{"type":"thinking_level_change","id":"b19c1fcd","parentId":null,"timestamp":"2026-03-29T23:49:18.400Z","thinkingLevel":"off"}
{"type":"model_change","id":"2d18ec84","parentId":"b19c1fcd","timestamp":"2026-03-29T23:49:18.401Z","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\nA researcher explored the codebase and a planner decomposed the work — you are the executor. The task plan below is your authoritative contract for the slice goal and verification bar, but it is not a substitute for local reality. Verify the referenced files and surrounding code before changing them. Do not do broad re-research or spontaneous re-planning. Small factual corrections, file-path fixes, and local implementation adaptations are part of execution. Escalate to `blocker_discovered: true` only when the slice contract or downstream task graph no longer holds.\n\n\n\n\n\n## Backing Source Artifacts\n- Slice plan: `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S05/S05-PLAN.md`\n- Task plan source: `.gsd/milestones/M001/slices/S05/tasks/T01-PLAN.md`\n- Prior task summaries in this slice:\n- (no prior tasks)\n\nThen:\n0. Narrate step transitions, key implementation decisions, and verification outcomes as you work. Keep it terse — one line between tool-call clusters, not between every call — but write complete sentences in user-facing prose, not shorthand notes or scratchpad fragments.\n1.  Follow any activated skills before writing code. If no skills match this task, skip this step.\n2. Execute the steps in the inlined task plan, adapting minor local mismatches when the surrounding code differs from the planner's snapshot\n3. Build the real thing. If the task plan says \"create login endpoint\", build an endpoint that actually authenticates against a real store, not one that returns a hardcoded success response. If the task plan says \"create dashboard page\", build a page that renders real data from the API, not a component with hardcoded props. Stubs and mocks are for tests, not for the shipped feature.\n4. Write or update tests as part of execution — tests are verification, not an afterthought. If the slice plan defines test files in its Verification section and this is the first task, create them (they should initially fail).\n5. When implementing non-trivial runtime behavior (async flows, API boundaries, background processes, error paths), add or preserve agent-usable observability. Skip this for simple changes where it doesn't apply.\n\n   **Background process rule:** Never use bare `command &` to run background processes. The shell's `&` operator leaves stdout/stderr attached to the parent, which causes the Bash tool to hang indefinitely waiting for those streams to close. Always redirect output before backgrounding:\n   - Correct: `command > /dev/null 2>&1 &` or `nohup command > /dev/null 2>&1 &`\n   - Example: `python -m http.server 8080 > /dev/null 2>&1 &` (NOT `python -m http.server 8080 &`)\n   - Preferred: use the `bg_shell` tool if available — it manages process lifecycle correctly without stream-inheritance issues\n6. If the task plan includes a **Failure Modes** section (Q5), implement the error/timeout/malformed handling specified. Verify each dependency's failure path is handled. Skip if the section is absent.\n7. If the task plan includes a **Load Profile** section (Q6), implement protections for the identified 10x breakpoint (connection pooling, rate limiting, pagination, etc.). Skip if absent.\n8. If the task plan includes a **Negative Tests** section (Q7), write the specified negative test cases alongside the happy-path tests — malformed inputs, error paths, and boundary conditions. Skip if absent.\n9. Verify must-haves are met by running concrete checks (tests, commands, observable behaviors)\n10. Run the slice-level verification checks defined in the slice plan's Verification section. Track which pass. On the final task of the slice, all must pass before marking done. On intermediate tasks, partial passes are expected — note which ones pass in the summary.\n11. After the verification gate runs (you'll see gate results in stderr/notify output), populate the `## Verification Evidence` table in your task summary with the check results. Use the `formatEvidenceTable` format: one row per check with command, exit code, verdict (✅ pass / ❌ fail), and duration. If no verification commands were discovered, note that in the section.\n12. If the task touches UI, browser flows, DOM behavior, or user-visible web state:\n   - exercise the real flow in the browser\n   - prefer `browser_batch` when the next few actions are obvious and sequential\n   - prefer `browser_assert` for explicit pass/fail verification of the intended outcome\n   - use `browser_diff` when an action's effect is ambiguous\n   - use console/network/dialog diagnostics when validating async, stateful, or failure-prone UI\n   - record verification in terms of explicit checks passed/failed, not only prose interpretation\n13. If the task plan includes an Observability Impact section, verify those signals directly. Skip this step if the task plan omits the section.\n14. **If execution is running long or verification fails:**\n\n    **Context budget:** You have approximately **~80K chars** reserved for verification context. If you've used most of your context and haven't finished all steps, stop implementing and prioritize writing the task summary with clear notes on what's done and what remains. A partial summary that enables clean resumption is more valuable than one more half-finished step with no documentation. Never sacrifice summary quality for one more implementation step.\n\n    **Debugging discipline:** If a verification check fails or implementation hits unexpected behavior:\n    - Form a hypothesis first. State what you think is wrong and why, then test that specific theory. Don't shotgun-fix.\n    - Change one variable at a time. Make one change, test, observe. Multiple simultaneous changes mean you can't attribute what worked.\n    - Read completely. When investigating, read entire functions and their imports, not just the line that looks relevant.\n    - Distinguish \"I know\" from \"I assume.\" Observable facts (the error says X) are strong evidence. Assumptions (this library should work this way) need verification.\n    - Know when to stop. If you've tried 3+ fixes without progress, your mental model is probably wrong. Stop. List what you know for certain. List what you've ruled out. Form fresh hypotheses from there.\n    - Don't fix symptoms. Understand *why* something fails before changing code. A test that passes after a change you don't understand is luck, not a fix.\n15. **Blocker discovery:** If execution reveals that the remaining slice plan is fundamentally invalid — not just a bug or minor deviation, but a plan-invalidating finding like a wrong API, missing capability, or architectural mismatch — set `blocker_discovered: true` in the task summary frontmatter and describe the blocker clearly in the summary narrative. Do NOT set `blocker_discovered: true` for ordinary debugging, minor deviations, or issues that can be fixed within the current task or the remaining plan. This flag triggers an automatic replan of the slice.\n16. If you made an architectural, pattern, library, or observability decision during this task that downstream work should know about, append it to `.gsd/DECISIONS.md` (read the template at `~/.gsd/agent/extensions/gsd/templates/decisions.md` if the file doesn't exist yet). Not every task produces decisions — only append when a meaningful choice was made.\n17. If you discover a non-obvious rule, recurring gotcha, or useful pattern during execution, append it to `.gsd/KNOWLEDGE.md`. Only add entries that would save future agents from repeating your investigation. Don't add obvious things.\n18. Read the template at `~/.gsd/agent/extensions/gsd/templates/task-summary.md`\n19. Write `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S05/tasks/T01-SUMMARY.md`\n20. Call `gsd_complete_task` with milestone_id, slice_id, task_id, and a summary of what was accomplished. This is your final required step — do NOT manually edit PLAN.md checkboxes. The tool marks the task complete, updates the DB, and renders PLAN.md automatically.\n21. Do not run git commands — the system reads your task summary after completion and creates a meaningful commit from it (type inferred from title, message from your one-liner, key files from frontmatter). Write a clear, specific one-liner in the summary — it becomes the commit message.\n\nAll work stays in your working directory: `/home/aux/projects/content-to-kb-automator`.\n\n**You MUST call `gsd_complete_task` AND write `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S05/tasks/T01-SUMMARY.md` before finishing.**\n\nWhen done, say: \"Task T01 complete.\"\n## Slice Plan Excerpt\nSource: `.gsd/milestones/M001/slices/S05/S05-PLAN.md`\n**Goal:** User searches for a technique, gets semantic results in <500ms, clicks through to a full technique page with study guide prose, key moments, and related links. Browse pages for creators (randomized default sort) and topics (two-level hierarchy) are navigable.\n**Demo:** After this: 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### Slice Verification\n- `cd backend && python -c \"from search_service import SearchService; print('OK')\"` — imports clean\n- `cd backend && python -c \"from routers.search import router; print(router.routes)\"` — search router has routes\n- `cd backend && python -c \"from routers.techniques import router; print(router.routes)\"` — techniques router has routes\n- `cd backend && python -c \"from routers.topics import router; print(router.routes)\"` — topics router has routes\n- `cd backend && python -c \"from main import app; routes = [r.path for r in app.routes]; assert '/api/v1/search' in str(routes) or any('search' in str(r.path) for r in app.routes); print('Mounted')\"` — routers mounted\n\n## UNIT: Execute Task T01 (\"Build async search service and all public API endpoints\") — Slice S05 (\"Search-First Web UI\"), Milestone M001\n\n## Resume State\n- No continue file present. Start from the top of the task plan.\n\n## Carry-Forward Context\n- No prior task summaries in this slice.\n\n## Inlined Task Plan (authoritative local execution contract)\nSource: `.gsd/milestones/M001/slices/S05/tasks/T01-PLAN.md`\n\n---\nestimated_steps: 68\nestimated_files: 7\nskills_used: []\n---\n\n# T01: Build async search service and all public API endpoints\n\n## Description\n\nCreate the backend API surface for S05: the async search service (embedding + Qdrant), search endpoint, technique pages CRUD, topics hierarchy, and enhanced creators endpoint. This is the highest-risk task because it introduces async embedding/Qdrant clients for the FastAPI request path (existing ones are sync for Celery).\n\n## Failure Modes\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| Embedding API (AsyncOpenAI) | Fall back to keyword-only search | 300ms timeout → keyword fallback | Return empty vectors → keyword fallback |\n| Qdrant (AsyncQdrantClient) | Fall back to keyword-only search | 300ms timeout → keyword fallback | Log warning, return empty results → keyword fallback |\n| PostgreSQL | Return 500 (standard FastAPI error handling) | Connection pool timeout → 500 | N/A (SQLAlchemy typed) |\n\n## Load Profile\n\n- **Shared resources**: AsyncQdrantClient connection pool, AsyncOpenAI HTTP pool, SQLAlchemy async session pool\n- **Per-operation cost**: Search = 1 embedding API call + 1 Qdrant query + 1-3 SQL queries for enrichment. Read endpoints = 1-2 SQL queries each.\n- **10x breakpoint**: Embedding API rate limiting (external dependency). Mitigated by client-side debounce (300ms) reducing request rate.\n\n## Negative Tests\n\n- **Malformed inputs**: Empty search query → return empty results. Query > 500 chars → truncate to 500. Invalid scope parameter → default to 'all'.\n- **Error paths**: Embedding API unreachable → keyword fallback. Qdrant unreachable → keyword fallback. Invalid slug → 404.\n- **Boundary conditions**: Empty Qdrant collection → keyword-only results. Zero matching techniques/creators → empty list.\n\n## Steps\n\n1. Create `backend/search_service.py` with `SearchService` class:\n   - `__init__` takes Settings, creates `openai.AsyncOpenAI` client and `qdrant_client.AsyncQdrantClient`\n   - `async embed_query(text: str) -> list[float] | None` — embeds query text with 300ms timeout, returns None on failure\n   - `async search_qdrant(vector: list[float], limit: int, type_filter: str | None) -> list[dict]` — queries Qdrant with optional payload type filter, returns scored results with payloads\n   - `async keyword_search(query: str, scope: str, limit: int, db: AsyncSession) -> list[dict]` — ILIKE search on technique_pages.title, key_moments.title, creators.name\n   - `async search(query: str, scope: str, limit: int, db: AsyncSession) -> dict` — orchestrates: embed → Qdrant → enrich with DB metadata → fallback to keyword if needed\n\n2. Add new Pydantic response schemas to `backend/schemas.py`:\n   - `SearchResultItem(title, slug, type, score, summary, creator_name, creator_slug, topic_category, topic_tags)`\n   - `SearchResponse(items: list[SearchResultItem], total: int, query: str, fallback_used: bool)`\n   - `TechniquePageDetail` (extends TechniquePageRead with nested key_moments, creator info, related links)\n   - `TopicCategory(name, description, sub_topics: list[TopicSubTopic])` and `TopicSubTopic(name, technique_count, creator_count)`\n   - `CreatorBrowseItem` (extends CreatorRead with technique_count, video_count)\n\n3. Create `backend/routers/search.py`:\n   - `GET /search?q=...&scope=all|topics|creators&limit=20`\n   - Instantiate SearchService from get_settings(), call search(), return SearchResponse\n   - Log query, latency_ms, result_count, fallback_used at INFO level\n\n4. Create `backend/routers/techniques.py`:\n   - `GET /techniques` — list technique pages with optional `category`, `creator_slug` query filters, pagination\n   - `GET /techniques/{slug}` — full detail with eager-loaded key_moments (ordered by start_time), creator info, outgoing+incoming related links\n   - Return 404 for unknown slug\n\n5. Create `backend/routers/topics.py`:\n   - `GET /topics` — load `canonical_tags.yaml`, for each category aggregate technique_count and creator_count per sub_topic from DB\n   - `GET /topics/{category_slug}` — return technique pages filtered by topic_category\n\n6. Extend `backend/routers/creators.py`:\n   - Add `sort` query param: `random` (default), `alpha`, `views`\n   - Add `genre` query param for filtering by genre\n   - Add technique_count and video_count subqueries to list endpoint\n   - For `sort=random`, use `func.random()` ORDER BY (dataset is small, <100 creators)\n\n7. Mount all new routers in `backend/main.py`:\n   - `from routers import search, techniques, topics`\n   - `app.include_router(search.router, prefix=\"/api/v1\")`\n   - `app.include_router(techniques.router, prefix=\"/api/v1\")`\n   - `app.include_router(topics.router, prefix=\"/api/v1\")`\n\n## Must-Haves\n\n- [ ] SearchService with async embedding + Qdrant + keyword fallback\n- [ ] GET /api/v1/search returns SearchResponse with enriched results\n- [ ] GET /api/v1/techniques and GET /api/v1/techniques/{slug} with full detail\n- [ ] GET /api/v1/topics returns category hierarchy with counts\n- [ ] GET /api/v1/creators supports sort=random (default), genre filter, technique/video counts\n- [ ] All new routers mounted in main.py\n- [ ] Embedding/Qdrant failures gracefully degrade to keyword search\n\n## Verification\n\n- `cd backend && python -c \"from search_service import SearchService; print('OK')\"` — imports clean\n- `cd backend && python -c \"from routers.search import router; print(router.routes)\"` — search router has routes\n- `cd backend && python -c \"from routers.techniques import router; print(router.routes)\"` — techniques router has routes\n- `cd backend && python -c \"from routers.topics import router; print(router.routes)\"` — topics router has routes\n- `cd backend && python -c \"from main import app; routes = [r.path for r in app.routes]; assert '/api/v1/search' in str(routes) or any('search' in str(r.path) for r in app.routes); print('Mounted')\"` — routers mounted\n\n## Observability Impact\n\n- Signals added: INFO log per search query with latency_ms, result_count, fallback_used. WARNING on embedding/Qdrant failure with error details.\n- How a future agent inspects this: `curl localhost:8001/api/v1/search?q=test` returns structured JSON with timing data\n- Failure state exposed: fallback_used=true in search response indicates Qdrant/embedding degradation\n\n## Inputs\n\n- ``backend/models.py` — all 7 ORM models (Creator, SourceVideo, TranscriptSegment, KeyMoment, TechniquePage, RelatedTechniqueLink, Tag)`\n- ``backend/database.py` — async engine, get_session dependency`\n- ``backend/config.py` — Settings with embedding_api_url, embedding_model, embedding_dimensions, qdrant_url, qdrant_collection`\n- ``backend/schemas.py` — existing Pydantic schemas to extend`\n- ``backend/routers/creators.py` — existing creators router to enhance`\n- ``backend/main.py` — existing router mounting to extend`\n- ``backend/pipeline/embedding_client.py` — reference for sync embedding pattern (async variant needed)`\n- ``backend/pipeline/qdrant_client.py` — reference for sync Qdrant pattern (async variant needed)`\n- ``config/canonical_tags.yaml` — tag taxonomy for topics endpoint`\n\n## Expected Output\n\n- ``backend/search_service.py` — async SearchService with embed_query, search_qdrant, keyword_search, search methods`\n- ``backend/schemas.py` — extended with SearchResultItem, SearchResponse, TechniquePageDetail, TopicCategory, TopicSubTopic, CreatorBrowseItem`\n- ``backend/routers/search.py` — GET /search endpoint with semantic + keyword fallback`\n- ``backend/routers/techniques.py` — GET /techniques and GET /techniques/{slug} endpoints`\n- ``backend/routers/topics.py` — GET /topics endpoint with category hierarchy`\n- ``backend/routers/creators.py` — enhanced with sort=random, genre filter, counts`\n- ``backend/main.py` — all new routers mounted`\n\n## Verification\n\ncd backend && python -c \"from search_service import SearchService; from routers.search import router as sr; from routers.techniques import router as tr; from routers.topics import router as tpr; print('All imports OK')\" && python -c \"from main import app; print([r.path for r in app.routes])\"\n","display":false,"id":"c4cc6989","parentId":"2d18ec84","timestamp":"2026-03-29T23:49:18.403Z"}
{"type":"message","id":"25df1c70","parentId":"c4cc6989","timestamp":"2026-03-29T23:49:23.772Z","message":{"role":"assistant","content":[{"type":"text","text":"\n\nI'll start by examining the existing codebase to understand the current state before implementing the task."},{"type":"toolCall","id":"toolu_019KuHQpAgYtViYFKLdFzysz","name":"async_bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator/backend -type f -name \"*.py\" | head -40","label":"list backend files"}},{"type":"toolCall","id":"toolu_01Q2hxEVZcbNn9mUdFnGeb7G","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S05/S05-PLAN.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":196,"cacheRead":49033,"cacheWrite":5176,"totalTokens":54407,"cost":{"input":0.00001,"output":0.0049,"cacheRead":0.0245165,"cacheWrite":0.032350000000000004,"total":0.061776500000000005}},"stopReason":"toolUse","timestamp":1774828158402}}
{"type":"message","id":"7106e743","parentId":"25df1c70","timestamp":"2026-03-29T23:49:23.779Z","message":{"role":"toolResult","toolCallId":"toolu_019KuHQpAgYtViYFKLdFzysz","toolName":"async_bash","content":[{"type":"text","text":"Background job started: **bg_1aa8a861**\nCommand: `list backend files`\n\nUse `await_job` to get results when ready, or `cancel_job` to stop."}],"isError":false,"timestamp":1774828163779}}
{"type":"message","id":"0cfa6720","parentId":"7106e743","timestamp":"2026-03-29T23:49:23.784Z","message":{"role":"toolResult","toolCallId":"toolu_01Q2hxEVZcbNn9mUdFnGeb7G","toolName":"read","content":[{"type":"text","text":"# S05: Search-First Web UI\n\n**Goal:** User searches for a technique, gets semantic results in <500ms, clicks through to a full technique page with study guide prose, key moments, and related links. Browse pages for creators (randomized default sort) and topics (two-level hierarchy) are navigable.\n**Demo:** After this: 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## Tasks\n- [ ] **T01: Build async search service and all public API endpoints** — ## Description\n\nCreate the backend API surface for S05: the async search service (embedding + Qdrant), search endpoint, technique pages CRUD, topics hierarchy, and enhanced creators endpoint. This is the highest-risk task because it introduces async embedding/Qdrant clients for the FastAPI request path (existing ones are sync for Celery).\n\n## Failure Modes\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| Embedding API (AsyncOpenAI) | Fall back to keyword-only search | 300ms timeout → keyword fallback | Return empty vectors → keyword fallback |\n| Qdrant (AsyncQdrantClient) | Fall back to keyword-only search | 300ms timeout → keyword fallback | Log warning, return empty results → keyword fallback |\n| PostgreSQL | Return 500 (standard FastAPI error handling) | Connection pool timeout → 500 | N/A (SQLAlchemy typed) |\n\n## Load Profile\n\n- **Shared resources**: AsyncQdrantClient connection pool, AsyncOpenAI HTTP pool, SQLAlchemy async session pool\n- **Per-operation cost**: Search = 1 embedding API call + 1 Qdrant query + 1-3 SQL queries for enrichment. Read endpoints = 1-2 SQL queries each.\n- **10x breakpoint**: Embedding API rate limiting (external dependency). Mitigated by client-side debounce (300ms) reducing request rate.\n\n## Negative Tests\n\n- **Malformed inputs**: Empty search query → return empty results. Query > 500 chars → truncate to 500. Invalid scope parameter → default to 'all'.\n- **Error paths**: Embedding API unreachable → keyword fallback. Qdrant unreachable → keyword fallback. Invalid slug → 404.\n- **Boundary conditions**: Empty Qdrant collection → keyword-only results. Zero matching techniques/creators → empty list.\n\n## Steps\n\n1. Create `backend/search_service.py` with `SearchService` class:\n   - `__init__` takes Settings, creates `openai.AsyncOpenAI` client and `qdrant_client.AsyncQdrantClient`\n   - `async embed_query(text: str) -> list[float] | None` — embeds query text with 300ms timeout, returns None on failure\n   - `async search_qdrant(vector: list[float], limit: int, type_filter: str | None) -> list[dict]` — queries Qdrant with optional payload type filter, returns scored results with payloads\n   - `async keyword_search(query: str, scope: str, limit: int, db: AsyncSession) -> list[dict]` — ILIKE search on technique_pages.title, key_moments.title, creators.name\n   - `async search(query: str, scope: str, limit: int, db: AsyncSession) -> dict` — orchestrates: embed → Qdrant → enrich with DB metadata → fallback to keyword if needed\n\n2. Add new Pydantic response schemas to `backend/schemas.py`:\n   - `SearchResultItem(title, slug, type, score, summary, creator_name, creator_slug, topic_category, topic_tags)`\n   - `SearchResponse(items: list[SearchResultItem], total: int, query: str, fallback_used: bool)`\n   - `TechniquePageDetail` (extends TechniquePageRead with nested key_moments, creator info, related links)\n   - `TopicCategory(name, description, sub_topics: list[TopicSubTopic])` and `TopicSubTopic(name, technique_count, creator_count)`\n   - `CreatorBrowseItem` (extends CreatorRead with technique_count, video_count)\n\n3. Create `backend/routers/search.py`:\n   - `GET /search?q=...&scope=all|topics|creators&limit=20`\n   - Instantiate SearchService from get_settings(), call search(), return SearchResponse\n   - Log query, latency_ms, result_count, fallback_used at INFO level\n\n4. Create `backend/routers/techniques.py`:\n   - `GET /techniques` — list technique pages with optional `category`, `creator_slug` query filters, pagination\n   - `GET /techniques/{slug}` — full detail with eager-loaded key_moments (ordered by start_time), creator info, outgoing+incoming related links\n   - Return 404 for unknown slug\n\n5. Create `backend/routers/topics.py`:\n   - `GET /topics` — load `canonical_tags.yaml`, for each category aggregate technique_count and creator_count per sub_topic from DB\n   - `GET /topics/{category_slug}` — return technique pages filtered by topic_category\n\n6. Extend `backend/routers/creators.py`:\n   - Add `sort` query param: `random` (default), `alpha`, `views`\n   - Add `genre` query param for filtering by genre\n   - Add technique_count and video_count subqueries to list endpoint\n   - For `sort=random`, use `func.random()` ORDER BY (dataset is small, <100 creators)\n\n7. Mount all new routers in `backend/main.py`:\n   - `from routers import search, techniques, topics`\n   - `app.include_router(search.router, prefix=\"/api/v1\")`\n   - `app.include_router(techniques.router, prefix=\"/api/v1\")`\n   - `app.include_router(topics.router, prefix=\"/api/v1\")`\n\n## Must-Haves\n\n- [ ] SearchService with async embedding + Qdrant + keyword fallback\n- [ ] GET /api/v1/search returns SearchResponse with enriched results\n- [ ] GET /api/v1/techniques and GET /api/v1/techniques/{slug} with full detail\n- [ ] GET /api/v1/topics returns category hierarchy with counts\n- [ ] GET /api/v1/creators supports sort=random (default), genre filter, technique/video counts\n- [ ] All new routers mounted in main.py\n- [ ] Embedding/Qdrant failures gracefully degrade to keyword search\n\n## Verification\n\n- `cd backend && python -c \"from search_service import SearchService; print('OK')\"` — imports clean\n- `cd backend && python -c \"from routers.search import router; print(router.routes)\"` — search router has routes\n- `cd backend && python -c \"from routers.techniques import router; print(router.routes)\"` — techniques router has routes\n- `cd backend && python -c \"from routers.topics import router; print(router.routes)\"` — topics router has routes\n- `cd backend && python -c \"from main import app; routes = [r.path for r in app.routes]; assert '/api/v1/search' in str(routes) or any('search' in str(r.path) for r in app.routes); print('Mounted')\"` — routers mounted\n\n## Observability Impact\n\n- Signals added: INFO log per search query with latency_ms, result_count, fallback_used. WARNING on embedding/Qdrant failure with error details.\n- How a future agent inspects this: `curl localhost:8001/api/v1/search?q=test` returns structured JSON with timing data\n- Failure state exposed: fallback_used=true in search response indicates Qdrant/embedding degradation\n  - Estimate: 2h\n  - Files: backend/search_service.py, backend/schemas.py, backend/routers/search.py, backend/routers/techniques.py, backend/routers/topics.py, backend/routers/creators.py, backend/main.py\n  - Verify: cd backend && python -c \"from search_service import SearchService; from routers.search import router as sr; from routers.techniques import router as tr; from routers.topics import router as tpr; print('All imports OK')\" && python -c \"from main import app; print([r.path for r in app.routes])\"\n- [ ] **T02: Add integration tests for search and public API endpoints** — ## Description\n\nWrite integration tests for all new S05 backend endpoints: search (with mocked embedding API and Qdrant), techniques list/detail, topics hierarchy, and enhanced creators (randomized sort, genre filter, counts). Tests run against real PostgreSQL with the existing conftest.py fixtures. All 40 existing tests must continue to pass.\n\n## Negative Tests\n\n- **Malformed inputs**: Empty search query returns empty results. Invalid technique slug returns 404. Invalid topic category returns empty list.\n- **Error paths**: Search with mocked embedding failure → keyword fallback results returned. Search with mocked Qdrant failure → keyword fallback.\n- **Boundary conditions**: Search with no matching results → empty items list. Topics with no technique pages → zero counts. Creators list with no creators → empty list.\n\n## Steps\n\n1. Create `backend/tests/test_search.py`:\n   - Fixture: seed DB with 2 creators, 3 technique pages (different categories/tags), 5 key moments\n   - Test search endpoint with mocked SearchService that returns canned results → verify response shape (items, total, query, fallback_used)\n   - Test search with empty query → returns empty results or validation error\n   - Test search keyword fallback: mock embedding to return None → verify keyword results returned and fallback_used=true\n   - Test search scope filtering (scope=topics returns only technique_page type results)\n\n2. Create `backend/tests/test_public_api.py`:\n   - Test GET /api/v1/techniques — returns list of technique pages, supports category filter\n   - Test GET /api/v1/techniques/{slug} — returns full detail with key_moments, creator info, related links\n   - Test GET /api/v1/techniques/{slug} with invalid slug → 404\n   - Test GET /api/v1/topics — returns category hierarchy with counts matching seeded data\n   - Test GET /api/v1/creators?sort=random — returns creators (verify all returned, order may vary)\n   - Test GET /api/v1/creators?sort=alpha — returns creators in alphabetical order\n   - Test GET /api/v1/creators?genre=Bass+music — returns only matching creators\n   - Test GET /api/v1/creators/{slug} — returns detail with technique_count, video_count\n\n3. Run full test suite: `cd backend && python -m pytest tests/ -v` — all 40 existing + new tests pass\n\n## Must-Haves\n\n- [ ] test_search.py with ≥4 tests covering happy path, empty query, keyword fallback, scope filter\n- [ ] test_public_api.py with ≥8 tests covering techniques list/detail/404, topics hierarchy, creators sort/filter/detail\n- [ ] All 40 existing tests still pass (regression)\n- [ ] Tests use real PostgreSQL with seeded data (not mocked DB)\n\n## Verification\n\n- `cd backend && python -m pytest tests/test_search.py tests/test_public_api.py -v` — all new tests pass\n- `cd backend && python -m pytest tests/ -v` — all tests pass (40 existing + new)\n  - Estimate: 1.5h\n  - Files: backend/tests/test_search.py, backend/tests/test_public_api.py, backend/tests/conftest.py\n  - Verify: cd backend && python -m pytest tests/test_search.py tests/test_public_api.py -v && python -m pytest tests/ -v\n- [ ] **T03: Build frontend search flow: landing page, search results, and technique page** — ## Description\n\nBuild the primary user flow: landing page with search bar → search results page → technique page detail. This is the R005/R006/R015 critical path. Includes the new typed API client for public endpoints, App.tsx routing with both admin and public routes, and 3 new page components with CSS.\n\nThe frontend uses React 18 + Vite + TypeScript with strict mode (`noUnusedLocals`, `noUnusedParameters`, `noUncheckedIndexedAccess`). Existing pattern: plain CSS in `App.css`, typed `fetch()` API client, React Router v6.\n\n## Steps\n\n1. Create `frontend/src/api/public-client.ts` — typed API client for public endpoints:\n   - Types: `SearchResultItem`, `SearchResponse`, `TechniquePageDetail`, `KeyMomentSummary`, `CreatorInfo`, `RelatedLink`, `TopicCategory`, `TopicSubTopic`, `CreatorBrowseItem`\n   - Functions: `searchApi(q, scope?, limit?)`, `fetchTechnique(slug)`, `fetchTechniques(params?)`, `fetchTopics()`, `fetchCreators(params?)`, `fetchCreator(slug)`\n   - Reuse the `request<T>` helper pattern from existing `client.ts` (or extract shared helper)\n\n2. Create `frontend/src/pages/Home.tsx` — landing page:\n   - Prominent search bar (auto-focus on mount) with debounce (300ms)\n   - Live typeahead: after 2+ chars, show top 5 results in dropdown below search bar\n   - On Enter or \"See all results\" link, navigate to `/search?q=...`\n   - Two navigation cards: \"Topics\" (links to `/topics`) and \"Creators\" (links to `/creators`)\n   - \"Recently Added\" section showing last 5 technique pages (fetch from `/api/v1/techniques?limit=5`)\n\n3. Create `frontend/src/pages/SearchResults.tsx` — full search results page:\n   - Read `q` from URL search params\n   - Display results grouped by type (technique_pages first, then key_moments)\n   - Each result: title (linked to technique page), summary snippet, creator name, category/tags\n   - Show \"No results found\" for empty results, \"Showing keyword results\" when fallback_used=true\n   - Search bar at top for refining query\n\n4. Create `frontend/src/pages/TechniquePage.tsx` — technique page display (R006):\n   - Fetch technique by slug from URL params via `fetchTechnique(slug)`\n   - Header: title, topic_category badge, topic_tags pills, creator name (linked to `/creators/{slug}`), source_quality indicator\n   - Amber banner if source_quality === 'unstructured' (livestream-sourced)\n   - Study guide prose: render `body_sections` JSONB — iterate Object.entries, render each section as `<h2>` + paragraph. Handle both string and object values gracefully.\n   - Key moments index: ordered list with title, start_time→end_time, content_type badge, summary\n   - Signal chains section (if present): render each chain as name + ordered steps\n   - Plugins referenced (if present): pill list\n   - Related techniques (if present): linked list\n   - Loading state and 404 error state\n\n5. Update `frontend/src/App.tsx` routing:\n   - Import new pages\n   - Add public routes: `/` → Home, `/search` → SearchResults, `/techniques/:slug` → TechniquePage\n   - Keep admin routes at `/admin/*`\n   - Update header: \"Chrysopedia\" title (not \"Chrysopedia Admin\"), nav links to Home, Topics, Creators, and Admin\n\n6. Add CSS to `frontend/src/App.css` for new pages:\n   - Search bar styles (large, centered on home, inline on results page)\n   - Typeahead dropdown styles\n   - Navigation cards (grid layout)\n   - Technique page layout (readable prose width, section spacing)\n   - Search result items (hover state, meta info)\n   - Tag/badge pill styles\n   - Loading and error states\n\n## Must-Haves\n\n- [ ] Typed public API client with all endpoint functions\n- [ ] Landing page with search bar, typeahead, navigation cards, recently added\n- [ ] Search results page with grouped results\n- [ ] Technique page with all sections (header, prose, key moments, related links)\n- [ ] App.tsx routes both public and admin paths\n- [ ] `cd frontend && npx tsc -b` passes with zero errors\n\n## Verification\n\n- `cd frontend && npx tsc -b` — zero TypeScript errors\n- `cd frontend && npm run build` — clean production build\n- Verify files exist: `test -f frontend/src/api/public-client.ts && test -f frontend/src/pages/Home.tsx && test -f frontend/src/pages/SearchResults.tsx && test -f frontend/src/pages/TechniquePage.tsx`\n  - Estimate: 2h\n  - Files: frontend/src/api/public-client.ts, frontend/src/pages/Home.tsx, frontend/src/pages/SearchResults.tsx, frontend/src/pages/TechniquePage.tsx, frontend/src/App.tsx, frontend/src/App.css\n  - Verify: cd frontend && npx tsc -b && npm run build && echo 'Frontend build OK'\n- [ ] **T04: Build frontend browse pages (creators, topics) and verify full build** — ## Description\n\nBuild the remaining browse pages: CreatorsBrowse (R007, R014 creator equity with randomized default sort), CreatorDetail, and TopicsBrowse (R008 two-level hierarchy). Then run final verification to confirm the full frontend builds cleanly and all requirements are covered.\n\n## Steps\n\n1. Create `frontend/src/pages/CreatorsBrowse.tsx` — creators browse page (R007, R014):\n   - Fetch creators from `/api/v1/creators?sort=random` (default) via `fetchCreators()`\n   - Genre filter pills at top (fetch unique genres from creator data, or hardcode from canonical_tags.yaml genres)\n   - Type-to-narrow input that filters displayed creators client-side by name\n   - Sort toggle: Random (default), Alphabetical, Views — each triggers re-fetch with `sort=random|alpha|views`\n   - Each creator row: name, genre tags (pills), technique_count, video_count, view_count\n   - Click row → navigate to `/creators/{slug}`\n   - All creators get equal visual weight (no featured/highlighted creators) per R014\n\n2. Create `frontend/src/pages/CreatorDetail.tsx` — creator detail page:\n   - Fetch creator by slug via `fetchCreator(slug)` — shows name, genres, video_count, technique_count\n   - Fetch creator's technique pages via `fetchTechniques({ creator_slug: slug })`\n   - List technique pages with title (linked to `/techniques/{slug}`), category, tags, summary\n   - Loading state and 404 error state\n\n3. Create `frontend/src/pages/TopicsBrowse.tsx` — topics browse page (R008):\n   - Fetch topics from `/api/v1/topics` via `fetchTopics()`\n   - Two-level hierarchy: 6 top-level categories (Sound design, Mixing, Synthesis, Arrangement, Workflow, Mastering)\n   - Each category expandable/collapsible, showing sub-topics\n   - Each sub-topic shows technique_count and creator_count\n   - Click sub-topic → navigate to `/search?q={sub_topic_name}&scope=topics` or filter technique list\n   - Filter input at top to narrow categories/sub-topics\n\n4. Update `frontend/src/App.tsx` — add routes for browse pages:\n   - `/creators` → CreatorsBrowse\n   - `/creators/:slug` → CreatorDetail\n   - `/topics` → TopicsBrowse\n   - Import new page components\n\n5. Add CSS to `frontend/src/App.css` for browse pages:\n   - Creator list styles (rows, genre pills, counts)\n   - Creator detail page layout\n   - Topics hierarchy styles (collapsible sections, sub-topic rows, counts)\n   - Filter input styles\n   - Sort toggle button group\n\n6. Final verification:\n   - `cd frontend && npx tsc -b` — zero errors\n   - `cd frontend && npm run build` — clean build\n   - Verify all 6 page files exist\n\n## Must-Haves\n\n- [ ] Creators browse page with randomized default sort (R014), genre filter, type-to-narrow, sort toggle (R007)\n- [ ] Creator detail page showing creator info and their technique pages\n- [ ] Topics browse page with two-level hierarchy, counts, clickable sub-topics (R008)\n- [ ] All routes registered in App.tsx\n- [ ] `cd frontend && npx tsc -b` passes with zero errors\n- [ ] `cd frontend && npm run build` succeeds\n\n## Verification\n\n- `cd frontend && npx tsc -b && npm run build && echo 'Build OK'`\n- `test -f frontend/src/pages/CreatorsBrowse.tsx && test -f frontend/src/pages/CreatorDetail.tsx && test -f frontend/src/pages/TopicsBrowse.tsx && echo 'All pages exist'`\n  - Estimate: 1.5h\n  - Files: frontend/src/pages/CreatorsBrowse.tsx, frontend/src/pages/CreatorDetail.tsx, frontend/src/pages/TopicsBrowse.tsx, frontend/src/App.tsx, frontend/src/App.css\n  - Verify: cd frontend && npx tsc -b && npm run build && test -f src/pages/CreatorsBrowse.tsx && test -f src/pages/CreatorDetail.tsx && test -f src/pages/TopicsBrowse.tsx && echo 'All browse pages built OK'\n"}],"isError":false,"timestamp":1774828163782}}
{"type":"message","id":"1bd809a4","parentId":"0cfa6720","timestamp":"2026-03-29T23:49:26.656Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01LZi9gUfykXPPRKkVsaTJNA","name":"await_job","arguments":{"jobs":["bg_1aa8a861"]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":61,"cacheRead":54209,"cacheWrite":5566,"totalTokens":59837,"cost":{"input":0.000005,"output":0.001525,"cacheRead":0.0271045,"cacheWrite":0.0347875,"total":0.063422}},"stopReason":"toolUse","timestamp":1774828163784}}
{"type":"message","id":"af718fb7","parentId":"1bd809a4","timestamp":"2026-03-29T23:49:26.658Z","message":{"role":"toolResult","toolCallId":"toolu_01LZi9gUfykXPPRKkVsaTJNA","toolName":"await_job","content":[{"type":"text","text":"### bg_1aa8a861 — list backend files (completed, 2.9s)\n\n/home/aux/projects/content-to-kb-automator/backend/models.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/llm_client.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/stages.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/qdrant_client.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/schemas.py\n/home/aux/projects/content-to-kb-automator/backend/main.py\n/home/aux/projects/content-to-kb-automator/backend/routers/health.py\n/home/aux/projects/content-to-kb-automator/backend/routers/videos.py\n/home/aux/projects/content-to-kb-automator/backend/routers/pipeline.py\n/home/aux/projects/content-to-kb-automator/backend/routers/__init__.py\n/home/aux/projects/content-to-kb-automator/backend/routers/creators.py\n/home/aux/projects/content-to-kb-automator/backend/routers/review.py\n/home/aux/projects/content-to-kb-automator/backend/routers/ingest.py\n/home/aux/projects/content-to-kb-automator/backend/redis_client.py\n/home/aux/projects/content-to-kb-automator/backend/database.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/tests/test_ingest.py\n/home/aux/projects/content-to-kb-automator/backend/tests/__init__.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/config.py\n/home/aux/projects/content-to-kb-automator/backend/worker.py\n/home/aux/projects/content-to-kb-automator/backend/schemas.py\n"}],"isError":false,"timestamp":1774828166657}}
{"type":"message","id":"7f77e06a","parentId":"af718fb7","timestamp":"2026-03-29T23:49:29.942Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me read the key input files to understand the existing patterns."},{"type":"toolCall","id":"toolu_01XNQ65xh31wRZedwEwqskqj","name":"read","arguments":{"path":"backend/models.py"}},{"type":"toolCall","id":"toolu_018irU4Fg8Tcvy2uUHvh9Vz8","name":"read","arguments":{"path":"backend/config.py"}},{"type":"toolCall","id":"toolu_016aiZXeDPfLAGHgZuGSQjLv","name":"read","arguments":{"path":"backend/database.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":146,"cacheRead":59775,"cacheWrite":740,"totalTokens":60662,"cost":{"input":0.000005,"output":0.00365,"cacheRead":0.029887499999999997,"cacheWrite":0.004625000000000001,"total":0.03816749999999999}},"stopReason":"toolUse","timestamp":1774828166657}}
{"type":"message","id":"a372d0b4","parentId":"7f77e06a","timestamp":"2026-03-29T23:49:29.944Z","message":{"role":"toolResult","toolCallId":"toolu_01XNQ65xh31wRZedwEwqskqj","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":1774828169944}}
{"type":"message","id":"a2157f80","parentId":"a372d0b4","timestamp":"2026-03-29T23:49:29.946Z","message":{"role":"toolResult","toolCallId":"toolu_018irU4Fg8Tcvy2uUHvh9Vz8","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":1774828169945}}
{"type":"message","id":"646cf130","parentId":"a2157f80","timestamp":"2026-03-29T23:49:29.947Z","message":{"role":"toolResult","toolCallId":"toolu_016aiZXeDPfLAGHgZuGSQjLv","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":1774828169946}}
{"type":"message","id":"e2edd37b","parentId":"646cf130","timestamp":"2026-03-29T23:49:33.098Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01JFHuye5iLiHHEmu4Su7SQG","name":"read","arguments":{"path":"backend/schemas.py"}},{"type":"toolCall","id":"toolu_011of4EewHYkZCR9H7a6fjQj","name":"read","arguments":{"path":"backend/routers/creators.py"}},{"type":"toolCall","id":"toolu_01Uy97iWtcvkCbf55YoxRNak","name":"read","arguments":{"path":"backend/main.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":134,"cacheRead":60515,"cacheWrite":4414,"totalTokens":65064,"cost":{"input":0.000005,"output":0.00335,"cacheRead":0.0302575,"cacheWrite":0.0275875,"total":0.0612}},"stopReason":"toolUse","timestamp":1774828169947}}
{"type":"message","id":"5a15d00f","parentId":"e2edd37b","timestamp":"2026-03-29T23:49:33.100Z","message":{"role":"toolResult","toolCallId":"toolu_01JFHuye5iLiHHEmu4Su7SQG","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":1774828173100}}
{"type":"message","id":"8bc97b67","parentId":"5a15d00f","timestamp":"2026-03-29T23:49:33.101Z","message":{"role":"toolResult","toolCallId":"toolu_011of4EewHYkZCR9H7a6fjQj","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":1774828173101}}
{"type":"message","id":"5aef9214","parentId":"8bc97b67","timestamp":"2026-03-29T23:49:33.103Z","message":{"role":"toolResult","toolCallId":"toolu_01Uy97iWtcvkCbf55YoxRNak","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":1774828173102}}
{"type":"message","id":"4192a8d2","parentId":"5aef9214","timestamp":"2026-03-29T23:49:38.829Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the existing sync embedding and Qdrant clients for reference, and the canonical tags file."},{"type":"toolCall","id":"toolu_011KZSMkwpVegi344DaXdtHA","name":"read","arguments":{"path":"backend/pipeline/embedding_client.py"}},{"type":"toolCall","id":"toolu_01UCiDqMj3sK5Rntu9uCEBju","name":"read","arguments":{"path":"backend/pipeline/qdrant_client.py"}},{"type":"toolCall","id":"toolu_01Y8vNsKc3NJa2KYD5gfBfZs","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator -name \"canonical_tags.yaml\" -type f 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":195,"cacheRead":64929,"cacheWrite":3459,"totalTokens":68584,"cost":{"input":0.000005,"output":0.004875,"cacheRead":0.0324645,"cacheWrite":0.021618750000000003,"total":0.05896325000000001}},"stopReason":"toolUse","timestamp":1774828173103}}
{"type":"message","id":"e6147823","parentId":"4192a8d2","timestamp":"2026-03-29T23:49:38.836Z","message":{"role":"toolResult","toolCallId":"toolu_011KZSMkwpVegi344DaXdtHA","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":1774828178836}}
{"type":"message","id":"92c1b755","parentId":"e6147823","timestamp":"2026-03-29T23:49:38.838Z","message":{"role":"toolResult","toolCallId":"toolu_01UCiDqMj3sK5Rntu9uCEBju","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":1774828178838}}
{"type":"message","id":"a5c13b18","parentId":"92c1b755","timestamp":"2026-03-29T23:49:38.867Z","message":{"role":"toolResult","toolCallId":"toolu_01Y8vNsKc3NJa2KYD5gfBfZs","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/config/canonical_tags.yaml\n"}],"isError":false,"timestamp":1774828178865}}
{"type":"message","id":"de8b793e","parentId":"a5c13b18","timestamp":"2026-03-29T23:49:42.218Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_019vkUMFrnv685GMoUesgMBE","name":"read","arguments":{"path":"config/canonical_tags.yaml"}},{"type":"toolCall","id":"toolu_01AffQefgJVmK9QN4wtJtAmY","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator/backend && pip list 2>/dev/null | grep -iE \"openai|qdrant|fastapi|sqlalchemy|pydantic\" "}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":141,"cacheRead":68388,"cacheWrite":2490,"totalTokens":71020,"cost":{"input":0.000005,"output":0.0035250000000000004,"cacheRead":0.034193999999999995,"cacheWrite":0.0155625,"total":0.053286499999999994}},"stopReason":"toolUse","timestamp":1774828178866}}
{"type":"message","id":"323c0a04","parentId":"de8b793e","timestamp":"2026-03-29T23:49:42.225Z","message":{"role":"toolResult","toolCallId":"toolu_019vkUMFrnv685GMoUesgMBE","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":1774828182225}}
{"type":"message","id":"fb46d935","parentId":"323c0a04","timestamp":"2026-03-29T23:49:42.681Z","message":{"role":"toolResult","toolCallId":"toolu_01AffQefgJVmK9QN4wtJtAmY","toolName":"bash","content":[{"type":"text","text":"fastapi               0.135.2\nopenai                2.30.0\npydantic              2.12.5\npydantic_core         2.41.5\npydantic-settings     2.13.1\nqdrant-client         1.17.1\nSQLAlchemy            2.0.48\n"}],"isError":false,"timestamp":1774828182679}}
{"type":"message","id":"a2c3bca8","parentId":"fb46d935","timestamp":"2026-03-29T23:50:23.183Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — all dependencies are installed. Now I have full context. Let me start implementing.\n\n**Step 1: Create `backend/search_service.py`** — the async search service with embedding, Qdrant, and keyword fallback."},{"type":"toolCall","id":"toolu_01A67AHXm8vtdSAkiYn17uSp","name":"write","arguments":{"content":"\"\"\"Async search service for the public search endpoint.\n\nOrchestrates semantic search (embedding + Qdrant) with keyword fallback.\nAll external calls have timeouts and graceful degradation — if embedding\nor Qdrant fail, the service falls back to keyword-only (ILIKE) search.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport logging\nimport time\nfrom typing import Any\n\nimport openai\nfrom qdrant_client import AsyncQdrantClient\nfrom qdrant_client.http import exceptions as qdrant_exceptions\nfrom qdrant_client.models import FieldCondition, Filter, MatchValue\nfrom sqlalchemy import or_, select\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom config import Settings\nfrom models import Creator, KeyMoment, TechniquePage\n\nlogger = logging.getLogger(\"chrysopedia.search\")\n\n# Timeout for external calls (embedding API, Qdrant) in seconds\n_EXTERNAL_TIMEOUT = 0.3  # 300ms per plan\n\n\nclass SearchService:\n    \"\"\"Async search service with semantic + keyword fallback.\n\n    Parameters\n    ----------\n    settings:\n        Application settings containing embedding and Qdrant config.\n    \"\"\"\n\n    def __init__(self, settings: Settings) -> None:\n        self.settings = settings\n        self._openai = openai.AsyncOpenAI(\n            base_url=settings.embedding_api_url,\n            api_key=settings.llm_api_key,\n        )\n        self._qdrant = AsyncQdrantClient(url=settings.qdrant_url)\n        self._collection = settings.qdrant_collection\n\n    # ── Embedding ────────────────────────────────────────────────────────\n\n    async def embed_query(self, text: str) -> list[float] | None:\n        \"\"\"Embed a query string into a vector.\n\n        Returns None on any failure (timeout, connection, malformed response)\n        so the caller can fall back to keyword search.\n        \"\"\"\n        try:\n            response = await asyncio.wait_for(\n                self._openai.embeddings.create(\n                    model=self.settings.embedding_model,\n                    input=text,\n                ),\n                timeout=_EXTERNAL_TIMEOUT,\n            )\n        except asyncio.TimeoutError:\n            logger.warning(\"Embedding API timeout (%.0fms limit) for query: %.50s…\", _EXTERNAL_TIMEOUT * 1000, text)\n            return None\n        except (openai.APIConnectionError, openai.APITimeoutError) as exc:\n            logger.warning(\"Embedding API connection error (%s: %s)\", type(exc).__name__, exc)\n            return None\n        except openai.APIError as exc:\n            logger.warning(\"Embedding API error (%s: %s)\", type(exc).__name__, exc)\n            return None\n\n        if not response.data:\n            logger.warning(\"Embedding API returned empty data for query: %.50s…\", text)\n            return None\n\n        vector = response.data[0].embedding\n        if len(vector) != self.settings.embedding_dimensions:\n            logger.warning(\n                \"Embedding dimension mismatch: expected %d, got %d\",\n                self.settings.embedding_dimensions,\n                len(vector),\n            )\n            return None\n\n        return vector\n\n    # ── Qdrant vector search ─────────────────────────────────────────────\n\n    async def search_qdrant(\n        self,\n        vector: list[float],\n        limit: int = 20,\n        type_filter: str | None = None,\n    ) -> list[dict[str, Any]]:\n        \"\"\"Search Qdrant for nearest neighbours.\n\n        Returns a list of dicts with 'score' and 'payload' keys.\n        Returns empty list on failure.\n        \"\"\"\n        query_filter = None\n        if type_filter:\n            query_filter = Filter(\n                must=[FieldCondition(key=\"type\", match=MatchValue(value=type_filter))]\n            )\n\n        try:\n            results = await asyncio.wait_for(\n                self._qdrant.query_points(\n                    collection_name=self._collection,\n                    query=vector,\n                    query_filter=query_filter,\n                    limit=limit,\n                    with_payload=True,\n                ),\n                timeout=_EXTERNAL_TIMEOUT,\n            )\n        except asyncio.TimeoutError:\n            logger.warning(\"Qdrant search timeout (%.0fms limit)\", _EXTERNAL_TIMEOUT * 1000)\n            return []\n        except qdrant_exceptions.UnexpectedResponse as exc:\n            logger.warning(\"Qdrant search error: %s\", exc)\n            return []\n        except Exception as exc:\n            logger.warning(\"Qdrant connection error (%s: %s)\", type(exc).__name__, exc)\n            return []\n\n        return [\n            {\"score\": point.score, \"payload\": point.payload}\n            for point in results.points\n        ]\n\n    # ── Keyword fallback ─────────────────────────────────────────────────\n\n    async def keyword_search(\n        self,\n        query: str,\n        scope: str,\n        limit: int,\n        db: AsyncSession,\n    ) -> list[dict[str, Any]]:\n        \"\"\"ILIKE keyword search across technique pages, key moments, and creators.\n\n        Searches title/name columns. Returns a unified list of result dicts.\n        \"\"\"\n        results: list[dict[str, Any]] = []\n        pattern = f\"%{query}%\"\n\n        if scope in (\"all\", \"topics\"):\n            stmt = (\n                select(TechniquePage)\n                .where(\n                    or_(\n                        TechniquePage.title.ilike(pattern),\n                        TechniquePage.summary.ilike(pattern),\n                    )\n                )\n                .limit(limit)\n            )\n            rows = await db.execute(stmt)\n            for tp in rows.scalars().all():\n                results.append({\n                    \"type\": \"technique_page\",\n                    \"title\": tp.title,\n                    \"slug\": tp.slug,\n                    \"summary\": tp.summary or \"\",\n                    \"topic_category\": tp.topic_category,\n                    \"topic_tags\": tp.topic_tags or [],\n                    \"creator_id\": str(tp.creator_id),\n                    \"score\": 0.0,\n                })\n\n        if scope in (\"all\",):\n            km_stmt = (\n                select(KeyMoment)\n                .where(KeyMoment.title.ilike(pattern))\n                .limit(limit)\n            )\n            km_rows = await db.execute(km_stmt)\n            for km in km_rows.scalars().all():\n                results.append({\n                    \"type\": \"key_moment\",\n                    \"title\": km.title,\n                    \"slug\": \"\",\n                    \"summary\": km.summary or \"\",\n                    \"topic_category\": \"\",\n                    \"topic_tags\": [],\n                    \"creator_id\": \"\",\n                    \"score\": 0.0,\n                })\n\n        if scope in (\"all\", \"creators\"):\n            cr_stmt = (\n                select(Creator)\n                .where(Creator.name.ilike(pattern))\n                .limit(limit)\n            )\n            cr_rows = await db.execute(cr_stmt)\n            for cr in cr_rows.scalars().all():\n                results.append({\n                    \"type\": \"creator\",\n                    \"title\": cr.name,\n                    \"slug\": cr.slug,\n                    \"summary\": \"\",\n                    \"topic_category\": \"\",\n                    \"topic_tags\": cr.genres or [],\n                    \"creator_id\": str(cr.id),\n                    \"score\": 0.0,\n                })\n\n        return results[:limit]\n\n    # ── Orchestrator ─────────────────────────────────────────────────────\n\n    async def search(\n        self,\n        query: str,\n        scope: str,\n        limit: int,\n        db: AsyncSession,\n    ) -> dict[str, Any]:\n        \"\"\"Run semantic search with keyword fallback.\n\n        Returns a dict matching the SearchResponse schema shape.\n        \"\"\"\n        start = time.monotonic()\n\n        # Validate / sanitize inputs\n        if not query or not query.strip():\n            return {\"items\": [], \"total\": 0, \"query\": query, \"fallback_used\": False}\n\n        # Truncate long queries\n        query = query.strip()[:500]\n\n        # Normalize scope\n        if scope not in (\"all\", \"topics\", \"creators\"):\n            scope = \"all\"\n\n        # Map scope to Qdrant type filter\n        type_filter_map = {\n            \"all\": None,\n            \"topics\": \"technique_page\",\n            \"creators\": None,  # creators aren't in Qdrant\n        }\n        qdrant_type_filter = type_filter_map.get(scope)\n\n        fallback_used = False\n        items: list[dict[str, Any]] = []\n\n        # Try semantic search\n        vector = await self.embed_query(query)\n        if vector is not None:\n            qdrant_results = await self.search_qdrant(vector, limit=limit, type_filter=qdrant_type_filter)\n            if qdrant_results:\n                # Enrich Qdrant results with DB metadata\n                items = await self._enrich_results(qdrant_results, db)\n\n        # Fallback to keyword search if semantic failed or returned nothing\n        if not items:\n            items = await self.keyword_search(query, scope, limit, db)\n            fallback_used = True\n\n        elapsed_ms = (time.monotonic() - start) * 1000\n\n        logger.info(\n            \"Search query=%r scope=%s results=%d fallback=%s latency_ms=%.1f\",\n            query,\n            scope,\n            len(items),\n            fallback_used,\n            elapsed_ms,\n        )\n\n        return {\n            \"items\": items,\n            \"total\": len(items),\n            \"query\": query,\n            \"fallback_used\": fallback_used,\n        }\n\n    # ── Result enrichment ────────────────────────────────────────────────\n\n    async def _enrich_results(\n        self,\n        qdrant_results: list[dict[str, Any]],\n        db: AsyncSession,\n    ) -> list[dict[str, Any]]:\n        \"\"\"Enrich Qdrant results with creator names and slugs from DB.\"\"\"\n        enriched: list[dict[str, Any]] = []\n\n        # Collect creator_ids to batch-fetch\n        creator_ids = set()\n        for r in qdrant_results:\n            payload = r.get(\"payload\", {})\n            cid = payload.get(\"creator_id\")\n            if cid:\n                creator_ids.add(cid)\n\n        # Batch fetch creators\n        creator_map: dict[str, dict[str, str]] = {}\n        if creator_ids:\n            from sqlalchemy.dialects.postgresql import UUID as PgUUID\n            import uuid as uuid_mod\n            valid_ids = []\n            for cid in creator_ids:\n                try:\n                    valid_ids.append(uuid_mod.UUID(cid))\n                except (ValueError, AttributeError):\n                    pass\n\n            if valid_ids:\n                stmt = select(Creator).where(Creator.id.in_(valid_ids))\n                result = await db.execute(stmt)\n                for c in result.scalars().all():\n                    creator_map[str(c.id)] = {\"name\": c.name, \"slug\": c.slug}\n\n        for r in qdrant_results:\n            payload = r.get(\"payload\", {})\n            cid = payload.get(\"creator_id\", \"\")\n            creator_info = creator_map.get(cid, {\"name\": \"\", \"slug\": \"\"})\n\n            enriched.append({\n                \"type\": payload.get(\"type\", \"\"),\n                \"title\": payload.get(\"title\", \"\"),\n                \"slug\": payload.get(\"slug\", payload.get(\"title\", \"\").lower().replace(\" \", \"-\")),\n                \"summary\": payload.get(\"summary\", \"\"),\n                \"topic_category\": payload.get(\"topic_category\", \"\"),\n                \"topic_tags\": payload.get(\"topic_tags\", []),\n                \"creator_id\": cid,\n                \"creator_name\": creator_info[\"name\"],\n                \"creator_slug\": creator_info[\"slug\"],\n                \"score\": r.get(\"score\", 0.0),\n            })\n\n        return enriched\n","path":"backend/search_service.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":3201,"cacheRead":70878,"cacheWrite":669,"totalTokens":74749,"cost":{"input":0.000005,"output":0.080025,"cacheRead":0.035439,"cacheWrite":0.00418125,"total":0.11965025}},"stopReason":"toolUse","timestamp":1774828182680}}
{"type":"message","id":"7bdd5a51","parentId":"a2c3bca8","timestamp":"2026-03-29T23:50:23.186Z","message":{"role":"toolResult","toolCallId":"toolu_01A67AHXm8vtdSAkiYn17uSp","toolName":"write","content":[{"type":"text","text":"Successfully wrote 11539 bytes to backend/search_service.py"}],"isError":false,"timestamp":1774828223184}}
{"type":"message","id":"80a46c9b","parentId":"7bdd5a51","timestamp":"2026-03-29T23:50:26.316Z","message":{"role":"assistant","content":[{"type":"text","text":"**Step 2: Add new Pydantic schemas to `backend/schemas.py`.**"},{"type":"toolCall","id":"toolu_01CdcLGJC3E1PapWigPhj6NQ","name":"read","arguments":{"limit":5,"offset":1,"path":"backend/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":111,"cacheRead":71547,"cacheWrite":3228,"totalTokens":74887,"cost":{"input":0.000005,"output":0.002775,"cacheRead":0.0357735,"cacheWrite":0.020175000000000002,"total":0.0587285}},"stopReason":"toolUse","timestamp":1774828223185}}
{"type":"message","id":"dd667c53","parentId":"80a46c9b","timestamp":"2026-03-29T23:50:26.319Z","message":{"role":"toolResult","toolCallId":"toolu_01CdcLGJC3E1PapWigPhj6NQ","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\n[246 more lines in file. Use offset=6 to continue.]"}],"isError":false,"timestamp":1774828226318}}
{"type":"message","id":"65ca9471","parentId":"dd667c53","timestamp":"2026-03-29T23:50:38.617Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01BU1Kz133TqtpRaywnkyXdi","name":"edit","arguments":{"newText":"class ReviewModeUpdate(BaseModel):\n    \"\"\"Request to update the review mode.\"\"\"\n    review_mode: bool\n\n\n# ── Search ───────────────────────────────────────────────────────────────────\n\nclass SearchResultItem(BaseModel):\n    \"\"\"A single search result.\"\"\"\n    title: str\n    slug: str = \"\"\n    type: str = \"\"\n    score: float = 0.0\n    summary: str = \"\"\n    creator_name: str = \"\"\n    creator_slug: str = \"\"\n    topic_category: str = \"\"\n    topic_tags: list[str] = Field(default_factory=list)\n\n\nclass SearchResponse(BaseModel):\n    \"\"\"Top-level search response with metadata.\"\"\"\n    items: list[SearchResultItem] = Field(default_factory=list)\n    total: int = 0\n    query: str = \"\"\n    fallback_used: bool = False\n\n\n# ── Technique Page Detail ────────────────────────────────────────────────────\n\nclass KeyMomentSummary(BaseModel):\n    \"\"\"Lightweight key moment for technique page detail.\"\"\"\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    title: str\n    summary: str\n    start_time: float\n    end_time: float\n    content_type: str\n    plugins: list[str] | None = None\n\n\nclass RelatedLinkItem(BaseModel):\n    \"\"\"A related technique link with target info.\"\"\"\n    model_config = ConfigDict(from_attributes=True)\n\n    target_title: str = \"\"\n    target_slug: str = \"\"\n    relationship: str = \"\"\n\n\nclass CreatorInfo(BaseModel):\n    \"\"\"Minimal creator info embedded in technique detail.\"\"\"\n    model_config = ConfigDict(from_attributes=True)\n\n    name: str\n    slug: str\n    genres: list[str] | None = None\n\n\nclass TechniquePageDetail(TechniquePageRead):\n    \"\"\"Technique page with nested key moments, creator, and related links.\"\"\"\n    key_moments: list[KeyMomentSummary] = Field(default_factory=list)\n    creator_info: CreatorInfo | None = None\n    related_links: list[RelatedLinkItem] = Field(default_factory=list)\n\n\n# ── Topics ───────────────────────────────────────────────────────────────────\n\nclass TopicSubTopic(BaseModel):\n    \"\"\"A sub-topic with aggregated counts.\"\"\"\n    name: str\n    technique_count: int = 0\n    creator_count: int = 0\n\n\nclass TopicCategory(BaseModel):\n    \"\"\"A top-level topic category with sub-topics.\"\"\"\n    name: str\n    description: str = \"\"\n    sub_topics: list[TopicSubTopic] = Field(default_factory=list)\n\n\n# ── Creator Browse ───────────────────────────────────────────────────────────\n\nclass CreatorBrowseItem(CreatorRead):\n    \"\"\"Creator with technique and video counts for browse pages.\"\"\"\n    technique_count: int = 0\n    video_count: int = 0","oldText":"class ReviewModeUpdate(BaseModel):\n    \"\"\"Request to update the review mode.\"\"\"\n    review_mode: bool","path":"backend/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":849,"cacheRead":74775,"cacheWrite":188,"totalTokens":75813,"cost":{"input":0.000005,"output":0.021225,"cacheRead":0.0373875,"cacheWrite":0.001175,"total":0.059792500000000005}},"stopReason":"toolUse","timestamp":1774828226318}}
{"type":"message","id":"9fae7707","parentId":"65ca9471","timestamp":"2026-03-29T23:50:38.623Z","message":{"role":"toolResult","toolCallId":"toolu_01BU1Kz133TqtpRaywnkyXdi","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/schemas.py."}],"details":{"diff":" 247 \n 248 class ReviewModeUpdate(BaseModel):\n 249     \"\"\"Request to update the review mode.\"\"\"\n 250     review_mode: bool\n+251 \n+252 \n+253 # ── Search ───────────────────────────────────────────────────────────────────\n+254 \n+255 class SearchResultItem(BaseModel):\n+256     \"\"\"A single search result.\"\"\"\n+257     title: str\n+258     slug: str = \"\"\n+259     type: str = \"\"\n+260     score: float = 0.0\n+261     summary: str = \"\"\n+262     creator_name: str = \"\"\n+263     creator_slug: str = \"\"\n+264     topic_category: str = \"\"\n+265     topic_tags: list[str] = Field(default_factory=list)\n+266 \n+267 \n+268 class SearchResponse(BaseModel):\n+269     \"\"\"Top-level search response with metadata.\"\"\"\n+270     items: list[SearchResultItem] = Field(default_factory=list)\n+271     total: int = 0\n+272     query: str = \"\"\n+273     fallback_used: bool = False\n+274 \n+275 \n+276 # ── Technique Page Detail ────────────────────────────────────────────────────\n+277 \n+278 class KeyMomentSummary(BaseModel):\n+279     \"\"\"Lightweight key moment for technique page detail.\"\"\"\n+280     model_config = ConfigDict(from_attributes=True)\n+281 \n+282     id: uuid.UUID\n+283     title: str\n+284     summary: str\n+285     start_time: float\n+286     end_time: float\n+287     content_type: str\n+288     plugins: list[str] | None = None\n+289 \n+290 \n+291 class RelatedLinkItem(BaseModel):\n+292     \"\"\"A related technique link with target info.\"\"\"\n+293     model_config = ConfigDict(from_attributes=True)\n+294 \n+295     target_title: str = \"\"\n+296     target_slug: str = \"\"\n+297     relationship: str = \"\"\n+298 \n+299 \n+300 class CreatorInfo(BaseModel):\n+301     \"\"\"Minimal creator info embedded in technique detail.\"\"\"\n+302     model_config = ConfigDict(from_attributes=True)\n+303 \n+304     name: str\n+305     slug: str\n+306     genres: list[str] | None = None\n+307 \n+308 \n+309 class TechniquePageDetail(TechniquePageRead):\n+310     \"\"\"Technique page with nested key moments, creator, and related links.\"\"\"\n+311     key_moments: list[KeyMomentSummary] = Field(default_factory=list)\n+312     creator_info: CreatorInfo | None = None\n+313     related_links: list[RelatedLinkItem] = Field(default_factory=list)\n+314 \n+315 \n+316 # ── Topics ───────────────────────────────────────────────────────────────────\n+317 \n+318 class TopicSubTopic(BaseModel):\n+319     \"\"\"A sub-topic with aggregated counts.\"\"\"\n+320     name: str\n+321     technique_count: int = 0\n+322     creator_count: int = 0\n+323 \n+324 \n+325 class TopicCategory(BaseModel):\n+326     \"\"\"A top-level topic category with sub-topics.\"\"\"\n+327     name: str\n+328     description: str = \"\"\n+329     sub_topics: list[TopicSubTopic] = Field(default_factory=list)\n+330 \n+331 \n+332 # ── Creator Browse ───────────────────────────────────────────────────────────\n+333 \n+334 class CreatorBrowseItem(CreatorRead):\n+335     \"\"\"Creator with technique and video counts for browse pages.\"\"\"\n+336     technique_count: int = 0\n+337     video_count: int = 0","firstChangedLine":251},"isError":false,"timestamp":1774828238622}}
{"type":"message","id":"2e32df3c","parentId":"9fae7707","timestamp":"2026-03-29T23:50:46.876Z","message":{"role":"assistant","content":[{"type":"text","text":"**Step 3: Create `backend/routers/search.py`.**"},{"type":"toolCall","id":"toolu_01TMRK6eFzt9HwLXX2SNa6n5","name":"write","arguments":{"content":"\"\"\"Search endpoint for semantic + keyword search with graceful fallback.\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nfrom typing import Annotated\n\nfrom fastapi import APIRouter, Depends, Query\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom config import get_settings\nfrom database import get_session\nfrom schemas import SearchResponse, SearchResultItem\nfrom search_service import SearchService\n\nlogger = logging.getLogger(\"chrysopedia.search.router\")\n\nrouter = APIRouter(prefix=\"/search\", tags=[\"search\"])\n\n\ndef _get_search_service() -> SearchService:\n    \"\"\"Build a SearchService from current settings.\"\"\"\n    return SearchService(get_settings())\n\n\n@router.get(\"\", response_model=SearchResponse)\nasync def search(\n    q: Annotated[str, Query(max_length=500)] = \"\",\n    scope: Annotated[str, Query()] = \"all\",\n    limit: Annotated[int, Query(ge=1, le=100)] = 20,\n    db: AsyncSession = Depends(get_session),\n) -> SearchResponse:\n    \"\"\"Semantic search with keyword fallback.\n\n    - **q**: Search query (max 500 chars). Empty → empty results.\n    - **scope**: ``all`` | ``topics`` | ``creators``. Invalid → defaults to ``all``.\n    - **limit**: Max results (1–100, default 20).\n    \"\"\"\n    svc = _get_search_service()\n    result = await svc.search(query=q, scope=scope, limit=limit, db=db)\n    return SearchResponse(\n        items=[SearchResultItem(**item) for item in result[\"items\"]],\n        total=result[\"total\"],\n        query=result[\"query\"],\n        fallback_used=result[\"fallback_used\"],\n    )\n","path":"backend/routers/search.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":517,"cacheRead":74963,"cacheWrite":870,"totalTokens":76351,"cost":{"input":0.000005,"output":0.012925,"cacheRead":0.0374815,"cacheWrite":0.0054375000000000005,"total":0.055848999999999996}},"stopReason":"toolUse","timestamp":1774828238623}}
{"type":"message","id":"dae2b9f3","parentId":"2e32df3c","timestamp":"2026-03-29T23:50:46.879Z","message":{"role":"toolResult","toolCallId":"toolu_01TMRK6eFzt9HwLXX2SNa6n5","toolName":"write","content":[{"type":"text","text":"Successfully wrote 1519 bytes to backend/routers/search.py"}],"isError":false,"timestamp":1774828246878}}
{"type":"message","id":"3bafa977","parentId":"dae2b9f3","timestamp":"2026-03-29T23:51:02.819Z","message":{"role":"assistant","content":[{"type":"text","text":"**Step 4: Create `backend/routers/techniques.py`.**"},{"type":"toolCall","id":"toolu_01H71BDNiCT8CaCrbADH1CoR","name":"write","arguments":{"content":"\"\"\"Technique page endpoints — list and detail with eager-loaded relations.\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nfrom typing import Annotated\n\nfrom fastapi import APIRouter, Depends, HTTPException, Query\nfrom sqlalchemy import select\nfrom sqlalchemy.ext.asyncio import AsyncSession\nfrom sqlalchemy.orm import selectinload\n\nfrom database import get_session\nfrom models import Creator, KeyMoment, RelatedTechniqueLink, TechniquePage\nfrom schemas import (\n    CreatorInfo,\n    KeyMomentSummary,\n    PaginatedResponse,\n    RelatedLinkItem,\n    TechniquePageDetail,\n    TechniquePageRead,\n)\n\nlogger = logging.getLogger(\"chrysopedia.techniques\")\n\nrouter = APIRouter(prefix=\"/techniques\", tags=[\"techniques\"])\n\n\n@router.get(\"\", response_model=PaginatedResponse)\nasync def list_techniques(\n    category: Annotated[str | None, Query()] = None,\n    creator_slug: Annotated[str | None, Query()] = None,\n    offset: Annotated[int, Query(ge=0)] = 0,\n    limit: Annotated[int, Query(ge=1, le=100)] = 50,\n    db: AsyncSession = Depends(get_session),\n) -> PaginatedResponse:\n    \"\"\"List technique pages with optional category/creator filtering.\"\"\"\n    stmt = select(TechniquePage)\n\n    if category:\n        stmt = stmt.where(TechniquePage.topic_category == category)\n\n    if creator_slug:\n        # Join to Creator to filter by slug\n        stmt = stmt.join(Creator, TechniquePage.creator_id == Creator.id).where(\n            Creator.slug == creator_slug\n        )\n\n    # Count total before pagination\n    from sqlalchemy import func\n\n    count_stmt = select(func.count()).select_from(stmt.subquery())\n    count_result = await db.execute(count_stmt)\n    total = count_result.scalar() or 0\n\n    stmt = stmt.order_by(TechniquePage.created_at.desc()).offset(offset).limit(limit)\n    result = await db.execute(stmt)\n    pages = result.scalars().all()\n\n    return PaginatedResponse(\n        items=[TechniquePageRead.model_validate(p) for p in pages],\n        total=total,\n        offset=offset,\n        limit=limit,\n    )\n\n\n@router.get(\"/{slug}\", response_model=TechniquePageDetail)\nasync def get_technique(\n    slug: str,\n    db: AsyncSession = Depends(get_session),\n) -> TechniquePageDetail:\n    \"\"\"Get full technique page detail with key moments, creator, and related links.\"\"\"\n    stmt = (\n        select(TechniquePage)\n        .where(TechniquePage.slug == slug)\n        .options(\n            selectinload(TechniquePage.key_moments),\n            selectinload(TechniquePage.creator),\n            selectinload(TechniquePage.outgoing_links).selectinload(\n                RelatedTechniqueLink.target_page\n            ),\n            selectinload(TechniquePage.incoming_links).selectinload(\n                RelatedTechniqueLink.source_page\n            ),\n        )\n    )\n    result = await db.execute(stmt)\n    page = result.scalar_one_or_none()\n\n    if page is None:\n        raise HTTPException(status_code=404, detail=f\"Technique '{slug}' not found\")\n\n    # Build key moments (ordered by start_time)\n    key_moments = sorted(page.key_moments, key=lambda km: km.start_time)\n    key_moment_items = [KeyMomentSummary.model_validate(km) for km in key_moments]\n\n    # Build creator info\n    creator_info = None\n    if page.creator:\n        creator_info = CreatorInfo(\n            name=page.creator.name,\n            slug=page.creator.slug,\n            genres=page.creator.genres,\n        )\n\n    # Build related links (outgoing + incoming)\n    related_links: list[RelatedLinkItem] = []\n    for link in page.outgoing_links:\n        if link.target_page:\n            related_links.append(\n                RelatedLinkItem(\n                    target_title=link.target_page.title,\n                    target_slug=link.target_page.slug,\n                    relationship=link.relationship.value if hasattr(link.relationship, 'value') else str(link.relationship),\n                )\n            )\n    for link in page.incoming_links:\n        if link.source_page:\n            related_links.append(\n                RelatedLinkItem(\n                    target_title=link.source_page.title,\n                    target_slug=link.source_page.slug,\n                    relationship=link.relationship.value if hasattr(link.relationship, 'value') else str(link.relationship),\n                )\n            )\n\n    base = TechniquePageRead.model_validate(page)\n    return TechniquePageDetail(\n        **base.model_dump(),\n        key_moments=key_moment_items,\n        creator_info=creator_info,\n        related_links=related_links,\n    )\n","path":"backend/routers/techniques.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":1380,"cacheRead":75833,"cacheWrite":545,"totalTokens":77759,"cost":{"input":0.000005,"output":0.0345,"cacheRead":0.0379165,"cacheWrite":0.00340625,"total":0.07582775}},"stopReason":"toolUse","timestamp":1774828246879}}
{"type":"message","id":"8d976763","parentId":"3bafa977","timestamp":"2026-03-29T23:51:02.822Z","message":{"role":"toolResult","toolCallId":"toolu_01H71BDNiCT8CaCrbADH1CoR","toolName":"write","content":[{"type":"text","text":"Successfully wrote 4508 bytes to backend/routers/techniques.py"}],"isError":false,"timestamp":1774828262821}}
{"type":"message","id":"3e3691cd","parentId":"8d976763","timestamp":"2026-03-29T23:51:22.924Z","message":{"role":"assistant","content":[{"type":"text","text":"**Step 5: Create `backend/routers/topics.py`.**"},{"type":"toolCall","id":"toolu_01TKqjh47zRYqMRDKcJ3i6Wn","name":"write","arguments":{"content":"\"\"\"Topics endpoint — two-level category hierarchy with aggregated counts.\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nimport os\nfrom typing import Annotated, Any\n\nimport yaml\nfrom fastapi import APIRouter, Depends, Query\nfrom sqlalchemy import func, select\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom database import get_session\nfrom models import Creator, TechniquePage\nfrom schemas import (\n    PaginatedResponse,\n    TechniquePageRead,\n    TopicCategory,\n    TopicSubTopic,\n)\n\nlogger = logging.getLogger(\"chrysopedia.topics\")\n\nrouter = APIRouter(prefix=\"/topics\", tags=[\"topics\"])\n\n# Path to canonical_tags.yaml relative to the backend directory\n_TAGS_PATH = os.path.join(os.path.dirname(__file__), \"..\", \"..\", \"config\", \"canonical_tags.yaml\")\n\n\ndef _load_canonical_tags() -> list[dict[str, Any]]:\n    \"\"\"Load the canonical tag categories from YAML.\"\"\"\n    path = os.path.normpath(_TAGS_PATH)\n    try:\n        with open(path) as f:\n            data = yaml.safe_load(f)\n        return data.get(\"categories\", [])\n    except FileNotFoundError:\n        logger.warning(\"canonical_tags.yaml not found at %s\", path)\n        return []\n\n\n@router.get(\"\", response_model=list[TopicCategory])\nasync def list_topics(\n    db: AsyncSession = Depends(get_session),\n) -> list[TopicCategory]:\n    \"\"\"Return the two-level topic hierarchy with technique/creator counts per sub-topic.\n\n    Categories come from ``canonical_tags.yaml``. Counts are computed\n    from live DB data by matching ``topic_tags`` array contents.\n    \"\"\"\n    categories = _load_canonical_tags()\n\n    # Pre-fetch all technique pages with their tags and creator_ids for counting\n    tp_stmt = select(\n        TechniquePage.topic_category,\n        TechniquePage.topic_tags,\n        TechniquePage.creator_id,\n    )\n    tp_result = await db.execute(tp_stmt)\n    tp_rows = tp_result.all()\n\n    # Build per-sub-topic counts\n    result: list[TopicCategory] = []\n    for cat in categories:\n        cat_name = cat.get(\"name\", \"\")\n        cat_desc = cat.get(\"description\", \"\")\n        sub_topic_names: list[str] = cat.get(\"sub_topics\", [])\n\n        sub_topics: list[TopicSubTopic] = []\n        for st_name in sub_topic_names:\n            technique_count = 0\n            creator_ids: set[str] = set()\n\n            for tp_cat, tp_tags, tp_creator_id in tp_rows:\n                tags = tp_tags or []\n                # Match if the sub-topic name appears in the technique's tags\n                # or if the category matches and tag is in sub-topics\n                if st_name.lower() in [t.lower() for t in tags]:\n                    technique_count += 1\n                    creator_ids.add(str(tp_creator_id))\n\n            sub_topics.append(\n                TopicSubTopic(\n                    name=st_name,\n                    technique_count=technique_count,\n                    creator_count=len(creator_ids),\n                )\n            )\n\n        result.append(\n            TopicCategory(\n                name=cat_name,\n                description=cat_desc,\n                sub_topics=sub_topics,\n            )\n        )\n\n    return result\n\n\n@router.get(\"/{category_slug}\", response_model=PaginatedResponse)\nasync def get_topic_techniques(\n    category_slug: str,\n    offset: Annotated[int, Query(ge=0)] = 0,\n    limit: Annotated[int, Query(ge=1, le=100)] = 50,\n    db: AsyncSession = Depends(get_session),\n) -> PaginatedResponse:\n    \"\"\"Return technique pages filtered by topic_category.\n\n    The ``category_slug`` is matched case-insensitively against\n    ``technique_pages.topic_category`` (e.g. 'sound-design' matches 'Sound design').\n    \"\"\"\n    # Normalize slug to category name: replace hyphens with spaces, title-case\n    category_name = category_slug.replace(\"-\", \" \").title()\n\n    # Also try exact match on the slug form\n    stmt = select(TechniquePage).where(\n        TechniquePage.topic_category.ilike(category_name)\n    )\n\n    count_stmt = select(func.count()).select_from(stmt.subquery())\n    count_result = await db.execute(count_stmt)\n    total = count_result.scalar() or 0\n\n    stmt = stmt.order_by(TechniquePage.title).offset(offset).limit(limit)\n    result = await db.execute(stmt)\n    pages = result.scalars().all()\n\n    return PaginatedResponse(\n        items=[TechniquePageRead.model_validate(p) for p in pages],\n        total=total,\n        offset=offset,\n        limit=limit,\n    )\n","path":"backend/routers/topics.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":1348,"cacheRead":76378,"cacheWrite":1408,"totalTokens":79135,"cost":{"input":0.000005,"output":0.0337,"cacheRead":0.038189,"cacheWrite":0.0088,"total":0.080694}},"stopReason":"toolUse","timestamp":1774828262821}}
{"type":"message","id":"47f97d02","parentId":"3e3691cd","timestamp":"2026-03-29T23:51:22.927Z","message":{"role":"toolResult","toolCallId":"toolu_01TKqjh47zRYqMRDKcJ3i6Wn","toolName":"write","content":[{"type":"text","text":"Successfully wrote 4375 bytes to backend/routers/topics.py"}],"isError":false,"timestamp":1774828282926}}
{"type":"message","id":"4d06de14","parentId":"47f97d02","timestamp":"2026-03-29T23:51:38.192Z","message":{"role":"assistant","content":[{"type":"text","text":"**Step 6: Enhance `backend/routers/creators.py` with sort, genre filter, and counts.**"},{"type":"toolCall","id":"toolu_01PdkFgF31nVWz7aju8ba8DF","name":"write","arguments":{"content":"\"\"\"Creator endpoints for Chrysopedia API.\n\nEnhanced with sort (random default per R014), genre filter, and\ntechnique/video counts for browse pages.\n\"\"\"\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, TechniquePage\nfrom schemas import CreatorBrowseItem, CreatorDetail, CreatorRead\n\nlogger = logging.getLogger(\"chrysopedia.creators\")\n\nrouter = APIRouter(prefix=\"/creators\", tags=[\"creators\"])\n\n\n@router.get(\"\", response_model=list[CreatorBrowseItem])\nasync def list_creators(\n    sort: Annotated[str, Query()] = \"random\",\n    genre: Annotated[str | None, Query()] = None,\n    offset: Annotated[int, Query(ge=0)] = 0,\n    limit: Annotated[int, Query(ge=1, le=100)] = 50,\n    db: AsyncSession = Depends(get_session),\n) -> list[CreatorBrowseItem]:\n    \"\"\"List creators with sort, genre filter, and technique/video counts.\n\n    - **sort**: ``random`` (default, R014 creator equity), ``alpha``, ``views``\n    - **genre**: filter by genre (matches against ARRAY column)\n    \"\"\"\n    # Subqueries for counts\n    technique_count_sq = (\n        select(func.count())\n        .where(TechniquePage.creator_id == Creator.id)\n        .correlate(Creator)\n        .scalar_subquery()\n    )\n    video_count_sq = (\n        select(func.count())\n        .where(SourceVideo.creator_id == Creator.id)\n        .correlate(Creator)\n        .scalar_subquery()\n    )\n\n    stmt = select(\n        Creator,\n        technique_count_sq.label(\"technique_count\"),\n        video_count_sq.label(\"video_count\"),\n    )\n\n    # Genre filter\n    if genre:\n        stmt = stmt.where(Creator.genres.any(genre))\n\n    # Sorting\n    if sort == \"alpha\":\n        stmt = stmt.order_by(Creator.name)\n    elif sort == \"views\":\n        stmt = stmt.order_by(Creator.view_count.desc())\n    else:\n        # Default: random (small dataset <100, func.random() is fine)\n        stmt = stmt.order_by(func.random())\n\n    stmt = stmt.offset(offset).limit(limit)\n    result = await db.execute(stmt)\n    rows = result.all()\n\n    items: list[CreatorBrowseItem] = []\n    for row in rows:\n        creator = row[0]\n        tc = row[1] or 0\n        vc = row[2] or 0\n        base = CreatorRead.model_validate(creator)\n        items.append(\n            CreatorBrowseItem(**base.model_dump(), technique_count=tc, video_count=vc)\n        )\n\n    logger.debug(\n        \"Listed %d creators (sort=%s, genre=%s, offset=%d, limit=%d)\",\n        len(items), sort, genre, offset, limit,\n    )\n    return items\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","path":"backend/routers/creators.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":1165,"cacheRead":77786,"cacheWrite":1376,"totalTokens":80328,"cost":{"input":0.000005,"output":0.029125,"cacheRead":0.038893,"cacheWrite":0.0086,"total":0.076623}},"stopReason":"toolUse","timestamp":1774828282927}}
{"type":"message","id":"f3763197","parentId":"4d06de14","timestamp":"2026-03-29T23:51:38.196Z","message":{"role":"toolResult","toolCallId":"toolu_01PdkFgF31nVWz7aju8ba8DF","toolName":"write","content":[{"type":"text","text":"Successfully wrote 3511 bytes to backend/routers/creators.py"}],"isError":false,"timestamp":1774828298194}}
{"type":"message","id":"0bdc8f84","parentId":"f3763197","timestamp":"2026-03-29T23:51:42.250Z","message":{"role":"assistant","content":[{"type":"text","text":"**Step 7: Mount new routers in `backend/main.py`.**"},{"type":"toolCall","id":"toolu_018upUYnh23BSzGYS6ADNrsG","name":"edit","arguments":{"newText":"from routers import creators, health, ingest, pipeline, review, search, techniques, topics, videos","oldText":"from routers import creators, health, ingest, pipeline, review, videos","path":"backend/main.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":146,"cacheRead":79162,"cacheWrite":1193,"totalTokens":80502,"cost":{"input":0.000005,"output":0.00365,"cacheRead":0.039581,"cacheWrite":0.00745625,"total":0.050692249999999994}},"stopReason":"toolUse","timestamp":1774828298195}}
{"type":"message","id":"e7e8accd","parentId":"0bdc8f84","timestamp":"2026-03-29T23:51:42.256Z","message":{"role":"toolResult","toolCallId":"toolu_018upUYnh23BSzGYS6ADNrsG","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/main.py."}],"details":{"diff":" 11 from fastapi import FastAPI\n 12 from fastapi.middleware.cors import CORSMiddleware\n 13 \n 14 from config import get_settings\n-15 from routers import creators, health, ingest, pipeline, review, videos\n+15 from routers import creators, health, ingest, pipeline, review, search, techniques, topics, videos\n 16 \n 17 \n 18 def _setup_logging() -> None:\n 19     \"\"\"Configure structured logging to stdout.\"\"\"","firstChangedLine":15},"isError":false,"timestamp":1774828302255}}
{"type":"message","id":"2e183537","parentId":"e7e8accd","timestamp":"2026-03-29T23:51:46.922Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01V19RumgWXdu2JYtLqvFSDe","name":"edit","arguments":{"newText":"# 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(search.router, prefix=\"/api/v1\")\napp.include_router(techniques.router, prefix=\"/api/v1\")\napp.include_router(topics.router, prefix=\"/api/v1\")\napp.include_router(videos.router, prefix=\"/api/v1\")","oldText":"# 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\")","path":"backend/main.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":335,"cacheRead":80355,"cacheWrite":167,"totalTokens":80858,"cost":{"input":0.000005,"output":0.008375,"cacheRead":0.0401775,"cacheWrite":0.00104375,"total":0.04960125}},"stopReason":"toolUse","timestamp":1774828302255}}
{"type":"message","id":"88b515d8","parentId":"2e183537","timestamp":"2026-03-29T23:51:46.927Z","message":{"role":"toolResult","toolCallId":"toolu_01V19RumgWXdu2JYtLqvFSDe","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/main.py."}],"details":{"diff":" 81 app.include_router(creators.router, prefix=\"/api/v1\")\n 82 app.include_router(ingest.router, prefix=\"/api/v1\")\n 83 app.include_router(pipeline.router, prefix=\"/api/v1\")\n 84 app.include_router(review.router, prefix=\"/api/v1\")\n+85 app.include_router(search.router, prefix=\"/api/v1\")\n+86 app.include_router(techniques.router, prefix=\"/api/v1\")\n+87 app.include_router(topics.router, prefix=\"/api/v1\")\n 88 app.include_router(videos.router, prefix=\"/api/v1\")\n 89 \n 90 \n 91 @app.get(\"/api/v1/health\")","firstChangedLine":85},"isError":false,"timestamp":1774828306926}}
{"type":"message","id":"a3f112aa","parentId":"88b515d8","timestamp":"2026-03-29T23:51:51.399Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check if `pyyaml` is installed since the topics router needs it."},{"type":"toolCall","id":"toolu_016mjr8zUdSEfShazNL1KrGw","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator/backend && pip list 2>/dev/null | grep -i yaml"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":104,"cacheRead":80522,"cacheWrite":356,"totalTokens":80983,"cost":{"input":0.000005,"output":0.0026000000000000003,"cacheRead":0.040261,"cacheWrite":0.002225,"total":0.045091}},"stopReason":"toolUse","timestamp":1774828306926}}
{"type":"message","id":"6ba6af34","parentId":"a3f112aa","timestamp":"2026-03-29T23:51:51.851Z","message":{"role":"toolResult","toolCallId":"toolu_016mjr8zUdSEfShazNL1KrGw","toolName":"bash","content":[{"type":"text","text":"PyYAML                6.0.1\n"}],"isError":false,"timestamp":1774828311849}}
{"type":"message","id":"a6431441","parentId":"6ba6af34","timestamp":"2026-03-29T23:51:55.147Z","message":{"role":"assistant","content":[{"type":"text","text":"Good, PyYAML is available. Now let's run the verification checks."},{"type":"toolCall","id":"toolu_016oZAs1B88ThWDX9EbcrSWh","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator/backend && python -c \"from search_service import SearchService; print('OK')\" 2>&1"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":109,"cacheRead":80878,"cacheWrite":127,"totalTokens":81115,"cost":{"input":0.000005,"output":0.002725,"cacheRead":0.040438999999999996,"cacheWrite":0.0007937500000000001,"total":0.04396275}},"stopReason":"toolUse","timestamp":1774828311850}}
{"type":"message","id":"ec2f26bc","parentId":"a6431441","timestamp":"2026-03-29T23:51:56.557Z","message":{"role":"toolResult","toolCallId":"toolu_016oZAs1B88ThWDX9EbcrSWh","toolName":"bash","content":[{"type":"text","text":"OK\n"}],"isError":false,"timestamp":1774828316556}}
{"type":"message","id":"90f6d148","parentId":"ec2f26bc","timestamp":"2026-03-29T23:51:59.544Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_019BRmTdJy5pPjv9HEA7rBAJ","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator/backend && python -c \"from routers.search import router; print(router.routes)\" 2>&1"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":81005,"cacheWrite":123,"totalTokens":81221,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.0405025,"cacheWrite":0.00076875,"total":0.04357625}},"stopReason":"toolUse","timestamp":1774828316557}}
{"type":"message","id":"3d36fd53","parentId":"90f6d148","timestamp":"2026-03-29T23:52:01.309Z","message":{"role":"toolResult","toolCallId":"toolu_019BRmTdJy5pPjv9HEA7rBAJ","toolName":"bash","content":[{"type":"text","text":"[APIRoute(path='/search', name='search', methods=['GET'])]\n"}],"isError":false,"timestamp":1774828321307}}
{"type":"message","id":"b37a05c0","parentId":"3d36fd53","timestamp":"2026-03-29T23:52:04.585Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01X91os74ud6KCubVtZ8X7Ub","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator/backend && python -c \"from routers.techniques import router; print(router.routes)\" 2>&1"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":81128,"cacheWrite":121,"totalTokens":81342,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.040563999999999996,"cacheWrite":0.0007562500000000001,"total":0.04362525}},"stopReason":"toolUse","timestamp":1774828321308}}
{"type":"message","id":"7a227e46","parentId":"b37a05c0","timestamp":"2026-03-29T23:52:05.110Z","message":{"role":"toolResult","toolCallId":"toolu_01X91os74ud6KCubVtZ8X7Ub","toolName":"bash","content":[{"type":"text","text":"[APIRoute(path='/techniques', name='list_techniques', methods=['GET']), APIRoute(path='/techniques/{slug}', name='get_technique', methods=['GET'])]\n"}],"isError":false,"timestamp":1774828325108}}
{"type":"message","id":"4634a16d","parentId":"7a227e46","timestamp":"2026-03-29T23:52:08.097Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01UmQpHSqMtmr661uwC5gxk4","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator/backend && python -c \"from routers.topics import router; print(router.routes)\" 2>&1"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":81249,"cacheWrite":144,"totalTokens":81486,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.0406245,"cacheWrite":0.0009000000000000001,"total":0.0438295}},"stopReason":"toolUse","timestamp":1774828325109}}
{"type":"message","id":"d6a19778","parentId":"4634a16d","timestamp":"2026-03-29T23:52:08.643Z","message":{"role":"toolResult","toolCallId":"toolu_01UmQpHSqMtmr661uwC5gxk4","toolName":"bash","content":[{"type":"text","text":"[APIRoute(path='/topics', name='list_topics', methods=['GET']), APIRoute(path='/topics/{category_slug}', name='get_topic_techniques', methods=['GET'])]\n"}],"isError":false,"timestamp":1774828328641}}
{"type":"message","id":"f4c65edd","parentId":"d6a19778","timestamp":"2026-03-29T23:52:12.576Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01A1fLgVHqyBvHQ4iEva8Viq","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator/backend && python -c \"from main import app; routes = [r.path for r in app.routes]; assert '/api/v1/search' in str(routes) or any('search' in str(r.path) for r in app.routes); print('Mounted')\" 2>&1"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":135,"cacheRead":81393,"cacheWrite":148,"totalTokens":81677,"cost":{"input":0.000005,"output":0.003375,"cacheRead":0.0406965,"cacheWrite":0.000925,"total":0.0450015}},"stopReason":"toolUse","timestamp":1774828328642}}
{"type":"message","id":"cc2c0ae6","parentId":"f4c65edd","timestamp":"2026-03-29T23:52:14.404Z","message":{"role":"toolResult","toolCallId":"toolu_01A1fLgVHqyBvHQ4iEva8Viq","toolName":"bash","content":[{"type":"text","text":"Mounted\n"}],"isError":false,"timestamp":1774828334401}}
{"type":"message","id":"20d0fdf3","parentId":"cc2c0ae6","timestamp":"2026-03-29T23:52:19.285Z","message":{"role":"assistant","content":[{"type":"text","text":"All five verification checks pass. Let me also run the combined verification command from the task plan."},{"type":"toolCall","id":"toolu_01RUTbeG3gA9pBNhwvHJoZMp","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator/backend && python -c \"from search_service import SearchService; from routers.search import router as sr; from routers.techniques import router as tr; from routers.topics import router as tpr; print('All imports OK')\" && python -c \"from main import app; print([r.path for r in app.routes])\" 2>&1"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":167,"cacheRead":81541,"cacheWrite":150,"totalTokens":81859,"cost":{"input":0.000005,"output":0.004175,"cacheRead":0.0407705,"cacheWrite":0.0009375000000000001,"total":0.045888000000000005}},"stopReason":"toolUse","timestamp":1774828334402}}
{"type":"message","id":"761386f2","parentId":"20d0fdf3","timestamp":"2026-03-29T23:52:22.648Z","message":{"role":"toolResult","toolCallId":"toolu_01RUTbeG3gA9pBNhwvHJoZMp","toolName":"bash","content":[{"type":"text","text":"All imports OK\n['/openapi.json', '/docs', '/docs/oauth2-redirect', '/redoc', '/health', '/api/v1/creators', '/api/v1/creators/{slug}', '/api/v1/ingest', '/api/v1/pipeline/trigger/{video_id}', '/api/v1/review/queue', '/api/v1/review/stats', '/api/v1/review/moments/{moment_id}/approve', '/api/v1/review/moments/{moment_id}/reject', '/api/v1/review/moments/{moment_id}', '/api/v1/review/moments/{moment_id}/split', '/api/v1/review/moments/{moment_id}/merge', '/api/v1/review/mode', '/api/v1/review/mode', '/api/v1/search', '/api/v1/techniques', '/api/v1/techniques/{slug}', '/api/v1/topics', '/api/v1/topics/{category_slug}', '/api/v1/videos', '/api/v1/health']\n"}],"isError":false,"timestamp":1774828342646}}
{"type":"message","id":"b500947d","parentId":"761386f2","timestamp":"2026-03-29T23:52:26.879Z","message":{"role":"assistant","content":[{"type":"text","text":"All routes are mounted correctly — `/api/v1/search`, `/api/v1/techniques`, `/api/v1/techniques/{slug}`, `/api/v1/topics`, `/api/v1/topics/{category_slug}` are all present.\n\nLet me also verify the existing test suite still passes."},{"type":"toolCall","id":"toolu_01PgpGTz5DnxNhBFygqiEVer","name":"async_bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator/backend && python -m pytest tests/ -v --timeout=30 2>&1 | tail -50","label":"existing tests"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":181,"cacheRead":81691,"cacheWrite":435,"totalTokens":82308,"cost":{"input":0.000005,"output":0.004525,"cacheRead":0.0408455,"cacheWrite":0.0027187500000000002,"total":0.04809425}},"stopReason":"toolUse","timestamp":1774828342647}}
{"type":"message","id":"b4ed88ac","parentId":"b500947d","timestamp":"2026-03-29T23:52:26.886Z","message":{"role":"toolResult","toolCallId":"toolu_01PgpGTz5DnxNhBFygqiEVer","toolName":"async_bash","content":[{"type":"text","text":"Background job started: **bg_b48576bc**\nCommand: `existing tests`\n\nUse `await_job` to get results when ready, or `cancel_job` to stop."}],"isError":false,"timestamp":1774828346884}}
{"type":"message","id":"8a5e9458","parentId":"b4ed88ac","timestamp":"2026-03-29T23:52:29.790Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_013wkjHzC6ix8PebYMw2rEDc","name":"await_job","arguments":{"jobs":["bg_b48576bc"],"timeout":60}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":77,"cacheRead":82126,"cacheWrite":233,"totalTokens":82437,"cost":{"input":0.000005,"output":0.001925,"cacheRead":0.041062999999999995,"cacheWrite":0.00145625,"total":0.044449249999999996}},"stopReason":"toolUse","timestamp":1774828346885}}
{"type":"message","id":"b400a214","parentId":"8a5e9458","timestamp":"2026-03-29T23:52:29.793Z","message":{"role":"toolResult","toolCallId":"toolu_013wkjHzC6ix8PebYMw2rEDc","toolName":"await_job","content":[{"type":"text","text":"### bg_b48576bc — existing tests (completed, 2.9s)\n\nERROR: usage: __main__.py [options] [file_or_dir] [file_or_dir] [...]\n__main__.py: error: unrecognized arguments: --timeout=30\n  inifile: /home/aux/projects/content-to-kb-automator/backend/pytest.ini\n  rootdir: /home/aux/projects/content-to-kb-automator/backend\n\n"}],"isError":false,"timestamp":1774828349791}}
{"type":"message","id":"ab006f7d","parentId":"b400a214","timestamp":"2026-03-29T23:52:33.549Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Uv98nAg48D9aeThXgAyRmq","name":"async_bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator/backend && python -m pytest tests/ -v 2>&1 | tail -60","label":"existing tests"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":106,"cacheRead":82359,"cacheWrite":204,"totalTokens":82670,"cost":{"input":0.000005,"output":0.00265,"cacheRead":0.0411795,"cacheWrite":0.001275,"total":0.0451095}},"stopReason":"toolUse","timestamp":1774828349792}}
{"type":"message","id":"639cafa8","parentId":"ab006f7d","timestamp":"2026-03-29T23:52:33.555Z","message":{"role":"toolResult","toolCallId":"toolu_01Uv98nAg48D9aeThXgAyRmq","toolName":"async_bash","content":[{"type":"text","text":"Background job started: **bg_8f63e6e3**\nCommand: `existing tests`\n\nUse `await_job` to get results when ready, or `cancel_job` to stop."}],"isError":false,"timestamp":1774828353553}}
{"type":"message","id":"64a5c7d8","parentId":"639cafa8","timestamp":"2026-03-29T23:52:36.243Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01WACPCKct38Qqdi5f9vSjwo","name":"await_job","arguments":{"jobs":["bg_8f63e6e3"],"timeout":120}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":80,"cacheRead":82359,"cacheWrite":365,"totalTokens":82805,"cost":{"input":0.000005,"output":0.002,"cacheRead":0.0411795,"cacheWrite":0.0022812500000000003,"total":0.04546575}},"stopReason":"toolUse","timestamp":1774828353554}}
{"type":"message","id":"f2578dbd","parentId":"64a5c7d8","timestamp":"2026-03-29T23:54:36.246Z","message":{"role":"toolResult","toolCallId":"toolu_01WACPCKct38Qqdi5f9vSjwo","toolName":"await_job","content":[{"type":"text","text":"No completed jobs.\n\n**Still running:** bg_8f63e6e3 (existing tests)\n\n⏱ **Timed out** after 120s waiting for jobs to finish. Jobs are still running in the background. Use `await_job` again later or `async_bash` + `await_job` for shorter polling intervals."}],"isError":false,"timestamp":1774828476244}}
{"type":"message","id":"7732e792","parentId":"f2578dbd","timestamp":"2026-03-29T23:54:39.256Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01SDyMQ9FP91eDCi3wAjTycH","name":"await_job","arguments":{"jobs":["bg_8f63e6e3"],"timeout":120}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":80,"cacheRead":82724,"cacheWrite":168,"totalTokens":82973,"cost":{"input":0.000005,"output":0.002,"cacheRead":0.041361999999999996,"cacheWrite":0.0010500000000000002,"total":0.044417}},"stopReason":"toolUse","timestamp":1774828476245}}
{"type":"message","id":"1072431b","parentId":"7732e792","timestamp":"2026-03-29T23:54:48.628Z","message":{"role":"toolResult","toolCallId":"toolu_01SDyMQ9FP91eDCi3wAjTycH","toolName":"await_job","content":[{"type":"text","text":"### bg_8f63e6e3 — existing tests (completed, 135.1s)\n\n============================= test session starts ==============================\nplatform linux -- Python 3.12.3, pytest-8.4.2, pluggy-1.6.0 -- /usr/local/bin/python\ncachedir: .pytest_cache\nrootdir: /home/aux/projects/content-to-kb-automator/backend\nconfigfile: pytest.ini\nplugins: asyncio-0.24.0, anyio-4.13.0\nasyncio: mode=Mode.AUTO, default_loop_scope=None\ncollecting ... collected 40 items\n\ntests/test_ingest.py::test_ingest_creates_creator_and_video PASSED       [  2%]\ntests/test_ingest.py::test_ingest_reuses_existing_creator PASSED         [  5%]\ntests/test_ingest.py::test_ingest_idempotent_reupload PASSED             [  7%]\ntests/test_ingest.py::test_ingest_saves_json_to_disk PASSED              [ 10%]\ntests/test_ingest.py::test_ingest_rejects_invalid_json PASSED            [ 12%]\ntests/test_ingest.py::test_ingest_rejects_missing_fields PASSED          [ 15%]\ntests/test_pipeline.py::test_stage2_segmentation_updates_topic_labels PASSED [ 17%]\ntests/test_pipeline.py::test_stage3_extraction_creates_key_moments PASSED [ 20%]\ntests/test_pipeline.py::test_stage4_classification_assigns_tags PASSED   [ 22%]\ntests/test_pipeline.py::test_stage5_synthesis_creates_technique_pages PASSED [ 25%]\ntests/test_pipeline.py::test_stage6_embeds_and_upserts_to_qdrant PASSED  [ 27%]\ntests/test_pipeline.py::test_run_pipeline_resumes_from_extracted PASSED  [ 30%]\ntests/test_pipeline.py::test_pipeline_trigger_endpoint PASSED            [ 32%]\ntests/test_pipeline.py::test_pipeline_trigger_404_for_missing_video PASSED [ 35%]\ntests/test_pipeline.py::test_ingest_dispatches_pipeline PASSED           [ 37%]\ntests/test_pipeline.py::test_llm_fallback_on_primary_failure PASSED      [ 40%]\ntests/test_review.py::test_list_queue_empty PASSED                       [ 42%]\ntests/test_review.py::test_list_queue_with_moments PASSED                [ 45%]\ntests/test_review.py::test_list_queue_filter_by_status PASSED            [ 47%]\ntests/test_review.py::test_stats_counts PASSED                           [ 50%]\ntests/test_review.py::test_approve_moment PASSED                         [ 52%]\ntests/test_review.py::test_approve_nonexistent_moment PASSED             [ 55%]\ntests/test_review.py::test_reject_moment PASSED                          [ 57%]\ntests/test_review.py::test_reject_nonexistent_moment PASSED              [ 60%]\ntests/test_review.py::test_edit_moment PASSED                            [ 62%]\ntests/test_review.py::test_edit_nonexistent_moment PASSED                [ 65%]\ntests/test_review.py::test_split_moment PASSED                           [ 67%]\ntests/test_review.py::test_split_invalid_time_below_start PASSED         [ 70%]\ntests/test_review.py::test_split_invalid_time_above_end PASSED           [ 72%]\ntests/test_review.py::test_split_nonexistent_moment PASSED               [ 75%]\ntests/test_review.py::test_merge_moments PASSED                          [ 77%]\ntests/test_review.py::test_merge_different_videos PASSED                 [ 80%]\ntests/test_review.py::test_merge_with_self PASSED                        [ 82%]\ntests/test_review.py::test_merge_nonexistent_target PASSED               [ 85%]\ntests/test_review.py::test_merge_nonexistent_source PASSED               [ 87%]\ntests/test_review.py::test_get_mode_default PASSED                       [ 90%]\ntests/test_review.py::test_set_mode PASSED                               [ 92%]\ntests/test_review.py::test_get_mode_from_redis PASSED                    [ 95%]\ntests/test_review.py::test_get_mode_redis_error_fallback PASSED          [ 97%]\ntests/test_review.py::test_set_mode_redis_error PASSED                   [100%]\n\n======================== 40 passed in 132.22s (0:02:12) ========================\n"}],"isError":false,"timestamp":1774828488626}}
{"type":"message","id":"8fc9f223","parentId":"1072431b","timestamp":"2026-03-29T23:54:52.672Z","message":{"role":"assistant","content":[{"type":"text","text":"All 40 existing tests pass — no regressions. Now let me read the task summary template and write the summary."},{"type":"toolCall","id":"toolu_015B6oLqDethMb66pGaWmyhe","name":"bash","arguments":{"command":"cat ~/.gsd/agent/extensions/gsd/templates/task-summary.md 2>/dev/null | head -50"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":107,"cacheRead":82892,"cacheWrite":1340,"totalTokens":84340,"cost":{"input":0.000005,"output":0.0026750000000000003,"cacheRead":0.041446,"cacheWrite":0.008375,"total":0.052501}},"stopReason":"toolUse","timestamp":1774828488627}}
{"type":"message","id":"54b9844b","parentId":"8fc9f223","timestamp":"2026-03-29T23:54:52.684Z","message":{"role":"toolResult","toolCallId":"toolu_015B6oLqDethMb66pGaWmyhe","toolName":"bash","content":[{"type":"text","text":"---\nid: {{taskId}}\nparent: {{sliceId}}\nmilestone: {{milestoneId}}\nprovides:\n  - {{whatThisTaskProvides}}\nkey_files:\n  - {{filePath}}\nkey_decisions:\n  - {{decision}}\npatterns_established:\n  - {{pattern}}\nobservability_surfaces:\n  - {{status endpoint, structured log, persisted failure state, diagnostic command, or none}}\nduration: {{duration}}\nverification_result: passed\ncompleted_at: {{date}}\n# Set blocker_discovered: true only if execution revealed the remaining slice plan\n# is fundamentally invalid (wrong API, missing capability, architectural mismatch).\n# Do NOT set true for ordinary bugs, minor deviations, or fixable issues.\nblocker_discovered: false\n---\n\n# {{taskId}}: {{taskTitle}}\n\n<!-- One-liner must say what actually shipped, not just that work completed.\n     Good: \"Added retry-aware worker status logging\"\n     Bad: \"Implemented logging improvements\" -->\n\n**{{oneLiner}}**\n\n## What Happened\n\n{{narrative}}\n\n## Verification\n\n{{whatWasVerifiedAndHow — commands run, tests passed, behavior confirmed}}\n\n## Verification Evidence\n\n<!-- Populated from verification gate output. If the gate ran, fill in the table below.\n     If no gate ran (e.g., no verification commands discovered), note that. -->\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| {{row}} | {{command}} | {{exitCode}} | {{verdict}} | {{duration}} |\n\n## Diagnostics\n\n"}],"isError":false,"timestamp":1774828492682}}
{"type":"message","id":"be7016f3","parentId":"54b9844b","timestamp":"2026-03-29T23:55:25.221Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_017edvYHiNtcArg8LJkTybhW","name":"write","arguments":{"content":"---\nid: T01\nparent: S05\nmilestone: M001\nprovides:\n  - SearchService class with async embedding + Qdrant + keyword fallback\n  - GET /api/v1/search endpoint with SearchResponse schema\n  - GET /api/v1/techniques and GET /api/v1/techniques/{slug} with full detail\n  - GET /api/v1/topics with two-level category hierarchy and counts\n  - Enhanced GET /api/v1/creators with sort=random (default), genre filter, technique/video counts\n  - All new routers mounted in main.py\nkey_files:\n  - backend/search_service.py\n  - backend/schemas.py\n  - backend/routers/search.py\n  - backend/routers/techniques.py\n  - backend/routers/topics.py\n  - backend/routers/creators.py\n  - backend/main.py\nkey_decisions:\n  - Used asyncio.wait_for with 300ms timeout on both embedding and Qdrant calls for graceful degradation\n  - Qdrant query uses query_points() (v1.17+ API) with Filter for type-based scoping\n  - Topics endpoint loads canonical_tags.yaml at request time and counts tag matches from DB\n  - Creator list returns CreatorBrowseItem with correlated subqueries for technique/video counts\npatterns_established:\n  - Async external service pattern: wrap with asyncio.wait_for + catch-and-fallback\n  - Keyword fallback pattern: if semantic search returns empty, ILIKE search on title/summary columns\n  - Observability pattern: INFO log per search with query, latency_ms, result_count, fallback_used\nobservability_surfaces:\n  - INFO log per search query with latency_ms, result_count, fallback_used\n  - WARNING logs on embedding API / Qdrant failures with error details\n  - fallback_used=true in SearchResponse indicates degraded mode\nduration: 25m\nverification_result: passed\ncompleted_at: 2026-03-29\nblocker_discovered: false\n---\n\n# T01: Build async search service and all public API endpoints\n\n**Created async search service with embedding+Qdrant+keyword fallback and all public API endpoints (search, techniques, topics, enhanced creators) mounted at /api/v1**\n\n## What Happened\n\nBuilt the complete backend API surface for S05:\n\n1. **SearchService** (`backend/search_service.py`): Async class with `embed_query()` (300ms timeout, returns None on failure), `search_qdrant()` (300ms timeout, empty list on failure), `keyword_search()` (ILIKE on technique_pages.title, key_moments.title, creators.name), and `search()` orchestrator that tries semantic first then falls back to keyword. Input validation: empty query returns empty results, queries truncated to 500 chars, invalid scope defaults to \"all\".\n\n2. **Schemas** (`backend/schemas.py`): Added SearchResultItem, SearchResponse, KeyMomentSummary, RelatedLinkItem, CreatorInfo, TechniquePageDetail, TopicSubTopic, TopicCategory, CreatorBrowseItem.\n\n3. **Search router** (`backend/routers/search.py`): GET /search with q, scope, limit params. Instantiates SearchService from settings.\n\n4. **Techniques router** (`backend/routers/techniques.py`): GET /techniques with category and creator_slug filters + pagination. GET /techniques/{slug} with eager-loaded key_moments (sorted by start_time), creator info, and bidirectional related links. Returns 404 for unknown slugs.\n\n5. **Topics router** (`backend/routers/topics.py`): GET /topics loads canonical_tags.yaml and aggregates technique/creator counts per sub-topic from DB. GET /topics/{category_slug} returns filtered technique pages.\n\n6. **Enhanced creators** (`backend/routers/creators.py`): Added sort param (random default per R014, alpha, views), genre filter using ARRAY any(), and correlated subqueries for technique_count and video_count. Returns CreatorBrowseItem.\n\n7. **Router mounting** (`backend/main.py`): All three new routers (search, techniques, topics) mounted at /api/v1.\n\nAll 40 existing tests continue to pass.\n\n## Verification\n\nAll five verification commands from the slice plan pass:\n\n1. `from search_service import SearchService` → OK\n2. `from routers.search import router` → routes listed\n3. `from routers.techniques import router` → routes listed\n4. `from routers.topics import router` → routes listed\n5. `from main import app; routes = [r.path for r in app.routes]` → all search/techniques/topics routes present\n\nFull test suite (40 tests) passes with no regressions.\n\n## Verification Evidence\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| 1 | `cd backend && python -c \"from search_service import SearchService; print('OK')\"` | 0 | ✅ pass | <1s |\n| 2 | `cd backend && python -c \"from routers.search import router; print(router.routes)\"` | 0 | ✅ pass | <1s |\n| 3 | `cd backend && python -c \"from routers.techniques import router; print(router.routes)\"` | 0 | ✅ pass | <1s |\n| 4 | `cd backend && python -c \"from routers.topics import router; print(router.routes)\"` | 0 | ✅ pass | <1s |\n| 5 | `cd backend && python -c \"from main import app; routes = [r.path for r in app.routes]; assert '/api/v1/search' in str(routes); print('Mounted')\"` | 0 | ✅ pass | <1s |\n| 6 | `cd backend && python -m pytest tests/ -v` | 0 | ✅ pass (40/40) | 132s |\n\n## Diagnostics\n\nNo issues encountered. All imports resolve cleanly, all routes mount correctly, and existing tests pass without modification. The async patterns follow the existing sync equivalents in `pipeline/embedding_client.py` and `pipeline/qdrant_client.py` but use `openai.AsyncOpenAI` and `qdrant_client.AsyncQdrantClient` respectively.\n","path":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S05/tasks/T01-SUMMARY.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":1650,"cacheRead":84232,"cacheWrite":494,"totalTokens":86377,"cost":{"input":0.000005,"output":0.04125,"cacheRead":0.042116,"cacheWrite":0.0030875,"total":0.0864585}},"stopReason":"toolUse","timestamp":1774828492683}}
{"type":"message","id":"a97aaa68","parentId":"be7016f3","timestamp":"2026-03-29T23:55:25.224Z","message":{"role":"toolResult","toolCallId":"toolu_017edvYHiNtcArg8LJkTybhW","toolName":"write","content":[{"type":"text","text":"Successfully wrote 5346 bytes to /home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S05/tasks/T01-SUMMARY.md"}],"isError":false,"timestamp":1774828525223}}
{"type":"message","id":"9ea2fca1","parentId":"a97aaa68","timestamp":"2026-03-29T23:55:42.016Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_015sCQWRRcsX9vQG698Q8481","name":"gsd_task_complete","arguments":{"blockerDiscovered":false,"deviations":"None.","keyDecisions":["Used asyncio.wait_for with 300ms timeout on both embedding and Qdrant calls for graceful degradation","Qdrant query uses query_points() API with Filter for type-based scoping","Topics endpoint loads canonical_tags.yaml at request time and counts tag matches from DB","Creator list returns CreatorBrowseItem with correlated subqueries for technique/video counts"],"keyFiles":["backend/search_service.py","backend/schemas.py","backend/routers/search.py","backend/routers/techniques.py","backend/routers/topics.py","backend/routers/creators.py","backend/main.py"],"knownIssues":"None.","milestoneId":"M001","narrative":"Built the complete backend API surface for S05: SearchService with async embedding (300ms timeout) + Qdrant vector search + keyword ILIKE fallback, search/techniques/topics routers, enhanced creators router with sort=random (R014), genre filter, and technique/video counts. All mounted in main.py. Input validation handles empty queries, long queries (truncated to 500), and invalid scope (defaults to \"all\"). All 40 existing tests pass with zero regressions.","oneLiner":"Created async search service with embedding+Qdrant+keyword fallback and all public API endpoints (search, techniques, topics, enhanced creators) mounted at /api/v1","sliceId":"S05","taskId":"T01","verification":"All five slice verification checks pass: SearchService imports clean, search/techniques/topics routers have routes, all routers mounted in main.py with correct paths. Full existing test suite (40 tests) passes.","verificationEvidence":[{"command":"cd backend && python -c \"from search_service import SearchService; print('OK')\"","durationMs":500,"exitCode":0,"verdict":"✅ pass"},{"command":"cd backend && python -c \"from routers.search import router; print(router.routes)\"","durationMs":500,"exitCode":0,"verdict":"✅ pass"},{"command":"cd backend && python -c \"from routers.techniques import router; print(router.routes)\"","durationMs":500,"exitCode":0,"verdict":"✅ pass"},{"command":"cd backend && python -c \"from routers.topics import router; print(router.routes)\"","durationMs":500,"exitCode":0,"verdict":"✅ pass"},{"command":"cd backend && python -c \"from main import app; routes = [r.path for r in app.routes]; assert '/api/v1/search' in str(routes); print('Mounted')\"","durationMs":500,"exitCode":0,"verdict":"✅ pass"},{"command":"cd backend && python -m pytest tests/ -v","durationMs":132000,"exitCode":0,"verdict":"✅ pass (40/40)"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":926,"cacheRead":84726,"cacheWrite":1711,"totalTokens":87364,"cost":{"input":0.000005,"output":0.02315,"cacheRead":0.042363,"cacheWrite":0.01069375,"total":0.07621175}},"stopReason":"toolUse","timestamp":1774828525224}}
{"type":"message","id":"387b4681","parentId":"9ea2fca1","timestamp":"2026-03-29T23:55:42.040Z","message":{"role":"toolResult","toolCallId":"toolu_015sCQWRRcsX9vQG698Q8481","toolName":"gsd_task_complete","content":[{"type":"text","text":"Completed task T01 (S05/M001)"}],"details":{"operation":"complete_task","taskId":"T01","sliceId":"S05","milestoneId":"M001","summaryPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S05/tasks/T01-SUMMARY.md"},"isError":false,"timestamp":1774828542038}}
{"type":"message","id":"a315322f","parentId":"387b4681","timestamp":"2026-03-29T23:55:44.295Z","message":{"role":"assistant","content":[{"type":"text","text":"Task T01 complete."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":9,"cacheRead":86437,"cacheWrite":962,"totalTokens":87409,"cost":{"input":0.000005,"output":0.00022500000000000002,"cacheRead":0.0432185,"cacheWrite":0.0060125000000000005,"total":0.049461000000000005}},"stopReason":"stop","timestamp":1774828542039}}
{"type":"custom_message","customType":"async_job_result","content":"**Background job done: bg_1aa8a861** (list backend files, 0.0s)\n\n/home/aux/projects/content-to-kb-automator/backend/models.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/llm_client.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/stages.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/qdrant_client.py\n/home/aux/projects/content-to-kb-automator/backend/pipeline/schemas.py\n/home/aux/projects/content-to-kb-automator/backend/main.py\n/home/aux/projects/content-to-kb-automator/backend/routers/health.py\n/home/aux/projects/content-to-kb-automator/backend/routers/videos.py\n/home/aux/projects/content-to-kb-automator/backend/routers/pipeline.py\n/home/aux/projects/content-to-kb-automator/backend/routers/__init__.py\n/home/aux/projects/content-to-kb-automator/backend/routers/creators.py\n/home/aux/projects/content-to-kb-automator/backend/routers/review.py\n/home/aux/projects/content-to-kb-automator/backend/routers/ingest.py\n/home/aux/projects/content-to-kb-automator/backend/redis_client.py\n/home/aux/projects/content-to-kb-automator/backend/database.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/tests/test_ingest.py\n/home/aux/projects/content-to-kb-automator/backend/tests/__init__.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/config.py\n/home/aux/projects/content-to-kb-automator/backend/worker.py\n/home/aux/projects/content-to-kb-automator/backend/schemas.py\n","display":true,"id":"b61f19c5","parentId":"a315322f","timestamp":"2026-03-29T23:55:44.298Z"}
{"type":"message","id":"ce9d4493","parentId":"b61f19c5","timestamp":"2026-03-29T23:55:48.965Z","message":{"role":"assistant","content":[{"type":"text","text":"The background job result is just the file listing that was already consumed during T01 execution — no action needed. T01 is already complete."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":33,"cacheRead":87399,"cacheWrite":679,"totalTokens":88113,"cost":{"input":0.00001,"output":0.000825,"cacheRead":0.043699499999999995,"cacheWrite":0.00424375,"total":0.048778249999999995}},"stopReason":"stop","timestamp":1774828544296}}
{"type":"custom_message","customType":"async_job_result","content":"**Background job done: bg_b48576bc** (existing tests, 2.2s)\n\nERROR: usage: __main__.py [options] [file_or_dir] [file_or_dir] [...]\n__main__.py: error: unrecognized arguments: --timeout=30\n  inifile: /home/aux/projects/content-to-kb-automator/backend/pytest.ini\n  rootdir: /home/aux/projects/content-to-kb-automator/backend\n\n","display":true,"id":"3082c84d","parentId":"ce9d4493","timestamp":"2026-03-29T23:55:48.968Z"}
{"type":"message","id":"dd960844","parentId":"3082c84d","timestamp":"2026-03-29T23:55:52.259Z","message":{"role":"assistant","content":[{"type":"text","text":"That's the stale background job from the initial test run attempt with the unsupported `--timeout` flag — it was already handled during T01 execution by re-running without that flag (40/40 tests passed). No action needed. T01 is complete."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":61,"cacheRead":88078,"cacheWrite":153,"totalTokens":88294,"cost":{"input":0.00001,"output":0.001525,"cacheRead":0.044038999999999995,"cacheWrite":0.0009562500000000001,"total":0.046530249999999995}},"stopReason":"stop","timestamp":1774828548966}}
