{"type":"thinking_level_change","id":"f7184521","parentId":null,"timestamp":"2026-03-29T23:39:14.762Z","thinkingLevel":"off"}
{"type":"model_change","id":"165a5e0d","parentId":"f7184521","timestamp":"2026-03-29T23:39:14.764Z","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 S05 (\"Search-First Web UI\") — Milestone M001\n\n## Inlined Context (preloaded — do not re-read these files)\n\n### Milestone Roadmap\nSource: `.gsd/milestones/M001/M001-ROADMAP.md`\n\n# M001: Chrysopedia Foundation — Infrastructure, Pipeline Core, and Skeleton UI\n\n## Vision\nStand up the complete Chrysopedia stack: Docker Compose deployment on ub01, PostgreSQL data model, FastAPI backend with transcript ingestion, Whisper transcription script for the desktop, LLM extraction pipeline (stages 2-5), review queue, Qdrant integration, and the search-first web UI with technique pages, creators, and topics browsing. By the end, a video file can be transcribed → ingested → extracted → reviewed → searched and read in the web UI.\n\n## Slice Overview\n| ID | Slice | Risk | Depends | Done | After this |\n|----|-------|------|---------|------|------------|\n| S01 | Docker Compose + Database + Whisper Script | low | — | ✅ | docker compose up -d starts all services on ub01; Whisper script transcribes a sample video to JSON |\n| S02 | Transcript Ingestion API | low | S01 | ✅ | POST a transcript JSON file to the API; Creator and Source Video records appear in PostgreSQL |\n| S03 | LLM Extraction Pipeline + Qdrant Integration | high | S02 | ✅ | A transcript JSON triggers stages 2-5: segmentation → extraction → classification → synthesis. Technique pages with key moments appear in DB. Qdrant has searchable embeddings. |\n| S04 | Review Queue Admin UI | medium | S03 | ✅ | Admin views pending key moments, approves/edits/rejects them, toggles between review and auto mode |\n| S05 | Search-First Web UI | medium | S03 | ⬜ | User searches for a technique, gets semantic results in <500ms, clicks through to a full technique page with study guide prose, key moments, and related links |\n\n---\n\n### Decisions\nSource: `.gsd/DECISIONS.md`\n\n# Decisions (compact)\nFields: id | when | scope | decision | choice | rationale | revisable | made_by\n\nD007 | M001/S04 | architecture | Runtime review mode toggle persistence mechanism | Store review mode toggle in Redis key `chrysopedia:review_mode` with async redis client. Fall back to `settings.review_mode` config default when key is absent. | The config.py `review_mode` setting is loaded via lru_cache from environment variables and cannot be mutated at runtime. Redis is already used by the project (Celery broker, stage 4 classification data) so it adds no new infrastructure. A system_settings DB table would work but Redis is simpler for a single boolean toggle on a single-admin tool. The pipeline's stages.py reads settings.review_mode from config — the admin toggle only affects new pipeline runs if stages.py is updated to check Redis too, but that's deferred since the toggle is primarily a UI-level concept for the review queue. | Yes | agent\nD008 | M001/S04 | requirement | R004 Review Queue UI status | validated | All R004 capabilities delivered and verified: 9 API endpoints (approve, reject, edit, split, merge, queue list, stats, mode get/set) with 24 passing integration tests covering happy paths and error boundaries. React+TypeScript frontend with queue page (filter tabs, stats, pagination), moment detail page (all review actions with modals), and review-vs-auto mode toggle. Frontend builds with zero TypeScript errors. | Yes | agent\n\n---\n\n### Requirements\nSource: `.gsd/REQUIREMENTS.md`\n\n# Requirements\n\n## R001 — Whisper Transcription Pipeline\n**Status:** active\n**Description:** Desktop Python script that accepts video files (MP4/MKV), extracts audio via ffmpeg, runs Whisper large-v3 on RTX 4090, and outputs timestamped transcript JSON with segment-level timestamps and word-level timing. Must be resumable.\n**Validation:** Script processes a sample video and produces valid JSON with timestamped segments.\n**Primary Owner:** M001/S01\n\n## R002 — Transcript Ingestion API\n**Status:** active\n**Description:** FastAPI endpoint that accepts transcript JSON uploads, creates/updates Creator and Source Video records, and stores transcript data in PostgreSQL. Handles new creator detection from folder names.\n**Validation:** POST transcript JSON → 200 OK, records created in DB, file stored on filesystem.\n**Primary Owner:** M001/S02\n\n## R003 — LLM-Powered Extraction Pipeline (Stages 2-5)\n**Status:** active\n**Description:** Background worker pipeline: transcript segmentation → key moment extraction → classification/tagging → technique page synthesis. Uses OpenAI-compatible API with primary (DGX Sparks Qwen) and fallback (local Ollama) endpoints. Pipeline must be resumable per-video per-stage.\n**Validation:** End-to-end: transcript JSON in → technique pages with key moments, tags, and cross-references out.\n**Primary Owner:** M001/S03\n\n## R004 — Review Queue UI\n**Status:** active\n**Description:** Admin interface for reviewing extracted key moments: approve, edit+approve, split, merge, reject. Organized by source video for contextual review. Includes mode toggle (review vs auto-publish).\n**Validation:** Admin can review, edit, and approve/reject moments; mode toggle controls whether new moments require review.\n**Primary Owner:** M001/S04\n\n## R005 — Search-First Web UI\n**Status:** active\n**Description:** Landing page with prominent search bar, live typeahead (results after 2-3 chars), scope toggle (All/Topics/Creators), and two navigation cards (Topics, Creators). Recently added section. Search powered by Qdrant semantic search with keyword fallback.\n**Validation:** User types query → results appear within 500ms, grouped by type, with clickable navigation.\n**Primary Owner:** M001/S05\n\n## R006 — Technique Page Display\n**Status:** active\n**Description:** Core content unit: header (tags, title, creator, meta), study guide prose (organized by sub-aspects with signal chain blocks and quotes), key moments index (timestamped list), related techniques, plugins referenced. Amber banner for livestream-sourced content.\n**Validation:** Technique page renders with all sections populated from synthesized data.\n**Primary Owner:** M001/S05\n\n## R007 — Creators Browse Page\n**Status:** active\n**Description:** Filterable creator list with genre filter pills, type-to-narrow, sort options (randomized default, alphabetical, view count). Each row: name, genre tags, technique count, video count, view count. Links to creator detail page.\n**Validation:** Page loads with randomized order, genre filtering works, clicking row navigates to creator detail.\n**Primary Owner:** M001/S05\n\n## R008 — Topics Browse Page\n**Status:** active\n**Description:** Two-level topic hierarchy (6 top-level categories → sub-topics). Filter input, genre filter pills. Each sub-topic shows technique count and creator count. Clicking sub-topic shows technique pages.\n**Validation:** Hierarchy renders, filtering works, sub-topic links show correct technique pages.\n**Primary Owner:** M001/S05\n\n## R009 — Qdrant Vector Search Integration\n**Status:** active\n**Description:** Embed key moment summaries, technique page content, and transcript segments in Qdrant using configurable embedding model (nomic-embed-text default). Power semantic search with metadata filtering.\n**Validation:** Semantic search returns relevant results for natural language queries; embeddings update when content changes.\n**Primary Owner:** M001/S03\n\n## R010 — Docker Compose Deployment\n**Status:** active\n**Description:** Single docker-compose.yml packaging API, web UI, PostgreSQL, and worker services. Follows XPLTD conventions: bind mounts at /vmPool/r/services/, compose at /vmPool/r/compose/chrysopedia/, xpltd_chrysopedia project name, dedicated Docker network.\n**Validation:** `docker compose up -d` brings up all services; data persists across restarts.\n**Primary Owner:** M001/S01\n\n## R011 — Canonical Tag System\n**Status:** active\n**Description:** Editable canonical tag list (config file) with aliases. Pipeline references tags during classification. New tags can be proposed by LLM and queued for admin approval or auto-added within existing categories.\n**Validation:** Tag list is editable; pipeline uses canonical tags consistently; alias normalization works.\n**Primary Owner:** M001/S03\n\n## R012 — Incremental Content Addition\n**Status:** active\n**Description:** System handles ongoing content: new videos processed through pipeline, new creators auto-detected, existing technique pages updated when new moments are added for same creator+topic.\n**Validation:** Adding a new video for an existing creator updates their technique pages; new creator folder creates new Creator record.\n**Primary Owner:** M001/S03\n\n## R013 — Prompt Template System\n**Status:** active\n**Description:** Extraction prompts (stages 2-5) stored as editable configuration files, not hardcoded. Admin can edit prompts and re-run extraction on specific or all videos for calibration.\n**Validation:** Prompt files are editable; re-processing a video with updated prompts produces different output.\n**Primary Owner:** M001/S03\n\n## R014 — Creator Equity\n**Status:** active\n**Description:** No creator is privileged in the UI. Default sort on Creators page is randomized on every page load. All creators get equal visual weight.\n**Validation:** Refreshing Creators page shows different order each time; no creator gets larger/bolder display.\n**Primary Owner:** M001/S05\n\n## R015 — 30-Second Retrieval Target\n**Status:** active\n**Description:** A producer mid-session can find a specific technique in under 30 seconds from Alt+Tab to reading the key insight.\n**Validation:** Timed test: Alt+Tab → search → read technique → under 30 seconds.\n**Primary Owner:** M001/S05\n\n---\n\n### Project Knowledge\nSource: `.gsd/KNOWLEDGE.md`\n\n# KNOWLEDGE\n\n## SQLAlchemy column names that shadow ORM functions\n\n**Context:** If a model has a column named `relationship` (or any other ORM function name like `query`, `metadata`), Python resolves the name to the column's `MappedColumn` descriptor within the class body. This causes `relationship(...)` calls below it to fail with \"MappedColumn object is not callable\".\n\n**Fix:** Import with an alias: `from sqlalchemy.orm import relationship as sa_relationship` and use `sa_relationship(...)` throughout. Alternatively, rename the database column, but the spec defines `relationship` as the column name so we preserved it.\n\n## Docker Compose variable interpolation and `:?` syntax\n\n**Context:** `${VAR:?error message}` in docker-compose.yml makes `docker compose config` fail when the variable is unset, even if `env_file: required: false` is used. The `required: false` only controls whether the `.env` file must exist — it does NOT provide defaults for variables referenced with `:?`.\n\n**Fix:** Use `${VAR:-default}` for variables that need a fallback, or ensure the variable is always set in the environment.\n\n## Host port 8000 conflict with kerf-engine\n\n**Context:** The kerf-engine Docker container (`kerf-engine-kerf-engine-1`) binds to `0.0.0.0:8000`. When testing the Chrysopedia API locally (outside Docker), curl requests to `localhost:8000` hit the kerf-engine container instead of the local uvicorn process, even when uvicorn binds to `127.0.0.1:8000`.\n\n**Fix:** Use an alternate port (e.g., 8001) for local testing. In Docker Compose, chrysopedia-api maps `127.0.0.1:8000:8000` which is fine because it goes through Docker's port forwarding, but will conflict with kerf-engine if both stacks run simultaneously. Consider changing one project's external port mapping.\n\n## asyncpg rejects timezone-aware datetimes for TIMESTAMP WITHOUT TIME ZONE columns\n\n**Context:** SQLAlchemy models using `default=datetime.now(timezone.utc)` produce timezone-aware Python datetimes. When the PostgreSQL column type is `TIMESTAMP WITHOUT TIME ZONE` (the default), asyncpg raises `DataError: can't subtract offset-naive and offset-aware datetimes`. This only surfaces when running against a real PostgreSQL database — SQLite-based tests won't catch it.\n\n**Fix:** Use `datetime.now(timezone.utc).replace(tzinfo=None)` in the `_now()` helper, or change the column type to `TIMESTAMP WITH TIME ZONE`. We chose the former since the existing schema uses naive timestamps.\n\n## asyncpg NullPool required for pytest-asyncio integration tests\n\n**Context:** When using a session-scoped SQLAlchemy async engine with asyncpg in pytest-asyncio tests, the connection pool reuses connections across fixtures and test functions. This causes `InterfaceError: cannot perform operation: another operation is in progress` because the ASGI test client's session holds a connection while cleanup/verification fixtures try to use the same pool.\n\n**Fix:** Use `poolclass=NullPool` when creating the test engine. Each connection is created fresh and immediately closed, eliminating contention. Performance cost is negligible for test suites.\n\n## Testing Celery tasks that use sync SQLAlchemy: patch module-level globals\n\n**Context:** Pipeline stages in `pipeline/stages.py` create their own sync SQLAlchemy engine/session via module-level `_engine` and `_SessionLocal` globals (because Celery is sync, not async). Tests need to redirect these to the test database, but the engine is created lazily at module scope.\n\n**Fix:** Patch the module globals directly: `unittest.mock.patch.object(stages, '_engine', test_engine)` and `unittest.mock.patch.object(stages, '_SessionLocal', test_session_factory)`. This redirects all DB access in stage functions to the test database without modifying production code.\n\n## Lazy imports in FastAPI handlers defeat simple mock patching\n\n**Context:** When a FastAPI handler imports a function lazily (inside the function body) like `from pipeline.stages import run_pipeline`, patching `routers.ingest.run_pipeline` has no effect because the name is re-bound on every call from the source module.\n\n**Fix:** Patch at the source module: `unittest.mock.patch('pipeline.stages.run_pipeline')`. The lazy import will pick up the mock from the source module. This applies to any handler that uses lazy imports to avoid circular dependencies at module load time.\n\n## Frontend detail page without a single-resource GET endpoint\n\n**Context:** The review queue backend has `GET /review/queue` (list, paginated) but no `GET /review/moments/{id}` for fetching a single moment. The MomentDetail page needs to display one specific moment by ID from the URL params.\n\n**Fix:** MomentDetail fetches the full queue with `limit=500` and filters client-side by ID. This works for a single-admin tool with small datasets but would not scale. If the moment count grows significantly, add a dedicated `GET /review/moments/{id}` endpoint to the review router.\n\n## Stage 4 classification data stored in Redis (not DB columns)\n\n**Context:** The KeyMoment SQLAlchemy model doesn't have `topic_tags` or `topic_category` columns. Stage 4 classification needs somewhere to store per-moment tag assignments that stage 5 can read.\n\n**Fix:** Store classification results in Redis under key `chrysopedia:classification:{video_id}` with a 24-hour TTL. Stage 5 reads from Redis. This avoids schema migrations during initial pipeline development. The data is ephemeral — if Redis loses it, re-running stage 4 regenerates it.\n\n---\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: S05`, `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/S05/`. Do NOT mkdir.\n\n**You MUST call `gsd_summary_save` with the research content before finishing.**\n\nWhen done, say: \"Slice S05 researched.\"","display":false,"id":"5cc3697a","parentId":"165a5e0d","timestamp":"2026-03-29T23:39:14.766Z"}
{"type":"message","id":"49cf2e6c","parentId":"5cc3697a","timestamp":"2026-03-29T23:39:27.346Z","message":{"role":"assistant","content":[{"type":"text","text":"\n\nI'll research Slice S05 (\"Search-First Web UI\") by exploring the existing codebase, understanding what's already built, and mapping out what needs to be created. This slice covers R005 (Search UI), R006 (Technique Page), R007 (Creators Browse), R008 (Topics Browse), R014 (Creator Equity), and R015 (30-Second Retrieval).\n\nLet me start by understanding the current project structure and existing frontend code."},{"type":"toolCall","id":"toolu_01E4H8pDnAek4PitSkfHkzzG","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator -type f -name \"*.ts\" -o -name \"*.tsx\" -o -name \"*.jsx\" -o -name \"*.js\" -o -name \"*.css\" -o -name \"*.html\" | grep -v node_modules | grep -v __pycache__ | head -60"}},{"type":"toolCall","id":"toolu_0141iSD5U3mW2J2XYBnfCdmx","name":"bash","arguments":{"command":"ls -la /home/aux/projects/content-to-kb-automator/"}},{"type":"toolCall","id":"toolu_012AxpqgMUfgw9isFEnVbBdf","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator -type f -name \"*.py\" | grep -v node_modules | grep -v __pycache__ | sort"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":374,"cacheRead":49033,"cacheWrite":9350,"totalTokens":58760,"cost":{"input":0.000015000000000000002,"output":0.00935,"cacheRead":0.0245165,"cacheWrite":0.0584375,"total":0.09231900000000001}},"stopReason":"toolUse","timestamp":1774827554764}}
{"type":"message","id":"fcb52180","parentId":"49cf2e6c","timestamp":"2026-03-29T23:39:27.398Z","message":{"role":"toolResult","toolCallId":"toolu_01E4H8pDnAek4PitSkfHkzzG","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/frontend/index.html\n/home/aux/projects/content-to-kb-automator/frontend/vite.config.ts\n/home/aux/projects/content-to-kb-automator/frontend/dist/index.html\n/home/aux/projects/content-to-kb-automator/frontend/dist/assets/index-CDU0piqy.js\n/home/aux/projects/content-to-kb-automator/frontend/dist/assets/index-GJQHNpHg.css\n/home/aux/projects/content-to-kb-automator/frontend/src/pages/ReviewQueue.tsx\n/home/aux/projects/content-to-kb-automator/frontend/src/pages/MomentDetail.tsx\n/home/aux/projects/content-to-kb-automator/frontend/src/vite-env.d.ts\n/home/aux/projects/content-to-kb-automator/frontend/src/App.tsx\n/home/aux/projects/content-to-kb-automator/frontend/src/api/client.ts\n/home/aux/projects/content-to-kb-automator/frontend/src/App.css\n/home/aux/projects/content-to-kb-automator/frontend/src/components/ModeToggle.tsx\n/home/aux/projects/content-to-kb-automator/frontend/src/components/StatusBadge.tsx\n/home/aux/projects/content-to-kb-automator/frontend/src/main.tsx\n"}],"isError":false,"timestamp":1774827567398}}
{"type":"message","id":"06e8d3ca","parentId":"fcb52180","timestamp":"2026-03-29T23:39:27.399Z","message":{"role":"toolResult","toolCallId":"toolu_0141iSD5U3mW2J2XYBnfCdmx","toolName":"bash","content":[{"type":"text","text":"total 136\ndrwxrwxr-x 16 aux aux  4096 Mar 29 22:51 .\ndrwxrwxr-x  4 aux aux  4096 Mar 29 17:47 ..\ndrwxrwxr-x  4 aux aux  4096 Mar 29 21:46 alembic\n-rw-rw-r--  1 aux aux   629 Mar 29 21:45 alembic.ini\ndrwxrwxr-x  7 aux aux  4096 Mar 29 23:05 backend\ndrwxrwxr-x  2 aux aux  4096 Mar 29 21:33 .bg-shell\n-rw-rw-r--  1 aux aux 37013 Mar 29 21:35 chrysopedia-spec.md\ndrwxrwxr-x  2 aux aux  4096 Mar 29 21:41 config\ndrwxrwxr-x  2 aux aux  4096 Mar 29 21:40 docker\n-rw-rw-r--  1 aux aux  3105 Mar 29 21:43 docker-compose.yml\n-rw-rw-r--  1 aux aux   964 Mar 29 21:40 .env.example\ndrwxrwxr-x  5 aux aux  4096 Mar 29 23:15 frontend\ndrwxrwxr-x  8 aux aux  4096 Mar 29 23:39 .git\n-rw-rw-r--  1 aux aux   353 Mar 29 21:39 .gitignore\ndrwxrwxr-x  6 aux aux  4096 Mar 29 23:39 .gsd\ndrwxrwxr-x  2 aux aux  4096 Mar 29 23:39 .gsd.lock\ndrwxrwxr-x  2 aux aux  4096 Mar 29 22:31 prompts\ndrwxrwxr-x  3 aux aux  4096 Mar 29 22:51 .pytest_cache\n-rw-rw-r--  1 aux aux 12726 Mar 29 21:59 README.md\ndrwxrwxr-x  3 aux aux  4096 Mar 29 21:40 tests\ndrwxrwxr-x  5 aux aux  4096 Mar 29 21:44 .venv\ndrwxrwxr-x  2 aux aux  4096 Mar 29 21:56 whisper\n"}],"isError":false,"timestamp":1774827567398}}
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{"type":"message","id":"bdeb73fd","parentId":"1c07e0f8","timestamp":"2026-03-29T23:39:35.774Z","message":{"role":"assistant","content":[{"type":"text","text":"The `find` for .py picked up .venv packages. Let me focus on actual project source files and explore the key pieces:"},{"type":"toolCall","id":"toolu_01UxqbCHHMNVcFimnfCDgJpS","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator -type f \\( -name \"*.py\" -o -name \"*.ts\" -o -name \"*.tsx\" -o -name \"*.js\" -o -name \"*.jsx\" -o -name \"*.css\" -o -name \"*.html\" -o -name \"*.json\" -o -name \"*.yaml\" -o -name \"*.yml\" \\) -not -path \"*/.venv/*\" -not -path \"*/node_modules/*\" -not -path \"*/__pycache__/*\" -not -path \"*/dist/*\" -not -path \"*/.gsd/*\" -not -path \"*/.pytest_cache/*\" | sort"}},{"type":"toolCall","id":"toolu_01XCtk4Y8g2bAcrPQh4XhEZy","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator/backend -type f -name \"*.py\" | sort"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":309,"cacheRead":58383,"cacheWrite":20723,"totalTokens":79416,"cost":{"input":0.000005,"output":0.007725,"cacheRead":0.0291915,"cacheWrite":0.12951875000000002,"total":0.16644025}},"stopReason":"toolUse","timestamp":1774827567439}}
{"type":"message","id":"00db977e","parentId":"bdeb73fd","timestamp":"2026-03-29T23:39:35.833Z","message":{"role":"toolResult","toolCallId":"toolu_01UxqbCHHMNVcFimnfCDgJpS","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/redis_client.py\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/review.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/tests/test_review.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/index.html\n/home/aux/projects/content-to-kb-automator/frontend/package.json\n/home/aux/projects/content-to-kb-automator/frontend/package-lock.json\n/home/aux/projects/content-to-kb-automator/frontend/src/api/client.ts\n/home/aux/projects/content-to-kb-automator/frontend/src/App.css\n/home/aux/projects/content-to-kb-automator/frontend/src/App.tsx\n/home/aux/projects/content-to-kb-automator/frontend/src/components/ModeToggle.tsx\n/home/aux/projects/content-to-kb-automator/frontend/src/components/StatusBadge.tsx\n/home/aux/projects/content-to-kb-automator/frontend/src/main.tsx\n/home/aux/projects/content-to-kb-automator/frontend/src/pages/MomentDetail.tsx\n/home/aux/projects/content-to-kb-automator/frontend/src/pages/ReviewQueue.tsx\n/home/aux/projects/content-to-kb-automator/frontend/src/vite-env.d.ts\n/home/aux/projects/content-to-kb-automator/frontend/tsconfig.app.json\n/home/aux/projects/content-to-kb-automator/frontend/tsconfig.json\n/home/aux/projects/content-to-kb-automator/frontend/vite.config.ts\n/home/aux/projects/content-to-kb-automator/tests/fixtures/sample_transcript.json\n/home/aux/projects/content-to-kb-automator/whisper/transcribe.py\n"}],"isError":false,"timestamp":1774827575832}}
{"type":"message","id":"75b9e11c","parentId":"00db977e","timestamp":"2026-03-29T23:39:35.835Z","message":{"role":"toolResult","toolCallId":"toolu_01XCtk4Y8g2bAcrPQh4XhEZy","toolName":"bash","content":[{"type":"text","text":"/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/redis_client.py\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/review.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/__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/tests/test_review.py\n/home/aux/projects/content-to-kb-automator/backend/worker.py\n"}],"isError":false,"timestamp":1774827575832}}
{"type":"message","id":"d5d58dff","parentId":"75b9e11c","timestamp":"2026-03-29T23:39:43.194Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me read the critical files — models, existing routers, frontend setup, Qdrant client, spec, and the existing frontend code:"},{"type":"toolCall","id":"toolu_01CmD2cCfToZDSwXUod3gULL","name":"read","arguments":{"path":"backend/models.py"}},{"type":"toolCall","id":"toolu_01P2SGnZpPeEanuKPmmotVej","name":"read","arguments":{"path":"backend/pipeline/qdrant_client.py"}},{"type":"toolCall","id":"toolu_01R2MjSBfvvPuKnKcPWM1dLt","name":"read","arguments":{"path":"backend/main.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":169,"cacheRead":79106,"cacheWrite":2238,"totalTokens":81514,"cost":{"input":0.000005,"output":0.0042250000000000005,"cacheRead":0.039553,"cacheWrite":0.0139875,"total":0.057770499999999995}},"stopReason":"toolUse","timestamp":1774827575833}}
{"type":"message","id":"329dc588","parentId":"d5d58dff","timestamp":"2026-03-29T23:39:43.197Z","message":{"role":"toolResult","toolCallId":"toolu_01CmD2cCfToZDSwXUod3gULL","toolName":"read","content":[{"type":"text","text":"\"\"\"SQLAlchemy ORM models for the Chrysopedia knowledge base.\n\nSeven entities matching chrysopedia-spec.md §6.1:\n  Creator, SourceVideo, TranscriptSegment, KeyMoment,\n  TechniquePage, RelatedTechniqueLink, Tag\n\"\"\"\n\nfrom __future__ import annotations\n\nimport enum\nimport uuid\nfrom datetime import datetime, timezone\n\nfrom sqlalchemy import (\n    Enum,\n    Float,\n    ForeignKey,\n    Integer,\n    String,\n    Text,\n    UniqueConstraint,\n    func,\n)\nfrom sqlalchemy.dialects.postgresql import ARRAY, JSONB, UUID\nfrom sqlalchemy.orm import Mapped, mapped_column\nfrom sqlalchemy.orm import relationship as sa_relationship\n\nfrom database import Base\n\n\n# ── Enums ────────────────────────────────────────────────────────────────────\n\nclass ContentType(str, enum.Enum):\n    \"\"\"Source video content type.\"\"\"\n    tutorial = \"tutorial\"\n    livestream = \"livestream\"\n    breakdown = \"breakdown\"\n    short_form = \"short_form\"\n\n\nclass ProcessingStatus(str, enum.Enum):\n    \"\"\"Pipeline processing status for a source video.\"\"\"\n    pending = \"pending\"\n    transcribed = \"transcribed\"\n    extracted = \"extracted\"\n    reviewed = \"reviewed\"\n    published = \"published\"\n\n\nclass KeyMomentContentType(str, enum.Enum):\n    \"\"\"Content classification for a key moment.\"\"\"\n    technique = \"technique\"\n    settings = \"settings\"\n    reasoning = \"reasoning\"\n    workflow = \"workflow\"\n\n\nclass ReviewStatus(str, enum.Enum):\n    \"\"\"Human review status for key moments.\"\"\"\n    pending = \"pending\"\n    approved = \"approved\"\n    edited = \"edited\"\n    rejected = \"rejected\"\n\n\nclass SourceQuality(str, enum.Enum):\n    \"\"\"Derived source quality for technique pages.\"\"\"\n    structured = \"structured\"\n    mixed = \"mixed\"\n    unstructured = \"unstructured\"\n\n\nclass PageReviewStatus(str, enum.Enum):\n    \"\"\"Review lifecycle for technique pages.\"\"\"\n    draft = \"draft\"\n    reviewed = \"reviewed\"\n    published = \"published\"\n\n\nclass RelationshipType(str, enum.Enum):\n    \"\"\"Types of links between technique pages.\"\"\"\n    same_technique_other_creator = \"same_technique_other_creator\"\n    same_creator_adjacent = \"same_creator_adjacent\"\n    general_cross_reference = \"general_cross_reference\"\n\n\n# ── Helpers ──────────────────────────────────────────────────────────────────\n\ndef _uuid_pk() -> Mapped[uuid.UUID]:\n    return mapped_column(\n        UUID(as_uuid=True),\n        primary_key=True,\n        default=uuid.uuid4,\n        server_default=func.gen_random_uuid(),\n    )\n\n\ndef _now() -> datetime:\n    \"\"\"Return current UTC time as a naive datetime (no tzinfo).\n\n    PostgreSQL TIMESTAMP WITHOUT TIME ZONE columns require naive datetimes.\n    asyncpg rejects timezone-aware datetimes for such columns.\n    \"\"\"\n    return datetime.now(timezone.utc).replace(tzinfo=None)\n\n\n# ── Models ───────────────────────────────────────────────────────────────────\n\nclass Creator(Base):\n    __tablename__ = \"creators\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    name: Mapped[str] = mapped_column(String(255), nullable=False)\n    slug: Mapped[str] = mapped_column(String(255), unique=True, nullable=False)\n    genres: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    folder_name: Mapped[str] = mapped_column(String(255), nullable=False)\n    view_count: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    videos: Mapped[list[SourceVideo]] = sa_relationship(back_populates=\"creator\")\n    technique_pages: Mapped[list[TechniquePage]] = sa_relationship(back_populates=\"creator\")\n\n\nclass SourceVideo(Base):\n    __tablename__ = \"source_videos\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    creator_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"creators.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    filename: Mapped[str] = mapped_column(String(500), nullable=False)\n    file_path: Mapped[str] = mapped_column(String(1000), nullable=False)\n    duration_seconds: Mapped[int] = mapped_column(Integer, nullable=True)\n    content_type: Mapped[ContentType] = mapped_column(\n        Enum(ContentType, name=\"content_type\", create_constraint=True),\n        nullable=False,\n    )\n    transcript_path: Mapped[str | None] = mapped_column(String(1000), nullable=True)\n    processing_status: Mapped[ProcessingStatus] = mapped_column(\n        Enum(ProcessingStatus, name=\"processing_status\", create_constraint=True),\n        default=ProcessingStatus.pending,\n        server_default=\"pending\",\n    )\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    creator: Mapped[Creator] = sa_relationship(back_populates=\"videos\")\n    segments: Mapped[list[TranscriptSegment]] = sa_relationship(back_populates=\"source_video\")\n    key_moments: Mapped[list[KeyMoment]] = sa_relationship(back_populates=\"source_video\")\n\n\nclass TranscriptSegment(Base):\n    __tablename__ = \"transcript_segments\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_video_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    start_time: Mapped[float] = mapped_column(Float, nullable=False)\n    end_time: Mapped[float] = mapped_column(Float, nullable=False)\n    text: Mapped[str] = mapped_column(Text, nullable=False)\n    segment_index: Mapped[int] = mapped_column(Integer, nullable=False)\n    topic_label: Mapped[str | None] = mapped_column(String(255), nullable=True)\n\n    # relationships\n    source_video: Mapped[SourceVideo] = sa_relationship(back_populates=\"segments\")\n\n\nclass KeyMoment(Base):\n    __tablename__ = \"key_moments\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_video_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    technique_page_id: Mapped[uuid.UUID | None] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"SET NULL\"), nullable=True\n    )\n    title: Mapped[str] = mapped_column(String(500), nullable=False)\n    summary: Mapped[str] = mapped_column(Text, nullable=False)\n    start_time: Mapped[float] = mapped_column(Float, nullable=False)\n    end_time: Mapped[float] = mapped_column(Float, nullable=False)\n    content_type: Mapped[KeyMomentContentType] = mapped_column(\n        Enum(KeyMomentContentType, name=\"key_moment_content_type\", create_constraint=True),\n        nullable=False,\n    )\n    plugins: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    review_status: Mapped[ReviewStatus] = mapped_column(\n        Enum(ReviewStatus, name=\"review_status\", create_constraint=True),\n        default=ReviewStatus.pending,\n        server_default=\"pending\",\n    )\n    raw_transcript: Mapped[str | None] = mapped_column(Text, nullable=True)\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    source_video: Mapped[SourceVideo] = sa_relationship(back_populates=\"key_moments\")\n    technique_page: Mapped[TechniquePage | None] = sa_relationship(\n        back_populates=\"key_moments\", foreign_keys=[technique_page_id]\n    )\n\n\nclass TechniquePage(Base):\n    __tablename__ = \"technique_pages\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    creator_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"creators.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    title: Mapped[str] = mapped_column(String(500), nullable=False)\n    slug: Mapped[str] = mapped_column(String(500), unique=True, nullable=False)\n    topic_category: Mapped[str] = mapped_column(String(255), nullable=False)\n    topic_tags: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    summary: Mapped[str | None] = mapped_column(Text, nullable=True)\n    body_sections: Mapped[dict | None] = mapped_column(JSONB, nullable=True)\n    signal_chains: Mapped[list | None] = mapped_column(JSONB, nullable=True)\n    plugins: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    source_quality: Mapped[SourceQuality | None] = mapped_column(\n        Enum(SourceQuality, name=\"source_quality\", create_constraint=True),\n        nullable=True,\n    )\n    view_count: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\n    review_status: Mapped[PageReviewStatus] = mapped_column(\n        Enum(PageReviewStatus, name=\"page_review_status\", create_constraint=True),\n        default=PageReviewStatus.draft,\n        server_default=\"draft\",\n    )\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    creator: Mapped[Creator] = sa_relationship(back_populates=\"technique_pages\")\n    key_moments: Mapped[list[KeyMoment]] = sa_relationship(\n        back_populates=\"technique_page\", foreign_keys=[KeyMoment.technique_page_id]\n    )\n    outgoing_links: Mapped[list[RelatedTechniqueLink]] = sa_relationship(\n        foreign_keys=\"RelatedTechniqueLink.source_page_id\", back_populates=\"source_page\"\n    )\n    incoming_links: Mapped[list[RelatedTechniqueLink]] = sa_relationship(\n        foreign_keys=\"RelatedTechniqueLink.target_page_id\", back_populates=\"target_page\"\n    )\n\n\nclass RelatedTechniqueLink(Base):\n    __tablename__ = \"related_technique_links\"\n    __table_args__ = (\n        UniqueConstraint(\"source_page_id\", \"target_page_id\", \"relationship\", name=\"uq_technique_link\"),\n    )\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    target_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    relationship: Mapped[RelationshipType] = mapped_column(\n        Enum(RelationshipType, name=\"relationship_type\", create_constraint=True),\n        nullable=False,\n    )\n\n    # relationships\n    source_page: Mapped[TechniquePage] = sa_relationship(\n        foreign_keys=[source_page_id], back_populates=\"outgoing_links\"\n    )\n    target_page: Mapped[TechniquePage] = sa_relationship(\n        foreign_keys=[target_page_id], back_populates=\"incoming_links\"\n    )\n\n\nclass Tag(Base):\n    __tablename__ = \"tags\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    name: Mapped[str] = mapped_column(String(255), unique=True, nullable=False)\n    category: Mapped[str] = mapped_column(String(255), nullable=False)\n    aliases: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n"}],"isError":false,"timestamp":1774827583197}}
{"type":"message","id":"958fcd9d","parentId":"329dc588","timestamp":"2026-03-29T23:39:43.199Z","message":{"role":"toolResult","toolCallId":"toolu_01P2SGnZpPeEanuKPmmotVej","toolName":"read","content":[{"type":"text","text":"\"\"\"Qdrant vector database manager for collection lifecycle and point upserts.\n\nHandles collection creation (idempotent) and batch upserts for technique pages\nand key moments. Connection failures are non-blocking — the pipeline continues\nwithout search indexing.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nimport uuid\n\nfrom qdrant_client import QdrantClient\nfrom qdrant_client.http import exceptions as qdrant_exceptions\nfrom qdrant_client.models import Distance, PointStruct, VectorParams\n\nfrom config import Settings\n\nlogger = logging.getLogger(__name__)\n\n\nclass QdrantManager:\n    \"\"\"Manages a Qdrant collection for Chrysopedia technique-page and key-moment vectors.\"\"\"\n\n    def __init__(self, settings: Settings) -> None:\n        self.settings = settings\n        self._client = QdrantClient(url=settings.qdrant_url)\n        self._collection = settings.qdrant_collection\n\n    # ── Collection management ────────────────────────────────────────────\n\n    def ensure_collection(self) -> None:\n        \"\"\"Create the collection if it does not already exist.\n\n        Uses cosine distance and the configured embedding dimensions.\n        \"\"\"\n        try:\n            if self._client.collection_exists(self._collection):\n                logger.info(\"Qdrant collection '%s' already exists.\", self._collection)\n                return\n\n            self._client.create_collection(\n                collection_name=self._collection,\n                vectors_config=VectorParams(\n                    size=self.settings.embedding_dimensions,\n                    distance=Distance.COSINE,\n                ),\n            )\n            logger.info(\n                \"Created Qdrant collection '%s' (dim=%d, cosine).\",\n                self._collection,\n                self.settings.embedding_dimensions,\n            )\n        except qdrant_exceptions.UnexpectedResponse as exc:\n            logger.warning(\n                \"Qdrant error during ensure_collection (%s). Skipping.\",\n                exc,\n            )\n        except Exception as exc:\n            logger.warning(\n                \"Qdrant connection failed during ensure_collection (%s: %s). Skipping.\",\n                type(exc).__name__,\n                exc,\n            )\n\n    # ── Low-level upsert ─────────────────────────────────────────────────\n\n    def upsert_points(self, points: list[PointStruct]) -> None:\n        \"\"\"Upsert a batch of pre-built PointStruct objects.\"\"\"\n        if not points:\n            return\n        try:\n            self._client.upsert(\n                collection_name=self._collection,\n                points=points,\n            )\n            logger.info(\n                \"Upserted %d points to Qdrant collection '%s'.\",\n                len(points),\n                self._collection,\n            )\n        except qdrant_exceptions.UnexpectedResponse as exc:\n            logger.warning(\n                \"Qdrant upsert failed (%s). %d points skipped.\",\n                exc,\n                len(points),\n            )\n        except Exception as exc:\n            logger.warning(\n                \"Qdrant upsert connection error (%s: %s). %d points skipped.\",\n                type(exc).__name__,\n                exc,\n                len(points),\n            )\n\n    # ── High-level upserts ───────────────────────────────────────────────\n\n    def upsert_technique_pages(\n        self,\n        pages: list[dict],\n        vectors: list[list[float]],\n    ) -> None:\n        \"\"\"Build and upsert PointStructs for technique pages.\n\n        Each page dict must contain:\n            page_id, creator_id, title, topic_category, topic_tags, summary\n\n        Parameters\n        ----------\n        pages:\n            Metadata dicts, one per technique page.\n        vectors:\n            Corresponding embedding vectors (same order as pages).\n        \"\"\"\n        if len(pages) != len(vectors):\n            logger.warning(\n                \"Technique-page count (%d) != vector count (%d). Skipping upsert.\",\n                len(pages),\n                len(vectors),\n            )\n            return\n\n        points = []\n        for page, vector in zip(pages, vectors):\n            point = PointStruct(\n                id=str(uuid.uuid4()),\n                vector=vector,\n                payload={\n                    \"type\": \"technique_page\",\n                    \"page_id\": page[\"page_id\"],\n                    \"creator_id\": page[\"creator_id\"],\n                    \"title\": page[\"title\"],\n                    \"topic_category\": page[\"topic_category\"],\n                    \"topic_tags\": page.get(\"topic_tags\") or [],\n                    \"summary\": page.get(\"summary\") or \"\",\n                },\n            )\n            points.append(point)\n\n        self.upsert_points(points)\n\n    def upsert_key_moments(\n        self,\n        moments: list[dict],\n        vectors: list[list[float]],\n    ) -> None:\n        \"\"\"Build and upsert PointStructs for key moments.\n\n        Each moment dict must contain:\n            moment_id, source_video_id, title, start_time, end_time, content_type\n\n        Parameters\n        ----------\n        moments:\n            Metadata dicts, one per key moment.\n        vectors:\n            Corresponding embedding vectors (same order as moments).\n        \"\"\"\n        if len(moments) != len(vectors):\n            logger.warning(\n                \"Key-moment count (%d) != vector count (%d). Skipping upsert.\",\n                len(moments),\n                len(vectors),\n            )\n            return\n\n        points = []\n        for moment, vector in zip(moments, vectors):\n            point = PointStruct(\n                id=str(uuid.uuid4()),\n                vector=vector,\n                payload={\n                    \"type\": \"key_moment\",\n                    \"moment_id\": moment[\"moment_id\"],\n                    \"source_video_id\": moment[\"source_video_id\"],\n                    \"title\": moment[\"title\"],\n                    \"start_time\": moment[\"start_time\"],\n                    \"end_time\": moment[\"end_time\"],\n                    \"content_type\": moment[\"content_type\"],\n                },\n            )\n            points.append(point)\n\n        self.upsert_points(points)\n"}],"isError":false,"timestamp":1774827583199}}
{"type":"message","id":"7da66563","parentId":"958fcd9d","timestamp":"2026-03-29T23:39:43.201Z","message":{"role":"toolResult","toolCallId":"toolu_01R2MjSBfvvPuKnKcPWM1dLt","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, review, videos\n\n\ndef _setup_logging() -> None:\n    \"\"\"Configure structured logging to stdout.\"\"\"\n    settings = get_settings()\n    level = getattr(logging, settings.app_log_level.upper(), logging.INFO)\n\n    handler = logging.StreamHandler(sys.stdout)\n    handler.setFormatter(\n        logging.Formatter(\n            fmt=\"%(asctime)s | %(levelname)-8s | %(name)s | %(message)s\",\n            datefmt=\"%Y-%m-%dT%H:%M:%S\",\n        )\n    )\n\n    root = logging.getLogger()\n    root.setLevel(level)\n    # Avoid duplicate handlers on reload\n    root.handlers.clear()\n    root.addHandler(handler)\n\n    # Quiet noisy libraries\n    logging.getLogger(\"uvicorn.access\").setLevel(logging.WARNING)\n    logging.getLogger(\"sqlalchemy.engine\").setLevel(logging.WARNING)\n\n\n@asynccontextmanager\nasync def lifespan(app: FastAPI):  # noqa: ARG001\n    \"\"\"Application lifespan: setup on startup, teardown on shutdown.\"\"\"\n    _setup_logging()\n    logger = logging.getLogger(\"chrysopedia\")\n    settings = get_settings()\n    logger.info(\n        \"Chrysopedia API starting (env=%s, log_level=%s)\",\n        settings.app_env,\n        settings.app_log_level,\n    )\n    yield\n    logger.info(\"Chrysopedia API shutting down\")\n\n\napp = FastAPI(\n    title=\"Chrysopedia API\",\n    description=\"Knowledge extraction and retrieval for music production content\",\n    version=\"0.1.0\",\n    lifespan=lifespan,\n)\n\n# ── Middleware ────────────────────────────────────────────────────────────────\n\nsettings = get_settings()\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=settings.cors_origins,\n    allow_credentials=True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"],\n)\n\n# ── Routers ──────────────────────────────────────────────────────────────────\n\n# Root-level health (no prefix)\napp.include_router(health.router)\n\n# Versioned API\napp.include_router(creators.router, prefix=\"/api/v1\")\napp.include_router(ingest.router, prefix=\"/api/v1\")\napp.include_router(pipeline.router, prefix=\"/api/v1\")\napp.include_router(review.router, prefix=\"/api/v1\")\napp.include_router(videos.router, prefix=\"/api/v1\")\n\n\n@app.get(\"/api/v1/health\")\nasync def api_health():\n    \"\"\"Lightweight version-prefixed health endpoint (no DB check).\"\"\"\n    return {\"status\": \"ok\", \"version\": \"0.1.0\"}\n"}],"isError":false,"timestamp":1774827583200}}
{"type":"message","id":"98bab5c6","parentId":"7da66563","timestamp":"2026-03-29T23:39:50.392Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. Now let me read the existing frontend code and the existing routers that will be relevant:"},{"type":"toolCall","id":"toolu_013cnbxZwVn7cM5pNdwU7d88","name":"read","arguments":{"path":"frontend/src/App.tsx"}},{"type":"toolCall","id":"toolu_014xtHSvN5FAfPRVCbGiGvuo","name":"read","arguments":{"path":"frontend/src/api/client.ts"}},{"type":"toolCall","id":"toolu_01PWSNZMDPnuw9vBw3seEiYz","name":"read","arguments":{"path":"frontend/package.json"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":158,"cacheRead":81344,"cacheWrite":5980,"totalTokens":87483,"cost":{"input":0.000005,"output":0.00395,"cacheRead":0.040672,"cacheWrite":0.037375,"total":0.08200199999999999}},"stopReason":"toolUse","timestamp":1774827583201}}
{"type":"message","id":"db16480d","parentId":"98bab5c6","timestamp":"2026-03-29T23:39:50.395Z","message":{"role":"toolResult","toolCallId":"toolu_013cnbxZwVn7cM5pNdwU7d88","toolName":"read","content":[{"type":"text","text":"import { Navigate, Route, Routes } from \"react-router-dom\";\nimport ReviewQueue from \"./pages/ReviewQueue\";\nimport MomentDetail from \"./pages/MomentDetail\";\nimport ModeToggle from \"./components/ModeToggle\";\n\nexport default function App() {\n  return (\n    <div className=\"app\">\n      <header className=\"app-header\">\n        <h1>Chrysopedia Admin</h1>\n        <div className=\"app-header__right\">\n          <ModeToggle />\n          <nav>\n            <a href=\"/admin/review\">Review Queue</a>\n          </nav>\n        </div>\n      </header>\n\n      <main className=\"app-main\">\n        <Routes>\n          <Route path=\"/admin/review\" element={<ReviewQueue />} />\n          <Route path=\"/admin/review/:momentId\" element={<MomentDetail />} />\n          <Route path=\"*\" element={<Navigate to=\"/admin/review\" replace />} />\n        </Routes>\n      </main>\n    </div>\n  );\n}\n"}],"isError":false,"timestamp":1774827590395}}
{"type":"message","id":"80f78bd7","parentId":"db16480d","timestamp":"2026-03-29T23:39:50.396Z","message":{"role":"toolResult","toolCallId":"toolu_014xtHSvN5FAfPRVCbGiGvuo","toolName":"read","content":[{"type":"text","text":"/**\n * Typed API client for Chrysopedia review queue endpoints.\n *\n * All functions use fetch() with JSON handling and throw on non-OK responses.\n * Base URL is empty so requests go through the Vite dev proxy or nginx in prod.\n */\n\n// ── Types ───────────────────────────────────────────────────────────────────\n\nexport interface KeyMomentRead {\n  id: string;\n  source_video_id: string;\n  technique_page_id: string | null;\n  title: string;\n  summary: string;\n  start_time: number;\n  end_time: number;\n  content_type: string;\n  plugins: string[] | null;\n  raw_transcript: string | null;\n  review_status: string;\n  created_at: string;\n  updated_at: string;\n}\n\nexport interface ReviewQueueItem extends KeyMomentRead {\n  video_filename: string;\n  creator_name: string;\n}\n\nexport interface ReviewQueueResponse {\n  items: ReviewQueueItem[];\n  total: number;\n  offset: number;\n  limit: number;\n}\n\nexport interface ReviewStatsResponse {\n  pending: number;\n  approved: number;\n  edited: number;\n  rejected: number;\n}\n\nexport interface ReviewModeResponse {\n  review_mode: boolean;\n}\n\nexport interface MomentEditRequest {\n  title?: string;\n  summary?: string;\n  start_time?: number;\n  end_time?: number;\n  content_type?: string;\n  plugins?: string[];\n}\n\nexport interface MomentSplitRequest {\n  split_time: number;\n}\n\nexport interface MomentMergeRequest {\n  target_moment_id: string;\n}\n\nexport interface QueueParams {\n  status?: string;\n  offset?: number;\n  limit?: number;\n}\n\n// ── Helpers ──────────────────────────────────────────────────────────────────\n\nconst BASE = \"/api/v1/review\";\n\nclass ApiError extends Error {\n  constructor(\n    public status: number,\n    public detail: string,\n  ) {\n    super(`API ${status}: ${detail}`);\n    this.name = \"ApiError\";\n  }\n}\n\nasync function request<T>(url: string, init?: RequestInit): Promise<T> {\n  const res = await fetch(url, {\n    ...init,\n    headers: {\n      \"Content-Type\": \"application/json\",\n      ...init?.headers,\n    },\n  });\n\n  if (!res.ok) {\n    let detail = res.statusText;\n    try {\n      const body = await res.json();\n      detail = body.detail ?? detail;\n    } catch {\n      // body not JSON — keep statusText\n    }\n    throw new ApiError(res.status, detail);\n  }\n\n  return res.json() as Promise<T>;\n}\n\n// ── Queue ────────────────────────────────────────────────────────────────────\n\nexport async function fetchQueue(\n  params: QueueParams = {},\n): Promise<ReviewQueueResponse> {\n  const qs = new URLSearchParams();\n  if (params.status) qs.set(\"status\", params.status);\n  if (params.offset !== undefined) qs.set(\"offset\", String(params.offset));\n  if (params.limit !== undefined) qs.set(\"limit\", String(params.limit));\n  const query = qs.toString();\n  return request<ReviewQueueResponse>(\n    `${BASE}/queue${query ? `?${query}` : \"\"}`,\n  );\n}\n\nexport async function fetchStats(): Promise<ReviewStatsResponse> {\n  return request<ReviewStatsResponse>(`${BASE}/stats`);\n}\n\n// ── Actions ──────────────────────────────────────────────────────────────────\n\nexport async function approveMoment(id: string): Promise<KeyMomentRead> {\n  return request<KeyMomentRead>(`${BASE}/moments/${id}/approve`, {\n    method: \"POST\",\n  });\n}\n\nexport async function rejectMoment(id: string): Promise<KeyMomentRead> {\n  return request<KeyMomentRead>(`${BASE}/moments/${id}/reject`, {\n    method: \"POST\",\n  });\n}\n\nexport async function editMoment(\n  id: string,\n  data: MomentEditRequest,\n): Promise<KeyMomentRead> {\n  return request<KeyMomentRead>(`${BASE}/moments/${id}`, {\n    method: \"PUT\",\n    body: JSON.stringify(data),\n  });\n}\n\nexport async function splitMoment(\n  id: string,\n  splitTime: number,\n): Promise<KeyMomentRead[]> {\n  const body: MomentSplitRequest = { split_time: splitTime };\n  return request<KeyMomentRead[]>(`${BASE}/moments/${id}/split`, {\n    method: \"POST\",\n    body: JSON.stringify(body),\n  });\n}\n\nexport async function mergeMoments(\n  id: string,\n  targetId: string,\n): Promise<KeyMomentRead> {\n  const body: MomentMergeRequest = { target_moment_id: targetId };\n  return request<KeyMomentRead>(`${BASE}/moments/${id}/merge`, {\n    method: \"POST\",\n    body: JSON.stringify(body),\n  });\n}\n\n// ── Mode ─────────────────────────────────────────────────────────────────────\n\nexport async function getReviewMode(): Promise<ReviewModeResponse> {\n  return request<ReviewModeResponse>(`${BASE}/mode`);\n}\n\nexport async function setReviewMode(\n  enabled: boolean,\n): Promise<ReviewModeResponse> {\n  return request<ReviewModeResponse>(`${BASE}/mode`, {\n    method: \"PUT\",\n    body: JSON.stringify({ review_mode: enabled }),\n  });\n}\n"}],"isError":false,"timestamp":1774827590396}}
{"type":"message","id":"34916955","parentId":"80f78bd7","timestamp":"2026-03-29T23:39:50.399Z","message":{"role":"toolResult","toolCallId":"toolu_01PWSNZMDPnuw9vBw3seEiYz","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\": \"vite\",\n    \"build\": \"tsc -b && vite build\",\n    \"preview\": \"vite preview\"\n  },\n  \"dependencies\": {\n    \"react\": \"^18.3.1\",\n    \"react-dom\": \"^18.3.1\",\n    \"react-router-dom\": \"^6.28.0\"\n  },\n  \"devDependencies\": {\n    \"@types/react\": \"^18.3.12\",\n    \"@types/react-dom\": \"^18.3.1\",\n    \"@vitejs/plugin-react\": \"^4.3.4\",\n    \"typescript\": \"~5.6.3\",\n    \"vite\": \"^6.0.3\"\n  }\n}\n"}],"isError":false,"timestamp":1774827590397}}
{"type":"message","id":"8624aa1a","parentId":"34916955","timestamp":"2026-03-29T23:39:56.562Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check existing backend routers relevant to search, creators, and the Qdrant search capability:"},{"type":"toolCall","id":"toolu_01AGWabtzhAeyMFNJymdXbCH","name":"read","arguments":{"path":"backend/routers/creators.py"}},{"type":"toolCall","id":"toolu_01NFevM9xiQBHRwb8agc71cj","name":"read","arguments":{"path":"backend/routers/videos.py"}},{"type":"toolCall","id":"toolu_01FsAw4XhfKf7XfWcfLdLa7s","name":"read","arguments":{"path":"backend/config.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":161,"cacheRead":87324,"cacheWrite":2172,"totalTokens":89658,"cost":{"input":0.000005,"output":0.004025,"cacheRead":0.043662,"cacheWrite":0.013575,"total":0.061267}},"stopReason":"toolUse","timestamp":1774827590398}}
{"type":"message","id":"dd7a900a","parentId":"8624aa1a","timestamp":"2026-03-29T23:39:56.564Z","message":{"role":"toolResult","toolCallId":"toolu_01AGWabtzhAeyMFNJymdXbCH","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":1774827596564}}
{"type":"message","id":"dad1764a","parentId":"dd7a900a","timestamp":"2026-03-29T23:39:56.566Z","message":{"role":"toolResult","toolCallId":"toolu_01NFevM9xiQBHRwb8agc71cj","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":1774827596565}}
{"type":"message","id":"94cfe120","parentId":"dad1764a","timestamp":"2026-03-29T23:39:56.568Z","message":{"role":"toolResult","toolCallId":"toolu_01FsAw4XhfKf7XfWcfLdLa7s","toolName":"read","content":[{"type":"text","text":"\"\"\"Application configuration loaded from environment variables.\"\"\"\n\nfrom functools import lru_cache\n\nfrom pydantic_settings import BaseSettings\n\n\nclass Settings(BaseSettings):\n    \"\"\"Chrysopedia API settings.\n\n    Values are loaded from environment variables (or .env file via\n    pydantic-settings' dotenv support).\n    \"\"\"\n\n    # Database\n    database_url: str = \"postgresql+asyncpg://chrysopedia:changeme@localhost:5433/chrysopedia\"\n\n    # Redis\n    redis_url: str = \"redis://localhost:6379/0\"\n\n    # Application\n    app_env: str = \"development\"\n    app_log_level: str = \"info\"\n    app_secret_key: str = \"changeme-generate-a-real-secret\"\n\n    # CORS\n    cors_origins: list[str] = [\"*\"]\n\n    # LLM endpoint (OpenAI-compatible)\n    llm_api_url: str = \"http://localhost:11434/v1\"\n    llm_api_key: str = \"sk-placeholder\"\n    llm_model: str = \"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":1774827596565}}
{"type":"message","id":"6eec6fe0","parentId":"94cfe120","timestamp":"2026-03-29T23:40:03.636Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the existing schemas, the database module, the review router, the spec, and the existing Qdrant client search capability:"},{"type":"toolCall","id":"toolu_01ARsjAUAvuEoDko66F43Fnm","name":"read","arguments":{"path":"backend/schemas.py"}},{"type":"toolCall","id":"toolu_01JS43mqAaarSfAzYfRB5BtU","name":"read","arguments":{"path":"backend/database.py"}},{"type":"toolCall","id":"toolu_01UVAfQHmTevFvab5DUm7poP","name":"read","arguments":{"path":"backend/pipeline/embedding_client.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":166,"cacheRead":89496,"cacheWrite":1737,"totalTokens":91400,"cost":{"input":0.000005,"output":0.00415,"cacheRead":0.044747999999999996,"cacheWrite":0.010856250000000001,"total":0.05975925}},"stopReason":"toolUse","timestamp":1774827596566}}
{"type":"message","id":"85c15a6b","parentId":"6eec6fe0","timestamp":"2026-03-29T23:40:03.639Z","message":{"role":"toolResult","toolCallId":"toolu_01ARsjAUAvuEoDko66F43Fnm","toolName":"read","content":[{"type":"text","text":"\"\"\"Pydantic schemas for the Chrysopedia API.\n\nRead-only schemas for list/detail endpoints and input schemas for creation.\nEach schema mirrors the corresponding SQLAlchemy model in models.py.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport uuid\nfrom datetime import datetime\n\nfrom pydantic import BaseModel, ConfigDict, Field\n\n\n# ── Health ───────────────────────────────────────────────────────────────────\n\nclass HealthResponse(BaseModel):\n    status: str = \"ok\"\n    service: str = \"chrysopedia-api\"\n    version: str = \"0.1.0\"\n    database: str = \"unknown\"\n\n\n# ── Creator ──────────────────────────────────────────────────────────────────\n\nclass CreatorBase(BaseModel):\n    name: str\n    slug: str\n    genres: list[str] | None = None\n    folder_name: str\n\nclass CreatorCreate(CreatorBase):\n    pass\n\nclass CreatorRead(CreatorBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    view_count: int = 0\n    created_at: datetime\n    updated_at: datetime\n\n\nclass CreatorDetail(CreatorRead):\n    \"\"\"Creator with nested video count.\"\"\"\n    video_count: int = 0\n\n\n# ── SourceVideo ──────────────────────────────────────────────────────────────\n\nclass SourceVideoBase(BaseModel):\n    filename: str\n    file_path: str\n    duration_seconds: int | None = None\n    content_type: str\n    transcript_path: str | None = None\n\nclass SourceVideoCreate(SourceVideoBase):\n    creator_id: uuid.UUID\n\nclass SourceVideoRead(SourceVideoBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    creator_id: uuid.UUID\n    processing_status: str = \"pending\"\n    created_at: datetime\n    updated_at: datetime\n\n\n# ── TranscriptSegment ────────────────────────────────────────────────────────\n\nclass TranscriptSegmentBase(BaseModel):\n    start_time: float\n    end_time: float\n    text: str\n    segment_index: int\n    topic_label: str | None = None\n\nclass TranscriptSegmentCreate(TranscriptSegmentBase):\n    source_video_id: uuid.UUID\n\nclass TranscriptSegmentRead(TranscriptSegmentBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    source_video_id: uuid.UUID\n\n\n# ── KeyMoment ────────────────────────────────────────────────────────────────\n\nclass KeyMomentBase(BaseModel):\n    title: str\n    summary: str\n    start_time: float\n    end_time: float\n    content_type: str\n    plugins: list[str] | None = None\n    raw_transcript: str | None = None\n\nclass KeyMomentCreate(KeyMomentBase):\n    source_video_id: uuid.UUID\n    technique_page_id: uuid.UUID | None = None\n\nclass KeyMomentRead(KeyMomentBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    source_video_id: uuid.UUID\n    technique_page_id: uuid.UUID | None = None\n    review_status: str = \"pending\"\n    created_at: datetime\n    updated_at: datetime\n\n\n# ── TechniquePage ────────────────────────────────────────────────────────────\n\nclass TechniquePageBase(BaseModel):\n    title: str\n    slug: str\n    topic_category: str\n    topic_tags: list[str] | None = None\n    summary: str | None = None\n    body_sections: dict | None = None\n    signal_chains: list | None = None\n    plugins: list[str] | None = None\n\nclass TechniquePageCreate(TechniquePageBase):\n    creator_id: uuid.UUID\n    source_quality: str | None = None\n\nclass TechniquePageRead(TechniquePageBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    creator_id: uuid.UUID\n    source_quality: str | None = None\n    view_count: int = 0\n    review_status: str = \"draft\"\n    created_at: datetime\n    updated_at: datetime\n\n\n# ── RelatedTechniqueLink ─────────────────────────────────────────────────────\n\nclass RelatedTechniqueLinkBase(BaseModel):\n    source_page_id: uuid.UUID\n    target_page_id: uuid.UUID\n    relationship: str\n\nclass RelatedTechniqueLinkCreate(RelatedTechniqueLinkBase):\n    pass\n\nclass RelatedTechniqueLinkRead(RelatedTechniqueLinkBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n\n\n# ── Tag ──────────────────────────────────────────────────────────────────────\n\nclass TagBase(BaseModel):\n    name: str\n    category: str\n    aliases: list[str] | None = None\n\nclass TagCreate(TagBase):\n    pass\n\nclass TagRead(TagBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n\n\n# ── Transcript Ingestion ─────────────────────────────────────────────────────\n\nclass TranscriptIngestResponse(BaseModel):\n    \"\"\"Response returned after successfully ingesting a transcript.\"\"\"\n    video_id: uuid.UUID\n    creator_id: uuid.UUID\n    creator_name: str\n    filename: str\n    segments_stored: int\n    processing_status: str\n    is_reupload: bool\n\n\n# ── Pagination wrapper ───────────────────────────────────────────────────────\n\nclass PaginatedResponse(BaseModel):\n    \"\"\"Generic paginated list response.\"\"\"\n    items: list = Field(default_factory=list)\n    total: int = 0\n    offset: int = 0\n    limit: int = 50\n\n\n# ── Review Queue ─────────────────────────────────────────────────────────────\n\nclass ReviewQueueItem(KeyMomentRead):\n    \"\"\"Key moment enriched with source video and creator info for review UI.\"\"\"\n    video_filename: str\n    creator_name: str\n\n\nclass ReviewQueueResponse(BaseModel):\n    \"\"\"Paginated response for the review queue.\"\"\"\n    items: list[ReviewQueueItem] = Field(default_factory=list)\n    total: int = 0\n    offset: int = 0\n    limit: int = 50\n\n\nclass ReviewStatsResponse(BaseModel):\n    \"\"\"Counts of key moments grouped by review status.\"\"\"\n    pending: int = 0\n    approved: int = 0\n    edited: int = 0\n    rejected: int = 0\n\n\nclass MomentEditRequest(BaseModel):\n    \"\"\"Editable fields for a key moment.\"\"\"\n    title: str | None = None\n    summary: str | None = None\n    start_time: float | None = None\n    end_time: float | None = None\n    content_type: str | None = None\n    plugins: list[str] | None = None\n\n\nclass MomentSplitRequest(BaseModel):\n    \"\"\"Request to split a moment at a given timestamp.\"\"\"\n    split_time: float\n\n\nclass MomentMergeRequest(BaseModel):\n    \"\"\"Request to merge two moments.\"\"\"\n    target_moment_id: uuid.UUID\n\n\nclass ReviewModeResponse(BaseModel):\n    \"\"\"Current review mode state.\"\"\"\n    review_mode: bool\n\n\nclass ReviewModeUpdate(BaseModel):\n    \"\"\"Request to update the review mode.\"\"\"\n    review_mode: bool\n"}],"isError":false,"timestamp":1774827603639}}
{"type":"message","id":"8ae4bb72","parentId":"85c15a6b","timestamp":"2026-03-29T23:40:03.641Z","message":{"role":"toolResult","toolCallId":"toolu_01JS43mqAaarSfAzYfRB5BtU","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":1774827603640}}
{"type":"message","id":"c823e66f","parentId":"8ae4bb72","timestamp":"2026-03-29T23:40:03.642Z","message":{"role":"toolResult","toolCallId":"toolu_01UVAfQHmTevFvab5DUm7poP","toolName":"read","content":[{"type":"text","text":"\"\"\"Synchronous embedding client using the OpenAI-compatible /v1/embeddings API.\n\nUses ``openai.OpenAI`` (sync) since Celery tasks run synchronously.\nHandles connection failures gracefully — embedding is non-blocking for the pipeline.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\n\nimport openai\n\nfrom config import Settings\n\nlogger = logging.getLogger(__name__)\n\n\nclass EmbeddingClient:\n    \"\"\"Sync embedding client backed by an OpenAI-compatible /v1/embeddings endpoint.\"\"\"\n\n    def __init__(self, settings: Settings) -> None:\n        self.settings = settings\n        self._client = openai.OpenAI(\n            base_url=settings.embedding_api_url,\n            api_key=settings.llm_api_key,\n        )\n\n    def embed(self, texts: list[str]) -> list[list[float]]:\n        \"\"\"Generate embedding vectors for a batch of texts.\n\n        Parameters\n        ----------\n        texts:\n            List of strings to embed.\n\n        Returns\n        -------\n        list[list[float]]\n            Embedding vectors. Returns empty list on connection/timeout errors\n            so the pipeline can continue without embeddings.\n        \"\"\"\n        if not texts:\n            return []\n\n        try:\n            response = self._client.embeddings.create(\n                model=self.settings.embedding_model,\n                input=texts,\n            )\n        except (openai.APIConnectionError, openai.APITimeoutError) as exc:\n            logger.warning(\n                \"Embedding API unavailable (%s: %s). Skipping %d texts.\",\n                type(exc).__name__,\n                exc,\n                len(texts),\n            )\n            return []\n        except openai.APIError as exc:\n            logger.warning(\n                \"Embedding API error (%s: %s). Skipping %d texts.\",\n                type(exc).__name__,\n                exc,\n                len(texts),\n            )\n            return []\n\n        vectors = [item.embedding for item in response.data]\n\n        # Validate dimensions\n        expected_dim = self.settings.embedding_dimensions\n        for i, vec in enumerate(vectors):\n            if len(vec) != expected_dim:\n                logger.warning(\n                    \"Embedding dimension mismatch at index %d: expected %d, got %d. \"\n                    \"Returning empty list.\",\n                    i,\n                    expected_dim,\n                    len(vec),\n                )\n                return []\n\n        logger.info(\n            \"Generated %d embeddings (dim=%d) using model=%s\",\n            len(vectors),\n            expected_dim,\n            self.settings.embedding_model,\n        )\n        return vectors\n"}],"isError":false,"timestamp":1774827603640}}
{"type":"message","id":"12de7955","parentId":"c823e66f","timestamp":"2026-03-29T23:40:11.197Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the spec for the Search UI requirements in detail:"},{"type":"toolCall","id":"toolu_012LY4R61Ar7brtDVWb1a3RY","name":"bash","arguments":{"command":"grep -n \"search\\|Search\\|typeahead\\|landing\\|browse\\|topic\\|creator.*page\\|technique.*page\" /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":126,"cacheRead":91233,"cacheWrite":2997,"totalTokens":94357,"cost":{"input":0.000005,"output":0.00315,"cacheRead":0.0456165,"cacheWrite":0.01873125,"total":0.06750275}},"stopReason":"toolUse","timestamp":1774827603641}}
{"type":"message","id":"722aba89","parentId":"12de7955","timestamp":"2026-03-29T23:40:11.210Z","message":{"role":"toolResult","toolCallId":"toolu_012LY4R61Ar7brtDVWb1a3RY","toolName":"bash","content":[{"type":"text","text":"3:> **Etymology:** From *chrysopoeia* (the alchemical transmutation of base material into gold) + *encyclopedia* (an organized body of knowledge). Chrysopedia transmutes raw video content into refined, searchable production knowledge.\n11:Hundreds of hours of educational video content from electronic music producers sit on local storage — tutorials, livestreams, track breakdowns, and deep dives covering techniques in sound design, mixing, arrangement, synthesis, and more. This content is extremely valuable but nearly impossible to retrieve: videos are unsearchable, unchaptered, and undocumented. A 4-hour livestream may contain 6 minutes of actionable gold buried among tangents and chat interaction. The current retrieval method is \"scrub through from memory and hope\" — or more commonly, the knowledge is simply lost.\n19:3. **Classifies** content by topic, creator, plugins, and production stage\n20:4. **Synthesizes** knowledge across multiple sources into coherent technique pages\n21:5. **Serves** a fast, search-first web UI for mid-session retrieval\n23:The system transforms raw video files into a browsable, searchable knowledge base with direct timestamp links back to source material.\n27:- **Search-first.** The primary interaction is typing a query and getting results in seconds. Browse is secondary, for exploration.\n60:**Structured content** (tutorials, breakdowns) tends to have natural topic boundaries — the producer announces what they're about to cover, then demonstrates. These are easier to segment.\n62:**Unstructured content** (livestreams) is chaotic: tangents, chat interaction, rambling, with gems appearing without warning. The extraction pipeline must handle both structured and unstructured content using semantic understanding, not just topic detection from speaker announcements.\n86:**Primary access method:** Same machine, Alt+Tab to browser.\n90:The landing page is a decision point, not a dashboard. Minimal, focused, fast.\n94:1. **Search bar** — prominent, full-width, with live typeahead (results appear after 2–3 characters). This is the primary interaction for most visits. Scope toggle tabs below the search input: `All | Topics | Creators`\n98:3. **Recently added** — a short list of the most recently processed/published technique pages with creator name, topic tag, and relative timestamp\n102:### 4.3 Live search (typeahead)\n104:The search bar is the primary interface. Behavior:\n109:  - **Topics** — technique pages matching the query, showing title, creator name(s), parent topic tag\n110:  - **Key moments** — individual timestamped insights matching the query, showing moment title, creator, source file, and timestamp. Clicking jumps to the technique page (or eventually direct to the video moment)\n112:- **\"Topics\" scope** — shows only technique pages\n115:- Search is semantic where possible (powered by Qdrant vector search), with keyword fallback\n119:The core content unit. Each technique page covers one technique or concept from one creator. The format adapts by content type but follows a consistent structure.\n145:   - Links to related technique pages — same technique by other creators, adjacent techniques by the same creator, general/cross-creator technique pages\n149:   - List of all plugins/tools mentioned in the technique page\n150:   - Each is a clickable tag that could lead to \"all techniques referencing this plugin\" (future: dedicated plugin pages)\n157:### 4.5 Creators browse page\n159:Accessed from the landing page \"Creators\" card.\n172:- Clicking a row navigates to that creator's detail page (list of all their technique pages)\n176:### 4.6 Topics browse page\n178:Accessed from the landing page \"Topics\" card.\n186:  - **Sub-topics within each:** clicking a top-level category expands or navigates to show sub-topics (e.g., Sound Design → Bass, Drums, Pads, Leads, FX, Foley; Drums → Kick, Snare, Hi-hat, Percussion)\n187:- Each sub-topic shows: technique count, number of creators covering it\n188:- Clicking a sub-topic shows all technique pages in that category, filterable by creator and genre\n190:### 4.7 Search results page\n192:For complex queries that go beyond typeahead (e.g., hitting Enter after typing a full query).\n195:- Search bar at top (retains query)\n199:  - **Also mentioned in** — cross-references where the search term appears inside other technique pages (e.g., searching \"snare\" surfaces \"drum bus processing\" because it mentions snare bus techniques)\n203:## 5. Taxonomy and topic hierarchy\n209:| Category | Description | Example sub-topics |\n218:### 5.2 Sub-topic management\n220:Sub-topics are not rigidly pre-defined. The extraction pipeline proposes sub-topic tags during classification, and the taxonomy grows organically as content is processed. However, the system maintains a **canonical tag list** that the LLM references during classification to ensure consistency (e.g., always \"snare\" not sometimes \"snare drum\" and sometimes \"snare design\").\n234:- Genre is available as a filter on both the Creators browse page and the Topics browse page (filtering Topics by genre shows techniques from creators tagged with that genre)\n250:view_count      integer (aggregated from child technique page views)\n277:topic_label     string (LLM-assigned topic label for this segment)\n284:technique_page_id FK → Technique Page (nullable until assigned)\n303:topic_category  string (top-level: \"sound design\")\n304:topic_tags      string[] (sub-topics: [\"drums\", \"snare\", \"layering\", \"saturation\"])\n336:| Relational DB | All structured data (creators, videos, moments, technique pages, tags) | PostgreSQL (preferred) or SQLite for initial simplicity |\n337:| Vector DB | Semantic search embeddings for transcripts, key moments, and technique page content | Qdrant (already running on hypervisor) |\n342:The following content gets embedded in Qdrant for semantic search:\n344:- Key moment summaries (with metadata: creator, topic, timestamp, source video)\n415:**Process:** The LLM analyzes the transcript to identify topic boundaries — points where the creator shifts from one subject to another. Output is a segmented transcript with topic labels per segment.\n437:- Top-level topic category\n438:- Sub-topic tags (referencing the canonical tag list)\n447:**Input:** All approved/published key moments for a given creator + topic combination\n448:**Process:** When multiple key moments from the same creator cover overlapping or related topics, the synthesis stage merges them into a coherent technique page. This includes:\n452:- Identifying related technique pages for cross-linking\n455:This stage runs whenever new key moments are approved for a creator+topic combination that already has a technique page (updating it), or when enough moments accumulate to warrant a new page.\n488:| Synthesis (LLM) | ~2 min per technique page | Varies by page count |\n547:6. **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.\n631:| Web UI | React or Svelte SPA | Fast, component-based, good for search-heavy UIs |\n655:Eventually: user-specific bookmarks, personal notes on technique pages, view history, and personalized \"trending\" based on individual usage patterns.\n"}],"isError":false,"timestamp":1774827611209}}
{"type":"message","id":"8900582c","parentId":"722aba89","timestamp":"2026-03-29T23:40:16.949Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me read the key sections of the spec more carefully:"},{"type":"toolCall","id":"toolu_01W1RpyMwM2c1ziV9XN6m1Yh","name":"read","arguments":{"limit":120,"offset":84,"path":"chrysopedia-spec.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":105,"cacheRead":94230,"cacheWrite":1888,"totalTokens":96224,"cost":{"input":0.000005,"output":0.002625,"cacheRead":0.047115,"cacheWrite":0.0118,"total":0.061544999999999996}},"stopReason":"toolUse","timestamp":1774827611210}}
{"type":"message","id":"8b41e7d6","parentId":"8900582c","timestamp":"2026-03-29T23:40:16.953Z","message":{"role":"toolResult","toolCallId":"toolu_01W1RpyMwM2c1ziV9XN6m1Yh","toolName":"read","content":[{"type":"text","text":"The system is accessed via Alt+Tab from a DAW on the same desktop machine. Every design decision optimizes for speed of retrieval and minimal cognitive load. The interface should feel like a tool, not a destination.\n\n**Primary access method:** Same machine, Alt+Tab to browser.\n\n### 4.2 Landing page (Launchpad)\n\nThe landing page is a decision point, not a dashboard. Minimal, focused, fast.\n\n**Layout (top to bottom):**\n\n1. **Search bar** — prominent, full-width, with live typeahead (results appear after 2–3 characters). This is the primary interaction for most visits. Scope toggle tabs below the search input: `All | Topics | Creators`\n2. **Two navigation cards** — side-by-side:\n   - **Topics** — \"Browse by technique, production stage, or concept\" with count of total techniques and categories\n   - **Creators** — \"Browse by artist, filterable by genre\" with count of total creators and genres\n3. **Recently added** — a short list of the most recently processed/published technique pages with creator name, topic tag, and relative timestamp\n\n**Future feature (not v1):** Trending / popular section alongside recently added, driven by view counts and cross-reference frequency.\n\n### 4.3 Live search (typeahead)\n\nThe search bar is the primary interface. Behavior:\n\n- Results begin appearing after 2–3 characters typed\n- Scope toggle: `All | Topics | Creators` — filters what types of results appear\n- **\"All\" scope** groups results by type:\n  - **Topics** — technique pages matching the query, showing title, creator name(s), parent topic tag\n  - **Key moments** — individual timestamped insights matching the query, showing moment title, creator, source file, and timestamp. Clicking jumps to the technique page (or eventually direct to the video moment)\n  - **Creators** — creator names matching the query\n- **\"Topics\" scope** — shows only technique pages\n- **\"Creators\" scope** — shows only creator matches\n- Genre filter is accessible on Creators scope and cross-filters Topics scope (using creator-level genre metadata)\n- Search is semantic where possible (powered by Qdrant vector search), with keyword fallback\n\n### 4.4 Technique page (A+C hybrid format)\n\nThe core content unit. Each technique page covers one technique or concept from one creator. The format adapts by content type but follows a consistent structure.\n\n**Layout (top to bottom):**\n\n1. **Header:**\n   - Topic tags (e.g., \"sound design,\" \"drums,\" \"snare\")\n   - Technique title (e.g., \"Snare design\")\n   - Creator name\n   - Meta line: \"Compiled from N sources · M key moments · Last updated [date]\"\n   - Source quality warning (amber banner) if content came from an unstructured livestream\n\n2. **Study guide prose (Section A):**\n   - Organized by sub-aspects of the technique (e.g., \"Layer construction,\" \"Saturation & character,\" \"Mix context\")\n   - Rich prose capturing:\n     - The specific technique/method described (highest priority)\n     - Exact settings, plugins, and parameters when the creator was *teaching* the setting (not incidental use)\n     - The reasoning/philosophy behind choices when the creator explains *why*\n   - Signal chain blocks rendered in monospace when a creator walks through a routing chain\n   - Direct quotes of creator opinions/warnings when they add value (e.g., \"He says it 'smears the transient into mush'\")\n\n3. **Key moments index (Section C):**\n   - Compact list of individual timestamped insights\n   - Each row: moment title, source video filename, clickable timestamp\n   - Sorted chronologically within each source video\n\n4. **Related techniques:**\n   - Links to related technique pages — same technique by other creators, adjacent techniques by the same creator, general/cross-creator technique pages\n   - Renders as clickable pill-shaped tags\n\n5. **Plugins referenced:**\n   - List of all plugins/tools mentioned in the technique page\n   - Each is a clickable tag that could lead to \"all techniques referencing this plugin\" (future: dedicated plugin pages)\n\n**Content type adaptation:**\n- **Technique-heavy content** (sound design, specific methods): Full A+C treatment with signal chains, plugin details, parameter specifics\n- **Philosophy/workflow content** (mixdown approach, creative process): More prose-heavy, fewer signal chain blocks, but same overall structure. These pages are still browsable but also serve as rich context for future RAG/chat retrieval\n- **Livestream-sourced content:** Amber warning banner noting source quality. Timestamps may land in messy context with tangents nearby\n\n### 4.5 Creators browse page\n\nAccessed from the landing page \"Creators\" card.\n\n**Layout:**\n- Page title: \"Creators\" with total count\n- Filter input: type-to-narrow the list\n- Genre filter pills: `All genres | Bass music | Drum & bass | Dubstep | Halftime | House | IDM | Neuro | Techno | ...` — clicking a genre filters the list to creators tagged with that genre\n- Sort options: Randomized (default, re-shuffled on every page load), Alphabetical, View count\n- Creator list: flat, equal-weight rows. Each row shows:\n  - Creator name\n  - Genre tags (multiple allowed)\n  - Technique count\n  - Video count\n  - View count (sum of activity across all content derived from this creator)\n- Clicking a row navigates to that creator's detail page (list of all their technique pages)\n\n**Default sort is randomized on every page load** to prevent discovery bias. Users can toggle to alphabetical or sort by view count.\n\n### 4.6 Topics browse page\n\nAccessed from the landing page \"Topics\" card.\n\n**Layout:**\n- Page title: \"Topics\" with total technique count\n- Filter input: type-to-narrow\n- Genre filter pills (uses creator-level genre metadata to filter): show only techniques from creators tagged with the selected genre\n- **Two-level hierarchy displayed:**\n  - **Top-level categories:** Sound design, Mixing, Synthesis, Arrangement, Workflow, Mastering\n  - **Sub-topics within each:** clicking a top-level category expands or navigates to show sub-topics (e.g., Sound Design → Bass, Drums, Pads, Leads, FX, Foley; Drums → Kick, Snare, Hi-hat, Percussion)\n- Each sub-topic shows: technique count, number of creators covering it\n- Clicking a sub-topic shows all technique pages in that category, filterable by creator and genre\n\n### 4.7 Search results page\n\nFor complex queries that go beyond typeahead (e.g., hitting Enter after typing a full query).\n\n**Layout:**\n- Search bar at top (retains query)\n- Scope tabs: `All results (N) | Techniques (N) | Key moments (N) | Creators (N)`\n- Results split into two tiers:\n  - **Technique pages** — first-class results with title, creator, summary snippet, tags, moment count, plugin list\n  - **Also mentioned in** — cross-references where the search term appears inside other technique pages (e.g., searching \"snare\" surfaces \"drum bus processing\" because it mentions snare bus techniques)\n\n---\n\n## 5. Taxonomy and topic hierarchy\n\n[511 more lines in file. Use offset=204 to continue.]"}],"isError":false,"timestamp":1774827616951}}
{"type":"message","id":"f6297634","parentId":"8b41e7d6","timestamp":"2026-03-29T23:40:23.349Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the canonical tags to understand the topic hierarchy:"},{"type":"toolCall","id":"toolu_01C1cG1qcaNmzzzAduNy1Kpg","name":"read","arguments":{"path":"config/canonical_tags.yaml"}},{"type":"toolCall","id":"toolu_01MXJvfkAStcKwENKDxPC8zu","name":"read","arguments":{"path":"frontend/vite.config.ts"}},{"type":"toolCall","id":"toolu_01Vuy9YqqoxqJyohjUCD7Qep","name":"read","arguments":{"path":"frontend/src/App.css"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":152,"cacheRead":96118,"cacheWrite":1853,"totalTokens":98124,"cost":{"input":0.000005,"output":0.0038,"cacheRead":0.048059,"cacheWrite":0.011581250000000001,"total":0.06344525000000001}},"stopReason":"toolUse","timestamp":1774827616952}}
{"type":"message","id":"9c6db60e","parentId":"f6297634","timestamp":"2026-03-29T23:40:23.351Z","message":{"role":"toolResult","toolCallId":"toolu_01C1cG1qcaNmzzzAduNy1Kpg","toolName":"read","content":[{"type":"text","text":"# Canonical tags — 6 top-level production categories\n# Sub-topics grow organically during pipeline extraction\ncategories:\n  - name: Sound design\n    description: Creating and shaping sounds from scratch or samples\n    sub_topics: [bass, drums, kick, snare, hi-hat, percussion, pads, leads, fx, foley, vocals, textures]\n\n  - name: Mixing\n    description: Balancing, processing, and spatializing elements\n    sub_topics: [eq, compression, bus processing, reverb, delay, stereo imaging, gain staging, automation]\n\n  - name: Synthesis\n    description: Methods of generating sound\n    sub_topics: [fm, wavetable, granular, additive, subtractive, modular, physical modeling]\n\n  - name: Arrangement\n    description: Structuring a track from intro to outro\n    sub_topics: [song structure, transitions, tension, energy flow, breakdowns, drops]\n\n  - name: Workflow\n    description: Creative process, session management, productivity\n    sub_topics: [daw setup, templates, creative process, collaboration, file management, resampling]\n\n  - name: Mastering\n    description: Final stage processing for release\n    sub_topics: [limiting, stereo width, loudness, format delivery, referencing]\n\n# Genre taxonomy (assigned to Creators, not techniques)\ngenres:\n  - Bass music\n  - Drum & bass\n  - Dubstep\n  - Halftime\n  - House\n  - Techno\n  - IDM\n  - Glitch\n  - Downtempo\n  - Neuro\n  - Ambient\n  - Experimental\n  - Cinematic\n"}],"isError":false,"timestamp":1774827623351}}
{"type":"message","id":"35f14931","parentId":"9c6db60e","timestamp":"2026-03-29T23:40:23.352Z","message":{"role":"toolResult","toolCallId":"toolu_01MXJvfkAStcKwENKDxPC8zu","toolName":"read","content":[{"type":"text","text":"import { defineConfig } from \"vite\";\nimport react from \"@vitejs/plugin-react\";\n\nexport default defineConfig({\n  plugins: [react()],\n  server: {\n    proxy: {\n      \"/api\": {\n        target: \"http://localhost:8001\",\n        changeOrigin: true,\n      },\n    },\n  },\n});\n"}],"isError":false,"timestamp":1774827623351}}
{"type":"message","id":"24994629","parentId":"35f14931","timestamp":"2026-03-29T23:40:23.354Z","message":{"role":"toolResult","toolCallId":"toolu_01Vuy9YqqoxqJyohjUCD7Qep","toolName":"read","content":[{"type":"text","text":"/* ── Base ─────────────────────────────────────────────────────────────────── */\n\n*,\n*::before,\n*::after {\n  box-sizing: border-box;\n  margin: 0;\n  padding: 0;\n}\n\nbody {\n  font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto,\n    \"Helvetica Neue\", Arial, sans-serif;\n  line-height: 1.5;\n  color: #1a1a2e;\n  background: #f4f4f8;\n}\n\n/* ── App shell ────────────────────────────────────────────────────────────── */\n\n.app-header {\n  display: flex;\n  align-items: center;\n  justify-content: space-between;\n  padding: 0.75rem 1.5rem;\n  background: #1a1a2e;\n  color: #fff;\n}\n\n.app-header h1 {\n  font-size: 1.125rem;\n  font-weight: 600;\n  letter-spacing: -0.01em;\n}\n\n.app-header__right {\n  display: flex;\n  align-items: center;\n  gap: 1.5rem;\n}\n\n.app-header nav a {\n  color: rgba(255, 255, 255, 0.8);\n  text-decoration: none;\n  font-size: 0.875rem;\n}\n\n.app-header nav a:hover {\n  color: #fff;\n}\n\n.app-main {\n  max-width: 72rem;\n  margin: 1.5rem auto;\n  padding: 0 1.5rem;\n}\n\n/* ── Queue header ─────────────────────────────────────────────────────────── */\n\n.queue-header {\n  display: flex;\n  align-items: center;\n  justify-content: space-between;\n  margin-bottom: 1rem;\n}\n\n.queue-header h2 {\n  font-size: 1.25rem;\n  font-weight: 700;\n}\n\n/* ── Stats bar ────────────────────────────────────────────────────────────── */\n\n.stats-bar {\n  display: flex;\n  gap: 0.75rem;\n  margin-bottom: 1rem;\n}\n\n.stats-card {\n  flex: 1;\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  padding: 0.75rem;\n  border-radius: 0.5rem;\n  background: #fff;\n  border: 1px solid #e2e2e8;\n  box-shadow: 0 1px 3px rgba(0, 0, 0, 0.04);\n}\n\n.stats-card__count {\n  font-size: 1.5rem;\n  font-weight: 700;\n  line-height: 1;\n}\n\n.stats-card__label {\n  font-size: 0.75rem;\n  text-transform: uppercase;\n  letter-spacing: 0.04em;\n  color: #6b7280;\n  margin-top: 0.25rem;\n}\n\n.stats-card--pending .stats-card__count { color: #92400e; }\n.stats-card--approved .stats-card__count { color: #065f46; }\n.stats-card--edited .stats-card__count { color: #1e40af; }\n.stats-card--rejected .stats-card__count { color: #991b1b; }\n\n/* ── Filter tabs ──────────────────────────────────────────────────────────── */\n\n.filter-tabs {\n  display: flex;\n  gap: 0;\n  border-bottom: 2px solid #e2e2e8;\n  margin-bottom: 1rem;\n}\n\n.filter-tab {\n  padding: 0.5rem 1rem;\n  border: none;\n  background: none;\n  font-size: 0.875rem;\n  font-weight: 500;\n  color: #6b7280;\n  cursor: pointer;\n  border-bottom: 2px solid transparent;\n  margin-bottom: -2px;\n  transition: color 0.15s, border-color 0.15s;\n}\n\n.filter-tab:hover {\n  color: #374151;\n}\n\n.filter-tab--active {\n  color: #1a1a2e;\n  border-bottom-color: #1a1a2e;\n}\n\n/* ── Cards ────────────────────────────────────────────────────────────────── */\n\n.card {\n  background: #fff;\n  border: 1px solid #e2e2e8;\n  border-radius: 0.5rem;\n  padding: 1.25rem;\n  margin-bottom: 1rem;\n  box-shadow: 0 1px 3px rgba(0, 0, 0, 0.04);\n}\n\n.card h2 {\n  font-size: 1rem;\n  font-weight: 600;\n  margin-bottom: 0.5rem;\n}\n\n.card p {\n  font-size: 0.875rem;\n  color: #555;\n}\n\n/* ── Queue cards ──────────────────────────────────────────────────────────── */\n\n.queue-list {\n  display: flex;\n  flex-direction: column;\n  gap: 0.5rem;\n}\n\n.queue-card {\n  display: block;\n  background: #fff;\n  border: 1px solid #e2e2e8;\n  border-radius: 0.5rem;\n  padding: 1rem 1.25rem;\n  text-decoration: none;\n  color: inherit;\n  box-shadow: 0 1px 3px rgba(0, 0, 0, 0.04);\n  transition: border-color 0.15s, box-shadow 0.15s;\n}\n\n.queue-card:hover {\n  border-color: #a5b4fc;\n  box-shadow: 0 2px 8px rgba(99, 102, 241, 0.1);\n}\n\n.queue-card__header {\n  display: flex;\n  align-items: center;\n  justify-content: space-between;\n  margin-bottom: 0.375rem;\n}\n\n.queue-card__title {\n  font-size: 0.9375rem;\n  font-weight: 600;\n}\n\n.queue-card__summary {\n  font-size: 0.8125rem;\n  color: #6b7280;\n  margin-bottom: 0.375rem;\n  line-height: 1.4;\n}\n\n.queue-card__meta {\n  display: flex;\n  align-items: center;\n  gap: 0.375rem;\n  font-size: 0.75rem;\n  color: #9ca3af;\n}\n\n.queue-card__separator {\n  color: #d1d5db;\n}\n\n/* ── Status badges ────────────────────────────────────────────────────────── */\n\n.badge {\n  display: inline-block;\n  padding: 0.125rem 0.5rem;\n  border-radius: 9999px;\n  font-size: 0.75rem;\n  font-weight: 600;\n  text-transform: capitalize;\n}\n\n.badge--pending {\n  background: #fef3c7;\n  color: #92400e;\n}\n\n.badge--approved {\n  background: #d1fae5;\n  color: #065f46;\n}\n\n.badge--edited {\n  background: #dbeafe;\n  color: #1e40af;\n}\n\n.badge--rejected {\n  background: #fee2e2;\n  color: #991b1b;\n}\n\n/* ── Buttons ──────────────────────────────────────────────────────────────── */\n\n.btn {\n  display: inline-flex;\n  align-items: center;\n  gap: 0.375rem;\n  padding: 0.5rem 1rem;\n  border: 1px solid #d1d5db;\n  border-radius: 0.375rem;\n  font-size: 0.8125rem;\n  font-weight: 500;\n  cursor: pointer;\n  background: #fff;\n  color: #374151;\n  transition: background 0.15s, border-color 0.15s, opacity 0.15s;\n}\n\n.btn:hover {\n  background: #f9fafb;\n  border-color: #9ca3af;\n}\n\n.btn:disabled {\n  opacity: 0.5;\n  cursor: not-allowed;\n}\n\n.btn--approve {\n  background: #059669;\n  color: #fff;\n  border-color: #059669;\n}\n\n.btn--approve:hover {\n  background: #047857;\n}\n\n.btn--reject {\n  background: #dc2626;\n  color: #fff;\n  border-color: #dc2626;\n}\n\n.btn--reject:hover {\n  background: #b91c1c;\n}\n\n/* ── Mode toggle ──────────────────────────────────────────────────────────── */\n\n.mode-toggle {\n  display: flex;\n  align-items: center;\n  gap: 0.5rem;\n  font-size: 0.8125rem;\n}\n\n.mode-toggle__dot {\n  display: inline-block;\n  width: 0.5rem;\n  height: 0.5rem;\n  border-radius: 50%;\n}\n\n.mode-toggle__dot--review {\n  background: #10b981;\n}\n\n.mode-toggle__dot--auto {\n  background: #f59e0b;\n}\n\n.mode-toggle__label {\n  color: rgba(255, 255, 255, 0.9);\n  white-space: nowrap;\n}\n\n.mode-toggle__switch {\n  position: relative;\n  width: 2.5rem;\n  height: 1.25rem;\n  background: #6b7280;\n  border: none;\n  border-radius: 9999px;\n  cursor: pointer;\n  transition: background 0.2s;\n  flex-shrink: 0;\n}\n\n.mode-toggle__switch--active {\n  background: #059669;\n}\n\n.mode-toggle__switch::after {\n  content: \"\";\n  position: absolute;\n  top: 0.125rem;\n  left: 0.125rem;\n  width: 1rem;\n  height: 1rem;\n  background: #fff;\n  border-radius: 50%;\n  transition: transform 0.2s;\n}\n\n.mode-toggle__switch--active::after {\n  transform: translateX(1.25rem);\n}\n\n.mode-toggle__switch:disabled {\n  opacity: 0.5;\n  cursor: not-allowed;\n}\n\n/* ── Pagination ───────────────────────────────────────────────────────────── */\n\n.pagination {\n  display: flex;\n  align-items: center;\n  justify-content: center;\n  gap: 1rem;\n  margin-top: 1.25rem;\n  padding: 0.75rem 0;\n}\n\n.pagination__info {\n  font-size: 0.8125rem;\n  color: #6b7280;\n}\n\n/* ── Detail page ──────────────────────────────────────────────────────────── */\n\n.back-link {\n  display: inline-block;\n  font-size: 0.875rem;\n  color: #6b7280;\n  text-decoration: none;\n  margin-bottom: 0.5rem;\n}\n\n.back-link:hover {\n  color: #374151;\n}\n\n.detail-header {\n  display: flex;\n  align-items: center;\n  gap: 0.75rem;\n  margin-bottom: 1rem;\n}\n\n.detail-header h2 {\n  font-size: 1.25rem;\n  font-weight: 700;\n}\n\n.detail-card {\n  display: grid;\n  grid-template-columns: 1fr 1fr;\n  gap: 1rem;\n}\n\n.detail-field {\n  display: flex;\n  flex-direction: column;\n  gap: 0.125rem;\n}\n\n.detail-field label {\n  font-size: 0.6875rem;\n  font-weight: 600;\n  text-transform: uppercase;\n  letter-spacing: 0.04em;\n  color: #9ca3af;\n}\n\n.detail-field span,\n.detail-field p {\n  font-size: 0.875rem;\n  color: #374151;\n}\n\n.detail-field--full {\n  grid-column: 1 / -1;\n}\n\n.detail-transcript {\n  background: #f9fafb;\n  padding: 0.75rem;\n  border-radius: 0.375rem;\n  font-size: 0.8125rem;\n  line-height: 1.6;\n  white-space: pre-wrap;\n  max-height: 20rem;\n  overflow-y: auto;\n}\n\n/* ── Action bar ───────────────────────────────────────────────────────────── */\n\n.action-bar {\n  display: flex;\n  flex-wrap: wrap;\n  gap: 0.5rem;\n  margin-top: 1rem;\n}\n\n.action-error {\n  background: #fef2f2;\n  border: 1px solid #fecaca;\n  border-radius: 0.375rem;\n  padding: 0.5rem 0.75rem;\n  color: #991b1b;\n  font-size: 0.8125rem;\n  margin-top: 0.75rem;\n  margin-bottom: 0.75rem;\n}\n\n/* ── Edit form ────────────────────────────────────────────────────────────── */\n\n.edit-form {\n  margin-top: 1rem;\n}\n\n.edit-form h3 {\n  font-size: 1rem;\n  font-weight: 600;\n  margin-bottom: 0.75rem;\n}\n\n.edit-field {\n  margin-bottom: 0.75rem;\n}\n\n.edit-field label {\n  display: block;\n  font-size: 0.75rem;\n  font-weight: 600;\n  color: #6b7280;\n  margin-bottom: 0.25rem;\n}\n\n.edit-field input,\n.edit-field textarea,\n.edit-field select {\n  width: 100%;\n  padding: 0.5rem 0.75rem;\n  border: 1px solid #d1d5db;\n  border-radius: 0.375rem;\n  font-size: 0.875rem;\n  font-family: inherit;\n  line-height: 1.5;\n  color: #374151;\n  background: #fff;\n  transition: border-color 0.15s;\n}\n\n.edit-field input:focus,\n.edit-field textarea:focus,\n.edit-field select:focus {\n  outline: none;\n  border-color: #6366f1;\n  box-shadow: 0 0 0 2px rgba(99, 102, 241, 0.15);\n}\n\n.edit-actions {\n  display: flex;\n  gap: 0.5rem;\n  margin-top: 1rem;\n}\n\n/* ── Dialogs (modal overlays) ─────────────────────────────────────────────── */\n\n.dialog-overlay {\n  position: fixed;\n  inset: 0;\n  background: rgba(0, 0, 0, 0.4);\n  display: flex;\n  align-items: center;\n  justify-content: center;\n  z-index: 100;\n}\n\n.dialog {\n  background: #fff;\n  border-radius: 0.75rem;\n  padding: 1.5rem;\n  width: 90%;\n  max-width: 28rem;\n  box-shadow: 0 10px 40px rgba(0, 0, 0, 0.15);\n}\n\n.dialog h3 {\n  font-size: 1.125rem;\n  font-weight: 600;\n  margin-bottom: 0.5rem;\n}\n\n.dialog__hint {\n  font-size: 0.8125rem;\n  color: #6b7280;\n  margin-bottom: 1rem;\n}\n\n.dialog__actions {\n  display: flex;\n  gap: 0.5rem;\n  margin-top: 1rem;\n}\n\n/* ── Loading / empty states ───────────────────────────────────────────────── */\n\n.loading {\n  text-align: center;\n  padding: 3rem 1rem;\n  color: #6b7280;\n  font-size: 0.875rem;\n}\n\n.empty-state {\n  text-align: center;\n  padding: 3rem 1rem;\n  color: #9ca3af;\n  font-size: 0.875rem;\n}\n\n.error-text {\n  color: #dc2626;\n}\n\n/* ── Responsive ───────────────────────────────────────────────────────────── */\n\n@media (max-width: 640px) {\n  .stats-bar {\n    flex-direction: column;\n    gap: 0.5rem;\n  }\n\n  .stats-card {\n    flex-direction: row;\n    justify-content: space-between;\n  }\n\n  .detail-card {\n    grid-template-columns: 1fr;\n  }\n\n  .queue-header {\n    flex-direction: column;\n    align-items: flex-start;\n    gap: 0.5rem;\n  }\n\n  .action-bar {\n    flex-direction: column;\n  }\n\n  .action-bar .btn {\n    width: 100%;\n    justify-content: center;\n  }\n\n  .app-header {\n    flex-direction: column;\n    gap: 0.5rem;\n  }\n\n  .app-header__right {\n    width: 100%;\n    justify-content: space-between;\n  }\n}\n"}],"isError":false,"timestamp":1774827623352}}
{"type":"message","id":"b94fd2c7","parentId":"24994629","timestamp":"2026-03-29T23:41:05.634Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the review router to understand the pattern already established:"},{"type":"toolCall","id":"toolu_01J26g3Y5NQGkTtGkg1ATPLy","name":"read","arguments":{"path":"backend/routers/review.py"}},{"type":"toolCall","id":"toolu_01QqFrcbpJ922gVYbj9pD9Wx","name":"read","arguments":{"path":"backend/redis_client.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":113,"cacheRead":97971,"cacheWrite":5282,"totalTokens":103367,"cost":{"input":0.000005,"output":0.0028250000000000003,"cacheRead":0.048985499999999994,"cacheWrite":0.0330125,"total":0.08482799999999999}},"stopReason":"toolUse","timestamp":1774827623353}}
{"type":"message","id":"3c3aef52","parentId":"b94fd2c7","timestamp":"2026-03-29T23:41:05.637Z","message":{"role":"toolResult","toolCallId":"toolu_01J26g3Y5NQGkTtGkg1ATPLy","toolName":"read","content":[{"type":"text","text":"\"\"\"Review queue endpoints for Chrysopedia API.\n\nProvides admin review workflow: list queue, stats, approve, reject,\nedit, split, merge key moments, and toggle review/auto mode via Redis.\n\"\"\"\n\nimport logging\nimport uuid\nfrom typing import Annotated\n\nfrom fastapi import APIRouter, Depends, HTTPException, Query\nfrom sqlalchemy import case, func, select\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom config import get_settings\nfrom database import get_session\nfrom models import Creator, KeyMoment, KeyMomentContentType, ReviewStatus, SourceVideo\nfrom redis_client import get_redis\nfrom schemas import (\n    KeyMomentRead,\n    MomentEditRequest,\n    MomentMergeRequest,\n    MomentSplitRequest,\n    ReviewModeResponse,\n    ReviewModeUpdate,\n    ReviewQueueItem,\n    ReviewQueueResponse,\n    ReviewStatsResponse,\n)\n\nlogger = logging.getLogger(\"chrysopedia.review\")\n\nrouter = APIRouter(prefix=\"/review\", tags=[\"review\"])\n\nREDIS_MODE_KEY = \"chrysopedia:review_mode\"\n\nVALID_STATUSES = {\"pending\", \"approved\", \"edited\", \"rejected\", \"all\"}\n\n\n# ── Helpers ──────────────────────────────────────────────────────────────────\n\n\ndef _moment_to_queue_item(\n    moment: KeyMoment, video_filename: str, creator_name: str\n) -> ReviewQueueItem:\n    \"\"\"Convert a KeyMoment ORM instance + joined fields to a ReviewQueueItem.\"\"\"\n    data = KeyMomentRead.model_validate(moment).model_dump()\n    data[\"video_filename\"] = video_filename\n    data[\"creator_name\"] = creator_name\n    return ReviewQueueItem(**data)\n\n\n# ── Endpoints ────────────────────────────────────────────────────────────────\n\n\n@router.get(\"/queue\", response_model=ReviewQueueResponse)\nasync def list_queue(\n    status: Annotated[str, Query()] = \"pending\",\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) -> ReviewQueueResponse:\n    \"\"\"List key moments in the review queue, filtered by status.\"\"\"\n    if status not in VALID_STATUSES:\n        raise HTTPException(\n            status_code=400,\n            detail=f\"Invalid status filter '{status}'. Must be one of: {', '.join(sorted(VALID_STATUSES))}\",\n        )\n\n    # Base query joining KeyMoment → SourceVideo → Creator\n    base = (\n        select(\n            KeyMoment,\n            SourceVideo.filename.label(\"video_filename\"),\n            Creator.name.label(\"creator_name\"),\n        )\n        .join(SourceVideo, KeyMoment.source_video_id == SourceVideo.id)\n        .join(Creator, SourceVideo.creator_id == Creator.id)\n    )\n\n    if status != \"all\":\n        base = base.where(KeyMoment.review_status == ReviewStatus(status))\n\n    # Count total matching rows\n    count_stmt = select(func.count()).select_from(base.subquery())\n    total = (await db.execute(count_stmt)).scalar_one()\n\n    # Fetch paginated results\n    stmt = base.order_by(KeyMoment.created_at.desc()).offset(offset).limit(limit)\n    rows = (await db.execute(stmt)).all()\n\n    items = [\n        _moment_to_queue_item(row.KeyMoment, row.video_filename, row.creator_name)\n        for row in rows\n    ]\n\n    return ReviewQueueResponse(items=items, total=total, offset=offset, limit=limit)\n\n\n@router.get(\"/stats\", response_model=ReviewStatsResponse)\nasync def get_stats(\n    db: AsyncSession = Depends(get_session),\n) -> ReviewStatsResponse:\n    \"\"\"Return counts of key moments grouped by review status.\"\"\"\n    stmt = (\n        select(\n            KeyMoment.review_status,\n            func.count().label(\"cnt\"),\n        )\n        .group_by(KeyMoment.review_status)\n    )\n    result = await db.execute(stmt)\n    counts = {row.review_status.value: row.cnt for row in result.all()}\n\n    return ReviewStatsResponse(\n        pending=counts.get(\"pending\", 0),\n        approved=counts.get(\"approved\", 0),\n        edited=counts.get(\"edited\", 0),\n        rejected=counts.get(\"rejected\", 0),\n    )\n\n\n@router.post(\"/moments/{moment_id}/approve\", response_model=KeyMomentRead)\nasync def approve_moment(\n    moment_id: uuid.UUID,\n    db: AsyncSession = Depends(get_session),\n) -> KeyMomentRead:\n    \"\"\"Approve a key moment for publishing.\"\"\"\n    moment = await db.get(KeyMoment, moment_id)\n    if moment is None:\n        raise HTTPException(\n            status_code=404,\n            detail=f\"Key moment {moment_id} not found\",\n        )\n\n    moment.review_status = ReviewStatus.approved\n    await db.commit()\n    await db.refresh(moment)\n\n    logger.info(\"Approved key moment %s\", moment_id)\n    return KeyMomentRead.model_validate(moment)\n\n\n@router.post(\"/moments/{moment_id}/reject\", response_model=KeyMomentRead)\nasync def reject_moment(\n    moment_id: uuid.UUID,\n    db: AsyncSession = Depends(get_session),\n) -> KeyMomentRead:\n    \"\"\"Reject a key moment.\"\"\"\n    moment = await db.get(KeyMoment, moment_id)\n    if moment is None:\n        raise HTTPException(\n            status_code=404,\n            detail=f\"Key moment {moment_id} not found\",\n        )\n\n    moment.review_status = ReviewStatus.rejected\n    await db.commit()\n    await db.refresh(moment)\n\n    logger.info(\"Rejected key moment %s\", moment_id)\n    return KeyMomentRead.model_validate(moment)\n\n\n@router.put(\"/moments/{moment_id}\", response_model=KeyMomentRead)\nasync def edit_moment(\n    moment_id: uuid.UUID,\n    body: MomentEditRequest,\n    db: AsyncSession = Depends(get_session),\n) -> KeyMomentRead:\n    \"\"\"Update editable fields of a key moment and set status to edited.\"\"\"\n    moment = await db.get(KeyMoment, moment_id)\n    if moment is None:\n        raise HTTPException(\n            status_code=404,\n            detail=f\"Key moment {moment_id} not found\",\n        )\n\n    update_data = body.model_dump(exclude_unset=True)\n    # Convert content_type string to enum if provided\n    if \"content_type\" in update_data and update_data[\"content_type\"] is not None:\n        try:\n            update_data[\"content_type\"] = KeyMomentContentType(update_data[\"content_type\"])\n        except ValueError:\n            raise HTTPException(\n                status_code=400,\n                detail=f\"Invalid content_type '{update_data['content_type']}'\",\n            )\n\n    for field, value in update_data.items():\n        setattr(moment, field, value)\n\n    moment.review_status = ReviewStatus.edited\n    await db.commit()\n    await db.refresh(moment)\n\n    logger.info(\"Edited key moment %s (fields: %s)\", moment_id, list(update_data.keys()))\n    return KeyMomentRead.model_validate(moment)\n\n\n@router.post(\"/moments/{moment_id}/split\", response_model=list[KeyMomentRead])\nasync def split_moment(\n    moment_id: uuid.UUID,\n    body: MomentSplitRequest,\n    db: AsyncSession = Depends(get_session),\n) -> list[KeyMomentRead]:\n    \"\"\"Split a key moment into two at the given timestamp.\"\"\"\n    moment = await db.get(KeyMoment, moment_id)\n    if moment is None:\n        raise HTTPException(\n            status_code=404,\n            detail=f\"Key moment {moment_id} not found\",\n        )\n\n    # Validate split_time is strictly between start_time and end_time\n    if body.split_time <= moment.start_time or body.split_time >= moment.end_time:\n        raise HTTPException(\n            status_code=400,\n            detail=(\n                f\"split_time ({body.split_time}) must be strictly between \"\n                f\"start_time ({moment.start_time}) and end_time ({moment.end_time})\"\n            ),\n        )\n\n    # Update original moment to [start_time, split_time)\n    original_end = moment.end_time\n    moment.end_time = body.split_time\n    moment.review_status = ReviewStatus.pending\n\n    # Create new moment for [split_time, end_time]\n    new_moment = KeyMoment(\n        source_video_id=moment.source_video_id,\n        technique_page_id=moment.technique_page_id,\n        title=f\"{moment.title} (split)\",\n        summary=moment.summary,\n        start_time=body.split_time,\n        end_time=original_end,\n        content_type=moment.content_type,\n        plugins=moment.plugins,\n        review_status=ReviewStatus.pending,\n        raw_transcript=moment.raw_transcript,\n    )\n    db.add(new_moment)\n\n    await db.commit()\n    await db.refresh(moment)\n    await db.refresh(new_moment)\n\n    logger.info(\n        \"Split key moment %s at %.2f → original [%.2f, %.2f), new [%.2f, %.2f]\",\n        moment_id, body.split_time,\n        moment.start_time, moment.end_time,\n        new_moment.start_time, new_moment.end_time,\n    )\n\n    return [\n        KeyMomentRead.model_validate(moment),\n        KeyMomentRead.model_validate(new_moment),\n    ]\n\n\n@router.post(\"/moments/{moment_id}/merge\", response_model=KeyMomentRead)\nasync def merge_moments(\n    moment_id: uuid.UUID,\n    body: MomentMergeRequest,\n    db: AsyncSession = Depends(get_session),\n) -> KeyMomentRead:\n    \"\"\"Merge two key moments into one.\"\"\"\n    if moment_id == body.target_moment_id:\n        raise HTTPException(\n            status_code=400,\n            detail=\"Cannot merge a moment with itself\",\n        )\n\n    source = await db.get(KeyMoment, moment_id)\n    if source is None:\n        raise HTTPException(\n            status_code=404,\n            detail=f\"Key moment {moment_id} not found\",\n        )\n\n    target = await db.get(KeyMoment, body.target_moment_id)\n    if target is None:\n        raise HTTPException(\n            status_code=404,\n            detail=f\"Target key moment {body.target_moment_id} not found\",\n        )\n\n    # Both must belong to the same source video\n    if source.source_video_id != target.source_video_id:\n        raise HTTPException(\n            status_code=400,\n            detail=\"Cannot merge moments from different source videos\",\n        )\n\n    # Merge: combined summary, min start, max end\n    source.summary = f\"{source.summary}\\n\\n{target.summary}\"\n    source.start_time = min(source.start_time, target.start_time)\n    source.end_time = max(source.end_time, target.end_time)\n    source.review_status = ReviewStatus.pending\n\n    # Delete target\n    await db.delete(target)\n    await db.commit()\n    await db.refresh(source)\n\n    logger.info(\n        \"Merged key moment %s with %s → [%.2f, %.2f]\",\n        moment_id, body.target_moment_id,\n        source.start_time, source.end_time,\n    )\n\n    return KeyMomentRead.model_validate(source)\n\n\n@router.get(\"/mode\", response_model=ReviewModeResponse)\nasync def get_mode() -> ReviewModeResponse:\n    \"\"\"Get the current review mode (review vs auto).\"\"\"\n    settings = get_settings()\n    try:\n        redis = await get_redis()\n        try:\n            value = await redis.get(REDIS_MODE_KEY)\n            if value is not None:\n                return ReviewModeResponse(review_mode=value.lower() == \"true\")\n        finally:\n            await redis.aclose()\n    except Exception as exc:\n        # Redis unavailable — fall back to config default\n        logger.warning(\"Redis unavailable for mode read, using config default: %s\", exc)\n\n    return ReviewModeResponse(review_mode=settings.review_mode)\n\n\n@router.put(\"/mode\", response_model=ReviewModeResponse)\nasync def set_mode(\n    body: ReviewModeUpdate,\n) -> ReviewModeResponse:\n    \"\"\"Set the review mode (review vs auto).\"\"\"\n    try:\n        redis = await get_redis()\n        try:\n            await redis.set(REDIS_MODE_KEY, str(body.review_mode))\n        finally:\n            await redis.aclose()\n    except Exception as exc:\n        logger.error(\"Failed to set review mode in Redis: %s\", exc)\n        raise HTTPException(\n            status_code=503,\n            detail=f\"Redis unavailable: {exc}\",\n        )\n\n    logger.info(\"Review mode set to %s\", body.review_mode)\n    return ReviewModeResponse(review_mode=body.review_mode)\n"}],"isError":false,"timestamp":1774827665636}}
{"type":"message","id":"fa57fe7f","parentId":"3c3aef52","timestamp":"2026-03-29T23:41:05.639Z","message":{"role":"toolResult","toolCallId":"toolu_01QqFrcbpJ922gVYbj9pD9Wx","toolName":"read","content":[{"type":"text","text":"\"\"\"Async Redis client helper for Chrysopedia.\"\"\"\n\nimport redis.asyncio as aioredis\n\nfrom config import get_settings\n\n\nasync def get_redis() -> aioredis.Redis:\n    \"\"\"Return an async Redis client from the configured URL.\n\n    Callers should close the connection when done, or use it\n    as a short-lived client within a request handler.\n    \"\"\"\n    settings = get_settings()\n    return aioredis.from_url(settings.redis_url, decode_responses=True)\n"}],"isError":false,"timestamp":1774827665638}}
{"type":"message","id":"87958c7d","parentId":"fa57fe7f","timestamp":"2026-03-29T23:41:55.498Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have a thorough understanding of the codebase. Let me check the Qdrant client library to understand what search methods are available:"},{"type":"toolCall","id":"toolu_01TZThFzPMHRp86hE5fY4bXe","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -r \"qdrant\" backend/requirements.txt"}},{"type":"toolCall","id":"toolu_011xvatXQyGwEUi22D2PKreX","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && python3 -c \"import qdrant_client; print(qdrant_client.__version__)\" 2>/dev/null || echo \"Not in venv\""}},{"type":"toolCall","id":"toolu_012YbWhxFYFF3pT4pjxAiozf","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && .venv/bin/python -c \"import qdrant_client; print(qdrant_client.__version__)\" 2>/dev/null || echo \"qdrant-client not installed\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":298,"cacheRead":103253,"cacheWrite":3722,"totalTokens":107274,"cost":{"input":0.000005,"output":0.00745,"cacheRead":0.0516265,"cacheWrite":0.023262500000000002,"total":0.082344}},"stopReason":"toolUse","timestamp":1774827665638}}
{"type":"message","id":"ae0c6232","parentId":"87958c7d","timestamp":"2026-03-29T23:41:55.513Z","message":{"role":"toolResult","toolCallId":"toolu_01TZThFzPMHRp86hE5fY4bXe","toolName":"bash","content":[{"type":"text","text":"qdrant-client>=1.9,<2.0\n"}],"isError":false,"timestamp":1774827715513}}
{"type":"message","id":"355ffbed","parentId":"ae0c6232","timestamp":"2026-03-29T23:41:56.369Z","message":{"role":"toolResult","toolCallId":"toolu_011xvatXQyGwEUi22D2PKreX","toolName":"bash","content":[{"type":"text","text":"Not in venv\n"}],"isError":false,"timestamp":1774827716368}}
{"type":"message","id":"a44c16bf","parentId":"355ffbed","timestamp":"2026-03-29T23:41:56.371Z","message":{"role":"toolResult","toolCallId":"toolu_012YbWhxFYFF3pT4pjxAiozf","toolName":"bash","content":[{"type":"text","text":"qdrant-client not installed\n"}],"isError":false,"timestamp":1774827716368}}
{"type":"message","id":"1b4c665d","parentId":"a44c16bf","timestamp":"2026-03-29T23:42:01.180Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01YFxSdKczctXQ8qnjwYCyDu","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && .venv/bin/pip list 2>/dev/null | grep -i qdrant"}},{"type":"toolCall","id":"toolu_013LxrbSbiKYV5EuSjGcd6r5","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/requirements.txt"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":146,"cacheRead":106975,"cacheWrite":411,"totalTokens":107533,"cost":{"input":0.000005,"output":0.00365,"cacheRead":0.0534875,"cacheWrite":0.0025687500000000003,"total":0.05971125}},"stopReason":"toolUse","timestamp":1774827716369}}
{"type":"message","id":"2f44db5d","parentId":"1b4c665d","timestamp":"2026-03-29T23:42:01.501Z","message":{"role":"toolResult","toolCallId":"toolu_01YFxSdKczctXQ8qnjwYCyDu","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1774827721500}}
{"type":"message","id":"2fb52eb9","parentId":"2f44db5d","timestamp":"2026-03-29T23:42:01.504Z","message":{"role":"toolResult","toolCallId":"toolu_013LxrbSbiKYV5EuSjGcd6r5","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":1774827721500}}
{"type":"message","id":"2a8720fc","parentId":"2fb52eb9","timestamp":"2026-03-29T23:42:05.288Z","message":{"role":"assistant","content":[{"type":"text","text":"Let me now look at how the qdrant-client search API works and check if there's an async variant:"},{"type":"toolCall","id":"toolu_01RoB6JjingnT3BRP9pYVFX4","name":"resolve_library","arguments":{"libraryName":"qdrant-client","query":"search query points semantic search with metadata filter"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":108,"cacheRead":107386,"cacheWrite":483,"totalTokens":107978,"cost":{"input":0.000005,"output":0.0027,"cacheRead":0.053693,"cacheWrite":0.00301875,"total":0.05941675}},"stopReason":"toolUse","timestamp":1774827721502}}
{"type":"message","id":"2c5126b1","parentId":"2a8720fc","timestamp":"2026-03-29T23:42:05.291Z","message":{"role":"toolResult","toolCallId":"toolu_01RoB6JjingnT3BRP9pYVFX4","toolName":"resolve_library","content":[{"type":"text","text":"Found 5 libraries matching \"qdrant-client\":\n\n• Qdrant Client  (ID: /qdrant/qdrant-client)\n  The Qdrant Python Client is a library for interacting with the Qdrant vector search engine, supporting both synchronous and asynchronous requests, local mode, and REST/gRPC communication.\n  trust: 9.8/10 · benchmark: 71.0 · 114 snippets · 24k tokens · updated: 2026-03-25\n• Qdrant Client  (ID: /websites/rs_qdrant-client_qdrant_client)\n  Qdrant Client is a Rust library that provides an easy-to-use API interface to connect to and interact with Qdrant vector search servers over gRPC.\n  trust: 9.7/10 · benchmark: 73.8 · 10,967 snippets · 1677k tokens · updated: 2026-02-15\n• Qdrant Rust Client  (ID: /qdrant/rust-client)\n  The official Rust client for Qdrant, a high-performance vector search engine, enabling efficient interaction with Qdrant for scalable vector search applications.\n  trust: 9.8/10 · benchmark: 89.6 · 40 snippets · 13k tokens · updated: 2026-02-05\n• Qdrant Go Client  (ID: /websites/pkg_go_dev_github_com_qdrant_go-client_v1_16_2)\n  A Go client library for the Qdrant vector search engine that provides utilities for creating collections, upserting vectors, and performing similarity searches with filtering capabilities.\n  trust: 10/10 · benchmark: 67.3 · 722 snippets · 118k tokens · updated: 2025-12-21\n• Qdrant  (ID: /websites/qdrant_tech)\n  Qdrant is an AI-native vector database and semantic search engine that extracts meaningful information from unstructured data using vector search capabilities.\n  trust: 10/10 · benchmark: 88.1 · 5,682 snippets · 1068k tokens · updated: 2026-03-25\n\nUse the ID (e.g. /websites/react_dev) with get_library_docs to fetch documentation."}],"details":{"query":"qdrant-client","resultCount":5,"cached":true},"isError":false,"timestamp":1774827725289}}
{"type":"message","id":"6ce7251f","parentId":"2c5126b1","timestamp":"2026-03-29T23:42:08.908Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01XkphredfqicPC9PvHVdUYp","name":"get_library_docs","arguments":{"libraryId":"/qdrant/qdrant-client","query":"search query points async client semantic search with filter metadata payload","tokens":5000}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":111,"cacheRead":107869,"cacheWrite":680,"totalTokens":108661,"cost":{"input":0.000005,"output":0.002775,"cacheRead":0.053934499999999996,"cacheWrite":0.00425,"total":0.06096449999999999}},"stopReason":"toolUse","timestamp":1774827725290}}
{"type":"message","id":"b390e8ca","parentId":"6ce7251f","timestamp":"2026-03-29T23:42:10.602Z","message":{"role":"toolResult","toolCallId":"toolu_01XkphredfqicPC9PvHVdUYp","toolName":"get_library_docs","content":[{"type":"text","text":"### Perform Universal Search with Qdrant Client\n\nSource: https://context7.com/qdrant/qdrant-client/llms.txt\n\nShows how to use the `query_points` method for universal search. This includes basic vector search, searching with filters based on payload fields, using an existing point ID as a query, and controlling the returned payload and vectors.\n\n```python\nfrom qdrant_client import QdrantClient\nfrom qdrant_client.models import VectorParams, Distance, PointStruct, Filter, FieldCondition, Range\n\nclient = QdrantClient(\":memory:\")\nclient.create_collection(\"articles\", vectors_config=VectorParams(size=4, distance=Distance.COSINE))\nclient.upsert(\"articles\", points=[\n    PointStruct(id=1, vector=[0.1, 0.2, 0.3, 0.4], payload={\"title\": \"AI News\", \"views\": 1000}),\n    PointStruct(id=2, vector=[0.15, 0.25, 0.35, 0.45], payload={\"title\": \"ML Guide\", \"views\": 500}),\n    PointStruct(id=3, vector=[0.9, 0.8, 0.7, 0.6], payload={\"title\": \"Sports\", \"views\": 2000}),\n])\n\n# Basic vector search\nresults = client.query_points(\n    collection_name=\"articles\",\n    query=[0.1, 0.2, 0.3, 0.4],\n    limit=2,\n)\nprint(results.points)\n\n# Search with filter\nresults = client.query_points(\n    collection_name=\"articles\",\n    query=[0.1, 0.2, 0.3, 0.4],\n    query_filter=Filter(\n        must=[\n            FieldCondition(key=\"views\", range=Range(gte=600))\n        ]\n    ),\n    limit=5,\n)\n\n# Search using existing point ID as query\nresults = client.query_points(\n    collection_name=\"articles\",\n    query=1,  # Use point with ID=1 as query\n    limit=2,\n)\n\n# Control payload and vector return\nresults = client.query_points(\n    collection_name=\"articles\",\n    query=[0.1, 0.2, 0.3, 0.4],\n    with_payload=[\"title\"],  # Only return title\n    with_vectors=True,       # Include vectors in response\n    score_threshold=0.5,     # Minimum similarity score\n    limit=10,\n)\n```\n\n--------------------------------\n\n### Querying with a Filter and Vector\n\nSource: https://github.com/qdrant/qdrant-client/blob/master/docs/source/quickstart.ipynb\n\nDemonstrates how to search for points in a Qdrant collection using a vector and applying a filter to narrow down the results. The filter uses a `FieldCondition` to match a specific value for the 'city' key.\n\n```APIDOC\n## POST /collections/{collection_name}/points/search\n\n### Description\nSearches for points in a collection based on a query vector and an optional filter. This endpoint allows for sophisticated querying by combining vector similarity search with attribute-based filtering.\n\n### Method\nPOST\n\n### Endpoint\n/collections/{collection_name}/points/search\n\n### Parameters\n#### Path Parameters\n- **collection_name** (string) - Required - The name of the collection to search within.\n\n#### Query Parameters\nNone\n\n#### Request Body\n- **vector** (list[float]) - Required - The query vector to search for.\n- **filter** (object) - Optional - A filter object to narrow down the search results. See [Filter Object Documentation](#filter-object) for details.\n- **limit** (integer) - Required - The maximum number of results to return.\n- **with_payload** (boolean) - Optional - If true, the payload of the points will be returned.\n- **with_vectors** (boolean) - Optional - If true, the vectors of the points will be returned.\n\n### Request Example\n```json\n{\n  \"vector\": [0.2, 0.1, 0.9, 0.7],\n  \"filter\": {\n    \"must\": [\n      {\n        \"key\": \"city\",\n        \"match\": {\n          \"value\": \"London\"\n        }\n      }\n    ]\n  },\n  \"limit\": 1\n}\n```\n\n### Response\n#### Success Response (200)\n- **points** (list[ScoredPoint]) - A list of scored points matching the query criteria.\n  - **id** (integer | string) - The ID of the point.\n  - **version** (integer) - The version of the point.\n  - **score** (float) - The similarity score of the point.\n  - **payload** (object) - The payload associated with the point (if requested).\n  - **vector** (object) - The vector associated with the point (if requested).\n\n#### Response Example\n```json\n{\n  \"points\": [\n    {\n      \"id\": 2,\n      \"version\": 0,\n      \"score\": 0.8709999993443489,\n      \"payload\": {\"city\": \"London\"},\n      \"vector\": null\n    }\n  ]\n}\n```\n```\n\n--------------------------------\n\n### Search with Filtering Conditions in Qdrant\n\nSource: https://github.com/qdrant/qdrant-client/blob/master/README.md\n\nExecutes a similarity search in Qdrant, applying a filter to narrow down the results. The example filters points based on a range condition on the 'rand_number' payload field.\n\n```python\nfrom qdrant_client.models import Filter, FieldCondition, Range\n\n# Assume client, 'my_collection', and query_vector are initialized\nhits = client.query_points(\n    collection_name=\"my_collection\",\n    query=query_vector,\n    query_filter=Filter(\n        must=[\n            FieldCondition(\n                key='rand_number',\n                range=Range(\n                    gte=3  # Select only those results where `rand_number` >= 3\n                )\n            )\n        ]\n    ),\n    limit=5  # Return 5 closest points\n)\n```\n\n--------------------------------\n\n### Python: Search Qdrant with Filter and Vector\n\nSource: https://github.com/qdrant/qdrant-client/blob/master/docs/source/quickstart.ipynb\n\nThis Python code snippet demonstrates how to search a Qdrant collection using a vector and a filter. The filter, defined using `Filter` and `FieldCondition`, restricts results to points where the 'city' field matches 'London'. The search is limited to one result.\n\n```python\nfrom qdrant_client.http.models import Filter, FieldCondition, MatchValue\n\nsearch_result = client.search(\n    collection_name=\"test_collection\",\n    query_vector=[0.2, 0.1, 0.9, 0.7], \n    query_filter=Filter(\n        must=[\n            FieldCondition(\n                key=\"city\",\n                match=MatchValue(value=\"London\")\n            )\n        ]\n    ),\n    limit=1\n)\nprint(search_result)\n```\n\n### Qdrant Client > Core Functionality > query_points\n\nSource: https://context7.com/qdrant/qdrant-client/llms.txt\n\nThe `query_points` endpoint is a versatile tool for performing various types of searches within your Qdrant collections. It supports nearest neighbor search for finding similar vectors, recommendation engine functionalities, and general discovery tasks. This method serves as the primary interface for retrieving vectors that are most similar to a given query vector. You can also apply filters to narrow down the search results based on payload conditions, use an existing point's ID as the query vector, and control which parts of the response are returned, such as specific payload fields or the vectors themselves. Additionally, you can set a score threshold to ensure only sufficiently similar results are returned."}],"details":{"libraryId":"/qdrant/qdrant-client","query":"search query points async client semantic search with filter metadata payload","tokens":5000,"cached":false,"truncated":false,"charCount":6669},"isError":false,"timestamp":1774827730600}}
{"type":"message","id":"8ba13c0e","parentId":"b390e8ca","timestamp":"2026-03-29T23:42:14.230Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have a clear picture. Let me also check the async variant of the Qdrant client since the FastAPI endpoints are async:"},{"type":"toolCall","id":"toolu_01VWtSarnr8RrWbqbGnZZBEu","name":"get_library_docs","arguments":{"libraryId":"/qdrant/qdrant-client","query":"async qdrant client AsyncQdrantClient query_points","tokens":3000}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":143,"cacheRead":108549,"cacheWrite":2027,"totalTokens":110720,"cost":{"input":0.000005,"output":0.003575,"cacheRead":0.054274499999999996,"cacheWrite":0.012668750000000001,"total":0.07052325}},"stopReason":"toolUse","timestamp":1774827730601}}
{"type":"message","id":"23c2b49d","parentId":"8ba13c0e","timestamp":"2026-03-29T23:42:15.686Z","message":{"role":"toolResult","toolCallId":"toolu_01VWtSarnr8RrWbqbGnZZBEu","toolName":"get_library_docs","content":[{"type":"text","text":"### Async Qdrant Client Operations in Python\n\nSource: https://github.com/qdrant/qdrant-client/blob/master/README.md\n\nDemonstrates how to initialize and use the `AsyncQdrantClient` for asynchronous interactions with a Qdrant instance. This includes creating a collection, upserting points, and querying for similar points. Requires the `qdrant-client` and `numpy` libraries.\n\n```python\nimport asyncio\n\nimport numpy as np\n\nfrom qdrant_client import AsyncQdrantClient, models\n\n\nasync def main():\n    # Your async code using QdrantClient might be put here\n    client = AsyncQdrantClient(url=\"http://localhost:6333\")\n\n    await client.create_collection(\n        collection_name=\"my_collection\",\n        vectors_config=models.VectorParams(size=10, distance=models.Distance.COSINE),\n    )\n\n    await client.upsert(\n        collection_name=\"my_collection\",\n        points=[\n            models.PointStruct(\n                id=i,\n                vector=np.random.rand(10).tolist(),\n            )\n            for i in range(100)\n        ],\n    )\n\n    res = await client.query_points(\n        collection_name=\"my_collection\",\n        query=np.random.rand(10).tolist(),  # type: ignore\n        limit=10,\n    )\n\n    print(res)\n\nasyncio.run(main())\n```\n\n--------------------------------\n\n### Asynchronous Vector Operations with Qdrant\n\nSource: https://context7.com/qdrant/qdrant-client/llms.txt\n\nDemonstrates a full lifecycle workflow using AsyncQdrantClient, including collection creation, data ingestion, similarity search, and filtered counting. This snippet requires the qdrant-client and numpy libraries.\n\n```python\nimport asyncio\nimport numpy as np\nfrom qdrant_client import AsyncQdrantClient, models\n\nasync def main():\n    client = AsyncQdrantClient(\":memory:\")\n\n    await client.create_collection(\n        collection_name=\"async_demo\",\n        vectors_config=models.VectorParams(size=128, distance=models.Distance.COSINE),\n    )\n\n    points = [\n        models.PointStruct(\n            id=i,\n            vector=np.random.rand(128).tolist(),\n            payload={\"index\": i, \"group\": i % 5}\n        )\n        for i in range(1000)\n    ]\n\n    await client.upsert(collection_name=\"async_demo\", points=points)\n\n    results = await client.query_points(\n        collection_name=\"async_demo\",\n        query=np.random.rand(128).tolist(),\n        limit=5,\n    )\n\n    print(f\"Found {len(results.points)} results\")\n\n    count = await client.count(\n        collection_name=\"async_demo\",\n        count_filter=models.Filter(\n            must=[models.FieldCondition(key=\"group\", match=models.MatchValue(value=0))]\n        ),\n    )\n    print(f\"Group 0 count: {count.count}\")\n\n    await client.close()\n\nasyncio.run(main())\n```\n\n--------------------------------\n\n### Perform Universal Search with Qdrant Client\n\nSource: https://context7.com/qdrant/qdrant-client/llms.txt\n\nShows how to use the `query_points` method for universal search. This includes basic vector search, searching with filters based on payload fields, using an existing point ID as a query, and controlling the returned payload and vectors.\n\n```python\nfrom qdrant_client import QdrantClient\nfrom qdrant_client.models import VectorParams, Distance, PointStruct, Filter, FieldCondition, Range\n\nclient = QdrantClient(\":memory:\")\nclient.create_collection(\"articles\", vectors_config=VectorParams(size=4, distance=Distance.COSINE))\nclient.upsert(\"articles\", points=[\n    PointStruct(id=1, vector=[0.1, 0.2, 0.3, 0.4], payload={\"title\": \"AI News\", \"views\": 1000}),\n    PointStruct(id=2, vector=[0.15, 0.25, 0.35, 0.45], payload={\"title\": \"ML Guide\", \"views\": 500}),\n    PointStruct(id=3, vector=[0.9, 0.8, 0.7, 0.6], payload={\"title\": \"Sports\", \"views\": 2000}),\n])\n\n# Basic vector search\nresults = client.query_points(\n    collection_name=\"articles\",\n    query=[0.1, 0.2, 0.3, 0.4],\n    limit=2,\n)\nprint(results.points)\n\n# Search with filter\nresults = client.query_points(\n    collection_name=\"articles\",\n    query=[0.1, 0.2, 0.3, 0.4],\n    query_filter=Filter(\n        must=[\n            FieldCondition(key=\"views\", range=Range(gte=600))\n        ]\n    ),\n    limit=5,\n)\n\n# Search using existing point ID as query\nresults = client.query_points(\n    collection_name=\"articles\",\n    query=1,  # Use point with ID=1 as query\n    limit=2,\n)\n\n# Control payload and vector return\nresults = client.query_points(\n    collection_name=\"articles\",\n    query=[0.1, 0.2, 0.3, 0.4],\n    with_payload=[\"title\"],  # Only return title\n    with_vectors=True,       # Include vectors in response\n    score_threshold=0.5,     # Minimum similarity score\n    limit=10,\n)\n```\n\n--------------------------------\n\n### Async Operations: Create Collection and Upsert\n\nSource: https://github.com/qdrant/qdrant-client/blob/master/docs/source/index.rst\n\nProvides an example of asynchronous operations for creating a collection and upserting points using the AsyncQdrantClient. It includes necessary imports and an asyncio event loop.\n\n```python\nfrom qdrant_client import AsyncQdrantClient, models\nimport numpy as np\nimport asyncio\n\nasync def main():\n   client = AsyncQdrantClient(url=\"http://localhost:6333\")\n\n   if not await client.collection_exists(\"my_collection\"):\n      await client.create_collection(\n         collection_name=\"my_collection\",\n         vectors_config=models.VectorParams(size=10, distance=models.Distance.COSINE),\n      )\n\n   await client.upsert(\n      collection_name=\"my_collection\",\n      points=[\n            models.PointStruct(\n               id=i,\n               vector=np.random.rand(10).tolist(),\n            )\n            for i in range(100)\n      ],\n   )\n\n   res = await client.search(\n      collection_name=\"my_collection\",\n      query_vector=np.random.rand(10).tolist(),  # type: ignore\n      limit=10,\n   )\n\n   print(res)\n\nasyncio.run(main())\n```\n\n--------------------------------\n\n### AsyncQdrantClient Overview\n\nSource: https://github.com/qdrant/qdrant-client/blob/master/docs/source/qdrant_client.async_qdrant_client.rst\n\nThe AsyncQdrantClient provides an asynchronous interface for performing vector operations, collection management, and point manipulation in Qdrant.\n\n```APIDOC\n## ASYNC OPERATIONS\n\n### Description\nThe AsyncQdrantClient allows for non-blocking execution of vector database operations. It supports all standard Qdrant operations including search, upsert, and collection management.\n\n### Method\nVarious (GET, POST, PUT, DELETE)\n\n### Endpoint\n/collections/{collection_name}/points\n\n### Parameters\n#### Path Parameters\n- **collection_name** (string) - Required - The name of the target collection.\n\n#### Request Body\n- **points** (list) - Required - A list of point objects to be upserted.\n\n### Request Example\n{\n  \"points\": [\n    {\"id\": 1, \"vector\": [0.1, 0.2, 0.3], \"payload\": {\"key\": \"value\"}}\n  ]\n}\n\n### Response\n#### Success Response (200)\n- **result** (object) - The operation status and result details.\n\n#### Response Example\n{\n  \"result\": {\n    \"operation_id\": 1,\n    \"status\": \"completed\"\n  }\n}\n```"}],"details":{"libraryId":"/qdrant/qdrant-client","query":"async qdrant client AsyncQdrantClient query_points","tokens":3000,"cached":false,"truncated":false,"charCount":6964},"isError":false,"timestamp":1774827735685}}
{"type":"message","id":"34ee5db1","parentId":"23c2b49d","timestamp":"2026-03-29T23:42:21.375Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have everything I need. The key insight: `AsyncQdrantClient` exists and mirrors the sync client perfectly — `query_points` works the same way. The search endpoint needs to embed the query text, then search Qdrant.\n\nLet me also check the existing frontend structure for how it handles routing and if there's a tsconfig to understand:"},{"type":"toolCall","id":"toolu_01HX1eB4FEC48vdSVkuSnQ6G","name":"read","arguments":{"path":"frontend/tsconfig.app.json"}},{"type":"toolCall","id":"toolu_01MMM8mYU8znr2QWWLdyeN5u","name":"read","arguments":{"path":"frontend/src/main.tsx"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":176,"cacheRead":110576,"cacheWrite":2222,"totalTokens":112975,"cost":{"input":0.000005,"output":0.0044,"cacheRead":0.055288,"cacheWrite":0.0138875,"total":0.0735805}},"stopReason":"toolUse","timestamp":1774827735685}}
{"type":"message","id":"03844f23","parentId":"34ee5db1","timestamp":"2026-03-29T23:42:21.377Z","message":{"role":"toolResult","toolCallId":"toolu_01HX1eB4FEC48vdSVkuSnQ6G","toolName":"read","content":[{"type":"text","text":"{\n  \"compilerOptions\": {\n    \"target\": \"ES2020\",\n    \"useDefineForClassFields\": true,\n    \"lib\": [\"ES2020\", \"DOM\", \"DOM.Iterable\"],\n    \"module\": \"ESNext\",\n    \"skipLibCheck\": true,\n\n    /* Bundler mode */\n    \"moduleResolution\": \"bundler\",\n    \"allowImportingTsExtensions\": true,\n    \"isolatedModules\": true,\n    \"moduleDetection\": \"force\",\n    \"noEmit\": true,\n    \"jsx\": \"react-jsx\",\n\n    /* Linting */\n    \"strict\": true,\n    \"noUnusedLocals\": true,\n    \"noUnusedParameters\": true,\n    \"noFallthroughCasesInSwitch\": true,\n    \"noUncheckedIndexedAccess\": true\n  },\n  \"include\": [\"src\"]\n}\n"}],"isError":false,"timestamp":1774827741377}}
{"type":"message","id":"edb4c7e1","parentId":"03844f23","timestamp":"2026-03-29T23:42:21.381Z","message":{"role":"toolResult","toolCallId":"toolu_01MMM8mYU8znr2QWWLdyeN5u","toolName":"read","content":[{"type":"text","text":"import { StrictMode } from \"react\";\nimport { createRoot } from \"react-dom/client\";\nimport { BrowserRouter } from \"react-router-dom\";\nimport App from \"./App\";\nimport \"./App.css\";\n\ncreateRoot(document.getElementById(\"root\")!).render(\n  <StrictMode>\n    <BrowserRouter>\n      <App />\n    </BrowserRouter>\n  </StrictMode>,\n);\n"}],"isError":false,"timestamp":1774827741379}}
{"type":"message","id":"b58713be","parentId":"edb4c7e1","timestamp":"2026-03-29T23:42:25.004Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check how the spec describes the topic hierarchy more precisely:"},{"type":"toolCall","id":"toolu_011Aj3o8zBdbes1v7vA2uoPD","name":"read","arguments":{"limit":60,"offset":204,"path":"chrysopedia-spec.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":106,"cacheRead":112798,"cacheWrite":561,"totalTokens":113466,"cost":{"input":0.000005,"output":0.00265,"cacheRead":0.056399,"cacheWrite":0.0035062500000000003,"total":0.06256025}},"stopReason":"toolUse","timestamp":1774827741380}}
{"type":"message","id":"41a2ad50","parentId":"b58713be","timestamp":"2026-03-29T23:42:25.008Z","message":{"role":"toolResult","toolCallId":"toolu_011Aj3o8zBdbes1v7vA2uoPD","toolName":"read","content":[{"type":"text","text":"\n### 5.1 Top-level categories\n\nThese are broad production stages/domains. They should cover the full scope of music production education:\n\n| Category | Description | Example sub-topics |\n|----------|-------------|-------------------|\n| Sound design | Creating and shaping sounds from scratch or samples | Bass, drums (kick, snare, hi-hat, percussion), pads, leads, FX, foley, vocals, textures |\n| Mixing | Balancing, processing, and spatializing elements in a session | EQ, compression, bus processing, reverb/delay, stereo imaging, gain staging, automation |\n| Synthesis | Methods of generating sound | FM, wavetable, granular, additive, subtractive, modular, physical modeling |\n| Arrangement | Structuring a track from intro to outro | Song structure, transitions, tension/release, energy flow, breakdowns, drops |\n| Workflow | Creative process, session management, productivity | DAW setup, templates, creative process, collaboration, file management, resampling |\n| Mastering | Final stage processing for release | Limiting, stereo width, loudness, format delivery, referencing |\n\n### 5.2 Sub-topic management\n\nSub-topics are not rigidly pre-defined. The extraction pipeline proposes sub-topic tags during classification, and the taxonomy grows organically as content is processed. However, the system maintains a **canonical tag list** that the LLM references during classification to ensure consistency (e.g., always \"snare\" not sometimes \"snare drum\" and sometimes \"snare design\").\n\nThe canonical tag list is editable by the administrator and should be stored as a configuration file that the pipeline references. New tags can be proposed by the pipeline and queued for admin approval, or auto-added if they fit within an existing top-level category.\n\n### 5.3 Genre taxonomy\n\nGenres are broad, general-level tags. Sub-genre classification is explicitly out of scope to avoid complexity.\n\n**Initial genre set (expandable):**\nBass music, Drum & bass, Dubstep, Halftime, House, Techno, IDM, Glitch, Downtempo, Neuro, Ambient, Experimental, Cinematic\n\n**Rules:**\n- Genres are metadata on Creators, not on techniques\n- A Creator can have multiple genre tags\n- Genre is available as a filter on both the Creators browse page and the Topics browse page (filtering Topics by genre shows techniques from creators tagged with that genre)\n- Genre tags are assigned during initial creator setup (manually or LLM-suggested based on content analysis) and can be edited by the administrator\n\n---\n\n## 6. Data model\n\n### 6.1 Core entities\n\n**Creator**\n```\nid              UUID\nname            string (display name, e.g., \"KOAN Sound\")\nslug            string (URL-safe, e.g., \"koan-sound\")\ngenres          string[] (e.g., [\"glitch hop\", \"neuro\", \"bass music\"])\nfolder_name     string (matches the folder name on disk for source mapping)\nview_count      integer (aggregated from child technique page views)\ncreated_at      timestamp\nupdated_at      timestamp\n```\n\n**Source Video**\n```\nid              UUID\ncreator_id      FK → Creator\nfilename        string (original filename)\nfile_path       string (path on disk)\nduration_seconds integer\ncontent_type    enum: tutorial | livestream | breakdown | short_form\ntranscript_path string (path to transcript JSON)\n\n[451 more lines in file. Use offset=264 to continue.]"}],"isError":false,"timestamp":1774827745006}}
{"type":"message","id":"0ea77cbc","parentId":"41a2ad50","timestamp":"2026-03-29T23:42:31.012Z","message":{"role":"assistant","content":[{"type":"text","text":"I now have all the context needed. Let me also quickly check how Qdrant-client does text/keyword search in case we need a keyword fallback:"},{"type":"toolCall","id":"toolu_01Kmhx8LmHHmwsMUe3m64Ffr","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -rn \"embedding_api_url\\|embedding_model\\|embedding_dimensions\\|openai.OpenAI\" backend/pipeline/embedding_client.py backend/config.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":150,"cacheRead":113359,"cacheWrite":930,"totalTokens":114440,"cost":{"input":0.000005,"output":0.0037500000000000003,"cacheRead":0.0566795,"cacheWrite":0.0058125,"total":0.066247}},"stopReason":"toolUse","timestamp":1774827745007}}
{"type":"message","id":"a7369d4d","parentId":"0ea77cbc","timestamp":"2026-03-29T23:42:31.024Z","message":{"role":"toolResult","toolCallId":"toolu_01Kmhx8LmHHmwsMUe3m64Ffr","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/embedding_client.py:3:Uses ``openai.OpenAI`` (sync) since Celery tasks run synchronously.\nbackend/pipeline/embedding_client.py:23:        self._client = openai.OpenAI(\nbackend/pipeline/embedding_client.py:24:            base_url=settings.embedding_api_url,\nbackend/pipeline/embedding_client.py:47:                model=self.settings.embedding_model,\nbackend/pipeline/embedding_client.py:70:        expected_dim = self.settings.embedding_dimensions\nbackend/pipeline/embedding_client.py:86:            self.settings.embedding_model,\nbackend/config.py:37:    embedding_api_url: str = \"http://localhost:11434/v1\"\nbackend/config.py:38:    embedding_model: str = \"nomic-embed-text\"\nbackend/config.py:39:    embedding_dimensions: int = 768\n"}],"isError":false,"timestamp":1774827751022}}
{"type":"message","id":"825aabab","parentId":"a7369d4d","timestamp":"2026-03-29T23:42:36.596Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have everything. Let me also quickly check what the existing stages.py looks like for embedding, to understand what metadata is stored in Qdrant payloads:"},{"type":"toolCall","id":"toolu_012J2P5tz8bT8x29PKCKRLM7","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"stage6\\|upsert_technique\\|upsert_key_moment\\|payload\\|\\\"type\\\"\" backend/pipeline/stages.py | head -30"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":147,"cacheRead":114289,"cacheWrite":389,"totalTokens":114826,"cost":{"input":0.000005,"output":0.0036750000000000003,"cacheRead":0.0571445,"cacheWrite":0.0024312500000000003,"total":0.06325575}},"stopReason":"toolUse","timestamp":1774827751023}}
{"type":"message","id":"a07b1605","parentId":"825aabab","timestamp":"2026-03-29T23:42:36.609Z","message":{"role":"toolResult","toolCallId":"toolu_012J2P5tz8bT8x29PKCKRLM7","toolName":"bash","content":[{"type":"text","text":"585:def stage6_embed_and_index(self, video_id: str) -> str:\n651:                qdrant.upsert_technique_pages(page_dicts, page_vectors)\n681:                qdrant.upsert_key_moments(moment_dicts, moment_vectors)\n754:            stage6_embed_and_index.s(),\n760:            stage6_embed_and_index.s(),\n"}],"isError":false,"timestamp":1774827756606}}
{"type":"message","id":"fae19bc3","parentId":"a07b1605","timestamp":"2026-03-29T23:42:40.090Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01JxpMBj6qzfNj7Nz3jFmHuP","name":"read","arguments":{"limit":100,"offset":585,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":91,"cacheRead":114678,"cacheWrite":263,"totalTokens":115033,"cost":{"input":0.000005,"output":0.002275,"cacheRead":0.057338999999999994,"cacheWrite":0.00164375,"total":0.06126274999999999}},"stopReason":"toolUse","timestamp":1774827756607}}
{"type":"message","id":"9cd6e00e","parentId":"fae19bc3","timestamp":"2026-03-29T23:42:40.094Z","message":{"role":"toolResult","toolCallId":"toolu_01JxpMBj6qzfNj7Nz3jFmHuP","toolName":"read","content":[{"type":"text","text":"def 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[94 more lines in file. Use offset=685 to continue.]"}],"isError":false,"timestamp":1774827760092}}
{"type":"message","id":"9c442406","parentId":"9cd6e00e","timestamp":"2026-03-29T23:42:44.744Z","message":{"role":"assistant","content":[{"type":"text","text":"Excellent. Now I have complete understanding. Let me also check the S04 summary for patterns established with the frontend:"},{"type":"toolCall","id":"toolu_01Q6nVJbeF373Xc3oHLjJ6WR","name":"bash","arguments":{"command":"head -50 /home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S04/S04-SUMMARY.md 2>/dev/null || echo \"No S04 summary\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":135,"cacheRead":114941,"cacheWrite":1165,"totalTokens":116242,"cost":{"input":0.000005,"output":0.003375,"cacheRead":0.0574705,"cacheWrite":0.00728125,"total":0.06813175}},"stopReason":"toolUse","timestamp":1774827760093}}
{"type":"message","id":"41f008f9","parentId":"9c442406","timestamp":"2026-03-29T23:42:44.755Z","message":{"role":"toolResult","toolCallId":"toolu_01Q6nVJbeF373Xc3oHLjJ6WR","toolName":"bash","content":[{"type":"text","text":"---\nid: S04\nparent: M001\nmilestone: M001\nprovides:\n  - 9 review queue API endpoints mounted at /api/v1/review/*\n  - React+Vite+TypeScript frontend with admin UI at /admin/review\n  - Typed API client (frontend/src/api/client.ts) for all review endpoints\n  - Reusable StatusBadge and ModeToggle components\n  - Redis-backed review mode toggle with config fallback\n  - 24 integration tests for review endpoints\nrequires:\n  - slice: S03\n    provides: KeyMoment model with review_status field, pipeline that creates moments in DB\naffects:\n  - S05\nkey_files:\n  - backend/routers/review.py\n  - backend/schemas.py\n  - backend/redis_client.py\n  - backend/main.py\n  - backend/tests/test_review.py\n  - frontend/package.json\n  - frontend/vite.config.ts\n  - frontend/tsconfig.json\n  - frontend/index.html\n  - frontend/src/main.tsx\n  - frontend/src/App.tsx\n  - frontend/src/App.css\n  - frontend/src/api/client.ts\n  - frontend/src/pages/ReviewQueue.tsx\n  - frontend/src/pages/MomentDetail.tsx\n  - frontend/src/components/StatusBadge.tsx\n  - frontend/src/components/ModeToggle.tsx\nkey_decisions:\n  - Redis mode toggle uses per-request get_redis() with aclose() — no connection pool (D007)\n  - API client uses bare fetch() with shared request() helper — no external HTTP library\n  - MomentDetail fetches full queue to find moment by ID since no single-moment GET endpoint exists\n  - Split creates new moment with '(split)' title suffix; merge combines summaries with double-newline separator\n  - Split dialog validates timestamp client-side before API call\npatterns_established:\n  - React + Vite + TypeScript frontend pattern: strict TS config, Vite dev proxy to backend, typed API client with fetch()-based request helper\n  - Reusable component extraction (StatusBadge, ModeToggle) for consistent styling across admin pages\n  - Review router pattern: async SQLAlchemy with joined loads for cross-table data (moment + video + creator)\n  - Redis as runtime config store with config.py fallback for settings that need to be mutable at runtime\nobservability_surfaces:\n  - GET /api/v1/review/stats — returns pending/approved/edited/rejected counts\n  - GET /api/v1/review/mode — returns current review/auto mode\n  - INFO log on each review action (approve/reject/edit/split/merge) with moment_id\n  - 404 responses include moment_id not found; 400 responses include validation details\n"}],"isError":false,"timestamp":1774827764753}}
{"type":"message","id":"5153dde7","parentId":"41f008f9","timestamp":"2026-03-29T23:44:00.438Z","message":{"role":"assistant","content":[{"type":"text","text":"I now have complete context. Let me write the research document and save it."},{"type":"toolCall","id":"toolu_015wSkDfKNTfrMkhW29bRVMQ","name":"gsd_summary_save","arguments":{"artifact_type":"RESEARCH","content":"# S05 — Search-First Web UI — Research\n\n**Date:** 2026-03-29\n\n## Summary\n\nS05 builds the public-facing web UI for Chrysopedia — the search-first landing page, technique page display, creators browse, and topics browse. This is a medium-complexity frontend + backend slice that wires together existing infrastructure (Qdrant vectors, PostgreSQL models, React+Vite+TypeScript frontend) into new pages and API endpoints. The backend needs a search endpoint that embeds query text via the embedding API, queries Qdrant for semantic results, and enriches them with DB metadata. The frontend needs 6 new pages/views and a new API client module.\n\nThe riskiest piece is the search endpoint: it requires an async embedding call (to convert query text to a vector) followed by an async Qdrant search, both of which must complete within 500ms (R015). The existing `EmbeddingClient` is sync (designed for Celery), so the search endpoint needs an async variant using `openai.AsyncOpenAI` and `AsyncQdrantClient`. Everything else — technique page display, creators browse, topics browse — is standard CRUD over existing DB models.\n\nThe frontend is a React 18 + Vite + TypeScript SPA. S04 established the pattern: typed API client with `fetch()`, React Router v6, plain CSS (no Tailwind/UI library). S05 extends this with new routes and pages. The existing `App.tsx` routes everything to `/admin/*` — S05 adds the public UI at root paths (`/`, `/search`, `/techniques/:slug`, `/creators`, `/creators/:slug`, `/topics`).\n\n## Recommendation\n\nBuild backend-first: start with the search API endpoint (highest risk — requires async embedding + Qdrant integration), then the read-only data endpoints (technique pages, creators list with counts, topics hierarchy), then the frontend pages. The search endpoint is the critical path — everything else is well-understood CRUD.\n\nUse `AsyncQdrantClient` from `qdrant-client` (already in requirements.txt, same package provides both sync and async) and `openai.AsyncOpenAI` (already in requirements.txt) for the search endpoint. Create a new `backend/routers/search.py` for the search API and new Pydantic response schemas. Keep the existing sync `EmbeddingClient` and `QdrantManager` for Celery — the async search service is a separate, thin layer for the FastAPI request path.\n\nFor keyword fallback (R005 says \"semantic where possible, with keyword fallback\"), use PostgreSQL `ILIKE` queries on technique_page.title, key_moment.title, and creator.name. This handles the case where the embedding service is unavailable or returns empty results.\n\n## Implementation Landscape\n\n### Key Files\n\n**Backend — existing (read, extend):**\n- `backend/config.py` — Settings with `qdrant_url`, `qdrant_collection`, `embedding_api_url`, `embedding_model`, `embedding_dimensions`. No changes needed.\n- `backend/models.py` — All 7 models already exist: Creator, SourceVideo, TranscriptSegment, KeyMoment, TechniquePage, RelatedTechniqueLink, Tag. TechniquePage has `body_sections` (JSONB), `signal_chains` (JSONB), `topic_tags` (ARRAY), `topic_category`, `plugins` (ARRAY). No schema changes needed.\n- `backend/schemas.py` — Has `CreatorRead`, `TechniquePageRead`, `KeyMomentRead`. Needs new response schemas for search results and enriched technique page detail.\n- `backend/database.py` — Async engine + `get_session` dependency. Used as-is.\n- `backend/main.py` — Mount new routers here (search, techniques, topics).\n- `backend/routers/creators.py` — Has `list_creators` (alphabetical order) and `get_creator` (by slug). Needs extension: randomized sort, genre filter, technique count, video count per creator.\n- `backend/pipeline/qdrant_client.py` — Sync `QdrantManager` for Celery pipeline write-path. Read-path (search) needs new async code.\n- `backend/pipeline/embedding_client.py` — Sync `EmbeddingClient`. Search needs async variant.\n\n**Backend — new files to create:**\n- `backend/routers/search.py` — `GET /api/v1/search?q=...&scope=all|topics|creators&limit=20` — orchestrates async embedding + Qdrant search + keyword fallback + DB enrichment.\n- `backend/routers/techniques.py` — `GET /api/v1/techniques` (list, filterable by category/creator), `GET /api/v1/techniques/{slug}` (full detail with key_moments, related links, creator info).\n- `backend/routers/topics.py` — `GET /api/v1/topics` (hierarchy: top-level categories with sub-topics, counts of technique pages and creators per sub-topic). Reads from `canonical_tags.yaml` + DB aggregation.\n- `backend/search_service.py` — Async search service class: `AsyncOpenAI` for embedding, `AsyncQdrantClient` for vector search. Thin wrapper, initialized from Settings.\n\n**Frontend — existing (modify):**\n- `frontend/src/App.tsx` — Add new routes for public pages (`/`, `/search`, `/techniques/:slug`, `/creators`, `/creators/:slug`, `/topics`). Keep admin routes as-is.\n- `frontend/src/App.css` — Extend with styles for new pages (search bar, technique page, browse lists).\n- `frontend/src/api/client.ts` — Extend (or create parallel `search-client.ts`) with typed functions for search, techniques, creators, topics endpoints.\n\n**Frontend — new files to create:**\n- `frontend/src/pages/Home.tsx` — Landing page: search bar, Topics card, Creators card, recently added section.\n- `frontend/src/pages/SearchResults.tsx` — Full search results page after Enter or \"See all results\".\n- `frontend/src/pages/TechniquePage.tsx` — Full technique page display (R006): header, study guide prose, key moments index, related techniques, plugins.\n- `frontend/src/pages/CreatorsBrowse.tsx` — Creators list (R007): genre filter pills, type-to-narrow, sort toggle (random/alpha/views), creator rows with counts.\n- `frontend/src/pages/CreatorDetail.tsx` — Creator's technique page list.\n- `frontend/src/pages/TopicsBrowse.tsx` — Two-level topic hierarchy (R008): top-level categories → sub-topics with counts.\n\n### Build Order\n\n1. **Search service + search endpoint** (highest risk, unblocks frontend search). Create `backend/search_service.py` with async embedding + Qdrant query. Create `backend/routers/search.py`. This proves the semantic search path works end-to-end. Verify with `curl`.\n\n2. **Technique page + topics + creators API endpoints** (unblocks all frontend pages). Create `backend/routers/techniques.py`, `backend/routers/topics.py`, extend `backend/routers/creators.py`. Add enriched response schemas to `backend/schemas.py`. Mount all new routers in `main.py`.\n\n3. **Frontend: Landing page + search + navigation shell** (proves the primary interaction). Build `Home.tsx` with search bar and live typeahead, `SearchResults.tsx` for full results. This is the highest-value UI — it's the R015 30-second retrieval path.\n\n4. **Frontend: Technique page display** (R006 — the core content unit). Build `TechniquePage.tsx` rendering all sections: header with tags, study guide prose from `body_sections` JSONB, key moments index, related links, plugins.\n\n5. **Frontend: Browse pages** (R007, R008, R014). Build `CreatorsBrowse.tsx` (randomized default sort for R014), `CreatorDetail.tsx`, `TopicsBrowse.tsx`.\n\n### Verification Approach\n\n**Backend verification:**\n- `cd backend && python -m pytest tests/ -v` — all existing 40 tests still pass (regression).\n- New integration tests for search endpoint: mock the embedding API and Qdrant, verify response shape and <500ms timing.\n- New integration tests for techniques/topics/creators endpoints: verify data shape, pagination, filtering.\n- `curl` smoke tests against running API: `GET /api/v1/search?q=snare`, `GET /api/v1/techniques`, `GET /api/v1/topics`, `GET /api/v1/creators?sort=random`.\n\n**Frontend verification:**\n- `cd frontend && npx tsc -b` — zero TypeScript errors.\n- `cd frontend && npm run build` — clean production build.\n- Manual browser verification: navigate to each page, confirm data renders, search returns results, technique page displays all sections.\n\n## Constraints\n\n- **Existing frontend uses plain CSS, no UI library.** S04 established this pattern — continue with plain CSS. Adding Tailwind or shadcn/ui would be a divergence. The existing `App.css` has ~350 lines of well-structured styles.\n- **EmbeddingClient is sync (Celery).** The search endpoint runs in async FastAPI — cannot reuse `EmbeddingClient` directly. Must create async embedding using `openai.AsyncOpenAI`.\n- **QdrantManager is sync (Celery).** Same issue — use `AsyncQdrantClient` for the search read path.\n- **Qdrant payloads have limited metadata.** Technique page payloads contain: `type`, `page_id`, `creator_id`, `title`, `topic_category`, `topic_tags`, `summary`. Key moment payloads contain: `type`, `moment_id`, `source_video_id`, `title`, `start_time`, `end_time`, `content_type`. The search endpoint must enrich results with DB data (creator names, slugs, etc.) after Qdrant returns IDs.\n- **Stage 4 classification in Redis, not DB.** The `KeyMoment` model lacks `topic_tags`/`topic_category` columns. However, `TechniquePage` has both. For search, technique pages are the primary entity — this is fine.\n- **No existing single-resource endpoints for technique pages or key moments.** These need to be created.\n- **TypeScript strict mode** — `noUnusedLocals`, `noUnusedParameters`, `noUncheckedIndexedAccess` are all enabled. Frontend code must be clean.\n\n## Common Pitfalls\n\n- **Async embedding timeout in search path.** The embedding API call adds latency to every search request. If the embedding service is slow (>200ms), the 500ms target becomes tight. Mitigation: set a short timeout (300ms) on the embedding call and fall back to keyword search on timeout.\n- **Qdrant connection failure in search.** Unlike the pipeline (where embedding/Qdrant failures are non-blocking side-effects), search failures are user-visible. The search endpoint must gracefully degrade to keyword-only search when Qdrant is unavailable.\n- **Random sort on creators page (R014) with pagination.** `ORDER BY random()` in SQL gives different results per page load, making offset-based pagination inconsistent. Since the creators list is likely <100 entries, fetch all and shuffle server-side, or use a seed-based random sort (`ORDER BY md5(id::text || seed)`) with the seed passed from the client.\n- **body_sections JSONB structure is undefined.** The `TechniquePage.body_sections` column stores the study guide prose as JSONB, but the exact schema depends on what stage 5 (synthesis) produces. The frontend must handle variable structures gracefully. Check `stage5_synthesis.txt` prompt to understand the expected format.\n- **Vite dev proxy only covers `/api`.** The existing proxy in `vite.config.ts` proxies `/api` to `http://localhost:8001`. This is sufficient for all new endpoints since they're all under `/api/v1/`.\n\n## Open Risks\n\n- **body_sections JSONB format is unknown until real pipeline output is examined.** The synthesis prompt (stage 5) defines the structure, but no real pipeline run has been done. The technique page frontend component must be flexible enough to handle whatever JSONB shape the LLM produces. Fallback: render as plain text if structure is unrecognized.\n- **Qdrant collection may be empty** if no pipeline runs have completed embedding. The search endpoint must handle empty results gracefully and fall back to keyword search.\n- **Embedding API latency** is unknown — it depends on the deployment (local Ollama vs DGX Sparks). The 500ms search target may not be achievable with slow embedding. Client-side debounce (300ms) helps, but server-side latency must be measured.\n","milestone_id":"M001","slice_id":"S05"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":3218,"cacheRead":116106,"cacheWrite":824,"totalTokens":120149,"cost":{"input":0.000005,"output":0.08045000000000001,"cacheRead":0.058053,"cacheWrite":0.00515,"total":0.143658}},"stopReason":"toolUse","timestamp":1774827764754}}
{"type":"message","id":"d090cb19","parentId":"5153dde7","timestamp":"2026-03-29T23:44:00.443Z","message":{"role":"toolResult","toolCallId":"toolu_015wSkDfKNTfrMkhW29bRVMQ","toolName":"gsd_summary_save","content":[{"type":"text","text":"Saved RESEARCH artifact to milestones/M001/slices/S05/S05-RESEARCH.md"}],"details":{"operation":"save_summary","path":"milestones/M001/slices/S05/S05-RESEARCH.md","artifact_type":"RESEARCH"},"isError":false,"timestamp":1774827840441}}
{"type":"message","id":"24ff2627","parentId":"d090cb19","timestamp":"2026-03-29T23:44:04.886Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S05 researched."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":11,"cacheRead":116930,"cacheWrite":3256,"totalTokens":120198,"cost":{"input":0.000005,"output":0.000275,"cacheRead":0.058464999999999996,"cacheWrite":0.02035,"total":0.079095}},"stopReason":"stop","timestamp":1774827840442}}
