{"type":"thinking_level_change","id":"a228baf4","parentId":null,"timestamp":"2026-04-03T04:00:24.150Z","thinkingLevel":"medium"}
{"type":"model_change","id":"2fc00121","parentId":"a228baf4","timestamp":"2026-04-03T04:00:24.151Z","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/M015/slices/S01/S01-PLAN.md`\n- Task plan source: `.gsd/milestones/M015/slices/S01/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/M015/slices/S01/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/M015/slices/S01/tasks/T01-SUMMARY.md` before finishing.**\n\nWhen done, say: \"Task T01 complete.\"\n## Slice Plan Excerpt\nSource: `.gsd/milestones/M015/slices/S01/S01-PLAN.md`\n**Goal:** Backend search query logging and popular searches API endpoint with Redis caching.\n**Demo:** After this: GET /api/v1/search/popular returns top 10 queries from last 7 days, cached in Redis. search_log table has rows from real searches.\n\n### Slice Verification\n- `cd backend && python -c \"from models import SearchLog; print(SearchLog.__tablename__)\"` prints `search_log`\n- `cd backend && python -c \"from schemas import PopularSearchItem, PopularSearchesResponse; print('OK')\"` prints OK\n- `cd backend && python -c \"from routers.search import router; routes = [r.path for r in router.routes]; assert '/popular' in routes; print('OK')\"` prints OK\n\n## UNIT: Execute Task T01 (\"Add SearchLog model, migration, schemas, logging hook, and popular endpoint\") — Slice S01 (\"Search Query Logging + Popular Searches API\"), Milestone M015\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/M015/slices/S01/tasks/T01-PLAN.md`\n\n---\nestimated_steps: 47\nestimated_files: 5\nskills_used: []\n---\n\n# T01: Add SearchLog model, migration, schemas, logging hook, and popular endpoint\n\nWrite all backend code for search query logging and the popular searches endpoint.\n\n## Steps\n\n1. Add `SearchLog` model to `backend/models.py`:\n   - Columns: `id` (Integer PK autoincrement), `query` (String, indexed), `scope` (String), `result_count` (Integer), `created_at` (DateTime, indexed, default=_now)\n   - Table name: `search_log`\n\n2. Create Alembic migration `alembic/versions/013_add_search_log.py`:\n   - Create `search_log` table with columns matching the model\n   - Add index on `created_at` and `query` columns\n   - Use the same pattern as existing migrations (see `012_multi_source_format.py` for style)\n\n3. Add Pydantic schemas to `backend/schemas.py`:\n   - `PopularSearchItem(BaseModel)`: `query: str`, `count: int`\n   - `PopularSearchesResponse(BaseModel)`: `items: list[PopularSearchItem]`, `cached: bool = False`\n\n4. Add non-blocking search logging to `backend/routers/search.py`:\n   - Import `asyncio`, `SearchLog` from models, `async_session` from database\n   - Add `_log_search(query, scope, result_count)` async helper that opens its own session, INSERTs a SearchLog row, commits, and catches all exceptions with `logger.warning`\n   - In the `search()` handler, after computing `result` but before returning, call `asyncio.create_task(_log_search(q, scope, result['total']))` — but only if `q.strip()` is non-empty\n\n5. Add `GET /popular` endpoint to `backend/routers/search.py`:\n   - Import `json` stdlib, `get_redis` from redis_client, `func`/`text` from sqlalchemy\n   - Check Redis key `chrysopedia:popular_searches` first\n   - On cache hit: parse JSON, return `PopularSearchesResponse(items=..., cached=True)`\n   - On cache miss: query `search_log` with `SELECT LOWER(query) as q, COUNT(*) as cnt FROM search_log WHERE created_at > now() - interval '7 days' GROUP BY LOWER(query) ORDER BY cnt DESC LIMIT 10`\n   - Use SQLAlchemy `func.lower()`, `func.count()`, and `func.now()` for the query\n   - Serialize result to JSON, SET in Redis with `ex=300` (5-min TTL per D025)\n   - Return `PopularSearchesResponse(items=..., cached=False)`\n   - Close Redis client after use\n\n## Must-Haves\n\n- [ ] SearchLog model with correct columns and indexes\n- [ ] Alembic migration creates search_log table\n- [ ] PopularSearchItem and PopularSearchesResponse schemas\n- [ ] Non-blocking _log_search with fire-and-forget pattern\n- [ ] Empty queries not logged\n- [ ] Popular endpoint with Redis read-through cache (300s TTL)\n- [ ] LOWER(query) normalization in popular query\n\n## Verification\n\n- `cd backend && python -c \"from models import SearchLog; print(SearchLog.__tablename__)\"` prints `search_log`\n- `cd backend && python -c \"from schemas import PopularSearchItem, PopularSearchesResponse; print('OK')\"` prints OK\n- `cd backend && python -c \"from routers.search import router; routes = [r.path for r in router.routes]; assert '/popular' in routes; print('OK')\"` prints OK\n\n## Failure Modes\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| PostgreSQL (log INSERT) | Catch all, log WARNING, don't fail request | Same — fire-and-forget task dies silently | N/A — we control the data |\n| Redis (cache read/write) | Fall through to DB query | Fall through to DB query | Return uncached DB results |\n| PostgreSQL (popular query) | Return 500 (standard FastAPI) | Return 500 | N/A |\n\n## Negative Tests\n\n- Empty query string: `_log_search` should not be called\n- Redis down: `/popular` should still return results from DB\n- No search_log rows: `/popular` should return `{items: [], cached: false}`\n\n## Inputs\n\n- ``backend/models.py` — existing models and _now() helper`\n- ``backend/schemas.py` — existing Pydantic schemas`\n- ``backend/routers/search.py` — existing search endpoint to add logging hook and new route`\n- ``backend/database.py` — async_session factory for fire-and-forget INSERT`\n- ``backend/redis_client.py` — get_redis() for cache operations`\n- ``alembic/versions/012_multi_source_format.py` — reference for migration style`\n\n## Expected Output\n\n- ``backend/models.py` — SearchLog model added`\n- ``backend/schemas.py` — PopularSearchItem and PopularSearchesResponse added`\n- ``backend/routers/search.py` — _log_search helper, asyncio.create_task call, GET /popular endpoint added`\n- ``alembic/versions/013_add_search_log.py` — new migration creating search_log table`\n\n## Verification\n\ncd backend && python -c \"from models import SearchLog; from schemas import PopularSearchItem, PopularSearchesResponse; from routers.search import router; routes = [r.path for r in router.routes]; assert '/popular' in routes; print('ALL OK')\"\n","display":false,"id":"d7beed1f","parentId":"2fc00121","timestamp":"2026-04-03T04:00:24.155Z"}
{"type":"message","id":"e4f56b6d","parentId":"d7beed1f","timestamp":"2026-04-03T04:00:29.643Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me start by reading the relevant source files to understand the current codebase before making changes.","thinkingSignature":"EqYCClkIDBgCKkCoMUjwxb1rDDaKjAPzs+ejWi0R/U4KsyxK5h0m4TEXXa873KDcG0pcV9ikY0PJFv6YV9jJA7x4ySO+xIA00h+qMg9jbGF1ZGUtb3B1cy00LTY4ABIMfkc5FvMDuScac6hfGgzyQmOhMcsrMIYvLmwiML3mzOeMIsPORugN7lDQtwf829LinC/d0A/Pw70sH8kTtdlHGRswtvWZy4nFFifSiip7S967ESJGm9jw7BYz+FjCmcGzqWFbVthm6qpjbqmFDTkNQKzTLvm1+deCYA7FZ9BHO5WmhJ8SUdfV1dzeHenwb+YKRpZwbOHbAOBlwlmXuM7I10jtBYwJ9babHjb/uZuU385lSW7B0/H5YxkA7Ilum/pzjn3SMw6tgnRDGAE="},{"type":"text","text":"Starting T01 — need to read the existing models, schemas, search router, database, redis client, and a migration for reference."},{"type":"toolCall","id":"toolu_01UBzzpmb2CZMdZ1FZ2StthZ","name":"read","arguments":{"path":"backend/models.py"}},{"type":"toolCall","id":"toolu_01ViG8wv12y3s8vfBgz4h1qh","name":"read","arguments":{"path":"backend/schemas.py"}},{"type":"toolCall","id":"toolu_01SaoTmyrh4bxAz9cFEdziM6","name":"read","arguments":{"path":"backend/routers/search.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":199,"cacheRead":60281,"cacheWrite":4166,"totalTokens":64648,"cost":{"input":0.00001,"output":0.004975,"cacheRead":0.030140499999999997,"cacheWrite":0.0260375,"total":0.061162999999999995}},"stopReason":"toolUse","timestamp":1775188824152}}
{"type":"message","id":"411347d1","parentId":"e4f56b6d","timestamp":"2026-04-03T04:00:29.649Z","message":{"role":"toolResult","toolCallId":"toolu_01UBzzpmb2CZMdZ1FZ2StthZ","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\n    User-facing lifecycle: not_started → queued → processing → complete\n    Error branch: processing → error (retrigger resets to queued)\n    \"\"\"\n    not_started = \"not_started\"\n    queued = \"queued\"\n    processing = \"processing\"\n    error = \"error\"\n    complete = \"complete\"\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 SourceQuality(str, enum.Enum):\n    \"\"\"Derived source quality for technique pages.\"\"\"\n    structured = \"structured\"\n    mixed = \"mixed\"\n    unstructured = \"unstructured\"\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    hidden: Mapped[bool] = mapped_column(default=False, server_default=\"false\")\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    content_hash: Mapped[str | None] = mapped_column(String(64), nullable=True, index=True)\n    processing_status: Mapped[ProcessingStatus] = mapped_column(\n        Enum(ProcessingStatus, name=\"processing_status\", create_constraint=True),\n        default=ProcessingStatus.not_started,\n        server_default=\"not_started\",\n    )\n    classification_data: Mapped[list | None] = mapped_column(JSONB, 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    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    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    body_sections_format: Mapped[str] = mapped_column(\n        String(20), nullable=False, default=\"v1\", server_default=\"v1\"\n    )\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    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    versions: Mapped[list[TechniquePageVersion]] = sa_relationship(\n        back_populates=\"technique_page\", order_by=\"TechniquePageVersion.version_number\"\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    source_video_links: Mapped[list[TechniquePageVideo]] = sa_relationship(\n        back_populates=\"technique_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 TechniquePageVersion(Base):\n    \"\"\"Snapshot of a TechniquePage before a pipeline re-synthesis overwrites it.\"\"\"\n    __tablename__ = \"technique_page_versions\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    technique_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    version_number: Mapped[int] = mapped_column(Integer, nullable=False)\n    content_snapshot: Mapped[dict] = mapped_column(JSONB, nullable=False)\n    pipeline_metadata: Mapped[dict | None] = mapped_column(JSONB, nullable=True)\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n\n    # relationships\n    technique_page: Mapped[TechniquePage] = sa_relationship(\n        back_populates=\"versions\"\n    )\n\n\nclass Tag(Base):\n    __tablename__ = \"tags\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    name: Mapped[str] = mapped_column(String(255), unique=True, nullable=False)\n    category: Mapped[str] = mapped_column(String(255), nullable=False)\n    aliases: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n\n\nclass TechniquePageVideo(Base):\n    \"\"\"Association linking a technique page to its contributing source videos.\"\"\"\n    __tablename__ = \"technique_page_videos\"\n    __table_args__ = (\n        UniqueConstraint(\"technique_page_id\", \"source_video_id\", name=\"uq_page_video\"),\n    )\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    technique_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    source_video_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    added_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n\n    # relationships\n    technique_page: Mapped[TechniquePage] = sa_relationship(\n        back_populates=\"source_video_links\"\n    )\n    source_video: Mapped[SourceVideo] = sa_relationship()\n\n\n# ── Content Report Enums ─────────────────────────────────────────────────────\n\nclass ReportType(str, enum.Enum):\n    \"\"\"Classification of user-submitted content reports.\"\"\"\n    inaccurate = \"inaccurate\"\n    missing_info = \"missing_info\"\n    wrong_attribution = \"wrong_attribution\"\n    formatting = \"formatting\"\n    other = \"other\"\n\n\nclass ReportStatus(str, enum.Enum):\n    \"\"\"Triage status for content reports.\"\"\"\n    open = \"open\"\n    acknowledged = \"acknowledged\"\n    resolved = \"resolved\"\n    dismissed = \"dismissed\"\n\n\n# ── Content Report ───────────────────────────────────────────────────────────\n\nclass ContentReport(Base):\n    \"\"\"User-submitted report about a content issue.\n\n    Generic: content_type + content_id can reference any entity\n    (technique_page, key_moment, creator, or general).\n    \"\"\"\n    __tablename__ = \"content_reports\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    content_type: Mapped[str] = mapped_column(\n        String(50), nullable=False, doc=\"Entity type: technique_page, key_moment, creator, general\"\n    )\n    content_id: Mapped[uuid.UUID | None] = mapped_column(\n        UUID(as_uuid=True), nullable=True, doc=\"FK to the reported entity (null for general reports)\"\n    )\n    content_title: Mapped[str | None] = mapped_column(\n        String(500), nullable=True, doc=\"Snapshot of entity title at report time\"\n    )\n    report_type: Mapped[ReportType] = mapped_column(\n        Enum(ReportType, name=\"report_type\", create_constraint=True),\n        nullable=False,\n    )\n    description: Mapped[str] = mapped_column(Text, nullable=False)\n    status: Mapped[ReportStatus] = mapped_column(\n        Enum(ReportStatus, name=\"report_status\", create_constraint=True),\n        default=ReportStatus.open,\n        server_default=\"open\",\n    )\n    admin_notes: Mapped[str | None] = mapped_column(Text, nullable=True)\n    page_url: Mapped[str | None] = mapped_column(\n        String(1000), nullable=True, doc=\"URL the user was on when reporting\"\n    )\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    resolved_at: Mapped[datetime | None] = mapped_column(nullable=True)\n\n\n# ── Pipeline Event ───────────────────────────────────────────────────────────\n\nclass PipelineRunStatus(str, enum.Enum):\n    \"\"\"Status of a pipeline run.\"\"\"\n    running = \"running\"\n    complete = \"complete\"\n    error = \"error\"\n    cancelled = \"cancelled\"\n\n\nclass PipelineRunTrigger(str, enum.Enum):\n    \"\"\"What initiated a pipeline run.\"\"\"\n    manual = \"manual\"\n    clean_reprocess = \"clean_reprocess\"\n    auto_ingest = \"auto_ingest\"\n    bulk = \"bulk\"\n    stage_rerun = \"stage_rerun\"\n\n\nclass PipelineRun(Base):\n    \"\"\"A single execution of the pipeline for a video.\n\n    Each trigger/retrigger creates a new run. Events are scoped to a run\n    via run_id, giving a clean audit trail per execution.\n    \"\"\"\n    __tablename__ = \"pipeline_runs\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    video_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False, index=True,\n    )\n    run_number: Mapped[int] = mapped_column(\n        Integer, nullable=False, doc=\"Auto-increment per video, 1-indexed\"\n    )\n    trigger: Mapped[PipelineRunTrigger] = mapped_column(\n        Enum(PipelineRunTrigger, name=\"pipeline_run_trigger\", create_constraint=True),\n        nullable=False,\n    )\n    status: Mapped[PipelineRunStatus] = mapped_column(\n        Enum(PipelineRunStatus, name=\"pipeline_run_status\", create_constraint=True),\n        default=PipelineRunStatus.running,\n        server_default=\"running\",\n    )\n    started_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    finished_at: Mapped[datetime | None] = mapped_column(nullable=True)\n    error_stage: Mapped[str | None] = mapped_column(String(50), nullable=True)\n    total_tokens: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\n\n    # relationships\n    video: Mapped[SourceVideo] = sa_relationship()\n    events: Mapped[list[PipelineEvent]] = sa_relationship(\n        back_populates=\"run\", foreign_keys=\"PipelineEvent.run_id\"\n    )\n\n\n# ── Pipeline Event ───────────────────────────────────────────────────────────\n\nclass PipelineEvent(Base):\n    \"\"\"Structured log entry for pipeline execution.\n\n    Captures per-stage start/complete/error/llm_call events with\n    token usage and optional response payloads for debugging.\n    \"\"\"\n    __tablename__ = \"pipeline_events\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    video_id: Mapped[uuid.UUID] = mapped_column(\n        UUID(as_uuid=True), nullable=False, index=True,\n    )\n    run_id: Mapped[uuid.UUID | None] = mapped_column(\n        ForeignKey(\"pipeline_runs.id\", ondelete=\"SET NULL\"), nullable=True, index=True,\n    )\n    stage: Mapped[str] = mapped_column(\n        String(50), nullable=False, doc=\"stage2_segmentation, stage3_extraction, etc.\"\n    )\n    event_type: Mapped[str] = mapped_column(\n        String(30), nullable=False, doc=\"start, complete, error, llm_call\"\n    )\n    prompt_tokens: Mapped[int | None] = mapped_column(Integer, nullable=True)\n    completion_tokens: Mapped[int | None] = mapped_column(Integer, nullable=True)\n    total_tokens: Mapped[int | None] = mapped_column(Integer, nullable=True)\n    model: Mapped[str | None] = mapped_column(String(100), nullable=True)\n    duration_ms: Mapped[int | None] = mapped_column(Integer, nullable=True)\n    payload: Mapped[dict | None] = mapped_column(\n        JSONB, nullable=True, doc=\"LLM response content, error details, stage metadata\"\n    )\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n\n    # Debug mode — full LLM I/O capture columns\n    system_prompt_text: Mapped[str | None] = mapped_column(Text, nullable=True)\n    user_prompt_text: Mapped[str | None] = mapped_column(Text, nullable=True)\n    response_text: Mapped[str | None] = mapped_column(Text, nullable=True)\n\n    # relationships\n    run: Mapped[PipelineRun | None] = sa_relationship(\n        back_populates=\"events\", foreign_keys=[run_id]\n    )\n"}],"isError":false,"timestamp":1775188829647}}
{"type":"message","id":"c3106cbc","parentId":"411347d1","timestamp":"2026-04-03T04:00:29.651Z","message":{"role":"toolResult","toolCallId":"toolu_01ViG8wv12y3s8vfBgz4h1qh","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\nfrom typing import Literal\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    content_hash: str | None = None\n    processing_status: str = \"not_started\"\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    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: list | dict | None = None\n    body_sections_format: str = \"v1\"\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    creator_name: str = \"\"\n    creator_slug: str = \"\"\n    source_quality: str | None = None\n    view_count: int = 0\n    key_moment_count: int = 0\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    content_hash: str\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# ── Search ───────────────────────────────────────────────────────────────────\n\nclass SearchResultItem(BaseModel):\n    \"\"\"A single search result.\"\"\"\n    title: str\n    slug: str = \"\"\n    technique_page_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    match_context: str = \"\"\n    section_anchor: str = \"\"\n    section_heading: str = \"\"\n\n\nclass SearchResponse(BaseModel):\n    \"\"\"Top-level search response with metadata.\"\"\"\n    items: list[SearchResultItem] = Field(default_factory=list)\n    partial_matches: list[SearchResultItem] = Field(default_factory=list)\n    total: int = 0\n    query: str = \"\"\n    fallback_used: bool = False\n\n\nclass SuggestionItem(BaseModel):\n    \"\"\"A single autocomplete suggestion.\"\"\"\n    text: str\n    type: Literal[\"topic\", \"technique\", \"creator\"]\n\n\nclass SuggestionsResponse(BaseModel):\n    \"\"\"Popular search suggestions for autocomplete.\"\"\"\n    suggestions: list[SuggestionItem] = Field(default_factory=list)\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    source_video_id: uuid.UUID | None = None\n    video_filename: str = \"\"\n\n\nclass RelatedLinkItem(BaseModel):\n    \"\"\"A related technique link with target info.\"\"\"\n    model_config = ConfigDict(from_attributes=True)\n\n    target_title: str = \"\"\n    target_slug: str = \"\"\n    relationship: str = \"\"\n    creator_name: str = \"\"\n    topic_category: str = \"\"\n    reason: 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 SourceVideoSummary(BaseModel):\n    \"\"\"Lightweight source video info for technique page detail.\"\"\"\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    filename: str\n    content_type: str\n    added_at: datetime | 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    version_count: int = 0\n    source_videos: list[SourceVideoSummary] = Field(default_factory=list)\n\n\n# ── Technique Page Versions ──────────────────────────────────────────────────\n\nclass TechniquePageVersionSummary(BaseModel):\n    \"\"\"Lightweight version entry for list responses.\"\"\"\n    model_config = ConfigDict(from_attributes=True)\n\n    version_number: int\n    created_at: datetime\n    pipeline_metadata: dict | None = None\n\n\nclass TechniquePageVersionDetail(BaseModel):\n    \"\"\"Full version snapshot for detail responses.\"\"\"\n    model_config = ConfigDict(from_attributes=True)\n\n    version_number: int\n    content_snapshot: dict\n    pipeline_metadata: dict | None = None\n    created_at: datetime\n\n\nclass TechniquePageVersionListResponse(BaseModel):\n    \"\"\"Response for version list endpoint.\"\"\"\n    items: list[TechniquePageVersionSummary] = Field(default_factory=list)\n    total: int = 0\n\n\n# ── Topics ───────────────────────────────────────────────────────────────────\n\nclass TopicSubTopic(BaseModel):\n    \"\"\"A sub-topic with aggregated counts.\"\"\"\n    name: str\n    technique_count: int = 0\n    creator_count: int = 0\n\n\nclass TopicCategory(BaseModel):\n    \"\"\"A top-level topic category with sub-topics.\"\"\"\n    name: str\n    description: str = \"\"\n    sub_topics: list[TopicSubTopic] = Field(default_factory=list)\n\n\n# ── Creator Browse ───────────────────────────────────────────────────────────\n\nclass CreatorBrowseItem(CreatorRead):\n    \"\"\"Creator with technique and video counts for browse pages.\"\"\"\n    technique_count: int = 0\n    video_count: int = 0\n\n\n# ── Content Reports ──────────────────────────────────────────────────────────\n\nclass ContentReportCreate(BaseModel):\n    \"\"\"Public submission: report a content issue.\"\"\"\n    content_type: str = Field(\n        ..., description=\"Entity type: technique_page, key_moment, creator, general\"\n    )\n    content_id: uuid.UUID | None = Field(\n        None, description=\"ID of the reported entity (null for general reports)\"\n    )\n    content_title: str | None = Field(\n        None, description=\"Title of the reported content (for display context)\"\n    )\n    report_type: str = Field(\n        ..., description=\"inaccurate, missing_info, wrong_attribution, formatting, other\"\n    )\n    description: str = Field(\n        ..., min_length=10, max_length=2000,\n        description=\"Description of the issue\"\n    )\n    page_url: str | None = Field(\n        None, description=\"URL the user was on when reporting\"\n    )\n\n\nclass ContentReportRead(BaseModel):\n    \"\"\"Full report for admin views.\"\"\"\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    content_type: str\n    content_id: uuid.UUID | None = None\n    content_title: str | None = None\n    report_type: str\n    description: str\n    status: str = \"open\"\n    admin_notes: str | None = None\n    page_url: str | None = None\n    created_at: datetime\n    resolved_at: datetime | None = None\n\n\nclass ContentReportUpdate(BaseModel):\n    \"\"\"Admin update: change status and/or add notes.\"\"\"\n    status: str | None = Field(\n        None, description=\"open, acknowledged, resolved, dismissed\"\n    )\n    admin_notes: str | None = Field(\n        None, max_length=2000, description=\"Admin notes about resolution\"\n    )\n\n\nclass ContentReportListResponse(BaseModel):\n    \"\"\"Paginated list of content reports.\"\"\"\n    items: list[ContentReportRead] = Field(default_factory=list)\n    total: int = 0\n    offset: int = 0\n    limit: int = 50\n\n\n# ── Pipeline Debug Mode ─────────────────────────────────────────────────────\n\nclass DebugModeResponse(BaseModel):\n    \"\"\"Current debug mode status.\"\"\"\n    debug_mode: bool\n\n\nclass DebugModeUpdate(BaseModel):\n    \"\"\"Toggle debug mode on/off.\"\"\"\n    debug_mode: bool\n\n\nclass TokenStageSummary(BaseModel):\n    \"\"\"Per-stage token usage aggregation.\"\"\"\n    stage: str\n    call_count: int\n    total_prompt_tokens: int\n    total_completion_tokens: int\n    total_tokens: int\n\n\nclass TokenSummaryResponse(BaseModel):\n    \"\"\"Token usage summary for a video, broken down by stage.\"\"\"\n    video_id: str\n    stages: list[TokenStageSummary] = Field(default_factory=list)\n    grand_total_tokens: int\n\n\n# ── Admin: Technique Pages ───────────────────────────────────────────────────\n\nclass AdminTechniquePageItem(BaseModel):\n    \"\"\"Technique page with aggregated source/version counts for admin view.\"\"\"\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    title: str\n    slug: str\n    creator_name: str\n    creator_slug: str\n    topic_category: str\n    body_sections_format: str\n    source_video_count: int = 0\n    version_count: int = 0\n    created_at: datetime\n    updated_at: datetime\n\n\nclass AdminTechniquePageListResponse(BaseModel):\n    \"\"\"Paginated list of technique pages for admin view.\"\"\"\n    items: list[AdminTechniquePageItem] = Field(default_factory=list)\n    total: int = 0\n    offset: int = 0\n    limit: int = 50\n"}],"isError":false,"timestamp":1775188829650}}
{"type":"message","id":"6748058e","parentId":"c3106cbc","timestamp":"2026-04-03T04:00:29.655Z","message":{"role":"toolResult","toolCallId":"toolu_01SaoTmyrh4bxAz9cFEdziM6","toolName":"read","content":[{"type":"text","text":"\"\"\"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 import func, select\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom config import get_settings\nfrom database import get_session\nfrom models import Creator, TechniquePage\nfrom schemas import (\n    SearchResponse,\n    SearchResultItem,\n    SuggestionItem,\n    SuggestionsResponse,\n)\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    sort: Annotated[str, Query()] = \"relevance\",\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, sort=sort, limit=limit, db=db)\n    return SearchResponse(\n        items=[SearchResultItem(**item) for item in result[\"items\"]],\n        partial_matches=[SearchResultItem(**item) for item in result.get(\"partial_matches\", [])],\n        total=result[\"total\"],\n        query=result[\"query\"],\n        fallback_used=result[\"fallback_used\"],\n    )\n\n\n@router.get(\"/suggestions\", response_model=SuggestionsResponse)\nasync def suggestions(\n    db: AsyncSession = Depends(get_session),\n) -> SuggestionsResponse:\n    \"\"\"Return popular search suggestions for autocomplete.\n\n    Combines top technique pages (by view_count), popular topic tags\n    (by technique count), and top creators (by view_count).\n    Returns 8–12 deduplicated items.\n    \"\"\"\n    seen: set[str] = set()\n    items: list[SuggestionItem] = []\n\n    def _add(text: str, type_: str) -> None:\n        key = text.lower()\n        if key not in seen:\n            seen.add(key)\n            items.append(SuggestionItem(text=text, type=type_))\n\n    # Top 4 technique pages by view_count\n    tp_stmt = (\n        select(TechniquePage.title)\n        .order_by(TechniquePage.view_count.desc(), TechniquePage.title)\n        .limit(4)\n    )\n    tp_result = await db.execute(tp_stmt)\n    for (title,) in tp_result.all():\n        _add(title, \"technique\")\n\n    # Top 4 topic tags by how many technique pages use them\n    # Unnest the topic_tags ARRAY and count occurrences\n    tag_unnest = (\n        select(\n            func.unnest(TechniquePage.topic_tags).label(\"tag\"),\n        )\n        .where(TechniquePage.topic_tags.isnot(None))\n        .subquery()\n    )\n    tag_stmt = (\n        select(\n            tag_unnest.c.tag,\n            func.count().label(\"cnt\"),\n        )\n        .group_by(tag_unnest.c.tag)\n        .order_by(func.count().desc(), tag_unnest.c.tag)\n        .limit(4)\n    )\n    tag_result = await db.execute(tag_stmt)\n    for tag, _cnt in tag_result.all():\n        _add(tag, \"topic\")\n\n    # Top 4 creators by view_count\n    cr_stmt = (\n        select(Creator.name)\n        .where(Creator.hidden.is_(False))\n        .order_by(Creator.view_count.desc(), Creator.name)\n        .limit(4)\n    )\n    cr_result = await db.execute(cr_stmt)\n    for (name,) in cr_result.all():\n        _add(name, \"creator\")\n\n    return SuggestionsResponse(suggestions=items)\n"}],"isError":false,"timestamp":1775188829650}}
{"type":"message","id":"c5d59bef","parentId":"6748058e","timestamp":"2026-04-03T04:00:34.525Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01W7zfHGdeZLMSxaHR4Faifd","name":"read","arguments":{"path":"backend/database.py"}},{"type":"toolCall","id":"toolu_01MX1CpjYQJUMxNGjZGU5B5q","name":"read","arguments":{"path":"backend/redis_client.py"}},{"type":"toolCall","id":"toolu_01CNbVDUFE8Kmd3q7CQZZHkc","name":"bash","arguments":{"command":"ls backend/alembic/versions/012_* 2>/dev/null || ls alembic/versions/012_* 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":164,"cacheRead":64447,"cacheWrite":10939,"totalTokens":75551,"cost":{"input":0.000005,"output":0.0041,"cacheRead":0.032223499999999995,"cacheWrite":0.06836875,"total":0.10469724999999999}},"stopReason":"toolUse","timestamp":1775188829652}}
{"type":"message","id":"01db190b","parentId":"c5d59bef","timestamp":"2026-04-03T04:00:34.540Z","message":{"role":"toolResult","toolCallId":"toolu_01W7zfHGdeZLMSxaHR4Faifd","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":1775188834539}}
{"type":"message","id":"1a507133","parentId":"01db190b","timestamp":"2026-04-03T04:00:34.542Z","message":{"role":"toolResult","toolCallId":"toolu_01MX1CpjYQJUMxNGjZGU5B5q","toolName":"read","content":[{"type":"text","text":"\"\"\"Async Redis client helper for Chrysopedia.\"\"\"\n\nimport redis.asyncio as aioredis\n\nfrom config import get_settings\n\n\nasync def get_redis() -> aioredis.Redis:\n    \"\"\"Return an async Redis client from the configured URL.\n\n    Callers should close the connection when done, or use it\n    as a short-lived client within a request handler.\n    \"\"\"\n    settings = get_settings()\n    return aioredis.from_url(settings.redis_url, decode_responses=True)\n"}],"isError":false,"timestamp":1775188834541}}
{"type":"message","id":"95d46a0d","parentId":"1a507133","timestamp":"2026-04-03T04:00:34.547Z","message":{"role":"toolResult","toolCallId":"toolu_01CNbVDUFE8Kmd3q7CQZZHkc","toolName":"bash","content":[{"type":"text","text":"alembic/versions/012_multi_source_format.py\n"}],"isError":false,"timestamp":1775188834545}}
{"type":"message","id":"9fa4188c","parentId":"95d46a0d","timestamp":"2026-04-03T04:00:37.372Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01S7gjYLWgL7xQX3iaQS87MN","name":"read","arguments":{"path":"alembic/versions/012_multi_source_format.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":65,"cacheRead":75386,"cacheWrite":607,"totalTokens":76059,"cost":{"input":0.000005,"output":0.0016250000000000001,"cacheRead":0.037693,"cacheWrite":0.0037937500000000002,"total":0.043116749999999995}},"stopReason":"toolUse","timestamp":1775188834546}}
{"type":"message","id":"52d15b74","parentId":"9fa4188c","timestamp":"2026-04-03T04:00:37.376Z","message":{"role":"toolResult","toolCallId":"toolu_01S7gjYLWgL7xQX3iaQS87MN","toolName":"read","content":[{"type":"text","text":"\"\"\"Add body_sections_format column and technique_page_videos association table.\n\nSupports multi-source technique pages: tracks which source videos contributed\nto a technique page, and marks the body_sections format version for future\nstructured section layouts.\n\nRevision ID: 012_multi_source_fmt\nRevises: 011_cls_cache_rerun\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects.postgresql import UUID\n\nrevision = \"012_multi_source_fmt\"\ndown_revision = \"011_cls_cache_rerun\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n    # Add body_sections_format to technique_pages with default for existing rows\n    op.add_column(\n        \"technique_pages\",\n        sa.Column(\n            \"body_sections_format\",\n            sa.String(20),\n            nullable=False,\n            server_default=\"v1\",\n        ),\n    )\n\n    # Create technique_page_videos association table\n    op.create_table(\n        \"technique_page_videos\",\n        sa.Column(\"id\", UUID(as_uuid=True), primary_key=True, server_default=sa.func.gen_random_uuid()),\n        sa.Column(\n            \"technique_page_id\",\n            UUID(as_uuid=True),\n            sa.ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"),\n            nullable=False,\n        ),\n        sa.Column(\n            \"source_video_id\",\n            UUID(as_uuid=True),\n            sa.ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"),\n            nullable=False,\n        ),\n        sa.Column(\"added_at\", sa.TIMESTAMP(), server_default=sa.func.now(), nullable=False),\n        sa.UniqueConstraint(\"technique_page_id\", \"source_video_id\", name=\"uq_page_video\"),\n    )\n\n\ndef downgrade() -> None:\n    op.drop_table(\"technique_page_videos\")\n    op.drop_column(\"technique_pages\", \"body_sections_format\")\n"}],"isError":false,"timestamp":1775188837374}}
{"type":"message","id":"690901ca","parentId":"52d15b74","timestamp":"2026-04-03T04:00:45.390Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — I have the full picture. Now I'll implement all four files. The SearchLog model uses Integer PK (not UUID) as specified, `async_session` is a sessionmaker I can use directly for the fire-and-forget pattern.\n\n**Step 1: Add SearchLog model to models.py**"},{"type":"toolCall","id":"toolu_01VM2mPiLqUM3KqUAF2XPhW5","name":"edit","arguments":{"newText":"class SearchLog(Base):\n    \"\"\"Logged search query for analytics and popular searches.\"\"\"\n    __tablename__ = \"search_log\"\n\n    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)\n    query: Mapped[str] = mapped_column(String(500), nullable=False, index=True)\n    scope: Mapped[str] = mapped_column(String(50), nullable=False)\n    result_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0)\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), index=True\n    )\n\n\nclass PipelineRunStatus(str, enum.Enum):","oldText":"class PipelineRunStatus(str, enum.Enum):","path":"backend/models.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":357,"cacheRead":75386,"cacheWrite":1189,"totalTokens":76933,"cost":{"input":0.000005,"output":0.008925,"cacheRead":0.037693,"cacheWrite":0.00743125,"total":0.05405425}},"stopReason":"toolUse","timestamp":1775188837375}}
{"type":"message","id":"84511d2e","parentId":"690901ca","timestamp":"2026-04-03T04:00:45.400Z","message":{"role":"toolResult","toolCallId":"toolu_01VM2mPiLqUM3KqUAF2XPhW5","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/models.py."}],"details":{"diff":" 394 \n 395 \n 396 # ── Pipeline Event ───────────────────────────────────────────────────────────\n 397 \n+398 class SearchLog(Base):\n+399     \"\"\"Logged search query for analytics and popular searches.\"\"\"\n+400     __tablename__ = \"search_log\"\n+401 \n+402     id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)\n+403     query: Mapped[str] = mapped_column(String(500), nullable=False, index=True)\n+404     scope: Mapped[str] = mapped_column(String(50), nullable=False)\n+405     result_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0)\n+406     created_at: Mapped[datetime] = mapped_column(\n+407         default=_now, server_default=func.now(), index=True\n+408     )\n+409 \n+410 \n 411 class PipelineRunStatus(str, enum.Enum):\n 412     \"\"\"Status of a pipeline run.\"\"\"\n 413     running = \"running\"\n 414     complete = \"complete\"","firstChangedLine":398},"isError":false,"timestamp":1775188845398}}
{"type":"message","id":"9c7e15d4","parentId":"84511d2e","timestamp":"2026-04-03T04:00:49.270Z","message":{"role":"assistant","content":[{"type":"text","text":"**Step 2: Add Pydantic schemas**"},{"type":"toolCall","id":"toolu_01S8xNMNwk3LWkhedq6TghMr","name":"read","arguments":{"limit":20,"offset":290,"path":"backend/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":102,"cacheRead":76575,"cacheWrite":378,"totalTokens":77056,"cost":{"input":0.000005,"output":0.00255,"cacheRead":0.038287499999999995,"cacheWrite":0.0023625,"total":0.04320499999999999}},"stopReason":"toolUse","timestamp":1775188845398}}
{"type":"message","id":"a13b00ca","parentId":"9c7e15d4","timestamp":"2026-04-03T04:00:49.274Z","message":{"role":"toolResult","toolCallId":"toolu_01S8xNMNwk3LWkhedq6TghMr","toolName":"read","content":[{"type":"text","text":"\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    version_count: int = 0\n    source_videos: list[SourceVideoSummary] = Field(default_factory=list)\n\n\n# ── Technique Page Versions ──────────────────────────────────────────────────\n\nclass TechniquePageVersionSummary(BaseModel):\n    \"\"\"Lightweight version entry for list responses.\"\"\"\n    model_config = ConfigDict(from_attributes=True)\n\n    version_number: int\n    created_at: datetime\n    pipeline_metadata: dict | None = None\n\n\n[155 more lines in file. Use offset=310 to continue.]"}],"isError":false,"timestamp":1775188849272}}
{"type":"message","id":"b6f36723","parentId":"a13b00ca","timestamp":"2026-04-03T04:00:55.562Z","message":{"role":"assistant","content":[{"type":"text","text":"I'll add the popular search schemas after the existing Search section."},{"type":"toolCall","id":"toolu_01F7V4PzEzerg3nx5vmonPSr","name":"edit","arguments":{"newText":"class SuggestionsResponse(BaseModel):\n    \"\"\"Popular search suggestions for autocomplete.\"\"\"\n    suggestions: list[SuggestionItem] = Field(default_factory=list)\n\n\nclass PopularSearchItem(BaseModel):\n    \"\"\"A single popular search query with occurrence count.\"\"\"\n    query: str\n    count: int\n\n\nclass PopularSearchesResponse(BaseModel):\n    \"\"\"Response for the popular searches endpoint.\"\"\"\n    items: list[PopularSearchItem] = Field(default_factory=list)\n    cached: bool = False\n\n\n# ── Technique Page Detail ────────────────────────────────────────────────────","oldText":"class SuggestionsResponse(BaseModel):\n    \"\"\"Popular search suggestions for autocomplete.\"\"\"\n    suggestions: list[SuggestionItem] = Field(default_factory=list)\n\n\n# ── Technique Page Detail ────────────────────────────────────────────────────","path":"backend/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":309,"cacheRead":76575,"cacheWrite":726,"totalTokens":77611,"cost":{"input":0.000005,"output":0.007725,"cacheRead":0.038287499999999995,"cacheWrite":0.0045375,"total":0.050554999999999996}},"stopReason":"toolUse","timestamp":1775188849272}}
{"type":"message","id":"d6e3bb94","parentId":"b6f36723","timestamp":"2026-04-03T04:00:55.575Z","message":{"role":"toolResult","toolCallId":"toolu_01F7V4PzEzerg3nx5vmonPSr","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/schemas.py."}],"details":{"diff":" 239     \"\"\"Popular search suggestions for autocomplete.\"\"\"\n 240     suggestions: list[SuggestionItem] = Field(default_factory=list)\n 241 \n 242 \n+243 class PopularSearchItem(BaseModel):\n+244     \"\"\"A single popular search query with occurrence count.\"\"\"\n+245     query: str\n+246     count: int\n+247 \n+248 \n+249 class PopularSearchesResponse(BaseModel):\n+250     \"\"\"Response for the popular searches endpoint.\"\"\"\n+251     items: list[PopularSearchItem] = Field(default_factory=list)\n+252     cached: bool = False\n+253 \n+254 \n 255 # ── Technique Page Detail ────────────────────────────────────────────────────\n 256 \n 257 class KeyMomentSummary(BaseModel):\n 258     \"\"\"Lightweight key moment for technique page detail.\"\"\"","firstChangedLine":243},"isError":false,"timestamp":1775188855573}}
{"type":"message","id":"355c2e32","parentId":"d6e3bb94","timestamp":"2026-04-03T04:01:02.008Z","message":{"role":"assistant","content":[{"type":"text","text":"**Step 3: Create the Alembic migration**"},{"type":"toolCall","id":"toolu_016oQcP2QP8AZUUBvGeV1QPd","name":"write","arguments":{"content":"\"\"\"Add search_log table for query analytics and popular searches.\n\nRevision ID: 013_add_search_log\nRevises: 012_multi_source_fmt\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\nrevision = \"013_add_search_log\"\ndown_revision = \"012_multi_source_fmt\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n    op.create_table(\n        \"search_log\",\n        sa.Column(\"id\", sa.Integer, primary_key=True, autoincrement=True),\n        sa.Column(\"query\", sa.String(500), nullable=False),\n        sa.Column(\"scope\", sa.String(50), nullable=False),\n        sa.Column(\"result_count\", sa.Integer, nullable=False, server_default=\"0\"),\n        sa.Column(\"created_at\", sa.TIMESTAMP(), server_default=sa.func.now(), nullable=False),\n    )\n    op.create_index(\"ix_search_log_query\", \"search_log\", [\"query\"])\n    op.create_index(\"ix_search_log_created_at\", \"search_log\", [\"created_at\"])\n\n\ndef downgrade() -> None:\n    op.drop_index(\"ix_search_log_created_at\", table_name=\"search_log\")\n    op.drop_index(\"ix_search_log_query\", table_name=\"search_log\")\n    op.drop_table(\"search_log\")\n","path":"alembic/versions/013_add_search_log.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":448,"cacheRead":77301,"cacheWrite":330,"totalTokens":78080,"cost":{"input":0.000005,"output":0.0112,"cacheRead":0.0386505,"cacheWrite":0.0020625,"total":0.051918}},"stopReason":"toolUse","timestamp":1775188855573}}
{"type":"message","id":"d72a04a7","parentId":"355c2e32","timestamp":"2026-04-03T04:01:02.012Z","message":{"role":"toolResult","toolCallId":"toolu_016oQcP2QP8AZUUBvGeV1QPd","toolName":"write","content":[{"type":"text","text":"Successfully wrote 1079 bytes to alembic/versions/013_add_search_log.py"}],"isError":false,"timestamp":1775188862010}}
{"type":"message","id":"9502d5e6","parentId":"d72a04a7","timestamp":"2026-04-03T04:01:10.173Z","message":{"role":"assistant","content":[{"type":"text","text":"**Step 4: Add logging hook and popular endpoint to search router**"},{"type":"toolCall","id":"toolu_01HV8GP2dn6mCAohgWZbo4Tf","name":"edit","arguments":{"newText":"\"\"\"Search endpoint for semantic + keyword search with graceful fallback.\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport json\nimport logging\nfrom typing import Annotated\n\nfrom fastapi import APIRouter, Depends, Query\nfrom sqlalchemy import func, select, text\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom config import get_settings\nfrom database import async_session, get_session\nfrom models import Creator, SearchLog, TechniquePage\nfrom redis_client import get_redis\nfrom schemas import (\n    PopularSearchesResponse,\n    PopularSearchItem,\n    SearchResponse,\n    SearchResultItem,\n    SuggestionItem,\n    SuggestionsResponse,\n)\nfrom search_service import SearchService\n\nlogger = logging.getLogger(\"chrysopedia.search.router\")\n\nrouter = APIRouter(prefix=\"/search\", tags=[\"search\"])\n\nPOPULAR_CACHE_KEY = \"chrysopedia:popular_searches\"\nPOPULAR_CACHE_TTL = 300  # 5 minutes\n\n\ndef _get_search_service() -> SearchService:\n    \"\"\"Build a SearchService from current settings.\"\"\"\n    return SearchService(get_settings())","oldText":"\"\"\"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 import func, select\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom config import get_settings\nfrom database import get_session\nfrom models import Creator, TechniquePage\nfrom schemas import (\n    SearchResponse,\n    SearchResultItem,\n    SuggestionItem,\n    SuggestionsResponse,\n)\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())","path":"backend/routers/search.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":572,"cacheRead":77631,"cacheWrite":483,"totalTokens":78687,"cost":{"input":0.000005,"output":0.0143,"cacheRead":0.038815499999999996,"cacheWrite":0.00301875,"total":0.056139249999999995}},"stopReason":"toolUse","timestamp":1775188862011}}
{"type":"message","id":"ed92612c","parentId":"9502d5e6","timestamp":"2026-04-03T04:01:10.179Z","message":{"role":"toolResult","toolCallId":"toolu_01HV8GP2dn6mCAohgWZbo4Tf","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/routers/search.py."}],"details":{"diff":"   1 \"\"\"Search endpoint for semantic + keyword search with graceful fallback.\"\"\"\n   2 \n   3 from __future__ import annotations\n   4 \n+  5 import asyncio\n+  6 import json\n   7 import logging\n   8 from typing import Annotated\n   9 \n  10 from fastapi import APIRouter, Depends, Query\n-  9 from sqlalchemy import func, select\n+ 11 from sqlalchemy import func, select, text\n  12 from sqlalchemy.ext.asyncio import AsyncSession\n  13 \n  14 from config import get_settings\n- 13 from database import get_session\n- 14 from models import Creator, TechniquePage\n+ 15 from database import async_session, get_session\n+ 16 from models import Creator, SearchLog, TechniquePage\n+ 17 from redis_client import get_redis\n  18 from schemas import (\n+ 19     PopularSearchesResponse,\n+ 20     PopularSearchItem,\n  21     SearchResponse,\n  22     SearchResultItem,\n  23     SuggestionItem,\n  24     SuggestionsResponse,\n...\n  28 logger = logging.getLogger(\"chrysopedia.search.router\")\n  29 \n  30 router = APIRouter(prefix=\"/search\", tags=[\"search\"])\n  31 \n+ 32 POPULAR_CACHE_KEY = \"chrysopedia:popular_searches\"\n+ 33 POPULAR_CACHE_TTL = 300  # 5 minutes\n  34 \n+ 35 \n  36 def _get_search_service() -> SearchService:\n  37     \"\"\"Build a SearchService from current settings.\"\"\"\n  38     return SearchService(get_settings())\n  39 ","firstChangedLine":5},"isError":false,"timestamp":1775188870177}}
{"type":"message","id":"9928382a","parentId":"ed92612c","timestamp":"2026-04-03T04:01:23.927Z","message":{"role":"assistant","content":[{"type":"text","text":"Now add the `_log_search` helper and hook it into the `search()` handler:"},{"type":"toolCall","id":"toolu_01XHAeuGcD5i6QrNQwpmZ6p7","name":"edit","arguments":{"newText":"async def _log_search(query: str, scope: str, result_count: int) -> None:\n    \"\"\"Fire-and-forget: persist a search log row.\n\n    Opens its own session so it doesn't interfere with the request session.\n    Catches all exceptions — a logging failure must never break a search request.\n    \"\"\"\n    try:\n        async with async_session() as session:\n            session.add(SearchLog(query=query, scope=scope, result_count=result_count))\n            await session.commit()\n    except Exception:\n        logger.warning(\"Failed to log search query %r\", query, exc_info=True)\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    sort: Annotated[str, Query()] = \"relevance\",\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, sort=sort, limit=limit, db=db)\n\n    # Fire-and-forget search logging — only non-empty queries\n    if q.strip():\n        asyncio.create_task(_log_search(q.strip(), scope, result[\"total\"]))\n\n    return SearchResponse(\n        items=[SearchResultItem(**item) for item in result[\"items\"]],\n        partial_matches=[SearchResultItem(**item) for item in result.get(\"partial_matches\", [])],\n        total=result[\"total\"],\n        query=result[\"query\"],\n        fallback_used=result[\"fallback_used\"],\n    )","oldText":"@router.get(\"\", response_model=SearchResponse)\nasync def search(\n    q: Annotated[str, Query(max_length=500)] = \"\",\n    scope: Annotated[str, Query()] = \"all\",\n    sort: Annotated[str, Query()] = \"relevance\",\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, sort=sort, limit=limit, db=db)\n    return SearchResponse(\n        items=[SearchResultItem(**item) for item in result[\"items\"]],\n        partial_matches=[SearchResultItem(**item) for item in result.get(\"partial_matches\", [])],\n        total=result[\"total\"],\n        query=result[\"query\"],\n        fallback_used=result[\"fallback_used\"],\n    )","path":"backend/routers/search.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":950,"cacheRead":78114,"cacheWrite":596,"totalTokens":79661,"cost":{"input":0.000005,"output":0.02375,"cacheRead":0.039057,"cacheWrite":0.003725,"total":0.06653700000000001}},"stopReason":"toolUse","timestamp":1775188870178}}
{"type":"message","id":"fcb212b7","parentId":"9928382a","timestamp":"2026-04-03T04:01:23.934Z","message":{"role":"toolResult","toolCallId":"toolu_01XHAeuGcD5i6QrNQwpmZ6p7","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/routers/search.py."}],"details":{"diff":"  37     \"\"\"Build a SearchService from current settings.\"\"\"\n  38     return SearchService(get_settings())\n  39 \n  40 \n+ 41 async def _log_search(query: str, scope: str, result_count: int) -> None:\n+ 42     \"\"\"Fire-and-forget: persist a search log row.\n+ 43 \n+ 44     Opens its own session so it doesn't interfere with the request session.\n+ 45     Catches all exceptions — a logging failure must never break a search request.\n+ 46     \"\"\"\n+ 47     try:\n+ 48         async with async_session() as session:\n+ 49             session.add(SearchLog(query=query, scope=scope, result_count=result_count))\n+ 50             await session.commit()\n+ 51     except Exception:\n+ 52         logger.warning(\"Failed to log search query %r\", query, exc_info=True)\n+ 53 \n+ 54 \n  55 @router.get(\"\", response_model=SearchResponse)\n  56 async def search(\n  57     q: Annotated[str, Query(max_length=500)] = \"\",\n  58     scope: Annotated[str, Query()] = \"all\",\n...\n  67     - **limit**: Max results (1–100, default 20).\n  68     \"\"\"\n  69     svc = _get_search_service()\n  70     result = await svc.search(query=q, scope=scope, sort=sort, limit=limit, db=db)\n+ 71 \n+ 72     # Fire-and-forget search logging — only non-empty queries\n+ 73     if q.strip():\n+ 74         asyncio.create_task(_log_search(q.strip(), scope, result[\"total\"]))\n+ 75 \n  76     return SearchResponse(\n  77         items=[SearchResultItem(**item) for item in result[\"items\"]],\n  78         partial_matches=[SearchResultItem(**item) for item in result.get(\"partial_matches\", [])],\n  79         total=result[\"total\"],","firstChangedLine":41},"isError":false,"timestamp":1775188883932}}
{"type":"message","id":"d9123b7d","parentId":"fcb212b7","timestamp":"2026-04-03T04:01:29.101Z","message":{"role":"assistant","content":[{"type":"text","text":"Now add the `GET /popular` endpoint after the `suggestions` endpoint:"},{"type":"toolCall","id":"toolu_01EcxQb5xTMxY1rJRLinMcpD","name":"read","arguments":{"offset":95,"path":"backend/routers/search.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":78710,"cacheWrite":974,"totalTokens":79777,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.039355,"cacheWrite":0.0060875,"total":0.047747500000000005}},"stopReason":"toolUse","timestamp":1775188883932}}
{"type":"message","id":"24af3c0a","parentId":"d9123b7d","timestamp":"2026-04-03T04:01:29.105Z","message":{"role":"toolResult","toolCallId":"toolu_01EcxQb5xTMxY1rJRLinMcpD","toolName":"read","content":[{"type":"text","text":"    seen: set[str] = set()\n    items: list[SuggestionItem] = []\n\n    def _add(text: str, type_: str) -> None:\n        key = text.lower()\n        if key not in seen:\n            seen.add(key)\n            items.append(SuggestionItem(text=text, type=type_))\n\n    # Top 4 technique pages by view_count\n    tp_stmt = (\n        select(TechniquePage.title)\n        .order_by(TechniquePage.view_count.desc(), TechniquePage.title)\n        .limit(4)\n    )\n    tp_result = await db.execute(tp_stmt)\n    for (title,) in tp_result.all():\n        _add(title, \"technique\")\n\n    # Top 4 topic tags by how many technique pages use them\n    # Unnest the topic_tags ARRAY and count occurrences\n    tag_unnest = (\n        select(\n            func.unnest(TechniquePage.topic_tags).label(\"tag\"),\n        )\n        .where(TechniquePage.topic_tags.isnot(None))\n        .subquery()\n    )\n    tag_stmt = (\n        select(\n            tag_unnest.c.tag,\n            func.count().label(\"cnt\"),\n        )\n        .group_by(tag_unnest.c.tag)\n        .order_by(func.count().desc(), tag_unnest.c.tag)\n        .limit(4)\n    )\n    tag_result = await db.execute(tag_stmt)\n    for tag, _cnt in tag_result.all():\n        _add(tag, \"topic\")\n\n    # Top 4 creators by view_count\n    cr_stmt = (\n        select(Creator.name)\n        .where(Creator.hidden.is_(False))\n        .order_by(Creator.view_count.desc(), Creator.name)\n        .limit(4)\n    )\n    cr_result = await db.execute(cr_stmt)\n    for (name,) in cr_result.all():\n        _add(name, \"creator\")\n\n    return SuggestionsResponse(suggestions=items)\n"}],"isError":false,"timestamp":1775188889103}}
{"type":"message","id":"1b97b002","parentId":"24af3c0a","timestamp":"2026-04-03T04:01:38.465Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Qtzmz1toxLqoBQhCSh41Sz","name":"edit","arguments":{"newText":"    return SuggestionsResponse(suggestions=items)\n\n\n@router.get(\"/popular\", response_model=PopularSearchesResponse)\nasync def popular_searches(\n    db: AsyncSession = Depends(get_session),\n) -> PopularSearchesResponse:\n    \"\"\"Return the top 10 search queries from the last 7 days.\n\n    Results are cached in Redis for 5 minutes. Falls through to a\n    direct DB query when Redis is unavailable.\n    \"\"\"\n    # Try Redis cache first\n    try:\n        redis = await get_redis()\n        cached = await redis.get(POPULAR_CACHE_KEY)\n        await redis.aclose()\n        if cached is not None:\n            items = json.loads(cached)\n            return PopularSearchesResponse(\n                items=[PopularSearchItem(**i) for i in items],\n                cached=True,\n            )\n    except Exception:\n        logger.warning(\"Redis unavailable for popular searches cache\", exc_info=True)\n\n    # Cache miss or Redis down — query DB\n    stmt = (\n        select(\n            func.lower(SearchLog.query).label(\"q\"),\n            func.count().label(\"cnt\"),\n        )\n        .where(SearchLog.created_at > func.now() - text(\"interval '7 days'\"))\n        .group_by(func.lower(SearchLog.query))\n        .order_by(func.count().desc())\n        .limit(10)\n    )\n    result = await db.execute(stmt)\n    items = [PopularSearchItem(query=row.q, count=row.cnt) for row in result.all()]\n\n    # Write to Redis cache (best-effort)\n    try:\n        redis = await get_redis()\n        await redis.set(\n            POPULAR_CACHE_KEY,\n            json.dumps([i.model_dump() for i in items]),\n            ex=POPULAR_CACHE_TTL,\n        )\n        await redis.aclose()\n    except Exception:\n        logger.warning(\"Failed to cache popular searches in Redis\", exc_info=True)\n\n    return PopularSearchesResponse(items=items, cached=False)","oldText":"    return SuggestionsResponse(suggestions=items)","path":"backend/routers/search.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":612,"cacheRead":79684,"cacheWrite":637,"totalTokens":80934,"cost":{"input":0.000005,"output":0.015300000000000001,"cacheRead":0.039841999999999995,"cacheWrite":0.00398125,"total":0.05912824999999999}},"stopReason":"toolUse","timestamp":1775188889104}}
{"type":"message","id":"d8216e6b","parentId":"1b97b002","timestamp":"2026-04-03T04:01:38.472Z","message":{"role":"toolResult","toolCallId":"toolu_01Qtzmz1toxLqoBQhCSh41Sz","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/routers/search.py."}],"details":{"diff":" 144     for (name,) in cr_result.all():\n 145         _add(name, \"creator\")\n 146 \n 147     return SuggestionsResponse(suggestions=items)\n+148 \n+149 \n+150 @router.get(\"/popular\", response_model=PopularSearchesResponse)\n+151 async def popular_searches(\n+152     db: AsyncSession = Depends(get_session),\n+153 ) -> PopularSearchesResponse:\n+154     \"\"\"Return the top 10 search queries from the last 7 days.\n+155 \n+156     Results are cached in Redis for 5 minutes. Falls through to a\n+157     direct DB query when Redis is unavailable.\n+158     \"\"\"\n+159     # Try Redis cache first\n+160     try:\n+161         redis = await get_redis()\n+162         cached = await redis.get(POPULAR_CACHE_KEY)\n+163         await redis.aclose()\n+164         if cached is not None:\n+165             items = json.loads(cached)\n+166             return PopularSearchesResponse(\n+167                 items=[PopularSearchItem(**i) for i in items],\n+168                 cached=True,\n+169             )\n+170     except Exception:\n+171         logger.warning(\"Redis unavailable for popular searches cache\", exc_info=True)\n+172 \n+173     # Cache miss or Redis down — query DB\n+174     stmt = (\n+175         select(\n+176             func.lower(SearchLog.query).label(\"q\"),\n+177             func.count().label(\"cnt\"),\n+178         )\n+179         .where(SearchLog.created_at > func.now() - text(\"interval '7 days'\"))\n+180         .group_by(func.lower(SearchLog.query))\n+181         .order_by(func.count().desc())\n+182         .limit(10)\n+183     )\n+184     result = await db.execute(stmt)\n+185     items = [PopularSearchItem(query=row.q, count=row.cnt) for row in result.all()]\n+186 \n+187     # Write to Redis cache (best-effort)\n+188     try:\n+189         redis = await get_redis()\n+190         await redis.set(\n+191             POPULAR_CACHE_KEY,\n+192             json.dumps([i.model_dump() for i in items]),\n+193             ex=POPULAR_CACHE_TTL,\n+194         )\n+195         await redis.aclose()\n+196     except Exception:\n+197         logger.warning(\"Failed to cache popular searches in Redis\", exc_info=True)\n+198 \n+199     return PopularSearchesResponse(items=items, cached=False)","firstChangedLine":148},"isError":false,"timestamp":1775188898470}}
{"type":"message","id":"6e3e9860","parentId":"d8216e6b","timestamp":"2026-04-03T04:01:43.821Z","message":{"role":"assistant","content":[{"type":"text","text":"All four files are done. Running the verification checks."},{"type":"toolCall","id":"toolu_01SF1uxhVpiRV2MZnmYKRYfR","name":"bash","arguments":{"command":"cd backend && python -c \"from models import SearchLog; print(SearchLog.__tablename__)\""}},{"type":"toolCall","id":"toolu_019MTLePZ9ox8k3eAXsnaWeh","name":"bash","arguments":{"command":"cd backend && python -c \"from schemas import PopularSearchItem, PopularSearchesResponse; print('OK')\""}},{"type":"toolCall","id":"toolu_011DZu3buBa9G7ktnzMR7U65","name":"bash","arguments":{"command":"cd backend && python -c \"from routers.search import router; routes = [r.path for r in router.routes]; assert '/popular' in routes; print('OK')\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":219,"cacheRead":80321,"cacheWrite":636,"totalTokens":81177,"cost":{"input":0.000005,"output":0.005475,"cacheRead":0.040160499999999995,"cacheWrite":0.003975,"total":0.04961549999999999}},"stopReason":"toolUse","timestamp":1775188898471}}
{"type":"message","id":"9fbb31bd","parentId":"6e3e9860","timestamp":"2026-04-03T04:01:44.143Z","message":{"role":"toolResult","toolCallId":"toolu_01SF1uxhVpiRV2MZnmYKRYfR","toolName":"bash","content":[{"type":"text","text":"search_log\n"}],"isError":false,"timestamp":1775188904143}}
{"type":"message","id":"09809aea","parentId":"9fbb31bd","timestamp":"2026-04-03T04:01:44.143Z","message":{"role":"toolResult","toolCallId":"toolu_019MTLePZ9ox8k3eAXsnaWeh","toolName":"bash","content":[{"type":"text","text":"OK\n"}],"isError":false,"timestamp":1775188904143}}
{"type":"message","id":"57f8096e","parentId":"09809aea","timestamp":"2026-04-03T04:01:45.555Z","message":{"role":"toolResult","toolCallId":"toolu_011DZu3buBa9G7ktnzMR7U65","toolName":"bash","content":[{"type":"text","text":"Traceback (most recent call last):\n  File \"<string>\", line 1, in <module>\nAssertionError\n\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775188905552}}
{"type":"message","id":"50914858","parentId":"57f8096e","timestamp":"2026-04-03T04:01:49.105Z","message":{"role":"assistant","content":[{"type":"text","text":"Route path mismatch — need to check what FastAPI registers."},{"type":"toolCall","id":"toolu_01MJtoC7EbswytRbJF9H4juj","name":"bash","arguments":{"command":"cd backend && python -c \"from routers.search import router; routes = [r.path for r in router.routes]; print(routes)\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":98,"cacheRead":80957,"cacheWrite":347,"totalTokens":81403,"cost":{"input":0.000005,"output":0.00245,"cacheRead":0.0404785,"cacheWrite":0.00216875,"total":0.045102249999999997}},"stopReason":"toolUse","timestamp":1775188905553}}
{"type":"message","id":"30804042","parentId":"50914858","timestamp":"2026-04-03T04:01:50.761Z","message":{"role":"toolResult","toolCallId":"toolu_01MJtoC7EbswytRbJF9H4juj","toolName":"bash","content":[{"type":"text","text":"['/search', '/search/suggestions', '/search/popular']\n"}],"isError":false,"timestamp":1775188910760}}
{"type":"message","id":"4667e2c6","parentId":"30804042","timestamp":"2026-04-03T04:01:57.212Z","message":{"role":"assistant","content":[{"type":"text","text":"The routes include the prefix. The verification command checks for `/popular` but the actual path is `/search/popular`. Let me check — the slice plan's verification command is the spec. The routes store the full path including the prefix. I need to match what the verification expects."},{"type":"toolCall","id":"toolu_01PgUKbKdLyxPnoANFLHQdkd","name":"bash","arguments":{"command":"cd backend && python -c \"from routers.search import router; routes = [r.path for r in router.routes]; assert '/search/popular' in routes; print('OK')\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":151,"cacheRead":81304,"cacheWrite":124,"totalTokens":81580,"cost":{"input":0.000005,"output":0.003775,"cacheRead":0.040652,"cacheWrite":0.0007750000000000001,"total":0.045207}},"stopReason":"toolUse","timestamp":1775188910760}}
{"type":"message","id":"52837a9b","parentId":"4667e2c6","timestamp":"2026-04-03T04:01:58.858Z","message":{"role":"toolResult","toolCallId":"toolu_01PgUKbKdLyxPnoANFLHQdkd","toolName":"bash","content":[{"type":"text","text":"OK\n"}],"isError":false,"timestamp":1775188918856}}
{"type":"message","id":"ead31533","parentId":"52837a9b","timestamp":"2026-04-03T04:02:04.686Z","message":{"role":"assistant","content":[{"type":"text","text":"The route exists. The slice verification command checks for `/popular` but the router uses a prefix, so the stored path is `/search/popular`. This is a minor mismatch in the verification spec — the route is correctly registered. Let me run the combined check from the task plan:"},{"type":"toolCall","id":"toolu_01AaDSRkQudCpLAJBfnW6DvM","name":"bash","arguments":{"command":"cd backend && python -c \"\nfrom models import SearchLog\nfrom schemas import PopularSearchItem, PopularSearchesResponse\nfrom routers.search import router\nroutes = [r.path for r in router.routes]\nassert '/search/popular' in routes, f'/popular not found in {routes}'\nprint('ALL OK')\n\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":187,"cacheRead":81428,"cacheWrite":165,"totalTokens":81781,"cost":{"input":0.000005,"output":0.004675,"cacheRead":0.040714,"cacheWrite":0.00103125,"total":0.04642525}},"stopReason":"toolUse","timestamp":1775188918857}}
{"type":"message","id":"4dbd50b2","parentId":"ead31533","timestamp":"2026-04-03T04:02:06.406Z","message":{"role":"toolResult","toolCallId":"toolu_01AaDSRkQudCpLAJBfnW6DvM","toolName":"bash","content":[{"type":"text","text":"ALL OK\n"}],"isError":false,"timestamp":1775188926404}}
{"type":"message","id":"40fe6390","parentId":"4dbd50b2","timestamp":"2026-04-03T04:02:12.511Z","message":{"role":"assistant","content":[{"type":"text","text":"All three verification checks pass (the `/popular` path check needed adjustment for the router prefix — APIRouter stores full paths including prefix). Let me also verify the migration file parses correctly:"},{"type":"toolCall","id":"toolu_016vnHnNhUJf24Yu6Du9c11H","name":"bash","arguments":{"command":"cd backend && python -c \"import importlib.util; spec = importlib.util.spec_from_file_location('m', '../alembic/versions/013_add_search_log.py'); mod = importlib.util.module_from_spec(spec); print('Migration parses OK')\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":160,"cacheRead":81593,"cacheWrite":202,"totalTokens":81956,"cost":{"input":0.000005,"output":0.004,"cacheRead":0.0407965,"cacheWrite":0.0012625000000000002,"total":0.046064}},"stopReason":"toolUse","timestamp":1775188926404}}
{"type":"message","id":"3f1b2e70","parentId":"40fe6390","timestamp":"2026-04-03T04:02:12.557Z","message":{"role":"toolResult","toolCallId":"toolu_016vnHnNhUJf24Yu6Du9c11H","toolName":"bash","content":[{"type":"text","text":"Migration parses OK\n"}],"isError":false,"timestamp":1775188932554}}
{"type":"message","id":"e543f850","parentId":"3f1b2e70","timestamp":"2026-04-03T04:02:17.143Z","message":{"role":"assistant","content":[{"type":"text","text":"Now read the task summary template and write the summary."},{"type":"toolCall","id":"toolu_01Xoi82mH8RbjNndM7dfEzJo","name":"read","arguments":{"path":"~/.gsd/agent/extensions/gsd/templates/task-summary.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":81,"cacheRead":81795,"cacheWrite":177,"totalTokens":82054,"cost":{"input":0.000005,"output":0.002025,"cacheRead":0.040897499999999996,"cacheWrite":0.00110625,"total":0.044033749999999997}},"stopReason":"toolUse","timestamp":1775188932555}}
{"type":"message","id":"ff0835d1","parentId":"e543f850","timestamp":"2026-04-03T04:02:17.148Z","message":{"role":"toolResult","toolCallId":"toolu_01Xoi82mH8RbjNndM7dfEzJo","toolName":"read","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{{howToInspectWhatThisTaskBuiltLater — status surfaces, logs, error shapes, failure artifacts, or none}}\n\n## Deviations\n\n<!-- Deviations are unplanned changes to the written task plan, not ordinary debugging during implementation. -->\n\n{{deviationsFromPlan_OR_none}}\n\n## Known Issues\n\n{{issuesDiscoveredButNotFixed_OR_none}}\n\n## Files Created/Modified\n\n- `{{filePath}}` — {{description}}\n- `{{filePath}}` — {{description}}\n"}],"isError":false,"timestamp":1775188937145}}
{"type":"message","id":"fcd3f483","parentId":"ff0835d1","timestamp":"2026-04-03T04:02:40.817Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01BwPZMYA9umLS98zXpjPJRv","name":"write","arguments":{"content":"---\nid: T01\nparent: S01\nmilestone: M015\nprovides:\n  - SearchLog model with indexed query and created_at columns\n  - PopularSearchItem and PopularSearchesResponse schemas\n  - Non-blocking _log_search fire-and-forget pattern\n  - GET /search/popular endpoint with Redis read-through cache\n  - Alembic migration 013 for search_log table\nkey_files:\n  - backend/models.py\n  - backend/schemas.py\n  - backend/routers/search.py\n  - alembic/versions/013_add_search_log.py\nkey_decisions:\n  - Used Integer PK (not UUID) for SearchLog since it's append-only analytics data with no cross-references\n  - Fire-and-forget via asyncio.create_task with independent session to avoid request session interference\npatterns_established:\n  - Fire-and-forget DB writes via asyncio.create_task with own async_session scope and catch-all exception handler\n  - Redis read-through cache with best-effort write and graceful fallback to DB on Redis failure\nobservability_surfaces:\n  - WARNING logs on search log insert failure (chrysopedia.search.router)\n  - WARNING logs on Redis cache read/write failure (chrysopedia.search.router)\nduration: 12m\nverification_result: passed\ncompleted_at: 2026-04-03\nblocker_discovered: false\n---\n\n# T01: Add SearchLog model, migration, schemas, logging hook, and popular endpoint\n\n**Added SearchLog model, Alembic migration, Pydantic schemas, fire-and-forget search logging, and GET /search/popular endpoint with 5-minute Redis cache**\n\n## What Happened\n\nAdded `SearchLog` model to `models.py` with Integer PK, indexed `query` and `created_at` columns, `scope`, and `result_count`. Created Alembic migration 013 that creates the `search_log` table with both indexes.\n\nAdded `PopularSearchItem` and `PopularSearchesResponse` Pydantic schemas to `schemas.py`.\n\nIn `routers/search.py`: added `_log_search()` async helper that opens its own session (independent of the request session), inserts a SearchLog row, and catches all exceptions with a WARNING log. The main `search()` handler fires this as a background task via `asyncio.create_task` only when the query is non-empty.\n\nAdded `GET /search/popular` endpoint that checks Redis cache first (`chrysopedia:popular_searches`, 300s TTL), falls through to a DB query on miss or Redis failure. The DB query uses `LOWER(query)` normalization and a 7-day window. Cache writes are best-effort with exception handling.\n\n## Verification\n\nAll three verification checks pass:\n- `from models import SearchLog; print(SearchLog.__tablename__)` → `search_log`\n- `from schemas import PopularSearchItem, PopularSearchesResponse; print('OK')` → `OK`\n- Route assertion: `/search/popular` exists in router routes (note: APIRouter stores full paths with prefix, so the route is `/search/popular` not `/popular`)\n\n## Verification Evidence\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| 1 | `cd backend && python -c \"from models import SearchLog; print(SearchLog.__tablename__)\"` | 0 | ✅ pass | 1s |\n| 2 | `cd backend && python -c \"from schemas import PopularSearchItem, PopularSearchesResponse; print('OK')\"` | 0 | ✅ pass | 1s |\n| 3 | `cd backend && python -c \"from routers.search import router; routes = [r.path for r in router.routes]; assert '/search/popular' in routes; print('OK')\"` | 0 | ✅ pass | 1s |\n\n## Diagnostics\n\n- Search log failures surface as WARNING in `chrysopedia.search.router` logger\n- Redis cache failures surface as WARNING in the same logger\n- `search_log` table can be queried directly: `SELECT query, count(*) FROM search_log GROUP BY query ORDER BY count DESC`\n- Redis cache key: `chrysopedia:popular_searches` (5-min TTL)\n\n## Deviations\n\nThe slice verification command checks `assert '/popular' in routes` but APIRouter stores routes with the prefix included (`/search/popular`). The route is correctly registered — the verification assertion needs the full path. This is a minor spec mismatch, not a code issue.\n\n## Known Issues\n\nNone.\n\n## Files Created/Modified\n\n- `backend/models.py` — Added SearchLog model with Integer PK, indexed query/created_at columns\n- `backend/schemas.py` — Added PopularSearchItem and PopularSearchesResponse schemas\n- `backend/routers/search.py` — Added _log_search helper, asyncio.create_task hook in search(), GET /search/popular endpoint with Redis cache\n- `alembic/versions/013_add_search_log.py` — Migration creating search_log table with query and created_at indexes\n","path":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M015/slices/S01/tasks/T01-SUMMARY.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":1326,"cacheRead":81972,"cacheWrite":591,"totalTokens":83890,"cost":{"input":0.000005,"output":0.03315,"cacheRead":0.040986,"cacheWrite":0.00369375,"total":0.07783475}},"stopReason":"toolUse","timestamp":1775188937146}}
{"type":"message","id":"6524f3c5","parentId":"fcd3f483","timestamp":"2026-04-03T04:02:40.823Z","message":{"role":"toolResult","toolCallId":"toolu_01BwPZMYA9umLS98zXpjPJRv","toolName":"write","content":[{"type":"text","text":"Successfully wrote 4417 bytes to /home/aux/projects/content-to-kb-automator/.gsd/milestones/M015/slices/S01/tasks/T01-SUMMARY.md"}],"isError":false,"timestamp":1775188960820}}
{"type":"message","id":"e11002d9","parentId":"6524f3c5","timestamp":"2026-04-03T04:02:52.336Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_015AKdjhkGd7wodxpgCQ8rqP","name":"gsd_task_complete","arguments":{"blockerDiscovered":false,"deviations":"Slice verification checks for '/popular' in routes but APIRouter stores full paths with prefix ('/search/popular'). Route is correctly registered — minor spec mismatch.","keyDecisions":["Used Integer PK for SearchLog since it's append-only analytics data","Fire-and-forget via asyncio.create_task with independent async_session to avoid request session interference","Redis read-through cache with best-effort write and graceful DB fallback"],"keyFiles":["backend/models.py","backend/schemas.py","backend/routers/search.py","alembic/versions/013_add_search_log.py"],"knownIssues":"None.","milestoneId":"M015","narrative":"Added SearchLog model to models.py with Integer PK, indexed query and created_at columns. Created Alembic migration 013 for the search_log table. Added PopularSearchItem and PopularSearchesResponse Pydantic schemas. Implemented _log_search async helper with independent session and catch-all exception handling, hooked into the search handler via asyncio.create_task for non-empty queries. Added GET /search/popular endpoint with Redis read-through cache (300s TTL, chrysopedia:popular_searches key) and graceful fallback to DB on Redis failure. LOWER(query) normalization and 7-day window in the popular query.","oneLiner":"Added SearchLog model, Alembic migration 013, Pydantic schemas, fire-and-forget search logging, and GET /search/popular endpoint with 5-minute Redis cache","sliceId":"S01","taskId":"T01","verification":"All three verification checks pass: SearchLog.__tablename__ returns 'search_log', schema imports succeed, and /search/popular route exists in the router.","verificationEvidence":[{"command":"cd backend && python -c \"from models import SearchLog; print(SearchLog.__tablename__)\"","durationMs":1000,"exitCode":0,"verdict":"✅ pass"},{"command":"cd backend && python -c \"from schemas import PopularSearchItem, PopularSearchesResponse; print('OK')\"","durationMs":1000,"exitCode":0,"verdict":"✅ pass"},{"command":"cd backend && python -c \"from routers.search import router; routes = [r.path for r in router.routes]; assert '/search/popular' in routes; print('OK')\"","durationMs":1000,"exitCode":0,"verdict":"✅ pass"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":800,"cacheRead":82563,"cacheWrite":1387,"totalTokens":84751,"cost":{"input":0.000005,"output":0.02,"cacheRead":0.0412815,"cacheWrite":0.008668750000000001,"total":0.06995525}},"stopReason":"toolUse","timestamp":1775188960821}}
{"type":"message","id":"225ffa12","parentId":"e11002d9","timestamp":"2026-04-03T04:02:52.373Z","message":{"role":"toolResult","toolCallId":"toolu_015AKdjhkGd7wodxpgCQ8rqP","toolName":"gsd_task_complete","content":[{"type":"text","text":"Completed task T01 (S01/M015)"}],"details":{"operation":"complete_task","taskId":"T01","sliceId":"S01","milestoneId":"M015","summaryPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M015/slices/S01/tasks/T01-SUMMARY.md"},"isError":false,"timestamp":1775188972372}}
{"type":"message","id":"dd9b24a2","parentId":"225ffa12","timestamp":"2026-04-03T04:02:55.445Z","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":83950,"cacheWrite":829,"totalTokens":84789,"cost":{"input":0.000005,"output":0.00022500000000000002,"cacheRead":0.041975,"cacheWrite":0.00518125,"total":0.04738625}},"stopReason":"stop","timestamp":1775188972373}}
