{"type":"thinking_level_change","id":"2cefbab8","parentId":null,"timestamp":"2026-04-03T01:10:20.361Z","thinkingLevel":"medium"}
{"type":"model_change","id":"4aa5a78c","parentId":"2cefbab8","timestamp":"2026-04-03T01:10:20.362Z","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## UNIT: Research Slice S03 (\"Data Model + Migration\") — 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- (no dependencies)\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: S03`, `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/S03/`. Do NOT mkdir.\n\n**You MUST call `gsd_summary_save` with the research content before finishing.**\n\nWhen done, say: \"Slice S03 researched.\"","display":false,"id":"139bc3cd","parentId":"4aa5a78c","timestamp":"2026-04-03T01:10:20.365Z"}
{"type":"message","id":"d61d7885","parentId":"139bc3cd","timestamp":"2026-04-03T01:10:29.337Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me research the Data Model + Migration slice for M014. This is about restructuring technique pages to support the new format (nested sections, citations, multi-source). Let me look at the current data model, migrations, and API to understand what needs to change.\n\nThis seems like targeted research — known technology (SQLAlchemy, Alembic, FastAPI) applied to this codebase with specific schema changes needed.","thinkingSignature":"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"},{"type":"text","text":"Let me explore the current data model and migration setup to understand what S03 needs."},{"type":"toolCall","id":"toolu_0116hgHM26rULGn6kY1x8zTr","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find backend/models -type f -name \"*.py\" | head -20 && echo \"---\" && find backend/alembic -type f -name \"*.py\" | head -10"}},{"type":"toolCall","id":"toolu_01EBgt5GMJiCGpsJHvGXFsZ5","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -l \"technique_page\" backend/models/ backend/routers/ backend/database/ 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":315,"cacheRead":59865,"cacheWrite":5234,"totalTokens":65417,"cost":{"input":0.000015000000000000002,"output":0.007875,"cacheRead":0.029932499999999997,"cacheWrite":0.0327125,"total":0.07053499999999999}},"stopReason":"toolUse","timestamp":1775178620363}}
{"type":"message","id":"7abc3469","parentId":"d61d7885","timestamp":"2026-04-03T01:10:29.355Z","message":{"role":"toolResult","toolCallId":"toolu_0116hgHM26rULGn6kY1x8zTr","toolName":"bash","content":[{"type":"text","text":"---\nfind: ‘backend/models’: No such file or directory\nfind: ‘backend/alembic’: No such file or directory\n"}],"isError":false,"timestamp":1775178629355}}
{"type":"message","id":"6cfcb94b","parentId":"7abc3469","timestamp":"2026-04-03T01:10:29.363Z","message":{"role":"toolResult","toolCallId":"toolu_01EBgt5GMJiCGpsJHvGXFsZ5","toolName":"bash","content":[{"type":"text","text":"backend/routers/topics.py\nbackend/routers/pipeline.py\nbackend/routers/techniques.py\n\n\nCommand exited with code 2"}],"details":{},"isError":true,"timestamp":1775178629361}}
{"type":"message","id":"ff39d7d0","parentId":"6cfcb94b","timestamp":"2026-04-03T01:10:33.152Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01KE3Z3BqinJ15JXLtBfLqie","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find backend -type f -name \"*.py\" | head -40 && echo \"===\" && ls backend/"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":93,"cacheRead":65099,"cacheWrite":338,"totalTokens":65531,"cost":{"input":0.000005,"output":0.0023250000000000002,"cacheRead":0.032549499999999995,"cacheWrite":0.0021125000000000002,"total":0.036992}},"stopReason":"toolUse","timestamp":1775178629362}}
{"type":"message","id":"2343ac24","parentId":"ff39d7d0","timestamp":"2026-04-03T01:10:33.170Z","message":{"role":"toolResult","toolCallId":"toolu_01KE3Z3BqinJ15JXLtBfLqie","toolName":"bash","content":[{"type":"text","text":"backend/models.py\nbackend/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\nbackend/main.py\nbackend/routers/techniques.py\nbackend/routers/topics.py\nbackend/routers/reports.py\nbackend/routers/health.py\nbackend/routers/videos.py\nbackend/routers/pipeline.py\nbackend/routers/__init__.py\nbackend/routers/creators.py\nbackend/routers/ingest.py\nbackend/routers/search.py\nbackend/redis_client.py\nbackend/database.py\nbackend/tests/test_pipeline.py\nbackend/tests/test_search.py\nbackend/tests/test_ingest.py\nbackend/tests/__init__.py\nbackend/tests/conftest.py\nbackend/tests/test_public_api.py\n===\nconfig.py\ndatabase.py\nmain.py\nmodels.py\npipeline\n__pycache__\npytest.ini\nredis_client.py\nrequirements.txt\nrouters\nschemas.py\nsearch_service.py\ntests\nwatcher.py\nworker.py\n"}],"isError":false,"timestamp":1775178633168}}
{"type":"message","id":"41831989","parentId":"2343ac24","timestamp":"2026-04-03T01:10:37.228Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_0178a24RKYfDtshJW9icCTxN","name":"read","arguments":{"path":"backend/models.py"}},{"type":"toolCall","id":"toolu_01321bMvRgYBfZPQNcK5Xq2T","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find . -path \"*/alembic*\" -o -path \"*/migrations*\" | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":130,"cacheRead":65099,"cacheWrite":899,"totalTokens":66129,"cost":{"input":0.000005,"output":0.0032500000000000003,"cacheRead":0.032549499999999995,"cacheWrite":0.0056187500000000005,"total":0.041423249999999995}},"stopReason":"toolUse","timestamp":1775178633168}}
{"type":"message","id":"bab95732","parentId":"41831989","timestamp":"2026-04-03T01:10:37.238Z","message":{"role":"toolResult","toolCallId":"toolu_0178a24RKYfDtshJW9icCTxN","toolName":"read","content":[{"type":"text","text":"\"\"\"SQLAlchemy ORM models for the Chrysopedia knowledge base.\n\nSeven entities matching chrysopedia-spec.md §6.1:\n  Creator, SourceVideo, TranscriptSegment, KeyMoment,\n  TechniquePage, RelatedTechniqueLink, Tag\n\"\"\"\n\nfrom __future__ import annotations\n\nimport enum\nimport uuid\nfrom datetime import datetime, timezone\n\nfrom sqlalchemy import (\n    Enum,\n    Float,\n    ForeignKey,\n    Integer,\n    String,\n    Text,\n    UniqueConstraint,\n    func,\n)\nfrom sqlalchemy.dialects.postgresql import ARRAY, JSONB, UUID\nfrom sqlalchemy.orm import Mapped, mapped_column\nfrom sqlalchemy.orm import relationship as sa_relationship\n\nfrom database import Base\n\n\n# ── Enums ────────────────────────────────────────────────────────────────────\n\nclass ContentType(str, enum.Enum):\n    \"\"\"Source video content type.\"\"\"\n    tutorial = \"tutorial\"\n    livestream = \"livestream\"\n    breakdown = \"breakdown\"\n    short_form = \"short_form\"\n\n\nclass ProcessingStatus(str, enum.Enum):\n    \"\"\"Pipeline processing status for a source video.\n\n    User-facing lifecycle: not_started → queued → processing → complete\n    Error branch: processing → error (retrigger resets to queued)\n    \"\"\"\n    not_started = \"not_started\"\n    queued = \"queued\"\n    processing = \"processing\"\n    error = \"error\"\n    complete = \"complete\"\n\n\nclass KeyMomentContentType(str, enum.Enum):\n    \"\"\"Content classification for a key moment.\"\"\"\n    technique = \"technique\"\n    settings = \"settings\"\n    reasoning = \"reasoning\"\n    workflow = \"workflow\"\n\n\nclass SourceQuality(str, enum.Enum):\n    \"\"\"Derived source quality for technique pages.\"\"\"\n    structured = \"structured\"\n    mixed = \"mixed\"\n    unstructured = \"unstructured\"\n\n\nclass RelationshipType(str, enum.Enum):\n    \"\"\"Types of links between technique pages.\"\"\"\n    same_technique_other_creator = \"same_technique_other_creator\"\n    same_creator_adjacent = \"same_creator_adjacent\"\n    general_cross_reference = \"general_cross_reference\"\n\n\n# ── Helpers ──────────────────────────────────────────────────────────────────\n\ndef _uuid_pk() -> Mapped[uuid.UUID]:\n    return mapped_column(\n        UUID(as_uuid=True),\n        primary_key=True,\n        default=uuid.uuid4,\n        server_default=func.gen_random_uuid(),\n    )\n\n\ndef _now() -> datetime:\n    \"\"\"Return current UTC time as a naive datetime (no tzinfo).\n\n    PostgreSQL TIMESTAMP WITHOUT TIME ZONE columns require naive datetimes.\n    asyncpg rejects timezone-aware datetimes for such columns.\n    \"\"\"\n    return datetime.now(timezone.utc).replace(tzinfo=None)\n\n\n# ── Models ───────────────────────────────────────────────────────────────────\n\nclass Creator(Base):\n    __tablename__ = \"creators\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    name: Mapped[str] = mapped_column(String(255), nullable=False)\n    slug: Mapped[str] = mapped_column(String(255), unique=True, nullable=False)\n    genres: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    folder_name: Mapped[str] = mapped_column(String(255), nullable=False)\n    view_count: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\n    hidden: Mapped[bool] = mapped_column(default=False, server_default=\"false\")\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    videos: Mapped[list[SourceVideo]] = sa_relationship(back_populates=\"creator\")\n    technique_pages: Mapped[list[TechniquePage]] = sa_relationship(back_populates=\"creator\")\n\n\nclass SourceVideo(Base):\n    __tablename__ = \"source_videos\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    creator_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"creators.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    filename: Mapped[str] = mapped_column(String(500), nullable=False)\n    file_path: Mapped[str] = mapped_column(String(1000), nullable=False)\n    duration_seconds: Mapped[int] = mapped_column(Integer, nullable=True)\n    content_type: Mapped[ContentType] = mapped_column(\n        Enum(ContentType, name=\"content_type\", create_constraint=True),\n        nullable=False,\n    )\n    transcript_path: Mapped[str | None] = mapped_column(String(1000), nullable=True)\n    content_hash: Mapped[str | None] = mapped_column(String(64), nullable=True, index=True)\n    processing_status: Mapped[ProcessingStatus] = mapped_column(\n        Enum(ProcessingStatus, name=\"processing_status\", create_constraint=True),\n        default=ProcessingStatus.not_started,\n        server_default=\"not_started\",\n    )\n    classification_data: Mapped[list | None] = mapped_column(JSONB, nullable=True)\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    creator: Mapped[Creator] = sa_relationship(back_populates=\"videos\")\n    segments: Mapped[list[TranscriptSegment]] = sa_relationship(back_populates=\"source_video\")\n    key_moments: Mapped[list[KeyMoment]] = sa_relationship(back_populates=\"source_video\")\n\n\nclass TranscriptSegment(Base):\n    __tablename__ = \"transcript_segments\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_video_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    start_time: Mapped[float] = mapped_column(Float, nullable=False)\n    end_time: Mapped[float] = mapped_column(Float, nullable=False)\n    text: Mapped[str] = mapped_column(Text, nullable=False)\n    segment_index: Mapped[int] = mapped_column(Integer, nullable=False)\n    topic_label: Mapped[str | None] = mapped_column(String(255), nullable=True)\n\n    # relationships\n    source_video: Mapped[SourceVideo] = sa_relationship(back_populates=\"segments\")\n\n\nclass KeyMoment(Base):\n    __tablename__ = \"key_moments\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_video_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    technique_page_id: Mapped[uuid.UUID | None] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"SET NULL\"), nullable=True\n    )\n    title: Mapped[str] = mapped_column(String(500), nullable=False)\n    summary: Mapped[str] = mapped_column(Text, nullable=False)\n    start_time: Mapped[float] = mapped_column(Float, nullable=False)\n    end_time: Mapped[float] = mapped_column(Float, nullable=False)\n    content_type: Mapped[KeyMomentContentType] = mapped_column(\n        Enum(KeyMomentContentType, name=\"key_moment_content_type\", create_constraint=True),\n        nullable=False,\n    )\n    plugins: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    raw_transcript: Mapped[str | None] = mapped_column(Text, nullable=True)\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    source_video: Mapped[SourceVideo] = sa_relationship(back_populates=\"key_moments\")\n    technique_page: Mapped[TechniquePage | None] = sa_relationship(\n        back_populates=\"key_moments\", foreign_keys=[technique_page_id]\n    )\n\n\nclass TechniquePage(Base):\n    __tablename__ = \"technique_pages\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    creator_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"creators.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    title: Mapped[str] = mapped_column(String(500), nullable=False)\n    slug: Mapped[str] = mapped_column(String(500), unique=True, nullable=False)\n    topic_category: Mapped[str] = mapped_column(String(255), nullable=False)\n    topic_tags: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    summary: Mapped[str | None] = mapped_column(Text, nullable=True)\n    body_sections: Mapped[dict | None] = mapped_column(JSONB, nullable=True)\n    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\nclass RelatedTechniqueLink(Base):\n    __tablename__ = \"related_technique_links\"\n    __table_args__ = (\n        UniqueConstraint(\"source_page_id\", \"target_page_id\", \"relationship\", name=\"uq_technique_link\"),\n    )\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    target_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    relationship: Mapped[RelationshipType] = mapped_column(\n        Enum(RelationshipType, name=\"relationship_type\", create_constraint=True),\n        nullable=False,\n    )\n\n    # relationships\n    source_page: Mapped[TechniquePage] = sa_relationship(\n        foreign_keys=[source_page_id], back_populates=\"outgoing_links\"\n    )\n    target_page: Mapped[TechniquePage] = sa_relationship(\n        foreign_keys=[target_page_id], back_populates=\"incoming_links\"\n    )\n\n\nclass TechniquePageVersion(Base):\n    \"\"\"Snapshot of a TechniquePage before a pipeline re-synthesis overwrites it.\"\"\"\n    __tablename__ = \"technique_page_versions\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    technique_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    version_number: Mapped[int] = mapped_column(Integer, nullable=False)\n    content_snapshot: Mapped[dict] = mapped_column(JSONB, nullable=False)\n    pipeline_metadata: Mapped[dict | None] = mapped_column(JSONB, nullable=True)\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n\n    # relationships\n    technique_page: Mapped[TechniquePage] = sa_relationship(\n        back_populates=\"versions\"\n    )\n\n\nclass Tag(Base):\n    __tablename__ = \"tags\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    name: Mapped[str] = mapped_column(String(255), unique=True, nullable=False)\n    category: Mapped[str] = mapped_column(String(255), nullable=False)\n    aliases: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n\n\n# ── Content Report Enums ─────────────────────────────────────────────────────\n\nclass ReportType(str, enum.Enum):\n    \"\"\"Classification of user-submitted content reports.\"\"\"\n    inaccurate = \"inaccurate\"\n    missing_info = \"missing_info\"\n    wrong_attribution = \"wrong_attribution\"\n    formatting = \"formatting\"\n    other = \"other\"\n\n\nclass ReportStatus(str, enum.Enum):\n    \"\"\"Triage status for content reports.\"\"\"\n    open = \"open\"\n    acknowledged = \"acknowledged\"\n    resolved = \"resolved\"\n    dismissed = \"dismissed\"\n\n\n# ── Content Report ───────────────────────────────────────────────────────────\n\nclass ContentReport(Base):\n    \"\"\"User-submitted report about a content issue.\n\n    Generic: content_type + content_id can reference any entity\n    (technique_page, key_moment, creator, or general).\n    \"\"\"\n    __tablename__ = \"content_reports\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    content_type: Mapped[str] = mapped_column(\n        String(50), nullable=False, doc=\"Entity type: technique_page, key_moment, creator, general\"\n    )\n    content_id: Mapped[uuid.UUID | None] = mapped_column(\n        UUID(as_uuid=True), nullable=True, doc=\"FK to the reported entity (null for general reports)\"\n    )\n    content_title: Mapped[str | None] = mapped_column(\n        String(500), nullable=True, doc=\"Snapshot of entity title at report time\"\n    )\n    report_type: Mapped[ReportType] = mapped_column(\n        Enum(ReportType, name=\"report_type\", create_constraint=True),\n        nullable=False,\n    )\n    description: Mapped[str] = mapped_column(Text, nullable=False)\n    status: Mapped[ReportStatus] = mapped_column(\n        Enum(ReportStatus, name=\"report_status\", create_constraint=True),\n        default=ReportStatus.open,\n        server_default=\"open\",\n    )\n    admin_notes: Mapped[str | None] = mapped_column(Text, nullable=True)\n    page_url: Mapped[str | None] = mapped_column(\n        String(1000), nullable=True, doc=\"URL the user was on when reporting\"\n    )\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    resolved_at: Mapped[datetime | None] = mapped_column(nullable=True)\n\n\n# ── Pipeline Event ───────────────────────────────────────────────────────────\n\nclass PipelineRunStatus(str, enum.Enum):\n    \"\"\"Status of a pipeline run.\"\"\"\n    running = \"running\"\n    complete = \"complete\"\n    error = \"error\"\n    cancelled = \"cancelled\"\n\n\nclass PipelineRunTrigger(str, enum.Enum):\n    \"\"\"What initiated a pipeline run.\"\"\"\n    manual = \"manual\"\n    clean_reprocess = \"clean_reprocess\"\n    auto_ingest = \"auto_ingest\"\n    bulk = \"bulk\"\n    stage_rerun = \"stage_rerun\"\n\n\nclass PipelineRun(Base):\n    \"\"\"A single execution of the pipeline for a video.\n\n    Each trigger/retrigger creates a new run. Events are scoped to a run\n    via run_id, giving a clean audit trail per execution.\n    \"\"\"\n    __tablename__ = \"pipeline_runs\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    video_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False, index=True,\n    )\n    run_number: Mapped[int] = mapped_column(\n        Integer, nullable=False, doc=\"Auto-increment per video, 1-indexed\"\n    )\n    trigger: Mapped[PipelineRunTrigger] = mapped_column(\n        Enum(PipelineRunTrigger, name=\"pipeline_run_trigger\", create_constraint=True),\n        nullable=False,\n    )\n    status: Mapped[PipelineRunStatus] = mapped_column(\n        Enum(PipelineRunStatus, name=\"pipeline_run_status\", create_constraint=True),\n        default=PipelineRunStatus.running,\n        server_default=\"running\",\n    )\n    started_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    finished_at: Mapped[datetime | None] = mapped_column(nullable=True)\n    error_stage: Mapped[str | None] = mapped_column(String(50), nullable=True)\n    total_tokens: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\n\n    # relationships\n    video: Mapped[SourceVideo] = sa_relationship()\n    events: Mapped[list[PipelineEvent]] = sa_relationship(\n        back_populates=\"run\", foreign_keys=\"PipelineEvent.run_id\"\n    )\n\n\n# ── Pipeline Event ───────────────────────────────────────────────────────────\n\nclass PipelineEvent(Base):\n    \"\"\"Structured log entry for pipeline execution.\n\n    Captures per-stage start/complete/error/llm_call events with\n    token usage and optional response payloads for debugging.\n    \"\"\"\n    __tablename__ = \"pipeline_events\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    video_id: Mapped[uuid.UUID] = mapped_column(\n        UUID(as_uuid=True), nullable=False, index=True,\n    )\n    run_id: Mapped[uuid.UUID | None] = mapped_column(\n        ForeignKey(\"pipeline_runs.id\", ondelete=\"SET NULL\"), nullable=True, index=True,\n    )\n    stage: Mapped[str] = mapped_column(\n        String(50), nullable=False, doc=\"stage2_segmentation, stage3_extraction, etc.\"\n    )\n    event_type: Mapped[str] = mapped_column(\n        String(30), nullable=False, doc=\"start, complete, error, llm_call\"\n    )\n    prompt_tokens: Mapped[int | None] = mapped_column(Integer, nullable=True)\n    completion_tokens: Mapped[int | None] = mapped_column(Integer, nullable=True)\n    total_tokens: Mapped[int | None] = mapped_column(Integer, nullable=True)\n    model: Mapped[str | None] = mapped_column(String(100), nullable=True)\n    duration_ms: Mapped[int | None] = mapped_column(Integer, nullable=True)\n    payload: Mapped[dict | None] = mapped_column(\n        JSONB, nullable=True, doc=\"LLM response content, error details, stage metadata\"\n    )\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n\n    # Debug mode — full LLM I/O capture columns\n    system_prompt_text: Mapped[str | None] = mapped_column(Text, nullable=True)\n    user_prompt_text: Mapped[str | None] = mapped_column(Text, nullable=True)\n    response_text: Mapped[str | None] = mapped_column(Text, nullable=True)\n\n    # relationships\n    run: Mapped[PipelineRun | None] = sa_relationship(\n        back_populates=\"events\", foreign_keys=[run_id]\n    )\n"}],"isError":false,"timestamp":1775178637235}}
{"type":"message","id":"0c21635d","parentId":"bab95732","timestamp":"2026-04-03T01:10:37.270Z","message":{"role":"toolResult","toolCallId":"toolu_01321bMvRgYBfZPQNcK5Xq2T","toolName":"bash","content":[{"type":"text","text":"./alembic.ini\n./alembic\n./alembic/script.py.mako\n./alembic/versions\n./alembic/versions/005_content_hash.py\n./alembic/versions/009_add_creator_hidden_flag.py\n./alembic/versions/004_pipeline_events.py\n./alembic/versions/011_classification_cache_and_stage_rerun.py\n./alembic/versions/010_add_pipeline_runs.py\n./alembic/versions/006_debug_columns.py\n./alembic/versions/001_initial.py\n./alembic/versions/003_content_reports.py\n./alembic/versions/002_technique_page_versions.py\n./alembic/versions/008_rename_processing_status.py\n./alembic/versions/007_drop_review_columns.py\n./alembic/__pycache__\n./alembic/__pycache__/env.cpython-312.pyc\n./alembic/env.py\n"}],"isError":false,"timestamp":1775178637267}}
{"type":"message","id":"e5b1b44c","parentId":"0c21635d","timestamp":"2026-04-03T01:10:44.170Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. Now let me check the S01/S02 prompt outputs to understand the new body_sections format and what fields are expected."},{"type":"toolCall","id":"toolu_017ZAjqvzWR8ghos2Cu9dCLa","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find . -path \"*M014*\" -name \"*.md\" | sort"}},{"type":"toolCall","id":"toolu_01SYWiTkxjygsTkHiXyADnLc","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -l \"body_sections_format\\|source_videos\\|technique_page_videos\\|format.*v2\\|format-2\" backend/ --type py 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":211,"cacheRead":65998,"cacheWrite":5914,"totalTokens":72124,"cost":{"input":0.000005,"output":0.005275,"cacheRead":0.032999,"cacheWrite":0.0369625,"total":0.0752415}},"stopReason":"toolUse","timestamp":1775178637268}}
{"type":"message","id":"5639ac3f","parentId":"e5b1b44c","timestamp":"2026-04-03T01:10:44.223Z","message":{"role":"toolResult","toolCallId":"toolu_017ZAjqvzWR8ghos2Cu9dCLa","toolName":"bash","content":[{"type":"text","text":"./.gsd/milestones/M014/M014-ROADMAP.md\n./.gsd/milestones/M014/slices/S01/S01-PLAN.md\n./.gsd/milestones/M014/slices/S01/S01-RESEARCH.md\n./.gsd/milestones/M014/slices/S01/S01-SUMMARY.md\n./.gsd/milestones/M014/slices/S01/S01-UAT.md\n./.gsd/milestones/M014/slices/S01/tasks/T01-PLAN.md\n./.gsd/milestones/M014/slices/S01/tasks/T01-SUMMARY.md\n./.gsd/milestones/M014/slices/S01/tasks/T02-PLAN.md\n./.gsd/milestones/M014/slices/S01/tasks/T02-SUMMARY.md\n./.gsd/milestones/M014/slices/S01/tasks/T03-PLAN.md\n./.gsd/milestones/M014/slices/S01/tasks/T03-SUMMARY.md\n./.gsd/milestones/M014/slices/S02/S02-PLAN.md\n./.gsd/milestones/M014/slices/S02/S02-RESEARCH.md\n./.gsd/milestones/M014/slices/S02/S02-SUMMARY.md\n./.gsd/milestones/M014/slices/S02/S02-UAT.md\n./.gsd/milestones/M014/slices/S02/tasks/T01-PLAN.md\n./.gsd/milestones/M014/slices/S02/tasks/T01-SUMMARY.md\n./.gsd/milestones/M014/slices/S02/tasks/T02-PLAN.md\n./.gsd/milestones/M014/slices/S02/tasks/T02-SUMMARY.md\n./.gsd/milestones/M014/slices/S02/tasks/T03-PLAN.md\n./.gsd/milestones/M014/slices/S02/tasks/T03-SUMMARY.md\n./.gsd/milestones/M014/slices/S03/S03-PLAN.md\n./.gsd/milestones/M014/slices/S04/S04-PLAN.md\n./.gsd/milestones/M014/slices/S05/S05-PLAN.md\n./.gsd/milestones/M014/slices/S06/S06-PLAN.md\n./.gsd/milestones/M014/slices/S07/S07-PLAN.md\n"}],"isError":false,"timestamp":1775178644222}}
{"type":"message","id":"56e01e89","parentId":"5639ac3f","timestamp":"2026-04-03T01:10:44.227Z","message":{"role":"toolResult","toolCallId":"toolu_01SYWiTkxjygsTkHiXyADnLc","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775178644222}}
{"type":"message","id":"6e64da01","parentId":"56e01e89","timestamp":"2026-04-03T01:10:48.573Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_019ziyRRNVFt8yTrkbaxYZmf","name":"read","arguments":{"path":".gsd/milestones/M014/slices/S01/S01-SUMMARY.md"}},{"type":"toolCall","id":"toolu_01QsvrcvyQzmHD1id6qsXkw6","name":"read","arguments":{"path":".gsd/milestones/M014/slices/S02/S02-SUMMARY.md"}},{"type":"toolCall","id":"toolu_01D1sNstYg87G5UKvqQjgMVe","name":"read","arguments":{"path":".gsd/milestones/M014/slices/S03/S03-PLAN.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":188,"cacheRead":71912,"cacheWrite":977,"totalTokens":73078,"cost":{"input":0.000005,"output":0.0047,"cacheRead":0.035955999999999995,"cacheWrite":0.0061062500000000006,"total":0.046767249999999996}},"stopReason":"toolUse","timestamp":1775178644224}}
{"type":"message","id":"9ea7a629","parentId":"6e64da01","timestamp":"2026-04-03T01:10:48.576Z","message":{"role":"toolResult","toolCallId":"toolu_019ziyRRNVFt8yTrkbaxYZmf","toolName":"read","content":[{"type":"text","text":"---\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## Requirements Advanced\n\nNone.\n\n## Requirements Validated\n\nNone.\n\n## New Requirements Surfaced\n\nNone.\n\n## Requirements Invalidated or Re-scoped\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"}],"isError":false,"timestamp":1775178648575}}
{"type":"message","id":"db8c3406","parentId":"9ea7a629","timestamp":"2026-04-03T01:10:48.579Z","message":{"role":"toolResult","toolCallId":"toolu_01QsvrcvyQzmHD1id6qsXkw6","toolName":"read","content":[{"type":"text","text":"---\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## 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## New Requirements Surfaced\n\nNone.\n\n## Requirements Invalidated or Re-scoped\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"}],"isError":false,"timestamp":1775178648578}}
{"type":"message","id":"8f99755d","parentId":"db8c3406","timestamp":"2026-04-03T01:10:48.582Z","message":{"role":"toolResult","toolCallId":"toolu_01D1sNstYg87G5UKvqQjgMVe","toolName":"read","content":[{"type":"text","text":"# S03: Data Model + Migration\n\n**Goal:** Add body_sections_format column, technique_page_videos association table, update models and API schemas.\n**Demo:** After this: Alembic migration runs clean. API response includes body_sections_format and source_videos fields.\n\n## Tasks\n"}],"isError":false,"timestamp":1775178648578}}
{"type":"message","id":"7d32f4d9","parentId":"8f99755d","timestamp":"2026-04-03T01:10:54.211Z","message":{"role":"assistant","content":[{"type":"text","text":"The S03 plan is empty. Let me look at the schemas, API responses, and the Alembic setup to understand what needs changing."},{"type":"toolCall","id":"toolu_01FLLDLtCHD8XvcyVxMUgGsw","name":"read","arguments":{"path":"backend/pipeline/schemas.py"}},{"type":"toolCall","id":"toolu_01CqMKdrcSkf3MMKzieWPjBH","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg \"class Technique\" backend/routers/techniques.py backend/schemas.py 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":166,"cacheRead":72889,"cacheWrite":3270,"totalTokens":76326,"cost":{"input":0.000005,"output":0.00415,"cacheRead":0.0364445,"cacheWrite":0.0204375,"total":0.061036999999999994}},"stopReason":"toolUse","timestamp":1775178648580}}
{"type":"message","id":"b05dc9ba","parentId":"7d32f4d9","timestamp":"2026-04-03T01:10:54.221Z","message":{"role":"toolResult","toolCallId":"toolu_01FLLDLtCHD8XvcyVxMUgGsw","toolName":"read","content":[{"type":"text","text":"\"\"\"Pydantic schemas for pipeline stage inputs and outputs.\n\nStage 2 — Segmentation: groups transcript segments by topic.\nStage 3 — Extraction: extracts key moments from segments.\nStage 4 — Classification: classifies moments by category/tags.\nStage 5 — Synthesis: generates technique pages from classified moments.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom pydantic import BaseModel, Field\n\n\n# ── Stage 2: Segmentation ───────────────────────────────────────────────────\n\nclass TopicSegment(BaseModel):\n    \"\"\"A contiguous group of transcript segments sharing a topic.\"\"\"\n\n    start_index: int = Field(description=\"First transcript segment index in this group\")\n    end_index: int = Field(description=\"Last transcript segment index in this group (inclusive)\")\n    topic_label: str = Field(description=\"Short label describing the topic\")\n    summary: str = Field(description=\"Brief summary of what is discussed\")\n\n\nclass SegmentationResult(BaseModel):\n    \"\"\"Full output of stage 2 (segmentation).\"\"\"\n\n    segments: list[TopicSegment]\n\n\n# ── Stage 3: Extraction ─────────────────────────────────────────────────────\n\nclass ExtractedMoment(BaseModel):\n    \"\"\"A single key moment extracted from a topic segment group.\"\"\"\n\n    title: str = Field(description=\"Concise title for the moment\")\n    summary: str = Field(description=\"Detailed summary of the technique/concept\")\n    start_time: float = Field(description=\"Start time in seconds\")\n    end_time: float = Field(description=\"End time in seconds\")\n    content_type: str = Field(description=\"One of: technique, settings, reasoning, workflow\")\n    plugins: list[str] = Field(default_factory=list, description=\"Plugins/tools mentioned\")\n    raw_transcript: str = Field(default=\"\", description=\"Raw transcript text for this moment\")\n\n\nclass ExtractionResult(BaseModel):\n    \"\"\"Full output of stage 3 (extraction).\"\"\"\n\n    moments: list[ExtractedMoment]\n\n\n# ── Stage 4: Classification ─────────────────────────────────────────────────\n\nclass ClassifiedMoment(BaseModel):\n    \"\"\"Classification metadata for a single extracted moment.\"\"\"\n\n    moment_index: int = Field(description=\"Index into ExtractionResult.moments\")\n    topic_category: str = Field(description=\"High-level topic category\")\n    topic_tags: list[str] = Field(default_factory=list, description=\"Specific topic tags\")\n    content_type_override: str | None = Field(\n        default=None,\n        description=\"Override for content_type if classification disagrees with extraction\",\n    )\n\n\nclass ClassificationResult(BaseModel):\n    \"\"\"Full output of stage 4 (classification).\"\"\"\n\n    classifications: list[ClassifiedMoment]\n\n\n# ── Stage 5: Synthesis ───────────────────────────────────────────────────────\n\nclass 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":1775178654221}}
{"type":"message","id":"dd4e0461","parentId":"b05dc9ba","timestamp":"2026-04-03T01:10:54.232Z","message":{"role":"toolResult","toolCallId":"toolu_01CqMKdrcSkf3MMKzieWPjBH","toolName":"bash","content":[{"type":"text","text":"backend/schemas.py:class TechniquePageBase(BaseModel):\nbackend/schemas.py:class TechniquePageCreate(TechniquePageBase):\nbackend/schemas.py:class TechniquePageRead(TechniquePageBase):\nbackend/schemas.py:class TechniquePageDetail(TechniquePageRead):\nbackend/schemas.py:class TechniquePageVersionSummary(BaseModel):\nbackend/schemas.py:class TechniquePageVersionDetail(BaseModel):\nbackend/schemas.py:class TechniquePageVersionListResponse(BaseModel):\n"}],"isError":false,"timestamp":1775178654230}}
{"type":"message","id":"ad90e62f","parentId":"dd4e0461","timestamp":"2026-04-03T01:10:58.273Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01AXXEnwKxw9Kpqx4Hda6HLh","name":"read","arguments":{"path":"backend/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":55,"cacheRead":76159,"cacheWrite":1611,"totalTokens":77826,"cost":{"input":0.000005,"output":0.0013750000000000001,"cacheRead":0.038079499999999995,"cacheWrite":0.010068750000000001,"total":0.049528249999999996}},"stopReason":"toolUse","timestamp":1775178654231}}
{"type":"message","id":"6ffde48f","parentId":"ad90e62f","timestamp":"2026-04-03T01:10:58.277Z","message":{"role":"toolResult","toolCallId":"toolu_01AXXEnwKxw9Kpqx4Hda6HLh","toolName":"read","content":[{"type":"text","text":"\"\"\"Pydantic schemas for the Chrysopedia API.\n\nRead-only schemas for list/detail endpoints and input schemas for creation.\nEach schema mirrors the corresponding SQLAlchemy model in models.py.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport uuid\nfrom datetime import datetime\nfrom typing import Literal\n\nfrom pydantic import BaseModel, ConfigDict, Field\n\n\n# ── Health ───────────────────────────────────────────────────────────────────\n\nclass HealthResponse(BaseModel):\n    status: str = \"ok\"\n    service: str = \"chrysopedia-api\"\n    version: str = \"0.1.0\"\n    database: str = \"unknown\"\n\n\n# ── Creator ──────────────────────────────────────────────────────────────────\n\nclass CreatorBase(BaseModel):\n    name: str\n    slug: str\n    genres: list[str] | None = None\n    folder_name: str\n\nclass CreatorCreate(CreatorBase):\n    pass\n\nclass CreatorRead(CreatorBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    view_count: int = 0\n    created_at: datetime\n    updated_at: datetime\n\n\nclass CreatorDetail(CreatorRead):\n    \"\"\"Creator with nested video count.\"\"\"\n    video_count: int = 0\n\n\n# ── SourceVideo ──────────────────────────────────────────────────────────────\n\nclass SourceVideoBase(BaseModel):\n    filename: str\n    file_path: str\n    duration_seconds: int | None = None\n    content_type: str\n    transcript_path: str | None = None\n\nclass SourceVideoCreate(SourceVideoBase):\n    creator_id: uuid.UUID\n\nclass SourceVideoRead(SourceVideoBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    creator_id: uuid.UUID\n    content_hash: str | None = None\n    processing_status: str = \"not_started\"\n    created_at: datetime\n    updated_at: datetime\n\n\n# ── TranscriptSegment ────────────────────────────────────────────────────────\n\nclass TranscriptSegmentBase(BaseModel):\n    start_time: float\n    end_time: float\n    text: str\n    segment_index: int\n    topic_label: str | None = None\n\nclass TranscriptSegmentCreate(TranscriptSegmentBase):\n    source_video_id: uuid.UUID\n\nclass TranscriptSegmentRead(TranscriptSegmentBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    source_video_id: uuid.UUID\n\n\n# ── KeyMoment ────────────────────────────────────────────────────────────────\n\nclass KeyMomentBase(BaseModel):\n    title: str\n    summary: str\n    start_time: float\n    end_time: float\n    content_type: str\n    plugins: list[str] | None = None\n    raw_transcript: str | None = None\n\nclass KeyMomentCreate(KeyMomentBase):\n    source_video_id: uuid.UUID\n    technique_page_id: uuid.UUID | None = None\n\nclass KeyMomentRead(KeyMomentBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    source_video_id: uuid.UUID\n    technique_page_id: uuid.UUID | None = None\n    created_at: datetime\n    updated_at: datetime\n\n\n# ── TechniquePage ────────────────────────────────────────────────────────────\n\nclass TechniquePageBase(BaseModel):\n    title: str\n    slug: str\n    topic_category: str\n    topic_tags: list[str] | None = None\n    summary: str | None = None\n    body_sections: dict | None = None\n    signal_chains: list | None = None\n    plugins: list[str] | None = None\n\nclass TechniquePageCreate(TechniquePageBase):\n    creator_id: uuid.UUID\n    source_quality: str | None = None\n\nclass TechniquePageRead(TechniquePageBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    creator_id: uuid.UUID\n    creator_name: str = \"\"\n    creator_slug: str = \"\"\n    source_quality: str | None = None\n    view_count: int = 0\n    key_moment_count: int = 0\n    created_at: datetime\n    updated_at: datetime\n\n\n# ── RelatedTechniqueLink ─────────────────────────────────────────────────────\n\nclass RelatedTechniqueLinkBase(BaseModel):\n    source_page_id: uuid.UUID\n    target_page_id: uuid.UUID\n    relationship: str\n\nclass RelatedTechniqueLinkCreate(RelatedTechniqueLinkBase):\n    pass\n\nclass RelatedTechniqueLinkRead(RelatedTechniqueLinkBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n\n\n# ── Tag ──────────────────────────────────────────────────────────────────────\n\nclass TagBase(BaseModel):\n    name: str\n    category: str\n    aliases: list[str] | None = None\n\nclass TagCreate(TagBase):\n    pass\n\nclass TagRead(TagBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n\n\n# ── Transcript Ingestion ─────────────────────────────────────────────────────\n\nclass TranscriptIngestResponse(BaseModel):\n    \"\"\"Response returned after successfully ingesting a transcript.\"\"\"\n    video_id: uuid.UUID\n    creator_id: uuid.UUID\n    creator_name: str\n    filename: str\n    segments_stored: int\n    processing_status: str\n    is_reupload: bool\n    content_hash: str\n\n\n# ── Pagination wrapper ───────────────────────────────────────────────────────\n\nclass PaginatedResponse(BaseModel):\n    \"\"\"Generic paginated list response.\"\"\"\n    items: list = Field(default_factory=list)\n    total: int = 0\n    offset: int = 0\n    limit: int = 50\n\n\n# ── Search ───────────────────────────────────────────────────────────────────\n\nclass SearchResultItem(BaseModel):\n    \"\"\"A single search result.\"\"\"\n    title: str\n    slug: str = \"\"\n    technique_page_slug: str = \"\"\n    type: str = \"\"\n    score: float = 0.0\n    summary: str = \"\"\n    creator_name: str = \"\"\n    creator_slug: str = \"\"\n    topic_category: str = \"\"\n    topic_tags: list[str] = Field(default_factory=list)\n    match_context: str = \"\"\n\n\nclass SearchResponse(BaseModel):\n    \"\"\"Top-level search response with metadata.\"\"\"\n    items: list[SearchResultItem] = Field(default_factory=list)\n    partial_matches: list[SearchResultItem] = Field(default_factory=list)\n    total: int = 0\n    query: str = \"\"\n    fallback_used: bool = False\n\n\nclass SuggestionItem(BaseModel):\n    \"\"\"A single autocomplete suggestion.\"\"\"\n    text: str\n    type: Literal[\"topic\", \"technique\", \"creator\"]\n\n\nclass SuggestionsResponse(BaseModel):\n    \"\"\"Popular search suggestions for autocomplete.\"\"\"\n    suggestions: list[SuggestionItem] = Field(default_factory=list)\n\n\n# ── Technique Page Detail ────────────────────────────────────────────────────\n\nclass KeyMomentSummary(BaseModel):\n    \"\"\"Lightweight key moment for technique page detail.\"\"\"\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    title: str\n    summary: str\n    start_time: float\n    end_time: float\n    content_type: str\n    plugins: list[str] | None = None\n    source_video_id: uuid.UUID | None = None\n    video_filename: str = \"\"\n\n\nclass RelatedLinkItem(BaseModel):\n    \"\"\"A related technique link with target info.\"\"\"\n    model_config = ConfigDict(from_attributes=True)\n\n    target_title: str = \"\"\n    target_slug: str = \"\"\n    relationship: str = \"\"\n    creator_name: str = \"\"\n    topic_category: str = \"\"\n    reason: str = \"\"\n\n\nclass CreatorInfo(BaseModel):\n    \"\"\"Minimal creator info embedded in technique detail.\"\"\"\n    model_config = ConfigDict(from_attributes=True)\n\n    name: str\n    slug: str\n    genres: list[str] | None = None\n\n\nclass TechniquePageDetail(TechniquePageRead):\n    \"\"\"Technique page with nested key moments, creator, and related links.\"\"\"\n    key_moments: list[KeyMomentSummary] = Field(default_factory=list)\n    creator_info: CreatorInfo | None = None\n    related_links: list[RelatedLinkItem] = Field(default_factory=list)\n    version_count: int = 0\n\n\n# ── Technique Page Versions ──────────────────────────────────────────────────\n\nclass TechniquePageVersionSummary(BaseModel):\n    \"\"\"Lightweight version entry for list responses.\"\"\"\n    model_config = ConfigDict(from_attributes=True)\n\n    version_number: int\n    created_at: datetime\n    pipeline_metadata: dict | None = None\n\n\nclass TechniquePageVersionDetail(BaseModel):\n    \"\"\"Full version snapshot for detail responses.\"\"\"\n    model_config = ConfigDict(from_attributes=True)\n\n    version_number: int\n    content_snapshot: dict\n    pipeline_metadata: dict | None = None\n    created_at: datetime\n\n\nclass TechniquePageVersionListResponse(BaseModel):\n    \"\"\"Response for version list endpoint.\"\"\"\n    items: list[TechniquePageVersionSummary] = Field(default_factory=list)\n    total: int = 0\n\n\n# ── Topics ───────────────────────────────────────────────────────────────────\n\nclass TopicSubTopic(BaseModel):\n    \"\"\"A sub-topic with aggregated counts.\"\"\"\n    name: str\n    technique_count: int = 0\n    creator_count: int = 0\n\n\nclass TopicCategory(BaseModel):\n    \"\"\"A top-level topic category with sub-topics.\"\"\"\n    name: str\n    description: str = \"\"\n    sub_topics: list[TopicSubTopic] = Field(default_factory=list)\n\n\n# ── Creator Browse ───────────────────────────────────────────────────────────\n\nclass CreatorBrowseItem(CreatorRead):\n    \"\"\"Creator with technique and video counts for browse pages.\"\"\"\n    technique_count: int = 0\n    video_count: int = 0\n\n\n# ── Content Reports ──────────────────────────────────────────────────────────\n\nclass ContentReportCreate(BaseModel):\n    \"\"\"Public submission: report a content issue.\"\"\"\n    content_type: str = Field(\n        ..., description=\"Entity type: technique_page, key_moment, creator, general\"\n    )\n    content_id: uuid.UUID | None = Field(\n        None, description=\"ID of the reported entity (null for general reports)\"\n    )\n    content_title: str | None = Field(\n        None, description=\"Title of the reported content (for display context)\"\n    )\n    report_type: str = Field(\n        ..., description=\"inaccurate, missing_info, wrong_attribution, formatting, other\"\n    )\n    description: str = Field(\n        ..., min_length=10, max_length=2000,\n        description=\"Description of the issue\"\n    )\n    page_url: str | None = Field(\n        None, description=\"URL the user was on when reporting\"\n    )\n\n\nclass ContentReportRead(BaseModel):\n    \"\"\"Full report for admin views.\"\"\"\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    content_type: str\n    content_id: uuid.UUID | None = None\n    content_title: str | None = None\n    report_type: str\n    description: str\n    status: str = \"open\"\n    admin_notes: str | None = None\n    page_url: str | None = None\n    created_at: datetime\n    resolved_at: datetime | None = None\n\n\nclass ContentReportUpdate(BaseModel):\n    \"\"\"Admin update: change status and/or add notes.\"\"\"\n    status: str | None = Field(\n        None, description=\"open, acknowledged, resolved, dismissed\"\n    )\n    admin_notes: str | None = Field(\n        None, max_length=2000, description=\"Admin notes about resolution\"\n    )\n\n\nclass ContentReportListResponse(BaseModel):\n    \"\"\"Paginated list of content reports.\"\"\"\n    items: list[ContentReportRead] = Field(default_factory=list)\n    total: int = 0\n    offset: int = 0\n    limit: int = 50\n\n\n# ── Pipeline Debug Mode ─────────────────────────────────────────────────────\n\nclass DebugModeResponse(BaseModel):\n    \"\"\"Current debug mode status.\"\"\"\n    debug_mode: bool\n\n\nclass DebugModeUpdate(BaseModel):\n    \"\"\"Toggle debug mode on/off.\"\"\"\n    debug_mode: bool\n\n\nclass TokenStageSummary(BaseModel):\n    \"\"\"Per-stage token usage aggregation.\"\"\"\n    stage: str\n    call_count: int\n    total_prompt_tokens: int\n    total_completion_tokens: int\n    total_tokens: int\n\n\nclass TokenSummaryResponse(BaseModel):\n    \"\"\"Token usage summary for a video, broken down by stage.\"\"\"\n    video_id: str\n    stages: list[TokenStageSummary] = Field(default_factory=list)\n    grand_total_tokens: int\n"}],"isError":false,"timestamp":1775178658275}}
{"type":"message","id":"ddddc84b","parentId":"6ffde48f","timestamp":"2026-04-03T01:11:03.005Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the technique page router to see how data is currently served."},{"type":"toolCall","id":"toolu_017Vqjm6xKQbP1yjbToJfNHF","name":"read","arguments":{"path":"backend/routers/techniques.py"}},{"type":"toolCall","id":"toolu_01YWKmxo8a5zUPtU95ZvM8dc","name":"read","arguments":{"path":"alembic/env.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":115,"cacheRead":77770,"cacheWrite":3423,"totalTokens":81309,"cost":{"input":0.000005,"output":0.002875,"cacheRead":0.038884999999999996,"cacheWrite":0.02139375,"total":0.06315875}},"stopReason":"toolUse","timestamp":1775178658276}}
{"type":"message","id":"accdc7a8","parentId":"ddddc84b","timestamp":"2026-04-03T01:11:03.008Z","message":{"role":"toolResult","toolCallId":"toolu_017Vqjm6xKQbP1yjbToJfNHF","toolName":"read","content":[{"type":"text","text":"\"\"\"Technique page endpoints — list and detail with eager-loaded relations.\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nfrom typing import Annotated\n\nfrom fastapi import APIRouter, Depends, HTTPException, Query\nfrom sqlalchemy import func, select\nfrom sqlalchemy.ext.asyncio import AsyncSession\nfrom sqlalchemy.orm import selectinload\n\nfrom database import get_session\nfrom models import Creator, KeyMoment, RelatedTechniqueLink, SourceVideo, TechniquePage, TechniquePageVersion\nfrom schemas import (\n    CreatorInfo,\n    KeyMomentSummary,\n    PaginatedResponse,\n    RelatedLinkItem,\n    TechniquePageDetail,\n    TechniquePageRead,\n    TechniquePageVersionDetail,\n    TechniquePageVersionListResponse,\n    TechniquePageVersionSummary,\n)\n\nlogger = logging.getLogger(\"chrysopedia.techniques\")\n\nrouter = APIRouter(prefix=\"/techniques\", tags=[\"techniques\"])\n\n\nasync def _find_dynamic_related(\n    db: AsyncSession,\n    page: TechniquePage,\n    exclude_slugs: set[str],\n    limit: int,\n) -> list[RelatedLinkItem]:\n    \"\"\"Score and return dynamically related technique pages.\n\n    Scoring:\n      - Same creator + same topic_category: +3\n      - Same creator, different category: +2\n      - Same topic_category, different creator: +2\n      - Each overlapping topic_tag: +1\n    \"\"\"\n    exclude_ids = {page.id}\n\n    # Base: all other technique pages, eagerly load creator for name\n    stmt = (\n        select(TechniquePage)\n        .options(selectinload(TechniquePage.creator))\n        .where(TechniquePage.id != page.id)\n    )\n    if exclude_slugs:\n        stmt = stmt.where(TechniquePage.slug.notin_(exclude_slugs))\n\n    result = await db.execute(stmt)\n    candidates = result.scalars().all()\n\n    if not candidates:\n        return []\n\n    current_tags = set(page.topic_tags) if page.topic_tags else set()\n\n    scored: list[tuple[int, str, TechniquePage]] = []\n    for cand in candidates:\n        score = 0\n        reasons: list[str] = []\n\n        same_creator = cand.creator_id == page.creator_id\n        same_category = cand.topic_category == page.topic_category\n\n        if same_creator and same_category:\n            score += 3\n            reasons.append(\"Same creator, same topic\")\n        elif same_creator:\n            score += 2\n            reasons.append(\"Same creator\")\n        elif same_category:\n            score += 2\n            reasons.append(f\"Also about {page.topic_category}\")\n\n        # Tag overlap scoring\n        if current_tags:\n            cand_tags = set(cand.topic_tags) if cand.topic_tags else set()\n            shared = current_tags & cand_tags\n            if shared:\n                score += len(shared)\n                reasons.append(f\"Shared tags: {', '.join(sorted(shared))}\")\n\n        if score > 0:\n            scored.append((score, \"; \".join(reasons), cand))\n\n    # Sort descending by score, then by title for determinism\n    scored.sort(key=lambda x: (-x[0], x[2].title))\n\n    results: list[RelatedLinkItem] = []\n    for score, reason, cand in scored[:limit]:\n        creator_name = cand.creator.name if cand.creator else \"\"\n        results.append(\n            RelatedLinkItem(\n                target_title=cand.title,\n                target_slug=cand.slug,\n                relationship=\"dynamic\",\n                creator_name=creator_name,\n                topic_category=cand.topic_category,\n                reason=reason,\n            )\n        )\n    return results\n\n\n@router.get(\"\", response_model=PaginatedResponse)\nasync def list_techniques(\n    category: Annotated[str | None, Query()] = None,\n    creator_slug: Annotated[str | None, Query()] = None,\n    sort: Annotated[str, Query()] = \"recent\",\n    offset: Annotated[int, Query(ge=0)] = 0,\n    limit: Annotated[int, Query(ge=1, le=100)] = 50,\n    db: AsyncSession = Depends(get_session),\n) -> PaginatedResponse:\n    \"\"\"List technique pages with optional category/creator filtering.\"\"\"\n    # Correlated subquery for key moment count (same pattern as creators.py)\n    key_moment_count_sq = (\n        select(func.count())\n        .where(KeyMoment.technique_page_id == TechniquePage.id)\n        .correlate(TechniquePage)\n        .scalar_subquery()\n    )\n\n    # Build base query with filters\n    base_stmt = select(TechniquePage.id)\n    if category:\n        base_stmt = base_stmt.where(TechniquePage.topic_category == category)\n    if creator_slug:\n        base_stmt = base_stmt.join(Creator, TechniquePage.creator_id == Creator.id).where(\n            Creator.slug == creator_slug\n        )\n\n    # Count total before pagination\n    count_stmt = select(func.count()).select_from(base_stmt.subquery())\n    count_result = await db.execute(count_stmt)\n    total = count_result.scalar() or 0\n\n    # Main query with subquery column\n    stmt = select(\n        TechniquePage,\n        key_moment_count_sq.label(\"key_moment_count\"),\n    )\n    if category:\n        stmt = stmt.where(TechniquePage.topic_category == category)\n    if creator_slug:\n        stmt = stmt.join(Creator, TechniquePage.creator_id == Creator.id).where(\n            Creator.slug == creator_slug\n        )\n\n    stmt = stmt.options(selectinload(TechniquePage.creator))\n    if sort == \"random\":\n        stmt = stmt.order_by(func.random())\n    elif sort == \"oldest\":\n        stmt = stmt.order_by(TechniquePage.created_at.asc())\n    elif sort == \"alpha\":\n        stmt = stmt.order_by(TechniquePage.title.asc())\n    elif sort == \"creator\":\n        # Need a join for creator name ordering; avoid duplicate join if creator_slug filter already joined\n        if not creator_slug:\n            stmt = stmt.join(Creator, TechniquePage.creator_id == Creator.id, isouter=True)\n        stmt = stmt.order_by(Creator.name.asc(), TechniquePage.title.asc())\n    else:\n        # Default: \"recent\" — newest first\n        stmt = stmt.order_by(TechniquePage.created_at.desc())\n    stmt = stmt.offset(offset).limit(limit)\n    result = await db.execute(stmt)\n    rows = result.all()\n\n    items = []\n    for row in rows:\n        p = row[0]\n        km_count = row[1] or 0\n        item = TechniquePageRead.model_validate(p)\n        if p.creator:\n            item.creator_name = p.creator.name\n            item.creator_slug = p.creator.slug\n        item.key_moment_count = km_count\n        items.append(item)\n\n    return PaginatedResponse(\n        items=items,\n        total=total,\n        offset=offset,\n        limit=limit,\n    )\n\n\n@router.get(\"/random\")\nasync def random_technique(\n    db: AsyncSession = Depends(get_session),\n) -> dict:\n    \"\"\"Return the slug of a single random technique page.\"\"\"\n    stmt = select(TechniquePage.slug).order_by(func.random()).limit(1)\n    result = await db.execute(stmt)\n    slug = result.scalar_one_or_none()\n    if slug is None:\n        raise HTTPException(status_code=404, detail=\"No techniques available\")\n    return {\"slug\": slug}\n\n\n@router.get(\"/{slug}\", response_model=TechniquePageDetail)\nasync def get_technique(\n    slug: str,\n    db: AsyncSession = Depends(get_session),\n) -> TechniquePageDetail:\n    \"\"\"Get full technique page detail with key moments, creator, and related links.\"\"\"\n    stmt = (\n        select(TechniquePage)\n        .where(TechniquePage.slug == slug)\n        .options(\n            selectinload(TechniquePage.key_moments).selectinload(KeyMoment.source_video),\n            selectinload(TechniquePage.creator),\n            selectinload(TechniquePage.outgoing_links).selectinload(\n                RelatedTechniqueLink.target_page\n            ),\n            selectinload(TechniquePage.incoming_links).selectinload(\n                RelatedTechniqueLink.source_page\n            ),\n        )\n    )\n    result = await db.execute(stmt)\n    page = result.scalar_one_or_none()\n\n    if page is None:\n        raise HTTPException(status_code=404, detail=f\"Technique '{slug}' not found\")\n\n    # Build key moments (ordered by start_time)\n    key_moments = sorted(page.key_moments, key=lambda km: km.start_time)\n    key_moment_items = []\n    for km in key_moments:\n        item = KeyMomentSummary.model_validate(km)\n        item.video_filename = km.source_video.filename if km.source_video else \"\"\n        key_moment_items.append(item)\n\n    # Build creator info\n    creator_info = None\n    if page.creator:\n        creator_info = CreatorInfo(\n            name=page.creator.name,\n            slug=page.creator.slug,\n            genres=page.creator.genres,\n        )\n\n    # Build related links (outgoing + incoming)\n    related_links: list[RelatedLinkItem] = []\n    for link in page.outgoing_links:\n        if link.target_page:\n            related_links.append(\n                RelatedLinkItem(\n                    target_title=link.target_page.title,\n                    target_slug=link.target_page.slug,\n                    relationship=link.relationship.value if hasattr(link.relationship, 'value') else str(link.relationship),\n                )\n            )\n    for link in page.incoming_links:\n        if link.source_page:\n            related_links.append(\n                RelatedLinkItem(\n                    target_title=link.source_page.title,\n                    target_slug=link.source_page.slug,\n                    relationship=link.relationship.value if hasattr(link.relationship, 'value') else str(link.relationship),\n                )\n            )\n\n    # Supplement with dynamic related techniques (up to 4 total)\n    curated_slugs = {link.target_slug for link in related_links}\n    max_related = 4\n    if len(related_links) < max_related:\n        remaining = max_related - len(related_links)\n        try:\n            dynamic_links = await _find_dynamic_related(\n                db, page, curated_slugs, remaining\n            )\n            related_links.extend(dynamic_links)\n        except Exception:\n            logger.warning(\n                \"Dynamic related query failed for %s, continuing with curated only\",\n                slug,\n                exc_info=True,\n            )\n\n    base = TechniquePageRead.model_validate(page)\n\n    # Count versions for this page\n    version_count_stmt = select(func.count()).where(\n        TechniquePageVersion.technique_page_id == page.id\n    )\n    version_count_result = await db.execute(version_count_stmt)\n    version_count = version_count_result.scalar() or 0\n\n    return TechniquePageDetail(\n        **base.model_dump(),\n        key_moments=key_moment_items,\n        creator_info=creator_info,\n        related_links=related_links,\n        version_count=version_count,\n    )\n\n\n@router.get(\"/{slug}/versions\", response_model=TechniquePageVersionListResponse)\nasync def list_technique_versions(\n    slug: str,\n    db: AsyncSession = Depends(get_session),\n) -> TechniquePageVersionListResponse:\n    \"\"\"List all version snapshots for a technique page, newest first.\"\"\"\n    # Resolve the technique page\n    page_stmt = select(TechniquePage).where(TechniquePage.slug == slug)\n    page_result = await db.execute(page_stmt)\n    page = page_result.scalar_one_or_none()\n    if page is None:\n        raise HTTPException(status_code=404, detail=f\"Technique '{slug}' not found\")\n\n    # Fetch versions ordered by version_number DESC\n    versions_stmt = (\n        select(TechniquePageVersion)\n        .where(TechniquePageVersion.technique_page_id == page.id)\n        .order_by(TechniquePageVersion.version_number.desc())\n    )\n    versions_result = await db.execute(versions_stmt)\n    versions = versions_result.scalars().all()\n\n    items = [TechniquePageVersionSummary.model_validate(v) for v in versions]\n    return TechniquePageVersionListResponse(items=items, total=len(items))\n\n\n@router.get(\"/{slug}/versions/{version_number}\", response_model=TechniquePageVersionDetail)\nasync def get_technique_version(\n    slug: str,\n    version_number: int,\n    db: AsyncSession = Depends(get_session),\n) -> TechniquePageVersionDetail:\n    \"\"\"Get a specific version snapshot by version number.\"\"\"\n    # Resolve the technique page\n    page_stmt = select(TechniquePage).where(TechniquePage.slug == slug)\n    page_result = await db.execute(page_stmt)\n    page = page_result.scalar_one_or_none()\n    if page is None:\n        raise HTTPException(status_code=404, detail=f\"Technique '{slug}' not found\")\n\n    # Fetch the specific version\n    version_stmt = (\n        select(TechniquePageVersion)\n        .where(\n            TechniquePageVersion.technique_page_id == page.id,\n            TechniquePageVersion.version_number == version_number,\n        )\n    )\n    version_result = await db.execute(version_stmt)\n    version = version_result.scalar_one_or_none()\n    if version is None:\n        raise HTTPException(\n            status_code=404,\n            detail=f\"Version {version_number} not found for technique '{slug}'\",\n        )\n\n    return TechniquePageVersionDetail.model_validate(version)\n"}],"isError":false,"timestamp":1775178663007}}
{"type":"message","id":"7186b62f","parentId":"accdc7a8","timestamp":"2026-04-03T01:11:03.012Z","message":{"role":"toolResult","toolCallId":"toolu_01YWKmxo8a5zUPtU95ZvM8dc","toolName":"read","content":[{"type":"text","text":"\"\"\"Alembic env.py — async migration runner for Chrysopedia.\"\"\"\n\nimport asyncio\nimport os\nimport sys\nfrom logging.config import fileConfig\n\nfrom alembic import context\nfrom sqlalchemy import pool\nfrom sqlalchemy.ext.asyncio import async_engine_from_config\n\n# Ensure the backend package is importable\n# When running locally: alembic/ sits beside backend/, so ../backend works\n# When running in Docker: alembic/ is inside /app/ alongside the backend modules\nsys.path.insert(0, os.path.join(os.path.dirname(__file__), \"..\", \"backend\"))\nsys.path.insert(0, os.path.join(os.path.dirname(__file__), \"..\"))\n\nfrom database import Base  # noqa: E402\nimport models  # noqa: E402, F401  — registers all tables on Base.metadata\n\nconfig = context.config\n\nif config.config_file_name is not None:\n    fileConfig(config.config_file_name)\n\ntarget_metadata = Base.metadata\n\n# Allow DATABASE_URL env var to override alembic.ini\nurl_override = os.getenv(\"DATABASE_URL\")\nif url_override:\n    config.set_main_option(\"sqlalchemy.url\", url_override)\n\n\ndef run_migrations_offline() -> None:\n    \"\"\"Run migrations in 'offline' mode — emit SQL to stdout.\"\"\"\n    url = config.get_main_option(\"sqlalchemy.url\")\n    context.configure(\n        url=url,\n        target_metadata=target_metadata,\n        literal_binds=True,\n        dialect_opts={\"paramstyle\": \"named\"},\n    )\n    with context.begin_transaction():\n        context.run_migrations()\n\n\ndef do_run_migrations(connection):\n    context.configure(connection=connection, target_metadata=target_metadata)\n    with context.begin_transaction():\n        context.run_migrations()\n\n\nasync def run_async_migrations() -> None:\n    \"\"\"Run migrations in 'online' mode with an async engine.\"\"\"\n    connectable = async_engine_from_config(\n        config.get_section(config.config_ini_section, {}),\n        prefix=\"sqlalchemy.\",\n        poolclass=pool.NullPool,\n    )\n    async with connectable.connect() as connection:\n        await connection.run_sync(do_run_migrations)\n    await connectable.dispose()\n\n\ndef run_migrations_online() -> None:\n    asyncio.run(run_async_migrations())\n\n\nif context.is_offline_mode():\n    run_migrations_offline()\nelse:\n    run_migrations_online()\n"}],"isError":false,"timestamp":1775178663010}}
{"type":"message","id":"a3dc18ee","parentId":"7186b62f","timestamp":"2026-04-03T01:11:10.133Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the latest migration to understand the revision chain, and look at the pipeline stages to see how technique pages are written."},{"type":"toolCall","id":"toolu_01NNgjBcNdrkk8RzxokUtbMS","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && head -30 alembic/versions/011_classification_cache_and_stage_rerun.py"}},{"type":"toolCall","id":"toolu_019P8zEaJ7kMYSfVvzKjDkvX","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg \"revision|down_revision|Revision\" alembic/versions/*.py | sort"}},{"type":"toolCall","id":"toolu_01FuTTFsPRokJDfKZCijFxL3","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg \"body_sections|TechniquePage|technique_page\" backend/pipeline/stages.py | head -30"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":270,"cacheRead":81193,"cacheWrite":4479,"totalTokens":85943,"cost":{"input":0.000005,"output":0.00675,"cacheRead":0.0405965,"cacheWrite":0.02799375,"total":0.07534525}},"stopReason":"toolUse","timestamp":1775178663011}}
{"type":"message","id":"c652fb15","parentId":"a3dc18ee","timestamp":"2026-04-03T01:11:10.153Z","message":{"role":"toolResult","toolCallId":"toolu_01NNgjBcNdrkk8RzxokUtbMS","toolName":"bash","content":[{"type":"text","text":"\"\"\"Add classification_data JSONB column to source_videos and stage_rerun trigger.\n\nPersists stage 4 classification data in PostgreSQL alongside Redis cache,\neliminating the 24-hour TTL data loss risk. Also adds the 'stage_rerun'\ntrigger value for single-stage re-run support.\n\nRevision ID: 011_cls_cache_rerun\nRevises: 010_add_pipeline_runs\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects.postgresql import JSONB\n\nrevision = \"011_cls_cache_rerun\"\ndown_revision = \"010_add_pipeline_runs\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n    # Add classification_data column to source_videos\n    op.add_column(\n        \"source_videos\",\n        sa.Column(\"classification_data\", JSONB, nullable=True),\n    )\n\n    # Add 'stage_rerun' to the pipeline_run_trigger enum\n    # PostgreSQL enums require ALTER TYPE to add values\n    op.execute(\"ALTER TYPE pipeline_run_trigger ADD VALUE IF NOT EXISTS 'stage_rerun'\")\n\n"}],"isError":false,"timestamp":1775178670153}}
{"type":"message","id":"39876d89","parentId":"c652fb15","timestamp":"2026-04-03T01:11:10.159Z","message":{"role":"toolResult","toolCallId":"toolu_019P8zEaJ7kMYSfVvzKjDkvX","toolName":"bash","content":[{"type":"text","text":"alembic/versions/001_initial.py:down_revision: Union[str, None] = None\nalembic/versions/001_initial.py:Revision ID: 001_initial\nalembic/versions/001_initial.py:# revision identifiers, used by Alembic.\nalembic/versions/001_initial.py:revision: str = \"001_initial\"\nalembic/versions/002_technique_page_versions.py:down_revision: Union[str, None] = \"001_initial\"\nalembic/versions/002_technique_page_versions.py:Revision ID: 002_technique_page_versions\nalembic/versions/002_technique_page_versions.py:# revision identifiers, used by Alembic.\nalembic/versions/002_technique_page_versions.py:revision: str = \"002_technique_page_versions\"\nalembic/versions/003_content_reports.py:down_revision = \"002_technique_page_versions\"\nalembic/versions/003_content_reports.py:revision = \"003_content_reports\"\nalembic/versions/003_content_reports.py:Revision ID: 003_content_reports\nalembic/versions/004_pipeline_events.py:down_revision = \"003_content_reports\"\nalembic/versions/004_pipeline_events.py:revision = \"004_pipeline_events\"\nalembic/versions/004_pipeline_events.py:Revision ID: 004_pipeline_events\nalembic/versions/005_content_hash.py:down_revision = \"004_pipeline_events\"\nalembic/versions/005_content_hash.py:revision = \"005_content_hash\"\nalembic/versions/005_content_hash.py:Revision ID: 005_content_hash\nalembic/versions/006_debug_columns.py:down_revision = \"005_content_hash\"\nalembic/versions/006_debug_columns.py:revision = \"006_debug_columns\"\nalembic/versions/006_debug_columns.py:Revision ID: 006_debug_columns\nalembic/versions/007_drop_review_columns.py:down_revision = \"006_debug_columns\"\nalembic/versions/007_drop_review_columns.py:revision = \"007_drop_review_columns\"\nalembic/versions/007_drop_review_columns.py:Revision ID: 007_drop_review_columns\nalembic/versions/008_rename_processing_status.py:down_revision = \"007_drop_review_columns\"\nalembic/versions/008_rename_processing_status.py:revision = \"008_rename_processing_status\"\nalembic/versions/008_rename_processing_status.py:Revision ID: 008_rename_processing_status\nalembic/versions/009_add_creator_hidden_flag.py:down_revision = \"008_rename_processing_status\"\nalembic/versions/009_add_creator_hidden_flag.py:revision = \"009_add_creator_hidden_flag\"\nalembic/versions/009_add_creator_hidden_flag.py:Revision ID: 009_add_creator_hidden_flag\nalembic/versions/010_add_pipeline_runs.py:down_revision = \"009_add_creator_hidden_flag\"\nalembic/versions/010_add_pipeline_runs.py:revision = \"010_add_pipeline_runs\"\nalembic/versions/010_add_pipeline_runs.py:Revision ID: 010_add_pipeline_runs\nalembic/versions/011_classification_cache_and_stage_rerun.py:down_revision = \"010_add_pipeline_runs\"\nalembic/versions/011_classification_cache_and_stage_rerun.py:revision = \"011_cls_cache_rerun\"\nalembic/versions/011_classification_cache_and_stage_rerun.py:Revision ID: 011_cls_cache_rerun\n"}],"isError":false,"timestamp":1775178670159}}
{"type":"message","id":"33e33c5c","parentId":"39876d89","timestamp":"2026-04-03T01:11:10.163Z","message":{"role":"toolResult","toolCallId":"toolu_01FuTTFsPRokJDfKZCijFxL3","toolName":"bash","content":[{"type":"text","text":"    TechniquePage,\n    TechniquePageVersion,\n    each group into a TechniquePage, creates/updates page rows, and links\n    KeyMoments to their TechniquePage.\n                        select(TechniquePage).where(TechniquePage.slug == page_data.slug)\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                            \"body_sections\": existing.body_sections,\n                                TechniquePageVersion.technique_page_id == existing.id\n                        version = TechniquePageVersion(\n                            technique_page_id=existing.id,\n                    existing.body_sections = page_data.body_sections\n                    page = TechniquePage(\n                        body_sections=page_data.body_sections,\n                            moment_group[idx][0].technique_page_id = page.id\n                        m.technique_page_id = page.id\n        page_ids = {m.technique_page_id for m in moments if m.technique_page_id is not None}\n                    select(TechniquePage).where(TechniquePage.id.in_(page_ids))\n                qdrant.upsert_technique_pages(page_dicts, page_vectors)\n                tp_id = str(m.technique_page_id) if m.technique_page_id else \"\"\n                    \"technique_page_id\": tp_id,\n                    \"technique_page_slug\": page_id_to_slug.get(tp_id, \"\"),\n    \"\"\"Save existing technique_page_ids linked to this video before pipeline resets them.\n            select(KeyMoment.technique_page_id)\n                KeyMoment.technique_page_id.isnot(None),\n"}],"isError":false,"timestamp":1775178670159}}
{"type":"message","id":"db45e5a5","parentId":"33e33c5c","timestamp":"2026-04-03T01:11:17.200Z","message":{"role":"assistant","content":[{"type":"text","text":"Let me look at the relevant section of stages.py where technique pages are created/updated to understand the current write path."},{"type":"toolCall","id":"toolu_01VpEYyjzccKWcYmc2ogLm1z","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"def stage5\\|def run_stage5\\|body_sections_format\\|source_video\" backend/pipeline/stages.py | head -20"}},{"type":"toolCall","id":"toolu_012zxbDE4y4779daxbyvZnPz","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"technique_page_videos\\|source_videos\" backend/ --type py 2>/dev/null | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":215,"cacheRead":85672,"cacheWrite":2097,"totalTokens":87985,"cost":{"input":0.000005,"output":0.0053750000000000004,"cacheRead":0.042836,"cacheWrite":0.01310625,"total":0.06132225}},"stopReason":"toolUse","timestamp":1775178670160}}
{"type":"message","id":"eb68206c","parentId":"db45e5a5","timestamp":"2026-04-03T01:11:17.212Z","message":{"role":"toolResult","toolCallId":"toolu_01VpEYyjzccKWcYmc2ogLm1z","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775178677211}}
{"type":"message","id":"487655ae","parentId":"eb68206c","timestamp":"2026-04-03T01:11:17.234Z","message":{"role":"toolResult","toolCallId":"toolu_012zxbDE4y4779daxbyvZnPz","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775178677232}}
{"type":"message","id":"6ef7d06b","parentId":"487655ae","timestamp":"2026-04-03T01:11:20.974Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_0157qmCrSbne9sT8JbhSU267","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"def stage5\\|def run_stage_5\\|def.*synthesis\" backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":97,"cacheRead":87769,"cacheWrite":280,"totalTokens":88147,"cost":{"input":0.000005,"output":0.002425,"cacheRead":0.0438845,"cacheWrite":0.00175,"total":0.0480645}},"stopReason":"toolUse","timestamp":1775178677232}}
{"type":"message","id":"5ac69b0f","parentId":"6ef7d06b","timestamp":"2026-04-03T01:11:20.990Z","message":{"role":"toolResult","toolCallId":"toolu_0157qmCrSbne9sT8JbhSU267","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775178680988}}
{"type":"message","id":"4470659b","parentId":"5ac69b0f","timestamp":"2026-04-03T01:11:24.452Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_0182FW9c1zEPBwpVDxtwjX1Q","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"^def \\|^class \\|^async def \" backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":88049,"cacheWrite":128,"totalTokens":88270,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.0440245,"cacheWrite":0.0008,"total":0.047129500000000005}},"stopReason":"toolUse","timestamp":1775178680989}}
{"type":"message","id":"f51aa79c","parentId":"4470659b","timestamp":"2026-04-03T01:11:24.461Z","message":{"role":"toolResult","toolCallId":"toolu_0182FW9c1zEPBwpVDxtwjX1Q","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775178684460}}
{"type":"message","id":"66b68f67","parentId":"f51aa79c","timestamp":"2026-04-03T01:11:27.894Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01JZqTGSq6HZ8vT18bmdpCsD","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"^def \\|^class \\|^    def \" backend/pipeline/stages.py | head -30"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":95,"cacheRead":88177,"cacheWrite":123,"totalTokens":88396,"cost":{"input":0.000005,"output":0.002375,"cacheRead":0.044088499999999996,"cacheWrite":0.00076875,"total":0.047237249999999995}},"stopReason":"toolUse","timestamp":1775178684460}}
{"type":"message","id":"a3bdd462","parentId":"66b68f67","timestamp":"2026-04-03T01:11:27.905Z","message":{"role":"toolResult","toolCallId":"toolu_01JZqTGSq6HZ8vT18bmdpCsD","toolName":"bash","content":[{"type":"text","text":"52:class LLMTruncationError(RuntimeError):\n59:def _set_error_status(video_id: str, stage_name: str, error: Exception) -> None:\n79:def _emit_event(\n122:def _is_debug_mode() -> bool:\n137:def _make_llm_callback(\n160:    def callback(*, model=None, prompt_tokens=None, completion_tokens=None,\n189:def _build_request_params(\n224:def _get_sync_engine():\n236:def _get_sync_session() -> Session:\n244: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]:\n298:def _load_canonical_tags() -> dict:\n314:def _format_taxonomy_for_prompt(tags_data: dict) -> str:\n325:def _safe_parse_llm_response(\n386: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:\n579:def _classify_moment_batch(\n636:def stage4_classification(self, video_id: str, run_id: str | None = None) -> str:\n741:def _store_classification_data(video_id: str, data: list[dict]) -> None:\n791:def _load_classification_data(video_id: str) -> list[dict]:\n847:def _get_git_commit_sha() -> str:\n884:def _capture_pipeline_metadata() -> dict:\n935:def _compute_page_tags(\n957:def _build_moments_text(\n983:def _synthesize_chunk(\n1025:def _slug_base(slug: str) -> str:\n1034:def _merge_pages_by_slug(\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:\n"}],"isError":false,"timestamp":1775178687904}}
{"type":"message","id":"123c51e1","parentId":"a3bdd462","timestamp":"2026-04-03T01:11:31.810Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01MykYkySFNZf2ffkvZ3Azx2","name":"read","arguments":{"limit":150,"offset":1127,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":88300,"cacheWrite":679,"totalTokens":89072,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.044149999999999995,"cacheWrite":0.00424375,"total":0.050698749999999994}},"stopReason":"toolUse","timestamp":1775178687904}}
{"type":"message","id":"9e06ceb2","parentId":"123c51e1","timestamp":"2026-04-03T01:11:31.817Z","message":{"role":"toolResult","toolCallId":"toolu_01MykYkySFNZf2ffkvZ3Azx2","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[827 more lines in file. Use offset=1277 to continue.]"}],"isError":false,"timestamp":1775178691814}}
{"type":"message","id":"52a890db","parentId":"9e06ceb2","timestamp":"2026-04-03T01:11:35.562Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_016u7g45HyXX84Yvdxj7AZFT","name":"read","arguments":{"limit":150,"offset":1277,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":88979,"cacheWrite":1981,"totalTokens":91053,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.0444895,"cacheWrite":0.01238125,"total":0.05917575}},"stopReason":"toolUse","timestamp":1775178691815}}
{"type":"message","id":"da3ee458","parentId":"52a890db","timestamp":"2026-04-03T01:11:35.567Z","message":{"role":"toolResult","toolCallId":"toolu_016u7g45HyXX84Yvdxj7AZFT","toolName":"read","content":[{"type":"text","text":"                )\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":1775178695565}}
{"type":"message","id":"367ea2d5","parentId":"da3ee458","timestamp":"2026-04-03T01:11:41.869Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have a clear picture. Let me check how the frontend currently uses `body_sections` and what the roadmap demo states require."},{"type":"toolCall","id":"toolu_01BvvXe1GXsAngt81au4qLPm","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"body_sections\" frontend/src/ --type ts --type tsx 2>/dev/null | head -20"}},{"type":"toolCall","id":"toolu_01XLC65tabQ2yupA9kVJTTTZ","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find frontend/src -name \"*.tsx\" -o -name \"*.ts\" | xargs grep -l \"body_sections\" 2>/dev/null | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":217,"cacheRead":88979,"cacheWrite":3786,"totalTokens":92983,"cost":{"input":0.000005,"output":0.005425,"cacheRead":0.0444895,"cacheWrite":0.0236625,"total":0.073582}},"stopReason":"toolUse","timestamp":1775178695566}}
{"type":"message","id":"337344e0","parentId":"367ea2d5","timestamp":"2026-04-03T01:11:41.884Z","message":{"role":"toolResult","toolCallId":"toolu_01BvvXe1GXsAngt81au4qLPm","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775178701883}}
{"type":"message","id":"8a157c50","parentId":"337344e0","timestamp":"2026-04-03T01:11:41.895Z","message":{"role":"toolResult","toolCallId":"toolu_01XLC65tabQ2yupA9kVJTTTZ","toolName":"bash","content":[{"type":"text","text":"frontend/src/pages/TechniquePage.tsx\nfrontend/src/api/public-client.ts\n"}],"isError":false,"timestamp":1775178701892}}
{"type":"message","id":"92f79a3d","parentId":"8a157c50","timestamp":"2026-04-03T01:11:46.149Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_019cGRVC5X2kmMhH4f3n18mx","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"body_sections\\|source_videos\\|SourceVideo\" frontend/src/api/public-client.ts | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":101,"cacheRead":92765,"cacheWrite":305,"totalTokens":93172,"cost":{"input":0.000005,"output":0.0025250000000000003,"cacheRead":0.0463825,"cacheWrite":0.0019062500000000002,"total":0.050818749999999996}},"stopReason":"toolUse","timestamp":1775178701893}}
{"type":"message","id":"a95c6bb4","parentId":"92f79a3d","timestamp":"2026-04-03T01:11:46.164Z","message":{"role":"toolResult","toolCallId":"toolu_019cGRVC5X2kmMhH4f3n18mx","toolName":"bash","content":[{"type":"text","text":"66:  body_sections: Record<string, unknown> | null;\n"}],"isError":false,"timestamp":1775178706161}}
{"type":"message","id":"465504a3","parentId":"a95c6bb4","timestamp":"2026-04-03T01:11:49.624Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Po5YJrPLBQ9WaXkgFt36pQ","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"body_sections\\|source_videos\\|body_sections_format\" frontend/src/pages/TechniquePage.tsx | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":104,"cacheRead":92765,"cacheWrite":435,"totalTokens":93305,"cost":{"input":0.000005,"output":0.0026000000000000003,"cacheRead":0.0463825,"cacheWrite":0.0027187500000000002,"total":0.05170625}},"stopReason":"toolUse","timestamp":1775178706162}}
{"type":"message","id":"175362d2","parentId":"465504a3","timestamp":"2026-04-03T01:11:49.639Z","message":{"role":"toolResult","toolCallId":"toolu_01Po5YJrPLBQ9WaXkgFt36pQ","toolName":"bash","content":[{"type":"text","text":"52:    body_sections:\n53:      typeof snapshot.body_sections === \"object\" && snapshot.body_sections !== null\n54:        ? (snapshot.body_sections as Record<string, unknown>)\n208:  const displaySections = overlay?.body_sections ?? technique.body_sections;\n405:      {/* Study guide prose — body_sections */}\n"}],"isError":false,"timestamp":1775178709636}}
{"type":"message","id":"1fd72686","parentId":"175362d2","timestamp":"2026-04-03T01:11:53.466Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. Now let me check the alembic.ini for the database URL pattern."},{"type":"toolCall","id":"toolu_01379WqY38TwE46X6CijzE3F","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep \"sqlalchemy.url\" alembic.ini"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":99,"cacheRead":93200,"cacheWrite":201,"totalTokens":93501,"cost":{"input":0.000005,"output":0.002475,"cacheRead":0.046599999999999996,"cacheWrite":0.00125625,"total":0.05033625}},"stopReason":"toolUse","timestamp":1775178709637}}
{"type":"message","id":"1c83424e","parentId":"1fd72686","timestamp":"2026-04-03T01:11:53.475Z","message":{"role":"toolResult","toolCallId":"toolu_01379WqY38TwE46X6CijzE3F","toolName":"bash","content":[{"type":"text","text":"sqlalchemy.url = postgresql+asyncpg://chrysopedia:changeme@localhost:5433/chrysopedia\n"}],"isError":false,"timestamp":1775178713473}}
{"type":"message","id":"0d8a6673","parentId":"1c83424e","timestamp":"2026-04-03T01:12:48.561Z","message":{"role":"assistant","content":[{"type":"text","text":"I have everything I need. Let me compile the research.\n\n**Summary of findings:**\n\n1. **TechniquePage model** (models.py) has `body_sections: JSONB` but no `body_sections_format` column. The pipeline schema (schemas.py) added `body_sections_format='v2'` in S01 but that only lives in the Pydantic pipeline model, not the DB.\n\n2. **No `technique_page_videos` table** exists. Currently, the many-to-one relationship is KeyMoment→SourceVideo + KeyMoment→TechniquePage, so you can derive which videos contributed to a page by joining through KeyMoment. But S04 needs a direct association table `technique_page_videos` for explicit multi-source tracking.\n\n3. **API schemas** (backend/schemas.py): `TechniquePageBase.body_sections` is typed as `dict | None` — needs to accept `list | dict | None` for v2 format. No `body_sections_format` or `source_videos` fields exist on any API response schema.\n\n4. **Migration chain**: latest is `011_cls_cache_rerun` → next will be `012`.\n\n5. **stages.py** writes `page_data.body_sections` (which is now `list[BodySection]` from pipeline schemas) directly to the DB JSONB column — Pydantic models serialize to dicts automatically. The `body_sections_format` field is not persisted.\n\n6. **Frontend** types `body_sections` as `Record<string, unknown> | null` — will need updating in S05, not S03."},{"type":"toolCall","id":"toolu_01W84RU7apVQnPmWg21E1UwM","name":"gsd_summary_save","arguments":{"artifact_type":"RESEARCH","content":"# S03 Research: Data Model + Migration\n\n## Summary\n\nStraightforward data model expansion. Three changes needed: (1) add `body_sections_format` column to `technique_pages`, (2) create `technique_page_videos` association table, (3) update API response schemas to expose both fields. No new technology, no ambiguous requirements — established Alembic + SQLAlchemy patterns already in codebase.\n\n## Recommendation\n\nLight-touch migration + model/schema update. Single Alembic migration file. Model changes in `models.py`, API schema changes in `schemas.py`, router query updates in `techniques.py` to populate `source_videos`.\n\n---\n\n## Implementation Landscape\n\n### Current State\n\n**TechniquePage model** (`backend/models.py`, line ~195):\n- `body_sections: Mapped[dict | None] = mapped_column(JSONB, nullable=True)` — stores either old flat dict or new v2 list-of-objects\n- No `body_sections_format` column — the pipeline Pydantic schema (`pipeline/schemas.py`) has `body_sections_format='v2'` but it's never persisted to DB\n- No association table linking technique pages to source videos\n\n**Video-to-page relationship today**: Indirect via `key_moments` table — `KeyMoment.source_video_id` + `KeyMoment.technique_page_id`. You can derive contributing videos with a JOIN, but there's no direct record. S04 needs explicit tracking for composition history.\n\n**API schemas** (`backend/schemas.py`):\n- `TechniquePageBase.body_sections: dict | None` — must become `list | dict | None` to accept v2 format\n- No `body_sections_format` field on any read schema\n- No `source_videos` field on detail response\n\n**Alembic chain**: `001_initial` → ... → `011_cls_cache_rerun`. Next revision = `012`.\n\n**Migration runner** (`alembic/env.py`): Async PostgreSQL via `asyncpg`, `sys.path` bootstrapped for both local and Docker. DB URL from `DATABASE_URL` env var or `alembic.ini` fallback (`postgresql+asyncpg://chrysopedia:changeme@localhost:5433/chrysopedia`).\n\n### What Needs to Change\n\n#### 1. New Alembic migration (`alembic/versions/012_multi_source_format.py`)\n\n```\ntechnique_pages:\n  + body_sections_format  VARCHAR(20)  DEFAULT 'v1'  NOT NULL\n\ntechnique_page_videos (new table):\n  id              UUID PK\n  technique_page_id  UUID FK → technique_pages.id ON DELETE CASCADE\n  source_video_id    UUID FK → source_videos.id ON DELETE CASCADE\n  added_at           TIMESTAMP DEFAULT now()\n  UNIQUE(technique_page_id, source_video_id)\n```\n\nDefault `body_sections_format` to `'v1'` for existing rows. New pages from the v2 pipeline will set `'v2'`. This lets the frontend discriminate rendering: format-1 pages use old dict iteration, format-2 pages use nested section rendering (S05).\n\nThe `technique_page_videos` table is a simple many-to-many join. `added_at` tracks when a video was composed into the page (useful for S06 admin UI composition history).\n\n#### 2. Model updates (`backend/models.py`)\n\nAdd to `TechniquePage`:\n```python\nbody_sections_format: Mapped[str] = mapped_column(\n    String(20), default=\"v1\", server_default=\"v1\", nullable=False\n)\n```\n\nNew association model:\n```python\nclass TechniquePageVideo(Base):\n    __tablename__ = \"technique_page_videos\"\n    __table_args__ = (\n        UniqueConstraint(\"technique_page_id\", \"source_video_id\", name=\"uq_page_video\"),\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(default=_now, server_default=func.now())\n```\n\nAdd relationships on `TechniquePage`:\n```python\nsource_video_links: Mapped[list[TechniquePageVideo]] = sa_relationship(back_populates=\"technique_page\")\n```\n\nAnd on `SourceVideo` (optional, for reverse navigation):\n```python\ntechnique_page_links: Mapped[list[TechniquePageVideo]] = sa_relationship(back_populates=\"source_video\")\n```\n\n#### 3. API schema updates (`backend/schemas.py`)\n\n`TechniquePageBase`:\n- Change `body_sections: dict | None = None` → `body_sections: list | dict | None = None`\n- Add `body_sections_format: str = \"v1\"`\n\n`TechniquePageRead` (inherits from Base, so gets both automatically).\n\n`TechniquePageDetail`:\n- Add `source_videos: list[SourceVideoSummary] = Field(default_factory=list)`\n- New lightweight schema:\n```python\nclass SourceVideoSummary(BaseModel):\n    model_config = ConfigDict(from_attributes=True)\n    id: uuid.UUID\n    filename: str\n    content_type: str\n    added_at: datetime | None = None\n```\n\n#### 4. Router query update (`backend/routers/techniques.py`)\n\nIn `get_technique()` (the detail endpoint): eagerly load `source_video_links` → `source_video`, build `source_videos` list on the response. The list endpoint doesn't need source_videos (too heavy for a list view).\n\n#### 5. Pipeline write path (`backend/pipeline/stages.py`)\n\nWhen creating/updating a TechniquePage in `stage5_synthesis`:\n- Set `body_sections_format = \"v2\"` on the page\n- After creating/updating the page, upsert a `TechniquePageVideo` row for the current video\n\nThis is S04 scope (pipeline compose-or-create logic), but the model/migration must support it. S03 just needs to ensure the columns and tables exist.\n\n### Constraints and Gotchas\n\n1. **Default value for existing rows**: `body_sections_format` must default to `'v1'` so existing pages don't break. The migration's `server_default='v1'` handles this.\n\n2. **JSONB type flexibility**: The `body_sections` DB column is JSONB, which already accepts any JSON type (list, dict, null). No DDL change needed — only the Pydantic schema type annotation needs widening.\n\n3. **Backfill `technique_page_videos`**: Existing pages don't have entries in the new table. We could backfill from `key_moments` JOINs, but the roadmap demo only requires the table to exist and API to expose it — actual population happens in S04. Empty `source_videos: []` for existing pages is fine.\n\n4. **Migration must run on production DB (ub01:5433)**: Use `docker exec chrysopedia-api alembic upgrade head` per the project's existing deployment pattern.\n\n### Task Decomposition Seams\n\nThree natural tasks:\n1. **Migration file** — `alembic/versions/012_multi_source_format.py` (standalone, can be verified with `alembic upgrade head`)\n2. **Model + Schema** — `models.py` (new column + model), `schemas.py` (response fields) — verifiable with import check\n3. **Router wiring** — `techniques.py` detail endpoint loads source_videos — verifiable with API call to `/techniques/{slug}`\n\nTasks 2 and 3 could be one task since they're small, but splitting keeps verification clean.\n\n### Verification Strategy\n\n- `alembic upgrade head` runs clean (no errors, table and column exist)\n- `python -c \"from models import TechniquePageVideo, TechniquePage\"` — imports succeed\n- `curl localhost:8000/techniques/{slug}` response includes `body_sections_format` and `source_videos` fields\n- For local testing without Docker: SQLAlchemy model import + Pydantic schema serialization test\n","milestone_id":"M014","slice_id":"S03"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":2548,"cacheRead":93401,"cacheWrite":138,"totalTokens":96088,"cost":{"input":0.000005,"output":0.0637,"cacheRead":0.0467005,"cacheWrite":0.0008625000000000001,"total":0.11126800000000002}},"stopReason":"toolUse","timestamp":1775178713474}}
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