{"type":"thinking_level_change","id":"5d1bc860","parentId":null,"timestamp":"2026-04-03T02:01:36.876Z","thinkingLevel":"medium"}
{"type":"model_change","id":"508c2189","parentId":"5d1bc860","timestamp":"2026-04-03T02:01:36.878Z","provider":"anthropic","modelId":"claude-opus-4-6"}
{"type":"custom_message","customType":"gsd-auto","content":"You are executing GSD auto-mode.\n\n## Working Directory\n\nYour working directory is `/home/aux/projects/content-to-kb-automator`. All file reads, writes, and shell commands MUST operate relative to this directory. Do NOT `cd` to any other directory.\n\nAll relevant context has been preloaded below — start working immediately without re-reading these files.\n\n## Requirements Advanced\n\n- R012 — Stage 5 now composes new video content into existing technique pages instead of overwriting, fulfilling the incremental update aspect of R012\n\n## Requirements Validated\n\nNone.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## Requirements Advanced\n\n- R006 — Technique page display now supports v2 nested sections with TOC and citations in addition to existing v1 format\n\n## Requirements Validated\n\nNone.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## UNIT: Research Slice S07 (\"Search — Per-Section Embeddings + Deep Linking\") — 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#### S04 Summary\nSource: `.gsd/milestones/M014/slices/S04/S04-SUMMARY.md`\n\n---\nid: S04\nparent: M014\nmilestone: M014\nprovides:\n  - Compose-or-create branching in stage5_synthesis\n  - body_sections_format='v2' on all technique pages\n  - TechniquePageVideo join table populated for every page+video combination\nrequires:\n  - slice: S01\n    provides: Synthesis prompt v5 with nested sections + citations format\n  - slice: S02\n    provides: Compose prompt + test harness validation\n  - slice: S03\n    provides: Data model with body_sections_format column and TechniquePageVideo table\naffects:\n  - S06\n  - S07\nkey_files:\n  - backend/pipeline/stages.py\n  - backend/pipeline/test_compose_pipeline.py\nkey_decisions:\n  - Compose detection queries all matching pages and warns on multiple matches, uses first\n  - pg_insert with on_conflict_do_nothing for idempotent TechniquePageVideo inserts\n  - Source-code assertions for branching logic tests instead of fragile full-session mocks\npatterns_established:\n  - Compose-or-create branching: query existing pages by creator_id + LOWER(category), compose into first match, fall through to standard synthesis otherwise\n  - XML-tagged compose prompt with offset-indexed moments: existing [0]-[N-1], new [N]-[N+M-1]\nobservability_surfaces:\n  - INFO log when compose path triggered: 'Stage 5: Composing into existing page ...'\n  - WARNING log when multiple pages match creator+category\ndrill_down_paths:\n  - .gsd/milestones/M014/slices/S04/tasks/T01-SUMMARY.md\n  - .gsd/milestones/M014/slices/S04/tasks/T02-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-03T01:34:24.402Z\nblocker_discovered: false\n---\n\n# S04: Pipeline Compose-or-Create Logic\n\n**Stage 5 now detects existing technique pages by creator+category and branches to a compose path that merges new video content into them, with body_sections_format='v2' and TechniquePageVideo tracking on all pages.**\n\n## What Happened\n\nTwo tasks delivered the compose-or-create pipeline logic and its test suite.\n\nT01 added `_build_compose_user_prompt()` and `_compose_into_existing()` helper functions to stages.py, then wired compose detection into the stage5_synthesis per-category loop. The detection query uses `creator_id + LOWER(topic_category)` for case-insensitive matching. When an existing page matches, the compose path loads its linked moments, builds an XML-tagged prompt with offset-indexed moment references, and calls the LLM with the stage5_compose.txt system prompt. If no match, the existing chunked synthesis path runs unchanged. All pages now get `body_sections_format='v2'` set unconditionally, and a `TechniquePageVideo` row is inserted via `pg_insert` with `on_conflict_do_nothing` for idempotency.\n\nT02 created 12 unit tests across 4 test classes: prompt construction (5 tests for XML structure, offset indices, empty existing moments, page JSON serialization, moment content), branching logic (3 tests using source-code assertions + focused mocks), format/tracking (3 tests for v2 flag, pg_insert usage, INSERT values), and case sensitivity (1 test verifying func.lower on both sides of the category comparison). All 12 pass.\n\n## Verification\n\nAll slice-level verification checks pass:\n1. `PYTHONPATH=backend python -c \"from pipeline.stages import _build_compose_user_prompt, _compose_into_existing; print('imports OK')\"` → exit 0\n2. `grep -q 'body_sections_format' backend/pipeline/stages.py` → exit 0\n3. `grep -q 'TechniquePageVideo' backend/pipeline/stages.py` → exit 0\n4. `grep -q 'stage5_compose' backend/pipeline/stages.py` → exit 0\n5. `PYTHONPATH=backend python -m pytest backend/pipeline/test_compose_pipeline.py -v` → 12 passed in 1.50s\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nUsed `default=str` in `json.dumps()` for page serialization in `_build_compose_user_prompt()` to handle UUID/datetime fields — not in plan but necessary for robustness. T02 replaced integration-level branching tests with source-code structure assertions + focused unit tests due to session mock fragility.\n\n## Known Limitations\n\nNone.\n\n## Follow-ups\n\nNone.\n\n## Files Created/Modified\n\n- `backend/pipeline/stages.py` — Added _build_compose_user_prompt(), _compose_into_existing(), compose-or-create branching in stage5_synthesis, body_sections_format='v2' setting, TechniquePageVideo insertion\n- `backend/pipeline/test_compose_pipeline.py` — New file: 12 unit tests covering compose prompt construction, branching logic, format tracking, and case-insensitive matching\n\n#### S05 Summary\nSource: `.gsd/milestones/M014/slices/S05/S05-SUMMARY.md`\n\n---\nid: S05\nparent: M014\nmilestone: M014\nprovides:\n  - V2 format-aware technique page rendering (TOC, nested sections, citations) for S07 deep linking\nrequires:\n  - slice: S03\n    provides: body_sections_format and source_videos fields in API response schema\naffects:\n  - S07\nkey_files:\n  - frontend/src/pages/TechniquePage.tsx\n  - frontend/src/components/TableOfContents.tsx\n  - frontend/src/utils/citations.tsx\n  - frontend/src/api/public-client.ts\n  - frontend/src/App.css\nkey_decisions:\n  - Subsection IDs use compound slugs (sectionSlug--subSlug) to avoid anchor collision between sections and subsections\n  - D024 enforced: sections with subsections skip empty content paragraph\n  - Invalid citation indices render as plain text rather than broken links\npatterns_established:\n  - Format-discriminated rendering: body_sections_format field selects v1 (dict) or v2 (array) renderer, keeping both paths independent\n  - Citation parsing as a standalone utility (parseCitations) reusable for any component rendering technique page content\nobservability_surfaces:\n  - none\ndrill_down_paths:\n  - .gsd/milestones/M014/slices/S05/tasks/T01-SUMMARY.md\n  - .gsd/milestones/M014/slices/S05/tasks/T02-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-04-03T01:47:43.749Z\nblocker_discovered: false\n---\n\n# S05: Frontend — Nested Rendering, TOC, Citations\n\n**Format-2 technique pages render with nested H2/H3 sections, a clickable TOC, and citation superscript links; format-1 pages unchanged; deployed to production.**\n\n## What Happened\n\nBuilt format-aware rendering in TechniquePage.tsx that detects body_sections_format=2 (list-of-objects) vs format=1 (dict) and renders accordingly. V2 path renders a TableOfContents component with CSS-counter-numbered nested anchor links, H2 sections with slugified IDs, H3 subsections with compound slug IDs (sectionSlug--subSlug), and citation parsing. The parseCitations utility in citations.tsx converts [N] and [N,M] markers to superscript anchor links targeting key moment IDs; invalid indices render as plain text. V1 dict rendering is completely untouched.\n\nTypeScript types updated with BodySectionV2, BodySubSectionV2 interfaces, body_sections_format discriminator, and source_videos field. snapshotToOverlay updated to pass through list-format body_sections.\n\nCSS additions: TOC card with sticky positioning, subsection left-border styling, citation superscript links with hover states, scroll-margin-top on anchored sections for smooth scrolling past the fixed header.\n\nDeployed to ub01 production: pushed 11 commits, built chrysopedia-web container (56 modules, 0 Vite/TS errors), verified HTTP 200 on site root and /health. No v2 pages exist in production yet (S04 populated them on test data), so v2 rendering is verified structurally via the TypeScript build. V1 pages render unchanged.\n\n## Verification\n\nFrontend build: `cd frontend && npm run build` exits 0, 56 modules, no errors. Production: curl http://ub01:8096/ returns 200. Health: curl http://ub01:8096/health returns 200. JS bundle hash matches build output.\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nDocker Compose service name is chrysopedia-web, not chrysopedia-web-8096 as listed in the slice plan verify command. The plan's verify command had shell quoting issues. T02 used the correct service name.\n\n## Known Limitations\n\nV2 rendering only verified structurally (TypeScript build) — no live v2 pages exist in production yet. Visual verification of TOC, citations, and nested sections requires S04 to populate v2 pages first. ub01 has a stashed git state with local backend edits from prior work.\n\n## Follow-ups\n\nVisual QA of v2 rendering once S04 data is in production. Review stashed edits on ub01.\n\n## Files Created/Modified\n\n- `frontend/src/pages/TechniquePage.tsx` — Format-aware rendering: v2 array → TOC + nested H2/H3 sections + citation links; v1 dict unchanged\n- `frontend/src/components/TableOfContents.tsx` — New component: CSS-counter-numbered nested anchor link list for v2 sections\n- `frontend/src/utils/citations.tsx` — New utility: parseCitations converts [N] and [N,M] markers to superscript anchor links\n- `frontend/src/api/public-client.ts` — Added BodySectionV2, BodySubSectionV2 types, body_sections_format, source_videos fields\n- `frontend/src/App.css` — TOC card styles, subsection borders, citation superscripts, scroll-margin-top for anchored sections\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: S07`, `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/S07/`. Do NOT mkdir.\n\n**You MUST call `gsd_summary_save` with the research content before finishing.**\n\nWhen done, say: \"Slice S07 researched.\"","display":false,"id":"991b499f","parentId":"508c2189","timestamp":"2026-04-03T02:01:36.882Z"}
{"type":"message","id":"acc7139a","parentId":"991b499f","timestamp":"2026-04-03T02:01:46.151Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me research S07: \"Search — Per-Section Embeddings + Deep Linking\". This involves updating the search system to embed at the section level (rather than full-page level) and enabling deep linking to specific sections when a user clicks a search result.\n\nLet me understand what's needed:\n1. Per-section embeddings in Qdrant (instead of full-page embeddings)\n2. Deep linking from search results to specific page sections (anchors)\n\nLet me explore the current search and embedding code.","thinkingSignature":"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"},{"type":"text","text":"Starting research on S07 — per-section embeddings and deep linking for search. Need to understand the current embedding/indexing pipeline, search API, and frontend search flow."},{"type":"toolCall","id":"toolu_01JqhWLZVX8FXeYyMrPtuoeq","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -l \"qdrant|embedding|embed\" backend/ --type py | head -20"}},{"type":"toolCall","id":"toolu_01H9wv4WRqarg43Ny592wAVE","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -l \"search\" frontend/src/ --type ts --type tsx 2>/dev/null; find frontend/src -name \"*search*\" -o -name \"*Search*\" | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":353,"cacheRead":59865,"cacheWrite":7920,"totalTokens":68141,"cost":{"input":0.000015000000000000002,"output":0.008825000000000001,"cacheRead":0.029932499999999997,"cacheWrite":0.0495,"total":0.0882725}},"stopReason":"toolUse","timestamp":1775181696879}}
{"type":"message","id":"2864c72a","parentId":"acc7139a","timestamp":"2026-04-03T02:01:46.175Z","message":{"role":"toolResult","toolCallId":"toolu_01JqhWLZVX8FXeYyMrPtuoeq","toolName":"bash","content":[{"type":"text","text":"backend/search_service.py\nbackend/schemas.py\nbackend/config.py\nbackend/routers/pipeline.py\nbackend/tests/fixtures/mock_llm_responses.py\nbackend/tests/test_search.py\nbackend/tests/test_pipeline.py\nbackend/pipeline/stages.py\nbackend/pipeline/qdrant_client.py\nbackend/pipeline/embedding_client.py\n"}],"isError":false,"timestamp":1775181706175}}
{"type":"message","id":"d62c05cc","parentId":"2864c72a","timestamp":"2026-04-03T02:01:46.180Z","message":{"role":"toolResult","toolCallId":"toolu_01H9wv4WRqarg43Ny592wAVE","toolName":"bash","content":[{"type":"text","text":"frontend/src/pages/SearchResults.tsx\nfrontend/src/components/SearchAutocomplete.tsx\n"}],"isError":false,"timestamp":1775181706179}}
{"type":"message","id":"ab1af24b","parentId":"d62c05cc","timestamp":"2026-04-03T02:01:49.910Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01AckfFoGapGHpE4o3sQrj44","name":"read","arguments":{"path":"backend/pipeline/qdrant_client.py"}},{"type":"toolCall","id":"toolu_01FhUjJiVqYgkdX8ditkHps3","name":"read","arguments":{"path":"backend/pipeline/embedding_client.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":103,"cacheRead":67785,"cacheWrite":406,"totalTokens":68295,"cost":{"input":0.000005,"output":0.002575,"cacheRead":0.0338925,"cacheWrite":0.0025375000000000003,"total":0.039009999999999996}},"stopReason":"toolUse","timestamp":1775181706179}}
{"type":"message","id":"97ecbe63","parentId":"ab1af24b","timestamp":"2026-04-03T02:01:49.913Z","message":{"role":"toolResult","toolCallId":"toolu_01AckfFoGapGHpE4o3sQrj44","toolName":"read","content":[{"type":"text","text":"\"\"\"Qdrant vector database manager for collection lifecycle and point upserts.\n\nHandles collection creation (idempotent) and batch upserts for technique pages\nand key moments. Connection failures are non-blocking — the pipeline continues\nwithout search indexing.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nimport uuid\n\nfrom qdrant_client import QdrantClient\nfrom qdrant_client.http import exceptions as qdrant_exceptions\nfrom qdrant_client.models import Distance, PointStruct, VectorParams\n\nfrom config import Settings\n\nlogger = logging.getLogger(__name__)\n\n# Namespace UUID for deterministic point IDs\n_QDRANT_NAMESPACE = uuid.UUID(\"a1b2c3d4-e5f6-7890-abcd-ef1234567890\")\n\n\nclass QdrantManager:\n    \"\"\"Manages a Qdrant collection for Chrysopedia technique-page and key-moment vectors.\"\"\"\n\n    def __init__(self, settings: Settings) -> None:\n        self.settings = settings\n        self._client = QdrantClient(url=settings.qdrant_url)\n        self._collection = settings.qdrant_collection\n\n    # ── Collection management ────────────────────────────────────────────\n\n    def ensure_collection(self) -> None:\n        \"\"\"Create the collection if it does not already exist.\n\n        Uses cosine distance and the configured embedding dimensions.\n        \"\"\"\n        try:\n            if self._client.collection_exists(self._collection):\n                logger.info(\"Qdrant collection '%s' already exists.\", self._collection)\n                return\n\n            self._client.create_collection(\n                collection_name=self._collection,\n                vectors_config=VectorParams(\n                    size=self.settings.embedding_dimensions,\n                    distance=Distance.COSINE,\n                ),\n            )\n            logger.info(\n                \"Created Qdrant collection '%s' (dim=%d, cosine).\",\n                self._collection,\n                self.settings.embedding_dimensions,\n            )\n        except qdrant_exceptions.UnexpectedResponse as exc:\n            logger.warning(\n                \"Qdrant error during ensure_collection (%s). Skipping.\",\n                exc,\n            )\n        except Exception as exc:\n            logger.warning(\n                \"Qdrant connection failed during ensure_collection (%s: %s). Skipping.\",\n                type(exc).__name__,\n                exc,\n            )\n\n    # ── Deletion ───────────────────────────────────────────────────────────\n\n    def delete_by_video_id(self, video_id: str) -> int:\n        \"\"\"Delete all points (key moments + technique pages) associated with a video.\n\n        Key moments have source_video_id in payload.\n        Technique pages don't have direct video linkage, so only moments are deleted.\n\n        Returns the count of deleted points (best-effort — Qdrant may not report exact counts).\n        \"\"\"\n        from qdrant_client.models import Filter, FieldCondition, MatchValue\n\n        try:\n            result = self._client.delete(\n                collection_name=self._collection,\n                points_selector=Filter(\n                    must=[\n                        FieldCondition(\n                            key=\"source_video_id\",\n                            match=MatchValue(value=video_id),\n                        ),\n                    ],\n                ),\n            )\n            logger.info(\n                \"Deleted Qdrant points for video_id=%s from collection '%s'.\",\n                video_id,\n                self._collection,\n            )\n            return 0  # Qdrant delete doesn't return count\n        except Exception as exc:\n            logger.warning(\n                \"Qdrant delete for video_id=%s failed (%s: %s). Skipping.\",\n                video_id,\n                type(exc).__name__,\n                exc,\n            )\n            return 0\n\n    # ── Low-level upsert ─────────────────────────────────────────────────\n\n    def upsert_points(self, points: list[PointStruct]) -> None:\n        \"\"\"Upsert a batch of pre-built PointStruct objects.\"\"\"\n        if not points:\n            return\n        try:\n            self._client.upsert(\n                collection_name=self._collection,\n                points=points,\n            )\n            logger.info(\n                \"Upserted %d points to Qdrant collection '%s'.\",\n                len(points),\n                self._collection,\n            )\n        except qdrant_exceptions.UnexpectedResponse as exc:\n            logger.warning(\n                \"Qdrant upsert failed (%s). %d points skipped.\",\n                exc,\n                len(points),\n            )\n        except Exception as exc:\n            logger.warning(\n                \"Qdrant upsert connection error (%s: %s). %d points skipped.\",\n                type(exc).__name__,\n                exc,\n                len(points),\n            )\n\n    # ── High-level upserts ───────────────────────────────────────────────\n\n    def upsert_technique_pages(\n        self,\n        pages: list[dict],\n        vectors: list[list[float]],\n    ) -> None:\n        \"\"\"Build and upsert PointStructs for technique pages.\n\n        Each page dict must contain:\n            page_id, creator_id, title, topic_category, topic_tags, summary\n\n        Parameters\n        ----------\n        pages:\n            Metadata dicts, one per technique page.\n        vectors:\n            Corresponding embedding vectors (same order as pages).\n        \"\"\"\n        if len(pages) != len(vectors):\n            logger.warning(\n                \"Technique-page count (%d) != vector count (%d). Skipping upsert.\",\n                len(pages),\n                len(vectors),\n            )\n            return\n\n        points = []\n        for page, vector in zip(pages, vectors):\n            # Deterministic UUID: same page always gets the same point ID\n            point_id = str(uuid.uuid5(_QDRANT_NAMESPACE, f\"tp:{page['page_id']}\"))\n            point = PointStruct(\n                id=point_id,\n                vector=vector,\n                payload={\n                    \"type\": \"technique_page\",\n                    \"page_id\": page[\"page_id\"],\n                    \"creator_id\": page[\"creator_id\"],\n                    \"creator_name\": page.get(\"creator_name\", \"\"),\n                    \"title\": page[\"title\"],\n                    \"slug\": page.get(\"slug\", \"\"),\n                    \"topic_category\": page[\"topic_category\"],\n                    \"topic_tags\": page.get(\"topic_tags\") or [],\n                    \"summary\": page.get(\"summary\") or \"\",\n                },\n            )\n            points.append(point)\n\n        self.upsert_points(points)\n\n    def upsert_key_moments(\n        self,\n        moments: list[dict],\n        vectors: list[list[float]],\n    ) -> None:\n        \"\"\"Build and upsert PointStructs for key moments.\n\n        Each moment dict must contain:\n            moment_id, source_video_id, title, start_time, end_time, content_type\n\n        Parameters\n        ----------\n        moments:\n            Metadata dicts, one per key moment.\n        vectors:\n            Corresponding embedding vectors (same order as moments).\n        \"\"\"\n        if len(moments) != len(vectors):\n            logger.warning(\n                \"Key-moment count (%d) != vector count (%d). Skipping upsert.\",\n                len(moments),\n                len(vectors),\n            )\n            return\n\n        points = []\n        for moment, vector in zip(moments, vectors):\n            # Deterministic UUID: same moment always gets the same point ID\n            point_id = str(uuid.uuid5(_QDRANT_NAMESPACE, f\"km:{moment['moment_id']}\"))\n            point = PointStruct(\n                id=point_id,\n                vector=vector,\n                payload={\n                    \"type\": \"key_moment\",\n                    \"moment_id\": moment[\"moment_id\"],\n                    \"source_video_id\": moment[\"source_video_id\"],\n                    \"technique_page_id\": moment.get(\"technique_page_id\", \"\"),\n                    \"technique_page_slug\": moment.get(\"technique_page_slug\", \"\"),\n                    \"title\": moment[\"title\"],\n                    \"creator_name\": moment.get(\"creator_name\", \"\"),\n                    \"start_time\": moment[\"start_time\"],\n                    \"end_time\": moment[\"end_time\"],\n                    \"content_type\": moment[\"content_type\"],\n                },\n            )\n            points.append(point)\n\n        self.upsert_points(points)\n"}],"isError":false,"timestamp":1775181709913}}
{"type":"message","id":"ae39d3b0","parentId":"97ecbe63","timestamp":"2026-04-03T02:01:49.916Z","message":{"role":"toolResult","toolCallId":"toolu_01FhUjJiVqYgkdX8ditkHps3","toolName":"read","content":[{"type":"text","text":"\"\"\"Synchronous embedding client using the OpenAI-compatible /v1/embeddings API.\n\nUses ``openai.OpenAI`` (sync) since Celery tasks run synchronously.\nHandles connection failures gracefully — embedding is non-blocking for the pipeline.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\n\nimport openai\n\nfrom config import Settings\n\nlogger = logging.getLogger(__name__)\n\n\nclass EmbeddingClient:\n    \"\"\"Sync embedding client backed by an OpenAI-compatible /v1/embeddings endpoint.\"\"\"\n\n    def __init__(self, settings: Settings) -> None:\n        self.settings = settings\n        self._client = openai.OpenAI(\n            base_url=settings.embedding_api_url,\n            api_key=settings.llm_api_key,\n        )\n\n    def embed(self, texts: list[str]) -> list[list[float]]:\n        \"\"\"Generate embedding vectors for a batch of texts.\n\n        Parameters\n        ----------\n        texts:\n            List of strings to embed.\n\n        Returns\n        -------\n        list[list[float]]\n            Embedding vectors. Returns empty list on connection/timeout errors\n            so the pipeline can continue without embeddings.\n        \"\"\"\n        if not texts:\n            return []\n\n        try:\n            response = self._client.embeddings.create(\n                model=self.settings.embedding_model,\n                input=texts,\n            )\n        except (openai.APIConnectionError, openai.APITimeoutError) as exc:\n            logger.warning(\n                \"Embedding API unavailable (%s: %s). Skipping %d texts.\",\n                type(exc).__name__,\n                exc,\n                len(texts),\n            )\n            return []\n        except openai.APIError as exc:\n            logger.warning(\n                \"Embedding API error (%s: %s). Skipping %d texts.\",\n                type(exc).__name__,\n                exc,\n                len(texts),\n            )\n            return []\n\n        vectors = [item.embedding for item in response.data]\n\n        # Validate dimensions\n        expected_dim = self.settings.embedding_dimensions\n        for i, vec in enumerate(vectors):\n            if len(vec) != expected_dim:\n                logger.warning(\n                    \"Embedding dimension mismatch at index %d: expected %d, got %d. \"\n                    \"Returning empty list.\",\n                    i,\n                    expected_dim,\n                    len(vec),\n                )\n                return []\n\n        logger.info(\n            \"Generated %d embeddings (dim=%d) using model=%s\",\n            len(vectors),\n            expected_dim,\n            self.settings.embedding_model,\n        )\n        return vectors\n"}],"isError":false,"timestamp":1775181709914}}
{"type":"message","id":"1616701e","parentId":"ae39d3b0","timestamp":"2026-04-03T02:01:54.694Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me look at how stage 6 (embed_and_index) currently works, and the search service:"},{"type":"toolCall","id":"toolu_01QvBm9PFFoJM2oQTXvgE484","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"stage6|embed_and_index|def.*embed\" backend/pipeline/stages.py | head -20"}},{"type":"toolCall","id":"toolu_01XiBgYVQxLQ9CQty8iHir8m","name":"read","arguments":{"path":"backend/search_service.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":163,"cacheRead":68191,"cacheWrite":2940,"totalTokens":71295,"cost":{"input":0.000005,"output":0.0040750000000000005,"cacheRead":0.0340955,"cacheWrite":0.018375000000000002,"total":0.056550500000000004}},"stopReason":"toolUse","timestamp":1775181709915}}
{"type":"message","id":"eed14dd2","parentId":"1616701e","timestamp":"2026-04-03T02:01:54.713Z","message":{"role":"toolResult","toolCallId":"toolu_01QvBm9PFFoJM2oQTXvgE484","toolName":"bash","content":[{"type":"text","text":"1595:def stage6_embed_and_index(self, video_id: str, run_id: str | None = None) -> str:\n1762:            _finish_run(run_id, \"complete\")  # Run is still \"complete\" — stage6 is best-effort\n1829:    \"stage6_embed_and_index\",\n1837:    \"stage6_embed_and_index\": stage6_embed_and_index,\n2265:        if stage_name == \"stage6_embed_and_index\":\n"}],"isError":false,"timestamp":1775181714712}}
{"type":"message","id":"54f6c72f","parentId":"eed14dd2","timestamp":"2026-04-03T02:01:54.718Z","message":{"role":"toolResult","toolCallId":"toolu_01XiBgYVQxLQ9CQty8iHir8m","toolName":"read","content":[{"type":"text","text":"\"\"\"Async search service for the public search endpoint.\n\nOrchestrates semantic search (embedding + Qdrant) with keyword search.\nBoth run in parallel — results are merged and deduplicated. Keyword matches\nget a match_context explaining WHY they matched. Semantic-only results get\na \"Semantic match\" context and are filtered by a minimum score threshold.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nimport logging\nimport time\nfrom typing import Any\n\nimport openai\nfrom qdrant_client import AsyncQdrantClient\nfrom qdrant_client.http import exceptions as qdrant_exceptions\nfrom qdrant_client.models import FieldCondition, Filter, MatchValue\nfrom sqlalchemy import and_, func, or_, select\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom config import Settings\nfrom models import Creator, KeyMoment, SourceVideo, TechniquePage\n\nlogger = logging.getLogger(\"chrysopedia.search\")\n\n# Timeout for external calls (embedding API, Qdrant) in seconds\n_EXTERNAL_TIMEOUT = 2.0  # 2s — Ollama local embedding needs more than 300ms\n\n# Minimum cosine similarity score for semantic results to be included\n_SEMANTIC_MIN_SCORE = 0.45\n\n\nclass SearchService:\n    \"\"\"Async search service with parallel semantic + keyword search.\n\n    Parameters\n    ----------\n    settings:\n        Application settings containing embedding and Qdrant config.\n    \"\"\"\n\n    def __init__(self, settings: Settings) -> None:\n        self.settings = settings\n        self._openai = openai.AsyncOpenAI(\n            base_url=settings.embedding_api_url,\n            api_key=settings.llm_api_key,\n        )\n        self._qdrant = AsyncQdrantClient(url=settings.qdrant_url)\n        self._collection = settings.qdrant_collection\n\n    # ── Embedding ────────────────────────────────────────────────────────\n\n    async def embed_query(self, text: str) -> list[float] | None:\n        \"\"\"Embed a query string into a vector.\n\n        Returns None on any failure (timeout, connection, malformed response)\n        so the caller can fall back to keyword search.\n        \"\"\"\n        try:\n            response = await asyncio.wait_for(\n                self._openai.embeddings.create(\n                    model=self.settings.embedding_model,\n                    input=text,\n                ),\n                timeout=_EXTERNAL_TIMEOUT,\n            )\n        except asyncio.TimeoutError:\n            logger.warning(\"Embedding API timeout (%.0fms limit) for query: %.50s…\", _EXTERNAL_TIMEOUT * 1000, text)\n            return None\n        except (openai.APIConnectionError, openai.APITimeoutError) as exc:\n            logger.warning(\"Embedding API connection error (%s: %s)\", type(exc).__name__, exc)\n            return None\n        except openai.APIError as exc:\n            logger.warning(\"Embedding API error (%s: %s)\", type(exc).__name__, exc)\n            return None\n\n        if not response.data:\n            logger.warning(\"Embedding API returned empty data for query: %.50s…\", text)\n            return None\n\n        vector = response.data[0].embedding\n        if len(vector) != self.settings.embedding_dimensions:\n            logger.warning(\n                \"Embedding dimension mismatch: expected %d, got %d\",\n                self.settings.embedding_dimensions,\n                len(vector),\n            )\n            return None\n\n        return vector\n\n    # ── Qdrant vector search ─────────────────────────────────────────────\n\n    async def search_qdrant(\n        self,\n        vector: list[float],\n        limit: int = 20,\n        type_filter: str | None = None,\n    ) -> list[dict[str, Any]]:\n        \"\"\"Search Qdrant for nearest neighbours.\n\n        Returns a list of dicts with 'score' and 'payload' keys.\n        Returns empty list on failure.\n        \"\"\"\n        query_filter = None\n        if type_filter:\n            query_filter = Filter(\n                must=[FieldCondition(key=\"type\", match=MatchValue(value=type_filter))]\n            )\n\n        try:\n            results = await asyncio.wait_for(\n                self._qdrant.query_points(\n                    collection_name=self._collection,\n                    query=vector,\n                    query_filter=query_filter,\n                    limit=limit,\n                    with_payload=True,\n                ),\n                timeout=_EXTERNAL_TIMEOUT,\n            )\n        except asyncio.TimeoutError:\n            logger.warning(\"Qdrant search timeout (%.0fms limit)\", _EXTERNAL_TIMEOUT * 1000)\n            return []\n        except qdrant_exceptions.UnexpectedResponse as exc:\n            logger.warning(\"Qdrant search error: %s\", exc)\n            return []\n        except Exception as exc:\n            logger.warning(\"Qdrant connection error (%s: %s)\", type(exc).__name__, exc)\n            return []\n\n        return [\n            {\"score\": point.score, \"payload\": point.payload}\n            for point in results.points\n        ]\n\n    # ── Token helpers ───────────────────────────────────────────────────\n\n    @staticmethod\n    def _tokenize(query: str) -> list[str]:\n        \"\"\"Split query into non-empty lowercase tokens.\"\"\"\n        return [t.lower() for t in query.split() if t]\n\n    @staticmethod\n    def _tp_token_condition(token: str):\n        \"\"\"Build an OR condition for a single token across TechniquePage + Creator fields.\"\"\"\n        pat = f\"%{token}%\"\n        return or_(\n            TechniquePage.title.ilike(pat),\n            TechniquePage.summary.ilike(pat),\n            TechniquePage.topic_category.ilike(pat),\n            func.array_to_string(TechniquePage.topic_tags, \" \").ilike(pat),\n            Creator.name.ilike(pat),\n        )\n\n    @staticmethod\n    def _km_token_condition(token: str):\n        \"\"\"Build an OR condition for a single token across KeyMoment + Creator fields.\"\"\"\n        pat = f\"%{token}%\"\n        return or_(\n            KeyMoment.title.ilike(pat),\n            KeyMoment.summary.ilike(pat),\n            Creator.name.ilike(pat),\n        )\n\n    @staticmethod\n    def _cr_token_condition(token: str):\n        \"\"\"Build an OR condition for a single token across Creator fields.\"\"\"\n        pat = f\"%{token}%\"\n        return or_(\n            Creator.name.ilike(pat),\n            func.array_to_string(Creator.genres, \" \").ilike(pat),\n        )\n\n    # ── Match context generation ────────────────────────────────────────\n\n    @staticmethod\n    def _build_match_context(item: dict[str, Any], tokens: list[str]) -> str:\n        \"\"\"Generate a human-readable string explaining which fields matched.\n\n        Checks each token against each field and returns a compact summary\n        like \"Creator: Keota · Tag: snare\" or \"Title: ...snare drum...\".\n        \"\"\"\n        if not tokens:\n            return \"\"\n\n        matches: list[str] = []\n        seen: set[str] = set()\n\n        for token in tokens:\n            t = token.lower()\n\n            # Check creator name\n            creator = item.get(\"creator_name\", \"\")\n            if creator and t in creator.lower() and \"creator\" not in seen:\n                matches.append(f\"Creator: {creator}\")\n                seen.add(\"creator\")\n                continue\n\n            # Check title\n            title = item.get(\"title\", \"\")\n            if title and t in title.lower() and \"title\" not in seen:\n                matches.append(f\"Title match\")\n                seen.add(\"title\")\n                continue\n\n            # Check topic_category\n            cat = item.get(\"topic_category\", \"\")\n            if cat and t in cat.lower() and \"category\" not in seen:\n                matches.append(f\"Category: {cat}\")\n                seen.add(\"category\")\n                continue\n\n            # Check topic_tags\n            tags = item.get(\"topic_tags\", [])\n            matched_tag = next((tag for tag in tags if t in tag.lower()), None)\n            if matched_tag and f\"tag:{matched_tag.lower()}\" not in seen:\n                matches.append(f\"Tag: {matched_tag}\")\n                seen.add(f\"tag:{matched_tag.lower()}\")\n                continue\n\n            # Check summary\n            summary = item.get(\"summary\", \"\")\n            if summary and t in summary.lower() and \"summary\" not in seen:\n                # Extract a small context window around the match\n                idx = summary.lower().find(t)\n                start = max(0, idx - 20)\n                end = min(len(summary), idx + len(t) + 20)\n                snippet = summary[start:end].strip()\n                if start > 0:\n                    snippet = \"…\" + snippet\n                if end < len(summary):\n                    snippet = snippet + \"…\"\n                matches.append(f\"Content: {snippet}\")\n                seen.add(\"summary\")\n                continue\n\n        return \" · \".join(matches) if matches else \"\"\n\n    # ── Keyword search (multi-token AND) ─────────────────────────────────\n\n    async def keyword_search(\n        self,\n        query: str,\n        scope: str,\n        limit: int,\n        db: AsyncSession,\n        sort: str = \"relevance\",\n    ) -> dict[str, list[dict[str, Any]]]:\n        \"\"\"Multi-token AND keyword search across technique pages, key moments, and creators.\n\n        Tokenizes the query by whitespace. Each token must match at least one\n        indexed field (title, summary, topic_category, topic_tags, creator name,\n        genres). All tokens must match for a row to be included.\n\n        If AND matching returns zero results but individual tokens would match,\n        returns up to 5 partial_matches scored by the number of tokens matched.\n\n        Returns ``{\"items\": [...], \"partial_matches\": [...]}``.\n        \"\"\"\n        tokens = self._tokenize(query)\n        if not tokens:\n            return {\"items\": [], \"partial_matches\": []}\n\n        items = await self._keyword_search_and(tokens, scope, limit, db)\n\n        # Add match_context to each item\n        for item in items:\n            item[\"match_context\"] = self._build_match_context(item, tokens)\n\n        partial: list[dict[str, Any]] = []\n        if not items and len(tokens) > 1:\n            partial = await self._keyword_partial_matches(tokens, scope, db)\n            for p in partial:\n                p[\"match_context\"] = self._build_match_context(p, tokens)\n\n        return {\"items\": items, \"partial_matches\": partial}\n\n    async def _keyword_search_and(\n        self,\n        tokens: list[str],\n        scope: str,\n        limit: int,\n        db: AsyncSession,\n    ) -> list[dict[str, Any]]:\n        \"\"\"Run AND-logic keyword search — every token must match at least one field.\"\"\"\n        results: list[dict[str, Any]] = []\n\n        if scope in (\"all\", \"topics\"):\n            tp_stmt = (\n                select(TechniquePage, Creator)\n                .join(Creator, TechniquePage.creator_id == Creator.id)\n                .where(and_(*(self._tp_token_condition(t) for t in tokens)))\n                .limit(limit)\n            )\n            tp_rows = await db.execute(tp_stmt)\n            for tp, cr in tp_rows.all():\n                results.append({\n                    \"type\": \"technique_page\",\n                    \"title\": tp.title,\n                    \"slug\": tp.slug,\n                    \"technique_page_slug\": tp.slug,\n                    \"summary\": tp.summary or \"\",\n                    \"topic_category\": tp.topic_category,\n                    \"topic_tags\": tp.topic_tags or [],\n                    \"creator_id\": str(tp.creator_id),\n                    \"creator_name\": cr.name,\n                    \"creator_slug\": cr.slug,\n                    \"created_at\": tp.created_at.isoformat() if tp.created_at else \"\",\n                    \"score\": 0.0,\n                })\n\n        if scope in (\"all\",):\n            km_stmt = (\n                select(KeyMoment, SourceVideo, Creator, TechniquePage)\n                .join(SourceVideo, KeyMoment.source_video_id == SourceVideo.id)\n                .join(Creator, SourceVideo.creator_id == Creator.id)\n                .outerjoin(TechniquePage, KeyMoment.technique_page_id == TechniquePage.id)\n                .where(and_(*(self._km_token_condition(t) for t in tokens)))\n                .limit(limit)\n            )\n            km_rows = await db.execute(km_stmt)\n            for km, sv, cr, tp in km_rows.all():\n                results.append({\n                    \"type\": \"key_moment\",\n                    \"title\": km.title,\n                    \"slug\": \"\",\n                    \"technique_page_slug\": tp.slug if tp else \"\",\n                    \"summary\": km.summary or \"\",\n                    \"topic_category\": \"\",\n                    \"topic_tags\": [],\n                    \"creator_id\": str(cr.id),\n                    \"creator_name\": cr.name,\n                    \"creator_slug\": cr.slug,\n                    \"created_at\": km.created_at.isoformat() if hasattr(km, \"created_at\") and km.created_at else \"\",\n                    \"score\": 0.0,\n                })\n\n        if scope in (\"all\", \"creators\"):\n            cr_stmt = (\n                select(Creator)\n                .where(and_(*(self._cr_token_condition(t) for t in tokens)))\n                .limit(limit)\n            )\n            cr_rows = await db.execute(cr_stmt)\n            for cr in cr_rows.scalars().all():\n                results.append({\n                    \"type\": \"creator\",\n                    \"title\": cr.name,\n                    \"slug\": cr.slug,\n                    \"technique_page_slug\": \"\",\n                    \"summary\": \"\",\n                    \"topic_category\": \"\",\n                    \"topic_tags\": cr.genres or [],\n                    \"creator_id\": str(cr.id),\n                    \"created_at\": cr.created_at.isoformat() if hasattr(cr, \"created_at\") and cr.created_at else \"\",\n                    \"score\": 0.0,\n                })\n\n        return results[:limit]\n\n    async def _keyword_partial_matches(\n        self,\n        tokens: list[str],\n        scope: str,\n        db: AsyncSession,\n    ) -> list[dict[str, Any]]:\n        \"\"\"When AND produces zero results, score rows by how many tokens match.\n\n        Returns the top 5 results ordered by match count descending.\n        \"\"\"\n        seen: dict[tuple[str, str], dict[str, Any]] = {}\n        match_counts: dict[tuple[str, str], int] = {}\n\n        for token in tokens:\n            single_results = await self._keyword_search_and([token], scope, 20, db)\n            for r in single_results:\n                key = (r[\"type\"], r.get(\"slug\") or r.get(\"title\", \"\"))\n                if key not in seen:\n                    seen[key] = r\n                    match_counts[key] = 0\n                match_counts[key] += 1\n\n        ranked = sorted(match_counts.keys(), key=lambda k: match_counts[k], reverse=True)\n        partial: list[dict[str, Any]] = []\n        for key in ranked[:5]:\n            item = seen[key]\n            item[\"score\"] = match_counts[key] / len(tokens)\n            partial.append(item)\n\n        return partial\n\n    # ── Orchestrator ─────────────────────────────────────────────────────\n\n    async def search(\n        self,\n        query: str,\n        scope: str,\n        limit: int,\n        db: AsyncSession,\n        sort: str = \"relevance\",\n    ) -> dict[str, Any]:\n        \"\"\"Run semantic and keyword search in parallel, merge and deduplicate.\n\n        Both engines run concurrently. Keyword results are always included\n        (with match_context). Semantic results above the score threshold are\n        merged in, deduplicated by (type, slug/title). Keyword matches rank\n        higher when they exist.\n        \"\"\"\n        start = time.monotonic()\n\n        if not query or not query.strip():\n            return {\"items\": [], \"partial_matches\": [], \"total\": 0, \"query\": query, \"fallback_used\": False}\n\n        query = query.strip()[:500]\n        if scope not in (\"all\", \"topics\", \"creators\"):\n            scope = \"all\"\n\n        # Map scope to Qdrant type filter\n        type_filter_map = {\n            \"all\": None,\n            \"topics\": \"technique_page\",\n            \"creators\": None,\n        }\n        qdrant_type_filter = type_filter_map.get(scope)\n\n        # Run both searches in parallel\n        async def _semantic():\n            vector = await self.embed_query(query)\n            if vector is None:\n                return []\n            results = await self.search_qdrant(vector, limit=limit, type_filter=qdrant_type_filter)\n            enriched = await self._enrich_qdrant_results(results, db)\n            # Filter by minimum score and add match_context\n            filtered = []\n            for item in enriched:\n                if item.get(\"score\", 0) >= _SEMANTIC_MIN_SCORE:\n                    if not item.get(\"match_context\"):\n                        item[\"match_context\"] = \"Semantic match\"\n                    filtered.append(item)\n            return filtered\n\n        async def _keyword():\n            return await self.keyword_search(query, scope, limit, db, sort=sort)\n\n        semantic_results, kw_result = await asyncio.gather(\n            _semantic(),\n            _keyword(),\n            return_exceptions=True,\n        )\n\n        # Handle exceptions gracefully\n        if isinstance(semantic_results, Exception):\n            logger.warning(\"Semantic search failed: %s\", semantic_results)\n            semantic_results = []\n        if isinstance(kw_result, Exception):\n            logger.warning(\"Keyword search failed: %s\", kw_result)\n            kw_result = {\"items\": [], \"partial_matches\": []}\n\n        kw_items = kw_result[\"items\"]\n        partial_matches = kw_result.get(\"partial_matches\", [])\n\n        # Merge: keyword results first (they have explicit match_context),\n        # then semantic results that aren't already present\n        seen_keys: set[str] = set()\n        merged: list[dict[str, Any]] = []\n\n        def _dedup_key(item: dict) -> str:\n            t = item.get(\"type\", \"\")\n            s = item.get(\"slug\") or item.get(\"technique_page_slug\") or \"\"\n            title = item.get(\"title\", \"\")\n            return f\"{t}:{s}:{title}\"\n\n        for item in kw_items:\n            key = _dedup_key(item)\n            if key not in seen_keys:\n                seen_keys.add(key)\n                merged.append(item)\n\n        for item in semantic_results:\n            key = _dedup_key(item)\n            if key not in seen_keys:\n                seen_keys.add(key)\n                merged.append(item)\n\n        # Apply sort\n        merged = self._apply_sort(merged, sort)\n\n        fallback_used = len(kw_items) > 0 and len(semantic_results) == 0\n\n        elapsed_ms = (time.monotonic() - start) * 1000\n        logger.info(\n            \"Search query=%r scope=%s keyword=%d semantic=%d merged=%d partial=%d latency_ms=%.1f\",\n            query, scope, len(kw_items), len(semantic_results),\n            len(merged), len(partial_matches), elapsed_ms,\n        )\n\n        return {\n            \"items\": merged[:limit],\n            \"partial_matches\": partial_matches,\n            \"total\": len(merged),\n            \"query\": query,\n            \"fallback_used\": fallback_used,\n        }\n\n    # ── Sort helpers ────────────────────────────────────────────────────\n\n    @staticmethod\n    def _apply_sort(items: list[dict[str, Any]], sort: str) -> list[dict[str, Any]]:\n        \"\"\"Sort enriched result dicts by the requested criterion.\"\"\"\n        if sort == \"relevance\" or not items:\n            return items\n        if sort == \"newest\":\n            return sorted(items, key=lambda r: r.get(\"created_at\", \"\"), reverse=True)\n        elif sort == \"oldest\":\n            return sorted(items, key=lambda r: r.get(\"created_at\") or \"9999\", reverse=False)\n        elif sort == \"alpha\":\n            return sorted(items, key=lambda r: (r.get(\"title\") or \"\").lower())\n        elif sort == \"creator\":\n            return sorted(\n                items,\n                key=lambda r: ((r.get(\"creator_name\") or \"\").lower(), (r.get(\"title\") or \"\").lower()),\n            )\n        return items\n\n    # ── Result enrichment ────────────────────────────────────────────────\n\n    async def _enrich_qdrant_results(\n        self,\n        qdrant_results: list[dict[str, Any]],\n        db: AsyncSession,\n    ) -> list[dict[str, Any]]:\n        \"\"\"Enrich Qdrant results with creator names and slugs from DB.\n\n        First reads creator_name from Qdrant payload; only hits DB for missing ones.\n        \"\"\"\n        enriched: list[dict[str, Any]] = []\n\n        # Collect creator_ids that need DB lookup\n        needs_db_lookup: set[str] = set()\n        for r in qdrant_results:\n            payload = r.get(\"payload\", {})\n            if not payload.get(\"creator_name\") and payload.get(\"creator_id\"):\n                needs_db_lookup.add(payload[\"creator_id\"])\n\n        # Batch fetch creators from DB\n        creator_map: dict[str, dict[str, str]] = {}\n        if needs_db_lookup:\n            import uuid as uuid_mod\n            valid_ids = []\n            for cid in needs_db_lookup:\n                try:\n                    valid_ids.append(uuid_mod.UUID(cid))\n                except (ValueError, AttributeError):\n                    pass\n            if valid_ids:\n                stmt = select(Creator).where(Creator.id.in_(valid_ids))\n                result = await db.execute(stmt)\n                for c in result.scalars().all():\n                    creator_map[str(c.id)] = {\"name\": c.name, \"slug\": c.slug}\n\n        for r in qdrant_results:\n            payload = r.get(\"payload\", {})\n            cid = payload.get(\"creator_id\", \"\")\n            result_type = payload.get(\"type\", \"\")\n\n            # Creator name: prefer payload, fall back to DB\n            creator_name = payload.get(\"creator_name\", \"\")\n            creator_slug = \"\"\n            if not creator_name and cid:\n                info = creator_map.get(cid, {\"name\": \"\", \"slug\": \"\"})\n                creator_name = info[\"name\"]\n                creator_slug = info[\"slug\"]\n            elif creator_name and cid:\n                # We have the name from payload but need the slug from DB\n                info = creator_map.get(cid, {})\n                creator_slug = info.get(\"slug\", \"\")\n\n            # Determine technique_page_slug based on result type\n            if result_type == \"technique_page\":\n                tp_slug = payload.get(\"slug\", payload.get(\"title\", \"\").lower().replace(\" \", \"-\"))\n            else:\n                tp_slug = payload.get(\"technique_page_slug\", \"\")\n\n            enriched.append({\n                \"type\": result_type,\n                \"title\": payload.get(\"title\", \"\"),\n                \"slug\": payload.get(\"slug\", payload.get(\"title\", \"\").lower().replace(\" \", \"-\")),\n                \"technique_page_slug\": tp_slug,\n                \"summary\": payload.get(\"summary\", \"\"),\n                \"topic_category\": payload.get(\"topic_category\", \"\"),\n                \"topic_tags\": payload.get(\"topic_tags\", []),\n                \"creator_id\": cid,\n                \"creator_name\": creator_name,\n                \"creator_slug\": creator_slug,\n                \"created_at\": payload.get(\"created_at\", \"\"),\n                \"score\": r.get(\"score\", 0.0),\n                \"match_context\": \"\",\n            })\n\n        return enriched\n"}],"isError":false,"timestamp":1775181714712}}
{"type":"message","id":"da472d56","parentId":"54f6c72f","timestamp":"2026-04-03T02:02:00.761Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me look at stage6 to see how indexing currently works, and the frontend search components:"},{"type":"toolCall","id":"toolu_01Qoouwe5X4jqf79R2VVezAb","name":"read","arguments":{"limit":170,"offset":1595,"path":"backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_01By3pkvuwfxpc17noYjyudM","name":"read","arguments":{"path":"frontend/src/components/SearchAutocomplete.tsx"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":160,"cacheRead":71131,"cacheWrite":6512,"totalTokens":77804,"cost":{"input":0.000005,"output":0.004,"cacheRead":0.0355655,"cacheWrite":0.0407,"total":0.0802705}},"stopReason":"toolUse","timestamp":1775181714713}}
{"type":"message","id":"1cd39de6","parentId":"da472d56","timestamp":"2026-04-03T02:02:00.765Z","message":{"role":"toolResult","toolCallId":"toolu_01Qoouwe5X4jqf79R2VVezAb","toolName":"read","content":[{"type":"text","text":"def stage6_embed_and_index(self, video_id: str, run_id: str | None = None) -> str:\n    \"\"\"Generate embeddings for technique pages and key moments, then upsert to Qdrant.\n\n    This is a non-blocking side-effect stage — failures are logged but do not\n    fail the pipeline. Embeddings can be regenerated later. Does NOT update\n    processing_status.\n\n    Returns the video_id for chain compatibility.\n    \"\"\"\n    start = time.monotonic()\n    logger.info(\"Stage 6 (embed & index) starting for video_id=%s\", video_id)\n\n    settings = get_settings()\n    session = _get_sync_session()\n    try:\n        # Load technique pages created for this video's moments\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        # Get unique technique page IDs from moments\n        page_ids = {m.technique_page_id for m in moments if m.technique_page_id is not None}\n        pages = []\n        if page_ids:\n            pages = (\n                session.execute(\n                    select(TechniquePage).where(TechniquePage.id.in_(page_ids))\n                )\n                .scalars()\n                .all()\n            )\n\n        if not moments and not pages:\n            logger.info(\"Stage 6: No moments or pages for video_id=%s, skipping.\", video_id)\n            if run_id:\n                _finish_run(run_id, \"complete\")\n            return video_id\n\n        # Resolve creator names for enriched embedding text\n        creator_ids = {p.creator_id for p in pages}\n        creator_map: dict[str, str] = {}\n        if creator_ids:\n            creators = (\n                session.execute(\n                    select(Creator).where(Creator.id.in_(creator_ids))\n                )\n                .scalars()\n                .all()\n            )\n            creator_map = {str(c.id): c.name for c in creators}\n\n        # Resolve creator name for key moments via source_video → creator\n        video_ids = {m.source_video_id for m in moments}\n        video_creator_map: dict[str, str] = {}\n        if video_ids:\n            rows = session.execute(\n                select(SourceVideo.id, Creator.name)\n                .join(Creator, SourceVideo.creator_id == Creator.id)\n                .where(SourceVideo.id.in_(video_ids))\n            ).all()\n            video_creator_map = {str(r[0]): r[1] for r in rows}\n\n        embed_client = EmbeddingClient(settings)\n        qdrant = QdrantManager(settings)\n\n        # Ensure collection exists before upserting\n        qdrant.ensure_collection()\n\n        # ── Embed & upsert technique pages ───────────────────────────────\n        if pages:\n            page_texts = []\n            page_dicts = []\n            for p in pages:\n                creator_name = creator_map.get(str(p.creator_id), \"\")\n                tags_joined = \" \".join(p.topic_tags) if p.topic_tags else \"\"\n                text = f\"{creator_name} {p.title} {p.topic_category or ''} {tags_joined} {p.summary or ''}\"\n                page_texts.append(text.strip())\n                page_dicts.append({\n                    \"page_id\": str(p.id),\n                    \"creator_id\": str(p.creator_id),\n                    \"creator_name\": creator_name,\n                    \"title\": p.title,\n                    \"slug\": p.slug,\n                    \"topic_category\": p.topic_category or \"\",\n                    \"topic_tags\": p.topic_tags or [],\n                    \"summary\": p.summary or \"\",\n                })\n\n            page_vectors = embed_client.embed(page_texts)\n            if page_vectors:\n                qdrant.upsert_technique_pages(page_dicts, page_vectors)\n                logger.info(\n                    \"Stage 6: Upserted %d technique page vectors for video_id=%s\",\n                    len(page_vectors), video_id,\n                )\n            else:\n                logger.warning(\n                    \"Stage 6: Embedding returned empty for %d technique pages (video_id=%s). \"\n                    \"Skipping page upsert.\",\n                    len(page_texts), video_id,\n                )\n\n        # ── Embed & upsert key moments ───────────────────────────────────\n        if moments:\n            # Build page_id → slug mapping for linking moments to technique pages\n            page_id_to_slug: dict[str, str] = {}\n            if pages:\n                for p in pages:\n                    page_id_to_slug[str(p.id)] = p.slug\n\n            moment_texts = []\n            moment_dicts = []\n            for m in moments:\n                creator_name = video_creator_map.get(str(m.source_video_id), \"\")\n                text = f\"{creator_name} {m.title} {m.summary or ''}\"\n                moment_texts.append(text.strip())\n                tp_id = str(m.technique_page_id) if m.technique_page_id else \"\"\n                moment_dicts.append({\n                    \"moment_id\": str(m.id),\n                    \"source_video_id\": str(m.source_video_id),\n                    \"technique_page_id\": tp_id,\n                    \"technique_page_slug\": page_id_to_slug.get(tp_id, \"\"),\n                    \"title\": m.title,\n                    \"creator_name\": creator_name,\n                    \"start_time\": m.start_time,\n                    \"end_time\": m.end_time,\n                    \"content_type\": m.content_type.value,\n                })\n\n            moment_vectors = embed_client.embed(moment_texts)\n            if moment_vectors:\n                qdrant.upsert_key_moments(moment_dicts, moment_vectors)\n                logger.info(\n                    \"Stage 6: Upserted %d key moment vectors for video_id=%s\",\n                    len(moment_vectors), video_id,\n                )\n            else:\n                logger.warning(\n                    \"Stage 6: Embedding returned empty for %d key moments (video_id=%s). \"\n                    \"Skipping moment upsert.\",\n                    len(moment_texts), video_id,\n                )\n\n        elapsed = time.monotonic() - start\n        logger.info(\n            \"Stage 6 (embed & index) completed for video_id=%s in %.1fs — \"\n            \"%d pages, %d moments processed\",\n            video_id, elapsed, len(pages), len(moments),\n        )\n        if run_id:\n            _finish_run(run_id, \"complete\")\n        return video_id\n\n    except Exception as exc:\n        # Non-blocking: log error but don't fail the pipeline\n        logger.error(\n            \"Stage 6 failed for video_id=%s: %s. \"\n            \"Pipeline continues — embeddings can be regenerated later.\",\n            video_id, exc,\n        )\n        if run_id:\n            _finish_run(run_id, \"complete\")  # Run is still \"complete\" — stage6 is best-effort\n        return video_id\n    finally:\n\n[507 more lines in file. Use offset=1765 to continue.]"}],"isError":false,"timestamp":1775181720764}}
{"type":"message","id":"5338f673","parentId":"1cd39de6","timestamp":"2026-04-03T02:02:00.769Z","message":{"role":"toolResult","toolCallId":"toolu_01By3pkvuwfxpc17noYjyudM","toolName":"read","content":[{"type":"text","text":"/**\n * Shared search autocomplete component.\n *\n * - On focus with empty input: shows popular suggestions from /api/v1/search/suggestions\n * - On 2+ chars: shows debounced typeahead search results (top 5)\n * - Escape / outside-click closes dropdown\n * - Enter submits via onSearch callback\n */\n\nimport { useCallback, useEffect, useRef, useState } from \"react\";\nimport { Link } from \"react-router-dom\";\nimport {\n  searchApi,\n  fetchSuggestions,\n  type SearchResultItem,\n  type SuggestionItem,\n} from \"../api/public-client\";\n\ninterface SearchAutocompleteProps {\n  onSearch: (query: string) => void;\n  placeholder?: string;\n  /** @deprecated Use variant=\"hero\" instead */\n  heroSize?: boolean;\n  variant?: 'hero' | 'inline' | 'nav';\n  initialQuery?: string;\n  autoFocus?: boolean;\n  /** When true, Ctrl+Shift+F focuses the input globally */\n  globalShortcut?: boolean;\n}\n\nexport default function SearchAutocomplete({\n  onSearch,\n  placeholder = \"Search techniques…\",\n  heroSize = false,\n  variant: variantProp,\n  initialQuery = \"\",\n  autoFocus = false,\n  globalShortcut = false,\n}: SearchAutocompleteProps) {\n  // Resolve variant: explicit prop wins, then legacy heroSize, then default 'inline'\n  const variant = variantProp ?? (heroSize ? 'hero' : 'inline');\n  const [query, setQuery] = useState(initialQuery);\n  const [searchResults, setSearchResults] = useState<SearchResultItem[]>([]);\n  const [popularSuggestions, setPopularSuggestions] = useState<SuggestionItem[]>([]);\n  const [showDropdown, setShowDropdown] = useState(false);\n  const inputRef = useRef<HTMLInputElement>(null);\n  const debounceRef = useRef<ReturnType<typeof setTimeout> | null>(null);\n  const dropdownRef = useRef<HTMLDivElement>(null);\n  const suggestionsLoadedRef = useRef(false);\n\n  // Sync initialQuery when URL changes (e.g. SearchResults page)\n  useEffect(() => {\n    setQuery(initialQuery);\n  }, [initialQuery]);\n\n  // Fetch popular suggestions once on mount\n  useEffect(() => {\n    if (suggestionsLoadedRef.current) return;\n    suggestionsLoadedRef.current = true;\n    void (async () => {\n      try {\n        const res = await fetchSuggestions();\n        setPopularSuggestions(res.suggestions);\n      } catch {\n        // Non-critical — autocomplete still works without popular suggestions\n      }\n    })();\n  }, []);\n\n  // Auto-focus\n  useEffect(() => {\n    if (autoFocus) inputRef.current?.focus();\n  }, [autoFocus]);\n\n  // Global Ctrl+Shift+F shortcut to focus input\n  useEffect(() => {\n    if (!globalShortcut) return;\n    function handleGlobalKey(e: KeyboardEvent) {\n      if (e.key === \"F\" && e.ctrlKey && e.shiftKey) {\n        e.preventDefault();\n        inputRef.current?.focus();\n      }\n    }\n    document.addEventListener(\"keydown\", handleGlobalKey);\n    return () => document.removeEventListener(\"keydown\", handleGlobalKey);\n  }, [globalShortcut]);\n\n  // Close dropdown on outside click\n  useEffect(() => {\n    function handleClick(e: MouseEvent) {\n      if (dropdownRef.current && !dropdownRef.current.contains(e.target as Node)) {\n        setShowDropdown(false);\n      }\n    }\n    document.addEventListener(\"mousedown\", handleClick);\n    return () => document.removeEventListener(\"mousedown\", handleClick);\n  }, []);\n\n  // Debounced typeahead search\n  const handleInputChange = useCallback((value: string) => {\n    setQuery(value);\n    if (debounceRef.current) clearTimeout(debounceRef.current);\n\n    if (value.length < 2) {\n      setSearchResults([]);\n      // Show popular suggestions if input is empty and we have them\n      if (value.length === 0 && popularSuggestions.length > 0) {\n        setShowDropdown(true);\n      } else {\n        setShowDropdown(false);\n      }\n      return;\n    }\n\n    debounceRef.current = setTimeout(() => {\n      void (async () => {\n        try {\n          const res = await searchApi(value, undefined, 5);\n          setSearchResults(res.items);\n          setShowDropdown(res.items.length > 0);\n        } catch {\n          setSearchResults([]);\n          setShowDropdown(false);\n        }\n      })();\n    }, 300);\n  }, [popularSuggestions.length]);\n\n  function handleFocus() {\n    if (query.length === 0 && popularSuggestions.length > 0) {\n      setShowDropdown(true);\n    } else if (query.length >= 2 && searchResults.length > 0) {\n      setShowDropdown(true);\n    }\n  }\n\n  function handleSubmit(e: React.FormEvent) {\n    e.preventDefault();\n    if (query.trim()) {\n      setShowDropdown(false);\n      onSearch(query.trim());\n    }\n  }\n\n  function handleKeyDown(e: React.KeyboardEvent) {\n    if (e.key === \"Escape\") {\n      setShowDropdown(false);\n    }\n  }\n\n  function handleSuggestionClick(text: string) {\n    setShowDropdown(false);\n    setQuery(text);\n    onSearch(text);\n  }\n\n  const showPopular = query.length < 2 && popularSuggestions.length > 0;\n  const showSearch = query.length >= 2 && searchResults.length > 0;\n\n  const typeLabel: Record<string, string> = {\n    technique: \"Technique\",\n    topic: \"Topic\",\n    creator: \"Creator\",\n    technique_page: \"Technique\",\n    key_moment: \"Key Moment\",\n  };\n\n  return (\n    <div className={`search-container${variant === 'nav' ? ' search-container--nav' : ''}`} ref={dropdownRef}>\n      <form\n        onSubmit={handleSubmit}\n        className={`search-form search-form--${variant}`}\n      >\n        <input\n          ref={inputRef}\n          type=\"search\"\n          className={`search-input search-input--${variant}`}\n          placeholder={placeholder}\n          value={query}\n          onChange={(e) => handleInputChange(e.target.value)}\n          onFocus={handleFocus}\n          onKeyDown={handleKeyDown}\n          aria-label=\"Search techniques\"\n        />\n        {variant !== 'nav' && (\n          <button type=\"submit\" className=\"btn btn--search\">\n            Search\n          </button>\n        )}\n        {variant === 'nav' && (\n          <kbd className=\"search-nav__shortcut\">Ctrl⇧F</kbd>\n        )}\n      </form>\n\n      {showDropdown && (showPopular || showSearch) && (\n        <div className=\"typeahead-dropdown\">\n          {showPopular && (\n            <>\n              <div className=\"typeahead-suggestions-header\">Popular</div>\n              {popularSuggestions.map((item) => (\n                <button\n                  key={`${item.type}-${item.text}`}\n                  className=\"typeahead-suggestion\"\n                  onClick={() => handleSuggestionClick(item.text)}\n                  type=\"button\"\n                >\n                  <span className=\"typeahead-suggestion__text\">{item.text}</span>\n                  <span className={`typeahead-item__type typeahead-item__type--${item.type}`}>\n                    {typeLabel[item.type] ?? item.type}\n                  </span>\n                </button>\n              ))}\n            </>\n          )}\n\n          {showSearch && (\n            <>\n              {searchResults.map((item) => (\n                <Link\n                  key={`${item.type}-${item.slug}`}\n                  to={`/techniques/${item.slug}`}\n                  className=\"typeahead-item\"\n                  onClick={() => setShowDropdown(false)}\n                >\n                  <span className=\"typeahead-item__title\">{item.title}</span>\n                  <span className=\"typeahead-item__meta\">\n                    <span className={`typeahead-item__type typeahead-item__type--${item.type}`}>\n                      {typeLabel[item.type] ?? item.type}\n                    </span>\n                    {item.creator_name && (\n                      <span className=\"typeahead-item__creator\">\n                        {item.creator_name}\n                      </span>\n                    )}\n                  </span>\n                </Link>\n              ))}\n              <Link\n                to={`/search?q=${encodeURIComponent(query)}`}\n                className=\"typeahead-see-all\"\n                onClick={() => setShowDropdown(false)}\n              >\n                See all results for &ldquo;{query}&rdquo;\n              </Link>\n            </>\n          )}\n        </div>\n      )}\n    </div>\n  );\n}\n"}],"isError":false,"timestamp":1775181720767}}
{"type":"message","id":"1f86ad3a","parentId":"5338f673","timestamp":"2026-04-03T02:02:05.776Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01EPqw9BtsVdq6PgzTrAkBQ2","name":"read","arguments":{"path":"frontend/src/pages/SearchResults.tsx"}},{"type":"toolCall","id":"toolu_01GJJy6XAHc7rWXvEfqy7ghL","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"scroll-margin\\|scrollIntoView\\|hash\\|anchor\\|section.*id\" frontend/src/pages/TechniquePage.tsx | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":153,"cacheRead":77643,"cacheWrite":4467,"totalTokens":82264,"cost":{"input":0.000005,"output":0.0038250000000000003,"cacheRead":0.038821499999999995,"cacheWrite":0.027918750000000003,"total":0.07057025}},"stopReason":"toolUse","timestamp":1775181720768}}
{"type":"message","id":"47fff00b","parentId":"1f86ad3a","timestamp":"2026-04-03T02:02:05.785Z","message":{"role":"toolResult","toolCallId":"toolu_01EPqw9BtsVdq6PgzTrAkBQ2","toolName":"read","content":[{"type":"text","text":"/**\n * Full search results page.\n *\n * Reads `q` from URL search params, calls searchApi, groups results by type\n * (technique_pages first, then key_moments). Shows fallback banner when\n * keyword search was used.\n */\n\nimport { useCallback, useEffect, useState } from \"react\";\nimport { Link, useSearchParams, useNavigate } from \"react-router-dom\";\nimport { searchApi, type SearchResultItem } from \"../api/public-client\";\nimport { catSlug } from \"../utils/catSlug\";\nimport SearchAutocomplete from \"../components/SearchAutocomplete\";\nimport SortDropdown from \"../components/SortDropdown\";\nimport TagList from \"../components/TagList\";\nimport { useDocumentTitle } from \"../hooks/useDocumentTitle\";\nimport { useSortPreference } from \"../hooks/useSortPreference\";\n\nconst SEARCH_SORT_OPTIONS = [\n  { value: \"relevance\", label: \"Relevance\" },\n  { value: \"newest\", label: \"Newest\" },\n  { value: \"oldest\", label: \"Oldest\" },\n  { value: \"alpha\", label: \"A–Z\" },\n  { value: \"creator\", label: \"Creator\" },\n];\n\nexport default function SearchResults() {\n  const [searchParams] = useSearchParams();\n  const navigate = useNavigate();\n  const q = searchParams.get(\"q\") ?? \"\";\n  const [sort, setSort] = useSortPreference(\"relevance\");\n\n  useDocumentTitle(q ? `Search: ${q} — Chrysopedia` : \"Search — Chrysopedia\");\n\n  const [results, setResults] = useState<SearchResultItem[]>([]);\n  const [partialMatches, setPartialMatches] = useState<SearchResultItem[]>([]);\n  const [loading, setLoading] = useState(false);\n  const [error, setError] = useState<string | null>(null);\n\n  const doSearch = useCallback(async (query: string, sortBy: string) => {\n    if (!query.trim()) {\n      setResults([]);\n      setPartialMatches([]);\n      return;\n    }\n\n    setLoading(true);\n    setError(null);\n    try {\n      const res = await searchApi(query.trim(), undefined, undefined, sortBy);\n      setResults(res.items);\n      setPartialMatches(res.partial_matches ?? []);\n    } catch (err) {\n      setError(err instanceof Error ? err.message : \"Search failed\");\n      setResults([]);\n      setPartialMatches([]);\n    } finally {\n      setLoading(false);\n    }\n  }, []);\n\n  // Search when URL param or sort changes\n  useEffect(() => {\n    if (q) void doSearch(q, sort);\n  }, [q, sort, doSearch]);\n\n  // Group results by type\n  const techniqueResults = results.filter((r) => r.type === \"technique_page\");\n  const momentResults = results.filter((r) => r.type === \"key_moment\");\n\n  return (\n    <div className=\"search-results-page\">\n      <h1 className=\"sr-only\">Search Results</h1>\n      {/* Inline search bar */}\n      <SearchAutocomplete\n        variant=\"inline\"\n        initialQuery={q}\n        onSearch={(newQ) =>\n          navigate(`/search?q=${encodeURIComponent(newQ)}`, { replace: true })\n        }\n      />\n\n      {/* Sort control */}\n      {q && (\n        <SortDropdown\n          options={SEARCH_SORT_OPTIONS}\n          value={sort}\n          onChange={setSort}\n        />\n      )}\n\n      {/* Status */}\n      {loading && <div className=\"loading\">Searching…</div>}\n      {error && <div className=\"loading error-text\">Error: {error}</div>}\n\n      {/* No exact results — partial match fallback */}\n      {!loading && !error && q && results.length === 0 && partialMatches.length > 0 && (\n        <>\n          <div className=\"partial-match-banner\">\n            <p>No exact matches for all terms in \"{q}\"</p>\n          </div>\n          <PartialMatchResults items={partialMatches} />\n        </>\n      )}\n\n      {/* No results at all */}\n      {!loading && !error && q && results.length === 0 && partialMatches.length === 0 && (\n        <div className=\"empty-state\">\n          <p>No results found for \"{q}\"</p>\n        </div>\n      )}\n\n      {/* Technique pages */}\n      {techniqueResults.length > 0 && (\n        <section className=\"search-group\">\n          <h3 className=\"search-group__title\">\n            Techniques ({techniqueResults.length})\n          </h3>\n          <div className=\"search-group__list\">\n            {techniqueResults.map((item, i) => (\n              <SearchResultCard key={`tp-${item.slug}`} item={item} staggerIndex={i} />\n            ))}\n          </div>\n        </section>\n      )}\n\n      {/* Key moments */}\n      {momentResults.length > 0 && (\n        <section className=\"search-group\">\n          <h3 className=\"search-group__title\">\n            Key Moments ({momentResults.length})\n          </h3>\n          <div className=\"search-group__list\">\n            {momentResults.map((item, i) => (\n              <SearchResultCard key={`km-${item.slug}-${i}`} item={item} staggerIndex={i} />\n            ))}\n          </div>\n        </section>\n      )}\n    </div>\n  );\n}\n\nfunction getSearchResultLink(item: SearchResultItem): string {\n  if (item.type === \"key_moment\") {\n    if (item.technique_page_slug) {\n      return `/techniques/${item.technique_page_slug}#km-${item.slug || item.title}`;\n    }\n    // Graceful fallback — re-search instead of 404\n    return `/search?q=${encodeURIComponent(item.title)}`;\n  }\n  return `/techniques/${item.slug}`;\n}\n\nfunction SearchResultCard({ item, staggerIndex }: { item: SearchResultItem; staggerIndex: number }) {\n  return (\n    <Link\n      to={getSearchResultLink(item)}\n      className=\"search-result-card card-stagger\"\n      style={{ '--stagger-index': staggerIndex } as React.CSSProperties}\n    >\n      <div className=\"search-result-card__header\">\n        <span className=\"search-result-card__title\">{item.title}</span>\n        <span className={`badge badge--type badge--type-${item.type}`}>\n          {item.type === \"technique_page\" ? \"Technique\" : \"Key Moment\"}\n        </span>\n      </div>\n      {item.match_context && (\n        <div className=\"search-result-card__match-context\">\n          <span className=\"match-context__icon\">⚡</span>\n          <span className=\"match-context__text\">{item.match_context}</span>\n        </div>\n      )}\n      {item.summary && (\n        <p className=\"search-result-card__summary\">\n          {item.summary.length > 200\n            ? `${item.summary.slice(0, 200)}…`\n            : item.summary}\n        </p>\n      )}\n      <div className=\"search-result-card__meta\">\n        {item.creator_name && <span className=\"search-result-card__creator\">{item.creator_name}</span>}\n        {item.topic_category && (\n          <>\n            <span className=\"queue-card__separator\">·</span>\n            <span className={`badge badge--cat-${catSlug(item.topic_category)}`}>{item.topic_category}</span>\n          </>\n        )}\n        {item.topic_tags.length > 0 && (\n          <span className=\"search-result-card__tags\">\n            <TagList tags={item.topic_tags} />\n          </span>\n        )}\n      </div>\n    </Link>\n  );\n}\n\nfunction PartialMatchResults({ items }: { items: SearchResultItem[] }) {\n  const techniqueResults = items.filter((r) => r.type === \"technique_page\");\n  const momentResults = items.filter((r) => r.type === \"key_moment\");\n\n  return (\n    <div className=\"partial-match-results\">\n      <h3 className=\"partial-match-results__header\">\n        Results matching some of your terms\n      </h3>\n\n      {techniqueResults.length > 0 && (\n        <section className=\"search-group\">\n          <h4 className=\"search-group__title\">\n            Techniques ({techniqueResults.length})\n          </h4>\n          <div className=\"search-group__list\">\n            {techniqueResults.map((item, i) => (\n              <SearchResultCard key={`partial-tp-${item.slug}`} item={item} staggerIndex={i} />\n            ))}\n          </div>\n        </section>\n      )}\n\n      {momentResults.length > 0 && (\n        <section className=\"search-group\">\n          <h4 className=\"search-group__title\">\n            Key Moments ({momentResults.length})\n          </h4>\n          <div className=\"search-group__list\">\n            {momentResults.map((item, i) => (\n              <SearchResultCard key={`partial-km-${item.slug}-${i}`} item={item} staggerIndex={i} />\n            ))}\n          </div>\n        </section>\n      )}\n    </div>\n  );\n}\n"}],"isError":false,"timestamp":1775181725784}}
{"type":"message","id":"78b9ecc3","parentId":"47fff00b","timestamp":"2026-04-03T02:02:05.792Z","message":{"role":"toolResult","toolCallId":"toolu_01GJJy6XAHc7rWXvEfqy7ghL","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775181725790}}
{"type":"message","id":"49cd1ed9","parentId":"78b9ecc3","timestamp":"2026-04-03T02:02:10.713Z","message":{"role":"assistant","content":[{"type":"text","text":"Let me check the TechniquePage for how anchors and scrolling work with v2 sections:"},{"type":"toolCall","id":"toolu_017pnND3T8FGvpWs7kxyyB9G","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"slug\\|id=\\|hash\\|anchor\\|scroll\" frontend/src/pages/TechniquePage.tsx | head -30"}},{"type":"toolCall","id":"toolu_01FdeAX9fLhZ9fKcGJvGHvCC","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"body_sections\\|BodySection\" frontend/src/pages/TechniquePage.tsx | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":208,"cacheRead":82110,"cacheWrite":2618,"totalTokens":84937,"cost":{"input":0.000005,"output":0.005200000000000001,"cacheRead":0.041055,"cacheWrite":0.016362500000000002,"total":0.0626225}},"stopReason":"toolUse","timestamp":1775181725790}}
{"type":"message","id":"21f1f10e","parentId":"49cd1ed9","timestamp":"2026-04-03T02:02:10.726Z","message":{"role":"toolResult","toolCallId":"toolu_017pnND3T8FGvpWs7kxyyB9G","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775181730726}}
{"type":"message","id":"483a7b1e","parentId":"21f1f10e","timestamp":"2026-04-03T02:02:10.732Z","message":{"role":"toolResult","toolCallId":"toolu_01FdeAX9fLhZ9fKcGJvGHvCC","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775181730730}}
{"type":"message","id":"4e444346","parentId":"483a7b1e","timestamp":"2026-04-03T02:02:14.981Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_017BGBFxFN6p3jsrkqsvY2vj","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && head -5 frontend/src/pages/TechniquePage.tsx"}},{"type":"toolCall","id":"toolu_01C1hUpCCCaKB256ML7aFrFf","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -rn \"slugify\\|section.*id\\|scrollInto\\|hash\\|anchor\" frontend/src/pages/TechniquePage.tsx frontend/src/components/TableOfContents.tsx frontend/src/utils/citations.tsx 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":199,"cacheRead":84728,"cacheWrite":273,"totalTokens":85201,"cost":{"input":0.000005,"output":0.004975,"cacheRead":0.042364,"cacheWrite":0.00170625,"total":0.04905025}},"stopReason":"toolUse","timestamp":1775181730730}}
{"type":"message","id":"a69216dc","parentId":"4e444346","timestamp":"2026-04-03T02:02:15.000Z","message":{"role":"toolResult","toolCallId":"toolu_017BGBFxFN6p3jsrkqsvY2vj","toolName":"bash","content":[{"type":"text","text":"/**\n * Technique page detail view with version switching.\n *\n * Fetches a single technique by slug. When historical versions exist,\n * shows a version switcher that lets admins view previous snapshots\n"}],"isError":false,"timestamp":1775181734999}}
{"type":"message","id":"018f0f44","parentId":"a69216dc","timestamp":"2026-04-03T02:02:15.011Z","message":{"role":"toolResult","toolCallId":"toolu_01C1hUpCCCaKB256ML7aFrFf","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775181735009}}
{"type":"message","id":"6a8d9794","parentId":"018f0f44","timestamp":"2026-04-03T02:02:17.975Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01QfdzMnjv3F6iHW83QFKKLS","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"slug\\|id=\" frontend/src/pages/TechniquePage.tsx | head -40"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":93,"cacheRead":85001,"cacheWrite":321,"totalTokens":85416,"cost":{"input":0.000005,"output":0.0023250000000000002,"cacheRead":0.0425005,"cacheWrite":0.0020062500000000002,"total":0.046836749999999996}},"stopReason":"toolUse","timestamp":1775181735010}}
{"type":"message","id":"7e57a500","parentId":"6a8d9794","timestamp":"2026-04-03T02:02:17.991Z","message":{"role":"toolResult","toolCallId":"toolu_01QfdzMnjv3F6iHW83QFKKLS","toolName":"bash","content":[{"type":"text","text":"4: * Fetches a single technique by slug. When historical versions exist,\n23:import TableOfContents, { slugify } from \"../components/TableOfContents\";\n82:  const { slug } = useParams<{ slug: string }>();\n100:    if (!slug) return;\n112:        const data = await fetchTechnique(slug);\n118:              const vRes = await fetchTechniqueVersions(slug);\n143:  }, [slug]);\n147:    if (!slug || selectedVersion === \"current\") {\n158:          slug,\n172:  }, [slug, selectedVersion]);\n194:        <p>The technique &ldquo;{slug}&rdquo; doesn&rsquo;t exist.</p>\n268:            <Link to={`/creators/${technique.creator_info.slug}`} className=\"technique-header__creator-link\">\n269:              <CreatorAvatar creatorId={String(technique.creator_info.slug)} name={technique.creator_info.name} size={22} />\n426:                  const sectionSlug = slugify(section.heading);\n428:                    <div key={sectionSlug} className=\"technique-prose__section\" id={sectionSlug}>\n434:                        const subSlug = `${sectionSlug}--${slugify(sub.heading)}`;\n436:                          <div key={subSlug} className=\"technique-prose__subsection\" id={subSlug}>\n478:              <li key={km.id} id={`km-${km.id}`} className=\"technique-moment\">\n545:              <div key={link.target_slug} className=\"related-card\">\n546:                <Link to={`/techniques/${link.target_slug}`} className=\"related-card__title\">\n"}],"isError":false,"timestamp":1775181737988}}
{"type":"message","id":"5d9b566b","parentId":"7e57a500","timestamp":"2026-04-03T02:02:23.481Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — sections already have `id={sectionSlug}` and subsections have `id={subSlug}`. The `scroll-margin-top` CSS is already in place from S05. Now let me check how the v2 body_sections data looks in the model:"},{"type":"toolCall","id":"toolu_01VpCLaLdEJPbrnJtdRq8oGL","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"body_sections\\|BodySection\" frontend/src/api/public-client.ts | head -20"}},{"type":"toolCall","id":"toolu_01H5nkBq6ZTcgBCUZ9oPf932","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"body_sections\" backend/models.py backend/schemas.py 2>/dev/null | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":237,"cacheRead":85001,"cacheWrite":847,"totalTokens":86086,"cost":{"input":0.000005,"output":0.005925000000000001,"cacheRead":0.0425005,"cacheWrite":0.00529375,"total":0.053724249999999994}},"stopReason":"toolUse","timestamp":1775181737989}}
{"type":"message","id":"f6f639c1","parentId":"5d9b566b","timestamp":"2026-04-03T02:02:23.497Z","message":{"role":"toolResult","toolCallId":"toolu_01VpCLaLdEJPbrnJtdRq8oGL","toolName":"bash","content":[{"type":"text","text":"64:export interface BodySectionV2 {\n84:  body_sections: BodySectionV2[] | Record<string, unknown> | null;\n85:  body_sections_format: string;\n752:  body_sections_format: string;\n"}],"isError":false,"timestamp":1775181743497}}
{"type":"message","id":"a5d5696a","parentId":"f6f639c1","timestamp":"2026-04-03T02:02:23.502Z","message":{"role":"toolResult","toolCallId":"toolu_01H5nkBq6ZTcgBCUZ9oPf932","toolName":"bash","content":[{"type":"text","text":"backend/models.py:218:    body_sections: Mapped[dict | None] = mapped_column(JSONB, nullable=True)\nbackend/models.py:219:    body_sections_format: Mapped[str] = mapped_column(\nbackend/schemas.py:125:    body_sections: list | dict | None = None\nbackend/schemas.py:126:    body_sections_format: str = \"v1\"\nbackend/schemas.py:449:    body_sections_format: str\n"}],"isError":false,"timestamp":1775181743500}}
{"type":"message","id":"319b89c7","parentId":"a5d5696a","timestamp":"2026-04-03T02:02:27.022Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01YJySmysnrsiRWWFeZnzRuB","name":"read","arguments":{"limit":40,"offset":60,"path":"frontend/src/api/public-client.ts"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":95,"cacheRead":85848,"cacheWrite":480,"totalTokens":86424,"cost":{"input":0.000005,"output":0.002375,"cacheRead":0.042924,"cacheWrite":0.003,"total":0.048304}},"stopReason":"toolUse","timestamp":1775181743501}}
{"type":"message","id":"3bf48c98","parentId":"319b89c7","timestamp":"2026-04-03T02:02:27.027Z","message":{"role":"toolResult","toolCallId":"toolu_01YJySmysnrsiRWWFeZnzRuB","toolName":"read","content":[{"type":"text","text":"  heading: string;\n  content: string;\n}\n\nexport interface BodySectionV2 {\n  heading: string;\n  content: string;\n  subsections: BodySubSectionV2[];\n}\n\nexport interface SourceVideoSummary {\n  id: string;\n  filename: string;\n  content_type: string;\n  added_at: string | null;\n}\n\nexport interface TechniquePageDetail {\n  id: string;\n  title: string;\n  slug: string;\n  topic_category: string;\n  topic_tags: string[] | null;\n  summary: string | null;\n  body_sections: BodySectionV2[] | Record<string, unknown> | null;\n  body_sections_format: string;\n  signal_chains: unknown[] | null;\n  plugins: string[] | null;\n  creator_id: string;\n  source_quality: string | null;\n  view_count: number;\n  created_at: string;\n  updated_at: string;\n  key_moments: KeyMomentSummary[];\n  creator_info: CreatorInfo | null;\n  related_links: RelatedLinkItem[];\n  version_count: number;\n  source_videos: SourceVideoSummary[];\n}\n\n\n[687 more lines in file. Use offset=100 to continue.]"}],"isError":false,"timestamp":1775181747024}}
{"type":"message","id":"91706c36","parentId":"3bf48c98","timestamp":"2026-04-03T02:02:31.598Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check how `slugify` works in TableOfContents and the SearchResultItem type:"},{"type":"toolCall","id":"toolu_019jaZgww6ULfk5vQfU48JXn","name":"read","arguments":{"path":"frontend/src/components/TableOfContents.tsx"}},{"type":"toolCall","id":"toolu_01NJ75UBTbfDPZ1YsoYgsdvY","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"SearchResultItem\\|search_section\\|section_heading\\|section_slug\\|section_anchor\\|deep_link\" frontend/src/api/public-client.ts | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":184,"cacheRead":86328,"cacheWrite":428,"totalTokens":86941,"cost":{"input":0.000005,"output":0.0046,"cacheRead":0.043164,"cacheWrite":0.0026750000000000003,"total":0.050444}},"stopReason":"toolUse","timestamp":1775181747025}}
{"type":"message","id":"904c0a24","parentId":"91706c36","timestamp":"2026-04-03T02:02:31.606Z","message":{"role":"toolResult","toolCallId":"toolu_019jaZgww6ULfk5vQfU48JXn","toolName":"read","content":[{"type":"text","text":"/**\n * Table of Contents for v2 technique pages with nested sections.\n *\n * Renders a nested list of anchor links matching the H2/H3 section structure.\n * Uses slugified headings as IDs for scroll targeting.\n */\n\nimport type { BodySectionV2 } from \"../api/public-client\";\n\nexport function slugify(text: string): string {\n  return text\n    .toLowerCase()\n    .replace(/[^a-z0-9]+/g, \"-\")\n    .replace(/^-|-$/g, \"\");\n}\n\ninterface TableOfContentsProps {\n  sections: BodySectionV2[];\n}\n\nexport default function TableOfContents({ sections }: TableOfContentsProps) {\n  if (sections.length === 0) return null;\n\n  return (\n    <nav className=\"technique-toc\" aria-label=\"Table of contents\">\n      <h3 className=\"technique-toc__title\">Contents</h3>\n      <ol className=\"technique-toc__list\">\n        {sections.map((section) => {\n          const sectionSlug = slugify(section.heading);\n          return (\n            <li key={sectionSlug} className=\"technique-toc__item\">\n              <a href={`#${sectionSlug}`} className=\"technique-toc__link\">\n                {section.heading}\n              </a>\n              {section.subsections.length > 0 && (\n                <ol className=\"technique-toc__sublist\">\n                  {section.subsections.map((sub) => {\n                    const subSlug = `${sectionSlug}--${slugify(sub.heading)}`;\n                    return (\n                      <li key={subSlug} className=\"technique-toc__subitem\">\n                        <a\n                          href={`#${subSlug}`}\n                          className=\"technique-toc__sublink\"\n                        >\n                          {sub.heading}\n                        </a>\n                      </li>\n                    );\n                  })}\n                </ol>\n              )}\n            </li>\n          );\n        })}\n      </ol>\n    </nav>\n  );\n}\n"}],"isError":false,"timestamp":1775181751606}}
{"type":"message","id":"4a4c8735","parentId":"904c0a24","timestamp":"2026-04-03T02:02:31.611Z","message":{"role":"toolResult","toolCallId":"toolu_01NJ75UBTbfDPZ1YsoYgsdvY","toolName":"bash","content":[{"type":"text","text":"10:export interface SearchResultItem {\n25:  items: SearchResultItem[];\n26:  partial_matches: SearchResultItem[];\n"}],"isError":false,"timestamp":1775181751610}}
{"type":"message","id":"e1c85b1d","parentId":"4a4c8735","timestamp":"2026-04-03T02:02:35.133Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_014gfBrJSsxNqTxPTURnB5EJ","name":"read","arguments":{"limit":25,"offset":10,"path":"frontend/src/api/public-client.ts"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":95,"cacheRead":86756,"cacheWrite":801,"totalTokens":87653,"cost":{"input":0.000005,"output":0.002375,"cacheRead":0.043378,"cacheWrite":0.00500625,"total":0.05076425}},"stopReason":"toolUse","timestamp":1775181751610}}
{"type":"message","id":"57ecbca8","parentId":"e1c85b1d","timestamp":"2026-04-03T02:02:35.139Z","message":{"role":"toolResult","toolCallId":"toolu_014gfBrJSsxNqTxPTURnB5EJ","toolName":"read","content":[{"type":"text","text":"export interface SearchResultItem {\n  title: string;\n  slug: string;\n  type: string;\n  score: number;\n  summary: string;\n  creator_name: string;\n  creator_slug: string;\n  topic_category: string;\n  topic_tags: string[];\n  technique_page_slug?: string;\n  match_context?: string;\n}\n\nexport interface SearchResponse {\n  items: SearchResultItem[];\n  partial_matches: SearchResultItem[];\n  total: number;\n  query: string;\n  fallback_used: boolean;\n}\n\nexport interface KeyMomentSummary {\n  id: string;\n  title: string;\n\n[752 more lines in file. Use offset=35 to continue.]"}],"isError":false,"timestamp":1775181755136}}
{"type":"message","id":"c2b872cd","parentId":"57ecbca8","timestamp":"2026-04-03T02:02:38.479Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the search router to understand the API schema:"},{"type":"toolCall","id":"toolu_016tA9YdBmnNL86gQH4CKVjN","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -rn \"search\" backend/routers/ --include=\"*.py\" -l"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":102,"cacheRead":86756,"cacheWrite":1083,"totalTokens":87942,"cost":{"input":0.000005,"output":0.00255,"cacheRead":0.043378,"cacheWrite":0.0067687500000000005,"total":0.052701750000000006}},"stopReason":"toolUse","timestamp":1775181755137}}
{"type":"message","id":"40018260","parentId":"c2b872cd","timestamp":"2026-04-03T02:02:38.495Z","message":{"role":"toolResult","toolCallId":"toolu_016tA9YdBmnNL86gQH4CKVjN","toolName":"bash","content":[{"type":"text","text":"backend/routers/search.py\n"}],"isError":false,"timestamp":1775181758493}}
{"type":"message","id":"09205379","parentId":"40018260","timestamp":"2026-04-03T02:02:44.959Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01EqMV1QUhbyPdEj8166pon9","name":"read","arguments":{"path":"backend/routers/search.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":58,"cacheRead":87839,"cacheWrite":123,"totalTokens":88021,"cost":{"input":0.000005,"output":0.0014500000000000001,"cacheRead":0.0439195,"cacheWrite":0.00076875,"total":0.04614325}},"stopReason":"toolUse","timestamp":1775181758494}}
{"type":"message","id":"69b27787","parentId":"09205379","timestamp":"2026-04-03T02:02:44.965Z","message":{"role":"toolResult","toolCallId":"toolu_01EqMV1QUhbyPdEj8166pon9","toolName":"read","content":[{"type":"text","text":"\"\"\"Search endpoint for semantic + keyword search with graceful fallback.\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nfrom typing import Annotated\n\nfrom fastapi import APIRouter, Depends, Query\nfrom sqlalchemy import func, select\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom config import get_settings\nfrom database import get_session\nfrom models import Creator, TechniquePage\nfrom schemas import (\n    SearchResponse,\n    SearchResultItem,\n    SuggestionItem,\n    SuggestionsResponse,\n)\nfrom search_service import SearchService\n\nlogger = logging.getLogger(\"chrysopedia.search.router\")\n\nrouter = APIRouter(prefix=\"/search\", tags=[\"search\"])\n\n\ndef _get_search_service() -> SearchService:\n    \"\"\"Build a SearchService from current settings.\"\"\"\n    return SearchService(get_settings())\n\n\n@router.get(\"\", response_model=SearchResponse)\nasync def search(\n    q: Annotated[str, Query(max_length=500)] = \"\",\n    scope: Annotated[str, Query()] = \"all\",\n    sort: Annotated[str, Query()] = \"relevance\",\n    limit: Annotated[int, Query(ge=1, le=100)] = 20,\n    db: AsyncSession = Depends(get_session),\n) -> SearchResponse:\n    \"\"\"Semantic search with keyword fallback.\n\n    - **q**: Search query (max 500 chars). Empty → empty results.\n    - **scope**: ``all`` | ``topics`` | ``creators``. Invalid → defaults to ``all``.\n    - **limit**: Max results (1–100, default 20).\n    \"\"\"\n    svc = _get_search_service()\n    result = await svc.search(query=q, scope=scope, sort=sort, limit=limit, db=db)\n    return SearchResponse(\n        items=[SearchResultItem(**item) for item in result[\"items\"]],\n        partial_matches=[SearchResultItem(**item) for item in result.get(\"partial_matches\", [])],\n        total=result[\"total\"],\n        query=result[\"query\"],\n        fallback_used=result[\"fallback_used\"],\n    )\n\n\n@router.get(\"/suggestions\", response_model=SuggestionsResponse)\nasync def suggestions(\n    db: AsyncSession = Depends(get_session),\n) -> SuggestionsResponse:\n    \"\"\"Return popular search suggestions for autocomplete.\n\n    Combines top technique pages (by view_count), popular topic tags\n    (by technique count), and top creators (by view_count).\n    Returns 8–12 deduplicated items.\n    \"\"\"\n    seen: set[str] = set()\n    items: list[SuggestionItem] = []\n\n    def _add(text: str, type_: str) -> None:\n        key = text.lower()\n        if key not in seen:\n            seen.add(key)\n            items.append(SuggestionItem(text=text, type=type_))\n\n    # Top 4 technique pages by view_count\n    tp_stmt = (\n        select(TechniquePage.title)\n        .order_by(TechniquePage.view_count.desc(), TechniquePage.title)\n        .limit(4)\n    )\n    tp_result = await db.execute(tp_stmt)\n    for (title,) in tp_result.all():\n        _add(title, \"technique\")\n\n    # Top 4 topic tags by how many technique pages use them\n    # Unnest the topic_tags ARRAY and count occurrences\n    tag_unnest = (\n        select(\n            func.unnest(TechniquePage.topic_tags).label(\"tag\"),\n        )\n        .where(TechniquePage.topic_tags.isnot(None))\n        .subquery()\n    )\n    tag_stmt = (\n        select(\n            tag_unnest.c.tag,\n            func.count().label(\"cnt\"),\n        )\n        .group_by(tag_unnest.c.tag)\n        .order_by(func.count().desc(), tag_unnest.c.tag)\n        .limit(4)\n    )\n    tag_result = await db.execute(tag_stmt)\n    for tag, _cnt in tag_result.all():\n        _add(tag, \"topic\")\n\n    # Top 4 creators by view_count\n    cr_stmt = (\n        select(Creator.name)\n        .where(Creator.hidden.is_(False))\n        .order_by(Creator.view_count.desc(), Creator.name)\n        .limit(4)\n    )\n    cr_result = await db.execute(cr_stmt)\n    for (name,) in cr_result.all():\n        _add(name, \"creator\")\n\n    return SuggestionsResponse(suggestions=items)\n"}],"isError":false,"timestamp":1775181764962}}
{"type":"message","id":"a87f8dd7","parentId":"69b27787","timestamp":"2026-04-03T02:02:48.575Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the SearchResponse schema to see what fields exist:"},{"type":"toolCall","id":"toolu_013FBqamifUHseLw2ThhXQdE","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -A 20 \"class SearchResultItem\" backend/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":100,"cacheRead":87962,"cacheWrite":1223,"totalTokens":89286,"cost":{"input":0.000005,"output":0.0025,"cacheRead":0.043981,"cacheWrite":0.00764375,"total":0.05412975}},"stopReason":"toolUse","timestamp":1775181764963}}
{"type":"message","id":"32ac0e1a","parentId":"a87f8dd7","timestamp":"2026-04-03T02:02:48.596Z","message":{"role":"toolResult","toolCallId":"toolu_013FBqamifUHseLw2ThhXQdE","toolName":"bash","content":[{"type":"text","text":"class 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"}],"isError":false,"timestamp":1775181768592}}
{"type":"message","id":"6ebfe680","parentId":"32ac0e1a","timestamp":"2026-04-03T02:02:52.901Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have a clear picture. Let me check how the v2 body_sections are stored in the DB (JSON format) to understand what we'd embed per-section:"},{"type":"toolCall","id":"toolu_01RyTo5uCNhxvVf9RVEVsEhJ","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"body_sections\\|heading\\|subsection\" backend/pipeline/stages.py | grep -i \"section\" | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":139,"cacheRead":89185,"cacheWrite":305,"totalTokens":89630,"cost":{"input":0.000005,"output":0.0034750000000000002,"cacheRead":0.0445925,"cacheWrite":0.0019062500000000002,"total":0.049978749999999995}},"stopReason":"toolUse","timestamp":1775181768593}}
{"type":"message","id":"f63b45e0","parentId":"6ebfe680","timestamp":"2026-04-03T02:02:52.930Z","message":{"role":"toolResult","toolCallId":"toolu_01RyTo5uCNhxvVf9RVEVsEhJ","toolName":"bash","content":[{"type":"text","text":"1008:        \"body_sections\": existing_page.body_sections,\n1482:                            \"body_sections\": existing.body_sections,\n1515:                    existing.body_sections = page_data.body_sections\n1530:                        body_sections=page_data.body_sections,\n1540:                # Set body_sections_format on every page (new or updated)\n1541:                page.body_sections_format = \"v2\"\n"}],"isError":false,"timestamp":1775181772928}}
{"type":"message","id":"ef8a8b39","parentId":"f63b45e0","timestamp":"2026-04-03T02:02:57.071Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check what the `upsert_technique_sections` method might need — do we need a new Qdrant upsert method for sections:"},{"type":"toolCall","id":"toolu_01CE88faypLegLDxsCySprJD","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"scroll-margin\" frontend/src/App.css | head -5"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":123,"cacheRead":89490,"cacheWrite":260,"totalTokens":89874,"cost":{"input":0.000005,"output":0.003075,"cacheRead":0.044745,"cacheWrite":0.0016250000000000001,"total":0.04945}},"stopReason":"toolUse","timestamp":1775181772929}}
{"type":"message","id":"df5e828f","parentId":"ef8a8b39","timestamp":"2026-04-03T02:02:57.091Z","message":{"role":"toolResult","toolCallId":"toolu_01CE88faypLegLDxsCySprJD","toolName":"bash","content":[{"type":"text","text":"2091:  scroll-margin-top: 5rem;\n"}],"isError":false,"timestamp":1775181777088}}
{"type":"message","id":"5016f8d0","parentId":"df5e828f","timestamp":"2026-04-03T02:03:02.026Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01JHnyGWhb3nBSFTn8CnBvaa","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -B2 -A2 \"scroll-margin\" frontend/src/App.css"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":89,"cacheRead":89750,"cacheWrite":150,"totalTokens":89990,"cost":{"input":0.000005,"output":0.002225,"cacheRead":0.044875,"cacheWrite":0.0009375000000000001,"total":0.0480425}},"stopReason":"toolUse","timestamp":1775181777089}}
{"type":"message","id":"5a0c896c","parentId":"5016f8d0","timestamp":"2026-04-03T02:03:02.042Z","message":{"role":"toolResult","toolCallId":"toolu_01JHnyGWhb3nBSFTn8CnBvaa","toolName":"bash","content":[{"type":"text","text":".technique-prose__section[id],\n.technique-prose__subsection[id] {\n  scroll-margin-top: 5rem;\n}\n\n"}],"isError":false,"timestamp":1775181782040}}
{"type":"message","id":"eddbe104","parentId":"5a0c896c","timestamp":"2026-04-03T02:03:07.308Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. The `scroll-margin-top` is already in place. Now let me verify the hash-based scrolling — does the browser natively scroll to `#section-slug` on page load, or is there manual JS needed?"},{"type":"toolCall","id":"toolu_01RncpZqP8wWcmMqpJPFdrth","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"useEffect.*hash\\|location.hash\\|scrollIntoView\\|window.location\" frontend/src/pages/TechniquePage.tsx | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":161,"cacheRead":89750,"cacheWrite":286,"totalTokens":90198,"cost":{"input":0.000005,"output":0.004025,"cacheRead":0.044875,"cacheWrite":0.0017875,"total":0.050692499999999995}},"stopReason":"toolUse","timestamp":1775181782041}}
{"type":"message","id":"40027ceb","parentId":"eddbe104","timestamp":"2026-04-03T02:03:07.326Z","message":{"role":"toolResult","toolCallId":"toolu_01RncpZqP8wWcmMqpJPFdrth","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775181787324}}
{"type":"message","id":"8dd88966","parentId":"40027ceb","timestamp":"2026-04-03T02:03:13.206Z","message":{"role":"assistant","content":[{"type":"text","text":"No hash-handling in TechniquePage. React Router's client-side navigation won't auto-scroll to hash anchors — the browser only does that on full page loads. This is a gap: navigating from search results to `/techniques/slug#section-id` won't scroll. We'll need a `useEffect` to handle hash scrolling.\n\nLet me also check how many sections a typical v2 page has, to estimate embedding volume:"},{"type":"toolCall","id":"toolu_011RwrFk2xcZNqvbZkPFwiV5","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat backend/pipeline/prompts/stage5_synthesis.txt | head -30"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":183,"cacheRead":90036,"cacheWrite":175,"totalTokens":90395,"cost":{"input":0.000005,"output":0.004575,"cacheRead":0.045017999999999996,"cacheWrite":0.00109375,"total":0.050691749999999994}},"stopReason":"toolUse","timestamp":1775181787324}}
{"type":"message","id":"81f91adf","parentId":"8dd88966","timestamp":"2026-04-03T02:03:13.220Z","message":{"role":"toolResult","toolCallId":"toolu_011RwrFk2xcZNqvbZkPFwiV5","toolName":"bash","content":[{"type":"text","text":"cat: backend/pipeline/prompts/stage5_synthesis.txt: No such file or directory\n"}],"isError":false,"timestamp":1775181793218}}
{"type":"message","id":"d32df43d","parentId":"81f91adf","timestamp":"2026-04-03T02:03:17.347Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01864psSb1RKTifPmuHHzJiX","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find backend/pipeline/prompts -name \"*stage5*\" -o -name \"*synth*\" 2>/dev/null; find backend -name \"*.txt\" -path \"*prompt*\" | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":116,"cacheRead":90211,"cacheWrite":217,"totalTokens":90545,"cost":{"input":0.000005,"output":0.0029000000000000002,"cacheRead":0.0451055,"cacheWrite":0.00135625,"total":0.04936675}},"stopReason":"toolUse","timestamp":1775181793219}}
{"type":"message","id":"ebc7b4fb","parentId":"d32df43d","timestamp":"2026-04-03T02:03:17.363Z","message":{"role":"toolResult","toolCallId":"toolu_01864psSb1RKTifPmuHHzJiX","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775181797361}}
{"type":"message","id":"ea9e132b","parentId":"ebc7b4fb","timestamp":"2026-04-03T02:03:22.193Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01GKTPYNHFZFCuYAxX3mnWmS","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find . -name \"*prompt*\" -type f 2>/dev/null | head -20"}},{"type":"toolCall","id":"toolu_018BZkRjRibaMXxGcEJAhuGh","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"prompt_dir\\|prompt_file\\|stage5.*prompt\\|stage5_synth\\|stage5_compose\" backend/pipeline/stages.py | head -15"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":183,"cacheRead":90211,"cacheWrite":347,"totalTokens":90742,"cost":{"input":0.000005,"output":0.004575,"cacheRead":0.0451055,"cacheWrite":0.00216875,"total":0.05185425}},"stopReason":"toolUse","timestamp":1775181797362}}
{"type":"message","id":"43856845","parentId":"ea9e132b","timestamp":"2026-04-03T02:03:22.245Z","message":{"role":"toolResult","toolCallId":"toolu_01GKTPYNHFZFCuYAxX3mnWmS","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775181802244}}
{"type":"message","id":"ac824b84","parentId":"43856845","timestamp":"2026-04-03T02:03:22.249Z","message":{"role":"toolResult","toolCallId":"toolu_018BZkRjRibaMXxGcEJAhuGh","toolName":"bash","content":[{"type":"text","text":"898:    prompt_files = [\n902:        \"stage5_synthesis.txt\",\n904:    for filename in prompt_files:\n1065:    compose_prompt = _load_prompt(\"stage5_compose.txt\", video_id=video_id)\n1072:        stage=\"stage5_synthesis\", hard_limit=hard_limit,\n1082:            video_id, \"stage5_synthesis\",\n1117:    estimated_input = estimate_max_tokens(system_prompt, user_prompt, stage=\"stage5_synthesis\", hard_limit=hard_limit)\n1126:            video_id, \"stage5_synthesis\",\n1196:            stage=\"stage5_synthesis\", hard_limit=hard_limit,\n1207:                video_id, \"stage5_synthesis\",\n1241:def stage5_synthesis(self, video_id: str, run_id: str | None = None) -> str:\n1258:    _emit_event(video_id, \"stage5_synthesis\", \"start\", run_id=run_id)\n1302:        system_prompt = _load_prompt(\"stage5_synthesis.txt\", video_id=video_id)\n1574:        _emit_event(video_id, \"stage5_synthesis\", \"complete\", run_id=run_id)\n1585:        _emit_event(video_id, \"stage5_synthesis\", \"error\", run_id=run_id, payload={\"error\": str(exc)})\n"}],"isError":false,"timestamp":1775181802244}}
{"type":"message","id":"bd1ab497","parentId":"ac824b84","timestamp":"2026-04-03T02:03:27.087Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_015gpMW4ZP2HCGFqCUtirZf1","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"_load_prompt\\|PROMPT_DIR\" backend/pipeline/stages.py | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":93,"cacheRead":90558,"cacheWrite":593,"totalTokens":91245,"cost":{"input":0.000005,"output":0.0023250000000000002,"cacheRead":0.045279,"cacheWrite":0.0037062500000000003,"total":0.05131525}},"stopReason":"toolUse","timestamp":1775181802246}}
{"type":"message","id":"8afc21af","parentId":"bd1ab497","timestamp":"2026-04-03T02:03:27.104Z","message":{"role":"toolResult","toolCallId":"toolu_015gpMW4ZP2HCGFqCUtirZf1","toolName":"bash","content":[{"type":"text","text":"247:def _load_prompt(template_name: str, video_id: str | None = None) -> str:\n427:        system_prompt = _load_prompt(\"stage2_segmentation.txt\", video_id=video_id)\n506:        system_prompt = _load_prompt(\"stage3_extraction.txt\", video_id=video_id)\n679:        system_prompt = _load_prompt(\"stage4_classification.txt\", video_id=video_id)\n1065:    compose_prompt = _load_prompt(\"stage5_compose.txt\", video_id=video_id)\n1191:        merge_system_prompt = _load_prompt(\"stage5_merge.txt\", video_id=video_id)\n1302:        system_prompt = _load_prompt(\"stage5_synthesis.txt\", video_id=video_id)\n2078:    per-video override that ``_load_prompt`` reads before falling back\n"}],"isError":false,"timestamp":1775181807100}}
{"type":"message","id":"79175e7a","parentId":"8afc21af","timestamp":"2026-04-03T02:03:30.051Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01G58nw2fPk7XpiS2a26rgj9","name":"read","arguments":{"limit":15,"offset":247,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":91,"cacheRead":90558,"cacheWrite":920,"totalTokens":91570,"cost":{"input":0.000005,"output":0.002275,"cacheRead":0.045279,"cacheWrite":0.00575,"total":0.053308999999999995}},"stopReason":"toolUse","timestamp":1775181807101}}
{"type":"message","id":"32e7befa","parentId":"79175e7a","timestamp":"2026-04-03T02:03:30.057Z","message":{"role":"toolResult","toolCallId":"toolu_01G58nw2fPk7XpiS2a26rgj9","toolName":"read","content":[{"type":"text","text":"def _load_prompt(template_name: str, video_id: str | None = None) -> str:\n    \"\"\"Read a prompt template from the prompts directory.\n\n    If ``video_id`` is provided, checks Redis for a per-video prompt override\n    (key: ``chrysopedia:prompt_override:{video_id}:{template_name}``) before\n    falling back to the on-disk template. Overrides are set by\n    ``run_single_stage`` for single-stage re-runs with custom prompts.\n\n    Raises FileNotFoundError if no override exists and the template is missing.\n    \"\"\"\n    # Check for per-video prompt override in Redis\n    if video_id:\n        try:\n            import redis\n            settings = get_settings()\n\n[2010 more lines in file. Use offset=262 to continue.]"}],"isError":false,"timestamp":1775181810054}}
{"type":"message","id":"aedbe8ec","parentId":"32e7befa","timestamp":"2026-04-03T02:03:33.093Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_013jYxhK4VsQEfqUYHE6vjEm","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find . -name \"stage5_synthesis.txt\" -type f 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":89,"cacheRead":91151,"cacheWrite":625,"totalTokens":91866,"cost":{"input":0.000005,"output":0.002225,"cacheRead":0.0455755,"cacheWrite":0.00390625,"total":0.05171175}},"stopReason":"toolUse","timestamp":1775181810055}}
{"type":"message","id":"4fd82d87","parentId":"aedbe8ec","timestamp":"2026-04-03T02:03:33.148Z","message":{"role":"toolResult","toolCallId":"toolu_013jYxhK4VsQEfqUYHE6vjEm","toolName":"bash","content":[{"type":"text","text":"./prompts/stage5_synthesis.txt\n"}],"isError":false,"timestamp":1775181813146}}
{"type":"message","id":"790c88fe","parentId":"4fd82d87","timestamp":"2026-04-03T02:03:36.255Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Ss5bWHR5cAKTRRkJCH6PJh","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && head -40 prompts/stage5_synthesis.txt"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":81,"cacheRead":91478,"cacheWrite":410,"totalTokens":91970,"cost":{"input":0.000005,"output":0.002025,"cacheRead":0.045738999999999995,"cacheWrite":0.0025625,"total":0.050331499999999994}},"stopReason":"toolUse","timestamp":1775181813147}}
{"type":"message","id":"a5de609f","parentId":"790c88fe","timestamp":"2026-04-03T02:03:36.268Z","message":{"role":"toolResult","toolCallId":"toolu_01Ss5bWHR5cAKTRRkJCH6PJh","toolName":"bash","content":[{"type":"text","text":"You are writing a technique reference page for music producers. Your job is to teach. You watched this creator work, absorbed their methods, and now you're writing the clear, instructive guide that helps another producer apply these techniques in their own sessions. Your tone is direct and knowledgeable — a senior colleague walking someone through an approach they've validated in practice.\n\n## What you are creating\n\nA Chrysopedia technique page teaches a specific production method. Not a video summary. Not a blog post. A clear, instructive reference that a producer can follow in their DAW.\n\nA producer should be able to read this page and apply the technique. If they can't, the page failed.\n\nThe page contains:\n1. **Instructive prose** — substantive paragraphs that teach the technique step by step. Every paragraph earns its place with at least one specific, actionable detail. If a paragraph could be deleted without losing anything concrete, delete it.\n2. **Key moments index** — reference list of the source moments for readers who want to trace information back to the original content.\n\n## Voice and tone\n\nYour tone is instructive and confident. You're teaching a technique, not narrating someone's session. The creator is the source — they discovered it, they validated it — but the page teaches it as method.\n\n- **Instructive, not narrative** — write \"Route the effect at 100% wet on a parallel lane, then control level via gain\" rather than \"He routes the effect at 100% wet.\" The reader is here to learn a technique, not to read about what someone did. Use imperative/instructive phrasing (\"set\", \"route\", \"use\", \"check\") for methods. The page reads like a lesson, not a biography.\n- **No hesitation** — state techniques as established method. \"Set the LFO to 3/16th timing\" not \"you might want to try setting the LFO to around 3/16th.\"\n- **Direct quotes for personality** — pull 2-3 direct quotes per page where the creator's phrasing is vivid or memorable. These give the page life. Introduce them with a brief attribution (\"as he puts it,\" or \"in his words,\") then let the quote do the work. Outside of quotes, the prose teaches — no attribution needed.\n- **Specific and grounded** — every technical claim needs concrete backing: plugin names, settings, frequencies, ratios, time values, routing decisions. \"Use some compression\" is worthless. \"OTT at 30% depth on the bus\" is useful.\n- **Match the energy of the source** — if the creator is hyped about a technique, let that land. If they're cautionary, make it feel urgent.\n- **Efficiency** — say it once, say it well. If a sentence doesn't teach something specific or make the next specific thing clearer, cut it.\n\n### Creator name usage — hard rule\n\nThe body sections are written as instructional content, not a biography. The creator's name appears in the **title** and **summary** for attribution — that's where the reader learns whose technique this is. The body sections then TEACH the technique directly, like a lesson book or a well-written blog post.\n\nThe creator's name should appear **at most once** in the entire body_sections content — and only when introducing a direct quote for the first time. Otherwise, the prose is instructive: \"do this\", \"set this to X\", \"route the signal through Y.\" Not \"he does this\", \"she sets this to X.\"\n\nBAD — third-person narration in every section:\n> **Section 1:** \"Keota solves this by creating dedicated analog modules...\"\n> **Section 2:** \"Keota uses Ableton's Remap plugin with a quantized staircase...\"\n> **Section 3:** \"Keota separates the modulation effect from the carrier...\"\n> **Section 4:** \"Keota compares options systematically...\"\n\nEach section re-introduces the creator as if the reader forgot whose page they're on. This reads like a Wikipedia article about a person, not a technique reference.\n\nGOOD — instructive voice, teaches the reader directly:\n> **Section 1:** \"The approach builds each harmonic as a separate parallel module with independent gain. Start with the 3rd harmonic, then optionally add the 5th, 6th, or 7th...\"\n> **Section 2:** \"Use Ableton's Remap with a quantized staircase curve to force any wavetable into discrete harmonic steps. Set up a harmonic control knob, map it to a quantized 8-step curve...\"\n"}],"isError":false,"timestamp":1775181816266}}
{"type":"message","id":"6b4c39a8","parentId":"a5de609f","timestamp":"2026-04-03T02:03:41.257Z","message":{"role":"assistant","content":[{"type":"text","text":"I have enough context. Let me now check what `type` values Qdrant currently stores (only `technique_page` and `key_moment`) to understand what new type we'd add:"},{"type":"toolCall","id":"toolu_01HCe6kKiwgrW6aT1NTSEuNG","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"type.*technique_section\\|section.*embed\\|per.section\" backend/ 2>/dev/null | head -5"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":144,"cacheRead":91888,"cacheWrite":1121,"totalTokens":93154,"cost":{"input":0.000005,"output":0.0036000000000000003,"cacheRead":0.045944,"cacheWrite":0.00700625,"total":0.056555249999999994}},"stopReason":"toolUse","timestamp":1775181816266}}
{"type":"message","id":"af361b80","parentId":"6b4c39a8","timestamp":"2026-04-03T02:03:41.283Z","message":{"role":"toolResult","toolCallId":"toolu_01HCe6kKiwgrW6aT1NTSEuNG","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775181821279}}
{"type":"message","id":"c97df83c","parentId":"af361b80","timestamp":"2026-04-03T02:03:47.246Z","message":{"role":"assistant","content":[{"type":"text","text":"No section-level embeddings exist yet. This is all greenfield. Let me also check the `getSearchResultLink` function more closely to understand the current deep-linking pattern:\n\nThe key findings are clear. Let me check one more thing — does the frontend handle hash-based scrolling anywhere else in the app?"},{"type":"toolCall","id":"toolu_01XNA3uR2PjEFYGFPFritfhK","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -rn \"scrollIntoView\\|location.hash\\|useHash\\|hash.*scroll\" frontend/src/ 2>/dev/null | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":175,"cacheRead":93009,"cacheWrite":158,"totalTokens":93343,"cost":{"input":0.000005,"output":0.004375,"cacheRead":0.0465045,"cacheWrite":0.0009875,"total":0.051872}},"stopReason":"toolUse","timestamp":1775181821281}}
{"type":"message","id":"0847064d","parentId":"c97df83c","timestamp":"2026-04-03T02:03:47.264Z","message":{"role":"toolResult","toolCallId":"toolu_01XNA3uR2PjEFYGFPFritfhK","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1775181827262}}
{"type":"message","id":"062f6bab","parentId":"0847064d","timestamp":"2026-04-03T02:04:38.749Z","message":{"role":"assistant","content":[{"type":"text","text":"No hash scrolling anywhere. Now I have all the context needed for the research document."},{"type":"toolCall","id":"toolu_015JGi47Zwmc56PXMag9fXSF","name":"gsd_summary_save","arguments":{"artifact_type":"RESEARCH","content":"# S07 Research: Search — Per-Section Embeddings + Deep Linking\n\n## Summary\n\nThis slice adds section-level search granularity and deep linking. Currently, stage 6 embeds whole technique pages and key moments as two point types in Qdrant. S07 adds a third point type (`technique_section`) that embeds individual v2 body_sections, enabling search results that link directly to a specific section within a technique page (e.g., `/techniques/lfo-modulation#grain-position-control`). The frontend already has section IDs and `scroll-margin-top` from S05 but lacks hash-based scroll handling — React Router client-side navigation doesn't auto-scroll to anchors.\n\nStraightforward extension of established patterns. No new technology, no ambiguous requirements.\n\n## Recommendation\n\nThree tasks:\n\n1. **Backend: Section embedding in stage 6 + new Qdrant upsert method** — Extend `stage6_embed_and_index` to iterate v2 `body_sections`, embed each section (heading + content + subsection content), and upsert to Qdrant with type `technique_section`. Add `upsert_technique_sections` to `QdrantManager`. Extend `SearchService._enrich_qdrant_results` to handle the new type. Update `SearchResultItem` schema with `section_anchor` field.\n\n2. **Frontend: Hash scroll handler + section result rendering** — Add `useHashScroll` hook to TechniquePage that scrolls to the hash anchor after content loads. Update `SearchResultItem` type with `section_anchor`. Update `getSearchResultLink` and `SearchResultCard` to show section context and link to `page#section-slug`.\n\n3. **Verification: End-to-end search → deep link test** — Verify stage 6 produces section points in Qdrant, search returns them with section_anchor, and clicking a result navigates to the correct section.\n\n## Implementation Landscape\n\n### Current Indexing Pipeline (stage 6)\n\n**File:** `backend/pipeline/stages.py` lines 1595–1762\n\nStage 6 loads technique pages and key moments for a video, embeds text via `EmbeddingClient`, and upserts to Qdrant via `QdrantManager`. Two point types exist:\n\n- `technique_page` — embed text: `\"{creator} {title} {category} {tags} {summary}\"`, payload: page_id, creator_id, creator_name, title, slug, topic_category, topic_tags, summary\n- `key_moment` — embed text: `\"{creator} {title} {summary}\"`, payload: moment_id, source_video_id, technique_page_id, technique_page_slug, title, creator_name, timestamps, content_type\n\nDeterministic UUIDs via `uuid5(_QDRANT_NAMESPACE, \"tp:{page_id}\")` / `\"km:{moment_id}\"`.\n\n### QdrantManager\n\n**File:** `backend/pipeline/qdrant_client.py`\n\nHas `upsert_technique_pages` and `upsert_key_moments` — both build `PointStruct` lists and call `upsert_points`. Adding `upsert_technique_sections` follows the identical pattern. Deterministic UUID would be `uuid5(_QDRANT_NAMESPACE, \"ts:{page_id}:{section_slug}\")`.\n\n### Search Service\n\n**File:** `backend/search_service.py`\n\n`search_qdrant()` returns payload dicts. `_enrich_qdrant_results()` maps payload fields to `SearchResultItem`-shaped dicts based on `payload[\"type\"]`. Currently handles `technique_page` and falls through for `key_moment`. Needs a `technique_section` branch that maps `section_heading`, `section_anchor`, and parent page slug.\n\nKeyword search (`_keyword_search_and`) queries PostgreSQL directly — no section-level keyword search needed since `body_sections` is a JSONB column and full-text indexing it would be complex with diminishing returns. Semantic search is the right tool for section-level granularity.\n\n### Search API Schema\n\n**File:** `backend/schemas.py`\n\n`SearchResultItem` needs a new optional field: `section_anchor: str = \"\"`. This carries the anchor slug (e.g., `grain-position-control`) so the frontend can build `page#anchor` links.\n\n### Frontend Search\n\n**File:** `frontend/src/pages/SearchResults.tsx`\n\n`getSearchResultLink()` already builds hash links for key_moments (`#km-{slug}`). Adding section deep links: if `item.type === \"technique_section\"`, return `/techniques/${item.technique_page_slug}#${item.section_anchor}`.\n\n**File:** `frontend/src/api/public-client.ts`\n\n`SearchResultItem` interface needs `section_anchor?: string`.\n\n### Frontend Hash Scrolling (GAP)\n\n**File:** `frontend/src/pages/TechniquePage.tsx`\n\nSections already have `id={sectionSlug}` and `scroll-margin-top: 5rem` CSS. But there is NO hash scroll handling — React Router client-side navigation won't auto-scroll to `#anchor`. Need a `useEffect` that reads `location.hash` after technique data loads and calls `document.getElementById(hash)?.scrollIntoView({ behavior: 'smooth' })`. Must run after render (when section elements exist in DOM).\n\n### Section ID Convention (from S05)\n\n`slugify()` in `frontend/src/components/TableOfContents.tsx`: lowercase, replace non-alphanumeric with hyphens, trim leading/trailing hyphens.\n\nSection IDs: `slugify(section.heading)` → e.g., `grain-position-control`\nSubsection IDs: `${sectionSlug}--${slugify(sub.heading)}` → e.g., `grain-position-control--lfo-routing`\n\nThe backend must replicate this slugify logic in Python for the `section_anchor` payload field. Python equivalent: `re.sub(r'[^a-z0-9]+', '-', heading.lower()).strip('-')`.\n\n### v2 Body Sections Structure\n\nStored as JSONB in `TechniquePage.body_sections`. Format when `body_sections_format='v2'`:\n\n```json\n[\n  {\n    \"heading\": \"Section Title\",\n    \"content\": \"Section prose...\",\n    \"subsections\": [\n      { \"heading\": \"Sub Title\", \"content\": \"Sub prose...\" }\n    ]\n  }\n]\n```\n\nPer D024: sections with subsections have empty-string content; substance lives in subsection content fields.\n\n### Embedding Text Construction for Sections\n\nFor each section, concatenate: `\"{creator_name} {page_title} — {section_heading}: {section_content} {subsection_contents}\"`. This gives the embedding model context about what page/creator the section belongs to while focusing on the section's specific content.\n\n### Volume Estimate\n\nTypical technique pages have 3-7 sections. With ~N pages in the system, this adds ~4N section points to Qdrant. The existing collection handles this without configuration changes — same vector dimensions, same distance metric.\n\n### Deduplication on Re-index\n\nDeterministic UUID `uuid5(namespace, \"ts:{page_id}:{section_slug}\")` ensures re-indexing the same page overwrites the same section points. However, if a compose operation changes section headings, orphan points from the old heading remain. Acceptable for now — a full re-index (`stage6_embed_and_index`) replaces all points for a video's pages anyway.\n\n### Risk: Section heading changes after compose\n\nWhen a page is composed with new video content, section headings may change. The old section points in Qdrant won't be deleted automatically. Mitigation: before upserting section points for a page, delete existing `technique_section` points with that `page_id`. This requires a `QdrantManager.delete_sections_by_page_id()` method using a filter on `payload.page_id`.\n\n## Constraints\n\n- Section embeddings only apply to v2 pages (`body_sections_format='v2'`). v1 pages continue with page-level embedding only.\n- The `slugify` function must be identical in Python (stage 6) and TypeScript (frontend) — any divergence breaks deep links.\n- Hash scrolling must wait for the technique page data to load and render before scrolling, otherwise the target element doesn't exist yet.\n","milestone_id":"M014","slice_id":"S07"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":2136,"cacheRead":93167,"cacheWrite":189,"totalTokens":95493,"cost":{"input":0.000005,"output":0.0534,"cacheRead":0.0465835,"cacheWrite":0.00118125,"total":0.10116975}},"stopReason":"toolUse","timestamp":1775181827263}}
{"type":"message","id":"8047f892","parentId":"062f6bab","timestamp":"2026-04-03T02:04:38.755Z","message":{"role":"toolResult","toolCallId":"toolu_015JGi47Zwmc56PXMag9fXSF","toolName":"gsd_summary_save","content":[{"type":"text","text":"Saved RESEARCH artifact to milestones/M014/slices/S07/S07-RESEARCH.md"}],"details":{"operation":"save_summary","path":"milestones/M014/slices/S07/S07-RESEARCH.md","artifact_type":"RESEARCH"},"isError":false,"timestamp":1775181878752}}
{"type":"message","id":"b38d5e45","parentId":"8047f892","timestamp":"2026-04-03T02:04:42.079Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S07 researched."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":11,"cacheRead":93356,"cacheWrite":2174,"totalTokens":95542,"cost":{"input":0.000005,"output":0.000275,"cacheRead":0.046678,"cacheWrite":0.0135875,"total":0.0605455}},"stopReason":"stop","timestamp":1775181878753}}
