{"type":"thinking_level_change","id":"0033bf58","parentId":null,"timestamp":"2026-03-30T18:54:50.672Z","thinkingLevel":"medium"}
{"type":"model_change","id":"39f6ef5a","parentId":"0033bf58","timestamp":"2026-03-30T18:54:50.674Z","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\n## UNIT: Complete Slice S01 (\"Pipeline Debug Mode — Full LLM I/O Capture and Token Accounting\") — Milestone M007\n\n## Your Role in the Pipeline\n\nExecutor agents built each task and wrote task summaries. You are the closer — verify the assembled work actually delivers the slice goal, then compress everything into a slice summary. After you finish, a **reassess-roadmap agent** reads your slice summary to decide if the remaining roadmap still makes sense. The slice summary is also the primary record of what this slice achieved — future slice researchers and planners read it as a dependency summary when their work builds on yours.\n\nWrite the summary for those downstream readers. What did this slice actually deliver? What patterns did it establish? What should the next slice know?\n\nAll relevant context has been preloaded below — the slice plan, all task summaries, and the milestone roadmap are inlined. Start working immediately without re-reading these files.\n\n## Inlined Context (preloaded — do not re-read these files)\n\n### Milestone Roadmap\nSource: `.gsd/milestones/M007/M007-ROADMAP.md`\n\n# M007: \n\n## Vision\nMake the pipeline fully transparent — every LLM call's full prompt and response viewable, copyable, exportable, with per-stage token accounting. Automate transcript ingestion via folder monitoring instead of manual POST. Tighten the admin UX: prune dead weight, improve workflow clarity. Fix frontend overflow and mobile responsiveness bugs. Set the foundation for iterating on content quality through better visibility into what the LLM is actually doing.\n\n## Slice Overview\n| ID | Slice | Risk | Depends | Done | After this |\n|----|-------|------|---------|------|------------|\n| S01 | Pipeline Debug Mode — Full LLM I/O Capture and Token Accounting | medium | — | ⬜ | Toggle debug mode on for a video, trigger pipeline, see full prompt + response stored for every LLM call with per-stage token breakdown |\n| S02 | Debug Payload Viewer — Inline View, Copy, and Export in Admin UI | low | S01 | ⬜ | Admin clicks an LLM call event, sees full prompt and response in a readable viewer, copies to clipboard, or downloads as JSON |\n| S03 | Transcript Folder Watcher — Auto-Ingest Service | medium | — | ⬜ | Drop a transcript JSON into the watch folder on ub01 → file is detected, validated, POSTed to /ingest, pipeline triggers automatically |\n| S04 | Admin UX Audit — Prune, Streamline, and Polish | low | S02 | ⬜ | Admin pipeline page is cleaner and more efficient for daily content management. Dead UI elements removed. Workflow improvements visible. |\n| S05 | Key Moment Card Text Overflow Fix | low | — | ⬜ | Key moment cards with long filenames and timestamp ranges display cleanly — text truncated with ellipsis or wrapped within card bounds, no horizontal bleed |\n| S06 | Mobile Viewport Overflow Fix — Technique Pages and Global Content | low | S05 | ⬜ | Technique page on Galaxy S25 Ultra renders cleanly — body text wraps, tags don't overflow, metadata stays within viewport, no horizontal scroll |\n\n---\n\n### Slice Plan\nSource: `.gsd/milestones/M007/slices/S01/S01-PLAN.md`\n\n# S01: Pipeline Debug Mode — Full LLM I/O Capture and Token Accounting\n\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## Tasks\n- [x] **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** — Set up all infrastructure for debug mode: Alembic migration adding 3 TEXT columns to pipeline_events, PipelineEvent model update, debug mode GET/PUT endpoints (Redis-backed, following review_mode pattern from D007), token summary aggregation endpoint, and extended event listing response.\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` columns (all `Text, nullable=True`) to `PipelineEvent` model in `backend/models.py` after the existing `payload` column.\n\n2. Create Alembic migration `alembic/versions/006_debug_columns.py` that adds the 3 TEXT columns to `pipeline_events`. Use `op.add_column()` with `nullable=True` — no data rewrite needed.\n\n3. Add `debug_mode: bool = False` to `Settings` class in `backend/config.py`.\n\n4. Add Pydantic schemas in `backend/schemas.py`:\n   - `DebugModeResponse(BaseModel)` with `debug_mode: bool`\n   - `DebugModeUpdate(BaseModel)` with `debug_mode: bool`\n   - `TokenStageSummary(BaseModel)` with `stage: str`, `call_count: int`, `total_prompt_tokens: int`, `total_completion_tokens: int`, `total_tokens: int`\n   - `TokenSummaryResponse(BaseModel)` with `video_id: str`, `stages: list[TokenStageSummary]`, `grand_total_tokens: int`\n\n5. Add 3 new endpoints in `backend/routers/pipeline.py`:\n   - `GET /admin/pipeline/debug-mode` — read from Redis key `chrysopedia:debug_mode`, fall back to `settings.debug_mode`. Follow exact pattern from review router's `get_mode()`.\n   - `PUT /admin/pipeline/debug-mode` — write to Redis. Follow exact pattern from review router's `set_mode()`.\n   - `GET /admin/pipeline/token-summary/{video_id}` — query `pipeline_events` grouped by stage where `event_type='llm_call'`, sum token columns, return `TokenSummaryResponse`.\n\n6. Extend the existing `list_pipeline_events` response dict to include `system_prompt_text`, `user_prompt_text`, `response_text` fields (nullable).\n\n7. Run migration: `docker exec chrysopedia-api alembic upgrade head`. Rebuild API container if needed: `docker compose build chrysopedia-api && docker compose up -d chrysopedia-api`.\n\n8. Verify: curl debug-mode GET returns `{\"debug_mode\": false}`, PUT sets to true, GET reads back true. Curl token-summary for any video_id returns valid (possibly empty) response.\n\n## Must-Haves\n\n- [ ] 3 TEXT columns added to pipeline_events table via migration\n- [ ] PipelineEvent model has matching mapped columns\n- [ ] Debug mode GET/PUT endpoints work with Redis (fall back to config)\n- [ ] Token summary endpoint returns per-stage aggregation\n- [ ] Event listing includes new fields\n\n## Failure Modes\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| Redis (debug mode read) | Fall back to config.debug_mode default | Same as error — use default | N/A (simple GET/SET of string) |\n| PostgreSQL (token summary) | Return 500 with error detail | Standard DB timeout handling | N/A (aggregation query) |\n  - Estimate: 1.5h\n  - Files: backend/models.py, backend/config.py, backend/schemas.py, backend/routers/pipeline.py, alembic/versions/006_debug_columns.py\n  - Verify: ssh ub01 'docker exec chrysopedia-api alembic upgrade head' && curl -sf http://ub01:8096/admin/pipeline/debug-mode | grep debug_mode\n- [x] **T02: Wired debug-mode-aware LLM I/O capture into pipeline callback — _emit_event accepts prompt/response text, _is_debug_mode reads sync Redis, _make_llm_callback conditionally captures full I/O, all 4 stage call sites updated** — Modify 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  - Estimate: 1.5h\n  - Files: backend/pipeline/stages.py\n  - Verify: ssh 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\n---\n\n### Requirements\nSource: `.gsd/REQUIREMENTS.md`\n\n# Requirements\n\n## R001 — Whisper Transcription Pipeline\n**Status:** validated\n**Description:** Desktop Python script that accepts video files (MP4/MKV), extracts audio via ffmpeg, runs Whisper large-v3 on RTX 4090, and outputs timestamped transcript JSON with segment-level timestamps and word-level timing. Must be resumable.\n**Validation:** Script processes a sample video and produces valid JSON with timestamped segments.\n**Primary Owner:** M001/S01\n\n## R002 — Transcript Ingestion API\n**Status:** validated\n**Description:** FastAPI endpoint that accepts transcript JSON uploads, creates/updates Creator and Source Video records, and stores transcript data in PostgreSQL. Handles new creator detection from folder names.\n**Validation:** POST transcript JSON → 200 OK, records created in DB, file stored on filesystem.\n**Primary Owner:** M001/S02\n\n## R003 — LLM-Powered Extraction Pipeline (Stages 2-5)\n**Status:** validated\n**Description:** Background worker pipeline: transcript segmentation → key moment extraction → classification/tagging → technique page synthesis. Uses OpenAI-compatible API with primary (DGX Sparks Qwen) and fallback (local Ollama) endpoints. Pipeline must be resumable per-video per-stage.\n**Validation:** End-to-end: transcript JSON in → technique pages with key moments, tags, and cross-references out.\n**Primary Owner:** M001/S03\n\n## R004 — Review Queue UI\n**Status:** validated\n**Description:** Admin interface for reviewing extracted key moments: approve, edit+approve, split, merge, reject. Organized by source video for contextual review. Includes mode toggle (review vs auto-publish).\n**Validation:** Admin can review, edit, and approve/reject moments; mode toggle controls whether new moments require review.\n**Primary Owner:** M001/S04\n\n## R005 — Search-First Web UI\n**Status:** validated\n**Description:** Landing page with prominent search bar, live typeahead (results after 2-3 chars), scope toggle (All/Topics/Creators), and two navigation cards (Topics, Creators). Recently added section. Search powered by Qdrant semantic search with keyword fallback.\n**Validation:** User types query → results appear within 500ms, grouped by type, with clickable navigation.\n**Primary Owner:** M001/S05\n\n## R006 — Technique Page Display\n**Status:** validated\n**Description:** Core content unit: header (tags, title, creator, meta), study guide prose (organized by sub-aspects with signal chain blocks and quotes), key moments index (timestamped list), related techniques, plugins referenced. Amber banner for livestream-sourced content.\n**Validation:** Technique page renders with all sections populated from synthesized data.\n**Primary Owner:** M001/S05\n\n## R007 — Creators Browse Page\n**Status:** validated\n**Description:** Filterable creator list with genre filter pills, type-to-narrow, sort options (randomized default, alphabetical, view count). Each row: name, genre tags, technique count, video count, view count. Links to creator detail page.\n**Validation:** Page loads with randomized order, genre filtering works, clicking row navigates to creator detail.\n**Primary Owner:** M001/S05\n\n## R008 — Topics Browse Page\n**Status:** validated\n**Description:** Two-level topic hierarchy (6 top-level categories → sub-topics). Filter input, genre filter pills. Each sub-topic shows technique count and creator count. Clicking sub-topic shows technique pages.\n**Validation:** Hierarchy renders, filtering works, sub-topic links show correct technique pages.\n**Primary Owner:** M001/S05\n\n## R009 — Qdrant Vector Search Integration\n**Status:** validated\n**Description:** Embed key moment summaries, technique page content, and transcript segments in Qdrant using configurable embedding model (nomic-embed-text default). Power semantic search with metadata filtering.\n**Validation:** Semantic search returns relevant results for natural language queries; embeddings update when content changes.\n**Primary Owner:** M001/S03\n\n## R010 — Docker Compose Deployment\n**Status:** validated\n**Description:** Single docker-compose.yml packaging API, web UI, PostgreSQL, and worker services. Follows XPLTD conventions: bind mounts at /vmPool/r/services/, compose at /vmPool/r/compose/chrysopedia/, xpltd_chrysopedia project name, dedicated Docker network.\n**Validation:** `docker compose up -d` brings up all services; data persists across restarts.\n**Primary Owner:** M001/S01\n\n## R011 — Canonical Tag System\n**Status:** validated\n**Description:** Editable canonical tag list (config file) with aliases. Pipeline references tags during classification. New tags can be proposed by LLM and queued for admin approval or auto-added within existing categories.\n**Validation:** Tag list is editable; pipeline uses canonical tags consistently; alias normalization works.\n**Primary Owner:** M001/S03\n\n## R012 — Incremental Content Addition\n**Status:** validated\n**Description:** System handles ongoing content: new videos processed through pipeline, new creators auto-detected, existing technique pages updated when new moments are added for same creator+topic.\n**Validation:** Adding a new video for an existing creator updates their technique pages; new creator folder creates new Creator record.\n**Primary Owner:** M001/S03\n\n## R013 — Prompt Template System\n**Status:** validated\n**Description:** Extraction prompts (stages 2-5) stored as editable configuration files, not hardcoded. Admin can edit prompts and re-run extraction on specific or all videos for calibration.\n**Validation:** Prompt files are editable; re-processing a video with updated prompts produces different output.\n**Primary Owner:** M001/S03\n\n## R014 — Creator Equity\n**Status:** validated\n**Description:** No creator is privileged in the UI. Default sort on Creators page is randomized on every page load. All creators get equal visual weight.\n**Validation:** Refreshing Creators page shows different order each time; no creator gets larger/bolder display.\n**Primary Owner:** M001/S05\n\n## R015 — 30-Second Retrieval Target\n**Status:** active\n**Description:** A producer mid-session can find a specific technique in under 30 seconds from Alt+Tab to reading the key insight.\n**Validation:** Timed test: Alt+Tab → search → read technique → under 30 seconds.\n**Primary Owner:** M001/S05\n\n---\n\n[...truncated 26 sections]\n\n**Match effort to complexity.** A simple slice with 1-2 tasks needs a brief summary and lightweight verification. A complex slice with 5 tasks across multiple subsystems needs thorough verification and a detailed summary. Scale the work below accordingly.\n\nThen:\n1. Use the **Slice Summary** and **UAT** output templates from the inlined context above\n2. If a `GSD Skill Preferences` block is present in system context, use it and the `<available_skills>` catalog in your system prompt to decide which skills to load and follow for this unit, without relaxing required verification or artifact rules.\n3. Run all slice-level verification checks defined in the slice plan. All must pass before marking the slice done. If any fail, fix them first.\n4. If the slice plan includes observability/diagnostic surfaces, confirm they work. Skip this for simple slices that don't have observability sections.\n5. If the slice involved runtime behavior, fill the **Operational Readiness** section (Q8) in the slice summary: health signal, failure signal, recovery procedure, and monitoring gaps. Omit entirely for simple slices with no runtime concerns.\n6. If this slice produced evidence that a requirement changed status (Active → Validated, Active → Deferred, etc.), call `gsd_save_decision` with scope=\"requirement\", decision=\"{requirement-id}\", choice=\"{new-status}\", rationale=\"{evidence}\". Do NOT write `.gsd/REQUIREMENTS.md` directly — the engine renders it from the database.\n7. Write `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M007/slices/S01/S01-SUMMARY.md` (compress all task summaries).\n8. Write `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M007/slices/S01/S01-UAT.md` — a concrete UAT script with real test cases derived from the slice plan and task summaries. Include preconditions, numbered steps with expected outcomes, and edge cases. This must NOT be a placeholder or generic template — tailor every test case to what this slice actually built.\n9. Review task summaries for `key_decisions`. Append any significant decisions to `.gsd/DECISIONS.md` if missing.\n10. Review task summaries for patterns, gotchas, or non-obvious lessons learned. If any would save future agents from repeating investigation or hitting the same issues, append them to `.gsd/KNOWLEDGE.md`. Only add entries that are genuinely useful — don't pad with obvious observations.\n11. Call `gsd_complete_slice` with milestone_id, slice_id, the slice summary, and the UAT result. Do NOT manually mark the roadmap checkbox — the tool writes to the DB and renders the ROADMAP.md projection automatically.\n12. Do not run git commands — the system commits your changes and handles any merge after this unit succeeds.\n13. Update `.gsd/PROJECT.md` if it exists — refresh current state if needed.\n\n**You MUST call `gsd_complete_slice` with the slice summary and UAT content before finishing. The tool persists to both DB and disk and renders `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M007/slices/S01/S01-SUMMARY.md` and `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M007/slices/S01/S01-UAT.md` automatically.**\n\nWhen done, say: \"Slice S01 complete.\"","display":false,"id":"1deb42e0","parentId":"39f6ef5a","timestamp":"2026-03-30T18:54:50.677Z"}
{"type":"message","id":"ad9ba04f","parentId":"1deb42e0","timestamp":"2026-03-30T18:54:55.371Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me start by reading the task summaries to understand what was actually delivered, then verify the slice-level checks.","thinkingSignature":"ErUCClkIDBgCKkAOSNExZstueKw8ZqPE/zZ16TlFYDjPoDzx65959T1LMFUjYZcJ3MLR67RNDk1aGgtE6bftoP5lY/4ycdqmGC0OMg9jbGF1ZGUtb3B1cy00LTY4ABIMTOaok5MEfTN/IY9LGgwFgu15ieAJV8g9QOEiMEgMzA8BNHInp4iZxJ63mSD8YpuPN+AXB+5Z/F+7rvhCgqB2eIP1XNjHZw4b/GpX5CqJAUwDqncBo2JDyOjMOqY3voI9tdaOWz1ujFaWtoT9OXYs6yX0WJW/yVlBT4TYLv8Ju3e+3v8jXzZAmLZdpKGFHNRlk//HCvg6pjcNR+RukSy7EmuG5oipAENXfgAWIelSduhQEQt/k+jNIyc3mwh3r/ujg4INA92pIVz2T3S7Wxk6rJOgOWhKOXS7GAE="},{"type":"text","text":"Let me read the task summaries and verify the delivered work."},{"type":"toolCall","id":"toolu_01XMBaKVS2PDKJHtRJMneX9g","name":"bash","arguments":{"command":"ls .gsd/milestones/M007/slices/S01/tasks/"}},{"type":"toolCall","id":"toolu_01KR1ZBad4M1ar8FgwHpwf4X","name":"read","arguments":{"path":".gsd/milestones/M007/slices/S01/tasks/T01-SUMMARY.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":184,"cacheRead":58112,"cacheWrite":6117,"totalTokens":64416,"cost":{"input":0.000015000000000000002,"output":0.0046,"cacheRead":0.029056,"cacheWrite":0.03823125,"total":0.07190225}},"stopReason":"toolUse","timestamp":1774896890675}}
{"type":"message","id":"b98366f6","parentId":"ad9ba04f","timestamp":"2026-03-30T18:54:55.382Z","message":{"role":"toolResult","toolCallId":"toolu_01XMBaKVS2PDKJHtRJMneX9g","toolName":"bash","content":[{"type":"text","text":"T01-PLAN.md\nT01-SUMMARY.md\nT01-VERIFY.json\nT02-PLAN.md\nT02-SUMMARY.md\nT02-VERIFY.json\n"}],"isError":false,"timestamp":1774896895382}}
{"type":"message","id":"29cc1ed4","parentId":"b98366f6","timestamp":"2026-03-30T18:54:55.386Z","message":{"role":"toolResult","toolCallId":"toolu_01KR1ZBad4M1ar8FgwHpwf4X","toolName":"read","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---\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\nAdded three nullable TEXT columns to PipelineEvent for full LLM I/O capture. Created Alembic migration 006. Added debug_mode config setting. Built three new endpoints: debug-mode GET/PUT (Redis-backed with config fallback, following review_mode pattern) and token-summary aggregation. Extended event listing to include new fields. All work deployed and verified on ub01.\n\n## Verification\n\nMigration 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.\n\n## Verification Evidence\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| 1 | `ssh ub01 'docker exec chrysopedia-api alembic upgrade head'` | 0 | ✅ pass | 3000ms |\n| 2 | `curl -sf http://ub01:8096/api/v1/admin/pipeline/debug-mode` | 0 | ✅ pass | 500ms |\n| 3 | `curl -sf -X PUT debug-mode with {debug_mode:true}` | 0 | ✅ pass | 500ms |\n| 4 | `curl -sf http://ub01:8096/api/v1/admin/pipeline/token-summary/{video_id}` | 0 | ✅ pass | 500ms |\n| 5 | `psql \\d pipeline_events | grep system_prompt` | 0 | ✅ pass | 500ms |\n\n\n## Deviations\n\nNone.\n\n## Known Issues\n\nNone.\n\n## Files Created/Modified\n\n- `backend/models.py`\n- `backend/config.py`\n- `backend/schemas.py`\n- `backend/routers/pipeline.py`\n- `alembic/versions/006_debug_columns.py`\n\n\n## Deviations\nNone.\n\n## Known Issues\nNone.\n"}],"isError":false,"timestamp":1774896895385}}
{"type":"message","id":"0be1493f","parentId":"29cc1ed4","timestamp":"2026-03-30T18:54:58.221Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01RY1d9zAy45CT4XFbCkPWAS","name":"read","arguments":{"path":".gsd/milestones/M007/slices/S01/tasks/T02-SUMMARY.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":76,"cacheRead":64229,"cacheWrite":1315,"totalTokens":65621,"cost":{"input":0.000005,"output":0.0019,"cacheRead":0.0321145,"cacheWrite":0.00821875,"total":0.04223824999999999}},"stopReason":"toolUse","timestamp":1774896895386}}
{"type":"message","id":"6a5e6dda","parentId":"0be1493f","timestamp":"2026-03-30T18:54:58.226Z","message":{"role":"toolResult","toolCallId":"toolu_01RY1d9zAy45CT4XFbCkPWAS","toolName":"read","content":[{"type":"text","text":"---\nid: T02\nparent: S01\nmilestone: M007\nprovides: []\nrequires: []\naffects: []\nkey_files: [\"backend/pipeline/stages.py\"]\nkey_decisions: [\"Debug mode checked once at callback creation time to avoid Redis round-trip per LLM callback\", \"Full response_text stored without truncation when debug on; existing content_preview in payload kept for non-debug use\"]\npatterns_established: []\ndrill_down_paths: []\nobservability_surfaces: []\nduration: \"\"\nverification_result: \"Syntax check passed (ast.parse). All 3 functions present. Exactly 4 call sites updated (grep count=4). Container rebuild succeeded. Worker running without import errors. Debug mode endpoint responds correctly. Events endpoint returns new nullable fields. End-to-end with populated debug fields not yet verified — test pipeline queued behind prior video.\"\ncompleted_at: 2026-03-30T18:54:43.467Z\nblocker_discovered: false\n---\n\n# T02: Wired debug-mode-aware LLM I/O capture into pipeline callback — _emit_event accepts prompt/response text, _is_debug_mode reads sync Redis, _make_llm_callback conditionally captures full I/O, all 4 stage call sites updated\n\n> Wired debug-mode-aware LLM I/O capture into pipeline callback — _emit_event accepts prompt/response text, _is_debug_mode reads sync Redis, _make_llm_callback conditionally captures full I/O, all 4 stage call sites updated\n\n## What Happened\n---\nid: T02\nparent: S01\nmilestone: M007\nkey_files:\n  - backend/pipeline/stages.py\nkey_decisions:\n  - Debug mode checked once at callback creation time to avoid Redis round-trip per LLM callback\n  - Full response_text stored without truncation when debug on; existing content_preview in payload kept for non-debug use\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-03-30T18:54:43.467Z\nblocker_discovered: false\n---\n\n# T02: Wired debug-mode-aware LLM I/O capture into pipeline callback — _emit_event accepts prompt/response text, _is_debug_mode reads sync Redis, _make_llm_callback conditionally captures full I/O, all 4 stage call sites updated\n\n**Wired debug-mode-aware LLM I/O capture into pipeline callback — _emit_event accepts prompt/response text, _is_debug_mode reads sync Redis, _make_llm_callback conditionally captures full I/O, all 4 stage call sites updated**\n\n## What Happened\n\nModified backend/pipeline/stages.py with: (1) _emit_event extended with system_prompt_text, user_prompt_text, response_text kwargs passed to PipelineEvent constructor; (2) _is_debug_mode() sync Redis helper that reads chrysopedia:debug_mode key with silent fallback to config; (3) _make_llm_callback now accepts system_prompt/user_prompt params and conditionally captures full I/O when debug mode is on; (4) all 4 stage call sites (stages 2-5) updated to pass prompt text through. Rebuilt and restarted containers on ub01. End-to-end verification partially complete — code deployed, worker running without errors, debug mode toggle works, events endpoint returns new fields. Full end-to-end with populated debug fields pending behind a long-running queued pipeline.\n\n## Verification\n\nSyntax check passed (ast.parse). All 3 functions present. Exactly 4 call sites updated (grep count=4). Container rebuild succeeded. Worker running without import errors. Debug mode endpoint responds correctly. Events endpoint returns new nullable fields. End-to-end with populated debug fields not yet verified — test pipeline queued behind prior video.\n\n## Verification Evidence\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| 1 | `python3 -c 'import ast; ast.parse(open(stages.py).read())'` | 0 | ✅ pass | 1000ms |\n| 2 | `grep -c 'system_prompt=system_prompt, user_prompt=user_prompt' stages.py (expected 4)` | 0 | ✅ pass | 1000ms |\n| 3 | `docker compose build chrysopedia-api chrysopedia-worker` | 0 | ✅ pass | 3000ms |\n| 4 | `curl debug-mode endpoint + json assert` | 0 | ✅ pass | 1000ms |\n| 5 | `docker logs chrysopedia-worker --tail 5 (no errors)` | 0 | ✅ pass | 1000ms |\n\n\n## Deviations\n\nEndpoint paths need /api/v1/ prefix (task plan omitted). Docker compose web service name is chrysopedia-web not chrysopedia-web-8096.\n\n## Known Issues\n\nEnd-to-end verification pending — test pipeline for video 5a91c885-7035-4ffb-bd4e-27ed093b373c triggered with debug mode on but queued behind prior pipeline. Once complete, verify events have non-null prompt/response fields.\n\n## Files Created/Modified\n\n- `backend/pipeline/stages.py`\n\n\n## Deviations\nEndpoint paths need /api/v1/ prefix (task plan omitted). Docker compose web service name is chrysopedia-web not chrysopedia-web-8096.\n\n## Known Issues\nEnd-to-end verification pending — test pipeline for video 5a91c885-7035-4ffb-bd4e-27ed093b373c triggered with debug mode on but queued behind prior pipeline. Once complete, verify events have non-null prompt/response fields.\n"}],"isError":false,"timestamp":1774896898223}}
{"type":"message","id":"e88df7bd","parentId":"6a5e6dda","timestamp":"2026-03-30T18:55:07.847Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"T02 has a pending end-to-end verification. Let me check if that pipeline has completed and verify the debug fields are populated. Let me also verify the key infrastructure pieces are working.","thinkingSignature":"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"},{"type":"text","text":"T02 noted end-to-end verification was pending — a pipeline run was queued. Let me verify the deployed state now."},{"type":"toolCall","id":"toolu_01JNdmeSRdhtekF2mpbhJp4n","name":"bash","arguments":{"command":"ssh ub01 'curl -sf http://ub01:8096/api/v1/admin/pipeline/debug-mode' 2>&1","timeout":15}},{"type":"toolCall","id":"toolu_01K6wzV1cY79F162hgHb2746","name":"bash","arguments":{"command":"ssh ub01 'curl -sf \"http://ub01:8096/api/v1/admin/pipeline/token-summary/5a91c885-7035-4ffb-bd4e-27ed093b373c\"' 2>&1","timeout":15}},{"type":"toolCall","id":"toolu_011PKPZmEtJU6FTigBA7uRaF","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&limit=2\" | python3 -c \"\nimport sys, json\ndata = json.load(sys.stdin)\nevents = data if isinstance(data, list) else data.get(\\\"events\\\", [])\nfor e in events[:2]:\n    print(f\\\"stage={e.get(\\\"stage\\\")}, has_system_prompt={e.get(\\\"system_prompt_text\\\") is not None}, has_user_prompt={e.get(\\\"user_prompt_text\\\") is not None}, has_response={e.get(\\\"response_text\\\") is not None}\\\")\nif not events:\n    print(\\\"NO LLM_CALL EVENTS FOUND\\\")\n\"' 2>&1","timeout":15}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":580,"cacheRead":65544,"cacheWrite":1468,"totalTokens":67593,"cost":{"input":0.000005,"output":0.0145,"cacheRead":0.032771999999999996,"cacheWrite":0.009175,"total":0.056452}},"stopReason":"toolUse","timestamp":1774896898224}}
{"type":"message","id":"64e17cf2","parentId":"e88df7bd","timestamp":"2026-03-30T18:55:08.745Z","message":{"role":"toolResult","toolCallId":"toolu_01JNdmeSRdhtekF2mpbhJp4n","toolName":"bash","content":[{"type":"text","text":"{\"debug_mode\":true}"}],"isError":false,"timestamp":1774896908745}}
{"type":"message","id":"7233b4f3","parentId":"64e17cf2","timestamp":"2026-03-30T18:55:08.746Z","message":{"role":"toolResult","toolCallId":"toolu_01K6wzV1cY79F162hgHb2746","toolName":"bash","content":[{"type":"text","text":"{\"video_id\":\"5a91c885-7035-4ffb-bd4e-27ed093b373c\",\"stages\":[{\"stage\":\"stage2_segmentation\",\"call_count\":1,\"total_prompt_tokens\":3635,\"total_completion_tokens\":183,\"total_tokens\":3818}],\"grand_total_tokens\":3818}"}],"isError":false,"timestamp":1774896908745}}
{"type":"message","id":"8436fe3e","parentId":"7233b4f3","timestamp":"2026-03-30T18:55:08.748Z","message":{"role":"toolResult","toolCallId":"toolu_011PKPZmEtJU6FTigBA7uRaF","toolName":"bash","content":[{"type":"text","text":"NO LLM_CALL EVENTS FOUND\n"}],"isError":false,"timestamp":1774896908745}}
{"type":"message","id":"c759a622","parentId":"8436fe3e","timestamp":"2026-03-30T18:55:16.398Z","message":{"role":"assistant","content":[{"type":"text","text":"Token summary works and returns real data. The events endpoint returned no llm_call events — let me check the event structure more carefully."},{"type":"toolCall","id":"toolu_01GGv6ptxNcGYnVv5nUifMEK","name":"bash","arguments":{"command":"ssh ub01 'curl -sf \"http://ub01:8096/api/v1/admin/pipeline/events/5a91c885-7035-4ffb-bd4e-27ed093b373c?limit=5\" | python3 -c \"\nimport sys, json\ndata = json.load(sys.stdin)\nevents = data if isinstance(data, list) else data.get(\\\"events\\\", [])\nfor e in events[:5]:\n    print(f\\\"event_type={e.get(\\\"event_type\\\")}, stage={e.get(\\\"stage\\\")}, has_sys={e.get(\\\"system_prompt_text\\\") is not None}, has_resp={e.get(\\\"response_text\\\") is not None}\\\")\nprint(f\\\"total events: {len(events)}\\\")\n\"' 2>&1","timeout":15}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":301,"cacheRead":67012,"cacheWrite":709,"totalTokens":68023,"cost":{"input":0.000005,"output":0.0075250000000000004,"cacheRead":0.033506,"cacheWrite":0.00443125,"total":0.04546725}},"stopReason":"toolUse","timestamp":1774896908746}}
{"type":"message","id":"10aeee19","parentId":"c759a622","timestamp":"2026-03-30T18:55:16.729Z","message":{"role":"toolResult","toolCallId":"toolu_01GGv6ptxNcGYnVv5nUifMEK","toolName":"bash","content":[{"type":"text","text":"total events: 0\n"}],"isError":false,"timestamp":1774896916727}}
{"type":"message","id":"63dd9e65","parentId":"10aeee19","timestamp":"2026-03-30T18:55:24.829Z","message":{"role":"assistant","content":[{"type":"text","text":"No events for that video. Let me check what videos have events."},{"type":"toolCall","id":"toolu_01STe8nKMj88eyrpRGe6dSii","name":"bash","arguments":{"command":"ssh ub01 'docker exec chrysopedia-api python3 -c \"\nfrom sqlalchemy import create_engine, text\nfrom config import get_settings\ns = get_settings()\ne = create_engine(s.database_url.replace(\\\"+asyncpg\\\", \\\"\\\"))\nwith e.connect() as c:\n    # Get video_ids with events\n    rows = c.execute(text(\\\"SELECT DISTINCT video_id, event_type, count(*) FROM pipeline_events GROUP BY video_id, event_type ORDER BY video_id LIMIT 20\\\")).fetchall()\n    for r in rows:\n        print(f\\\"video={r[0]}, type={r[1]}, count={r[2]}\\\")\n    # Check for any events with populated debug fields\n    debug_rows = c.execute(text(\\\"SELECT video_id, stage, system_prompt_text IS NOT NULL as has_sys, response_text IS NOT NULL as has_resp FROM pipeline_events WHERE system_prompt_text IS NOT NULL LIMIT 5\\\")).fetchall()\n    print(f\\\"\\\\nEvents with debug data: {len(debug_rows)}\\\")\n    for r in debug_rows:\n        print(f\\\"  video={r[0]}, stage={r[1]}, has_sys={r[2]}, has_resp={r[3]}\\\")\n\"' 2>&1","timeout":15}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":409,"cacheRead":67721,"cacheWrite":319,"totalTokens":68450,"cost":{"input":0.000005,"output":0.010225,"cacheRead":0.0338605,"cacheWrite":0.0019937500000000003,"total":0.04608425000000001}},"stopReason":"toolUse","timestamp":1774896916727}}
{"type":"message","id":"4bd4230d","parentId":"63dd9e65","timestamp":"2026-03-30T18:55:25.542Z","message":{"role":"toolResult","toolCallId":"toolu_01STe8nKMj88eyrpRGe6dSii","toolName":"bash","content":[{"type":"text","text":"video=021002d0-449c-4ae5-b0df-bbd3af3be85a, type=complete, count=1\nvideo=021002d0-449c-4ae5-b0df-bbd3af3be85a, type=llm_call, count=1\nvideo=021002d0-449c-4ae5-b0df-bbd3af3be85a, type=start, count=1\nvideo=119bfcac-1c30-42ec-a09b-ebf48fa34e33, type=complete, count=4\nvideo=119bfcac-1c30-42ec-a09b-ebf48fa34e33, type=llm_call, count=9\nvideo=119bfcac-1c30-42ec-a09b-ebf48fa34e33, type=start, count=4\nvideo=230d748f-b11b-45f3-9e26-6698c76bd48f, type=complete, count=1\nvideo=230d748f-b11b-45f3-9e26-6698c76bd48f, type=llm_call, count=3\nvideo=230d748f-b11b-45f3-9e26-6698c76bd48f, type=start, count=2\nvideo=2e86a985-cbde-4ae1-9347-e46ce1fc025a, type=complete, count=4\nvideo=2e86a985-cbde-4ae1-9347-e46ce1fc025a, type=llm_call, count=15\nvideo=2e86a985-cbde-4ae1-9347-e46ce1fc025a, type=start, count=4\nvideo=3195c90e-0d81-46b6-a523-6a119134a497, type=complete, count=1\nvideo=3195c90e-0d81-46b6-a523-6a119134a497, type=llm_call, count=8\nvideo=3195c90e-0d81-46b6-a523-6a119134a497, type=start, count=2\nvideo=515b7881-6c5c-44df-84cc-9bf11aeab9a0, type=complete, count=1\nvideo=515b7881-6c5c-44df-84cc-9bf11aeab9a0, type=llm_call, count=1\nvideo=515b7881-6c5c-44df-84cc-9bf11aeab9a0, type=start, count=1\nvideo=5a91c885-7035-4ffb-bd4e-27ed093b373c, type=complete, count=1\nvideo=5a91c885-7035-4ffb-bd4e-27ed093b373c, type=llm_call, count=1\n\nEvents with debug data: 5\n  video=7afc5a42-b201-4457-802d-6440cf0314b2, stage=stage3_extraction, has_sys=True, has_resp=True\n  video=7afc5a42-b201-4457-802d-6440cf0314b2, stage=stage3_extraction, has_sys=True, has_resp=True\n  video=7afc5a42-b201-4457-802d-6440cf0314b2, stage=stage3_extraction, has_sys=True, has_resp=True\n  video=7afc5a42-b201-4457-802d-6440cf0314b2, stage=stage3_extraction, has_sys=True, has_resp=True\n  video=7afc5a42-b201-4457-802d-6440cf0314b2, stage=stage3_extraction, has_sys=True, has_resp=True\n"}],"isError":false,"timestamp":1774896925539}}
{"type":"message","id":"f78095c7","parentId":"4bd4230d","timestamp":"2026-03-30T18:55:33.587Z","message":{"role":"assistant","content":[{"type":"text","text":"Debug data is populated. Video `7afc5a42...` has events with `system_prompt_text` and `response_text` filled. The pipeline ran with debug mode on and captured LLM I/O. Let me verify the events endpoint works for that video and confirm the token summary too."},{"type":"toolCall","id":"toolu_012sLU1x5QZX6C2CjaAfkhnR","name":"bash","arguments":{"command":"ssh ub01 'curl -sf \"http://ub01:8096/api/v1/admin/pipeline/events/7afc5a42-b201-4457-802d-6440cf0314b2?event_type=llm_call&limit=2\" | python3 -c \"\nimport sys, json\ndata = json.load(sys.stdin)\nevents = data if isinstance(data, list) else data.get(\\\"events\\\", [])\nfor e in events[:2]:\n    sp = e.get(\\\"system_prompt_text\\\", \\\"\\\")\n    up = e.get(\\\"user_prompt_text\\\", \\\"\\\")\n    rt = e.get(\\\"response_text\\\", \\\"\\\")\n    print(f\\\"stage={e.get(\\\"stage\\\")}\\\")\n    print(f\\\"  system_prompt: {len(sp) if sp else 0} chars, starts: {sp[:80] if sp else \\\"NULL\\\"}...\\\")\n    print(f\\\"  user_prompt: {len(up) if up else 0} chars\\\")\n    print(f\\\"  response: {len(rt) if rt else 0} chars, starts: {rt[:80] if rt else \\\"NULL\\\"}...\\\")\n\"' 2>&1","timeout":15}},{"type":"toolCall","id":"toolu_0137MxCCUocZUx5aczr7mhYf","name":"bash","arguments":{"command":"ssh ub01 'curl -sf \"http://ub01:8096/api/v1/admin/pipeline/token-summary/7afc5a42-b201-4457-802d-6440cf0314b2\"' 2>&1","timeout":15}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":552,"cacheRead":68040,"cacheWrite":1375,"totalTokens":69968,"cost":{"input":0.000005,"output":0.013800000000000002,"cacheRead":0.03402,"cacheWrite":0.00859375,"total":0.056418750000000004}},"stopReason":"toolUse","timestamp":1774896925540}}
{"type":"message","id":"2641a376","parentId":"f78095c7","timestamp":"2026-03-30T18:55:33.955Z","message":{"role":"toolResult","toolCallId":"toolu_012sLU1x5QZX6C2CjaAfkhnR","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774896933955}}
{"type":"message","id":"83810204","parentId":"2641a376","timestamp":"2026-03-30T18:55:33.957Z","message":{"role":"toolResult","toolCallId":"toolu_0137MxCCUocZUx5aczr7mhYf","toolName":"bash","content":[{"type":"text","text":"{\"video_id\":\"7afc5a42-b201-4457-802d-6440cf0314b2\",\"stages\":[{\"stage\":\"stage2_segmentation\",\"call_count\":1,\"total_prompt_tokens\":15694,\"total_completion_tokens\":1233,\"total_tokens\":16927},{\"stage\":\"stage3_extraction\",\"call_count\":15,\"total_prompt_tokens\":53562,\"total_completion_tokens\":13100,\"total_tokens\":66662}],\"grand_total_tokens\":83589}"}],"isError":false,"timestamp":1774896933955}}
{"type":"message","id":"a02fcd78","parentId":"83810204","timestamp":"2026-03-30T18:55:40.588Z","message":{"role":"assistant","content":[{"type":"text","text":"Token summary works perfectly — stage2 (1 call, 16K tokens) and stage3 (15 calls, 66K tokens), grand total 83K. The events endpoint returned empty for the API — let me check the routing."},{"type":"toolCall","id":"toolu_01RAUnW5TibfE113ZRuRfBD9","name":"bash","arguments":{"command":"ssh ub01 'curl -sf \"http://ub01:8096/api/v1/admin/pipeline/events/7afc5a42-b201-4457-802d-6440cf0314b2?limit=2\" | python3 -c \"\nimport sys, json\ndata = json.load(sys.stdin)\nprint(type(data).__name__, list(data.keys()) if isinstance(data, dict) else len(data))\nevents = data.get(\\\"events\\\", data) if isinstance(data, dict) else data\nprint(f\\\"events count: {len(events)}\\\")\nif events:\n    e = events[0]\n    print(f\\\"keys: {list(e.keys())}\\\")\n    print(f\\\"system_prompt_text present: {\\\"system_prompt_text\\\" in e}\\\")\n\"' 2>&1","timeout":15}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":328,"cacheRead":69415,"cacheWrite":736,"totalTokens":70480,"cost":{"input":0.000005,"output":0.0082,"cacheRead":0.034707499999999995,"cacheWrite":0.0046,"total":0.04751249999999999}},"stopReason":"toolUse","timestamp":1774896933956}}
{"type":"message","id":"5892f694","parentId":"a02fcd78","timestamp":"2026-03-30T18:55:40.993Z","message":{"role":"toolResult","toolCallId":"toolu_01RAUnW5TibfE113ZRuRfBD9","toolName":"bash","content":[{"type":"text","text":"dict ['items', 'total', 'offset', 'limit']\nevents count: 4\nTraceback (most recent call last):\n  File \"<string>\", line 8, in <module>\nKeyError: 0\n\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1774896940991}}
{"type":"message","id":"940efefc","parentId":"5892f694","timestamp":"2026-03-30T18:55:46.312Z","message":{"role":"assistant","content":[{"type":"text","text":"The response is paginated with `items` key. Let me fix the script."},{"type":"toolCall","id":"toolu_01AEspgbnnntBNk8ezRtQJMo","name":"bash","arguments":{"command":"ssh ub01 'curl -sf \"http://ub01:8096/api/v1/admin/pipeline/events/7afc5a42-b201-4457-802d-6440cf0314b2?limit=2\" | python3 -c \"\nimport sys, json\ndata = json.load(sys.stdin)\nevents = data.get(\\\"items\\\", [])\nfor e in events[:2]:\n    sp = e.get(\\\"system_prompt_text\\\") or \\\"\\\"\n    up = e.get(\\\"user_prompt_text\\\") or \\\"\\\"\n    rt = e.get(\\\"response_text\\\") or \\\"\\\"\n    print(f\\\"stage={e.get(\\\"stage\\\")}, type={e.get(\\\"event_type\\\")}\\\")\n    print(f\\\"  system_prompt: {len(sp)} chars\\\")\n    print(f\\\"  user_prompt: {len(up)} chars\\\")\n    print(f\\\"  response: {len(rt)} chars\\\")\n\"' 2>&1","timeout":15}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":336,"cacheRead":70151,"cacheWrite":409,"totalTokens":70897,"cost":{"input":0.000005,"output":0.008400000000000001,"cacheRead":0.035075499999999996,"cacheWrite":0.00255625,"total":0.04603675}},"stopReason":"toolUse","timestamp":1774896940992}}
{"type":"message","id":"fbaa26cb","parentId":"940efefc","timestamp":"2026-03-30T18:55:46.641Z","message":{"role":"toolResult","toolCallId":"toolu_01AEspgbnnntBNk8ezRtQJMo","toolName":"bash","content":[{"type":"text","text":"stage=stage3_extraction, type=complete\n  system_prompt: 0 chars\n  user_prompt: 0 chars\n  response: 0 chars\nstage=stage3_extraction, type=llm_call\n  system_prompt: 7752 chars\n  user_prompt: 971 chars\n  response: 15 chars\n"}],"isError":false,"timestamp":1774896946640}}
{"type":"message","id":"0b2de780","parentId":"fbaa26cb","timestamp":"2026-03-30T18:55:53.540Z","message":{"role":"assistant","content":[{"type":"text","text":"All three fields working. The `llm_call` event has 7752 chars of system prompt, 971 chars of user prompt, and 15 chars of response text. The `complete` events correctly have empty debug fields. Let me also verify the debug mode toggle round-trip."},{"type":"toolCall","id":"toolu_01Cyr8S8RYBeygi9EpMkHLT3","name":"bash","arguments":{"command":"ssh ub01 '\n# Verify toggle: set to false, read back, set to true, read back\ncurl -sf -X PUT http://ub01:8096/api/v1/admin/pipeline/debug-mode -H \"Content-Type: application/json\" -d \"{\\\"debug_mode\\\":false}\" && echo \"\"\ncurl -sf http://ub01:8096/api/v1/admin/pipeline/debug-mode && echo \"\"\ncurl -sf -X PUT http://ub01:8096/api/v1/admin/pipeline/debug-mode -H \"Content-Type: application/json\" -d \"{\\\"debug_mode\\\":true}\" && echo \"\"\ncurl -sf http://ub01:8096/api/v1/admin/pipeline/debug-mode\n' 2>&1","timeout":15}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":320,"cacheRead":70560,"cacheWrite":430,"totalTokens":71311,"cost":{"input":0.000005,"output":0.008,"cacheRead":0.03528,"cacheWrite":0.0026875000000000002,"total":0.0459725}},"stopReason":"toolUse","timestamp":1774896946640}}
{"type":"message","id":"875e861f","parentId":"0b2de780","timestamp":"2026-03-30T18:55:53.906Z","message":{"role":"toolResult","toolCallId":"toolu_01Cyr8S8RYBeygi9EpMkHLT3","toolName":"bash","content":[{"type":"text","text":"{\"debug_mode\":false}\n{\"debug_mode\":false}\n{\"debug_mode\":true}\n{\"debug_mode\":true}"}],"isError":false,"timestamp":1774896953905}}
{"type":"message","id":"976ec0e5","parentId":"875e861f","timestamp":"2026-03-30T18:56:01.216Z","message":{"role":"assistant","content":[{"type":"text","text":"All slice-level verification passes:\n\n1. ✅ 3 TEXT columns added to pipeline_events via migration 006\n2. ✅ PipelineEvent model has matching columns\n3. ✅ Debug mode GET/PUT endpoints work with Redis toggle\n4. ✅ Token summary returns per-stage aggregation (verified with real data: 83K tokens across 16 calls)\n5. ✅ Event listing includes new fields (system_prompt_text, user_prompt_text, response_text populated when debug on)\n6. ✅ End-to-end: debug on → pipeline ran → events have full prompt/response text\n\nNow let me check for decisions that need recording, then complete the slice."},{"type":"toolCall","id":"toolu_01VMJ4AdFiHmYdFQAPbf5kqS","name":"read","arguments":{"limit":30,"path":".gsd/DECISIONS.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":242,"cacheRead":70990,"cacheWrite":362,"totalTokens":71595,"cost":{"input":0.000005,"output":0.006050000000000001,"cacheRead":0.035495,"cacheWrite":0.0022625,"total":0.0438125}},"stopReason":"toolUse","timestamp":1774896953906}}
{"type":"message","id":"75380f3d","parentId":"976ec0e5","timestamp":"2026-03-30T18:56:01.224Z","message":{"role":"toolResult","toolCallId":"toolu_01VMJ4AdFiHmYdFQAPbf5kqS","toolName":"read","content":[{"type":"text","text":"# Decisions Register\n\n<!-- Append-only. Never edit or remove existing rows.\n     To reverse a decision, add a new row that supersedes it.\n     Read this file at the start of any planning or research phase. -->\n\n| # | When | Scope | Decision | Choice | Rationale | Revisable? | Made By |\n|---|------|-------|----------|--------|-----------|------------|---------|\n| D001 |  | architecture | Storage layer selection | PostgreSQL for structured data, Qdrant (existing instance at 10.0.0.10) for vector embeddings, local filesystem for transcript JSON files | Spec specifies PostgreSQL for JSONB support and Qdrant already running on hypervisor. File store on local filesystem organized by creator slug. Qdrant gets a dedicated collection, not a new instance. | Yes | collaborative |\n| D002 |  | architecture | Timestamp handling for asyncpg with TIMESTAMP WITHOUT TIME ZONE columns | Use datetime.now(timezone.utc).replace(tzinfo=None) in _now() helper | asyncpg rejects timezone-aware datetimes for TIMESTAMP WITHOUT TIME ZONE columns. Stripping tzinfo at the application layer preserves UTC semantics while staying compatible with the existing schema. | Yes | agent |\n| D003 |  | requirement | R002 Transcript Ingestion API status | validated | 6 passing integration tests prove the full POST /api/v1/ingest flow: creator auto-detection, SourceVideo upsert, TranscriptSegment bulk insert, raw JSON persistence, idempotent re-upload, and invalid input rejection. | Yes | agent |\n| D004 |  | architecture | Sync vs async approach for Celery worker pipeline tasks | Use sync openai.OpenAI, sync QdrantClient, and sync SQLAlchemy (create_engine with psycopg2) inside Celery tasks. Convert DATABASE_URL from postgresql+asyncpg:// to postgresql:// for the sync engine. | Celery workers run in a synchronous context. Using asyncio.run() inside tasks risks nested event loop errors with gevent/eventlet workers. Using sync clients throughout eliminates this class of bug entirely. The async engine/session from database.py is only used by FastAPI (ASGI); the worker gets its own sync engine. | Yes | agent |\n| D005 |  | architecture | Embedding/Qdrant failure handling strategy in pipeline | Embedding/Qdrant failures (stage 6) log errors but do not fail the pipeline. Processing_status is set by stages 2-5 only. Embeddings can be regenerated by manual re-trigger. | Qdrant is at 10.0.0.10 on the hypervisor network and may not be reachable during all pipeline runs. Making embedding a non-blocking side-effect ensures core pipeline output (KeyMoments, TechniquePages in PostgreSQL) is never lost due to vector store issues. The manual re-trigger endpoint allows regenerating embeddings at any time. | Yes | agent |\n| D006 |  | requirement | R013 Prompt Template System status | validated | 4 prompt template files in prompts/ directory loaded from configurable settings.prompts_path. Templates use XML-style content fencing. Pipeline stages read templates from disk at runtime, enabling edits without code changes. Manual re-trigger endpoint (POST /api/v1/pipeline/trigger/{video_id}) allows re-processing after prompt edits. | Yes | agent |\n| D007 | M001/S04 | architecture | Runtime review mode toggle persistence mechanism | Store review mode toggle in Redis key `chrysopedia:review_mode` with async redis client. Fall back to `settings.review_mode` config default when key is absent. | The config.py `review_mode` setting is loaded via lru_cache from environment variables and cannot be mutated at runtime. Redis is already used by the project (Celery broker, stage 4 classification data) so it adds no new infrastructure. A system_settings DB table would work but Redis is simpler for a single boolean toggle on a single-admin tool. The pipeline's stages.py reads settings.review_mode from config — the admin toggle only affects new pipeline runs if stages.py is updated to check Redis too, but that's deferred since the toggle is primarily a UI-level concept for the review queue. | Yes | agent |\n| D008 | M001/S04 | requirement | R004 Review Queue UI status | validated | All R004 capabilities delivered and verified: 9 API endpoints (approve, reject, edit, split, merge, queue list, stats, mode get/set) with 24 passing integration tests covering happy paths and error boundaries. React+TypeScript frontend with queue page (filter tabs, stats, pagination), moment detail page (all review actions with modals), and review-vs-auto mode toggle. Frontend builds with zero TypeScript errors. | Yes | agent |\n| D009 | M001/S05 | architecture | Async search service pattern for FastAPI request path | Create a separate `SearchService` class using `openai.AsyncOpenAI` and `qdrant_client.AsyncQdrantClient` for the search endpoint. Keep existing sync `EmbeddingClient` and `QdrantManager` for Celery pipeline. Search endpoint has 300ms timeout on embedding API and falls back to SQL ILIKE keyword search on Qdrant/embedding failure. | The existing EmbeddingClient and QdrantManager are sync (using `openai.OpenAI` and `QdrantClient`) because Celery tasks run synchronously. FastAPI request handlers are async — reusing sync clients would block the event loop. Creating a thin async wrapper avoids modifying the battle-tested pipeline code while providing non-blocking search. The 300ms timeout and keyword fallback ensure the search endpoint always returns results, even when Qdrant or the embedding service is degraded. | Yes | agent |\n| D010 | M001/S05 | requirement | R005 Search-First Web UI status | validated | Search endpoint (GET /api/v1/search) with async embedding + Qdrant + keyword fallback implemented and tested. Frontend Home.tsx has prominent search bar with 300ms debounced typeahead, scope toggle via URL params, nav cards for Topics/Creators, Recently Added section. SearchResults.tsx displays grouped results. 5 integration tests verify search happy path, empty query, keyword fallback, scope filter, and no-results. Frontend production build succeeds with zero TypeScript errors. | Yes | agent |\n| D011 | M001/S05 | requirement | R006 Technique Page Display status | validated | TechniquePage.tsx renders all specified sections: header with topic_category badge and topic_tags pills, creator name linked to creator detail, source_quality indicator, amber banner for unstructured content, body_sections JSONB prose (handles both string and object values), key moments index ordered by start_time, signal chains, plugins pill list, and related techniques links. Backend GET /api/v1/techniques/{slug} returns full detail with eager-loaded key_moments and related links. 404 for unknown slug tested. | Yes | agent |\n| D012 | M001/S05 | requirement | R007 Creators Browse Page status | validated | CreatorsBrowse.tsx implements genre filter pills, type-to-narrow name filter, sort toggle (Random default/A-Z/Views). Each creator row shows name, genre tags, technique_count, video_count. Links to CreatorDetail page. Backend GET /api/v1/creators supports sort=random\\|alpha\\|views and genre filter. Integration tests verify random sort, alpha sort, genre filter, detail endpoint, 404, and counts. | Yes | agent |\n| D013 | M001/S05 | requirement | R008 Topics Browse Page status | validated | TopicsBrowse.tsx renders two-level topic hierarchy (6 categories from canonical_tags.yaml with expandable sub-topics showing technique_count and creator_count). Filter input narrows categories/sub-topics. Clicking sub-topic navigates to search with scope=topics. Backend GET /api/v1/topics aggregates counts from DB per sub-topic. Integration test verifies topic hierarchy response shape. | Yes | agent |\n| D014 | M001/S05 | requirement | R014 Creator Equity status | validated | CreatorsBrowse.tsx defaults to sort=random. Backend uses func.random() ORDER BY for randomized sort. Integration test verifies random sort returns all creators (order may vary). All creators get equal visual weight in the UI — no featured/highlighted treatment. Equal-weight row layout confirmed in CSS. | Yes | agent |\n| D015 | M002/S01 | architecture | Docker network subnet for Chrysopedia on ub01 | 172.32.0.0/24 — free on ub01, replaces 172.24.0.0/24 which is used by xpltd_docs_default | 172.24.0.0/24 was already allocated to xpltd_docs_default network on ub01. Scanned all existing subnets and 172.32.0.0/24 was the first unoccupied /24 in the 172.x range. | Yes | agent |\n| D016 | M002/S01 | architecture | Embedding service for the pipeline and search — OpenWebUI doesn't serve /v1/embeddings | Add Ollama container (ollama/ollama:latest) to the compose stack running nomic-embed-text on CPU | The FYN OpenWebUI instance at chat.forgetyour.name returns 404/500 on /v1/embeddings. No Ollama instance exists on the network. Ollama provides the standard OpenAI-compatible /v1/embeddings endpoint that both EmbeddingClient and SearchService expect. | Yes | agent |\n| D017 |  | frontend | CSS theming approach for dark mode | 77 semantic CSS custom properties in :root block, all hardcoded colors replaced with var(--*) references | A complete variable-based palette enables theme switching, ensures consistency, and makes future color changes single-point edits. 77 tokens (vs ~30 initially planned) were needed to cover all semantic contexts — badges, buttons, surfaces, text, accents, shadows, and status states. Cyan (#22d3ee) replaces indigo as the accent color throughout. | Yes | agent |\n| D018 | M004/S04 | architecture | Version snapshot failure handling strategy in stage 5 | Best-effort versioning — snapshot INSERT failure logs ERROR but does not block page update (existing content overwrites proceed regardless) | Versioning is a diagnostic/benchmarking feature. Losing a version snapshot is far less damaging than failing the entire page synthesis. Follows the same non-blocking side-effect pattern established in D005 for embedding/Qdrant failures. | Yes | agent |\n| D019 | M005/S02 | frontend-layout | Technique page layout structure | CSS grid 2-column layout: 1fr main + 22rem sticky sidebar, collapsing to single column at 768px. Page max-width widened from 48rem to 64rem. | 22rem sidebar provides enough room for moment cards and plugin lists without cramming. 64rem total width accommodates both columns comfortably on standard desktop displays. Sticky sidebar keeps navigation aids visible while scrolling prose. 768px breakpoint aligns with existing mobile styles in the codebase. | Yes | agent |\n| D020 | M006/S05 | frontend | Topics page card layout visual differentiation approach | 3px colored left border + small colored dot next to category name, using existing badge CSS custom properties per category | Subtler than a full colored header — maintains dark theme cohesion while providing clear visual differentiation between 7 category cards. Reuses existing --color-badge-cat-*-bg/text custom properties, avoiding new color definitions. | Yes | agent |\n"}],"isError":false,"timestamp":1774896961220}}
{"type":"message","id":"a6f710b7","parentId":"75380f3d","timestamp":"2026-03-30T18:56:11.722Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The T01 key decisions are \"Followed exact review_mode Redis pattern for debug_mode toggle\" and \"Used coalesce in token aggregation to handle NULL token columns\". The first is basically D007 extended — not a new decision worth recording. The coalesce for NULL is a technique detail.\n\nT02 decisions: \"Debug mode checked once at callback creation time to avoid Redis round-trip per LLM callback\" and \"Full response_text stored without truncation when debug on\". The first is an interesting architectural choice. Let me record it.\n\nActually, looking more closely — these are implementation details that follow established patterns. The review_mode Redis pattern was already documented in D007. \n\nThese don't warrant new entries in DECISIONS.md, so I'll move on to checking PROJECT.md and KNOWLEDGE.md for any necessary updates.","thinkingSignature":"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"},{"type":"text","text":"The key decisions from T01/T02 follow established patterns (D007 Redis toggle, D004 sync pipeline). The \"debug mode checked once at callback creation time\" optimization and \"coalesce for NULL tokens\" are implementation details, not new architectural decisions. No new entries needed in DECISIONS.md.\n\nLet me check if there are knowledge entries worth adding."},{"type":"toolCall","id":"toolu_01FJiGBD57KkDD2edkWoQrwt","name":"read","arguments":{"path":".gsd/PROJECT.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":339,"cacheRead":71352,"cacheWrite":3006,"totalTokens":74698,"cost":{"input":0.000005,"output":0.008475,"cacheRead":0.035676,"cacheWrite":0.018787500000000002,"total":0.0629435}},"stopReason":"toolUse","timestamp":1774896961222}}
{"type":"message","id":"727c3287","parentId":"a6f710b7","timestamp":"2026-03-30T18:56:11.727Z","message":{"role":"toolResult","toolCallId":"toolu_01FJiGBD57KkDD2edkWoQrwt","toolName":"read","content":[{"type":"text","text":"# Chrysopedia\n\n**Knowledge base for music production techniques**, extracted from video content via an LLM-powered pipeline. Producers can search for specific techniques and find timestamped key moments, signal chains, and synthesized study guides — all derived from creator videos.\n\n## Current State\n\nFive milestones complete plus a sixth refinement milestone. The system is deployed and running on ub01 at `http://ub01:8096`.\n\n### What's Built\n\n- **Transcription pipeline** — Desktop script: video → ffmpeg audio extraction → Whisper large-v3 → timestamped transcript JSON\n- **6-stage LLM extraction pipeline** — Transcript segmentation → key moment extraction → classification/tagging → technique page synthesis → embedding/indexing. Per-stage model routing (chat vs thinking models). Resumable per-video per-stage.\n- **Article versioning** — Pre-overwrite snapshots with pipeline metadata (prompt SHA-256 hashes, model config) for benchmarking extraction quality across prompt iterations.\n- **Review queue UI** — Admin interface for approving/editing/rejecting extracted key moments.\n- **Search-first web UI** — Dark theme with cyan accents. Semantic search (Qdrant), topic/creator browse pages, technique detail pages with signal chain blocks and video source metadata.\n- **Docker Compose deployment** — API, worker, web, PostgreSQL, Redis, Qdrant, Ollama on ub01 with bind mounts at `/vmPool/r/services/chrysopedia_*`.\n- **Prompt template system** — Editable per-stage prompt files with SHA-256 tracking for reproducibility.\n- **Pipeline admin dashboard** — Admin page at `/admin/pipeline` with video list, status badges, retrigger/revoke controls, expandable event log with token usage, collapsible JSON response viewer, and live worker status indicator. Pipeline events instrumented via `PipelineEvent` model (stage, event_type, token counts, JSONB payload).\n- **Technique page 2-column layout** — Prose content (summary + study guide) on left, sidebar (key moments, signal chains, plugins, related techniques) on right at desktop widths. Sticky sidebar. Collapses to single column at ≤768px.\n- **Key moment card redesign** — Cards in sidebar show title prominently on its own line (h3), with source file, timestamp, and content type badge on a clean metadata row below.\n- **Admin navigation dropdown** — Header consolidated: Home, Topics, Creators visible; Admin dropdown reveals Review, Reports, Pipeline. ModeToggle removed from header.\n- **Pipeline head/tail log view** — Pipeline event log shows Head/Tail toggle with configurable event count. Token counts visible per event and per video.\n- **Git commit SHA tracking** — Pipeline captures current git commit SHA. Technique page versions display commit hash in metadata.\n- **Technique page tag polish** — Sidebar reordered (Plugins Referenced at top), creator name prominent, tags use coherent color system.\n- **Topics page redesign** — 7 categories (including Music Theory) with card layout, descriptions, sub-topic counts, colored left borders.\n- **App footer** — Every page shows version, build date, commit SHA (linked to GitHub), and repo link. Build-time constants via Vite define with Docker ARG/ENV passthrough.\n\n### Stack\n\n- **Backend:** FastAPI + Celery + SQLAlchemy (async) + PostgreSQL + Redis + Qdrant\n- **Frontend:** React + TypeScript + Vite\n- **LLM:** OpenAI-compatible API (DGX Sparks Qwen primary, local Ollama fallback)\n- **Deployment:** Docker Compose on ub01, nginx reverse proxy on nuc01\n\n### Milestone History\n\n| ID | Title | Status |\n|----|-------|--------|\n| M001 | Chrysopedia Foundation — Infrastructure, Pipeline Core, and Skeleton UI | ✅ Complete |\n| M002 | Deployment — GitHub, ub01 Docker Stack, and Production Wiring | ✅ Complete |\n| M003 | Domain + DNS + Per-Stage LLM Model Routing | ✅ Complete |\n| M004 | UI Polish, Bug Fixes, Technique Page Redesign, and Article Versioning | ✅ Complete |\n| M005 | Pipeline Dashboard, Technique Page Redesign, Key Moment Cards | ✅ Complete |\n| M006 | Admin Nav, Pipeline Log Views, Commit SHA, Tag Polish, Topics Redesign, Footer | ✅ Complete |\n"}],"isError":false,"timestamp":1774896971725}}
{"type":"message","id":"089ccc75","parentId":"727c3287","timestamp":"2026-03-30T18:57:12.915Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have everything needed. Let me complete the slice."},{"type":"toolCall","id":"toolu_01RXVAEqLSguKNEjbpUtHUyC","name":"gsd_complete_slice","arguments":{"affects":["S02"],"deviations":"T02 noted endpoint paths need /api/v1/ prefix (omitted in plan) and Docker compose web service name is chrysopedia-web not chrysopedia-web-8096. Minor naming discrepancies, no functional impact.","drillDownPaths":[".gsd/milestones/M007/slices/S01/tasks/T01-SUMMARY.md",".gsd/milestones/M007/slices/S01/tasks/T02-SUMMARY.md"],"filesModified":[{"description":"Added system_prompt_text, user_prompt_text, response_text columns to PipelineEvent","path":"backend/models.py"},{"description":"Added debug_mode: bool = False to Settings","path":"backend/config.py"},{"description":"Added DebugModeResponse, DebugModeUpdate, TokenStageSummary, TokenSummaryResponse schemas","path":"backend/schemas.py"},{"description":"Added debug-mode GET/PUT endpoints, token-summary endpoint, extended event listing response","path":"backend/routers/pipeline.py"},{"description":"Migration adding 3 TEXT columns to pipeline_events","path":"alembic/versions/006_debug_columns.py"},{"description":"Added _is_debug_mode(), extended _emit_event and _make_llm_callback for conditional I/O capture, updated 4 stage call sites","path":"backend/pipeline/stages.py"}],"followUps":"S02 builds the admin UI viewer for these debug payloads — inline view, copy, and export.","keyDecisions":["Followed review_mode Redis pattern (D007) for debug_mode toggle — consistent approach across all admin toggles","Debug mode checked once at callback creation time to avoid Redis round-trip per LLM call","Full response_text stored without truncation when debug on; existing content_preview in JSONB payload kept for non-debug use","Used SQL coalesce for NULL token columns in aggregation query"],"keyFiles":["backend/models.py","backend/config.py","backend/schemas.py","backend/routers/pipeline.py","alembic/versions/006_debug_columns.py","backend/pipeline/stages.py"],"knownLimitations":"Debug mode is global (all videos), not per-video. Full prompt/response text stored without truncation — for very long prompts this could grow the pipeline_events table significantly if debug is left on permanently. Intended for diagnostic use, not always-on.","milestoneId":"M007","narrative":"Two tasks delivered the full debug mode feature.\n\nT01 added infrastructure: Alembic migration 006 with 3 nullable TEXT columns (system_prompt_text, user_prompt_text, response_text) on pipeline_events, PipelineEvent model update, debug_mode config setting, Redis-backed GET/PUT toggle endpoints (following the review_mode pattern from D007), and a token-summary aggregation endpoint that groups by stage.\n\nT02 wired the capture into the pipeline: _emit_event extended to accept the 3 text fields, _is_debug_mode() reads sync Redis with config fallback, _make_llm_callback conditionally captures full I/O when debug is on (checked once at callback creation, not per-call), and all 4 stage call sites (stages 2-5) pass system_prompt and user_prompt through. The existing content_preview in the JSONB payload is preserved for non-debug use.\n\nEnd-to-end verified on ub01: debug mode toggled on, pipeline ran for a video, events have populated system_prompt_text (7752 chars), user_prompt_text (971 chars), and response_text. Token summary returns per-stage breakdown (e.g., stage2: 1 call / 16K tokens, stage3: 15 calls / 66K tokens, grand total 83K). When debug is off, fields remain NULL — no storage overhead.","observabilitySurfaces":["GET /api/v1/admin/pipeline/debug-mode — current debug toggle state","GET /api/v1/admin/pipeline/token-summary/{video_id} — per-stage token accounting","pipeline_events.system_prompt_text/user_prompt_text/response_text — full LLM I/O when debug mode was on during pipeline run"],"oneLiner":"Added debug mode toggle (Redis-backed) that captures full LLM system prompt, user prompt, and response text in pipeline_events, plus per-stage token summary endpoint.","patternsEstablished":["Redis-backed admin toggle with config fallback: GET reads Redis key, falls back to settings default; PUT writes to Redis. Pattern now used by review_mode and debug_mode.","Conditional pipeline instrumentation: _make_llm_callback checks mode once at creation, closure captures the decision, no per-call overhead when off."],"provides":["Debug mode toggle API (GET/PUT /api/v1/admin/pipeline/debug-mode)","Full LLM I/O stored in pipeline_events when debug on","Per-stage token summary API (GET /api/v1/admin/pipeline/token-summary/{video_id})","Extended event listing with system_prompt_text, user_prompt_text, response_text fields"],"requirementsAdvanced":[],"requirementsInvalidated":[],"requirementsSurfaced":[],"requirementsValidated":[],"requires":[],"sliceId":"S01","sliceTitle":"Pipeline Debug Mode — Full LLM I/O Capture and Token Accounting","uatContent":"## UAT: Pipeline Debug Mode — Full LLM I/O Capture and Token Accounting\n\n### Preconditions\n- Chrysopedia stack running on ub01 (docker ps shows chrysopedia-api, chrysopedia-worker, chrysopedia-db, chrysopedia-redis healthy)\n- At least one video with completed pipeline events in database\n- SSH access to ub01\n\n### Test 1: Debug Mode Toggle Round-Trip\n\n1. GET debug mode state: `curl -sf http://ub01:8096/api/v1/admin/pipeline/debug-mode`\n   - **Expected:** JSON with `debug_mode` boolean field\n2. PUT debug mode to true: `curl -sf -X PUT http://ub01:8096/api/v1/admin/pipeline/debug-mode -H 'Content-Type: application/json' -d '{\"debug_mode\":true}'`\n   - **Expected:** `{\"debug_mode\": true}`\n3. GET debug mode again\n   - **Expected:** `{\"debug_mode\": true}` — persisted in Redis\n4. PUT debug mode to false\n   - **Expected:** `{\"debug_mode\": false}`\n5. GET debug mode again\n   - **Expected:** `{\"debug_mode\": false}`\n\n### Test 2: Token Summary for Existing Video\n\n1. Pick a video_id with pipeline events (e.g., one shown in admin pipeline page)\n2. GET token summary: `curl -sf http://ub01:8096/api/v1/admin/pipeline/token-summary/{video_id}`\n   - **Expected:** JSON with `video_id`, `stages` array (each with stage name, call_count, total_prompt_tokens, total_completion_tokens, total_tokens), and `grand_total_tokens`\n   - **Expected:** grand_total_tokens equals sum of all stage total_tokens\n\n### Test 3: Token Summary for Non-Existent Video\n\n1. GET token summary with fake ID: `curl -sf http://ub01:8096/api/v1/admin/pipeline/token-summary/00000000-0000-0000-0000-000000000000`\n   - **Expected:** `{\"video_id\": \"00000000-...\", \"stages\": [], \"grand_total_tokens\": 0}` — empty, not 404\n\n### Test 4: End-to-End Debug Capture\n\n1. Enable debug mode: PUT `{\"debug_mode\": true}`\n2. Trigger pipeline for a video: `curl -X POST http://ub01:8096/api/v1/admin/pipeline/trigger/{video_id}`\n3. Wait for pipeline to complete (check events or worker logs)\n4. GET events: `curl -sf http://ub01:8096/api/v1/admin/pipeline/events/{video_id}`\n5. Find an `llm_call` event in the items array\n   - **Expected:** `system_prompt_text` is a non-empty string (the system prompt sent to the LLM)\n   - **Expected:** `user_prompt_text` is a non-empty string (the user prompt with transcript content)\n   - **Expected:** `response_text` is a non-empty string (the LLM's response)\n6. Find a `start` or `complete` event\n   - **Expected:** `system_prompt_text`, `user_prompt_text`, `response_text` are all null (only llm_call events carry debug data)\n\n### Test 5: Debug Off Produces No Capture\n\n1. Disable debug mode: PUT `{\"debug_mode\": false}`\n2. Trigger pipeline for a different video (or re-trigger same one)\n3. Wait for pipeline to complete\n4. GET events for that video, find llm_call events\n   - **Expected:** `system_prompt_text`, `user_prompt_text`, `response_text` are all null\n   - **Expected:** Other event fields (stage, event_type, prompt_tokens, completion_tokens, payload) are still populated normally\n\n### Test 6: Event Listing Includes Debug Fields\n\n1. GET events for any video: `curl -sf http://ub01:8096/api/v1/admin/pipeline/events/{video_id}`\n   - **Expected:** Each event object in `items` array has keys `system_prompt_text`, `user_prompt_text`, `response_text` (may be null)\n   - **Expected:** Response shape is `{items: [...], total: N, offset: 0, limit: N}`\n\n### Edge Cases\n\n- **Redis down during debug mode read:** _is_debug_mode() silently falls back to config.debug_mode (default false). Pipeline continues without capturing I/O.\n- **Debug mode toggled mid-pipeline:** Events before toggle use old state, events after use new state. The check happens once per _make_llm_callback creation (once per stage invocation), not per LLM call within a stage.\n- **Very large prompts:** No truncation — full text stored. Monitor pipeline_events table size if debug is left on for many pipeline runs.","verification":"All slice-level checks passed:\n\n1. **Migration**: `docker exec chrysopedia-api alembic upgrade head` — 006_debug_columns applied, 3 TEXT columns confirmed via psql \\d pipeline_events\n2. **Debug mode toggle**: GET returns {\"debug_mode\":false}, PUT sets to true, GET reads back true, PUT sets to false, GET confirms false — full round-trip\n3. **Token summary**: GET /api/v1/admin/pipeline/token-summary/{video_id} returns per-stage aggregation with call_count, prompt/completion/total tokens, and grand_total_tokens\n4. **Event listing**: GET /api/v1/admin/pipeline/events/{video_id} items include system_prompt_text, user_prompt_text, response_text (populated for llm_call events when debug was on, NULL otherwise)\n5. **End-to-end capture**: Video 7afc5a42 processed with debug on — stage3_extraction events contain 7752 chars of system prompt and 971 chars of user prompt\n6. **Worker health**: chrysopedia-worker running without import errors after rebuild"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":3123,"cacheRead":74358,"cacheWrite":1208,"totalTokens":78690,"cost":{"input":0.000005,"output":0.078075,"cacheRead":0.037179,"cacheWrite":0.00755,"total":0.122809}},"stopReason":"toolUse","timestamp":1774896971726}}
{"type":"message","id":"98fba8c8","parentId":"089ccc75","timestamp":"2026-03-30T18:57:12.951Z","message":{"role":"toolResult","toolCallId":"toolu_01RXVAEqLSguKNEjbpUtHUyC","toolName":"gsd_complete_slice","content":[{"type":"text","text":"Completed slice S01 (M007)"}],"details":{"operation":"complete_slice","sliceId":"S01","milestoneId":"M007","summaryPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M007/slices/S01/S01-SUMMARY.md","uatPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M007/slices/S01/S01-UAT.md"},"isError":false,"timestamp":1774897032949}}
{"type":"message","id":"e7679962","parentId":"98fba8c8","timestamp":"2026-03-30T18:57:17.869Z","message":{"role":"assistant","content":[{"type":"text","text":"Now update PROJECT.md to reflect the new capability."},{"type":"toolCall","id":"toolu_0187vfpGZ3nxN3TAqtC8ncnU","name":"edit","arguments":{"newText":"- **Pipeline debug mode** — Redis-backed toggle captures full LLM system prompt, user prompt, and response text in pipeline_events. Per-stage token summary endpoint for cost/usage analysis.\n- **Git commit SHA tracking** — Pipeline captures current git commit SHA. Technique page versions display commit hash in metadata.","oldText":"- **Git commit SHA tracking** — Pipeline captures current git commit SHA. Technique page versions display commit hash in metadata.","path":".gsd/PROJECT.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":194,"cacheRead":75566,"cacheWrite":3160,"totalTokens":78921,"cost":{"input":0.000005,"output":0.00485,"cacheRead":0.037783,"cacheWrite":0.01975,"total":0.062388}},"stopReason":"toolUse","timestamp":1774897032950}}
{"type":"message","id":"9a24e6d6","parentId":"e7679962","timestamp":"2026-03-30T18:57:17.884Z","message":{"role":"toolResult","toolCallId":"toolu_0187vfpGZ3nxN3TAqtC8ncnU","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in .gsd/PROJECT.md."}],"details":{"diff":" 19 - **Technique page 2-column layout** — Prose content (summary + study guide) on left, sidebar (key moments, signal chains, plugins, related techniques) on right at desktop widths. Sticky sidebar. Collapses to single column at ≤768px.\n 20 - **Key moment card redesign** — Cards in sidebar show title prominently on its own line (h3), with source file, timestamp, and content type badge on a clean metadata row below.\n 21 - **Admin navigation dropdown** — Header consolidated: Home, Topics, Creators visible; Admin dropdown reveals Review, Reports, Pipeline. ModeToggle removed from header.\n 22 - **Pipeline head/tail log view** — Pipeline event log shows Head/Tail toggle with configurable event count. Token counts visible per event and per video.\n+23 - **Pipeline debug mode** — Redis-backed toggle captures full LLM system prompt, user prompt, and response text in pipeline_events. Per-stage token summary endpoint for cost/usage analysis.\n 24 - **Git commit SHA tracking** — Pipeline captures current git commit SHA. Technique page versions display commit hash in metadata.\n 25 - **Technique page tag polish** — Sidebar reordered (Plugins Referenced at top), creator name prominent, tags use coherent color system.\n 26 - **Topics page redesign** — 7 categories (including Music Theory) with card layout, descriptions, sub-topic counts, colored left borders.\n 27 - **App footer** — Every page shows version, build date, commit SHA (linked to GitHub), and repo link. Build-time constants via Vite define with Docker ARG/ENV passthrough.","firstChangedLine":23},"isError":false,"timestamp":1774897037882}}
{"type":"message","id":"0c3a4b8c","parentId":"9a24e6d6","timestamp":"2026-03-30T18:57:20.775Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S01 complete."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":10,"cacheRead":78726,"cacheWrite":217,"totalTokens":78954,"cost":{"input":0.000005,"output":0.00025,"cacheRead":0.039362999999999995,"cacheWrite":0.00135625,"total":0.04097425}},"stopReason":"stop","timestamp":1774897037883}}
