** — Build the queue view (list moments, filter tabs, status counts) and the moment review page (display moment + raw transcript, action buttons). Wire to the backend API.\n\n4. **Mode toggle + integration** — Add the mode toggle UI, connect to the backend toggle endpoint. Verify the full flow: pipeline produces moments → admin reviews/approves → status updates correctly.\n\n### Verification Approach\n\n**Backend:**\n- `cd backend && python -m pytest tests/test_review.py -v` — All review API tests pass\n- `cd backend && python -m pytest tests/ -v` — All existing tests still pass (no regressions)\n- Manual curl: `GET /api/v1/review/stats` returns counts, `POST .../approve` changes status\n\n**Frontend:**\n- `cd frontend && npm run build` — Build succeeds, produces `dist/index.html`\n- `cd frontend && npm run dev` — Dev server starts, admin UI renders\n- Navigate to admin review queue page, see list of moments (requires seeded data or mocked API)\n\n**Integration:**\n- Docker compose builds both services successfully\n- Nginx proxies `/api/` to backend, serves frontend SPA on `/`\n\n## Constraints\n\n- **Async-only in FastAPI handlers** — All review endpoints must use async SQLAlchemy (`AsyncSession`), following the pattern in existing routers. The sync engine/session pattern is only for Celery tasks.\n- **No auth** — The spec doesn't mention authentication for S04. This is a single-admin internal tool. Auth can be added later if needed.\n- **Mode toggle persistence** — `config.py`'s `review_mode` comes from environment variable and is cached via `lru_cache`. A runtime toggle needs Redis or a DB table; changing env vars at runtime is fragile. Redis is the simpler choice — the project already uses it for stage 4 classification data.\n- **KeyMoment lacks topic_tags/topic_category columns** — Classification data is in Redis (24h TTL). The review UI should display tags from Redis if available, or show \"not classified\" if Redis data has expired. This is a read-only concern for S04.\n- **Existing Docker build expects `npm run build` to produce `dist/`** — The frontend build must output to `frontend/dist/` for the nginx Dockerfile to work.\n\n## Common Pitfalls\n\n- **Split/merge moment complexity** — Splitting a moment requires creating a new `KeyMoment` row and adjusting timestamps on both the original and new row. Merging requires combining summaries and extending timestamp ranges, then deleting one row. Both operations must handle the `technique_page_id` foreign key — split moments should keep the same page link, merged moments should keep one.\n- **Redis mode toggle vs config.py** — If the mode toggle is stored in Redis but `pipeline/stages.py` reads `settings.review_mode` from config, changing the toggle won't affect running pipeline tasks. The pipeline needs to read the toggle from the same source as the admin UI. Either the pipeline reads from Redis too, or the toggle updates the Settings object.\n- **Frontend build from zero** — Installing React + Vite + TypeScript from scratch in the existing `frontend/` directory. The `package.json` exists but has no deps. Need to be careful not to break the Docker build — the Dockerfile runs `npm ci` then `npm run build`.\n\n---\n\n### Decisions\nSource: `.gsd/DECISIONS.md`\n\n# Decisions Register\n\n<!-- Append-only. Never edit or remove existing rows.\n     To reverse a decision, add a new row that supersedes it.\n     Read this file at the start of any planning or research phase. -->\n\n| # | When | Scope | Decision | Choice | Rationale | Revisable? | Made By |\n|---|------|-------|----------|--------|-----------|------------|---------|\n| D001 |  | architecture | Storage layer selection | PostgreSQL for structured data, Qdrant (existing instance at 10.0.0.10) for vector embeddings, local filesystem for transcript JSON files | Spec specifies PostgreSQL for JSONB support and Qdrant already running on hypervisor. File store on local filesystem organized by creator slug. Qdrant gets a dedicated collection, not a new instance. | Yes | collaborative |\n| D002 |  | architecture | Timestamp handling for asyncpg with TIMESTAMP WITHOUT TIME ZONE columns | Use datetime.now(timezone.utc).replace(tzinfo=None) in _now() helper | asyncpg rejects timezone-aware datetimes for TIMESTAMP WITHOUT TIME ZONE columns. Stripping tzinfo at the application layer preserves UTC semantics while staying compatible with the existing schema. | Yes | agent |\n| D003 |  | requirement | R002 Transcript Ingestion API status | validated | 6 passing integration tests prove the full POST /api/v1/ingest flow: creator auto-detection, SourceVideo upsert, TranscriptSegment bulk insert, raw JSON persistence, idempotent re-upload, and invalid input rejection. | Yes | agent |\n| D004 |  | architecture | Sync vs async approach for Celery worker pipeline tasks | Use sync openai.OpenAI, sync QdrantClient, and sync SQLAlchemy (create_engine with psycopg2) inside Celery tasks. Convert DATABASE_URL from postgresql+asyncpg:// to postgresql:// for the sync engine. | Celery workers run in a synchronous context. Using asyncio.run() inside tasks risks nested event loop errors with gevent/eventlet workers. Using sync clients throughout eliminates this class of bug entirely. The async engine/session from database.py is only used by FastAPI (ASGI); the worker gets its own sync engine. | Yes | agent |\n| D005 |  | architecture | Embedding/Qdrant failure handling strategy in pipeline | Embedding/Qdrant failures (stage 6) log errors but do not fail the pipeline. Processing_status is set by stages 2-5 only. Embeddings can be regenerated by manual re-trigger. | Qdrant is at 10.0.0.10 on the hypervisor network and may not be reachable during all pipeline runs. Making embedding a non-blocking side-effect ensures core pipeline output (KeyMoments, TechniquePages in PostgreSQL) is never lost due to vector store issues. The manual re-trigger endpoint allows regenerating embeddings at any time. | Yes | agent |\n| D006 |  | requirement | R009 Qdrant Vector Search Integration status | advanced | EmbeddingClient and QdrantManager created with idempotent collection management, metadata-rich upserts for technique pages and key moments. stage6_embed_and_index wired into pipeline as non-blocking side-effect. Integration test verifies embedding and upsert calls. Full validation deferred to S05 when search UI exercises the query path. | Yes | agent |\n\n---\n\n### Requirements\nSource: `.gsd/REQUIREMENTS.md`\n\n# Requirements\n\n## R001 — Whisper Transcription Pipeline\n**Status:** active\n**Description:** Desktop Python script that accepts video files (MP4/MKV), extracts audio via ffmpeg, runs Whisper large-v3 on RTX 4090, and outputs timestamped transcript JSON with segment-level timestamps and word-level timing. Must be resumable.\n**Validation:** Script processes a sample video and produces valid JSON with timestamped segments.\n**Primary Owner:** M001/S01\n\n## R002 — Transcript Ingestion API\n**Status:** active\n**Description:** FastAPI endpoint that accepts transcript JSON uploads, creates/updates Creator and Source Video records, and stores transcript data in PostgreSQL. Handles new creator detection from folder names.\n**Validation:** POST transcript JSON → 200 OK, records created in DB, file stored on filesystem.\n**Primary Owner:** M001/S02\n\n## R003 — LLM-Powered Extraction Pipeline (Stages 2-5)\n**Status:** active\n**Description:** Background worker pipeline: transcript segmentation → key moment extraction → classification/tagging → technique page synthesis. Uses OpenAI-compatible API with primary (DGX Sparks Qwen) and fallback (local Ollama) endpoints. Pipeline must be resumable per-video per-stage.\n**Validation:** End-to-end: transcript JSON in → technique pages with key moments, tags, and cross-references out.\n**Primary Owner:** M001/S03\n\n## R004 — Review Queue UI\n**Status:** active\n**Description:** Admin interface for reviewing extracted key moments: approve, edit+approve, split, merge, reject. Organized by source video for contextual review. Includes mode toggle (review vs auto-publish).\n**Validation:** Admin can review, edit, and approve/reject moments; mode toggle controls whether new moments require review.\n**Primary Owner:** M001/S04\n\n## R005 — Search-First Web UI\n**Status:** active\n**Description:** Landing page with prominent search bar, live typeahead (results after 2-3 chars), scope toggle (All/Topics/Creators), and two navigation cards (Topics, Creators). Recently added section. Search powered by Qdrant semantic search with keyword fallback.\n**Validation:** User types query → results appear within 500ms, grouped by type, with clickable navigation.\n**Primary Owner:** M001/S05\n\n## R006 — Technique Page Display\n**Status:** active\n**Description:** Core content unit: header (tags, title, creator, meta), study guide prose (organized by sub-aspects with signal chain blocks and quotes), key moments index (timestamped list), related techniques, plugins referenced. Amber banner for livestream-sourced content.\n**Validation:** Technique page renders with all sections populated from synthesized data.\n**Primary Owner:** M001/S05\n\n## R007 — Creators Browse Page\n**Status:** active\n**Description:** Filterable creator list with genre filter pills, type-to-narrow, sort options (randomized default, alphabetical, view count). Each row: name, genre tags, technique count, video count, view count. Links to creator detail page.\n**Validation:** Page loads with randomized order, genre filtering works, clicking row navigates to creator detail.\n**Primary Owner:** M001/S05\n\n## R008 — Topics Browse Page\n**Status:** active\n**Description:** Two-level topic hierarchy (6 top-level categories → sub-topics). Filter input, genre filter pills. Each sub-topic shows technique count and creator count. Clicking sub-topic shows technique pages.\n**Validation:** Hierarchy renders, filtering works, sub-topic links show correct technique pages.\n**Primary Owner:** M001/S05\n\n## R009 — Qdrant Vector Search Integration\n**Status:** active\n**Description:** Embed key moment summaries, technique page content, and transcript segments in Qdrant using configurable embedding model (nomic-embed-text default). Power semantic search with metadata filtering.\n**Validation:** Semantic search returns relevant results for natural language queries; embeddings update when content changes.\n**Primary Owner:** M001/S03\n\n## R010 — Docker Compose Deployment\n**Status:** active\n**Description:** Single docker-compose.yml packaging API, web UI, PostgreSQL, and worker services. Follows XPLTD conventions: bind mounts at /vmPool/r/services/, compose at /vmPool/r/compose/chrysopedia/, xpltd_chrysopedia project name, dedicated Docker network.\n**Validation:** `docker compose up -d` brings up all services; data persists across restarts.\n**Primary Owner:** M001/S01\n\n## R011 — Canonical Tag System\n**Status:** active\n**Description:** Editable canonical tag list (config file) with aliases. Pipeline references tags during classification. New tags can be proposed by LLM and queued for admin approval or auto-added within existing categories.\n**Validation:** Tag list is editable; pipeline uses canonical tags consistently; alias normalization works.\n**Primary Owner:** M001/S03\n\n## R012 — Incremental Content Addition\n**Status:** active\n**Description:** System handles ongoing content: new videos processed through pipeline, new creators auto-detected, existing technique pages updated when new moments are added for same creator+topic.\n**Validation:** Adding a new video for an existing creator updates their technique pages; new creator folder creates new Creator record.\n**Primary Owner:** M001/S03\n\n## R013 — Prompt Template System\n**Status:** active\n**Description:** Extraction prompts (stages 2-5) stored as editable configuration files, not hardcoded. Admin can edit prompts and re-run extraction on specific or all videos for calibration.\n**Validation:** Prompt files are editable; re-processing a video with updated prompts produces different output.\n**Primary Owner:** M001/S03\n\n## R014 — Creator Equity\n**Status:** active\n**Description:** No creator is privileged in the UI. Default sort on Creators page is randomized on every page load. All creators get equal visual weight.\n**Validation:** Refreshing Creators page shows different order each time; no creator gets larger/bolder display.\n**Primary Owner:** M001/S05\n\n## R015 — 30-Second Retrieval Target\n**Status:** active\n**Description:** A producer mid-session can find a specific technique in under 30 seconds from Alt+Tab to reading the key insight.\n**Validation:** Timed test: Alt+Tab → search → read technique → under 30 seconds.\n**Primary Owner:** M001/S05\n\n---\n\n### Project Knowledge\nSource: `.gsd/KNOWLEDGE.md`\n\n# KNOWLEDGE\n\n## SQLAlchemy column names that shadow ORM functions\n\n**Context:** If a model has a column named `relationship` (or any other ORM function name like `query`, `metadata`), Python resolves the name to the column's `MappedColumn` descriptor within the class body. This causes `relationship(...)` calls below it to fail with \"MappedColumn object is not callable\".\n\n**Fix:** Import with an alias: `from sqlalchemy.orm import relationship as sa_relationship` and use `sa_relationship(...)` throughout. Alternatively, rename the database column, but the spec defines `relationship` as the column name so we preserved it.\n\n## Docker Compose variable interpolation and `:?` syntax\n\n**Context:** `${VAR:?error message}` in docker-compose.yml makes `docker compose config` fail when the variable is unset, even if `env_file: required: false` is used. The `required: false` only controls whether the `.env` file must exist — it does NOT provide defaults for variables referenced with `:?`.\n\n**Fix:** Use `${VAR:-default}` for variables that need a fallback, or ensure the variable is always set in the environment.\n\n## Host port 8000 conflict with kerf-engine\n\n**Context:** The kerf-engine Docker container (`kerf-engine-kerf-engine-1`) binds to `0.0.0.0:8000`. When testing the Chrysopedia API locally (outside Docker), curl requests to `localhost:8000` hit the kerf-engine container instead of the local uvicorn process, even when uvicorn binds to `127.0.0.1:8000`.\n\n**Fix:** Use an alternate port (e.g., 8001) for local testing. In Docker Compose, chrysopedia-api maps `127.0.0.1:8000:8000` which is fine because it goes through Docker's port forwarding, but will conflict with kerf-engine if both stacks run simultaneously. Consider changing one project's external port mapping.\n\n## asyncpg rejects timezone-aware datetimes for TIMESTAMP WITHOUT TIME ZONE columns\n\n**Context:** SQLAlchemy models using `default=datetime.now(timezone.utc)` produce timezone-aware Python datetimes. When the PostgreSQL column type is `TIMESTAMP WITHOUT TIME ZONE` (the default), asyncpg raises `DataError: can't subtract offset-naive and offset-aware datetimes`. This only surfaces when running against a real PostgreSQL database — SQLite-based tests won't catch it.\n\n**Fix:** Use `datetime.now(timezone.utc).replace(tzinfo=None)` in the `_now()` helper, or change the column type to `TIMESTAMP WITH TIME ZONE`. We chose the former since the existing schema uses naive timestamps.\n\n## asyncpg NullPool required for pytest-asyncio integration tests\n\n**Context:** When using a session-scoped SQLAlchemy async engine with asyncpg in pytest-asyncio tests, the connection pool reuses connections across fixtures and test functions. This causes `InterfaceError: cannot perform operation: another operation is in progress` because the ASGI test client's session holds a connection while cleanup/verification fixtures try to use the same pool.\n\n**Fix:** Use `poolclass=NullPool` when creating the test engine. Each connection is created fresh and immediately closed, eliminating contention. Performance cost is negligible for test suites.\n\n## Testing Celery tasks that use sync SQLAlchemy: patch module-level globals\n\n**Context:** Pipeline stages in `pipeline/stages.py` create their own sync SQLAlchemy engine/session via module-level `_engine` and `_SessionLocal` globals (because Celery is sync, not async). Tests need to redirect these to the test database, but the engine is created lazily at module scope.\n\n**Fix:** Patch the module globals directly: `unittest.mock.patch.object(stages, '_engine', test_engine)` and `unittest.mock.patch.object(stages, '_SessionLocal', test_session_factory)`. This redirects all DB access in stage functions to the test database without modifying production code.\n\n## Lazy imports in FastAPI handlers defeat simple mock patching\n\n**Context:** When a FastAPI handler imports a function lazily (inside the function body) like `from pipeline.stages import run_pipeline`, patching `routers.ingest.run_pipeline` has no effect because the name is re-bound on every call from the source module.\n\n**Fix:** Patch at the source module: `unittest.mock.patch('pipeline.stages.run_pipeline')`. The lazy import will pick up the mock from the source module. This applies to any handler that uses lazy imports to avoid circular dependencies at module load time.\n\n## Stage 4 classification data stored in Redis (not DB columns)\n\n**Context:** The KeyMoment SQLAlchemy model doesn't have `topic_tags` or `topic_category` columns. Stage 4 classification needs somewhere to store per-moment tag assignments that stage 5 can read.\n\n**Fix:** Store classification results in Redis under key `chrysopedia:classification:{video_id}` with a 24-hour TTL. Stage 5 reads from Redis. This avoids schema migrations during initial pipeline development. The data is ephemeral — if Redis loses it, re-running stage 4 regenerates it.\n\n[...truncated 2 sections]\n\n### Dependency Slice Summaries\n\nPay particular attention to **Forward Intelligence** sections — they contain hard-won knowledge about what's fragile, what assumptions changed, and what this slice should watch out for.\n\n#### S03 Summary\nSource: `.gsd/milestones/M001/slices/S03/S03-SUMMARY.md`\n\n---\nid: S03\nparent: M001\nmilestone: M001\nprovides:\n  - 6 Celery tasks: stage2-6 + run_pipeline orchestrator\n  - LLMClient with primary/fallback for downstream use\n  - EmbeddingClient for vector generation\n  - QdrantManager for vector store operations\n  - POST /api/v1/pipeline/trigger/{video_id} manual re-trigger endpoint\n  - 8 Pydantic schemas for pipeline stage I/O\n  - 4 editable prompt templates in prompts/\n  - 10 integration tests with mock fixtures\nrequires:\n  - slice: S02\n    provides: Ingest endpoint, database models (SourceVideo, TranscriptSegment, KeyMoment, TechniquePage, Creator), async SQLAlchemy engine, test infrastructure\naffects:\n  - S04\n  - S05\nkey_files:\n  - backend/config.py\n  - backend/worker.py\n  - backend/pipeline/__init__.py\n  - backend/pipeline/schemas.py\n  - backend/pipeline/llm_client.py\n  - backend/pipeline/embedding_client.py\n  - backend/pipeline/qdrant_client.py\n  - backend/pipeline/stages.py\n  - backend/routers/pipeline.py\n  - backend/routers/ingest.py\n  - backend/main.py\n  - prompts/stage2_segmentation.txt\n  - prompts/stage3_extraction.txt\n  - prompts/stage4_classification.txt\n  - prompts/stage5_synthesis.txt\n  - backend/tests/test_pipeline.py\n  - backend/tests/fixtures/mock_llm_responses.py\n  - backend/tests/conftest.py\n  - backend/requirements.txt\nkey_decisions:\n  - Sync OpenAI/SQLAlchemy/Qdrant throughout Celery tasks — no async in worker context (D004)\n  - Embedding/Qdrant stage is non-blocking side-effect — failures don't break pipeline (D005)\n  - Stage 4 classification stored in Redis (24h TTL) due to missing KeyMoment columns\n  - Pipeline dispatch from ingest is best-effort; manual trigger returns 503 on Celery failure\n  - LLMClient retries once with JSON nudge on malformed LLM output before failing\npatterns_established:\n  - Celery task pattern: @celery_app.task(bind=True, max_retries=3) with sync SQLAlchemy session per task\n  - LLM client pattern: primary → fallback → fail, with Pydantic response parsing\n  - Non-blocking side-effect pattern: max_retries=0, catch-all exception handler, pipeline continues\n  - Prompt template pattern: plain text files in prompts/ dir, XML-style content fencing, loaded at runtime\n  - Pipeline test pattern: patch module-level _engine/_SessionLocal globals to redirect stages to test DB\nobservability_surfaces:\n  - INFO log at start/end of each stage with video_id and duration\n  - WARNING on LLM fallback trigger\n  - ERROR on LLM parse failure with raw response excerpt\n  - WARNING on embedding/Qdrant failures with error details\n  - source_videos.processing_status tracks pipeline progress per video\n  - Celery task registry shows all 6 registered tasks\n  - POST /api/v1/pipeline/trigger/{video_id} returns current processing_status\ndrill_down_paths:\n  - .gsd/milestones/M001/slices/S03/tasks/T01-SUMMARY.md\n  - .gsd/milestones/M001/slices/S03/tasks/T02-SUMMARY.md\n  - .gsd/milestones/M001/slices/S03/tasks/T03-SUMMARY.md\n  - .gsd/milestones/M001/slices/S03/tasks/T04-SUMMARY.md\n  - .gsd/milestones/M001/slices/S03/tasks/T05-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-03-29T22:59:23.268Z\nblocker_discovered: false\n---\n\n# S03: LLM Extraction Pipeline + Qdrant Integration\n\n**Built the complete 6-stage LLM extraction pipeline (segmentation → extraction → classification → synthesis → embedding) with Celery workers, sync SQLAlchemy, primary/fallback LLM endpoints, Qdrant vector indexing, configurable prompt templates, auto-dispatch from ingest, manual re-trigger API, and 10 integration tests — all 16 tests pass.**\n\n## What Happened\n\n## What This Slice Delivered\n\nS03 implemented the core intelligence of Chrysopedia: the background worker pipeline that transforms raw transcripts into structured knowledge (technique pages, key moments, topic tags, embeddings).\n\n### T01 — Infrastructure Foundation\nExtended Settings with 12 config fields (LLM primary/fallback endpoints, embedding config, Qdrant connection, prompt path, review mode). Created the Celery app in `worker.py` using Redis as broker. Built `LLMClient` with sync `openai.OpenAI` and primary→fallback logic that catches `APIConnectionError` and `APITimeoutError`. Defined 8 Pydantic schemas (`TopicSegment`, `SegmentationResult`, `ExtractedMoment`, `ExtractionResult`, `ClassifiedMoment`, `ClassificationResult`, `SynthesizedPage`, `SynthesisResult`) matching the pipeline stage inputs/outputs.\n\n### T02 — Pipeline Stages + Prompt Templates\nCreated 4 prompt template files in `prompts/` with XML-style content fencing. Implemented 5 Celery tasks in `pipeline/stages.py`:\n- **stage2_segmentation**: Groups transcript segments into topic boundaries, updates `topic_label` on TranscriptSegment rows\n- **stage3_extraction**: Extracts key moments from topic groups, creates KeyMoment rows, sets `processing_status=extracted`\n- **stage4_classification**: Classifies moments against `canonical_tags.yaml`, stores results in Redis (24h TTL) since KeyMoment lacks tag columns\n- **stage5_synthesis**: Synthesizes TechniquePage rows from grouped moments, links KeyMoments, sets `processing_status=reviewed` (or `published` if `review_mode=False`)\n- **run_pipeline**: Orchestrator that checks `processing_status` and chains only the remaining stages for resumability\n\nAll tasks use sync SQLAlchemy sessions (psycopg2) with `bind=True, max_retries=3`. `_safe_parse_llm_response` retries once with a JSON nudge on malformed output.\n\n### T03 — Embedding & Qdrant Integration\nCreated `EmbeddingClient` (sync `openai.OpenAI` for `/v1/embeddings`) that returns empty list on errors. Created `QdrantManager` with idempotent `ensure_collection()` (cosine distance, config-driven dimensions) and `upsert_technique_pages()`/`upsert_key_moments()` with full metadata payloads. Added `stage6_embed_and_index` as a non-blocking side-effect (`max_retries=0`, catches all exceptions) appended to the pipeline chain.\n\n### T04 — API Wiring\nWired `run_pipeline.delay()` dispatch after ingest commit (best-effort — failures don't break ingest response). Added `POST /api/v1/pipeline/trigger/{video_id}` for manual re-processing (returns 404 for missing video, 503 on Celery failure). Mounted pipeline router in `main.py`.\n\n### T05 — Integration Tests\nCreated 10 integration tests with mocked LLM/Qdrant and real PostgreSQL: stages 2-6 produce correct DB records, pipeline resumes from `extracted` status, trigger endpoint returns 200/404, ingest dispatches pipeline, LLM falls back on primary failure. All 16 tests (6 ingest + 10 pipeline) pass.\n\n### Key Deviation\nStage 4 stores classification data in Redis rather than DB columns because `KeyMoment` model lacks `topic_tags`/`topic_category` columns. This is an intentional simplification — stage 5 reads from Redis during synthesis.\n\n## Verification\n\nAll slice verification checks pass:\n\n**T01 verification (5/5):** Settings prints correct defaults, all 8 schema classes import, LLMClient imports clean, celery_app.main prints 'chrysopedia', openai/qdrant-client in requirements.txt.\n\n**T02 verification (3/3):** 4 prompt files exist, all 5 stage functions import, worker shows 6 registered tasks.\n\n**T03 verification (3/3):** EmbeddingClient, QdrantManager, stage6_embed_and_index all import successfully.\n\n**T04 verification (3/3):** Pipeline router has /trigger/{video_id} route, pipeline in main.py, run_pipeline in ingest.py.\n\n**T05 verification (2/2):** `cd backend && python -m pytest tests/test_pipeline.py -v` — 10/10 pass. `cd backend && python -m pytest tests/ -v` — 16/16 pass (122s).\n\nAll 16 registered tests pass. 6 Celery tasks registered in worker.\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nStage 4 classification data stored in Redis (not DB columns) because KeyMoment model lacks topic_tags/topic_category columns. Added psycopg2-binary to requirements.txt for sync SQLAlchemy. Created pipeline/stages.py stub in T01 so worker.py import chain succeeds ahead of T02. Pipeline router uses lazy import of run_pipeline inside handler to avoid circular imports.\n\n## Known Limitations\n\nStage 4 classification stored in Redis with 24h TTL — if Redis is flushed between stage 4 and stage 5, classification data is lost. QdrantManager uses random UUIDs for point IDs — re-indexing creates duplicates rather than updating existing points. KeyMoment model needs topic_tags/topic_category columns for a permanent solution.\n\n## Follow-ups\n\nAdd topic_tags and topic_category columns to KeyMoment model to eliminate Redis dependency for classification. Add deterministic point IDs to QdrantManager based on content hash for idempotent re-indexing. Consider adding a /api/v1/pipeline/status/{video_id} endpoint for monitoring pipeline progress.\n\n## Files Created/Modified\n\n- `backend/config.py` — Extended Settings with 12 LLM/embedding/Qdrant/prompt config fields\n- `backend/requirements.txt` — Added openai, qdrant-client, pyyaml, psycopg2-binary\n- `backend/worker.py` — Created Celery app with Redis broker, imports pipeline.stages\n- `backend/pipeline/__init__.py` — Created empty package init\n- `backend/pipeline/schemas.py` — 8 Pydantic models for pipeline stage I/O\n- `backend/pipeline/llm_client.py` — Sync LLMClient with primary/fallback logic\n- `backend/pipeline/embedding_client.py` — Sync EmbeddingClient for /v1/embeddings\n- `backend/pipeline/qdrant_client.py` — QdrantManager with idempotent collection mgmt and metadata upserts\n- `backend/pipeline/stages.py` — 6 Celery tasks: stages 2-6 + run_pipeline orchestrator\n- `backend/routers/pipeline.py` — POST /trigger/{video_id} manual re-trigger endpoint\n- `backend/routers/ingest.py` — Added run_pipeline.delay() dispatch after ingest commit\n- `backend/main.py` — Mounted pipeline router under /api/v1\n- `prompts/stage2_segmentation.txt` — LLM prompt for topic boundary detection\n- `prompts/stage3_extraction.txt` — LLM prompt for key moment extraction\n- `prompts/stage4_classification.txt` — LLM prompt for canonical tag classification\n- `prompts/stage5_synthesis.txt` — LLM prompt for technique page synthesis\n- `backend/tests/test_pipeline.py` — 10 integration tests covering all pipeline stages\n- `backend/tests/fixtures/mock_llm_responses.py` — Mock LLM response fixtures for all stages\n- `backend/tests/conftest.py` — Added sync engine/session fixtures and pre_ingested_video fixture\n\n## Your Role in the Pipeline\n\nYou have full tool access. Before decomposing, explore the relevant code to ground your plan in reality.\n\n### Verify Roadmap Assumptions\n\nCheck prior slice summaries (inlined above as dependency summaries, if present). If prior slices discovered constraints, changed approaches, or flagged fragility, adjust your plan accordingly. The roadmap description may be stale — verify it against the current codebase state.\n\n### Explore Slice Scope\n\nRead the code files relevant to this slice. Confirm the roadmap's description of what exists, what needs to change, and what boundaries apply. Use `rg`, `find`, and targeted reads.\n\n### Source Files\n\n- **Requirements**: `.gsd/REQUIREMENTS.md`\n- **Decisions**: `.gsd/DECISIONS.md`\n- **Roadmap**: `.gsd/milestones/M001/M001-ROADMAP.md`\n- **Slice Research**: `.gsd/milestones/M001/slices/S04/S04-RESEARCH.md`\n\nIf slice research exists (inlined above), trust those findings and skip redundant exploration.\n\nAfter you finish, **executor agents** implement each task in isolated fresh context windows. They see only their task plan, the slice plan excerpt (goal/demo/verification), and compressed summaries of prior tasks. They do not see the research doc, the roadmap, or REQUIREMENTS.md. Everything an executor needs must be in the task plan itself — file paths, specific steps, expected inputs and outputs.\n\nNarrate your decomposition reasoning — why you're grouping work this way, what risks are driving the order, what verification strategy you're choosing and why. Keep the narration proportional to the work — a simple slice doesn't need a long justification — but write in complete sentences, not planner shorthand.\n\n**Right-size the plan.** If the slice is simple enough to be 1 task, plan 1 task. Don't split into multiple tasks just because you can identify sub-steps. Don't fill in sections with \"None\" when the section doesn't apply — omit them entirely. The plan's job is to guide execution, not to fill a template.\n","display":false,"id":"b968e316","parentId":"d45de452","timestamp":"2026-03-29T23:02:09.932Z"}
{"type":"message","id":"7bb177e5","parentId":"b968e316","timestamp":"2026-03-29T23:02:16.407Z","message":{"role":"assistant","content":[{"type":"text","text":"\n\nI'll start by exploring the current codebase to ground the plan in reality — checking what exists in the backend models, schemas, routers, and the frontend directory."},{"type":"toolCall","id":"toolu_01GPXgbM6qibfJ9K19FcjHYe","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find backend/routers -type f -name '*.py' | head -20 && echo \"---\" && find backend/models* -type f 2>/dev/null && echo \"---\" && find frontend -type f 2>/dev/null | head -30"}},{"type":"toolCall","id":"toolu_017ZHr2AXnwnyuXkE1QtkDba","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/models.py | head -120"}},{"type":"toolCall","id":"toolu_019Y4yorfVy6uppxztqPyYiC","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":283,"cacheRead":49033,"cacheWrite":12079,"totalTokens":61397,"cost":{"input":0.00001,"output":0.0070750000000000006,"cacheRead":0.0245165,"cacheWrite":0.07549375,"total":0.10709525}},"stopReason":"toolUse","timestamp":1774825329931}}
{"type":"message","id":"e84bd33e","parentId":"7bb177e5","timestamp":"2026-03-29T23:02:16.430Z","message":{"role":"toolResult","toolCallId":"toolu_01GPXgbM6qibfJ9K19FcjHYe","toolName":"bash","content":[{"type":"text","text":"backend/routers/health.py\nbackend/routers/videos.py\nbackend/routers/pipeline.py\nbackend/routers/__init__.py\nbackend/routers/creators.py\nbackend/routers/ingest.py\n---\nbackend/models.py\n---\nfrontend/package.json\n"}],"isError":false,"timestamp":1774825336430}}
{"type":"message","id":"6b0236ab","parentId":"e84bd33e","timestamp":"2026-03-29T23:02:16.431Z","message":{"role":"toolResult","toolCallId":"toolu_017ZHr2AXnwnyuXkE1QtkDba","toolName":"bash","content":[{"type":"text","text":"\"\"\"SQLAlchemy ORM models for the Chrysopedia knowledge base.\n\nSeven entities matching chrysopedia-spec.md §6.1:\n  Creator, SourceVideo, TranscriptSegment, KeyMoment,\n  TechniquePage, RelatedTechniqueLink, Tag\n\"\"\"\n\nfrom __future__ import annotations\n\nimport enum\nimport uuid\nfrom datetime import datetime, timezone\n\nfrom sqlalchemy import (\n    Enum,\n    Float,\n    ForeignKey,\n    Integer,\n    String,\n    Text,\n    UniqueConstraint,\n    func,\n)\nfrom sqlalchemy.dialects.postgresql import ARRAY, JSONB, UUID\nfrom sqlalchemy.orm import Mapped, mapped_column\nfrom sqlalchemy.orm import relationship as sa_relationship\n\nfrom database import Base\n\n\n# ── Enums ────────────────────────────────────────────────────────────────────\n\nclass ContentType(str, enum.Enum):\n    \"\"\"Source video content type.\"\"\"\n    tutorial = \"tutorial\"\n    livestream = \"livestream\"\n    breakdown = \"breakdown\"\n    short_form = \"short_form\"\n\n\nclass ProcessingStatus(str, enum.Enum):\n    \"\"\"Pipeline processing status for a source video.\"\"\"\n    pending = \"pending\"\n    transcribed = \"transcribed\"\n    extracted = \"extracted\"\n    reviewed = \"reviewed\"\n    published = \"published\"\n\n\nclass KeyMomentContentType(str, enum.Enum):\n    \"\"\"Content classification for a key moment.\"\"\"\n    technique = \"technique\"\n    settings = \"settings\"\n    reasoning = \"reasoning\"\n    workflow = \"workflow\"\n\n\nclass ReviewStatus(str, enum.Enum):\n    \"\"\"Human review status for key moments.\"\"\"\n    pending = \"pending\"\n    approved = \"approved\"\n    edited = \"edited\"\n    rejected = \"rejected\"\n\n\nclass SourceQuality(str, enum.Enum):\n    \"\"\"Derived source quality for technique pages.\"\"\"\n    structured = \"structured\"\n    mixed = \"mixed\"\n    unstructured = \"unstructured\"\n\n\nclass PageReviewStatus(str, enum.Enum):\n    \"\"\"Review lifecycle for technique pages.\"\"\"\n    draft = \"draft\"\n    reviewed = \"reviewed\"\n    published = \"published\"\n\n\nclass RelationshipType(str, enum.Enum):\n    \"\"\"Types of links between technique pages.\"\"\"\n    same_technique_other_creator = \"same_technique_other_creator\"\n    same_creator_adjacent = \"same_creator_adjacent\"\n    general_cross_reference = \"general_cross_reference\"\n\n\n# ── Helpers ──────────────────────────────────────────────────────────────────\n\ndef _uuid_pk() -> Mapped[uuid.UUID]:\n    return mapped_column(\n        UUID(as_uuid=True),\n        primary_key=True,\n        default=uuid.uuid4,\n        server_default=func.gen_random_uuid(),\n    )\n\n\ndef _now() -> datetime:\n    \"\"\"Return current UTC time as a naive datetime (no tzinfo).\n\n    PostgreSQL TIMESTAMP WITHOUT TIME ZONE columns require naive datetimes.\n    asyncpg rejects timezone-aware datetimes for such columns.\n    \"\"\"\n    return datetime.now(timezone.utc).replace(tzinfo=None)\n\n\n# ── Models ───────────────────────────────────────────────────────────────────\n\nclass Creator(Base):\n    __tablename__ = \"creators\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    name: Mapped[str] = mapped_column(String(255), nullable=False)\n    slug: Mapped[str] = mapped_column(String(255), unique=True, nullable=False)\n    genres: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    folder_name: Mapped[str] = mapped_column(String(255), nullable=False)\n    view_count: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n"}],"isError":false,"timestamp":1774825336430}}
{"type":"message","id":"90b9946d","parentId":"6b0236ab","timestamp":"2026-03-29T23:02:16.433Z","message":{"role":"toolResult","toolCallId":"toolu_019Y4yorfVy6uppxztqPyYiC","toolName":"bash","content":[{"type":"text","text":"\"\"\"Pydantic schemas for the Chrysopedia API.\n\nRead-only schemas for list/detail endpoints and input schemas for creation.\nEach schema mirrors the corresponding SQLAlchemy model in models.py.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport uuid\nfrom datetime import datetime\n\nfrom pydantic import BaseModel, ConfigDict, Field\n\n\n# ── Health ───────────────────────────────────────────────────────────────────\n\nclass HealthResponse(BaseModel):\n    status: str = \"ok\"\n    service: str = \"chrysopedia-api\"\n    version: str = \"0.1.0\"\n    database: str = \"unknown\"\n\n\n# ── Creator ──────────────────────────────────────────────────────────────────\n\nclass CreatorBase(BaseModel):\n    name: str\n    slug: str\n    genres: list[str] | None = None\n    folder_name: str\n\nclass CreatorCreate(CreatorBase):\n    pass\n\nclass CreatorRead(CreatorBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    view_count: int = 0\n    created_at: datetime\n    updated_at: datetime\n\n\nclass CreatorDetail(CreatorRead):\n    \"\"\"Creator with nested video count.\"\"\"\n    video_count: int = 0\n\n\n# ── SourceVideo ──────────────────────────────────────────────────────────────\n\nclass SourceVideoBase(BaseModel):\n    filename: str\n    file_path: str\n    duration_seconds: int | None = None\n    content_type: str\n    transcript_path: str | None = None\n\nclass SourceVideoCreate(SourceVideoBase):\n    creator_id: uuid.UUID\n\nclass SourceVideoRead(SourceVideoBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    creator_id: uuid.UUID\n    processing_status: str = \"pending\"\n    created_at: datetime\n    updated_at: datetime\n\n\n# ── TranscriptSegment ────────────────────────────────────────────────────────\n\nclass TranscriptSegmentBase(BaseModel):\n    start_time: float\n    end_time: float\n    text: str\n    segment_index: int\n    topic_label: str | None = None\n\nclass TranscriptSegmentCreate(TranscriptSegmentBase):\n    source_video_id: uuid.UUID\n\nclass TranscriptSegmentRead(TranscriptSegmentBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    source_video_id: uuid.UUID\n\n\n# ── KeyMoment ────────────────────────────────────────────────────────────────\n\nclass KeyMomentBase(BaseModel):\n    title: str\n    summary: str\n    start_time: float\n    end_time: float\n    content_type: str\n    plugins: list[str] | None = None\n    raw_transcript: str | None = None\n\nclass KeyMomentCreate(KeyMomentBase):\n    source_video_id: uuid.UUID\n    technique_page_id: uuid.UUID | None = None\n\nclass KeyMomentRead(KeyMomentBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    source_video_id: uuid.UUID\n    technique_page_id: uuid.UUID | None = None\n    review_status: str = \"pending\"\n    created_at: datetime\n    updated_at: datetime\n\n\n# ── TechniquePage ────────────────────────────────────────────────────────────\n\nclass TechniquePageBase(BaseModel):\n    title: str\n    slug: str\n    topic_category: str\n    topic_tags: list[str] | None = None\n    summary: str | None = None\n    body_sections: dict | None = None\n    signal_chains: list | None = None\n    plugins: list[str] | None = None\n\nclass TechniquePageCreate(TechniquePageBase):\n    creator_id: uuid.UUID\n    source_quality: str | None = None\n\nclass TechniquePageRead(TechniquePageBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    creator_id: uuid.UUID\n    source_quality: str | None = None\n    view_count: int = 0\n    review_status: str = \"draft\"\n    created_at: datetime\n    updated_at: datetime\n\n\n# ── RelatedTechniqueLink ─────────────────────────────────────────────────────\n\nclass RelatedTechniqueLinkBase(BaseModel):\n    source_page_id: uuid.UUID\n    target_page_id: uuid.UUID\n    relationship: str\n\nclass RelatedTechniqueLinkCreate(RelatedTechniqueLinkBase):\n    pass\n\nclass RelatedTechniqueLinkRead(RelatedTechniqueLinkBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n\n\n# ── Tag ──────────────────────────────────────────────────────────────────────\n\nclass TagBase(BaseModel):\n    name: str\n    category: str\n    aliases: list[str] | None = None\n\nclass TagCreate(TagBase):\n    pass\n\nclass TagRead(TagBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n\n\n# ── Transcript Ingestion ─────────────────────────────────────────────────────\n\nclass TranscriptIngestResponse(BaseModel):\n    \"\"\"Response returned after successfully ingesting a transcript.\"\"\"\n    video_id: uuid.UUID\n    creator_id: uuid.UUID\n    creator_name: str\n    filename: str\n    segments_stored: int\n    processing_status: str\n    is_reupload: bool\n\n\n# ── Pagination wrapper ───────────────────────────────────────────────────────\n\nclass PaginatedResponse(BaseModel):\n    \"\"\"Generic paginated list response.\"\"\"\n    items: list = Field(default_factory=list)\n    total: int = 0\n    offset: int = 0\n    limit: int = 50\n"}],"isError":false,"timestamp":1774825336431}}
{"type":"message","id":"d8e4558e","parentId":"90b9946d","timestamp":"2026-03-29T23:02:23.032Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_011JjYH39migXoQnEwbMDBu2","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/models.py | tail -200"}},{"type":"toolCall","id":"toolu_011MQpubsnEatxUyKZdH2xmr","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/main.py"}},{"type":"toolCall","id":"toolu_01NRzk8uBQPjx4f4PDRQYEtt","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/config.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":192,"cacheRead":61112,"cacheWrite":2913,"totalTokens":64218,"cost":{"input":0.000005,"output":0.0048000000000000004,"cacheRead":0.030556,"cacheWrite":0.01820625,"total":0.053567250000000004}},"stopReason":"toolUse","timestamp":1774825336432}}
{"type":"message","id":"abc61c59","parentId":"d8e4558e","timestamp":"2026-03-29T23:02:23.058Z","message":{"role":"toolResult","toolCallId":"toolu_011JjYH39migXoQnEwbMDBu2","toolName":"bash","content":[{"type":"text","text":"def _now() -> datetime:\n    \"\"\"Return current UTC time as a naive datetime (no tzinfo).\n\n    PostgreSQL TIMESTAMP WITHOUT TIME ZONE columns require naive datetimes.\n    asyncpg rejects timezone-aware datetimes for such columns.\n    \"\"\"\n    return datetime.now(timezone.utc).replace(tzinfo=None)\n\n\n# ── Models ───────────────────────────────────────────────────────────────────\n\nclass Creator(Base):\n    __tablename__ = \"creators\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    name: Mapped[str] = mapped_column(String(255), nullable=False)\n    slug: Mapped[str] = mapped_column(String(255), unique=True, nullable=False)\n    genres: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    folder_name: Mapped[str] = mapped_column(String(255), nullable=False)\n    view_count: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    videos: Mapped[list[SourceVideo]] = sa_relationship(back_populates=\"creator\")\n    technique_pages: Mapped[list[TechniquePage]] = sa_relationship(back_populates=\"creator\")\n\n\nclass SourceVideo(Base):\n    __tablename__ = \"source_videos\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    creator_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"creators.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    filename: Mapped[str] = mapped_column(String(500), nullable=False)\n    file_path: Mapped[str] = mapped_column(String(1000), nullable=False)\n    duration_seconds: Mapped[int] = mapped_column(Integer, nullable=True)\n    content_type: Mapped[ContentType] = mapped_column(\n        Enum(ContentType, name=\"content_type\", create_constraint=True),\n        nullable=False,\n    )\n    transcript_path: Mapped[str | None] = mapped_column(String(1000), nullable=True)\n    processing_status: Mapped[ProcessingStatus] = mapped_column(\n        Enum(ProcessingStatus, name=\"processing_status\", create_constraint=True),\n        default=ProcessingStatus.pending,\n        server_default=\"pending\",\n    )\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    creator: Mapped[Creator] = sa_relationship(back_populates=\"videos\")\n    segments: Mapped[list[TranscriptSegment]] = sa_relationship(back_populates=\"source_video\")\n    key_moments: Mapped[list[KeyMoment]] = sa_relationship(back_populates=\"source_video\")\n\n\nclass TranscriptSegment(Base):\n    __tablename__ = \"transcript_segments\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_video_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    start_time: Mapped[float] = mapped_column(Float, nullable=False)\n    end_time: Mapped[float] = mapped_column(Float, nullable=False)\n    text: Mapped[str] = mapped_column(Text, nullable=False)\n    segment_index: Mapped[int] = mapped_column(Integer, nullable=False)\n    topic_label: Mapped[str | None] = mapped_column(String(255), nullable=True)\n\n    # relationships\n    source_video: Mapped[SourceVideo] = sa_relationship(back_populates=\"segments\")\n\n\nclass KeyMoment(Base):\n    __tablename__ = \"key_moments\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_video_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    technique_page_id: Mapped[uuid.UUID | None] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"SET NULL\"), nullable=True\n    )\n    title: Mapped[str] = mapped_column(String(500), nullable=False)\n    summary: Mapped[str] = mapped_column(Text, nullable=False)\n    start_time: Mapped[float] = mapped_column(Float, nullable=False)\n    end_time: Mapped[float] = mapped_column(Float, nullable=False)\n    content_type: Mapped[KeyMomentContentType] = mapped_column(\n        Enum(KeyMomentContentType, name=\"key_moment_content_type\", create_constraint=True),\n        nullable=False,\n    )\n    plugins: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    review_status: Mapped[ReviewStatus] = mapped_column(\n        Enum(ReviewStatus, name=\"review_status\", create_constraint=True),\n        default=ReviewStatus.pending,\n        server_default=\"pending\",\n    )\n    raw_transcript: Mapped[str | None] = mapped_column(Text, nullable=True)\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    source_video: Mapped[SourceVideo] = sa_relationship(back_populates=\"key_moments\")\n    technique_page: Mapped[TechniquePage | None] = sa_relationship(\n        back_populates=\"key_moments\", foreign_keys=[technique_page_id]\n    )\n\n\nclass TechniquePage(Base):\n    __tablename__ = \"technique_pages\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    creator_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"creators.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    title: Mapped[str] = mapped_column(String(500), nullable=False)\n    slug: Mapped[str] = mapped_column(String(500), unique=True, nullable=False)\n    topic_category: Mapped[str] = mapped_column(String(255), nullable=False)\n    topic_tags: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    summary: Mapped[str | None] = mapped_column(Text, nullable=True)\n    body_sections: Mapped[dict | None] = mapped_column(JSONB, nullable=True)\n    signal_chains: Mapped[list | None] = mapped_column(JSONB, nullable=True)\n    plugins: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    source_quality: Mapped[SourceQuality | None] = mapped_column(\n        Enum(SourceQuality, name=\"source_quality\", create_constraint=True),\n        nullable=True,\n    )\n    view_count: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\n    review_status: Mapped[PageReviewStatus] = mapped_column(\n        Enum(PageReviewStatus, name=\"page_review_status\", create_constraint=True),\n        default=PageReviewStatus.draft,\n        server_default=\"draft\",\n    )\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    creator: Mapped[Creator] = sa_relationship(back_populates=\"technique_pages\")\n    key_moments: Mapped[list[KeyMoment]] = sa_relationship(\n        back_populates=\"technique_page\", foreign_keys=[KeyMoment.technique_page_id]\n    )\n    outgoing_links: Mapped[list[RelatedTechniqueLink]] = sa_relationship(\n        foreign_keys=\"RelatedTechniqueLink.source_page_id\", back_populates=\"source_page\"\n    )\n    incoming_links: Mapped[list[RelatedTechniqueLink]] = sa_relationship(\n        foreign_keys=\"RelatedTechniqueLink.target_page_id\", back_populates=\"target_page\"\n    )\n\n\nclass RelatedTechniqueLink(Base):\n    __tablename__ = \"related_technique_links\"\n    __table_args__ = (\n        UniqueConstraint(\"source_page_id\", \"target_page_id\", \"relationship\", name=\"uq_technique_link\"),\n    )\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    target_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    relationship: Mapped[RelationshipType] = mapped_column(\n        Enum(RelationshipType, name=\"relationship_type\", create_constraint=True),\n        nullable=False,\n    )\n\n    # relationships\n    source_page: Mapped[TechniquePage] = sa_relationship(\n        foreign_keys=[source_page_id], back_populates=\"outgoing_links\"\n    )\n    target_page: Mapped[TechniquePage] = sa_relationship(\n        foreign_keys=[target_page_id], back_populates=\"incoming_links\"\n    )\n\n\nclass Tag(Base):\n    __tablename__ = \"tags\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    name: Mapped[str] = mapped_column(String(255), unique=True, nullable=False)\n    category: Mapped[str] = mapped_column(String(255), nullable=False)\n    aliases: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n"}],"isError":false,"timestamp":1774825343058}}
{"type":"message","id":"f4473d80","parentId":"abc61c59","timestamp":"2026-03-29T23:02:23.060Z","message":{"role":"toolResult","toolCallId":"toolu_011MQpubsnEatxUyKZdH2xmr","toolName":"bash","content":[{"type":"text","text":"\"\"\"Chrysopedia API — Knowledge extraction and retrieval system.\n\nEntry point for the FastAPI application. Configures middleware,\nstructured logging, and mounts versioned API routers.\n\"\"\"\n\nimport logging\nimport sys\nfrom contextlib import asynccontextmanager\n\nfrom fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\n\nfrom config import get_settings\nfrom routers import creators, health, ingest, pipeline, videos\n\n\ndef _setup_logging() -> None:\n    \"\"\"Configure structured logging to stdout.\"\"\"\n    settings = get_settings()\n    level = getattr(logging, settings.app_log_level.upper(), logging.INFO)\n\n    handler = logging.StreamHandler(sys.stdout)\n    handler.setFormatter(\n        logging.Formatter(\n            fmt=\"%(asctime)s | %(levelname)-8s | %(name)s | %(message)s\",\n            datefmt=\"%Y-%m-%dT%H:%M:%S\",\n        )\n    )\n\n    root = logging.getLogger()\n    root.setLevel(level)\n    # Avoid duplicate handlers on reload\n    root.handlers.clear()\n    root.addHandler(handler)\n\n    # Quiet noisy libraries\n    logging.getLogger(\"uvicorn.access\").setLevel(logging.WARNING)\n    logging.getLogger(\"sqlalchemy.engine\").setLevel(logging.WARNING)\n\n\n@asynccontextmanager\nasync def lifespan(app: FastAPI):  # noqa: ARG001\n    \"\"\"Application lifespan: setup on startup, teardown on shutdown.\"\"\"\n    _setup_logging()\n    logger = logging.getLogger(\"chrysopedia\")\n    settings = get_settings()\n    logger.info(\n        \"Chrysopedia API starting (env=%s, log_level=%s)\",\n        settings.app_env,\n        settings.app_log_level,\n    )\n    yield\n    logger.info(\"Chrysopedia API shutting down\")\n\n\napp = FastAPI(\n    title=\"Chrysopedia API\",\n    description=\"Knowledge extraction and retrieval for music production content\",\n    version=\"0.1.0\",\n    lifespan=lifespan,\n)\n\n# ── Middleware ────────────────────────────────────────────────────────────────\n\nsettings = get_settings()\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=settings.cors_origins,\n    allow_credentials=True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"],\n)\n\n# ── Routers ──────────────────────────────────────────────────────────────────\n\n# Root-level health (no prefix)\napp.include_router(health.router)\n\n# Versioned API\napp.include_router(creators.router, prefix=\"/api/v1\")\napp.include_router(ingest.router, prefix=\"/api/v1\")\napp.include_router(pipeline.router, prefix=\"/api/v1\")\napp.include_router(videos.router, prefix=\"/api/v1\")\n\n\n@app.get(\"/api/v1/health\")\nasync def api_health():\n    \"\"\"Lightweight version-prefixed health endpoint (no DB check).\"\"\"\n    return {\"status\": \"ok\", \"version\": \"0.1.0\"}\n"}],"isError":false,"timestamp":1774825343058}}
{"type":"message","id":"19c073eb","parentId":"f4473d80","timestamp":"2026-03-29T23:02:23.062Z","message":{"role":"toolResult","toolCallId":"toolu_01NRzk8uBQPjx4f4PDRQYEtt","toolName":"bash","content":[{"type":"text","text":"\"\"\"Application configuration loaded from environment variables.\"\"\"\n\nfrom functools import lru_cache\n\nfrom pydantic_settings import BaseSettings\n\n\nclass Settings(BaseSettings):\n    \"\"\"Chrysopedia API settings.\n\n    Values are loaded from environment variables (or .env file via\n    pydantic-settings' dotenv support).\n    \"\"\"\n\n    # Database\n    database_url: str = \"postgresql+asyncpg://chrysopedia:changeme@localhost:5433/chrysopedia\"\n\n    # Redis\n    redis_url: str = \"redis://localhost:6379/0\"\n\n    # Application\n    app_env: str = \"development\"\n    app_log_level: str = \"info\"\n    app_secret_key: str = \"changeme-generate-a-real-secret\"\n\n    # CORS\n    cors_origins: list[str] = [\"*\"]\n\n    # LLM endpoint (OpenAI-compatible)\n    llm_api_url: str = \"http://localhost:11434/v1\"\n    llm_api_key: str = \"sk-placeholder\"\n    llm_model: str = \"qwen2.5:14b-q8_0\"\n    llm_fallback_url: str = \"http://localhost:11434/v1\"\n    llm_fallback_model: str = \"qwen2.5:14b-q8_0\"\n\n    # Embedding endpoint\n    embedding_api_url: str = \"http://localhost:11434/v1\"\n    embedding_model: str = \"nomic-embed-text\"\n    embedding_dimensions: int = 768\n\n    # Qdrant\n    qdrant_url: str = \"http://localhost:6333\"\n    qdrant_collection: str = \"chrysopedia\"\n\n    # Prompt templates\n    prompts_path: str = \"./prompts\"\n\n    # Review mode — when True, extracted moments go to review queue before publishing\n    review_mode: bool = True\n\n    # File storage\n    transcript_storage_path: str = \"/data/transcripts\"\n    video_metadata_path: str = \"/data/video_meta\"\n\n    model_config = {\n        \"env_file\": \".env\",\n        \"env_file_encoding\": \"utf-8\",\n        \"case_sensitive\": False,\n    }\n\n\n@lru_cache\ndef get_settings() -> Settings:\n    \"\"\"Return cached application settings (singleton).\"\"\"\n    return Settings()\n"}],"isError":false,"timestamp":1774825343059}}
{"type":"message","id":"ccb442e0","parentId":"19c073eb","timestamp":"2026-03-29T23:02:27.439Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Vc77m5zqXmm55tGNqVVHou","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/routers/creators.py"}},{"type":"toolCall","id":"toolu_011bgDsh5kTVLHWFTMoAW4C8","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/tests/conftest.py"}},{"type":"toolCall","id":"toolu_01Jp5oJqkWQ8EaymKqWcbFC7","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat frontend/package.json"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":194,"cacheRead":64025,"cacheWrite":4334,"totalTokens":68554,"cost":{"input":0.000005,"output":0.00485,"cacheRead":0.0320125,"cacheWrite":0.0270875,"total":0.063955}},"stopReason":"toolUse","timestamp":1774825343061}}
{"type":"message","id":"e418dd58","parentId":"ccb442e0","timestamp":"2026-03-29T23:02:27.461Z","message":{"role":"toolResult","toolCallId":"toolu_01Vc77m5zqXmm55tGNqVVHou","toolName":"bash","content":[{"type":"text","text":"\"\"\"Creator endpoints for Chrysopedia API.\"\"\"\n\nimport logging\nfrom typing import Annotated\n\nfrom fastapi import APIRouter, Depends, HTTPException, Query\nfrom sqlalchemy import func, select\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom database import get_session\nfrom models import Creator, SourceVideo\nfrom schemas import CreatorDetail, CreatorRead\n\nlogger = logging.getLogger(\"chrysopedia.creators\")\n\nrouter = APIRouter(prefix=\"/creators\", tags=[\"creators\"])\n\n\n@router.get(\"\", response_model=list[CreatorRead])\nasync def list_creators(\n    offset: Annotated[int, Query(ge=0)] = 0,\n    limit: Annotated[int, Query(ge=1, le=100)] = 50,\n    db: AsyncSession = Depends(get_session),\n) -> list[CreatorRead]:\n    \"\"\"List all creators with pagination.\"\"\"\n    stmt = select(Creator).order_by(Creator.name).offset(offset).limit(limit)\n    result = await db.execute(stmt)\n    creators = result.scalars().all()\n    logger.debug(\"Listed %d creators (offset=%d, limit=%d)\", len(creators), offset, limit)\n    return [CreatorRead.model_validate(c) for c in creators]\n\n\n@router.get(\"/{slug}\", response_model=CreatorDetail)\nasync def get_creator(\n    slug: str,\n    db: AsyncSession = Depends(get_session),\n) -> CreatorDetail:\n    \"\"\"Get a single creator by slug, including video count.\"\"\"\n    stmt = select(Creator).where(Creator.slug == slug)\n    result = await db.execute(stmt)\n    creator = result.scalar_one_or_none()\n\n    if creator is None:\n        raise HTTPException(status_code=404, detail=f\"Creator '{slug}' not found\")\n\n    # Count videos for this creator\n    count_stmt = (\n        select(func.count())\n        .select_from(SourceVideo)\n        .where(SourceVideo.creator_id == creator.id)\n    )\n    count_result = await db.execute(count_stmt)\n    video_count = count_result.scalar() or 0\n\n    creator_data = CreatorRead.model_validate(creator)\n    return CreatorDetail(**creator_data.model_dump(), video_count=video_count)\n"}],"isError":false,"timestamp":1774825347461}}
{"type":"message","id":"f44a92a4","parentId":"e418dd58","timestamp":"2026-03-29T23:02:27.462Z","message":{"role":"toolResult","toolCallId":"toolu_011bgDsh5kTVLHWFTMoAW4C8","toolName":"bash","content":[{"type":"text","text":"\"\"\"Shared fixtures for Chrysopedia integration tests.\n\nProvides:\n- Async SQLAlchemy engine/session against a real PostgreSQL test database\n- Sync SQLAlchemy engine/session for pipeline stage tests (Celery stages are sync)\n- httpx.AsyncClient wired to the FastAPI app with dependency overrides\n- Pre-ingest fixture for pipeline tests\n- Sample transcript fixture path and temporary storage directory\n\nKey design choice: function-scoped engine with NullPool avoids asyncpg\n\"another operation in progress\" errors caused by session-scoped connection\nreuse between the ASGI test client and verification queries.\n\"\"\"\n\nimport json\nimport os\nimport pathlib\nimport uuid\n\nimport pytest\nimport pytest_asyncio\nfrom httpx import ASGITransport, AsyncClient\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine\nfrom sqlalchemy.orm import Session, sessionmaker\nfrom sqlalchemy.pool import NullPool\n\n# Ensure backend/ is on sys.path so \"from models import ...\" works\nimport sys\nsys.path.insert(0, str(pathlib.Path(__file__).resolve().parent.parent))\n\nfrom database import Base, get_session  # noqa: E402\nfrom main import app  # noqa: E402\nfrom models import (  # noqa: E402\n    ContentType,\n    Creator,\n    ProcessingStatus,\n    SourceVideo,\n    TranscriptSegment,\n)\n\nTEST_DATABASE_URL = os.getenv(\n    \"TEST_DATABASE_URL\",\n    \"postgresql+asyncpg://chrysopedia:changeme@localhost:5433/chrysopedia_test\",\n)\n\nTEST_DATABASE_URL_SYNC = TEST_DATABASE_URL.replace(\n    \"postgresql+asyncpg://\", \"postgresql+psycopg2://\"\n)\n\n\n@pytest_asyncio.fixture()\nasync def db_engine():\n    \"\"\"Create a per-test async engine (NullPool) and create/drop all tables.\"\"\"\n    engine = create_async_engine(TEST_DATABASE_URL, echo=False, poolclass=NullPool)\n\n    # Create all tables fresh for each test\n    async with engine.begin() as conn:\n        await conn.run_sync(Base.metadata.drop_all)\n        await conn.run_sync(Base.metadata.create_all)\n\n    yield engine\n\n    # Drop all tables after test\n    async with engine.begin() as conn:\n        await conn.run_sync(Base.metadata.drop_all)\n\n    await engine.dispose()\n\n\n@pytest_asyncio.fixture()\nasync def client(db_engine, tmp_path):\n    \"\"\"Async HTTP test client wired to FastAPI with dependency overrides.\"\"\"\n    session_factory = async_sessionmaker(\n        db_engine, class_=AsyncSession, expire_on_commit=False\n    )\n\n    async def _override_get_session():\n        async with session_factory() as session:\n            yield session\n\n    # Override DB session dependency\n    app.dependency_overrides[get_session] = _override_get_session\n\n    # Override transcript_storage_path via environment variable\n    os.environ[\"TRANSCRIPT_STORAGE_PATH\"] = str(tmp_path)\n    # Clear the lru_cache so Settings picks up the new env var\n    from config import get_settings\n    get_settings.cache_clear()\n\n    transport = ASGITransport(app=app)\n    async with AsyncClient(transport=transport, base_url=\"http://testserver\") as ac:\n        yield ac\n\n    # Teardown: clean overrides and restore settings cache\n    app.dependency_overrides.clear()\n    os.environ.pop(\"TRANSCRIPT_STORAGE_PATH\", None)\n    get_settings.cache_clear()\n\n\n@pytest.fixture()\ndef sample_transcript_path() -> pathlib.Path:\n    \"\"\"Path to the sample 5-segment transcript JSON fixture.\"\"\"\n    return pathlib.Path(__file__).parent / \"fixtures\" / \"sample_transcript.json\"\n\n\n@pytest.fixture()\ndef tmp_transcript_dir(tmp_path) -> pathlib.Path:\n    \"\"\"Temporary directory for transcript storage during tests.\"\"\"\n    return tmp_path\n\n\n# ── Sync engine/session for pipeline stages ──────────────────────────────────\n\n\n@pytest.fixture()\ndef sync_engine(db_engine):\n    \"\"\"Create a sync SQLAlchemy engine pointing at the test database.\n\n    Tables are already created/dropped by the async ``db_engine`` fixture,\n    so this fixture just wraps a sync engine around the same DB URL.\n    \"\"\"\n    engine = create_engine(TEST_DATABASE_URL_SYNC, echo=False, poolclass=NullPool)\n    yield engine\n    engine.dispose()\n\n\n@pytest.fixture()\ndef sync_session(sync_engine) -> Session:\n    \"\"\"Create a sync SQLAlchemy session for pipeline stage tests.\"\"\"\n    factory = sessionmaker(bind=sync_engine)\n    session = factory()\n    yield session\n    session.close()\n\n\n# ── Pre-ingest fixture for pipeline tests ────────────────────────────────────\n\n\n@pytest.fixture()\ndef pre_ingested_video(sync_engine):\n    \"\"\"Ingest the sample transcript directly into the test DB via sync ORM.\n\n    Returns a dict with ``video_id``, ``creator_id``, and ``segment_count``.\n    \"\"\"\n    factory = sessionmaker(bind=sync_engine)\n    session = factory()\n    try:\n        # Create creator\n        creator = Creator(\n            name=\"Skope\",\n            slug=\"skope\",\n            folder_name=\"Skope\",\n        )\n        session.add(creator)\n        session.flush()\n\n        # Create video\n        video = SourceVideo(\n            creator_id=creator.id,\n            filename=\"mixing-basics-ep1.mp4\",\n            file_path=\"Skope/mixing-basics-ep1.mp4\",\n            duration_seconds=1234,\n            content_type=ContentType.tutorial,\n            processing_status=ProcessingStatus.transcribed,\n        )\n        session.add(video)\n        session.flush()\n\n        # Create transcript segments\n        sample = pathlib.Path(__file__).parent / \"fixtures\" / \"sample_transcript.json\"\n        data = json.loads(sample.read_text())\n        for idx, seg in enumerate(data[\"segments\"]):\n            session.add(TranscriptSegment(\n                source_video_id=video.id,\n                start_time=float(seg[\"start\"]),\n                end_time=float(seg[\"end\"]),\n                text=str(seg[\"text\"]),\n                segment_index=idx,\n            ))\n\n        session.commit()\n\n        result = {\n            \"video_id\": str(video.id),\n            \"creator_id\": str(creator.id),\n            \"segment_count\": len(data[\"segments\"]),\n        }\n    finally:\n        session.close()\n\n    return result\n"}],"isError":false,"timestamp":1774825347461}}
{"type":"message","id":"03d8d04f","parentId":"f44a92a4","timestamp":"2026-03-29T23:02:27.463Z","message":{"role":"toolResult","toolCallId":"toolu_01Jp5oJqkWQ8EaymKqWcbFC7","toolName":"bash","content":[{"type":"text","text":"{\n  \"name\": \"chrysopedia-web\",\n  \"private\": true,\n  \"version\": \"0.1.0\",\n  \"type\": \"module\",\n  \"scripts\": {\n    \"dev\": \"echo 'placeholder — install a framework first'\",\n    \"build\": \"echo 'placeholder build' && mkdir -p dist && echo '<!DOCTYPE html><html><head><title>Chrysopedia</title></head><body><h1>Chrysopedia</h1><p>Web UI placeholder</p></body></html>' > dist/index.html\"\n  }\n}\n"}],"isError":false,"timestamp":1774825347461}}
{"type":"message","id":"cad5e9ef","parentId":"03d8d04f","timestamp":"2026-03-29T23:02:32.996Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the existing routers and the Dockerfile for the frontend build:"},{"type":"toolCall","id":"toolu_01JxGLLZP3yFZ9FbTiN83u5K","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/routers/pipeline.py"}},{"type":"toolCall","id":"toolu_01Qpfa32nGEyUjELXN1eQyNM","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find . -name 'Dockerfile*' -o -name 'docker-compose*' | head -10 && echo \"---\" && cat docker/frontend/Dockerfile 2>/dev/null || find . -path '*/Dockerfile' -exec grep -l 'frontend\\|npm\\|nginx' {} \\;"}},{"type":"toolCall","id":"toolu_01A27k2TwoQgrejzsphazrf9","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/database.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":275,"cacheRead":68359,"cacheWrite":2644,"totalTokens":71279,"cost":{"input":0.000005,"output":0.006875,"cacheRead":0.0341795,"cacheWrite":0.016525,"total":0.0575845}},"stopReason":"toolUse","timestamp":1774825347462}}
{"type":"message","id":"a588694e","parentId":"cad5e9ef","timestamp":"2026-03-29T23:02:33.017Z","message":{"role":"toolResult","toolCallId":"toolu_01JxGLLZP3yFZ9FbTiN83u5K","toolName":"bash","content":[{"type":"text","text":"\"\"\"Pipeline management endpoints for manual re-trigger and status inspection.\"\"\"\n\nimport logging\n\nfrom fastapi import APIRouter, Depends, HTTPException\nfrom sqlalchemy import select\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom database import get_session\nfrom models import SourceVideo\n\nlogger = logging.getLogger(\"chrysopedia.pipeline\")\n\nrouter = APIRouter(prefix=\"/pipeline\", tags=[\"pipeline\"])\n\n\n@router.post(\"/trigger/{video_id}\")\nasync def trigger_pipeline(\n    video_id: str,\n    db: AsyncSession = Depends(get_session),\n):\n    \"\"\"Manually trigger (or re-trigger) the LLM extraction pipeline for a video.\n\n    Looks up the SourceVideo by ID, dispatches ``run_pipeline.delay()``,\n    and returns the current processing status. Returns 404 if the video\n    does not exist.\n    \"\"\"\n    stmt = select(SourceVideo).where(SourceVideo.id == video_id)\n    result = await db.execute(stmt)\n    video = result.scalar_one_or_none()\n\n    if video is None:\n        raise HTTPException(status_code=404, detail=f\"Video not found: {video_id}\")\n\n    # Import inside handler to avoid circular import at module level\n    from pipeline.stages import run_pipeline\n\n    try:\n        run_pipeline.delay(str(video.id))\n        logger.info(\"Pipeline manually triggered for video_id=%s\", video_id)\n    except Exception as exc:\n        logger.warning(\n            \"Failed to dispatch pipeline for video_id=%s: %s\", video_id, exc\n        )\n        raise HTTPException(\n            status_code=503,\n            detail=\"Pipeline dispatch failed — Celery/Redis may be unavailable\",\n        ) from exc\n\n    return {\n        \"status\": \"triggered\",\n        \"video_id\": str(video.id),\n        \"current_processing_status\": video.processing_status.value,\n    }\n"}],"isError":false,"timestamp":1774825353016}}
{"type":"message","id":"b5dfcb74","parentId":"a588694e","timestamp":"2026-03-29T23:02:33.055Z","message":{"role":"toolResult","toolCallId":"toolu_01Qpfa32nGEyUjELXN1eQyNM","toolName":"bash","content":[{"type":"text","text":"./docker/Dockerfile.api\n./docker/Dockerfile.web\n./docker-compose.yml\n---\n"}],"isError":false,"timestamp":1774825353055}}
{"type":"message","id":"5568cd0c","parentId":"b5dfcb74","timestamp":"2026-03-29T23:02:33.057Z","message":{"role":"toolResult","toolCallId":"toolu_01A27k2TwoQgrejzsphazrf9","toolName":"bash","content":[{"type":"text","text":"\"\"\"Database engine, session factory, and declarative base for Chrysopedia.\"\"\"\n\nimport os\n\nfrom sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine\nfrom sqlalchemy.orm import DeclarativeBase\n\nDATABASE_URL = os.getenv(\n    \"DATABASE_URL\",\n    \"postgresql+asyncpg://chrysopedia:changeme@localhost:5433/chrysopedia\",\n)\n\nengine = create_async_engine(DATABASE_URL, echo=False, pool_pre_ping=True)\n\nasync_session = async_sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)\n\n\nclass Base(DeclarativeBase):\n    \"\"\"Declarative base for all ORM models.\"\"\"\n    pass\n\n\nasync def get_session() -> AsyncSession:  # type: ignore[misc]\n    \"\"\"FastAPI dependency that yields an async DB session.\"\"\"\n    async with async_session() as session:\n        yield session\n"}],"isError":false,"timestamp":1774825353055}}
{"type":"message","id":"613d7a31","parentId":"5568cd0c","timestamp":"2026-03-29T23:02:35.850Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_017ycXBrobseK3gfG5e1G7eW","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat docker/Dockerfile.web"}},{"type":"toolCall","id":"toolu_016T4NzzXMGeQDWBzTcJQ29z","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat docker-compose.yml | head -80"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":136,"cacheRead":71003,"cacheWrite":1071,"totalTokens":72211,"cost":{"input":0.000005,"output":0.0034000000000000002,"cacheRead":0.0355015,"cacheWrite":0.00669375,"total":0.045600249999999995}},"stopReason":"toolUse","timestamp":1774825353056}}
{"type":"message","id":"f6f3d2ba","parentId":"613d7a31","timestamp":"2026-03-29T23:02:35.871Z","message":{"role":"toolResult","toolCallId":"toolu_017ycXBrobseK3gfG5e1G7eW","toolName":"bash","content":[{"type":"text","text":"FROM node:22-alpine AS build\n\nWORKDIR /app\nCOPY frontend/package*.json ./\nRUN npm ci --ignore-scripts\nCOPY frontend/ .\nRUN npm run build\n\nFROM nginx:1.27-alpine\n\nCOPY --from=build /app/dist /usr/share/nginx/html\nCOPY docker/nginx.conf /etc/nginx/conf.d/default.conf\n\nEXPOSE 80\n\nCMD [\"nginx\", \"-g\", \"daemon off;\"]\n"}],"isError":false,"timestamp":1774825355870}}
{"type":"message","id":"9e6aee54","parentId":"f6f3d2ba","timestamp":"2026-03-29T23:02:35.873Z","message":{"role":"toolResult","toolCallId":"toolu_016T4NzzXMGeQDWBzTcJQ29z","toolName":"bash","content":[{"type":"text","text":"# Chrysopedia — Docker Compose\n# XPLTD convention: xpltd_chrysopedia project, bind mounts, dedicated bridge\nname: xpltd_chrysopedia\n\nservices:\n  # ── PostgreSQL 16 ──\n  chrysopedia-db:\n    image: postgres:16-alpine\n    container_name: chrysopedia-db\n    restart: unless-stopped\n    environment:\n      POSTGRES_USER: ${POSTGRES_USER:-chrysopedia}\n      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-changeme}\n      POSTGRES_DB: ${POSTGRES_DB:-chrysopedia}\n    volumes:\n      - /vmPool/r/services/chrysopedia_db:/var/lib/postgresql/data\n    ports:\n      - \"127.0.0.1:5433:5432\"\n    networks:\n      - chrysopedia\n    healthcheck:\n      test: [\"CMD-SHELL\", \"pg_isready -U ${POSTGRES_USER:-chrysopedia}\"]\n      interval: 10s\n      timeout: 5s\n      retries: 5\n\n  # ── Redis (Celery broker) ──\n  chrysopedia-redis:\n    image: redis:7-alpine\n    container_name: chrysopedia-redis\n    restart: unless-stopped\n    volumes:\n      - /vmPool/r/services/chrysopedia_redis:/data\n    networks:\n      - chrysopedia\n    healthcheck:\n      test: [\"CMD\", \"redis-cli\", \"ping\"]\n      interval: 10s\n      timeout: 5s\n      retries: 5\n\n  # ── FastAPI application ──\n  chrysopedia-api:\n    build:\n      context: .\n      dockerfile: docker/Dockerfile.api\n    container_name: chrysopedia-api\n    restart: unless-stopped\n    env_file:\n      - path: .env\n        required: false\n    environment:\n      DATABASE_URL: postgresql+asyncpg://${POSTGRES_USER:-chrysopedia}:${POSTGRES_PASSWORD}@chrysopedia-db:5432/${POSTGRES_DB:-chrysopedia}\n      REDIS_URL: redis://chrysopedia-redis:6379/0\n    volumes:\n      - ./backend:/app\n      - /vmPool/r/services/chrysopedia_data:/data\n    ports:\n      - \"127.0.0.1:8000:8000\"\n    depends_on:\n      chrysopedia-db:\n        condition: service_healthy\n      chrysopedia-redis:\n        condition: service_healthy\n    networks:\n      - chrysopedia\n\n  # ── Celery worker (pipeline stages 2-5) ──\n  chrysopedia-worker:\n    build:\n      context: .\n      dockerfile: docker/Dockerfile.api\n    container_name: chrysopedia-worker\n    restart: unless-stopped\n    env_file:\n      - path: .env\n        required: false\n    environment:\n      DATABASE_URL: postgresql+asyncpg://${POSTGRES_USER:-chrysopedia}:${POSTGRES_PASSWORD}@chrysopedia-db:5432/${POSTGRES_DB:-chrysopedia}\n      REDIS_URL: redis://chrysopedia-redis:6379/0\n"}],"isError":false,"timestamp":1774825355870}}
{"type":"message","id":"c619622e","parentId":"9e6aee54","timestamp":"2026-03-29T23:02:39.007Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01XNKfpjikXDHfVNuiobv4ac","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat docker/nginx.conf 2>/dev/null || echo \"No nginx.conf found\""}},{"type":"toolCall","id":"toolu_01SyVedSvgd5QTMoXjHpKbJb","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/pipeline/stages.py | head -60"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":152,"cacheRead":72074,"cacheWrite":1087,"totalTokens":73314,"cost":{"input":0.000005,"output":0.0038,"cacheRead":0.036037,"cacheWrite":0.00679375,"total":0.046635750000000004}},"stopReason":"toolUse","timestamp":1774825355871}}
{"type":"message","id":"3c56c3bd","parentId":"c619622e","timestamp":"2026-03-29T23:02:39.025Z","message":{"role":"toolResult","toolCallId":"toolu_01XNKfpjikXDHfVNuiobv4ac","toolName":"bash","content":[{"type":"text","text":"server {\n    listen 80;\n    server_name _;\n    root /usr/share/nginx/html;\n    index index.html;\n\n    # SPA fallback\n    location / {\n        try_files $uri $uri/ /index.html;\n    }\n\n    # API proxy\n    location /api/ {\n        proxy_pass http://chrysopedia-api:8000;\n        proxy_set_header Host $host;\n        proxy_set_header X-Real-IP $remote_addr;\n        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;\n        proxy_set_header X-Forwarded-Proto $scheme;\n    }\n\n    location /health {\n        proxy_pass http://chrysopedia-api:8000;\n    }\n}\n"}],"isError":false,"timestamp":1774825359024}}
{"type":"message","id":"87f36792","parentId":"3c56c3bd","timestamp":"2026-03-29T23:02:39.027Z","message":{"role":"toolResult","toolCallId":"toolu_01SyVedSvgd5QTMoXjHpKbJb","toolName":"bash","content":[{"type":"text","text":"\"\"\"Pipeline stage tasks (stages 2-5) and run_pipeline orchestrator.\n\nEach stage reads from PostgreSQL via sync SQLAlchemy, loads its prompt\ntemplate from disk, calls the LLM client, parses the response, writes\nresults back, and updates processing_status on SourceVideo.\n\nCelery tasks are synchronous — all DB access uses ``sqlalchemy.orm.Session``.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport logging\nimport time\nfrom collections import defaultdict\nfrom pathlib import Path\n\nimport yaml\nfrom celery import chain as celery_chain\nfrom pydantic import ValidationError\nfrom sqlalchemy import create_engine, select\nfrom sqlalchemy.orm import Session, sessionmaker\n\nfrom config import get_settings\nfrom models import (\n    KeyMoment,\n    KeyMomentContentType,\n    ProcessingStatus,\n    SourceVideo,\n    TechniquePage,\n    TranscriptSegment,\n)\nfrom pipeline.embedding_client import EmbeddingClient\nfrom pipeline.llm_client import LLMClient\nfrom pipeline.qdrant_client import QdrantManager\nfrom pipeline.schemas import (\n    ClassificationResult,\n    ExtractionResult,\n    SegmentationResult,\n    SynthesisResult,\n)\nfrom worker import celery_app\n\nlogger = logging.getLogger(__name__)\n\n# ── Helpers ──────────────────────────────────────────────────────────────────\n\n_engine = None\n_SessionLocal = None\n\n\ndef _get_sync_engine():\n    \"\"\"Create a sync SQLAlchemy engine, converting the async URL if needed.\"\"\"\n    global _engine\n    if _engine is None:\n        settings = get_settings()\n        url = settings.database_url\n        # Convert async driver to sync driver\n        url = url.replace(\"postgresql+asyncpg://\", \"postgresql+psycopg2://\")\n        _engine = create_engine(url, pool_pre_ping=True, pool_size=5, max_overflow=10)\n"}],"isError":false,"timestamp":1774825359024}}
{"type":"message","id":"10018572","parentId":"87f36792","timestamp":"2026-03-29T23:02:42.608Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me read the templates:"},{"type":"toolCall","id":"toolu_018B8cmj3Gh72wujp9c5nyWm","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/plan.md"}},{"type":"toolCall","id":"toolu_0128EX9Yryn9z8vB9ozqvJPy","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/task-plan.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":127,"cacheRead":73161,"cacheWrite":881,"totalTokens":74170,"cost":{"input":0.000005,"output":0.0031750000000000003,"cacheRead":0.036580499999999995,"cacheWrite":0.00550625,"total":0.045266749999999994}},"stopReason":"toolUse","timestamp":1774825359025}}
{"type":"message","id":"acf692fb","parentId":"10018572","timestamp":"2026-03-29T23:02:42.613Z","message":{"role":"toolResult","toolCallId":"toolu_018B8cmj3Gh72wujp9c5nyWm","toolName":"read","content":[{"type":"text","text":"# {{sliceId}}: {{sliceTitle}}\n\n**Goal:** {{goal}}\n**Demo:** {{demo}}\n\n## Must-Haves\n\n- {{mustHave}}\n- {{mustHave}}\n\n## Threat Surface\n\n<!-- Q3: How can this be exploited? OMIT ENTIRELY for simple slices with no auth, user input, or data exposure. -->\n\n- **Abuse**: {{abuseScenarios — parameter tampering, replay, privilege escalation, or N/A}}\n- **Data exposure**: {{sensitiveDataAccessible — PII, tokens, secrets, or none}}\n- **Input trust**: {{untrustedInput — user input reaching DB/API/filesystem, or none}}\n\n## Requirement Impact\n\n<!-- Q4: What existing promises does this break? OMIT ENTIRELY if no existing requirements are affected. -->\n\n- **Requirements touched**: {{requirementIds — e.g. R001, R003, or none}}\n- **Re-verify**: {{whatMustBeRetested — e.g. login flow, API contract, or N/A}}\n- **Decisions revisited**: {{decisionIds — e.g. D002, or none}}\n\n## Proof Level\n\n<!-- Omit this section entirely for simple slices where the answer is trivially obvious. -->\n\n- This slice proves: {{contract | integration | operational | final-assembly}}\n- Real runtime required: {{yes/no}}\n- Human/UAT required: {{yes/no}}\n\n## Verification\n\n<!-- Define what \"done\" looks like BEFORE detailing tasks.\n     This section is the slice's objective stopping condition — execution isn't done\n     until everything here passes.\n\n     For non-trivial projects:\n     - Write actual test files into the codebase during the first task\n     - Tests should assert on the slice's demo outcome and boundary contracts\n     - Name the test files here so execution has an unambiguous target\n\n     For simple projects or scripts:\n     - Executable verification commands (bash assertions, curl checks, etc.) are sufficient\n\n     If the project has no test framework and the work is non-trivial,\n     the first task should set one up. A test runner costs 2 minutes\n     and pays for itself immediately.\n\n     For non-trivial backend, integration, async, stateful, or UI work:\n     - Include at least one verification check for an observability or failure-path signal\n     - Verify not just that the feature works, but that a future agent can inspect its state when it fails -->\n\n- {{testFileOrCommand — e.g. `npm test -- --grep \"auth flow\"` or `bash scripts/verify-s01.sh`}}\n- {{testFileOrCommand}}\n\n## Observability / Diagnostics\n\n<!-- Include this section for non-trivial backend, integration, async, stateful, or UI slices.\n     OMIT ENTIRELY for simple slices where all fields would be \"none\".\n\n     When included, describe how a future agent will inspect current state, detect failure,\n     and localize the problem with minimal ambiguity. Keep it concise and high-signal. -->\n\n- Runtime signals: {{structured log/event, state transition, metric, or none}}\n- Inspection surfaces: {{status endpoint, CLI command, script, UI state, DB table, or none}}\n- Failure visibility: {{last error, retry count, phase, timestamp, correlation id, or none}}\n- Redaction constraints: {{secret/PII boundary or none}}\n\n## Integration Closure\n\n<!-- Omit this section entirely for simple slices with no meaningful integration concerns. -->\n\n- Upstream surfaces consumed: {{specific files / modules / contracts}}\n- New wiring introduced in this slice: {{entrypoint / composition / runtime hookup, or none}}\n- What remains before the milestone is truly usable end-to-end: {{list or \"nothing\"}}\n\n## Tasks\n\n<!--\n  If every task below is completed exactly as written, the Goal and Demo above\n  should be true at the stated proof level. Tasks should close the loop on the\n  slice, not merely prepare for later work unless the Demo truthfully says the\n  slice only proves fixture/contract-level behavior.\n\n  Write each task as an executable increment, not a vague intention.\n\n  Prefer action-oriented titles:\n  - \"Wire real auth middleware into dashboard routes\"\n  - \"Persist job status and expose failure diagnostics\"\n  - \"Add browser test covering empty-state recovery\"\n\n  Avoid vague titles:\n  - \"Set up auth\"\n  - \"Handle errors\"\n  - \"Improve UI\"\n\n  Each task should usually include:\n  - Why: why this task exists / what part of the slice it closes\n  - Files: the main files likely touched\n  - Do: concrete implementation steps and important constraints\n  - Verify: the command, test, or runtime check that proves it worked\n  - Done when: a measurable acceptance condition\n\n  Keep the checkbox line format exactly:\n  - [ ] **T01: Title** `est:30m`\n-->\n\n- [ ] **T01: {{taskTitle}}** `est:{{estimate}}`\n  - Why: {{whyThisTaskExists}}\n  - Files: `{{filePath}}`, `{{filePath}}`\n  - Do: {{specificImplementationStepsAndConstraints}}\n  - Verify: {{testCommandOrRuntimeCheck}}\n  - Done when: {{measurableAcceptanceCondition}}\n- [ ] **T02: {{taskTitle}}** `est:{{estimate}}`\n  - Why: {{whyThisTaskExists}}\n  - Files: `{{filePath}}`, `{{filePath}}`\n  - Do: {{specificImplementationStepsAndConstraints}}\n  - Verify: {{testCommandOrRuntimeCheck}}\n  - Done when: {{measurableAcceptanceCondition}}\n<!--\n  Format rules (parsers depend on this exact structure):\n  - Checkbox line: - [ ] **T01: Title** `est:30m`\n  - Description:   indented text on the next line(s)\n  - Mark done:     change [ ] to [x]\n  - Tasks execute sequentially in order (T01, T02, T03, ...)\n  - est: is informational (e.g. 30m, 1h, 2h) and optional\n\n  Verify field rules:\n  - MUST be a mechanically executable command: `npm test`, `grep -q \"pattern\" file`, `test -f path`\n  - For content/document tasks: verify file existence, section count, YAML validity, or word count\n    NOT exact phrasing, specific formulas, or \"zero TBD\" aspirational criteria\n  - If no command can verify the output, write: \"Manual review — file exists and is non-empty\"\n  - BAD: \"Sections 3.1 and 3.2 exist with exact formulas. Zero TBD/TODO.\"\n  - GOOD: `grep -c \"^## \" doc.md` returns >= 4 (4+ sections), `! grep -q \"TBD\\|TODO\" doc.md`\n\n  Integration closure rule:\n  - At least one slice in any multi-boundary milestone should perform real composition/wiring, not just contract hardening\n  - For the final assembly slice, verification must exercise the real entrypoint or runtime path\n-->\n\n## Files Likely Touched\n\n- `{{filePath}}`\n- `{{filePath}}`\n"}],"isError":false,"timestamp":1774825362613}}
{"type":"message","id":"b3371c52","parentId":"acf692fb","timestamp":"2026-03-29T23:02:42.616Z","message":{"role":"toolResult","toolCallId":"toolu_0128EX9Yryn9z8vB9ozqvJPy","toolName":"read","content":[{"type":"text","text":"---\n# Optional scope estimate — helps the plan quality validator detect over-scoped tasks.\n# Tasks with 10+ estimated steps or 12+ estimated files trigger a warning to consider splitting.\nestimated_steps: {{estimatedSteps}}\nestimated_files: {{estimatedFiles}}\n# Installed skills the planner expects the executor to load before coding.\nskills_used:\n  - {{skillName}}\n---\n\n# {{taskId}}: {{taskTitle}}\n\n**Slice:** {{sliceId}} — {{sliceTitle}}\n**Milestone:** {{milestoneId}}\n\n## Description\n\n{{description}}\n\n## Failure Modes\n\n<!-- Q5: What breaks when dependencies fail? OMIT ENTIRELY for tasks with no external dependencies. -->\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| {{dependency}} | {{errorStrategy}} | {{timeoutStrategy}} | {{malformedStrategy}} |\n\n## Load Profile\n\n<!-- Q6: What breaks at 10x load? OMIT ENTIRELY for tasks with no shared resources or scaling concerns. -->\n\n- **Shared resources**: {{sharedResources — DB connections, caches, rate limiters, or none}}\n- **Per-operation cost**: {{perOpCost — N API calls, M DB queries, K bytes, or trivial}}\n- **10x breakpoint**: {{whatBreaksFirst — pool exhaustion, rate limit, memory, or N/A}}\n\n## Negative Tests\n\n<!-- Q7: What negative tests prove robustness? OMIT ENTIRELY for trivial tasks. -->\n\n- **Malformed inputs**: {{malformedInputTests — empty string, null, oversized, wrong type}}\n- **Error paths**: {{errorPathTests — network timeout, auth failure, 5xx, invalid JSON}}\n- **Boundary conditions**: {{boundaryTests — empty list, max length, zero, off-by-one}}\n\n## Steps\n\n1. {{step}}\n2. {{step}}\n3. {{step}}\n\n## Must-Haves\n\n- [ ] {{mustHave}}\n- [ ] {{mustHave}}\n\n## Verification\n\n- {{howToVerifyThisTaskIsActuallyDone}}\n- {{commandToRun_OR_behaviorToCheck}}\n\n## Observability Impact\n\n<!-- OMIT THIS SECTION ENTIRELY for simple tasks that don't touch runtime boundaries,\n     async flows, APIs, background processes, or error paths.\n     Include it only when the task meaningfully changes how failures are detected or diagnosed. -->\n\n- Signals added/changed: {{structured logs, statuses, errors, metrics}}\n- How a future agent inspects this: {{command, endpoint, file, UI state}}\n- Failure state exposed: {{what becomes visible on failure}}\n\n## Inputs\n\n<!-- Every input MUST be a backtick-wrapped file path. These paths are machine-parsed to\n     derive task dependencies — vague descriptions without paths break dependency detection.\n     For the first task in a slice with no prior task outputs, list the existing source files\n     this task reads or modifies. -->\n\n- `{{filePath}}` — {{whatThisTaskNeedsFromPriorWork}}\n\n## Expected Output\n\n<!-- Every output MUST be a backtick-wrapped file path — the specific files this task creates\n     or modifies. These paths are machine-parsed to derive task dependencies.\n     This task should produce a real increment toward making the slice goal/demo true. A full\n     slice plan should not be able to mark every task complete while the claimed slice behavior\n     still does not work at the stated proof level. -->\n\n- `{{filePath}}` — {{whatThisTaskCreatesOrModifies}}\n"}],"isError":false,"timestamp":1774825362613}}
{"type":"message","id":"44ca8fb8","parentId":"b3371c52","timestamp":"2026-03-29T23:02:47.308Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have a thorough picture of the codebase. Let me check the existing tests to understand patterns:"},{"type":"toolCall","id":"toolu_018Fvmp2RVG5eqD6Pe8B25gz","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && wc -l backend/tests/test_pipeline.py && head -50 backend/tests/test_pipeline.py"}},{"type":"toolCall","id":"toolu_017wjpdVVFfXRmTaWZ4W5YA9","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n 'review_mode\\|review_status\\|ReviewStatus' backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":195,"cacheRead":74042,"cacheWrite":2735,"totalTokens":76973,"cost":{"input":0.000005,"output":0.004875,"cacheRead":0.037021,"cacheWrite":0.01709375,"total":0.05899475}},"stopReason":"toolUse","timestamp":1774825362614}}
{"type":"message","id":"449f4ebe","parentId":"44ca8fb8","timestamp":"2026-03-29T23:02:47.330Z","message":{"role":"toolResult","toolCallId":"toolu_018Fvmp2RVG5eqD6Pe8B25gz","toolName":"bash","content":[{"type":"text","text":"739 backend/tests/test_pipeline.py\n\"\"\"Integration tests for the LLM extraction pipeline.\n\nTests run against a real PostgreSQL test database with mocked LLM and Qdrant\nclients. Pipeline stages are sync (Celery tasks), so tests call stage\nfunctions directly with sync SQLAlchemy sessions.\n\nTests (a)–(f) call pipeline stages directly. Tests (g)–(i) use the async\nHTTP client. Test (j) verifies LLM fallback logic.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport os\nimport pathlib\nimport uuid\nfrom unittest.mock import MagicMock, patch, PropertyMock\n\nimport openai\nimport pytest\nfrom sqlalchemy import create_engine, select\nfrom sqlalchemy.orm import Session, sessionmaker\nfrom sqlalchemy.pool import NullPool\n\nfrom models import (\n    Creator,\n    KeyMoment,\n    KeyMomentContentType,\n    ProcessingStatus,\n    SourceVideo,\n    TechniquePage,\n    TranscriptSegment,\n)\nfrom pipeline.schemas import (\n    ClassificationResult,\n    ExtractionResult,\n    SegmentationResult,\n    SynthesisResult,\n)\n\nfrom tests.fixtures.mock_llm_responses import (\n    STAGE2_SEGMENTATION_RESPONSE,\n    STAGE3_EXTRACTION_RESPONSE,\n    STAGE4_CLASSIFICATION_RESPONSE,\n    STAGE5_SYNTHESIS_RESPONSE,\n    make_mock_embeddings,\n)\n\n# ── Test database URL ────────────────────────────────────────────────────────\n\n"}],"isError":false,"timestamp":1774825367330}}
{"type":"message","id":"5e6ceff9","parentId":"449f4ebe","timestamp":"2026-03-29T23:02:47.332Z","message":{"role":"toolResult","toolCallId":"toolu_017wjpdVVFfXRmTaWZ4W5YA9","toolName":"bash","content":[{"type":"text","text":"451:    Sets processing_status to 'reviewed' (or 'published' if review_mode is False).\n559:        if settings.review_mode:\n"}],"isError":false,"timestamp":1774825367330}}
{"type":"message","id":"f43df0fd","parentId":"5e6ceff9","timestamp":"2026-03-29T23:04:49.075Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. Now I have complete understanding of the codebase. Let me reason through the decomposition.\n\n## Decomposition Reasoning\n\n**Slice scope**: S04 builds the Review Queue Admin UI — a backend review API + React frontend for reviewing key moments. The requirement (R004) is clear: approve/edit/reject moments, mode toggle. The slice also touches R003 indirectly (the pipeline produces moments with `review_status`).\n\n**Key constraints discovered**:\n1. `KeyMoment` model already has `review_status` enum (pending/approved/edited/rejected) — no schema changes needed.\n2. `config.py` has `review_mode: bool = True` but it's `lru_cache`-bound from env — runtime toggle needs Redis.\n3. Frontend is a bare `package.json` placeholder with no framework installed. The Docker build expects `npm run build` → `dist/`.\n4. Existing patterns: async SQLAlchemy in routers, Pydantic schemas, CORS middleware already set to `[\"*\"]`.\n5. The nginx conf already proxies `/api/` to the backend — frontend just needs to be built correctly.\n\n**Task decomposition**: I'm splitting into 3 tasks:\n1. **Backend review API + tests** — This is the highest-risk, highest-value work. Build all review endpoints, schemas, Redis mode toggle, and integration tests. Without this, the frontend has nothing to consume. ~12 endpoints + test file.\n2. **React frontend bootstrap** — Initialize Vite + React + TypeScript, get build working, verify Docker compatibility. This is a clean infrastructure task that unblocks the UI work.\n3. **Review queue UI pages** — Build the actual admin UI pages (queue list, moment review, mode toggle) connected to the backend API. This is the user-facing payoff.\n\nThis split gives clean dependency ordering (T01 → T02 → T03), each task is independently verifiable, and each fits comfortably in one context window.\n\n**Why not 2 tasks?** Combining the React bootstrap with the UI pages would create a ~15-file task that's hard to verify incrementally. The bootstrap needs to be proven (build works, Docker compatible) before writing pages on top.\n\n**Why not 4+ tasks?** Split/merge endpoints and mode toggle are naturally part of the review API — separating them would fragment the router into multiple tasks with shared schema dependencies.\n\n**Verification strategy**: Backend tests use the established pytest + real PostgreSQL pattern. Frontend uses `npm run build` success + `npm run dev` serving. No browser test framework since this is a single-admin internal tool — the UAT will be manual verification via the browser."},{"type":"toolCall","id":"toolu_01PhDM5cQDZtdiEvo5joPnfo","name":"gsd_plan_slice","arguments":{"goal":"Admin can review, edit, approve, reject, split, and merge extracted key moments via a web UI. Mode toggle switches between review mode (moments queued for human review) and auto mode (moments publish directly).","integrationClosure":"- Upstream surfaces consumed: `backend/models.py` (KeyMoment with review_status, SourceVideo with processing_status), `backend/database.py` (get_session), `backend/schemas.py` (KeyMomentRead), `backend/config.py` (review_mode setting), Redis for runtime mode toggle\n- New wiring introduced: `backend/routers/review.py` mounted in `backend/main.py`, React app in `frontend/` replacing placeholder\n- What remains before milestone is truly usable end-to-end: S05 (Search-First Web UI) for the public-facing search and browse experience","milestoneId":"M001","observabilityImpact":"- Runtime signals: INFO logs on approve/reject/edit/split/merge actions with moment_id and admin action, WARNING on mode toggle changes\n- Inspection surfaces: `GET /api/v1/review/stats` returns count breakdown by review_status, `GET /api/v1/review/mode` returns current mode\n- Failure visibility: 404 on missing moment_id, 400 on invalid split/merge parameters, Redis connection errors surface as 503 on mode toggle\n- Redaction constraints: none (no PII or secrets in review data)","proofLevel":"This slice proves: integration (backend API with real DB) + operational (frontend builds and renders). Real runtime required: yes (PostgreSQL for tests, browser for UI). Human/UAT required: yes (visual review of admin UI).","sliceId":"S04","successCriteria":"- `cd backend && python -m pytest tests/test_review.py -v` — all review API tests pass\n- `cd backend && python -m pytest tests/ -v` — all existing + new tests pass (no regressions)\n- `cd frontend && npm run build` — produces `dist/index.html` with React app\n- `curl http://localhost:8001/api/v1/review/stats` returns status counts JSON\n- `curl -X POST http://localhost:8001/api/v1/review/moments/{id}/approve` changes review_status\n- `curl http://localhost:8001/api/v1/review/mode` returns current mode\n- Review queue page renders in browser with moment list, filter tabs, and action buttons","tasks":[{"description":"Create the complete review queue backend: new Pydantic schemas for review actions, a review router with 9 endpoints (list queue, stats, approve, reject, edit, split, merge, get mode, set mode), Redis-backed runtime mode toggle, mount in main.py, and comprehensive integration tests. Follow existing async SQLAlchemy patterns from routers/creators.py.\n\n## Steps\n\n1. Add review-specific Pydantic schemas to `backend/schemas.py`: `ReviewQueueItem` (KeyMomentRead + video title + creator name), `ReviewQueueResponse` (paginated), `ReviewStatsResponse` (counts per status), `MomentEditRequest` (editable fields: title, summary, start_time, end_time, content_type, plugins), `MomentSplitRequest` (split_time: float), `ReviewModeResponse` and `ReviewModeUpdate` (mode: bool).\n\n2. Create `backend/routers/review.py` with these async endpoints:\n   - `GET /review/queue` — List key moments filtered by `status` query param (pending/approved/edited/rejected/all), paginated with `offset`/`limit`, joined with SourceVideo.filename and Creator.name. Default filter: pending. Order by created_at desc.\n   - `GET /review/stats` — Return counts grouped by review_status (pending, approved, edited, rejected) using SQL count + group by.\n   - `POST /review/moments/{moment_id}/approve` — Set review_status=approved, return updated moment. 404 if not found.\n   - `POST /review/moments/{moment_id}/reject` — Set review_status=rejected, return updated moment. 404 if not found.\n   - `PUT /review/moments/{moment_id}` — Update editable fields from MomentEditRequest, set review_status=edited, return updated moment. 404 if not found.\n   - `POST /review/moments/{moment_id}/split` — Split moment at `split_time` into two moments. Validate split_time is between start_time and end_time. Original keeps [start_time, split_time), new gets [split_time, end_time]. Both keep same source_video_id and technique_page_id. Return both moments. 400 on invalid split_time.\n   - `POST /review/moments/{moment_id}/merge` — Accept `target_moment_id` in body. Merge two moments: combined summary, min(start_time), max(end_time), delete target, return merged result. Both must belong to same source_video. 400 if different videos. 404 if either not found.\n   - `GET /review/mode` — Read current mode from Redis key `chrysopedia:review_mode`. If not in Redis, fall back to `settings.review_mode` default.\n   - `PUT /review/mode` — Set mode in Redis key `chrysopedia:review_mode`. Return new mode.\n\n3. Add Redis client helper. Create a small `backend/redis_client.py` module with `get_redis()` async function using `redis.asyncio.Redis.from_url(settings.redis_url)`. Import in review router.\n\n4. Mount the review router in `backend/main.py`: `app.include_router(review.router, prefix=\"/api/v1\")`.\n\n5. Add `redis` (async redis client) to `backend/requirements.txt` if not already present.\n\n6. Create `backend/tests/test_review.py` with integration tests using the established conftest patterns (async client, real PostgreSQL):\n   - Test list queue returns empty when no moments exist\n   - Test list queue returns moments with video/creator info after seeding\n   - Test filter by status works (seed moments with different statuses)\n   - Test stats endpoint returns correct counts\n   - Test approve sets review_status=approved\n   - Test reject sets review_status=rejected\n   - Test edit updates fields and sets review_status=edited\n   - Test split creates two moments with correct timestamps\n   - Test split returns 400 for invalid split_time (outside range)\n   - Test merge combines two moments correctly\n   - Test merge returns 400 for moments from different videos\n   - Test approve/reject/edit return 404 for nonexistent moment\n   - Test mode get/set (mock Redis)\n\n## Must-Haves\n\n- [ ] All 9 review endpoints return correct HTTP status codes and response bodies\n- [ ] Split validates split_time is strictly between start_time and end_time\n- [ ] Merge validates both moments belong to same source_video\n- [ ] Mode toggle reads/writes Redis, falls back to config default\n- [ ] All review tests pass alongside existing test suite\n- [ ] Review router mounted in main.py\n\n## Failure Modes\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| PostgreSQL | SQLAlchemy raises, FastAPI returns 500 | Connection timeout → 500 | N/A (ORM handles) |\n| Redis (mode toggle) | Return 503 with error detail | Timeout → fall back to config default | N/A (simple get/set) |\n\n## Negative Tests\n\n- **Malformed inputs**: split_time outside moment range → 400, merge moments from different videos → 400, edit with empty title → validation error\n- **Error paths**: approve/reject/edit/split nonexistent moment → 404, merge with nonexistent target → 404\n- **Boundary conditions**: split at exact start_time or end_time → 400, merge moment with itself → 400, empty queue → empty list\n\n## Verification\n\n- `cd backend && python -m pytest tests/test_review.py -v` — all tests pass\n- `cd backend && python -m pytest tests/ -v` — no regressions (all existing tests still pass)\n- `python -c \"from routers.review import router; print(len(router.routes))\"` — prints 9 (routes registered)\n\n## Observability Impact\n\n- Signals added: INFO log on each review action (approve/reject/edit/split/merge) with moment_id\n- How a future agent inspects: `GET /api/v1/review/stats` shows pending/approved/edited/rejected counts\n- Failure state exposed: 404 responses include moment_id that was not found, 400 responses include validation details","estimate":"2h","expectedOutput":["`backend/routers/review.py` — review queue API router with 9 endpoints","`backend/redis_client.py` — async Redis client helper","`backend/schemas.py` — extended with review-specific Pydantic schemas","`backend/main.py` — updated to mount review router","`backend/requirements.txt` — updated with redis dependency","`backend/tests/test_review.py` — integration tests for all review endpoints"],"files":["backend/schemas.py","backend/routers/review.py","backend/redis_client.py","backend/main.py","backend/requirements.txt","backend/tests/test_review.py"],"inputs":["`backend/models.py` — KeyMoment, SourceVideo, Creator models with review_status enum","`backend/schemas.py` — existing Pydantic schemas to extend","`backend/database.py` — get_session async dependency","`backend/config.py` — Settings with review_mode and redis_url","`backend/main.py` — router mount point","`backend/routers/creators.py` — pattern reference for async SQLAlchemy endpoints","`backend/tests/conftest.py` — test fixtures (db_engine, client, sync_engine, pre_ingested_video)"],"taskId":"T01","title":"Build review queue API endpoints with Redis mode toggle and integration tests","verify":"cd backend && python -m pytest tests/test_review.py -v && python -m pytest tests/ -v"},{"description":"Replace the placeholder frontend with a real React + Vite + TypeScript application. Install dependencies, configure Vite with API proxy for development, create the app shell with React Router, and build a typed API client module for the review endpoints. Verify `npm run build` produces `dist/index.html` compatible with the existing Docker build pipeline.\n\n## Steps\n\n1. Initialize the React app in `frontend/`. Replace `package.json` with proper dependencies:\n   - `react`, `react-dom`, `react-router-dom` for the app\n   - `typescript`, `@types/react`, `@types/react-dom` for types\n   - `vite`, `@vitejs/plugin-react` for build tooling\n   - Scripts: `dev` → `vite`, `build` → `tsc -b && vite build`, `preview` → `vite preview`\n\n2. Create `frontend/vite.config.ts` with React plugin and dev server proxy (`/api` → `http://localhost:8001`) so the frontend dev server can reach the backend during development.\n\n3. Create `frontend/tsconfig.json` and `frontend/tsconfig.app.json` with strict TypeScript config targeting ES2020+ and JSX.\n\n4. Create `frontend/index.html` — Vite entry point with `<div id=\"root\">` and `<script type=\"module\" src=\"/src/main.tsx\">`.\n\n5. Create app shell files:\n   - `frontend/src/main.tsx` — ReactDOM.createRoot, render App with BrowserRouter\n   - `frontend/src/App.tsx` — Routes: `/admin/review` → ReviewQueue page, `/admin/review/:momentId` → MomentDetail page, `/` → redirect to `/admin/review`. Simple nav header with \"Chrysopedia Admin\" title.\n   - `frontend/src/App.css` — Minimal admin styles: clean sans-serif typography, card-based layout, status badge colors (pending=amber, approved=green, edited=blue, rejected=red)\n\n6. Create `frontend/src/api/client.ts` — Typed API client with functions for all review endpoints:\n   - `fetchQueue(params)` → GET /api/v1/review/queue\n   - `fetchStats()` → GET /api/v1/review/stats\n   - `approveMoment(id)` → POST /api/v1/review/moments/{id}/approve\n   - `rejectMoment(id)` → POST /api/v1/review/moments/{id}/reject\n   - `editMoment(id, data)` → PUT /api/v1/review/moments/{id}\n   - `splitMoment(id, splitTime)` → POST /api/v1/review/moments/{id}/split\n   - `mergeMoments(id, targetId)` → POST /api/v1/review/moments/{id}/merge\n   - `getReviewMode()` → GET /api/v1/review/mode\n   - `setReviewMode(enabled)` → PUT /api/v1/review/mode\n   All functions use fetch() with proper error handling. TypeScript interfaces for all request/response types.\n\n7. Create placeholder page components (just enough to verify routing works):\n   - `frontend/src/pages/ReviewQueue.tsx` — renders \"Review Queue\" heading + \"Loading...\" text\n   - `frontend/src/pages/MomentDetail.tsx` — renders \"Moment Detail\" heading + shows moment ID from URL params\n\n8. Run `npm install` and `npm run build` to verify the build produces `dist/index.html`. Verify the output directory structure matches what `docker/Dockerfile.web` expects.\n\n## Must-Haves\n\n- [ ] `npm run build` succeeds and produces `dist/index.html`\n- [ ] `npm run dev` starts Vite dev server\n- [ ] React Router routes `/admin/review` and `/admin/review/:momentId` render correctly\n- [ ] API client module exports typed functions for all 9 review endpoints\n- [ ] TypeScript compilation passes with no errors\n- [ ] Build output is compatible with existing `docker/Dockerfile.web` (files in `dist/`)\n\n## Verification\n\n- `cd frontend && npm run build && test -f dist/index.html` — build succeeds\n- `cd frontend && npx tsc --noEmit` — TypeScript has no errors\n- `grep -q 'fetchQueue\\|approveMoment\\|getReviewMode' frontend/src/api/client.ts` — API client has key functions","estimate":"1h","expectedOutput":["`frontend/package.json` — updated with React, Vite, TypeScript dependencies","`frontend/vite.config.ts` — Vite config with React plugin and API proxy","`frontend/tsconfig.json` — TypeScript project config","`frontend/tsconfig.app.json` — TypeScript app config","`frontend/index.html` — SPA entry point","`frontend/src/main.tsx` — React app entry","`frontend/src/App.tsx` — App shell with React Router","`frontend/src/App.css` — Admin UI styles","`frontend/src/api/client.ts` — Typed API client for review endpoints","`frontend/src/pages/ReviewQueue.tsx` — Queue page placeholder","`frontend/src/pages/MomentDetail.tsx` — Moment detail page placeholder"],"files":["frontend/package.json","frontend/vite.config.ts","frontend/tsconfig.json","frontend/tsconfig.app.json","frontend/index.html","frontend/src/main.tsx","frontend/src/App.tsx","frontend/src/App.css","frontend/src/api/client.ts","frontend/src/pages/ReviewQueue.tsx","frontend/src/pages/MomentDetail.tsx"],"inputs":["`frontend/package.json` — existing placeholder to replace","`docker/Dockerfile.web` — Docker build expects npm ci + npm run build → dist/","`docker/nginx.conf` — SPA serving with /api/ proxy","`backend/routers/review.py` — API endpoint signatures to match in client"],"taskId":"T02","title":"Bootstrap React + Vite + TypeScript frontend with API client","verify":"cd frontend && npm run build && test -f dist/index.html && npx tsc --noEmit"},{"description":"Implement the full admin review queue UI: the queue list page with status filter tabs and stats summary, the moment detail/review page with approve/reject/edit/split/merge actions, and the review mode toggle. Wire all pages to the API client from T02.\n\n## Steps\n\n1. Build `frontend/src/pages/ReviewQueue.tsx` — the main admin page:\n   - Stats bar at top showing counts per status (pending, approved, edited, rejected) fetched from `/api/v1/review/stats`\n   - Filter tabs: All, Pending, Approved, Edited, Rejected — clicking a tab filters the queue list\n   - Queue list: cards showing moment title, summary excerpt (first 150 chars), video filename, creator name, review_status badge, timestamps. Click card → navigate to `/admin/review/{momentId}`\n   - Pagination: Previous/Next buttons with offset/limit\n   - Mode toggle in header area: switch between Review Mode and Auto Mode, calls `PUT /api/v1/review/mode`. Show current mode with visual indicator (green dot for review, amber for auto)\n   - Empty state: show message when no moments match the current filter\n   - Use `useEffect` + `useState` for data fetching (no external state library needed for a single-admin tool)\n\n2. Build `frontend/src/pages/MomentDetail.tsx` — individual moment review page:\n   - Display full moment data: title, summary, content_type, start_time/end_time (formatted as mm:ss), plugins list, raw_transcript (if available), review_status badge\n   - Show source video filename and creator name\n   - Action buttons row:\n     - Approve (green) — calls `POST .../approve`, navigates back to queue on success\n     - Reject (red) — calls `POST .../reject`, navigates back to queue on success\n     - Edit — toggles inline edit mode for title, summary, content_type fields. Save button calls `PUT .../` with edited data\n     - Split — opens a split dialog: text input for split timestamp (validated between start_time and end_time), calls `POST .../split`\n     - Merge — opens a merge dialog: dropdown to select another moment from same video, calls `POST .../merge`\n   - Back link to queue\n   - Loading and error states for all API calls\n\n3. Create `frontend/src/components/StatusBadge.tsx` — reusable status badge component with color coding (pending=amber, approved=green, edited=blue, rejected=red).\n\n4. Create `frontend/src/components/ModeToggle.tsx` — review/auto mode toggle component extracted from the queue page for reuse in the header.\n\n5. Update `frontend/src/App.tsx` if needed to add the mode toggle to the global nav header.\n\n6. Update `frontend/src/App.css` with styles for:\n   - Stats bar (flex row of count cards)\n   - Filter tabs (horizontal tab bar with active indicator)\n   - Queue cards (bordered cards with hover effect)\n   - Status badges (colored pill shapes)\n   - Action buttons (colored, with hover/disabled states)\n   - Edit form (inline fields with save/cancel)\n   - Split/merge dialogs (modal overlays)\n   - Responsive layout (single column on narrow screens)\n\n7. Verify `npm run build` still succeeds after all UI changes.\n\n## Must-Haves\n\n- [ ] Queue page loads and displays moments from API with status filter tabs\n- [ ] Stats bar shows correct counts per review status\n- [ ] Clicking a moment navigates to detail page\n- [ ] Approve, reject actions work and navigate back to queue\n- [ ] Edit mode allows inline editing of title/summary/content_type with save\n- [ ] Split dialog validates split_time and creates two moments\n- [ ] Merge dialog shows moments from same video and merges on confirm\n- [ ] Mode toggle reads and updates review/auto mode via API\n- [ ] Build succeeds with no TypeScript errors\n\n## Verification\n\n- `cd frontend && npm run build && test -f dist/index.html` — build succeeds\n- `cd frontend && npx tsc --noEmit` — no TypeScript errors\n- `grep -q 'StatusBadge\\|ModeToggle' frontend/src/pages/ReviewQueue.tsx` — components integrated\n- `grep -q 'approve\\|reject\\|split\\|merge' frontend/src/pages/MomentDetail.tsx` — all actions present","estimate":"2h","expectedOutput":["`frontend/src/pages/ReviewQueue.tsx` — full queue page with stats, filters, moment list, mode toggle","`frontend/src/pages/MomentDetail.tsx` — full detail page with approve/reject/edit/split/merge actions","`frontend/src/components/StatusBadge.tsx` — reusable status badge component","`frontend/src/components/ModeToggle.tsx` — review/auto mode toggle component","`frontend/src/App.tsx` — updated with mode toggle in header if needed","`frontend/src/App.css` — complete admin UI styles"],"files":["frontend/src/pages/ReviewQueue.tsx","frontend/src/pages/MomentDetail.tsx","frontend/src/components/StatusBadge.tsx","frontend/src/components/ModeToggle.tsx","frontend/src/App.tsx","frontend/src/App.css"],"inputs":["`frontend/src/api/client.ts` — typed API client functions from T02","`frontend/src/App.tsx` — app shell with routes from T02","`frontend/src/App.css` — base styles from T02","`frontend/src/pages/ReviewQueue.tsx` — placeholder from T02 to replace","`frontend/src/pages/MomentDetail.tsx` — placeholder from T02 to replace"],"taskId":"T03","title":"Build review queue UI pages with status filters, moment actions, and mode toggle","verify":"cd frontend && npm run build && test -f dist/index.html && npx tsc --noEmit"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":6554,"cacheRead":76777,"cacheWrite":673,"totalTokens":84005,"cost":{"input":0.000005,"output":0.16385,"cacheRead":0.0383885,"cacheWrite":0.00420625,"total":0.20644975}},"stopReason":"toolUse","timestamp":1774825367331}}
{"type":"message","id":"a29f4742","parentId":"f43df0fd","timestamp":"2026-03-29T23:04:49.097Z","message":{"role":"toolResult","toolCallId":"toolu_01PhDM5cQDZtdiEvo5joPnfo","toolName":"gsd_plan_slice","content":[{"type":"text","text":"Planned slice S04 (M001)"}],"details":{"operation":"plan_slice","milestoneId":"M001","sliceId":"S04","planPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S04/S04-PLAN.md","taskPlanPaths":["/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S04/tasks/T01-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S04/tasks/T02-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S04/tasks/T03-PLAN.md"]},"isError":false,"timestamp":1774825489095}}
{"type":"message","id":"8f3b5a30","parentId":"a29f4742","timestamp":"2026-03-29T23:05:13.067Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me self-audit the plan against all checks:\n\n**Completion semantics**: If T01 (backend API + tests), T02 (React bootstrap + API client), and T03 (UI pages with all actions) are all completed, the slice goal — \"Admin can review, edit, approve, reject, split, and merge extracted key moments via a web UI. Mode toggle switches between review mode and auto mode.\" — is true. ✅\n\n**Requirement coverage**: R004 (\"Admin interface for reviewing extracted key moments: approve, edit+approve, split, merge, reject. Mode toggle.\") maps to T01 (API) + T03 (UI). Every must-have in the slice has at least one task. ✅\n\n**Task completeness**: All tasks have steps, must-haves, verification commands, inputs (backtick-wrapped file paths), and expected output (backtick-wrapped file paths). ✅\n\n**Dependency correctness**: T01 → T02 → T03 is sequential. T02 reads from T01's `backend/routers/review.py` for API signatures. T03 reads from T02's `frontend/src/api/client.ts` and placeholder pages. No circular or backwards references. ✅\n\n**Key links planned**: T01 mounts router in main.py. T02 creates API client matching T01's endpoints. T03 wires UI to API client. ✅\n\n**Scope sanity**: T01 has 6 steps, 7 files. T02 has 8 steps, 11 files. T03 has 7 steps, 6 files. All within bounds. ✅\n\n**Feature completeness**: Each task produces user-facing progress — T01 gives working API, T02 gives buildable frontend shell, T03 gives the complete admin UI. ✅\n\n**Quality gate coverage**: Threat Surface (Q3) applies — the review API handles user input that modifies DB state (approve/reject/edit/split/merge). Failure Modes (Q5) and Negative Tests (Q7) are included in T01 where they matter most. ✅\n\nNow let me record the key structural decision about the mode toggle:"},{"type":"toolCall","id":"toolu_01Jw1c9w9yWvfXksuw3qfBaY","name":"gsd_decision_save","arguments":{"choice":"Store review mode toggle in Redis key `chrysopedia:review_mode` with async redis client. Fall back to `settings.review_mode` config default when key is absent.","decision":"Runtime review mode toggle persistence mechanism","made_by":"agent","rationale":"The config.py `review_mode` setting is loaded via lru_cache from environment variables and cannot be mutated at runtime. Redis is already used by the project (Celery broker, stage 4 classification data) so it adds no new infrastructure. A system_settings DB table would work but Redis is simpler for a single boolean toggle on a single-admin tool. The pipeline's stages.py reads settings.review_mode from config — the admin toggle only affects new pipeline runs if stages.py is updated to check Redis too, but that's deferred since the toggle is primarily a UI-level concept for the review queue.","revisable":"Yes","scope":"architecture","when_context":"M001/S04"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":845,"cacheRead":77450,"cacheWrite":6756,"totalTokens":85052,"cost":{"input":0.000005,"output":0.021125,"cacheRead":0.038724999999999996,"cacheWrite":0.042225,"total":0.10208}},"stopReason":"toolUse","timestamp":1774825489096}}
{"type":"message","id":"7230726f","parentId":"8f3b5a30","timestamp":"2026-03-29T23:05:13.076Z","message":{"role":"toolResult","toolCallId":"toolu_01Jw1c9w9yWvfXksuw3qfBaY","toolName":"gsd_decision_save","content":[{"type":"text","text":"Saved decision D007"}],"details":{"operation":"save_decision","id":"D007"},"isError":false,"timestamp":1774825513074}}
{"type":"message","id":"7708ab06","parentId":"7230726f","timestamp":"2026-03-29T23:05:15.058Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S04 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":10,"cacheRead":84206,"cacheWrite":862,"totalTokens":85079,"cost":{"input":0.000005,"output":0.00025,"cacheRead":0.042103,"cacheWrite":0.0053875,"total":0.047745499999999996}},"stopReason":"stop","timestamp":1774825513075}}
