{"type":"thinking_level_change","id":"657ef493","parentId":null,"timestamp":"2026-03-29T22:59:30.744Z","thinkingLevel":"off"}
{"type":"model_change","id":"07b7a533","parentId":"657ef493","timestamp":"2026-03-29T22:59:30.745Z","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- R003 — Full pipeline stages 2-6 implemented and tested: segmentation, extraction, classification, synthesis, embedding. 10 integration tests verify all stages with mocked LLM and real PostgreSQL.\n- R009 — Write path implemented: EmbeddingClient generates vectors, QdrantManager upserts with metadata payloads. Read path (search query) deferred to S05.\n- R011 — Stage 4 loads canonical_tags.yaml for classification. Tag taxonomy is config-driven.\n- R012 — run_pipeline orchestrator resumes from last completed stage. Auto-dispatch from ingest handles new videos. Manual trigger supports re-processing.\n- R013 — 4 prompt template files loaded from configurable prompts_path. Manual trigger enables re-processing after prompt edits.\n\n## Requirements Validated\n\n- R003 — 10 integration tests prove full pipeline: stage2 updates topic_labels, stage3 creates KeyMoments, stage4 classifies tags, stage5 creates TechniquePages, stage6 embeds to Qdrant. Resumability and LLM fallback tested.\n- R013 — 4 prompt files in prompts/, loaded from configurable path, POST /api/v1/pipeline/trigger/{video_id} enables re-processing.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## UNIT: Research Slice S04 (\"Review Queue Admin UI\") — Milestone M001\n\n## Inlined Context (preloaded — do not re-read these files)\n\n### Milestone Roadmap\nSource: `.gsd/milestones/M001/M001-ROADMAP.md`\n\n# M001: Chrysopedia Foundation — Infrastructure, Pipeline Core, and Skeleton UI\n\n## Vision\nStand up the complete Chrysopedia stack: Docker Compose deployment on ub01, PostgreSQL data model, FastAPI backend with transcript ingestion, Whisper transcription script for the desktop, LLM extraction pipeline (stages 2-5), review queue, Qdrant integration, and the search-first web UI with technique pages, creators, and topics browsing. By the end, a video file can be transcribed → ingested → extracted → reviewed → searched and read in the web UI.\n\n## Slice Overview\n| ID | Slice | Risk | Depends | Done | After this |\n|----|-------|------|---------|------|------------|\n| S01 | Docker Compose + Database + Whisper Script | low | — | ✅ | docker compose up -d starts all services on ub01; Whisper script transcribes a sample video to JSON |\n| S02 | Transcript Ingestion API | low | S01 | ✅ | POST a transcript JSON file to the API; Creator and Source Video records appear in PostgreSQL |\n| S03 | LLM Extraction Pipeline + Qdrant Integration | high | S02 | ✅ | A transcript JSON triggers stages 2-5: segmentation → extraction → classification → synthesis. Technique pages with key moments appear in DB. Qdrant has searchable embeddings. |\n| S04 | Review Queue Admin UI | medium | S03 | ⬜ | Admin views pending key moments, approves/edits/rejects them, toggles between review and auto mode |\n| S05 | Search-First Web UI | medium | S03 | ⬜ | User searches for a technique, gets semantic results in <500ms, clicks through to a full technique page with study guide prose, key moments, and related links |\n\n---\n\n### Decisions\nSource: `.gsd/DECISIONS.md`\n\n# Decisions Register\n\n<!-- Append-only. Never edit or remove existing rows.\n     To reverse a decision, add a new row that supersedes it.\n     Read this file at the start of any planning or research phase. -->\n\n| # | When | Scope | Decision | Choice | Rationale | Revisable? | Made By |\n|---|------|-------|----------|--------|-----------|------------|---------|\n| D001 |  | architecture | Storage layer selection | PostgreSQL for structured data, Qdrant (existing instance at 10.0.0.10) for vector embeddings, local filesystem for transcript JSON files | Spec specifies PostgreSQL for JSONB support and Qdrant already running on hypervisor. File store on local filesystem organized by creator slug. Qdrant gets a dedicated collection, not a new instance. | Yes | collaborative |\n| D002 |  | architecture | Timestamp handling for asyncpg with TIMESTAMP WITHOUT TIME ZONE columns | Use datetime.now(timezone.utc).replace(tzinfo=None) in _now() helper | asyncpg rejects timezone-aware datetimes for TIMESTAMP WITHOUT TIME ZONE columns. Stripping tzinfo at the application layer preserves UTC semantics while staying compatible with the existing schema. | Yes | agent |\n| D003 |  | requirement | R002 Transcript Ingestion API status | validated | 6 passing integration tests prove the full POST /api/v1/ingest flow: creator auto-detection, SourceVideo upsert, TranscriptSegment bulk insert, raw JSON persistence, idempotent re-upload, and invalid input rejection. | Yes | agent |\n| D004 |  | architecture | Sync vs async approach for Celery worker pipeline tasks | Use sync openai.OpenAI, sync QdrantClient, and sync SQLAlchemy (create_engine with psycopg2) inside Celery tasks. Convert DATABASE_URL from postgresql+asyncpg:// to postgresql:// for the sync engine. | Celery workers run in a synchronous context. Using asyncio.run() inside tasks risks nested event loop errors with gevent/eventlet workers. Using sync clients throughout eliminates this class of bug entirely. The async engine/session from database.py is only used by FastAPI (ASGI); the worker gets its own sync engine. | Yes | agent |\n| D005 |  | architecture | Embedding/Qdrant failure handling strategy in pipeline | Embedding/Qdrant failures (stage 6) log errors but do not fail the pipeline. Processing_status is set by stages 2-5 only. Embeddings can be regenerated by manual re-trigger. | Qdrant is at 10.0.0.10 on the hypervisor network and may not be reachable during all pipeline runs. Making embedding a non-blocking side-effect ensures core pipeline output (KeyMoments, TechniquePages in PostgreSQL) is never lost due to vector store issues. The manual re-trigger endpoint allows regenerating embeddings at any time. | Yes | agent |\n| D006 |  | requirement | R009 Qdrant Vector Search Integration status | advanced | EmbeddingClient and QdrantManager created with idempotent collection management, metadata-rich upserts for technique pages and key moments. stage6_embed_and_index wired into pipeline as non-blocking side-effect. Integration test verifies embedding and upsert calls. Full validation deferred to S05 when search UI exercises the query path. | Yes | agent |\n\n---\n\n### Requirements\nSource: `.gsd/REQUIREMENTS.md`\n\n# Requirements\n\n## R001 — Whisper Transcription Pipeline\n**Status:** active\n**Description:** Desktop Python script that accepts video files (MP4/MKV), extracts audio via ffmpeg, runs Whisper large-v3 on RTX 4090, and outputs timestamped transcript JSON with segment-level timestamps and word-level timing. Must be resumable.\n**Validation:** Script processes a sample video and produces valid JSON with timestamped segments.\n**Primary Owner:** M001/S01\n\n## R002 — Transcript Ingestion API\n**Status:** active\n**Description:** FastAPI endpoint that accepts transcript JSON uploads, creates/updates Creator and Source Video records, and stores transcript data in PostgreSQL. Handles new creator detection from folder names.\n**Validation:** POST transcript JSON → 200 OK, records created in DB, file stored on filesystem.\n**Primary Owner:** M001/S02\n\n## R003 — LLM-Powered Extraction Pipeline (Stages 2-5)\n**Status:** active\n**Description:** Background worker pipeline: transcript segmentation → key moment extraction → classification/tagging → technique page synthesis. Uses OpenAI-compatible API with primary (DGX Sparks Qwen) and fallback (local Ollama) endpoints. Pipeline must be resumable per-video per-stage.\n**Validation:** End-to-end: transcript JSON in → technique pages with key moments, tags, and cross-references out.\n**Primary Owner:** M001/S03\n\n## R004 — Review Queue UI\n**Status:** active\n**Description:** Admin interface for reviewing extracted key moments: approve, edit+approve, split, merge, reject. Organized by source video for contextual review. Includes mode toggle (review vs auto-publish).\n**Validation:** Admin can review, edit, and approve/reject moments; mode toggle controls whether new moments require review.\n**Primary Owner:** M001/S04\n\n## R005 — Search-First Web UI\n**Status:** active\n**Description:** Landing page with prominent search bar, live typeahead (results after 2-3 chars), scope toggle (All/Topics/Creators), and two navigation cards (Topics, Creators). Recently added section. Search powered by Qdrant semantic search with keyword fallback.\n**Validation:** User types query → results appear within 500ms, grouped by type, with clickable navigation.\n**Primary Owner:** M001/S05\n\n## R006 — Technique Page Display\n**Status:** active\n**Description:** Core content unit: header (tags, title, creator, meta), study guide prose (organized by sub-aspects with signal chain blocks and quotes), key moments index (timestamped list), related techniques, plugins referenced. Amber banner for livestream-sourced content.\n**Validation:** Technique page renders with all sections populated from synthesized data.\n**Primary Owner:** M001/S05\n\n## R007 — Creators Browse Page\n**Status:** active\n**Description:** Filterable creator list with genre filter pills, type-to-narrow, sort options (randomized default, alphabetical, view count). Each row: name, genre tags, technique count, video count, view count. Links to creator detail page.\n**Validation:** Page loads with randomized order, genre filtering works, clicking row navigates to creator detail.\n**Primary Owner:** M001/S05\n\n## R008 — Topics Browse Page\n**Status:** active\n**Description:** Two-level topic hierarchy (6 top-level categories → sub-topics). Filter input, genre filter pills. Each sub-topic shows technique count and creator count. Clicking sub-topic shows technique pages.\n**Validation:** Hierarchy renders, filtering works, sub-topic links show correct technique pages.\n**Primary Owner:** M001/S05\n\n## R009 — Qdrant Vector Search Integration\n**Status:** active\n**Description:** Embed key moment summaries, technique page content, and transcript segments in Qdrant using configurable embedding model (nomic-embed-text default). Power semantic search with metadata filtering.\n**Validation:** Semantic search returns relevant results for natural language queries; embeddings update when content changes.\n**Primary Owner:** M001/S03\n\n## R010 — Docker Compose Deployment\n**Status:** active\n**Description:** Single docker-compose.yml packaging API, web UI, PostgreSQL, and worker services. Follows XPLTD conventions: bind mounts at /vmPool/r/services/, compose at /vmPool/r/compose/chrysopedia/, xpltd_chrysopedia project name, dedicated Docker network.\n**Validation:** `docker compose up -d` brings up all services; data persists across restarts.\n**Primary Owner:** M001/S01\n\n## R011 — Canonical Tag System\n**Status:** active\n**Description:** Editable canonical tag list (config file) with aliases. Pipeline references tags during classification. New tags can be proposed by LLM and queued for admin approval or auto-added within existing categories.\n**Validation:** Tag list is editable; pipeline uses canonical tags consistently; alias normalization works.\n**Primary Owner:** M001/S03\n\n## R012 — Incremental Content Addition\n**Status:** active\n**Description:** System handles ongoing content: new videos processed through pipeline, new creators auto-detected, existing technique pages updated when new moments are added for same creator+topic.\n**Validation:** Adding a new video for an existing creator updates their technique pages; new creator folder creates new Creator record.\n**Primary Owner:** M001/S03\n\n## R013 — Prompt Template System\n**Status:** active\n**Description:** Extraction prompts (stages 2-5) stored as editable configuration files, not hardcoded. Admin can edit prompts and re-run extraction on specific or all videos for calibration.\n**Validation:** Prompt files are editable; re-processing a video with updated prompts produces different output.\n**Primary Owner:** M001/S03\n\n## R014 — Creator Equity\n**Status:** active\n**Description:** No creator is privileged in the UI. Default sort on Creators page is randomized on every page load. All creators get equal visual weight.\n**Validation:** Refreshing Creators page shows different order each time; no creator gets larger/bolder display.\n**Primary Owner:** M001/S05\n\n## R015 — 30-Second Retrieval Target\n**Status:** active\n**Description:** A producer mid-session can find a specific technique in under 30 seconds from Alt+Tab to reading the key insight.\n**Validation:** Timed test: Alt+Tab → search → read technique → under 30 seconds.\n**Primary Owner:** M001/S05\n\n---\n\n### Project Knowledge\nSource: `.gsd/KNOWLEDGE.md`\n\n# KNOWLEDGE\n\n## SQLAlchemy column names that shadow ORM functions\n\n**Context:** If a model has a column named `relationship` (or any other ORM function name like `query`, `metadata`), Python resolves the name to the column's `MappedColumn` descriptor within the class body. This causes `relationship(...)` calls below it to fail with \"MappedColumn object is not callable\".\n\n**Fix:** Import with an alias: `from sqlalchemy.orm import relationship as sa_relationship` and use `sa_relationship(...)` throughout. Alternatively, rename the database column, but the spec defines `relationship` as the column name so we preserved it.\n\n## Docker Compose variable interpolation and `:?` syntax\n\n**Context:** `${VAR:?error message}` in docker-compose.yml makes `docker compose config` fail when the variable is unset, even if `env_file: required: false` is used. The `required: false` only controls whether the `.env` file must exist — it does NOT provide defaults for variables referenced with `:?`.\n\n**Fix:** Use `${VAR:-default}` for variables that need a fallback, or ensure the variable is always set in the environment.\n\n## Host port 8000 conflict with kerf-engine\n\n**Context:** The kerf-engine Docker container (`kerf-engine-kerf-engine-1`) binds to `0.0.0.0:8000`. When testing the Chrysopedia API locally (outside Docker), curl requests to `localhost:8000` hit the kerf-engine container instead of the local uvicorn process, even when uvicorn binds to `127.0.0.1:8000`.\n\n**Fix:** Use an alternate port (e.g., 8001) for local testing. In Docker Compose, chrysopedia-api maps `127.0.0.1:8000:8000` which is fine because it goes through Docker's port forwarding, but will conflict with kerf-engine if both stacks run simultaneously. Consider changing one project's external port mapping.\n\n## asyncpg rejects timezone-aware datetimes for TIMESTAMP WITHOUT TIME ZONE columns\n\n**Context:** SQLAlchemy models using `default=datetime.now(timezone.utc)` produce timezone-aware Python datetimes. When the PostgreSQL column type is `TIMESTAMP WITHOUT TIME ZONE` (the default), asyncpg raises `DataError: can't subtract offset-naive and offset-aware datetimes`. This only surfaces when running against a real PostgreSQL database — SQLite-based tests won't catch it.\n\n**Fix:** Use `datetime.now(timezone.utc).replace(tzinfo=None)` in the `_now()` helper, or change the column type to `TIMESTAMP WITH TIME ZONE`. We chose the former since the existing schema uses naive timestamps.\n\n## asyncpg NullPool required for pytest-asyncio integration tests\n\n**Context:** When using a session-scoped SQLAlchemy async engine with asyncpg in pytest-asyncio tests, the connection pool reuses connections across fixtures and test functions. This causes `InterfaceError: cannot perform operation: another operation is in progress` because the ASGI test client's session holds a connection while cleanup/verification fixtures try to use the same pool.\n\n**Fix:** Use `poolclass=NullPool` when creating the test engine. Each connection is created fresh and immediately closed, eliminating contention. Performance cost is negligible for test suites.\n\n## Testing Celery tasks that use sync SQLAlchemy: patch module-level globals\n\n**Context:** Pipeline stages in `pipeline/stages.py` create their own sync SQLAlchemy engine/session via module-level `_engine` and `_SessionLocal` globals (because Celery is sync, not async). Tests need to redirect these to the test database, but the engine is created lazily at module scope.\n\n**Fix:** Patch the module globals directly: `unittest.mock.patch.object(stages, '_engine', test_engine)` and `unittest.mock.patch.object(stages, '_SessionLocal', test_session_factory)`. This redirects all DB access in stage functions to the test database without modifying production code.\n\n## Lazy imports in FastAPI handlers defeat simple mock patching\n\n**Context:** When a FastAPI handler imports a function lazily (inside the function body) like `from pipeline.stages import run_pipeline`, patching `routers.ingest.run_pipeline` has no effect because the name is re-bound on every call from the source module.\n\n**Fix:** Patch at the source module: `unittest.mock.patch('pipeline.stages.run_pipeline')`. The lazy import will pick up the mock from the source module. This applies to any handler that uses lazy imports to avoid circular dependencies at module load time.\n\n## Stage 4 classification data stored in Redis (not DB columns)\n\n**Context:** The KeyMoment SQLAlchemy model doesn't have `topic_tags` or `topic_category` columns. Stage 4 classification needs somewhere to store per-moment tag assignments that stage 5 can read.\n\n**Fix:** Store classification results in Redis under key `chrysopedia:classification:{video_id}` with a 24-hour TTL. Stage 5 reads from Redis. This avoids schema migrations during initial pipeline development. The data is ephemeral — if Redis loses it, re-running stage 4 regenerates it.\n\n---\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/M001/slices/S03/S03-SUMMARY.md`\n\n---\nid: S03\nparent: M001\nmilestone: M001\nprovides:\n  - 6 Celery tasks: stage2-6 + run_pipeline orchestrator\n  - LLMClient with primary/fallback for downstream use\n  - EmbeddingClient for vector generation\n  - QdrantManager for vector store operations\n  - POST /api/v1/pipeline/trigger/{video_id} manual re-trigger endpoint\n  - 8 Pydantic schemas for pipeline stage I/O\n  - 4 editable prompt templates in prompts/\n  - 10 integration tests with mock fixtures\nrequires:\n  - slice: S02\n    provides: Ingest endpoint, database models (SourceVideo, TranscriptSegment, KeyMoment, TechniquePage, Creator), async SQLAlchemy engine, test infrastructure\naffects:\n  - S04\n  - S05\nkey_files:\n  - backend/config.py\n  - backend/worker.py\n  - backend/pipeline/__init__.py\n  - backend/pipeline/schemas.py\n  - backend/pipeline/llm_client.py\n  - backend/pipeline/embedding_client.py\n  - backend/pipeline/qdrant_client.py\n  - backend/pipeline/stages.py\n  - backend/routers/pipeline.py\n  - backend/routers/ingest.py\n  - backend/main.py\n  - prompts/stage2_segmentation.txt\n  - prompts/stage3_extraction.txt\n  - prompts/stage4_classification.txt\n  - prompts/stage5_synthesis.txt\n  - backend/tests/test_pipeline.py\n  - backend/tests/fixtures/mock_llm_responses.py\n  - backend/tests/conftest.py\n  - backend/requirements.txt\nkey_decisions:\n  - Sync OpenAI/SQLAlchemy/Qdrant throughout Celery tasks — no async in worker context (D004)\n  - Embedding/Qdrant stage is non-blocking side-effect — failures don't break pipeline (D005)\n  - Stage 4 classification stored in Redis (24h TTL) due to missing KeyMoment columns\n  - Pipeline dispatch from ingest is best-effort; manual trigger returns 503 on Celery failure\n  - LLMClient retries once with JSON nudge on malformed LLM output before failing\npatterns_established:\n  - Celery task pattern: @celery_app.task(bind=True, max_retries=3) with sync SQLAlchemy session per task\n  - LLM client pattern: primary → fallback → fail, with Pydantic response parsing\n  - Non-blocking side-effect pattern: max_retries=0, catch-all exception handler, pipeline continues\n  - Prompt template pattern: plain text files in prompts/ dir, XML-style content fencing, loaded at runtime\n  - Pipeline test pattern: patch module-level _engine/_SessionLocal globals to redirect stages to test DB\nobservability_surfaces:\n  - INFO log at start/end of each stage with video_id and duration\n  - WARNING on LLM fallback trigger\n  - ERROR on LLM parse failure with raw response excerpt\n  - WARNING on embedding/Qdrant failures with error details\n  - source_videos.processing_status tracks pipeline progress per video\n  - Celery task registry shows all 6 registered tasks\n  - POST /api/v1/pipeline/trigger/{video_id} returns current processing_status\ndrill_down_paths:\n  - .gsd/milestones/M001/slices/S03/tasks/T01-SUMMARY.md\n  - .gsd/milestones/M001/slices/S03/tasks/T02-SUMMARY.md\n  - .gsd/milestones/M001/slices/S03/tasks/T03-SUMMARY.md\n  - .gsd/milestones/M001/slices/S03/tasks/T04-SUMMARY.md\n  - .gsd/milestones/M001/slices/S03/tasks/T05-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-03-29T22:59:23.268Z\nblocker_discovered: false\n---\n\n# S03: LLM Extraction Pipeline + Qdrant Integration\n\n**Built the complete 6-stage LLM extraction pipeline (segmentation → extraction → classification → synthesis → embedding) with Celery workers, sync SQLAlchemy, primary/fallback LLM endpoints, Qdrant vector indexing, configurable prompt templates, auto-dispatch from ingest, manual re-trigger API, and 10 integration tests — all 16 tests pass.**\n\n## What Happened\n\n## What This Slice Delivered\n\nS03 implemented the core intelligence of Chrysopedia: the background worker pipeline that transforms raw transcripts into structured knowledge (technique pages, key moments, topic tags, embeddings).\n\n### T01 — Infrastructure Foundation\nExtended Settings with 12 config fields (LLM primary/fallback endpoints, embedding config, Qdrant connection, prompt path, review mode). Created the Celery app in `worker.py` using Redis as broker. Built `LLMClient` with sync `openai.OpenAI` and primary→fallback logic that catches `APIConnectionError` and `APITimeoutError`. Defined 8 Pydantic schemas (`TopicSegment`, `SegmentationResult`, `ExtractedMoment`, `ExtractionResult`, `ClassifiedMoment`, `ClassificationResult`, `SynthesizedPage`, `SynthesisResult`) matching the pipeline stage inputs/outputs.\n\n### T02 — Pipeline Stages + Prompt Templates\nCreated 4 prompt template files in `prompts/` with XML-style content fencing. Implemented 5 Celery tasks in `pipeline/stages.py`:\n- **stage2_segmentation**: Groups transcript segments into topic boundaries, updates `topic_label` on TranscriptSegment rows\n- **stage3_extraction**: Extracts key moments from topic groups, creates KeyMoment rows, sets `processing_status=extracted`\n- **stage4_classification**: Classifies moments against `canonical_tags.yaml`, stores results in Redis (24h TTL) since KeyMoment lacks tag columns\n- **stage5_synthesis**: Synthesizes TechniquePage rows from grouped moments, links KeyMoments, sets `processing_status=reviewed` (or `published` if `review_mode=False`)\n- **run_pipeline**: Orchestrator that checks `processing_status` and chains only the remaining stages for resumability\n\nAll tasks use sync SQLAlchemy sessions (psycopg2) with `bind=True, max_retries=3`. `_safe_parse_llm_response` retries once with a JSON nudge on malformed output.\n\n### T03 — Embedding & Qdrant Integration\nCreated `EmbeddingClient` (sync `openai.OpenAI` for `/v1/embeddings`) that returns empty list on errors. Created `QdrantManager` with idempotent `ensure_collection()` (cosine distance, config-driven dimensions) and `upsert_technique_pages()`/`upsert_key_moments()` with full metadata payloads. Added `stage6_embed_and_index` as a non-blocking side-effect (`max_retries=0`, catches all exceptions) appended to the pipeline chain.\n\n### T04 — API Wiring\nWired `run_pipeline.delay()` dispatch after ingest commit (best-effort — failures don't break ingest response). Added `POST /api/v1/pipeline/trigger/{video_id}` for manual re-processing (returns 404 for missing video, 503 on Celery failure). Mounted pipeline router in `main.py`.\n\n### T05 — Integration Tests\nCreated 10 integration tests with mocked LLM/Qdrant and real PostgreSQL: stages 2-6 produce correct DB records, pipeline resumes from `extracted` status, trigger endpoint returns 200/404, ingest dispatches pipeline, LLM falls back on primary failure. All 16 tests (6 ingest + 10 pipeline) pass.\n\n### Key Deviation\nStage 4 stores classification data in Redis rather than DB columns because `KeyMoment` model lacks `topic_tags`/`topic_category` columns. This is an intentional simplification — stage 5 reads from Redis during synthesis.\n\n## Verification\n\nAll slice verification checks pass:\n\n**T01 verification (5/5):** Settings prints correct defaults, all 8 schema classes import, LLMClient imports clean, celery_app.main prints 'chrysopedia', openai/qdrant-client in requirements.txt.\n\n**T02 verification (3/3):** 4 prompt files exist, all 5 stage functions import, worker shows 6 registered tasks.\n\n**T03 verification (3/3):** EmbeddingClient, QdrantManager, stage6_embed_and_index all import successfully.\n\n**T04 verification (3/3):** Pipeline router has /trigger/{video_id} route, pipeline in main.py, run_pipeline in ingest.py.\n\n**T05 verification (2/2):** `cd backend && python -m pytest tests/test_pipeline.py -v` — 10/10 pass. `cd backend && python -m pytest tests/ -v` — 16/16 pass (122s).\n\nAll 16 registered tests pass. 6 Celery tasks registered in worker.\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nStage 4 classification data stored in Redis (not DB columns) because KeyMoment model lacks topic_tags/topic_category columns. Added psycopg2-binary to requirements.txt for sync SQLAlchemy. Created pipeline/stages.py stub in T01 so worker.py import chain succeeds ahead of T02. Pipeline router uses lazy import of run_pipeline inside handler to avoid circular imports.\n\n## Known Limitations\n\nStage 4 classification stored in Redis with 24h TTL — if Redis is flushed between stage 4 and stage 5, classification data is lost. QdrantManager uses random UUIDs for point IDs — re-indexing creates duplicates rather than updating existing points. KeyMoment model needs topic_tags/topic_category columns for a permanent solution.\n\n## Follow-ups\n\nAdd topic_tags and topic_category columns to KeyMoment model to eliminate Redis dependency for classification. Add deterministic point IDs to QdrantManager based on content hash for idempotent re-indexing. Consider adding a /api/v1/pipeline/status/{video_id} endpoint for monitoring pipeline progress.\n\n## Files Created/Modified\n\n- `backend/config.py` — Extended Settings with 12 LLM/embedding/Qdrant/prompt config fields\n- `backend/requirements.txt` — Added openai, qdrant-client, pyyaml, psycopg2-binary\n- `backend/worker.py` — Created Celery app with Redis broker, imports pipeline.stages\n- `backend/pipeline/__init__.py` — Created empty package init\n- `backend/pipeline/schemas.py` — 8 Pydantic models for pipeline stage I/O\n- `backend/pipeline/llm_client.py` — Sync LLMClient with primary/fallback logic\n- `backend/pipeline/embedding_client.py` — Sync EmbeddingClient for /v1/embeddings\n- `backend/pipeline/qdrant_client.py` — QdrantManager with idempotent collection mgmt and metadata upserts\n- `backend/pipeline/stages.py` — 6 Celery tasks: stages 2-6 + run_pipeline orchestrator\n- `backend/routers/pipeline.py` — POST /trigger/{video_id} manual re-trigger endpoint\n- `backend/routers/ingest.py` — Added run_pipeline.delay() dispatch after ingest commit\n- `backend/main.py` — Mounted pipeline router under /api/v1\n- `prompts/stage2_segmentation.txt` — LLM prompt for topic boundary detection\n- `prompts/stage3_extraction.txt` — LLM prompt for key moment extraction\n- `prompts/stage4_classification.txt` — LLM prompt for canonical tag classification\n- `prompts/stage5_synthesis.txt` — LLM prompt for technique page synthesis\n- `backend/tests/test_pipeline.py` — 10 integration tests covering all pipeline stages\n- `backend/tests/fixtures/mock_llm_responses.py` — Mock LLM response fixtures for all stages\n- `backend/tests/conftest.py` — Added sync engine/session fixtures and pre_ingested_video fixture\n\n## Your Role in the Pipeline\n\nYou 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: M001`, `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/M001/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":"dfbc69f9","parentId":"07b7a533","timestamp":"2026-03-29T22:59:30.747Z"}
{"type":"message","id":"117d5188","parentId":"dfbc69f9","timestamp":"2026-03-29T22:59:39.524Z","message":{"role":"assistant","content":[{"type":"text","text":"\n\nI'll research S04 \"Review Queue Admin UI\" by exploring the existing codebase to understand what's available and what needs to be built.\n\n**Target requirements:** R004 (Review Queue UI) is the primary owner. R011 (Canonical Tag System) and R012 (Incremental Content Addition) are supporting — the review queue interacts with processing status and review mode.\n\nLet me start by understanding the current state of the codebase:"},{"type":"toolCall","id":"toolu_015wXwbw9aQ8JxeStqbT5nJN","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator -type f -name '*.py' -o -name '*.html' -o -name '*.js' -o -name '*.ts' -o -name '*.yaml' -o -name '*.yml' | grep -v __pycache__ | grep -v node_modules | grep -v .git | sort"}},{"type":"toolCall","id":"toolu_01PM1MmxHup3rCgG1DA8FJdV","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/backend/models.py"}},{"type":"toolCall","id":"toolu_011xrG3KoBnh44e65xf7JR5H","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/backend/config.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":353,"cacheRead":49033,"cacheWrite":9667,"totalTokens":59056,"cost":{"input":0.000015000000000000002,"output":0.008825000000000001,"cacheRead":0.0245165,"cacheWrite":0.06041875,"total":0.09377525}},"stopReason":"toolUse","timestamp":1774825170746}}
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{"type":"message","id":"42c646bb","parentId":"65c5cb74","timestamp":"2026-03-29T22:59:39.624Z","message":{"role":"toolResult","toolCallId":"toolu_01PM1MmxHup3rCgG1DA8FJdV","toolName":"bash","content":[{"type":"text","text":"\"\"\"SQLAlchemy ORM models for the Chrysopedia knowledge base.\n\nSeven entities matching chrysopedia-spec.md §6.1:\n  Creator, SourceVideo, TranscriptSegment, KeyMoment,\n  TechniquePage, RelatedTechniqueLink, Tag\n\"\"\"\n\nfrom __future__ import annotations\n\nimport enum\nimport uuid\nfrom datetime import datetime, timezone\n\nfrom sqlalchemy import (\n    Enum,\n    Float,\n    ForeignKey,\n    Integer,\n    String,\n    Text,\n    UniqueConstraint,\n    func,\n)\nfrom sqlalchemy.dialects.postgresql import ARRAY, JSONB, UUID\nfrom sqlalchemy.orm import Mapped, mapped_column\nfrom sqlalchemy.orm import relationship as sa_relationship\n\nfrom database import Base\n\n\n# ── Enums ────────────────────────────────────────────────────────────────────\n\nclass ContentType(str, enum.Enum):\n    \"\"\"Source video content type.\"\"\"\n    tutorial = \"tutorial\"\n    livestream = \"livestream\"\n    breakdown = \"breakdown\"\n    short_form = \"short_form\"\n\n\nclass ProcessingStatus(str, enum.Enum):\n    \"\"\"Pipeline processing status for a source video.\"\"\"\n    pending = \"pending\"\n    transcribed = \"transcribed\"\n    extracted = \"extracted\"\n    reviewed = \"reviewed\"\n    published = \"published\"\n\n\nclass KeyMomentContentType(str, enum.Enum):\n    \"\"\"Content classification for a key moment.\"\"\"\n    technique = \"technique\"\n    settings = \"settings\"\n    reasoning = \"reasoning\"\n    workflow = \"workflow\"\n\n\nclass ReviewStatus(str, enum.Enum):\n    \"\"\"Human review status for key moments.\"\"\"\n    pending = \"pending\"\n    approved = \"approved\"\n    edited = \"edited\"\n    rejected = \"rejected\"\n\n\nclass SourceQuality(str, enum.Enum):\n    \"\"\"Derived source quality for technique pages.\"\"\"\n    structured = \"structured\"\n    mixed = \"mixed\"\n    unstructured = \"unstructured\"\n\n\nclass PageReviewStatus(str, enum.Enum):\n    \"\"\"Review lifecycle for technique pages.\"\"\"\n    draft = \"draft\"\n    reviewed = \"reviewed\"\n    published = \"published\"\n\n\nclass RelationshipType(str, enum.Enum):\n    \"\"\"Types of links between technique pages.\"\"\"\n    same_technique_other_creator = \"same_technique_other_creator\"\n    same_creator_adjacent = \"same_creator_adjacent\"\n    general_cross_reference = \"general_cross_reference\"\n\n\n# ── Helpers ──────────────────────────────────────────────────────────────────\n\ndef _uuid_pk() -> Mapped[uuid.UUID]:\n    return mapped_column(\n        UUID(as_uuid=True),\n        primary_key=True,\n        default=uuid.uuid4,\n        server_default=func.gen_random_uuid(),\n    )\n\n\ndef _now() -> datetime:\n    \"\"\"Return current UTC time as a naive datetime (no tzinfo).\n\n    PostgreSQL TIMESTAMP WITHOUT TIME ZONE columns require naive datetimes.\n    asyncpg rejects timezone-aware datetimes for such columns.\n    \"\"\"\n    return datetime.now(timezone.utc).replace(tzinfo=None)\n\n\n# ── Models ───────────────────────────────────────────────────────────────────\n\nclass Creator(Base):\n    __tablename__ = \"creators\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    name: Mapped[str] = mapped_column(String(255), nullable=False)\n    slug: Mapped[str] = mapped_column(String(255), unique=True, nullable=False)\n    genres: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    folder_name: Mapped[str] = mapped_column(String(255), nullable=False)\n    view_count: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    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":1774825179619}}
{"type":"message","id":"8fbdf8df","parentId":"42c646bb","timestamp":"2026-03-29T22:59:39.625Z","message":{"role":"toolResult","toolCallId":"toolu_011xrG3KoBnh44e65xf7JR5H","toolName":"bash","content":[{"type":"text","text":"\"\"\"Application configuration loaded from environment variables.\"\"\"\n\nfrom functools import lru_cache\n\nfrom pydantic_settings import BaseSettings\n\n\nclass Settings(BaseSettings):\n    \"\"\"Chrysopedia API settings.\n\n    Values are loaded from environment variables (or .env file via\n    pydantic-settings' dotenv support).\n    \"\"\"\n\n    # Database\n    database_url: str = \"postgresql+asyncpg://chrysopedia:changeme@localhost:5433/chrysopedia\"\n\n    # Redis\n    redis_url: str = \"redis://localhost:6379/0\"\n\n    # Application\n    app_env: str = \"development\"\n    app_log_level: str = \"info\"\n    app_secret_key: str = \"changeme-generate-a-real-secret\"\n\n    # CORS\n    cors_origins: list[str] = [\"*\"]\n\n    # LLM endpoint (OpenAI-compatible)\n    llm_api_url: str = \"http://localhost:11434/v1\"\n    llm_api_key: str = \"sk-placeholder\"\n    llm_model: str = \"qwen2.5:14b-q8_0\"\n    llm_fallback_url: str = \"http://localhost:11434/v1\"\n    llm_fallback_model: str = \"qwen2.5:14b-q8_0\"\n\n    # Embedding endpoint\n    embedding_api_url: str = \"http://localhost:11434/v1\"\n    embedding_model: str = \"nomic-embed-text\"\n    embedding_dimensions: int = 768\n\n    # Qdrant\n    qdrant_url: str = \"http://localhost:6333\"\n    qdrant_collection: str = \"chrysopedia\"\n\n    # Prompt templates\n    prompts_path: str = \"./prompts\"\n\n    # Review mode — when True, extracted moments go to review queue before publishing\n    review_mode: bool = True\n\n    # File storage\n    transcript_storage_path: str = \"/data/transcripts\"\n    video_metadata_path: str = \"/data/video_meta\"\n\n    model_config = {\n        \"env_file\": \".env\",\n        \"env_file_encoding\": \"utf-8\",\n        \"case_sensitive\": False,\n    }\n\n\n@lru_cache\ndef get_settings() -> Settings:\n    \"\"\"Return cached application settings (singleton).\"\"\"\n    return Settings()\n"}],"isError":false,"timestamp":1774825179623}}
{"type":"message","id":"5330ed24","parentId":"8fbdf8df","timestamp":"2026-03-29T22:59:46.403Z","message":{"role":"assistant","content":[{"type":"text","text":"The find output is overwhelmed by .venv files. Let me get just the project source files:"},{"type":"toolCall","id":"toolu_01SSWdqnxfMcB4TvnBjs9f1z","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator -type f \\( -name '*.py' -o -name '*.html' -o -name '*.js' -o -name '*.ts' -o -name '*.yaml' -o -name '*.yml' -o -name '*.txt' -o -name '*.css' -o -name '*.json' \\) -not -path '*/.venv/*' -not -path '*/__pycache__/*' -not -path '*/.git/*' -not -path '*/node_modules/*' -not -path '*/.gsd/*' | sort"}},{"type":"toolCall","id":"toolu_01DuPUVJFWuZK6X8M6WDs7Kx","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/backend/main.py"}},{"type":"toolCall","id":"toolu_01Exg2DyfkFx4EP5TPxwnAMU","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/backend/routers/pipeline.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":331,"cacheRead":58700,"cacheWrite":23632,"totalTokens":82664,"cost":{"input":0.000005,"output":0.008275000000000001,"cacheRead":0.029349999999999998,"cacheWrite":0.1477,"total":0.18533}},"stopReason":"toolUse","timestamp":1774825179624}}
{"type":"message","id":"3ff6fc2a","parentId":"5330ed24","timestamp":"2026-03-29T22:59:46.449Z","message":{"role":"toolResult","toolCallId":"toolu_01SSWdqnxfMcB4TvnBjs9f1z","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/alembic/env.py\n/home/aux/projects/content-to-kb-automator/alembic/versions/001_initial.py\n/home/aux/projects/content-to-kb-automator/backend/config.py\n/home/aux/projects/content-to-kb-automator/backend/database.py\n/home/aux/projects/content-to-kb-automator/backend/main.py\n/home/aux/projects/content-to-kb-automator/backend/models.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/embedding_client.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/__init__.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/llm_client.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/qdrant_client.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/schemas.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py\n/home/aux/projects/content-to-kb-automator/backend/requirements.txt\n/home/aux/projects/content-to-kb-automator/backend/routers/creators.py\n/home/aux/projects/content-to-kb-automator/backend/routers/health.py\n/home/aux/projects/content-to-kb-automator/backend/routers/ingest.py\n/home/aux/projects/content-to-kb-automator/backend/routers/__init__.py\n/home/aux/projects/content-to-kb-automator/backend/routers/pipeline.py\n/home/aux/projects/content-to-kb-automator/backend/routers/videos.py\n/home/aux/projects/content-to-kb-automator/backend/schemas.py\n/home/aux/projects/content-to-kb-automator/backend/tests/conftest.py\n/home/aux/projects/content-to-kb-automator/backend/tests/fixtures/mock_llm_responses.py\n/home/aux/projects/content-to-kb-automator/backend/tests/fixtures/sample_transcript.json\n/home/aux/projects/content-to-kb-automator/backend/tests/__init__.py\n/home/aux/projects/content-to-kb-automator/backend/tests/test_ingest.py\n/home/aux/projects/content-to-kb-automator/backend/tests/test_pipeline.py\n/home/aux/projects/content-to-kb-automator/backend/worker.py\n/home/aux/projects/content-to-kb-automator/.bg-shell/manifest.json\n/home/aux/projects/content-to-kb-automator/config/canonical_tags.yaml\n/home/aux/projects/content-to-kb-automator/docker-compose.yml\n/home/aux/projects/content-to-kb-automator/frontend/package.json\n/home/aux/projects/content-to-kb-automator/prompts/stage2_segmentation.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage3_extraction.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage4_classification.txt\n/home/aux/projects/content-to-kb-automator/prompts/stage5_synthesis.txt\n/home/aux/projects/content-to-kb-automator/tests/fixtures/sample_transcript.json\n/home/aux/projects/content-to-kb-automator/whisper/requirements.txt\n/home/aux/projects/content-to-kb-automator/whisper/transcribe.py\n"}],"isError":false,"timestamp":1774825186448}}
{"type":"message","id":"c1fdbc28","parentId":"3ff6fc2a","timestamp":"2026-03-29T22:59:46.450Z","message":{"role":"toolResult","toolCallId":"toolu_01DuPUVJFWuZK6X8M6WDs7Kx","toolName":"read","content":[{"type":"text","text":"\"\"\"Chrysopedia API — Knowledge extraction and retrieval system.\n\nEntry point for the FastAPI application. Configures middleware,\nstructured logging, and mounts versioned API routers.\n\"\"\"\n\nimport logging\nimport sys\nfrom contextlib import asynccontextmanager\n\nfrom fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\n\nfrom config import get_settings\nfrom routers import creators, health, ingest, pipeline, videos\n\n\ndef _setup_logging() -> None:\n    \"\"\"Configure structured logging to stdout.\"\"\"\n    settings = get_settings()\n    level = getattr(logging, settings.app_log_level.upper(), logging.INFO)\n\n    handler = logging.StreamHandler(sys.stdout)\n    handler.setFormatter(\n        logging.Formatter(\n            fmt=\"%(asctime)s | %(levelname)-8s | %(name)s | %(message)s\",\n            datefmt=\"%Y-%m-%dT%H:%M:%S\",\n        )\n    )\n\n    root = logging.getLogger()\n    root.setLevel(level)\n    # Avoid duplicate handlers on reload\n    root.handlers.clear()\n    root.addHandler(handler)\n\n    # Quiet noisy libraries\n    logging.getLogger(\"uvicorn.access\").setLevel(logging.WARNING)\n    logging.getLogger(\"sqlalchemy.engine\").setLevel(logging.WARNING)\n\n\n@asynccontextmanager\nasync def lifespan(app: FastAPI):  # noqa: ARG001\n    \"\"\"Application lifespan: setup on startup, teardown on shutdown.\"\"\"\n    _setup_logging()\n    logger = logging.getLogger(\"chrysopedia\")\n    settings = get_settings()\n    logger.info(\n        \"Chrysopedia API starting (env=%s, log_level=%s)\",\n        settings.app_env,\n        settings.app_log_level,\n    )\n    yield\n    logger.info(\"Chrysopedia API shutting down\")\n\n\napp = FastAPI(\n    title=\"Chrysopedia API\",\n    description=\"Knowledge extraction and retrieval for music production content\",\n    version=\"0.1.0\",\n    lifespan=lifespan,\n)\n\n# ── Middleware ────────────────────────────────────────────────────────────────\n\nsettings = get_settings()\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=settings.cors_origins,\n    allow_credentials=True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"],\n)\n\n# ── Routers ──────────────────────────────────────────────────────────────────\n\n# Root-level health (no prefix)\napp.include_router(health.router)\n\n# Versioned API\napp.include_router(creators.router, prefix=\"/api/v1\")\napp.include_router(ingest.router, prefix=\"/api/v1\")\napp.include_router(pipeline.router, prefix=\"/api/v1\")\napp.include_router(videos.router, prefix=\"/api/v1\")\n\n\n@app.get(\"/api/v1/health\")\nasync def api_health():\n    \"\"\"Lightweight version-prefixed health endpoint (no DB check).\"\"\"\n    return {\"status\": \"ok\", \"version\": \"0.1.0\"}\n"}],"isError":false,"timestamp":1774825186448}}
{"type":"message","id":"417fe93f","parentId":"c1fdbc28","timestamp":"2026-03-29T22:59:46.452Z","message":{"role":"toolResult","toolCallId":"toolu_01Exg2DyfkFx4EP5TPxwnAMU","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":1774825186449}}
{"type":"message","id":"a98c12b8","parentId":"417fe93f","timestamp":"2026-03-29T22:59:53.316Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. Now let me examine the existing routers and schemas to understand the API patterns, plus the KeyMoment model closely and the existing review_mode config:"},{"type":"toolCall","id":"toolu_01PVEBtoAMAbKMedWi4LZzEo","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/backend/schemas.py"}},{"type":"toolCall","id":"toolu_01KjsZQEcpQDH5ycAsWt6fRJ","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/backend/routers/videos.py"}},{"type":"toolCall","id":"toolu_01ALpzbSNVnDyTLYkQTcQV4i","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/backend/routers/creators.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":220,"cacheRead":82332,"cacheWrite":2597,"totalTokens":85150,"cost":{"input":0.000005,"output":0.0055000000000000005,"cacheRead":0.041166,"cacheWrite":0.01623125,"total":0.06290225}},"stopReason":"toolUse","timestamp":1774825186450}}
{"type":"message","id":"b283e185","parentId":"a98c12b8","timestamp":"2026-03-29T22:59:53.322Z","message":{"role":"toolResult","toolCallId":"toolu_01PVEBtoAMAbKMedWi4LZzEo","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"}],"isError":false,"timestamp":1774825193321}}
{"type":"message","id":"2d312c4a","parentId":"b283e185","timestamp":"2026-03-29T22:59:53.324Z","message":{"role":"toolResult","toolCallId":"toolu_01KjsZQEcpQDH5ycAsWt6fRJ","toolName":"read","content":[{"type":"text","text":"\"\"\"Source video endpoints for Chrysopedia API.\"\"\"\n\nimport logging\nfrom typing import Annotated\n\nfrom fastapi import APIRouter, Depends, Query\nfrom sqlalchemy import select\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom database import get_session\nfrom models import SourceVideo\nfrom schemas import SourceVideoRead\n\nlogger = logging.getLogger(\"chrysopedia.videos\")\n\nrouter = APIRouter(prefix=\"/videos\", tags=[\"videos\"])\n\n\n@router.get(\"\", response_model=list[SourceVideoRead])\nasync def list_videos(\n    offset: Annotated[int, Query(ge=0)] = 0,\n    limit: Annotated[int, Query(ge=1, le=100)] = 50,\n    creator_id: str | None = None,\n    db: AsyncSession = Depends(get_session),\n) -> list[SourceVideoRead]:\n    \"\"\"List source videos with optional filtering by creator.\"\"\"\n    stmt = select(SourceVideo).order_by(SourceVideo.created_at.desc())\n\n    if creator_id:\n        stmt = stmt.where(SourceVideo.creator_id == creator_id)\n\n    stmt = stmt.offset(offset).limit(limit)\n    result = await db.execute(stmt)\n    videos = result.scalars().all()\n    logger.debug(\"Listed %d videos (offset=%d, limit=%d)\", len(videos), offset, limit)\n    return [SourceVideoRead.model_validate(v) for v in videos]\n"}],"isError":false,"timestamp":1774825193324}}
{"type":"message","id":"08bcb600","parentId":"2d312c4a","timestamp":"2026-03-29T22:59:53.326Z","message":{"role":"toolResult","toolCallId":"toolu_01ALpzbSNVnDyTLYkQTcQV4i","toolName":"read","content":[{"type":"text","text":"\"\"\"Creator endpoints for Chrysopedia API.\"\"\"\n\nimport logging\nfrom typing import Annotated\n\nfrom fastapi import APIRouter, Depends, HTTPException, Query\nfrom sqlalchemy import func, select\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom database import get_session\nfrom models import Creator, SourceVideo\nfrom schemas import CreatorDetail, CreatorRead\n\nlogger = logging.getLogger(\"chrysopedia.creators\")\n\nrouter = APIRouter(prefix=\"/creators\", tags=[\"creators\"])\n\n\n@router.get(\"\", response_model=list[CreatorRead])\nasync def list_creators(\n    offset: Annotated[int, Query(ge=0)] = 0,\n    limit: Annotated[int, Query(ge=1, le=100)] = 50,\n    db: AsyncSession = Depends(get_session),\n) -> list[CreatorRead]:\n    \"\"\"List all creators with pagination.\"\"\"\n    stmt = select(Creator).order_by(Creator.name).offset(offset).limit(limit)\n    result = await db.execute(stmt)\n    creators = result.scalars().all()\n    logger.debug(\"Listed %d creators (offset=%d, limit=%d)\", len(creators), offset, limit)\n    return [CreatorRead.model_validate(c) for c in creators]\n\n\n@router.get(\"/{slug}\", response_model=CreatorDetail)\nasync def get_creator(\n    slug: str,\n    db: AsyncSession = Depends(get_session),\n) -> CreatorDetail:\n    \"\"\"Get a single creator by slug, including video count.\"\"\"\n    stmt = select(Creator).where(Creator.slug == slug)\n    result = await db.execute(stmt)\n    creator = result.scalar_one_or_none()\n\n    if creator is None:\n        raise HTTPException(status_code=404, detail=f\"Creator '{slug}' not found\")\n\n    # Count videos for this creator\n    count_stmt = (\n        select(func.count())\n        .select_from(SourceVideo)\n        .where(SourceVideo.creator_id == creator.id)\n    )\n    count_result = await db.execute(count_stmt)\n    video_count = count_result.scalar() or 0\n\n    creator_data = CreatorRead.model_validate(creator)\n    return CreatorDetail(**creator_data.model_dump(), video_count=video_count)\n"}],"isError":false,"timestamp":1774825193324}}
{"type":"message","id":"e4457229","parentId":"08bcb600","timestamp":"2026-03-29T22:59:57.032Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me look at the pipeline stages to understand the `review_mode` interaction and how key moments get their status:"},{"type":"toolCall","id":"toolu_01Fmu4Jt7vxVkxoAoG9cnSsm","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":99,"cacheRead":84929,"cacheWrite":2708,"totalTokens":87737,"cost":{"input":0.000005,"output":0.002475,"cacheRead":0.042464499999999995,"cacheWrite":0.016925000000000003,"total":0.0618695}},"stopReason":"toolUse","timestamp":1774825193325}}
{"type":"message","id":"9db384d8","parentId":"e4457229","timestamp":"2026-03-29T22:59:57.039Z","message":{"role":"toolResult","toolCallId":"toolu_01Fmu4Jt7vxVkxoAoG9cnSsm","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 _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(raw: str, model_cls, llm: LLMClient, system_prompt: str, user_prompt: str):\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(system_prompt, nudge_prompt, response_model=model_cls)\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        raw = llm.complete(system_prompt, user_prompt, response_model=SegmentationResult)\n        result = _safe_parse_llm_response(raw, SegmentationResult, llm, system_prompt, user_prompt)\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        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            result = _safe_parse_llm_response(raw, ExtractionResult, llm, system_prompt, user_prompt)\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        raw = llm.complete(system_prompt, user_prompt, response_model=ClassificationResult)\n        result = _safe_parse_llm_response(raw, ClassificationResult, llm, system_prompt, user_prompt)\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        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            result = _safe_parse_llm_response(raw, SynthesisResult, llm, system_prompt, user_prompt)\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":1774825197036}}
{"type":"message","id":"96971656","parentId":"9db384d8","timestamp":"2026-03-29T23:00:09.673Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the database.py and frontend/package.json to understand the existing frontend setup:"},{"type":"toolCall","id":"toolu_01RiAzNJCm7c6CDGQgtSKZV9","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/backend/database.py"}},{"type":"toolCall","id":"toolu_01CvKrzTDjpxbFLccjNfrj5X","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/frontend/package.json"}},{"type":"toolCall","id":"toolu_0133hSVdPYf6szTPEN8JN95X","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/backend/routers/ingest.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":205,"cacheRead":87637,"cacheWrite":7962,"totalTokens":95805,"cost":{"input":0.000005,"output":0.005125,"cacheRead":0.043818499999999996,"cacheWrite":0.0497625,"total":0.098711}},"stopReason":"toolUse","timestamp":1774825197038}}
{"type":"message","id":"b1d33f91","parentId":"96971656","timestamp":"2026-03-29T23:00:09.677Z","message":{"role":"toolResult","toolCallId":"toolu_01RiAzNJCm7c6CDGQgtSKZV9","toolName":"read","content":[{"type":"text","text":"\"\"\"Database engine, session factory, and declarative base for Chrysopedia.\"\"\"\n\nimport os\n\nfrom sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine\nfrom sqlalchemy.orm import DeclarativeBase\n\nDATABASE_URL = os.getenv(\n    \"DATABASE_URL\",\n    \"postgresql+asyncpg://chrysopedia:changeme@localhost:5433/chrysopedia\",\n)\n\nengine = create_async_engine(DATABASE_URL, echo=False, pool_pre_ping=True)\n\nasync_session = async_sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)\n\n\nclass Base(DeclarativeBase):\n    \"\"\"Declarative base for all ORM models.\"\"\"\n    pass\n\n\nasync def get_session() -> AsyncSession:  # type: ignore[misc]\n    \"\"\"FastAPI dependency that yields an async DB session.\"\"\"\n    async with async_session() as session:\n        yield session\n"}],"isError":false,"timestamp":1774825209677}}
{"type":"message","id":"2428dbbc","parentId":"b1d33f91","timestamp":"2026-03-29T23:00:09.681Z","message":{"role":"toolResult","toolCallId":"toolu_01CvKrzTDjpxbFLccjNfrj5X","toolName":"read","content":[{"type":"text","text":"{\n  \"name\": \"chrysopedia-web\",\n  \"private\": true,\n  \"version\": \"0.1.0\",\n  \"type\": \"module\",\n  \"scripts\": {\n    \"dev\": \"echo 'placeholder — install a framework first'\",\n    \"build\": \"echo 'placeholder build' && mkdir -p dist && echo '<!DOCTYPE html><html><head><title>Chrysopedia</title></head><body><h1>Chrysopedia</h1><p>Web UI placeholder</p></body></html>' > dist/index.html\"\n  }\n}\n"}],"isError":false,"timestamp":1774825209681}}
{"type":"message","id":"66bbe938","parentId":"2428dbbc","timestamp":"2026-03-29T23:00:09.686Z","message":{"role":"toolResult","toolCallId":"toolu_0133hSVdPYf6szTPEN8JN95X","toolName":"read","content":[{"type":"text","text":"\"\"\"Transcript ingestion endpoint for the Chrysopedia API.\n\nAccepts a Whisper-format transcript JSON via multipart file upload, finds or\ncreates a Creator, upserts a SourceVideo, bulk-inserts TranscriptSegments,\npersists the raw JSON to disk, and returns a structured response.\n\"\"\"\n\nimport json\nimport logging\nimport os\nimport re\nimport uuid\n\nfrom fastapi import APIRouter, Depends, HTTPException, UploadFile\nfrom sqlalchemy import delete, select\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom config import get_settings\nfrom database import get_session\nfrom models import ContentType, Creator, ProcessingStatus, SourceVideo, TranscriptSegment\nfrom schemas import TranscriptIngestResponse\n\nlogger = logging.getLogger(\"chrysopedia.ingest\")\n\nrouter = APIRouter(prefix=\"/ingest\", tags=[\"ingest\"])\n\nREQUIRED_KEYS = {\"source_file\", \"creator_folder\", \"duration_seconds\", \"segments\"}\n\n\ndef slugify(value: str) -> str:\n    \"\"\"Lowercase, replace non-alphanumeric chars with hyphens, collapse/strip.\"\"\"\n    value = value.lower()\n    value = re.sub(r\"[^a-z0-9]+\", \"-\", value)\n    value = value.strip(\"-\")\n    value = re.sub(r\"-{2,}\", \"-\", value)\n    return value\n\n\n@router.post(\"\", response_model=TranscriptIngestResponse)\nasync def ingest_transcript(\n    file: UploadFile,\n    db: AsyncSession = Depends(get_session),\n) -> TranscriptIngestResponse:\n    \"\"\"Ingest a Whisper transcript JSON file.\n\n    Workflow:\n      1. Parse and validate the uploaded JSON.\n      2. Find-or-create a Creator by folder_name.\n      3. Upsert a SourceVideo by (creator_id, filename).\n      4. Bulk-insert TranscriptSegment rows.\n      5. Save raw JSON to transcript_storage_path.\n      6. Return structured response.\n    \"\"\"\n    settings = get_settings()\n\n    # ── 1. Read & parse JSON ─────────────────────────────────────────────\n    try:\n        raw_bytes = await file.read()\n        raw_text = raw_bytes.decode(\"utf-8\")\n    except Exception as exc:\n        raise HTTPException(status_code=400, detail=f\"Invalid file: {exc}\") from exc\n\n    try:\n        data = json.loads(raw_text)\n    except json.JSONDecodeError as exc:\n        raise HTTPException(\n            status_code=422, detail=f\"JSON parse error: {exc}\"\n        ) from exc\n\n    if not isinstance(data, dict):\n        raise HTTPException(status_code=422, detail=\"Expected a JSON object at the top level\")\n\n    missing = REQUIRED_KEYS - data.keys()\n    if missing:\n        raise HTTPException(\n            status_code=422,\n            detail=f\"Missing required keys: {', '.join(sorted(missing))}\",\n        )\n\n    source_file: str = data[\"source_file\"]\n    creator_folder: str = data[\"creator_folder\"]\n    duration_seconds: int | None = data.get(\"duration_seconds\")\n    segments_data: list = data[\"segments\"]\n\n    if not isinstance(segments_data, list):\n        raise HTTPException(status_code=422, detail=\"'segments' must be an array\")\n\n    # ── 2. Find-or-create Creator ────────────────────────────────────────\n    stmt = select(Creator).where(Creator.folder_name == creator_folder)\n    result = await db.execute(stmt)\n    creator = result.scalar_one_or_none()\n\n    if creator is None:\n        creator = Creator(\n            name=creator_folder,\n            slug=slugify(creator_folder),\n            folder_name=creator_folder,\n        )\n        db.add(creator)\n        await db.flush()  # assign id\n\n    # ── 3. Upsert SourceVideo ────────────────────────────────────────────\n    stmt = select(SourceVideo).where(\n        SourceVideo.creator_id == creator.id,\n        SourceVideo.filename == source_file,\n    )\n    result = await db.execute(stmt)\n    existing_video = result.scalar_one_or_none()\n\n    is_reupload = existing_video is not None\n\n    if is_reupload:\n        video = existing_video\n        # Delete old segments for idempotent re-upload\n        await db.execute(\n            delete(TranscriptSegment).where(\n                TranscriptSegment.source_video_id == video.id\n            )\n        )\n        video.duration_seconds = duration_seconds\n        video.processing_status = ProcessingStatus.transcribed\n    else:\n        video = SourceVideo(\n            creator_id=creator.id,\n            filename=source_file,\n            file_path=f\"{creator_folder}/{source_file}\",\n            duration_seconds=duration_seconds,\n            content_type=ContentType.tutorial,\n            processing_status=ProcessingStatus.transcribed,\n        )\n        db.add(video)\n        await db.flush()  # assign id\n\n    # ── 4. Bulk-insert TranscriptSegments ────────────────────────────────\n    segment_objs = [\n        TranscriptSegment(\n            source_video_id=video.id,\n            start_time=float(seg[\"start\"]),\n            end_time=float(seg[\"end\"]),\n            text=str(seg[\"text\"]),\n            segment_index=idx,\n        )\n        for idx, seg in enumerate(segments_data)\n    ]\n    db.add_all(segment_objs)\n\n    # ── 5. Save raw JSON to disk ─────────────────────────────────────────\n    transcript_dir = os.path.join(\n        settings.transcript_storage_path, creator_folder\n    )\n    transcript_path = os.path.join(transcript_dir, f\"{source_file}.json\")\n\n    try:\n        os.makedirs(transcript_dir, exist_ok=True)\n        with open(transcript_path, \"w\", encoding=\"utf-8\") as f:\n            f.write(raw_text)\n    except OSError as exc:\n        raise HTTPException(\n            status_code=500, detail=f\"Failed to save transcript: {exc}\"\n        ) from exc\n\n    video.transcript_path = transcript_path\n\n    # ── 6. Commit & respond ──────────────────────────────────────────────\n    try:\n        await db.commit()\n    except Exception as exc:\n        await db.rollback()\n        logger.error(\"Database commit failed during ingest: %s\", exc)\n        raise HTTPException(\n            status_code=500, detail=\"Database error during ingest\"\n        ) from exc\n\n    await db.refresh(video)\n    await db.refresh(creator)\n\n    # ── 7. Dispatch LLM pipeline (best-effort) ──────────────────────────\n    try:\n        from pipeline.stages import run_pipeline\n\n        run_pipeline.delay(str(video.id))\n        logger.info(\"Pipeline dispatched for video_id=%s\", video.id)\n    except Exception as exc:\n        logger.warning(\n            \"Pipeline dispatch failed for video_id=%s (ingest still succeeds): %s\",\n            video.id,\n            exc,\n        )\n\n    logger.info(\n        \"Ingested transcript: creator=%s, file=%s, segments=%d, reupload=%s\",\n        creator.name,\n        source_file,\n        len(segment_objs),\n        is_reupload,\n    )\n\n    return TranscriptIngestResponse(\n        video_id=video.id,\n        creator_id=creator.id,\n        creator_name=creator.name,\n        filename=source_file,\n        segments_stored=len(segment_objs),\n        processing_status=video.processing_status.value,\n        is_reupload=is_reupload,\n    )\n"}],"isError":false,"timestamp":1774825209684}}
{"type":"message","id":"4722b7a6","parentId":"66bbe938","timestamp":"2026-03-29T23:00:23.418Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the test infrastructure to understand testing patterns:"},{"type":"toolCall","id":"toolu_015msF61gQ256NqwSohAYKxz","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/backend/tests/conftest.py"}},{"type":"toolCall","id":"toolu_01PyE26MNrygA6g5STfnLhH4","name":"bash","arguments":{"command":"ls -la /home/aux/projects/content-to-kb-automator/frontend/"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":142,"cacheRead":95599,"cacheWrite":2623,"totalTokens":98365,"cost":{"input":0.000005,"output":0.00355,"cacheRead":0.047799499999999995,"cacheWrite":0.016393750000000002,"total":0.06774825}},"stopReason":"toolUse","timestamp":1774825209685}}
{"type":"message","id":"92c82f21","parentId":"4722b7a6","timestamp":"2026-03-29T23:00:23.431Z","message":{"role":"toolResult","toolCallId":"toolu_015msF61gQ256NqwSohAYKxz","toolName":"read","content":[{"type":"text","text":"\"\"\"Shared fixtures for Chrysopedia integration tests.\n\nProvides:\n- Async SQLAlchemy engine/session against a real PostgreSQL test database\n- Sync SQLAlchemy engine/session for pipeline stage tests (Celery stages are sync)\n- httpx.AsyncClient wired to the FastAPI app with dependency overrides\n- Pre-ingest fixture for pipeline tests\n- Sample transcript fixture path and temporary storage directory\n\nKey design choice: function-scoped engine with NullPool avoids asyncpg\n\"another operation in progress\" errors caused by session-scoped connection\nreuse between the ASGI test client and verification queries.\n\"\"\"\n\nimport json\nimport os\nimport pathlib\nimport uuid\n\nimport pytest\nimport pytest_asyncio\nfrom httpx import ASGITransport, AsyncClient\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine\nfrom sqlalchemy.orm import Session, sessionmaker\nfrom sqlalchemy.pool import NullPool\n\n# Ensure backend/ is on sys.path so \"from models import ...\" works\nimport sys\nsys.path.insert(0, str(pathlib.Path(__file__).resolve().parent.parent))\n\nfrom database import Base, get_session  # noqa: E402\nfrom main import app  # noqa: E402\nfrom models import (  # noqa: E402\n    ContentType,\n    Creator,\n    ProcessingStatus,\n    SourceVideo,\n    TranscriptSegment,\n)\n\nTEST_DATABASE_URL = os.getenv(\n    \"TEST_DATABASE_URL\",\n    \"postgresql+asyncpg://chrysopedia:changeme@localhost:5433/chrysopedia_test\",\n)\n\nTEST_DATABASE_URL_SYNC = TEST_DATABASE_URL.replace(\n    \"postgresql+asyncpg://\", \"postgresql+psycopg2://\"\n)\n\n\n@pytest_asyncio.fixture()\nasync def db_engine():\n    \"\"\"Create a per-test async engine (NullPool) and create/drop all tables.\"\"\"\n    engine = create_async_engine(TEST_DATABASE_URL, echo=False, poolclass=NullPool)\n\n    # Create all tables fresh for each test\n    async with engine.begin() as conn:\n        await conn.run_sync(Base.metadata.drop_all)\n        await conn.run_sync(Base.metadata.create_all)\n\n    yield engine\n\n    # Drop all tables after test\n    async with engine.begin() as conn:\n        await conn.run_sync(Base.metadata.drop_all)\n\n    await engine.dispose()\n\n\n@pytest_asyncio.fixture()\nasync def client(db_engine, tmp_path):\n    \"\"\"Async HTTP test client wired to FastAPI with dependency overrides.\"\"\"\n    session_factory = async_sessionmaker(\n        db_engine, class_=AsyncSession, expire_on_commit=False\n    )\n\n    async def _override_get_session():\n        async with session_factory() as session:\n            yield session\n\n    # Override DB session dependency\n    app.dependency_overrides[get_session] = _override_get_session\n\n    # Override transcript_storage_path via environment variable\n    os.environ[\"TRANSCRIPT_STORAGE_PATH\"] = str(tmp_path)\n    # Clear the lru_cache so Settings picks up the new env var\n    from config import get_settings\n    get_settings.cache_clear()\n\n    transport = ASGITransport(app=app)\n    async with AsyncClient(transport=transport, base_url=\"http://testserver\") as ac:\n        yield ac\n\n    # Teardown: clean overrides and restore settings cache\n    app.dependency_overrides.clear()\n    os.environ.pop(\"TRANSCRIPT_STORAGE_PATH\", None)\n    get_settings.cache_clear()\n\n\n@pytest.fixture()\ndef sample_transcript_path() -> pathlib.Path:\n    \"\"\"Path to the sample 5-segment transcript JSON fixture.\"\"\"\n    return pathlib.Path(__file__).parent / \"fixtures\" / \"sample_transcript.json\"\n\n\n@pytest.fixture()\ndef tmp_transcript_dir(tmp_path) -> pathlib.Path:\n    \"\"\"Temporary directory for transcript storage during tests.\"\"\"\n    return tmp_path\n\n\n# ── Sync engine/session for pipeline stages ──────────────────────────────────\n\n\n@pytest.fixture()\ndef sync_engine(db_engine):\n    \"\"\"Create a sync SQLAlchemy engine pointing at the test database.\n\n    Tables are already created/dropped by the async ``db_engine`` fixture,\n    so this fixture just wraps a sync engine around the same DB URL.\n    \"\"\"\n    engine = create_engine(TEST_DATABASE_URL_SYNC, echo=False, poolclass=NullPool)\n    yield engine\n    engine.dispose()\n\n\n@pytest.fixture()\ndef sync_session(sync_engine) -> Session:\n    \"\"\"Create a sync SQLAlchemy session for pipeline stage tests.\"\"\"\n    factory = sessionmaker(bind=sync_engine)\n    session = factory()\n    yield session\n    session.close()\n\n\n# ── Pre-ingest fixture for pipeline tests ────────────────────────────────────\n\n\n@pytest.fixture()\ndef pre_ingested_video(sync_engine):\n    \"\"\"Ingest the sample transcript directly into the test DB via sync ORM.\n\n    Returns a dict with ``video_id``, ``creator_id``, and ``segment_count``.\n    \"\"\"\n    factory = sessionmaker(bind=sync_engine)\n    session = factory()\n    try:\n        # Create creator\n        creator = Creator(\n            name=\"Skope\",\n            slug=\"skope\",\n            folder_name=\"Skope\",\n        )\n        session.add(creator)\n        session.flush()\n\n        # Create video\n        video = SourceVideo(\n            creator_id=creator.id,\n            filename=\"mixing-basics-ep1.mp4\",\n            file_path=\"Skope/mixing-basics-ep1.mp4\",\n            duration_seconds=1234,\n            content_type=ContentType.tutorial,\n            processing_status=ProcessingStatus.transcribed,\n        )\n        session.add(video)\n        session.flush()\n\n        # Create transcript segments\n        sample = pathlib.Path(__file__).parent / \"fixtures\" / \"sample_transcript.json\"\n        data = json.loads(sample.read_text())\n        for idx, seg in enumerate(data[\"segments\"]):\n            session.add(TranscriptSegment(\n                source_video_id=video.id,\n                start_time=float(seg[\"start\"]),\n                end_time=float(seg[\"end\"]),\n                text=str(seg[\"text\"]),\n                segment_index=idx,\n            ))\n\n        session.commit()\n\n        result = {\n            \"video_id\": str(video.id),\n            \"creator_id\": str(creator.id),\n            \"segment_count\": len(data[\"segments\"]),\n        }\n    finally:\n        session.close()\n\n    return result\n"}],"isError":false,"timestamp":1774825223430}}
{"type":"message","id":"9c58baa2","parentId":"92c82f21","timestamp":"2026-03-29T23:00:23.432Z","message":{"role":"toolResult","toolCallId":"toolu_01PyE26MNrygA6g5STfnLhH4","toolName":"bash","content":[{"type":"text","text":"total 12\ndrwxrwxr-x  2 aux aux 4096 Mar 29 21:41 .\ndrwxrwxr-x 16 aux aux 4096 Mar 29 22:51 ..\n-rw-rw-r--  1 aux aux  387 Mar 29 21:41 package.json\n"}],"isError":false,"timestamp":1774825223430}}
{"type":"message","id":"eb66b467","parentId":"9c58baa2","timestamp":"2026-03-29T23:00:29.193Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — the frontend is just a placeholder. Now I have a clear picture. Let me check for any spec document that might detail the review queue requirements more:"},{"type":"toolCall","id":"toolu_013d6GhxAv4MtQzRSaPmkeQG","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator -name '*spec*' -o -name '*chrysopedia*spec*' -o -name 'SPEC*' -o -name 'spec*' 2>/dev/null | grep -v .venv | grep -v __pycache__ | grep -v .git | grep -v .gsd"}},{"type":"toolCall","id":"toolu_01BV12eHWhEyaK66iUVwUqZu","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/backend/requirements.txt"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":228,"cacheRead":98222,"cacheWrite":1953,"totalTokens":100404,"cost":{"input":0.000005,"output":0.0057,"cacheRead":0.049110999999999995,"cacheWrite":0.01220625,"total":0.06702224999999999}},"stopReason":"toolUse","timestamp":1774825223431}}
{"type":"message","id":"bd0f5348","parentId":"eb66b467","timestamp":"2026-03-29T23:00:29.236Z","message":{"role":"toolResult","toolCallId":"toolu_013d6GhxAv4MtQzRSaPmkeQG","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/chrysopedia-spec.md\n"}],"isError":false,"timestamp":1774825229236}}
{"type":"message","id":"134095ab","parentId":"bd0f5348","timestamp":"2026-03-29T23:00:29.238Z","message":{"role":"toolResult","toolCallId":"toolu_01BV12eHWhEyaK66iUVwUqZu","toolName":"bash","content":[{"type":"text","text":"fastapi>=0.115.0,<1.0\nuvicorn[standard]>=0.32.0,<1.0\nsqlalchemy[asyncio]>=2.0,<3.0\nasyncpg>=0.30.0,<1.0\nalembic>=1.14.0,<2.0\npydantic>=2.0,<3.0\npydantic-settings>=2.0,<3.0\ncelery[redis]>=5.4.0,<6.0\nredis>=5.0,<6.0\npython-dotenv>=1.0,<2.0\npython-multipart>=0.0.9,<1.0\nhttpx>=0.27.0,<1.0\nopenai>=1.0,<2.0\nqdrant-client>=1.9,<2.0\npyyaml>=6.0,<7.0\npsycopg2-binary>=2.9,<3.0\n# Test dependencies\npytest>=8.0,<10.0\npytest-asyncio>=0.24,<1.0\n"}],"isError":false,"timestamp":1774825229236}}
{"type":"message","id":"dc1c401a","parentId":"134095ab","timestamp":"2026-03-29T23:00:37.738Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01G3YvsHj6bw7jbaBmRCMQDT","name":"bash","arguments":{"command":"grep -n -i \"review\" /home/aux/projects/content-to-kb-automator/chrysopedia-spec.md | head -60"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":86,"cacheRead":100175,"cacheWrite":577,"totalTokens":100839,"cost":{"input":0.000005,"output":0.00215,"cacheRead":0.0500875,"cacheWrite":0.00360625,"total":0.055848749999999996}},"stopReason":"toolUse","timestamp":1774825229237}}
{"type":"message","id":"d9ada109","parentId":"dc1c401a","timestamp":"2026-03-29T23:00:37.758Z","message":{"role":"toolResult","toolCallId":"toolu_01G3YvsHj6bw7jbaBmRCMQDT","toolName":"bash","content":[{"type":"text","text":"264:processing_status enum: pending | transcribed | extracted | reviewed | published\n291:review_status   enum: pending | approved | edited | rejected\n311:review_status   enum: draft | reviewed | published\n494:## 8. Review and approval workflow\n500:- **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.\n501:- **Auto mode (post-calibration):** Extracted moments are published automatically. The review queue still exists but functions as an audit log rather than a gate.\n503:The 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.\n505:### 8.2 Review queue interface\n507:The review UI is part of the Chrysopedia web application (an admin section, not a separate tool).\n512:- Items organized by source video (review all moments from one video in sequence for context)\n514:**Individual moment review:**\n526:The 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:\n530:3. Review the new output\n532:This is the \"calibration loop\" — run pipeline, review output, tune prompts, re-run, repeat until quality is sufficient for auto mode.\n546:5. **Review or auto-publish:** Depending on mode, moments enter the review queue or publish directly\n689:- Build the review queue UI\n691:- Tune extraction prompts based on review feedback\n701:- Review and approve extractions (transitioning toward auto mode)\n"}],"isError":false,"timestamp":1774825237756}}
{"type":"message","id":"10121994","parentId":"d9ada109","timestamp":"2026-03-29T23:00:40.725Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01KTJR1pna9yKB9tPYMq8y1y","name":"read","arguments":{"limit":55,"offset":494,"path":"/home/aux/projects/content-to-kb-automator/chrysopedia-spec.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":107,"cacheRead":100175,"cacheWrite":1087,"totalTokens":101370,"cost":{"input":0.000005,"output":0.0026750000000000003,"cacheRead":0.0500875,"cacheWrite":0.00679375,"total":0.05956125}},"stopReason":"toolUse","timestamp":1774825237757}}
{"type":"message","id":"62cc97b6","parentId":"10121994","timestamp":"2026-03-29T23:00:40.732Z","message":{"role":"toolResult","toolCallId":"toolu_01KTJR1pna9yKB9tPYMq8y1y","toolName":"read","content":[{"type":"text","text":"## 8. Review and approval workflow\n\n### 8.1 Modes\n\nThe system supports two modes:\n\n- **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.\n\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\n### 8.2 Review queue interface\n\nThe review UI is part of the Chrysopedia web application (an admin section, not a separate tool).\n\n**Queue view:**\n- Counts: pending, approved, edited, rejected\n- Filter tabs: Pending | Approved | Edited | Rejected\n- Items organized by source video (review all moments from one video in sequence for context)\n\n**Individual moment review:**\n- Extracted moment: title, timestamp range, summary, tags, plugins detected\n- Raw transcript segment displayed alongside for comparison\n- Five actions:\n  - **Approve** — publish as-is\n  - **Edit & approve** — modify summary, tags, timestamp, or plugins, then publish\n  - **Split** — the moment actually contains two distinct insights; split into two separate moments\n  - **Merge with adjacent** — the system over-segmented; combine with the next or previous moment\n  - **Reject** — not a key moment; discard\n\n### 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\n## 9. New content ingestion workflow\n\n### 9.1 Adding new videos\n\nThe ongoing workflow for adding new content after initial seeding:\n\n1. **Drop file:** Place new video file(s) in the appropriate creator folder on the desktop (or create a new folder for a new creator)\n2. **Trigger transcription:** Run the Whisper transcription stage on the new file(s). This could be a manual CLI command, a watched-folder daemon, or an n8n workflow trigger.\n3. **Ship transcript:** Transfer the transcript JSON to the hypervisor (automated via the pipeline)\n4. **Process:** Stages 2-5 run automatically on the new transcript\n5. **Review or auto-publish:** Depending on mode, moments enter the review queue or publish directly\n6. **Synthesis update:** If the new content covers a topic that already has a technique page for this creator, the synthesis stage updates the existing page. If it's a new topic, a new technique page is created.\n\n\n[166 more lines in file. Use offset=549 to continue.]"}],"isError":false,"timestamp":1774825240730}}
{"type":"message","id":"0f06aeeb","parentId":"62cc97b6","timestamp":"2026-03-29T23:00:45.494Z","message":{"role":"assistant","content":[{"type":"text","text":"Excellent — I now have the full picture. The spec defines the review queue very precisely. Let me check what frontend approach makes sense — the frontend is just a placeholder package.json with no framework yet. This is an admin UI, so let me check the Docker setup for how the web UI is served:"},{"type":"toolCall","id":"toolu_01U7Mfq14tnV5HPumnfn8TSN","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/docker-compose.yml"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":135,"cacheRead":101262,"cacheWrite":843,"totalTokens":102241,"cost":{"input":0.000005,"output":0.003375,"cacheRead":0.050630999999999995,"cacheWrite":0.00526875,"total":0.05927975}},"stopReason":"toolUse","timestamp":1774825240731}}
{"type":"message","id":"9e9eb581","parentId":"0f06aeeb","timestamp":"2026-03-29T23:00:45.509Z","message":{"role":"toolResult","toolCallId":"toolu_01U7Mfq14tnV5HPumnfn8TSN","toolName":"read","content":[{"type":"text","text":"# Chrysopedia — Docker Compose\n# XPLTD convention: xpltd_chrysopedia project, bind mounts, dedicated bridge\nname: xpltd_chrysopedia\n\nservices:\n  # ── PostgreSQL 16 ──\n  chrysopedia-db:\n    image: postgres:16-alpine\n    container_name: chrysopedia-db\n    restart: unless-stopped\n    environment:\n      POSTGRES_USER: ${POSTGRES_USER:-chrysopedia}\n      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-changeme}\n      POSTGRES_DB: ${POSTGRES_DB:-chrysopedia}\n    volumes:\n      - /vmPool/r/services/chrysopedia_db:/var/lib/postgresql/data\n    ports:\n      - \"127.0.0.1:5433:5432\"\n    networks:\n      - chrysopedia\n    healthcheck:\n      test: [\"CMD-SHELL\", \"pg_isready -U ${POSTGRES_USER:-chrysopedia}\"]\n      interval: 10s\n      timeout: 5s\n      retries: 5\n\n  # ── Redis (Celery broker) ──\n  chrysopedia-redis:\n    image: redis:7-alpine\n    container_name: chrysopedia-redis\n    restart: unless-stopped\n    volumes:\n      - /vmPool/r/services/chrysopedia_redis:/data\n    networks:\n      - chrysopedia\n    healthcheck:\n      test: [\"CMD\", \"redis-cli\", \"ping\"]\n      interval: 10s\n      timeout: 5s\n      retries: 5\n\n  # ── FastAPI application ──\n  chrysopedia-api:\n    build:\n      context: .\n      dockerfile: docker/Dockerfile.api\n    container_name: chrysopedia-api\n    restart: unless-stopped\n    env_file:\n      - path: .env\n        required: false\n    environment:\n      DATABASE_URL: postgresql+asyncpg://${POSTGRES_USER:-chrysopedia}:${POSTGRES_PASSWORD}@chrysopedia-db:5432/${POSTGRES_DB:-chrysopedia}\n      REDIS_URL: redis://chrysopedia-redis:6379/0\n    volumes:\n      - ./backend:/app\n      - /vmPool/r/services/chrysopedia_data:/data\n    ports:\n      - \"127.0.0.1:8000:8000\"\n    depends_on:\n      chrysopedia-db:\n        condition: service_healthy\n      chrysopedia-redis:\n        condition: service_healthy\n    networks:\n      - chrysopedia\n\n  # ── Celery worker (pipeline stages 2-5) ──\n  chrysopedia-worker:\n    build:\n      context: .\n      dockerfile: docker/Dockerfile.api\n    container_name: chrysopedia-worker\n    restart: unless-stopped\n    env_file:\n      - path: .env\n        required: false\n    environment:\n      DATABASE_URL: postgresql+asyncpg://${POSTGRES_USER:-chrysopedia}:${POSTGRES_PASSWORD}@chrysopedia-db:5432/${POSTGRES_DB:-chrysopedia}\n      REDIS_URL: redis://chrysopedia-redis:6379/0\n    command: [\"celery\", \"-A\", \"worker\", \"worker\", \"--loglevel=info\"]\n    volumes:\n      - ./backend:/app\n      - /vmPool/r/services/chrysopedia_data:/data\n      - ./prompts:/prompts:ro\n    depends_on:\n      chrysopedia-db:\n        condition: service_healthy\n      chrysopedia-redis:\n        condition: service_healthy\n    networks:\n      - chrysopedia\n\n  # ── React web UI (nginx) ──\n  chrysopedia-web:\n    build:\n      context: .\n      dockerfile: docker/Dockerfile.web\n    container_name: chrysopedia-web\n    restart: unless-stopped\n    ports:\n      - \"127.0.0.1:3000:80\"\n    depends_on:\n      - chrysopedia-api\n    networks:\n      - chrysopedia\n\nnetworks:\n  chrysopedia:\n    driver: bridge\n    ipam:\n      config:\n        - subnet: 172.24.0.0/24\n"}],"isError":false,"timestamp":1774825245506}}
{"type":"message","id":"b293abf9","parentId":"9e9eb581","timestamp":"2026-03-29T23:00:54.388Z","message":{"role":"assistant","content":[{"type":"text","text":"The compose file references a React web UI with nginx. The frontend is still a placeholder. Since S04 is about the admin review queue UI, and S05 is the public search UI, I need to understand the approach: should S04 build a full React app or can we use a simpler server-rendered approach?\n\nGiven that:\n1. The frontend is currently just a placeholder package.json (no React, no build tooling)\n2. S04 needs an admin UI for reviewing key moments\n3. S05 will need a full search-first React UI\n4. The spec says \"The review UI is part of the Chrysopedia web application (an admin section, not a separate tool)\"\n\nThe review queue has clear API needs. Let me look at what the admin UI needs more carefully from the spec:"},{"type":"toolCall","id":"toolu_01K1vFL5QXAA2gYBotscKqYC","name":"read","arguments":{"limit":30,"offset":507,"path":"/home/aux/projects/content-to-kb-automator/chrysopedia-spec.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":285,"cacheRead":102105,"cacheWrite":1177,"totalTokens":103568,"cost":{"input":0.000005,"output":0.007125,"cacheRead":0.0510525,"cacheWrite":0.00735625,"total":0.06553874999999999}},"stopReason":"toolUse","timestamp":1774825245507}}
{"type":"message","id":"6c224fb0","parentId":"b293abf9","timestamp":"2026-03-29T23:00:54.395Z","message":{"role":"toolResult","toolCallId":"toolu_01K1vFL5QXAA2gYBotscKqYC","toolName":"read","content":[{"type":"text","text":"The review UI is part of the Chrysopedia web application (an admin section, not a separate tool).\n\n**Queue view:**\n- Counts: pending, approved, edited, rejected\n- Filter tabs: Pending | Approved | Edited | Rejected\n- Items organized by source video (review all moments from one video in sequence for context)\n\n**Individual moment review:**\n- Extracted moment: title, timestamp range, summary, tags, plugins detected\n- Raw transcript segment displayed alongside for comparison\n- Five actions:\n  - **Approve** — publish as-is\n  - **Edit & approve** — modify summary, tags, timestamp, or plugins, then publish\n  - **Split** — the moment actually contains two distinct insights; split into two separate moments\n  - **Merge with adjacent** — the system over-segmented; combine with the next or previous moment\n  - **Reject** — not a key moment; discard\n\n### 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\n## 9. New content ingestion workflow\n\n[178 more lines in file. Use offset=537 to continue.]"}],"isError":false,"timestamp":1774825254393}}
{"type":"message","id":"954e1279","parentId":"6c224fb0","timestamp":"2026-03-29T23:00:59.022Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have everything I need. Let me also check if Jinja2 is already a dependency (for potential SSR templates as an alternative to React for admin):"},{"type":"toolCall","id":"toolu_01Jc7izs67GRPn9EMGTKyQB6","name":"bash","arguments":{"command":"grep -i jinja /home/aux/projects/content-to-kb-automator/backend/requirements.txt 2>/dev/null; pip list 2>/dev/null | grep -i jinja || true"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":136,"cacheRead":103282,"cacheWrite":658,"totalTokens":104077,"cost":{"input":0.000005,"output":0.0034000000000000002,"cacheRead":0.051641,"cacheWrite":0.0041125,"total":0.059158499999999996}},"stopReason":"toolUse","timestamp":1774825254394}}
{"type":"message","id":"2a151ca7","parentId":"954e1279","timestamp":"2026-03-29T23:00:59.470Z","message":{"role":"toolResult","toolCallId":"toolu_01Jc7izs67GRPn9EMGTKyQB6","toolName":"bash","content":[{"type":"text","text":"Jinja2                3.1.2\n"}],"isError":false,"timestamp":1774825259468}}
{"type":"message","id":"090d40cf","parentId":"2a151ca7","timestamp":"2026-03-29T23:01:03.680Z","message":{"role":"assistant","content":[{"type":"text","text":"Jinja2 is available (transitive dependency from FastAPI/Starlette). Now let me check the docker Dockerfiles to understand the frontend build:"},{"type":"toolCall","id":"toolu_01Fsf4geicpyxvPvSmkG5uM2","name":"bash","arguments":{"command":"ls /home/aux/projects/content-to-kb-automator/docker/ 2>/dev/null && cat /home/aux/projects/content-to-kb-automator/docker/Dockerfile.web 2>/dev/null && cat /home/aux/projects/content-to-kb-automator/docker/Dockerfile.api 2>/dev/null || echo \"No docker/ dir\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":180,"cacheRead":103940,"cacheWrite":159,"totalTokens":104280,"cost":{"input":0.000005,"output":0.0045000000000000005,"cacheRead":0.051969999999999995,"cacheWrite":0.00099375,"total":0.05746875}},"stopReason":"toolUse","timestamp":1774825259469}}
{"type":"message","id":"e1ee303e","parentId":"090d40cf","timestamp":"2026-03-29T23:01:03.698Z","message":{"role":"toolResult","toolCallId":"toolu_01Fsf4geicpyxvPvSmkG5uM2","toolName":"bash","content":[{"type":"text","text":"Dockerfile.api\nDockerfile.web\nnginx.conf\nFROM node:22-alpine AS build\n\nWORKDIR /app\nCOPY frontend/package*.json ./\nRUN npm ci --ignore-scripts\nCOPY frontend/ .\nRUN npm run build\n\nFROM nginx:1.27-alpine\n\nCOPY --from=build /app/dist /usr/share/nginx/html\nCOPY docker/nginx.conf /etc/nginx/conf.d/default.conf\n\nEXPOSE 80\n\nCMD [\"nginx\", \"-g\", \"daemon off;\"]\nFROM python:3.12-slim\n\nWORKDIR /app\n\n# System deps\nRUN apt-get update && apt-get install -y --no-install-recommends \\\n    gcc libpq-dev \\\n    && rm -rf /var/lib/apt/lists/*\n\n# Python deps (cached layer)\nCOPY backend/requirements.txt /app/requirements.txt\nRUN pip install --no-cache-dir -r requirements.txt\n\n# Application code\nCOPY backend/ /app/\n\nEXPOSE 8000\n\nCMD [\"uvicorn\", \"main:app\", \"--host\", \"0.0.0.0\", \"--port\", \"8000\"]\n"}],"isError":false,"timestamp":1774825263696}}
{"type":"message","id":"94fde663","parentId":"e1ee303e","timestamp":"2026-03-29T23:01:05.993Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_015KEwKmWyC9S6CtJt5E9iSV","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/docker/nginx.conf"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":73,"cacheRead":104099,"cacheWrite":469,"totalTokens":104642,"cost":{"input":0.000005,"output":0.001825,"cacheRead":0.0520495,"cacheWrite":0.0029312500000000003,"total":0.05681075}},"stopReason":"toolUse","timestamp":1774825263697}}
{"type":"message","id":"63babd2f","parentId":"94fde663","timestamp":"2026-03-29T23:01:06.012Z","message":{"role":"toolResult","toolCallId":"toolu_015KEwKmWyC9S6CtJt5E9iSV","toolName":"bash","content":[{"type":"text","text":"server {\n    listen 80;\n    server_name _;\n    root /usr/share/nginx/html;\n    index index.html;\n\n    # SPA fallback\n    location / {\n        try_files $uri $uri/ /index.html;\n    }\n\n    # API proxy\n    location /api/ {\n        proxy_pass http://chrysopedia-api:8000;\n        proxy_set_header Host $host;\n        proxy_set_header X-Real-IP $remote_addr;\n        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;\n        proxy_set_header X-Forwarded-Proto $scheme;\n    }\n\n    location /health {\n        proxy_pass http://chrysopedia-api:8000;\n    }\n}\n"}],"isError":false,"timestamp":1774825266011}}
{"type":"message","id":"f1a8e5e1","parentId":"63babd2f","timestamp":"2026-03-29T23:02:06.883Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have the complete picture. Let me write the research document.\n\nThe approach is clear: S04 needs to deliver both backend API endpoints (for review queue CRUD operations) and a frontend admin UI. The frontend currently has zero framework code — just a placeholder. The natural approach is:\n\n1. **Backend**: New review router with endpoints for listing/filtering key moments, approving/rejecting/editing them, split/merge actions, and a mode toggle endpoint.\n2. **Frontend**: Initialize a React SPA (the Docker setup already expects this — Node build + nginx serving). Build the admin review queue pages.\n\nThis is straightforward CRUD + admin UI work using established patterns in the codebase."},{"type":"toolCall","id":"toolu_016jBwf6M4EVrjYGKJCkxqJ1","name":"gsd_summary_save","arguments":{"artifact_type":"RESEARCH","content":"# S04 — Review Queue Admin UI — Research\n\n**Date:** 2026-03-29\n\n## Summary\n\nS04 builds the admin review queue for Chrysopedia — the UI and API that let an administrator review, edit, approve, reject, split, and merge extracted key moments before they're published. The spec (§8) defines two modes: **review mode** (all moments queued for human review) and **auto mode** (moments publish directly, queue becomes an audit log). A system-level mode toggle switches between them. The `review_mode: bool = True` setting already exists in `config.py`.\n\nThe work naturally splits into two halves: **backend API endpoints** for review queue operations (list/filter moments, approve/reject/edit, split/merge, mode toggle, status counts) and a **React frontend** admin UI. The frontend is currently a bare placeholder (`package.json` with no framework) but the Docker build pipeline (Node build → nginx SPA serving with `/api/` proxy) is already wired. The backend has established patterns for async SQLAlchemy endpoints, Pydantic schemas, and test fixtures.\n\nThis is medium-complexity CRUD + admin UI work. The data model already has `KeyMoment.review_status` (pending/approved/edited/rejected) and `SourceVideo.processing_status`. The riskiest parts are: (1) split/merge operations on key moments (modifying timestamps and creating/deleting rows), and (2) bootstrapping the React app from zero. No novel technology or unfamiliar APIs involved.\n\n## Recommendation\n\nBuild the backend review API first (new `routers/review.py`), then initialize the React frontend with a minimal admin UI. Use the existing async SQLAlchemy patterns from `routers/creators.py` and `routers/videos.py`. For the frontend, use React + React Router + a lightweight fetching approach (fetch or a small library). No heavy framework needed — this is a single-user admin tool, not a high-traffic public site.\n\nThe mode toggle should be a runtime-mutable setting, not just the config file default. Since there's only one admin, store the toggle in Redis (like stage 4's classification data) or add a `system_settings` table. Redis is simpler and already used by the project.\n\n## Implementation Landscape\n\n### Key Files\n\n**Existing (read/extend):**\n- `backend/models.py` — Has `KeyMoment` (with `review_status: ReviewStatus`), `SourceVideo` (with `processing_status`), `TechniquePage`. All the DB models needed for review are already defined.\n- `backend/schemas.py` — Has `KeyMomentRead`, `KeyMomentBase`. Needs new schemas for review actions (approve, edit, split, merge responses).\n- `backend/config.py` — Has `review_mode: bool = True`. This is the default; runtime toggle needs a separate mechanism.\n- `backend/database.py` — `get_session` async dependency. Used by all routers.\n- `backend/main.py` — Mount point for new review router.\n- `backend/tests/conftest.py` — Test infrastructure with async/sync fixtures, `pre_ingested_video`.\n- `backend/pipeline/stages.py` — `stage5_synthesis` reads `settings.review_mode` to set processing_status. The mode toggle affects new pipeline runs, not existing moments.\n- `backend/routers/pipeline.py` — `POST /pipeline/trigger/{video_id}` for re-processing after prompt edits.\n\n**New files to create:**\n- `backend/routers/review.py` — Review queue API endpoints:\n  - `GET /api/v1/review/queue` — List key moments with filter (status), pagination, grouped by video\n  - `GET /api/v1/review/stats` — Counts by review_status (pending, approved, edited, rejected)\n  - `POST /api/v1/review/moments/{moment_id}/approve` — Set review_status=approved\n  - `POST /api/v1/review/moments/{moment_id}/reject` — Set review_status=rejected\n  - `PUT /api/v1/review/moments/{moment_id}` — Edit fields + set review_status=edited\n  - `POST /api/v1/review/moments/{moment_id}/split` — Split into two moments\n  - `POST /api/v1/review/moments/{moment_id}/merge` — Merge with adjacent moment\n  - `GET /api/v1/review/mode` — Get current review/auto mode\n  - `PUT /api/v1/review/mode` — Toggle review/auto mode\n- `backend/tests/test_review.py` — Integration tests for review API\n- `frontend/src/` — React app source (App, Router, pages)\n- `frontend/package.json` — Updated with React, build tooling\n- `frontend/vite.config.ts` — Vite config for React build\n- `frontend/index.html` — SPA entry point\n- `frontend/src/pages/ReviewQueue.tsx` — Queue view with filter tabs, status counts\n- `frontend/src/pages/MomentReview.tsx` — Individual moment review with actions\n- `frontend/src/components/` — Shared UI components\n\n### Build Order\n\n1. **Backend review API + tests** — Build `routers/review.py` with all endpoints, add schemas, mount in `main.py`, write integration tests. This is the foundation — the frontend is just a consumer of these endpoints. Prove the API works with tests before touching the frontend.\n\n2. **React app bootstrap** — Initialize the React app in `frontend/` with Vite + TypeScript. Get `npm run dev` and `npm run build` working. Verify the Docker build pipeline produces a working SPA.\n\n3. **Review queue UI pages** — Build the queue view (list moments, filter tabs, status counts) and the moment review page (display moment + raw transcript, action buttons). Wire to the backend API.\n\n4. **Mode toggle + integration** — Add the mode toggle UI, connect to the backend toggle endpoint. Verify the full flow: pipeline produces moments → admin reviews/approves → status updates correctly.\n\n### Verification Approach\n\n**Backend:**\n- `cd backend && python -m pytest tests/test_review.py -v` — All review API tests pass\n- `cd backend && python -m pytest tests/ -v` — All existing tests still pass (no regressions)\n- Manual curl: `GET /api/v1/review/stats` returns counts, `POST .../approve` changes status\n\n**Frontend:**\n- `cd frontend && npm run build` — Build succeeds, produces `dist/index.html`\n- `cd frontend && npm run dev` — Dev server starts, admin UI renders\n- Navigate to admin review queue page, see list of moments (requires seeded data or mocked API)\n\n**Integration:**\n- Docker compose builds both services successfully\n- Nginx proxies `/api/` to backend, serves frontend SPA on `/`\n\n## Constraints\n\n- **Async-only in FastAPI handlers** — All review endpoints must use async SQLAlchemy (`AsyncSession`), following the pattern in existing routers. The sync engine/session pattern is only for Celery tasks.\n- **No auth** — The spec doesn't mention authentication for S04. This is a single-admin internal tool. Auth can be added later if needed.\n- **Mode toggle persistence** — `config.py`'s `review_mode` comes from environment variable and is cached via `lru_cache`. A runtime toggle needs Redis or a DB table; changing env vars at runtime is fragile. Redis is the simpler choice — the project already uses it for stage 4 classification data.\n- **KeyMoment lacks topic_tags/topic_category columns** — Classification data is in Redis (24h TTL). The review UI should display tags from Redis if available, or show \"not classified\" if Redis data has expired. This is a read-only concern for S04.\n- **Existing Docker build expects `npm run build` to produce `dist/`** — The frontend build must output to `frontend/dist/` for the nginx Dockerfile to work.\n\n## Common Pitfalls\n\n- **Split/merge moment complexity** — Splitting a moment requires creating a new `KeyMoment` row and adjusting timestamps on both the original and new row. Merging requires combining summaries and extending timestamp ranges, then deleting one row. Both operations must handle the `technique_page_id` foreign key — split moments should keep the same page link, merged moments should keep one.\n- **Redis mode toggle vs config.py** — If the mode toggle is stored in Redis but `pipeline/stages.py` reads `settings.review_mode` from config, changing the toggle won't affect running pipeline tasks. The pipeline needs to read the toggle from the same source as the admin UI. Either the pipeline reads from Redis too, or the toggle updates the Settings object.\n- **Frontend build from zero** — Installing React + Vite + TypeScript from scratch in the existing `frontend/` directory. The `package.json` exists but has no deps. Need to be careful not to break the Docker build — the Dockerfile runs `npm ci` then `npm run build`.\n","milestone_id":"M001","slice_id":"S04"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":2401,"cacheRead":104568,"cacheWrite":279,"totalTokens":107249,"cost":{"input":0.000005,"output":0.060025,"cacheRead":0.052284,"cacheWrite":0.00174375,"total":0.11405775}},"stopReason":"toolUse","timestamp":1774825266011}}
{"type":"message","id":"10fb7008","parentId":"f1a8e5e1","timestamp":"2026-03-29T23:02:06.891Z","message":{"role":"toolResult","toolCallId":"toolu_016jBwf6M4EVrjYGKJCkxqJ1","toolName":"gsd_summary_save","content":[{"type":"text","text":"Saved RESEARCH artifact to milestones/M001/slices/S04/S04-RESEARCH.md"}],"details":{"operation":"save_summary","path":"milestones/M001/slices/S04/S04-RESEARCH.md","artifact_type":"RESEARCH"},"isError":false,"timestamp":1774825326889}}
{"type":"message","id":"d0a5e4dc","parentId":"10fb7008","timestamp":"2026-03-29T23:02:09.616Z","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":104847,"cacheWrite":2439,"totalTokens":107298,"cost":{"input":0.000005,"output":0.000275,"cacheRead":0.0524235,"cacheWrite":0.01524375,"total":0.06794725}},"stopReason":"stop","timestamp":1774825326890}}
