{"type":"thinking_level_change","id":"14a935ea","parentId":null,"timestamp":"2026-03-29T23:44:05.190Z","thinkingLevel":"off"}
{"type":"model_change","id":"a576872c","parentId":"14a935ea","timestamp":"2026-03-29T23:44:05.191Z","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/S05/S05-PLAN.md` and `.gsd/milestones/M001/slices/S05/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 S05 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 S05 (\"Search-First Web 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/S05/S05-RESEARCH.md`\n\n# S05 — Search-First Web UI — Research\n\n**Date:** 2026-03-29\n\n## Summary\n\nS05 builds the public-facing web UI for Chrysopedia — the search-first landing page, technique page display, creators browse, and topics browse. This is a medium-complexity frontend + backend slice that wires together existing infrastructure (Qdrant vectors, PostgreSQL models, React+Vite+TypeScript frontend) into new pages and API endpoints. The backend needs a search endpoint that embeds query text via the embedding API, queries Qdrant for semantic results, and enriches them with DB metadata. The frontend needs 6 new pages/views and a new API client module.\n\nThe riskiest piece is the search endpoint: it requires an async embedding call (to convert query text to a vector) followed by an async Qdrant search, both of which must complete within 500ms (R015). The existing `EmbeddingClient` is sync (designed for Celery), so the search endpoint needs an async variant using `openai.AsyncOpenAI` and `AsyncQdrantClient`. Everything else — technique page display, creators browse, topics browse — is standard CRUD over existing DB models.\n\nThe frontend is a React 18 + Vite + TypeScript SPA. S04 established the pattern: typed API client with `fetch()`, React Router v6, plain CSS (no Tailwind/UI library). S05 extends this with new routes and pages. The existing `App.tsx` routes everything to `/admin/*` — S05 adds the public UI at root paths (`/`, `/search`, `/techniques/:slug`, `/creators`, `/creators/:slug`, `/topics`).\n\n## Recommendation\n\nBuild backend-first: start with the search API endpoint (highest risk — requires async embedding + Qdrant integration), then the read-only data endpoints (technique pages, creators list with counts, topics hierarchy), then the frontend pages. The search endpoint is the critical path — everything else is well-understood CRUD.\n\nUse `AsyncQdrantClient` from `qdrant-client` (already in requirements.txt, same package provides both sync and async) and `openai.AsyncOpenAI` (already in requirements.txt) for the search endpoint. Create a new `backend/routers/search.py` for the search API and new Pydantic response schemas. Keep the existing sync `EmbeddingClient` and `QdrantManager` for Celery — the async search service is a separate, thin layer for the FastAPI request path.\n\nFor keyword fallback (R005 says \"semantic where possible, with keyword fallback\"), use PostgreSQL `ILIKE` queries on technique_page.title, key_moment.title, and creator.name. This handles the case where the embedding service is unavailable or returns empty results.\n\n## Implementation Landscape\n\n### Key Files\n\n**Backend — existing (read, extend):**\n- `backend/config.py` — Settings with `qdrant_url`, `qdrant_collection`, `embedding_api_url`, `embedding_model`, `embedding_dimensions`. No changes needed.\n- `backend/models.py` — All 7 models already exist: Creator, SourceVideo, TranscriptSegment, KeyMoment, TechniquePage, RelatedTechniqueLink, Tag. TechniquePage has `body_sections` (JSONB), `signal_chains` (JSONB), `topic_tags` (ARRAY), `topic_category`, `plugins` (ARRAY). No schema changes needed.\n- `backend/schemas.py` — Has `CreatorRead`, `TechniquePageRead`, `KeyMomentRead`. Needs new response schemas for search results and enriched technique page detail.\n- `backend/database.py` — Async engine + `get_session` dependency. Used as-is.\n- `backend/main.py` — Mount new routers here (search, techniques, topics).\n- `backend/routers/creators.py` — Has `list_creators` (alphabetical order) and `get_creator` (by slug). Needs extension: randomized sort, genre filter, technique count, video count per creator.\n- `backend/pipeline/qdrant_client.py` — Sync `QdrantManager` for Celery pipeline write-path. Read-path (search) needs new async code.\n- `backend/pipeline/embedding_client.py` — Sync `EmbeddingClient`. Search needs async variant.\n\n**Backend — new files to create:**\n- `backend/routers/search.py` — `GET /api/v1/search?q=...&scope=all|topics|creators&limit=20` — orchestrates async embedding + Qdrant search + keyword fallback + DB enrichment.\n- `backend/routers/techniques.py` — `GET /api/v1/techniques` (list, filterable by category/creator), `GET /api/v1/techniques/{slug}` (full detail with key_moments, related links, creator info).\n- `backend/routers/topics.py` — `GET /api/v1/topics` (hierarchy: top-level categories with sub-topics, counts of technique pages and creators per sub-topic). Reads from `canonical_tags.yaml` + DB aggregation.\n- `backend/search_service.py` — Async search service class: `AsyncOpenAI` for embedding, `AsyncQdrantClient` for vector search. Thin wrapper, initialized from Settings.\n\n**Frontend — existing (modify):**\n- `frontend/src/App.tsx` — Add new routes for public pages (`/`, `/search`, `/techniques/:slug`, `/creators`, `/creators/:slug`, `/topics`). Keep admin routes as-is.\n- `frontend/src/App.css` — Extend with styles for new pages (search bar, technique page, browse lists).\n- `frontend/src/api/client.ts` — Extend (or create parallel `search-client.ts`) with typed functions for search, techniques, creators, topics endpoints.\n\n**Frontend — new files to create:**\n- `frontend/src/pages/Home.tsx` — Landing page: search bar, Topics card, Creators card, recently added section.\n- `frontend/src/pages/SearchResults.tsx` — Full search results page after Enter or \"See all results\".\n- `frontend/src/pages/TechniquePage.tsx` — Full technique page display (R006): header, study guide prose, key moments index, related techniques, plugins.\n- `frontend/src/pages/CreatorsBrowse.tsx` — Creators list (R007): genre filter pills, type-to-narrow, sort toggle (random/alpha/views), creator rows with counts.\n- `frontend/src/pages/CreatorDetail.tsx` — Creator's technique page list.\n- `frontend/src/pages/TopicsBrowse.tsx` — Two-level topic hierarchy (R008): top-level categories → sub-topics with counts.\n\n### Build Order\n\n1. **Search service + search endpoint** (highest risk, unblocks frontend search). Create `backend/search_service.py` with async embedding + Qdrant query. Create `backend/routers/search.py`. This proves the semantic search path works end-to-end. Verify with `curl`.\n\n2. **Technique page + topics + creators API endpoints** (unblocks all frontend pages). Create `backend/routers/techniques.py`, `backend/routers/topics.py`, extend `backend/routers/creators.py`. Add enriched response schemas to `backend/schemas.py`. Mount all new routers in `main.py`.\n\n3. **Frontend: Landing page + search + navigation shell** (proves the primary interaction). Build `Home.tsx` with search bar and live typeahead, `SearchResults.tsx` for full results. This is the highest-value UI — it's the R015 30-second retrieval path.\n\n4. **Frontend: Technique page display** (R006 — the core content unit). Build `TechniquePage.tsx` rendering all sections: header with tags, study guide prose from `body_sections` JSONB, key moments index, related links, plugins.\n\n5. **Frontend: Browse pages** (R007, R008, R014). Build `CreatorsBrowse.tsx` (randomized default sort for R014), `CreatorDetail.tsx`, `TopicsBrowse.tsx`.\n\n### Verification Approach\n\n**Backend verification:**\n- `cd backend && python -m pytest tests/ -v` — all existing 40 tests still pass (regression).\n- New integration tests for search endpoint: mock the embedding API and Qdrant, verify response shape and <500ms timing.\n- New integration tests for techniques/topics/creators endpoints: verify data shape, pagination, filtering.\n- `curl` smoke tests against running API: `GET /api/v1/search?q=snare`, `GET /api/v1/techniques`, `GET /api/v1/topics`, `GET /api/v1/creators?sort=random`.\n\n**Frontend verification:**\n- `cd frontend && npx tsc -b` — zero TypeScript errors.\n- `cd frontend && npm run build` — clean production build.\n- Manual browser verification: navigate to each page, confirm data renders, search returns results, technique page displays all sections.\n\n## Constraints\n\n- **Existing frontend uses plain CSS, no UI library.** S04 established this pattern — continue with plain CSS. Adding Tailwind or shadcn/ui would be a divergence. The existing `App.css` has ~350 lines of well-structured styles.\n- **EmbeddingClient is sync (Celery).** The search endpoint runs in async FastAPI — cannot reuse `EmbeddingClient` directly. Must create async embedding using `openai.AsyncOpenAI`.\n- **QdrantManager is sync (Celery).** Same issue — use `AsyncQdrantClient` for the search read path.\n- **Qdrant payloads have limited metadata.** Technique page payloads contain: `type`, `page_id`, `creator_id`, `title`, `topic_category`, `topic_tags`, `summary`. Key moment payloads contain: `type`, `moment_id`, `source_video_id`, `title`, `start_time`, `end_time`, `content_type`. The search endpoint must enrich results with DB data (creator names, slugs, etc.) after Qdrant returns IDs.\n- **Stage 4 classification in Redis, not DB.** The `KeyMoment` model lacks `topic_tags`/`topic_category` columns. However, `TechniquePage` has both. For search, technique pages are the primary entity — this is fine.\n- **No existing single-resource endpoints for technique pages or key moments.** These need to be created.\n- **TypeScript strict mode** — `noUnusedLocals`, `noUnusedParameters`, `noUncheckedIndexedAccess` are all enabled. Frontend code must be clean.\n\n## Common Pitfalls\n\n- **Async embedding timeout in search path.** The embedding API call adds latency to every search request. If the embedding service is slow (>200ms), the 500ms target becomes tight. Mitigation: set a short timeout (300ms) on the embedding call and fall back to keyword search on timeout.\n- **Qdrant connection failure in search.** Unlike the pipeline (where embedding/Qdrant failures are non-blocking side-effects), search failures are user-visible. The search endpoint must gracefully degrade to keyword-only search when Qdrant is unavailable.\n- **Random sort on creators page (R014) with pagination.** `ORDER BY random()` in SQL gives different results per page load, making offset-based pagination inconsistent. Since the creators list is likely <100 entries, fetch all and shuffle server-side, or use a seed-based random sort (`ORDER BY md5(id::text || seed)`) with the seed passed from the client.\n- **body_sections JSONB structure is undefined.** The `TechniquePage.body_sections` column stores the study guide prose as JSONB, but the exact schema depends on what stage 5 (synthesis) produces. The frontend must handle variable structures gracefully. Check `stage5_synthesis.txt` prompt to understand the expected format.\n- **Vite dev proxy only covers `/api`.** The existing proxy in `vite.config.ts` proxies `/api` to `http://localhost:8001`. This is sufficient for all new endpoints since they're all under `/api/v1/`.\n\n## Open Risks\n\n- **body_sections JSONB format is unknown until real pipeline output is examined.** The synthesis prompt (stage 5) defines the structure, but no real pipeline run has been done. The technique page frontend component must be flexible enough to handle whatever JSONB shape the LLM produces. Fallback: render as plain text if structure is unrecognized.\n- **Qdrant collection may be empty** if no pipeline runs have completed embedding. The search endpoint must handle empty results gracefully and fall back to keyword search.\n- **Embedding API latency** is unknown — it depends on the deployment (local Ollama vs DGX Sparks). The 500ms search target may not be achievable with slow embedding. Client-side debounce (300ms) helps, but server-side latency must be measured.\n\n---\n\n### Decisions\nSource: `.gsd/DECISIONS.md`\n\n# Decisions (compact)\nFields: id | when | scope | decision | choice | rationale | revisable | made_by\n\nD007 | M001/S04 | architecture | Runtime review mode toggle persistence mechanism | 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. | 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. | Yes | agent\nD008 | M001/S04 | requirement | R004 Review Queue UI status | validated | All R004 capabilities delivered and verified: 9 API endpoints (approve, reject, edit, split, merge, queue list, stats, mode get/set) with 24 passing integration tests covering happy paths and error boundaries. React+TypeScript frontend with queue page (filter tabs, stats, pagination), moment detail page (all review actions with modals), and review-vs-auto mode toggle. Frontend builds with zero TypeScript errors. | 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## Frontend detail page without a single-resource GET endpoint\n\n**Context:** The review queue backend has `GET /review/queue` (list, paginated) but no `GET /review/moments/{id}` for fetching a single moment. The MomentDetail page needs to display one specific moment by ID from the URL params.\n\n**Fix:** MomentDetail fetches the full queue with `limit=500` and filters client-side by ID. This works for a single-admin tool with small datasets but would not scale. If the moment count grows significantly, add a dedicated `GET /review/moments/{id}` endpoint to the review router.\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/S05/S05-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":"154b1689","parentId":"a576872c","timestamp":"2026-03-29T23:44:05.192Z"}
{"type":"message","id":"55a01a58","parentId":"154b1689","timestamp":"2026-03-29T23:44:13.082Z","message":{"role":"assistant","content":[{"type":"text","text":"\n\nI'll start by exploring the current codebase to ground the plan in reality, then read the templates."},{"type":"toolCall","id":"toolu_01CXBXKLEbSCDVmh3WUEQkuG","name":"async_bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find backend/routers -type f -name '*.py' | sort && echo \"---\" && find frontend/src -type f | sort && echo \"---\" && cat backend/main.py","label":"explore structure"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":154,"cacheRead":49033,"cacheWrite":12714,"totalTokens":61903,"cost":{"input":0.00001,"output":0.00385,"cacheRead":0.0245165,"cacheWrite":0.0794625,"total":0.107839}},"stopReason":"toolUse","timestamp":1774827845191}}
{"type":"message","id":"9609b06f","parentId":"55a01a58","timestamp":"2026-03-29T23:44:13.089Z","message":{"role":"toolResult","toolCallId":"toolu_01CXBXKLEbSCDVmh3WUEQkuG","toolName":"async_bash","content":[{"type":"text","text":"Background job started: **bg_89de94ce**\nCommand: `explore structure`\n\nUse `await_job` to get results when ready, or `cancel_job` to stop."}],"isError":false,"timestamp":1774827853088}}
{"type":"message","id":"f6b54528","parentId":"9609b06f","timestamp":"2026-03-29T23:44:16.453Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Cz4CDa8VdKerPAApbYzsjY","name":"async_bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/schemas.py","label":"schemas"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":61747,"cacheWrite":208,"totalTokens":62048,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.030873499999999998,"cacheWrite":0.0013000000000000002,"total":0.0344785}},"stopReason":"toolUse","timestamp":1774827853088}}
{"type":"message","id":"92d74eb2","parentId":"f6b54528","timestamp":"2026-03-29T23:44:16.460Z","message":{"role":"toolResult","toolCallId":"toolu_01Cz4CDa8VdKerPAApbYzsjY","toolName":"async_bash","content":[{"type":"text","text":"Background job started: **bg_d2c2ce43**\nCommand: `schemas`\n\nUse `await_job` to get results when ready, or `cancel_job` to stop."}],"isError":false,"timestamp":1774827856458}}
{"type":"message","id":"45d28f6e","parentId":"92d74eb2","timestamp":"2026-03-29T23:44:19.790Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_015yfgkGH3Wewh1QD8W8Lc9E","name":"async_bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/models.py","label":"models"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":61955,"cacheWrite":145,"totalTokens":62193,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.030977499999999998,"cacheWrite":0.00090625,"total":0.03418875}},"stopReason":"toolUse","timestamp":1774827856459}}
{"type":"message","id":"bc3cdd68","parentId":"45d28f6e","timestamp":"2026-03-29T23:44:19.796Z","message":{"role":"toolResult","toolCallId":"toolu_015yfgkGH3Wewh1QD8W8Lc9E","toolName":"async_bash","content":[{"type":"text","text":"Background job started: **bg_be9e25cd**\nCommand: `models`\n\nUse `await_job` to get results when ready, or `cancel_job` to stop."}],"isError":false,"timestamp":1774827859795}}
{"type":"message","id":"f8d781eb","parentId":"bc3cdd68","timestamp":"2026-03-29T23:44:22.668Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_011Peyuzwf8RDAwyF2WLjs41","name":"async_bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/routers/creators.py","label":"creators router"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":96,"cacheRead":62100,"cacheWrite":144,"totalTokens":62341,"cost":{"input":0.000005,"output":0.0024000000000000002,"cacheRead":0.031049999999999998,"cacheWrite":0.0009000000000000001,"total":0.034355}},"stopReason":"toolUse","timestamp":1774827859795}}
{"type":"message","id":"6f471d82","parentId":"f8d781eb","timestamp":"2026-03-29T23:44:22.674Z","message":{"role":"toolResult","toolCallId":"toolu_011Peyuzwf8RDAwyF2WLjs41","toolName":"async_bash","content":[{"type":"text","text":"Background job started: **bg_ad40d79c**\nCommand: `creators router`\n\nUse `await_job` to get results when ready, or `cancel_job` to stop."}],"isError":false,"timestamp":1774827862673}}
{"type":"message","id":"c45c2545","parentId":"6f471d82","timestamp":"2026-03-29T23:44:26.004Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01HukKAUVKgS5Xof5fJHcnTM","name":"async_bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/pipeline/embedding_client.py && echo \"===\" && cat backend/pipeline/qdrant_client.py","label":"embedding+qdrant"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":118,"cacheRead":62100,"cacheWrite":293,"totalTokens":62512,"cost":{"input":0.000005,"output":0.00295,"cacheRead":0.031049999999999998,"cacheWrite":0.0018312500000000002,"total":0.03583625}},"stopReason":"toolUse","timestamp":1774827862673}}
{"type":"message","id":"3f8cad18","parentId":"c45c2545","timestamp":"2026-03-29T23:44:26.010Z","message":{"role":"toolResult","toolCallId":"toolu_01HukKAUVKgS5Xof5fJHcnTM","toolName":"async_bash","content":[{"type":"text","text":"Background job started: **bg_ea8ea4f8**\nCommand: `embedding+qdrant`\n\nUse `await_job` to get results when ready, or `cancel_job` to stop."}],"isError":false,"timestamp":1774827866009}}
{"type":"message","id":"8e6bdc92","parentId":"3f8cad18","timestamp":"2026-03-29T23:44:29.572Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01QEAKtC46T7axVjwqmqrJiJ","name":"async_bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat frontend/src/App.tsx && echo \"===\" && cat frontend/src/App.css | head -50 && echo \"===\" && cat frontend/vite.config.ts && echo \"===\" && cat frontend/tsconfig.json","label":"frontend core"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":142,"cacheRead":62393,"cacheWrite":175,"totalTokens":62711,"cost":{"input":0.000005,"output":0.00355,"cacheRead":0.0311965,"cacheWrite":0.00109375,"total":0.035845249999999995}},"stopReason":"toolUse","timestamp":1774827866009}}
{"type":"message","id":"fc7e4f24","parentId":"8e6bdc92","timestamp":"2026-03-29T23:44:29.578Z","message":{"role":"toolResult","toolCallId":"toolu_01QEAKtC46T7axVjwqmqrJiJ","toolName":"async_bash","content":[{"type":"text","text":"Background job started: **bg_880523fd**\nCommand: `frontend core`\n\nUse `await_job` to get results when ready, or `cancel_job` to stop."}],"isError":false,"timestamp":1774827869576}}
{"type":"message","id":"e68834f2","parentId":"fc7e4f24","timestamp":"2026-03-29T23:44:32.364Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_0199xBG3981MZyf7hvKxBDRe","name":"async_bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/config.py","label":"config"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":62568,"cacheWrite":193,"totalTokens":62854,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.031284,"cacheWrite":0.00120625,"total":0.03479525}},"stopReason":"toolUse","timestamp":1774827869577}}
{"type":"message","id":"c375a427","parentId":"e68834f2","timestamp":"2026-03-29T23:44:32.371Z","message":{"role":"toolResult","toolCallId":"toolu_0199xBG3981MZyf7hvKxBDRe","toolName":"async_bash","content":[{"type":"text","text":"Background job started: **bg_4226df48**\nCommand: `config`\n\nUse `await_job` to get results when ready, or `cancel_job` to stop."}],"isError":false,"timestamp":1774827872369}}
{"type":"message","id":"fb14824c","parentId":"c375a427","timestamp":"2026-03-29T23:44:35.344Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01TgW4jcUD9UXvfrA7YPF7Bb","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/plan.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":67,"cacheRead":62761,"cacheWrite":143,"totalTokens":62972,"cost":{"input":0.000005,"output":0.001675,"cacheRead":0.0313805,"cacheWrite":0.00089375,"total":0.03395425}},"stopReason":"toolUse","timestamp":1774827872370}}
{"type":"message","id":"9710d9d9","parentId":"fb14824c","timestamp":"2026-03-29T23:44:35.347Z","message":{"role":"toolResult","toolCallId":"toolu_01TgW4jcUD9UXvfrA7YPF7Bb","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":1774827875345}}
{"type":"message","id":"2a5462f4","parentId":"9710d9d9","timestamp":"2026-03-29T23:44:38.058Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01S5m8knR38GDaddtrxj5YuZ","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":69,"cacheRead":62904,"cacheWrite":1786,"totalTokens":64760,"cost":{"input":0.000005,"output":0.0017250000000000002,"cacheRead":0.031452,"cacheWrite":0.0111625,"total":0.0443445}},"stopReason":"toolUse","timestamp":1774827875346}}
{"type":"message","id":"22283a90","parentId":"2a5462f4","timestamp":"2026-03-29T23:44:38.062Z","message":{"role":"toolResult","toolCallId":"toolu_01S5m8knR38GDaddtrxj5YuZ","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":1774827878060}}
{"type":"message","id":"f5150e86","parentId":"22283a90","timestamp":"2026-03-29T23:44:41.171Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me wait for all the exploration jobs to complete:"},{"type":"toolCall","id":"toolu_01EMNvRHjDRvVyfhPmM37FJJ","name":"await_job","arguments":{"jobs":["bg_89de94ce","bg_d2c2ce43","bg_be9e25cd","bg_ad40d79c","bg_ea8ea4f8","bg_880523fd","bg_4226df48"]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":126,"cacheRead":64690,"cacheWrite":923,"totalTokens":65740,"cost":{"input":0.000005,"output":0.00315,"cacheRead":0.032345,"cacheWrite":0.0057687500000000004,"total":0.04126875}},"stopReason":"toolUse","timestamp":1774827878061}}
{"type":"message","id":"6e8113a7","parentId":"f5150e86","timestamp":"2026-03-29T23:44:41.175Z","message":{"role":"toolResult","toolCallId":"toolu_01EMNvRHjDRvVyfhPmM37FJJ","toolName":"await_job","content":[{"type":"text","text":"### bg_89de94ce — explore structure (completed, 28.1s)\n\nbackend/routers/creators.py\nbackend/routers/health.py\nbackend/routers/ingest.py\nbackend/routers/__init__.py\nbackend/routers/pipeline.py\nbackend/routers/review.py\nbackend/routers/videos.py\n---\nfrontend/src/api/client.ts\nfrontend/src/App.css\nfrontend/src/App.tsx\nfrontend/src/components/ModeToggle.tsx\nfrontend/src/components/StatusBadge.tsx\nfrontend/src/main.tsx\nfrontend/src/pages/MomentDetail.tsx\nfrontend/src/pages/ReviewQueue.tsx\nfrontend/src/vite-env.d.ts\n---\n\"\"\"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, review, 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(review.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\n\n---\n\n### bg_d2c2ce43 — schemas (completed, 24.7s)\n\n\"\"\"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\n\n# ── Review Queue ─────────────────────────────────────────────────────────────\n\nclass ReviewQueueItem(KeyMomentRead):\n    \"\"\"Key moment enriched with source video and creator info for review UI.\"\"\"\n    video_filename: str\n    creator_name: str\n\n\nclass ReviewQueueResponse(BaseModel):\n    \"\"\"Paginated response for the review queue.\"\"\"\n    items: list[ReviewQueueItem] = Field(default_factory=list)\n    total: int = 0\n    offset: int = 0\n    limit: int = 50\n\n\nclass ReviewStatsResponse(BaseModel):\n    \"\"\"Counts of key moments grouped by review status.\"\"\"\n    pending: int = 0\n    approved: int = 0\n    edited: int = 0\n    rejected: int = 0\n\n\nclass MomentEditRequest(BaseModel):\n    \"\"\"Editable fields for a key moment.\"\"\"\n    title: str | None = None\n    summary: str | None = None\n    start_time: float | None = None\n    end_time: float | None = None\n    content_type: str | None = None\n    plugins: list[str] | None = None\n\n\nclass MomentSplitRequest(BaseModel):\n    \"\"\"Request to split a moment at a given timestamp.\"\"\"\n    split_time: float\n\n\nclass MomentMergeRequest(BaseModel):\n    \"\"\"Request to merge two moments.\"\"\"\n    target_moment_id: uuid.UUID\n\n\nclass ReviewModeResponse(BaseModel):\n    \"\"\"Current review mode state.\"\"\"\n    review_mode: bool\n\n\nclass ReviewModeUpdate(BaseModel):\n    \"\"\"Request to update the review mode.\"\"\"\n    review_mode: bool\n\n\n---\n\n### bg_be9e25cd — models (completed, 21.4s)\n\n\"\"\"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    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\n\n---\n\n### bg_ad40d79c — creators router (completed, 18.5s)\n\n\"\"\"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\n\n---\n\n### bg_ea8ea4f8 — embedding+qdrant (completed, 15.2s)\n\n\"\"\"Synchronous embedding client using the OpenAI-compatible /v1/embeddings API.\n\nUses ``openai.OpenAI`` (sync) since Celery tasks run synchronously.\nHandles connection failures gracefully — embedding is non-blocking for the pipeline.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\n\nimport openai\n\nfrom config import Settings\n\nlogger = logging.getLogger(__name__)\n\n\nclass EmbeddingClient:\n    \"\"\"Sync embedding client backed by an OpenAI-compatible /v1/embeddings endpoint.\"\"\"\n\n    def __init__(self, settings: Settings) -> None:\n        self.settings = settings\n        self._client = openai.OpenAI(\n            base_url=settings.embedding_api_url,\n            api_key=settings.llm_api_key,\n        )\n\n    def embed(self, texts: list[str]) -> list[list[float]]:\n        \"\"\"Generate embedding vectors for a batch of texts.\n\n        Parameters\n        ----------\n        texts:\n            List of strings to embed.\n\n        Returns\n        -------\n        list[list[float]]\n            Embedding vectors. Returns empty list on connection/timeout errors\n            so the pipeline can continue without embeddings.\n        \"\"\"\n        if not texts:\n            return []\n\n        try:\n            response = self._client.embeddings.create(\n                model=self.settings.embedding_model,\n                input=texts,\n            )\n        except (openai.APIConnectionError, openai.APITimeoutError) as exc:\n            logger.warning(\n                \"Embedding API unavailable (%s: %s). Skipping %d texts.\",\n                type(exc).__name__,\n                exc,\n                len(texts),\n            )\n            return []\n        except openai.APIError as exc:\n            logger.warning(\n                \"Embedding API error (%s: %s). Skipping %d texts.\",\n                type(exc).__name__,\n                exc,\n                len(texts),\n            )\n            return []\n\n        vectors = [item.embedding for item in response.data]\n\n        # Validate dimensions\n        expected_dim = self.settings.embedding_dimensions\n        for i, vec in enumerate(vectors):\n            if len(vec) != expected_dim:\n                logger.warning(\n                    \"Embedding dimension mismatch at index %d: expected %d, got %d. \"\n                    \"Returning empty list.\",\n                    i,\n                    expected_dim,\n                    len(vec),\n                )\n                return []\n\n        logger.info(\n            \"Generated %d embeddings (dim=%d) using model=%s\",\n            len(vectors),\n            expected_dim,\n            self.settings.embedding_model,\n        )\n        return vectors\n===\n\"\"\"Qdrant vector database manager for collection lifecycle and point upserts.\n\nHandles collection creation (idempotent) and batch upserts for technique pages\nand key moments. Connection failures are non-blocking — the pipeline continues\nwithout search indexing.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nimport uuid\n\nfrom qdrant_client import QdrantClient\nfrom qdrant_client.http import exceptions as qdrant_exceptions\nfrom qdrant_client.models import Distance, PointStruct, VectorParams\n\nfrom config import Settings\n\nlogger = logging.getLogger(__name__)\n\n\nclass QdrantManager:\n    \"\"\"Manages a Qdrant collection for Chrysopedia technique-page and key-moment vectors.\"\"\"\n\n    def __init__(self, settings: Settings) -> None:\n        self.settings = settings\n        self._client = QdrantClient(url=settings.qdrant_url)\n        self._collection = settings.qdrant_collection\n\n    # ── Collection management ────────────────────────────────────────────\n\n    def ensure_collection(self) -> None:\n        \"\"\"Create the collection if it does not already exist.\n\n        Uses cosine distance and the configured embedding dimensions.\n        \"\"\"\n        try:\n            if self._client.collection_exists(self._collection):\n                logger.info(\"Qdrant collection '%s' already exists.\", self._collection)\n                return\n\n            self._client.create_collection(\n                collection_name=self._collection,\n                vectors_config=VectorParams(\n                    size=self.settings.embedding_dimensions,\n                    distance=Distance.COSINE,\n                ),\n            )\n            logger.info(\n                \"Created Qdrant collection '%s' (dim=%d, cosine).\",\n                self._collection,\n                self.settings.embedding_dimensions,\n            )\n        except qdrant_exceptions.UnexpectedResponse as exc:\n            logger.warning(\n                \"Qdrant error during ensure_collection (%s). Skipping.\",\n                exc,\n            )\n        except Exception as exc:\n            logger.warning(\n                \"Qdrant connection failed during ensure_collection (%s: %s). Skipping.\",\n                type(exc).__name__,\n                exc,\n            )\n\n    # ── Low-level upsert ─────────────────────────────────────────────────\n\n    def upsert_points(self, points: list[PointStruct]) -> None:\n        \"\"\"Upsert a batch of pre-built PointStruct objects.\"\"\"\n        if not points:\n            return\n        try:\n            self._client.upsert(\n                collection_name=self._collection,\n                points=points,\n            )\n            logger.info(\n                \"Upserted %d points to Qdrant collection '%s'.\",\n                len(points),\n                self._collection,\n            )\n        except qdrant_exceptions.UnexpectedResponse as exc:\n            logger.warning(\n                \"Qdrant upsert failed (%s). %d points skipped.\",\n                exc,\n                len(points),\n            )\n        except Exception as exc:\n            logger.warning(\n                \"Qdrant upsert connection error (%s: %s). %d points skipped.\",\n                type(exc).__name__,\n                exc,\n                len(points),\n            )\n\n    # ── High-level upserts ───────────────────────────────────────────────\n\n    def upsert_technique_pages(\n        self,\n        pages: list[dict],\n        vectors: list[list[float]],\n    ) -> None:\n        \"\"\"Build and upsert PointStructs for technique pages.\n\n        Each page dict must contain:\n            page_id, creator_id, title, topic_category, topic_tags, summary\n\n        Parameters\n        ----------\n        pages:\n            Metadata dicts, one per technique page.\n        vectors:\n            Corresponding embedding vectors (same order as pages).\n        \"\"\"\n        if len(pages) != len(vectors):\n            logger.warning(\n                \"Technique-page count (%d) != vector count (%d). Skipping upsert.\",\n                len(pages),\n                len(vectors),\n            )\n            return\n\n        points = []\n        for page, vector in zip(pages, vectors):\n            point = PointStruct(\n                id=str(uuid.uuid4()),\n                vector=vector,\n                payload={\n                    \"type\": \"technique_page\",\n                    \"page_id\": page[\"page_id\"],\n                    \"creator_id\": page[\"creator_id\"],\n                    \"title\": page[\"title\"],\n                    \"topic_category\": page[\"topic_category\"],\n                    \"topic_tags\": page.get(\"topic_tags\") or [],\n                    \"summary\": page.get(\"summary\") or \"\",\n                },\n            )\n            points.append(point)\n\n        self.upsert_points(points)\n\n    def upsert_key_moments(\n        self,\n        moments: list[dict],\n        vectors: list[list[float]],\n    ) -> None:\n        \"\"\"Build and upsert PointStructs for key moments.\n\n        Each moment dict must contain:\n            moment_id, source_video_id, title, start_time, end_time, content_type\n\n        Parameters\n        ----------\n        moments:\n            Metadata dicts, one per key moment.\n        vectors:\n            Corresponding embedding vectors (same order as moments).\n        \"\"\"\n        if len(moments) != len(vectors):\n            logger.warning(\n                \"Key-moment count (%d) != vector count (%d). Skipping upsert.\",\n                len(moments),\n                len(vectors),\n            )\n            return\n\n        points = []\n        for moment, vector in zip(moments, vectors):\n            point = PointStruct(\n                id=str(uuid.uuid4()),\n                vector=vector,\n                payload={\n                    \"type\": \"key_moment\",\n                    \"moment_id\": moment[\"moment_id\"],\n                    \"source_video_id\": moment[\"source_video_id\"],\n                    \"title\": moment[\"title\"],\n                    \"start_time\": moment[\"start_time\"],\n                    \"end_time\": moment[\"end_time\"],\n                    \"content_type\": moment[\"content_type\"],\n                },\n            )\n            points.append(point)\n\n        self.upsert_points(points)\n\n\n---\n\n### bg_880523fd — frontend core (completed, 11.6s)\n\nimport { Navigate, Route, Routes } from \"react-router-dom\";\nimport ReviewQueue from \"./pages/ReviewQueue\";\nimport MomentDetail from \"./pages/MomentDetail\";\nimport ModeToggle from \"./components/ModeToggle\";\n\nexport default function App() {\n  return (\n    <div className=\"app\">\n      <header className=\"app-header\">\n        <h1>Chrysopedia Admin</h1>\n        <div className=\"app-header__right\">\n          <ModeToggle />\n          <nav>\n            <a href=\"/admin/review\">Review Queue</a>\n          </nav>\n        </div>\n      </header>\n\n      <main className=\"app-main\">\n        <Routes>\n          <Route path=\"/admin/review\" element={<ReviewQueue />} />\n          <Route path=\"/admin/review/:momentId\" element={<MomentDetail />} />\n          <Route path=\"*\" element={<Navigate to=\"/admin/review\" replace />} />\n        </Routes>\n      </main>\n    </div>\n  );\n}\n===\n/* ── Base ─────────────────────────────────────────────────────────────────── */\n\n*,\n*::before,\n*::after {\n  box-sizing: border-box;\n  margin: 0;\n  padding: 0;\n}\n\nbody {\n  font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto,\n    \"Helvetica Neue\", Arial, sans-serif;\n  line-height: 1.5;\n  color: #1a1a2e;\n  background: #f4f4f8;\n}\n\n/* ── App shell ────────────────────────────────────────────────────────────── */\n\n.app-header {\n  display: flex;\n  align-items: center;\n  justify-content: space-between;\n  padding: 0.75rem 1.5rem;\n  background: #1a1a2e;\n  color: #fff;\n}\n\n.app-header h1 {\n  font-size: 1.125rem;\n  font-weight: 600;\n  letter-spacing: -0.01em;\n}\n\n.app-header__right {\n  display: flex;\n  align-items: center;\n  gap: 1.5rem;\n}\n\n.app-header nav a {\n  color: rgba(255, 255, 255, 0.8);\n  text-decoration: none;\n  font-size: 0.875rem;\n}\n\n.app-header nav a:hover {\n  color: #fff;\n}\n===\nimport { defineConfig } from \"vite\";\nimport react from \"@vitejs/plugin-react\";\n\nexport default defineConfig({\n  plugins: [react()],\n  server: {\n    proxy: {\n      \"/api\": {\n        target: \"http://localhost:8001\",\n        changeOrigin: true,\n      },\n    },\n  },\n});\n===\n{\n  \"files\": [],\n  \"references\": [{ \"path\": \"./tsconfig.app.json\" }]\n}\n\n\n---\n\n### bg_4226df48 — config (completed, 8.8s)\n\n\"\"\"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":1774827881172}}
{"type":"message","id":"ab2f66e2","parentId":"6e8113a7","timestamp":"2026-03-29T23:44:46.333Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me examine a few more files to understand the frontend API client and existing tests:"},{"type":"toolCall","id":"toolu_01Kgz62UiMzC9YNH43AJxeng","name":"async_bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat frontend/src/api/client.ts && echo \"===\" && cat frontend/tsconfig.app.json && echo \"===\" && cat frontend/package.json","label":"frontend api+config"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":145,"cacheRead":65613,"cacheWrite":10619,"totalTokens":76378,"cost":{"input":0.000005,"output":0.003625,"cacheRead":0.032806499999999995,"cacheWrite":0.06636875,"total":0.10280525}},"stopReason":"toolUse","timestamp":1774827881174}}
{"type":"message","id":"f72732c4","parentId":"ab2f66e2","timestamp":"2026-03-29T23:44:46.340Z","message":{"role":"toolResult","toolCallId":"toolu_01Kgz62UiMzC9YNH43AJxeng","toolName":"async_bash","content":[{"type":"text","text":"Background job started: **bg_1783fe85**\nCommand: `frontend api+config`\n\nUse `await_job` to get results when ready, or `cancel_job` to stop."}],"isError":false,"timestamp":1774827886338}}
{"type":"message","id":"0c96a56c","parentId":"f72732c4","timestamp":"2026-03-29T23:44:50.870Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_016rE2zgRpjVz9biQu3VuFV8","name":"async_bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/database.py && echo \"===\" && ls backend/tests/ && echo \"===\" && head -80 backend/tests/conftest.py","label":"db+tests"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":123,"cacheRead":76232,"cacheWrite":199,"totalTokens":76555,"cost":{"input":0.000005,"output":0.003075,"cacheRead":0.038116,"cacheWrite":0.00124375,"total":0.04243975}},"stopReason":"toolUse","timestamp":1774827886339}}
{"type":"message","id":"7ee99c4b","parentId":"0c96a56c","timestamp":"2026-03-29T23:44:50.877Z","message":{"role":"toolResult","toolCallId":"toolu_016rE2zgRpjVz9biQu3VuFV8","toolName":"async_bash","content":[{"type":"text","text":"Background job started: **bg_78dddfdc**\nCommand: `db+tests`\n\nUse `await_job` to get results when ready, or `cancel_job` to stop."}],"isError":false,"timestamp":1774827890875}}
{"type":"message","id":"690f1f4a","parentId":"7ee99c4b","timestamp":"2026-03-29T23:44:54.701Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01K9SWkQx9Ckhf8Le99vWWDe","name":"async_bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/canonical_tags.yaml 2>/dev/null || cat canonical_tags.yaml 2>/dev/null || find . -name 'canonical_tags.yaml' -type f 2>/dev/null","label":"tags yaml"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":136,"cacheRead":76431,"cacheWrite":176,"totalTokens":76744,"cost":{"input":0.000005,"output":0.0034000000000000002,"cacheRead":0.0382155,"cacheWrite":0.0011,"total":0.042720499999999995}},"stopReason":"toolUse","timestamp":1774827890876}}
{"type":"message","id":"38f64cc6","parentId":"690f1f4a","timestamp":"2026-03-29T23:44:54.707Z","message":{"role":"toolResult","toolCallId":"toolu_01K9SWkQx9Ckhf8Le99vWWDe","toolName":"async_bash","content":[{"type":"text","text":"Background job started: **bg_b4f8c61d**\nCommand: `tags yaml`\n\nUse `await_job` to get results when ready, or `cancel_job` to stop."}],"isError":false,"timestamp":1774827894706}}
{"type":"message","id":"b73fab3a","parentId":"38f64cc6","timestamp":"2026-03-29T23:44:58.497Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01DoyymSpSShohC24XGa6pHt","name":"async_bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat prompts/stage5_synthesis.txt","label":"synthesis prompt"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":97,"cacheRead":76607,"cacheWrite":191,"totalTokens":76896,"cost":{"input":0.000005,"output":0.002425,"cacheRead":0.0383035,"cacheWrite":0.0011937500000000001,"total":0.04192725}},"stopReason":"toolUse","timestamp":1774827894707}}
{"type":"message","id":"5bfc1c9b","parentId":"b73fab3a","timestamp":"2026-03-29T23:44:58.504Z","message":{"role":"toolResult","toolCallId":"toolu_01DoyymSpSShohC24XGa6pHt","toolName":"async_bash","content":[{"type":"text","text":"Background job started: **bg_ff61735d**\nCommand: `synthesis prompt`\n\nUse `await_job` to get results when ready, or `cancel_job` to stop."}],"isError":false,"timestamp":1774827898502}}
{"type":"message","id":"447c8f30","parentId":"5bfc1c9b","timestamp":"2026-03-29T23:45:01.702Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_014otrMXEExuY7mDYKS95LX6","name":"await_job","arguments":{"jobs":["bg_1783fe85","bg_78dddfdc","bg_b4f8c61d","bg_ff61735d"]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":87,"cacheRead":76798,"cacheWrite":149,"totalTokens":77035,"cost":{"input":0.000005,"output":0.0021750000000000003,"cacheRead":0.038398999999999996,"cacheWrite":0.00093125,"total":0.04151025}},"stopReason":"toolUse","timestamp":1774827898503}}
{"type":"message","id":"32b3f642","parentId":"447c8f30","timestamp":"2026-03-29T23:45:01.705Z","message":{"role":"toolResult","toolCallId":"toolu_014otrMXEExuY7mDYKS95LX6","toolName":"await_job","content":[{"type":"text","text":"### bg_1783fe85 — frontend api+config (completed, 15.4s)\n\n/**\n * Typed API client for Chrysopedia review queue endpoints.\n *\n * All functions use fetch() with JSON handling and throw on non-OK responses.\n * Base URL is empty so requests go through the Vite dev proxy or nginx in prod.\n */\n\n// ── Types ───────────────────────────────────────────────────────────────────\n\nexport interface KeyMomentRead {\n  id: string;\n  source_video_id: string;\n  technique_page_id: string | null;\n  title: string;\n  summary: string;\n  start_time: number;\n  end_time: number;\n  content_type: string;\n  plugins: string[] | null;\n  raw_transcript: string | null;\n  review_status: string;\n  created_at: string;\n  updated_at: string;\n}\n\nexport interface ReviewQueueItem extends KeyMomentRead {\n  video_filename: string;\n  creator_name: string;\n}\n\nexport interface ReviewQueueResponse {\n  items: ReviewQueueItem[];\n  total: number;\n  offset: number;\n  limit: number;\n}\n\nexport interface ReviewStatsResponse {\n  pending: number;\n  approved: number;\n  edited: number;\n  rejected: number;\n}\n\nexport interface ReviewModeResponse {\n  review_mode: boolean;\n}\n\nexport interface MomentEditRequest {\n  title?: string;\n  summary?: string;\n  start_time?: number;\n  end_time?: number;\n  content_type?: string;\n  plugins?: string[];\n}\n\nexport interface MomentSplitRequest {\n  split_time: number;\n}\n\nexport interface MomentMergeRequest {\n  target_moment_id: string;\n}\n\nexport interface QueueParams {\n  status?: string;\n  offset?: number;\n  limit?: number;\n}\n\n// ── Helpers ──────────────────────────────────────────────────────────────────\n\nconst BASE = \"/api/v1/review\";\n\nclass ApiError extends Error {\n  constructor(\n    public status: number,\n    public detail: string,\n  ) {\n    super(`API ${status}: ${detail}`);\n    this.name = \"ApiError\";\n  }\n}\n\nasync function request<T>(url: string, init?: RequestInit): Promise<T> {\n  const res = await fetch(url, {\n    ...init,\n    headers: {\n      \"Content-Type\": \"application/json\",\n      ...init?.headers,\n    },\n  });\n\n  if (!res.ok) {\n    let detail = res.statusText;\n    try {\n      const body = await res.json();\n      detail = body.detail ?? detail;\n    } catch {\n      // body not JSON — keep statusText\n    }\n    throw new ApiError(res.status, detail);\n  }\n\n  return res.json() as Promise<T>;\n}\n\n// ── Queue ────────────────────────────────────────────────────────────────────\n\nexport async function fetchQueue(\n  params: QueueParams = {},\n): Promise<ReviewQueueResponse> {\n  const qs = new URLSearchParams();\n  if (params.status) qs.set(\"status\", params.status);\n  if (params.offset !== undefined) qs.set(\"offset\", String(params.offset));\n  if (params.limit !== undefined) qs.set(\"limit\", String(params.limit));\n  const query = qs.toString();\n  return request<ReviewQueueResponse>(\n    `${BASE}/queue${query ? `?${query}` : \"\"}`,\n  );\n}\n\nexport async function fetchStats(): Promise<ReviewStatsResponse> {\n  return request<ReviewStatsResponse>(`${BASE}/stats`);\n}\n\n// ── Actions ──────────────────────────────────────────────────────────────────\n\nexport async function approveMoment(id: string): Promise<KeyMomentRead> {\n  return request<KeyMomentRead>(`${BASE}/moments/${id}/approve`, {\n    method: \"POST\",\n  });\n}\n\nexport async function rejectMoment(id: string): Promise<KeyMomentRead> {\n  return request<KeyMomentRead>(`${BASE}/moments/${id}/reject`, {\n    method: \"POST\",\n  });\n}\n\nexport async function editMoment(\n  id: string,\n  data: MomentEditRequest,\n): Promise<KeyMomentRead> {\n  return request<KeyMomentRead>(`${BASE}/moments/${id}`, {\n    method: \"PUT\",\n    body: JSON.stringify(data),\n  });\n}\n\nexport async function splitMoment(\n  id: string,\n  splitTime: number,\n): Promise<KeyMomentRead[]> {\n  const body: MomentSplitRequest = { split_time: splitTime };\n  return request<KeyMomentRead[]>(`${BASE}/moments/${id}/split`, {\n    method: \"POST\",\n    body: JSON.stringify(body),\n  });\n}\n\nexport async function mergeMoments(\n  id: string,\n  targetId: string,\n): Promise<KeyMomentRead> {\n  const body: MomentMergeRequest = { target_moment_id: targetId };\n  return request<KeyMomentRead>(`${BASE}/moments/${id}/merge`, {\n    method: \"POST\",\n    body: JSON.stringify(body),\n  });\n}\n\n// ── Mode ─────────────────────────────────────────────────────────────────────\n\nexport async function getReviewMode(): Promise<ReviewModeResponse> {\n  return request<ReviewModeResponse>(`${BASE}/mode`);\n}\n\nexport async function setReviewMode(\n  enabled: boolean,\n): Promise<ReviewModeResponse> {\n  return request<ReviewModeResponse>(`${BASE}/mode`, {\n    method: \"PUT\",\n    body: JSON.stringify({ review_mode: enabled }),\n  });\n}\n===\n{\n  \"compilerOptions\": {\n    \"target\": \"ES2020\",\n    \"useDefineForClassFields\": true,\n    \"lib\": [\"ES2020\", \"DOM\", \"DOM.Iterable\"],\n    \"module\": \"ESNext\",\n    \"skipLibCheck\": true,\n\n    /* Bundler mode */\n    \"moduleResolution\": \"bundler\",\n    \"allowImportingTsExtensions\": true,\n    \"isolatedModules\": true,\n    \"moduleDetection\": \"force\",\n    \"noEmit\": true,\n    \"jsx\": \"react-jsx\",\n\n    /* Linting */\n    \"strict\": true,\n    \"noUnusedLocals\": true,\n    \"noUnusedParameters\": true,\n    \"noFallthroughCasesInSwitch\": true,\n    \"noUncheckedIndexedAccess\": true\n  },\n  \"include\": [\"src\"]\n}\n===\n{\n  \"name\": \"chrysopedia-web\",\n  \"private\": true,\n  \"version\": \"0.1.0\",\n  \"type\": \"module\",\n  \"scripts\": {\n    \"dev\": \"vite\",\n    \"build\": \"tsc -b && vite build\",\n    \"preview\": \"vite preview\"\n  },\n  \"dependencies\": {\n    \"react\": \"^18.3.1\",\n    \"react-dom\": \"^18.3.1\",\n    \"react-router-dom\": \"^6.28.0\"\n  },\n  \"devDependencies\": {\n    \"@types/react\": \"^18.3.12\",\n    \"@types/react-dom\": \"^18.3.1\",\n    \"@vitejs/plugin-react\": \"^4.3.4\",\n    \"typescript\": \"~5.6.3\",\n    \"vite\": \"^6.0.3\"\n  }\n}\n\n\n---\n\n### bg_78dddfdc — db+tests (completed, 10.8s)\n\n\"\"\"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===\nconftest.py\nfixtures\n__init__.py\n__pycache__\ntest_ingest.py\ntest_pipeline.py\ntest_review.py\n===\n\"\"\"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\n---\n\n### bg_b4f8c61d — tags yaml (completed, 7.0s)\n\n./config/canonical_tags.yaml\n\n\n---\n\n### bg_ff61735d — synthesis prompt (completed, 3.2s)\n\nYou are a music production knowledge synthesizer. Your task is to create a comprehensive technique page from a group of related key moments by the same creator on the same topic.\n\n## Instructions\n\n1. Read the key moments and their metadata provided inside the <moments> tags.\n2. Synthesize them into a single, coherent technique page.\n3. Organize the content into logical body sections.\n\n## Output Format\n\nReturn a JSON object with a single key \"pages\" containing a list of synthesized pages:\n\n```json\n{\n  \"pages\": [\n    {\n      \"title\": \"Descriptive page title\",\n      \"slug\": \"url-safe-slug\",\n      \"topic_category\": \"Sound design\",\n      \"topic_tags\": [\"bass\", \"synthesis\"],\n      \"summary\": \"A concise overview paragraph (2-3 sentences).\",\n      \"body_sections\": {\n        \"Overview\": \"Introduction to the technique...\",\n        \"Step-by-Step Process\": \"Detailed walkthrough...\",\n        \"Key Settings\": \"Specific parameter values...\",\n        \"Tips and Variations\": \"Additional tips...\"\n      },\n      \"signal_chains\": [\n        {\n          \"name\": \"Main bass chain\",\n          \"steps\": [\"Serum (oscillator)\", \"OTT (compression)\", \"EQ8 (low cut)\"]\n        }\n      ],\n      \"plugins\": [\"Serum\", \"OTT\", \"EQ8\"],\n      \"source_quality\": \"structured\"\n    }\n  ]\n}\n```\n\n## Field Rules\n\n- **title**: Clear, search-friendly title (e.g., \"Neuro Bass Design with Serum by CreatorName\").\n- **slug**: URL-safe version of the title using hyphens (e.g., \"neuro-bass-design-serum-creatorname\").\n- **topic_category**: The primary category from the canonical taxonomy.\n- **topic_tags**: All relevant tags aggregated from the classified moments.\n- **summary**: 2-3 sentence overview that captures the essence of the technique.\n- **body_sections**: A dictionary of section titles to section content. Use clear, educational prose. Include specific values and settings when available. Suggested sections: Overview, Step-by-Step Process, Key Settings, Tips and Variations. Adapt section names to fit the content.\n- **signal_chains**: List of signal chain objects with name and ordered steps. Empty list if not applicable.\n- **plugins**: Deduplicated list of all plugins/tools mentioned across the moments.\n- **source_quality**: One of \"structured\" (clear tutorial), \"mixed\" (some structure), \"unstructured\" (conversation/livestream).\n\n## Rules\n\n- Synthesize, don't just concatenate. Create coherent prose from potentially fragmented moments.\n- Preserve specific technical details (frequencies, ratios, plugin settings).\n- If moments conflict, note both approaches.\n- Output ONLY the JSON object, no other text.\n"}],"isError":false,"timestamp":1774827901703}}
{"type":"message","id":"f98a8c2e","parentId":"32b3f642","timestamp":"2026-03-29T23:45:06.362Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the canonical tags structure and the test count:"},{"type":"toolCall","id":"toolu_011k3qYnsR2wPTcJ8kqyBeqY","name":"async_bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat config/canonical_tags.yaml && echo \"===\" && cd backend && python -m pytest tests/ --collect-only 2>&1 | tail -20","label":"tags+test count"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":139,"cacheRead":76947,"cacheWrite":3753,"totalTokens":80840,"cost":{"input":0.000005,"output":0.0034750000000000002,"cacheRead":0.0384735,"cacheWrite":0.02345625,"total":0.06540975}},"stopReason":"toolUse","timestamp":1774827901704}}
{"type":"message","id":"fdef412c","parentId":"f98a8c2e","timestamp":"2026-03-29T23:45:06.369Z","message":{"role":"toolResult","toolCallId":"toolu_011k3qYnsR2wPTcJ8kqyBeqY","toolName":"async_bash","content":[{"type":"text","text":"Background job started: **bg_4caae85d**\nCommand: `tags+test count`\n\nUse `await_job` to get results when ready, or `cancel_job` to stop."}],"isError":false,"timestamp":1774827906367}}
{"type":"message","id":"a5b7156f","parentId":"fdef412c","timestamp":"2026-03-29T23:45:10.008Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01G29ZdxmAY5bjnB9vqWqTgC","name":"async_bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && wc -l frontend/src/App.css && echo \"===\" && cat frontend/src/main.tsx","label":"css+main"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":113,"cacheRead":80700,"cacheWrite":194,"totalTokens":81008,"cost":{"input":0.000005,"output":0.0028250000000000003,"cacheRead":0.04035,"cacheWrite":0.0012125,"total":0.044392499999999994}},"stopReason":"toolUse","timestamp":1774827906368}}
{"type":"message","id":"c223c7c9","parentId":"a5b7156f","timestamp":"2026-03-29T23:45:10.015Z","message":{"role":"toolResult","toolCallId":"toolu_01G29ZdxmAY5bjnB9vqWqTgC","toolName":"async_bash","content":[{"type":"text","text":"Background job started: **bg_0633848c**\nCommand: `css+main`\n\nUse `await_job` to get results when ready, or `cancel_job` to stop."}],"isError":false,"timestamp":1774827910013}}
{"type":"message","id":"a5b1d42b","parentId":"c223c7c9","timestamp":"2026-03-29T23:45:12.827Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01XpbjGNjb6JifNFPKiTiVe2","name":"await_job","arguments":{"jobs":["bg_4caae85d","bg_0633848c"]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":69,"cacheRead":80894,"cacheWrite":166,"totalTokens":81130,"cost":{"input":0.000005,"output":0.0017250000000000002,"cacheRead":0.040447,"cacheWrite":0.0010375,"total":0.043214499999999996}},"stopReason":"toolUse","timestamp":1774827910014}}
{"type":"message","id":"74efe151","parentId":"a5b1d42b","timestamp":"2026-03-29T23:45:12.829Z","message":{"role":"toolResult","toolCallId":"toolu_01XpbjGNjb6JifNFPKiTiVe2","toolName":"await_job","content":[{"type":"text","text":"### bg_4caae85d — tags+test count (completed, 6.5s)\n\n# Canonical tags — 6 top-level production categories\n# Sub-topics grow organically during pipeline extraction\ncategories:\n  - name: Sound design\n    description: Creating and shaping sounds from scratch or samples\n    sub_topics: [bass, drums, kick, snare, hi-hat, percussion, pads, leads, fx, foley, vocals, textures]\n\n  - name: Mixing\n    description: Balancing, processing, and spatializing elements\n    sub_topics: [eq, compression, bus processing, reverb, delay, stereo imaging, gain staging, automation]\n\n  - name: Synthesis\n    description: Methods of generating sound\n    sub_topics: [fm, wavetable, granular, additive, subtractive, modular, physical modeling]\n\n  - name: Arrangement\n    description: Structuring a track from intro to outro\n    sub_topics: [song structure, transitions, tension, energy flow, breakdowns, drops]\n\n  - name: Workflow\n    description: Creative process, session management, productivity\n    sub_topics: [daw setup, templates, creative process, collaboration, file management, resampling]\n\n  - name: Mastering\n    description: Final stage processing for release\n    sub_topics: [limiting, stereo width, loudness, format delivery, referencing]\n\n# Genre taxonomy (assigned to Creators, not techniques)\ngenres:\n  - Bass music\n  - Drum & bass\n  - Dubstep\n  - Halftime\n  - House\n  - Techno\n  - IDM\n  - Glitch\n  - Downtempo\n  - Neuro\n  - Ambient\n  - Experimental\n  - Cinematic\n===\n      <Coroutine test_reject_moment>\n      <Coroutine test_reject_nonexistent_moment>\n      <Coroutine test_edit_moment>\n      <Coroutine test_edit_nonexistent_moment>\n      <Coroutine test_split_moment>\n      <Coroutine test_split_invalid_time_below_start>\n      <Coroutine test_split_invalid_time_above_end>\n      <Coroutine test_split_nonexistent_moment>\n      <Coroutine test_merge_moments>\n      <Coroutine test_merge_different_videos>\n      <Coroutine test_merge_with_self>\n      <Coroutine test_merge_nonexistent_target>\n      <Coroutine test_merge_nonexistent_source>\n      <Coroutine test_get_mode_default>\n      <Coroutine test_set_mode>\n      <Coroutine test_get_mode_from_redis>\n      <Coroutine test_get_mode_redis_error_fallback>\n      <Coroutine test_set_mode_redis_error>\n\n========================= 40 tests collected in 0.33s ==========================\n\n\n---\n\n### bg_0633848c — css+main (completed, 2.8s)\n\n620 frontend/src/App.css\n===\nimport { StrictMode } from \"react\";\nimport { createRoot } from \"react-dom/client\";\nimport { BrowserRouter } from \"react-router-dom\";\nimport App from \"./App\";\nimport \"./App.css\";\n\ncreateRoot(document.getElementById(\"root\")!).render(\n  <StrictMode>\n    <BrowserRouter>\n      <App />\n    </BrowserRouter>\n  </StrictMode>,\n);\n"}],"isError":false,"timestamp":1774827912827}}
{"type":"message","id":"4260deb7","parentId":"74efe151","timestamp":"2026-03-29T23:48:07.407Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. Now I have a thorough understanding of the codebase. Let me reason through the decomposition.\n\n## Decomposition Reasoning\n\n**Requirements owned by S05:** R005 (Search-First Web UI), R006 (Technique Page Display), R007 (Creators Browse), R008 (Topics Browse), R014 (Creator Equity), R015 (30-Second Retrieval Target). S05 also supports R009 (read path — search query was deferred to S05).\n\n**What exists:** \n- Backend: 7 models, async SQLAlchemy, existing creators router (alphabetical only), sync EmbeddingClient/QdrantManager (Celery-only), 40 tests\n- Frontend: React 18 + Vite + TS, react-router v6, plain CSS (620 lines), admin-only routes, typed API client for review queue only\n- Config: canonical_tags.yaml with 6 categories, embedding/Qdrant settings ready\n\n**What's needed:**\n1. Backend: async search service, search endpoint, techniques CRUD, topics endpoint, enhanced creators endpoint\n2. Backend tests: search + read endpoint integration tests  \n3. Frontend: 6 new pages (Home, SearchResults, TechniquePage, CreatorsBrowse, CreatorDetail, TopicsBrowse), API client for public endpoints, navigation shell, CSS\n4. Frontend verification: TypeScript compiles, production build succeeds\n\n**Task grouping strategy:**\n\nThe research doc suggests 5 build phases, but the executor context budget (200K tokens, 2-6 tasks recommended) and the scope sanity rule (3-8 files per task) push me toward 4 tasks:\n\n- **T01: Backend search service + all API endpoints** — This is the highest-risk and most cohesive backend unit. The search service needs async embedding + Qdrant, and the read endpoints (techniques, topics, enhanced creators) are straightforward CRUD. Grouping all backend endpoints in one task keeps the API surface coherent and avoids splitting router+schema work across tasks. ~8 files touched.\n\n- **T02: Backend integration tests** — Separate from T01 because tests require all endpoints to exist, and isolating them makes test failures diagnostic rather than mixed with implementation bugs. Tests for search (mocked embedding + Qdrant), techniques, topics, creators endpoints. Verifies R009 read path. ~4 files.\n\n- **T03: Frontend navigation shell + search + technique page** — The primary user flow: land → search → see results → view technique page. This is the R005/R006/R015 critical path. Includes the API client, App.tsx routing, Home, SearchResults, TechniquePage components, and the CSS for these. ~8 files.\n\n- **T04: Frontend browse pages + final verification** — CreatorsBrowse (R007/R014), CreatorDetail, TopicsBrowse (R008), and remaining CSS. Then full build verification. ~6 files.\n\nThis 4-task split keeps each task well within context limits, maintains clear dependencies (T01→T02, T01→T03→T04), and covers every requirement.\n\nNow let me persist the plan:"},{"type":"toolCall","id":"toolu_015rFZLnan56Zx68hZEW2xXa","name":"gsd_plan_slice","arguments":{"goal":"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. Browse pages for creators (randomized default sort) and topics (two-level hierarchy) are navigable.","integrationClosure":"Upstream: models.py, database.py, config.py, pipeline/embedding_client.py, pipeline/qdrant_client.py, config/canonical_tags.yaml, frontend/src/api/client.ts. New wiring: search_service.py, 3 new routers mounted in main.py, 6 frontend pages + routing. Nothing remains after this slice — it is the final assembly for M001.","milestoneId":"M001","observabilityImpact":"Search endpoint logs query latency and fallback status. Qdrant/embedding failures trigger keyword fallback with WARNING logs. All new endpoints return structured JSON for inspection.","proofLevel":"integration + final-assembly","sliceId":"S05","successCriteria":"## Must-Haves\n\n- `GET /api/v1/search?q=...` returns semantic results from Qdrant with keyword fallback, enriched with DB metadata (R005, R009)\n- `GET /api/v1/techniques/{slug}` returns full technique page with key moments, creator info, related links (R006)\n- `GET /api/v1/techniques` returns filterable technique page list\n- `GET /api/v1/topics` returns two-level category→sub-topic hierarchy with technique/creator counts (R008)\n- `GET /api/v1/creators` supports `sort=random` (R014), genre filter, technique/video counts (R007)\n- Landing page with search bar, topics card, creators card, recently added section (R005)\n- Search results page with grouped results and clickable navigation (R005, R015)\n- Technique page renders header, study guide prose from `body_sections` JSONB, key moments index, related links, plugins (R006)\n- Creators browse page with randomized default sort, genre filter pills, type-to-narrow (R007, R014)\n- Topics browse page with two-level hierarchy, counts, clickable sub-topics (R008)\n- All 40 existing backend tests still pass (regression)\n- New integration tests for search, techniques, topics, creators endpoints\n- `cd frontend && npx tsc -b` — zero TypeScript errors\n- `cd frontend && npm run build` — clean production build\n\n## Threat Surface\n\n- **Abuse**: Search `q` parameter is user-controlled text passed to embedding API and used in SQL ILIKE — must sanitize/parameterize. Slug parameters in technique/creator detail endpoints are user-controlled path params used in DB WHERE clauses — use parameterized queries (SQLAlchemy handles this). No authentication required (public read-only UI).\n- **Data exposure**: No PII or secrets. All data is public knowledge content (technique descriptions, creator names, tags). Qdrant payloads contain only content metadata.\n- **Input trust**: Search query text → embedding API (length-bounded, timeout-protected) → Qdrant vector query (limit-bounded) → SQL ILIKE fallback (parameterized). Slug path params → SQL WHERE (parameterized via SQLAlchemy).\n\n## Requirement Impact\n\n- **Requirements touched**: R005, R006, R007, R008, R009 (read path), R014, R015\n- **Re-verify**: All 40 existing tests must pass. Existing creators endpoint behavior (alphabetical list, detail by slug) must remain functional.\n- **Decisions revisited**: None — this slice extends existing patterns without changing prior decisions.\n\n## Proof Level\n\n- This slice proves: integration (backend API + Qdrant + PostgreSQL) and final-assembly (full frontend UI renders real API data)\n- Real runtime required: yes (PostgreSQL for tests, Qdrant mocked in tests but real in deployment)\n- Human/UAT required: yes (visual verification of rendered pages, search latency check)\n\n## Verification\n\n- `cd backend && python -m pytest tests/ -v` — all existing 40 tests plus new S05 tests pass\n- `cd backend && python -m pytest tests/test_search.py tests/test_public_api.py -v` — new endpoint tests pass\n- `cd frontend && npx tsc -b` — zero TypeScript errors\n- `cd frontend && npm run build` — clean production build (exit code 0)\n\n## Observability / Diagnostics\n\n- Runtime signals: INFO log on search query with latency_ms, result_count, fallback_used fields; WARNING on embedding timeout/failure triggering keyword fallback; WARNING on Qdrant unavailable\n- Inspection surfaces: `GET /api/v1/search?q=test` returns result count and timing in response; `GET /api/v1/topics` shows category hierarchy; `GET /api/v1/creators?sort=random` verifies randomization\n- Failure visibility: Search endpoint returns results even on Qdrant/embedding failure (keyword fallback); 500 errors logged with stack trace; empty Qdrant collection handled gracefully with keyword-only results\n- Redaction constraints: None — all data is public content\n\n## Integration Closure\n\n- Upstream surfaces consumed: `backend/pipeline/embedding_client.py` (sync — need async variant), `backend/pipeline/qdrant_client.py` (sync — need async variant), `backend/models.py` (all 7 models), `backend/database.py` (async engine/session), `backend/config.py` (embedding/Qdrant settings), `config/canonical_tags.yaml` (tag taxonomy), `frontend/src/api/client.ts` (API client pattern)\n- New wiring introduced in this slice: `backend/search_service.py` (async embedding + Qdrant search), 3 new routers (search, techniques, topics) mounted in `main.py`, 6 new frontend pages + API client + routing in `App.tsx`\n- What remains before the milestone is truly usable end-to-end: Nothing — S05 is the final assembly slice for M001","tasks":[{"description":"## Description\n\nCreate the backend API surface for S05: the async search service (embedding + Qdrant), search endpoint, technique pages CRUD, topics hierarchy, and enhanced creators endpoint. This is the highest-risk task because it introduces async embedding/Qdrant clients for the FastAPI request path (existing ones are sync for Celery).\n\n## Failure Modes\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| Embedding API (AsyncOpenAI) | Fall back to keyword-only search | 300ms timeout → keyword fallback | Return empty vectors → keyword fallback |\n| Qdrant (AsyncQdrantClient) | Fall back to keyword-only search | 300ms timeout → keyword fallback | Log warning, return empty results → keyword fallback |\n| PostgreSQL | Return 500 (standard FastAPI error handling) | Connection pool timeout → 500 | N/A (SQLAlchemy typed) |\n\n## Load Profile\n\n- **Shared resources**: AsyncQdrantClient connection pool, AsyncOpenAI HTTP pool, SQLAlchemy async session pool\n- **Per-operation cost**: Search = 1 embedding API call + 1 Qdrant query + 1-3 SQL queries for enrichment. Read endpoints = 1-2 SQL queries each.\n- **10x breakpoint**: Embedding API rate limiting (external dependency). Mitigated by client-side debounce (300ms) reducing request rate.\n\n## Negative Tests\n\n- **Malformed inputs**: Empty search query → return empty results. Query > 500 chars → truncate to 500. Invalid scope parameter → default to 'all'.\n- **Error paths**: Embedding API unreachable → keyword fallback. Qdrant unreachable → keyword fallback. Invalid slug → 404.\n- **Boundary conditions**: Empty Qdrant collection → keyword-only results. Zero matching techniques/creators → empty list.\n\n## Steps\n\n1. Create `backend/search_service.py` with `SearchService` class:\n   - `__init__` takes Settings, creates `openai.AsyncOpenAI` client and `qdrant_client.AsyncQdrantClient`\n   - `async embed_query(text: str) -> list[float] | None` — embeds query text with 300ms timeout, returns None on failure\n   - `async search_qdrant(vector: list[float], limit: int, type_filter: str | None) -> list[dict]` — queries Qdrant with optional payload type filter, returns scored results with payloads\n   - `async keyword_search(query: str, scope: str, limit: int, db: AsyncSession) -> list[dict]` — ILIKE search on technique_pages.title, key_moments.title, creators.name\n   - `async search(query: str, scope: str, limit: int, db: AsyncSession) -> dict` — orchestrates: embed → Qdrant → enrich with DB metadata → fallback to keyword if needed\n\n2. Add new Pydantic response schemas to `backend/schemas.py`:\n   - `SearchResultItem(title, slug, type, score, summary, creator_name, creator_slug, topic_category, topic_tags)`\n   - `SearchResponse(items: list[SearchResultItem], total: int, query: str, fallback_used: bool)`\n   - `TechniquePageDetail` (extends TechniquePageRead with nested key_moments, creator info, related links)\n   - `TopicCategory(name, description, sub_topics: list[TopicSubTopic])` and `TopicSubTopic(name, technique_count, creator_count)`\n   - `CreatorBrowseItem` (extends CreatorRead with technique_count, video_count)\n\n3. Create `backend/routers/search.py`:\n   - `GET /search?q=...&scope=all|topics|creators&limit=20`\n   - Instantiate SearchService from get_settings(), call search(), return SearchResponse\n   - Log query, latency_ms, result_count, fallback_used at INFO level\n\n4. Create `backend/routers/techniques.py`:\n   - `GET /techniques` — list technique pages with optional `category`, `creator_slug` query filters, pagination\n   - `GET /techniques/{slug}` — full detail with eager-loaded key_moments (ordered by start_time), creator info, outgoing+incoming related links\n   - Return 404 for unknown slug\n\n5. Create `backend/routers/topics.py`:\n   - `GET /topics` — load `canonical_tags.yaml`, for each category aggregate technique_count and creator_count per sub_topic from DB\n   - `GET /topics/{category_slug}` — return technique pages filtered by topic_category\n\n6. Extend `backend/routers/creators.py`:\n   - Add `sort` query param: `random` (default), `alpha`, `views`\n   - Add `genre` query param for filtering by genre\n   - Add technique_count and video_count subqueries to list endpoint\n   - For `sort=random`, use `func.random()` ORDER BY (dataset is small, <100 creators)\n\n7. Mount all new routers in `backend/main.py`:\n   - `from routers import search, techniques, topics`\n   - `app.include_router(search.router, prefix=\"/api/v1\")`\n   - `app.include_router(techniques.router, prefix=\"/api/v1\")`\n   - `app.include_router(topics.router, prefix=\"/api/v1\")`\n\n## Must-Haves\n\n- [ ] SearchService with async embedding + Qdrant + keyword fallback\n- [ ] GET /api/v1/search returns SearchResponse with enriched results\n- [ ] GET /api/v1/techniques and GET /api/v1/techniques/{slug} with full detail\n- [ ] GET /api/v1/topics returns category hierarchy with counts\n- [ ] GET /api/v1/creators supports sort=random (default), genre filter, technique/video counts\n- [ ] All new routers mounted in main.py\n- [ ] Embedding/Qdrant failures gracefully degrade to keyword search\n\n## Verification\n\n- `cd backend && python -c \"from search_service import SearchService; print('OK')\"` — imports clean\n- `cd backend && python -c \"from routers.search import router; print(router.routes)\"` — search router has routes\n- `cd backend && python -c \"from routers.techniques import router; print(router.routes)\"` — techniques router has routes\n- `cd backend && python -c \"from routers.topics import router; print(router.routes)\"` — topics router has routes\n- `cd backend && python -c \"from main import app; routes = [r.path for r in app.routes]; assert '/api/v1/search' in str(routes) or any('search' in str(r.path) for r in app.routes); print('Mounted')\"` — routers mounted\n\n## Observability Impact\n\n- Signals added: INFO log per search query with latency_ms, result_count, fallback_used. WARNING on embedding/Qdrant failure with error details.\n- How a future agent inspects this: `curl localhost:8001/api/v1/search?q=test` returns structured JSON with timing data\n- Failure state exposed: fallback_used=true in search response indicates Qdrant/embedding degradation","estimate":"2h","expectedOutput":["`backend/search_service.py` — async SearchService with embed_query, search_qdrant, keyword_search, search methods","`backend/schemas.py` — extended with SearchResultItem, SearchResponse, TechniquePageDetail, TopicCategory, TopicSubTopic, CreatorBrowseItem","`backend/routers/search.py` — GET /search endpoint with semantic + keyword fallback","`backend/routers/techniques.py` — GET /techniques and GET /techniques/{slug} endpoints","`backend/routers/topics.py` — GET /topics endpoint with category hierarchy","`backend/routers/creators.py` — enhanced with sort=random, genre filter, counts","`backend/main.py` — all new routers mounted"],"files":["backend/search_service.py","backend/schemas.py","backend/routers/search.py","backend/routers/techniques.py","backend/routers/topics.py","backend/routers/creators.py","backend/main.py"],"inputs":["`backend/models.py` — all 7 ORM models (Creator, SourceVideo, TranscriptSegment, KeyMoment, TechniquePage, RelatedTechniqueLink, Tag)","`backend/database.py` — async engine, get_session dependency","`backend/config.py` — Settings with embedding_api_url, embedding_model, embedding_dimensions, qdrant_url, qdrant_collection","`backend/schemas.py` — existing Pydantic schemas to extend","`backend/routers/creators.py` — existing creators router to enhance","`backend/main.py` — existing router mounting to extend","`backend/pipeline/embedding_client.py` — reference for sync embedding pattern (async variant needed)","`backend/pipeline/qdrant_client.py` — reference for sync Qdrant pattern (async variant needed)","`config/canonical_tags.yaml` — tag taxonomy for topics endpoint"],"taskId":"T01","title":"Build async search service and all public API endpoints","verify":"cd backend && python -c \"from search_service import SearchService; from routers.search import router as sr; from routers.techniques import router as tr; from routers.topics import router as tpr; print('All imports OK')\" && python -c \"from main import app; print([r.path for r in app.routes])\""},{"description":"## Description\n\nWrite integration tests for all new S05 backend endpoints: search (with mocked embedding API and Qdrant), techniques list/detail, topics hierarchy, and enhanced creators (randomized sort, genre filter, counts). Tests run against real PostgreSQL with the existing conftest.py fixtures. All 40 existing tests must continue to pass.\n\n## Negative Tests\n\n- **Malformed inputs**: Empty search query returns empty results. Invalid technique slug returns 404. Invalid topic category returns empty list.\n- **Error paths**: Search with mocked embedding failure → keyword fallback results returned. Search with mocked Qdrant failure → keyword fallback.\n- **Boundary conditions**: Search with no matching results → empty items list. Topics with no technique pages → zero counts. Creators list with no creators → empty list.\n\n## Steps\n\n1. Create `backend/tests/test_search.py`:\n   - Fixture: seed DB with 2 creators, 3 technique pages (different categories/tags), 5 key moments\n   - Test search endpoint with mocked SearchService that returns canned results → verify response shape (items, total, query, fallback_used)\n   - Test search with empty query → returns empty results or validation error\n   - Test search keyword fallback: mock embedding to return None → verify keyword results returned and fallback_used=true\n   - Test search scope filtering (scope=topics returns only technique_page type results)\n\n2. Create `backend/tests/test_public_api.py`:\n   - Test GET /api/v1/techniques — returns list of technique pages, supports category filter\n   - Test GET /api/v1/techniques/{slug} — returns full detail with key_moments, creator info, related links\n   - Test GET /api/v1/techniques/{slug} with invalid slug → 404\n   - Test GET /api/v1/topics — returns category hierarchy with counts matching seeded data\n   - Test GET /api/v1/creators?sort=random — returns creators (verify all returned, order may vary)\n   - Test GET /api/v1/creators?sort=alpha — returns creators in alphabetical order\n   - Test GET /api/v1/creators?genre=Bass+music — returns only matching creators\n   - Test GET /api/v1/creators/{slug} — returns detail with technique_count, video_count\n\n3. Run full test suite: `cd backend && python -m pytest tests/ -v` — all 40 existing + new tests pass\n\n## Must-Haves\n\n- [ ] test_search.py with ≥4 tests covering happy path, empty query, keyword fallback, scope filter\n- [ ] test_public_api.py with ≥8 tests covering techniques list/detail/404, topics hierarchy, creators sort/filter/detail\n- [ ] All 40 existing tests still pass (regression)\n- [ ] Tests use real PostgreSQL with seeded data (not mocked DB)\n\n## Verification\n\n- `cd backend && python -m pytest tests/test_search.py tests/test_public_api.py -v` — all new tests pass\n- `cd backend && python -m pytest tests/ -v` — all tests pass (40 existing + new)","estimate":"1.5h","expectedOutput":["`backend/tests/test_search.py` — ≥4 integration tests for search endpoint","`backend/tests/test_public_api.py` — ≥8 integration tests for techniques, topics, creators endpoints","`backend/tests/conftest.py` — possibly extended with shared seed fixtures for S05 tests"],"files":["backend/tests/test_search.py","backend/tests/test_public_api.py","backend/tests/conftest.py"],"inputs":["`backend/search_service.py` — SearchService class to mock for search tests","`backend/routers/search.py` — search endpoint under test","`backend/routers/techniques.py` — techniques endpoints under test","`backend/routers/topics.py` — topics endpoint under test","`backend/routers/creators.py` — enhanced creators endpoint under test","`backend/schemas.py` — response schemas for assertion shapes","`backend/models.py` — ORM models for seeding test data","`backend/tests/conftest.py` — existing test fixtures (db_engine, client)","`config/canonical_tags.yaml` — expected tag structure for topics test assertions"],"taskId":"T02","title":"Add integration tests for search and public API endpoints","verify":"cd backend && python -m pytest tests/test_search.py tests/test_public_api.py -v && python -m pytest tests/ -v"},{"description":"## Description\n\nBuild the primary user flow: landing page with search bar → search results page → technique page detail. This is the R005/R006/R015 critical path. Includes the new typed API client for public endpoints, App.tsx routing with both admin and public routes, and 3 new page components with CSS.\n\nThe frontend uses React 18 + Vite + TypeScript with strict mode (`noUnusedLocals`, `noUnusedParameters`, `noUncheckedIndexedAccess`). Existing pattern: plain CSS in `App.css`, typed `fetch()` API client, React Router v6.\n\n## Steps\n\n1. Create `frontend/src/api/public-client.ts` — typed API client for public endpoints:\n   - Types: `SearchResultItem`, `SearchResponse`, `TechniquePageDetail`, `KeyMomentSummary`, `CreatorInfo`, `RelatedLink`, `TopicCategory`, `TopicSubTopic`, `CreatorBrowseItem`\n   - Functions: `searchApi(q, scope?, limit?)`, `fetchTechnique(slug)`, `fetchTechniques(params?)`, `fetchTopics()`, `fetchCreators(params?)`, `fetchCreator(slug)`\n   - Reuse the `request<T>` helper pattern from existing `client.ts` (or extract shared helper)\n\n2. Create `frontend/src/pages/Home.tsx` — landing page:\n   - Prominent search bar (auto-focus on mount) with debounce (300ms)\n   - Live typeahead: after 2+ chars, show top 5 results in dropdown below search bar\n   - On Enter or \"See all results\" link, navigate to `/search?q=...`\n   - Two navigation cards: \"Topics\" (links to `/topics`) and \"Creators\" (links to `/creators`)\n   - \"Recently Added\" section showing last 5 technique pages (fetch from `/api/v1/techniques?limit=5`)\n\n3. Create `frontend/src/pages/SearchResults.tsx` — full search results page:\n   - Read `q` from URL search params\n   - Display results grouped by type (technique_pages first, then key_moments)\n   - Each result: title (linked to technique page), summary snippet, creator name, category/tags\n   - Show \"No results found\" for empty results, \"Showing keyword results\" when fallback_used=true\n   - Search bar at top for refining query\n\n4. Create `frontend/src/pages/TechniquePage.tsx` — technique page display (R006):\n   - Fetch technique by slug from URL params via `fetchTechnique(slug)`\n   - Header: title, topic_category badge, topic_tags pills, creator name (linked to `/creators/{slug}`), source_quality indicator\n   - Amber banner if source_quality === 'unstructured' (livestream-sourced)\n   - Study guide prose: render `body_sections` JSONB — iterate Object.entries, render each section as `<h2>` + paragraph. Handle both string and object values gracefully.\n   - Key moments index: ordered list with title, start_time→end_time, content_type badge, summary\n   - Signal chains section (if present): render each chain as name + ordered steps\n   - Plugins referenced (if present): pill list\n   - Related techniques (if present): linked list\n   - Loading state and 404 error state\n\n5. Update `frontend/src/App.tsx` routing:\n   - Import new pages\n   - Add public routes: `/` → Home, `/search` → SearchResults, `/techniques/:slug` → TechniquePage\n   - Keep admin routes at `/admin/*`\n   - Update header: \"Chrysopedia\" title (not \"Chrysopedia Admin\"), nav links to Home, Topics, Creators, and Admin\n\n6. Add CSS to `frontend/src/App.css` for new pages:\n   - Search bar styles (large, centered on home, inline on results page)\n   - Typeahead dropdown styles\n   - Navigation cards (grid layout)\n   - Technique page layout (readable prose width, section spacing)\n   - Search result items (hover state, meta info)\n   - Tag/badge pill styles\n   - Loading and error states\n\n## Must-Haves\n\n- [ ] Typed public API client with all endpoint functions\n- [ ] Landing page with search bar, typeahead, navigation cards, recently added\n- [ ] Search results page with grouped results\n- [ ] Technique page with all sections (header, prose, key moments, related links)\n- [ ] App.tsx routes both public and admin paths\n- [ ] `cd frontend && npx tsc -b` passes with zero errors\n\n## Verification\n\n- `cd frontend && npx tsc -b` — zero TypeScript errors\n- `cd frontend && npm run build` — clean production build\n- Verify files exist: `test -f frontend/src/api/public-client.ts && test -f frontend/src/pages/Home.tsx && test -f frontend/src/pages/SearchResults.tsx && test -f frontend/src/pages/TechniquePage.tsx`","estimate":"2h","expectedOutput":["`frontend/src/api/public-client.ts` — typed API client for search, techniques, topics, creators endpoints","`frontend/src/pages/Home.tsx` — landing page with search bar, typeahead, nav cards, recently added","`frontend/src/pages/SearchResults.tsx` — search results page with grouped results","`frontend/src/pages/TechniquePage.tsx` — full technique page display with all sections","`frontend/src/App.tsx` — updated with public + admin routes and navigation","`frontend/src/App.css` — extended with styles for all new components"],"files":["frontend/src/api/public-client.ts","frontend/src/pages/Home.tsx","frontend/src/pages/SearchResults.tsx","frontend/src/pages/TechniquePage.tsx","frontend/src/App.tsx","frontend/src/App.css"],"inputs":["`frontend/src/api/client.ts` — existing API client pattern (request helper, typed functions)","`frontend/src/App.tsx` — existing routing structure to extend","`frontend/src/App.css` — existing styles to extend (620 lines)","`frontend/src/main.tsx` — entry point (BrowserRouter already configured)","`frontend/tsconfig.app.json` — strict TS config (noUnusedLocals, noUnusedParameters, noUncheckedIndexedAccess)","`frontend/package.json` — dependencies (react 18, react-router-dom 6, vite 6)","`backend/schemas.py` — response schemas defining API contract shapes (SearchResponse, TechniquePageDetail, etc.)"],"taskId":"T03","title":"Build frontend search flow: landing page, search results, and technique page","verify":"cd frontend && npx tsc -b && npm run build && echo 'Frontend build OK'"},{"description":"## Description\n\nBuild the remaining browse pages: CreatorsBrowse (R007, R014 creator equity with randomized default sort), CreatorDetail, and TopicsBrowse (R008 two-level hierarchy). Then run final verification to confirm the full frontend builds cleanly and all requirements are covered.\n\n## Steps\n\n1. Create `frontend/src/pages/CreatorsBrowse.tsx` — creators browse page (R007, R014):\n   - Fetch creators from `/api/v1/creators?sort=random` (default) via `fetchCreators()`\n   - Genre filter pills at top (fetch unique genres from creator data, or hardcode from canonical_tags.yaml genres)\n   - Type-to-narrow input that filters displayed creators client-side by name\n   - Sort toggle: Random (default), Alphabetical, Views — each triggers re-fetch with `sort=random|alpha|views`\n   - Each creator row: name, genre tags (pills), technique_count, video_count, view_count\n   - Click row → navigate to `/creators/{slug}`\n   - All creators get equal visual weight (no featured/highlighted creators) per R014\n\n2. Create `frontend/src/pages/CreatorDetail.tsx` — creator detail page:\n   - Fetch creator by slug via `fetchCreator(slug)` — shows name, genres, video_count, technique_count\n   - Fetch creator's technique pages via `fetchTechniques({ creator_slug: slug })`\n   - List technique pages with title (linked to `/techniques/{slug}`), category, tags, summary\n   - Loading state and 404 error state\n\n3. Create `frontend/src/pages/TopicsBrowse.tsx` — topics browse page (R008):\n   - Fetch topics from `/api/v1/topics` via `fetchTopics()`\n   - Two-level hierarchy: 6 top-level categories (Sound design, Mixing, Synthesis, Arrangement, Workflow, Mastering)\n   - Each category expandable/collapsible, showing sub-topics\n   - Each sub-topic shows technique_count and creator_count\n   - Click sub-topic → navigate to `/search?q={sub_topic_name}&scope=topics` or filter technique list\n   - Filter input at top to narrow categories/sub-topics\n\n4. Update `frontend/src/App.tsx` — add routes for browse pages:\n   - `/creators` → CreatorsBrowse\n   - `/creators/:slug` → CreatorDetail\n   - `/topics` → TopicsBrowse\n   - Import new page components\n\n5. Add CSS to `frontend/src/App.css` for browse pages:\n   - Creator list styles (rows, genre pills, counts)\n   - Creator detail page layout\n   - Topics hierarchy styles (collapsible sections, sub-topic rows, counts)\n   - Filter input styles\n   - Sort toggle button group\n\n6. Final verification:\n   - `cd frontend && npx tsc -b` — zero errors\n   - `cd frontend && npm run build` — clean build\n   - Verify all 6 page files exist\n\n## Must-Haves\n\n- [ ] Creators browse page with randomized default sort (R014), genre filter, type-to-narrow, sort toggle (R007)\n- [ ] Creator detail page showing creator info and their technique pages\n- [ ] Topics browse page with two-level hierarchy, counts, clickable sub-topics (R008)\n- [ ] All routes registered in App.tsx\n- [ ] `cd frontend && npx tsc -b` passes with zero errors\n- [ ] `cd frontend && npm run build` succeeds\n\n## Verification\n\n- `cd frontend && npx tsc -b && npm run build && echo 'Build OK'`\n- `test -f frontend/src/pages/CreatorsBrowse.tsx && test -f frontend/src/pages/CreatorDetail.tsx && test -f frontend/src/pages/TopicsBrowse.tsx && echo 'All pages exist'`","estimate":"1.5h","expectedOutput":["`frontend/src/pages/CreatorsBrowse.tsx` — creators browse page with randomized sort, genre filter, type-to-narrow","`frontend/src/pages/CreatorDetail.tsx` — creator detail page with technique page list","`frontend/src/pages/TopicsBrowse.tsx` — two-level topic hierarchy with counts","`frontend/src/App.tsx` — all 9 routes registered (3 public + 3 browse + 2 admin + fallback)","`frontend/src/App.css` — complete CSS for all S05 pages"],"files":["frontend/src/pages/CreatorsBrowse.tsx","frontend/src/pages/CreatorDetail.tsx","frontend/src/pages/TopicsBrowse.tsx","frontend/src/App.tsx","frontend/src/App.css"],"inputs":["`frontend/src/api/public-client.ts` — typed API client with fetchCreators, fetchCreator, fetchTopics, fetchTechniques","`frontend/src/App.tsx` — routing structure from T03 to extend with browse routes","`frontend/src/App.css` — styles from T03 to extend with browse page styles","`frontend/src/pages/Home.tsx` — reference for component patterns established in T03","`config/canonical_tags.yaml` — genre list for creator filter pills (Bass music, Drum & bass, etc.)"],"taskId":"T04","title":"Build frontend browse pages (creators, topics) and verify full build","verify":"cd frontend && npx tsc -b && npm run build && test -f src/pages/CreatorsBrowse.tsx && test -f src/pages/CreatorDetail.tsx && test -f src/pages/TopicsBrowse.tsx && echo 'All browse pages built OK'"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":9322,"cacheRead":81060,"cacheWrite":941,"totalTokens":91324,"cost":{"input":0.000005,"output":0.23305,"cacheRead":0.04053,"cacheWrite":0.00588125,"total":0.27946625}},"stopReason":"toolUse","timestamp":1774827912828}}
{"type":"message","id":"ee81f5da","parentId":"4260deb7","timestamp":"2026-03-29T23:48:07.433Z","message":{"role":"toolResult","toolCallId":"toolu_015rFZLnan56Zx68hZEW2xXa","toolName":"gsd_plan_slice","content":[{"type":"text","text":"Planned slice S05 (M001)"}],"details":{"operation":"plan_slice","milestoneId":"M001","sliceId":"S05","planPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S05/S05-PLAN.md","taskPlanPaths":["/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S05/tasks/T01-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S05/tasks/T02-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S05/tasks/T03-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S05/tasks/T04-PLAN.md"]},"isError":false,"timestamp":1774828087431}}
{"type":"message","id":"75fdb420","parentId":"ee81f5da","timestamp":"2026-03-29T23:48:30.358Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me self-audit the plan against the checklist:\n\n**Completion semantics:** If T01 (backend endpoints), T02 (backend tests), T03 (frontend search flow), and T04 (frontend browse pages) all complete, the demo statement holds: user can search → get semantic results → click through to technique page → browse creators (randomized) → browse topics.\n\n**Requirement coverage:**\n- R005 (Search-First Web UI) → T01 (search endpoint), T03 (Home + SearchResults pages)\n- R006 (Technique Page Display) → T01 (techniques endpoint), T03 (TechniquePage component)\n- R007 (Creators Browse) → T01 (enhanced creators endpoint), T04 (CreatorsBrowse)\n- R008 (Topics Browse) → T01 (topics endpoint), T04 (TopicsBrowse)\n- R009 (Qdrant read path) → T01 (SearchService with async Qdrant), T02 (tests)\n- R014 (Creator Equity) → T01 (sort=random default), T04 (equal visual weight)\n- R015 (30-Second Retrieval) → T03 (search flow with debounce + typeahead)\n\nAll owned requirements have at least one task. ✓\n\n**Task completeness:** All 4 tasks have steps, must-haves, verification commands, input/output file paths with backtick wrapping. ✓\n\n**Dependency correctness:** T01 → T02 (tests need endpoints), T01 → T03 (frontend needs API), T03 → T04 (browse pages extend T03's routing/client). ✓\n\n**Scope sanity:** T01: 7 steps, 7 files. T02: 3 steps, 3 files. T03: 6 steps, 6 files. T04: 6 steps, 5 files. All within limits. ✓\n\n**Quality gates:** Threat Surface, Requirement Impact present in slice plan. Failure Modes, Load Profile, Negative Tests present in T01 and T02 (the non-trivial tasks). ✓\n\nNow let me record the key architectural decision about the async search service:"},{"type":"toolCall","id":"toolu_01YbeMPJqpHP5wjGVeE7nFs9","name":"gsd_decision_save","arguments":{"choice":"Create a separate `SearchService` class using `openai.AsyncOpenAI` and `qdrant_client.AsyncQdrantClient` for the search endpoint. Keep existing sync `EmbeddingClient` and `QdrantManager` for Celery pipeline. Search endpoint has 300ms timeout on embedding API and falls back to SQL ILIKE keyword search on Qdrant/embedding failure.","decision":"Async search service pattern for FastAPI request path","made_by":"agent","rationale":"The existing EmbeddingClient and QdrantManager are sync (using `openai.OpenAI` and `QdrantClient`) because Celery tasks run synchronously. FastAPI request handlers are async — reusing sync clients would block the event loop. Creating a thin async wrapper avoids modifying the battle-tested pipeline code while providing non-blocking search. The 300ms timeout and keyword fallback ensure the search endpoint always returns results, even when Qdrant or the embedding service is degraded.","scope":"architecture","when_context":"M001/S05"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":881,"cacheRead":82001,"cacheWrite":9560,"totalTokens":92443,"cost":{"input":0.000005,"output":0.022025,"cacheRead":0.041000499999999995,"cacheWrite":0.059750000000000004,"total":0.12278049999999999}},"stopReason":"toolUse","timestamp":1774828087432}}
{"type":"message","id":"1fff706d","parentId":"75fdb420","timestamp":"2026-03-29T23:48:30.365Z","message":{"role":"toolResult","toolCallId":"toolu_01YbeMPJqpHP5wjGVeE7nFs9","toolName":"gsd_decision_save","content":[{"type":"text","text":"Saved decision D009"}],"details":{"operation":"save_decision","id":"D009"},"isError":false,"timestamp":1774828110363}}
{"type":"message","id":"5f9eba55","parentId":"1fff706d","timestamp":"2026-03-29T23:48:41.840Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S05 planned.\n\n**Summary of the plan:**\n\n- **T01 (2h):** Backend — creates the async `SearchService` (embed query → Qdrant search → keyword fallback), search endpoint, techniques CRUD, topics hierarchy, and enhanced creators endpoint (random sort, genre filter, counts). 7 files, highest risk.\n- **T02 (1.5h):** Backend tests — ≥4 search tests (happy path, empty query, keyword fallback, scope filter) + ≥8 public API tests (techniques list/detail/404, topics hierarchy, creators sort/filter/detail). Verifies regression: all 40 existing tests still pass.\n- **T03 (2h):** Frontend primary flow — typed API client, landing page with search bar + typeahead + nav cards, search results page with grouped results, technique page display with all sections (R006), App.tsx routing for public + admin. 6 files.\n- **T04 (1.5h):** Frontend browse pages — creators browse (randomized default sort per R014, genre filter, sort toggle), creator detail, topics browse (two-level hierarchy), final build verification. 5 files.\n\nRequirements R005, R006, R007, R008, R009, R014, R015 are all covered with explicit task mappings. Decision D009 records the async search service pattern."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":320,"cacheRead":91561,"cacheWrite":898,"totalTokens":92780,"cost":{"input":0.000005,"output":0.008,"cacheRead":0.045780499999999995,"cacheWrite":0.0056125,"total":0.05939799999999999}},"stopReason":"stop","timestamp":1774828110364}}
{"type":"custom_message","customType":"async_job_result","content":"**Background job done: bg_89de94ce** (explore structure, 0.0s)\n\nbackend/routers/creators.py\nbackend/routers/health.py\nbackend/routers/ingest.py\nbackend/routers/__init__.py\nbackend/routers/pipeline.py\nbackend/routers/review.py\nbackend/routers/videos.py\n---\nfrontend/src/api/client.ts\nfrontend/src/App.css\nfrontend/src/App.tsx\nfrontend/src/components/ModeToggle.tsx\nfrontend/src/components/StatusBadge.tsx\nfrontend/src/main.tsx\nfrontend/src/pages/MomentDetail.tsx\nfrontend/src/pages/ReviewQueue.tsx\nfrontend/src/vite-env.d.ts\n---\n\"\"\"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, review, 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        settin\n\n[... truncated, use await_job for full output]","display":true,"id":"ef5f8218","parentId":"5f9eba55","timestamp":"2026-03-29T23:48:41.844Z"}
{"type":"message","id":"9d066007","parentId":"ef5f8218","timestamp":"2026-03-29T23:48:45.995Z","message":{"role":"assistant","content":[{"type":"text","text":"The plan is already complete. The `gsd_plan_slice` call was persisted successfully, and all 4 tasks (T01–T04) are written to the database with their plan files rendered. Decision D009 was also recorded.\n\nSlice S05 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":64,"cacheRead":92459,"cacheWrite":958,"totalTokens":93484,"cost":{"input":0.000015000000000000002,"output":0.0016,"cacheRead":0.0462295,"cacheWrite":0.005987500000000001,"total":0.053832}},"stopReason":"stop","timestamp":1774828121841}}
{"type":"custom_message","customType":"async_job_result","content":"**Background job done: bg_d2c2ce43** (schemas, 0.0s)\n\n\"\"\"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(TranscriptSegme\n\n[... truncated, use await_job for full output]","display":true,"id":"931e973d","parentId":"9d066007","timestamp":"2026-03-29T23:48:45.998Z"}
{"type":"message","id":"10848f3c","parentId":"931e973d","timestamp":"2026-03-29T23:48:49.580Z","message":{"role":"assistant","content":[{"type":"text","text":"The plan is already complete and persisted. All background jobs from the exploration phase have finished — these are just the delayed notifications for jobs that were already consumed via `await_job` during planning.\n\nSlice S05 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":51,"cacheRead":93417,"cacheWrite":694,"totalTokens":94165,"cost":{"input":0.000015000000000000002,"output":0.001275,"cacheRead":0.0467085,"cacheWrite":0.0043375,"total":0.052336}},"stopReason":"stop","timestamp":1774828125996}}
{"type":"custom_message","customType":"async_job_result","content":"**Background job done: bg_be9e25cd** (models, 0.0s)\n\n\"\"\"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_creat\n\n[... truncated, use await_job for full output]","display":true,"id":"8a883af9","parentId":"10848f3c","timestamp":"2026-03-29T23:48:49.583Z"}
{"type":"message","id":"dd48122a","parentId":"8a883af9","timestamp":"2026-03-29T23:48:51.918Z","message":{"role":"assistant","content":[{"type":"text","text":"Already done — these are stale background job notifications from the exploration phase. The slice plan is fully persisted.\n\nSlice S05 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":34,"cacheRead":94111,"cacheWrite":647,"totalTokens":94795,"cost":{"input":0.000015000000000000002,"output":0.0008500000000000001,"cacheRead":0.0470555,"cacheWrite":0.0040437500000000005,"total":0.05196425}},"stopReason":"stop","timestamp":1774828129581}}
{"type":"custom_message","customType":"async_job_result","content":"**Background job done: bg_ad40d79c** (creators router, 0.0s)\n\n\"\"\"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","display":true,"id":"274e5144","parentId":"dd48122a","timestamp":"2026-03-29T23:48:51.921Z"}
{"type":"message","id":"a7943379","parentId":"274e5144","timestamp":"2026-03-29T23:48:53.955Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S05 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":10,"cacheRead":94111,"cacheWrite":1272,"totalTokens":95395,"cost":{"input":0.00001,"output":0.00025,"cacheRead":0.0470555,"cacheWrite":0.00795,"total":0.0552655}},"stopReason":"stop","timestamp":1774828131919}}
{"type":"custom_message","customType":"async_job_result","content":"**Background job done: bg_ea8ea4f8** (embedding+qdrant, 0.0s)\n\n\"\"\"Synchronous embedding client using the OpenAI-compatible /v1/embeddings API.\n\nUses ``openai.OpenAI`` (sync) since Celery tasks run synchronously.\nHandles connection failures gracefully — embedding is non-blocking for the pipeline.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\n\nimport openai\n\nfrom config import Settings\n\nlogger = logging.getLogger(__name__)\n\n\nclass EmbeddingClient:\n    \"\"\"Sync embedding client backed by an OpenAI-compatible /v1/embeddings endpoint.\"\"\"\n\n    def __init__(self, settings: Settings) -> None:\n        self.settings = settings\n        self._client = openai.OpenAI(\n            base_url=settings.embedding_api_url,\n            api_key=settings.llm_api_key,\n        )\n\n    def embed(self, texts: list[str]) -> list[list[float]]:\n        \"\"\"Generate embedding vectors for a batch of texts.\n\n        Parameters\n        ----------\n        texts:\n            List of strings to embed.\n\n        Returns\n        -------\n        list[list[float]]\n            Embedding vectors. Returns empty list on connection/timeout errors\n            so the pipeline can continue without embeddings.\n        \"\"\"\n        if not texts:\n            return []\n\n        try:\n            response = self._client.embeddings.create(\n                model=self.settings.embedding_model,\n                input=texts,\n            )\n        except (openai.APIConnectionError, openai.APITimeoutError) as exc:\n            logger.warning(\n                \"Embedding API unavailable (%s: %s). Skipping %d texts.\",\n                type(exc).__name__,\n                exc,\n                len(texts),\n            )\n            return []\n        except openai.APIError as exc:\n            logger.warning(\n                \"Embedding API error (%s: %s). Skipping %d texts.\",\n                type(exc).__name__,\n                exc,\n                len(texts),\n            )\n            return []\n\n        vectors = [item.embedding for item in response.data]\n\n        # Validate dimensions\n        ex\n\n[... truncated, use await_job for full output]","display":true,"id":"a2b66894","parentId":"a7943379","timestamp":"2026-03-29T23:48:53.957Z"}
{"type":"message","id":"98eb40d4","parentId":"a2b66894","timestamp":"2026-03-29T23:48:56.249Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S05 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":10,"cacheRead":95383,"cacheWrite":549,"totalTokens":95945,"cost":{"input":0.000015000000000000002,"output":0.00025,"cacheRead":0.0476915,"cacheWrite":0.0034312500000000003,"total":0.051387749999999996}},"stopReason":"stop","timestamp":1774828133956}}
{"type":"custom_message","customType":"async_job_result","content":"**Background job done: bg_880523fd** (frontend core, 0.0s)\n\nimport { Navigate, Route, Routes } from \"react-router-dom\";\nimport ReviewQueue from \"./pages/ReviewQueue\";\nimport MomentDetail from \"./pages/MomentDetail\";\nimport ModeToggle from \"./components/ModeToggle\";\n\nexport default function App() {\n  return (\n    <div className=\"app\">\n      <header className=\"app-header\">\n        <h1>Chrysopedia Admin</h1>\n        <div className=\"app-header__right\">\n          <ModeToggle />\n          <nav>\n            <a href=\"/admin/review\">Review Queue</a>\n          </nav>\n        </div>\n      </header>\n\n      <main className=\"app-main\">\n        <Routes>\n          <Route path=\"/admin/review\" element={<ReviewQueue />} />\n          <Route path=\"/admin/review/:momentId\" element={<MomentDetail />} />\n          <Route path=\"*\" element={<Navigate to=\"/admin/review\" replace />} />\n        </Routes>\n      </main>\n    </div>\n  );\n}\n===\n/* ── Base ─────────────────────────────────────────────────────────────────── */\n\n*,\n*::before,\n*::after {\n  box-sizing: border-box;\n  margin: 0;\n  padding: 0;\n}\n\nbody {\n  font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto,\n    \"Helvetica Neue\", Arial, sans-serif;\n  line-height: 1.5;\n  color: #1a1a2e;\n  background: #f4f4f8;\n}\n\n/* ── App shell ────────────────────────────────────────────────────────────── */\n\n.app-header {\n  display: flex;\n  align-items: center;\n  justify-content: space-between;\n  padding: 0.75rem 1.5rem;\n  background: #1a1a2e;\n  color: #fff;\n}\n\n.app-header h1 {\n  font-size: 1.125rem;\n  font-weight: 600;\n  letter-spacing: -0.01em;\n}\n\n.app-header__right {\n  display: flex;\n  align-items: center;\n  gap: 1.5rem;\n}\n\n.app-header nav a {\n  color: rgba(255, 255, 255, 0.8);\n  text-decoration: none;\n  font-size: 0.875rem;\n}\n\n.app-header nav a:hover {\n  color: #fff;\n}\n===\nimport { defineConfig } from \"vite\";\nimport react from \"@vitejs/plugin-react\";\n\nexport default defineConfig({\n  plugins: [react()],\n  server: {\n    proxy: {\n      \"/api\": {\n        target: \"http://localhost:8001\",\n        change\n\n[... truncated, use await_job for full output]","display":true,"id":"12fdc1ac","parentId":"98eb40d4","timestamp":"2026-03-29T23:48:56.252Z"}
{"type":"message","id":"efb61603","parentId":"12fdc1ac","timestamp":"2026-03-29T23:48:58.636Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S05 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":10,"cacheRead":95932,"cacheWrite":758,"totalTokens":96703,"cost":{"input":0.000015000000000000002,"output":0.00025,"cacheRead":0.047965999999999995,"cacheWrite":0.0047375,"total":0.052968499999999995}},"stopReason":"stop","timestamp":1774828136250}}
{"type":"custom_message","customType":"async_job_result","content":"**Background job done: bg_4226df48** (config, 0.0s)\n\n\"\"\"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","display":true,"id":"3f1014fb","parentId":"efb61603","timestamp":"2026-03-29T23:48:58.638Z"}
{"type":"message","id":"0ccf6287","parentId":"3f1014fb","timestamp":"2026-03-29T23:49:00.458Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S05 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":10,"cacheRead":96690,"cacheWrite":611,"totalTokens":97313,"cost":{"input":0.00001,"output":0.00025,"cacheRead":0.048345,"cacheWrite":0.00381875,"total":0.052423750000000005}},"stopReason":"stop","timestamp":1774828138636}}
{"type":"custom_message","customType":"async_job_result","content":"**Background job done: bg_1783fe85** (frontend api+config, 0.0s)\n\n/**\n * Typed API client for Chrysopedia review queue endpoints.\n *\n * All functions use fetch() with JSON handling and throw on non-OK responses.\n * Base URL is empty so requests go through the Vite dev proxy or nginx in prod.\n */\n\n// ── Types ───────────────────────────────────────────────────────────────────\n\nexport interface KeyMomentRead {\n  id: string;\n  source_video_id: string;\n  technique_page_id: string | null;\n  title: string;\n  summary: string;\n  start_time: number;\n  end_time: number;\n  content_type: string;\n  plugins: string[] | null;\n  raw_transcript: string | null;\n  review_status: string;\n  created_at: string;\n  updated_at: string;\n}\n\nexport interface ReviewQueueItem extends KeyMomentRead {\n  video_filename: string;\n  creator_name: string;\n}\n\nexport interface ReviewQueueResponse {\n  items: ReviewQueueItem[];\n  total: number;\n  offset: number;\n  limit: number;\n}\n\nexport interface ReviewStatsResponse {\n  pending: number;\n  approved: number;\n  edited: number;\n  rejected: number;\n}\n\nexport interface ReviewModeResponse {\n  review_mode: boolean;\n}\n\nexport interface MomentEditRequest {\n  title?: string;\n  summary?: string;\n  start_time?: number;\n  end_time?: number;\n  content_type?: string;\n  plugins?: string[];\n}\n\nexport interface MomentSplitRequest {\n  split_time: number;\n}\n\nexport interface MomentMergeRequest {\n  target_moment_id: string;\n}\n\nexport interface QueueParams {\n  status?: string;\n  offset?: number;\n  limit?: number;\n}\n\n// ── Helpers ──────────────────────────────────────────────────────────────────\n\nconst BASE = \"/api/v1/review\";\n\nclass ApiError extends Error {\n  constructor(\n    public status: number,\n    public detail: string,\n  ) {\n    super(`API ${status}: ${detail}`);\n    this.name = \"ApiError\";\n  }\n}\n\nasync function request<T>(url: string, init?: RequestInit): Promise<T> {\n  const res = await fetch(url, {\n    ...init,\n    headers: {\n      \"Content-Type\": \"application/json\",\n      ...init?.headers,\n    },\n  });\n\n  if (!res.ok) {\n    let de\n\n[... truncated, use await_job for full output]","display":true,"id":"1fa1e937","parentId":"0ccf6287","timestamp":"2026-03-29T23:49:00.461Z"}
{"type":"message","id":"bfe90e4f","parentId":"1fa1e937","timestamp":"2026-03-29T23:49:02.984Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S05 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":10,"cacheRead":97301,"cacheWrite":673,"totalTokens":97987,"cost":{"input":0.000015000000000000002,"output":0.00025,"cacheRead":0.0486505,"cacheWrite":0.00420625,"total":0.05312175}},"stopReason":"stop","timestamp":1774828140459}}
{"type":"custom_message","customType":"async_job_result","content":"**Background job done: bg_78dddfdc** (db+tests, 0.0s)\n\n\"\"\"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===\nconftest.py\nfixtures\n__init__.py\n__pycache__\ntest_ingest.py\ntest_pipeline.py\ntest_review.py\n===\n\"\"\"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\nfro\n\n[... truncated, use await_job for full output]","display":true,"id":"26e3cfce","parentId":"bfe90e4f","timestamp":"2026-03-29T23:49:02.986Z"}
{"type":"message","id":"2033657a","parentId":"26e3cfce","timestamp":"2026-03-29T23:49:07.075Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S05 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":10,"cacheRead":97301,"cacheWrite":1269,"totalTokens":98583,"cost":{"input":0.000015000000000000002,"output":0.00025,"cacheRead":0.0486505,"cacheWrite":0.00793125,"total":0.05684675}},"stopReason":"stop","timestamp":1774828142984}}
{"type":"custom_message","customType":"async_job_result","content":"**Background job done: bg_b4f8c61d** (tags yaml, 0.0s)\n\n./config/canonical_tags.yaml\n","display":true,"id":"09534e3e","parentId":"2033657a","timestamp":"2026-03-29T23:49:07.077Z"}
{"type":"message","id":"9c0f9fb1","parentId":"09534e3e","timestamp":"2026-03-29T23:49:09.188Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S05 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":10,"cacheRead":98570,"cacheWrite":48,"totalTokens":98630,"cost":{"input":0.00001,"output":0.00025,"cacheRead":0.049284999999999995,"cacheWrite":0.00030000000000000003,"total":0.049845}},"stopReason":"stop","timestamp":1774828147075}}
{"type":"custom_message","customType":"async_job_result","content":"**Background job done: bg_ff61735d** (synthesis prompt, 0.0s)\n\nYou are a music production knowledge synthesizer. Your task is to create a comprehensive technique page from a group of related key moments by the same creator on the same topic.\n\n## Instructions\n\n1. Read the key moments and their metadata provided inside the <moments> tags.\n2. Synthesize them into a single, coherent technique page.\n3. Organize the content into logical body sections.\n\n## Output Format\n\nReturn a JSON object with a single key \"pages\" containing a list of synthesized pages:\n\n```json\n{\n  \"pages\": [\n    {\n      \"title\": \"Descriptive page title\",\n      \"slug\": \"url-safe-slug\",\n      \"topic_category\": \"Sound design\",\n      \"topic_tags\": [\"bass\", \"synthesis\"],\n      \"summary\": \"A concise overview paragraph (2-3 sentences).\",\n      \"body_sections\": {\n        \"Overview\": \"Introduction to the technique...\",\n        \"Step-by-Step Process\": \"Detailed walkthrough...\",\n        \"Key Settings\": \"Specific parameter values...\",\n        \"Tips and Variations\": \"Additional tips...\"\n      },\n      \"signal_chains\": [\n        {\n          \"name\": \"Main bass chain\",\n          \"steps\": [\"Serum (oscillator)\", \"OTT (compression)\", \"EQ8 (low cut)\"]\n        }\n      ],\n      \"plugins\": [\"Serum\", \"OTT\", \"EQ8\"],\n      \"source_quality\": \"structured\"\n    }\n  ]\n}\n```\n\n## Field Rules\n\n- **title**: Clear, search-friendly title (e.g., \"Neuro Bass Design with Serum by CreatorName\").\n- **slug**: URL-safe version of the title using hyphens (e.g., \"neuro-bass-design-serum-creatorname\").\n- **topic_category**: The primary category from the canonical taxonomy.\n- **topic_tags**: All relevant tags aggregated from the classified moments.\n- **summary**: 2-3 sentence overview that captures the essence of the technique.\n- **body_sections**: A dictionary of section titles to section content. Use clear, educational prose. Include specific values and settings when available. Suggested sections: Overview, Step-by-Step Process, Key Settings, Tips and Variations. Adapt section names to fit the content.\n- **s\n\n[... truncated, use await_job for full output]","display":true,"id":"28e05112","parentId":"9c0f9fb1","timestamp":"2026-03-29T23:49:09.190Z"}
{"type":"message","id":"7ad5f10c","parentId":"28e05112","timestamp":"2026-03-29T23:49:11.425Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S05 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":10,"cacheRead":98618,"cacheWrite":586,"totalTokens":99217,"cost":{"input":0.000015000000000000002,"output":0.00025,"cacheRead":0.049309,"cacheWrite":0.0036625000000000004,"total":0.0532365}},"stopReason":"stop","timestamp":1774828149188}}
{"type":"custom_message","customType":"async_job_result","content":"**Background job done: bg_4caae85d** (tags+test count, 1.6s)\n\n# Canonical tags — 6 top-level production categories\n# Sub-topics grow organically during pipeline extraction\ncategories:\n  - name: Sound design\n    description: Creating and shaping sounds from scratch or samples\n    sub_topics: [bass, drums, kick, snare, hi-hat, percussion, pads, leads, fx, foley, vocals, textures]\n\n  - name: Mixing\n    description: Balancing, processing, and spatializing elements\n    sub_topics: [eq, compression, bus processing, reverb, delay, stereo imaging, gain staging, automation]\n\n  - name: Synthesis\n    description: Methods of generating sound\n    sub_topics: [fm, wavetable, granular, additive, subtractive, modular, physical modeling]\n\n  - name: Arrangement\n    description: Structuring a track from intro to outro\n    sub_topics: [song structure, transitions, tension, energy flow, breakdowns, drops]\n\n  - name: Workflow\n    description: Creative process, session management, productivity\n    sub_topics: [daw setup, templates, creative process, collaboration, file management, resampling]\n\n  - name: Mastering\n    description: Final stage processing for release\n    sub_topics: [limiting, stereo width, loudness, format delivery, referencing]\n\n# Genre taxonomy (assigned to Creators, not techniques)\ngenres:\n  - Bass music\n  - Drum & bass\n  - Dubstep\n  - Halftime\n  - House\n  - Techno\n  - IDM\n  - Glitch\n  - Downtempo\n  - Neuro\n  - Ambient\n  - Experimental\n  - Cinematic\n===\n      <Coroutine test_reject_moment>\n      <Coroutine test_reject_nonexistent_moment>\n      <Coroutine test_edit_moment>\n      <Coroutine test_edit_nonexistent_moment>\n      <Coroutine test_split_moment>\n      <Coroutine test_split_invalid_time_below_start>\n      <Coroutine test_split_invalid_time_above_end>\n      <Coroutine test_split_nonexistent_moment>\n      <Coroutine test_merge_moments>\n      <Coroutine test_merge_different_videos>\n      <Coroutine test_merge_with_self>\n      <Coroutine test_merge_nonexistent_target>\n      <Coroutine test_merge_nonexistent_source>\n      <Corout\n\n[... truncated, use await_job for full output]","display":true,"id":"78979e41","parentId":"7ad5f10c","timestamp":"2026-03-29T23:49:11.427Z"}
{"type":"message","id":"d7ac62fc","parentId":"78979e41","timestamp":"2026-03-29T23:49:13.654Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S05 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":10,"cacheRead":99204,"cacheWrite":659,"totalTokens":99876,"cost":{"input":0.000015000000000000002,"output":0.00025,"cacheRead":0.049602,"cacheWrite":0.00411875,"total":0.05398575}},"stopReason":"stop","timestamp":1774828151425}}
{"type":"custom_message","customType":"async_job_result","content":"**Background job done: bg_0633848c** (css+main, 0.0s)\n\n620 frontend/src/App.css\n===\nimport { StrictMode } from \"react\";\nimport { createRoot } from \"react-dom/client\";\nimport { BrowserRouter } from \"react-router-dom\";\nimport App from \"./App\";\nimport \"./App.css\";\n\ncreateRoot(document.getElementById(\"root\")!).render(\n  <StrictMode>\n    <BrowserRouter>\n      <App />\n    </BrowserRouter>\n  </StrictMode>,\n);\n","display":true,"id":"7be9d290","parentId":"d7ac62fc","timestamp":"2026-03-29T23:49:13.658Z"}
{"type":"message","id":"248205d3","parentId":"7be9d290","timestamp":"2026-03-29T23:49:17.944Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S05 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":10,"cacheRead":99863,"cacheWrite":152,"totalTokens":100027,"cost":{"input":0.00001,"output":0.00025,"cacheRead":0.0499315,"cacheWrite":0.00095,"total":0.0511415}},"stopReason":"stop","timestamp":1774828153655}}
