{"type":"thinking_level_change","id":"9d2898aa","parentId":null,"timestamp":"2026-04-03T01:20:30.712Z","thinkingLevel":"medium"}
{"type":"model_change","id":"3c4c748c","parentId":"9d2898aa","timestamp":"2026-04-03T01:20:30.714Z","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\nAll relevant context has been preloaded below — start working immediately without re-reading these files.\n\n## Requirements Advanced\n\nNone.\n\n## Requirements Validated\n\nNone.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## Requirements Advanced\n\n- R012 — Composition prompt and harness provide the offline-testable merge mechanism for updating existing technique pages with new video content\n\n## Requirements Validated\n\nNone.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## Requirements Advanced\n\nNone.\n\n## Requirements Validated\n\nNone.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## UNIT: Research Slice S04 (\"Pipeline Compose-or-Create Logic\") — Milestone M014\n\n## Inlined Context (preloaded — do not re-read these files)\n\n### Milestone Roadmap\nSource: `.gsd/milestones/M014/M014-ROADMAP.md`\n\n# M014: \n\n## Vision\nRestructure technique pages to be broader (per-creator+category across videos), deeper (H2/H3 nesting with TOC), multi-source (compose new video content into existing pages), with inline citations linking prose claims to source moments, admin multi-source management, and section-level search with deep linking.\n\n## Slice Overview\n| ID | Slice | Risk | Depends | Done | After this |\n|----|-------|------|---------|------|------------|\n| S01 | Synthesis Prompt v5 — Nested Sections + Citations | high | — | ✅ | Run test harness with new prompt → output has list-of-objects body_sections with H2/H3 nesting, citation markers on key claims, broader page scope. |\n| S02 | Composition Prompt + Test Harness Compose Mode | high | S01 | ✅ | Run test harness --compose mode with existing page + new moments → merged output with deduplication, new sections, updated citations. |\n| S03 | Data Model + Migration | low | — | ✅ | Alembic migration runs clean. API response includes body_sections_format and source_videos fields. |\n| S04 | Pipeline Compose-or-Create Logic | high | S01, S02, S03 | ⬜ | Process two COPYCATT videos. Second video's moments composed into existing page. technique_page_videos has both video IDs. |\n| S05 | Frontend — Nested Rendering, TOC, Citations | medium | S03 | ⬜ | Format-2 page renders with TOC, nested sections, clickable citations. Format-1 pages unchanged. |\n| S06 | Admin UI — Multi-Source Pipeline Management | medium | S03, S04 | ⬜ | Admin view for multi-source page shows source dropdown, composition history, per-video chunking inspection. |\n| S07 | Search — Per-Section Embeddings + Deep Linking | medium | S04, S05 | ⬜ | Search 'LFO grain position' → section-level result → click → navigates to page#section and scrolls. |\n\n---\n\n### Decisions\nSource: `.gsd/DECISIONS.md`\n\n# Decisions (compact)\nFields: id | when | scope | decision | choice | rationale | revisable | made_by\n\nD024 | M014/S01 | architecture | Content model for sections with subsections | Sections with subsections use empty-string content field; substance lives in subsections | Avoids duplication between section-level content and subsection content. The section heading serves as H2 container; all prose lives in subsection content fields. Sections without subsections use the section content field directly. | Yes | agent\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 (7 top-level categories → sub-topics). Filter input, genre filter pills. Each sub-topic shows technique count and creator count. Clicking sub-topic loads a dedicated page with creator-grouped techniques and breadcrumbs.\n**Validation:** Hierarchy renders, filtering works, sub-topics have dedicated pages at /topics/:category/:subtopic with backend filtering and creator grouping. Verified: M010/S01.\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## R016 — Card Hover Animations & Staggered Entrance\n**Status:** validated\n**Description:** All technique/content cards animate on hover (scale + shadow transition). Card grids use staggered fade-in-up entrance animations on page load.\n**Validation:** Cards visually respond to hover with smooth 200ms transition. Grid cards appear sequentially on page load.\n**Primary Owner:** M011/S01\n\n## R017 — Featured Technique Visual Redesign\n**Status:** validated\n**Description:** Featured Technique section on homepage has a visually distinct treatment (gradient border, glow, or full-bleed) that differentiates it from ordinary cards.\n**Validation:** Featured technique is visually prominent and clearly distinct from regular technique cards.\n**Primary Owner:** M011/S01\n\n## R018 — Random Technique Discovery\n**Status:** validated\n**Description:** A \"Random Technique\" button exists that navigates to a randomly selected technique page.\n**Validation:** Clicking the button loads a different technique each time.\n**Primary Owner:** M011/S01\n\n## R019 — Topics Page Default-Collapsed\n**Status:** validated\n**Description:** Topics page loads with all categories collapsed, showing only category cards. Clicking a category expands its sub-topics with a smooth slide animation.\n**Validation:** Page loads collapsed. Click expands with animation. Click again collapses.\n**Primary Owner:** M011/S02\n\n## R020 — Global Search in Navigation\n**Status:** validated\n**Description:** A compact search input in the navigation bar is available on all pages except the homepage. Cmd+K (or /) keyboard shortcut focuses it from any page.\n**Validation:** Search bar visible in nav on Topics, Creators, Technique, About pages. Cmd+K focuses it.\n**Primary Owner:** M011/S03\n\n## R021 — Mobile Hamburger Menu\n**Status:** validated\n**Description:** Navigation uses a hamburger menu on viewports below 768px with generous touch targets (44×44px minimum).\n**Validation:** Mobile viewport shows hamburger icon. Tapping reveals nav items. Touch targets meet minimum size.\n**Primary Owner:** M011/S03\n\n## R022 — Heading Hierarchy Fix\n**Status:** validated\n**Description:** Every page has exactly one H1 element. Heading levels are sequential (no skips from H1 to H3).\n**Validation:** DOM inspection confirms single H1 per page and sequential heading levels.\n**Primary Owner:** M011/S04\n\n## R023 — Skip-to-Content Link\n**Status:** validated\n**Description:** A visually hidden skip link is the first focusable element on every page, visible on keyboard focus.\n**Validation:** Tab from page load shows \"Skip to content\" link. Clicking it jumps to main content area.\n**Primary Owner:** M011/S04\n\n## R024 — Text Contrast AA Compliance\n**Status:** validated\n**Description:** All text meets WCAG AA contrast ratios (4.5:1 for normal text, 3:1 for large text) against the dark background.\n**Validation:** Secondary text color passes 4.5:1 contrast check against page background.\n**Primary Owner:** M011/S04\n\n## R025 — Page-Specific Document Titles\n**Status:** validated\n**Description:** Each route sets a descriptive document title (e.g., \"Bass — Sound Design — Chrysopedia\", \"COPYCATT — Chrysopedia\").\n**Validation:** Browser tab shows distinct title per page. Navigating between pages updates the title.\n**Primary Owner:** M011/S04\n\n## R026 — Creator Stats Topic-Colored Pills\n**Status:** validated\n**Description:** Creator detail page stats line rendered as topic-colored pill badges instead of run-on text.\n**Validation:** Stats show as visually distinct colored pills grouped by topic category.\n**Primary Owner:** M011/S02\n\n## R027 — Tag Overflow Limit on Cards\n**Status:** validated\n**Description:** Technique cards show a maximum of 4 tags, with a \"+N more\" indicator for additional tags.\n**Validation:** Cards with >4 tags show exactly 4 plus a \"+N more\" badge.\n**Primary Owner:** M011/S02\n\n## R028 — Empty Subtopic Handling\n**Status:** validated\n**Description:** Sub-topics with 0 techniques either hidden by default or display a \"Coming soon\" badge.\n**Validation:** Empty subtopics are visually distinguished or hidden. No dead-end clicks.\n**Primary Owner:** M011/S02\n\n## Denied / Out of Scope\n\n## R029 — Beginner Learning Paths\n**Status:** out-of-scope\n**Description:** Curated beginner learning paths with skill-level tags and \"New to Production?\" section.\n**Validation:** n/a\n**Notes:** Denied — audience knows what they're looking for. Assessment F01.\n\n## R030 — YouTube Deep Links on Key Moments\n**Status:** out-of-scope\n**Description:** Key moment timestamps link to source YouTube videos at the correct time.\n**Validation:** n/a\n**Notes:** Denied — no video URLs in data model / don't want to link out. Assessment F02.\n\n## R031 — Hide Admin Dropdown\n**Status:** out-of-scope\n**Description:** Admin dropdown hidden from public visitors behind auth or flag.\n**Validation:** n/a\n**Notes:** Denied — fine as-is. Assessment F03.\n\n## R032 — CTA Label Changes\n**Status:** out-of-scope\n**Description:** Change \"Start Exploring\" to more descriptive CTA text.\n**Validation:** n/a\n**Notes:** Denied — skipped. Assessment F15.\n\n---\n\n[...truncated 6 sections]\n\n### Dependency Slice Summaries\n\nPay particular attention to **Forward Intelligence** sections — they contain hard-won knowledge about what's fragile, what assumptions changed, and what to watch out for.\n\n#### S01 Summary\nSource: `.gsd/milestones/M014/slices/S01/S01-SUMMARY.md`\n\n---\nid: S01\nparent: M014\nmilestone: M014\nprovides:\n  - BodySection/BodySubSection Pydantic models for downstream rendering (S05) and DB storage (S03)\n  - citation_utils.extract_citations() and validate_citations() for pipeline quality checks\n  - stage5_synthesis.txt v5 prompt with v2 output format for pipeline execution (S04)\n  - test_harness v2 format support for composition testing (S02)\nrequires:\n  []\naffects:\n  - S02 — uses updated test harness and prompt for compose mode testing\n  - S04 — pipeline uses the v2 prompt and parses v2 output via BodySection models\n  - S05 — frontend renders list[BodySection] with TOC and citation links\nkey_files:\n  - backend/pipeline/schemas.py\n  - backend/pipeline/citation_utils.py\n  - backend/pipeline/test_citation_utils.py\n  - backend/pipeline/test_harness.py\n  - backend/pipeline/test_harness_v2_format.py\n  - prompts/stage5_synthesis.txt\nkey_decisions:\n  - D024: Citations use 0-based indices matching moment list position\n  - D024: Sections with subsections use empty-string content; substance lives in subsections\n  - Word count includes section.content + subsection.content; heading text excluded\n  - Citation report uses CITE log tag for grep-friendly structured output\npatterns_established:\n  - body_sections v2 format: list[BodySection] with heading/content/subsections, discriminated by body_sections_format='v2'\n  - Citation validation as reusable utility — validate_citations() returns a dict with coverage_pct, invalid_indices, uncited_moments for any consumer\n  - Prompt backup convention: .bak with timestamp suffix before destructive edits\nobservability_surfaces:\n  - CITE log tag in test harness output for per-page citation coverage reporting\ndrill_down_paths:\n  - .gsd/milestones/M014/slices/S01/tasks/T01-SUMMARY.md\n  - .gsd/milestones/M014/slices/S01/tasks/T02-SUMMARY.md\n  - .gsd/milestones/M014/slices/S01/tasks/T03-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-03T00:55:48.823Z\nblocker_discovered: false\n---\n\n# S01: Synthesis Prompt v5 — Nested Sections + Citations\n\n**Introduced body_sections v2 format (list-of-objects with H2/H3 nesting and inline [N] citation markers), backed by Pydantic schema, citation validation utility, updated synthesis prompt, and v2-aware test harness — all verified with 28 passing tests.**\n\n## What Happened\n\nThis slice replaced the flat dict-based body_sections format with a structured list-of-objects format supporting H2/H3 nesting and inline citation markers.\n\n**T01 — Schema & Citation Utils:** Added BodySection and BodySubSection Pydantic models to schemas.py. BodySection has heading, content, and optional subsections list. Changed SynthesizedPage.body_sections from dict to list[BodySection] and added body_sections_format='v2' discriminator field. Created citation_utils.py with extract_citations() (regex parser for [N] and [N,M] markers) and validate_citations() (returns coverage stats, invalid indices, uncited moments). 15 unit tests cover all edge cases including multi-citation markers, out-of-range indices, subsection citations, and zero-moment edge case.\n\n**T02 — Prompt Rewrite:** Backed up the existing prompt, then made four targeted edits to stage5_synthesis.txt: (1) H3 subsection guidance in the body sections structure section, (2) new Citation rules section defining [N] and [N,M] format with placement conventions, (3) rewrote the Output format JSON example with v2 structure showing both flat and nested sections with inline citations, (4) updated Field rules to describe the new format. All non-output sections (voice, tone, synthesis philosophy) preserved unchanged.\n\n**T03 — Test Harness Update:** Updated two locations in test_harness.py where body_sections was iterated as a dict — now walks BodySection objects for word counting and metadata. Added subsection count reporting and per-page citation coverage logging with a CITE log tag for grep-friendly output. Created test_harness_v2_format.py with 13 tests covering word counting (flat and nested), section counting, citation integration, and SynthesisResult round-trip serialization.\n\n## Verification\n\nAll three task verification commands pass:\n- `python -m pytest backend/pipeline/test_citation_utils.py -v` — 15/15 passed (0.08s)\n- `python -m pytest backend/pipeline/test_harness_v2_format.py -v` — 13/13 passed (0.08s)\n- Prompt structure assertion (body_sections_format, Citation rules, subsections present) — passed\n\nTotal: 28 tests passing, prompt validates structurally.\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nNone.\n\n## Known Limitations\n\nThe v2 format is schema-validated and test-harnessed but has not been run against a live LLM yet — that happens when the pipeline compose logic (S04) exercises the prompt end-to-end.\n\n## Follow-ups\n\nNone.\n\n## Files Created/Modified\n\n- `backend/pipeline/schemas.py` — Added BodySubSection, BodySection models; changed SynthesizedPage.body_sections to list[BodySection]; added body_sections_format='v2'\n- `backend/pipeline/citation_utils.py` — New file: extract_citations() and validate_citations() functions\n- `backend/pipeline/test_citation_utils.py` — New file: 15 unit tests for citation utilities\n- `backend/pipeline/test_harness.py` — Updated word-count and metadata logic from dict iteration to BodySection object walking; added citation coverage reporting\n- `backend/pipeline/test_harness_v2_format.py` — New file: 13 tests for v2 format word counting, section counting, citation integration, round-trip\n- `prompts/stage5_synthesis.txt` — Added Citation rules section, H3 subsection guidance, rewrote Output format to v2 list-of-objects with citations, updated Field rules\n\n#### S02 Summary\nSource: `.gsd/milestones/M014/slices/S02/S02-SUMMARY.md`\n\n---\nid: S02\nparent: M014\nmilestone: M014\nprovides:\n  - stage5_compose.txt prompt for S04 pipeline compose-or-create logic\n  - build_compose_prompt() function for S04 to call from pipeline stages\n  - compose CLI subcommand for offline testing of composition before pipeline integration\nrequires:\n  - slice: S01\n    provides: v2 SynthesisResult schema and body_sections format used by composition output\naffects:\n  - S04\nkey_files:\n  - prompts/stage5_compose.txt\n  - backend/pipeline/test_harness.py\n  - backend/pipeline/test_harness_compose.py\n  - conftest.py\nkey_decisions:\n  - Composition prompt is self-contained — carries its own writing standards rather than importing from synthesis prompt at runtime\n  - Offset-based citation scheme: existing keep [0]-[N-1], new get [N]-[N+M-1], no renumbering of existing citations\n  - Compose accepts both harness output (with .pages[]) and raw SynthesizedPage JSON\n  - Root conftest.py bootstraps backend/ onto sys.path for project-root test discovery via pipeline symlink\npatterns_established:\n  - Compose prompt pattern: XML-tagged inputs with offset-based citation indexing for multi-source content merging\n  - Test harness subcommand pattern: build_*_prompt() pure function + run_*() orchestrator + CLI wiring\nobservability_surfaces:\n  - none\ndrill_down_paths:\n  - .gsd/milestones/M014/slices/S02/tasks/T01-SUMMARY.md\n  - .gsd/milestones/M014/slices/S02/tasks/T02-SUMMARY.md\n  - .gsd/milestones/M014/slices/S02/tasks/T03-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-03T01:10:16.667Z\nblocker_discovered: false\n---\n\n# S02: Composition Prompt + Test Harness Compose Mode\n\n**Composition prompt, test harness compose subcommand, and 16 unit tests enable offline testing of merging new video moments into existing technique pages with correct citation re-indexing.**\n\n## What Happened\n\nThree tasks delivered the composition pipeline's offline testing surface:\n\nT01 wrote `prompts/stage5_compose.txt` — a self-contained LLM prompt for merging new video moments into existing technique pages. It defines four XML input tags (`<existing_page>`, `<existing_moments>`, `<new_moments>`, `<creator>`), offset-based citation re-indexing (existing moments keep [0]-[N-1], new moments get [N]-[N+M-1]), merge/enrich/dedup rules with concrete examples, workflow-ordered section placement, and v2 SynthesisResult output format with two validated JSON examples.\n\nT02 added `build_compose_prompt()` and `run_compose()` to the test harness, plus a `compose` CLI subcommand. The prompt builder constructs the XML-tagged user prompt with correct citation index offsets. `run_compose()` loads an existing page JSON + two fixture files (original moments and new moments), filters new moments by category, calls the LLM with the compose system prompt, validates the output as SynthesisResult, and logs compose-specific metrics (word count before/after, sections before/after).\n\nT03 created 16 unit tests in `test_harness_compose.py` covering prompt XML structure (6 tests), citation offset arithmetic (4 tests), category filtering (2 tests), and edge cases (4 tests including empty moments, single moment, no subsections, large offsets). All pass in 0.4s.\n\nA root-level `conftest.py` was added to bootstrap `backend/` onto `sys.path`, fixing test discovery when running from the project root via the `pipeline` symlink.\n\n## Verification\n\nAll three verification gates pass:\n1. `python -m pytest pipeline/test_harness_compose.py -v` — 16/16 passed (0.40s) from project root\n2. `cd backend && python -m pipeline.test_harness compose --help` — exits 0, correct arg listing\n3. Structural validation of `prompts/stage5_compose.txt` — 13053 chars, all required sections present, JSON examples parse valid\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nAdded root-level conftest.py to fix sys.path for project-root test execution — not in original plan but required for the verification command to work without cd backend prefix.\n\n## Known Limitations\n\nrun_compose() requires an LLM endpoint to actually test composition end-to-end. The unit tests validate prompt construction and plumbing only.\n\n## Follow-ups\n\nNone.\n\n## Files Created/Modified\n\n- `prompts/stage5_compose.txt` — New composition prompt with merge rules, citation re-indexing, dedup guidance, two v2 JSON examples\n- `backend/pipeline/test_harness.py` — Added build_compose_prompt(), run_compose(), and compose CLI subcommand\n- `backend/pipeline/test_harness_compose.py` — 16 unit tests for compose prompt construction, citation math, category filtering, edge cases\n- `conftest.py` — Root-level sys.path bootstrap for project-root test discovery via pipeline symlink\n\n#### S03 Summary\nSource: `.gsd/milestones/M014/slices/S03/S03-SUMMARY.md`\n\n---\nid: S03\nparent: M014\nmilestone: M014\nprovides:\n  - body_sections_format column on technique_pages (default 'v1')\n  - technique_page_videos association table\n  - SourceVideoSummary schema\n  - source_videos field on TechniquePageDetail API response\n  - body_sections accepts list | dict | None\nrequires:\n  []\naffects:\n  - S04\n  - S05\n  - S06\nkey_files:\n  - alembic/versions/012_multi_source_format.py\n  - backend/models.py\n  - backend/schemas.py\n  - backend/routers/techniques.py\nkey_decisions:\n  - Used TIMESTAMP (not WITH TIME ZONE) for added_at to stay consistent with existing schema convention\npatterns_established:\n  - Association table pattern with dual CASCADE FKs and unique constraint for many-to-many with metadata (added_at)\n  - body_sections_format discriminator column for handling multiple content formats in the same table\nobservability_surfaces:\n  - none\ndrill_down_paths:\n  - .gsd/milestones/M014/slices/S03/tasks/T01-SUMMARY.md\n  - .gsd/milestones/M014/slices/S03/tasks/T02-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-03T01:20:27.897Z\nblocker_discovered: false\n---\n\n# S03: Data Model + Migration\n\n**Added body_sections_format column, technique_page_videos association table, and wired both into the API response for multi-source technique pages.**\n\n## What Happened\n\nThis slice laid the data foundation for M014's multi-source, nested-section technique pages. Two tasks delivered three changes:\n\n**T01 — Schema + Migration:** Created Alembic migration 012 adding `body_sections_format` (VARCHAR(20), NOT NULL, default 'v1') to technique_pages and a new `technique_page_videos` association table with dual CASCADE foreign keys and a unique constraint on (technique_page_id, source_video_id). Updated SQLAlchemy models with the new `TechniquePageVideo` class and `body_sections_format` column on `TechniquePage`. Widened the Pydantic `body_sections` type from `dict | None` to `list | dict | None` to support both v1 (dict) and v2 (list-of-objects) formats. Added `SourceVideoSummary` schema and `source_videos` field to `TechniquePageDetail`.\n\n**T02 — API Wiring:** Updated `get_technique()` to eagerly load `source_video_links` → `source_video` via chained `selectinload`. Builds the `source_videos` list from association table rows. Ran migration on ub01 and verified the API returns both new fields with correct defaults (`body_sections_format: \"v1\"`, `source_videos: []`).\n\nAll existing technique pages continue to work unchanged — the v1 default ensures backward compatibility.\n\n## Verification\n\nAll slice-level checks passed:\n1. `from models import TechniquePageVideo, TechniquePage; assert hasattr(TechniquePage, 'body_sections_format')` → OK\n2. `from schemas import SourceVideoSummary, TechniquePageDetail` → OK\n3. `alembic upgrade head` on ub01 Docker → clean (migration 012 applied)\n4. `curl` to live API for existing technique → `body_sections_format: \"v1\"` and `source_videos: []` present in response\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nNone.\n\n## Known Limitations\n\nNone.\n\n## Follow-ups\n\nNone.\n\n## Files Created/Modified\n\n- `alembic/versions/012_multi_source_format.py` — New migration: body_sections_format column + technique_page_videos table\n- `backend/models.py` — Added TechniquePageVideo model, body_sections_format column, source_video_links relationship\n- `backend/schemas.py` — Widened body_sections type, added SourceVideoSummary, added source_videos to TechniquePageDetail\n- `backend/routers/techniques.py` — Eager-load source_video_links, build source_videos list in technique detail response\n\n## Your Role in the Pipeline\n\nYou are the scout. After you finish, a **planner agent** reads your output in a fresh context with no memory of your exploration. It uses your findings to decompose this slice into executable tasks — deciding what files change, what order to build things, how to verify the work. Then **executor agents** build each task in isolated context windows.\n\nWrite for the planner, not for a human. The planner needs:\n- **What files exist and what they do** — so it can scope tasks to specific files\n- **Where the natural seams are** — where work divides into independent units\n- **What to build or prove first** — what's riskiest, what unblocks everything else\n- **How to verify the result** — what commands, tests, or checks confirm the slice works\n\nIf the research doc is vague, the planner will waste its context re-exploring code you already read. If it's precise, the planner can decompose immediately.\n\n## Calibrate Depth\n\nRead the slice title, the roadmap excerpt, and any milestone research above. Ask: does this slice involve unfamiliar technology, risky integration, novel architecture, or ambiguous requirements? Or is it straightforward application of known patterns to known code?\n\n- **Deep research** — new technology, unfamiliar APIs, risky integration, multiple viable approaches, or ambiguous scope. Explore broadly, look up docs, check libraries, investigate constraints. Write all template sections. This is the default when you're genuinely uncertain.\n- **Targeted research** — known technology but new to this codebase, or moderately complex integration. Explore the relevant code, check one or two libraries, identify constraints. Omit Don't Hand-Roll and Sources if nothing applies.\n- **Light research** — well-understood work using established patterns already in the codebase (wiring up existing APIs, adding standard UI components, CRUD operations, configuration changes). Read the relevant files to confirm the pattern, note any constraints, write Summary + Recommendation + Implementation Landscape. Skip the rest. A light research doc can be 15-20 lines. Don't manufacture pitfalls or risks for work that doesn't have them.\n\nAn honest \"this is straightforward, here's the pattern to follow\" is more valuable than invented complexity.\n\n## Steps\n\nResearch what this slice needs. Narrate key findings and surprises as you go — what exists, what's missing, what constrains the approach.\n0. If `REQUIREMENTS.md` was preloaded above, identify which Active requirements this slice owns or supports. Research should target these requirements — surfacing risks, unknowns, and implementation constraints that could affect whether the slice actually delivers them.\n1.  Reference specific rules from loaded skills in your findings where they inform the implementation approach.\n2. **Skill Discovery (suggest):**\n   Identify the key technologies, frameworks, and services this work depends on (e.g. Stripe, Clerk, Supabase, JUCE, SwiftUI).\n   For each, check if a professional agent skill already exists:\n   - First check `<available_skills>` in your system prompt — a skill may already be installed.\n   - For technologies without an installed skill, run: `npx skills find \"<technology>\"`\n   - Only consider skills that are **directly relevant** to core technologies — not tangentially related.\n   - Evaluate results by install count and relevance to the actual work.\n   - Note promising skills in your research output with their install commands, but do NOT install them.\n   - The user will decide which to install.\n3. Explore relevant code for this slice's scope. For targeted exploration, use `rg`, `find`, and reads. For broad or unfamiliar subsystems, use `scout` to map the relevant area first.\n4. Use `resolve_library` / `get_library_docs` for unfamiliar libraries — skip this for libraries already used in the codebase\n5. **Web search budget:** You have a limited budget of web searches (max ~15 per session). Use them strategically — prefer `resolve_library` / `get_library_docs` for library documentation. Do NOT repeat the same or similar queries. If a search didn't find what you need, rephrase once or move on. Target 3-5 total web searches for a typical research unit.\n6. Use the **Research** output template from the inlined context above — include only sections that have real content. The template is already inlined above; do NOT attempt to read any template file from disk (there is no `templates/SLICE-RESEARCH.md` — the correct template is already present in this prompt).\n7. Call `gsd_summary_save` with `milestone_id: M014`, `slice_id: S04`, `artifact_type: \"RESEARCH\"`, and the full research markdown as `content` — the tool computes the file path and persists to both DB and disk.\n\nThe slice directory already exists at `.gsd/milestones/M014/slices/S04/`. Do NOT mkdir.\n\n**You MUST call `gsd_summary_save` with the research content before finishing.**\n\nWhen done, say: \"Slice S04 researched.\"","display":false,"id":"9ba132bc","parentId":"3c4c748c","timestamp":"2026-04-03T01:20:30.717Z"}
{"type":"message","id":"4860ba09","parentId":"9ba132bc","timestamp":"2026-04-03T01:20:44.006Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me research the S04 slice: \"Pipeline Compose-or-Create Logic\". This is about integrating the composition prompt into the actual pipeline so that when a second video is processed for the same creator+category, it composes into the existing page rather than creating a new one.\n\nLet me understand the dependencies:\n- S01: v2 prompt and schema (done)\n- S02: Composition prompt and test harness (done)\n- S03: Data model with technique_page_videos association table (done)\n\nThe slice demo: \"Process two COPYCATT videos. Second video's moments composed into existing page. technique_page_videos has both video IDs.\"\n\n\nNow I'm diving into the pipeline code to see where the compose-or-create decision point needs to be inserted and how it fits into the current stage 5 flow.","thinkingSignature":"EpkHClkIDBgCKkD4fvZIJxqsPXsGNaW78kWVbRUmsDfU11i/EeL2sNwpcCAu7A/EThEd7ubEGD7y34HKMCQ3bRq6qYDYCjk7gFVkMg9jbGF1ZGUtb3B1cy00LTY4ABIMJ9QuKP8o0i64Sf3cGgztOIiOh9Vnpd2Hnf4iMI6U40hMxXbHdECOtuemh261eTXwHtzvqaP71wIqZ7d+yhgXDOnG7OCAd3yxF+yQ3yrtBaM62y64W0Imwm+quDJuWHFGWLumpQJGDDslqTk7hPnl8xEBoClMD1W1pD/58M6lg1PuG7RjCQX+4QYjv9KofxXbevG8n1q6GAI+jSQqgL1sL3pZ7bBprPLBUMMmFXuSLm3EyT1zCRvxZRIvT39JVC0+nagHjbPW5xb9DLsl8wVvlSYelzS41df7c9KyO87QXwj68E3yTE6e9JBopmUo73XfL/XpX208lPhyv904H0Gfnrj04zr5v6lMPaKdYuRENy4yiuJb/AobfhfAHmsAASACp8Mf/LNsHF9TTXbeyCrbpINaV3tGj/aRb9wWrPzKvTX8j/b0TcttnPYPNWs/Pdu4sVfu8CtbdhZDRY9tUNTwYFDAhwl5cLtEePfuPBbLNOeen3SA3nh6ysBrpXOsab665VmROGVqd8d7jyrihdQruAE2yYABAwQbm2+0+fHHQ8+b3a1KEvgb7PsD4OSL/X62zE4C6rItCZCw/zTIO0gfH3ZsMLHCJ0S5YCZ4Y0BvvpF8V8A9RVBxh5L/72Kn3lk0xIq9yvwM2QhOyxYHIGm5noloi+zg7iUr42zaVVcQAEpPbDDjpq6bDSdhMZCNGEUDy4E8Sct1Z7lgkT+2kw7/O8mZWAgsnPIGCiDD/2dbYPMUZQ5qbFXgrkGCqSYM8oCZPrM8cpLGjuLYaZ8iK3xFPelavZI9Wv+kikCO/xQeucIYkvYxDFSzuvgu3Ie9OcWHzUKDHq67anF1ZZw3R16gGua2rFqYCtpSuIU4tXiMqr46qvZYaqzLMNq0kmohgiG1/3mc5xDQunUgmVM+FWuzUDNb9SDRPP1mhBT0MA+BlNyl3AnEs2xsbHSVyzm3XH3fb6njTq0Mz5SgtLYYZ92QQBKo+6FZNdFEpPTfj4ULcgmGngM9yJD5ILdw+JOGjLP4hcAekfx1eeLvjWPYVSNHoxY4/i20CaHTgztQsrZNWakH9l+b0GOqNp3qhyIyl+I9UQn85Gup8ieYbVEAGAE="},{"type":"text","text":"Starting with the pipeline stage code to understand the current stage 5 flow and where compose-or-create logic fits."},{"type":"toolCall","id":"toolu_01YYMHnbqjKVywVE9woZtGWF","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find backend/pipeline -type f -name '*.py' | head -30 && echo \"---\" && ls -la backend/pipeline/stages*.py 2>/dev/null || echo \"no stages files\" && echo \"---\" && ls -la backend/pipeline/stage*.py 2>/dev/null || echo \"no stage files\""}},{"type":"toolCall","id":"toolu_0161wDbN1EA19ZZ6zQ173YCs","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -l \"stage.?5\\|synthesis\\|synthesize\\|compose\" backend/pipeline/ --type py"}},{"type":"toolCall","id":"toolu_012YhqP6f9oDNnsHcttQBs8W","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -l \"technique_page\\|TechniquePage\\|technique_page_videos\" backend/pipeline/ --type py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":520,"cacheRead":59865,"cacheWrite":9347,"totalTokens":69735,"cost":{"input":0.000015000000000000002,"output":0.013000000000000001,"cacheRead":0.029932499999999997,"cacheWrite":0.058418750000000005,"total":0.10136625}},"stopReason":"toolUse","timestamp":1775179230715}}
{"type":"message","id":"65393c60","parentId":"4860ba09","timestamp":"2026-04-03T01:20:44.024Z","message":{"role":"toolResult","toolCallId":"toolu_01YYMHnbqjKVywVE9woZtGWF","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/test_citation_utils.py\nbackend/pipeline/test_harness_compose.py\nbackend/pipeline/llm_client.py\nbackend/pipeline/test_harness.py\nbackend/pipeline/stages.py\nbackend/pipeline/export_fixture.py\nbackend/pipeline/citation_utils.py\nbackend/pipeline/embedding_client.py\nbackend/pipeline/__init__.py\nbackend/pipeline/test_harness_v2_format.py\nbackend/pipeline/qdrant_client.py\nbackend/pipeline/quality/optimizer.py\nbackend/pipeline/quality/__main__.py\nbackend/pipeline/quality/scorer.py\nbackend/pipeline/quality/fitness.py\nbackend/pipeline/quality/variant_generator.py\nbackend/pipeline/quality/__init__.py\nbackend/pipeline/quality/voice_dial.py\nbackend/pipeline/quality/fixtures/__init__.py\nbackend/pipeline/schemas.py\n---\n-rw-rw-r-- 1 aux aux 82490 Apr  2 05:07 backend/pipeline/stages.py\n---\n-rw-rw-r-- 1 aux aux 82490 Apr  2 05:07 backend/pipeline/stages.py\n"}],"isError":false,"timestamp":1775179244024}}
{"type":"message","id":"ca6b7574","parentId":"65393c60","timestamp":"2026-04-03T01:20:44.025Z","message":{"role":"toolResult","toolCallId":"toolu_0161wDbN1EA19ZZ6zQ173YCs","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775179244025}}
{"type":"message","id":"0be22994","parentId":"ca6b7574","timestamp":"2026-04-03T01:20:44.028Z","message":{"role":"toolResult","toolCallId":"toolu_012YhqP6f9oDNnsHcttQBs8W","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775179244027}}
{"type":"message","id":"35a96c39","parentId":"0be22994","timestamp":"2026-04-03T01:20:48.285Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01AcDaCsW7L1PmHXf93oQ3Gy","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"stage.?5\\|synthesis\\|synthesize\\|compose\\|technique_page\\|TechniquePage\" backend/pipeline/stages.py | head -50"}},{"type":"toolCall","id":"toolu_01W3jmmwtCmuPvto43qYcgMH","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"def (run_|stage)\" backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":180,"cacheRead":69212,"cacheWrite":724,"totalTokens":70117,"cost":{"input":0.000005,"output":0.0045000000000000005,"cacheRead":0.034606,"cacheWrite":0.004525,"total":0.043636}},"stopReason":"toolUse","timestamp":1775179244028}}
{"type":"message","id":"11e37e96","parentId":"35a96c39","timestamp":"2026-04-03T01:20:48.301Z","message":{"role":"toolResult","toolCallId":"toolu_01AcDaCsW7L1PmHXf93oQ3Gy","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775179248300}}
{"type":"message","id":"fcaa5eee","parentId":"11e37e96","timestamp":"2026-04-03T01:20:48.308Z","message":{"role":"toolResult","toolCallId":"toolu_01W3jmmwtCmuPvto43qYcgMH","toolName":"bash","content":[{"type":"text","text":"386:def stage2_segmentation(self, video_id: str, run_id: str | None = None) -> str:\n468:def stage3_extraction(self, video_id: str, run_id: str | None = None) -> str:\n636:def stage4_classification(self, video_id: str, run_id: str | None = None) -> str:\n1127:def stage5_synthesis(self, video_id: str, run_id: str | None = None) -> str:\n1427:def stage6_embed_and_index(self, video_id: str, run_id: str | None = None) -> str:\n1787:def run_pipeline(video_id: str, trigger: str = \"manual\") -> str:\n1897:def run_single_stage(\n"}],"isError":false,"timestamp":1775179248306}}
{"type":"message","id":"0af348af","parentId":"fcaa5eee","timestamp":"2026-04-03T01:20:52.123Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_012oN1XYyMskKm4dnhvGjDJ6","name":"read","arguments":{"limit":300,"offset":1127,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":69936,"cacheWrite":436,"totalTokens":70465,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.034968,"cacheWrite":0.002725,"total":0.039998}},"stopReason":"toolUse","timestamp":1775179248306}}
{"type":"message","id":"7b2f2ef5","parentId":"0af348af","timestamp":"2026-04-03T01:20:52.129Z","message":{"role":"toolResult","toolCallId":"toolu_012oN1XYyMskKm4dnhvGjDJ6","toolName":"read","content":[{"type":"text","text":"def stage5_synthesis(self, video_id: str, run_id: str | None = None) -> str:\n    \"\"\"Synthesize technique pages from classified key moments.\n\n    Groups moments by (creator, topic_category), calls the LLM to synthesize\n    each group into a TechniquePage, creates/updates page rows, and links\n    KeyMoments to their TechniquePage.\n\n    For large category groups (exceeding synthesis_chunk_size), moments are\n    split into chronological chunks, synthesized independently, then pages\n    with matching slugs are merged via a dedicated merge LLM call.\n\n    Sets processing_status to 'complete'.\n\n    Returns the video_id for chain compatibility.\n    \"\"\"\n    start = time.monotonic()\n    logger.info(\"Stage 5 (synthesis) starting for video_id=%s\", video_id)\n    _emit_event(video_id, \"stage5_synthesis\", \"start\", run_id=run_id)\n\n    settings = get_settings()\n    chunk_size = settings.synthesis_chunk_size\n    session = _get_sync_session()\n    try:\n        # Load video and moments\n        video = session.execute(\n            select(SourceVideo).where(SourceVideo.id == video_id)\n        ).scalar_one()\n\n        moments = (\n            session.execute(\n                select(KeyMoment)\n                .where(KeyMoment.source_video_id == video_id)\n                .order_by(KeyMoment.start_time)\n            )\n            .scalars()\n            .all()\n        )\n\n        # Resolve creator name for the LLM prompt\n        creator = session.execute(\n            select(Creator).where(Creator.id == video.creator_id)\n        ).scalar_one_or_none()\n        creator_name = creator.name if creator else \"Unknown\"\n\n        if not moments:\n            logger.info(\"Stage 5: No moments found for video_id=%s, skipping.\", video_id)\n            return video_id\n\n        # Load classification data from stage 4\n        classification_data = _load_classification_data(video_id)\n        cls_by_moment_id = {c[\"moment_id\"]: c for c in classification_data}\n\n        # Group moments by topic_category (from classification)\n        # Normalize category casing to prevent near-duplicate groups\n        # (e.g., \"Sound design\" vs \"Sound Design\")\n        groups: dict[str, list[tuple[KeyMoment, dict]]] = defaultdict(list)\n        for moment in moments:\n            cls_info = cls_by_moment_id.get(str(moment.id), {})\n            category = cls_info.get(\"topic_category\", \"Uncategorized\").strip().title()\n            groups[category].append((moment, cls_info))\n\n        system_prompt = _load_prompt(\"stage5_synthesis.txt\", video_id=video_id)\n        llm = _get_llm_client()\n        model_override, modality = _get_stage_config(5)\n        hard_limit = settings.llm_max_tokens_hard_limit\n        logger.info(\"Stage 5 using model=%s, modality=%s\", model_override or \"default\", modality)\n        pages_created = 0\n\n        for category, moment_group in groups.items():\n            # Collect all tags across the full group (used for DB writes later)\n            all_tags: set[str] = set()\n            for _, cls_info in moment_group:\n                all_tags.update(cls_info.get(\"topic_tags\", []))\n\n            # ── Chunked synthesis with truncation recovery ─────────\n            if len(moment_group) <= chunk_size:\n                # Small group — try single LLM call first\n                try:\n                    result = _synthesize_chunk(\n                        moment_group, category, creator_name,\n                        system_prompt, llm, model_override, modality, hard_limit,\n                        video_id, run_id, f\"category:{category}\",\n                    )\n                    synthesized_pages = list(result.pages)\n                    logger.info(\n                        \"Stage 5: category '%s' — %d moments, %d page(s) from single call\",\n                        category, len(moment_group), len(synthesized_pages),\n                    )\n                except LLMTruncationError:\n                    # Output too large for model context — split in half and retry\n                    logger.warning(\n                        \"Stage 5: category '%s' truncated with %d moments. \"\n                        \"Splitting into smaller chunks and retrying.\",\n                        category, len(moment_group),\n                    )\n                    half = max(1, len(moment_group) // 2)\n                    chunk_pages = []\n                    for sub_start in range(0, len(moment_group), half):\n                        sub_chunk = moment_group[sub_start:sub_start + half]\n                        sub_label = f\"category:{category} recovery-chunk:{sub_start // half + 1}\"\n                        sub_result = _synthesize_chunk(\n                            sub_chunk, category, creator_name,\n                            system_prompt, llm, model_override, modality, hard_limit,\n                            video_id, run_id, sub_label,\n                        )\n                        # Reindex moment_indices to global offsets\n                        for p in sub_result.pages:\n                            if p.moment_indices:\n                                p.moment_indices = [idx + sub_start for idx in p.moment_indices]\n                        chunk_pages.extend(sub_result.pages)\n                    synthesized_pages = chunk_pages\n                    logger.info(\n                        \"Stage 5: category '%s' — %d page(s) from recovery split\",\n                        category, len(synthesized_pages),\n                    )\n            else:\n                # Large group — split into chunks, synthesize each, then merge\n                num_chunks = (len(moment_group) + chunk_size - 1) // chunk_size\n                logger.info(\n                    \"Stage 5: category '%s' has %d moments — splitting into %d chunks of ≤%d\",\n                    category, len(moment_group), num_chunks, chunk_size,\n                )\n\n                chunk_pages = []\n                for chunk_idx in range(num_chunks):\n                    chunk_start = chunk_idx * chunk_size\n                    chunk_end = min(chunk_start + chunk_size, len(moment_group))\n                    chunk = moment_group[chunk_start:chunk_end]\n                    chunk_label = f\"category:{category} chunk:{chunk_idx + 1}/{num_chunks}\"\n\n                    result = _synthesize_chunk(\n                        chunk, category, creator_name,\n                        system_prompt, llm, model_override, modality, hard_limit,\n                        video_id, run_id, chunk_label,\n                    )\n                    chunk_pages.extend(result.pages)\n                    logger.info(\n                        \"Stage 5: %s produced %d page(s)\",\n                        chunk_label, len(result.pages),\n                    )\n\n                # Merge pages with matching slugs across chunks\n                logger.info(\n                    \"Stage 5: category '%s' — %d total pages from %d chunks, checking for merges\",\n                    category, len(chunk_pages), num_chunks,\n                )\n                synthesized_pages = _merge_pages_by_slug(\n                    chunk_pages, creator_name,\n                    llm, model_override, modality, hard_limit,\n                    video_id, run_id,\n                )\n                logger.info(\n                    \"Stage 5: category '%s' — %d final page(s) after merge\",\n                    category, len(synthesized_pages),\n                )\n\n            # ── Persist pages to DB ──────────────────────────────────\n            # Load prior pages from this video (snapshot taken before pipeline reset)\n            prior_page_ids = _load_prior_pages(video_id)\n\n            for page_data in synthesized_pages:\n                page_moment_indices = getattr(page_data, \"moment_indices\", None) or []\n                existing = None\n\n                # First: check by slug (most specific match)\n                if existing is None:\n                    existing = session.execute(\n                        select(TechniquePage).where(TechniquePage.slug == page_data.slug)\n                    ).scalar_one_or_none()\n\n                # Fallback: check prior pages from this video by creator + category\n                # Use .first() since multiple pages may share a category\n                if existing is None and prior_page_ids:\n                    existing = session.execute(\n                        select(TechniquePage).where(\n                            TechniquePage.id.in_(prior_page_ids),\n                            TechniquePage.creator_id == video.creator_id,\n                            func.lower(TechniquePage.topic_category) == func.lower(page_data.topic_category or category),\n                        )\n                    ).scalars().first()\n                    if existing:\n                        logger.info(\n                            \"Stage 5: Matched prior page '%s' (id=%s) by creator+category for video_id=%s\",\n                            existing.slug, existing.id, video_id,\n                        )\n\n                if existing:\n                    # Snapshot existing content before overwriting\n                    try:\n                        sq = existing.source_quality\n                        sq_value = sq.value if hasattr(sq, 'value') else sq\n                        snapshot = {\n                            \"title\": existing.title,\n                            \"slug\": existing.slug,\n                            \"topic_category\": existing.topic_category,\n                            \"topic_tags\": existing.topic_tags,\n                            \"summary\": existing.summary,\n                            \"body_sections\": existing.body_sections,\n                            \"signal_chains\": existing.signal_chains,\n                            \"plugins\": existing.plugins,\n                            \"source_quality\": sq_value,\n                        }\n                        version_count = session.execute(\n                            select(func.count()).where(\n                                TechniquePageVersion.technique_page_id == existing.id\n                            )\n                        ).scalar()\n                        version_number = version_count + 1\n\n                        version = TechniquePageVersion(\n                            technique_page_id=existing.id,\n                            version_number=version_number,\n                            content_snapshot=snapshot,\n                            pipeline_metadata=_capture_pipeline_metadata(),\n                        )\n                        session.add(version)\n                        logger.info(\n                            \"Version snapshot v%d created for page slug=%s\",\n                            version_number, existing.slug,\n                        )\n                    except Exception as snap_exc:\n                        logger.error(\n                            \"Failed to create version snapshot for page slug=%s: %s\",\n                            existing.slug, snap_exc,\n                        )\n                        # Best-effort versioning — continue with page update\n\n                    # Update existing page\n                    existing.title = page_data.title\n                    existing.summary = page_data.summary\n                    existing.body_sections = page_data.body_sections\n                    existing.signal_chains = page_data.signal_chains\n                    existing.plugins = page_data.plugins if page_data.plugins else None\n                    page_tags = _compute_page_tags(page_moment_indices, moment_group, all_tags)\n                    existing.topic_tags = page_tags\n                    existing.source_quality = page_data.source_quality\n                    page = existing\n                else:\n                    page = TechniquePage(\n                        creator_id=video.creator_id,\n                        title=page_data.title,\n                        slug=page_data.slug,\n                        topic_category=page_data.topic_category or category,\n                        topic_tags=_compute_page_tags(page_moment_indices, moment_group, all_tags),\n                        summary=page_data.summary,\n                        body_sections=page_data.body_sections,\n                        signal_chains=page_data.signal_chains,\n                        plugins=page_data.plugins if page_data.plugins else None,\n                        source_quality=page_data.source_quality,\n                    )\n                    session.add(page)\n                    session.flush()  # Get the page.id assigned\n\n                pages_created += 1\n\n                # Link moments to the technique page using moment_indices\n\n                if page_moment_indices:\n                    # LLM specified which moments belong to this page\n                    for idx in page_moment_indices:\n                        if 0 <= idx < len(moment_group):\n                            moment_group[idx][0].technique_page_id = page.id\n                elif len(synthesized_pages) == 1:\n                    # Single page — link all moments (safe fallback)\n                    for m, _ in moment_group:\n                        m.technique_page_id = page.id\n                else:\n                    # Multiple pages but no moment_indices — log warning\n                    logger.warning(\n                        \"Stage 5: page '%s' has no moment_indices and is one of %d pages \"\n                        \"for category '%s'. Moments will not be linked to this page.\",\n                        page_data.slug, len(synthesized_pages), category,\n                    )\n\n        # Update processing_status\n        video.processing_status = ProcessingStatus.complete\n\n        session.commit()\n        elapsed = time.monotonic() - start\n        _emit_event(video_id, \"stage5_synthesis\", \"complete\", run_id=run_id)\n        logger.info(\n            \"Stage 5 (synthesis) completed for video_id=%s in %.1fs — %d pages created/updated\",\n            video_id, elapsed, pages_created,\n        )\n        return video_id\n\n    except FileNotFoundError:\n        raise\n    except Exception as exc:\n        session.rollback()\n        _emit_event(video_id, \"stage5_synthesis\", \"error\", run_id=run_id, payload={\"error\": str(exc)})\n        logger.error(\"Stage 5 failed for video_id=%s: %s\", video_id, exc)\n        raise self.retry(exc=exc)\n    finally:\n        session.close()\n\n\n# ── Stage 6: Embed & Index ───────────────────────────────────────────────────\n\n@celery_app.task(bind=True, max_retries=0)\n\n[677 more lines in file. Use offset=1427 to continue.]"}],"isError":false,"timestamp":1775179252126}}
{"type":"message","id":"faa443a0","parentId":"7b2f2ef5","timestamp":"2026-04-03T01:21:01.732Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I need to understand:\n1. The current stage 5 flow - it creates new pages or updates existing ones by slug, but doesn't use the compose prompt\n2. The new compose-or-create logic needs to: detect existing pages for the same creator+category, use the compose prompt to merge, and populate technique_page_videos\n3. Key files: stages.py (stage5_synthesis), test_harness.py (build_compose_prompt), models.py (TechniquePageVideo), schemas.py\n\nLet me check the compose prompt builder and the models for the association table.","thinkingSignature":"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"},{"type":"text","text":"Now let me check the compose prompt builder and models."},{"type":"toolCall","id":"toolu_01KikQTdW8m47ezPsLz8cZ2Z","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"def build_compose_prompt\\|def run_compose\" backend/pipeline/test_harness.py"}},{"type":"toolCall","id":"toolu_012hr4Cy4wdBD31b8DRjRGyr","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"TechniquePageVideo\\|technique_page_videos\\|body_sections_format\" backend/models.py"}},{"type":"toolCall","id":"toolu_01K3EoGADPkChdxpWBUTXZGF","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"def _synthesize_chunk\\|def _merge_pages_by_slug\\|def _load_prior_pages\\|def _load_classification_data\\|def _load_prompt\\|def _get_llm_client\\|def _get_stage_config\\|def _compute_page_tags\\|def _capture_pipeline_metadata\" backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":479,"cacheRead":69936,"cacheWrite":4101,"totalTokens":74517,"cost":{"input":0.000005,"output":0.011975000000000001,"cacheRead":0.034968,"cacheWrite":0.02563125,"total":0.07257925000000001}},"stopReason":"toolUse","timestamp":1775179252127}}
{"type":"message","id":"7a8288b5","parentId":"faa443a0","timestamp":"2026-04-03T01:21:01.746Z","message":{"role":"toolResult","toolCallId":"toolu_01KikQTdW8m47ezPsLz8cZ2Z","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775179261745}}
{"type":"message","id":"730fa2ce","parentId":"7a8288b5","timestamp":"2026-04-03T01:21:01.746Z","message":{"role":"toolResult","toolCallId":"toolu_012hr4Cy4wdBD31b8DRjRGyr","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775179261745}}
{"type":"message","id":"577619e8","parentId":"730fa2ce","timestamp":"2026-04-03T01:21:01.752Z","message":{"role":"toolResult","toolCallId":"toolu_01K3EoGADPkChdxpWBUTXZGF","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775179261751}}
{"type":"message","id":"c62933db","parentId":"577619e8","timestamp":"2026-04-03T01:21:08.581Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The grep results are empty - these files might be on ub01, not here. Let me check what's actually in this directory.","thinkingSignature":"ErACClkIDBgCKkB1kktm1u0zk5x3oUQZ8N4bNY/TU+Rusox8dP3lI9hnNxTc1Q3M3ZJs2ocKFXlAmzLna+HZBz2lo3HvYo1vDh7vMg9jbGF1ZGUtb3B1cy00LTY4ABIMEK9lCC8XVAhueYqQGgx9nurPV6nVR8V1ARkiMCXOMJ7KQA7WFO0h5uOzbnZ+1B9/qNuH+pj76ALCnklix7H6ymVNneU4wOyaVszn4SqEAeLBIwHo0LHt7TSYO5hFv9oikkUi/tQV29eJu7xNtcPU8i5anVz8d3AVBhX8QORlhygCsBxgAVMotvNYG07aNeZlnrcYMtjJ6830srsoyus5RlzOvEm7WrYmD9uv7B++jtUSf0Xvadg/iiWyKoS54xGa8BuVaxebvTMGd2ZN6f7qt1kvChgB"},{"type":"text","text":"Interesting — greps return empty. The CLAUDE.md says ub01 is the canonical dev directory. Let me check what's actually present locally."},{"type":"toolCall","id":"toolu_01LJvHstsZZ7yqwNUopRWzMi","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls backend/pipeline/stages.py && wc -l backend/pipeline/stages.py && head -5 backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_01Rt6VY2paDBE3isHAG3UUe8","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls backend/models.py backend/schemas.py 2>/dev/null && wc -l backend/models.py backend/schemas.py 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":266,"cacheRead":74037,"cacheWrite":451,"totalTokens":74755,"cost":{"input":0.000005,"output":0.0066500000000000005,"cacheRead":0.037018499999999996,"cacheWrite":0.00281875,"total":0.04649225}},"stopReason":"toolUse","timestamp":1775179261751}}
{"type":"message","id":"46fa70f5","parentId":"c62933db","timestamp":"2026-04-03T01:21:08.595Z","message":{"role":"toolResult","toolCallId":"toolu_01LJvHstsZZ7yqwNUopRWzMi","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/stages.py\n2102 backend/pipeline/stages.py\n\"\"\"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"}],"isError":false,"timestamp":1775179268595}}
{"type":"message","id":"9d8798c9","parentId":"46fa70f5","timestamp":"2026-04-03T01:21:08.599Z","message":{"role":"toolResult","toolCallId":"toolu_01Rt6VY2paDBE3isHAG3UUe8","toolName":"bash","content":[{"type":"text","text":"backend/models.py\nbackend/schemas.py\n  496 backend/models.py\n  434 backend/schemas.py\n  930 total\n"}],"isError":false,"timestamp":1775179268598}}
{"type":"message","id":"ff976f74","parentId":"9d8798c9","timestamp":"2026-04-03T01:21:13.380Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01So9Xu1KjTbxQ2WyWjoRNhX","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"TechniquePageVideo\\|technique_page_videos\\|body_sections_format\" backend/models.py"}},{"type":"toolCall","id":"toolu_013sz6MGovzN82YxYBuw3dqZ","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"def build_compose_prompt\\|def run_compose\" backend/pipeline/test_harness.py"}},{"type":"toolCall","id":"toolu_015rbsLVgLYgYVu6s83Vxi82","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"def _synthesize_chunk\\|def _merge_pages\\|def _load_prior\\|def _load_class\\|def _load_prompt\\|def _get_llm\\|def _get_stage\\|def _compute_page\\|def _capture_pipeline\" backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":302,"cacheRead":74488,"cacheWrite":406,"totalTokens":75197,"cost":{"input":0.000005,"output":0.00755,"cacheRead":0.037244,"cacheWrite":0.0025375000000000003,"total":0.0473365}},"stopReason":"toolUse","timestamp":1775179268598}}
{"type":"message","id":"65b0816a","parentId":"ff976f74","timestamp":"2026-04-03T01:21:13.400Z","message":{"role":"toolResult","toolCallId":"toolu_01So9Xu1KjTbxQ2WyWjoRNhX","toolName":"bash","content":[{"type":"text","text":"219:    body_sections_format: Mapped[str] = mapped_column(\n250:    source_video_links: Mapped[list[TechniquePageVideo]] = sa_relationship(\n312:class TechniquePageVideo(Base):\n314:    __tablename__ = \"technique_page_videos\"\n"}],"isError":false,"timestamp":1775179273400}}
{"type":"message","id":"611b4a7f","parentId":"65b0816a","timestamp":"2026-04-03T01:21:13.401Z","message":{"role":"toolResult","toolCallId":"toolu_013sz6MGovzN82YxYBuw3dqZ","toolName":"bash","content":[{"type":"text","text":"332:def build_compose_prompt(\n376:def run_compose(\n"}],"isError":false,"timestamp":1775179273400}}
{"type":"message","id":"c09d09a8","parentId":"611b4a7f","timestamp":"2026-04-03T01:21:13.405Z","message":{"role":"toolResult","toolCallId":"toolu_015rbsLVgLYgYVu6s83Vxi82","toolName":"bash","content":[{"type":"text","text":"244:def _load_prompt(template_name: str, video_id: str | None = None) -> str:\n280:def _get_llm_client() -> LLMClient:\n285:def _get_stage_config(stage_num: int) -> tuple[str | None, str]:\n791:def _load_classification_data(video_id: str) -> list[dict]:\n884:def _capture_pipeline_metadata() -> dict:\n935:def _compute_page_tags(\n983:def _synthesize_chunk(\n1034:def _merge_pages_by_slug(\n1640:def _load_prior_pages(video_id: str) -> list[str]:\n"}],"isError":false,"timestamp":1775179273403}}
{"type":"message","id":"fe0ff1cb","parentId":"c09d09a8","timestamp":"2026-04-03T01:21:18.733Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — now I can see the full picture. Let me read the key helper functions and understand the compose prompt builder."},{"type":"toolCall","id":"toolu_0194VujWYsW4NRKuhKejx4mA","name":"read","arguments":{"limit":100,"offset":332,"path":"backend/pipeline/test_harness.py"}},{"type":"toolCall","id":"toolu_01XWb5CvA6D3QFFktdrxW9xf","name":"read","arguments":{"limit":60,"offset":983,"path":"backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_01YXa9m3p3FLtzX42a4KJJNp","name":"read","arguments":{"limit":40,"offset":1640,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":268,"cacheRead":74894,"cacheWrite":647,"totalTokens":75810,"cost":{"input":0.000005,"output":0.0067,"cacheRead":0.037447,"cacheWrite":0.0040437500000000005,"total":0.04819575}},"stopReason":"toolUse","timestamp":1775179273404}}
{"type":"message","id":"98a016ff","parentId":"fe0ff1cb","timestamp":"2026-04-03T01:21:18.737Z","message":{"role":"toolResult","toolCallId":"toolu_0194VujWYsW4NRKuhKejx4mA","toolName":"read","content":[{"type":"text","text":"def build_compose_prompt(\n    existing_page: dict,\n    existing_moments: list[tuple[MockKeyMoment, dict]],\n    new_moments: list[tuple[MockKeyMoment, dict]],\n    creator_name: str,\n) -> str:\n    \"\"\"Build the user prompt for composition (merging new moments into an existing page).\n\n    Existing moments keep indices [0]-[N-1].\n    New moments get indices [N]-[N+M-1].\n    Uses build_moments_text() for formatting, with index offsets applied for new moments.\n    \"\"\"\n    category = existing_page.get(\"topic_category\", \"Uncategorized\")\n\n    # Format existing moments [0]-[N-1]\n    existing_text, _ = build_moments_text(existing_moments, category)\n\n    # Format new moments with offset indices [N]-[N+M-1]\n    n = len(existing_moments)\n    new_lines = []\n    for i, (m, cls_info) in enumerate(new_moments):\n        tags = cls_info.get(\"topic_tags\", [])\n        new_lines.append(\n            f\"[{n + 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    new_text = \"\\n\\n\".join(new_lines)\n\n    page_json = json.dumps(existing_page, indent=2, ensure_ascii=False)\n\n    return (\n        f\"<existing_page>\\n{page_json}\\n</existing_page>\\n\"\n        f\"<existing_moments>\\n{existing_text}\\n</existing_moments>\\n\"\n        f\"<new_moments>\\n{new_text}\\n</new_moments>\\n\"\n        f\"<creator>{creator_name}</creator>\"\n    )\n\n\ndef run_compose(\n    existing_page_path: str,\n    existing_fixture_path: str,\n    new_fixture_path: str,\n    prompt_path: str,\n    category_filter: str | None = None,\n    model_override: str | None = None,\n    modality: str | None = None,\n) -> tuple[list[dict], int]:\n    \"\"\"Run composition: merge new fixture moments into an existing page.\n\n    Returns (pages, exit_code) — same shape as run_synthesis().\n    \"\"\"\n    # Load existing page JSON\n    existing_page_file = Path(existing_page_path)\n    if not existing_page_file.exists():\n        _log(\"ERROR\", f\"Existing page not found: {existing_page_path}\", level=\"ERROR\")\n        return [], 3\n\n    try:\n        existing_raw = json.loads(existing_page_file.read_text(encoding=\"utf-8\"))\n    except json.JSONDecodeError as exc:\n        _log(\"ERROR\", f\"Invalid JSON in existing page: {exc}\", level=\"ERROR\")\n        return [], 3\n\n    # The existing page file might be a harness output (with .pages[]) or a raw SynthesizedPage\n    if \"pages\" in existing_raw and isinstance(existing_raw[\"pages\"], list):\n        page_dicts = existing_raw[\"pages\"]\n        _log(\"COMPOSE\", f\"Loaded harness output with {len(page_dicts)} pages\")\n    elif \"title\" in existing_raw and \"body_sections\" in existing_raw:\n        page_dicts = [existing_raw]\n        _log(\"COMPOSE\", \"Loaded single SynthesizedPage\")\n    else:\n        _log(\"ERROR\", \"Existing page JSON must be a SynthesizedPage or harness output with 'pages' key\", level=\"ERROR\")\n        return [], 3\n\n    # Validate each page against SynthesizedPage\n    validated_pages: list[dict] = []\n    for pd in page_dicts:\n        try:\n            SynthesizedPage.model_validate(pd)\n            validated_pages.append(pd)\n        except ValidationError as exc:\n            _log(\"WARN\", f\"Skipping invalid page '{pd.get('title', '?')}': {exc}\", level=\"WARN\")\n\n    if not validated_pages:\n        _log(\"ERROR\", \"No valid SynthesizedPage found in existing page file\", level=\"ERROR\")\n        return [], 3\n\n    # Apply category filter\n    if category_filter:\n        validated_pages = [p for p in validated_pages if p.get(\"topic_category\") == category_filter]\n        if not validated_pages:\n            _log(\"ERROR\", f\"No pages match category '{category_filter}'\", level=\"ERROR\")\n            return [], 3\n\n\n[400 more lines in file. Use offset=432 to continue.]"}],"isError":false,"timestamp":1775179278737}}
{"type":"message","id":"b0460d4f","parentId":"98a016ff","timestamp":"2026-04-03T01:21:18.740Z","message":{"role":"toolResult","toolCallId":"toolu_01XWb5CvA6D3QFFktdrxW9xf","toolName":"read","content":[{"type":"text","text":"def _synthesize_chunk(\n    chunk: list[tuple[KeyMoment, dict]],\n    category: str,\n    creator_name: str,\n    system_prompt: str,\n    llm: LLMClient,\n    model_override: str | None,\n    modality: str,\n    hard_limit: int,\n    video_id: str,\n    run_id: str | None,\n    chunk_label: str,\n) -> SynthesisResult:\n    \"\"\"Run a single synthesis LLM call for a chunk of moments.\n\n    Returns the parsed SynthesisResult.\n    \"\"\"\n    moments_text, _ = _build_moments_text(chunk, category)\n    user_prompt = f\"<creator>{creator_name}</creator>\\n<moments>\\n{moments_text}\\n</moments>\"\n\n    estimated_input = estimate_max_tokens(system_prompt, user_prompt, stage=\"stage5_synthesis\", hard_limit=hard_limit)\n    logger.info(\n        \"Stage 5: Synthesizing %s — %d moments, max_tokens=%d\",\n        chunk_label, len(chunk), estimated_input,\n    )\n\n    raw = llm.complete(\n        system_prompt, user_prompt, response_model=SynthesisResult,\n        on_complete=_make_llm_callback(\n            video_id, \"stage5_synthesis\",\n            system_prompt=system_prompt, user_prompt=user_prompt,\n            run_id=run_id, context_label=chunk_label,\n            request_params=_build_request_params(estimated_input, model_override, modality, \"SynthesisResult\", hard_limit),\n        ),\n        modality=modality, model_override=model_override, max_tokens=estimated_input,\n    )\n    return _safe_parse_llm_response(\n        raw, SynthesisResult, llm, system_prompt, user_prompt,\n        modality=modality, model_override=model_override, max_tokens=estimated_input,\n    )\n\n\ndef _slug_base(slug: str) -> str:\n    \"\"\"Extract the slug prefix before the creator name suffix for merge grouping.\n\n    E.g. 'wavetable-sound-design-copycatt' → 'wavetable-sound-design'\n    Also normalizes casing.\n    \"\"\"\n    return slug.lower().strip()\n\n\ndef _merge_pages_by_slug(\n    all_pages: list,\n    creator_name: str,\n    llm: LLMClient,\n    model_override: str | None,\n    modality: str,\n    hard_limit: int,\n    video_id: str,\n    run_id: str | None,\n\n[1061 more lines in file. Use offset=1043 to continue.]"}],"isError":false,"timestamp":1775179278740}}
{"type":"message","id":"9d345175","parentId":"b0460d4f","timestamp":"2026-04-03T01:21:18.748Z","message":{"role":"toolResult","toolCallId":"toolu_01YXa9m3p3FLtzX42a4KJJNp","toolName":"read","content":[{"type":"text","text":"def _load_prior_pages(video_id: str) -> list[str]:\n    \"\"\"Load prior technique page IDs from Redis.\"\"\"\n    import redis\n\n    settings = get_settings()\n    r = redis.Redis.from_url(settings.redis_url)\n    key = f\"chrysopedia:prior_pages:{video_id}\"\n    raw = r.get(key)\n    if raw is None:\n        return []\n    return json.loads(raw)\n\n\n# ── Stage completion detection for auto-resume ───────────────────────────────\n\n# Ordered list of pipeline stages for resumability logic\n_PIPELINE_STAGES = [\n    \"stage2_segmentation\",\n    \"stage3_extraction\",\n    \"stage4_classification\",\n    \"stage5_synthesis\",\n    \"stage6_embed_and_index\",\n]\n\n_STAGE_TASKS = {\n    \"stage2_segmentation\": stage2_segmentation,\n    \"stage3_extraction\": stage3_extraction,\n    \"stage4_classification\": stage4_classification,\n    \"stage5_synthesis\": stage5_synthesis,\n    \"stage6_embed_and_index\": stage6_embed_and_index,\n}\n\n\ndef _get_last_completed_stage(video_id: str) -> str | None:\n    \"\"\"Find the last stage that completed successfully for this video.\n\n    Queries pipeline_events for the most recent run, looking for 'complete'\n    events. Returns the stage name (e.g. 'stage3_extraction') or None if\n    no stages have completed.\n    \"\"\"\n\n[424 more lines in file. Use offset=1680 to continue.]"}],"isError":false,"timestamp":1775179278744}}
{"type":"message","id":"d50a74be","parentId":"9d345175","timestamp":"2026-04-03T01:21:23.051Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I understand the current architecture. Let me check the models for TechniquePageVideo and see how the body_sections_format is stored."},{"type":"toolCall","id":"toolu_01TfmB7RTaZR9LgYxX42WxDH","name":"read","arguments":{"limit":50,"offset":200,"path":"backend/models.py"}},{"type":"toolCall","id":"toolu_01FHR2MJMEjb2pEkoKUVmXk8","name":"read","arguments":{"limit":40,"offset":305,"path":"backend/models.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":193,"cacheRead":75541,"cacheWrite":2679,"totalTokens":78414,"cost":{"input":0.000005,"output":0.004825,"cacheRead":0.0377705,"cacheWrite":0.01674375,"total":0.05934425}},"stopReason":"toolUse","timestamp":1775179278745}}
{"type":"message","id":"c87d63e9","parentId":"d50a74be","timestamp":"2026-04-03T01:21:23.054Z","message":{"role":"toolResult","toolCallId":"toolu_01TfmB7RTaZR9LgYxX42WxDH","toolName":"read","content":[{"type":"text","text":"    source_video: Mapped[SourceVideo] = sa_relationship(back_populates=\"key_moments\")\n    technique_page: Mapped[TechniquePage | None] = sa_relationship(\n        back_populates=\"key_moments\", foreign_keys=[technique_page_id]\n    )\n\n\nclass TechniquePage(Base):\n    __tablename__ = \"technique_pages\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    creator_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"creators.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    title: Mapped[str] = mapped_column(String(500), nullable=False)\n    slug: Mapped[str] = mapped_column(String(500), unique=True, nullable=False)\n    topic_category: Mapped[str] = mapped_column(String(255), nullable=False)\n    topic_tags: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    summary: Mapped[str | None] = mapped_column(Text, nullable=True)\n    body_sections: Mapped[dict | None] = mapped_column(JSONB, nullable=True)\n    body_sections_format: Mapped[str] = mapped_column(\n        String(20), nullable=False, default=\"v1\", server_default=\"v1\"\n    )\n    signal_chains: Mapped[list | None] = mapped_column(JSONB, nullable=True)\n    plugins: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    source_quality: Mapped[SourceQuality | None] = mapped_column(\n        Enum(SourceQuality, name=\"source_quality\", create_constraint=True),\n        nullable=True,\n    )\n    view_count: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    creator: Mapped[Creator] = sa_relationship(back_populates=\"technique_pages\")\n    key_moments: Mapped[list[KeyMoment]] = sa_relationship(\n        back_populates=\"technique_page\", foreign_keys=[KeyMoment.technique_page_id]\n    )\n    versions: Mapped[list[TechniquePageVersion]] = sa_relationship(\n        back_populates=\"technique_page\", order_by=\"TechniquePageVersion.version_number\"\n    )\n    outgoing_links: Mapped[list[RelatedTechniqueLink]] = sa_relationship(\n        foreign_keys=\"RelatedTechniqueLink.source_page_id\", back_populates=\"source_page\"\n    )\n    incoming_links: Mapped[list[RelatedTechniqueLink]] = sa_relationship(\n        foreign_keys=\"RelatedTechniqueLink.target_page_id\", back_populates=\"target_page\"\n    )\n\n[248 more lines in file. Use offset=250 to continue.]"}],"isError":false,"timestamp":1775179283054}}
{"type":"message","id":"bf081d03","parentId":"c87d63e9","timestamp":"2026-04-03T01:21:23.061Z","message":{"role":"toolResult","toolCallId":"toolu_01FHR2MJMEjb2pEkoKUVmXk8","toolName":"read","content":[{"type":"text","text":"\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    name: Mapped[str] = mapped_column(String(255), unique=True, nullable=False)\n    category: Mapped[str] = mapped_column(String(255), nullable=False)\n    aliases: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n\n\nclass TechniquePageVideo(Base):\n    \"\"\"Association linking a technique page to its contributing source videos.\"\"\"\n    __tablename__ = \"technique_page_videos\"\n    __table_args__ = (\n        UniqueConstraint(\"technique_page_id\", \"source_video_id\", name=\"uq_page_video\"),\n    )\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    technique_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    source_video_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    added_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n\n    # relationships\n    technique_page: Mapped[TechniquePage] = sa_relationship(\n        back_populates=\"source_video_links\"\n    )\n    source_video: Mapped[SourceVideo] = sa_relationship()\n\n\n# ── Content Report Enums ─────────────────────────────────────────────────────\n\nclass ReportType(str, enum.Enum):\n    \"\"\"Classification of user-submitted content reports.\"\"\"\n    inaccurate = \"inaccurate\"\n    missing_info = \"missing_info\"\n    wrong_attribution = \"wrong_attribution\"\n    formatting = \"formatting\"\n\n[153 more lines in file. Use offset=345 to continue.]"}],"isError":false,"timestamp":1775179283058}}
{"type":"message","id":"591e163a","parentId":"bf081d03","timestamp":"2026-04-03T01:21:27.940Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the schemas for SynthesizedPage and BodySection to understand the v2 format."},{"type":"toolCall","id":"toolu_01Dwf6wAhsqGHhouNVkWtMKy","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"class BodySection\\|class BodySubSection\\|class SynthesizedPage\\|class SynthesisResult\\|body_sections_format\\|body_sections\" backend/pipeline/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":144,"cacheRead":78220,"cacheWrite":1535,"totalTokens":79900,"cost":{"input":0.000005,"output":0.0036000000000000003,"cacheRead":0.03911,"cacheWrite":0.00959375,"total":0.052308749999999994}},"stopReason":"toolUse","timestamp":1775179283058}}
{"type":"message","id":"7459a346","parentId":"591e163a","timestamp":"2026-04-03T01:21:27.952Z","message":{"role":"toolResult","toolCallId":"toolu_01Dwf6wAhsqGHhouNVkWtMKy","toolName":"bash","content":[{"type":"text","text":"73:class BodySubSection(BaseModel):\n80:class BodySection(BaseModel):\n91:class SynthesizedPage(BaseModel):\n99:    body_sections: list[BodySection] = Field(\n103:    body_sections_format: str = Field(\n105:        description=\"Schema version for body_sections ('v2' = list[BodySection])\",\n122:class SynthesisResult(BaseModel):\n"}],"isError":false,"timestamp":1775179287951}}
{"type":"message","id":"4ff668ea","parentId":"7459a346","timestamp":"2026-04-03T01:21:31.190Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01EEqeozqBUVKLyPBKpRf5HU","name":"read","arguments":{"limit":65,"offset":73,"path":"backend/pipeline/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":91,"cacheRead":79755,"cacheWrite":260,"totalTokens":80107,"cost":{"input":0.000005,"output":0.002275,"cacheRead":0.039877499999999996,"cacheWrite":0.0016250000000000001,"total":0.043782499999999995}},"stopReason":"toolUse","timestamp":1775179287951}}
{"type":"message","id":"bcf8fed8","parentId":"4ff668ea","timestamp":"2026-04-03T01:21:31.195Z","message":{"role":"toolResult","toolCallId":"toolu_01EEqeozqBUVKLyPBKpRf5HU","toolName":"read","content":[{"type":"text","text":"class BodySubSection(BaseModel):\n    \"\"\"An H3-level subsection within a body section.\"\"\"\n\n    heading: str = Field(description=\"H3 subsection heading\")\n    content: str = Field(description=\"Subsection body text (may contain [N] citation markers)\")\n\n\nclass BodySection(BaseModel):\n    \"\"\"An H2-level section of a technique page body.\"\"\"\n\n    heading: str = Field(description=\"H2 section heading\")\n    content: str = Field(description=\"Section body text (may contain [N] citation markers)\")\n    subsections: list[BodySubSection] = Field(\n        default_factory=list,\n        description=\"Optional H3-level subsections\",\n    )\n\n\nclass SynthesizedPage(BaseModel):\n    \"\"\"A technique page synthesized from classified moments.\"\"\"\n\n    title: str = Field(description=\"Page title\")\n    slug: str = Field(description=\"URL-safe slug\")\n    topic_category: str = Field(description=\"Primary topic category\")\n    topic_tags: list[str] = Field(default_factory=list, description=\"Associated tags\")\n    summary: str = Field(description=\"Page summary / overview paragraph\")\n    body_sections: list[BodySection] = Field(\n        default_factory=list,\n        description=\"Structured body content as H2 sections with optional H3 subsections\",\n    )\n    body_sections_format: str = Field(\n        default=\"v2\",\n        description=\"Schema version for body_sections ('v2' = list[BodySection])\",\n    )\n    signal_chains: list[dict] = Field(\n        default_factory=list,\n        description=\"Signal chain descriptions (for audio/music production contexts)\",\n    )\n    plugins: list[str] = Field(default_factory=list, description=\"Plugins/tools referenced\")\n    source_quality: str = Field(\n        default=\"mixed\",\n        description=\"One of: structured, mixed, unstructured\",\n    )\n    moment_indices: list[int] = Field(\n        default_factory=list,\n        description=\"Indices of source moments (from the input list) that this page covers\",\n    )\n\n\nclass SynthesisResult(BaseModel):\n    \"\"\"Full output of stage 5 (synthesis).\"\"\"\n\n    pages: list[SynthesizedPage]\n"}],"isError":false,"timestamp":1775179291192}}
{"type":"message","id":"6658cd3c","parentId":"bcf8fed8","timestamp":"2026-04-03T01:21:36.259Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me understand what compose-or-create logic means — I need to check the _build_moments_text helper used in stage 5, and see the existing pipeline_events model."},{"type":"toolCall","id":"toolu_01JausVVMkC5AJgmTbeBtJZk","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"def _build_moments_text\" backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_015cdQh1ephgVdgMMBp4SqTD","name":"read","arguments":{"limit":50,"offset":884,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":202,"cacheRead":79755,"cacheWrite":906,"totalTokens":80864,"cost":{"input":0.000005,"output":0.005050000000000001,"cacheRead":0.039877499999999996,"cacheWrite":0.0056625,"total":0.050595}},"stopReason":"toolUse","timestamp":1775179291193}}
{"type":"message","id":"11ac9509","parentId":"6658cd3c","timestamp":"2026-04-03T01:21:36.274Z","message":{"role":"toolResult","toolCallId":"toolu_01JausVVMkC5AJgmTbeBtJZk","toolName":"bash","content":[{"type":"text","text":"957:def _build_moments_text(\n"}],"isError":false,"timestamp":1775179296273}}
{"type":"message","id":"5668a976","parentId":"11ac9509","timestamp":"2026-04-03T01:21:36.278Z","message":{"role":"toolResult","toolCallId":"toolu_015cdQh1ephgVdgMMBp4SqTD","toolName":"read","content":[{"type":"text","text":"def _capture_pipeline_metadata() -> dict:\n    \"\"\"Capture current pipeline configuration for version metadata.\n\n    Returns a dict with model names, prompt file SHA-256 hashes, and stage\n    modality settings. Handles missing prompt files gracefully.\n    \"\"\"\n    settings = get_settings()\n    prompts_path = Path(settings.prompts_path)\n\n    # Hash each prompt template file\n    prompt_hashes: dict[str, str] = {}\n    prompt_files = [\n        \"stage2_segmentation.txt\",\n        \"stage3_extraction.txt\",\n        \"stage4_classification.txt\",\n        \"stage5_synthesis.txt\",\n    ]\n    for filename in prompt_files:\n        filepath = prompts_path / filename\n        try:\n            content = filepath.read_bytes()\n            prompt_hashes[filename] = hashlib.sha256(content).hexdigest()\n        except FileNotFoundError:\n            logger.warning(\"Prompt file not found for metadata capture: %s\", filepath)\n            prompt_hashes[filename] = \"\"\n        except OSError as exc:\n            logger.warning(\"Could not read prompt file %s: %s\", filepath, exc)\n            prompt_hashes[filename] = \"\"\n\n    return {\n        \"git_commit_sha\": _get_git_commit_sha(),\n        \"models\": {\n            \"stage2\": settings.llm_stage2_model,\n            \"stage3\": settings.llm_stage3_model,\n            \"stage4\": settings.llm_stage4_model,\n            \"stage5\": settings.llm_stage5_model,\n            \"embedding\": settings.embedding_model,\n        },\n        \"modalities\": {\n            \"stage2\": settings.llm_stage2_modality,\n            \"stage3\": settings.llm_stage3_modality,\n            \"stage4\": settings.llm_stage4_modality,\n            \"stage5\": settings.llm_stage5_modality,\n        },\n        \"prompt_hashes\": prompt_hashes,\n    }\n\n\n# ── Stage 5: Synthesis ───────────────────────────────────────────────────────\n\n\n[1170 more lines in file. Use offset=934 to continue.]"}],"isError":false,"timestamp":1775179296273}}
{"type":"message","id":"2ad0ba84","parentId":"5668a976","timestamp":"2026-04-03T01:21:39.438Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Sx3TtWrMy9DKkGnHvQbUTB","name":"read","arguments":{"limit":30,"offset":957,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":91,"cacheRead":80661,"cacheWrite":808,"totalTokens":81561,"cost":{"input":0.000005,"output":0.002275,"cacheRead":0.0403305,"cacheWrite":0.005050000000000001,"total":0.047660499999999995}},"stopReason":"toolUse","timestamp":1775179296275}}
{"type":"message","id":"ee329da7","parentId":"2ad0ba84","timestamp":"2026-04-03T01:21:39.443Z","message":{"role":"toolResult","toolCallId":"toolu_01Sx3TtWrMy9DKkGnHvQbUTB","toolName":"read","content":[{"type":"text","text":"def _build_moments_text(\n    moment_group: list[tuple[KeyMoment, dict]],\n    category: str,\n) -> tuple[str, set[str]]:\n    \"\"\"Build the moments prompt text and collect all tags for a group of moments.\n\n    Returns (moments_text, all_tags).\n    \"\"\"\n    moments_lines = []\n    all_tags: set[str] = set()\n    for i, (m, cls_info) in enumerate(moment_group):\n        tags = cls_info.get(\"topic_tags\", [])\n        all_tags.update(tags)\n        moments_lines.append(\n            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    return \"\\n\\n\".join(moments_lines), all_tags\n\n\ndef _synthesize_chunk(\n    chunk: list[tuple[KeyMoment, dict]],\n    category: str,\n    creator_name: str,\n\n[1117 more lines in file. Use offset=987 to continue.]"}],"isError":false,"timestamp":1775179299441}}
{"type":"message","id":"e9c2ee2b","parentId":"ee329da7","timestamp":"2026-04-03T01:21:43.165Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the run_pipeline function to understand the orchestration flow."},{"type":"toolCall","id":"toolu_01WNexbxz4EgpYiA4HLkovkp","name":"read","arguments":{"limit":120,"offset":1787,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":109,"cacheRead":81469,"cacheWrite":500,"totalTokens":82079,"cost":{"input":0.000005,"output":0.002725,"cacheRead":0.0407345,"cacheWrite":0.003125,"total":0.046589500000000006}},"stopReason":"toolUse","timestamp":1775179299441}}
{"type":"message","id":"ca92efef","parentId":"e9c2ee2b","timestamp":"2026-04-03T01:21:43.170Z","message":{"role":"toolResult","toolCallId":"toolu_01WNexbxz4EgpYiA4HLkovkp","toolName":"read","content":[{"type":"text","text":"def run_pipeline(video_id: str, trigger: str = \"manual\") -> str:\n    \"\"\"Orchestrate the full pipeline (stages 2-6) with auto-resume.\n\n    For error/processing status, queries pipeline_events to find the last\n    stage that completed successfully and resumes from the next stage.\n    This avoids re-running expensive LLM stages that already succeeded.\n\n    For clean_reprocess trigger, always starts from stage 2.\n\n    Returns the video_id.\n    \"\"\"\n    logger.info(\"run_pipeline starting for video_id=%s\", video_id)\n\n    session = _get_sync_session()\n    try:\n        video = session.execute(\n            select(SourceVideo).where(SourceVideo.id == video_id)\n        ).scalar_one_or_none()\n\n        if video is None:\n            logger.error(\"run_pipeline: video_id=%s not found\", video_id)\n            raise ValueError(f\"Video not found: {video_id}\")\n\n        status = video.processing_status\n        logger.info(\n            \"run_pipeline: video_id=%s current status=%s\", video_id, status.value\n        )\n    finally:\n        session.close()\n\n    if status == ProcessingStatus.complete:\n        logger.info(\n            \"run_pipeline: video_id=%s already at status=%s, nothing to do.\",\n            video_id, status.value,\n        )\n        return video_id\n\n    # Snapshot prior technique pages before pipeline resets key_moments\n    _snapshot_prior_pages(video_id)\n\n    # Create a pipeline run record\n    run_id = _create_run(video_id, trigger)\n    logger.info(\"run_pipeline: created run_id=%s for video_id=%s (trigger=%s)\", run_id, video_id, trigger)\n\n    # Determine which stages to run\n    resume_from_idx = 0  # Default: start from stage 2\n\n    if trigger != \"clean_reprocess\" and status in (ProcessingStatus.processing, ProcessingStatus.error):\n        # Try to resume from where we left off\n        last_completed = _get_last_completed_stage(video_id)\n        if last_completed and last_completed in _PIPELINE_STAGES:\n            completed_idx = _PIPELINE_STAGES.index(last_completed)\n            resume_from_idx = completed_idx + 1\n            if resume_from_idx >= len(_PIPELINE_STAGES):\n                logger.info(\n                    \"run_pipeline: all stages already completed for video_id=%s\",\n                    video_id,\n                )\n                return video_id\n\n    stages_to_run = _PIPELINE_STAGES[resume_from_idx:]\n    logger.info(\n        \"run_pipeline: video_id=%s will run stages: %s (resume_from_idx=%d)\",\n        video_id, stages_to_run, resume_from_idx,\n    )\n\n    # Run stages inline (synchronously) so each video completes fully\n    # before the worker picks up the next queued video.\n    # This replaces the previous celery_chain dispatch which caused\n    # interleaved execution when multiple videos were queued.\n    if stages_to_run:\n        # Mark as processing before starting\n        session = _get_sync_session()\n        try:\n            video = session.execute(\n                select(SourceVideo).where(SourceVideo.id == video_id)\n            ).scalar_one()\n            video.processing_status = ProcessingStatus.processing\n            session.commit()\n        finally:\n            session.close()\n\n        logger.info(\n            \"run_pipeline: executing %d stages inline for video_id=%s (run_id=%s, starting at %s)\",\n            len(stages_to_run), video_id, run_id, stages_to_run[0],\n        )\n\n        try:\n            for stage_name in stages_to_run:\n                task_func = _STAGE_TASKS[stage_name]\n                # Call the task directly — runs synchronously in this worker\n                # process. bind=True tasks receive the task instance as self\n                # automatically when called this way.\n                task_func(video_id, run_id=run_id)\n        except Exception as exc:\n            logger.error(\n                \"run_pipeline: stage %s failed for video_id=%s: %s\",\n                stage_name, video_id, exc,\n            )\n            _set_error_status(video_id, stage_name, exc)\n            if run_id:\n                _finish_run(run_id, \"error\", error_stage=stage_name)\n            raise\n\n    return video_id\n\n\n# ── Single-Stage Re-Run ─────────────────────────────────────────────────────\n\n@celery_app.task\ndef run_single_stage(\n    video_id: str,\n    stage_name: str,\n    trigger: str = \"stage_rerun\",\n    prompt_override: str | None = None,\n) -> str:\n    \"\"\"Re-run a single pipeline stage without running predecessors.\n\n    Designed for fast prompt iteration — especially stage 5 synthesis.\n    Bypasses the processing_status==complete guard, creates a proper\n\n[197 more lines in file. Use offset=1907 to continue.]"}],"isError":false,"timestamp":1775179303168}}
{"type":"message","id":"d5bffb8a","parentId":"ca92efef","timestamp":"2026-04-03T01:21:47.375Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me see the existing `_snapshot_prior_pages` function and how Redis is used for classification data."},{"type":"toolCall","id":"toolu_01GqNJ7BzcP8YSDDRMBFMFKu","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"def _snapshot_prior_pages\" backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_01Jt7reNqqcR9HYCY5NDVNc3","name":"read","arguments":{"limit":50,"offset":791,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":186,"cacheRead":81969,"cacheWrite":1393,"totalTokens":83549,"cost":{"input":0.000005,"output":0.0046500000000000005,"cacheRead":0.0409845,"cacheWrite":0.00870625,"total":0.05434575}},"stopReason":"toolUse","timestamp":1775179303169}}
{"type":"message","id":"b86a2a55","parentId":"d5bffb8a","timestamp":"2026-04-03T01:21:47.387Z","message":{"role":"toolResult","toolCallId":"toolu_01GqNJ7BzcP8YSDDRMBFMFKu","toolName":"bash","content":[{"type":"text","text":"1602:def _snapshot_prior_pages(video_id: str) -> None:\n"}],"isError":false,"timestamp":1775179307386}}
{"type":"message","id":"b643b7b3","parentId":"b86a2a55","timestamp":"2026-04-03T01:21:47.391Z","message":{"role":"toolResult","toolCallId":"toolu_01Jt7reNqqcR9HYCY5NDVNc3","toolName":"read","content":[{"type":"text","text":"def _load_classification_data(video_id: str) -> list[dict]:\n    \"\"\"Load classification data from Redis (fast path) or PostgreSQL (fallback).\n\n    Tries Redis first. If the key has expired or Redis is unavailable, falls\n    back to the durable SourceVideo.classification_data column.\n    \"\"\"\n    import redis\n\n    settings = get_settings()\n\n    # Try Redis first (fast path)\n    try:\n        r = redis.Redis.from_url(settings.redis_url)\n        key = f\"chrysopedia:classification:{video_id}\"\n        raw = r.get(key)\n        if raw is not None:\n            data = json.loads(raw)\n            logger.info(\n                \"[CLASSIFY-LOAD] Source: redis, video_id=%s, %d entries\",\n                video_id, len(data),\n            )\n            return data\n    except Exception as exc:\n        logger.warning(\n            \"[CLASSIFY-LOAD] Redis unavailable for video_id=%s: %s\", video_id, exc,\n        )\n\n    # Fallback to PostgreSQL\n    logger.info(\"[CLASSIFY-LOAD] Redis miss, falling back to PostgreSQL for video_id=%s\", video_id)\n    session = _get_sync_session()\n    try:\n        video = session.execute(\n            select(SourceVideo).where(SourceVideo.id == video_id)\n        ).scalar_one_or_none()\n        if video and video.classification_data:\n            data = video.classification_data\n            logger.info(\n                \"[CLASSIFY-LOAD] Source: postgresql, video_id=%s, %d entries\",\n                video_id, len(data),\n            )\n            return data\n    except Exception as exc:\n        logger.warning(\n            \"[CLASSIFY-LOAD] PostgreSQL fallback failed for video_id=%s: %s\", video_id, exc,\n        )\n    finally:\n        session.close()\n\n    logger.warning(\n        \"[CLASSIFY-LOAD] No classification data found in Redis or PostgreSQL for video_id=%s\",\n\n[1263 more lines in file. Use offset=841 to continue.]"}],"isError":false,"timestamp":1775179307386}}
{"type":"message","id":"73fdb763","parentId":"b643b7b3","timestamp":"2026-04-03T01:21:51.306Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01UaFVaY3BoQsS8GGR7FKZNb","name":"read","arguments":{"limit":40,"offset":1602,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":83362,"cacheWrite":786,"totalTokens":84241,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.041680999999999996,"cacheWrite":0.0049125,"total":0.0488985}},"stopReason":"toolUse","timestamp":1775179307388}}
{"type":"message","id":"8559a31c","parentId":"73fdb763","timestamp":"2026-04-03T01:21:51.311Z","message":{"role":"toolResult","toolCallId":"toolu_01UaFVaY3BoQsS8GGR7FKZNb","toolName":"read","content":[{"type":"text","text":"def _snapshot_prior_pages(video_id: str) -> None:\n    \"\"\"Save existing technique_page_ids linked to this video before pipeline resets them.\n\n    When a video is reprocessed, stage 3 deletes and recreates key_moments,\n    breaking the link to technique pages. This snapshots the page IDs to Redis\n    so stage 5 can find and update prior pages instead of creating duplicates.\n    \"\"\"\n    import redis\n\n    session = _get_sync_session()\n    try:\n        # Find technique pages linked via this video's key moments\n        rows = session.execute(\n            select(KeyMoment.technique_page_id)\n            .where(\n                KeyMoment.source_video_id == video_id,\n                KeyMoment.technique_page_id.isnot(None),\n            )\n            .distinct()\n        ).scalars().all()\n\n        page_ids = [str(pid) for pid in rows]\n\n        if page_ids:\n            settings = get_settings()\n            r = redis.Redis.from_url(settings.redis_url)\n            key = f\"chrysopedia:prior_pages:{video_id}\"\n            r.set(key, json.dumps(page_ids), ex=86400)\n            logger.info(\n                \"Snapshot %d prior technique pages for video_id=%s: %s\",\n                len(page_ids), video_id, page_ids,\n            )\n        else:\n            logger.info(\"No prior technique pages for video_id=%s\", video_id)\n    finally:\n        session.close()\n\n\ndef _load_prior_pages(video_id: str) -> list[str]:\n    \"\"\"Load prior technique page IDs from Redis.\"\"\"\n\n[462 more lines in file. Use offset=1642 to continue.]"}],"isError":false,"timestamp":1775179311309}}
{"type":"message","id":"61f15406","parentId":"8559a31c","timestamp":"2026-04-03T01:21:55.324Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me look at the compose prompt itself to understand what inputs are needed."},{"type":"toolCall","id":"toolu_01VU2FPoHj2H6EibSVV3T45r","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && head -30 prompts/stage5_compose.txt"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":98,"cacheRead":84148,"cacheWrite":534,"totalTokens":84781,"cost":{"input":0.000005,"output":0.00245,"cacheRead":0.042074,"cacheWrite":0.0033375,"total":0.0478665}},"stopReason":"toolUse","timestamp":1775179311310}}
{"type":"message","id":"1c161867","parentId":"61f15406","timestamp":"2026-04-03T01:21:55.339Z","message":{"role":"toolResult","toolCallId":"toolu_01VU2FPoHj2H6EibSVV3T45r","toolName":"bash","content":[{"type":"text","text":"You are updating an existing Chrysopedia technique page with new source material. A technique page already exists — it was synthesized from earlier video moments. Now new moments from additional videos are available. Your job is to compose a unified page that integrates the new material into the existing page, preserving what's already good while enriching it with genuinely new detail.\n\nThis is NOT a rewrite. This is a merge. The existing page has been reviewed and published. Treat its prose as the baseline — preserve its voice, structure, and specific details. Your job is to strengthen it with new information, not to rephrase what's already well-written.\n\n## What you receive\n\nYou receive four inputs in XML tags:\n\n1. `<existing_page>` — the current SynthesizedPage JSON (v2 format: body_sections as list of BodySection objects with heading, content, subsections)\n2. `<existing_moments>` — JSON array of the key moments that the existing page was built from (indices [0] through [N-1])\n3. `<new_moments>` — JSON array of new key moments to integrate (these will be indexed [N] through [N+M-1] in the composed output)\n4. `<creator>` — the creator name (same creator as the existing page)\n\n## Citation re-indexing — critical\n\nThe composed page uses a unified citation index space across both old and new moments:\n\n- **Existing moments** keep their original indices: `[0]`, `[1]`, ..., `[N-1]` where N is the length of `<existing_moments>`.\n- **New moments** are assigned indices starting at N: the first new moment is `[N]`, the second is `[N+1]`, ..., the last is `[N+M-1]` where M is the length of `<new_moments>`.\n- All citation markers `[N]` in the existing page prose remain valid and unchanged.\n- New citations referencing new moments use the offset indices: if there are 5 existing moments, the first new moment is cited as `[5]`, the second as `[6]`, etc.\n- When new information reinforces an existing claim, you may add a new citation alongside the existing one: `[2,7]` means the claim is supported by both existing moment 2 and new moment 7.\n\n**Never renumber existing citations.** The existing `[0]`, `[1]`, etc. in the prose must remain exactly as they are. Only add new `[N+...]` markers where new material contributes.\n\n## Merge rules\n\n### Preserve existing prose\nKeep existing section headings, prose, citations, and structure intact unless you have a specific reason to change something. Reasons to change existing prose:\n- A new moment directly contradicts an existing claim (explain the contradiction, don't silently overwrite)\n"}],"isError":false,"timestamp":1775179315336}}
{"type":"message","id":"525293cd","parentId":"1c161867","timestamp":"2026-04-03T01:21:59.677Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. Now I have a thorough understanding of the full architecture. Let me check how SourceVideo links to creators to understand the compose-or-create decision point."},{"type":"toolCall","id":"toolu_01Xt6c8prkQLJWYWgLYmVE5M","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"class SourceVideo\\|creator_id\\|class Creator\" backend/models.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":131,"cacheRead":84682,"cacheWrite":755,"totalTokens":85569,"cost":{"input":0.000005,"output":0.003275,"cacheRead":0.042341,"cacheWrite":0.00471875,"total":0.050339749999999996}},"stopReason":"toolUse","timestamp":1775179315337}}
{"type":"message","id":"64ccc04b","parentId":"525293cd","timestamp":"2026-04-03T01:21:59.696Z","message":{"role":"toolResult","toolCallId":"toolu_01Xt6c8prkQLJWYWgLYmVE5M","toolName":"bash","content":[{"type":"text","text":"98:class Creator(Base):\n120:class SourceVideo(Base):\n124:    creator_id: Mapped[uuid.UUID] = mapped_column(\n210:    creator_id: Mapped[uuid.UUID] = mapped_column(\n"}],"isError":false,"timestamp":1775179319693}}
{"type":"message","id":"b47ef45f","parentId":"64ccc04b","timestamp":"2026-04-03T01:22:06.262Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check what the slice plan says — the roadmap demo says: \"Process two COPYCATT videos. Second video's moments composed into existing page. technique_page_videos has both video IDs.\""},{"type":"toolCall","id":"toolu_015n3EWLDY6QqJnbNw22PZrS","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls .gsd/milestones/M014/slices/S04/"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":132,"cacheRead":85437,"cacheWrite":201,"totalTokens":85771,"cost":{"input":0.000005,"output":0.0033,"cacheRead":0.0427185,"cacheWrite":0.00125625,"total":0.04727975}},"stopReason":"toolUse","timestamp":1775179319694}}
{"type":"message","id":"9562fb03","parentId":"b47ef45f","timestamp":"2026-04-03T01:22:06.276Z","message":{"role":"toolResult","toolCallId":"toolu_015n3EWLDY6QqJnbNw22PZrS","toolName":"bash","content":[{"type":"text","text":"S04-PLAN.md\ntasks\n"}],"isError":false,"timestamp":1775179326273}}
{"type":"message","id":"dc91011a","parentId":"9562fb03","timestamp":"2026-04-03T01:22:09.947Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01FCVhZLVbJVT27YhxXPRTZ9","name":"read","arguments":{"path":".gsd/milestones/M014/slices/S04/S04-PLAN.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":74,"cacheRead":85638,"cacheWrite":154,"totalTokens":85867,"cost":{"input":0.000005,"output":0.00185,"cacheRead":0.042818999999999996,"cacheWrite":0.0009625,"total":0.045636499999999997}},"stopReason":"toolUse","timestamp":1775179326274}}
{"type":"message","id":"cf872234","parentId":"dc91011a","timestamp":"2026-04-03T01:22:09.950Z","message":{"role":"toolResult","toolCallId":"toolu_01FCVhZLVbJVT27YhxXPRTZ9","toolName":"read","content":[{"type":"text","text":"# S04: Pipeline Compose-or-Create Logic\n\n**Goal:** Stage 5 uses new prompt (format-2), detects existing pages for compose, tracks video associations.\n**Demo:** After this: Process two COPYCATT videos. Second video's moments composed into existing page. technique_page_videos has both video IDs.\n\n## Tasks\n"}],"isError":false,"timestamp":1775179329949}}
{"type":"message","id":"0d5066b8","parentId":"cf872234","timestamp":"2026-04-03T01:22:15.295Z","message":{"role":"assistant","content":[{"type":"text","text":"Skeleton plan — no tasks yet. That's what the planner will fill in after this research. Let me now understand the full compose-or-create decision flow that needs to happen in stage 5."},{"type":"toolCall","id":"toolu_01CsDgzJDiKv4DRNY3s7JBVz","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"source_video_links\\|TechniquePageVideo\" backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":137,"cacheRead":85792,"cacheWrite":163,"totalTokens":86093,"cost":{"input":0.000005,"output":0.003425,"cacheRead":0.042896,"cacheWrite":0.0010187500000000001,"total":0.04734475}},"stopReason":"toolUse","timestamp":1775179329949}}
{"type":"message","id":"e5577c25","parentId":"0d5066b8","timestamp":"2026-04-03T01:22:15.309Z","message":{"role":"toolResult","toolCallId":"toolu_01CsDgzJDiKv4DRNY3s7JBVz","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775179335306}}
{"type":"message","id":"74addd5d","parentId":"e5577c25","timestamp":"2026-04-03T01:22:20.873Z","message":{"role":"assistant","content":[{"type":"text","text":"As expected — stages.py currently has no compose logic and no TechniquePageVideo usage. This is the new work. Let me also check the existing _merge_pages_by_slug to understand how within-video merging works (since compose is cross-video merging)."},{"type":"toolCall","id":"toolu_011rDSUmCkT2XZi4zkMA3adT","name":"read","arguments":{"limit":80,"offset":1034,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":153,"cacheRead":85955,"cacheWrite":168,"totalTokens":86277,"cost":{"input":0.000005,"output":0.0038250000000000003,"cacheRead":0.042977499999999995,"cacheWrite":0.0010500000000000002,"total":0.0478575}},"stopReason":"toolUse","timestamp":1775179335307}}
{"type":"message","id":"0cb2103e","parentId":"74addd5d","timestamp":"2026-04-03T01:22:20.881Z","message":{"role":"toolResult","toolCallId":"toolu_011rDSUmCkT2XZi4zkMA3adT","toolName":"read","content":[{"type":"text","text":"def _merge_pages_by_slug(\n    all_pages: list,\n    creator_name: str,\n    llm: LLMClient,\n    model_override: str | None,\n    modality: str,\n    hard_limit: int,\n    video_id: str,\n    run_id: str | None,\n) -> list:\n    \"\"\"Detect pages with the same slug across chunks and merge them via LLM.\n\n    Pages with unique slugs pass through unchanged. Pages sharing a slug\n    get sent to a merge prompt that combines them into one cohesive page.\n\n    Returns the final list of SynthesizedPage objects.\n    \"\"\"\n    from pipeline.schemas import SynthesizedPage\n\n    # Group pages by slug\n    by_slug: dict[str, list] = defaultdict(list)\n    for page in all_pages:\n        by_slug[_slug_base(page.slug)].append(page)\n\n    final_pages = []\n    for slug, pages_group in by_slug.items():\n        if len(pages_group) == 1:\n            # Unique slug — no merge needed\n            final_pages.append(pages_group[0])\n            continue\n\n        # Multiple pages share this slug — merge via LLM\n        logger.info(\n            \"Stage 5: Merging %d partial pages with slug '%s' for video_id=%s\",\n            len(pages_group), slug, video_id,\n        )\n\n        # Serialize partial pages to JSON for the merge prompt\n        pages_json = json.dumps(\n            [p.model_dump() for p in pages_group],\n            indent=2, ensure_ascii=False,\n        )\n\n        merge_system_prompt = _load_prompt(\"stage5_merge.txt\", video_id=video_id)\n        merge_user_prompt = f\"<creator>{creator_name}</creator>\\n<pages>\\n{pages_json}\\n</pages>\"\n\n        max_tokens = estimate_max_tokens(\n            merge_system_prompt, merge_user_prompt,\n            stage=\"stage5_synthesis\", hard_limit=hard_limit,\n        )\n        logger.info(\n            \"Stage 5: Merge call for slug '%s' — %d partial pages, max_tokens=%d\",\n            slug, len(pages_group), max_tokens,\n        )\n\n        raw = llm.complete(\n            merge_system_prompt, merge_user_prompt,\n            response_model=SynthesisResult,\n            on_complete=_make_llm_callback(\n                video_id, \"stage5_synthesis\",\n                system_prompt=merge_system_prompt,\n                user_prompt=merge_user_prompt,\n                run_id=run_id, context_label=f\"merge:{slug}\",\n                request_params=_build_request_params(max_tokens, model_override, modality, \"SynthesisResult\", hard_limit),\n            ),\n            modality=modality, model_override=model_override,\n            max_tokens=max_tokens,\n        )\n        merge_result = _safe_parse_llm_response(\n            raw, SynthesisResult, llm,\n            merge_system_prompt, merge_user_prompt,\n            modality=modality, model_override=model_override,\n            max_tokens=max_tokens,\n        )\n\n        if merge_result.pages:\n            final_pages.extend(merge_result.pages)\n            logger.info(\n                \"Stage 5: Merge produced %d page(s) for slug '%s'\",\n                len(merge_result.pages), slug,\n\n[990 more lines in file. Use offset=1114 to continue.]"}],"isError":false,"timestamp":1775179340877}}
{"type":"message","id":"6b668834","parentId":"0cb2103e","timestamp":"2026-04-03T01:22:25.839Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check how the LLM client works, particularly the `_safe_parse_llm_response` function which the compose call will need."},{"type":"toolCall","id":"toolu_01Qe68WZLXquskmYx5cjVC2V","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"def _safe_parse_llm_response\\|def estimate_max_tokens\\|def _make_llm_callback\\|def _build_request_params\" backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":151,"cacheRead":86123,"cacheWrite":1047,"totalTokens":87322,"cost":{"input":0.000005,"output":0.003775,"cacheRead":0.043061499999999996,"cacheWrite":0.00654375,"total":0.053385249999999995}},"stopReason":"toolUse","timestamp":1775179340879}}
{"type":"message","id":"8fdbd4ce","parentId":"6b668834","timestamp":"2026-04-03T01:22:25.852Z","message":{"role":"toolResult","toolCallId":"toolu_01Qe68WZLXquskmYx5cjVC2V","toolName":"bash","content":[{"type":"text","text":"137:def _make_llm_callback(\n189:def _build_request_params(\n325:def _safe_parse_llm_response(\n"}],"isError":false,"timestamp":1775179345850}}
{"type":"message","id":"a47933aa","parentId":"8fdbd4ce","timestamp":"2026-04-03T01:22:29.185Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01PgW5M26zASVXpST89eBCWh","name":"read","arguments":{"limit":30,"offset":325,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":91,"cacheRead":87170,"cacheWrite":200,"totalTokens":87462,"cost":{"input":0.000005,"output":0.002275,"cacheRead":0.043585,"cacheWrite":0.00125,"total":0.047115}},"stopReason":"toolUse","timestamp":1775179345851}}
{"type":"message","id":"df624840","parentId":"a47933aa","timestamp":"2026-04-03T01:22:29.191Z","message":{"role":"toolResult","toolCallId":"toolu_01PgW5M26zASVXpST89eBCWh","toolName":"read","content":[{"type":"text","text":"def _safe_parse_llm_response(\n    raw,\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    max_tokens: int | None = None,\n):\n    \"\"\"Parse LLM response with truncation detection and one retry on failure.\n\n    If the response was truncated (finish_reason=length), raises\n    LLMTruncationError immediately — retrying with a JSON nudge would only\n    make things worse by adding tokens to an already-too-large prompt.\n\n    For non-truncation parse failures: retry once with a JSON nudge, then\n    raise on second failure.\n    \"\"\"\n    # Check for truncation before attempting parse\n    is_truncated = isinstance(raw, LLMResponse) and raw.truncated\n    if is_truncated:\n        logger.warning(\n            \"LLM response truncated (finish=length) for %s. \"\n            \"prompt_tokens=%s, completion_tokens=%s. Will not retry with nudge.\",\n            model_cls.__name__,\n            getattr(raw, \"prompt_tokens\", \"?\"),\n            getattr(raw, \"completion_tokens\", \"?\"),\n        )\n\n\n[1749 more lines in file. Use offset=355 to continue.]"}],"isError":false,"timestamp":1775179349188}}
{"type":"message","id":"d5431975","parentId":"df624840","timestamp":"2026-04-03T01:22:35.381Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the imports and key functions to understand `estimate_max_tokens`."},{"type":"toolCall","id":"toolu_01Dn5r27rZEo3kzjFv1QjeKp","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"def estimate_max_tokens\" backend/pipeline/stages.py backend/pipeline/llm_client.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":115,"cacheRead":87370,"cacheWrite":433,"totalTokens":87919,"cost":{"input":0.000005,"output":0.002875,"cacheRead":0.043684999999999995,"cacheWrite":0.0027062500000000003,"total":0.049271249999999996}},"stopReason":"toolUse","timestamp":1775179349189}}
{"type":"message","id":"65608c31","parentId":"d5431975","timestamp":"2026-04-03T01:22:35.397Z","message":{"role":"toolResult","toolCallId":"toolu_01Dn5r27rZEo3kzjFv1QjeKp","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/llm_client.py:118:def estimate_max_tokens(\n"}],"isError":false,"timestamp":1775179355393}}
{"type":"message","id":"2e89c345","parentId":"65608c31","timestamp":"2026-04-03T01:22:39.408Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — I have the full picture now. Let me also quickly check the prompts directory for what exists."},{"type":"toolCall","id":"toolu_01LpJbw82sgCBSeTDm7nbrBS","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls prompts/stage5*"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":98,"cacheRead":87803,"cacheWrite":148,"totalTokens":88050,"cost":{"input":0.000005,"output":0.00245,"cacheRead":0.043901499999999996,"cacheWrite":0.000925,"total":0.0472815}},"stopReason":"toolUse","timestamp":1775179355395}}
{"type":"message","id":"45bb4b8e","parentId":"2e89c345","timestamp":"2026-04-03T01:22:39.419Z","message":{"role":"toolResult","toolCallId":"toolu_01LpJbw82sgCBSeTDm7nbrBS","toolName":"bash","content":[{"type":"text","text":"prompts/stage5_compose.txt\nprompts/stage5_synthesis.20260401_101518.bak\nprompts/stage5_synthesis.20260403_005044.bak\nprompts/stage5_synthesis.txt\n\nprompts/stage5_variants:\nmanifest.json\nv001.txt\nv002.txt\nv003.txt\nv004.txt\nv005.txt\nv006.txt\nv007.txt\nv008.txt\nv009.txt\nv010.txt\nv011.txt\nv012.txt\nv013.txt\nv014.txt\nv015.txt\nv016.txt\nv017.txt\nv018.txt\nv019.txt\nv020.txt\nv021.txt\nv022.txt\nv023.txt\nv024.txt\nv025.txt\nv026.txt\nv027.txt\nv028.txt\nv029.txt\nv030.txt\nv031.txt\nv032.txt\nv033.txt\nv034.txt\nv035.txt\nv036.txt\nv037.txt\nv038.txt\nv039.txt\nv040.txt\nv041.txt\nv042.txt\nv043.txt\nv044.txt\nv045.txt\nv046.txt\nv047.txt\nv048.txt\nv049.txt\nv050.txt\nv051.txt\nv052.txt\nv053.txt\nv054.txt\nv055.txt\nv056.txt\nv057.txt\nv058.txt\nv059.txt\nv060.txt\nv061.txt\nv062.txt\nv063.txt\nv064.txt\nv065.txt\nv066.txt\nv067.txt\nv068.txt\nv069.txt\nv070.txt\nv071.txt\nv072.txt\nv073.txt\nv074.txt\nv075.txt\nv076.txt\nv077.txt\nv078.txt\nv079.txt\nv080.txt\nv081.txt\nv082.txt\nv083.txt\nv084.txt\nv085.txt\nv086.txt\nv087.txt\nv088.txt\nv089.txt\nv090.txt\nv091.txt\nv092.txt\nv093.txt\nv094.txt\nv095.txt\nv096.txt\nv097.txt\nv098.txt\nv099.txt\nv100.txt\n"}],"isError":false,"timestamp":1775179359418}}
{"type":"message","id":"7c2a3cce","parentId":"45bb4b8e","timestamp":"2026-04-03T01:22:42.982Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the existing test files to understand what tests exist and what verification needs to look like."},{"type":"toolCall","id":"toolu_01RxZCB1VrtCb1yPGwuT62K1","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls backend/pipeline/test_*.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":99,"cacheRead":87951,"cacheWrite":679,"totalTokens":88730,"cost":{"input":0.000005,"output":0.002475,"cacheRead":0.0439755,"cacheWrite":0.00424375,"total":0.05069925}},"stopReason":"toolUse","timestamp":1775179359418}}
{"type":"message","id":"e2b48341","parentId":"7c2a3cce","timestamp":"2026-04-03T01:22:43.000Z","message":{"role":"toolResult","toolCallId":"toolu_01RxZCB1VrtCb1yPGwuT62K1","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/test_citation_utils.py\nbackend/pipeline/test_harness_compose.py\nbackend/pipeline/test_harness.py\nbackend/pipeline/test_harness_v2_format.py\n"}],"isError":false,"timestamp":1775179362996}}
{"type":"message","id":"6d4728ee","parentId":"e2b48341","timestamp":"2026-04-03T01:22:48.532Z","message":{"role":"assistant","content":[{"type":"text","text":"Let me check if there's a stage5_merge.txt (referenced in the _merge_pages_by_slug function)."},{"type":"toolCall","id":"toolu_01F83aBsVBSu5x8jGGVy7sjT","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls prompts/stage5_merge.txt 2>/dev/null && echo \"exists\" || echo \"not found\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":123,"cacheRead":88630,"cacheWrite":163,"totalTokens":88917,"cost":{"input":0.000005,"output":0.003075,"cacheRead":0.044315,"cacheWrite":0.0010187500000000001,"total":0.04841375}},"stopReason":"toolUse","timestamp":1775179362997}}
{"type":"message","id":"0ac97c59","parentId":"6d4728ee","timestamp":"2026-04-03T01:22:48.548Z","message":{"role":"toolResult","toolCallId":"toolu_01F83aBsVBSu5x8jGGVy7sjT","toolName":"bash","content":[{"type":"text","text":"not found\n"}],"isError":false,"timestamp":1775179368545}}
{"type":"message","id":"dd30eaa7","parentId":"0ac97c59","timestamp":"2026-04-03T01:22:53.855Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01ErkSrDRg98RG1vsAUfT7T4","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls prompts/*.txt"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":73,"cacheRead":88793,"cacheWrite":138,"totalTokens":89005,"cost":{"input":0.000005,"output":0.001825,"cacheRead":0.0443965,"cacheWrite":0.0008625000000000001,"total":0.047089}},"stopReason":"toolUse","timestamp":1775179368546}}
{"type":"message","id":"3225e745","parentId":"dd30eaa7","timestamp":"2026-04-03T01:22:53.869Z","message":{"role":"toolResult","toolCallId":"toolu_01ErkSrDRg98RG1vsAUfT7T4","toolName":"bash","content":[{"type":"text","text":"prompts/stage2_segmentation.txt\nprompts/stage3_extraction.txt\nprompts/stage4_classification.txt\nprompts/stage5_compose.txt\nprompts/stage5_synthesis.txt\n"}],"isError":false,"timestamp":1775179373867}}
{"type":"message","id":"8e3fcf8b","parentId":"3225e745","timestamp":"2026-04-03T01:22:58.689Z","message":{"role":"assistant","content":[{"type":"text","text":"Interesting — `stage5_merge.txt` doesn't exist but is referenced by `_merge_pages_by_slug`. That's an existing issue, not our concern.\n\nNow let me check the imports at the top of stages.py to understand what's available."},{"type":"toolCall","id":"toolu_013qyM5LBxxH7zvL44kdSLeu","name":"read","arguments":{"limit":60,"offset":1,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":150,"cacheRead":88931,"cacheWrite":136,"totalTokens":89218,"cost":{"input":0.000005,"output":0.0037500000000000003,"cacheRead":0.0444655,"cacheWrite":0.0008500000000000001,"total":0.0490705}},"stopReason":"toolUse","timestamp":1775179373868}}
{"type":"message","id":"c0c1487c","parentId":"8e3fcf8b","timestamp":"2026-04-03T01:22:58.694Z","message":{"role":"toolResult","toolCallId":"toolu_013qyM5LBxxH7zvL44kdSLeu","toolName":"read","content":[{"type":"text","text":"\"\"\"Pipeline stage tasks (stages 2-5) and run_pipeline orchestrator.\n\nEach stage reads from PostgreSQL via sync SQLAlchemy, loads its prompt\ntemplate from disk, calls the LLM client, parses the response, writes\nresults back, and updates processing_status on SourceVideo.\n\nCelery tasks are synchronous — all DB access uses ``sqlalchemy.orm.Session``.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport hashlib\nimport json\nimport logging\nimport subprocess\nimport time\nfrom collections import defaultdict\nfrom pathlib import Path\n\nimport yaml\n\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    Creator,\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, LLMResponse, 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\nclass LLMTruncationError(RuntimeError):\n    \"\"\"Raised when the LLM response was truncated (finish_reason=length).\"\"\"\n    pass\n\n\n# ── Error status helper ──────────────────────────────────────────────────────\n\ndef _set_error_status(video_id: str, stage_name: str, error: Exception) -> None:\n    \"\"\"Mark a video as errored when a pipeline stage fails permanently.\"\"\"\n\n[2043 more lines in file. Use offset=61 to continue.]"}],"isError":false,"timestamp":1775179378692}}
{"type":"message","id":"30987872","parentId":"c0c1487c","timestamp":"2026-04-03T01:24:01.144Z","message":{"role":"assistant","content":[{"type":"text","text":"Note that `TechniquePageVideo` is NOT imported yet. That'll need to be added.\n\nNow I have the complete picture. Let me write the research."},{"type":"toolCall","id":"toolu_01CEHu9jx2bb7SU8yi5zLKx9","name":"gsd_summary_save","arguments":{"artifact_type":"RESEARCH","content":"# S04 Research: Pipeline Compose-or-Create Logic\n\n## Summary\n\nThis slice modifies stage 5 (synthesis) in `backend/pipeline/stages.py` to:\n1. Detect when a technique page already exists for the same creator+category (from a prior video)\n2. Use the compose prompt (`stage5_compose.txt`) instead of synthesis prompt when composing\n3. Store the v2 `body_sections_format` on newly created/updated pages\n4. Populate the `technique_page_videos` association table so pages track all contributing videos\n\nThe compose prompt, Pydantic schemas, and test harness are all done (S01, S02). The data model and migration are done (S03). This slice wires them together in the live pipeline.\n\n## Recommendation\n\nTargeted research. The pattern is clear from reading the existing code — the compose-or-create decision is a conditional branch inside the per-category loop in `stage5_synthesis()`. The main risk is getting the LLM call plumbing right (same shape as `_synthesize_chunk` but using compose prompt and different user prompt format). No new technology. No ambiguous requirements.\n\n## Implementation Landscape\n\n### File: `backend/pipeline/stages.py` (2102 lines)\n\n**Current flow (stage5_synthesis, line 1127):**\n1. Load video, moments, creator, classification data\n2. Group moments by topic_category\n3. For each category group: synthesize via `_synthesize_chunk()` (or chunk+merge for large groups)\n4. Persist pages: check for existing page by slug or by prior_page_ids (from Redis snapshot), create or update\n5. Link moments to pages, set `processing_status = complete`\n\n**What changes:**\n- After grouping moments, before calling LLM: query DB for existing `TechniquePage` with same `creator_id` + `topic_category`\n- If found → compose path: load existing page's body_sections + moments, build compose prompt via logic similar to `build_compose_prompt()` from test_harness.py, call LLM with `stage5_compose.txt` system prompt, parse result as `SynthesisResult`\n- If not found → create path: existing synthesis flow (unchanged)\n- After persisting page: INSERT into `technique_page_videos` (upsert pattern due to unique constraint)\n- Set `body_sections_format = 'v2'` on all newly created/updated pages (both compose and create paths)\n\n**Key helpers to reuse:**\n- `_build_moments_text()` (line 957) — formats moments for prompt, returns (text, tags)\n- `_synthesize_chunk()` (line 983) — single-category synthesis LLM call\n- `_safe_parse_llm_response()` (line 325) — parse + truncation detection + retry\n- `_load_prompt()` (line 244) — loads from prompts/ directory\n- `estimate_max_tokens()` — from `pipeline.llm_client`\n- `_make_llm_callback()` (line 137) — observability callback\n- `_build_request_params()` (line 189) — LLM request config\n- `_capture_pipeline_metadata()` (line 884) — for version snapshots\n\n**New import needed:** `TechniquePageVideo` from `models`\n\n### File: `backend/pipeline/test_harness.py`\n\nContains `build_compose_prompt()` (line 332) which is the reference implementation for building the compose user prompt. The pipeline stage will need its own version that works with real `KeyMoment` ORM objects instead of `MockKeyMoment` test doubles. The structure is identical — XML tags with `<existing_page>`, `<existing_moments>`, `<new_moments>`, `<creator>`.\n\n### Compose prompt inputs needed:\n1. **existing_page** — JSON of the existing TechniquePage's SynthesizedPage-compatible dict (title, slug, topic_category, summary, body_sections, signal_chains, plugins, source_quality)\n2. **existing_moments** — formatted text of key moments already linked to this page (from previous video(s))\n3. **new_moments** — formatted text of moments from the current video, with offset indices starting at N\n4. **creator** — creator name\n\n### The compose-or-create decision point:\n\nFor each `(creator_id, topic_category)` group in the current video's moments:\n```\nexisting_page = SELECT FROM technique_pages \n  WHERE creator_id = ? AND LOWER(topic_category) = LOWER(?)\n  LIMIT 1\n```\n- If `existing_page` exists → compose path\n- If not → create path (current synthesis flow)\n\nThe existing code already does slug-based and prior_page_ids-based matching at *persist* time (line ~1320). The compose decision needs to happen *before* the LLM call, not after. This is the key architectural change — the detection moves earlier in the flow.\n\n### TechniquePageVideo population:\n\nAfter creating or updating a `TechniquePage`, insert a row:\n```python\nfrom sqlalchemy.dialects.postgresql import insert as pg_insert\n\nstmt = pg_insert(TechniquePageVideo).values(\n    technique_page_id=page.id,\n    source_video_id=video_id,\n).on_conflict_do_nothing()\nsession.execute(stmt)\n```\n\nThe `on_conflict_do_nothing` handles the unique constraint gracefully for reprocessing scenarios.\n\n### Loading existing moments for compose prompt:\n\nWhen composing, we need the existing page's linked moments:\n```python\nexisting_moments = session.execute(\n    select(KeyMoment)\n    .where(KeyMoment.technique_page_id == existing_page.id)\n    .order_by(KeyMoment.start_time)\n).scalars().all()\n```\n\nThese get formatted as indices [0]-[N-1]. The new video's moments for this category get indices [N]-[N+M-1].\n\n### body_sections_format tracking:\n\n- New pages created via synthesis → `body_sections_format = 'v2'` (the v5 prompt always outputs v2)\n- Pages updated via compose → `body_sections_format = 'v2'` (compose prompt also outputs v2)\n- Existing v1 pages NOT processed in this pipeline run → unchanged (backward compatible)\n\nThe column already has `default='v1'` and `server_default='v1'` from S03 migration.\n\n## Seams / Task Decomposition\n\n### T01: Compose detection + compose LLM call helper\n- Add `_build_compose_user_prompt()` helper (similar to test harness's `build_compose_prompt()` but using real ORM objects)\n- Add `_compose_into_existing()` helper that takes an existing page + existing moments + new moments, calls LLM with compose system prompt, returns `SynthesisResult`\n- This is the riskiest piece — get it testable before wiring into the main flow\n\n### T02: Wire compose-or-create into stage5_synthesis + TechniquePageVideo\n- Modify the per-category loop: before calling `_synthesize_chunk()`, check for existing page\n- If found and has moments → call `_compose_into_existing()`\n- If not → existing synthesis path\n- After persisting page: insert `TechniquePageVideo` row\n- Set `body_sections_format = 'v2'` on all pages touched\n- Add `TechniquePageVideo` to imports\n\n### T03: Integration test / verification\n- Test with mock LLM to verify compose path is triggered correctly\n- Verify TechniquePageVideo rows are created\n- Verify body_sections_format is set\n- The roadmap demo says \"Process two COPYCATT videos\" — this is an end-to-end verification on ub01, not something we can unit test here\n\n## Constraints\n\n1. **Sync SQLAlchemy only** — stages.py uses sync sessions (Celery is sync). No async.\n2. **stage5_merge.txt doesn't exist** — `_merge_pages_by_slug` references it but the file is missing. This is pre-existing; don't fix it in this slice.\n3. **Redis classification data** — stage 4 stores classification in Redis with 24h TTL. If the second video is processed >24h after the first, classification data for the first video may be gone. The compose flow loads existing *moments from the DB*, not classification data — so this isn't a blocker.\n4. **Existing page detection must be case-insensitive** — KNOWLEDGE.md notes \"LLM-generated topic categories have inconsistent casing\". Use `func.lower()` for category matching.\n5. **The compose prompt** (`stage5_compose.txt`) is self-contained — no runtime import from synthesis prompt needed (per S02 decision).\n6. **Existing _load_prior_pages Redis snapshot** — currently used for reprocessing the *same* video. The compose flow is for a *different* video contributing to the same page. These are separate mechanisms — compose uses DB query, reprocess uses Redis snapshot. Don't conflate them.\n\n## Risks\n\n1. **Compose LLM output quality** — First time the compose prompt runs against real LLM. May need prompt tuning. Mitigated by: the prompt was carefully designed in S02, and `_safe_parse_llm_response` handles parse failures with retry.\n2. **Multiple pages per category** — If a creator has 2+ pages in the same category (from chunked synthesis), the compose detection query returns only one. The current code handles this with `LIMIT 1` / `.first()`. Worth logging when >1 exists but proceeding with the first match.\n3. **Moment count growth** — Composing many moments into a single page may exceed context limits. The existing truncation recovery (split-in-half retry) doesn't apply to compose since we can't split the existing page. Mitigated by: compose prompts are typically shorter than synthesis (existing page is already summarized).\n\n## Verification Strategy\n\n1. **Unit tests** for `_build_compose_user_prompt()` — XML structure, citation offset math (same pattern as test_harness_compose.py tests)\n2. **Unit test** for compose-or-create branching logic — mock the DB query result\n3. **Integration check** on ub01: process a video, verify page created with format v2 and TechniquePageVideo row. Then process a second video for same creator — verify compose path triggered, page updated, second TechniquePageVideo row added.\n","milestone_id":"M014","slice_id":"S04"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":2663,"cacheRead":89067,"cacheWrite":629,"totalTokens":92360,"cost":{"input":0.000005,"output":0.06657500000000001,"cacheRead":0.0445335,"cacheWrite":0.00393125,"total":0.11504475}},"stopReason":"toolUse","timestamp":1775179378693}}
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{"type":"message","id":"f4b3517e","parentId":"277478ae","timestamp":"2026-04-03T01:24:04.496Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S04 researched."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":11,"cacheRead":89696,"cacheWrite":2701,"totalTokens":92409,"cost":{"input":0.000005,"output":0.000275,"cacheRead":0.044848,"cacheWrite":0.01688125,"total":0.06200925}},"stopReason":"stop","timestamp":1775179441148}}
