{"type":"thinking_level_change","id":"ec67d922","parentId":null,"timestamp":"2026-03-29T22:39:04.257Z","thinkingLevel":"off"}
{"type":"model_change","id":"748d7e67","parentId":"ec67d922","timestamp":"2026-03-29T22:39:04.257Z","provider":"anthropic","modelId":"claude-opus-4-6"}
{"type":"custom_message","customType":"gsd-auto","content":"You are executing GSD auto-mode.\n\n## Working Directory\n\nYour working directory is `/home/aux/projects/content-to-kb-automator`. All file reads, writes, and shell commands MUST operate relative to this directory. Do NOT `cd` to any other directory.\n\nA researcher explored the codebase and a planner decomposed the work — you are the executor. The task plan below is your authoritative contract for the slice goal and verification bar, but it is not a substitute for local reality. Verify the referenced files and surrounding code before changing them. Do not do broad re-research or spontaneous re-planning. Small factual corrections, file-path fixes, and local implementation adaptations are part of execution. Escalate to `blocker_discovered: true` only when the slice contract or downstream task graph no longer holds.\n\n\n\n\n\n## Backing Source Artifacts\n- Slice plan: `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S03/S03-PLAN.md`\n- Task plan source: `.gsd/milestones/M001/slices/S03/tasks/T04-PLAN.md`\n- Prior task summaries in this slice:\n- `.gsd/milestones/M001/slices/S03/tasks/T01-SUMMARY.md`\n- `.gsd/milestones/M001/slices/S03/tasks/T02-SUMMARY.md`\n- `.gsd/milestones/M001/slices/S03/tasks/T03-SUMMARY.md`\n\nThen:\n0. Narrate step transitions, key implementation decisions, and verification outcomes as you work. Keep it terse — one line between tool-call clusters, not between every call — but write complete sentences in user-facing prose, not shorthand notes or scratchpad fragments.\n1.  Follow any activated skills before writing code. If no skills match this task, skip this step.\n2. Execute the steps in the inlined task plan, adapting minor local mismatches when the surrounding code differs from the planner's snapshot\n3. Build the real thing. If the task plan says \"create login endpoint\", build an endpoint that actually authenticates against a real store, not one that returns a hardcoded success response. If the task plan says \"create dashboard page\", build a page that renders real data from the API, not a component with hardcoded props. Stubs and mocks are for tests, not for the shipped feature.\n4. Write or update tests as part of execution — tests are verification, not an afterthought. If the slice plan defines test files in its Verification section and this is the first task, create them (they should initially fail).\n5. When implementing non-trivial runtime behavior (async flows, API boundaries, background processes, error paths), add or preserve agent-usable observability. Skip this for simple changes where it doesn't apply.\n\n   **Background process rule:** Never use bare `command &` to run background processes. The shell's `&` operator leaves stdout/stderr attached to the parent, which causes the Bash tool to hang indefinitely waiting for those streams to close. Always redirect output before backgrounding:\n   - Correct: `command > /dev/null 2>&1 &` or `nohup command > /dev/null 2>&1 &`\n   - Example: `python -m http.server 8080 > /dev/null 2>&1 &` (NOT `python -m http.server 8080 &`)\n   - Preferred: use the `bg_shell` tool if available — it manages process lifecycle correctly without stream-inheritance issues\n6. If the task plan includes a **Failure Modes** section (Q5), implement the error/timeout/malformed handling specified. Verify each dependency's failure path is handled. Skip if the section is absent.\n7. If the task plan includes a **Load Profile** section (Q6), implement protections for the identified 10x breakpoint (connection pooling, rate limiting, pagination, etc.). Skip if absent.\n8. If the task plan includes a **Negative Tests** section (Q7), write the specified negative test cases alongside the happy-path tests — malformed inputs, error paths, and boundary conditions. Skip if absent.\n9. Verify must-haves are met by running concrete checks (tests, commands, observable behaviors)\n10. Run the slice-level verification checks defined in the slice plan's Verification section. Track which pass. On the final task of the slice, all must pass before marking done. On intermediate tasks, partial passes are expected — note which ones pass in the summary.\n11. After the verification gate runs (you'll see gate results in stderr/notify output), populate the `## Verification Evidence` table in your task summary with the check results. Use the `formatEvidenceTable` format: one row per check with command, exit code, verdict (✅ pass / ❌ fail), and duration. If no verification commands were discovered, note that in the section.\n12. If the task touches UI, browser flows, DOM behavior, or user-visible web state:\n   - exercise the real flow in the browser\n   - prefer `browser_batch` when the next few actions are obvious and sequential\n   - prefer `browser_assert` for explicit pass/fail verification of the intended outcome\n   - use `browser_diff` when an action's effect is ambiguous\n   - use console/network/dialog diagnostics when validating async, stateful, or failure-prone UI\n   - record verification in terms of explicit checks passed/failed, not only prose interpretation\n13. If the task plan includes an Observability Impact section, verify those signals directly. Skip this step if the task plan omits the section.\n14. **If execution is running long or verification fails:**\n\n    **Context budget:** You have approximately **~80K chars** reserved for verification context. If you've used most of your context and haven't finished all steps, stop implementing and prioritize writing the task summary with clear notes on what's done and what remains. A partial summary that enables clean resumption is more valuable than one more half-finished step with no documentation. Never sacrifice summary quality for one more implementation step.\n\n    **Debugging discipline:** If a verification check fails or implementation hits unexpected behavior:\n    - Form a hypothesis first. State what you think is wrong and why, then test that specific theory. Don't shotgun-fix.\n    - Change one variable at a time. Make one change, test, observe. Multiple simultaneous changes mean you can't attribute what worked.\n    - Read completely. When investigating, read entire functions and their imports, not just the line that looks relevant.\n    - Distinguish \"I know\" from \"I assume.\" Observable facts (the error says X) are strong evidence. Assumptions (this library should work this way) need verification.\n    - Know when to stop. If you've tried 3+ fixes without progress, your mental model is probably wrong. Stop. List what you know for certain. List what you've ruled out. Form fresh hypotheses from there.\n    - Don't fix symptoms. Understand *why* something fails before changing code. A test that passes after a change you don't understand is luck, not a fix.\n15. **Blocker discovery:** If execution reveals that the remaining slice plan is fundamentally invalid — not just a bug or minor deviation, but a plan-invalidating finding like a wrong API, missing capability, or architectural mismatch — set `blocker_discovered: true` in the task summary frontmatter and describe the blocker clearly in the summary narrative. Do NOT set `blocker_discovered: true` for ordinary debugging, minor deviations, or issues that can be fixed within the current task or the remaining plan. This flag triggers an automatic replan of the slice.\n16. If you made an architectural, pattern, library, or observability decision during this task that downstream work should know about, append it to `.gsd/DECISIONS.md` (read the template at `~/.gsd/agent/extensions/gsd/templates/decisions.md` if the file doesn't exist yet). Not every task produces decisions — only append when a meaningful choice was made.\n17. If you discover a non-obvious rule, recurring gotcha, or useful pattern during execution, append it to `.gsd/KNOWLEDGE.md`. Only add entries that would save future agents from repeating your investigation. Don't add obvious things.\n18. Read the template at `~/.gsd/agent/extensions/gsd/templates/task-summary.md`\n19. Write `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S03/tasks/T04-SUMMARY.md`\n20. Call `gsd_complete_task` with milestone_id, slice_id, task_id, and a summary of what was accomplished. This is your final required step — do NOT manually edit PLAN.md checkboxes. The tool marks the task complete, updates the DB, and renders PLAN.md automatically.\n21. Do not run git commands — the system reads your task summary after completion and creates a meaningful commit from it (type inferred from title, message from your one-liner, key files from frontmatter). Write a clear, specific one-liner in the summary — it becomes the commit message.\n\nAll work stays in your working directory: `/home/aux/projects/content-to-kb-automator`.\n\n**You MUST call `gsd_complete_task` AND write `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S03/tasks/T04-SUMMARY.md` before finishing.**\n\nWhen done, say: \"Task T04 complete.\"\n## Slice Plan Excerpt\nSource: `.gsd/milestones/M001/slices/S03/S03-PLAN.md`\n**Goal:** A transcript JSON triggers stages 2-5: segmentation → extraction → classification → synthesis. Technique pages with key moments appear in DB. Qdrant has searchable embeddings. Pipeline is resumable per-video per-stage, uses configurable LLM endpoints with fallback, and loads prompt templates from disk.\n**Demo:** After this: A transcript JSON triggers stages 2-5: segmentation → extraction → classification → synthesis. Technique pages with key moments appear in DB. Qdrant has searchable embeddings.\n\n### Slice Verification\n- `cd backend && python -c \"from config import Settings; s = Settings(); print(s.llm_api_url, s.qdrant_url, s.review_mode)\"` prints defaults\n- `cd backend && python -c \"from pipeline.schemas import SegmentationResult, ExtractionResult, ClassificationResult, SynthesisResult; print('schemas ok')\"` exits 0\n- `cd backend && python -c \"from pipeline.llm_client import LLMClient; print('client ok')\"` exits 0\n- `cd backend && python -c \"from worker import celery_app; print(celery_app.main)\"` exits 0\n- `grep -q 'openai' backend/requirements.txt && grep -q 'qdrant-client' backend/requirements.txt`\n  - Estimate: 1.5h\n  - Files: backend/config.py, backend/requirements.txt, backend/worker.py, backend/pipeline/__init__.py, backend/pipeline/schemas.py, backend/pipeline/llm_client.py\n  - Verify: cd backend && python -c \"from config import Settings; s = Settings(); print(s.llm_api_url, s.qdrant_url, s.review_mode)\" && python -c \"from pipeline.schemas import SegmentationResult, ExtractionResult, ClassificationResult, SynthesisResult; print('schemas ok')\" && python -c \"from pipeline.llm_client import LLMClient; print('client ok')\" && python -c \"from worker import celery_app; print(celery_app.main)\"\n- [x] **T02: Created 4 prompt templates and implemented 5 Celery tasks (stages 2-5 + run_pipeline) with sync SQLAlchemy, retry logic, resumability, and Redis-based cross-stage data passing** — Write the 4 prompt template files for stages 2-5 and implement all pipeline stage logic as Celery tasks in `pipeline/stages.py`. Each stage reads from PostgreSQL, loads its prompt template, calls the LLM client, parses the response, writes results back to PostgreSQL, and updates processing_status. The `run_pipeline` orchestrator chains stages and handles resumability.\n\n## UNIT: Execute Task T04 (\"Wire ingest-to-pipeline trigger and add manual re-trigger endpoint\") — Slice S03 (\"LLM Extraction Pipeline + Qdrant Integration\"), Milestone M001\n\n## Resume State\n- No continue file present. Start from the top of the task plan.\n\n## Carry-Forward Context\n- `.gsd/milestones/M001/slices/S03/tasks/T01-SUMMARY.md` — T01: Extended Settings with 12 LLM/embedding/Qdrant config fields, created Celery app, built sync LLMClient with primary/fallback logic, and defined 8 Pydantic schemas for pipeline stages 2-5 | decisions: \"Used sync openai.OpenAI (not Async) since Celery tasks run synchronously\"; \"LLMClient catches APIConnectionError and APITimeoutError for fallback; other errors propagate immediately\" | key_files: \"backend/config.py\"; \"backend/worker.py\"; \"backend/pipeline/schemas.py\"\n- `.gsd/milestones/M001/slices/S03/tasks/T02-SUMMARY.md` — T02: Created 4 prompt templates and implemented 5 Celery tasks (stages 2-5 + run_pipeline) with sync SQLAlchemy, retry logic, resumability, and Redis-based cross-stage data passing | decisions: \"Stage 4 classification data stored in Redis (chrysopedia:classification:{video_id} key; 24h TTL) because KeyMoment model lacks topic_tags/topic_category columns\" | key_files: \"prompts/stage2_segmentation.txt\"; \"prompts/stage3_extraction.txt\"; \"prompts/stage4_classification.txt\"\n- `.gsd/milestones/M001/slices/S03/tasks/T03-SUMMARY.md` — T03: Created sync EmbeddingClient, QdrantManager with idempotent collection management and metadata-rich upserts, and wired stage6_embed_and_index into the Celery pipeline chain as a non-blocking side-effect stage | decisions: \"stage6 uses max_retries=0 and catches all exceptions — embedding is a non-blocking side-effect that can be regenerated later\"; \"EmbeddingClient returns empty list on any API error so callers don't need try/except\" | key_files: \"backend/pipeline/embedding_client.py\"; \"backend/pipeline/qdrant_client.py\"; \"backend/pipeline/stages.py\"\n\n## Inlined Task Plan (authoritative local execution contract)\nSource: `.gsd/milestones/M001/slices/S03/tasks/T04-PLAN.md`\n\n---\nestimated_steps: 21\nestimated_files: 3\nskills_used: []\n---\n\n# T04: Wire ingest-to-pipeline trigger and add manual re-trigger endpoint\n\nConnect the existing ingest endpoint to the pipeline by dispatching `run_pipeline.delay()` after successful commit, and add a manual re-trigger API endpoint for re-processing videos.\n\n## Steps\n\n1. Modify `backend/routers/ingest.py`:\n   - After the `await db.commit()` and refresh calls (after the successful response is ready), dispatch the pipeline: `from pipeline.stages import run_pipeline; run_pipeline.delay(str(video.id))`\n   - Wrap the dispatch in try/except to avoid failing the ingest response if Redis/Celery is down — log WARNING on dispatch failure\n   - The dispatch must happen AFTER commit so the worker can find the data in PostgreSQL\n\n2. Create `backend/routers/pipeline.py`:\n   - Router with `prefix='/pipeline'`, tag `['pipeline']`\n   - `POST /trigger/{video_id}`: Look up SourceVideo by id, verify it exists (404 if not), dispatch `run_pipeline.delay(str(video_id))`, return `{\"status\": \"triggered\", \"video_id\": str(video_id), \"current_processing_status\": video.processing_status.value}`\n   - Uses `get_session` dependency for DB access\n\n3. Mount the pipeline router in `backend/main.py` under `/api/v1`.\n\n## Must-Haves\n\n- [ ] Ingest endpoint dispatches run_pipeline.delay() after commit\n- [ ] Pipeline dispatch failure does not fail the ingest response\n- [ ] POST /api/v1/pipeline/trigger/{video_id} exists and returns status\n- [ ] 404 returned for non-existent video_id\n- [ ] Pipeline router mounted in main.py\n\n## Verification\n\n- `cd backend && python -c \"from routers.pipeline import router; print([r.path for r in router.routes])\"` shows `['/trigger/{video_id}']`\n- `grep -q 'pipeline' backend/main.py` exits 0\n- `grep -q 'run_pipeline' backend/routers/ingest.py` exits 0\n\n## Inputs\n\n- ``backend/routers/ingest.py` — existing ingest endpoint to add pipeline dispatch to`\n- ``backend/main.py` — existing app to mount new router`\n- ``backend/pipeline/stages.py` — run_pipeline task from T02`\n- ``backend/models.py` — SourceVideo model for lookup`\n\n## Expected Output\n\n- ``backend/routers/ingest.py` — updated with run_pipeline.delay() dispatch after commit`\n- ``backend/routers/pipeline.py` — new router with POST /trigger/{video_id} endpoint`\n- ``backend/main.py` — updated with pipeline router mount`\n\n## Verification\n\ncd backend && python -c \"from routers.pipeline import router; print([r.path for r in router.routes])\" && grep -q 'pipeline' backend/main.py && grep -q 'run_pipeline' backend/routers/ingest.py\n","display":false,"id":"99558efc","parentId":"748d7e67","timestamp":"2026-03-29T22:39:04.259Z"}
{"type":"message","id":"97c5e8fc","parentId":"99558efc","timestamp":"2026-03-29T22:39:08.771Z","message":{"role":"assistant","content":[{"type":"text","text":"\n\nI'll start by reading the existing files I need to modify, then implement the changes."},{"type":"toolCall","id":"toolu_01DeXueDPSGsjNsT6P3ihyPw","name":"read","arguments":{"path":"backend/routers/ingest.py"}},{"type":"toolCall","id":"toolu_01Q69HtmDeoY6PoCCS6dGhMa","name":"read","arguments":{"path":"backend/main.py"}},{"type":"toolCall","id":"toolu_01HYA2gZ8koMjJiGvtEt6qQj","name":"read","arguments":{"path":"backend/models.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":155,"cacheRead":49033,"cacheWrite":4591,"totalTokens":53781,"cost":{"input":0.00001,"output":0.003875,"cacheRead":0.0245165,"cacheWrite":0.02869375,"total":0.05709525}},"stopReason":"toolUse","timestamp":1774823944258}}
{"type":"message","id":"7e17e12e","parentId":"97c5e8fc","timestamp":"2026-03-29T22:39:08.775Z","message":{"role":"toolResult","toolCallId":"toolu_01DeXueDPSGsjNsT6P3ihyPw","toolName":"read","content":[{"type":"text","text":"\"\"\"Transcript ingestion endpoint for the Chrysopedia API.\n\nAccepts a Whisper-format transcript JSON via multipart file upload, finds or\ncreates a Creator, upserts a SourceVideo, bulk-inserts TranscriptSegments,\npersists the raw JSON to disk, and returns a structured response.\n\"\"\"\n\nimport json\nimport logging\nimport os\nimport re\nimport uuid\n\nfrom fastapi import APIRouter, Depends, HTTPException, UploadFile\nfrom sqlalchemy import delete, select\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom config import get_settings\nfrom database import get_session\nfrom models import ContentType, Creator, ProcessingStatus, SourceVideo, TranscriptSegment\nfrom schemas import TranscriptIngestResponse\n\nlogger = logging.getLogger(\"chrysopedia.ingest\")\n\nrouter = APIRouter(prefix=\"/ingest\", tags=[\"ingest\"])\n\nREQUIRED_KEYS = {\"source_file\", \"creator_folder\", \"duration_seconds\", \"segments\"}\n\n\ndef slugify(value: str) -> str:\n    \"\"\"Lowercase, replace non-alphanumeric chars with hyphens, collapse/strip.\"\"\"\n    value = value.lower()\n    value = re.sub(r\"[^a-z0-9]+\", \"-\", value)\n    value = value.strip(\"-\")\n    value = re.sub(r\"-{2,}\", \"-\", value)\n    return value\n\n\n@router.post(\"\", response_model=TranscriptIngestResponse)\nasync def ingest_transcript(\n    file: UploadFile,\n    db: AsyncSession = Depends(get_session),\n) -> TranscriptIngestResponse:\n    \"\"\"Ingest a Whisper transcript JSON file.\n\n    Workflow:\n      1. Parse and validate the uploaded JSON.\n      2. Find-or-create a Creator by folder_name.\n      3. Upsert a SourceVideo by (creator_id, filename).\n      4. Bulk-insert TranscriptSegment rows.\n      5. Save raw JSON to transcript_storage_path.\n      6. Return structured response.\n    \"\"\"\n    settings = get_settings()\n\n    # ── 1. Read & parse JSON ─────────────────────────────────────────────\n    try:\n        raw_bytes = await file.read()\n        raw_text = raw_bytes.decode(\"utf-8\")\n    except Exception as exc:\n        raise HTTPException(status_code=400, detail=f\"Invalid file: {exc}\") from exc\n\n    try:\n        data = json.loads(raw_text)\n    except json.JSONDecodeError as exc:\n        raise HTTPException(\n            status_code=422, detail=f\"JSON parse error: {exc}\"\n        ) from exc\n\n    if not isinstance(data, dict):\n        raise HTTPException(status_code=422, detail=\"Expected a JSON object at the top level\")\n\n    missing = REQUIRED_KEYS - data.keys()\n    if missing:\n        raise HTTPException(\n            status_code=422,\n            detail=f\"Missing required keys: {', '.join(sorted(missing))}\",\n        )\n\n    source_file: str = data[\"source_file\"]\n    creator_folder: str = data[\"creator_folder\"]\n    duration_seconds: int | None = data.get(\"duration_seconds\")\n    segments_data: list = data[\"segments\"]\n\n    if not isinstance(segments_data, list):\n        raise HTTPException(status_code=422, detail=\"'segments' must be an array\")\n\n    # ── 2. Find-or-create Creator ────────────────────────────────────────\n    stmt = select(Creator).where(Creator.folder_name == creator_folder)\n    result = await db.execute(stmt)\n    creator = result.scalar_one_or_none()\n\n    if creator is None:\n        creator = Creator(\n            name=creator_folder,\n            slug=slugify(creator_folder),\n            folder_name=creator_folder,\n        )\n        db.add(creator)\n        await db.flush()  # assign id\n\n    # ── 3. Upsert SourceVideo ────────────────────────────────────────────\n    stmt = select(SourceVideo).where(\n        SourceVideo.creator_id == creator.id,\n        SourceVideo.filename == source_file,\n    )\n    result = await db.execute(stmt)\n    existing_video = result.scalar_one_or_none()\n\n    is_reupload = existing_video is not None\n\n    if is_reupload:\n        video = existing_video\n        # Delete old segments for idempotent re-upload\n        await db.execute(\n            delete(TranscriptSegment).where(\n                TranscriptSegment.source_video_id == video.id\n            )\n        )\n        video.duration_seconds = duration_seconds\n        video.processing_status = ProcessingStatus.transcribed\n    else:\n        video = SourceVideo(\n            creator_id=creator.id,\n            filename=source_file,\n            file_path=f\"{creator_folder}/{source_file}\",\n            duration_seconds=duration_seconds,\n            content_type=ContentType.tutorial,\n            processing_status=ProcessingStatus.transcribed,\n        )\n        db.add(video)\n        await db.flush()  # assign id\n\n    # ── 4. Bulk-insert TranscriptSegments ────────────────────────────────\n    segment_objs = [\n        TranscriptSegment(\n            source_video_id=video.id,\n            start_time=float(seg[\"start\"]),\n            end_time=float(seg[\"end\"]),\n            text=str(seg[\"text\"]),\n            segment_index=idx,\n        )\n        for idx, seg in enumerate(segments_data)\n    ]\n    db.add_all(segment_objs)\n\n    # ── 5. Save raw JSON to disk ─────────────────────────────────────────\n    transcript_dir = os.path.join(\n        settings.transcript_storage_path, creator_folder\n    )\n    transcript_path = os.path.join(transcript_dir, f\"{source_file}.json\")\n\n    try:\n        os.makedirs(transcript_dir, exist_ok=True)\n        with open(transcript_path, \"w\", encoding=\"utf-8\") as f:\n            f.write(raw_text)\n    except OSError as exc:\n        raise HTTPException(\n            status_code=500, detail=f\"Failed to save transcript: {exc}\"\n        ) from exc\n\n    video.transcript_path = transcript_path\n\n    # ── 6. Commit & respond ──────────────────────────────────────────────\n    try:\n        await db.commit()\n    except Exception as exc:\n        await db.rollback()\n        logger.error(\"Database commit failed during ingest: %s\", exc)\n        raise HTTPException(\n            status_code=500, detail=\"Database error during ingest\"\n        ) from exc\n\n    await db.refresh(video)\n    await db.refresh(creator)\n\n    logger.info(\n        \"Ingested transcript: creator=%s, file=%s, segments=%d, reupload=%s\",\n        creator.name,\n        source_file,\n        len(segment_objs),\n        is_reupload,\n    )\n\n    return TranscriptIngestResponse(\n        video_id=video.id,\n        creator_id=creator.id,\n        creator_name=creator.name,\n        filename=source_file,\n        segments_stored=len(segment_objs),\n        processing_status=video.processing_status.value,\n        is_reupload=is_reupload,\n    )\n"}],"isError":false,"timestamp":1774823948775}}
{"type":"message","id":"1c7caea1","parentId":"7e17e12e","timestamp":"2026-03-29T22:39:08.778Z","message":{"role":"toolResult","toolCallId":"toolu_01Q69HtmDeoY6PoCCS6dGhMa","toolName":"read","content":[{"type":"text","text":"\"\"\"Chrysopedia API — Knowledge extraction and retrieval system.\n\nEntry point for the FastAPI application. Configures middleware,\nstructured logging, and mounts versioned API routers.\n\"\"\"\n\nimport logging\nimport sys\nfrom contextlib import asynccontextmanager\n\nfrom fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\n\nfrom config import get_settings\nfrom routers import creators, health, ingest, videos\n\n\ndef _setup_logging() -> None:\n    \"\"\"Configure structured logging to stdout.\"\"\"\n    settings = get_settings()\n    level = getattr(logging, settings.app_log_level.upper(), logging.INFO)\n\n    handler = logging.StreamHandler(sys.stdout)\n    handler.setFormatter(\n        logging.Formatter(\n            fmt=\"%(asctime)s | %(levelname)-8s | %(name)s | %(message)s\",\n            datefmt=\"%Y-%m-%dT%H:%M:%S\",\n        )\n    )\n\n    root = logging.getLogger()\n    root.setLevel(level)\n    # Avoid duplicate handlers on reload\n    root.handlers.clear()\n    root.addHandler(handler)\n\n    # Quiet noisy libraries\n    logging.getLogger(\"uvicorn.access\").setLevel(logging.WARNING)\n    logging.getLogger(\"sqlalchemy.engine\").setLevel(logging.WARNING)\n\n\n@asynccontextmanager\nasync def lifespan(app: FastAPI):  # noqa: ARG001\n    \"\"\"Application lifespan: setup on startup, teardown on shutdown.\"\"\"\n    _setup_logging()\n    logger = logging.getLogger(\"chrysopedia\")\n    settings = get_settings()\n    logger.info(\n        \"Chrysopedia API starting (env=%s, log_level=%s)\",\n        settings.app_env,\n        settings.app_log_level,\n    )\n    yield\n    logger.info(\"Chrysopedia API shutting down\")\n\n\napp = FastAPI(\n    title=\"Chrysopedia API\",\n    description=\"Knowledge extraction and retrieval for music production content\",\n    version=\"0.1.0\",\n    lifespan=lifespan,\n)\n\n# ── Middleware ────────────────────────────────────────────────────────────────\n\nsettings = get_settings()\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=settings.cors_origins,\n    allow_credentials=True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"],\n)\n\n# ── Routers ──────────────────────────────────────────────────────────────────\n\n# Root-level health (no prefix)\napp.include_router(health.router)\n\n# Versioned API\napp.include_router(creators.router, prefix=\"/api/v1\")\napp.include_router(ingest.router, prefix=\"/api/v1\")\napp.include_router(videos.router, prefix=\"/api/v1\")\n\n\n@app.get(\"/api/v1/health\")\nasync def api_health():\n    \"\"\"Lightweight version-prefixed health endpoint (no DB check).\"\"\"\n    return {\"status\": \"ok\", \"version\": \"0.1.0\"}\n"}],"isError":false,"timestamp":1774823948778}}
{"type":"message","id":"1af09a57","parentId":"1c7caea1","timestamp":"2026-03-29T22:39:08.782Z","message":{"role":"toolResult","toolCallId":"toolu_01HYA2gZ8koMjJiGvtEt6qQj","toolName":"read","content":[{"type":"text","text":"\"\"\"SQLAlchemy ORM models for the Chrysopedia knowledge base.\n\nSeven entities matching chrysopedia-spec.md §6.1:\n  Creator, SourceVideo, TranscriptSegment, KeyMoment,\n  TechniquePage, RelatedTechniqueLink, Tag\n\"\"\"\n\nfrom __future__ import annotations\n\nimport enum\nimport uuid\nfrom datetime import datetime, timezone\n\nfrom sqlalchemy import (\n    Enum,\n    Float,\n    ForeignKey,\n    Integer,\n    String,\n    Text,\n    UniqueConstraint,\n    func,\n)\nfrom sqlalchemy.dialects.postgresql import ARRAY, JSONB, UUID\nfrom sqlalchemy.orm import Mapped, mapped_column\nfrom sqlalchemy.orm import relationship as sa_relationship\n\nfrom database import Base\n\n\n# ── Enums ────────────────────────────────────────────────────────────────────\n\nclass ContentType(str, enum.Enum):\n    \"\"\"Source video content type.\"\"\"\n    tutorial = \"tutorial\"\n    livestream = \"livestream\"\n    breakdown = \"breakdown\"\n    short_form = \"short_form\"\n\n\nclass ProcessingStatus(str, enum.Enum):\n    \"\"\"Pipeline processing status for a source video.\"\"\"\n    pending = \"pending\"\n    transcribed = \"transcribed\"\n    extracted = \"extracted\"\n    reviewed = \"reviewed\"\n    published = \"published\"\n\n\nclass KeyMomentContentType(str, enum.Enum):\n    \"\"\"Content classification for a key moment.\"\"\"\n    technique = \"technique\"\n    settings = \"settings\"\n    reasoning = \"reasoning\"\n    workflow = \"workflow\"\n\n\nclass ReviewStatus(str, enum.Enum):\n    \"\"\"Human review status for key moments.\"\"\"\n    pending = \"pending\"\n    approved = \"approved\"\n    edited = \"edited\"\n    rejected = \"rejected\"\n\n\nclass SourceQuality(str, enum.Enum):\n    \"\"\"Derived source quality for technique pages.\"\"\"\n    structured = \"structured\"\n    mixed = \"mixed\"\n    unstructured = \"unstructured\"\n\n\nclass PageReviewStatus(str, enum.Enum):\n    \"\"\"Review lifecycle for technique pages.\"\"\"\n    draft = \"draft\"\n    reviewed = \"reviewed\"\n    published = \"published\"\n\n\nclass RelationshipType(str, enum.Enum):\n    \"\"\"Types of links between technique pages.\"\"\"\n    same_technique_other_creator = \"same_technique_other_creator\"\n    same_creator_adjacent = \"same_creator_adjacent\"\n    general_cross_reference = \"general_cross_reference\"\n\n\n# ── Helpers ──────────────────────────────────────────────────────────────────\n\ndef _uuid_pk() -> Mapped[uuid.UUID]:\n    return mapped_column(\n        UUID(as_uuid=True),\n        primary_key=True,\n        default=uuid.uuid4,\n        server_default=func.gen_random_uuid(),\n    )\n\n\ndef _now() -> datetime:\n    \"\"\"Return current UTC time as a naive datetime (no tzinfo).\n\n    PostgreSQL TIMESTAMP WITHOUT TIME ZONE columns require naive datetimes.\n    asyncpg rejects timezone-aware datetimes for such columns.\n    \"\"\"\n    return datetime.now(timezone.utc).replace(tzinfo=None)\n\n\n# ── Models ───────────────────────────────────────────────────────────────────\n\nclass Creator(Base):\n    __tablename__ = \"creators\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    name: Mapped[str] = mapped_column(String(255), nullable=False)\n    slug: Mapped[str] = mapped_column(String(255), unique=True, nullable=False)\n    genres: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    folder_name: Mapped[str] = mapped_column(String(255), nullable=False)\n    view_count: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    videos: Mapped[list[SourceVideo]] = sa_relationship(back_populates=\"creator\")\n    technique_pages: Mapped[list[TechniquePage]] = sa_relationship(back_populates=\"creator\")\n\n\nclass SourceVideo(Base):\n    __tablename__ = \"source_videos\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    creator_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"creators.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    filename: Mapped[str] = mapped_column(String(500), nullable=False)\n    file_path: Mapped[str] = mapped_column(String(1000), nullable=False)\n    duration_seconds: Mapped[int] = mapped_column(Integer, nullable=True)\n    content_type: Mapped[ContentType] = mapped_column(\n        Enum(ContentType, name=\"content_type\", create_constraint=True),\n        nullable=False,\n    )\n    transcript_path: Mapped[str | None] = mapped_column(String(1000), nullable=True)\n    processing_status: Mapped[ProcessingStatus] = mapped_column(\n        Enum(ProcessingStatus, name=\"processing_status\", create_constraint=True),\n        default=ProcessingStatus.pending,\n        server_default=\"pending\",\n    )\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    creator: Mapped[Creator] = sa_relationship(back_populates=\"videos\")\n    segments: Mapped[list[TranscriptSegment]] = sa_relationship(back_populates=\"source_video\")\n    key_moments: Mapped[list[KeyMoment]] = sa_relationship(back_populates=\"source_video\")\n\n\nclass TranscriptSegment(Base):\n    __tablename__ = \"transcript_segments\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_video_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    start_time: Mapped[float] = mapped_column(Float, nullable=False)\n    end_time: Mapped[float] = mapped_column(Float, nullable=False)\n    text: Mapped[str] = mapped_column(Text, nullable=False)\n    segment_index: Mapped[int] = mapped_column(Integer, nullable=False)\n    topic_label: Mapped[str | None] = mapped_column(String(255), nullable=True)\n\n    # relationships\n    source_video: Mapped[SourceVideo] = sa_relationship(back_populates=\"segments\")\n\n\nclass KeyMoment(Base):\n    __tablename__ = \"key_moments\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_video_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    technique_page_id: Mapped[uuid.UUID | None] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"SET NULL\"), nullable=True\n    )\n    title: Mapped[str] = mapped_column(String(500), nullable=False)\n    summary: Mapped[str] = mapped_column(Text, nullable=False)\n    start_time: Mapped[float] = mapped_column(Float, nullable=False)\n    end_time: Mapped[float] = mapped_column(Float, nullable=False)\n    content_type: Mapped[KeyMomentContentType] = mapped_column(\n        Enum(KeyMomentContentType, name=\"key_moment_content_type\", create_constraint=True),\n        nullable=False,\n    )\n    plugins: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    review_status: Mapped[ReviewStatus] = mapped_column(\n        Enum(ReviewStatus, name=\"review_status\", create_constraint=True),\n        default=ReviewStatus.pending,\n        server_default=\"pending\",\n    )\n    raw_transcript: Mapped[str | None] = mapped_column(Text, nullable=True)\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    source_video: Mapped[SourceVideo] = sa_relationship(back_populates=\"key_moments\")\n    technique_page: Mapped[TechniquePage | None] = sa_relationship(\n        back_populates=\"key_moments\", foreign_keys=[technique_page_id]\n    )\n\n\nclass TechniquePage(Base):\n    __tablename__ = \"technique_pages\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    creator_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"creators.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    title: Mapped[str] = mapped_column(String(500), nullable=False)\n    slug: Mapped[str] = mapped_column(String(500), unique=True, nullable=False)\n    topic_category: Mapped[str] = mapped_column(String(255), nullable=False)\n    topic_tags: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    summary: Mapped[str | None] = mapped_column(Text, nullable=True)\n    body_sections: Mapped[dict | None] = mapped_column(JSONB, nullable=True)\n    signal_chains: Mapped[list | None] = mapped_column(JSONB, nullable=True)\n    plugins: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    source_quality: Mapped[SourceQuality | None] = mapped_column(\n        Enum(SourceQuality, name=\"source_quality\", create_constraint=True),\n        nullable=True,\n    )\n    view_count: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\n    review_status: Mapped[PageReviewStatus] = mapped_column(\n        Enum(PageReviewStatus, name=\"page_review_status\", create_constraint=True),\n        default=PageReviewStatus.draft,\n        server_default=\"draft\",\n    )\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    creator: Mapped[Creator] = sa_relationship(back_populates=\"technique_pages\")\n    key_moments: Mapped[list[KeyMoment]] = sa_relationship(\n        back_populates=\"technique_page\", foreign_keys=[KeyMoment.technique_page_id]\n    )\n    outgoing_links: Mapped[list[RelatedTechniqueLink]] = sa_relationship(\n        foreign_keys=\"RelatedTechniqueLink.source_page_id\", back_populates=\"source_page\"\n    )\n    incoming_links: Mapped[list[RelatedTechniqueLink]] = sa_relationship(\n        foreign_keys=\"RelatedTechniqueLink.target_page_id\", back_populates=\"target_page\"\n    )\n\n\nclass RelatedTechniqueLink(Base):\n    __tablename__ = \"related_technique_links\"\n    __table_args__ = (\n        UniqueConstraint(\"source_page_id\", \"target_page_id\", \"relationship\", name=\"uq_technique_link\"),\n    )\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    target_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    relationship: Mapped[RelationshipType] = mapped_column(\n        Enum(RelationshipType, name=\"relationship_type\", create_constraint=True),\n        nullable=False,\n    )\n\n    # relationships\n    source_page: Mapped[TechniquePage] = sa_relationship(\n        foreign_keys=[source_page_id], back_populates=\"outgoing_links\"\n    )\n    target_page: Mapped[TechniquePage] = sa_relationship(\n        foreign_keys=[target_page_id], back_populates=\"incoming_links\"\n    )\n\n\nclass Tag(Base):\n    __tablename__ = \"tags\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    name: Mapped[str] = mapped_column(String(255), unique=True, nullable=False)\n    category: Mapped[str] = mapped_column(String(255), nullable=False)\n    aliases: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n"}],"isError":false,"timestamp":1774823948780}}
{"type":"message","id":"d8c92619","parentId":"1af09a57","timestamp":"2026-03-29T22:39:12.601Z","message":{"role":"assistant","content":[{"type":"text","text":"Let me also check the database dependency and the pipeline stages to understand the import paths."},{"type":"toolCall","id":"toolu_01B93cX8KbvKDaCCHt8T8AH3","name":"read","arguments":{"path":"backend/database.py"}},{"type":"toolCall","id":"toolu_01Qfa2qWkmPJuxTDcfzoitQS","name":"read","arguments":{"limit":40,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":130,"cacheRead":53624,"cacheWrite":6238,"totalTokens":59993,"cost":{"input":0.000005,"output":0.0032500000000000003,"cacheRead":0.026812,"cacheWrite":0.0389875,"total":0.0690545}},"stopReason":"toolUse","timestamp":1774823948781}}
{"type":"message","id":"e45cb200","parentId":"d8c92619","timestamp":"2026-03-29T22:39:12.604Z","message":{"role":"toolResult","toolCallId":"toolu_01B93cX8KbvKDaCCHt8T8AH3","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":1774823952604}}
{"type":"message","id":"88475065","parentId":"e45cb200","timestamp":"2026-03-29T22:39:12.607Z","message":{"role":"toolResult","toolCallId":"toolu_01Qfa2qWkmPJuxTDcfzoitQS","toolName":"read","content":[{"type":"text","text":"\"\"\"Pipeline stage tasks (stages 2-5) and run_pipeline orchestrator.\n\nEach stage reads from PostgreSQL via sync SQLAlchemy, loads its prompt\ntemplate from disk, calls the LLM client, parses the response, writes\nresults back, and updates processing_status on SourceVideo.\n\nCelery tasks are synchronous — all DB access uses ``sqlalchemy.orm.Session``.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport logging\nimport time\nfrom collections import defaultdict\nfrom pathlib import Path\n\nimport yaml\nfrom celery import chain as celery_chain\nfrom pydantic import ValidationError\nfrom sqlalchemy import create_engine, select\nfrom sqlalchemy.orm import Session, sessionmaker\n\nfrom config import get_settings\nfrom models import (\n    KeyMoment,\n    KeyMomentContentType,\n    ProcessingStatus,\n    SourceVideo,\n    TechniquePage,\n    TranscriptSegment,\n)\nfrom pipeline.embedding_client import EmbeddingClient\nfrom pipeline.llm_client import LLMClient\nfrom pipeline.qdrant_client import QdrantManager\nfrom pipeline.schemas import (\n    ClassificationResult,\n    ExtractionResult,\n    SegmentationResult,\n    SynthesisResult,\n\n[738 more lines in file. Use offset=41 to continue.]"}],"isError":false,"timestamp":1774823952606}}
{"type":"message","id":"d81d4af1","parentId":"88475065","timestamp":"2026-03-29T22:39:16.636Z","message":{"role":"assistant","content":[{"type":"text","text":"Let me see the `run_pipeline` task definition to confirm the import path and signature."},{"type":"toolCall","id":"toolu_014WBFXqH6UA4KPDFZUDxhYS","name":"bash","arguments":{"command":"cd backend && grep -n 'def run_pipeline\\|@celery_app.task.*run_pipeline\\|run_pipeline' pipeline/stages.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":111,"cacheRead":59862,"cacheWrite":723,"totalTokens":60697,"cost":{"input":0.000005,"output":0.002775,"cacheRead":0.029931,"cacheWrite":0.00451875,"total":0.03722975}},"stopReason":"toolUse","timestamp":1774823952607}}
{"type":"message","id":"ed68f09b","parentId":"d81d4af1","timestamp":"2026-03-29T22:39:16.649Z","message":{"role":"toolResult","toolCallId":"toolu_014WBFXqH6UA4KPDFZUDxhYS","toolName":"bash","content":[{"type":"text","text":"1:\"\"\"Pipeline stage tasks (stages 2-5) and run_pipeline orchestrator.\n716:def run_pipeline(video_id: str) -> str:\n727:    logger.info(\"run_pipeline starting for video_id=%s\", video_id)\n736:            logger.error(\"run_pipeline: video_id=%s not found\", video_id)\n741:            \"run_pipeline: video_id=%s current status=%s\", video_id, status.value\n764:            \"run_pipeline: video_id=%s already at status=%s, nothing to do.\",\n773:            \"run_pipeline: dispatched %d stages for video_id=%s\",\n"}],"isError":false,"timestamp":1774823956648}}
{"type":"message","id":"947915b6","parentId":"ed68f09b","timestamp":"2026-03-29T22:39:19.269Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_014hB8R1jDC4WsS3uZ1A37dQ","name":"read","arguments":{"limit":70,"offset":710,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":91,"cacheRead":60585,"cacheWrite":279,"totalTokens":60956,"cost":{"input":0.000005,"output":0.002275,"cacheRead":0.0302925,"cacheWrite":0.00174375,"total":0.03431625}},"stopReason":"toolUse","timestamp":1774823956648}}
{"type":"message","id":"a8797ec3","parentId":"947915b6","timestamp":"2026-03-29T22:39:19.281Z","message":{"role":"toolResult","toolCallId":"toolu_014hB8R1jDC4WsS3uZ1A37dQ","toolName":"read","content":[{"type":"text","text":"        session.close()\n\n\n# ── Orchestrator ─────────────────────────────────────────────────────────────\n\n@celery_app.task\ndef run_pipeline(video_id: str) -> str:\n    \"\"\"Orchestrate the full pipeline (stages 2-5) with resumability.\n\n    Checks the current processing_status of the video and chains only the\n    stages that still need to run. For example:\n      - pending/transcribed → stages 2, 3, 4, 5\n      - extracted → stages 4, 5\n      - reviewed/published → no-op\n\n    Returns the video_id.\n    \"\"\"\n    logger.info(\"run_pipeline starting for video_id=%s\", video_id)\n\n    session = _get_sync_session()\n    try:\n        video = session.execute(\n            select(SourceVideo).where(SourceVideo.id == video_id)\n        ).scalar_one_or_none()\n\n        if video is None:\n            logger.error(\"run_pipeline: video_id=%s not found\", video_id)\n            raise ValueError(f\"Video not found: {video_id}\")\n\n        status = video.processing_status\n        logger.info(\n            \"run_pipeline: video_id=%s current status=%s\", video_id, status.value\n        )\n    finally:\n        session.close()\n\n    # Build the chain based on current status\n    stages = []\n    if status in (ProcessingStatus.pending, ProcessingStatus.transcribed):\n        stages = [\n            stage2_segmentation.s(video_id),\n            stage3_extraction.s(),  # receives video_id from previous\n            stage4_classification.s(),\n            stage5_synthesis.s(),\n            stage6_embed_and_index.s(),\n        ]\n    elif status == ProcessingStatus.extracted:\n        stages = [\n            stage4_classification.s(video_id),\n            stage5_synthesis.s(),\n            stage6_embed_and_index.s(),\n        ]\n    elif status in (ProcessingStatus.reviewed, ProcessingStatus.published):\n        logger.info(\n            \"run_pipeline: video_id=%s already at status=%s, nothing to do.\",\n            video_id, status.value,\n        )\n        return video_id\n\n    if stages:\n        pipeline = celery_chain(*stages)\n        pipeline.apply_async()\n        logger.info(\n            \"run_pipeline: dispatched %d stages for video_id=%s\",\n            len(stages), video_id,\n        )\n\n    return video_id\n"}],"isError":false,"timestamp":1774823959280}}
{"type":"message","id":"c05a4831","parentId":"a8797ec3","timestamp":"2026-03-29T22:39:23.481Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have the full picture. Let me check what schemas are in the `backend/schemas.py` to see existing patterns for response models."},{"type":"toolCall","id":"toolu_01YRKPRGLJL1c1GqMH3ty7EP","name":"bash","arguments":{"command":"cd backend && grep -n 'class.*Model\\|class.*Response\\|class.*Request' schemas.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":111,"cacheRead":60864,"cacheWrite":719,"totalTokens":61695,"cost":{"input":0.000005,"output":0.002775,"cacheRead":0.030431999999999997,"cacheWrite":0.00449375,"total":0.037705749999999996}},"stopReason":"toolUse","timestamp":1774823959280}}
{"type":"message","id":"fb8b5eda","parentId":"c05a4831","timestamp":"2026-03-29T22:39:23.495Z","message":{"role":"toolResult","toolCallId":"toolu_01YRKPRGLJL1c1GqMH3ty7EP","toolName":"bash","content":[{"type":"text","text":"17:class HealthResponse(BaseModel):\n26:class CreatorBase(BaseModel):\n51:class SourceVideoBase(BaseModel):\n73:class TranscriptSegmentBase(BaseModel):\n92:class KeyMomentBase(BaseModel):\n118:class TechniquePageBase(BaseModel):\n146:class RelatedTechniqueLinkBase(BaseModel):\n162:class TagBase(BaseModel):\n178:class TranscriptIngestResponse(BaseModel):\n191:class PaginatedResponse(BaseModel):\n"}],"isError":false,"timestamp":1774823963493}}
{"type":"message","id":"e37297cc","parentId":"fb8b5eda","timestamp":"2026-03-29T22:39:33.915Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. Now I'll implement all three changes. Let me start with creating the pipeline router, then modify the ingest endpoint, and finally mount the router in main.py.\n\n**Step 1: Create `backend/routers/pipeline.py`**"},{"type":"toolCall","id":"toolu_01KpCxKmRC4sVgFVn5zZ7XpW","name":"write","arguments":{"content":"\"\"\"Pipeline management endpoints for manual re-trigger and status inspection.\"\"\"\n\nimport logging\n\nfrom fastapi import APIRouter, Depends, HTTPException\nfrom sqlalchemy import select\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom database import get_session\nfrom models import SourceVideo\n\nlogger = logging.getLogger(\"chrysopedia.pipeline\")\n\nrouter = APIRouter(prefix=\"/pipeline\", tags=[\"pipeline\"])\n\n\n@router.post(\"/trigger/{video_id}\")\nasync def trigger_pipeline(\n    video_id: str,\n    db: AsyncSession = Depends(get_session),\n):\n    \"\"\"Manually trigger (or re-trigger) the LLM extraction pipeline for a video.\n\n    Looks up the SourceVideo by ID, dispatches ``run_pipeline.delay()``,\n    and returns the current processing status. Returns 404 if the video\n    does not exist.\n    \"\"\"\n    stmt = select(SourceVideo).where(SourceVideo.id == video_id)\n    result = await db.execute(stmt)\n    video = result.scalar_one_or_none()\n\n    if video is None:\n        raise HTTPException(status_code=404, detail=f\"Video not found: {video_id}\")\n\n    # Import inside handler to avoid circular import at module level\n    from pipeline.stages import run_pipeline\n\n    try:\n        run_pipeline.delay(str(video.id))\n        logger.info(\"Pipeline manually triggered for video_id=%s\", video_id)\n    except Exception as exc:\n        logger.warning(\n            \"Failed to dispatch pipeline for video_id=%s: %s\", video_id, exc\n        )\n        raise HTTPException(\n            status_code=503,\n            detail=\"Pipeline dispatch failed — Celery/Redis may be unavailable\",\n        ) from exc\n\n    return {\n        \"status\": \"triggered\",\n        \"video_id\": str(video.id),\n        \"current_processing_status\": video.processing_status.value,\n    }\n","path":"backend/routers/pipeline.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":593,"cacheRead":61583,"cacheWrite":250,"totalTokens":62427,"cost":{"input":0.000005,"output":0.014825000000000001,"cacheRead":0.0307915,"cacheWrite":0.0015625,"total":0.047184000000000004}},"stopReason":"toolUse","timestamp":1774823963494}}
{"type":"message","id":"feaba045","parentId":"e37297cc","timestamp":"2026-03-29T22:39:33.920Z","message":{"role":"toolResult","toolCallId":"toolu_01KpCxKmRC4sVgFVn5zZ7XpW","toolName":"write","content":[{"type":"text","text":"Successfully wrote 1738 bytes to backend/routers/pipeline.py"}],"isError":false,"timestamp":1774823973919}}
{"type":"message","id":"eebd7f3d","parentId":"feaba045","timestamp":"2026-03-29T22:39:41.579Z","message":{"role":"assistant","content":[{"type":"text","text":"**Step 2: Modify `backend/routers/ingest.py` — add pipeline dispatch after commit**"},{"type":"toolCall","id":"toolu_01ENjUCv6C4S6C5q7yzYRuiD","name":"edit","arguments":{"newText":"    await db.refresh(video)\n    await db.refresh(creator)\n\n    # ── 7. Dispatch LLM pipeline (best-effort) ──────────────────────────\n    try:\n        from pipeline.stages import run_pipeline\n\n        run_pipeline.delay(str(video.id))\n        logger.info(\"Pipeline dispatched for video_id=%s\", video.id)\n    except Exception as exc:\n        logger.warning(\n            \"Pipeline dispatch failed for video_id=%s (ingest still succeeds): %s\",\n            video.id,\n            exc,\n        )\n\n    logger.info(\n        \"Ingested transcript: creator=%s, file=%s, segments=%d, reupload=%s\",\n        creator.name,\n        source_file,\n        len(segment_objs),\n        is_reupload,\n    )\n\n    return TranscriptIngestResponse(","oldText":"    await db.refresh(video)\n    await db.refresh(creator)\n\n    logger.info(\n        \"Ingested transcript: creator=%s, file=%s, segments=%d, reupload=%s\",\n        creator.name,\n        source_file,\n        len(segment_objs),\n        is_reupload,\n    )\n\n    return TranscriptIngestResponse(","path":"backend/routers/ingest.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":424,"cacheRead":61833,"cacheWrite":621,"totalTokens":62879,"cost":{"input":0.000005,"output":0.0106,"cacheRead":0.0309165,"cacheWrite":0.00388125,"total":0.045402750000000006}},"stopReason":"toolUse","timestamp":1774823973920}}
{"type":"message","id":"c37bf6d9","parentId":"eebd7f3d","timestamp":"2026-03-29T22:39:41.589Z","message":{"role":"toolResult","toolCallId":"toolu_01ENjUCv6C4S6C5q7yzYRuiD","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/routers/ingest.py."}],"details":{"diff":" 173 \n 174     await db.refresh(video)\n 175     await db.refresh(creator)\n 176 \n+177     # ── 7. Dispatch LLM pipeline (best-effort) ──────────────────────────\n+178     try:\n+179         from pipeline.stages import run_pipeline\n+180 \n+181         run_pipeline.delay(str(video.id))\n+182         logger.info(\"Pipeline dispatched for video_id=%s\", video.id)\n+183     except Exception as exc:\n+184         logger.warning(\n+185             \"Pipeline dispatch failed for video_id=%s (ingest still succeeds): %s\",\n+186             video.id,\n+187             exc,\n+188         )\n+189 \n 190     logger.info(\n 191         \"Ingested transcript: creator=%s, file=%s, segments=%d, reupload=%s\",\n 192         creator.name,\n 193         source_file,","firstChangedLine":177},"isError":false,"timestamp":1774823981588}}
{"type":"message","id":"5c78a7d2","parentId":"c37bf6d9","timestamp":"2026-03-29T22:39:45.039Z","message":{"role":"assistant","content":[{"type":"text","text":"**Step 3: Mount the pipeline router in `backend/main.py`**"},{"type":"toolCall","id":"toolu_01364t4mSkc7ZZ9V5dYLw9SU","name":"edit","arguments":{"newText":"from routers import creators, health, ingest, pipeline, videos","oldText":"from routers import creators, health, ingest, videos","path":"backend/main.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":134,"cacheRead":62454,"cacheWrite":449,"totalTokens":63038,"cost":{"input":0.000005,"output":0.00335,"cacheRead":0.031226999999999998,"cacheWrite":0.00280625,"total":0.03738825}},"stopReason":"toolUse","timestamp":1774823981588}}
{"type":"message","id":"d995673c","parentId":"5c78a7d2","timestamp":"2026-03-29T22:39:45.053Z","message":{"role":"toolResult","toolCallId":"toolu_01364t4mSkc7ZZ9V5dYLw9SU","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/main.py."}],"details":{"diff":" 11 from fastapi import FastAPI\n 12 from fastapi.middleware.cors import CORSMiddleware\n 13 \n 14 from config import get_settings\n-15 from routers import creators, health, ingest, videos\n+15 from routers import creators, health, ingest, pipeline, videos\n 16 \n 17 \n 18 def _setup_logging() -> None:\n 19     \"\"\"Configure structured logging to stdout.\"\"\"","firstChangedLine":15},"isError":false,"timestamp":1774823985051}}
{"type":"message","id":"e9bf6127","parentId":"d995673c","timestamp":"2026-03-29T22:39:48.231Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01TnYBDzcgjqJoAMvG7zn895","name":"edit","arguments":{"newText":"app.include_router(ingest.router, prefix=\"/api/v1\")\napp.include_router(pipeline.router, prefix=\"/api/v1\")\napp.include_router(videos.router, prefix=\"/api/v1\")","oldText":"app.include_router(ingest.router, prefix=\"/api/v1\")\napp.include_router(videos.router, prefix=\"/api/v1\")","path":"backend/main.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":179,"cacheRead":62903,"cacheWrite":155,"totalTokens":63238,"cost":{"input":0.000005,"output":0.004475,"cacheRead":0.0314515,"cacheWrite":0.00096875,"total":0.036900249999999996}},"stopReason":"toolUse","timestamp":1774823985052}}
{"type":"message","id":"e3ccdd11","parentId":"e9bf6127","timestamp":"2026-03-29T22:39:48.241Z","message":{"role":"toolResult","toolCallId":"toolu_01TnYBDzcgjqJoAMvG7zn895","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/main.py."}],"details":{"diff":" 79 \n 80 # Versioned API\n 81 app.include_router(creators.router, prefix=\"/api/v1\")\n 82 app.include_router(ingest.router, prefix=\"/api/v1\")\n+83 app.include_router(pipeline.router, prefix=\"/api/v1\")\n 84 app.include_router(videos.router, prefix=\"/api/v1\")\n 85 \n 86 \n 87 @app.get(\"/api/v1/health\")","firstChangedLine":83},"isError":false,"timestamp":1774823988239}}
{"type":"message","id":"4f1d9edc","parentId":"e3ccdd11","timestamp":"2026-03-29T22:39:52.386Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me run the verification commands."},{"type":"toolCall","id":"toolu_018N7Us8p4qTous5gtnAoEAQ","name":"bash","arguments":{"command":"cd backend && python -c \"from routers.pipeline import router; print([r.path for r in router.routes])\""}},{"type":"toolCall","id":"toolu_01FHwFvRgbn3agh8K3FkW3st","name":"bash","arguments":{"command":"grep -q 'pipeline' backend/main.py && echo \"pipeline found in main.py\""}},{"type":"toolCall","id":"toolu_01Wneg8DK41zduiT9rN6DJVg","name":"bash","arguments":{"command":"grep -q 'run_pipeline' backend/routers/ingest.py && echo \"run_pipeline found in ingest.py\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":209,"cacheRead":63058,"cacheWrite":200,"totalTokens":63468,"cost":{"input":0.000005,"output":0.0052250000000000005,"cacheRead":0.031529,"cacheWrite":0.00125,"total":0.038009}},"stopReason":"toolUse","timestamp":1774823988240}}
{"type":"message","id":"58e27783","parentId":"4f1d9edc","timestamp":"2026-03-29T22:39:52.894Z","message":{"role":"toolResult","toolCallId":"toolu_018N7Us8p4qTous5gtnAoEAQ","toolName":"bash","content":[{"type":"text","text":"['/pipeline/trigger/{video_id}']\n"}],"isError":false,"timestamp":1774823992894}}
{"type":"message","id":"8515c5a9","parentId":"58e27783","timestamp":"2026-03-29T22:39:52.895Z","message":{"role":"toolResult","toolCallId":"toolu_01FHwFvRgbn3agh8K3FkW3st","toolName":"bash","content":[{"type":"text","text":"pipeline found in main.py\n"}],"isError":false,"timestamp":1774823992894}}
{"type":"message","id":"2b804629","parentId":"8515c5a9","timestamp":"2026-03-29T22:39:52.896Z","message":{"role":"toolResult","toolCallId":"toolu_01Wneg8DK41zduiT9rN6DJVg","toolName":"bash","content":[{"type":"text","text":"run_pipeline found in ingest.py\n"}],"isError":false,"timestamp":1774823992894}}
{"type":"message","id":"be28dcf2","parentId":"2b804629","timestamp":"2026-03-29T22:39:59.712Z","message":{"role":"assistant","content":[{"type":"text","text":"All three verification checks pass. Now let me run the slice-level verification commands as well."},{"type":"toolCall","id":"toolu_01P5qrurh3gSMBPTnFVSrQJY","name":"bash","arguments":{"command":"cd backend && python -c \"from config import Settings; s = Settings(); print(s.llm_api_url, s.qdrant_url, s.review_mode)\""}},{"type":"toolCall","id":"toolu_01SVryeieFsKvFYtbzDRGxKU","name":"bash","arguments":{"command":"cd backend && python -c \"from pipeline.schemas import SegmentationResult, ExtractionResult, ClassificationResult, SynthesisResult; print('schemas ok')\""}},{"type":"toolCall","id":"toolu_01N5ZRrRcg3DKYwZWAkAEePj","name":"bash","arguments":{"command":"cd backend && python -c \"from pipeline.llm_client import LLMClient; print('client ok')\""}},{"type":"toolCall","id":"toolu_0117NZ6ByynxZgDibZ7eWBrz","name":"bash","arguments":{"command":"cd backend && python -c \"from worker import celery_app; print(celery_app.main)\""}},{"type":"toolCall","id":"toolu_014CUoqGfwKvcoe5NtvXyNDM","name":"bash","arguments":{"command":"grep -q 'openai' backend/requirements.txt && grep -q 'qdrant-client' backend/requirements.txt && echo \"deps ok\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":373,"cacheRead":63258,"cacheWrite":322,"totalTokens":63954,"cost":{"input":0.000005,"output":0.009325,"cacheRead":0.031629,"cacheWrite":0.0020125,"total":0.042971499999999996}},"stopReason":"toolUse","timestamp":1774823992895}}
{"type":"message","id":"4656ac5c","parentId":"be28dcf2","timestamp":"2026-03-29T22:39:59.894Z","message":{"role":"toolResult","toolCallId":"toolu_01P5qrurh3gSMBPTnFVSrQJY","toolName":"bash","content":[{"type":"text","text":"http://localhost:11434/v1 http://localhost:6333 True\n"}],"isError":false,"timestamp":1774823999894}}
{"type":"message","id":"3d9b100f","parentId":"4656ac5c","timestamp":"2026-03-29T22:39:59.916Z","message":{"role":"toolResult","toolCallId":"toolu_01SVryeieFsKvFYtbzDRGxKU","toolName":"bash","content":[{"type":"text","text":"schemas ok\n"}],"isError":false,"timestamp":1774823999916}}
{"type":"message","id":"78134295","parentId":"3d9b100f","timestamp":"2026-03-29T22:40:00.389Z","message":{"role":"toolResult","toolCallId":"toolu_01N5ZRrRcg3DKYwZWAkAEePj","toolName":"bash","content":[{"type":"text","text":"client ok\n"}],"isError":false,"timestamp":1774824000389}}
{"type":"message","id":"3111f837","parentId":"78134295","timestamp":"2026-03-29T22:40:01.720Z","message":{"role":"toolResult","toolCallId":"toolu_0117NZ6ByynxZgDibZ7eWBrz","toolName":"bash","content":[{"type":"text","text":"chrysopedia\n"}],"isError":false,"timestamp":1774824001720}}
{"type":"message","id":"c9d39bdc","parentId":"3111f837","timestamp":"2026-03-29T22:40:01.722Z","message":{"role":"toolResult","toolCallId":"toolu_014CUoqGfwKvcoe5NtvXyNDM","toolName":"bash","content":[{"type":"text","text":"deps ok\n"}],"isError":false,"timestamp":1774824001720}}
{"type":"message","id":"3b41ac28","parentId":"c9d39bdc","timestamp":"2026-03-29T22:40:05.624Z","message":{"role":"assistant","content":[{"type":"text","text":"All slice-level verification checks pass. Let me quickly review the final state of all three modified/created files to ensure correctness."},{"type":"toolCall","id":"toolu_01EvgowaGi8C5WSDfrp18p6i","name":"read","arguments":{"path":"backend/routers/pipeline.py"}},{"type":"toolCall","id":"toolu_019sJXuLePN8YEk9YFFXcaER","name":"read","arguments":{"limit":30,"offset":105,"path":"backend/routers/ingest.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":162,"cacheRead":63580,"cacheWrite":538,"totalTokens":64281,"cost":{"input":0.000005,"output":0.00405,"cacheRead":0.03179,"cacheWrite":0.0033625,"total":0.0392075}},"stopReason":"toolUse","timestamp":1774824001721}}
{"type":"message","id":"7c481184","parentId":"3b41ac28","timestamp":"2026-03-29T22:40:05.626Z","message":{"role":"toolResult","toolCallId":"toolu_01EvgowaGi8C5WSDfrp18p6i","toolName":"read","content":[{"type":"text","text":"\"\"\"Pipeline management endpoints for manual re-trigger and status inspection.\"\"\"\n\nimport logging\n\nfrom fastapi import APIRouter, Depends, HTTPException\nfrom sqlalchemy import select\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom database import get_session\nfrom models import SourceVideo\n\nlogger = logging.getLogger(\"chrysopedia.pipeline\")\n\nrouter = APIRouter(prefix=\"/pipeline\", tags=[\"pipeline\"])\n\n\n@router.post(\"/trigger/{video_id}\")\nasync def trigger_pipeline(\n    video_id: str,\n    db: AsyncSession = Depends(get_session),\n):\n    \"\"\"Manually trigger (or re-trigger) the LLM extraction pipeline for a video.\n\n    Looks up the SourceVideo by ID, dispatches ``run_pipeline.delay()``,\n    and returns the current processing status. Returns 404 if the video\n    does not exist.\n    \"\"\"\n    stmt = select(SourceVideo).where(SourceVideo.id == video_id)\n    result = await db.execute(stmt)\n    video = result.scalar_one_or_none()\n\n    if video is None:\n        raise HTTPException(status_code=404, detail=f\"Video not found: {video_id}\")\n\n    # Import inside handler to avoid circular import at module level\n    from pipeline.stages import run_pipeline\n\n    try:\n        run_pipeline.delay(str(video.id))\n        logger.info(\"Pipeline manually triggered for video_id=%s\", video_id)\n    except Exception as exc:\n        logger.warning(\n            \"Failed to dispatch pipeline for video_id=%s: %s\", video_id, exc\n        )\n        raise HTTPException(\n            status_code=503,\n            detail=\"Pipeline dispatch failed — Celery/Redis may be unavailable\",\n        ) from exc\n\n    return {\n        \"status\": \"triggered\",\n        \"video_id\": str(video.id),\n        \"current_processing_status\": video.processing_status.value,\n    }\n"}],"isError":false,"timestamp":1774824005625}}
{"type":"message","id":"bd719907","parentId":"7c481184","timestamp":"2026-03-29T22:40:05.628Z","message":{"role":"toolResult","toolCallId":"toolu_019sJXuLePN8YEk9YFFXcaER","toolName":"read","content":[{"type":"text","text":"        SourceVideo.filename == source_file,\n    )\n    result = await db.execute(stmt)\n    existing_video = result.scalar_one_or_none()\n\n    is_reupload = existing_video is not None\n\n    if is_reupload:\n        video = existing_video\n        # Delete old segments for idempotent re-upload\n        await db.execute(\n            delete(TranscriptSegment).where(\n                TranscriptSegment.source_video_id == video.id\n            )\n        )\n        video.duration_seconds = duration_seconds\n        video.processing_status = ProcessingStatus.transcribed\n    else:\n        video = SourceVideo(\n            creator_id=creator.id,\n            filename=source_file,\n            file_path=f\"{creator_folder}/{source_file}\",\n            duration_seconds=duration_seconds,\n            content_type=ContentType.tutorial,\n            processing_status=ProcessingStatus.transcribed,\n        )\n        db.add(video)\n        await db.flush()  # assign id\n\n    # ── 4. Bulk-insert TranscriptSegments ────────────────────────────────\n\n[73 more lines in file. Use offset=135 to continue.]"}],"isError":false,"timestamp":1774824005627}}
{"type":"message","id":"50e8fb61","parentId":"bd719907","timestamp":"2026-03-29T22:40:07.960Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01QfozNnGKkQacDrGK95Q9vS","name":"read","arguments":{"limit":30,"offset":150,"path":"backend/routers/ingest.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":93,"cacheRead":64118,"cacheWrite":983,"totalTokens":65195,"cost":{"input":0.000005,"output":0.0023250000000000002,"cacheRead":0.032059,"cacheWrite":0.00614375,"total":0.04053274999999999}},"stopReason":"toolUse","timestamp":1774824005627}}
{"type":"message","id":"ee008f20","parentId":"50e8fb61","timestamp":"2026-03-29T22:40:07.967Z","message":{"role":"toolResult","toolCallId":"toolu_01QfozNnGKkQacDrGK95Q9vS","toolName":"read","content":[{"type":"text","text":"    )\n    transcript_path = os.path.join(transcript_dir, f\"{source_file}.json\")\n\n    try:\n        os.makedirs(transcript_dir, exist_ok=True)\n        with open(transcript_path, \"w\", encoding=\"utf-8\") as f:\n            f.write(raw_text)\n    except OSError as exc:\n        raise HTTPException(\n            status_code=500, detail=f\"Failed to save transcript: {exc}\"\n        ) from exc\n\n    video.transcript_path = transcript_path\n\n    # ── 6. Commit & respond ──────────────────────────────────────────────\n    try:\n        await db.commit()\n    except Exception as exc:\n        await db.rollback()\n        logger.error(\"Database commit failed during ingest: %s\", exc)\n        raise HTTPException(\n            status_code=500, detail=\"Database error during ingest\"\n        ) from exc\n\n    await db.refresh(video)\n    await db.refresh(creator)\n\n    # ── 7. Dispatch LLM pipeline (best-effort) ──────────────────────────\n    try:\n        from pipeline.stages import run_pipeline\n\n[28 more lines in file. Use offset=180 to continue.]"}],"isError":false,"timestamp":1774824007966}}
{"type":"message","id":"e41b7638","parentId":"ee008f20","timestamp":"2026-03-29T22:40:12.223Z","message":{"role":"assistant","content":[{"type":"text","text":"Everything is correctly wired. Now let me read the task summary template, then write the summary and complete the task."},{"type":"toolCall","id":"toolu_018ybLrxzgpznknRPeDDoqbA","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":94,"cacheRead":64118,"cacheWrite":1388,"totalTokens":65601,"cost":{"input":0.000005,"output":0.00235,"cacheRead":0.032059,"cacheWrite":0.008675,"total":0.043089}},"stopReason":"toolUse","timestamp":1774824007966}}
{"type":"message","id":"d40e787f","parentId":"e41b7638","timestamp":"2026-03-29T22:40:12.226Z","message":{"role":"toolResult","toolCallId":"toolu_018ybLrxzgpznknRPeDDoqbA","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":1774824012225}}
{"type":"message","id":"e74684dc","parentId":"d40e787f","timestamp":"2026-03-29T22:40:42.138Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01NxmyY5BqHvTEZZDTPvhQJE","name":"write","arguments":{"content":"---\nid: T04\nparent: S03\nmilestone: M001\nprovides:\n  - Automatic pipeline dispatch on transcript ingest\n  - Manual pipeline re-trigger endpoint at POST /api/v1/pipeline/trigger/{video_id}\nkey_files:\n  - backend/routers/pipeline.py\n  - backend/routers/ingest.py\n  - backend/main.py\nkey_decisions:\n  - \"Pipeline dispatch in ingest is best-effort: wrapped in try/except, logs WARNING on failure, ingest response still succeeds\"\n  - \"Pipeline router import of run_pipeline is inside the handler to avoid circular imports at module level\"\n  - \"Manual trigger returns 503 (not swallowed) when Celery/Redis is down — deliberate distinction from ingest which silently swallows dispatch failures\"\npatterns_established:\n  - \"Best-effort Celery dispatch pattern: import inside function, try/except with WARNING log, never fail the primary operation\"\nobservability_surfaces:\n  - \"chrysopedia.pipeline logger: INFO on successful manual trigger, WARNING on dispatch failure\"\n  - \"chrysopedia.ingest logger: INFO on successful pipeline dispatch, WARNING on dispatch failure\"\nduration: 10m\nverification_result: passed\ncompleted_at: 2026-03-29\nblocker_discovered: false\n---\n\n# T04: Wire ingest-to-pipeline trigger and add manual re-trigger endpoint\n\n**Wired automatic run_pipeline.delay() dispatch after ingest commit and added POST /api/v1/pipeline/trigger/{video_id} for manual re-processing**\n\n## What Happened\n\nAdded pipeline dispatch to two entry points:\n\n1. **Ingest endpoint** (`backend/routers/ingest.py`): After `db.commit()` and `db.refresh()`, the endpoint now calls `run_pipeline.delay(str(video.id))` wrapped in try/except. On failure, it logs a WARNING but the ingest response still returns successfully — the pipeline can always be triggered manually later.\n\n2. **Pipeline router** (`backend/routers/pipeline.py`): New router with `POST /trigger/{video_id}` that looks up the SourceVideo by ID, returns 404 if not found, dispatches `run_pipeline.delay()`, and returns `{\"status\": \"triggered\", \"video_id\": ..., \"current_processing_status\": ...}`. Unlike the ingest endpoint, this endpoint returns 503 if dispatch fails — since the user explicitly requested a trigger, they should know it failed.\n\n3. **Main app** (`backend/main.py`): Mounted the pipeline router under `/api/v1`.\n\n## Verification\n\nAll task-level and slice-level verification commands pass:\n\n```\ncd backend && python -c \"from routers.pipeline import router; print([r.path for r in router.routes])\"\n# Output: ['/pipeline/trigger/{video_id}']\n\ngrep -q 'pipeline' backend/main.py  # exits 0\ngrep -q 'run_pipeline' backend/routers/ingest.py  # exits 0\n```\n\nSlice-level checks (all pass — this is the final task):\n```\ncd backend && python -c \"from config import Settings; s = Settings(); print(s.llm_api_url, s.qdrant_url, s.review_mode)\"\n# Output: http://localhost:11434/v1 http://localhost:6333 True\n\npython -c \"from pipeline.schemas import SegmentationResult, ExtractionResult, ClassificationResult, SynthesisResult; print('schemas ok')\"\n# Output: schemas ok\n\npython -c \"from pipeline.llm_client import LLMClient; print('client ok')\"\n# Output: client ok\n\npython -c \"from worker import celery_app; print(celery_app.main)\"\n# Output: chrysopedia\n\ngrep -q 'openai' backend/requirements.txt && grep -q 'qdrant-client' backend/requirements.txt\n# exits 0\n```\n\n## Verification Evidence\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| 1 | `cd backend && python -c \"from routers.pipeline import router; print([r.path for r in router.routes])\"` | 0 | ✅ pass | <1s |\n| 2 | `grep -q 'pipeline' backend/main.py` | 0 | ✅ pass | <1s |\n| 3 | `grep -q 'run_pipeline' backend/routers/ingest.py` | 0 | ✅ pass | <1s |\n| 4 | `cd backend && python -c \"from config import Settings; s = Settings(); print(s.llm_api_url, s.qdrant_url, s.review_mode)\"` | 0 | ✅ pass | <1s |\n| 5 | `cd backend && python -c \"from pipeline.schemas import SegmentationResult, ExtractionResult, ClassificationResult, SynthesisResult; print('schemas ok')\"` | 0 | ✅ pass | <1s |\n| 6 | `cd backend && python -c \"from pipeline.llm_client import LLMClient; print('client ok')\"` | 0 | ✅ pass | <1s |\n| 7 | `cd backend && python -c \"from worker import celery_app; print(celery_app.main)\"` | 0 | ✅ pass | <1s |\n| 8 | `grep -q 'openai' backend/requirements.txt && grep -q 'qdrant-client' backend/requirements.txt` | 0 | ✅ pass | <1s |\n\n## Diagnostics\n\n- `chrysopedia.ingest` logger emits INFO on successful dispatch, WARNING on failure\n- `chrysopedia.pipeline` logger emits INFO on manual trigger, WARNING on dispatch failure\n- Pipeline trigger endpoint returns `current_processing_status` to inspect video state\n\n## Deviations\n\nNone.\n\n## Known Issues\n\nNone.\n\n## Files Created/Modified\n\n- `backend/routers/pipeline.py` — New router with POST /trigger/{video_id} endpoint for manual pipeline re-trigger\n- `backend/routers/ingest.py` — Added best-effort run_pipeline.delay() dispatch after commit (step 7)\n- `backend/main.py` — Mounted pipeline router under /api/v1\n","path":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S03/tasks/T04-SUMMARY.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":1648,"cacheRead":65506,"cacheWrite":604,"totalTokens":67759,"cost":{"input":0.000005,"output":0.0412,"cacheRead":0.032753,"cacheWrite":0.003775,"total":0.077733}},"stopReason":"toolUse","timestamp":1774824012225}}
{"type":"message","id":"0bdbf2d9","parentId":"e74684dc","timestamp":"2026-03-29T22:40:42.140Z","message":{"role":"toolResult","toolCallId":"toolu_01NxmyY5BqHvTEZZDTPvhQJE","toolName":"write","content":[{"type":"text","text":"Successfully wrote 5018 bytes to /home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S03/tasks/T04-SUMMARY.md"}],"isError":false,"timestamp":1774824042139}}
{"type":"message","id":"c38298ed","parentId":"0bdbf2d9","timestamp":"2026-03-29T22:41:00.018Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01SW83QjsQj9Y7zbvybrjego","name":"gsd_complete_task","arguments":{"blockerDiscovered":false,"deviations":"None.","keyDecisions":["Pipeline dispatch in ingest is best-effort: wrapped in try/except, logs WARNING on failure, ingest response still succeeds","Manual trigger returns 503 (not swallowed) when Celery/Redis is down — deliberate distinction from ingest which silently swallows dispatch failures","Pipeline router import of run_pipeline is inside the handler to avoid circular imports at module level"],"keyFiles":["backend/routers/pipeline.py","backend/routers/ingest.py","backend/main.py"],"knownIssues":"None.","milestoneId":"M001","narrative":"Added pipeline dispatch to two entry points: (1) the ingest endpoint now calls run_pipeline.delay() after db.commit(), wrapped in try/except so dispatch failures never fail the ingest response; (2) a new pipeline router with POST /trigger/{video_id} that looks up the video, returns 404 if missing, dispatches run_pipeline.delay(), and returns the current processing status (returns 503 on dispatch failure since it's an explicit user action). Mounted the pipeline router in main.py under /api/v1.","oneLiner":"Wired automatic run_pipeline.delay() dispatch after ingest commit and added POST /api/v1/pipeline/trigger/{video_id} for manual re-processing","sliceId":"S03","taskId":"T04","verification":"All 3 task-level verification commands pass: pipeline router has /pipeline/trigger/{video_id} route, 'pipeline' found in main.py, 'run_pipeline' found in ingest.py. All 5 slice-level verification commands also pass: Settings defaults print correctly, pipeline schemas import, LLMClient imports, celery_app.main prints 'chrysopedia', and openai/qdrant-client are in requirements.txt.","verificationEvidence":[{"command":"cd backend && python -c \"from routers.pipeline import router; print([r.path for r in router.routes])\"","durationMs":500,"exitCode":0,"verdict":"✅ pass"},{"command":"grep -q 'pipeline' backend/main.py","durationMs":50,"exitCode":0,"verdict":"✅ pass"},{"command":"grep -q 'run_pipeline' backend/routers/ingest.py","durationMs":50,"exitCode":0,"verdict":"✅ pass"},{"command":"cd backend && python -c \"from config import Settings; s = Settings(); print(s.llm_api_url, s.qdrant_url, s.review_mode)\"","durationMs":500,"exitCode":0,"verdict":"✅ pass"},{"command":"cd backend && python -c \"from pipeline.schemas import SegmentationResult, ExtractionResult, ClassificationResult, SynthesisResult; print('schemas ok')\"","durationMs":500,"exitCode":0,"verdict":"✅ pass"},{"command":"cd backend && python -c \"from pipeline.llm_client import LLMClient; print('client ok')\"","durationMs":500,"exitCode":0,"verdict":"✅ pass"},{"command":"cd backend && python -c \"from worker import celery_app; print(celery_app.main)\"","durationMs":500,"exitCode":0,"verdict":"✅ pass"},{"command":"grep -q 'openai' backend/requirements.txt && grep -q 'qdrant-client' backend/requirements.txt","durationMs":50,"exitCode":0,"verdict":"✅ pass"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":1087,"cacheRead":66110,"cacheWrite":1709,"totalTokens":68907,"cost":{"input":0.000005,"output":0.027175,"cacheRead":0.033055,"cacheWrite":0.01068125,"total":0.07091625}},"stopReason":"toolUse","timestamp":1774824042139}}
{"type":"message","id":"a5607220","parentId":"c38298ed","timestamp":"2026-03-29T22:41:00.040Z","message":{"role":"toolResult","toolCallId":"toolu_01SW83QjsQj9Y7zbvybrjego","toolName":"gsd_complete_task","content":[{"type":"text","text":"Completed task T04 (S03/M001)"}],"details":{"operation":"complete_task","taskId":"T04","sliceId":"S03","milestoneId":"M001","summaryPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S03/tasks/T04-SUMMARY.md"},"isError":false,"timestamp":1774824060039}}
{"type":"message","id":"3c9a9480","parentId":"a5607220","timestamp":"2026-03-29T22:41:01.900Z","message":{"role":"assistant","content":[{"type":"text","text":"Task T04 complete."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":9,"cacheRead":67819,"cacheWrite":1130,"totalTokens":68959,"cost":{"input":0.000005,"output":0.00022500000000000002,"cacheRead":0.033909499999999995,"cacheWrite":0.0070625,"total":0.041201999999999996}},"stopReason":"stop","timestamp":1774824060039}}
