{"type":"thinking_level_change","id":"5806e9c1","parentId":null,"timestamp":"2026-03-30T06:58:46.403Z","thinkingLevel":"off"}
{"type":"model_change","id":"9796418d","parentId":"5806e9c1","timestamp":"2026-03-30T06:58:46.404Z","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## Requirements Advanced\n\n- R006 — Technique page now renders meta stats line, video source filenames on key moments, and signal chain flow blocks with arrow separators — matching the reference layout specification\n\n## Requirements Validated\n\nNone.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## UNIT: Research Slice S04 (\"Article Versioning + Pipeline Tuning Metadata\") — Milestone M004\n\n## Inlined Context (preloaded — do not re-read these files)\n\n### Milestone Roadmap\nSource: `.gsd/milestones/M004/M004-ROADMAP.md`\n\n# M004: \n\n## Vision\nFix the immediate bugs (422 errors, creators page), apply a dark mode theme with cyan accents and fix mobile responsive issues, redesign the technique page to match the reference HTML (proper sections, signal chain blocks, key moments with video source), and introduce article versioning with pipeline tuning metadata for benchmarking extraction quality across prompt iterations.\n\n## Slice Overview\n| ID | Slice | Risk | Depends | Done | After this |\n|----|-------|------|---------|------|------------|\n| S01 | Fix API Bugs — Review Detail 422 + Creators Page | low | — | ✅ | Review detail page and creators page load without errors using real pipeline data |\n| S02 | Dark Theme + Cyan Accents + Mobile Responsive Fix | medium | — | ✅ | App uses dark theme with cyan accents, no horizontal scroll on mobile |\n| S03 | Technique Page Redesign + Video Source on Moments | medium | S01 | ✅ | Technique page matches reference HTML layout with video source on key moments, signal chain blocks, and proper section structure |\n| S04 | Article Versioning + Pipeline Tuning Metadata | high | S03 | ⬜ | Technique pages track version history with pipeline metadata; API returns version list |\n\n---\n\n### Decisions\nSource: `.gsd/DECISIONS.md`\n\n# Decisions Register\n\n<!-- Append-only. Never edit or remove existing rows.\n     To reverse a decision, add a new row that supersedes it.\n     Read this file at the start of any planning or research phase. -->\n\n| # | When | Scope | Decision | Choice | Rationale | Revisable? | Made By |\n|---|------|-------|----------|--------|-----------|------------|---------|\n| D001 |  | architecture | Storage layer selection | PostgreSQL for structured data, Qdrant (existing instance at 10.0.0.10) for vector embeddings, local filesystem for transcript JSON files | Spec specifies PostgreSQL for JSONB support and Qdrant already running on hypervisor. File store on local filesystem organized by creator slug. Qdrant gets a dedicated collection, not a new instance. | Yes | collaborative |\n| D002 |  | architecture | Timestamp handling for asyncpg with TIMESTAMP WITHOUT TIME ZONE columns | Use datetime.now(timezone.utc).replace(tzinfo=None) in _now() helper | asyncpg rejects timezone-aware datetimes for TIMESTAMP WITHOUT TIME ZONE columns. Stripping tzinfo at the application layer preserves UTC semantics while staying compatible with the existing schema. | Yes | agent |\n| D003 |  | requirement | R002 Transcript Ingestion API status | validated | 6 passing integration tests prove the full POST /api/v1/ingest flow: creator auto-detection, SourceVideo upsert, TranscriptSegment bulk insert, raw JSON persistence, idempotent re-upload, and invalid input rejection. | Yes | agent |\n| D004 |  | architecture | Sync vs async approach for Celery worker pipeline tasks | Use sync openai.OpenAI, sync QdrantClient, and sync SQLAlchemy (create_engine with psycopg2) inside Celery tasks. Convert DATABASE_URL from postgresql+asyncpg:// to postgresql:// for the sync engine. | Celery workers run in a synchronous context. Using asyncio.run() inside tasks risks nested event loop errors with gevent/eventlet workers. Using sync clients throughout eliminates this class of bug entirely. The async engine/session from database.py is only used by FastAPI (ASGI); the worker gets its own sync engine. | Yes | agent |\n| D005 |  | architecture | Embedding/Qdrant failure handling strategy in pipeline | Embedding/Qdrant failures (stage 6) log errors but do not fail the pipeline. Processing_status is set by stages 2-5 only. Embeddings can be regenerated by manual re-trigger. | Qdrant is at 10.0.0.10 on the hypervisor network and may not be reachable during all pipeline runs. Making embedding a non-blocking side-effect ensures core pipeline output (KeyMoments, TechniquePages in PostgreSQL) is never lost due to vector store issues. The manual re-trigger endpoint allows regenerating embeddings at any time. | Yes | agent |\n| D006 |  | requirement | R013 Prompt Template System status | validated | 4 prompt template files in prompts/ directory loaded from configurable settings.prompts_path. Templates use XML-style content fencing. Pipeline stages read templates from disk at runtime, enabling edits without code changes. Manual re-trigger endpoint (POST /api/v1/pipeline/trigger/{video_id}) allows re-processing after prompt edits. | Yes | agent |\n| D007 | 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 |\n| D008 | 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| D009 | M001/S05 | architecture | Async search service pattern for FastAPI request path | 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. | 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. | Yes | agent |\n| D010 | M001/S05 | requirement | R005 Search-First Web UI status | validated | Search endpoint (GET /api/v1/search) with async embedding + Qdrant + keyword fallback implemented and tested. Frontend Home.tsx has prominent search bar with 300ms debounced typeahead, scope toggle via URL params, nav cards for Topics/Creators, Recently Added section. SearchResults.tsx displays grouped results. 5 integration tests verify search happy path, empty query, keyword fallback, scope filter, and no-results. Frontend production build succeeds with zero TypeScript errors. | Yes | agent |\n| D011 | M001/S05 | requirement | R006 Technique Page Display status | validated | TechniquePage.tsx renders all specified sections: header with topic_category badge and topic_tags pills, creator name linked to creator detail, source_quality indicator, amber banner for unstructured content, body_sections JSONB prose (handles both string and object values), key moments index ordered by start_time, signal chains, plugins pill list, and related techniques links. Backend GET /api/v1/techniques/{slug} returns full detail with eager-loaded key_moments and related links. 404 for unknown slug tested. | Yes | agent |\n| D012 | M001/S05 | requirement | R007 Creators Browse Page status | validated | CreatorsBrowse.tsx implements genre filter pills, type-to-narrow name filter, sort toggle (Random default/A-Z/Views). Each creator row shows name, genre tags, technique_count, video_count. Links to CreatorDetail page. Backend GET /api/v1/creators supports sort=random\\|alpha\\|views and genre filter. Integration tests verify random sort, alpha sort, genre filter, detail endpoint, 404, and counts. | Yes | agent |\n| D013 | M001/S05 | requirement | R008 Topics Browse Page status | validated | TopicsBrowse.tsx renders two-level topic hierarchy (6 categories from canonical_tags.yaml with expandable sub-topics showing technique_count and creator_count). Filter input narrows categories/sub-topics. Clicking sub-topic navigates to search with scope=topics. Backend GET /api/v1/topics aggregates counts from DB per sub-topic. Integration test verifies topic hierarchy response shape. | Yes | agent |\n| D014 | M001/S05 | requirement | R014 Creator Equity status | validated | CreatorsBrowse.tsx defaults to sort=random. Backend uses func.random() ORDER BY for randomized sort. Integration test verifies random sort returns all creators (order may vary). All creators get equal visual weight in the UI — no featured/highlighted treatment. Equal-weight row layout confirmed in CSS. | Yes | agent |\n| D015 | M002/S01 | architecture | Docker network subnet for Chrysopedia on ub01 | 172.32.0.0/24 — free on ub01, replaces 172.24.0.0/24 which is used by xpltd_docs_default | 172.24.0.0/24 was already allocated to xpltd_docs_default network on ub01. Scanned all existing subnets and 172.32.0.0/24 was the first unoccupied /24 in the 172.x range. | Yes | agent |\n| D016 | M002/S01 | architecture | Embedding service for the pipeline and search — OpenWebUI doesn't serve /v1/embeddings | Add Ollama container (ollama/ollama:latest) to the compose stack running nomic-embed-text on CPU | The FYN OpenWebUI instance at chat.forgetyour.name returns 404/500 on /v1/embeddings. No Ollama instance exists on the network. Ollama provides the standard OpenAI-compatible /v1/embeddings endpoint that both EmbeddingClient and SearchService expect. | Yes | agent |\n| D017 |  | frontend | CSS theming approach for dark mode | 77 semantic CSS custom properties in :root block, all hardcoded colors replaced with var(--*) references | A complete variable-based palette enables theme switching, ensures consistency, and makes future color changes single-point edits. 77 tokens (vs ~30 initially planned) were needed to cover all semantic contexts — badges, buttons, surfaces, text, accents, shadows, and status states. Cyan (#22d3ee) replaces indigo as the accent color throughout. | Yes | agent |\n\n---\n\n### Requirements\nSource: `.gsd/REQUIREMENTS.md`\n\n# Requirements\n\n## R001 — Whisper Transcription Pipeline\n**Status:** validated\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:** validated\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:** validated\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:** validated\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:** validated\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:** validated\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:** validated\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:** validated\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:** validated\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:** validated\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:** validated\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:** validated\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:** validated\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:** validated\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## Separate async/sync clients for FastAPI vs Celery\n\n**Context:** The Chrysopedia backend has both sync Celery tasks (pipeline stages using `openai.OpenAI`, `QdrantClient`, sync SQLAlchemy) and async FastAPI handlers. Reusing sync clients in async handlers blocks the event loop; reusing async clients in Celery risks nested event loop errors.\n\n**Fix:** Create separate client classes: `SearchService` (async, for FastAPI request path) wraps `openai.AsyncOpenAI` and `AsyncQdrantClient`. The pipeline's `EmbeddingClient` and `QdrantManager` (sync, for Celery) remain untouched. This doubles the client code surface but eliminates the async/sync mismatch class of bugs entirely.\n\n## Mocking SearchService at the router dependency level for tests\n\n**Context:** The search endpoint creates a `SearchService` instance internally. Testing search results with real embedding API and Qdrant is fragile (external dependencies). Mocking individual `openai.AsyncOpenAI` or `AsyncQdrantClient` is complex.\n\n**Fix:** Mock `SearchService` at the router level by patching the service instance in the endpoint function. This gives full control over search results in tests without complex async mock setup. Used in `test_search.py` — mock returns canned `SearchResponse` dicts.\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## QdrantManager uses random UUIDs for point IDs\n\n**Context:** `QdrantManager.upsert_technique_pages()` and `upsert_key_moments()` generate `uuid4()` for each Qdrant point. Re-indexing the same content creates duplicate points rather than updating existing ones.\n\n**Fix (deferred):** Use deterministic UUIDs based on content hash (e.g., `uuid5(NAMESPACE, f\"{technique_slug}:{section}\")`) so re-indexing overwrites the same points. This should be addressed before running the pipeline on production data to avoid index bloat.\n\n## Non-blocking side-effect pattern for external service calls in pipelines\n\n**Context:** Celery pipeline stages that call external services (embedding API, Qdrant) should not fail the entire pipeline if those services are down. Stage 6 (embed_and_index) is valuable but not critical — the pipeline's primary output (technique pages in PostgreSQL) doesn't depend on it.\n\n**Fix:** Use `max_retries=0` and a catch-all exception handler that logs WARNING and returns without raising. The pipeline orchestrator chains stage 6 after stage 5 but a failure there doesn't prevent `processing_status` from reaching its final state. This pattern applies to any \"best-effort enrichment\" stage in a pipeline.\n\n## Container healthcheck tool availability varies by base image\n\n**Context:** Qdrant (distroless-ish), Ollama, and nginx-alpine images don't have curl or wget. Standard healthcheck patterns like `curl -f http://localhost:PORT/health` fail silently inside these containers.\n\n**Fix by image:**\n- **Ollama:** Use `ollama list` (built-in CLI)\n- **Qdrant:** Use `bash -c 'echo > /dev/tcp/localhost/6333'` (bash is available)\n- **nginx-alpine:** Use `curl` (available) but NOT busybox `wget` (fails on localhost)\n- **Celery worker (same image as API):** Use `celery -A worker inspect ping` instead of HTTP healthcheck\n\n**Rule:** Always `docker exec <container> <healthcheck-cmd>` to verify the command works before deploying.\n\n## XPLTD domain setup flow\n\n**Context:** Adding a new external domain (like chrysopedia.com) to the XPLTD infrastructure requires three steps across two machines.\n\n**Flow:**\n1. **AdGuard on ub01 (10.0.0.10):** Add DNS rewrite via API: `POST /control/rewrite/add` with `{domain, answer: \"10.0.0.9\"}`\n2. **nginx on nuc01 (10.0.0.9):** Create server block in `/etc/nginx/sites-enabled/{domain}.conf` with `proxy_pass http://10.0.0.10:{port}`\n3. **Certbot on nuc01:** `sudo certbot --nginx -d {domain}` (requires external DNS A record or CNAME pointing to public IP first)\n\n**Note:** The AD DNS servers (10.0.0.15/.16) may already have CNAME entries for domains. Check `nslookup {domain} 10.0.0.15` before assuming setup is needed. ub01's resolv.conf uses AD DNS, not AdGuard, so `curl {domain}` from ub01 requires the AD DNS chain to resolve.\n\n## Alembic env.py sys.path needs both local and Docker paths\n\n**Context:** Alembic's env.py imports from `database` and `models`, which live in `backend/` locally but at `/app/` (the WORKDIR) inside Docker. The original `sys.path.insert(0, \"../backend\")` works locally but fails in Docker.\n\n**Fix:** Add both paths: `sys.path.insert(0, os.path.join(os.path.dirname(__file__), \"..\", \"backend\"))` AND `sys.path.insert(0, os.path.join(os.path.dirname(__file__), \"..\"))`. The second resolves to `/app/` inside Docker.\n\n---\n\n# {{scope}} — Research\n\n**Date:** {{date}}\n\n<!-- Required sections: Summary, Recommendation, Implementation Landscape.\n     All other sections: include only when they have real content.\n     For light research (straightforward work with known patterns),\n     the required sections alone are sufficient. -->\n\n## Summary\n\n{{summary — 2-3 paragraphs with primary recommendation}}\n\n## Recommendation\n\n{{whatApproachToTake_AND_why}}\n\n## Implementation Landscape\n\n<!-- This section is the primary input for the planner agent.\n     Be specific — file paths, function names, patterns to follow.\n     The planner uses this to scope tasks to files and decide build order. -->\n\n### Key Files\n\n- `{{filePath}}` — {{whatItDoesAndHowItRelates}}\n- `{{filePath}}` — {{whatNeedsToChange}}\n\n### Build Order\n\n{{whatToProveOrBuildFirst_AND_why — whatUnblocksDownstreamWork}}\n\n### Verification Approach\n\n{{howToConfirmTheSliceWorks — commands, tests, observable behaviors}}\n\n<!-- Sections below: include when applicable, omit entirely when not. -->\n\n## Don't Hand-Roll\n\n<!-- Include when existing libraries/tools solve problems the slice would otherwise reimplement. -->\n\n| Problem | Existing Solution | Why Use It |\n|---------|------------------|------------|\n| {{problem}} | {{solution}} | {{why}} |\n\n## Constraints\n\n<!-- Include when the codebase, runtime, or dependencies impose hard limits on approach. -->\n\n- {{hardConstraintFromCodebaseOrRuntime}}\n- {{constraintFromDependencies}}\n\n## Common Pitfalls\n\n<!-- Include when there are non-obvious failure modes worth flagging. -->\n\n- **{{pitfall}}** — {{howToAvoid}}\n- **{{pitfall}}** — {{howToAvoid}}\n\n## Open Risks\n\n<!-- Include when execution could hit unknowns that affect the plan. -->\n\n- {{riskThatCouldSurfaceDuringExecution}}\n\n## Skills Discovered\n\n<!-- Include when skill discovery found relevant skills. -->\n\n| Technology | Skill | Status |\n|------------|-------|--------|\n| {{technology}} | {{owner/repo@skill}} | {{installed / available / none found}} |\n\n## Sources\n\n<!-- Include when external docs, articles, or references informed the research. -->\n\n- {{whatWasLearned}} (source: [{{title}}]({{url}}))\n\n### Output Template: Research\nSource: `templates/research.md`\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 to watch out for.\n\n#### S03 Summary\nSource: `.gsd/milestones/M004/slices/S03/S03-SUMMARY.md`\n\n---\nid: S03\nparent: M004\nmilestone: M004\nprovides:\n  - Technique detail API returns video_filename on each key moment\n  - Technique page renders meta stats, video sources, and signal chain flow blocks matching reference layout\nrequires:\n  - slice: S01\n    provides: Fixed technique detail endpoint (422 bug resolved)\naffects:\n  - S04\nkey_files:\n  - backend/schemas.py\n  - backend/routers/techniques.py\n  - frontend/src/api/public-client.ts\n  - frontend/src/pages/TechniquePage.tsx\n  - frontend/src/App.css\nkey_decisions:\n  - Populate video_filename via post-processing loop rather than Pydantic from_attributes auto-mapping, since the field comes from a joined relation not a direct column\n  - Used text-overflow ellipsis on video filename with max-width 20rem to prevent layout breakage on long filenames\npatterns_established:\n  - Chained selectinload for cross-relation field population in technique detail endpoint — pattern for adding more joined fields to API responses\nobservability_surfaces:\n  - none\ndrill_down_paths:\n  - .gsd/milestones/M004/slices/S03/tasks/T01-SUMMARY.md\n  - .gsd/milestones/M004/slices/S03/tasks/T02-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-03-30T06:58:43.576Z\nblocker_discovered: false\n---\n\n# S03: Technique Page Redesign + Video Source on Moments\n\n**Technique detail pages now show meta stats, video filenames on key moments, and monospace signal chain flow blocks with arrow separators — matching the reference layout spec.**\n\n## What Happened\n\nThis slice delivered three visual changes to the technique page plus the backend data prerequisite to power them.\n\n**T01 (Backend):** Added `video_filename: str = \"\"` to the `KeyMomentSummary` Pydantic schema. Chained `.selectinload(KeyMoment.source_video)` onto the existing key_moments eager load in the technique detail endpoint. Added a post-processing loop that populates `video_filename` from the joined `source_video.filename` relation. Deployed to ub01 and verified all 13 key moments on the wave-shaping technique page return populated video filenames.\n\n**T02 (Frontend):** Three changes to TechniquePage.tsx plus corresponding CSS:\n1. **Meta stats line** below the title — shows source count (unique video filenames), moment count, and last-updated date. Computed inline via an IIFE for readability.\n2. **Video filename on key moments** — each moment row displays the source video filename in muted monospace text, truncated with ellipsis at 20rem to prevent layout breakage.\n3. **Signal chain flow blocks** — replaced the numbered `<ol>` list with a horizontal-wrap monospace `<div>` where steps are separated by cyan arrow (`→`) separators. Uses `var(--color-accent)` for the arrows, `var(--color-bg-surface)` background, and proper border-radius.\n\nAll CSS uses `var(--*)` tokens exclusively — zero hardcoded hex colors outside `:root`. The TypeScript `KeyMomentSummary` interface was updated to include `video_filename: string`. Build passes cleanly with zero errors.\n\n## Verification\n\n1. API returns video_filename on all key moments: `curl` to wave-shaping technique endpoint → 13 moments, all with `video_filename` populated as \"Skope - Understanding Waveshapers (2160p).mp4\".\n2. Frontend builds cleanly: `npm run build` exits 0 with zero TypeScript errors.\n3. No hardcoded colors: grep for hex colors outside `:root` returns zero matches.\n4. Live deployment on ub01:8096 serves the updated frontend with all three visual changes rendering correctly.\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nNone.\n\n## Known Limitations\n\nThe meta stats line computes source count client-side from video filenames — if two videos have the same filename (different paths), they'd be counted as one source. Unlikely in practice.\n\n## Follow-ups\n\nNone.\n\n## Files Created/Modified\n\n- `backend/schemas.py` — Added video_filename: str = '' to KeyMomentSummary schema\n- `backend/routers/techniques.py` — Chained selectinload for source_video; post-processing loop populates video_filename\n- `frontend/src/api/public-client.ts` — Added video_filename: string to KeyMomentSummary TypeScript interface\n- `frontend/src/pages/TechniquePage.tsx` — Added meta stats line, video filename display on moments, monospace signal chain flow blocks\n- `frontend/src/App.css` — Added CSS for technique-header__stats, technique-moment__source, technique-chain__flow/arrow/step classes\n\n## Your Role in the Pipeline\n\nYou are the scout. After you finish, a **planner agent** reads your output in a fresh context with no memory of your exploration. It uses your findings to decompose this slice into executable tasks — deciding what files change, what order to build things, how to verify the work. Then **executor agents** build each task in isolated context windows.\n\nWrite for the planner, not for a human. The planner needs:\n- **What files exist and what they do** — so it can scope tasks to specific files\n- **Where the natural seams are** — where work divides into independent units\n- **What to build or prove first** — what's riskiest, what unblocks everything else\n- **How to verify the result** — what commands, tests, or checks confirm the slice works\n\nIf the research doc is vague, the planner will waste its context re-exploring code you already read. If it's precise, the planner can decompose immediately.\n\n## Calibrate Depth\n\nRead the slice title, the roadmap excerpt, and any milestone research above. Ask: does this slice involve unfamiliar technology, risky integration, novel architecture, or ambiguous requirements? Or is it straightforward application of known patterns to known code?\n\n- **Deep research** — new technology, unfamiliar APIs, risky integration, multiple viable approaches, or ambiguous scope. Explore broadly, look up docs, check libraries, investigate constraints. Write all template sections. This is the default when you're genuinely uncertain.\n- **Targeted research** — known technology but new to this codebase, or moderately complex integration. Explore the relevant code, check one or two libraries, identify constraints. Omit Don't Hand-Roll and Sources if nothing applies.\n- **Light research** — well-understood work using established patterns already in the codebase (wiring up existing APIs, adding standard UI components, CRUD operations, configuration changes). Read the relevant files to confirm the pattern, note any constraints, write Summary + Recommendation + Implementation Landscape. Skip the rest. A light research doc can be 15-20 lines. Don't manufacture pitfalls or risks for work that doesn't have them.\n\nAn honest \"this is straightforward, here's the pattern to follow\" is more valuable than invented complexity.\n\n## Steps\n\nResearch what this slice needs. Narrate key findings and surprises as you go — what exists, what's missing, what constrains the approach.\n0. If `REQUIREMENTS.md` was preloaded above, identify which Active requirements this slice owns or supports. Research should target these requirements — surfacing risks, unknowns, and implementation constraints that could affect whether the slice actually delivers them.\n1.  Reference specific rules from loaded skills in your findings where they inform the implementation approach.\n2. **Skill Discovery (suggest):**\n   Identify the key technologies, frameworks, and services this work depends on (e.g. Stripe, Clerk, Supabase, JUCE, SwiftUI).\n   For each, check if a professional agent skill already exists:\n   - First check `<available_skills>` in your system prompt — a skill may already be installed.\n   - For technologies without an installed skill, run: `npx skills find \"<technology>\"`\n   - Only consider skills that are **directly relevant** to core technologies — not tangentially related.\n   - Evaluate results by install count and relevance to the actual work.\n   - Note promising skills in your research output with their install commands, but do NOT install them.\n   - The user will decide which to install.\n3. Explore relevant code for this slice's scope. For targeted exploration, use `rg`, `find`, and reads. For broad or unfamiliar subsystems, use `scout` to map the relevant area first.\n4. Use `resolve_library` / `get_library_docs` for unfamiliar libraries — skip this for libraries already used in the codebase\n5. **Web search budget:** You have a limited budget of web searches (max ~15 per session). Use them strategically — prefer `resolve_library` / `get_library_docs` for library documentation. Do NOT repeat the same or similar queries. If a search didn't find what you need, rephrase once or move on. Target 3-5 total web searches for a typical research unit.\n6. Use the **Research** output template from the inlined context above — include only sections that have real content. The template is already inlined above; do NOT attempt to read any template file from disk (there is no `templates/SLICE-RESEARCH.md` — the correct template is already present in this prompt).\n7. Call `gsd_summary_save` with `milestone_id: M004`, `slice_id: S04`, `artifact_type: \"RESEARCH\"`, and the full research markdown as `content` — the tool computes the file path and persists to both DB and disk.\n\nThe slice directory already exists at `.gsd/milestones/M004/slices/S04/`. Do NOT mkdir.\n\n**You MUST call `gsd_summary_save` with the research content before finishing.**\n\nWhen done, say: \"Slice S04 researched.\"","display":false,"id":"fc7bffd1","parentId":"9796418d","timestamp":"2026-03-30T06:58:46.406Z"}
{"type":"message","id":"6cb22abc","parentId":"fc7bffd1","timestamp":"2026-03-30T06:58:53.995Z","message":{"role":"assistant","content":[{"type":"text","text":"\n\nI'll research what's needed for article versioning and pipeline tuning metadata. Let me start by understanding the current data model, pipeline architecture, and what \"versioning\" means in this context."},{"type":"toolCall","id":"toolu_01ML4BuWhwMu1DwtWm7XFKYN","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find backend/models -type f -name '*.py' | head -20 && echo \"---\" && ls backend/models/ 2>/dev/null || echo \"No models dir\" && echo \"---\" && ls backend/ | head -30"}},{"type":"toolCall","id":"toolu_018uf3JKw3JN82mdCapvoNFA","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -l \"TechniquePage\" backend/ --type py | head -20"}},{"type":"toolCall","id":"toolu_01ANgYPdu78yeRUrGQUydVVx","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls .gsd/milestones/M004/M004-CONTEXT.md 2>/dev/null && echo \"EXISTS\" || echo \"NO CONTEXT\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":323,"cacheRead":56783,"cacheWrite":10459,"totalTokens":67568,"cost":{"input":0.000015000000000000002,"output":0.008075,"cacheRead":0.0283915,"cacheWrite":0.06536875,"total":0.10185025}},"stopReason":"toolUse","timestamp":1774853926405}}
{"type":"message","id":"3738d082","parentId":"6cb22abc","timestamp":"2026-03-30T06:58:54.019Z","message":{"role":"toolResult","toolCallId":"toolu_01ML4BuWhwMu1DwtWm7XFKYN","toolName":"bash","content":[{"type":"text","text":"---\nNo models dir\n---\nconfig.py\ndatabase.py\nmain.py\nmodels.py\npipeline\n__pycache__\npytest.ini\nredis_client.py\nrequirements.txt\nrouters\nschemas.py\nsearch_service.py\ntests\nworker.py\nfind: ‘backend/models’: No such file or directory\n"}],"isError":false,"timestamp":1774853934019}}
{"type":"message","id":"3d946643","parentId":"3738d082","timestamp":"2026-03-30T06:58:54.021Z","message":{"role":"toolResult","toolCallId":"toolu_018uf3JKw3JN82mdCapvoNFA","toolName":"bash","content":[{"type":"text","text":"backend/search_service.py\nbackend/schemas.py\nbackend/tests/test_public_api.py\nbackend/tests/test_search.py\nbackend/tests/test_pipeline.py\nbackend/routers/topics.py\nbackend/pipeline/stages.py\nbackend/routers/techniques.py\nbackend/models.py\nbackend/routers/creators.py\n"}],"isError":false,"timestamp":1774853934021}}
{"type":"message","id":"f9a0f3b6","parentId":"3d946643","timestamp":"2026-03-30T06:58:54.024Z","message":{"role":"toolResult","toolCallId":"toolu_01ANgYPdu78yeRUrGQUydVVx","toolName":"bash","content":[{"type":"text","text":"NO CONTEXT\n"}],"isError":false,"timestamp":1774853934022}}
{"type":"message","id":"5a41bf06","parentId":"f9a0f3b6","timestamp":"2026-03-30T06:58:58.930Z","message":{"role":"assistant","content":[{"type":"text","text":"No milestone context file. Let me read the core model and schema files to understand the TechniquePage data structure."},{"type":"toolCall","id":"toolu_01Prhae4fkxoVnHcgKUrMNrA","name":"read","arguments":{"path":"backend/models.py"}},{"type":"toolCall","id":"toolu_01DKRWyA3uG9mvFZs5cTsZNQ","name":"read","arguments":{"path":"backend/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":118,"cacheRead":67242,"cacheWrite":581,"totalTokens":67942,"cost":{"input":0.000005,"output":0.00295,"cacheRead":0.033621,"cacheWrite":0.0036312500000000004,"total":0.04020725}},"stopReason":"toolUse","timestamp":1774853934022}}
{"type":"message","id":"7f17f1db","parentId":"5a41bf06","timestamp":"2026-03-30T06:58:58.933Z","message":{"role":"toolResult","toolCallId":"toolu_01Prhae4fkxoVnHcgKUrMNrA","toolName":"read","content":[{"type":"text","text":"\"\"\"SQLAlchemy ORM models for the Chrysopedia knowledge base.\n\nSeven entities matching chrysopedia-spec.md §6.1:\n  Creator, SourceVideo, TranscriptSegment, KeyMoment,\n  TechniquePage, RelatedTechniqueLink, Tag\n\"\"\"\n\nfrom __future__ import annotations\n\nimport enum\nimport uuid\nfrom datetime import datetime, timezone\n\nfrom sqlalchemy import (\n    Enum,\n    Float,\n    ForeignKey,\n    Integer,\n    String,\n    Text,\n    UniqueConstraint,\n    func,\n)\nfrom sqlalchemy.dialects.postgresql import ARRAY, JSONB, UUID\nfrom sqlalchemy.orm import Mapped, mapped_column\nfrom sqlalchemy.orm import relationship as sa_relationship\n\nfrom database import Base\n\n\n# ── Enums ────────────────────────────────────────────────────────────────────\n\nclass ContentType(str, enum.Enum):\n    \"\"\"Source video content type.\"\"\"\n    tutorial = \"tutorial\"\n    livestream = \"livestream\"\n    breakdown = \"breakdown\"\n    short_form = \"short_form\"\n\n\nclass ProcessingStatus(str, enum.Enum):\n    \"\"\"Pipeline processing status for a source video.\"\"\"\n    pending = \"pending\"\n    transcribed = \"transcribed\"\n    extracted = \"extracted\"\n    reviewed = \"reviewed\"\n    published = \"published\"\n\n\nclass KeyMomentContentType(str, enum.Enum):\n    \"\"\"Content classification for a key moment.\"\"\"\n    technique = \"technique\"\n    settings = \"settings\"\n    reasoning = \"reasoning\"\n    workflow = \"workflow\"\n\n\nclass ReviewStatus(str, enum.Enum):\n    \"\"\"Human review status for key moments.\"\"\"\n    pending = \"pending\"\n    approved = \"approved\"\n    edited = \"edited\"\n    rejected = \"rejected\"\n\n\nclass SourceQuality(str, enum.Enum):\n    \"\"\"Derived source quality for technique pages.\"\"\"\n    structured = \"structured\"\n    mixed = \"mixed\"\n    unstructured = \"unstructured\"\n\n\nclass PageReviewStatus(str, enum.Enum):\n    \"\"\"Review lifecycle for technique pages.\"\"\"\n    draft = \"draft\"\n    reviewed = \"reviewed\"\n    published = \"published\"\n\n\nclass RelationshipType(str, enum.Enum):\n    \"\"\"Types of links between technique pages.\"\"\"\n    same_technique_other_creator = \"same_technique_other_creator\"\n    same_creator_adjacent = \"same_creator_adjacent\"\n    general_cross_reference = \"general_cross_reference\"\n\n\n# ── Helpers ──────────────────────────────────────────────────────────────────\n\ndef _uuid_pk() -> Mapped[uuid.UUID]:\n    return mapped_column(\n        UUID(as_uuid=True),\n        primary_key=True,\n        default=uuid.uuid4,\n        server_default=func.gen_random_uuid(),\n    )\n\n\ndef _now() -> datetime:\n    \"\"\"Return current UTC time as a naive datetime (no tzinfo).\n\n    PostgreSQL TIMESTAMP WITHOUT TIME ZONE columns require naive datetimes.\n    asyncpg rejects timezone-aware datetimes for such columns.\n    \"\"\"\n    return datetime.now(timezone.utc).replace(tzinfo=None)\n\n\n# ── Models ───────────────────────────────────────────────────────────────────\n\nclass Creator(Base):\n    __tablename__ = \"creators\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    name: Mapped[str] = mapped_column(String(255), nullable=False)\n    slug: Mapped[str] = mapped_column(String(255), unique=True, nullable=False)\n    genres: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    folder_name: Mapped[str] = mapped_column(String(255), nullable=False)\n    view_count: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    videos: Mapped[list[SourceVideo]] = sa_relationship(back_populates=\"creator\")\n    technique_pages: Mapped[list[TechniquePage]] = sa_relationship(back_populates=\"creator\")\n\n\nclass SourceVideo(Base):\n    __tablename__ = \"source_videos\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    creator_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"creators.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    filename: Mapped[str] = mapped_column(String(500), nullable=False)\n    file_path: Mapped[str] = mapped_column(String(1000), nullable=False)\n    duration_seconds: Mapped[int] = mapped_column(Integer, nullable=True)\n    content_type: Mapped[ContentType] = mapped_column(\n        Enum(ContentType, name=\"content_type\", create_constraint=True),\n        nullable=False,\n    )\n    transcript_path: Mapped[str | None] = mapped_column(String(1000), nullable=True)\n    processing_status: Mapped[ProcessingStatus] = mapped_column(\n        Enum(ProcessingStatus, name=\"processing_status\", create_constraint=True),\n        default=ProcessingStatus.pending,\n        server_default=\"pending\",\n    )\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    creator: Mapped[Creator] = sa_relationship(back_populates=\"videos\")\n    segments: Mapped[list[TranscriptSegment]] = sa_relationship(back_populates=\"source_video\")\n    key_moments: Mapped[list[KeyMoment]] = sa_relationship(back_populates=\"source_video\")\n\n\nclass TranscriptSegment(Base):\n    __tablename__ = \"transcript_segments\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_video_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    start_time: Mapped[float] = mapped_column(Float, nullable=False)\n    end_time: Mapped[float] = mapped_column(Float, nullable=False)\n    text: Mapped[str] = mapped_column(Text, nullable=False)\n    segment_index: Mapped[int] = mapped_column(Integer, nullable=False)\n    topic_label: Mapped[str | None] = mapped_column(String(255), nullable=True)\n\n    # relationships\n    source_video: Mapped[SourceVideo] = sa_relationship(back_populates=\"segments\")\n\n\nclass KeyMoment(Base):\n    __tablename__ = \"key_moments\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_video_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    technique_page_id: Mapped[uuid.UUID | None] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"SET NULL\"), nullable=True\n    )\n    title: Mapped[str] = mapped_column(String(500), nullable=False)\n    summary: Mapped[str] = mapped_column(Text, nullable=False)\n    start_time: Mapped[float] = mapped_column(Float, nullable=False)\n    end_time: Mapped[float] = mapped_column(Float, nullable=False)\n    content_type: Mapped[KeyMomentContentType] = mapped_column(\n        Enum(KeyMomentContentType, name=\"key_moment_content_type\", create_constraint=True),\n        nullable=False,\n    )\n    plugins: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    review_status: Mapped[ReviewStatus] = mapped_column(\n        Enum(ReviewStatus, name=\"review_status\", create_constraint=True),\n        default=ReviewStatus.pending,\n        server_default=\"pending\",\n    )\n    raw_transcript: Mapped[str | None] = mapped_column(Text, nullable=True)\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    source_video: Mapped[SourceVideo] = sa_relationship(back_populates=\"key_moments\")\n    technique_page: Mapped[TechniquePage | None] = sa_relationship(\n        back_populates=\"key_moments\", foreign_keys=[technique_page_id]\n    )\n\n\nclass TechniquePage(Base):\n    __tablename__ = \"technique_pages\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    creator_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"creators.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    title: Mapped[str] = mapped_column(String(500), nullable=False)\n    slug: Mapped[str] = mapped_column(String(500), unique=True, nullable=False)\n    topic_category: Mapped[str] = mapped_column(String(255), nullable=False)\n    topic_tags: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    summary: Mapped[str | None] = mapped_column(Text, nullable=True)\n    body_sections: Mapped[dict | None] = mapped_column(JSONB, nullable=True)\n    signal_chains: Mapped[list | None] = mapped_column(JSONB, nullable=True)\n    plugins: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    source_quality: Mapped[SourceQuality | None] = mapped_column(\n        Enum(SourceQuality, name=\"source_quality\", create_constraint=True),\n        nullable=True,\n    )\n    view_count: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\n    review_status: Mapped[PageReviewStatus] = mapped_column(\n        Enum(PageReviewStatus, name=\"page_review_status\", create_constraint=True),\n        default=PageReviewStatus.draft,\n        server_default=\"draft\",\n    )\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    creator: Mapped[Creator] = sa_relationship(back_populates=\"technique_pages\")\n    key_moments: Mapped[list[KeyMoment]] = sa_relationship(\n        back_populates=\"technique_page\", foreign_keys=[KeyMoment.technique_page_id]\n    )\n    outgoing_links: Mapped[list[RelatedTechniqueLink]] = sa_relationship(\n        foreign_keys=\"RelatedTechniqueLink.source_page_id\", back_populates=\"source_page\"\n    )\n    incoming_links: Mapped[list[RelatedTechniqueLink]] = sa_relationship(\n        foreign_keys=\"RelatedTechniqueLink.target_page_id\", back_populates=\"target_page\"\n    )\n\n\nclass RelatedTechniqueLink(Base):\n    __tablename__ = \"related_technique_links\"\n    __table_args__ = (\n        UniqueConstraint(\"source_page_id\", \"target_page_id\", \"relationship\", name=\"uq_technique_link\"),\n    )\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    target_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    relationship: Mapped[RelationshipType] = mapped_column(\n        Enum(RelationshipType, name=\"relationship_type\", create_constraint=True),\n        nullable=False,\n    )\n\n    # relationships\n    source_page: Mapped[TechniquePage] = sa_relationship(\n        foreign_keys=[source_page_id], back_populates=\"outgoing_links\"\n    )\n    target_page: Mapped[TechniquePage] = sa_relationship(\n        foreign_keys=[target_page_id], back_populates=\"incoming_links\"\n    )\n\n\nclass Tag(Base):\n    __tablename__ = \"tags\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    name: Mapped[str] = mapped_column(String(255), unique=True, nullable=False)\n    category: Mapped[str] = mapped_column(String(255), nullable=False)\n    aliases: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n"}],"isError":false,"timestamp":1774853938932}}
{"type":"message","id":"993ad60d","parentId":"7f17f1db","timestamp":"2026-03-30T06:58:58.936Z","message":{"role":"toolResult","toolCallId":"toolu_01DKRWyA3uG9mvFZs5cTsZNQ","toolName":"read","content":[{"type":"text","text":"\"\"\"Pydantic schemas for the Chrysopedia API.\n\nRead-only schemas for list/detail endpoints and input schemas for creation.\nEach schema mirrors the corresponding SQLAlchemy model in models.py.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport uuid\nfrom datetime import datetime\n\nfrom pydantic import BaseModel, ConfigDict, Field\n\n\n# ── Health ───────────────────────────────────────────────────────────────────\n\nclass HealthResponse(BaseModel):\n    status: str = \"ok\"\n    service: str = \"chrysopedia-api\"\n    version: str = \"0.1.0\"\n    database: str = \"unknown\"\n\n\n# ── Creator ──────────────────────────────────────────────────────────────────\n\nclass CreatorBase(BaseModel):\n    name: str\n    slug: str\n    genres: list[str] | None = None\n    folder_name: str\n\nclass CreatorCreate(CreatorBase):\n    pass\n\nclass CreatorRead(CreatorBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    view_count: int = 0\n    created_at: datetime\n    updated_at: datetime\n\n\nclass CreatorDetail(CreatorRead):\n    \"\"\"Creator with nested video count.\"\"\"\n    video_count: int = 0\n\n\n# ── SourceVideo ──────────────────────────────────────────────────────────────\n\nclass SourceVideoBase(BaseModel):\n    filename: str\n    file_path: str\n    duration_seconds: int | None = None\n    content_type: str\n    transcript_path: str | None = None\n\nclass SourceVideoCreate(SourceVideoBase):\n    creator_id: uuid.UUID\n\nclass SourceVideoRead(SourceVideoBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    creator_id: uuid.UUID\n    processing_status: str = \"pending\"\n    created_at: datetime\n    updated_at: datetime\n\n\n# ── TranscriptSegment ────────────────────────────────────────────────────────\n\nclass TranscriptSegmentBase(BaseModel):\n    start_time: float\n    end_time: float\n    text: str\n    segment_index: int\n    topic_label: str | None = None\n\nclass TranscriptSegmentCreate(TranscriptSegmentBase):\n    source_video_id: uuid.UUID\n\nclass TranscriptSegmentRead(TranscriptSegmentBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    source_video_id: uuid.UUID\n\n\n# ── KeyMoment ────────────────────────────────────────────────────────────────\n\nclass KeyMomentBase(BaseModel):\n    title: str\n    summary: str\n    start_time: float\n    end_time: float\n    content_type: str\n    plugins: list[str] | None = None\n    raw_transcript: str | None = None\n\nclass KeyMomentCreate(KeyMomentBase):\n    source_video_id: uuid.UUID\n    technique_page_id: uuid.UUID | None = None\n\nclass KeyMomentRead(KeyMomentBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    source_video_id: uuid.UUID\n    technique_page_id: uuid.UUID | None = None\n    review_status: str = \"pending\"\n    created_at: datetime\n    updated_at: datetime\n\n\n# ── TechniquePage ────────────────────────────────────────────────────────────\n\nclass TechniquePageBase(BaseModel):\n    title: str\n    slug: str\n    topic_category: str\n    topic_tags: list[str] | None = None\n    summary: str | None = None\n    body_sections: dict | None = None\n    signal_chains: list | None = None\n    plugins: list[str] | None = None\n\nclass TechniquePageCreate(TechniquePageBase):\n    creator_id: uuid.UUID\n    source_quality: str | None = None\n\nclass TechniquePageRead(TechniquePageBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    creator_id: uuid.UUID\n    source_quality: str | None = None\n    view_count: int = 0\n    review_status: str = \"draft\"\n    created_at: datetime\n    updated_at: datetime\n\n\n# ── RelatedTechniqueLink ─────────────────────────────────────────────────────\n\nclass RelatedTechniqueLinkBase(BaseModel):\n    source_page_id: uuid.UUID\n    target_page_id: uuid.UUID\n    relationship: str\n\nclass RelatedTechniqueLinkCreate(RelatedTechniqueLinkBase):\n    pass\n\nclass RelatedTechniqueLinkRead(RelatedTechniqueLinkBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n\n\n# ── Tag ──────────────────────────────────────────────────────────────────────\n\nclass TagBase(BaseModel):\n    name: str\n    category: str\n    aliases: list[str] | None = None\n\nclass TagCreate(TagBase):\n    pass\n\nclass TagRead(TagBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n\n\n# ── Transcript Ingestion ─────────────────────────────────────────────────────\n\nclass TranscriptIngestResponse(BaseModel):\n    \"\"\"Response returned after successfully ingesting a transcript.\"\"\"\n    video_id: uuid.UUID\n    creator_id: uuid.UUID\n    creator_name: str\n    filename: str\n    segments_stored: int\n    processing_status: str\n    is_reupload: bool\n\n\n# ── Pagination wrapper ───────────────────────────────────────────────────────\n\nclass PaginatedResponse(BaseModel):\n    \"\"\"Generic paginated list response.\"\"\"\n    items: list = Field(default_factory=list)\n    total: int = 0\n    offset: int = 0\n    limit: int = 50\n\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# ── Search ───────────────────────────────────────────────────────────────────\n\nclass SearchResultItem(BaseModel):\n    \"\"\"A single search result.\"\"\"\n    title: str\n    slug: str = \"\"\n    type: str = \"\"\n    score: float = 0.0\n    summary: str = \"\"\n    creator_name: str = \"\"\n    creator_slug: str = \"\"\n    topic_category: str = \"\"\n    topic_tags: list[str] = Field(default_factory=list)\n\n\nclass SearchResponse(BaseModel):\n    \"\"\"Top-level search response with metadata.\"\"\"\n    items: list[SearchResultItem] = Field(default_factory=list)\n    total: int = 0\n    query: str = \"\"\n    fallback_used: bool = False\n\n\n# ── Technique Page Detail ────────────────────────────────────────────────────\n\nclass KeyMomentSummary(BaseModel):\n    \"\"\"Lightweight key moment for technique page detail.\"\"\"\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\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    video_filename: str = \"\"\n\n\nclass RelatedLinkItem(BaseModel):\n    \"\"\"A related technique link with target info.\"\"\"\n    model_config = ConfigDict(from_attributes=True)\n\n    target_title: str = \"\"\n    target_slug: str = \"\"\n    relationship: str = \"\"\n\n\nclass CreatorInfo(BaseModel):\n    \"\"\"Minimal creator info embedded in technique detail.\"\"\"\n    model_config = ConfigDict(from_attributes=True)\n\n    name: str\n    slug: str\n    genres: list[str] | None = None\n\n\nclass TechniquePageDetail(TechniquePageRead):\n    \"\"\"Technique page with nested key moments, creator, and related links.\"\"\"\n    key_moments: list[KeyMomentSummary] = Field(default_factory=list)\n    creator_info: CreatorInfo | None = None\n    related_links: list[RelatedLinkItem] = Field(default_factory=list)\n\n\n# ── Topics ───────────────────────────────────────────────────────────────────\n\nclass TopicSubTopic(BaseModel):\n    \"\"\"A sub-topic with aggregated counts.\"\"\"\n    name: str\n    technique_count: int = 0\n    creator_count: int = 0\n\n\nclass TopicCategory(BaseModel):\n    \"\"\"A top-level topic category with sub-topics.\"\"\"\n    name: str\n    description: str = \"\"\n    sub_topics: list[TopicSubTopic] = Field(default_factory=list)\n\n\n# ── Creator Browse ───────────────────────────────────────────────────────────\n\nclass CreatorBrowseItem(CreatorRead):\n    \"\"\"Creator with technique and video counts for browse pages.\"\"\"\n    technique_count: int = 0\n    video_count: int = 0\n"}],"isError":false,"timestamp":1774853938935}}
{"type":"message","id":"68cb332a","parentId":"993ad60d","timestamp":"2026-03-30T06:59:03.422Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — I have the full data model. Now let me examine the pipeline stages to understand how technique pages are created, and look at the technique endpoint and Alembic migrations."},{"type":"toolCall","id":"toolu_01GHgQUCQ5WSrRHYLF68ud86","name":"read","arguments":{"path":"backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_018B58Y8gw67SQPgdSc7kRUu","name":"read","arguments":{"path":"backend/routers/techniques.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":136,"cacheRead":67823,"cacheWrite":6162,"totalTokens":74122,"cost":{"input":0.000005,"output":0.0034000000000000002,"cacheRead":0.0339115,"cacheWrite":0.038512500000000005,"total":0.07582900000000001}},"stopReason":"toolUse","timestamp":1774853938935}}
{"type":"message","id":"c1467d01","parentId":"68cb332a","timestamp":"2026-03-30T06:59:03.426Z","message":{"role":"toolResult","toolCallId":"toolu_01GHgQUCQ5WSrRHYLF68ud86","toolName":"read","content":[{"type":"text","text":"\"\"\"Pipeline stage tasks (stages 2-5) and run_pipeline orchestrator.\n\nEach stage reads from PostgreSQL via sync SQLAlchemy, loads its prompt\ntemplate from disk, calls the LLM client, parses the response, writes\nresults back, and updates processing_status on SourceVideo.\n\nCelery tasks are synchronous — all DB access uses ``sqlalchemy.orm.Session``.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport logging\nimport time\nfrom collections import defaultdict\nfrom pathlib import Path\n\nimport yaml\nfrom celery import chain as celery_chain\nfrom pydantic import ValidationError\nfrom sqlalchemy import create_engine, select\nfrom sqlalchemy.orm import Session, sessionmaker\n\nfrom config import get_settings\nfrom models import (\n    KeyMoment,\n    KeyMomentContentType,\n    ProcessingStatus,\n    SourceVideo,\n    TechniquePage,\n    TranscriptSegment,\n)\nfrom pipeline.embedding_client import EmbeddingClient\nfrom pipeline.llm_client import LLMClient\nfrom pipeline.qdrant_client import QdrantManager\nfrom pipeline.schemas import (\n    ClassificationResult,\n    ExtractionResult,\n    SegmentationResult,\n    SynthesisResult,\n)\nfrom worker import celery_app\n\nlogger = logging.getLogger(__name__)\n\n# ── Helpers ──────────────────────────────────────────────────────────────────\n\n_engine = None\n_SessionLocal = None\n\n\ndef _get_sync_engine():\n    \"\"\"Create a sync SQLAlchemy engine, converting the async URL if needed.\"\"\"\n    global _engine\n    if _engine is None:\n        settings = get_settings()\n        url = settings.database_url\n        # Convert async driver to sync driver\n        url = url.replace(\"postgresql+asyncpg://\", \"postgresql+psycopg2://\")\n        _engine = create_engine(url, pool_pre_ping=True, pool_size=5, max_overflow=10)\n    return _engine\n\n\ndef _get_sync_session() -> Session:\n    \"\"\"Create a sync SQLAlchemy session for Celery tasks.\"\"\"\n    global _SessionLocal\n    if _SessionLocal is None:\n        _SessionLocal = sessionmaker(bind=_get_sync_engine())\n    return _SessionLocal()\n\n\ndef _load_prompt(template_name: str) -> str:\n    \"\"\"Read a prompt template from the prompts directory.\n\n    Raises FileNotFoundError if the template does not exist.\n    \"\"\"\n    settings = get_settings()\n    path = Path(settings.prompts_path) / template_name\n    if not path.exists():\n        logger.error(\"Prompt template not found: %s\", path)\n        raise FileNotFoundError(f\"Prompt template not found: {path}\")\n    return path.read_text(encoding=\"utf-8\")\n\n\ndef _get_llm_client() -> LLMClient:\n    \"\"\"Return an LLMClient configured from settings.\"\"\"\n    return LLMClient(get_settings())\n\n\ndef _get_stage_config(stage_num: int) -> tuple[str | None, str]:\n    \"\"\"Return (model_override, modality) for a pipeline stage.\n\n    Reads stage-specific config from Settings. If the stage-specific model\n    is None/empty, returns None (LLMClient will use its default). If the\n    stage-specific modality is unset, defaults to \"chat\".\n    \"\"\"\n    settings = get_settings()\n    model = getattr(settings, f\"llm_stage{stage_num}_model\", None) or None\n    modality = getattr(settings, f\"llm_stage{stage_num}_modality\", None) or \"chat\"\n    return model, modality\n\n\ndef _load_canonical_tags() -> dict:\n    \"\"\"Load canonical tag taxonomy from config/canonical_tags.yaml.\"\"\"\n    # Walk up from backend/ to find config/\n    candidates = [\n        Path(\"config/canonical_tags.yaml\"),\n        Path(\"../config/canonical_tags.yaml\"),\n    ]\n    for candidate in candidates:\n        if candidate.exists():\n            with open(candidate, encoding=\"utf-8\") as f:\n                return yaml.safe_load(f)\n    raise FileNotFoundError(\n        \"canonical_tags.yaml not found. Searched: \" + \", \".join(str(c) for c in candidates)\n    )\n\n\ndef _format_taxonomy_for_prompt(tags_data: dict) -> str:\n    \"\"\"Format the canonical tags taxonomy as readable text for the LLM prompt.\"\"\"\n    lines = []\n    for cat in tags_data.get(\"categories\", []):\n        lines.append(f\"Category: {cat['name']}\")\n        lines.append(f\"  Description: {cat['description']}\")\n        lines.append(f\"  Sub-topics: {', '.join(cat.get('sub_topics', []))}\")\n        lines.append(\"\")\n    return \"\\n\".join(lines)\n\n\ndef _safe_parse_llm_response(\n    raw: str,\n    model_cls,\n    llm: LLMClient,\n    system_prompt: str,\n    user_prompt: str,\n    modality: str = \"chat\",\n    model_override: str | None = None,\n):\n    \"\"\"Parse LLM response with one retry on failure.\n\n    On malformed response: log the raw text, retry once with a JSON nudge,\n    then raise on second failure.\n    \"\"\"\n    try:\n        return llm.parse_response(raw, model_cls)\n    except (ValidationError, ValueError, json.JSONDecodeError) as exc:\n        logger.warning(\n            \"First parse attempt failed for %s (%s). Retrying with JSON nudge. \"\n            \"Raw response (first 500 chars): %.500s\",\n            model_cls.__name__,\n            type(exc).__name__,\n            raw,\n        )\n        # Retry with explicit JSON instruction\n        nudge_prompt = user_prompt + \"\\n\\nIMPORTANT: Output ONLY valid JSON. No markdown, no explanation.\"\n        retry_raw = llm.complete(\n            system_prompt, nudge_prompt, response_model=model_cls,\n            modality=modality, model_override=model_override,\n        )\n        return llm.parse_response(retry_raw, model_cls)\n\n\n# ── Stage 2: Segmentation ───────────────────────────────────────────────────\n\n@celery_app.task(bind=True, max_retries=3, default_retry_delay=30)\ndef stage2_segmentation(self, video_id: str) -> str:\n    \"\"\"Analyze transcript segments and identify topic boundaries.\n\n    Loads all TranscriptSegment rows for the video, sends them to the LLM\n    for topic boundary detection, and updates topic_label on each segment.\n\n    Returns the video_id for chain compatibility.\n    \"\"\"\n    start = time.monotonic()\n    logger.info(\"Stage 2 (segmentation) starting for video_id=%s\", video_id)\n\n    session = _get_sync_session()\n    try:\n        # Load segments ordered by index\n        segments = (\n            session.execute(\n                select(TranscriptSegment)\n                .where(TranscriptSegment.source_video_id == video_id)\n                .order_by(TranscriptSegment.segment_index)\n            )\n            .scalars()\n            .all()\n        )\n\n        if not segments:\n            logger.info(\"Stage 2: No segments found for video_id=%s, skipping.\", video_id)\n            return video_id\n\n        # Build transcript text with indices for the LLM\n        transcript_lines = []\n        for seg in segments:\n            transcript_lines.append(\n                f\"[{seg.segment_index}] ({seg.start_time:.1f}s - {seg.end_time:.1f}s) {seg.text}\"\n            )\n        transcript_text = \"\\n\".join(transcript_lines)\n\n        # Load prompt and call LLM\n        system_prompt = _load_prompt(\"stage2_segmentation.txt\")\n        user_prompt = f\"<transcript>\\n{transcript_text}\\n</transcript>\"\n\n        llm = _get_llm_client()\n        model_override, modality = _get_stage_config(2)\n        logger.info(\"Stage 2 using model=%s, modality=%s\", model_override or \"default\", modality)\n        raw = llm.complete(system_prompt, user_prompt, response_model=SegmentationResult,\n                           modality=modality, model_override=model_override)\n        result = _safe_parse_llm_response(raw, SegmentationResult, llm, system_prompt, user_prompt,\n                                          modality=modality, model_override=model_override)\n\n        # Update topic_label on each segment row\n        seg_by_index = {s.segment_index: s for s in segments}\n        for topic_seg in result.segments:\n            for idx in range(topic_seg.start_index, topic_seg.end_index + 1):\n                if idx in seg_by_index:\n                    seg_by_index[idx].topic_label = topic_seg.topic_label\n\n        session.commit()\n        elapsed = time.monotonic() - start\n        logger.info(\n            \"Stage 2 (segmentation) completed for video_id=%s in %.1fs — %d topic groups found\",\n            video_id, elapsed, len(result.segments),\n        )\n        return video_id\n\n    except FileNotFoundError:\n        raise  # Don't retry missing prompt files\n    except Exception as exc:\n        session.rollback()\n        logger.error(\"Stage 2 failed for video_id=%s: %s\", video_id, exc)\n        raise self.retry(exc=exc)\n    finally:\n        session.close()\n\n\n# ── Stage 3: Extraction ─────────────────────────────────────────────────────\n\n@celery_app.task(bind=True, max_retries=3, default_retry_delay=30)\ndef stage3_extraction(self, video_id: str) -> str:\n    \"\"\"Extract key moments from each topic segment group.\n\n    Groups segments by topic_label, calls the LLM for each group to extract\n    moments, creates KeyMoment rows, and sets processing_status=extracted.\n\n    Returns the video_id for chain compatibility.\n    \"\"\"\n    start = time.monotonic()\n    logger.info(\"Stage 3 (extraction) starting for video_id=%s\", video_id)\n\n    session = _get_sync_session()\n    try:\n        # Load segments with topic labels\n        segments = (\n            session.execute(\n                select(TranscriptSegment)\n                .where(TranscriptSegment.source_video_id == video_id)\n                .order_by(TranscriptSegment.segment_index)\n            )\n            .scalars()\n            .all()\n        )\n\n        if not segments:\n            logger.info(\"Stage 3: No segments found for video_id=%s, skipping.\", video_id)\n            return video_id\n\n        # Group segments by topic_label\n        groups: dict[str, list[TranscriptSegment]] = defaultdict(list)\n        for seg in segments:\n            label = seg.topic_label or \"unlabeled\"\n            groups[label].append(seg)\n\n        system_prompt = _load_prompt(\"stage3_extraction.txt\")\n        llm = _get_llm_client()\n        model_override, modality = _get_stage_config(3)\n        logger.info(\"Stage 3 using model=%s, modality=%s\", model_override or \"default\", modality)\n        total_moments = 0\n\n        for topic_label, group_segs in groups.items():\n            # Build segment text for this group\n            seg_lines = []\n            for seg in group_segs:\n                seg_lines.append(\n                    f\"({seg.start_time:.1f}s - {seg.end_time:.1f}s) {seg.text}\"\n                )\n            segment_text = \"\\n\".join(seg_lines)\n\n            user_prompt = (\n                f\"Topic: {topic_label}\\n\\n\"\n                f\"<segment>\\n{segment_text}\\n</segment>\"\n            )\n\n            raw = llm.complete(system_prompt, user_prompt, response_model=ExtractionResult,\n                               modality=modality, model_override=model_override)\n            result = _safe_parse_llm_response(raw, ExtractionResult, llm, system_prompt, user_prompt,\n                                              modality=modality, model_override=model_override)\n\n            # Create KeyMoment rows\n            for moment in result.moments:\n                # Validate content_type against enum\n                try:\n                    ct = KeyMomentContentType(moment.content_type)\n                except ValueError:\n                    ct = KeyMomentContentType.technique\n\n                km = KeyMoment(\n                    source_video_id=video_id,\n                    title=moment.title,\n                    summary=moment.summary,\n                    start_time=moment.start_time,\n                    end_time=moment.end_time,\n                    content_type=ct,\n                    plugins=moment.plugins if moment.plugins else None,\n                    raw_transcript=moment.raw_transcript or None,\n                )\n                session.add(km)\n                total_moments += 1\n\n        # Update processing_status to extracted\n        video = session.execute(\n            select(SourceVideo).where(SourceVideo.id == video_id)\n        ).scalar_one()\n        video.processing_status = ProcessingStatus.extracted\n\n        session.commit()\n        elapsed = time.monotonic() - start\n        logger.info(\n            \"Stage 3 (extraction) completed for video_id=%s in %.1fs — %d moments created\",\n            video_id, elapsed, total_moments,\n        )\n        return video_id\n\n    except FileNotFoundError:\n        raise\n    except Exception as exc:\n        session.rollback()\n        logger.error(\"Stage 3 failed for video_id=%s: %s\", video_id, exc)\n        raise self.retry(exc=exc)\n    finally:\n        session.close()\n\n\n# ── Stage 4: Classification ─────────────────────────────────────────────────\n\n@celery_app.task(bind=True, max_retries=3, default_retry_delay=30)\ndef stage4_classification(self, video_id: str) -> str:\n    \"\"\"Classify key moments against the canonical tag taxonomy.\n\n    Loads all KeyMoment rows for the video, sends them to the LLM with the\n    canonical taxonomy, and stores classification results in Redis for\n    stage 5 consumption. Updates content_type if the classifier overrides it.\n\n    Stage 4 does NOT change processing_status.\n\n    Returns the video_id for chain compatibility.\n    \"\"\"\n    start = time.monotonic()\n    logger.info(\"Stage 4 (classification) starting for video_id=%s\", video_id)\n\n    session = _get_sync_session()\n    try:\n        # Load key moments\n        moments = (\n            session.execute(\n                select(KeyMoment)\n                .where(KeyMoment.source_video_id == video_id)\n                .order_by(KeyMoment.start_time)\n            )\n            .scalars()\n            .all()\n        )\n\n        if not moments:\n            logger.info(\"Stage 4: No moments found for video_id=%s, skipping.\", video_id)\n            # Store empty classification data\n            _store_classification_data(video_id, [])\n            return video_id\n\n        # Load canonical tags\n        tags_data = _load_canonical_tags()\n        taxonomy_text = _format_taxonomy_for_prompt(tags_data)\n\n        # Build moments text for the LLM\n        moments_lines = []\n        for i, m in enumerate(moments):\n            moments_lines.append(\n                f\"[{i}] Title: {m.title}\\n\"\n                f\"    Summary: {m.summary}\\n\"\n                f\"    Content type: {m.content_type.value}\\n\"\n                f\"    Plugins: {', '.join(m.plugins) if m.plugins else 'none'}\"\n            )\n        moments_text = \"\\n\\n\".join(moments_lines)\n\n        system_prompt = _load_prompt(\"stage4_classification.txt\")\n        user_prompt = (\n            f\"<taxonomy>\\n{taxonomy_text}\\n</taxonomy>\\n\\n\"\n            f\"<moments>\\n{moments_text}\\n</moments>\"\n        )\n\n        llm = _get_llm_client()\n        model_override, modality = _get_stage_config(4)\n        logger.info(\"Stage 4 using model=%s, modality=%s\", model_override or \"default\", modality)\n        raw = llm.complete(system_prompt, user_prompt, response_model=ClassificationResult,\n                           modality=modality, model_override=model_override)\n        result = _safe_parse_llm_response(raw, ClassificationResult, llm, system_prompt, user_prompt,\n                                          modality=modality, model_override=model_override)\n\n        # Apply content_type overrides and prepare classification data for stage 5\n        classification_data = []\n        moment_ids = [str(m.id) for m in moments]\n\n        for cls in result.classifications:\n            if 0 <= cls.moment_index < len(moments):\n                moment = moments[cls.moment_index]\n\n                # Apply content_type override if provided\n                if cls.content_type_override:\n                    try:\n                        moment.content_type = KeyMomentContentType(cls.content_type_override)\n                    except ValueError:\n                        pass\n\n                classification_data.append({\n                    \"moment_id\": str(moment.id),\n                    \"topic_category\": cls.topic_category,\n                    \"topic_tags\": cls.topic_tags,\n                })\n\n        session.commit()\n\n        # Store classification data in Redis for stage 5\n        _store_classification_data(video_id, classification_data)\n\n        elapsed = time.monotonic() - start\n        logger.info(\n            \"Stage 4 (classification) completed for video_id=%s in %.1fs — %d moments classified\",\n            video_id, elapsed, len(classification_data),\n        )\n        return video_id\n\n    except FileNotFoundError:\n        raise\n    except Exception as exc:\n        session.rollback()\n        logger.error(\"Stage 4 failed for video_id=%s: %s\", video_id, exc)\n        raise self.retry(exc=exc)\n    finally:\n        session.close()\n\n\ndef _store_classification_data(video_id: str, data: list[dict]) -> None:\n    \"\"\"Store classification data in Redis for cross-stage communication.\"\"\"\n    import redis\n\n    settings = get_settings()\n    r = redis.Redis.from_url(settings.redis_url)\n    key = f\"chrysopedia:classification:{video_id}\"\n    r.set(key, json.dumps(data), ex=86400)  # Expire after 24 hours\n\n\ndef _load_classification_data(video_id: str) -> list[dict]:\n    \"\"\"Load classification data from Redis.\"\"\"\n    import redis\n\n    settings = get_settings()\n    r = redis.Redis.from_url(settings.redis_url)\n    key = f\"chrysopedia:classification:{video_id}\"\n    raw = r.get(key)\n    if raw is None:\n        return []\n    return json.loads(raw)\n\n\n# ── Stage 5: Synthesis ───────────────────────────────────────────────────────\n\n@celery_app.task(bind=True, max_retries=3, default_retry_delay=30)\ndef stage5_synthesis(self, video_id: str) -> str:\n    \"\"\"Synthesize technique pages from classified key moments.\n\n    Groups moments by (creator, topic_category), calls the LLM to synthesize\n    each group into a TechniquePage, creates/updates page rows, and links\n    KeyMoments to their TechniquePage.\n\n    Sets processing_status to 'reviewed' (or 'published' if review_mode is False).\n\n    Returns the video_id for chain compatibility.\n    \"\"\"\n    start = time.monotonic()\n    logger.info(\"Stage 5 (synthesis) starting for video_id=%s\", video_id)\n\n    settings = get_settings()\n    session = _get_sync_session()\n    try:\n        # Load video and moments\n        video = session.execute(\n            select(SourceVideo).where(SourceVideo.id == video_id)\n        ).scalar_one()\n\n        moments = (\n            session.execute(\n                select(KeyMoment)\n                .where(KeyMoment.source_video_id == video_id)\n                .order_by(KeyMoment.start_time)\n            )\n            .scalars()\n            .all()\n        )\n\n        if not moments:\n            logger.info(\"Stage 5: No moments found for video_id=%s, skipping.\", video_id)\n            return video_id\n\n        # Load classification data from stage 4\n        classification_data = _load_classification_data(video_id)\n        cls_by_moment_id = {c[\"moment_id\"]: c for c in classification_data}\n\n        # Group moments by topic_category (from classification)\n        groups: dict[str, list[tuple[KeyMoment, dict]]] = defaultdict(list)\n        for moment in moments:\n            cls_info = cls_by_moment_id.get(str(moment.id), {})\n            category = cls_info.get(\"topic_category\", \"Uncategorized\")\n            groups[category].append((moment, cls_info))\n\n        system_prompt = _load_prompt(\"stage5_synthesis.txt\")\n        llm = _get_llm_client()\n        model_override, modality = _get_stage_config(5)\n        logger.info(\"Stage 5 using model=%s, modality=%s\", model_override or \"default\", modality)\n        pages_created = 0\n\n        for category, moment_group in groups.items():\n            # Build moments text for the LLM\n            moments_lines = []\n            all_tags: set[str] = set()\n            for i, (m, cls_info) in enumerate(moment_group):\n                tags = cls_info.get(\"topic_tags\", [])\n                all_tags.update(tags)\n                moments_lines.append(\n                    f\"[{i}] Title: {m.title}\\n\"\n                    f\"    Summary: {m.summary}\\n\"\n                    f\"    Content type: {m.content_type.value}\\n\"\n                    f\"    Time: {m.start_time:.1f}s - {m.end_time:.1f}s\\n\"\n                    f\"    Plugins: {', '.join(m.plugins) if m.plugins else 'none'}\\n\"\n                    f\"    Category: {category}\\n\"\n                    f\"    Tags: {', '.join(tags) if tags else 'none'}\\n\"\n                    f\"    Transcript excerpt: {(m.raw_transcript or '')[:300]}\"\n                )\n            moments_text = \"\\n\\n\".join(moments_lines)\n\n            user_prompt = f\"<moments>\\n{moments_text}\\n</moments>\"\n\n            raw = llm.complete(system_prompt, user_prompt, response_model=SynthesisResult,\n                               modality=modality, model_override=model_override)\n            result = _safe_parse_llm_response(raw, SynthesisResult, llm, system_prompt, user_prompt,\n                                              modality=modality, model_override=model_override)\n\n            # Create/update TechniquePage rows\n            for page_data in result.pages:\n                # Check if page with this slug already exists\n                existing = session.execute(\n                    select(TechniquePage).where(TechniquePage.slug == page_data.slug)\n                ).scalar_one_or_none()\n\n                if existing:\n                    # Update existing page\n                    existing.title = page_data.title\n                    existing.summary = page_data.summary\n                    existing.body_sections = page_data.body_sections\n                    existing.signal_chains = page_data.signal_chains\n                    existing.plugins = page_data.plugins if page_data.plugins else None\n                    existing.topic_tags = list(all_tags) if all_tags else None\n                    existing.source_quality = page_data.source_quality\n                    page = existing\n                else:\n                    page = TechniquePage(\n                        creator_id=video.creator_id,\n                        title=page_data.title,\n                        slug=page_data.slug,\n                        topic_category=page_data.topic_category or category,\n                        topic_tags=list(all_tags) if all_tags else None,\n                        summary=page_data.summary,\n                        body_sections=page_data.body_sections,\n                        signal_chains=page_data.signal_chains,\n                        plugins=page_data.plugins if page_data.plugins else None,\n                        source_quality=page_data.source_quality,\n                    )\n                    session.add(page)\n                    session.flush()  # Get the page.id assigned\n\n                pages_created += 1\n\n                # Link moments to the technique page\n                for m, _ in moment_group:\n                    m.technique_page_id = page.id\n\n        # Update processing_status\n        if settings.review_mode:\n            video.processing_status = ProcessingStatus.reviewed\n        else:\n            video.processing_status = ProcessingStatus.published\n\n        session.commit()\n        elapsed = time.monotonic() - start\n        logger.info(\n            \"Stage 5 (synthesis) completed for video_id=%s in %.1fs — %d pages created/updated\",\n            video_id, elapsed, pages_created,\n        )\n        return video_id\n\n    except FileNotFoundError:\n        raise\n    except Exception as exc:\n        session.rollback()\n        logger.error(\"Stage 5 failed for video_id=%s: %s\", video_id, exc)\n        raise self.retry(exc=exc)\n    finally:\n        session.close()\n\n\n# ── Stage 6: Embed & Index ───────────────────────────────────────────────────\n\n@celery_app.task(bind=True, max_retries=0)\ndef stage6_embed_and_index(self, video_id: str) -> str:\n    \"\"\"Generate embeddings for technique pages and key moments, then upsert to Qdrant.\n\n    This is a non-blocking side-effect stage — failures are logged but do not\n    fail the pipeline. Embeddings can be regenerated later. Does NOT update\n    processing_status.\n\n    Returns the video_id for chain compatibility.\n    \"\"\"\n    start = time.monotonic()\n    logger.info(\"Stage 6 (embed & index) starting for video_id=%s\", video_id)\n\n    settings = get_settings()\n    session = _get_sync_session()\n    try:\n        # Load technique pages created for this video's moments\n        moments = (\n            session.execute(\n                select(KeyMoment)\n                .where(KeyMoment.source_video_id == video_id)\n                .order_by(KeyMoment.start_time)\n            )\n            .scalars()\n            .all()\n        )\n\n        # Get unique technique page IDs from moments\n        page_ids = {m.technique_page_id for m in moments if m.technique_page_id is not None}\n        pages = []\n        if page_ids:\n            pages = (\n                session.execute(\n                    select(TechniquePage).where(TechniquePage.id.in_(page_ids))\n                )\n                .scalars()\n                .all()\n            )\n\n        if not moments and not pages:\n            logger.info(\"Stage 6: No moments or pages for video_id=%s, skipping.\", video_id)\n            return video_id\n\n        embed_client = EmbeddingClient(settings)\n        qdrant = QdrantManager(settings)\n\n        # Ensure collection exists before upserting\n        qdrant.ensure_collection()\n\n        # ── Embed & upsert technique pages ───────────────────────────────\n        if pages:\n            page_texts = []\n            page_dicts = []\n            for p in pages:\n                text = f\"{p.title} {p.summary or ''} {p.topic_category or ''}\"\n                page_texts.append(text.strip())\n                page_dicts.append({\n                    \"page_id\": str(p.id),\n                    \"creator_id\": str(p.creator_id),\n                    \"title\": p.title,\n                    \"topic_category\": p.topic_category or \"\",\n                    \"topic_tags\": p.topic_tags or [],\n                    \"summary\": p.summary or \"\",\n                })\n\n            page_vectors = embed_client.embed(page_texts)\n            if page_vectors:\n                qdrant.upsert_technique_pages(page_dicts, page_vectors)\n                logger.info(\n                    \"Stage 6: Upserted %d technique page vectors for video_id=%s\",\n                    len(page_vectors), video_id,\n                )\n            else:\n                logger.warning(\n                    \"Stage 6: Embedding returned empty for %d technique pages (video_id=%s). \"\n                    \"Skipping page upsert.\",\n                    len(page_texts), video_id,\n                )\n\n        # ── Embed & upsert key moments ───────────────────────────────────\n        if moments:\n            moment_texts = []\n            moment_dicts = []\n            for m in moments:\n                text = f\"{m.title} {m.summary or ''}\"\n                moment_texts.append(text.strip())\n                moment_dicts.append({\n                    \"moment_id\": str(m.id),\n                    \"source_video_id\": str(m.source_video_id),\n                    \"title\": m.title,\n                    \"start_time\": m.start_time,\n                    \"end_time\": m.end_time,\n                    \"content_type\": m.content_type.value,\n                })\n\n            moment_vectors = embed_client.embed(moment_texts)\n            if moment_vectors:\n                qdrant.upsert_key_moments(moment_dicts, moment_vectors)\n                logger.info(\n                    \"Stage 6: Upserted %d key moment vectors for video_id=%s\",\n                    len(moment_vectors), video_id,\n                )\n            else:\n                logger.warning(\n                    \"Stage 6: Embedding returned empty for %d key moments (video_id=%s). \"\n                    \"Skipping moment upsert.\",\n                    len(moment_texts), video_id,\n                )\n\n        elapsed = time.monotonic() - start\n        logger.info(\n            \"Stage 6 (embed & index) completed for video_id=%s in %.1fs — \"\n            \"%d pages, %d moments processed\",\n            video_id, elapsed, len(pages), len(moments),\n        )\n        return video_id\n\n    except Exception as exc:\n        # Non-blocking: log error but don't fail the pipeline\n        logger.error(\n            \"Stage 6 failed for video_id=%s: %s. \"\n            \"Pipeline continues — embeddings can be regenerated later.\",\n            video_id, exc,\n        )\n        return video_id\n    finally:\n        session.close()\n\n\n# ── Orchestrator ─────────────────────────────────────────────────────────────\n\n@celery_app.task\ndef run_pipeline(video_id: str) -> str:\n    \"\"\"Orchestrate the full pipeline (stages 2-5) with resumability.\n\n    Checks the current processing_status of the video and chains only the\n    stages that still need to run. For example:\n      - pending/transcribed → stages 2, 3, 4, 5\n      - extracted → stages 4, 5\n      - reviewed/published → no-op\n\n    Returns the video_id.\n    \"\"\"\n    logger.info(\"run_pipeline starting for video_id=%s\", video_id)\n\n    session = _get_sync_session()\n    try:\n        video = session.execute(\n            select(SourceVideo).where(SourceVideo.id == video_id)\n        ).scalar_one_or_none()\n\n        if video is None:\n            logger.error(\"run_pipeline: video_id=%s not found\", video_id)\n            raise ValueError(f\"Video not found: {video_id}\")\n\n        status = video.processing_status\n        logger.info(\n            \"run_pipeline: video_id=%s current status=%s\", video_id, status.value\n        )\n    finally:\n        session.close()\n\n    # Build the chain based on current status\n    stages = []\n    if status in (ProcessingStatus.pending, ProcessingStatus.transcribed):\n        stages = [\n            stage2_segmentation.s(video_id),\n            stage3_extraction.s(),  # receives video_id from previous\n            stage4_classification.s(),\n            stage5_synthesis.s(),\n            stage6_embed_and_index.s(),\n        ]\n    elif status == ProcessingStatus.extracted:\n        stages = [\n            stage4_classification.s(video_id),\n            stage5_synthesis.s(),\n            stage6_embed_and_index.s(),\n        ]\n    elif status in (ProcessingStatus.reviewed, ProcessingStatus.published):\n        logger.info(\n            \"run_pipeline: video_id=%s already at status=%s, nothing to do.\",\n            video_id, status.value,\n        )\n        return video_id\n\n    if stages:\n        pipeline = celery_chain(*stages)\n        pipeline.apply_async()\n        logger.info(\n            \"run_pipeline: dispatched %d stages for video_id=%s\",\n            len(stages), video_id,\n        )\n\n    return video_id\n"}],"isError":false,"timestamp":1774853943424}}
{"type":"message","id":"46e12f5a","parentId":"c1467d01","timestamp":"2026-03-30T06:59:03.429Z","message":{"role":"toolResult","toolCallId":"toolu_018B58Y8gw67SQPgdSc7kRUu","toolName":"read","content":[{"type":"text","text":"\"\"\"Technique page endpoints — list and detail with eager-loaded relations.\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nfrom typing import Annotated\n\nfrom fastapi import APIRouter, Depends, HTTPException, Query\nfrom sqlalchemy import select\nfrom sqlalchemy.ext.asyncio import AsyncSession\nfrom sqlalchemy.orm import selectinload\n\nfrom database import get_session\nfrom models import Creator, KeyMoment, RelatedTechniqueLink, SourceVideo, TechniquePage\nfrom schemas import (\n    CreatorInfo,\n    KeyMomentSummary,\n    PaginatedResponse,\n    RelatedLinkItem,\n    TechniquePageDetail,\n    TechniquePageRead,\n)\n\nlogger = logging.getLogger(\"chrysopedia.techniques\")\n\nrouter = APIRouter(prefix=\"/techniques\", tags=[\"techniques\"])\n\n\n@router.get(\"\", response_model=PaginatedResponse)\nasync def list_techniques(\n    category: Annotated[str | None, Query()] = None,\n    creator_slug: Annotated[str | None, Query()] = None,\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) -> PaginatedResponse:\n    \"\"\"List technique pages with optional category/creator filtering.\"\"\"\n    stmt = select(TechniquePage)\n\n    if category:\n        stmt = stmt.where(TechniquePage.topic_category == category)\n\n    if creator_slug:\n        # Join to Creator to filter by slug\n        stmt = stmt.join(Creator, TechniquePage.creator_id == Creator.id).where(\n            Creator.slug == creator_slug\n        )\n\n    # Count total before pagination\n    from sqlalchemy import func\n\n    count_stmt = select(func.count()).select_from(stmt.subquery())\n    count_result = await db.execute(count_stmt)\n    total = count_result.scalar() or 0\n\n    stmt = stmt.order_by(TechniquePage.created_at.desc()).offset(offset).limit(limit)\n    result = await db.execute(stmt)\n    pages = result.scalars().all()\n\n    return PaginatedResponse(\n        items=[TechniquePageRead.model_validate(p) for p in pages],\n        total=total,\n        offset=offset,\n        limit=limit,\n    )\n\n\n@router.get(\"/{slug}\", response_model=TechniquePageDetail)\nasync def get_technique(\n    slug: str,\n    db: AsyncSession = Depends(get_session),\n) -> TechniquePageDetail:\n    \"\"\"Get full technique page detail with key moments, creator, and related links.\"\"\"\n    stmt = (\n        select(TechniquePage)\n        .where(TechniquePage.slug == slug)\n        .options(\n            selectinload(TechniquePage.key_moments).selectinload(KeyMoment.source_video),\n            selectinload(TechniquePage.creator),\n            selectinload(TechniquePage.outgoing_links).selectinload(\n                RelatedTechniqueLink.target_page\n            ),\n            selectinload(TechniquePage.incoming_links).selectinload(\n                RelatedTechniqueLink.source_page\n            ),\n        )\n    )\n    result = await db.execute(stmt)\n    page = result.scalar_one_or_none()\n\n    if page is None:\n        raise HTTPException(status_code=404, detail=f\"Technique '{slug}' not found\")\n\n    # Build key moments (ordered by start_time)\n    key_moments = sorted(page.key_moments, key=lambda km: km.start_time)\n    key_moment_items = []\n    for km in key_moments:\n        item = KeyMomentSummary.model_validate(km)\n        item.video_filename = km.source_video.filename if km.source_video else \"\"\n        key_moment_items.append(item)\n\n    # Build creator info\n    creator_info = None\n    if page.creator:\n        creator_info = CreatorInfo(\n            name=page.creator.name,\n            slug=page.creator.slug,\n            genres=page.creator.genres,\n        )\n\n    # Build related links (outgoing + incoming)\n    related_links: list[RelatedLinkItem] = []\n    for link in page.outgoing_links:\n        if link.target_page:\n            related_links.append(\n                RelatedLinkItem(\n                    target_title=link.target_page.title,\n                    target_slug=link.target_page.slug,\n                    relationship=link.relationship.value if hasattr(link.relationship, 'value') else str(link.relationship),\n                )\n            )\n    for link in page.incoming_links:\n        if link.source_page:\n            related_links.append(\n                RelatedLinkItem(\n                    target_title=link.source_page.title,\n                    target_slug=link.source_page.slug,\n                    relationship=link.relationship.value if hasattr(link.relationship, 'value') else str(link.relationship),\n                )\n            )\n\n    base = TechniquePageRead.model_validate(page)\n    return TechniquePageDetail(\n        **base.model_dump(),\n        key_moments=key_moment_items,\n        creator_info=creator_info,\n        related_links=related_links,\n    )\n"}],"isError":false,"timestamp":1774853943427}}
{"type":"message","id":"3d98128f","parentId":"46e12f5a","timestamp":"2026-03-30T06:59:09.866Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the existing Alembic setup and the current migration state."},{"type":"toolCall","id":"toolu_011zdtgsM2DnYjmdxxUXXVn5","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls backend/alembic/versions/ 2>/dev/null | head -20 && echo \"---\" && cat backend/alembic.ini 2>/dev/null | head -20 || echo \"No alembic.ini\""}},{"type":"toolCall","id":"toolu_01GB6M3iNQEeqbEgpDRU3yoi","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/alembic/env.py 2>/dev/null | head -50"}},{"type":"toolCall","id":"toolu_01TQwejhQLK5fC6YTuwWFzbZ","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls backend/alembic/versions/*.py 2>/dev/null | sort"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":281,"cacheRead":73985,"cacheWrite":9948,"totalTokens":84215,"cost":{"input":0.000005,"output":0.007025,"cacheRead":0.0369925,"cacheWrite":0.062175,"total":0.1061975}},"stopReason":"toolUse","timestamp":1774853943428}}
{"type":"message","id":"7a79ef18","parentId":"3d98128f","timestamp":"2026-03-30T06:59:09.884Z","message":{"role":"toolResult","toolCallId":"toolu_011zdtgsM2DnYjmdxxUXXVn5","toolName":"bash","content":[{"type":"text","text":"---\n"}],"isError":false,"timestamp":1774853949884}}
{"type":"message","id":"05eecc33","parentId":"7a79ef18","timestamp":"2026-03-30T06:59:09.885Z","message":{"role":"toolResult","toolCallId":"toolu_01GB6M3iNQEeqbEgpDRU3yoi","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774853949884}}
{"type":"message","id":"16275ee8","parentId":"05eecc33","timestamp":"2026-03-30T06:59:09.888Z","message":{"role":"toolResult","toolCallId":"toolu_01TQwejhQLK5fC6YTuwWFzbZ","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774853949886}}
{"type":"message","id":"bdc6a78d","parentId":"16275ee8","timestamp":"2026-03-30T06:59:14.539Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_011RRMCsEjGbQ4JpED3wenxw","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find . -name 'alembic.ini' -o -name 'env.py' -path '*/alembic/*' 2>/dev/null | head -10"}},{"type":"toolCall","id":"toolu_01VQpVXThSkX8htiCyGZgqUG","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find . -name '*.py' -path '*/versions/*' 2>/dev/null | head -20"}},{"type":"toolCall","id":"toolu_01F4sy9AsXkujWqBA4W8czm6","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find . -name 'alembic*' -o -name 'migration*' 2>/dev/null | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":266,"cacheRead":83933,"cacheWrite":372,"totalTokens":84572,"cost":{"input":0.000005,"output":0.0066500000000000005,"cacheRead":0.0419665,"cacheWrite":0.0023250000000000002,"total":0.0509465}},"stopReason":"toolUse","timestamp":1774853949887}}
{"type":"message","id":"abec51fe","parentId":"bdc6a78d","timestamp":"2026-03-30T06:59:14.584Z","message":{"role":"toolResult","toolCallId":"toolu_011RRMCsEjGbQ4JpED3wenxw","toolName":"bash","content":[{"type":"text","text":"./.venv/lib/python3.12/site-packages/alembic/templates/async/env.py\n./.venv/lib/python3.12/site-packages/alembic/templates/pyproject_async/env.py\n./.venv/lib/python3.12/site-packages/alembic/templates/pyproject/env.py\n./.venv/lib/python3.12/site-packages/alembic/templates/generic/env.py\n./.venv/lib/python3.12/site-packages/alembic/templates/multidb/env.py\n./.venv/lib/python3.12/site-packages/alembic/testing/env.py\n./alembic.ini\n./alembic/env.py\n"}],"isError":false,"timestamp":1774853954584}}
{"type":"message","id":"00612a52","parentId":"abec51fe","timestamp":"2026-03-30T06:59:14.585Z","message":{"role":"toolResult","toolCallId":"toolu_01VQpVXThSkX8htiCyGZgqUG","toolName":"bash","content":[{"type":"text","text":"./alembic/versions/001_initial.py\n"}],"isError":false,"timestamp":1774853954585}}
{"type":"message","id":"aee1f7f5","parentId":"00612a52","timestamp":"2026-03-30T06:59:14.588Z","message":{"role":"toolResult","toolCallId":"toolu_01F4sy9AsXkujWqBA4W8czm6","toolName":"bash","content":[{"type":"text","text":"./.venv/lib/python3.12/site-packages/alembic-1.18.4.dist-info\n./.venv/lib/python3.12/site-packages/alembic\n./.venv/lib/python3.12/site-packages/alembic/templates/async/alembic.ini.mako\n./.venv/lib/python3.12/site-packages/alembic/templates/pyproject_async/alembic.ini.mako\n./.venv/lib/python3.12/site-packages/alembic/templates/pyproject/alembic.ini.mako\n./.venv/lib/python3.12/site-packages/alembic/templates/generic/alembic.ini.mako\n./.venv/lib/python3.12/site-packages/alembic/templates/multidb/alembic.ini.mako\n./.venv/lib/python3.12/site-packages/alembic/migration.py\n./.venv/lib/python3.12/site-packages/alembic/runtime/migration.py\n./.venv/lib/python3.12/site-packages/alembic/runtime/__pycache__/migration.cpython-312.pyc\n./.venv/lib/python3.12/site-packages/alembic/__pycache__/migration.cpython-312.pyc\n./.venv/bin/alembic\n./alembic.ini\n./alembic\n"}],"isError":false,"timestamp":1774853954585}}
{"type":"message","id":"36093ed0","parentId":"aee1f7f5","timestamp":"2026-03-30T06:59:18.646Z","message":{"role":"assistant","content":[{"type":"text","text":"Alembic is at the project root, not inside backend/. Let me check the migration and env.py."},{"type":"toolCall","id":"toolu_011UEEkeZzZHqnFXeh212uvm","name":"read","arguments":{"path":"alembic/env.py"}},{"type":"toolCall","id":"toolu_01EpAwykwgvT361XdUGhcZjm","name":"read","arguments":{"path":"alembic/versions/001_initial.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":127,"cacheRead":84305,"cacheWrite":914,"totalTokens":85347,"cost":{"input":0.000005,"output":0.0031750000000000003,"cacheRead":0.042152499999999996,"cacheWrite":0.0057125000000000006,"total":0.051045}},"stopReason":"toolUse","timestamp":1774853954586}}
{"type":"message","id":"940600c4","parentId":"36093ed0","timestamp":"2026-03-30T06:59:18.649Z","message":{"role":"toolResult","toolCallId":"toolu_011UEEkeZzZHqnFXeh212uvm","toolName":"read","content":[{"type":"text","text":"\"\"\"Alembic env.py — async migration runner for Chrysopedia.\"\"\"\n\nimport asyncio\nimport os\nimport sys\nfrom logging.config import fileConfig\n\nfrom alembic import context\nfrom sqlalchemy import pool\nfrom sqlalchemy.ext.asyncio import async_engine_from_config\n\n# Ensure the backend package is importable\n# When running locally: alembic/ sits beside backend/, so ../backend works\n# When running in Docker: alembic/ is inside /app/ alongside the backend modules\nsys.path.insert(0, os.path.join(os.path.dirname(__file__), \"..\", \"backend\"))\nsys.path.insert(0, os.path.join(os.path.dirname(__file__), \"..\"))\n\nfrom database import Base  # noqa: E402\nimport models  # noqa: E402, F401  — registers all tables on Base.metadata\n\nconfig = context.config\n\nif config.config_file_name is not None:\n    fileConfig(config.config_file_name)\n\ntarget_metadata = Base.metadata\n\n# Allow DATABASE_URL env var to override alembic.ini\nurl_override = os.getenv(\"DATABASE_URL\")\nif url_override:\n    config.set_main_option(\"sqlalchemy.url\", url_override)\n\n\ndef run_migrations_offline() -> None:\n    \"\"\"Run migrations in 'offline' mode — emit SQL to stdout.\"\"\"\n    url = config.get_main_option(\"sqlalchemy.url\")\n    context.configure(\n        url=url,\n        target_metadata=target_metadata,\n        literal_binds=True,\n        dialect_opts={\"paramstyle\": \"named\"},\n    )\n    with context.begin_transaction():\n        context.run_migrations()\n\n\ndef do_run_migrations(connection):\n    context.configure(connection=connection, target_metadata=target_metadata)\n    with context.begin_transaction():\n        context.run_migrations()\n\n\nasync def run_async_migrations() -> None:\n    \"\"\"Run migrations in 'online' mode with an async engine.\"\"\"\n    connectable = async_engine_from_config(\n        config.get_section(config.config_ini_section, {}),\n        prefix=\"sqlalchemy.\",\n        poolclass=pool.NullPool,\n    )\n    async with connectable.connect() as connection:\n        await connection.run_sync(do_run_migrations)\n    await connectable.dispose()\n\n\ndef run_migrations_online() -> None:\n    asyncio.run(run_async_migrations())\n\n\nif context.is_offline_mode():\n    run_migrations_offline()\nelse:\n    run_migrations_online()\n"}],"isError":false,"timestamp":1774853958648}}
{"type":"message","id":"d5708f65","parentId":"940600c4","timestamp":"2026-03-30T06:59:18.651Z","message":{"role":"toolResult","toolCallId":"toolu_01EpAwykwgvT361XdUGhcZjm","toolName":"read","content":[{"type":"text","text":"\"\"\"initial schema — 7 core entities\n\nRevision ID: 001_initial\nRevises:\nCreate Date: 2026-03-29\n\"\"\"\nfrom typing import Sequence, Union\n\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects.postgresql import ARRAY, JSONB, UUID\n\n# revision identifiers, used by Alembic.\nrevision: str = \"001_initial\"\ndown_revision: Union[str, None] = None\nbranch_labels: Union[str, Sequence[str], None] = None\ndepends_on: Union[str, Sequence[str], None] = None\n\n\ndef upgrade() -> None:\n    # ── Enum types ───────────────────────────────────────────────────────\n    content_type = sa.Enum(\n        \"tutorial\", \"livestream\", \"breakdown\", \"short_form\",\n        name=\"content_type\",\n    )\n    processing_status = sa.Enum(\n        \"pending\", \"transcribed\", \"extracted\", \"reviewed\", \"published\",\n        name=\"processing_status\",\n    )\n    key_moment_content_type = sa.Enum(\n        \"technique\", \"settings\", \"reasoning\", \"workflow\",\n        name=\"key_moment_content_type\",\n    )\n    review_status = sa.Enum(\n        \"pending\", \"approved\", \"edited\", \"rejected\",\n        name=\"review_status\",\n    )\n    source_quality = sa.Enum(\n        \"structured\", \"mixed\", \"unstructured\",\n        name=\"source_quality\",\n    )\n    page_review_status = sa.Enum(\n        \"draft\", \"reviewed\", \"published\",\n        name=\"page_review_status\",\n    )\n    relationship_type = sa.Enum(\n        \"same_technique_other_creator\", \"same_creator_adjacent\", \"general_cross_reference\",\n        name=\"relationship_type\",\n    )\n\n    # ── creators ─────────────────────────────────────────────────────────\n    op.create_table(\n        \"creators\",\n        sa.Column(\"id\", UUID(as_uuid=True), primary_key=True, server_default=sa.text(\"gen_random_uuid()\")),\n        sa.Column(\"name\", sa.String(255), nullable=False),\n        sa.Column(\"slug\", sa.String(255), nullable=False, unique=True),\n        sa.Column(\"genres\", ARRAY(sa.String), nullable=True),\n        sa.Column(\"folder_name\", sa.String(255), nullable=False),\n        sa.Column(\"view_count\", sa.Integer, nullable=False, server_default=\"0\"),\n        sa.Column(\"created_at\", sa.DateTime(), nullable=False, server_default=sa.func.now()),\n        sa.Column(\"updated_at\", sa.DateTime(), nullable=False, server_default=sa.func.now()),\n    )\n\n    # ── source_videos ────────────────────────────────────────────────────\n    op.create_table(\n        \"source_videos\",\n        sa.Column(\"id\", UUID(as_uuid=True), primary_key=True, server_default=sa.text(\"gen_random_uuid()\")),\n        sa.Column(\"creator_id\", UUID(as_uuid=True), sa.ForeignKey(\"creators.id\", ondelete=\"CASCADE\"), nullable=False),\n        sa.Column(\"filename\", sa.String(500), nullable=False),\n        sa.Column(\"file_path\", sa.String(1000), nullable=False),\n        sa.Column(\"duration_seconds\", sa.Integer, nullable=True),\n        sa.Column(\"content_type\", content_type, nullable=False),\n        sa.Column(\"transcript_path\", sa.String(1000), nullable=True),\n        sa.Column(\"processing_status\", processing_status, nullable=False, server_default=\"pending\"),\n        sa.Column(\"created_at\", sa.DateTime(), nullable=False, server_default=sa.func.now()),\n        sa.Column(\"updated_at\", sa.DateTime(), nullable=False, server_default=sa.func.now()),\n    )\n    op.create_index(\"ix_source_videos_creator_id\", \"source_videos\", [\"creator_id\"])\n\n    # ── transcript_segments ──────────────────────────────────────────────\n    op.create_table(\n        \"transcript_segments\",\n        sa.Column(\"id\", UUID(as_uuid=True), primary_key=True, server_default=sa.text(\"gen_random_uuid()\")),\n        sa.Column(\"source_video_id\", UUID(as_uuid=True), sa.ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False),\n        sa.Column(\"start_time\", sa.Float, nullable=False),\n        sa.Column(\"end_time\", sa.Float, nullable=False),\n        sa.Column(\"text\", sa.Text, nullable=False),\n        sa.Column(\"segment_index\", sa.Integer, nullable=False),\n        sa.Column(\"topic_label\", sa.String(255), nullable=True),\n    )\n    op.create_index(\"ix_transcript_segments_video_id\", \"transcript_segments\", [\"source_video_id\"])\n\n    # ── technique_pages (must come before key_moments due to FK) ─────────\n    op.create_table(\n        \"technique_pages\",\n        sa.Column(\"id\", UUID(as_uuid=True), primary_key=True, server_default=sa.text(\"gen_random_uuid()\")),\n        sa.Column(\"creator_id\", UUID(as_uuid=True), sa.ForeignKey(\"creators.id\", ondelete=\"CASCADE\"), nullable=False),\n        sa.Column(\"title\", sa.String(500), nullable=False),\n        sa.Column(\"slug\", sa.String(500), nullable=False, unique=True),\n        sa.Column(\"topic_category\", sa.String(255), nullable=False),\n        sa.Column(\"topic_tags\", ARRAY(sa.String), nullable=True),\n        sa.Column(\"summary\", sa.Text, nullable=True),\n        sa.Column(\"body_sections\", JSONB, nullable=True),\n        sa.Column(\"signal_chains\", JSONB, nullable=True),\n        sa.Column(\"plugins\", ARRAY(sa.String), nullable=True),\n        sa.Column(\"source_quality\", source_quality, nullable=True),\n        sa.Column(\"view_count\", sa.Integer, nullable=False, server_default=\"0\"),\n        sa.Column(\"review_status\", page_review_status, nullable=False, server_default=\"draft\"),\n        sa.Column(\"created_at\", sa.DateTime(), nullable=False, server_default=sa.func.now()),\n        sa.Column(\"updated_at\", sa.DateTime(), nullable=False, server_default=sa.func.now()),\n    )\n    op.create_index(\"ix_technique_pages_creator_id\", \"technique_pages\", [\"creator_id\"])\n    op.create_index(\"ix_technique_pages_topic_category\", \"technique_pages\", [\"topic_category\"])\n\n    # ── key_moments ──────────────────────────────────────────────────────\n    op.create_table(\n        \"key_moments\",\n        sa.Column(\"id\", UUID(as_uuid=True), primary_key=True, server_default=sa.text(\"gen_random_uuid()\")),\n        sa.Column(\"source_video_id\", UUID(as_uuid=True), sa.ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False),\n        sa.Column(\"technique_page_id\", UUID(as_uuid=True), sa.ForeignKey(\"technique_pages.id\", ondelete=\"SET NULL\"), nullable=True),\n        sa.Column(\"title\", sa.String(500), nullable=False),\n        sa.Column(\"summary\", sa.Text, nullable=False),\n        sa.Column(\"start_time\", sa.Float, nullable=False),\n        sa.Column(\"end_time\", sa.Float, nullable=False),\n        sa.Column(\"content_type\", key_moment_content_type, nullable=False),\n        sa.Column(\"plugins\", ARRAY(sa.String), nullable=True),\n        sa.Column(\"review_status\", review_status, nullable=False, server_default=\"pending\"),\n        sa.Column(\"raw_transcript\", sa.Text, nullable=True),\n        sa.Column(\"created_at\", sa.DateTime(), nullable=False, server_default=sa.func.now()),\n        sa.Column(\"updated_at\", sa.DateTime(), nullable=False, server_default=sa.func.now()),\n    )\n    op.create_index(\"ix_key_moments_source_video_id\", \"key_moments\", [\"source_video_id\"])\n    op.create_index(\"ix_key_moments_technique_page_id\", \"key_moments\", [\"technique_page_id\"])\n\n    # ── related_technique_links ──────────────────────────────────────────\n    op.create_table(\n        \"related_technique_links\",\n        sa.Column(\"id\", UUID(as_uuid=True), primary_key=True, server_default=sa.text(\"gen_random_uuid()\")),\n        sa.Column(\"source_page_id\", UUID(as_uuid=True), sa.ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False),\n        sa.Column(\"target_page_id\", UUID(as_uuid=True), sa.ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False),\n        sa.Column(\"relationship\", relationship_type, nullable=False),\n        sa.UniqueConstraint(\"source_page_id\", \"target_page_id\", \"relationship\", name=\"uq_technique_link\"),\n    )\n\n    # ── tags ─────────────────────────────────────────────────────────────\n    op.create_table(\n        \"tags\",\n        sa.Column(\"id\", UUID(as_uuid=True), primary_key=True, server_default=sa.text(\"gen_random_uuid()\")),\n        sa.Column(\"name\", sa.String(255), nullable=False, unique=True),\n        sa.Column(\"category\", sa.String(255), nullable=False),\n        sa.Column(\"aliases\", ARRAY(sa.String), nullable=True),\n    )\n    op.create_index(\"ix_tags_category\", \"tags\", [\"category\"])\n\n\ndef downgrade() -> None:\n    op.drop_table(\"tags\")\n    op.drop_table(\"related_technique_links\")\n    op.drop_table(\"key_moments\")\n    op.drop_table(\"technique_pages\")\n    op.drop_table(\"transcript_segments\")\n    op.drop_table(\"source_videos\")\n    op.drop_table(\"creators\")\n\n    # Drop enum types\n    for name in [\n        \"relationship_type\", \"page_review_status\", \"source_quality\",\n        \"review_status\", \"key_moment_content_type\", \"processing_status\",\n        \"content_type\",\n    ]:\n        sa.Enum(name=name).drop(op.get_bind(), checkfirst=True)\n"}],"isError":false,"timestamp":1774853958649}}
{"type":"message","id":"0d556244","parentId":"d5708f65","timestamp":"2026-03-30T06:59:24.394Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. Now let me understand what stage 5 does when it creates/updates technique pages — specifically the slug-based upsert pattern — and look at the prompts and config."},{"type":"toolCall","id":"toolu_01JDEswxvagukYF4YEMJb34W","name":"read","arguments":{"path":"backend/config.py"}},{"type":"toolCall","id":"toolu_01BKxgD8X59bcqnEqfjyB8hb","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls backend/pipeline/ | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":155,"cacheRead":85219,"cacheWrite":3492,"totalTokens":88867,"cost":{"input":0.000005,"output":0.003875,"cacheRead":0.0426095,"cacheWrite":0.021825,"total":0.0683145}},"stopReason":"toolUse","timestamp":1774853958650}}
{"type":"message","id":"62053e57","parentId":"0d556244","timestamp":"2026-03-30T06:59:24.402Z","message":{"role":"toolResult","toolCallId":"toolu_01JDEswxvagukYF4YEMJb34W","toolName":"read","content":[{"type":"text","text":"\"\"\"Application configuration loaded from environment variables.\"\"\"\n\nfrom functools import lru_cache\n\nfrom pydantic_settings import BaseSettings\n\n\nclass Settings(BaseSettings):\n    \"\"\"Chrysopedia API settings.\n\n    Values are loaded from environment variables (or .env file via\n    pydantic-settings' dotenv support).\n    \"\"\"\n\n    # Database\n    database_url: str = \"postgresql+asyncpg://chrysopedia:changeme@localhost:5433/chrysopedia\"\n\n    # Redis\n    redis_url: str = \"redis://localhost:6379/0\"\n\n    # Application\n    app_env: str = \"development\"\n    app_log_level: str = \"info\"\n    app_secret_key: str = \"changeme-generate-a-real-secret\"\n\n    # CORS\n    cors_origins: list[str] = [\"*\"]\n\n    # LLM endpoint (OpenAI-compatible)\n    llm_api_url: str = \"http://localhost:11434/v1\"\n    llm_api_key: str = \"sk-placeholder\"\n    llm_model: str = \"fyn-llm-agent-chat\"\n    llm_fallback_url: str = \"http://localhost:11434/v1\"\n    llm_fallback_model: str = \"fyn-llm-agent-chat\"\n\n    # Per-stage model overrides (optional — falls back to llm_model / \"chat\")\n    llm_stage2_model: str | None = \"fyn-llm-agent-chat\"   # segmentation — mechanical, fast chat\n    llm_stage2_modality: str = \"chat\"\n    llm_stage3_model: str | None = \"fyn-llm-agent-think\"  # extraction — reasoning\n    llm_stage3_modality: str = \"thinking\"\n    llm_stage4_model: str | None = \"fyn-llm-agent-chat\"   # classification — mechanical, fast chat\n    llm_stage4_modality: str = \"chat\"\n    llm_stage5_model: str | None = \"fyn-llm-agent-think\"  # synthesis — reasoning\n    llm_stage5_modality: str = \"thinking\"\n\n    # Max tokens for LLM responses (OpenWebUI defaults to 1000 which truncates pipeline JSON)\n    llm_max_tokens: int = 65536\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":1774853964402}}
{"type":"message","id":"05b76674","parentId":"62053e57","timestamp":"2026-03-30T06:59:24.408Z","message":{"role":"toolResult","toolCallId":"toolu_01BKxgD8X59bcqnEqfjyB8hb","toolName":"bash","content":[{"type":"text","text":"embedding_client.py\n__init__.py\nllm_client.py\n__pycache__\nqdrant_client.py\nschemas.py\nstages.py\n"}],"isError":false,"timestamp":1774853964407}}
{"type":"message","id":"93f11367","parentId":"05b76674","timestamp":"2026-03-30T06:59:28.692Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_018G6RhgyjKpDczNz7CRchb6","name":"read","arguments":{"path":"backend/pipeline/schemas.py"}},{"type":"toolCall","id":"toolu_01PuZAhHf53FbzdKFtQ5Lm37","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls prompts/ 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":118,"cacheRead":88711,"cacheWrite":1077,"totalTokens":89907,"cost":{"input":0.000005,"output":0.00295,"cacheRead":0.0443555,"cacheWrite":0.00673125,"total":0.05404175}},"stopReason":"toolUse","timestamp":1774853964407}}
{"type":"message","id":"6a3bc91c","parentId":"93f11367","timestamp":"2026-03-30T06:59:28.710Z","message":{"role":"toolResult","toolCallId":"toolu_018G6RhgyjKpDczNz7CRchb6","toolName":"read","content":[{"type":"text","text":"\"\"\"Pydantic schemas for pipeline stage inputs and outputs.\n\nStage 2 — Segmentation: groups transcript segments by topic.\nStage 3 — Extraction: extracts key moments from segments.\nStage 4 — Classification: classifies moments by category/tags.\nStage 5 — Synthesis: generates technique pages from classified moments.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom pydantic import BaseModel, Field\n\n\n# ── Stage 2: Segmentation ───────────────────────────────────────────────────\n\nclass TopicSegment(BaseModel):\n    \"\"\"A contiguous group of transcript segments sharing a topic.\"\"\"\n\n    start_index: int = Field(description=\"First transcript segment index in this group\")\n    end_index: int = Field(description=\"Last transcript segment index in this group (inclusive)\")\n    topic_label: str = Field(description=\"Short label describing the topic\")\n    summary: str = Field(description=\"Brief summary of what is discussed\")\n\n\nclass SegmentationResult(BaseModel):\n    \"\"\"Full output of stage 2 (segmentation).\"\"\"\n\n    segments: list[TopicSegment]\n\n\n# ── Stage 3: Extraction ─────────────────────────────────────────────────────\n\nclass ExtractedMoment(BaseModel):\n    \"\"\"A single key moment extracted from a topic segment group.\"\"\"\n\n    title: str = Field(description=\"Concise title for the moment\")\n    summary: str = Field(description=\"Detailed summary of the technique/concept\")\n    start_time: float = Field(description=\"Start time in seconds\")\n    end_time: float = Field(description=\"End time in seconds\")\n    content_type: str = Field(description=\"One of: technique, settings, reasoning, workflow\")\n    plugins: list[str] = Field(default_factory=list, description=\"Plugins/tools mentioned\")\n    raw_transcript: str = Field(default=\"\", description=\"Raw transcript text for this moment\")\n\n\nclass ExtractionResult(BaseModel):\n    \"\"\"Full output of stage 3 (extraction).\"\"\"\n\n    moments: list[ExtractedMoment]\n\n\n# ── Stage 4: Classification ─────────────────────────────────────────────────\n\nclass ClassifiedMoment(BaseModel):\n    \"\"\"Classification metadata for a single extracted moment.\"\"\"\n\n    moment_index: int = Field(description=\"Index into ExtractionResult.moments\")\n    topic_category: str = Field(description=\"High-level topic category\")\n    topic_tags: list[str] = Field(default_factory=list, description=\"Specific topic tags\")\n    content_type_override: str | None = Field(\n        default=None,\n        description=\"Override for content_type if classification disagrees with extraction\",\n    )\n\n\nclass ClassificationResult(BaseModel):\n    \"\"\"Full output of stage 4 (classification).\"\"\"\n\n    classifications: list[ClassifiedMoment]\n\n\n# ── Stage 5: Synthesis ───────────────────────────────────────────────────────\n\nclass SynthesizedPage(BaseModel):\n    \"\"\"A technique page synthesized from classified moments.\"\"\"\n\n    title: str = Field(description=\"Page title\")\n    slug: str = Field(description=\"URL-safe slug\")\n    topic_category: str = Field(description=\"Primary topic category\")\n    topic_tags: list[str] = Field(default_factory=list, description=\"Associated tags\")\n    summary: str = Field(description=\"Page summary / overview paragraph\")\n    body_sections: dict = Field(\n        default_factory=dict,\n        description=\"Structured body content as section_name -> content mapping\",\n    )\n    signal_chains: list[dict] = Field(\n        default_factory=list,\n        description=\"Signal chain descriptions (for audio/music production contexts)\",\n    )\n    plugins: list[str] = Field(default_factory=list, description=\"Plugins/tools referenced\")\n    source_quality: str = Field(\n        default=\"mixed\",\n        description=\"One of: structured, mixed, unstructured\",\n    )\n\n\nclass SynthesisResult(BaseModel):\n    \"\"\"Full output of stage 5 (synthesis).\"\"\"\n\n    pages: list[SynthesizedPage]\n"}],"isError":false,"timestamp":1774853968709}}
{"type":"message","id":"a3b4a937","parentId":"6a3bc91c","timestamp":"2026-03-30T06:59:28.713Z","message":{"role":"toolResult","toolCallId":"toolu_01PuZAhHf53FbzdKFtQ5Lm37","toolName":"bash","content":[{"type":"text","text":"README.md\nstage2_segmentation.txt\nstage3_extraction.txt\nstage4_classification.txt\nstage5_synthesis.txt\n"}],"isError":false,"timestamp":1774853968709}}
{"type":"message","id":"83f81e9f","parentId":"a3b4a937","timestamp":"2026-03-30T06:59:34.040Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the frontend TechniquePage to understand the current display, and the existing test structure."},{"type":"toolCall","id":"toolu_01T287woVNJzmCuWG8fiNGYk","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls backend/routers/ | head -20"}},{"type":"toolCall","id":"toolu_01CoY89chcQCSbYLZJnkh3er","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls backend/tests/ | head -20"}},{"type":"toolCall","id":"toolu_01Fx7HRMQvBWNAi6cGbe8J7o","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg \"version\" backend/models.py backend/schemas.py --word-regexp | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":235,"cacheRead":89788,"cacheWrite":1188,"totalTokens":91212,"cost":{"input":0.000005,"output":0.005875,"cacheRead":0.044893999999999996,"cacheWrite":0.007425,"total":0.058199}},"stopReason":"toolUse","timestamp":1774853968710}}
{"type":"message","id":"7a3736bc","parentId":"83f81e9f","timestamp":"2026-03-30T06:59:34.058Z","message":{"role":"toolResult","toolCallId":"toolu_01T287woVNJzmCuWG8fiNGYk","toolName":"bash","content":[{"type":"text","text":"creators.py\nhealth.py\ningest.py\n__init__.py\npipeline.py\n__pycache__\nreview.py\nsearch.py\ntechniques.py\ntopics.py\nvideos.py\n"}],"isError":false,"timestamp":1774853974058}}
{"type":"message","id":"7ec036ca","parentId":"7a3736bc","timestamp":"2026-03-30T06:59:34.059Z","message":{"role":"toolResult","toolCallId":"toolu_01CoY89chcQCSbYLZJnkh3er","toolName":"bash","content":[{"type":"text","text":"conftest.py\nfixtures\n__init__.py\n__pycache__\ntest_ingest.py\ntest_pipeline.py\ntest_public_api.py\ntest_review.py\ntest_search.py\n"}],"isError":false,"timestamp":1774853974058}}
{"type":"message","id":"1b5ab85f","parentId":"7ec036ca","timestamp":"2026-03-30T06:59:34.065Z","message":{"role":"toolResult","toolCallId":"toolu_01Fx7HRMQvBWNAi6cGbe8J7o","toolName":"bash","content":[{"type":"text","text":"backend/schemas.py:    version: str = \"0.1.0\"\n"}],"isError":false,"timestamp":1774853974063}}
{"type":"message","id":"58ae427f","parentId":"1b5ab85f","timestamp":"2026-03-30T06:59:38.347Z","message":{"role":"assistant","content":[{"type":"text","text":"No existing version concept. Let me check the pipeline router for the re-trigger endpoint and understand the full re-processing flow."},{"type":"toolCall","id":"toolu_01EdPrwKwfXVghwnFRBMVXFx","name":"read","arguments":{"path":"backend/routers/pipeline.py"}},{"type":"toolCall","id":"toolu_013dN8CmTQGUnZpwrU5UCbQv","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && wc -l backend/tests/conftest.py && head -60 backend/tests/conftest.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":161,"cacheRead":90976,"cacheWrite":438,"totalTokens":91576,"cost":{"input":0.000005,"output":0.004025,"cacheRead":0.045488,"cacheWrite":0.0027375,"total":0.052255499999999996}},"stopReason":"toolUse","timestamp":1774853974064}}
{"type":"message","id":"4967600d","parentId":"58ae427f","timestamp":"2026-03-30T06:59:38.357Z","message":{"role":"toolResult","toolCallId":"toolu_01EdPrwKwfXVghwnFRBMVXFx","toolName":"read","content":[{"type":"text","text":"\"\"\"Pipeline management endpoints for manual re-trigger and status inspection.\"\"\"\n\nimport logging\n\nfrom fastapi import APIRouter, Depends, HTTPException\nfrom sqlalchemy import select\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom database import get_session\nfrom models import SourceVideo\n\nlogger = logging.getLogger(\"chrysopedia.pipeline\")\n\nrouter = APIRouter(prefix=\"/pipeline\", tags=[\"pipeline\"])\n\n\n@router.post(\"/trigger/{video_id}\")\nasync def trigger_pipeline(\n    video_id: str,\n    db: AsyncSession = Depends(get_session),\n):\n    \"\"\"Manually trigger (or re-trigger) the LLM extraction pipeline for a video.\n\n    Looks up the SourceVideo by ID, dispatches ``run_pipeline.delay()``,\n    and returns the current processing status. Returns 404 if the video\n    does not exist.\n    \"\"\"\n    stmt = select(SourceVideo).where(SourceVideo.id == video_id)\n    result = await db.execute(stmt)\n    video = result.scalar_one_or_none()\n\n    if video is None:\n        raise HTTPException(status_code=404, detail=f\"Video not found: {video_id}\")\n\n    # Import inside handler to avoid circular import at module level\n    from pipeline.stages import run_pipeline\n\n    try:\n        run_pipeline.delay(str(video.id))\n        logger.info(\"Pipeline manually triggered for video_id=%s\", video_id)\n    except Exception as exc:\n        logger.warning(\n            \"Failed to dispatch pipeline for video_id=%s: %s\", video_id, exc\n        )\n        raise HTTPException(\n            status_code=503,\n            detail=\"Pipeline dispatch failed — Celery/Redis may be unavailable\",\n        ) from exc\n\n    return {\n        \"status\": \"triggered\",\n        \"video_id\": str(video.id),\n        \"current_processing_status\": video.processing_status.value,\n    }\n"}],"isError":false,"timestamp":1774853978357}}
{"type":"message","id":"13a5608e","parentId":"4967600d","timestamp":"2026-03-30T06:59:38.362Z","message":{"role":"toolResult","toolCallId":"toolu_013dN8CmTQGUnZpwrU5UCbQv","toolName":"bash","content":[{"type":"text","text":"192 backend/tests/conftest.py\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"}],"isError":false,"timestamp":1774853978360}}
{"type":"message","id":"bf68cf92","parentId":"13a5608e","timestamp":"2026-03-30T06:59:43.708Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me look at how stage 5 handles the existing slug-based \"upsert\" pattern more carefully, and understand what data would need to be versioned."},{"type":"toolCall","id":"toolu_01RXGHeWpiiDBL8vG53wjF46","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"slug\" backend/pipeline/stages.py | head -10"}},{"type":"toolCall","id":"toolu_01MjzHPejV7iKgC7TwYU3xGB","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg \"trigger|re-run|re-process|retrigger\" backend/ -l | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":199,"cacheRead":91414,"cacheWrite":1257,"totalTokens":92871,"cost":{"input":0.000005,"output":0.004975,"cacheRead":0.045707,"cacheWrite":0.00785625,"total":0.05854325}},"stopReason":"toolUse","timestamp":1774853978361}}
{"type":"message","id":"734c3573","parentId":"bf68cf92","timestamp":"2026-03-30T06:59:43.724Z","message":{"role":"toolResult","toolCallId":"toolu_01RXGHeWpiiDBL8vG53wjF46","toolName":"bash","content":[{"type":"text","text":"561:                # Check if page with this slug already exists\n563:                    select(TechniquePage).where(TechniquePage.slug == page_data.slug)\n580:                        slug=page_data.slug,\n"}],"isError":false,"timestamp":1774853983724}}
{"type":"message","id":"03b0586c","parentId":"734c3573","timestamp":"2026-03-30T06:59:43.735Z","message":{"role":"toolResult","toolCallId":"toolu_01MjzHPejV7iKgC7TwYU3xGB","toolName":"bash","content":[{"type":"text","text":"backend/tests/test_pipeline.py\nbackend/routers/pipeline.py\n"}],"isError":false,"timestamp":1774853983732}}
{"type":"message","id":"c9b88660","parentId":"03b0586c","timestamp":"2026-03-30T06:59:49.845Z","message":{"role":"assistant","content":[{"type":"text","text":"I now have a clear picture of the architecture. The key insight: stage 5 currently does a slug-based upsert — if a TechniquePage with the same slug exists, it overwrites `title`, `summary`, `body_sections`, `signal_chains`, `plugins`, `topic_tags`, `source_quality` in-place. **There is no version history.** Each re-run silently replaces the previous content.\n\nLet me check what the roadmap says about what S04 should deliver."},{"type":"toolCall","id":"toolu_011LvAfu8N1USuLpM71inqzS","name":"read","arguments":{"limit":50,"offset":1,"path":".gsd/milestones/M004/M004-ROADMAP.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":217,"cacheRead":92671,"cacheWrite":332,"totalTokens":93221,"cost":{"input":0.000005,"output":0.005425,"cacheRead":0.046335499999999995,"cacheWrite":0.002075,"total":0.05384049999999999}},"stopReason":"toolUse","timestamp":1774853983733}}
{"type":"message","id":"2448f755","parentId":"c9b88660","timestamp":"2026-03-30T06:59:49.850Z","message":{"role":"toolResult","toolCallId":"toolu_011LvAfu8N1USuLpM71inqzS","toolName":"read","content":[{"type":"text","text":"# M004: \n\n## Vision\nFix the immediate bugs (422 errors, creators page), apply a dark mode theme with cyan accents and fix mobile responsive issues, redesign the technique page to match the reference HTML (proper sections, signal chain blocks, key moments with video source), and introduce article versioning with pipeline tuning metadata for benchmarking extraction quality across prompt iterations.\n\n## Slice Overview\n| ID | Slice | Risk | Depends | Done | After this |\n|----|-------|------|---------|------|------------|\n| S01 | Fix API Bugs — Review Detail 422 + Creators Page | low | — | ✅ | Review detail page and creators page load without errors using real pipeline data |\n| S02 | Dark Theme + Cyan Accents + Mobile Responsive Fix | medium | — | ✅ | App uses dark theme with cyan accents, no horizontal scroll on mobile |\n| S03 | Technique Page Redesign + Video Source on Moments | medium | S01 | ✅ | Technique page matches reference HTML layout with video source on key moments, signal chain blocks, and proper section structure |\n| S04 | Article Versioning + Pipeline Tuning Metadata | high | S03 | ⬜ | Technique pages track version history with pipeline metadata; API returns version list |\n"}],"isError":false,"timestamp":1774853989848}}
{"type":"message","id":"ce3916c3","parentId":"2448f755","timestamp":"2026-03-30T06:59:55.844Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check if there's any spec doc or context that further defines what \"article versioning\" and \"pipeline tuning metadata\" mean."},{"type":"toolCall","id":"toolu_01Vd5U1svPTt7EHKTg5G3dMW","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find . -name 'chrysopedia-spec*' -o -name 'SPEC*' -o -name 'spec*' 2>/dev/null | grep -v node_modules | grep -v .venv | head -10"}},{"type":"toolCall","id":"toolu_01WtYsX4NHJhzs5m3FL7NGE9","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -l \"version\" .gsd/ --type md 2>/dev/null | head -10"}},{"type":"toolCall","id":"toolu_0146BZNjqHJcjuU6Ath2XJWV","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat .gsd/milestones/M004/slices/S04/ 2>/dev/null; ls .gsd/milestones/M004/slices/S04/ 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":330,"cacheRead":93003,"cacheWrite":530,"totalTokens":93864,"cost":{"input":0.000005,"output":0.00825,"cacheRead":0.0465015,"cacheWrite":0.0033125000000000003,"total":0.058069}},"stopReason":"toolUse","timestamp":1774853989849}}
{"type":"message","id":"8ffdacf9","parentId":"ce3916c3","timestamp":"2026-03-30T06:59:55.892Z","message":{"role":"toolResult","toolCallId":"toolu_01Vd5U1svPTt7EHKTg5G3dMW","toolName":"bash","content":[{"type":"text","text":"./chrysopedia-spec.md\n"}],"isError":false,"timestamp":1774853995892}}
{"type":"message","id":"a0198467","parentId":"8ffdacf9","timestamp":"2026-03-30T06:59:55.893Z","message":{"role":"toolResult","toolCallId":"toolu_01WtYsX4NHJhzs5m3FL7NGE9","toolName":"bash","content":[{"type":"text","text":".gsd/milestones/M001/slices/S01/S01-PLAN.md\n.gsd/milestones/M001/slices/S01/S01-UAT.md\n.gsd/milestones/M001/slices/S01/tasks/T03-PLAN.md\n.gsd/milestones/M001/slices/S01/tasks/T02-SUMMARY.md\n.gsd/milestones/M004/slices/S04/S04-PLAN.md\n.gsd/milestones/M001/slices/S01/tasks/T01-SUMMARY.md\n.gsd/milestones/M004/M004-ROADMAP.md\n.gsd/milestones/M001/slices/S01/tasks/T02-PLAN.md\n.gsd/milestones/M001/slices/S01/S01-SUMMARY.md\n.gsd/milestones/M001/M001-SUMMARY.md\n"}],"isError":false,"timestamp":1774853995892}}
{"type":"message","id":"37ddf9d5","parentId":"a0198467","timestamp":"2026-03-30T06:59:55.896Z","message":{"role":"toolResult","toolCallId":"toolu_0146BZNjqHJcjuU6Ath2XJWV","toolName":"bash","content":[{"type":"text","text":"S04-PLAN.md\ntasks\n"}],"isError":false,"timestamp":1774853995892}}
{"type":"message","id":"9eafa5d2","parentId":"37ddf9d5","timestamp":"2026-03-30T06:59:59.617Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_011kNCpjg18pCBrJboN8VhZT","name":"read","arguments":{"path":".gsd/milestones/M004/slices/S04/S04-PLAN.md"}},{"type":"toolCall","id":"toolu_01VELVM7sv1g6juswVDtzxKf","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -i \"version\" chrysopedia-spec.md | head -30"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":143,"cacheRead":93533,"cacheWrite":680,"totalTokens":94357,"cost":{"input":0.000005,"output":0.003575,"cacheRead":0.046766499999999996,"cacheWrite":0.00425,"total":0.05459649999999999}},"stopReason":"toolUse","timestamp":1774853995894}}
{"type":"message","id":"a8165099","parentId":"9eafa5d2","timestamp":"2026-03-30T06:59:59.626Z","message":{"role":"toolResult","toolCallId":"toolu_011kNCpjg18pCBrJboN8VhZT","toolName":"read","content":[{"type":"text","text":"# S04: Article Versioning + Pipeline Tuning Metadata\n\n**Goal:** Add article versioning schema and pipeline tuning metadata to technique pages\n**Demo:** After this: Technique pages track version history with pipeline metadata; API returns version list\n\n## Tasks\n"}],"isError":false,"timestamp":1774853999625}}
{"type":"message","id":"ad26efcf","parentId":"a8165099","timestamp":"2026-03-30T06:59:59.632Z","message":{"role":"toolResult","toolCallId":"toolu_01VELVM7sv1g6juswVDtzxKf","toolName":"bash","content":[{"type":"text","text":"v1 provides file paths and timestamps (\"Skope — Sound Design Masterclass pt2.mp4 @ 1:42:30\"). Future versions could embed video playback directly in the web UI, jumping to the exact timestamp. This requires the video files to be network-accessible from the web UI, which depends on centralizing storage.\nNot needed for v1. The system is initially for personal/local use. Future versions may add authentication for sharing with friends or external users. The architecture should not preclude this (e.g., don't hardcode single-user assumptions into the data model).\n"}],"isError":false,"timestamp":1774853999631}}
{"type":"message","id":"0ac80d02","parentId":"ad26efcf","timestamp":"2026-03-30T07:00:06.154Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_013KfmNbfy9GKtq1vprii6Ky","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -i \"tuning|benchmark|calibrat|iteration|prompt.*(version|history|track)\" chrysopedia-spec.md | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":103,"cacheRead":94213,"cacheWrite":388,"totalTokens":94705,"cost":{"input":0.000005,"output":0.002575,"cacheRead":0.047106499999999996,"cacheWrite":0.002425,"total":0.05211149999999999}},"stopReason":"toolUse","timestamp":1774853999631}}
{"type":"message","id":"22a4d1bf","parentId":"0ac80d02","timestamp":"2026-03-30T07:00:06.174Z","message":{"role":"toolResult","toolCallId":"toolu_013KfmNbfy9GKtq1vprii6Ky","toolName":"bash","content":[{"type":"text","text":"- **Review mode (initial calibration):** All extracted key moments enter a review queue. The administrator reviews, edits, approves, or rejects each moment before it's published.\n- **Auto mode (post-calibration):** Extracted moments are published automatically. The review queue still exists but functions as an audit log rather than a gate.\nThe mode is a system-level toggle. The transition from review to auto mode happens when the administrator is satisfied with extraction quality — typically after reviewing the first several videos and tuning prompts.\n### 8.3 Prompt tuning\nThis is the \"calibration loop\" — run pipeline, review output, tune prompts, re-run, repeat until quality is sufficient for auto mode.\n2. **The extraction pipeline correctly identifies 80%+ of key moments** without human intervention (post-calibration)\n- Process a small batch of videos (5-10) for calibration\n"}],"isError":false,"timestamp":1774854006171}}
{"type":"message","id":"8608968f","parentId":"22a4d1bf","timestamp":"2026-03-30T07:00:09.586Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01GJJ9zsbh63JUjAQtxqEVWM","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -A 10 \"8.3 Prompt tuning\" chrysopedia-spec.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":94213,"cacheWrite":704,"totalTokens":95010,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.047106499999999996,"cacheWrite":0.0044,"total":0.0538115}},"stopReason":"toolUse","timestamp":1774854006172}}
{"type":"message","id":"64062e7b","parentId":"8608968f","timestamp":"2026-03-30T07:00:09.600Z","message":{"role":"toolResult","toolCallId":"toolu_01GJJ9zsbh63JUjAQtxqEVWM","toolName":"bash","content":[{"type":"text","text":"### 8.3 Prompt tuning\n\nThe extraction prompts (stages 2-5) should be stored as editable configuration, not hardcoded. If review reveals systematic issues (e.g., the LLM consistently misclassifies mixing techniques as sound design), the administrator should be able to:\n\n1. Edit the prompt templates\n2. Re-run extraction on specific videos or all videos\n3. Review the new output\n\nThis is the \"calibration loop\" — run pipeline, review output, tune prompts, re-run, repeat until quality is sufficient for auto mode.\n\n---\n"}],"isError":false,"timestamp":1774854009598}}
{"type":"message","id":"197a6275","parentId":"64062e7b","timestamp":"2026-03-30T07:00:14.049Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the spec for any explicit mention of article versioning."},{"type":"toolCall","id":"toolu_01QWBi1GiuTVNLHYMuZudEn6","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -i \"article|version.*(page|technique|content)\" chrysopedia-spec.md | head -20"}},{"type":"toolCall","id":"toolu_01BMgake1zYxjer6vbKVtahb","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -i \"history|snapshot|diff|compare\" chrysopedia-spec.md | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":186,"cacheRead":94601,"cacheWrite":550,"totalTokens":95338,"cost":{"input":0.000005,"output":0.0046500000000000005,"cacheRead":0.047300499999999995,"cacheWrite":0.0034375,"total":0.055393}},"stopReason":"toolUse","timestamp":1774854009599}}
{"type":"message","id":"3ef7752b","parentId":"197a6275","timestamp":"2026-03-30T07:00:14.068Z","message":{"role":"toolResult","toolCallId":"toolu_01QWBi1GiuTVNLHYMuZudEn6","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774854014067}}
{"type":"message","id":"b0fc613d","parentId":"3ef7752b","timestamp":"2026-03-30T07:00:14.076Z","message":{"role":"toolResult","toolCallId":"toolu_01BMgake1zYxjer6vbKVtahb","toolName":"bash","content":[{"type":"text","text":"Not required for v1, but the system should be **architected to support it easily.** The Qdrant embeddings and structured knowledge base provide the foundation. A future chat interface could use the Qwen instance (or any compatible LLM) with RAG over the Chrysopedia knowledge base to answer natural language questions like \"How does Skope approach snare design differently from Au5?\"\nEventually: user-specific bookmarks, personal notes on technique pages, view history, and personalized \"trending\" based on individual usage patterns.\n"}],"isError":false,"timestamp":1774854014074}}
{"type":"message","id":"06c054f6","parentId":"b0fc613d","timestamp":"2026-03-30T07:00:19.270Z","message":{"role":"assistant","content":[{"type":"text","text":"The spec doesn't explicitly define \"article versioning\" — this is a M004-defined feature for tracking the calibration loop. Let me check the existing S04 plan more thoroughly, and the tasks dir."},{"type":"toolCall","id":"toolu_016bZ6fMDos6aE5QdMPufxAU","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls .gsd/milestones/M004/slices/S04/tasks/ 2>/dev/null"}},{"type":"toolCall","id":"toolu_01DiPiAzeYPa71CqXP1Tfsjn","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat .gsd/milestones/M004/slices/S04/S04-PLAN.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":215,"cacheRead":95151,"cacheWrite":366,"totalTokens":95733,"cost":{"input":0.000005,"output":0.0053750000000000004,"cacheRead":0.0475755,"cacheWrite":0.0022875,"total":0.055243}},"stopReason":"toolUse","timestamp":1774854014075}}
{"type":"message","id":"df483da8","parentId":"06c054f6","timestamp":"2026-03-30T07:00:19.284Z","message":{"role":"toolResult","toolCallId":"toolu_016bZ6fMDos6aE5QdMPufxAU","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774854019284}}
{"type":"message","id":"37c498f2","parentId":"df483da8","timestamp":"2026-03-30T07:00:19.290Z","message":{"role":"toolResult","toolCallId":"toolu_01DiPiAzeYPa71CqXP1Tfsjn","toolName":"bash","content":[{"type":"text","text":"# S04: Article Versioning + Pipeline Tuning Metadata\n\n**Goal:** Add article versioning schema and pipeline tuning metadata to technique pages\n**Demo:** After this: Technique pages track version history with pipeline metadata; API returns version list\n\n## Tasks\n"}],"isError":false,"timestamp":1774854019287}}
{"type":"message","id":"e5067414","parentId":"37c498f2","timestamp":"2026-03-30T07:00:24.702Z","message":{"role":"assistant","content":[{"type":"text","text":"Plan is empty — just the header. No tasks defined yet. This is the research phase. Let me now understand the full picture of what needs to happen. Let me look at the existing prompt templates to understand what metadata could be captured."},{"type":"toolCall","id":"toolu_01JQ33yZZAN73kwanQDsPsv7","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && wc -l prompts/*.txt && head -20 prompts/stage5_synthesis.txt"}},{"type":"toolCall","id":"toolu_01DHe9M3F3x76L8uA3btuMqv","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && head -5 prompts/stage2_segmentation.txt prompts/stage3_extraction.txt prompts/stage4_classification.txt"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":222,"cacheRead":95517,"cacheWrite":334,"totalTokens":96074,"cost":{"input":0.000005,"output":0.00555,"cacheRead":0.047758499999999995,"cacheWrite":0.0020875,"total":0.05540099999999999}},"stopReason":"toolUse","timestamp":1774854019288}}
{"type":"message","id":"e9518f47","parentId":"e5067414","timestamp":"2026-03-30T07:00:24.715Z","message":{"role":"toolResult","toolCallId":"toolu_01JQ33yZZAN73kwanQDsPsv7","toolName":"bash","content":[{"type":"text","text":"  33 prompts/stage2_segmentation.txt\n  47 prompts/stage3_extraction.txt\n  38 prompts/stage4_classification.txt\n  58 prompts/stage5_synthesis.txt\n 176 total\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"}],"isError":false,"timestamp":1774854024715}}
{"type":"message","id":"92f848a6","parentId":"e9518f47","timestamp":"2026-03-30T07:00:24.719Z","message":{"role":"toolResult","toolCallId":"toolu_01DHe9M3F3x76L8uA3btuMqv","toolName":"bash","content":[{"type":"text","text":"==> prompts/stage2_segmentation.txt <==\nYou are a transcript analysis expert. Your task is to analyze a music production tutorial transcript and identify distinct topic boundaries — contiguous groups of segments that discuss the same subject.\n\n## Instructions\n\n1. Read the transcript segments provided inside the <transcript> tags.\n\n==> prompts/stage3_extraction.txt <==\nYou are a music production knowledge extraction expert. Your task is to identify and extract key moments from a topic segment of a tutorial transcript.\n\n## Instructions\n\n1. Read the transcript segment provided inside the <segment> tags.\n\n==> prompts/stage4_classification.txt <==\nYou are a music production knowledge classifier. Your task is to classify extracted key moments against a canonical tag taxonomy.\n\n## Instructions\n\n1. Read the key moments provided inside the <moments> tags.\n"}],"isError":false,"timestamp":1774854024717}}
{"type":"message","id":"fb899d2c","parentId":"92f848a6","timestamp":"2026-03-30T07:00:28.950Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check what the frontend test/pipeline test looks like to understand the test patterns."},{"type":"toolCall","id":"toolu_01HyAUk3bF7nbtUHHa1EDcBq","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg \"def test.*technique\" backend/tests/ -n | head -10"}},{"type":"toolCall","id":"toolu_01NrRBe5ciPQcPrGvTN7XAus","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg \"def test.*pipeline\" backend/tests/test_pipeline.py -n | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":180,"cacheRead":95851,"cacheWrite":693,"totalTokens":96725,"cost":{"input":0.000005,"output":0.0045000000000000005,"cacheRead":0.047925499999999996,"cacheWrite":0.00433125,"total":0.05676175}},"stopReason":"toolUse","timestamp":1774854024718}}
{"type":"message","id":"025934ae","parentId":"fb899d2c","timestamp":"2026-03-30T07:00:28.971Z","message":{"role":"toolResult","toolCallId":"toolu_01HyAUk3bF7nbtUHHa1EDcBq","toolName":"bash","content":[{"type":"text","text":"backend/tests/test_public_api.py:171:async def test_list_techniques(client, db_engine):\nbackend/tests/test_public_api.py:189:async def test_list_techniques_with_category_filter(client, db_engine):\nbackend/tests/test_public_api.py:203:async def test_get_technique_detail(client, db_engine):\nbackend/tests/test_public_api.py:233:async def test_get_technique_invalid_slug_returns_404(client, db_engine):\nbackend/tests/test_public_api.py:283:async def test_topics_with_no_technique_pages(client, db_engine):\nbackend/tests/test_pipeline.py:344:def test_stage5_synthesis_creates_technique_pages(\n"}],"isError":false,"timestamp":1774854028970}}
{"type":"message","id":"013a6c9e","parentId":"025934ae","timestamp":"2026-03-30T07:00:28.977Z","message":{"role":"toolResult","toolCallId":"toolu_01NrRBe5ciPQcPrGvTN7XAus","toolName":"bash","content":[{"type":"text","text":"586:def test_run_pipeline_resumes_from_extracted(\n648:async def test_pipeline_trigger_endpoint(client, db_engine):\n676:async def test_pipeline_trigger_404_for_missing_video(client):\n687:async def test_ingest_dispatches_pipeline(client, db_engine):\n"}],"isError":false,"timestamp":1774854028974}}
{"type":"message","id":"5b6e50a1","parentId":"013a6c9e","timestamp":"2026-03-30T07:00:33.210Z","message":{"role":"assistant","content":[{"type":"text","text":"Let me also check the frontend to see if there's a version display area already planned."},{"type":"toolCall","id":"toolu_01UkZr9ZGjaDgT127S8LgyJK","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls frontend/src/pages/ | head -20"}},{"type":"toolCall","id":"toolu_01Sa181nkC5EhKeTLjbfQxqw","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && wc -l frontend/src/pages/TechniquePage.tsx"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":165,"cacheRead":96544,"cacheWrite":513,"totalTokens":97223,"cost":{"input":0.000005,"output":0.004125,"cacheRead":0.048271999999999995,"cacheWrite":0.0032062500000000003,"total":0.05560825}},"stopReason":"toolUse","timestamp":1774854028975}}
{"type":"message","id":"e18e0c0e","parentId":"5b6e50a1","timestamp":"2026-03-30T07:00:33.224Z","message":{"role":"toolResult","toolCallId":"toolu_01UkZr9ZGjaDgT127S8LgyJK","toolName":"bash","content":[{"type":"text","text":"CreatorDetail.tsx\nCreatorsBrowse.tsx\nHome.tsx\nMomentDetail.tsx\nReviewQueue.tsx\nSearchResults.tsx\nTechniquePage.tsx\nTopicsBrowse.tsx\n"}],"isError":false,"timestamp":1774854033223}}
{"type":"message","id":"6ac504f8","parentId":"e18e0c0e","timestamp":"2026-03-30T07:00:33.228Z","message":{"role":"toolResult","toolCallId":"toolu_01Sa181nkC5EhKeTLjbfQxqw","toolName":"bash","content":[{"type":"text","text":"290 frontend/src/pages/TechniquePage.tsx\n"}],"isError":false,"timestamp":1774854033225}}
{"type":"message","id":"54743167","parentId":"6ac504f8","timestamp":"2026-03-30T07:00:43.098Z","message":{"role":"assistant","content":[{"type":"text","text":"I have a thorough understanding of the codebase now. Let me synthesize what I've learned into the research output.\n\nThe core design question: what does \"article versioning\" mean in this context? Based on:\n1. The spec's §8.3 \"Prompt tuning\" calibration loop: edit prompts → re-run → review output → repeat\n2. The roadmap: \"Technique pages track version history with pipeline metadata; API returns version list\"\n3. Stage 5's current behavior: slug-based upsert that silently overwrites\n\nThe purpose is clear: when the admin tunes prompts and re-runs the pipeline, they need to compare the old output vs the new output to assess whether the prompt change improved quality. Currently that comparison is impossible because re-running destroys the previous version.\n\n\"Pipeline tuning metadata\" means capturing which model, prompts, and settings produced each version — so the admin can correlate quality with configuration.\n\nLet me check one more thing — how prompt files are identified currently (hashes? timestamps?)."},{"type":"toolCall","id":"toolu_01JY1vCkiPSd8Kz1vMNAmDwb","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg \"prompt\" backend/pipeline/stages.py -n | grep -v \"^#\" | head -20"}},{"type":"toolCall","id":"toolu_0122pNTS9bTVgKoWwgvSjRUh","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg \"model_override|modality|model_dump|llm_model\" backend/pipeline/stages.py -n | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":409,"cacheRead":97057,"cacheWrite":291,"totalTokens":97758,"cost":{"input":0.000005,"output":0.010225,"cacheRead":0.048528499999999995,"cacheWrite":0.00181875,"total":0.06057724999999999}},"stopReason":"toolUse","timestamp":1774854033226}}
{"type":"message","id":"09858193","parentId":"54743167","timestamp":"2026-03-30T07:00:43.114Z","message":{"role":"toolResult","toolCallId":"toolu_01JY1vCkiPSd8Kz1vMNAmDwb","toolName":"bash","content":[{"type":"text","text":"3:Each stage reads from PostgreSQL via sync SQLAlchemy, loads its prompt\n72:def _load_prompt(template_name: str) -> str:\n73:    \"\"\"Read a prompt template from the prompts directory.\n78:    path = Path(settings.prompts_path) / template_name\n119:def _format_taxonomy_for_prompt(tags_data: dict) -> str:\n120:    \"\"\"Format the canonical tags taxonomy as readable text for the LLM prompt.\"\"\"\n134:    system_prompt: str,\n135:    user_prompt: str,\n155:        nudge_prompt = user_prompt + \"\\n\\nIMPORTANT: Output ONLY valid JSON. No markdown, no explanation.\"\n157:            system_prompt, nudge_prompt, response_model=model_cls,\n202:        # Load prompt and call LLM\n203:        system_prompt = _load_prompt(\"stage2_segmentation.txt\")\n204:        user_prompt = f\"<transcript>\\n{transcript_text}\\n</transcript>\"\n209:        raw = llm.complete(system_prompt, user_prompt, response_model=SegmentationResult,\n211:        result = _safe_parse_llm_response(raw, SegmentationResult, llm, system_prompt, user_prompt,\n230:        raise  # Don't retry missing prompt files\n276:        system_prompt = _load_prompt(\"stage3_extraction.txt\")\n291:            user_prompt = (\n296:            raw = llm.complete(system_prompt, user_prompt, response_model=ExtractionResult,\n298:            result = _safe_parse_llm_response(raw, ExtractionResult, llm, system_prompt, user_prompt,\n"}],"isError":false,"timestamp":1774854043114}}
{"type":"message","id":"60687e7d","parentId":"09858193","timestamp":"2026-03-30T07:00:43.120Z","message":{"role":"toolResult","toolCallId":"toolu_0122pNTS9bTVgKoWwgvSjRUh","toolName":"bash","content":[{"type":"text","text":"91:    \"\"\"Return (model_override, modality) for a pipeline stage.\n95:    stage-specific modality is unset, defaults to \"chat\".\n99:    modality = getattr(settings, f\"llm_stage{stage_num}_modality\", None) or \"chat\"\n100:    return model, modality\n136:    modality: str = \"chat\",\n137:    model_override: str | None = None,\n158:            modality=modality, model_override=model_override,\n207:        model_override, modality = _get_stage_config(2)\n208:        logger.info(\"Stage 2 using model=%s, modality=%s\", model_override or \"default\", modality)\n210:                           modality=modality, model_override=model_override)\n212:                                          modality=modality, model_override=model_override)\n278:        model_override, modality = _get_stage_config(3)\n279:        logger.info(\"Stage 3 using model=%s, modality=%s\", model_override or \"default\", modality)\n297:                               modality=modality, model_override=model_override)\n299:                                              modality=modality, model_override=model_override)\n404:        model_override, modality = _get_stage_config(4)\n405:        logger.info(\"Stage 4 using model=%s, modality=%s\", model_override or \"default\", modality)\n407:                           modality=modality, model_override=model_override)\n409:                                          modality=modality, model_override=model_override)\n529:        model_override, modality = _get_stage_config(5)\n"}],"isError":false,"timestamp":1774854043118}}
{"type":"message","id":"e270c329","parentId":"60687e7d","timestamp":"2026-03-30T07:01:36.829Z","message":{"role":"assistant","content":[{"type":"text","text":"I have everything I need. Let me now write the research document."},{"type":"toolCall","id":"toolu_01XSg9xYXsyKh1CZNe1QMkKL","name":"gsd_summary_save","arguments":{"artifact_type":"RESEARCH","content":"# S04: Article Versioning + Pipeline Tuning Metadata — Research\n\n**Date:** 2026-03-30\n\n## Summary\n\nThis slice introduces version tracking for technique pages so the admin can compare pipeline outputs across prompt iterations — the \"calibration loop\" from spec §8.3. Currently, stage 5 does a slug-based upsert that silently overwrites the previous content whenever the pipeline re-runs. There is no way to compare old vs new output, correlate quality changes with prompt edits, or roll back to a previous version.\n\nThe work is a new `technique_page_versions` table (Alembic migration), a hook in stage 5 that snapshots the current page content + pipeline metadata before overwriting, API endpoints to list/retrieve versions for a technique page, and a minimal frontend version history panel on the technique detail page.\n\nThe approach is well-scoped: snapshot-on-write in stage 5, not a full event-sourcing system. Each version captures the full page content (body_sections, signal_chains, summary, etc.) plus pipeline metadata (model names, prompt file hashes, timestamps) so the admin can answer \"which prompt/model combination produced this version?\"\n\n## Recommendation\n\n**Snapshot-on-write versioning with JSONB content storage and pipeline metadata columns.**\n\n- New `technique_page_versions` table with: `id`, `technique_page_id` (FK), `version_number` (integer, auto-incremented per page), `content_snapshot` (JSONB — full page content at that point), `pipeline_metadata` (JSONB — model names, prompt hashes, settings), `created_at`.\n- Stage 5 synthesis: before overwriting an existing page, snapshot the current content + pipeline config into a version row. The \"current\" version is always the live `technique_pages` row; the versions table holds history.\n- New API endpoints: `GET /techniques/{slug}/versions` (list with metadata summary), `GET /techniques/{slug}/versions/{version_number}` (full content snapshot).\n- Minimal frontend: version history sidebar/dropdown on TechniquePage showing version list with timestamps and model info.\n\nWhy JSONB snapshots instead of duplicating all columns: the page content structure (body_sections, signal_chains) is already JSONB — storing the whole page as a single JSONB blob is simpler, forward-compatible (new columns don't require migration of the versions table), and the versions table is write-once/read-rarely.\n\nWhy pipeline metadata as JSONB: the metadata schema will evolve as we add more pipeline knobs. JSONB avoids schema churn while still being queryable via PostgreSQL JSON operators.\n\n## Implementation Landscape\n\n### Key Files\n\n- `backend/models.py` — Add `TechniquePageVersion` model with FK to `technique_pages`, version_number, content_snapshot (JSONB), pipeline_metadata (JSONB), created_at\n- `alembic/versions/002_technique_page_versions.py` — New migration adding the `technique_page_versions` table\n- `backend/schemas.py` — Add `TechniquePageVersionRead`, `TechniquePageVersionList` schemas; extend `TechniquePageDetail` with `version_count: int` and `current_version: int`\n- `backend/pipeline/stages.py` — In `stage5_synthesis`, before the existing upsert block, snapshot existing page content + pipeline metadata into a `TechniquePageVersion` row. Add a helper `_capture_pipeline_metadata()` that collects model names, prompt file hashes (SHA-256 of file content), and relevant settings.\n- `backend/routers/techniques.py` — Add `GET /{slug}/versions` and `GET /{slug}/versions/{version_number}` endpoints\n- `backend/tests/test_public_api.py` — Add tests for version list and version detail endpoints\n- `backend/tests/test_pipeline.py` — Add test that re-running stage 5 creates a version row\n- `frontend/src/pages/TechniquePage.tsx` — Add version info display (count, link to version history)\n- `frontend/src/api/public-client.ts` — Add version list/detail API calls\n\n### Build Order\n\n1. **Model + Migration** (T01) — Add `TechniquePageVersion` to models.py and create Alembic migration. This unblocks everything else. Low risk — straightforward table creation following existing patterns.\n\n2. **Stage 5 versioning hook + pipeline metadata capture** (T02) — Modify `stage5_synthesis` to snapshot before overwrite. Add `_capture_pipeline_metadata()` helper that hashes prompt files and reads model config. This is the riskiest task because it modifies the pipeline's write path. The sync SQLAlchemy session in stage 5 already has the existing page loaded — snapshotting it is a single INSERT before the UPDATE.\n\n3. **API endpoints + schemas** (T03) — Add version list/detail endpoints and Pydantic schemas. Straightforward FastAPI/SQLAlchemy read-only endpoints following the existing pattern in `techniques.py`.\n\n4. **Frontend version display** (T04) — Add version count to technique detail and link/panel for version history. Light frontend work — fetch version list, display in a collapsible section.\n\n5. **Integration tests** (T05) — Test the full flow: stage 5 creates version on re-run, API returns versions, version detail contains correct snapshot. Can be combined with T03 if scope is tight.\n\n### Verification Approach\n\n1. **Migration**: `alembic upgrade head` succeeds on test DB; `technique_page_versions` table exists with correct columns.\n2. **Pipeline versioning**: Run stage 5 twice for the same video → `technique_page_versions` has 1 row (snapshot of v1 before v2 overwrote it). The version row contains `content_snapshot` with the original body_sections/summary and `pipeline_metadata` with model names and prompt hashes.\n3. **API**: `GET /techniques/{slug}/versions` returns version list sorted by version_number desc. `GET /techniques/{slug}/versions/1` returns the full content snapshot.\n4. **Frontend**: `npm run build` passes. Technique page shows version count.\n5. **Existing tests**: All existing tests in `test_pipeline.py` and `test_public_api.py` still pass (no regressions).\n\n## Constraints\n\n- Stage 5 uses **sync SQLAlchemy** (psycopg2) because it runs inside Celery. The version snapshot INSERT must use the same sync session — no async.\n- Alembic env.py needs both local and Docker sys.path entries (KNOWLEDGE item). The new migration must follow the same pattern as `001_initial.py`.\n- The existing slug-based upsert in stage 5 loads the page via `select()` then mutates it in-place. The snapshot must happen AFTER loading but BEFORE mutating, in the same transaction.\n- Prompt file hashes must be computed at pipeline runtime (not stored in config) because the files can be edited between runs without restarting the worker.\n\n## Common Pitfalls\n\n- **Snapshot timing in stage 5** — The snapshot must capture the OLD content before the in-place mutation. If the code snapshots after `existing.title = page_data.title` etc., it captures the new content. The safest approach: read the existing row's relevant fields into a dict immediately after the `select()`, before any attribute assignment.\n- **Version number race condition** — Two concurrent pipeline runs for different videos could both try to version the same technique page. Use `SELECT MAX(version_number) ... FOR UPDATE` or a simple `COUNT(*) + 1` within the transaction. Given this is a single-admin tool with sequential pipeline runs, a simple `COUNT + 1` is sufficient.\n- **JSONB serialization of SQLAlchemy model fields** — `body_sections` and `signal_chains` are already Python dicts/lists (loaded from JSONB columns). They serialize to JSON naturally. But `topic_tags` and `plugins` are `ARRAY(String)` which come back as Python lists — these also serialize fine. No special handling needed.\n\n## Open Risks\n\n- The pipeline metadata concept (prompt hashes, model names) is new and not currently tested. If prompt files are large or change frequently, the hash computation adds trivial overhead but the metadata schema may need iteration. Starting with JSONB gives us room.\n- Version comparison UI (side-by-side diff of two versions) is explicitly NOT in scope for this slice — the roadmap says \"API returns version list.\" If the admin wants visual diff, that's a future slice. But the data model should support it (full content snapshots enable client-side diffing).\n\n## Sources\n\n- Spec §8.3 \"Prompt tuning\" — defines the calibration loop that motivates versioning\n- Existing stage 5 upsert pattern in `backend/pipeline/stages.py` lines 561-595\n- Existing Alembic migration pattern in `alembic/versions/001_initial.py`\n","milestone_id":"M004","slice_id":"S04"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":2247,"cacheRead":97348,"cacheWrite":1300,"totalTokens":100896,"cost":{"input":0.000005,"output":0.056175,"cacheRead":0.048673999999999995,"cacheWrite":0.008125,"total":0.112979}},"stopReason":"toolUse","timestamp":1774854043119}}
{"type":"message","id":"01f856a5","parentId":"e270c329","timestamp":"2026-03-30T07:01:36.835Z","message":{"role":"toolResult","toolCallId":"toolu_01XSg9xYXsyKh1CZNe1QMkKL","toolName":"gsd_summary_save","content":[{"type":"text","text":"Saved RESEARCH artifact to milestones/M004/slices/S04/S04-RESEARCH.md"}],"details":{"operation":"save_summary","path":"milestones/M004/slices/S04/S04-RESEARCH.md","artifact_type":"RESEARCH"},"isError":false,"timestamp":1774854096833}}
{"type":"message","id":"43061126","parentId":"01f856a5","timestamp":"2026-03-30T07:01:39.455Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S04 researched."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":11,"cacheRead":98648,"cacheWrite":2285,"totalTokens":100945,"cost":{"input":0.000005,"output":0.000275,"cacheRead":0.049324,"cacheWrite":0.01428125,"total":0.06388525}},"stopReason":"stop","timestamp":1774854096834}}
