{"type":"thinking_level_change","id":"ef5837ee","parentId":null,"timestamp":"2026-03-30T18:23:39.712Z","thinkingLevel":"medium"}
{"type":"model_change","id":"9655b22e","parentId":"ef5837ee","timestamp":"2026-03-30T18:23:39.714Z","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/M007/slices/S01/S01-PLAN.md`\n- Task plan source: `.gsd/milestones/M007/slices/S01/tasks/T02-PLAN.md`\n- Prior task summaries in this slice:\n- `.gsd/milestones/M007/slices/S01/tasks/T01-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/M007/slices/S01/tasks/T02-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/M007/slices/S01/tasks/T02-SUMMARY.md` before finishing.**\n\nWhen done, say: \"Task T02 complete.\"\n## Slice Plan Excerpt\nSource: `.gsd/milestones/M007/slices/S01/S01-PLAN.md`\n**Goal:** Debug mode toggle (Redis-backed) controls full LLM I/O capture in the pipeline. When on, every LLM call stores system prompt, user prompt, and full response text. Per-stage token summary available via API.\n**Demo:** After this: Toggle debug mode on for a video, trigger pipeline, see full prompt + response stored for every LLM call with per-stage token breakdown\n\n## UNIT: Execute Task T02 (\"Wire debug-mode-aware LLM I/O capture into pipeline callback and verify end-to-end\") — Slice S01 (\"Pipeline Debug Mode — Full LLM I/O Capture and Token Accounting\"), Milestone M007\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/M007/slices/S01/tasks/T01-SUMMARY.md` — T01: Added 3 TEXT columns to pipeline_events (system_prompt_text, user_prompt_text, response_text), Redis-backed debug mode toggle, and per-stage token summary endpoint | decisions: \"Followed exact review_mode Redis pattern for debug_mode toggle\"; \"Used coalesce in token aggregation to handle NULL token columns\" | key_files: \"backend/models.py\"; \"backend/config.py\"; \"backend/schemas.py\"\n\n## Inlined Task Plan (authoritative local execution contract)\nSource: `.gsd/milestones/M007/slices/S01/tasks/T02-PLAN.md`\n\n---\nestimated_steps: 56\nestimated_files: 1\nskills_used: []\n---\n\n# T02: Wire debug-mode-aware LLM I/O capture into pipeline callback and verify end-to-end\n\nModify the pipeline's `_make_llm_callback` to conditionally capture full prompt and response text when debug mode is enabled. Thread system_prompt and user_prompt through to the callback at all 4 stage call sites. Rebuild worker container and verify end-to-end on ub01.\n\nAll work is on ub01 via SSH at /vmPool/r/repos/xpltdco/chrysopedia.\n\n## Steps\n\n1. Add `system_prompt_text`, `user_prompt_text`, `response_text` kwargs to `_emit_event()` in `backend/pipeline/stages.py`. Pass them through to the `PipelineEvent` constructor.\n\n2. Add a sync Redis helper at the top of `backend/pipeline/stages.py` to read debug mode:\n   ```python\n   def _is_debug_mode() -> bool:\n       \"\"\"Check if debug mode is enabled via Redis. Falls back to config.\"\"\"\n       try:\n           import redis\n           settings = get_settings()\n           r = redis.from_url(settings.redis_url)\n           val = r.get(\"chrysopedia:debug_mode\")\n           r.close()\n           if val is not None:\n               return val.decode().lower() == \"true\"\n       except Exception:\n           pass\n       return getattr(get_settings(), 'debug_mode', False)\n   ```\n   Note: uses sync `redis.from_url()` because Celery worker is sync (D004). The `redis` package is already installed (Celery broker dependency).\n\n3. Modify `_make_llm_callback(video_id, stage)` to accept `system_prompt: str | None = None` and `user_prompt: str | None = None` parameters. Inside the callback closure:\n   - Check `_is_debug_mode()`\n   - If debug on: pass `system_prompt_text=system_prompt`, `user_prompt_text=user_prompt`, `response_text=content` to `_emit_event()`\n   - If debug off: pass None for all three (existing behavior preserved)\n\n4. Update all 4 `llm.complete()` call sites to pass prompt text to the callback factory:\n   - Stage 2 (~line 278): `_make_llm_callback(video_id, \"stage2_segmentation\", system_prompt=system_prompt, user_prompt=user_prompt)`\n   - Stage 3 (~line 370): same pattern\n   - Stage 4 (~line 485): same pattern  \n   - Stage 5 (~line 724): same pattern\n\n5. Rebuild and restart worker + API containers:\n   ```bash\n   cd /vmPool/r/repos/xpltdco/chrysopedia\n   docker compose build chrysopedia-api chrysopedia-worker\n   docker compose up -d\n   docker compose restart chrysopedia-web-8096  # nginx DNS cache\n   ```\n\n6. End-to-end verification:\n   a. Enable debug mode: `curl -X PUT http://ub01:8096/admin/pipeline/debug-mode -H 'Content-Type: application/json' -d '{\"debug_mode\":true}'`\n   b. Trigger pipeline for a video: `curl -X POST http://ub01:8096/admin/pipeline/trigger/{video_id}`\n   c. Wait for pipeline completion (check logs or events)\n   d. Query events: `curl http://ub01:8096/admin/pipeline/events/{video_id}?event_type=llm_call` — verify `system_prompt_text`, `user_prompt_text`, `response_text` are populated\n   e. Query token summary: `curl http://ub01:8096/admin/pipeline/token-summary/{video_id}` — verify per-stage breakdown\n   f. Disable debug mode: `curl -X PUT http://ub01:8096/admin/pipeline/debug-mode -H 'Content-Type: application/json' -d '{\"debug_mode\":false}'`\n   g. (Optional) Trigger another run — verify new events have null prompt/response columns\n\n## Must-Haves\n\n- [ ] `_emit_event` accepts and stores prompt/response text columns\n- [ ] `_is_debug_mode()` reads from sync Redis, falls back to config\n- [ ] `_make_llm_callback` conditionally captures full I/O when debug is on\n- [ ] All 4 stage call sites pass system_prompt and user_prompt to callback\n- [ ] Pipeline still works when debug is off (existing behavior preserved)\n- [ ] End-to-end: debug on → pipeline → events have full prompt/response text\n\n## Observability Impact\n\n- Signals added: Full LLM system prompt, user prompt, and response text stored per llm_call event when debug mode is on\n- How a future agent inspects this: `curl http://ub01:8096/admin/pipeline/events/{video_id}?event_type=llm_call` — check system_prompt_text/user_prompt_text/response_text fields\n- Failure state exposed: `_is_debug_mode()` failure is silent (falls back to off) — matches existing best-effort pattern in `_emit_event`\n\n## Inputs\n\n- ``backend/pipeline/stages.py` — existing _emit_event, _make_llm_callback, and 4 stage call sites`\n- ``backend/models.py` — PipelineEvent model with new columns (from T01)`\n- ``backend/routers/pipeline.py` — debug-mode and token-summary endpoints (from T01)`\n- ``backend/config.py` — Settings.debug_mode field (from T01)`\n\n## Expected Output\n\n- ``backend/pipeline/stages.py` — _emit_event with prompt/response params, _is_debug_mode() helper, _make_llm_callback with conditional capture, all 4 call sites updated`\n\n## Verification\n\nssh ub01 'curl -sf http://ub01:8096/admin/pipeline/debug-mode | python3 -c \"import sys,json; assert json.load(sys.stdin)[\\\"debug_mode\\\"] in [True,False]\"' && ssh ub01 'docker logs chrysopedia-worker --tail 5 2>&1 | head -5'\n","display":false,"id":"fe7c5670","parentId":"9655b22e","timestamp":"2026-03-30T18:23:39.716Z"}
{"type":"message","id":"3b3c04bb","parentId":"fe7c5670","timestamp":"2026-03-30T18:23:52.145Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me start by reading the task plan, T01 summary, and the current state of the pipeline stages file to understand what needs to be done.","thinkingSignature":"EsYCClkIDBgCKkChqBpLDva+BJxxXY9zJShJcNf/jy6ZPA5/MUL4FsYrjfe11HF0Gz2UBCwSPJBH4yPj6p/W0lGbEBfDgQSwW629Mg9jbGF1ZGUtb3B1cy00LTY4ABIMZYw1FkpeyMnBiVVeGgwMlRYLcVqrDfbFWgkiMOONoDgmkjuNffKxweYFdJoTLc2Ng/0blVcXFAVpvY25zW8Dcz6F4xs5byhhoFP0UCqaATSIbezxFwmQEGR16wzhb40yoSieFwz6LQHKrxqfjGcasxjfWvcX9UmeejWvWfeKN8u11F9RntTxMncVgkxiFNWKvGF8QBPhmVmLdy9ijYsuaVDc7hPM8G6qGNjHZv7+qf+QqR5PSsMQ67E/kejuVdicU/3r7RpobZt9QqyDpeM7ZVDJCG1zveINtnvgnjDxeJMKn8xESWhbO98YAQ=="},{"type":"text","text":"Starting T02 — wiring debug-mode-aware LLM I/O capture into the pipeline callback. Let me read the prior task summary and current state of the target file."},{"type":"toolCall","id":"toolu_01CDEKxBEfwwP8svdoyVFMzQ","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/.gsd/milestones/M007/slices/S01/tasks/T01-SUMMARY.md | head -40"}},{"type":"toolCall","id":"toolu_012DknQ8GELcqW8Mq7yzumip","name":"bash","arguments":{"command":"ssh ub01 'cat /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py' | wc -l"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":256,"cacheRead":58112,"cacheWrite":4387,"totalTokens":62757,"cost":{"input":0.00001,"output":0.0064,"cacheRead":0.029056,"cacheWrite":0.027418750000000002,"total":0.06288475}},"stopReason":"toolUse","timestamp":1774895019714}}
{"type":"message","id":"2da8457d","parentId":"3b3c04bb","timestamp":"2026-03-30T18:23:52.161Z","message":{"role":"toolResult","toolCallId":"toolu_01CDEKxBEfwwP8svdoyVFMzQ","toolName":"bash","content":[{"type":"text","text":"---\nid: T01\nparent: S01\nmilestone: M007\nprovides: []\nrequires: []\naffects: []\nkey_files: [\"backend/models.py\", \"backend/config.py\", \"backend/schemas.py\", \"backend/routers/pipeline.py\", \"alembic/versions/006_debug_columns.py\"]\nkey_decisions: [\"Followed exact review_mode Redis pattern for debug_mode toggle\", \"Used coalesce in token aggregation to handle NULL token columns\"]\npatterns_established: []\ndrill_down_paths: []\nobservability_surfaces: []\nduration: \"\"\nverification_result: \"Migration 006 applied successfully. Debug mode GET returns false (default), PUT toggles to true, GET reads back true. Token summary returns real per-stage aggregation for existing video data. DB columns confirmed via psql. Event listing response includes new nullable text fields.\"\ncompleted_at: 2026-03-30T18:23:26.734Z\nblocker_discovered: false\n---\n\n# T01: Added 3 TEXT columns to pipeline_events (system_prompt_text, user_prompt_text, response_text), Redis-backed debug mode toggle, and per-stage token summary endpoint\n\n> Added 3 TEXT columns to pipeline_events (system_prompt_text, user_prompt_text, response_text), Redis-backed debug mode toggle, and per-stage token summary endpoint\n\n## What Happened\n---\nid: T01\nparent: S01\nmilestone: M007\nkey_files:\n  - backend/models.py\n  - backend/config.py\n  - backend/schemas.py\n  - backend/routers/pipeline.py\n  - alembic/versions/006_debug_columns.py\nkey_decisions:\n  - Followed exact review_mode Redis pattern for debug_mode toggle\n  - Used coalesce in token aggregation to handle NULL token columns\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-03-30T18:23:26.734Z\nblocker_discovered: false\n"}],"isError":false,"timestamp":1774895032161}}
{"type":"message","id":"133b9edb","parentId":"2da8457d","timestamp":"2026-03-30T18:23:52.499Z","message":{"role":"toolResult","toolCallId":"toolu_012DknQ8GELcqW8Mq7yzumip","toolName":"bash","content":[{"type":"text","text":"1103\n"}],"isError":false,"timestamp":1774895032498}}
{"type":"message","id":"a72c6538","parentId":"133b9edb","timestamp":"2026-03-30T18:23:57.802Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me read the stages.py file to understand the current structure — specifically `_emit_event`, `_make_llm_callback`, and the 4 LLM call sites.","thinkingSignature":"Es4CClkIDBgCKkDxnK5BHafR5z0cRxqhUBd3G8TabsVgj+J4K6wsOXri4iZ9YIINB85VVssZ8/eLYuYQ6eiJDTipui/wX4qSELrFMg9jbGF1ZGUtb3B1cy00LTY4ABIMWqhTUqY8MQoQzV0XGgyJLASQkRz5F9sutrQiMOt9C3huhqhtooW5FqqDsgg0jt45B6elkCaChvsoIxAwj/NiF3AcovY9s+DWrwMscyqiAdV9xst8iqmn/aOe5I0z3zji2AT9ipRGBiYq/gzzdCUOFPuAR1WUYjb5wHcd5uOLICK0whhA7UieAbfZdI+j8RGpvP7BrJMQD8+CosFtkKPGlgBlzm1dYllqa2tfkU4n/yeevZTUTA45xszd2aL5FP8sUXjyeFTSjlDafSBAvin7KOXo0CqebmWrbyM85ofFRqIRPIY30j8xr+bS/7jdHL3uxBgB"},{"type":"text","text":"1103 lines — need to find the key functions. Let me locate `_emit_event`, `_make_llm_callback`, and the LLM call sites."},{"type":"toolCall","id":"toolu_01H9TUXfwCfJkQRj8RVSkSCy","name":"bash","arguments":{"command":"ssh ub01 'cd /vmPool/r/repos/xpltdco/chrysopedia && grep -n \"_emit_event\\|_make_llm_callback\\|llm\\.complete\\|def stage\" backend/pipeline/stages.py'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":206,"cacheRead":62499,"cacheWrite":748,"totalTokens":63454,"cost":{"input":0.000005,"output":0.00515,"cacheRead":0.0312495,"cacheWrite":0.004675,"total":0.0410795}},"stopReason":"toolUse","timestamp":1774895032499}}
{"type":"message","id":"799e401e","parentId":"a72c6538","timestamp":"2026-03-30T18:23:58.125Z","message":{"role":"toolResult","toolCallId":"toolu_01H9TUXfwCfJkQRj8RVSkSCy","toolName":"bash","content":[{"type":"text","text":"53:def _emit_event(\n88:def _make_llm_callback(video_id: str, stage: str):\n95:        _emit_event(\n222:        retry_raw = llm.complete(\n232:def stage2_segmentation(self, video_id: str) -> str:\n242:    _emit_event(video_id, \"stage2_segmentation\", \"start\")\n278:        raw = llm.complete(system_prompt, user_prompt, response_model=SegmentationResult, on_complete=_make_llm_callback(video_id, \"stage2_segmentation\"),\n292:        _emit_event(video_id, \"stage2_segmentation\", \"complete\")\n303:        _emit_event(video_id, \"stage2_segmentation\", \"error\", payload={\"error\": str(exc)})\n313:def stage3_extraction(self, video_id: str) -> str:\n323:    _emit_event(video_id, \"stage3_extraction\", \"start\")\n370:            raw = llm.complete(system_prompt, user_prompt, response_model=ExtractionResult, on_complete=_make_llm_callback(video_id, \"stage3_extraction\"),\n404:        _emit_event(video_id, \"stage3_extraction\", \"complete\")\n415:        _emit_event(video_id, \"stage3_extraction\", \"error\", payload={\"error\": str(exc)})\n425:def stage4_classification(self, video_id: str) -> str:\n438:    _emit_event(video_id, \"stage4_classification\", \"start\")\n485:        raw = llm.complete(system_prompt, user_prompt, response_model=ClassificationResult, on_complete=_make_llm_callback(video_id, \"stage4_classification\"),\n517:        _emit_event(video_id, \"stage4_classification\", \"complete\")\n528:        _emit_event(video_id, \"stage4_classification\", \"error\", payload={\"error\": str(exc)})\n647:def stage5_synthesis(self, video_id: str) -> str:\n660:    _emit_event(video_id, \"stage5_synthesis\", \"start\")\n724:            raw = llm.complete(system_prompt, user_prompt, response_model=SynthesisResult, on_complete=_make_llm_callback(video_id, \"stage5_synthesis\"),\n835:        _emit_event(video_id, \"stage5_synthesis\", \"complete\")\n846:        _emit_event(video_id, \"stage5_synthesis\", \"error\", payload={\"error\": str(exc)})\n856:def stage6_embed_and_index(self, video_id: str) -> str:\n"}],"isError":false,"timestamp":1774895038123}}
{"type":"message","id":"6d9ca361","parentId":"799e401e","timestamp":"2026-03-30T18:24:08.972Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — I can see the 4 call sites (lines 278, 370, 485, 724) and the two target functions. Let me read them."},{"type":"toolCall","id":"toolu_013nGygd1jjvD1upLbs1eWxc","name":"bash","arguments":{"command":"ssh ub01 'sed -n \"1,15p\" /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py'"}},{"type":"toolCall","id":"toolu_018eCB8a8f4LAMADdgxYzpHW","name":"bash","arguments":{"command":"ssh ub01 'sed -n \"50,120p\" /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":204,"cacheRead":63247,"cacheWrite":829,"totalTokens":64281,"cost":{"input":0.000005,"output":0.0051,"cacheRead":0.0316235,"cacheWrite":0.00518125,"total":0.041909749999999996}},"stopReason":"toolUse","timestamp":1774895038124}}
{"type":"message","id":"bbae22e7","parentId":"6d9ca361","timestamp":"2026-03-30T18:24:09.265Z","message":{"role":"toolResult","toolCallId":"toolu_013nGygd1jjvD1upLbs1eWxc","toolName":"bash","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 hashlib\nimport json\nimport logging\nimport subprocess\n"}],"isError":false,"timestamp":1774895049265}}
{"type":"message","id":"a627f127","parentId":"bbae22e7","timestamp":"2026-03-30T18:24:09.280Z","message":{"role":"toolResult","toolCallId":"toolu_018eCB8a8f4LAMADdgxYzpHW","toolName":"bash","content":[{"type":"text","text":"\n# ── Pipeline event persistence ───────────────────────────────────────────────\n\ndef _emit_event(\n    video_id: str,\n    stage: str,\n    event_type: str,\n    *,\n    prompt_tokens: int | None = None,\n    completion_tokens: int | None = None,\n    total_tokens: int | None = None,\n    model: str | None = None,\n    duration_ms: int | None = None,\n    payload: dict | None = None,\n) -> None:\n    \"\"\"Persist a pipeline event to the DB. Best-effort -- failures logged, not raised.\"\"\"\n    try:\n        session = _get_sync_session()\n        try:\n            event = PipelineEvent(\n                video_id=video_id,\n                stage=stage,\n                event_type=event_type,\n                prompt_tokens=prompt_tokens,\n                completion_tokens=completion_tokens,\n                total_tokens=total_tokens,\n                model=model,\n                duration_ms=duration_ms,\n                payload=payload,\n            )\n            session.add(event)\n            session.commit()\n        finally:\n            session.close()\n    except Exception as exc:\n        logger.warning(\"Failed to emit pipeline event: %s\", exc)\n\n\ndef _make_llm_callback(video_id: str, stage: str):\n    \"\"\"Create an on_complete callback for LLMClient that emits llm_call events.\"\"\"\n    def callback(*, model=None, prompt_tokens=None, completion_tokens=None,\n                 total_tokens=None, content=None, finish_reason=None,\n                 is_fallback=False, **_kwargs):\n        # Truncate content for storage — keep first 2000 chars for debugging\n        truncated = content[:2000] if content and len(content) > 2000 else content\n        _emit_event(\n            video_id=video_id,\n            stage=stage,\n            event_type=\"llm_call\",\n            model=model,\n            prompt_tokens=prompt_tokens,\n            completion_tokens=completion_tokens,\n            total_tokens=total_tokens,\n            payload={\n                \"content_preview\": truncated,\n                \"content_length\": len(content) if content else 0,\n                \"finish_reason\": finish_reason,\n                \"is_fallback\": is_fallback,\n            },\n        )\n    return callback\n\n# ── Helpers ──────────────────────────────────────────────────────────────────\n\n_engine = None\n_SessionLocal = None\n\n\ndef _get_sync_engine():\n    \"\"\"Create a sync SQLAlchemy engine, converting the async URL if needed.\"\"\"\n    global _engine\n"}],"isError":false,"timestamp":1774895049279}}
{"type":"message","id":"fe931ce9","parentId":"a627f127","timestamp":"2026-03-30T18:24:14.581Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me see the call sites to understand how `system_prompt` and `user_prompt` are constructed at each stage."},{"type":"toolCall","id":"toolu_01B8Dn6RXNJNF9wzym4NuZg3","name":"bash","arguments":{"command":"ssh ub01 'sed -n \"265,290p\" /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py'"}},{"type":"toolCall","id":"toolu_01Hny4pQ5TvZmkUCpTEXeSWp","name":"bash","arguments":{"command":"ssh ub01 'sed -n \"355,385p\" /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py'"}},{"type":"toolCall","id":"toolu_01MBT4G2b5rhtFjNUUxd3Hbx","name":"bash","arguments":{"command":"ssh ub01 'sed -n \"470,500p\" /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py'"}},{"type":"toolCall","id":"toolu_01XAoyMZxZMqs8yo9yx2qTjf","name":"bash","arguments":{"command":"ssh ub01 'sed -n \"710,740p\" /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":345,"cacheRead":64076,"cacheWrite":1027,"totalTokens":65449,"cost":{"input":0.000005,"output":0.008625,"cacheRead":0.032038,"cacheWrite":0.00641875,"total":0.04708675}},"stopReason":"toolUse","timestamp":1774895049280}}
{"type":"message","id":"357ac18b","parentId":"fe931ce9","timestamp":"2026-03-30T18:24:14.919Z","message":{"role":"toolResult","toolCallId":"toolu_01B8Dn6RXNJNF9wzym4NuZg3","toolName":"bash","content":[{"type":"text","text":"                f\"[{seg.segment_index}] ({seg.start_time:.1f}s - {seg.end_time:.1f}s) {seg.text}\"\n            )\n        transcript_text = \"\\n\".join(transcript_lines)\n\n        # Load prompt and call LLM\n        system_prompt = _load_prompt(\"stage2_segmentation.txt\")\n        user_prompt = f\"<transcript>\\n{transcript_text}\\n</transcript>\"\n\n        llm = _get_llm_client()\n        model_override, modality = _get_stage_config(2)\n        hard_limit = get_settings().llm_max_tokens_hard_limit\n        max_tokens = estimate_max_tokens(system_prompt, user_prompt, stage=\"stage2_segmentation\", hard_limit=hard_limit)\n        logger.info(\"Stage 2 using model=%s, modality=%s, max_tokens=%d\", model_override or \"default\", modality, max_tokens)\n        raw = llm.complete(system_prompt, user_prompt, response_model=SegmentationResult, on_complete=_make_llm_callback(video_id, \"stage2_segmentation\"),\n                           modality=modality, model_override=model_override, max_tokens=max_tokens)\n        result = _safe_parse_llm_response(raw, SegmentationResult, llm, system_prompt, user_prompt,\n                                          modality=modality, model_override=model_override)\n\n        # Update topic_label on each segment row\n        seg_by_index = {s.segment_index: s for s in segments}\n        for topic_seg in result.segments:\n            for idx in range(topic_seg.start_index, topic_seg.end_index + 1):\n                if idx in seg_by_index:\n                    seg_by_index[idx].topic_label = topic_seg.topic_label\n\n        session.commit()\n"}],"isError":false,"timestamp":1774895054919}}
{"type":"message","id":"21a3bc83","parentId":"357ac18b","timestamp":"2026-03-30T18:24:14.920Z","message":{"role":"toolResult","toolCallId":"toolu_01Hny4pQ5TvZmkUCpTEXeSWp","toolName":"bash","content":[{"type":"text","text":"        for topic_label, group_segs in groups.items():\n            # Build segment text for this group\n            seg_lines = []\n            for seg in group_segs:\n                seg_lines.append(\n                    f\"({seg.start_time:.1f}s - {seg.end_time:.1f}s) {seg.text}\"\n                )\n            segment_text = \"\\n\".join(seg_lines)\n\n            user_prompt = (\n                f\"Topic: {topic_label}\\n\\n\"\n                f\"<segment>\\n{segment_text}\\n</segment>\"\n            )\n\n            max_tokens = estimate_max_tokens(system_prompt, user_prompt, stage=\"stage3_extraction\", hard_limit=hard_limit)\n            raw = llm.complete(system_prompt, user_prompt, response_model=ExtractionResult, on_complete=_make_llm_callback(video_id, \"stage3_extraction\"),\n                               modality=modality, model_override=model_override, max_tokens=max_tokens)\n            result = _safe_parse_llm_response(raw, ExtractionResult, llm, system_prompt, user_prompt,\n                                              modality=modality, model_override=model_override)\n\n            # Create KeyMoment rows\n            for moment in result.moments:\n                # Validate content_type against enum\n                try:\n                    ct = KeyMomentContentType(moment.content_type)\n                except ValueError:\n                    ct = KeyMomentContentType.technique\n\n                km = KeyMoment(\n                    source_video_id=video_id,\n                    title=moment.title,\n"}],"isError":false,"timestamp":1774895054919}}
{"type":"message","id":"a81a44ed","parentId":"21a3bc83","timestamp":"2026-03-30T18:24:14.924Z","message":{"role":"toolResult","toolCallId":"toolu_01MBT4G2b5rhtFjNUUxd3Hbx","toolName":"bash","content":[{"type":"text","text":"                f\"    Plugins: {', '.join(m.plugins) if m.plugins else 'none'}\"\n            )\n        moments_text = \"\\n\\n\".join(moments_lines)\n\n        system_prompt = _load_prompt(\"stage4_classification.txt\")\n        user_prompt = (\n            f\"<taxonomy>\\n{taxonomy_text}\\n</taxonomy>\\n\\n\"\n            f\"<moments>\\n{moments_text}\\n</moments>\"\n        )\n\n        llm = _get_llm_client()\n        model_override, modality = _get_stage_config(4)\n        hard_limit = get_settings().llm_max_tokens_hard_limit\n        max_tokens = estimate_max_tokens(system_prompt, user_prompt, stage=\"stage4_classification\", hard_limit=hard_limit)\n        logger.info(\"Stage 4 using model=%s, modality=%s, max_tokens=%d\", model_override or \"default\", modality, max_tokens)\n        raw = llm.complete(system_prompt, user_prompt, response_model=ClassificationResult, on_complete=_make_llm_callback(video_id, \"stage4_classification\"),\n                           modality=modality, model_override=model_override, max_tokens=max_tokens)\n        result = _safe_parse_llm_response(raw, ClassificationResult, llm, system_prompt, user_prompt,\n                                          modality=modality, model_override=model_override)\n\n        # Apply content_type overrides and prepare classification data for stage 5\n        classification_data = []\n        moment_ids = [str(m.id) for m in moments]\n\n        for cls in result.classifications:\n            if 0 <= cls.moment_index < len(moments):\n                moment = moments[cls.moment_index]\n\n                # Apply content_type override if provided\n                if cls.content_type_override:\n                    try:\n"}],"isError":false,"timestamp":1774895054924}}
{"type":"message","id":"25fa357e","parentId":"a81a44ed","timestamp":"2026-03-30T18:24:14.927Z","message":{"role":"toolResult","toolCallId":"toolu_01XAoyMZxZMqs8yo9yx2qTjf","toolName":"bash","content":[{"type":"text","text":"                    f\"[{i}] Title: {m.title}\\n\"\n                    f\"    Summary: {m.summary}\\n\"\n                    f\"    Content type: {m.content_type.value}\\n\"\n                    f\"    Time: {m.start_time:.1f}s - {m.end_time:.1f}s\\n\"\n                    f\"    Plugins: {', '.join(m.plugins) if m.plugins else 'none'}\\n\"\n                    f\"    Category: {category}\\n\"\n                    f\"    Tags: {', '.join(tags) if tags else 'none'}\\n\"\n                    f\"    Transcript excerpt: {(m.raw_transcript or '')[:300]}\"\n                )\n            moments_text = \"\\n\\n\".join(moments_lines)\n\n            user_prompt = f\"<moments>\\n{moments_text}\\n</moments>\"\n\n            max_tokens = estimate_max_tokens(system_prompt, user_prompt, stage=\"stage5_synthesis\", hard_limit=hard_limit)\n            raw = llm.complete(system_prompt, user_prompt, response_model=SynthesisResult, on_complete=_make_llm_callback(video_id, \"stage5_synthesis\"),\n                               modality=modality, model_override=model_override, max_tokens=max_tokens)\n            result = _safe_parse_llm_response(raw, SynthesisResult, llm, system_prompt, user_prompt,\n                                              modality=modality, model_override=model_override)\n\n            # Load prior pages from this video (snapshot taken before pipeline reset)\n            prior_page_ids = _load_prior_pages(video_id)\n\n            # Create/update TechniquePage rows\n            for page_data in result.pages:\n                existing = None\n\n                # First: check prior pages from this video by creator + category\n                if prior_page_ids:\n                    existing = session.execute(\n                        select(TechniquePage).where(\n                            TechniquePage.id.in_(prior_page_ids),\n"}],"isError":false,"timestamp":1774895054925}}
{"type":"message","id":"c8f2b38e","parentId":"25fa357e","timestamp":"2026-03-30T18:24:19.029Z","message":{"role":"assistant","content":[{"type":"text","text":"Clear picture. Also need to check line ~222 for the retry `llm.complete` call:"},{"type":"toolCall","id":"toolu_01QSeviU7rUtBy2qm6ixFExu","name":"bash","arguments":{"command":"ssh ub01 'sed -n \"195,230p\" /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":115,"cacheRead":65103,"cacheWrite":2297,"totalTokens":67516,"cost":{"input":0.000005,"output":0.002875,"cacheRead":0.0325515,"cacheWrite":0.014356250000000001,"total":0.04978775}},"stopReason":"toolUse","timestamp":1774895054926}}
{"type":"message","id":"301f36c7","parentId":"c8f2b38e","timestamp":"2026-03-30T18:24:19.340Z","message":{"role":"toolResult","toolCallId":"toolu_01QSeviU7rUtBy2qm6ixFExu","toolName":"bash","content":[{"type":"text","text":"\ndef _safe_parse_llm_response(\n    raw: str,\n    model_cls,\n    llm: LLMClient,\n    system_prompt: str,\n    user_prompt: str,\n    modality: str = \"chat\",\n    model_override: str | None = None,\n):\n    \"\"\"Parse LLM response with one retry on failure.\n\n    On malformed response: log the raw text, retry once with a JSON nudge,\n    then raise on second failure.\n    \"\"\"\n    try:\n        return llm.parse_response(raw, model_cls)\n    except (ValidationError, ValueError, json.JSONDecodeError) as exc:\n        logger.warning(\n            \"First parse attempt failed for %s (%s). Retrying with JSON nudge. \"\n            \"Raw response (first 500 chars): %.500s\",\n            model_cls.__name__,\n            type(exc).__name__,\n            raw,\n        )\n        # Retry with explicit JSON instruction\n        nudge_prompt = user_prompt + \"\\n\\nIMPORTANT: Output ONLY valid JSON. No markdown, no explanation.\"\n        retry_raw = llm.complete(\n            system_prompt, nudge_prompt, response_model=model_cls,\n            modality=modality, model_override=model_override,\n        )\n        return llm.parse_response(retry_raw, model_cls)\n\n\n# ── Stage 2: Segmentation ───────────────────────────────────────────────────\n\n"}],"isError":false,"timestamp":1774895059339}}
{"type":"message","id":"19889934","parentId":"301f36c7","timestamp":"2026-03-30T18:24:30.439Z","message":{"role":"assistant","content":[{"type":"text","text":"The retry call at line 222 doesn't use `_make_llm_callback` — no callback needed there, it's a parse-retry. Now let me check `_get_sync_session` and the imports area:"},{"type":"toolCall","id":"toolu_0138wiTPPLEUr1kLpMRyVRyq","name":"bash","arguments":{"command":"ssh ub01 'sed -n \"15,50p\" /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py'"}},{"type":"toolCall","id":"toolu_018mTLdTotJMiUXGhjr9Qkmw","name":"bash","arguments":{"command":"ssh ub01 'sed -n \"120,170p\" /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":218,"cacheRead":67400,"cacheWrite":492,"totalTokens":68111,"cost":{"input":0.000005,"output":0.00545,"cacheRead":0.0337,"cacheWrite":0.003075,"total":0.042230000000000004}},"stopReason":"toolUse","timestamp":1774895059339}}
{"type":"message","id":"bac81f95","parentId":"19889934","timestamp":"2026-03-30T18:24:30.768Z","message":{"role":"toolResult","toolCallId":"toolu_0138wiTPPLEUr1kLpMRyVRyq","toolName":"bash","content":[{"type":"text","text":"import subprocess\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, func, select\nfrom sqlalchemy.orm import Session, sessionmaker\n\nfrom config import get_settings\nfrom models import (\n    KeyMoment,\n    KeyMomentContentType,\n    PipelineEvent,\n    ProcessingStatus,\n    SourceVideo,\n    TechniquePage,\n    TechniquePageVersion,\n    TranscriptSegment,\n)\nfrom pipeline.embedding_client import EmbeddingClient\nfrom pipeline.llm_client import LLMClient, estimate_max_tokens\nfrom pipeline.qdrant_client import QdrantManager\nfrom pipeline.schemas import (\n    ClassificationResult,\n    ExtractionResult,\n    SegmentationResult,\n    SynthesisResult,\n)\nfrom worker import celery_app\n\nlogger = logging.getLogger(__name__)\n\n\n"}],"isError":false,"timestamp":1774895070768}}
{"type":"message","id":"3e82b944","parentId":"bac81f95","timestamp":"2026-03-30T18:24:30.770Z","message":{"role":"toolResult","toolCallId":"toolu_018mTLdTotJMiUXGhjr9Qkmw","toolName":"bash","content":[{"type":"text","text":"    global _engine\n    if _engine is None:\n        settings = get_settings()\n        url = settings.database_url\n        # Convert async driver to sync driver\n        url = url.replace(\"postgresql+asyncpg://\", \"postgresql+psycopg2://\")\n        _engine = create_engine(url, pool_pre_ping=True, pool_size=5, max_overflow=10)\n    return _engine\n\n\ndef _get_sync_session() -> Session:\n    \"\"\"Create a sync SQLAlchemy session for Celery tasks.\"\"\"\n    global _SessionLocal\n    if _SessionLocal is None:\n        _SessionLocal = sessionmaker(bind=_get_sync_engine())\n    return _SessionLocal()\n\n\ndef _load_prompt(template_name: str) -> str:\n    \"\"\"Read a prompt template from the prompts directory.\n\n    Raises FileNotFoundError if the template does not exist.\n    \"\"\"\n    settings = get_settings()\n    path = Path(settings.prompts_path) / template_name\n    if not path.exists():\n        logger.error(\"Prompt template not found: %s\", path)\n        raise FileNotFoundError(f\"Prompt template not found: {path}\")\n    return path.read_text(encoding=\"utf-8\")\n\n\ndef _get_llm_client() -> LLMClient:\n    \"\"\"Return an LLMClient configured from settings.\"\"\"\n    return LLMClient(get_settings())\n\n\ndef _get_stage_config(stage_num: int) -> tuple[str | None, str]:\n    \"\"\"Return (model_override, modality) for a pipeline stage.\n\n    Reads stage-specific config from Settings. If the stage-specific model\n    is None/empty, returns None (LLMClient will use its default). If the\n    stage-specific modality is unset, defaults to \"chat\".\n    \"\"\"\n    settings = get_settings()\n    model = getattr(settings, f\"llm_stage{stage_num}_model\", None) or None\n    modality = getattr(settings, f\"llm_stage{stage_num}_modality\", None) or \"chat\"\n    return model, modality\n\n\ndef _load_canonical_tags() -> dict:\n    \"\"\"Load canonical tag taxonomy from config/canonical_tags.yaml.\"\"\"\n"}],"isError":false,"timestamp":1774895070768}}
{"type":"message","id":"78692e3c","parentId":"3e82b944","timestamp":"2026-03-30T18:24:34.984Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — I have full context. Now let me also verify the PipelineEvent model has the new columns from T01:"},{"type":"toolCall","id":"toolu_01GMgQ7QTevLMrCrbTqbgt7V","name":"bash","arguments":{"command":"ssh ub01 'grep -n \"system_prompt_text\\|user_prompt_text\\|response_text\" /vmPool/r/repos/xpltdco/chrysopedia/backend/models.py'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":130,"cacheRead":67892,"cacheWrite":1055,"totalTokens":69078,"cost":{"input":0.000005,"output":0.0032500000000000003,"cacheRead":0.033946,"cacheWrite":0.006593750000000001,"total":0.04379475}},"stopReason":"toolUse","timestamp":1774895070769}}
{"type":"message","id":"fa699b21","parentId":"78692e3c","timestamp":"2026-03-30T18:24:35.308Z","message":{"role":"toolResult","toolCallId":"toolu_01GMgQ7QTevLMrCrbTqbgt7V","toolName":"bash","content":[{"type":"text","text":"417:    system_prompt_text: Mapped[str | None] = mapped_column(Text, nullable=True)\n418:    user_prompt_text: Mapped[str | None] = mapped_column(Text, nullable=True)\n419:    response_text: Mapped[str | None] = mapped_column(Text, nullable=True)\n"}],"isError":false,"timestamp":1774895075307}}
{"type":"message","id":"f6eb4574","parentId":"fa699b21","timestamp":"2026-03-30T18:24:45.468Z","message":{"role":"assistant","content":[{"type":"text","text":"Columns exist on the model. Now I'll implement the changes. The plan is straightforward:\n\n1. Add `system_prompt_text`, `user_prompt_text`, `response_text` kwargs to `_emit_event`\n2. Add `_is_debug_mode()` sync Redis helper\n3. Modify `_make_llm_callback` to accept prompt params and conditionally capture\n4. Update all 4 call sites\n\nLet me build the patch file and apply it via SSH."},{"type":"toolCall","id":"toolu_01UdZ3WFhdMdgipXH8EUnTGm","name":"bash","arguments":{"command":"ssh ub01 'sed -n \"53,86p\" /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":205,"cacheRead":68947,"cacheWrite":224,"totalTokens":69377,"cost":{"input":0.000005,"output":0.005125,"cacheRead":0.0344735,"cacheWrite":0.0014,"total":0.0410035}},"stopReason":"toolUse","timestamp":1774895075307}}
{"type":"message","id":"decdae00","parentId":"f6eb4574","timestamp":"2026-03-30T18:24:46.239Z","message":{"role":"toolResult","toolCallId":"toolu_01UdZ3WFhdMdgipXH8EUnTGm","toolName":"bash","content":[{"type":"text","text":"def _emit_event(\n    video_id: str,\n    stage: str,\n    event_type: str,\n    *,\n    prompt_tokens: int | None = None,\n    completion_tokens: int | None = None,\n    total_tokens: int | None = None,\n    model: str | None = None,\n    duration_ms: int | None = None,\n    payload: dict | None = None,\n) -> None:\n    \"\"\"Persist a pipeline event to the DB. Best-effort -- failures logged, not raised.\"\"\"\n    try:\n        session = _get_sync_session()\n        try:\n            event = PipelineEvent(\n                video_id=video_id,\n                stage=stage,\n                event_type=event_type,\n                prompt_tokens=prompt_tokens,\n                completion_tokens=completion_tokens,\n                total_tokens=total_tokens,\n                model=model,\n                duration_ms=duration_ms,\n                payload=payload,\n            )\n            session.add(event)\n            session.commit()\n        finally:\n            session.close()\n    except Exception as exc:\n        logger.warning(\"Failed to emit pipeline event: %s\", exc)\n\n"}],"isError":false,"timestamp":1774895086238}}
{"type":"message","id":"463d09de","parentId":"decdae00","timestamp":"2026-03-30T18:25:09.834Z","message":{"role":"assistant","content":[{"type":"text","text":"I'll create a Python script to apply the edits precisely via SSH."},{"type":"toolCall","id":"toolu_01M442sBwyqrqbntMMAw8C8v","name":"write","arguments":{"content":"\"\"\"Apply T02 edits to stages.py — debug-mode-aware LLM I/O capture.\"\"\"\nimport re\n\nwith open(\"/vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py\", \"r\") as f:\n    content = f.read()\n\n# 1. Add system_prompt_text, user_prompt_text, response_text to _emit_event\nold_emit = '''def _emit_event(\n    video_id: str,\n    stage: str,\n    event_type: str,\n    *,\n    prompt_tokens: int | None = None,\n    completion_tokens: int | None = None,\n    total_tokens: int | None = None,\n    model: str | None = None,\n    duration_ms: int | None = None,\n    payload: dict | None = None,\n) -> None:\n    \"\"\"Persist a pipeline event to the DB. Best-effort -- failures logged, not raised.\"\"\"\n    try:\n        session = _get_sync_session()\n        try:\n            event = PipelineEvent(\n                video_id=video_id,\n                stage=stage,\n                event_type=event_type,\n                prompt_tokens=prompt_tokens,\n                completion_tokens=completion_tokens,\n                total_tokens=total_tokens,\n                model=model,\n                duration_ms=duration_ms,\n                payload=payload,\n            )\n            session.add(event)\n            session.commit()\n        finally:\n            session.close()\n    except Exception as exc:\n        logger.warning(\"Failed to emit pipeline event: %s\", exc)'''\n\nnew_emit = '''def _emit_event(\n    video_id: str,\n    stage: str,\n    event_type: str,\n    *,\n    prompt_tokens: int | None = None,\n    completion_tokens: int | None = None,\n    total_tokens: int | None = None,\n    model: str | None = None,\n    duration_ms: int | None = None,\n    payload: dict | None = None,\n    system_prompt_text: str | None = None,\n    user_prompt_text: str | None = None,\n    response_text: str | None = None,\n) -> None:\n    \"\"\"Persist a pipeline event to the DB. Best-effort -- failures logged, not raised.\"\"\"\n    try:\n        session = _get_sync_session()\n        try:\n            event = PipelineEvent(\n                video_id=video_id,\n                stage=stage,\n                event_type=event_type,\n                prompt_tokens=prompt_tokens,\n                completion_tokens=completion_tokens,\n                total_tokens=total_tokens,\n                model=model,\n                duration_ms=duration_ms,\n                payload=payload,\n                system_prompt_text=system_prompt_text,\n                user_prompt_text=user_prompt_text,\n                response_text=response_text,\n            )\n            session.add(event)\n            session.commit()\n        finally:\n            session.close()\n    except Exception as exc:\n        logger.warning(\"Failed to emit pipeline event: %s\", exc)'''\n\nassert old_emit in content, \"_emit_event not found\"\ncontent = content.replace(old_emit, new_emit)\n\n# 2. Replace _make_llm_callback with debug-mode-aware version and add _is_debug_mode\nold_callback = '''def _make_llm_callback(video_id: str, stage: str):\n    \"\"\"Create an on_complete callback for LLMClient that emits llm_call events.\"\"\"\n    def callback(*, model=None, prompt_tokens=None, completion_tokens=None,\n                 total_tokens=None, content=None, finish_reason=None,\n                 is_fallback=False, **_kwargs):\n        # Truncate content for storage — keep first 2000 chars for debugging\n        truncated = content[:2000] if content and len(content) > 2000 else content\n        _emit_event(\n            video_id=video_id,\n            stage=stage,\n            event_type=\"llm_call\",\n            model=model,\n            prompt_tokens=prompt_tokens,\n            completion_tokens=completion_tokens,\n            total_tokens=total_tokens,\n            payload={\n                \"content_preview\": truncated,\n                \"content_length\": len(content) if content else 0,\n                \"finish_reason\": finish_reason,\n                \"is_fallback\": is_fallback,\n            },\n        )\n    return callback'''\n\nnew_callback = '''def _is_debug_mode() -> bool:\n    \"\"\"Check if debug mode is enabled via Redis. Falls back to config setting.\"\"\"\n    try:\n        import redis\n        settings = get_settings()\n        r = redis.from_url(settings.redis_url)\n        val = r.get(\"chrysopedia:debug_mode\")\n        r.close()\n        if val is not None:\n            return val.decode().lower() == \"true\"\n    except Exception:\n        pass\n    return getattr(get_settings(), \"debug_mode\", False)\n\n\ndef _make_llm_callback(\n    video_id: str,\n    stage: str,\n    system_prompt: str | None = None,\n    user_prompt: str | None = None,\n):\n    \"\"\"Create an on_complete callback for LLMClient that emits llm_call events.\n\n    When debug mode is enabled, captures full system prompt, user prompt,\n    and response text on each llm_call event.\n    \"\"\"\n    debug = _is_debug_mode()\n\n    def callback(*, model=None, prompt_tokens=None, completion_tokens=None,\n                 total_tokens=None, content=None, finish_reason=None,\n                 is_fallback=False, **_kwargs):\n        # Truncate content for storage — keep first 2000 chars for debugging\n        truncated = content[:2000] if content and len(content) > 2000 else content\n        _emit_event(\n            video_id=video_id,\n            stage=stage,\n            event_type=\"llm_call\",\n            model=model,\n            prompt_tokens=prompt_tokens,\n            completion_tokens=completion_tokens,\n            total_tokens=total_tokens,\n            payload={\n                \"content_preview\": truncated,\n                \"content_length\": len(content) if content else 0,\n                \"finish_reason\": finish_reason,\n                \"is_fallback\": is_fallback,\n            },\n            system_prompt_text=system_prompt if debug else None,\n            user_prompt_text=user_prompt if debug else None,\n            response_text=content if debug else None,\n        )\n    return callback'''\n\nassert old_callback in content, \"_make_llm_callback not found\"\ncontent = content.replace(old_callback, new_callback)\n\n# 3. Update all 4 call sites to pass system_prompt and user_prompt\n# Stage 2\nold_s2 = '_make_llm_callback(video_id, \"stage2_segmentation\")'\nnew_s2 = '_make_llm_callback(video_id, \"stage2_segmentation\", system_prompt=system_prompt, user_prompt=user_prompt)'\nassert old_s2 in content, \"Stage 2 call site not found\"\ncontent = content.replace(old_s2, new_s2)\n\n# Stage 3\nold_s3 = '_make_llm_callback(video_id, \"stage3_extraction\")'\nnew_s3 = '_make_llm_callback(video_id, \"stage3_extraction\", system_prompt=system_prompt, user_prompt=user_prompt)'\nassert old_s3 in content, \"Stage 3 call site not found\"\ncontent = content.replace(old_s3, new_s3)\n\n# Stage 4\nold_s4 = '_make_llm_callback(video_id, \"stage4_classification\")'\nnew_s4 = '_make_llm_callback(video_id, \"stage4_classification\", system_prompt=system_prompt, user_prompt=user_prompt)'\nassert old_s4 in content, \"Stage 4 call site not found\"\ncontent = content.replace(old_s4, new_s4)\n\n# Stage 5\nold_s5 = '_make_llm_callback(video_id, \"stage5_synthesis\")'\nnew_s5 = '_make_llm_callback(video_id, \"stage5_synthesis\", system_prompt=system_prompt, user_prompt=user_prompt)'\nassert old_s5 in content, \"Stage 5 call site not found\"\ncontent = content.replace(old_s5, new_s5)\n\nwith open(\"/vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py\", \"w\") as f:\n    f.write(content)\n\nprint(\"All edits applied successfully.\")\n","path":"/tmp/patch_stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":2261,"cacheRead":69171,"cacheWrite":502,"totalTokens":71935,"cost":{"input":0.000005,"output":0.056525000000000006,"cacheRead":0.0345855,"cacheWrite":0.0031375,"total":0.094253}},"stopReason":"toolUse","timestamp":1774895086238}}
{"type":"message","id":"0e20ffc6","parentId":"463d09de","timestamp":"2026-03-30T18:25:09.837Z","message":{"role":"toolResult","toolCallId":"toolu_01M442sBwyqrqbntMMAw8C8v","toolName":"write","content":[{"type":"text","text":"Successfully wrote 7324 bytes to /tmp/patch_stages.py"}],"isError":false,"timestamp":1774895109836}}
{"type":"message","id":"6948626c","parentId":"0e20ffc6","timestamp":"2026-03-30T18:25:14.802Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01U5Ys62UjVoG1WhvZLx3VF8","name":"bash","arguments":{"command":"scp /tmp/patch_stages.py ub01:/tmp/patch_stages.py && ssh ub01 'python3 /tmp/patch_stages.py'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":91,"cacheRead":69673,"cacheWrite":2289,"totalTokens":72054,"cost":{"input":0.000005,"output":0.002275,"cacheRead":0.0348365,"cacheWrite":0.014306250000000001,"total":0.051422749999999996}},"stopReason":"toolUse","timestamp":1774895109836}}
{"type":"message","id":"a581abd6","parentId":"6948626c","timestamp":"2026-03-30T18:25:15.430Z","message":{"role":"toolResult","toolCallId":"toolu_01U5Ys62UjVoG1WhvZLx3VF8","toolName":"bash","content":[{"type":"text","text":"All edits applied successfully.\n"}],"isError":false,"timestamp":1774895115428}}
{"type":"message","id":"ac7fd8fb","parentId":"a581abd6","timestamp":"2026-03-30T18:25:19.989Z","message":{"role":"assistant","content":[{"type":"text","text":"Edits applied. Let me verify the changes look correct:"},{"type":"toolCall","id":"toolu_011FfT1pDzSPjFry7qzZfPGy","name":"bash","arguments":{"command":"ssh ub01 'sed -n \"53,120p\" /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":106,"cacheRead":71962,"cacheWrite":110,"totalTokens":72179,"cost":{"input":0.000005,"output":0.00265,"cacheRead":0.035981,"cacheWrite":0.0006875000000000001,"total":0.0393235}},"stopReason":"toolUse","timestamp":1774895115429}}
{"type":"message","id":"26c44946","parentId":"ac7fd8fb","timestamp":"2026-03-30T18:25:20.305Z","message":{"role":"toolResult","toolCallId":"toolu_011FfT1pDzSPjFry7qzZfPGy","toolName":"bash","content":[{"type":"text","text":"def _emit_event(\n    video_id: str,\n    stage: str,\n    event_type: str,\n    *,\n    prompt_tokens: int | None = None,\n    completion_tokens: int | None = None,\n    total_tokens: int | None = None,\n    model: str | None = None,\n    duration_ms: int | None = None,\n    payload: dict | None = None,\n    system_prompt_text: str | None = None,\n    user_prompt_text: str | None = None,\n    response_text: str | None = None,\n) -> None:\n    \"\"\"Persist a pipeline event to the DB. Best-effort -- failures logged, not raised.\"\"\"\n    try:\n        session = _get_sync_session()\n        try:\n            event = PipelineEvent(\n                video_id=video_id,\n                stage=stage,\n                event_type=event_type,\n                prompt_tokens=prompt_tokens,\n                completion_tokens=completion_tokens,\n                total_tokens=total_tokens,\n                model=model,\n                duration_ms=duration_ms,\n                payload=payload,\n                system_prompt_text=system_prompt_text,\n                user_prompt_text=user_prompt_text,\n                response_text=response_text,\n            )\n            session.add(event)\n            session.commit()\n        finally:\n            session.close()\n    except Exception as exc:\n        logger.warning(\"Failed to emit pipeline event: %s\", exc)\n\n\ndef _is_debug_mode() -> bool:\n    \"\"\"Check if debug mode is enabled via Redis. Falls back to config setting.\"\"\"\n    try:\n        import redis\n        settings = get_settings()\n        r = redis.from_url(settings.redis_url)\n        val = r.get(\"chrysopedia:debug_mode\")\n        r.close()\n        if val is not None:\n            return val.decode().lower() == \"true\"\n    except Exception:\n        pass\n    return getattr(get_settings(), \"debug_mode\", False)\n\n\ndef _make_llm_callback(\n    video_id: str,\n    stage: str,\n    system_prompt: str | None = None,\n    user_prompt: str | None = None,\n):\n    \"\"\"Create an on_complete callback for LLMClient that emits llm_call events.\n\n    When debug mode is enabled, captures full system prompt, user prompt,\n    and response text on each llm_call event.\n    \"\"\"\n    debug = _is_debug_mode()\n"}],"isError":false,"timestamp":1774895120303}}
{"type":"message","id":"8e37fd0b","parentId":"26c44946","timestamp":"2026-03-30T18:25:25.357Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01H3Pht4WUustRNk8aYNj66Z","name":"bash","arguments":{"command":"ssh ub01 'grep \"_make_llm_callback\" /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":72072,"cacheWrite":727,"totalTokens":72892,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.036036,"cacheWrite":0.00454375,"total":0.04288475}},"stopReason":"toolUse","timestamp":1774895120304}}
{"type":"message","id":"e93afa00","parentId":"8e37fd0b","timestamp":"2026-03-30T18:25:25.663Z","message":{"role":"toolResult","toolCallId":"toolu_01H3Pht4WUustRNk8aYNj66Z","toolName":"bash","content":[{"type":"text","text":"def _make_llm_callback(\n        raw = llm.complete(system_prompt, user_prompt, response_model=SegmentationResult, on_complete=_make_llm_callback(video_id, \"stage2_segmentation\", system_prompt=system_prompt, user_prompt=user_prompt),\n            raw = llm.complete(system_prompt, user_prompt, response_model=ExtractionResult, on_complete=_make_llm_callback(video_id, \"stage3_extraction\", system_prompt=system_prompt, user_prompt=user_prompt),\n        raw = llm.complete(system_prompt, user_prompt, response_model=ClassificationResult, on_complete=_make_llm_callback(video_id, \"stage4_classification\", system_prompt=system_prompt, user_prompt=user_prompt),\n            raw = llm.complete(system_prompt, user_prompt, response_model=SynthesisResult, on_complete=_make_llm_callback(video_id, \"stage5_synthesis\", system_prompt=system_prompt, user_prompt=user_prompt),\n"}],"isError":false,"timestamp":1774895125660}}
{"type":"message","id":"4a21aa1b","parentId":"e93afa00","timestamp":"2026-03-30T18:25:34.451Z","message":{"role":"assistant","content":[{"type":"text","text":"All 4 call sites updated. Now let me do a quick syntax check and rebuild the containers:"},{"type":"toolCall","id":"toolu_017HznwEu39bUCyNGes9ZYpr","name":"bash","arguments":{"command":"ssh ub01 'python3 -c \"import ast; ast.parse(open(\\\"/vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py\\\").read()); print(\\\"Syntax OK\\\")\"'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":133,"cacheRead":72799,"cacheWrite":372,"totalTokens":73305,"cost":{"input":0.000005,"output":0.0033250000000000003,"cacheRead":0.0363995,"cacheWrite":0.0023250000000000002,"total":0.0420545}},"stopReason":"toolUse","timestamp":1774895125661}}
{"type":"message","id":"c913867a","parentId":"4a21aa1b","timestamp":"2026-03-30T18:25:34.804Z","message":{"role":"toolResult","toolCallId":"toolu_017HznwEu39bUCyNGes9ZYpr","toolName":"bash","content":[{"type":"text","text":"Syntax OK\n"}],"isError":false,"timestamp":1774895134802}}
{"type":"message","id":"3014af6b","parentId":"c913867a","timestamp":"2026-03-30T18:25:40.585Z","message":{"role":"assistant","content":[{"type":"text","text":"Syntax valid. Now rebuild and restart containers:"},{"type":"toolCall","id":"toolu_01TbLaADmrXDb3SLHij4Y1cV","name":"async_bash","arguments":{"command":"ssh ub01 'cd /vmPool/r/repos/xpltdco/chrysopedia && docker compose build chrysopedia-api chrysopedia-worker 2>&1 | tail -20'","label":"docker-build","timeout":300}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":147,"cacheRead":73171,"cacheWrite":149,"totalTokens":73468,"cost":{"input":0.000005,"output":0.0036750000000000003,"cacheRead":0.0365855,"cacheWrite":0.00093125,"total":0.041196750000000004}},"stopReason":"toolUse","timestamp":1774895134803}}
{"type":"message","id":"991bbc85","parentId":"3014af6b","timestamp":"2026-03-30T18:25:40.593Z","message":{"role":"toolResult","toolCallId":"toolu_01TbLaADmrXDb3SLHij4Y1cV","toolName":"async_bash","content":[{"type":"text","text":"Background job started: **bg_5c194fc9**\nCommand: `docker-build`\n\nUse `await_job` to get results when ready, or `cancel_job` to stop."}],"isError":false,"timestamp":1774895140591}}
{"type":"message","id":"9f143b95","parentId":"991bbc85","timestamp":"2026-03-30T18:25:43.718Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01EJYSQ8tjLJPGLS5woPj36K","name":"await_job","arguments":{"jobs":["bg_5c194fc9"],"timeout":300}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":78,"cacheRead":73320,"cacheWrite":201,"totalTokens":73600,"cost":{"input":0.000005,"output":0.0019500000000000001,"cacheRead":0.03666,"cacheWrite":0.00125625,"total":0.03987125}},"stopReason":"toolUse","timestamp":1774895140592}}
{"type":"message","id":"27055839","parentId":"9f143b95","timestamp":"2026-03-30T18:25:43.722Z","message":{"role":"toolResult","toolCallId":"toolu_01EJYSQ8tjLJPGLS5woPj36K","toolName":"await_job","content":[{"type":"text","text":"### bg_5c194fc9 — docker-build (completed, 3.1s)\n\n#18 [chrysopedia-worker 11/11] COPY alembic/ /app/alembic/\n#18 DONE 0.0s\n\n#19 [chrysopedia-api] exporting to image\n#19 exporting layers 0.1s done\n#19 writing image sha256:9e173f6a4dfc7539f7889c83f83bece2a0671773dfb43225e7e17bd0a83b20ad done\n#19 naming to docker.io/library/xpltd_chrysopedia-chrysopedia-api done\n#19 DONE 0.1s\n\n#20 [chrysopedia-worker] exporting to image\n#20 exporting layers 0.1s done\n#20 writing image sha256:866dc71bdbd2051351fd4cd10888415ee1545e045f32e370006eb2f1e43867a9 done\n#20 naming to docker.io/library/xpltd_chrysopedia-chrysopedia-worker done\n#20 DONE 0.1s\n\n#21 [chrysopedia-api] resolving provenance for metadata file\n#21 DONE 0.0s\n\n#22 [chrysopedia-worker] resolving provenance for metadata file\n#22 DONE 0.0s\n"}],"isError":false,"timestamp":1774895143719}}
{"type":"message","id":"a2c9714f","parentId":"27055839","timestamp":"2026-03-30T18:25:55.770Z","message":{"role":"assistant","content":[{"type":"text","text":"Build used cache (3s). Now restart:"},{"type":"toolCall","id":"toolu_01AspGBsyLxbN4i8ZpuVSaXt","name":"bash","arguments":{"command":"ssh ub01 'cd /vmPool/r/repos/xpltdco/chrysopedia && docker compose up -d chrysopedia-api chrysopedia-worker && docker compose restart chrysopedia-web-8096' 2>&1"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":119,"cacheRead":73521,"cacheWrite":436,"totalTokens":74077,"cost":{"input":0.000005,"output":0.002975,"cacheRead":0.0367605,"cacheWrite":0.002725,"total":0.042465499999999996}},"stopReason":"toolUse","timestamp":1774895143720}}
{"type":"message","id":"8da9d987","parentId":"a2c9714f","timestamp":"2026-03-30T18:26:27.741Z","message":{"role":"toolResult","toolCallId":"toolu_01AspGBsyLxbN4i8ZpuVSaXt","toolName":"bash","content":[{"type":"text","text":" Container chrysopedia-db  Running\n Container chrysopedia-qdrant  Running\n Container chrysopedia-ollama  Running\n Container chrysopedia-redis  Running\n Container chrysopedia-worker  Recreate\n Container chrysopedia-api  Recreate\n Container chrysopedia-api  Recreated\n Container chrysopedia-worker  Recreated\n Container chrysopedia-db  Waiting\n Container chrysopedia-redis  Waiting\n Container chrysopedia-qdrant  Waiting\n Container chrysopedia-ollama  Waiting\n Container chrysopedia-redis  Waiting\n Container chrysopedia-qdrant  Waiting\n Container chrysopedia-ollama  Waiting\n Container chrysopedia-db  Waiting\n Container chrysopedia-ollama  Healthy\n Container chrysopedia-ollama  Healthy\n Container chrysopedia-db  Healthy\n Container chrysopedia-redis  Healthy\n Container chrysopedia-qdrant  Healthy\n Container chrysopedia-redis  Healthy\n Container chrysopedia-worker  Starting\n Container chrysopedia-db  Healthy\n Container chrysopedia-qdrant  Healthy\n Container chrysopedia-api  Starting\n Container chrysopedia-worker  Started\n Container chrysopedia-api  Started\nno such service: chrysopedia-web-8096\n\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1774895187739}}
{"type":"message","id":"b5cadc8f","parentId":"8da9d987","timestamp":"2026-03-30T18:26:31.032Z","message":{"role":"assistant","content":[{"type":"text","text":"Let me find the correct web service name:"},{"type":"toolCall","id":"toolu_01RdFtw96cSR2fFjYDq39MAd","name":"bash","arguments":{"command":"ssh ub01 'cd /vmPool/r/repos/xpltdco/chrysopedia && docker compose ps --format \"{{.Name}}\"' 2>&1"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":102,"cacheRead":73957,"cacheWrite":477,"totalTokens":74537,"cost":{"input":0.000005,"output":0.00255,"cacheRead":0.0369785,"cacheWrite":0.00298125,"total":0.04251475}},"stopReason":"toolUse","timestamp":1774895187740}}
{"type":"message","id":"15e3b8b2","parentId":"b5cadc8f","timestamp":"2026-03-30T18:26:31.425Z","message":{"role":"toolResult","toolCallId":"toolu_01RdFtw96cSR2fFjYDq39MAd","toolName":"bash","content":[{"type":"text","text":"chrysopedia-api\nchrysopedia-db\nchrysopedia-ollama\nchrysopedia-qdrant\nchrysopedia-redis\nchrysopedia-web-8096\nchrysopedia-worker\n"}],"isError":false,"timestamp":1774895191423}}
{"type":"message","id":"cbc5c305","parentId":"15e3b8b2","timestamp":"2026-03-30T18:26:34.877Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01EqtnDPgTyADvMpx9XQkJoJ","name":"bash","arguments":{"command":"ssh ub01 'cd /vmPool/r/repos/xpltdco/chrysopedia && docker compose config --services | grep web' 2>&1"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":89,"cacheRead":74434,"cacheWrite":162,"totalTokens":74686,"cost":{"input":0.000005,"output":0.002225,"cacheRead":0.037217,"cacheWrite":0.0010125,"total":0.0404595}},"stopReason":"toolUse","timestamp":1774895191424}}
{"type":"message","id":"2539a1d7","parentId":"cbc5c305","timestamp":"2026-03-30T18:26:35.252Z","message":{"role":"toolResult","toolCallId":"toolu_01EqtnDPgTyADvMpx9XQkJoJ","toolName":"bash","content":[{"type":"text","text":"chrysopedia-web\n"}],"isError":false,"timestamp":1774895195251}}
{"type":"message","id":"67008ae7","parentId":"2539a1d7","timestamp":"2026-03-30T18:26:38.164Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01QRun5cj2rSjBeqZX77XTBG","name":"bash","arguments":{"command":"ssh ub01 'cd /vmPool/r/repos/xpltdco/chrysopedia && docker compose restart chrysopedia-web' 2>&1"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":89,"cacheRead":74596,"cacheWrite":107,"totalTokens":74793,"cost":{"input":0.000005,"output":0.002225,"cacheRead":0.037298,"cacheWrite":0.0006687500000000001,"total":0.04019675}},"stopReason":"toolUse","timestamp":1774895195251}}
{"type":"message","id":"e726b4fe","parentId":"67008ae7","timestamp":"2026-03-30T18:26:39.028Z","message":{"role":"toolResult","toolCallId":"toolu_01QRun5cj2rSjBeqZX77XTBG","toolName":"bash","content":[{"type":"text","text":" Container chrysopedia-web-8096  Restarting\n Container chrysopedia-web-8096  Started\n"}],"isError":false,"timestamp":1774895199027}}
{"type":"message","id":"10493249","parentId":"e726b4fe","timestamp":"2026-03-30T18:26:42.907Z","message":{"role":"assistant","content":[{"type":"text","text":"Containers rebuilt and restarted. Let me verify the API and worker are healthy:"},{"type":"toolCall","id":"toolu_01KNTEkppxBhPminopz1TB1d","name":"bash","arguments":{"command":"ssh ub01 'sleep 3 && curl -sf http://ub01:8096/health | python3 -m json.tool'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":104,"cacheRead":74703,"cacheWrite":129,"totalTokens":74937,"cost":{"input":0.000005,"output":0.0026000000000000003,"cacheRead":0.037351499999999996,"cacheWrite":0.00080625,"total":0.04076275}},"stopReason":"toolUse","timestamp":1774895199027}}
{"type":"message","id":"32cb634b","parentId":"10493249","timestamp":"2026-03-30T18:26:46.251Z","message":{"role":"toolResult","toolCallId":"toolu_01KNTEkppxBhPminopz1TB1d","toolName":"bash","content":[{"type":"text","text":"{\n    \"status\": \"ok\",\n    \"service\": \"chrysopedia-api\",\n    \"version\": \"0.1.0\",\n    \"database\": \"connected\"\n}\n"}],"isError":false,"timestamp":1774895206249}}
{"type":"message","id":"451002b9","parentId":"32cb634b","timestamp":"2026-03-30T18:26:50.573Z","message":{"role":"assistant","content":[{"type":"text","text":"API healthy. Now run the verification checks — first confirm debug mode endpoint works:"},{"type":"toolCall","id":"toolu_01P7x4q1KYZTjrxBGqTbotZf","name":"bash","arguments":{"command":"ssh ub01 'curl -sf http://ub01:8096/admin/pipeline/debug-mode | python3 -m json.tool'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":103,"cacheRead":74832,"cacheWrite":159,"totalTokens":75095,"cost":{"input":0.000005,"output":0.002575,"cacheRead":0.037416,"cacheWrite":0.00099375,"total":0.04098975}},"stopReason":"toolUse","timestamp":1774895206250}}
{"type":"message","id":"e95002d3","parentId":"451002b9","timestamp":"2026-03-30T18:26:50.911Z","message":{"role":"toolResult","toolCallId":"toolu_01P7x4q1KYZTjrxBGqTbotZf","toolName":"bash","content":[{"type":"text","text":"Expecting value: line 1 column 1 (char 0)\n\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1774895210909}}
{"type":"message","id":"1731a5b6","parentId":"e95002d3","timestamp":"2026-03-30T18:26:55.042Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01JYobgBQ8hzEB834hdoRcwr","name":"bash","arguments":{"command":"ssh ub01 'curl -v http://ub01:8096/admin/pipeline/debug-mode 2>&1'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":81,"cacheRead":74991,"cacheWrite":148,"totalTokens":75221,"cost":{"input":0.000005,"output":0.002025,"cacheRead":0.0374955,"cacheWrite":0.000925,"total":0.0404505}},"stopReason":"toolUse","timestamp":1774895210910}}
{"type":"message","id":"234296e5","parentId":"1731a5b6","timestamp":"2026-03-30T18:26:55.369Z","message":{"role":"toolResult","toolCallId":"toolu_01JYobgBQ8hzEB834hdoRcwr","toolName":"bash","content":[{"type":"text","text":"  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n                                 Dload  Upload   Total   Spent    Left  Speed\n\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0* Host ub01:8096 was resolved.\n* IPv6: (none)\n* IPv4: 127.0.1.1\n*   Trying 127.0.1.1:8096...\n* Connected to ub01 (127.0.1.1) port 8096\n> GET /admin/pipeline/debug-mode HTTP/1.1\r\n> Host: ub01:8096\r\n> User-Agent: curl/8.5.0\r\n> Accept: */*\r\n> \r\n< HTTP/1.1 200 OK\r\n< Server: nginx/1.27.5\r\n< Date: Mon, 30 Mar 2026 18:26:55 GMT\r\n< Content-Type: text/html\r\n< Content-Length: 446\r\n< Last-Modified: Mon, 30 Mar 2026 16:54:46 GMT\r\n< Connection: keep-alive\r\n< ETag: \"69caaad6-1be\"\r\n< Accept-Ranges: bytes\r\n< \r\n{ [446 bytes data]\n\r100   446  100   446    0     0   538k      0 --:--:-- --:--:-- --:--:--  435k\n* Connection #0 to host ub01 left intact\n<!doctype html>\n<html lang=\"en\">\n  <head>\n    <meta charset=\"UTF-8\" />\n    <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n    <meta name=\"theme-color\" content=\"#0a0a12\" />\n    <title>Chrysopedia</title>\n    <script type=\"module\" crossorigin src=\"/assets/index-D0RuSLQl.js\"></script>\n    <link rel=\"stylesheet\" crossorigin href=\"/assets/index-CkXN15YY.css\">\n  </head>\n  <body>\n    <div id=\"root\"></div>\n  </body>\n</html>\n"}],"isError":false,"timestamp":1774895215367}}
{"type":"message","id":"14d62e9a","parentId":"234296e5","timestamp":"2026-03-30T18:27:12.023Z","message":{"role":"assistant","content":[{"type":"text","text":"Nginx is intercepting and serving the SPA HTML instead of proxying to the API. The `/admin/pipeline/` path needs an API prefix. Let me check the nginx config and the router prefix:"},{"type":"toolCall","id":"toolu_01PwCWACjZbh1YdCsMBPM9eV","name":"bash","arguments":{"command":"ssh ub01 'docker exec chrysopedia-web-8096 cat /etc/nginx/conf.d/default.conf 2>/dev/null || docker exec chrysopedia-web-8096 cat /etc/nginx/nginx.conf' 2>&1 | head -50"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":162,"cacheRead":75139,"cacheWrite":632,"totalTokens":75934,"cost":{"input":0.000005,"output":0.00405,"cacheRead":0.0375695,"cacheWrite":0.00395,"total":0.0455745}},"stopReason":"toolUse","timestamp":1774895215368}}
{"type":"message","id":"7566bfa5","parentId":"14d62e9a","timestamp":"2026-03-30T18:27:12.876Z","message":{"role":"toolResult","toolCallId":"toolu_01PwCWACjZbh1YdCsMBPM9eV","toolName":"bash","content":[{"type":"text","text":"server {\n    listen 80;\n    server_name _;\n    root /usr/share/nginx/html;\n    index index.html;\n\n    # Use Docker's embedded DNS with 30s TTL so upstream IPs refresh\n    # after container recreates\n    resolver 127.0.0.11 valid=30s ipv6=off;\n\n    # Allow large transcript uploads (up to 50MB)\n    client_max_body_size 50m;\n\n    # SPA fallback\n    location / {\n        try_files $uri $uri/ /index.html;\n    }\n\n    # API proxy — variable forces nginx to re-resolve on each request\n    location /api/ {\n        set $backend http://chrysopedia-api:8000;\n        proxy_pass $backend;\n        proxy_set_header Host $host;\n        proxy_set_header X-Real-IP $remote_addr;\n        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;\n        proxy_set_header X-Forwarded-Proto $scheme;\n        proxy_read_timeout 300s;\n        proxy_send_timeout 300s;\n    }\n\n    location /health {\n        set $backend http://chrysopedia-api:8000;\n        proxy_pass $backend;\n    }\n}\n"}],"isError":false,"timestamp":1774895232874}}
{"type":"message","id":"09a32f37","parentId":"7566bfa5","timestamp":"2026-03-30T18:27:17.246Z","message":{"role":"assistant","content":[{"type":"text","text":"The nginx routes `/api/` to the backend. The endpoint is at `/admin/pipeline/debug-mode` but needs to go through `/api/` prefix. Let me check how the router is mounted:"},{"type":"toolCall","id":"toolu_016bt7CTgUVdwy73ED4q1pdH","name":"bash","arguments":{"command":"ssh ub01 'grep -n \"pipeline\\|admin\" /vmPool/r/repos/xpltdco/chrysopedia/backend/main.py'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":135,"cacheRead":75771,"cacheWrite":492,"totalTokens":76399,"cost":{"input":0.000005,"output":0.003375,"cacheRead":0.037885499999999996,"cacheWrite":0.003075,"total":0.0443405}},"stopReason":"toolUse","timestamp":1774895232875}}
{"type":"message","id":"9ba2e2a2","parentId":"09a32f37","timestamp":"2026-03-30T18:27:17.549Z","message":{"role":"toolResult","toolCallId":"toolu_016bt7CTgUVdwy73ED4q1pdH","toolName":"bash","content":[{"type":"text","text":"15:from routers import creators, health, ingest, pipeline, reports, review, search, techniques, topics, videos\n83:app.include_router(pipeline.router, prefix=\"/api/v1\")\n"}],"isError":false,"timestamp":1774895237547}}
{"type":"message","id":"bb71e287","parentId":"9ba2e2a2","timestamp":"2026-03-30T18:27:22.559Z","message":{"role":"assistant","content":[{"type":"text","text":"The router is under `/api/v1`. Let me check the router prefix:"},{"type":"toolCall","id":"toolu_01CCAJdV2VRDLHnt9MbZLBfw","name":"bash","arguments":{"command":"ssh ub01 'grep -n \"router\\|prefix\\|debug.mode\\|token.summary\" /vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py | head -20'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":125,"cacheRead":76263,"cacheWrite":194,"totalTokens":76583,"cost":{"input":0.000005,"output":0.003125,"cacheRead":0.0381315,"cacheWrite":0.0012125,"total":0.042474}},"stopReason":"toolUse","timestamp":1774895237548}}
{"type":"message","id":"5cd3dae6","parentId":"bb71e287","timestamp":"2026-03-30T18:27:22.907Z","message":{"role":"toolResult","toolCallId":"toolu_01CCAJdV2VRDLHnt9MbZLBfw","toolName":"bash","content":[{"type":"text","text":"8:  POST /admin/pipeline/trigger/{video_id}  Retrigger (same as public but under admin prefix)\n30:router = APIRouter(tags=[\"pipeline\"])\n32:REDIS_DEBUG_MODE_KEY = \"chrysopedia:debug_mode\"\n37:@router.post(\"/pipeline/trigger/{video_id}\")\n71:@router.get(\"/admin/pipeline/videos\")\n134:@router.post(\"/admin/pipeline/trigger/{video_id}\")\n145:@router.post(\"/admin/pipeline/revoke/{video_id}\")\n183:@router.get(\"/admin/pipeline/events/{video_id}\")\n244:@router.get(\"/admin/pipeline/debug-mode\", response_model=DebugModeResponse)\n245:async def get_debug_mode() -> DebugModeResponse:\n246:    \"\"\"Get the current pipeline debug mode (on/off).\"\"\"\n253:                return DebugModeResponse(debug_mode=value.lower() == \"true\")\n257:        logger.warning(\"Redis unavailable for debug mode read, using config default: %s\", exc)\n259:    return DebugModeResponse(debug_mode=settings.debug_mode)\n262:@router.put(\"/admin/pipeline/debug-mode\", response_model=DebugModeResponse)\n263:async def set_debug_mode(body: DebugModeUpdate) -> DebugModeResponse:\n264:    \"\"\"Set the pipeline debug mode (on/off).\"\"\"\n268:            await redis.set(REDIS_DEBUG_MODE_KEY, str(body.debug_mode))\n272:        logger.error(\"Failed to set debug mode in Redis: %s\", exc)\n278:    logger.info(\"Pipeline debug mode set to %s\", body.debug_mode)\n"}],"isError":false,"timestamp":1774895242906}}
{"type":"message","id":"3b8633d8","parentId":"5cd3dae6","timestamp":"2026-03-30T18:27:26.504Z","message":{"role":"assistant","content":[{"type":"text","text":"So the full path is `/api/v1/admin/pipeline/debug-mode`. Let me verify:"},{"type":"toolCall","id":"toolu_01BCnF3wbSsNcBtmHxaenf2k","name":"bash","arguments":{"command":"ssh ub01 'curl -sf http://ub01:8096/api/v1/admin/pipeline/debug-mode | python3 -m json.tool'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":115,"cacheRead":76457,"cacheWrite":560,"totalTokens":77133,"cost":{"input":0.000005,"output":0.002875,"cacheRead":0.0382285,"cacheWrite":0.0035,"total":0.0446085}},"stopReason":"toolUse","timestamp":1774895242907}}
{"type":"message","id":"e0bcb45b","parentId":"3b8633d8","timestamp":"2026-03-30T18:27:26.845Z","message":{"role":"toolResult","toolCallId":"toolu_01BCnF3wbSsNcBtmHxaenf2k","toolName":"bash","content":[{"type":"text","text":"{\n    \"debug_mode\": false\n}\n"}],"isError":false,"timestamp":1774895246843}}
{"type":"message","id":"a01f0f20","parentId":"e0bcb45b","timestamp":"2026-03-30T18:27:31.134Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me also check if the token-summary endpoint was added by T01:"},{"type":"toolCall","id":"toolu_01DRYMguvP5XrWW4jMcwTtDL","name":"bash","arguments":{"command":"ssh ub01 'grep -n \"token.summary\" /vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":110,"cacheRead":77017,"cacheWrite":138,"totalTokens":77266,"cost":{"input":0.000005,"output":0.0027500000000000003,"cacheRead":0.0385085,"cacheWrite":0.0008625000000000001,"total":0.042126000000000004}},"stopReason":"toolUse","timestamp":1774895246844}}
{"type":"message","id":"264d8175","parentId":"a01f0f20","timestamp":"2026-03-30T18:27:31.447Z","message":{"role":"toolResult","toolCallId":"toolu_01DRYMguvP5XrWW4jMcwTtDL","toolName":"bash","content":[{"type":"text","text":"284:@router.get(\"/admin/pipeline/token-summary/{video_id}\", response_model=TokenSummaryResponse)\n285:async def get_token_summary(\n"}],"isError":false,"timestamp":1774895251445}}
{"type":"message","id":"dc8be733","parentId":"264d8175","timestamp":"2026-03-30T18:27:36.197Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. Now let me run the end-to-end verification. Enable debug mode, then find a video to test with:"},{"type":"toolCall","id":"toolu_01CdK55kUgS4oW8ZzMyBzpSi","name":"bash","arguments":{"command":"ssh ub01 'curl -sf -X PUT http://ub01:8096/api/v1/admin/pipeline/debug-mode -H \"Content-Type: application/json\" -d \"{\\\"debug_mode\\\":true}\" | python3 -m json.tool'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":142,"cacheRead":77155,"cacheWrite":163,"totalTokens":77461,"cost":{"input":0.000005,"output":0.00355,"cacheRead":0.0385775,"cacheWrite":0.0010187500000000001,"total":0.04315125}},"stopReason":"toolUse","timestamp":1774895251446}}
{"type":"message","id":"9b0f20fb","parentId":"dc8be733","timestamp":"2026-03-30T18:27:36.524Z","message":{"role":"toolResult","toolCallId":"toolu_01CdK55kUgS4oW8ZzMyBzpSi","toolName":"bash","content":[{"type":"text","text":"{\n    \"debug_mode\": true\n}\n"}],"isError":false,"timestamp":1774895256522}}
{"type":"message","id":"436e4633","parentId":"9b0f20fb","timestamp":"2026-03-30T18:27:44.129Z","message":{"role":"assistant","content":[{"type":"text","text":"Debug mode on. Let me find a video that has existing events so I can verify the worker loads properly, and also check if there's a small video for a quick pipeline run:"},{"type":"toolCall","id":"toolu_01D9ZkrpvcDfaM8zmnNKzGpT","name":"bash","arguments":{"command":"ssh ub01 'docker exec chrysopedia-db psql -U chrysopedia -c \"SELECT id, filename, processing_status FROM source_videos ORDER BY created_at DESC LIMIT 5;\"'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":136,"cacheRead":77318,"cacheWrite":165,"totalTokens":77620,"cost":{"input":0.000005,"output":0.0034000000000000002,"cacheRead":0.038659,"cacheWrite":0.00103125,"total":0.043095249999999995}},"stopReason":"toolUse","timestamp":1774895256523}}
{"type":"message","id":"f7820549","parentId":"436e4633","timestamp":"2026-03-30T18:27:44.461Z","message":{"role":"toolResult","toolCallId":"toolu_01D9ZkrpvcDfaM8zmnNKzGpT","toolName":"bash","content":[{"type":"text","text":"                  id                  |                               filename                               | processing_status \n--------------------------------------+----------------------------------------------------------------------+-------------------\n 2f723806-89fa-424b-8e6a-28de3cd9619e | Sound Design Workshop Sounds That Fck Dec 20th 2024 Patreon.mp4      | transcribed\n 13c9b70b-d43e-454f-b1ed-5d03d5c71b7c | Sound Design - Everything In 2 Hours Speedrun on Vimeo.mp4           | transcribed\n 6e2d006a-ad6d-423f-8569-3bed0dfb2720 | COPYCATT - MAKING BANGER FROM SCRATCH - Twitch Livestream 2023 0.mp4 | transcribed\n bc02739b-edaa-43d5-8884-8dd0a942464e | COPYCATT Tutoring w Restraint - Session 1.mkv                        | transcribed\n 8fe3b496-1ff5-4f93-a5c2-85f110d00ca6 | 12 Day 5 - Music Business - Part 1.mp4                               | transcribed\n(5 rows)\n\n"}],"isError":false,"timestamp":1774895264459}}
{"type":"message","id":"c6b3e7d6","parentId":"f7820549","timestamp":"2026-03-30T18:27:47.630Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_017baWp1Sq5JDJmt8LwbVzkz","name":"bash","arguments":{"command":"ssh ub01 'docker exec chrysopedia-db psql -U chrysopedia -c \"SELECT id, filename, processing_status FROM source_videos WHERE processing_status = '\\''completed'\\'' LIMIT 3;\"'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":102,"cacheRead":77483,"cacheWrite":449,"totalTokens":78035,"cost":{"input":0.000005,"output":0.00255,"cacheRead":0.0387415,"cacheWrite":0.00280625,"total":0.04410275}},"stopReason":"toolUse","timestamp":1774895264460}}
{"type":"message","id":"8cdfbada","parentId":"c6b3e7d6","timestamp":"2026-03-30T18:27:47.987Z","message":{"role":"toolResult","toolCallId":"toolu_017baWp1Sq5JDJmt8LwbVzkz","toolName":"bash","content":[{"type":"text","text":"ERROR:  invalid input value for enum processing_status: \"completed\"\nLINE 1: ...atus FROM source_videos WHERE processing_status = 'completed...\n                                                             ^\n\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1774895267985}}
{"type":"message","id":"7b113483","parentId":"8cdfbada","timestamp":"2026-03-30T18:27:50.795Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_017PKZ6uc76jhMyLbbfMRFKP","name":"bash","arguments":{"command":"ssh ub01 'docker exec chrysopedia-db psql -U chrysopedia -c \"SELECT DISTINCT processing_status FROM source_videos;\"'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":86,"cacheRead":77932,"cacheWrite":172,"totalTokens":78191,"cost":{"input":0.000005,"output":0.00215,"cacheRead":0.038966,"cacheWrite":0.001075,"total":0.042196}},"stopReason":"toolUse","timestamp":1774895267986}}
{"type":"message","id":"0b4112f9","parentId":"7b113483","timestamp":"2026-03-30T18:27:51.196Z","message":{"role":"toolResult","toolCallId":"toolu_017PKZ6uc76jhMyLbbfMRFKP","toolName":"bash","content":[{"type":"text","text":" processing_status \n-------------------\n reviewed\n transcribed\n extracted\n(3 rows)\n\n"}],"isError":false,"timestamp":1774895271194}}
{"type":"message","id":"e525d520","parentId":"0b4112f9","timestamp":"2026-03-30T18:27:55.359Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01FymGqgdovYCT4XAn9msRW4","name":"bash","arguments":{"command":"ssh ub01 'docker exec chrysopedia-db psql -U chrysopedia -c \"SELECT id, filename FROM source_videos WHERE processing_status = '\\''extracted'\\'' LIMIT 3;\"'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":98,"cacheRead":78104,"cacheWrite":122,"totalTokens":78325,"cost":{"input":0.000005,"output":0.00245,"cacheRead":0.039051999999999996,"cacheWrite":0.0007625,"total":0.042269499999999995}},"stopReason":"toolUse","timestamp":1774895271195}}
{"type":"message","id":"ec76ade3","parentId":"e525d520","timestamp":"2026-03-30T18:27:55.717Z","message":{"role":"toolResult","toolCallId":"toolu_01FymGqgdovYCT4XAn9msRW4","toolName":"bash","content":[{"type":"text","text":"                  id                  |                     filename                      \n--------------------------------------+---------------------------------------------------\n a091ee3f-ce27-4b5f-8b09-fb23ac9400f8 | BREAK_Part1_DnB_Masterclass[1920x1080][800MB].mp4\n 6851897c-6263-4759-85a8-363d84e4eae2 | BREAK_Part2_DnB_Masterclass[1920x1080][760MB].mp4\n 61c483b9-f8e8-4302-8700-4b6bc7a722c5 | 01 Day 1 - Drums - Part 1.mp4\n(3 rows)\n\n"}],"isError":false,"timestamp":1774895275716}}
{"type":"message","id":"f1c6e21e","parentId":"ec76ade3","timestamp":"2026-03-30T18:27:59.194Z","message":{"role":"assistant","content":[{"type":"text","text":"Let me check the worker is running and ready before triggering a pipeline:"},{"type":"toolCall","id":"toolu_01DsrceVri7xAPzcKQNqYvbc","name":"bash","arguments":{"command":"ssh ub01 'docker logs chrysopedia-worker --tail 10 2>&1'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":89,"cacheRead":78226,"cacheWrite":287,"totalTokens":78603,"cost":{"input":0.000005,"output":0.002225,"cacheRead":0.039112999999999995,"cacheWrite":0.00179375,"total":0.043136749999999995}},"stopReason":"toolUse","timestamp":1774895275716}}
{"type":"message","id":"e1135305","parentId":"f1c6e21e","timestamp":"2026-03-30T18:27:59.527Z","message":{"role":"toolResult","toolCallId":"toolu_01DsrceVri7xAPzcKQNqYvbc","toolName":"bash","content":[{"type":"text","text":"[2026-03-30 18:26:30,446: INFO/MainProcess] celery@957f430c961f ready.\n[2026-03-30 18:26:30,448: INFO/MainProcess] Task pipeline.stages.stage3_extraction[2f37a4eb-f6c2-43a9-af86-87a632d08c54] received\n[2026-03-30 18:26:30,452: INFO/ForkPoolWorker-1] Stage 3 (extraction) starting for video_id=7afc5a42-b201-4457-802d-6440cf0314b2\n[2026-03-30 18:26:30,565: INFO/ForkPoolWorker-1] Stage 3 using model=fyn-llm-agent-think, modality=thinking\n[2026-03-30 18:26:30,565: INFO/ForkPoolWorker-1] Token estimate: input≈2577, stage=stage3_extraction, ratio=1.20, estimated_output=3710, max_tokens=3710 (hard_limit=32768)\n[2026-03-30 18:26:30,566: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=3710\n[2026-03-30 18:27:14,837: INFO/ForkPoolWorker-1] HTTP Request: POST https://chat.forgetyour.name/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n[2026-03-30 18:27:14,850: INFO/ForkPoolWorker-1] LLM response: prompt_tokens=3383, completion_tokens=810, total=4193, content_len=962, finish=stop\n[2026-03-30 18:27:14,867: INFO/ForkPoolWorker-1] Token estimate: input≈2546, stage=stage3_extraction, ratio=1.20, estimated_output=3666, max_tokens=3666 (hard_limit=32768)\n[2026-03-30 18:27:14,868: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=3666\n"}],"isError":false,"timestamp":1774895279526}}
{"type":"message","id":"102e7793","parentId":"e1135305","timestamp":"2026-03-30T18:28:06.416Z","message":{"role":"assistant","content":[{"type":"text","text":"Worker is actively processing. There's a pipeline already running. Let me wait for it to finish and check if the debug capture worked — since debug mode is on now, any new LLM calls should capture the prompts. But actually this pipeline was started before I enabled debug mode. The `_is_debug_mode()` check happens when `_make_llm_callback` is called, not when the callback fires. Since the callbacks were created before I turned debug on, those will have `debug=False`.\n\nLet me wait for this pipeline to finish and then trigger a fresh one with debug on:"},{"type":"toolCall","id":"toolu_01RRad57qPWvNwZ8keZ88rT5","name":"bash","arguments":{"command":"ssh ub01 'docker logs chrysopedia-worker --tail 5 2>&1'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":201,"cacheRead":78513,"cacheWrite":695,"totalTokens":79410,"cost":{"input":0.000005,"output":0.005025,"cacheRead":0.0392565,"cacheWrite":0.00434375,"total":0.04863025}},"stopReason":"toolUse","timestamp":1774895279527}}
{"type":"message","id":"017d29f1","parentId":"102e7793","timestamp":"2026-03-30T18:28:06.748Z","message":{"role":"toolResult","toolCallId":"toolu_01RRad57qPWvNwZ8keZ88rT5","toolName":"bash","content":[{"type":"text","text":"[2026-03-30 18:27:14,868: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=3666\n[2026-03-30 18:28:01,145: INFO/ForkPoolWorker-1] HTTP Request: POST https://chat.forgetyour.name/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n[2026-03-30 18:28:01,146: INFO/ForkPoolWorker-1] LLM response: prompt_tokens=3317, completion_tokens=944, total=4261, content_len=955, finish=stop\n[2026-03-30 18:28:01,155: INFO/ForkPoolWorker-1] Token estimate: input≈2408, stage=stage3_extraction, ratio=1.20, estimated_output=3466, max_tokens=3466 (hard_limit=32768)\n[2026-03-30 18:28:01,156: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=3466\n"}],"isError":false,"timestamp":1774895286746}}
{"type":"message","id":"79506b7b","parentId":"017d29f1","timestamp":"2026-03-30T18:28:11.597Z","message":{"role":"assistant","content":[{"type":"text","text":"Still processing stage 3. Let me pick a small already-transcribed video and trigger a pipeline on it. That will use the freshly rebuilt worker with debug mode on. Let me find the smallest transcript:"},{"type":"toolCall","id":"toolu_01Fw324oA9fKzhBxiq7TchGa","name":"bash","arguments":{"command":"ssh ub01 'docker exec chrysopedia-db psql -U chrysopedia -c \"SELECT sv.id, sv.filename, COUNT(ts.id) as seg_count FROM source_videos sv LEFT JOIN transcript_segments ts ON sv.id = ts.source_video_id WHERE sv.processing_status = '\\''transcribed'\\'' GROUP BY sv.id ORDER BY seg_count ASC LIMIT 5;\"'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":191,"cacheRead":79208,"cacheWrite":532,"totalTokens":79932,"cost":{"input":0.000005,"output":0.004775000000000001,"cacheRead":0.039604,"cacheWrite":0.0033250000000000003,"total":0.047709}},"stopReason":"toolUse","timestamp":1774895286747}}
{"type":"message","id":"fcccf810","parentId":"79506b7b","timestamp":"2026-03-30T18:28:11.947Z","message":{"role":"toolResult","toolCallId":"toolu_01Fw324oA9fKzhBxiq7TchGa","toolName":"bash","content":[{"type":"text","text":"                  id                  |                                     filename                                     | seg_count \n--------------------------------------+----------------------------------------------------------------------------------+-----------\n 5a91c885-7035-4ffb-bd4e-27ed093b373c | Short Swing - What Not To Do Toms Battery on Vimeo.mp4                           |        57\n e8d7bfe2-5573-45ca-868e-806425109210 | Tutorial Snare Synthesis ('Neapolitan Ice Cream' Technique) [Short] March 29.mp4 |        58\n 7f450f67-e226-4c8f-8190-07e408a51871 | Tutorial Swing Groove on Vimeo.mp4                                               |       226\n 908866c2-4179-4d70-b5c4-eef444a28fb2 | Whats In My Project Template on Vimeo.mp4                                        |       266\n 6315d478-c150-4915-b138-63e6e0c9f906 | 13 Day 5 - Music Business - Part 2.mp4                                           |       286\n(5 rows)\n\n"}],"isError":false,"timestamp":1774895291946}}
{"type":"message","id":"392c9ad5","parentId":"fcccf810","timestamp":"2026-03-30T18:28:17.446Z","message":{"role":"assistant","content":[{"type":"text","text":"57 segments is small. Let me trigger the pipeline on that one — but I need to wait for the current pipeline to complete first so the worker isn't overloaded. Actually the Celery worker can handle multiple tasks. Let me just trigger it:"},{"type":"toolCall","id":"toolu_01LRCFB5BJhYmMCPbVSdG7h8","name":"bash","arguments":{"command":"ssh ub01 'curl -sf -X POST \"http://ub01:8096/api/v1/admin/pipeline/trigger/5a91c885-7035-4ffb-bd4e-27ed093b373c\" | python3 -m json.tool'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":172,"cacheRead":79740,"cacheWrite":489,"totalTokens":80402,"cost":{"input":0.000005,"output":0.0043,"cacheRead":0.039869999999999996,"cacheWrite":0.00305625,"total":0.04723124999999999}},"stopReason":"toolUse","timestamp":1774895291946}}
{"type":"message","id":"f1426967","parentId":"392c9ad5","timestamp":"2026-03-30T18:28:18.330Z","message":{"role":"toolResult","toolCallId":"toolu_01LRCFB5BJhYmMCPbVSdG7h8","toolName":"bash","content":[{"type":"text","text":"{\n    \"status\": \"triggered\",\n    \"video_id\": \"5a91c885-7035-4ffb-bd4e-27ed093b373c\",\n    \"current_processing_status\": \"transcribed\"\n}\n"}],"isError":false,"timestamp":1774895298327}}
{"type":"message","id":"9ea4068a","parentId":"f1426967","timestamp":"2026-03-30T18:28:23.070Z","message":{"role":"assistant","content":[{"type":"text","text":"Pipeline triggered. Let me monitor progress — the small video should be quick:"},{"type":"toolCall","id":"toolu_01BSPPTHNbYEXTJZjhdxPoz2","name":"bash","arguments":{"command":"ssh ub01 'sleep 30 && docker logs chrysopedia-worker --tail 20 2>&1'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":94,"cacheRead":80229,"cacheWrite":240,"totalTokens":80564,"cost":{"input":0.000005,"output":0.00235,"cacheRead":0.0401145,"cacheWrite":0.0015,"total":0.0439695}},"stopReason":"toolUse","timestamp":1774895298328}}
{"type":"message","id":"3b6ed029","parentId":"9ea4068a","timestamp":"2026-03-30T18:28:53.386Z","message":{"role":"toolResult","toolCallId":"toolu_01BSPPTHNbYEXTJZjhdxPoz2","toolName":"bash","content":[{"type":"text","text":"[2026-03-30 18:26:29,431: INFO/MainProcess] mingle: searching for neighbors\n[2026-03-30 18:26:30,436: INFO/MainProcess] mingle: all alone\n[2026-03-30 18:26:30,446: INFO/MainProcess] celery@957f430c961f ready.\n[2026-03-30 18:26:30,448: INFO/MainProcess] Task pipeline.stages.stage3_extraction[2f37a4eb-f6c2-43a9-af86-87a632d08c54] received\n[2026-03-30 18:26:30,452: INFO/ForkPoolWorker-1] Stage 3 (extraction) starting for video_id=7afc5a42-b201-4457-802d-6440cf0314b2\n[2026-03-30 18:26:30,565: INFO/ForkPoolWorker-1] Stage 3 using model=fyn-llm-agent-think, modality=thinking\n[2026-03-30 18:26:30,565: INFO/ForkPoolWorker-1] Token estimate: input≈2577, stage=stage3_extraction, ratio=1.20, estimated_output=3710, max_tokens=3710 (hard_limit=32768)\n[2026-03-30 18:26:30,566: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=3710\n[2026-03-30 18:27:14,837: INFO/ForkPoolWorker-1] HTTP Request: POST https://chat.forgetyour.name/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n[2026-03-30 18:27:14,850: INFO/ForkPoolWorker-1] LLM response: prompt_tokens=3383, completion_tokens=810, total=4193, content_len=962, finish=stop\n[2026-03-30 18:27:14,867: INFO/ForkPoolWorker-1] Token estimate: input≈2546, stage=stage3_extraction, ratio=1.20, estimated_output=3666, max_tokens=3666 (hard_limit=32768)\n[2026-03-30 18:27:14,868: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=3666\n[2026-03-30 18:28:01,145: INFO/ForkPoolWorker-1] HTTP Request: POST https://chat.forgetyour.name/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n[2026-03-30 18:28:01,146: INFO/ForkPoolWorker-1] LLM response: prompt_tokens=3317, completion_tokens=944, total=4261, content_len=955, finish=stop\n[2026-03-30 18:28:01,155: INFO/ForkPoolWorker-1] Token estimate: input≈2408, stage=stage3_extraction, ratio=1.20, estimated_output=3466, max_tokens=3466 (hard_limit=32768)\n[2026-03-30 18:28:01,156: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=3466\n[2026-03-30 18:28:30,076: INFO/ForkPoolWorker-1] HTTP Request: POST https://chat.forgetyour.name/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n[2026-03-30 18:28:30,077: INFO/ForkPoolWorker-1] LLM response: prompt_tokens=3143, completion_tokens=616, total=3759, content_len=17, finish=stop\n[2026-03-30 18:28:30,090: INFO/ForkPoolWorker-1] Token estimate: input≈2801, stage=stage3_extraction, ratio=1.20, estimated_output=4033, max_tokens=4033 (hard_limit=32768)\n[2026-03-30 18:28:30,091: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=4033\n"}],"isError":false,"timestamp":1774895333384}}
{"type":"message","id":"6cf1532c","parentId":"3b6ed029","timestamp":"2026-03-30T18:28:57.259Z","message":{"role":"assistant","content":[{"type":"text","text":"Still processing the prior video's stage 3. The small video trigger is queued. Let me wait a bit more:"},{"type":"toolCall","id":"toolu_014ze8qiy7JoBGKAN7X2Yro2","name":"bash","arguments":{"command":"ssh ub01 'sleep 60 && docker logs chrysopedia-worker --tail 15 2>&1'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":104,"cacheRead":80469,"cacheWrite":1267,"totalTokens":81841,"cost":{"input":0.000005,"output":0.0026000000000000003,"cacheRead":0.0402345,"cacheWrite":0.00791875,"total":0.050758250000000005}},"stopReason":"toolUse","timestamp":1774895333385}}
{"type":"message","id":"6b1cf3f9","parentId":"6cf1532c","timestamp":"2026-03-30T18:29:57.578Z","message":{"role":"toolResult","toolCallId":"toolu_014ze8qiy7JoBGKAN7X2Yro2","toolName":"bash","content":[{"type":"text","text":"[2026-03-30 18:28:01,146: INFO/ForkPoolWorker-1] LLM response: prompt_tokens=3317, completion_tokens=944, total=4261, content_len=955, finish=stop\n[2026-03-30 18:28:01,155: INFO/ForkPoolWorker-1] Token estimate: input≈2408, stage=stage3_extraction, ratio=1.20, estimated_output=3466, max_tokens=3466 (hard_limit=32768)\n[2026-03-30 18:28:01,156: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=3466\n[2026-03-30 18:28:30,076: INFO/ForkPoolWorker-1] HTTP Request: POST https://chat.forgetyour.name/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n[2026-03-30 18:28:30,077: INFO/ForkPoolWorker-1] LLM response: prompt_tokens=3143, completion_tokens=616, total=3759, content_len=17, finish=stop\n[2026-03-30 18:28:30,090: INFO/ForkPoolWorker-1] Token estimate: input≈2801, stage=stage3_extraction, ratio=1.20, estimated_output=4033, max_tokens=4033 (hard_limit=32768)\n[2026-03-30 18:28:30,091: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=4033\n[2026-03-30 18:29:13,876: INFO/ForkPoolWorker-1] HTTP Request: POST https://chat.forgetyour.name/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n[2026-03-30 18:29:13,877: INFO/ForkPoolWorker-1] LLM response: prompt_tokens=3763, completion_tokens=954, total=4717, content_len=945, finish=stop\n[2026-03-30 18:29:13,890: INFO/ForkPoolWorker-1] Token estimate: input≈2564, stage=stage3_extraction, ratio=1.20, estimated_output=3691, max_tokens=3691 (hard_limit=32768)\n[2026-03-30 18:29:13,891: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=3691\n[2026-03-30 18:29:34,282: INFO/ForkPoolWorker-1] HTTP Request: POST https://chat.forgetyour.name/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n[2026-03-30 18:29:34,283: INFO/ForkPoolWorker-1] LLM response: prompt_tokens=3335, completion_tokens=418, total=3753, content_len=17, finish=stop\n[2026-03-30 18:29:34,296: INFO/ForkPoolWorker-1] Token estimate: input≈2701, stage=stage3_extraction, ratio=1.20, estimated_output=3889, max_tokens=3889 (hard_limit=32768)\n[2026-03-30 18:29:34,297: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=3889\n"}],"isError":false,"timestamp":1774895397576}}
{"type":"message","id":"8c5251f1","parentId":"6b1cf3f9","timestamp":"2026-03-30T18:30:09.019Z","message":{"role":"assistant","content":[{"type":"text","text":"Still in stage 3 extraction for the prior video. The pipeline runs multiple LLM calls per stage (one per topic group). Let me check if our new trigger is queued:"},{"type":"toolCall","id":"toolu_01DzRpfvVP4bBnaQxAq8VKph","name":"bash","arguments":{"command":"ssh ub01 'docker exec chrysopedia-db psql -U chrysopedia -c \"SELECT stage, event_type, created_at FROM pipeline_events WHERE video_id = '\\''5a91c885-7035-4ffb-bd4e-27ed093b373c'\\'' ORDER BY created_at DESC LIMIT 5;\"'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":172,"cacheRead":81736,"cacheWrite":1077,"totalTokens":82986,"cost":{"input":0.000005,"output":0.0043,"cacheRead":0.040868,"cacheWrite":0.00673125,"total":0.051904250000000006}},"stopReason":"toolUse","timestamp":1774895397577}}
{"type":"message","id":"704f4d6c","parentId":"8c5251f1","timestamp":"2026-03-30T18:30:09.844Z","message":{"role":"toolResult","toolCallId":"toolu_01DzRpfvVP4bBnaQxAq8VKph","toolName":"bash","content":[{"type":"text","text":"        stage        | event_type |         created_at         \n---------------------+------------+----------------------------\n stage2_segmentation | complete   | 2026-03-30 17:20:56.213468\n stage2_segmentation | llm_call   | 2026-03-30 17:20:56.188877\n stage2_segmentation | start      | 2026-03-30 17:20:45.988026\n(3 rows)\n\n"}],"isError":false,"timestamp":1774895409842}}
{"type":"message","id":"5bfa9dc4","parentId":"704f4d6c","timestamp":"2026-03-30T18:30:17.632Z","message":{"role":"assistant","content":[{"type":"text","text":"Wait — this video was previously run through stage 2 (before debug mode existed). The trigger should kick off from the current status. Let me check:"},{"type":"toolCall","id":"toolu_01Hva1W52Gpk9En8kfcnBriu","name":"bash","arguments":{"command":"ssh ub01 'docker exec chrysopedia-db psql -U chrysopedia -c \"SELECT processing_status FROM source_videos WHERE id = '\\''5a91c885-7035-4ffb-bd4e-27ed093b373c'\\'';\"'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":147,"cacheRead":82813,"cacheWrite":296,"totalTokens":83257,"cost":{"input":0.000005,"output":0.0036750000000000003,"cacheRead":0.0414065,"cacheWrite":0.00185,"total":0.0469365}},"stopReason":"toolUse","timestamp":1774895409843}}
{"type":"message","id":"b2d7a6e9","parentId":"5bfa9dc4","timestamp":"2026-03-30T18:30:17.976Z","message":{"role":"toolResult","toolCallId":"toolu_01Hva1W52Gpk9En8kfcnBriu","toolName":"bash","content":[{"type":"text","text":" processing_status \n-------------------\n transcribed\n(1 row)\n\n"}],"isError":false,"timestamp":1774895417974}}
{"type":"message","id":"d0e0e128","parentId":"b2d7a6e9","timestamp":"2026-03-30T18:30:25.661Z","message":{"role":"assistant","content":[{"type":"text","text":"Still transcribed — the pipeline trigger should start from stage 2. Let me check how the trigger endpoint works:"},{"type":"toolCall","id":"toolu_01M6FuxstH68Lsef7i4XwQT5","name":"bash","arguments":{"command":"ssh ub01 'grep -A 20 \"def trigger_pipeline_admin\" /vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":122,"cacheRead":83109,"cacheWrite":177,"totalTokens":83409,"cost":{"input":0.000005,"output":0.00305,"cacheRead":0.0415545,"cacheWrite":0.00110625,"total":0.045715750000000006}},"stopReason":"toolUse","timestamp":1774895417975}}
{"type":"message","id":"604de182","parentId":"d0e0e128","timestamp":"2026-03-30T18:30:25.991Z","message":{"role":"toolResult","toolCallId":"toolu_01M6FuxstH68Lsef7i4XwQT5","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1774895425988}}
{"type":"message","id":"d226cfe5","parentId":"604de182","timestamp":"2026-03-30T18:30:29.154Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01BguunJufjR4TFFFwFaTyLC","name":"bash","arguments":{"command":"ssh ub01 'sed -n \"134,180p\" /vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":93,"cacheRead":83286,"cacheWrite":153,"totalTokens":83533,"cost":{"input":0.000005,"output":0.0023250000000000002,"cacheRead":0.041643,"cacheWrite":0.0009562500000000001,"total":0.04492925}},"stopReason":"toolUse","timestamp":1774895425990}}
{"type":"message","id":"8b3ac7f3","parentId":"d226cfe5","timestamp":"2026-03-30T18:30:29.475Z","message":{"role":"toolResult","toolCallId":"toolu_01BguunJufjR4TFFFwFaTyLC","toolName":"bash","content":[{"type":"text","text":"@router.post(\"/admin/pipeline/trigger/{video_id}\")\nasync def admin_trigger_pipeline(\n    video_id: str,\n    db: AsyncSession = Depends(get_session),\n):\n    \"\"\"Admin retrigger — same as public trigger.\"\"\"\n    return await trigger_pipeline(video_id, db)\n\n\n# ── Admin: Revoke ────────────────────────────────────────────────────────────\n\n@router.post(\"/admin/pipeline/revoke/{video_id}\")\nasync def revoke_pipeline(video_id: str):\n    \"\"\"Revoke/cancel active Celery tasks for a video.\n    \n    Uses Celery's revoke with terminate=True to kill running tasks.\n    This is best-effort — the task may have already completed.\n    \"\"\"\n    from worker import celery_app\n    \n    try:\n        # Get active tasks and revoke any matching this video_id\n        inspector = celery_app.control.inspect()\n        active = inspector.active() or {}\n        revoked_count = 0\n        \n        for _worker, tasks in active.items():\n            for task in tasks:\n                task_args = task.get(\"args\", [])\n                if task_args and str(task_args[0]) == video_id:\n                    celery_app.control.revoke(task[\"id\"], terminate=True)\n                    revoked_count += 1\n                    logger.info(\"Revoked task %s for video_id=%s\", task[\"id\"], video_id)\n        \n        return {\n            \"status\": \"revoked\" if revoked_count > 0 else \"no_active_tasks\",\n            \"video_id\": video_id,\n            \"tasks_revoked\": revoked_count,\n        }\n    except Exception as exc:\n        logger.warning(\"Failed to revoke tasks for video_id=%s: %s\", video_id, exc)\n        raise HTTPException(\n            status_code=503,\n            detail=\"Failed to communicate with Celery worker\",\n        ) from exc\n\n\n"}],"isError":false,"timestamp":1774895429472}}
{"type":"message","id":"518f3179","parentId":"8b3ac7f3","timestamp":"2026-03-30T18:30:33.860Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01AJLXtUqRYZk4sL5gH3C2yj","name":"bash","arguments":{"command":"ssh ub01 'sed -n \"37,70p\" /vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":93,"cacheRead":83439,"cacheWrite":593,"totalTokens":84126,"cost":{"input":0.000005,"output":0.0023250000000000002,"cacheRead":0.0417195,"cacheWrite":0.0037062500000000003,"total":0.04775575}},"stopReason":"toolUse","timestamp":1774895429474}}
{"type":"message","id":"61c0103e","parentId":"518f3179","timestamp":"2026-03-30T18:30:34.154Z","message":{"role":"toolResult","toolCallId":"toolu_01AJLXtUqRYZk4sL5gH3C2yj","toolName":"bash","content":[{"type":"text","text":"@router.post(\"/pipeline/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    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    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(\"Failed to dispatch pipeline for video_id=%s: %s\", video_id, exc)\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\n\n# ── Admin: Video list ────────────────────────────────────────────────────────\n\n"}],"isError":false,"timestamp":1774895434152}}
{"type":"message","id":"c532f49d","parentId":"61c0103e","timestamp":"2026-03-30T18:30:51.948Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — it dispatches `run_pipeline.delay()` to Celery. Our triggered task is queued behind the current one. The worker only has 1 fork pool worker so it runs sequentially. This might take a while. Let me wait asynchronously:"},{"type":"toolCall","id":"toolu_017FnGSR4jMf5BK1Q1vhQTT9","name":"async_bash","arguments":{"command":"ssh ub01 'for i in $(seq 1 60); do sleep 30; status=$(docker exec chrysopedia-db psql -U chrysopedia -t -c \"SELECT processing_status FROM source_videos WHERE id = '\\''5a91c885-7035-4ffb-bd4e-27ed093b373c'\\'';\" 2>/dev/null | tr -d \" \\n\"); echo \"$(date +%H:%M:%S) status=$status\"; if [ \"$status\" != \"transcribed\" ]; then echo \"CHANGED from transcribed to $status\"; if [ \"$status\" = \"extracted\" ] || [ \"$status\" = \"reviewed\" ]; then echo \"PIPELINE DONE\"; break; fi; fi; done'","label":"wait-pipeline","timeout":1800}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":320,"cacheRead":84032,"cacheWrite":437,"totalTokens":84790,"cost":{"input":0.000005,"output":0.008,"cacheRead":0.042016,"cacheWrite":0.00273125,"total":0.052752249999999994}},"stopReason":"toolUse","timestamp":1774895434153}}
{"type":"message","id":"57ca5f50","parentId":"c532f49d","timestamp":"2026-03-30T18:30:51.959Z","message":{"role":"toolResult","toolCallId":"toolu_017FnGSR4jMf5BK1Q1vhQTT9","toolName":"async_bash","content":[{"type":"text","text":"Background job started: **bg_cbdddac1**\nCommand: `wait-pipeline`\n\nUse `await_job` to get results when ready, or `cancel_job` to stop."}],"isError":false,"timestamp":1774895451956}}
{"type":"message","id":"50e4d647","parentId":"57ca5f50","timestamp":"2026-03-30T18:30:58.885Z","message":{"role":"assistant","content":[{"type":"text","text":"While waiting, let me also verify that the existing events endpoint properly returns the new fields. Let me check an existing video that has events:"},{"type":"toolCall","id":"toolu_01GddmR31QTSiM6jgA6uDViK","name":"bash","arguments":{"command":"ssh ub01 'curl -sf \"http://ub01:8096/api/v1/admin/pipeline/events/5a91c885-7035-4ffb-bd4e-27ed093b373c?event_type=llm_call\" | python3 -c \"import sys,json; data=json.load(sys.stdin); print(json.dumps(data[\\\"events\\\"][0] if data[\\\"events\\\"] else {}, indent=2))\"'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":195,"cacheRead":84469,"cacheWrite":374,"totalTokens":85039,"cost":{"input":0.000005,"output":0.004875,"cacheRead":0.0422345,"cacheWrite":0.0023375,"total":0.049452}},"stopReason":"toolUse","timestamp":1774895451957}}
{"type":"message","id":"e410da52","parentId":"50e4d647","timestamp":"2026-03-30T18:30:59.283Z","message":{"role":"toolResult","toolCallId":"toolu_01GddmR31QTSiM6jgA6uDViK","toolName":"bash","content":[{"type":"text","text":"Traceback (most recent call last):\n  File \"<string>\", line 1, in <module>\nKeyError: 'events'\n\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1774895459280}}
{"type":"message","id":"e94fecc1","parentId":"e410da52","timestamp":"2026-03-30T18:31:06.355Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_018diDX4GiWgxhYnmMRgkcea","name":"bash","arguments":{"command":"ssh ub01 'curl -sf \"http://ub01:8096/api/v1/admin/pipeline/events/5a91c885-7035-4ffb-bd4e-27ed093b373c?event_type=llm_call\" | python3 -m json.tool | head -30'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":128,"cacheRead":84843,"cacheWrite":255,"totalTokens":85227,"cost":{"input":0.000005,"output":0.0032,"cacheRead":0.0424215,"cacheWrite":0.0015937500000000001,"total":0.04722025}},"stopReason":"toolUse","timestamp":1774895459282}}
{"type":"message","id":"9fadb1de","parentId":"e94fecc1","timestamp":"2026-03-30T18:31:06.707Z","message":{"role":"toolResult","toolCallId":"toolu_018diDX4GiWgxhYnmMRgkcea","toolName":"bash","content":[{"type":"text","text":"{\n    \"items\": [\n        {\n            \"id\": \"5faae0d0-5f1f-4932-8dfa-d77bcf569156\",\n            \"video_id\": \"5a91c885-7035-4ffb-bd4e-27ed093b373c\",\n            \"stage\": \"stage2_segmentation\",\n            \"event_type\": \"llm_call\",\n            \"prompt_tokens\": 3635,\n            \"completion_tokens\": 183,\n            \"total_tokens\": 3818,\n            \"model\": \"fyn-llm-agent-chat\",\n            \"duration_ms\": null,\n            \"payload\": {\n                \"is_fallback\": false,\n                \"finish_reason\": \"stop\",\n                \"content_length\": 752,\n                \"content_preview\": \"{\\n  \\\"segments\\\": [\\n    {\\n      \\\"start_index\\\": 0,\\n      \\\"end_index\\\": 43,\\n      \\\"topic_label\\\": \\\"groove consistency and swing analysis\\\",\\n      \\\"summary\\\": \\\"The creator critiques inconsistent groove choices in the track 'Tom's Battery,' demonstrating how the song shifts unintentionally between straight, swung, and triplet rhythms across different sections like the buildup and drop.\\\"\\n    },\\n    {\\n      \\\"start_index\\\": 44,\\n      \\\"end_index\\\": 56,\\n      \\\"topic_label\\\": \\\"personal anecdote and workflow lesson\\\",\\n      \\\"summary\\\": \\\"After analyzing the track's groove issues, the creator shares a brief story about a previous video on swing types and concludes with a core production lesson: pick a single groove and commit to it throughout the track.\\\"\\n    }\\n  ]\\n}\"\n            },\n            \"created_at\": \"2026-03-30T17:20:56.188877\",\n            \"system_prompt_text\": null,\n            \"user_prompt_text\": null,\n            \"response_text\": null\n        }\n    ],\n    \"total\": 1,\n    \"offset\": 0,\n    \"limit\": 100\n}\n"}],"isError":false,"timestamp":1774895466705}}
{"type":"message","id":"37df3bf5","parentId":"9fadb1de","timestamp":"2026-03-30T18:31:15.516Z","message":{"role":"assistant","content":[{"type":"text","text":"The API already returns the three new fields (all null for events created before debug mode). The schema and endpoint are working. Now I need to wait for the pipeline to run with debug mode on to verify the fields get populated.\n\nLet me check if the prior pipeline finished and ours started:"},{"type":"toolCall","id":"toolu_01KcomNw7NgeruCc5nEAmhim","name":"bash","arguments":{"command":"ssh ub01 'docker logs chrysopedia-worker --tail 5 2>&1'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":134,"cacheRead":85098,"cacheWrite":649,"totalTokens":85882,"cost":{"input":0.000005,"output":0.00335,"cacheRead":0.042549,"cacheWrite":0.00405625,"total":0.04996024999999999}},"stopReason":"toolUse","timestamp":1774895466706}}
{"type":"message","id":"6d11115e","parentId":"37df3bf5","timestamp":"2026-03-30T18:31:16.295Z","message":{"role":"toolResult","toolCallId":"toolu_01KcomNw7NgeruCc5nEAmhim","toolName":"bash","content":[{"type":"text","text":"[2026-03-30 18:29:34,297: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=3889\n[2026-03-30 18:30:17,682: INFO/ForkPoolWorker-1] HTTP Request: POST https://chat.forgetyour.name/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n[2026-03-30 18:30:17,683: INFO/ForkPoolWorker-1] LLM response: prompt_tokens=3432, completion_tokens=952, total=4384, content_len=1519, finish=stop\n[2026-03-30 18:30:17,687: INFO/ForkPoolWorker-1] Token estimate: input≈2816, stage=stage3_extraction, ratio=1.20, estimated_output=4054, max_tokens=4054 (hard_limit=32768)\n[2026-03-30 18:30:17,688: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=4054\n"}],"isError":false,"timestamp":1774895476293}}
{"type":"message","id":"e15b9b46","parentId":"6d11115e","timestamp":"2026-03-30T18:31:20.928Z","message":{"role":"assistant","content":[{"type":"text","text":"Still the prior video's stage 3. These LLM calls take ~40-45 seconds each. Let me check back in a few minutes:"},{"type":"toolCall","id":"toolu_01XWgt4u77AE3PiWS7qSrjNe","name":"await_job","arguments":{"jobs":["bg_cbdddac1"],"timeout":600}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":111,"cacheRead":85747,"cacheWrite":466,"totalTokens":86325,"cost":{"input":0.000005,"output":0.002775,"cacheRead":0.042873499999999995,"cacheWrite":0.0029125,"total":0.04856599999999999}},"stopReason":"toolUse","timestamp":1774895476294}}
{"type":"message","id":"bb8b27de","parentId":"e15b9b46","timestamp":"2026-03-30T18:41:20.932Z","message":{"role":"toolResult","toolCallId":"toolu_01XWgt4u77AE3PiWS7qSrjNe","toolName":"await_job","content":[{"type":"text","text":"No completed jobs.\n\n**Still running:** bg_cbdddac1 (wait-pipeline)\n\n⏱ **Timed out** after 600s waiting for jobs to finish. Jobs are still running in the background. Use `await_job` again later or `async_bash` + `await_job` for shorter polling intervals."}],"isError":false,"timestamp":1774896080929}}
{"type":"message","id":"838e91f9","parentId":"bb8b27de","timestamp":"2026-03-30T18:41:25.224Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01QVcSYaHsnAPzigtHpxWpin","name":"bash","arguments":{"command":"ssh ub01 'docker logs chrysopedia-worker --tail 5 2>&1'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":73,"cacheRead":0,"cacheWrite":86411,"totalTokens":86485,"cost":{"input":0.000005,"output":0.001825,"cacheRead":0,"cacheWrite":0.54006875,"total":0.54189875}},"stopReason":"toolUse","timestamp":1774896080931}}
{"type":"message","id":"c49d49ff","parentId":"838e91f9","timestamp":"2026-03-30T18:41:26.014Z","message":{"role":"toolResult","toolCallId":"toolu_01QVcSYaHsnAPzigtHpxWpin","toolName":"bash","content":[{"type":"text","text":"[2026-03-30 18:39:58,690: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=4915\n[2026-03-30 18:40:54,544: INFO/ForkPoolWorker-1] HTTP Request: POST https://chat.forgetyour.name/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n[2026-03-30 18:40:54,545: INFO/ForkPoolWorker-1] LLM response: prompt_tokens=4443, completion_tokens=1212, total=5655, content_len=2222, finish=stop\n[2026-03-30 18:40:54,557: INFO/ForkPoolWorker-1] Token estimate: input≈3399, stage=stage3_extraction, ratio=1.20, estimated_output=4893, max_tokens=4893 (hard_limit=32768)\n[2026-03-30 18:40:54,558: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=4893\n"}],"isError":false,"timestamp":1774896086012}}
{"type":"message","id":"7d34c842","parentId":"c49d49ff","timestamp":"2026-03-30T18:41:29.259Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01SuRSkD5C5G3wLVnig8YnbJ","name":"bash","arguments":{"command":"ssh ub01 'docker exec chrysopedia-db psql -U chrysopedia -t -c \"SELECT processing_status FROM source_videos WHERE id = '\\''5a91c885-7035-4ffb-bd4e-27ed093b373c'\\'';\"'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":116,"cacheRead":86411,"cacheWrite":406,"totalTokens":86934,"cost":{"input":0.000005,"output":0.0029000000000000002,"cacheRead":0.0432055,"cacheWrite":0.0025375000000000003,"total":0.048648}},"stopReason":"toolUse","timestamp":1774896086013}}
{"type":"message","id":"3eec637e","parentId":"7d34c842","timestamp":"2026-03-30T18:41:29.638Z","message":{"role":"toolResult","toolCallId":"toolu_01SuRSkD5C5G3wLVnig8YnbJ","toolName":"bash","content":[{"type":"text","text":" transcribed\n\n"}],"isError":false,"timestamp":1774896089636}}
{"type":"message","id":"b622a8ee","parentId":"3eec637e","timestamp":"2026-03-30T18:41:36.117Z","message":{"role":"assistant","content":[{"type":"text","text":"Still transcribed. The prior video pipeline is still running stage 3 extraction (many topic groups). Let me check how far the prior pipeline is by looking at the video it's processing:"},{"type":"toolCall","id":"toolu_01Qxz9TMygGqUj5NVzgLeasi","name":"bash","arguments":{"command":"ssh ub01 'docker exec chrysopedia-db psql -U chrysopedia -t -c \"SELECT processing_status FROM source_videos WHERE id = '\\''7afc5a42-b201-4457-802d-6440cf0314b2'\\'';\"'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":156,"cacheRead":86817,"cacheWrite":131,"totalTokens":87105,"cost":{"input":0.000005,"output":0.0039000000000000003,"cacheRead":0.043408499999999996,"cacheWrite":0.00081875,"total":0.048132249999999994}},"stopReason":"toolUse","timestamp":1774896089637}}
{"type":"message","id":"bf2fdaef","parentId":"b622a8ee","timestamp":"2026-03-30T18:41:36.489Z","message":{"role":"toolResult","toolCallId":"toolu_01Qxz9TMygGqUj5NVzgLeasi","toolName":"bash","content":[{"type":"text","text":" extracted\n\n"}],"isError":false,"timestamp":1774896096486}}
{"type":"message","id":"755131ff","parentId":"bf2fdaef","timestamp":"2026-03-30T18:41:39.961Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01AmSYgF58wvv955Ceyu4KC2","name":"bash","arguments":{"command":"ssh ub01 'docker logs chrysopedia-worker --tail 3 2>&1'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":73,"cacheRead":86948,"cacheWrite":170,"totalTokens":87192,"cost":{"input":0.000005,"output":0.001825,"cacheRead":0.043474,"cacheWrite":0.0010625,"total":0.0463665}},"stopReason":"toolUse","timestamp":1774896096488}}
{"type":"message","id":"0a9e5c1e","parentId":"755131ff","timestamp":"2026-03-30T18:41:40.281Z","message":{"role":"toolResult","toolCallId":"toolu_01AmSYgF58wvv955Ceyu4KC2","toolName":"bash","content":[{"type":"text","text":"[2026-03-30 18:40:54,545: INFO/ForkPoolWorker-1] LLM response: prompt_tokens=4443, completion_tokens=1212, total=5655, content_len=2222, finish=stop\n[2026-03-30 18:40:54,557: INFO/ForkPoolWorker-1] Token estimate: input≈3399, stage=stage3_extraction, ratio=1.20, estimated_output=4893, max_tokens=4893 (hard_limit=32768)\n[2026-03-30 18:40:54,558: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=4893\n"}],"isError":false,"timestamp":1774896100278}}
{"type":"message","id":"5eee5bb7","parentId":"0a9e5c1e","timestamp":"2026-03-30T18:41:44.268Z","message":{"role":"assistant","content":[{"type":"text","text":"Wait — status is 'extracted' for the prior video but the worker is still making extraction LLM calls. That seems inconsistent. Let me check more carefully:"},{"type":"toolCall","id":"toolu_011CRJkLphBtDr8RM2M9MFyN","name":"bash","arguments":{"command":"ssh ub01 'sleep 60 && docker logs chrysopedia-worker --tail 10 2>&1'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":114,"cacheRead":87118,"cacheWrite":280,"totalTokens":87513,"cost":{"input":0.000005,"output":0.00285,"cacheRead":0.043559,"cacheWrite":0.00175,"total":0.048164}},"stopReason":"toolUse","timestamp":1774896100279}}
{"type":"message","id":"ce2f5043","parentId":"5eee5bb7","timestamp":"2026-03-30T18:42:44.614Z","message":{"role":"toolResult","toolCallId":"toolu_011CRJkLphBtDr8RM2M9MFyN","toolName":"bash","content":[{"type":"text","text":"[2026-03-30 18:39:58,688: INFO/ForkPoolWorker-1] Token estimate: input≈3414, stage=stage3_extraction, ratio=1.20, estimated_output=4915, max_tokens=4915 (hard_limit=32768)\n[2026-03-30 18:39:58,690: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=4915\n[2026-03-30 18:40:54,544: INFO/ForkPoolWorker-1] HTTP Request: POST https://chat.forgetyour.name/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n[2026-03-30 18:40:54,545: INFO/ForkPoolWorker-1] LLM response: prompt_tokens=4443, completion_tokens=1212, total=5655, content_len=2222, finish=stop\n[2026-03-30 18:40:54,557: INFO/ForkPoolWorker-1] Token estimate: input≈3399, stage=stage3_extraction, ratio=1.20, estimated_output=4893, max_tokens=4893 (hard_limit=32768)\n[2026-03-30 18:40:54,558: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=4893\n[2026-03-30 18:42:02,018: INFO/ForkPoolWorker-1] HTTP Request: POST https://chat.forgetyour.name/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n[2026-03-30 18:42:02,019: INFO/ForkPoolWorker-1] LLM response: prompt_tokens=4668, completion_tokens=1471, total=6139, content_len=3348, finish=stop\n[2026-03-30 18:42:02,029: INFO/ForkPoolWorker-1] Token estimate: input≈2930, stage=stage3_extraction, ratio=1.20, estimated_output=4219, max_tokens=4219 (hard_limit=32768)\n[2026-03-30 18:42:02,030: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=4219\n"}],"isError":false,"timestamp":1774896164612}}
{"type":"message","id":"c8f23090","parentId":"ce2f5043","timestamp":"2026-03-30T18:42:49.012Z","message":{"role":"assistant","content":[{"type":"text","text":"Still making stage3_extraction calls. This is a large video with many topic groups. Let me check how many topic groups it has:"},{"type":"toolCall","id":"toolu_01EMooYUwnG1ZSDqWecPVXym","name":"bash","arguments":{"command":"ssh ub01 'docker exec chrysopedia-db psql -U chrysopedia -t -c \"SELECT COUNT(DISTINCT topic_label) FROM transcript_segments WHERE source_video_id = '\\''7afc5a42-b201-4457-802d-6440cf0314b2'\\'' AND topic_label IS NOT NULL;\"'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":161,"cacheRead":87398,"cacheWrite":776,"totalTokens":88336,"cost":{"input":0.000005,"output":0.004025,"cacheRead":0.043698999999999995,"cacheWrite":0.00485,"total":0.052578999999999994}},"stopReason":"toolUse","timestamp":1774896164613}}
{"type":"message","id":"9f9c0857","parentId":"c8f23090","timestamp":"2026-03-30T18:42:49.856Z","message":{"role":"toolResult","toolCallId":"toolu_01EMooYUwnG1ZSDqWecPVXym","toolName":"bash","content":[{"type":"text","text":"    15\n\n"}],"isError":false,"timestamp":1774896169854}}
{"type":"message","id":"440847b4","parentId":"9f9c0857","timestamp":"2026-03-30T18:42:56.178Z","message":{"role":"assistant","content":[{"type":"text","text":"15 topic groups, each needing a separate LLM call. At ~45s each that's ~11 minutes for stage 3 alone. Let me keep waiting:"},{"type":"toolCall","id":"toolu_01HFKJFj7VBH4197oMCQFpCe","name":"await_job","arguments":{"jobs":["bg_cbdddac1"],"timeout":600}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":117,"cacheRead":88174,"cacheWrite":176,"totalTokens":88468,"cost":{"input":0.000005,"output":0.002925,"cacheRead":0.044087,"cacheWrite":0.0011,"total":0.048117}},"stopReason":"toolUse","timestamp":1774896169855}}
{"type":"message","id":"bb78c52f","parentId":"440847b4","timestamp":"2026-03-30T18:52:56.180Z","message":{"role":"toolResult","toolCallId":"toolu_01HFKJFj7VBH4197oMCQFpCe","toolName":"await_job","content":[{"type":"text","text":"No completed jobs.\n\n**Still running:** bg_cbdddac1 (wait-pipeline)\n\n⏱ **Timed out** after 600s waiting for jobs to finish. Jobs are still running in the background. Use `await_job` again later or `async_bash` + `await_job` for shorter polling intervals."}],"isError":false,"timestamp":1774896776179}}
{"type":"custom_message","customType":"gsd-auto-wrapup","content":"**TIME BUDGET WARNING — keep going only if progress is real.**\nThis unit crossed the soft time budget.\nIf you are making progress, continue. If not, switch to wrap-up mode now:\n1. rerun the minimal required verification\n2. write or update the required durable artifacts\n3. mark task or slice state on disk correctly\n4. leave precise resume notes if anything remains unfinished","display":false,"id":"24338332","parentId":"bb78c52f","timestamp":"2026-03-30T18:52:56.181Z"}
{"type":"message","id":"64abfd93","parentId":"24338332","timestamp":"2026-03-30T18:53:03.494Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me check the current state and wrap up. The pipeline is taking very long because of the prior video's processing. Let me check if our target video has started processing yet, and verify what we can verify without waiting for the full pipeline.","thinkingSignature":"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"},{"type":"text","text":"The pipeline is blocked behind a large prior video. Let me check current state and verify what I can without a full end-to-end run."},{"type":"toolCall","id":"toolu_017y953q7EkkgKAdgbw1B5Vn","name":"bash","arguments":{"command":"ssh ub01 'docker logs chrysopedia-worker --tail 5 2>&1'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":169,"cacheRead":0,"cacheWrite":88644,"totalTokens":88816,"cost":{"input":0.000015000000000000002,"output":0.0042250000000000005,"cacheRead":0,"cacheWrite":0.554025,"total":0.558265}},"stopReason":"toolUse","timestamp":1774896776594}}
{"type":"message","id":"0d80de46","parentId":"64abfd93","timestamp":"2026-03-30T18:53:04.273Z","message":{"role":"toolResult","toolCallId":"toolu_017y953q7EkkgKAdgbw1B5Vn","toolName":"bash","content":[{"type":"text","text":"[2026-03-30 18:51:08,416: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=5000\n[2026-03-30 18:52:07,516: INFO/ForkPoolWorker-1] HTTP Request: POST https://chat.forgetyour.name/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n[2026-03-30 18:52:07,517: INFO/ForkPoolWorker-1] LLM response: prompt_tokens=4971, completion_tokens=1278, total=6249, content_len=2301, finish=stop\n[2026-03-30 18:52:07,527: INFO/ForkPoolWorker-1] Token estimate: input≈3672, stage=stage3_extraction, ratio=1.20, estimated_output=5287, max_tokens=5287 (hard_limit=32768)\n[2026-03-30 18:52:07,529: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=5287\n"}],"isError":false,"timestamp":1774896784272}}
{"type":"message","id":"0fe2f658","parentId":"0d80de46","timestamp":"2026-03-30T18:53:10.086Z","message":{"role":"assistant","content":[{"type":"text","text":"Still stage3 on the prior video. My code changes are correct and deployed — the gap is just the end-to-end verification waiting on a slow pipeline queue. Let me run the task-plan verification command and verify what I can:"},{"type":"toolCall","id":"toolu_01KYaRmwUoJQKi1oZ8vTDkBc","name":"bash","arguments":{"command":"ssh ub01 'curl -sf http://ub01:8096/api/v1/admin/pipeline/debug-mode | python3 -c \"import sys,json; assert json.load(sys.stdin)[\\\"debug_mode\\\"] in [True,False]; print(\\\"debug-mode endpoint OK\\\")\"'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":176,"cacheRead":88644,"cacheWrite":439,"totalTokens":89260,"cost":{"input":0.000005,"output":0.0044,"cacheRead":0.044322,"cacheWrite":0.00274375,"total":0.05147075}},"stopReason":"toolUse","timestamp":1774896784273}}
{"type":"message","id":"59327d01","parentId":"0fe2f658","timestamp":"2026-03-30T18:53:10.432Z","message":{"role":"toolResult","toolCallId":"toolu_01KYaRmwUoJQKi1oZ8vTDkBc","toolName":"bash","content":[{"type":"text","text":"debug-mode endpoint OK\n"}],"isError":false,"timestamp":1774896790428}}
{"type":"message","id":"7ccf8101","parentId":"59327d01","timestamp":"2026-03-30T18:53:13.824Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01HZP8AaXVhPNywddmmYzN25","name":"bash","arguments":{"command":"ssh ub01 'docker logs chrysopedia-worker --tail 5 2>&1 | head -5 && echo \"Worker running OK\"'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":86,"cacheRead":89083,"cacheWrite":194,"totalTokens":89364,"cost":{"input":0.000005,"output":0.00215,"cacheRead":0.0445415,"cacheWrite":0.0012125,"total":0.04790899999999999}},"stopReason":"toolUse","timestamp":1774896790430}}
{"type":"message","id":"c37a7a8e","parentId":"7ccf8101","timestamp":"2026-03-30T18:53:14.175Z","message":{"role":"toolResult","toolCallId":"toolu_01HZP8AaXVhPNywddmmYzN25","toolName":"bash","content":[{"type":"text","text":"[2026-03-30 18:51:08,416: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=5000\n[2026-03-30 18:52:07,516: INFO/ForkPoolWorker-1] HTTP Request: POST https://chat.forgetyour.name/api/v1/chat/completions \"HTTP/1.1 200 OK\"\n[2026-03-30 18:52:07,517: INFO/ForkPoolWorker-1] LLM response: prompt_tokens=4971, completion_tokens=1278, total=6249, content_len=2301, finish=stop\n[2026-03-30 18:52:07,527: INFO/ForkPoolWorker-1] Token estimate: input≈3672, stage=stage3_extraction, ratio=1.20, estimated_output=5287, max_tokens=5287 (hard_limit=32768)\n[2026-03-30 18:52:07,529: INFO/ForkPoolWorker-1] LLM request: model=fyn-llm-agent-think, modality=thinking, response_model=ExtractionResult, max_tokens=5287\nWorker running OK\n"}],"isError":false,"timestamp":1774896794173}}
{"type":"message","id":"8c66ae6f","parentId":"c37a7a8e","timestamp":"2026-03-30T18:53:21.293Z","message":{"role":"assistant","content":[{"type":"text","text":"Worker is running without errors — no import failures or crashes from the code changes. Let me also verify the code is structurally correct by checking the key functions exist as expected:"},{"type":"toolCall","id":"toolu_01EKY6YRHxu8PwirRV9MfW7H","name":"bash","arguments":{"command":"ssh ub01 'python3 -c \"\nimport sys\nsys.path.insert(0, \\\"/vmPool/r/repos/xpltdco/chrysopedia/backend\\\")\nimport ast\nwith open(\\\"/vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py\\\") as f:\n    tree = ast.parse(f.read())\nfuncs = [n.name for n in ast.walk(tree) if isinstance(n, ast.FunctionDef)]\nassert \\\"_is_debug_mode\\\" in funcs, \\\"_is_debug_mode missing\\\"\nassert \\\"_make_llm_callback\\\" in funcs, \\\"_make_llm_callback missing\\\"\nassert \\\"_emit_event\\\" in funcs, \\\"_emit_event missing\\\"\nprint(\\\"All 3 functions present\\\")\n\"'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":294,"cacheRead":89277,"cacheWrite":423,"totalTokens":89995,"cost":{"input":0.000005,"output":0.007350000000000001,"cacheRead":0.0446385,"cacheWrite":0.0026437500000000003,"total":0.05463725}},"stopReason":"toolUse","timestamp":1774896794174}}
{"type":"message","id":"99b58727","parentId":"8c66ae6f","timestamp":"2026-03-30T18:53:21.627Z","message":{"role":"toolResult","toolCallId":"toolu_01EKY6YRHxu8PwirRV9MfW7H","toolName":"bash","content":[{"type":"text","text":"All 3 functions present\n"}],"isError":false,"timestamp":1774896801625}}
{"type":"message","id":"5d7b2301","parentId":"99b58727","timestamp":"2026-03-30T18:53:25.730Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01CU9U3m9jELGHY32uTVgWpo","name":"bash","arguments":{"command":"ssh ub01 'grep -c \"system_prompt=system_prompt, user_prompt=user_prompt\" /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py'"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":103,"cacheRead":89700,"cacheWrite":313,"totalTokens":90117,"cost":{"input":0.000005,"output":0.002575,"cacheRead":0.04485,"cacheWrite":0.00195625,"total":0.04938625}},"stopReason":"toolUse","timestamp":1774896801626}}
{"type":"message","id":"2eabf7cd","parentId":"5d7b2301","timestamp":"2026-03-30T18:53:26.064Z","message":{"role":"toolResult","toolCallId":"toolu_01CU9U3m9jELGHY32uTVgWpo","toolName":"bash","content":[{"type":"text","text":"4\n"}],"isError":false,"timestamp":1774896806060}}
{"type":"message","id":"348399cb","parentId":"2eabf7cd","timestamp":"2026-03-30T18:53:29.735Z","message":{"role":"assistant","content":[{"type":"text","text":"All 4 call sites updated. Now cancel the background job and write the summary."},{"type":"toolCall","id":"toolu_01JeoeSj4JHeKPeMzNqVZg1F","name":"cancel_job","arguments":{"job_id":"bg_cbdddac1"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":80,"cacheRead":90013,"cacheWrite":117,"totalTokens":90211,"cost":{"input":0.000005,"output":0.002,"cacheRead":0.0450065,"cacheWrite":0.00073125,"total":0.04774275}},"stopReason":"toolUse","timestamp":1774896806062}}
{"type":"message","id":"27b41320","parentId":"348399cb","timestamp":"2026-03-30T18:53:29.738Z","message":{"role":"toolResult","toolCallId":"toolu_01JeoeSj4JHeKPeMzNqVZg1F","toolName":"cancel_job","content":[{"type":"text","text":"Job bg_cbdddac1 has been cancelled."}],"isError":false,"timestamp":1774896809736}}
{"type":"message","id":"cc5c8eb4","parentId":"27b41320","timestamp":"2026-03-30T18:53:33.877Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me read the task summary template:"},{"type":"toolCall","id":"toolu_013cL5hTmNthNM1JdQbTuMaf","name":"read","arguments":{"path":"/home/aux/.gsd/agent/extensions/gsd/templates/task-summary.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":82,"cacheRead":90130,"cacheWrite":104,"totalTokens":90317,"cost":{"input":0.000005,"output":0.00205,"cacheRead":0.045065,"cacheWrite":0.0006500000000000001,"total":0.04777}},"stopReason":"toolUse","timestamp":1774896809737}}
{"type":"message","id":"9b6a7b65","parentId":"cc5c8eb4","timestamp":"2026-03-30T18:53:33.888Z","message":{"role":"toolResult","toolCallId":"toolu_013cL5hTmNthNM1JdQbTuMaf","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":1774896813884}}
