{"type":"thinking_level_change","id":"42bb93e8","parentId":null,"timestamp":"2026-03-31T06:28:14.943Z","thinkingLevel":"medium"}
{"type":"model_change","id":"62be96c9","parentId":"42bb93e8","timestamp":"2026-03-31T06:28:14.944Z","provider":"anthropic","modelId":"claude-opus-4-6"}
{"type":"custom_message","customType":"gsd-auto","content":"You are executing GSD auto-mode.\n\n## Working Directory\n\nYour working directory is `/home/aux/projects/content-to-kb-automator`. All file reads, writes, and shell commands MUST operate relative to this directory. Do NOT `cd` to any other directory.\n\nAll relevant context has been preloaded below — start working immediately without re-reading these files.\n\n## UNIT: Research Slice S04 (\"Search Autocomplete & Suggestions\") — Milestone M010\n\n## Inlined Context (preloaded — do not re-read these files)\n\n### Milestone Roadmap\nSource: `.gsd/milestones/M010/M010-ROADMAP.md`\n\n# M010: Discovery, Navigation & Visual Identity\n\n## Vision\nChrysopedia should feel like exploring a music production library, not querying a database. This milestone adds the navigation structure (sub-topic pages, cross-links), visual identity (topic colors, creator differentiation), and interaction polish (autocomplete, transitions) that transform browsing from utilitarian to engaging. A user who reads one technique should naturally discover three more.\n\n## Slice Overview\n| ID | Slice | Risk | Depends | Done | After this |\n|----|-------|------|---------|------|------------|\n| S01 | Dedicated Sub-Topic Pages | high | — | ✅ | Clicking 'Compression' on Topics page loads /topics/mixing/compression with techniques grouped by creator, description, breadcrumbs |\n| S02 | Related Techniques Cross-Linking | medium | — | ✅ | Bottom of every technique page shows 3-4 related techniques from same creator or same sub-topic |\n| S03 | Topic Color Coding & Visual Polish | medium | S01 | ✅ | Each of 7 topic categories has a distinct accent color in badges, cards, and sub-topic pages. Page transitions are smooth. Creator avatars are visually unique. |\n| S04 | Search Autocomplete & Suggestions | medium | — | ⬜ | Typing in search shows autocomplete suggestions. Empty search box shows popular search terms. |\n\n---\n\n### Decisions\nSource: `.gsd/DECISIONS.md`\n\n# Decisions Register\n\n<!-- Append-only. Never edit or remove existing rows.\n     To reverse a decision, add a new row that supersedes it.\n     Read this file at the start of any planning or research phase. -->\n\n| # | When | Scope | Decision | Choice | Rationale | Revisable? | Made By |\n|---|------|-------|----------|--------|-----------|------------|---------|\n| D001 |  | architecture | Storage layer selection | PostgreSQL for structured data, Qdrant (existing instance at 10.0.0.10) for vector embeddings, local filesystem for transcript JSON files | Spec specifies PostgreSQL for JSONB support and Qdrant already running on hypervisor. File store on local filesystem organized by creator slug. Qdrant gets a dedicated collection, not a new instance. | Yes | collaborative |\n| D002 |  | architecture | Timestamp handling for asyncpg with TIMESTAMP WITHOUT TIME ZONE columns | Use datetime.now(timezone.utc).replace(tzinfo=None) in _now() helper | asyncpg rejects timezone-aware datetimes for TIMESTAMP WITHOUT TIME ZONE columns. Stripping tzinfo at the application layer preserves UTC semantics while staying compatible with the existing schema. | Yes | agent |\n| D003 |  | requirement | R002 Transcript Ingestion API status | validated | 6 passing integration tests prove the full POST /api/v1/ingest flow: creator auto-detection, SourceVideo upsert, TranscriptSegment bulk insert, raw JSON persistence, idempotent re-upload, and invalid input rejection. | Yes | agent |\n| D004 |  | architecture | Sync vs async approach for Celery worker pipeline tasks | Use sync openai.OpenAI, sync QdrantClient, and sync SQLAlchemy (create_engine with psycopg2) inside Celery tasks. Convert DATABASE_URL from postgresql+asyncpg:// to postgresql:// for the sync engine. | Celery workers run in a synchronous context. Using asyncio.run() inside tasks risks nested event loop errors with gevent/eventlet workers. Using sync clients throughout eliminates this class of bug entirely. The async engine/session from database.py is only used by FastAPI (ASGI); the worker gets its own sync engine. | Yes | agent |\n| D005 |  | architecture | Embedding/Qdrant failure handling strategy in pipeline | Embedding/Qdrant failures (stage 6) log errors but do not fail the pipeline. Processing_status is set by stages 2-5 only. Embeddings can be regenerated by manual re-trigger. | Qdrant is at 10.0.0.10 on the hypervisor network and may not be reachable during all pipeline runs. Making embedding a non-blocking side-effect ensures core pipeline output (KeyMoments, TechniquePages in PostgreSQL) is never lost due to vector store issues. The manual re-trigger endpoint allows regenerating embeddings at any time. | Yes | agent |\n| D006 |  | requirement | R013 Prompt Template System status | validated | 4 prompt template files in prompts/ directory loaded from configurable settings.prompts_path. Templates use XML-style content fencing. Pipeline stages read templates from disk at runtime, enabling edits without code changes. Manual re-trigger endpoint (POST /api/v1/pipeline/trigger/{video_id}) allows re-processing after prompt edits. | Yes | agent |\n| D007 | M001/S04 | architecture | Runtime review mode toggle persistence mechanism | Store review mode toggle in Redis key `chrysopedia:review_mode` with async redis client. Fall back to `settings.review_mode` config default when key is absent. | The config.py `review_mode` setting is loaded via lru_cache from environment variables and cannot be mutated at runtime. Redis is already used by the project (Celery broker, stage 4 classification data) so it adds no new infrastructure. A system_settings DB table would work but Redis is simpler for a single boolean toggle on a single-admin tool. The pipeline's stages.py reads settings.review_mode from config — the admin toggle only affects new pipeline runs if stages.py is updated to check Redis too, but that's deferred since the toggle is primarily a UI-level concept for the review queue. | Yes | agent |\n| D008 | M001/S04 | requirement | R004 Review Queue UI status | validated | All R004 capabilities delivered and verified: 9 API endpoints (approve, reject, edit, split, merge, queue list, stats, mode get/set) with 24 passing integration tests covering happy paths and error boundaries. React+TypeScript frontend with queue page (filter tabs, stats, pagination), moment detail page (all review actions with modals), and review-vs-auto mode toggle. Frontend builds with zero TypeScript errors. | Yes | agent |\n| D009 | M001/S05 | architecture | Async search service pattern for FastAPI request path | Create a separate `SearchService` class using `openai.AsyncOpenAI` and `qdrant_client.AsyncQdrantClient` for the search endpoint. Keep existing sync `EmbeddingClient` and `QdrantManager` for Celery pipeline. Search endpoint has 300ms timeout on embedding API and falls back to SQL ILIKE keyword search on Qdrant/embedding failure. | The existing EmbeddingClient and QdrantManager are sync (using `openai.OpenAI` and `QdrantClient`) because Celery tasks run synchronously. FastAPI request handlers are async — reusing sync clients would block the event loop. Creating a thin async wrapper avoids modifying the battle-tested pipeline code while providing non-blocking search. The 300ms timeout and keyword fallback ensure the search endpoint always returns results, even when Qdrant or the embedding service is degraded. | Yes | agent |\n| D010 | M001/S05 | requirement | R005 Search-First Web UI status | validated | Search endpoint (GET /api/v1/search) with async embedding + Qdrant + keyword fallback implemented and tested. Frontend Home.tsx has prominent search bar with 300ms debounced typeahead, scope toggle via URL params, nav cards for Topics/Creators, Recently Added section. SearchResults.tsx displays grouped results. 5 integration tests verify search happy path, empty query, keyword fallback, scope filter, and no-results. Frontend production build succeeds with zero TypeScript errors. | Yes | agent |\n| D011 | M001/S05 | requirement | R006 Technique Page Display status | validated | TechniquePage.tsx renders all specified sections: header with topic_category badge and topic_tags pills, creator name linked to creator detail, source_quality indicator, amber banner for unstructured content, body_sections JSONB prose (handles both string and object values), key moments index ordered by start_time, signal chains, plugins pill list, and related techniques links. Backend GET /api/v1/techniques/{slug} returns full detail with eager-loaded key_moments and related links. 404 for unknown slug tested. | Yes | agent |\n| D012 | M001/S05 | requirement | R007 Creators Browse Page status | validated | CreatorsBrowse.tsx implements genre filter pills, type-to-narrow name filter, sort toggle (Random default/A-Z/Views). Each creator row shows name, genre tags, technique_count, video_count. Links to CreatorDetail page. Backend GET /api/v1/creators supports sort=random\\|alpha\\|views and genre filter. Integration tests verify random sort, alpha sort, genre filter, detail endpoint, 404, and counts. | Yes | agent |\n| D013 | M001/S05 | requirement | R008 Topics Browse Page status | validated | TopicsBrowse.tsx renders two-level topic hierarchy (6 categories from canonical_tags.yaml with expandable sub-topics showing technique_count and creator_count). Filter input narrows categories/sub-topics. Clicking sub-topic navigates to search with scope=topics. Backend GET /api/v1/topics aggregates counts from DB per sub-topic. Integration test verifies topic hierarchy response shape. | Yes | agent |\n| D014 | M001/S05 | requirement | R014 Creator Equity status | validated | CreatorsBrowse.tsx defaults to sort=random. Backend uses func.random() ORDER BY for randomized sort. Integration test verifies random sort returns all creators (order may vary). All creators get equal visual weight in the UI — no featured/highlighted treatment. Equal-weight row layout confirmed in CSS. | Yes | agent |\n| D015 | M002/S01 | architecture | Docker network subnet for Chrysopedia on ub01 | 172.32.0.0/24 — free on ub01, replaces 172.24.0.0/24 which is used by xpltd_docs_default | 172.24.0.0/24 was already allocated to xpltd_docs_default network on ub01. Scanned all existing subnets and 172.32.0.0/24 was the first unoccupied /24 in the 172.x range. | Yes | agent |\n| D016 | M002/S01 | architecture | Embedding service for the pipeline and search — OpenWebUI doesn't serve /v1/embeddings | Add Ollama container (ollama/ollama:latest) to the compose stack running nomic-embed-text on CPU | The FYN OpenWebUI instance at chat.forgetyour.name returns 404/500 on /v1/embeddings. No Ollama instance exists on the network. Ollama provides the standard OpenAI-compatible /v1/embeddings endpoint that both EmbeddingClient and SearchService expect. | Yes | agent |\n| D017 |  | frontend | CSS theming approach for dark mode | 77 semantic CSS custom properties in :root block, all hardcoded colors replaced with var(--*) references | A complete variable-based palette enables theme switching, ensures consistency, and makes future color changes single-point edits. 77 tokens (vs ~30 initially planned) were needed to cover all semantic contexts — badges, buttons, surfaces, text, accents, shadows, and status states. Cyan (#22d3ee) replaces indigo as the accent color throughout. | Yes | agent |\n| D018 | M004/S04 | architecture | Version snapshot failure handling strategy in stage 5 | Best-effort versioning — snapshot INSERT failure logs ERROR but does not block page update (existing content overwrites proceed regardless) | Versioning is a diagnostic/benchmarking feature. Losing a version snapshot is far less damaging than failing the entire page synthesis. Follows the same non-blocking side-effect pattern established in D005 for embedding/Qdrant failures. | Yes | agent |\n| D019 | M005/S02 | frontend-layout | Technique page layout structure | CSS grid 2-column layout: 1fr main + 22rem sticky sidebar, collapsing to single column at 768px. Page max-width widened from 48rem to 64rem. | 22rem sidebar provides enough room for moment cards and plugin lists without cramming. 64rem total width accommodates both columns comfortably on standard desktop displays. Sticky sidebar keeps navigation aids visible while scrolling prose. 768px breakpoint aligns with existing mobile styles in the codebase. | Yes | agent |\n| D020 | M006/S05 | frontend | Topics page card layout visual differentiation approach | 3px colored left border + small colored dot next to category name, using existing badge CSS custom properties per category | Subtler than a full colored header — maintains dark theme cohesion while providing clear visual differentiation between 7 category cards. Reuses existing --color-badge-cat-*-bg/text custom properties, avoiding new color definitions. | Yes | agent |\n\n---\n\n### Requirements\nSource: `.gsd/REQUIREMENTS.md`\n\n# Requirements\n\n## R001 — Whisper Transcription Pipeline\n**Status:** validated\n**Description:** Desktop Python script that accepts video files (MP4/MKV), extracts audio via ffmpeg, runs Whisper large-v3 on RTX 4090, and outputs timestamped transcript JSON with segment-level timestamps and word-level timing. Must be resumable.\n**Validation:** Script processes a sample video and produces valid JSON with timestamped segments.\n**Primary Owner:** M001/S01\n\n## R002 — Transcript Ingestion API\n**Status:** validated\n**Description:** FastAPI endpoint that accepts transcript JSON uploads, creates/updates Creator and Source Video records, and stores transcript data in PostgreSQL. Handles new creator detection from folder names.\n**Validation:** POST transcript JSON → 200 OK, records created in DB, file stored on filesystem.\n**Primary Owner:** M001/S02\n\n## R003 — LLM-Powered Extraction Pipeline (Stages 2-5)\n**Status:** validated\n**Description:** Background worker pipeline: transcript segmentation → key moment extraction → classification/tagging → technique page synthesis. Uses OpenAI-compatible API with primary (DGX Sparks Qwen) and fallback (local Ollama) endpoints. Pipeline must be resumable per-video per-stage.\n**Validation:** End-to-end: transcript JSON in → technique pages with key moments, tags, and cross-references out.\n**Primary Owner:** M001/S03\n\n## R004 — Review Queue UI\n**Status:** validated\n**Description:** Admin interface for reviewing extracted key moments: approve, edit+approve, split, merge, reject. Organized by source video for contextual review. Includes mode toggle (review vs auto-publish).\n**Validation:** Admin can review, edit, and approve/reject moments; mode toggle controls whether new moments require review.\n**Primary Owner:** M001/S04\n\n## R005 — Search-First Web UI\n**Status:** validated\n**Description:** Landing page with prominent search bar, live typeahead (results after 2-3 chars), scope toggle (All/Topics/Creators), and two navigation cards (Topics, Creators). Recently added section. Search powered by Qdrant semantic search with keyword fallback.\n**Validation:** User types query → results appear within 500ms, grouped by type, with clickable navigation.\n**Primary Owner:** M001/S05\n\n## R006 — Technique Page Display\n**Status:** validated\n**Description:** Core content unit: header (tags, title, creator, meta), study guide prose (organized by sub-aspects with signal chain blocks and quotes), key moments index (timestamped list), related techniques, plugins referenced. Amber banner for livestream-sourced content.\n**Validation:** Technique page renders with all sections populated from synthesized data.\n**Primary Owner:** M001/S05\n\n## R007 — Creators Browse Page\n**Status:** validated\n**Description:** Filterable creator list with genre filter pills, type-to-narrow, sort options (randomized default, alphabetical, view count). Each row: name, genre tags, technique count, video count, view count. Links to creator detail page.\n**Validation:** Page loads with randomized order, genre filtering works, clicking row navigates to creator detail.\n**Primary Owner:** M001/S05\n\n## R008 — Topics Browse Page\n**Status:** validated\n**Description:** Two-level topic hierarchy (6 top-level categories → sub-topics). Filter input, genre filter pills. Each sub-topic shows technique count and creator count. Clicking sub-topic shows technique pages.\n**Validation:** Hierarchy renders, filtering works, sub-topic links show correct technique pages.\n**Primary Owner:** M001/S05\n\n## R009 — Qdrant Vector Search Integration\n**Status:** validated\n**Description:** Embed key moment summaries, technique page content, and transcript segments in Qdrant using configurable embedding model (nomic-embed-text default). Power semantic search with metadata filtering.\n**Validation:** Semantic search returns relevant results for natural language queries; embeddings update when content changes.\n**Primary Owner:** M001/S03\n\n## R010 — Docker Compose Deployment\n**Status:** validated\n**Description:** Single docker-compose.yml packaging API, web UI, PostgreSQL, and worker services. Follows XPLTD conventions: bind mounts at /vmPool/r/services/, compose at /vmPool/r/compose/chrysopedia/, xpltd_chrysopedia project name, dedicated Docker network.\n**Validation:** `docker compose up -d` brings up all services; data persists across restarts.\n**Primary Owner:** M001/S01\n\n## R011 — Canonical Tag System\n**Status:** validated\n**Description:** Editable canonical tag list (config file) with aliases. Pipeline references tags during classification. New tags can be proposed by LLM and queued for admin approval or auto-added within existing categories.\n**Validation:** Tag list is editable; pipeline uses canonical tags consistently; alias normalization works.\n**Primary Owner:** M001/S03\n\n## R012 — Incremental Content Addition\n**Status:** validated\n**Description:** System handles ongoing content: new videos processed through pipeline, new creators auto-detected, existing technique pages updated when new moments are added for same creator+topic.\n**Validation:** Adding a new video for an existing creator updates their technique pages; new creator folder creates new Creator record.\n**Primary Owner:** M001/S03\n\n## R013 — Prompt Template System\n**Status:** validated\n**Description:** Extraction prompts (stages 2-5) stored as editable configuration files, not hardcoded. Admin can edit prompts and re-run extraction on specific or all videos for calibration.\n**Validation:** Prompt files are editable; re-processing a video with updated prompts produces different output.\n**Primary Owner:** M001/S03\n\n## R014 — Creator Equity\n**Status:** validated\n**Description:** No creator is privileged in the UI. Default sort on Creators page is randomized on every page load. All creators get equal visual weight.\n**Validation:** Refreshing Creators page shows different order each time; no creator gets larger/bolder display.\n**Primary Owner:** M001/S05\n\n## R015 — 30-Second Retrieval Target\n**Status:** active\n**Description:** A producer mid-session can find a specific technique in under 30 seconds from Alt+Tab to reading the key insight.\n**Validation:** Timed test: Alt+Tab → search → read technique → under 30 seconds.\n**Primary Owner:** M001/S05\n\n---\n\n[...truncated 6 sections]\n\n### Dependency Slice Summaries\n\nPay particular attention to **Forward Intelligence** sections — they contain hard-won knowledge about what's fragile, what assumptions changed, and what to watch out for.\n\n- (no dependencies)\n\n## Your Role in the Pipeline\n\nYou are the scout. After you finish, a **planner agent** reads your output in a fresh context with no memory of your exploration. It uses your findings to decompose this slice into executable tasks — deciding what files change, what order to build things, how to verify the work. Then **executor agents** build each task in isolated context windows.\n\nWrite for the planner, not for a human. The planner needs:\n- **What files exist and what they do** — so it can scope tasks to specific files\n- **Where the natural seams are** — where work divides into independent units\n- **What to build or prove first** — what's riskiest, what unblocks everything else\n- **How to verify the result** — what commands, tests, or checks confirm the slice works\n\nIf the research doc is vague, the planner will waste its context re-exploring code you already read. If it's precise, the planner can decompose immediately.\n\n## Calibrate Depth\n\nRead the slice title, the roadmap excerpt, and any milestone research above. Ask: does this slice involve unfamiliar technology, risky integration, novel architecture, or ambiguous requirements? Or is it straightforward application of known patterns to known code?\n\n- **Deep research** — new technology, unfamiliar APIs, risky integration, multiple viable approaches, or ambiguous scope. Explore broadly, look up docs, check libraries, investigate constraints. Write all template sections. This is the default when you're genuinely uncertain.\n- **Targeted research** — known technology but new to this codebase, or moderately complex integration. Explore the relevant code, check one or two libraries, identify constraints. Omit Don't Hand-Roll and Sources if nothing applies.\n- **Light research** — well-understood work using established patterns already in the codebase (wiring up existing APIs, adding standard UI components, CRUD operations, configuration changes). Read the relevant files to confirm the pattern, note any constraints, write Summary + Recommendation + Implementation Landscape. Skip the rest. A light research doc can be 15-20 lines. Don't manufacture pitfalls or risks for work that doesn't have them.\n\nAn honest \"this is straightforward, here's the pattern to follow\" is more valuable than invented complexity.\n\n## Steps\n\nResearch what this slice needs. Narrate key findings and surprises as you go — what exists, what's missing, what constrains the approach.\n0. If `REQUIREMENTS.md` was preloaded above, identify which Active requirements this slice owns or supports. Research should target these requirements — surfacing risks, unknowns, and implementation constraints that could affect whether the slice actually delivers them.\n1.  Reference specific rules from loaded skills in your findings where they inform the implementation approach.\n2. **Skill Discovery (suggest):**\n   Identify the key technologies, frameworks, and services this work depends on (e.g. Stripe, Clerk, Supabase, JUCE, SwiftUI).\n   For each, check if a professional agent skill already exists:\n   - First check `<available_skills>` in your system prompt — a skill may already be installed.\n   - For technologies without an installed skill, run: `npx skills find \"<technology>\"`\n   - Only consider skills that are **directly relevant** to core technologies — not tangentially related.\n   - Evaluate results by install count and relevance to the actual work.\n   - Note promising skills in your research output with their install commands, but do NOT install them.\n   - The user will decide which to install.\n3. Explore relevant code for this slice's scope. For targeted exploration, use `rg`, `find`, and reads. For broad or unfamiliar subsystems, use `scout` to map the relevant area first.\n4. Use `resolve_library` / `get_library_docs` for unfamiliar libraries — skip this for libraries already used in the codebase\n5. **Web search budget:** You have a limited budget of web searches (max ~15 per session). Use them strategically — prefer `resolve_library` / `get_library_docs` for library documentation. Do NOT repeat the same or similar queries. If a search didn't find what you need, rephrase once or move on. Target 3-5 total web searches for a typical research unit.\n6. Use the **Research** output template from the inlined context above — include only sections that have real content. The template is already inlined above; do NOT attempt to read any template file from disk (there is no `templates/SLICE-RESEARCH.md` — the correct template is already present in this prompt).\n7. Call `gsd_summary_save` with `milestone_id: M010`, `slice_id: S04`, `artifact_type: \"RESEARCH\"`, and the full research markdown as `content` — the tool computes the file path and persists to both DB and disk.\n\nThe slice directory already exists at `.gsd/milestones/M010/slices/S04/`. Do NOT mkdir.\n\n**You MUST call `gsd_summary_save` with the research content before finishing.**\n\nWhen done, say: \"Slice S04 researched.\"","display":false,"id":"1a36b76a","parentId":"62be96c9","timestamp":"2026-03-31T06:28:14.947Z"}
{"type":"message","id":"ce0459c6","parentId":"1a36b76a","timestamp":"2026-03-31T06:28:23.343Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me research the Search Autocomplete & Suggestions slice. The roadmap says: \"Typing in search shows autocomplete suggestions. Empty search box shows popular search terms.\"\n\nThis is a frontend-focused feature building on the existing search infrastructure. Let me explore the current search implementation to understand what exists.","thinkingSignature":"EogEClkIDBgCKkCpStksvhjIBpv8ufTolT94cyCuDFBrDrYyGvFRwz0dB9r4sLhLgp30IvN/d0ahwTjBbRU2U32+o1Hx6kVE9KmwMg9jbGF1ZGUtb3B1cy00LTY4ABIMJJZ4TK1ugI9tgcQBGgxmydmjIQ5Sp6XEIQciMC5dqfG4zex1zyQnNa84W3+keDIwHmVYwdgSMXCmpheTog0nak8nJtTh41nrIVyyBircAixF7ImyA56yn+7L+RW8Dvi0fvRDvOko4skhKfEyh6VpeOOOdLq514Y/IKrRQxW0Yyad2b79jrAHhE6sZ30bW5w7MXhxBq0KeWFdtYIFtgQah9C0ZBA9rZpzGfBc6wsofYgZAAFb9lYiQipKya9QRLOyLfj6MU08XlQ844qveBeWtM136ZIx89OU1lhpeF8WH/GnNlG6gNzR4YEucfXNV/tGyfuzF+WPCVoXSJFXTcGDcYz4xvphl/OCncLX4ZsJziHPqWRUupydIBT6D/CxUPaAcniHiRX1uGZ7uzCb90uSg3vYkxMRLyUUIxFFeIfTDz/NII+zNyFo12O8YoV/cMqoPrvkeuCm/JjKA/OvbJIQyI+z+hFef8f7utZq8ly+ubvBNhZCBTvYeslChGd37U6mH371uRrKy4wXigPI/hcI5ItcCHUivLX3py6M3pdYBJkpKPqKZjCutkT3/hgB"},{"type":"text","text":"Let me explore the current search implementation — both backend and frontend — to understand what we're building on."},{"type":"toolCall","id":"toolu_01QZKKvkaGnKBr4rCcY8gmou","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -l \"search\" backend/routers/ frontend/src/ --type py --type ts --type tsx 2>/dev/null; find frontend/src -name \"*.tsx\" -o -name \"*.ts\" | head -30; find backend/routers -name \"*.py\" | head -10"}},{"type":"toolCall","id":"toolu_01LAimxx2nFodUQyKhunk8gm","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -l \"autocomplete\\|suggest\\|popular\\|typeahead\\|debounce\" frontend/src/ backend/ --type py --type ts 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":332,"cacheRead":58653,"cacheWrite":6329,"totalTokens":65317,"cost":{"input":0.000015000000000000002,"output":0.0083,"cacheRead":0.0293265,"cacheWrite":0.03955625,"total":0.07719775}},"stopReason":"toolUse","timestamp":1774938494945}}
{"type":"message","id":"c9465aac","parentId":"ce0459c6","timestamp":"2026-03-31T06:28:23.364Z","message":{"role":"toolResult","toolCallId":"toolu_01QZKKvkaGnKBr4rCcY8gmou","toolName":"bash","content":[{"type":"text","text":"frontend/src/pages/AdminPipeline.tsx\nfrontend/src/pages/CreatorDetail.tsx\nfrontend/src/pages/CreatorsBrowse.tsx\nfrontend/src/pages/AdminReports.tsx\nfrontend/src/pages/Home.tsx\nfrontend/src/pages/TechniquePage.tsx\nfrontend/src/pages/TopicsBrowse.tsx\nfrontend/src/pages/About.tsx\nfrontend/src/pages/SubTopicPage.tsx\nfrontend/src/pages/SearchResults.tsx\nfrontend/src/vite-env.d.ts\nfrontend/src/utils/catSlug.ts\nfrontend/src/App.tsx\nfrontend/src/api/public-client.ts\nfrontend/src/components/CategoryIcons.tsx\nfrontend/src/components/CreatorAvatar.tsx\nfrontend/src/components/AppFooter.tsx\nfrontend/src/components/CopyLinkButton.tsx\nfrontend/src/components/AdminDropdown.tsx\nfrontend/src/components/ReportIssueModal.tsx\nfrontend/src/main.tsx\nbackend/routers/techniques.py\nbackend/routers/topics.py\nbackend/routers/reports.py\nbackend/routers/health.py\nbackend/routers/videos.py\nbackend/routers/pipeline.py\nbackend/routers/__init__.py\nbackend/routers/creators.py\nbackend/routers/ingest.py\nbackend/routers/search.py\n"}],"isError":false,"timestamp":1774938503364}}
{"type":"message","id":"05bad94e","parentId":"c9465aac","timestamp":"2026-03-31T06:28:23.372Z","message":{"role":"toolResult","toolCallId":"toolu_01LAimxx2nFodUQyKhunk8gm","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1774938503370}}
{"type":"message","id":"0289a047","parentId":"05bad94e","timestamp":"2026-03-31T06:28:26.944Z","message":{"role":"assistant","content":[{"type":"text","text":"No autocomplete/suggest/typeahead code exists yet. Let me read the current search backend and frontend."},{"type":"toolCall","id":"toolu_01Emkxy4Ls9bz7qaPBkLXVRL","name":"read","arguments":{"path":"backend/routers/search.py"}},{"type":"toolCall","id":"toolu_01SAUyMwuL5nc1etbGuuFKYr","name":"read","arguments":{"path":"frontend/src/pages/Home.tsx"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":124,"cacheRead":64982,"cacheWrite":671,"totalTokens":65778,"cost":{"input":0.000005,"output":0.0031000000000000003,"cacheRead":0.032491,"cacheWrite":0.00419375,"total":0.039789750000000006}},"stopReason":"toolUse","timestamp":1774938503371}}
{"type":"message","id":"4d00f1ff","parentId":"0289a047","timestamp":"2026-03-31T06:28:26.946Z","message":{"role":"toolResult","toolCallId":"toolu_01Emkxy4Ls9bz7qaPBkLXVRL","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.ext.asyncio import AsyncSession\n\nfrom config import get_settings\nfrom database import get_session\nfrom schemas import SearchResponse, SearchResultItem\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    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, limit=limit, db=db)\n    return SearchResponse(\n        items=[SearchResultItem(**item) for item in result[\"items\"]],\n        total=result[\"total\"],\n        query=result[\"query\"],\n        fallback_used=result[\"fallback_used\"],\n    )\n"}],"isError":false,"timestamp":1774938506945}}
{"type":"message","id":"8f05f566","parentId":"4d00f1ff","timestamp":"2026-03-31T06:28:26.948Z","message":{"role":"toolResult","toolCallId":"toolu_01SAUyMwuL5nc1etbGuuFKYr","toolName":"read","content":[{"type":"text","text":"/**\n * Home / landing page.\n *\n * Prominent search bar with 300ms debounced typeahead (top 5 results after 2+ chars),\n * navigation cards for Topics and Creators, and a \"Recently Added\" section.\n */\n\nimport { IconTopics, IconCreators } from \"../components/CategoryIcons\";\nimport { useCallback, useEffect, useRef, useState } from \"react\";\nimport { Link, useNavigate } from \"react-router-dom\";\nimport {\n  searchApi,\n  fetchTechniques,\n  fetchTopics,\n  type SearchResultItem,\n  type TechniqueListItem,\n} from \"../api/public-client\";\n\nexport default function Home() {\n  const [query, setQuery] = useState(\"\");\n  const [suggestions, setSuggestions] = useState<SearchResultItem[]>([]);\n  const [showDropdown, setShowDropdown] = useState(false);\n  const [featured, setFeatured] = useState<TechniqueListItem | null>(null);\n  const [recent, setRecent] = useState<TechniqueListItem[]>([]);\n  const [recentLoading, setRecentLoading] = useState(true);\n  const [popularTopics, setPopularTopics] = useState<{name: string; count: number}[]>([]);\n  const navigate = useNavigate();\n  const inputRef = useRef<HTMLInputElement>(null);\n  const debounceRef = useRef<ReturnType<typeof setTimeout> | null>(null);\n  const dropdownRef = useRef<HTMLDivElement>(null);\n\n  // Auto-focus search on mount\n  useEffect(() => {\n    inputRef.current?.focus();\n  }, []);\n\n  // Load featured technique (random)\n  useEffect(() => {\n    let cancelled = false;\n    void (async () => {\n      try {\n        const res = await fetchTechniques({ sort: \"random\", limit: 1 });\n        if (!cancelled && res.items.length > 0) setFeatured(res.items[0] ?? null);\n      } catch {\n        // silently ignore — optional section\n      }\n    })();\n    return () => {\n      cancelled = true;\n    };\n  }, []);\n\n  // Load recently added techniques\n  useEffect(() => {\n    let cancelled = false;\n    void (async () => {\n      try {\n        const res = await fetchTechniques({ sort: \"recent\", limit: 6 });\n        if (!cancelled) setRecent(res.items);\n      } catch {\n        // silently ignore — not critical\n      } finally {\n        if (!cancelled) setRecentLoading(false);\n      }\n    })();\n    return () => {\n      cancelled = true;\n    };\n  }, []);\n\n  // Load popular topics\n  useEffect(() => {\n    let cancelled = false;\n    void (async () => {\n      try {\n        const categories = await fetchTopics();\n        const all = categories.flatMap((cat) =>\n          cat.sub_topics.map((st) => ({ name: st.name, count: st.technique_count }))\n        );\n        all.sort((a, b) => b.count - a.count);\n        if (!cancelled) setPopularTopics(all.slice(0, 8));\n      } catch {\n        // optional section — silently ignore\n      }\n    })();\n    return () => {\n      cancelled = true;\n    };\n  }, []);\n\n  // Close dropdown on outside click\n  useEffect(() => {\n    function handleClick(e: MouseEvent) {\n      if (\n        dropdownRef.current &&\n        !dropdownRef.current.contains(e.target as Node)\n      ) {\n        setShowDropdown(false);\n      }\n    }\n    document.addEventListener(\"mousedown\", handleClick);\n    return () => document.removeEventListener(\"mousedown\", handleClick);\n  }, []);\n\n  // Debounced typeahead\n  const handleInputChange = useCallback(\n    (value: string) => {\n      setQuery(value);\n\n      if (debounceRef.current) clearTimeout(debounceRef.current);\n\n      if (value.length < 2) {\n        setSuggestions([]);\n        setShowDropdown(false);\n        return;\n      }\n\n      debounceRef.current = setTimeout(() => {\n        void (async () => {\n          try {\n            const res = await searchApi(value, undefined, 5);\n            setSuggestions(res.items);\n            setShowDropdown(res.items.length > 0);\n          } catch {\n            setSuggestions([]);\n            setShowDropdown(false);\n          }\n        })();\n      }, 300);\n    },\n    [],\n  );\n\n  function handleSubmit(e: React.FormEvent) {\n    e.preventDefault();\n    if (query.trim()) {\n      setShowDropdown(false);\n      navigate(`/search?q=${encodeURIComponent(query.trim())}`);\n    }\n  }\n\n  function handleKeyDown(e: React.KeyboardEvent) {\n    if (e.key === \"Escape\") {\n      setShowDropdown(false);\n    }\n  }\n\n  return (\n    <div className=\"home\">\n      {/* Hero search */}\n      <section className=\"home-hero\">\n        <h1 className=\"home-hero__title\">Production Knowledge, Distilled</h1>\n        <p className=\"home-hero__subtitle\">\n          Search techniques, key moments, and creators\n        </p>\n\n        <div className=\"search-container\" ref={dropdownRef}>\n          <form onSubmit={handleSubmit} className=\"search-form search-form--hero\">\n            <input\n              ref={inputRef}\n              type=\"search\"\n              className=\"search-input search-input--hero\"\n              placeholder=\"Search techniques…\"\n              value={query}\n              onChange={(e) => handleInputChange(e.target.value)}\n              onFocus={() => {\n                if (suggestions.length > 0) setShowDropdown(true);\n              }}\n              onKeyDown={handleKeyDown}\n              aria-label=\"Search techniques\"\n            />\n            <button type=\"submit\" className=\"btn btn--search\">\n              Search\n            </button>\n          </form>\n\n          {showDropdown && suggestions.length > 0 && (\n            <div className=\"typeahead-dropdown\">\n              {suggestions.map((item) => (\n                <Link\n                  key={`${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--${item.type}`}>\n                      {item.type === \"technique_page\" ? \"Technique\" : \"Key Moment\"}\n                    </span>\n                    {item.creator_name && (\n                      <span className=\"typeahead-item__creator\">\n                        {item.creator_name}\n                      </span>\n                    )}\n                  </span>\n                </Link>\n              ))}\n              <Link\n                to={`/search?q=${encodeURIComponent(query)}`}\n                className=\"typeahead-see-all\"\n                onClick={() => setShowDropdown(false)}\n              >\n                See all results for \"{query}\"\n              </Link>\n            </div>\n          )}\n        </div>\n\n        <p className=\"home-hero__value-prop\">\n          Real music production techniques extracted from creator tutorials.\n          Skip the 4-hour videos — find the insight you need in seconds.\n        </p>\n\n        <div className=\"home-how-it-works\">\n          <div className=\"home-how-it-works__step\">\n            <span className=\"home-how-it-works__number\">1</span>\n            <h3 className=\"home-how-it-works__title\">Creators Share Techniques</h3>\n            <p className=\"home-how-it-works__desc\">\n              Real producers and sound designers publish in-depth tutorials\n            </p>\n          </div>\n          <div className=\"home-how-it-works__step\">\n            <span className=\"home-how-it-works__number\">2</span>\n            <h3 className=\"home-how-it-works__title\">AI Extracts Key Moments</h3>\n            <p className=\"home-how-it-works__desc\">\n              We distill hours of video into structured, searchable knowledge\n            </p>\n          </div>\n          <div className=\"home-how-it-works__step\">\n            <span className=\"home-how-it-works__number\">3</span>\n            <h3 className=\"home-how-it-works__title\">You Find Answers Fast</h3>\n            <p className=\"home-how-it-works__desc\">\n              Search by topic, technique, or creator — get straight to the insight\n            </p>\n          </div>\n        </div>\n\n        <Link to=\"/topics\" className=\"btn home-cta\">Start Exploring</Link>\n\n        {popularTopics.length > 0 && (\n          <section className=\"home-popular-topics\">\n            <h2 className=\"home-popular-topics__title\">Popular Topics</h2>\n            <div className=\"home-popular-topics__list\">\n              {popularTopics.map((topic) => (\n                <Link\n                  key={topic.name}\n                  to={`/search?q=${encodeURIComponent(topic.name)}&scope=topics`}\n                  className=\"pill pill--topic-quick\"\n                >\n                  {topic.name}\n                </Link>\n              ))}\n            </div>\n          </section>\n        )}\n      </section>\n\n      {/* Navigation cards */}\n      <section className=\"nav-cards\">\n        <Link to=\"/topics\" className=\"nav-card\">\n          <h3 className=\"nav-card__title\"><IconTopics /> Topics</h3>\n          <p className=\"nav-card__desc\">\n            Browse techniques organized by category and sub-topic\n          </p>\n        </Link>\n        <Link to=\"/creators\" className=\"nav-card\">\n          <h3 className=\"nav-card__title\"><IconCreators /> Creators</h3>\n          <p className=\"nav-card__desc\">\n            Discover creators and their technique libraries\n          </p>\n        </Link>\n      </section>\n\n      {/* Featured Technique Spotlight */}\n      {featured && (\n        <section className=\"home-featured\">\n          <h3 className=\"home-featured__label\">Featured Technique</h3>\n          <Link to={`/techniques/${featured.slug}`} className=\"home-featured__title\">\n            {featured.title}\n          </Link>\n          {featured.summary && (\n            <p className=\"home-featured__summary\">{featured.summary}</p>\n          )}\n          <div className=\"home-featured__meta\">\n            {featured.topic_category && (\n              <span className=\"badge badge--category\">{featured.topic_category}</span>\n            )}\n            {featured.topic_tags && featured.topic_tags.length > 0 && featured.topic_tags.map(tag => (\n              <span key={tag} className=\"pill pill--tag\">{tag}</span>\n            ))}\n            {featured.key_moment_count > 0 && (\n              <span className=\"home-featured__moments\">\n                {featured.key_moment_count} moment{featured.key_moment_count !== 1 ? \"s\" : \"\"}\n              </span>\n            )}\n          </div>\n          {featured.creator_name && (\n            <Link to={`/creators/${featured.creator_slug}`} className=\"home-featured__creator\">\n              by {featured.creator_name}\n            </Link>\n          )}\n        </section>\n      )}\n\n      {/* Recently Added */}\n      <section className=\"recent-section\">\n        <h3 className=\"recent-section__title\">Recently Added</h3>\n        {recentLoading ? (\n          <div className=\"loading\">Loading…</div>\n        ) : recent.length === 0 ? (\n          <div className=\"empty-state\">No techniques yet.</div>\n        ) : (\n          <div className=\"recent-list\">\n            {recent\n              .filter((t) => t.id !== featured?.id)\n              .slice(0, 4)\n              .map((t) => (\n              <Link\n                key={t.id}\n                to={`/techniques/${t.slug}`}\n                className=\"recent-card\"\n              >\n                <span className=\"recent-card__header\">\n                  <span className=\"recent-card__title\">{t.title}</span>\n                  {t.creator_name && (\n                    <span className=\"recent-card__creator\">{t.creator_name}</span>\n                  )}\n                </span>\n                <span className=\"recent-card__meta\">\n                  <span className=\"badge badge--category\">\n                    {t.topic_category}\n                  </span>\n                  {t.topic_tags && t.topic_tags.length > 0 && t.topic_tags.map(tag => (\n                    <span key={tag} className=\"pill pill--tag\">{tag}</span>\n                  ))}\n                  {t.summary && (\n                    <span className=\"recent-card__summary\">\n                      {t.summary.length > 150\n                        ? `${t.summary.slice(0, 150)}…`\n                        : t.summary}\n                    </span>\n                  )}\n                  {t.key_moment_count > 0 && (\n                    <span className=\"recent-card__moments\">\n                      {t.key_moment_count} moment{t.key_moment_count !== 1 ? 's' : ''}\n                    </span>\n                  )}\n                </span>\n              </Link>\n            ))}\n          </div>\n        )}\n      </section>\n    </div>\n  );\n}\n"}],"isError":false,"timestamp":1774938506947}}
{"type":"message","id":"457b9fd4","parentId":"8f05f566","timestamp":"2026-03-31T06:28:30.754Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. Home.tsx already has debounced typeahead showing top-5 results after 2+ chars, and a popular topics section. Now let me check the search service and the SearchResults page."},{"type":"toolCall","id":"toolu_015n6uYsY5bkJ8rCyA6zyVov","name":"read","arguments":{"path":"backend/search_service.py"}},{"type":"toolCall","id":"toolu_01Xpp5JDy7Efosj4Lu6wpmMr","name":"read","arguments":{"path":"frontend/src/pages/SearchResults.tsx"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":145,"cacheRead":65653,"cacheWrite":4074,"totalTokens":69873,"cost":{"input":0.000005,"output":0.003625,"cacheRead":0.0328265,"cacheWrite":0.025462500000000002,"total":0.061919}},"stopReason":"toolUse","timestamp":1774938506947}}
{"type":"message","id":"63a69b89","parentId":"457b9fd4","timestamp":"2026-03-31T06:28:30.758Z","message":{"role":"toolResult","toolCallId":"toolu_015n6uYsY5bkJ8rCyA6zyVov","toolName":"read","content":[{"type":"text","text":"\"\"\"Async search service for the public search endpoint.\n\nOrchestrates semantic search (embedding + Qdrant) with keyword fallback.\nAll external calls have timeouts and graceful degradation — if embedding\nor Qdrant fail, the service falls back to keyword-only (ILIKE) search.\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 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 = 0.3  # 300ms per plan\n\n\nclass SearchService:\n    \"\"\"Async search service with semantic + keyword fallback.\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    # ── Keyword fallback ─────────────────────────────────────────────────\n\n    async def keyword_search(\n        self,\n        query: str,\n        scope: str,\n        limit: int,\n        db: AsyncSession,\n    ) -> list[dict[str, Any]]:\n        \"\"\"ILIKE keyword search across technique pages, key moments, and creators.\n\n        Searches title/name columns. Returns a unified list of result dicts.\n        \"\"\"\n        results: list[dict[str, Any]] = []\n        pattern = f\"%{query}%\"\n\n        if scope in (\"all\", \"topics\"):\n            stmt = (\n                select(TechniquePage)\n                .where(\n                    or_(\n                        TechniquePage.title.ilike(pattern),\n                        TechniquePage.summary.ilike(pattern),\n                    )\n                )\n                .limit(limit)\n            )\n            rows = await db.execute(stmt)\n            for tp in rows.scalars().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                    \"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(KeyMoment.title.ilike(pattern))\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                    \"score\": 0.0,\n                })\n\n        if scope in (\"all\", \"creators\"):\n            cr_stmt = (\n                select(Creator)\n                .where(Creator.name.ilike(pattern))\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                    \"score\": 0.0,\n                })\n\n        # Enrich keyword results with creator names\n        kw_creator_ids = {r[\"creator_id\"] for r in results if r.get(\"creator_id\")}\n        kw_creator_map: dict[str, dict[str, str]] = {}\n        if kw_creator_ids:\n            import uuid as _uuid_mod\n            valid = []\n            for cid in kw_creator_ids:\n                try:\n                    valid.append(_uuid_mod.UUID(cid))\n                except (ValueError, AttributeError):\n                    pass\n            if valid:\n                cr_stmt = select(Creator).where(Creator.id.in_(valid))\n                cr_result = await db.execute(cr_stmt)\n                for c in cr_result.scalars().all():\n                    kw_creator_map[str(c.id)] = {\"name\": c.name, \"slug\": c.slug}\n        for r in results:\n            info = kw_creator_map.get(r.get(\"creator_id\", \"\"), {\"name\": \"\", \"slug\": \"\"})\n            r[\"creator_name\"] = info[\"name\"]\n            r[\"creator_slug\"] = info[\"slug\"]\n\n        return results[:limit]\n\n    # ── Orchestrator ─────────────────────────────────────────────────────\n\n    async def search(\n        self,\n        query: str,\n        scope: str,\n        limit: int,\n        db: AsyncSession,\n    ) -> dict[str, Any]:\n        \"\"\"Run semantic search with keyword fallback.\n\n        Returns a dict matching the SearchResponse schema shape.\n        \"\"\"\n        start = time.monotonic()\n\n        # Validate / sanitize inputs\n        if not query or not query.strip():\n            return {\"items\": [], \"total\": 0, \"query\": query, \"fallback_used\": False}\n\n        # Truncate long queries\n        query = query.strip()[:500]\n\n        # Normalize scope\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,  # creators aren't in Qdrant\n        }\n        qdrant_type_filter = type_filter_map.get(scope)\n\n        fallback_used = False\n        items: list[dict[str, Any]] = []\n\n        # Try semantic search\n        vector = await self.embed_query(query)\n        if vector is not None:\n            qdrant_results = await self.search_qdrant(vector, limit=limit, type_filter=qdrant_type_filter)\n            if qdrant_results:\n                # Enrich Qdrant results with DB metadata\n                items = await self._enrich_results(qdrant_results, db)\n\n        # Fallback to keyword search if semantic failed or returned nothing\n        if not items:\n            items = await self.keyword_search(query, scope, limit, db)\n            fallback_used = True\n\n        elapsed_ms = (time.monotonic() - start) * 1000\n\n        logger.info(\n            \"Search query=%r scope=%s results=%d fallback=%s latency_ms=%.1f\",\n            query,\n            scope,\n            len(items),\n            fallback_used,\n            elapsed_ms,\n        )\n\n        return {\n            \"items\": items,\n            \"total\": len(items),\n            \"query\": query,\n            \"fallback_used\": fallback_used,\n        }\n\n    # ── Result enrichment ────────────────────────────────────────────────\n\n    async def _enrich_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        enriched: list[dict[str, Any]] = []\n\n        # Collect creator_ids to batch-fetch\n        creator_ids = set()\n        for r in qdrant_results:\n            payload = r.get(\"payload\", {})\n            cid = payload.get(\"creator_id\")\n            if cid:\n                creator_ids.add(cid)\n\n        # Batch fetch creators\n        creator_map: dict[str, dict[str, str]] = {}\n        if creator_ids:\n            from sqlalchemy.dialects.postgresql import UUID as PgUUID\n            import uuid as uuid_mod\n            valid_ids = []\n            for cid in creator_ids:\n                try:\n                    valid_ids.append(uuid_mod.UUID(cid))\n                except (ValueError, AttributeError):\n                    pass\n\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            creator_info = creator_map.get(cid, {\"name\": \"\", \"slug\": \"\"})\n            result_type = payload.get(\"type\", \"\")\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_info[\"name\"],\n                \"creator_slug\": creator_info[\"slug\"],\n                \"score\": r.get(\"score\", 0.0),\n            })\n\n        return enriched\n"}],"isError":false,"timestamp":1774938510757}}
{"type":"message","id":"e19b588f","parentId":"63a69b89","timestamp":"2026-03-31T06:28:30.762Z","message":{"role":"toolResult","toolCallId":"toolu_01Xpp5JDy7Efosj4Lu6wpmMr","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, useRef, useState } from \"react\";\nimport { Link, useSearchParams, useNavigate } from \"react-router-dom\";\nimport { searchApi, type SearchResultItem } from \"../api/public-client\";\nimport { catSlug } from \"../utils/catSlug\";\n\nexport default function SearchResults() {\n  const [searchParams] = useSearchParams();\n  const navigate = useNavigate();\n  const q = searchParams.get(\"q\") ?? \"\";\n\n  const [results, setResults] = useState<SearchResultItem[]>([]);\n  const [loading, setLoading] = useState(false);\n  const [error, setError] = useState<string | null>(null);\n  const [localQuery, setLocalQuery] = useState(q);\n  const debounceRef = useRef<ReturnType<typeof setTimeout> | null>(null);\n\n  const doSearch = useCallback(async (query: string) => {\n    if (!query.trim()) {\n      setResults([]);\n      return;\n    }\n\n    setLoading(true);\n    setError(null);\n    try {\n      const res = await searchApi(query.trim());\n      setResults(res.items);\n    } catch (err) {\n      setError(err instanceof Error ? err.message : \"Search failed\");\n      setResults([]);\n    } finally {\n      setLoading(false);\n    }\n  }, []);\n\n  // Search when URL param changes\n  useEffect(() => {\n    setLocalQuery(q);\n    if (q) void doSearch(q);\n  }, [q, doSearch]);\n\n  function handleInputChange(value: string) {\n    setLocalQuery(value);\n\n    if (debounceRef.current) clearTimeout(debounceRef.current);\n    debounceRef.current = setTimeout(() => {\n      if (value.trim()) {\n        navigate(`/search?q=${encodeURIComponent(value.trim())}`, {\n          replace: true,\n        });\n      }\n    }, 400);\n  }\n\n  function handleSubmit(e: React.FormEvent) {\n    e.preventDefault();\n    if (debounceRef.current) clearTimeout(debounceRef.current);\n    if (localQuery.trim()) {\n      navigate(`/search?q=${encodeURIComponent(localQuery.trim())}`, {\n        replace: true,\n      });\n    }\n  }\n\n  // Group results by type\n  const techniqueResults = results.filter((r) => r.type === \"technique_page\");\n  const momentResults = results.filter((r) => r.type === \"key_moment\");\n\n  return (\n    <div className=\"search-results-page\">\n      {/* Inline search bar */}\n      <form onSubmit={handleSubmit} className=\"search-form search-form--inline\">\n        <input\n          type=\"search\"\n          className=\"search-input search-input--inline\"\n          placeholder=\"Search techniques…\"\n          value={localQuery}\n          onChange={(e) => handleInputChange(e.target.value)}\n          aria-label=\"Refine search\"\n        />\n        <button type=\"submit\" className=\"btn btn--search\">\n          Search\n        </button>\n      </form>\n\n      {/* Status */}\n      {loading && <div className=\"loading\">Searching…</div>}\n      {error && <div className=\"loading error-text\">Error: {error}</div>}\n\n      {/* No results */}\n      {!loading && !error && q && results.length === 0 && (\n        <div className=\"empty-state\">\n          <p>No results found for \"{q}\"</p>\n        </div>\n      )}\n\n      {/* Technique pages */}\n      {techniqueResults.length > 0 && (\n        <section className=\"search-group\">\n          <h3 className=\"search-group__title\">\n            Techniques ({techniqueResults.length})\n          </h3>\n          <div className=\"search-group__list\">\n            {techniqueResults.map((item) => (\n              <SearchResultCard key={`tp-${item.slug}`} item={item} />\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} />\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 }: { item: SearchResultItem }) {\n  return (\n    <Link\n      to={getSearchResultLink(item)}\n      className=\"search-result-card\"\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-${item.type}`}>\n          {item.type === \"technique_page\" ? \"Technique\" : \"Key Moment\"}\n        </span>\n      </div>\n      {item.summary && (\n        <p className=\"search-result-card__summary\">\n          {item.summary.length > 200\n            ? `${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            {item.topic_tags.map((tag) => (\n              <span key={tag} className=\"pill\">\n                {tag}\n              </span>\n            ))}\n          </span>\n        )}\n      </div>\n    </Link>\n  );\n}\n"}],"isError":false,"timestamp":1774938510760}}
{"type":"message","id":"c5da2170","parentId":"e19b588f","timestamp":"2026-03-31T06:28:36.715Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the API client and the existing CSS for search/typeahead styles."},{"type":"toolCall","id":"toolu_01KnP4GTEajRwWCK1a9MxmeF","name":"read","arguments":{"path":"frontend/src/api/public-client.ts"}},{"type":"toolCall","id":"toolu_01NVtA4fYyqsoN4PhyuYspj9","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"typeahead\\|autocomplete\\|search-input\\|search-form\\|search-container\" frontend/src/styles/ 2>/dev/null; find frontend/src/styles -name \"*.css\" | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":189,"cacheRead":69727,"cacheWrite":5576,"totalTokens":75493,"cost":{"input":0.000005,"output":0.004725,"cacheRead":0.0348635,"cacheWrite":0.03485,"total":0.0744435}},"stopReason":"toolUse","timestamp":1774938510761}}
{"type":"message","id":"22c74c1e","parentId":"c5da2170","timestamp":"2026-03-31T06:28:36.727Z","message":{"role":"toolResult","toolCallId":"toolu_01KnP4GTEajRwWCK1a9MxmeF","toolName":"read","content":[{"type":"text","text":"/**\n * Typed API client for Chrysopedia public endpoints.\n *\n * Mirrors backend schemas: SearchResponse, TechniquePageDetail, TopicCategory, CreatorBrowseItem.\n * Uses the same request<T> pattern as client.ts.\n */\n\n// ── Types ───────────────────────────────────────────────────────────────────\n\nexport 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}\n\nexport interface SearchResponse {\n  items: SearchResultItem[];\n  total: number;\n  query: string;\n  fallback_used: boolean;\n}\n\nexport interface KeyMomentSummary {\n  id: string;\n  title: string;\n  summary: string;\n  start_time: number;\n  end_time: number;\n  content_type: string;\n  plugins: string[] | null;\n  source_video_id: string;\n  video_filename: string;\n}\n\nexport interface CreatorInfo {\n  name: string;\n  slug: string;\n  genres: string[] | null;\n}\n\nexport interface RelatedLinkItem {\n  target_title: string;\n  target_slug: string;\n  relationship: string;\n  creator_name: string;\n  topic_category: string;\n  reason: string;\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: Record<string, unknown> | null;\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}\n\nexport interface TechniquePageVersionSummary {\n  version_number: number;\n  created_at: string;\n  pipeline_metadata: Record<string, unknown> | null;\n}\n\nexport interface TechniquePageVersionListResponse {\n  items: TechniquePageVersionSummary[];\n  total: number;\n}\n\nexport interface TechniquePageVersionDetail {\n  version_number: number;\n  content_snapshot: Record<string, unknown>;\n  pipeline_metadata: Record<string, unknown> | null;\n  created_at: string;\n}\n\nexport interface TechniqueListItem {\n  id: string;\n  title: string;\n  slug: string;\n  topic_category: string;\n  topic_tags: string[] | null;\n  summary: string | null;\n  creator_id: string;\n  creator_name: string;\n  creator_slug: string;\n  source_quality: string | null;\n  view_count: number;\n  key_moment_count: number;\n  created_at: string;\n  updated_at: string;\n}\n\nexport interface TechniqueListResponse {\n  items: TechniqueListItem[];\n  total: number;\n  offset: number;\n  limit: number;\n}\n\nexport interface TopicSubTopic {\n  name: string;\n  technique_count: number;\n  creator_count: number;\n}\n\nexport interface TopicCategory {\n  name: string;\n  description: string;\n  sub_topics: TopicSubTopic[];\n}\n\nexport interface CreatorBrowseItem {\n  id: string;\n  name: string;\n  slug: string;\n  genres: string[] | null;\n  folder_name: string;\n  view_count: number;\n  created_at: string;\n  updated_at: string;\n  technique_count: number;\n  video_count: number;\n}\n\nexport interface CreatorBrowseResponse {\n  items: CreatorBrowseItem[];\n  total: number;\n  offset: number;\n  limit: number;\n}\n\nexport interface CreatorDetailResponse {\n  id: string;\n  name: string;\n  slug: string;\n  genres: string[] | null;\n  folder_name: string;\n  view_count: number;\n  created_at: string;\n  updated_at: string;\n  video_count: number;\n}\n\n// ── Helpers ──────────────────────────────────────────────────────────────────\n\nconst BASE = \"/api/v1\";\n\nclass ApiError extends Error {\n  constructor(\n    public status: number,\n    public detail: string,\n  ) {\n    super(`API ${status}: ${detail}`);\n    this.name = \"ApiError\";\n  }\n}\n\nasync function request<T>(url: string, init?: RequestInit): Promise<T> {\n  const res = await fetch(url, {\n    ...init,\n    headers: {\n      \"Content-Type\": \"application/json\",\n      ...init?.headers,\n    },\n  });\n\n  if (!res.ok) {\n    let detail = res.statusText;\n    try {\n      const body: unknown = await res.json();\n      if (typeof body === \"object\" && body !== null && \"detail\" in body) {\n        const d = (body as { detail: unknown }).detail;\n        detail = typeof d === \"string\" ? d : Array.isArray(d) ? d.map((e: any) => e.msg || JSON.stringify(e)).join(\"; \") : JSON.stringify(d);\n      }\n    } catch {\n      // body not JSON — keep statusText\n    }\n    throw new ApiError(res.status, detail);\n  }\n\n  return res.json() as Promise<T>;\n}\n\n// ── Search ───────────────────────────────────────────────────────────────────\n\nexport async function searchApi(\n  q: string,\n  scope?: string,\n  limit?: number,\n): Promise<SearchResponse> {\n  const qs = new URLSearchParams({ q });\n  if (scope) qs.set(\"scope\", scope);\n  if (limit !== undefined) qs.set(\"limit\", String(limit));\n  return request<SearchResponse>(`${BASE}/search?${qs.toString()}`);\n}\n\n// ── Techniques ───────────────────────────────────────────────────────────────\n\nexport interface TechniqueListParams {\n  limit?: number;\n  offset?: number;\n  category?: string;\n  creator_slug?: string;\n  sort?: string;\n}\n\nexport async function fetchTechniques(\n  params: TechniqueListParams = {},\n): Promise<TechniqueListResponse> {\n  const qs = new URLSearchParams();\n  if (params.limit !== undefined) qs.set(\"limit\", String(params.limit));\n  if (params.offset !== undefined) qs.set(\"offset\", String(params.offset));\n  if (params.category) qs.set(\"category\", params.category);\n  if (params.creator_slug) qs.set(\"creator_slug\", params.creator_slug);\n  if (params.sort) qs.set(\"sort\", params.sort);\n  const query = qs.toString();\n  return request<TechniqueListResponse>(\n    `${BASE}/techniques${query ? `?${query}` : \"\"}`,\n  );\n}\n\nexport async function fetchTechnique(\n  slug: string,\n): Promise<TechniquePageDetail> {\n  return request<TechniquePageDetail>(`${BASE}/techniques/${slug}`);\n}\n\nexport async function fetchTechniqueVersions(\n  slug: string,\n): Promise<TechniquePageVersionListResponse> {\n  return request<TechniquePageVersionListResponse>(\n    `${BASE}/techniques/${slug}/versions`,\n  );\n}\n\nexport async function fetchTechniqueVersion(\n  slug: string,\n  versionNumber: number,\n): Promise<TechniquePageVersionDetail> {\n  return request<TechniquePageVersionDetail>(\n    `${BASE}/techniques/${slug}/versions/${versionNumber}`,\n  );\n}\n\n// ── Topics ───────────────────────────────────────────────────────────────────\n\nexport async function fetchTopics(): Promise<TopicCategory[]> {\n  return request<TopicCategory[]>(`${BASE}/topics`);\n}\n\nexport async function fetchSubTopicTechniques(\n  categorySlug: string,\n  subtopicSlug: string,\n  params: { limit?: number; offset?: number } = {},\n): Promise<TechniqueListResponse> {\n  const qs = new URLSearchParams();\n  if (params.limit !== undefined) qs.set(\"limit\", String(params.limit));\n  if (params.offset !== undefined) qs.set(\"offset\", String(params.offset));\n  const query = qs.toString();\n  return request<TechniqueListResponse>(\n    `${BASE}/topics/${encodeURIComponent(categorySlug)}/${encodeURIComponent(subtopicSlug)}${query ? `?${query}` : \"\"}`,\n  );\n}\n\n// ── Creators ─────────────────────────────────────────────────────────────────\n\nexport interface CreatorListParams {\n  sort?: string;\n  genre?: string;\n  limit?: number;\n  offset?: number;\n}\n\nexport async function fetchCreators(\n  params: CreatorListParams = {},\n): Promise<CreatorBrowseResponse> {\n  const qs = new URLSearchParams();\n  if (params.sort) qs.set(\"sort\", params.sort);\n  if (params.genre) qs.set(\"genre\", params.genre);\n  if (params.limit !== undefined) qs.set(\"limit\", String(params.limit));\n  if (params.offset !== undefined) qs.set(\"offset\", String(params.offset));\n  const query = qs.toString();\n  return request<CreatorBrowseResponse>(\n    `${BASE}/creators${query ? `?${query}` : \"\"}`,\n  );\n}\n\nexport async function fetchCreator(\n  slug: string,\n): Promise<CreatorDetailResponse> {\n  return request<CreatorDetailResponse>(`${BASE}/creators/${slug}`);\n}\n\n\n// ── Content Reports ─────────────────────────────────────────────────────────\n\nexport interface ContentReportCreate {\n  content_type: string;\n  content_id?: string | null;\n  content_title?: string | null;\n  report_type: string;\n  description: string;\n  page_url?: string | null;\n}\n\nexport interface ContentReport {\n  id: string;\n  content_type: string;\n  content_id: string | null;\n  content_title: string | null;\n  report_type: string;\n  description: string;\n  status: string;\n  admin_notes: string | null;\n  page_url: string | null;\n  created_at: string;\n  resolved_at: string | null;\n}\n\nexport interface ContentReportListResponse {\n  items: ContentReport[];\n  total: number;\n  offset: number;\n  limit: number;\n}\n\nexport async function submitReport(\n  body: ContentReportCreate,\n): Promise<ContentReport> {\n  return request<ContentReport>(`${BASE}/reports`, {\n    method: \"POST\",\n    body: JSON.stringify(body),\n  });\n}\n\nexport async function fetchReports(params: {\n  status?: string;\n  content_type?: string;\n  offset?: number;\n  limit?: number;\n} = {}): Promise<ContentReportListResponse> {\n  const qs = new URLSearchParams();\n  if (params.status) qs.set(\"status\", params.status);\n  if (params.content_type) qs.set(\"content_type\", params.content_type);\n  if (params.offset !== undefined) qs.set(\"offset\", String(params.offset));\n  if (params.limit !== undefined) qs.set(\"limit\", String(params.limit));\n  const query = qs.toString();\n  return request<ContentReportListResponse>(\n    `${BASE}/admin/reports${query ? `?${query}` : \"\"}`,\n  );\n}\n\nexport async function updateReport(\n  id: string,\n  body: { status?: string; admin_notes?: string },\n): Promise<ContentReport> {\n  return request<ContentReport>(`${BASE}/admin/reports/${id}`, {\n    method: \"PATCH\",\n    body: JSON.stringify(body),\n  });\n}\n\n\n// ── Pipeline Admin ──────────────────────────────────────────────────────────\n\nexport interface PipelineVideoItem {\n  id: string;\n  filename: string;\n  processing_status: string;\n  creator_name: string;\n  created_at: string | null;\n  updated_at: string | null;\n  event_count: number;\n  total_tokens_used: number;\n  last_event_at: string | null;\n}\n\nexport interface PipelineVideoListResponse {\n  items: PipelineVideoItem[];\n  total: number;\n}\n\nexport interface PipelineEvent {\n  id: string;\n  video_id: string;\n  stage: string;\n  event_type: string;\n  prompt_tokens: number | null;\n  completion_tokens: number | null;\n  total_tokens: number | null;\n  model: string | null;\n  duration_ms: number | null;\n  payload: Record<string, unknown> | null;\n  system_prompt_text: string | null;\n  user_prompt_text: string | null;\n  response_text: string | null;\n  created_at: string | null;\n}\n\nexport interface PipelineEventListResponse {\n  items: PipelineEvent[];\n  total: number;\n  offset: number;\n  limit: number;\n}\n\nexport interface WorkerTask {\n  id: string;\n  name: string;\n  args: unknown[];\n  time_start: number | null;\n}\n\nexport interface WorkerInfo {\n  name: string;\n  active_tasks: WorkerTask[];\n  reserved_tasks: number;\n  total_completed: number;\n  uptime: string | null;\n  pool_size: number | null;\n}\n\nexport interface WorkerStatusResponse {\n  online: boolean;\n  workers: WorkerInfo[];\n  error?: string;\n}\n\nexport interface TriggerResponse {\n  status: string;\n  video_id: string;\n  current_processing_status?: string;\n}\n\nexport interface RevokeResponse {\n  status: string;\n  video_id: string;\n  tasks_revoked: number;\n}\n\nexport async function fetchPipelineVideos(): Promise<PipelineVideoListResponse> {\n  return request<PipelineVideoListResponse>(`${BASE}/admin/pipeline/videos`);\n}\n\nexport async function fetchPipelineEvents(\n  videoId: string,\n  params: { offset?: number; limit?: number; stage?: string; event_type?: string; order?: \"asc\" | \"desc\" } = {},\n): Promise<PipelineEventListResponse> {\n  const qs = new URLSearchParams();\n  if (params.offset !== undefined) qs.set(\"offset\", String(params.offset));\n  if (params.limit !== undefined) qs.set(\"limit\", String(params.limit));\n  if (params.stage) qs.set(\"stage\", params.stage);\n  if (params.event_type) qs.set(\"event_type\", params.event_type);\n  if (params.order) qs.set(\"order\", params.order);\n  const query = qs.toString();\n  return request<PipelineEventListResponse>(\n    `${BASE}/admin/pipeline/events/${videoId}${query ? `?${query}` : \"\"}`,\n  );\n}\n\nexport async function fetchWorkerStatus(): Promise<WorkerStatusResponse> {\n  return request<WorkerStatusResponse>(`${BASE}/admin/pipeline/worker-status`);\n}\n\nexport async function triggerPipeline(videoId: string): Promise<TriggerResponse> {\n  return request<TriggerResponse>(`${BASE}/admin/pipeline/trigger/${videoId}`, {\n    method: \"POST\",\n  });\n}\n\nexport async function revokePipeline(videoId: string): Promise<RevokeResponse> {\n  return request<RevokeResponse>(`${BASE}/admin/pipeline/revoke/${videoId}`, {\n    method: \"POST\",\n  });\n}\n\n// ── Debug Mode ──────────────────────────────────────────────────────────────\n\nexport interface DebugModeResponse {\n  debug_mode: boolean;\n}\n\nexport async function fetchDebugMode(): Promise<DebugModeResponse> {\n  return request<DebugModeResponse>(`${BASE}/admin/pipeline/debug-mode`);\n}\n\nexport async function setDebugMode(enabled: boolean): Promise<DebugModeResponse> {\n  return request<DebugModeResponse>(`${BASE}/admin/pipeline/debug-mode`, {\n    method: \"PUT\",\n    body: JSON.stringify({ debug_mode: enabled }),\n  });\n}\n"}],"isError":false,"timestamp":1774938516725}}
{"type":"message","id":"a72814c1","parentId":"22c74c1e","timestamp":"2026-03-31T06:28:36.735Z","message":{"role":"toolResult","toolCallId":"toolu_01NVtA4fYyqsoN4PhyuYspj9","toolName":"bash","content":[{"type":"text","text":"find: ‘frontend/src/styles’: No such file or directory\n"}],"isError":false,"timestamp":1774938516732}}
{"type":"message","id":"5f6d4e56","parentId":"a72814c1","timestamp":"2026-03-31T06:28:41.615Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01QV3taoRZsGZF998ARc9krV","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find frontend/src -name \"*.css\" | head -10; rg -n \"typeahead\\|search-container\\|search-form\\|search-input\" frontend/src/ --include \"*.css\" 2>/dev/null || rg -rn \"typeahead\\|search-container\\|search-form\\|search-input\" frontend/src/ -g \"*.css\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":159,"cacheRead":75303,"cacheWrite":4511,"totalTokens":79974,"cost":{"input":0.000005,"output":0.003975,"cacheRead":0.0376515,"cacheWrite":0.02819375,"total":0.06982524999999999}},"stopReason":"toolUse","timestamp":1774938516733}}
{"type":"message","id":"80ffe740","parentId":"5f6d4e56","timestamp":"2026-03-31T06:28:41.642Z","message":{"role":"toolResult","toolCallId":"toolu_01QV3taoRZsGZF998ARc9krV","toolName":"bash","content":[{"type":"text","text":"frontend/src/App.css\n\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1774938521639}}
{"type":"message","id":"67f8f962","parentId":"80ffe740","timestamp":"2026-03-31T06:28:44.233Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01AKznRqSGjHH9HBbP2X5qZA","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"typeahead\\|search-container\\|search-form\\|search-input\" frontend/src/App.css"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":99,"cacheRead":79814,"cacheWrite":194,"totalTokens":80108,"cost":{"input":0.000005,"output":0.002475,"cacheRead":0.039907,"cacheWrite":0.0012125,"total":0.0435995}},"stopReason":"toolUse","timestamp":1774938521640}}
{"type":"message","id":"159538fb","parentId":"67f8f962","timestamp":"2026-03-31T06:28:44.250Z","message":{"role":"toolResult","toolCallId":"toolu_01AKznRqSGjHH9HBbP2X5qZA","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1774938524248}}
{"type":"message","id":"5384a98c","parentId":"159538fb","timestamp":"2026-03-31T06:28:47.114Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01LMh4SF68NW5MNHeVmYYT5g","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -rn \"typeahead\\|search-container\\|search-form\\|search-input\" frontend/"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":95,"cacheRead":80008,"cacheWrite":130,"totalTokens":80234,"cost":{"input":0.000005,"output":0.002375,"cacheRead":0.040004,"cacheWrite":0.0008125000000000001,"total":0.0431965}},"stopReason":"toolUse","timestamp":1774938524249}}
{"type":"message","id":"28b77c81","parentId":"5384a98c","timestamp":"2026-03-31T06:28:47.133Z","message":{"role":"toolResult","toolCallId":"toolu_01LMh4SF68NW5MNHeVmYYT5g","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1774938527131}}
{"type":"message","id":"f06b0a2f","parentId":"28b77c81","timestamp":"2026-03-31T06:28:50.191Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01YB5juhopMPmsBPpPuj5JRf","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -rn \"typeahead\\|search-container\\|search-form\\|search-input\\|\\.search\" frontend/ -g \"*.css\" | head -40"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":109,"cacheRead":80138,"cacheWrite":126,"totalTokens":80374,"cost":{"input":0.000005,"output":0.002725,"cacheRead":0.040069,"cacheWrite":0.0007875,"total":0.0435865}},"stopReason":"toolUse","timestamp":1774938527132}}
{"type":"message","id":"b2fbc781","parentId":"f06b0a2f","timestamp":"2026-03-31T06:28:50.209Z","message":{"role":"toolResult","toolCallId":"toolu_01YB5juhopMPmsBPpPuj5JRf","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774938530207}}
{"type":"message","id":"0b2aec72","parentId":"b2fbc781","timestamp":"2026-03-31T06:28:53.340Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01CsS1uW2XvZSywFVCqU1672","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find frontend -name \"*.css\" -o -name \"*.scss\" | head -20; ls frontend/src/"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":94,"cacheRead":80264,"cacheWrite":123,"totalTokens":80482,"cost":{"input":0.000005,"output":0.00235,"cacheRead":0.040132,"cacheWrite":0.00076875,"total":0.04325575}},"stopReason":"toolUse","timestamp":1774938530208}}
{"type":"message","id":"2170e56f","parentId":"0b2aec72","timestamp":"2026-03-31T06:28:53.372Z","message":{"role":"toolResult","toolCallId":"toolu_01CsS1uW2XvZSywFVCqU1672","toolName":"bash","content":[{"type":"text","text":"frontend/dist/assets/index-D2zBUDi3.css\nfrontend/src/App.css\napi\nApp.css\nApp.tsx\ncomponents\nmain.tsx\npages\nutils\nvite-env.d.ts\n"}],"isError":false,"timestamp":1774938533370}}
{"type":"message","id":"75d777b2","parentId":"2170e56f","timestamp":"2026-03-31T06:28:58.050Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01MQ6BWv8pNN1hYppmQRaJof","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && wc -l frontend/src/App.css && rg -n \"typeahead\\|autocomplete\\|search\" frontend/src/App.css | head -30"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":107,"cacheRead":80387,"cacheWrite":162,"totalTokens":80657,"cost":{"input":0.000005,"output":0.0026750000000000003,"cacheRead":0.0401935,"cacheWrite":0.0010125,"total":0.043886}},"stopReason":"toolUse","timestamp":1774938533371}}
{"type":"message","id":"dcdfa125","parentId":"75d777b2","timestamp":"2026-03-31T06:28:58.071Z","message":{"role":"toolResult","toolCallId":"toolu_01MQ6BWv8pNN1hYppmQRaJof","toolName":"bash","content":[{"type":"text","text":"3753 frontend/src/App.css\n"}],"isError":false,"timestamp":1774938538068}}
{"type":"message","id":"d9cbb942","parentId":"dcdfa125","timestamp":"2026-03-31T06:29:02.283Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01NP8SEVZWRZ7kyFmD1aWz8n","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"typeahead\\|autocomplete\\|search-\\|\\.search\" frontend/src/App.css | head -40"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":100,"cacheRead":80549,"cacheWrite":130,"totalTokens":80780,"cost":{"input":0.000005,"output":0.0025,"cacheRead":0.0402745,"cacheWrite":0.0008125000000000001,"total":0.043592}},"stopReason":"toolUse","timestamp":1774938538069}}
{"type":"message","id":"feb25e61","parentId":"d9cbb942","timestamp":"2026-03-31T06:29:02.301Z","message":{"role":"toolResult","toolCallId":"toolu_01NP8SEVZWRZ7kyFmD1aWz8n","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774938542299}}
{"type":"message","id":"f24e985e","parentId":"feb25e61","timestamp":"2026-03-31T06:29:05.563Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01C3GGQEvNCDcZrhzFQDiRk6","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"search\" frontend/src/App.css | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":86,"cacheRead":80679,"cacheWrite":114,"totalTokens":80880,"cost":{"input":0.000005,"output":0.00215,"cacheRead":0.0403395,"cacheWrite":0.0007125,"total":0.043206999999999995}},"stopReason":"toolUse","timestamp":1774938542299}}
{"type":"message","id":"9b1616ee","parentId":"f24e985e","timestamp":"2026-03-31T06:29:05.582Z","message":{"role":"toolResult","toolCallId":"toolu_01C3GGQEvNCDcZrhzFQDiRk6","toolName":"bash","content":[{"type":"text","text":"127:  --color-btn-search-bg: #22d3ee;\n128:  --color-btn-search-text: #0f0f14;\n129:  --color-btn-search-hover-bg: #67e8f9;\n1002:.search-container {\n1008:.search-form {\n1013:.search-form--hero {\n1017:.search-form--inline {\n1021:.search-input {\n1033:.search-input:focus {\n1039:.search-input--hero {\n1045:.btn--search {\n1046:  background: var(--color-btn-search-bg);\n1047:  color: var(--color-btn-search-text);\n1048:  border-color: var(--color-btn-search-bg);\n1054:.btn--search:hover {\n1055:  background: var(--color-btn-search-hover-bg);\n1312:.search-results-page {\n1316:.search-group {\n1320:.search-group__title {\n1327:.search-group__list {\n"}],"isError":false,"timestamp":1774938545579}}
{"type":"message","id":"bd5730f8","parentId":"9b1616ee","timestamp":"2026-03-31T06:29:09.817Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01WeNLPcFbUkxuh2tz2crvYz","name":"read","arguments":{"limit":100,"offset":1002,"path":"frontend/src/App.css"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":80793,"cacheWrite":355,"totalTokens":81241,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.040396499999999995,"cacheWrite":0.0022187500000000002,"total":0.044920249999999995}},"stopReason":"toolUse","timestamp":1774938545580}}
{"type":"message","id":"31a34d70","parentId":"bd5730f8","timestamp":"2026-03-31T06:29:09.824Z","message":{"role":"toolResult","toolCallId":"toolu_01WeNLPcFbUkxuh2tz2crvYz","toolName":"read","content":[{"type":"text","text":".search-container {\n  position: relative;\n  max-width: 36rem;\n  margin: 0 auto;\n}\n\n.search-form {\n  display: flex;\n  gap: 0.5rem;\n}\n\n.search-form--hero {\n  justify-content: center;\n}\n\n.search-form--inline {\n  margin-bottom: 1.25rem;\n}\n\n.search-input {\n  flex: 1;\n  padding: 0.625rem 1rem;\n  border: 1px solid var(--color-border);\n  border-radius: 0.5rem;\n  font-size: 0.9375rem;\n  font-family: inherit;\n  color: var(--color-text-primary);\n  background: var(--color-bg-input);\n  transition: border-color 0.15s, box-shadow 0.15s;\n}\n\n.search-input:focus {\n  outline: none;\n  border-color: var(--color-accent);\n  box-shadow: 0 0 0 3px var(--color-accent-focus);\n}\n\n.search-input--hero {\n  padding: 0.75rem 1.25rem;\n  font-size: 1.0625rem;\n  border-radius: 0.625rem;\n}\n\n.btn--search {\n  background: var(--color-btn-search-bg);\n  color: var(--color-btn-search-text);\n  border-color: var(--color-btn-search-bg);\n  border-radius: 0.5rem;\n  padding: 0.625rem 1.25rem;\n  font-weight: 600;\n}\n\n.btn--search:hover {\n  background: var(--color-btn-search-hover-bg);\n}\n\n/* ── Typeahead dropdown ───────────────────────────────────────────────────── */\n\n.typeahead-dropdown {\n  position: absolute;\n  top: calc(100% + 0.25rem);\n  left: 0;\n  right: 0;\n  background: var(--color-bg-surface);\n  border: 1px solid var(--color-border);\n  border-radius: 0.5rem;\n  box-shadow: 0 8px 24px var(--color-shadow-heavy);\n  z-index: 50;\n  overflow: hidden;\n}\n\n.typeahead-item {\n  display: flex;\n  justify-content: space-between;\n  align-items: center;\n  padding: 0.625rem 1rem;\n  text-decoration: none;\n  color: inherit;\n  transition: background 0.1s;\n}\n\n.typeahead-item:hover {\n  background: var(--color-bg-surface-hover);\n}\n\n.typeahead-item__title {\n  font-size: 0.875rem;\n  font-weight: 500;\n}\n\n.typeahead-item__meta {\n  display: flex;\n  align-items: center;\n  gap: 0.5rem;\n  font-size: 0.75rem;\n  color: var(--color-text-secondary);\n}\n\n.typeahead-item__type {\n  padding: 0.0625rem 0.375rem;\n\n[2653 more lines in file. Use offset=1102 to continue.]"}],"isError":false,"timestamp":1774938549821}}
{"type":"message","id":"578978ae","parentId":"31a34d70","timestamp":"2026-03-31T06:29:14.333Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — typeahead CSS already exists. Now let me check what the slice specifically asks for: \"Typing in search shows autocomplete suggestions. Empty search box shows popular search terms.\"\n\nLet me check what already exists vs what's needed:"},{"type":"toolCall","id":"toolu_01HTbLVvmb5ps9CV7eEWy4Up","name":"read","arguments":{"limit":40,"offset":1100,"path":"frontend/src/App.css"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":143,"cacheRead":81148,"cacheWrite":930,"totalTokens":82222,"cost":{"input":0.000005,"output":0.003575,"cacheRead":0.040574,"cacheWrite":0.0058125,"total":0.0499665}},"stopReason":"toolUse","timestamp":1774938549822}}
{"type":"message","id":"b5e66621","parentId":"578978ae","timestamp":"2026-03-31T06:29:14.337Z","message":{"role":"toolResult","toolCallId":"toolu_01HTbLVvmb5ps9CV7eEWy4Up","toolName":"read","content":[{"type":"text","text":".typeahead-item__type {\n  padding: 0.0625rem 0.375rem;\n  border-radius: 0.25rem;\n  font-size: 0.6875rem;\n  font-weight: 600;\n  text-transform: uppercase;\n  letter-spacing: 0.02em;\n}\n\n.typeahead-item__type--technique_page {\n  background: var(--color-badge-type-technique-bg);\n  color: var(--color-badge-type-technique-text);\n}\n\n.typeahead-item__type--key_moment {\n  background: var(--color-badge-type-moment-bg);\n  color: var(--color-badge-type-moment-text);\n}\n\n.typeahead-see-all {\n  display: block;\n  padding: 0.5rem 1rem;\n  text-align: center;\n  font-size: 0.8125rem;\n  color: var(--color-typeahead-see-all);\n  text-decoration: none;\n  border-top: 1px solid var(--color-border);\n  transition: background 0.1s;\n}\n\n.typeahead-see-all:hover {\n  background: var(--color-bg-surface-hover);\n}\n\n/* ── Navigation cards ─────────────────────────────────────────────────────── */\n\n.nav-cards {\n  display: grid;\n  grid-template-columns: repeat(2, 1fr);\n  gap: 1rem;\n\n[2615 more lines in file. Use offset=1140 to continue.]"}],"isError":false,"timestamp":1774938554335}}
{"type":"message","id":"e3ec5a8e","parentId":"b5e66621","timestamp":"2026-03-31T06:29:28.573Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I have a clear picture. Let me assess what exists and what's needed:\n\n**Already exists:**\n1. Home.tsx has debounced typeahead (300ms, 2+ chars, top 5 results) with dropdown\n2. Typeahead dropdown CSS is complete\n3. Search backend with semantic + keyword fallback\n4. Popular topics section on home page (derived from topic counts)\n5. SearchResults.tsx has inline search bar with debounced navigation\n\n**What the slice asks for:**\n1. \"Typing in search shows autocomplete suggestions\" — ✅ Already done in Home.tsx\n2. \"Empty search box shows popular search terms\" — Partially done (popular topics section exists below hero, but NOT in the typeahead dropdown when focused with empty input)\n\n\nThe real gap is showing popular searches in the dropdown when the input is focused but empty, and possibly extending that same pattern to the SearchResults page's search bar. I should check whether we already have a backend endpoint for suggestions or if I need to create one.","thinkingSignature":"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"},{"type":"text","text":"Now I have a clear picture. The existing typeahead only shows results when 2+ chars are typed. The gap is: when the input is focused but empty, show popular/suggested terms in the dropdown. Let me check if there's any backend for suggestions."},{"type":"toolCall","id":"toolu_01DgKCxhTPnLrsCSA6HCBYSn","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -rn \"suggest\\|popular\\|trending\\|recent_search\" backend/ -g \"*.py\" | grep -v __pycache__ | head -20"}},{"type":"toolCall","id":"toolu_01DbaKtzJDzmCb3VyBJPG2xY","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"prefix_search\\|autocomplete\\|startswith\\|starts_with\" backend/ -g \"*.py\" | grep -v __pycache__ | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":557,"cacheRead":82078,"cacheWrite":547,"totalTokens":83183,"cost":{"input":0.000005,"output":0.013925,"cacheRead":0.041039,"cacheWrite":0.0034187500000000003,"total":0.058387749999999995}},"stopReason":"toolUse","timestamp":1774938554335}}
{"type":"message","id":"0e14a1ae","parentId":"e3ec5a8e","timestamp":"2026-03-31T06:29:28.595Z","message":{"role":"toolResult","toolCallId":"toolu_01DgKCxhTPnLrsCSA6HCBYSn","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774938568595}}
{"type":"message","id":"09c203dc","parentId":"0e14a1ae","timestamp":"2026-03-31T06:29:28.601Z","message":{"role":"toolResult","toolCallId":"toolu_01DbaKtzJDzmCb3VyBJPG2xY","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774938568598}}
{"type":"message","id":"739f9b0a","parentId":"09c203dc","timestamp":"2026-03-31T06:29:32.340Z","message":{"role":"assistant","content":[{"type":"text","text":"No backend suggestions endpoint exists. Let me check the existing models and the existing test files."},{"type":"toolCall","id":"toolu_01SZo4DRSefoetricrADrS2T","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -l \"test.*search\" tests/ -g \"*.py\" | head -5; ls tests/ 2>/dev/null | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":122,"cacheRead":82625,"cacheWrite":323,"totalTokens":83071,"cost":{"input":0.000005,"output":0.00305,"cacheRead":0.041312499999999995,"cacheWrite":0.00201875,"total":0.04638625}},"stopReason":"toolUse","timestamp":1774938568599}}
{"type":"message","id":"e07f6c99","parentId":"739f9b0a","timestamp":"2026-03-31T06:29:32.360Z","message":{"role":"toolResult","toolCallId":"toolu_01SZo4DRSefoetricrADrS2T","toolName":"bash","content":[{"type":"text","text":"fixtures\n"}],"isError":false,"timestamp":1774938572357}}
{"type":"message","id":"05ee5bd0","parentId":"e07f6c99","timestamp":"2026-03-31T06:29:35.707Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01XCJJpLJmZ4cRKPWmqYjARh","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find . -name \"test_search*\" -o -name \"*test*search*\" 2>/dev/null | grep -v __pycache__ | grep -v node_modules | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":114,"cacheRead":82948,"cacheWrite":136,"totalTokens":83199,"cost":{"input":0.000005,"output":0.00285,"cacheRead":0.041474,"cacheWrite":0.0008500000000000001,"total":0.045179}},"stopReason":"toolUse","timestamp":1774938572358}}
{"type":"message","id":"dac30137","parentId":"05ee5bd0","timestamp":"2026-03-31T06:29:35.755Z","message":{"role":"toolResult","toolCallId":"toolu_01XCJJpLJmZ4cRKPWmqYjARh","toolName":"bash","content":[{"type":"text","text":"./backend/tests/test_search.py\n"}],"isError":false,"timestamp":1774938575752}}
{"type":"message","id":"4ac27867","parentId":"dac30137","timestamp":"2026-03-31T06:29:38.633Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01HhjrVG3zYzCrLwekXZkagv","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && head -30 backend/tests/test_search.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":81,"cacheRead":83084,"cacheWrite":137,"totalTokens":83303,"cost":{"input":0.000005,"output":0.002025,"cacheRead":0.041541999999999996,"cacheWrite":0.00085625,"total":0.044428249999999996}},"stopReason":"toolUse","timestamp":1774938575753}}
{"type":"message","id":"f9cdd7e4","parentId":"4ac27867","timestamp":"2026-03-31T06:29:38.648Z","message":{"role":"toolResult","toolCallId":"toolu_01HhjrVG3zYzCrLwekXZkagv","toolName":"bash","content":[{"type":"text","text":"\"\"\"Integration tests for the /api/v1/search endpoint.\n\nTests run against a real PostgreSQL test database via httpx.AsyncClient.\nSearchService is mocked at the router dependency level so we can test\nendpoint behavior without requiring external embedding API or Qdrant.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport uuid\nfrom unittest.mock import AsyncMock, MagicMock, patch\n\nimport pytest\nimport pytest_asyncio\nfrom httpx import AsyncClient\nfrom sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker\n\nfrom models import (\n    ContentType,\n    Creator,\n    KeyMoment,\n    KeyMomentContentType,\n    ProcessingStatus,\n    SourceVideo,\n    TechniquePage,\n)\nfrom search_service import SearchService\n\nSEARCH_URL = \"/api/v1/search\"\n\n"}],"isError":false,"timestamp":1774938578645}}
{"type":"message","id":"68e96532","parentId":"f9cdd7e4","timestamp":"2026-03-31T06:29:41.946Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check what schemas exist for the search response."},{"type":"toolCall","id":"toolu_018Hk41sZjdk6uKzKwWNLBzT","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"SearchResponse\\|SearchResultItem\\|SearchSuggestion\" backend/schemas.py | head -15"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":110,"cacheRead":83221,"cacheWrite":289,"totalTokens":83621,"cost":{"input":0.000005,"output":0.0027500000000000003,"cacheRead":0.041610499999999995,"cacheWrite":0.0018062500000000001,"total":0.04617175}},"stopReason":"toolUse","timestamp":1774938578646}}
{"type":"message","id":"a7aec7d3","parentId":"68e96532","timestamp":"2026-03-31T06:29:41.964Z","message":{"role":"toolResult","toolCallId":"toolu_018Hk41sZjdk6uKzKwWNLBzT","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774938581961}}
{"type":"message","id":"4bd0d7d6","parentId":"a7aec7d3","timestamp":"2026-03-31T06:29:48.078Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01XygtE5oCW2ieiSR8B2NxNc","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -rn \"SearchResponse\\|SearchResultItem\" backend/ -g \"*.py\" | grep -v __pycache__ | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":103,"cacheRead":83510,"cacheWrite":124,"totalTokens":83738,"cost":{"input":0.000005,"output":0.002575,"cacheRead":0.041755,"cacheWrite":0.0007750000000000001,"total":0.04511}},"stopReason":"toolUse","timestamp":1774938581962}}
{"type":"message","id":"a9f1efe8","parentId":"4bd0d7d6","timestamp":"2026-03-31T06:29:48.095Z","message":{"role":"toolResult","toolCallId":"toolu_01XygtE5oCW2ieiSR8B2NxNc","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774938588093}}
{"type":"message","id":"51545d90","parentId":"a9f1efe8","timestamp":"2026-03-31T06:29:50.964Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_014AjMmxdwVCyJsna1ZRPW7k","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -rn \"class Search\" backend/ -g \"*.py\" | grep -v __pycache__"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":94,"cacheRead":83634,"cacheWrite":117,"totalTokens":83846,"cost":{"input":0.000005,"output":0.00235,"cacheRead":0.041817,"cacheWrite":0.00073125,"total":0.044903250000000006}},"stopReason":"toolUse","timestamp":1774938588094}}
{"type":"message","id":"6742dd0b","parentId":"51545d90","timestamp":"2026-03-31T06:29:50.980Z","message":{"role":"toolResult","toolCallId":"toolu_014AjMmxdwVCyJsna1ZRPW7k","toolName":"bash","content":[{"type":"text","text":"backend/search_service.py:nService:\nbackend/schemas.py:nResultItem(BaseModel):\nbackend/schemas.py:nResponse(BaseModel):\n"}],"isError":false,"timestamp":1774938590979}}
{"type":"message","id":"b9381d8f","parentId":"6742dd0b","timestamp":"2026-03-31T06:29:54.252Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01JXbznahTvRmj7vj2nYAbxY","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"class Search\" backend/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":81,"cacheRead":83634,"cacheWrite":262,"totalTokens":83978,"cost":{"input":0.000005,"output":0.002025,"cacheRead":0.041817,"cacheWrite":0.0016375,"total":0.0454845}},"stopReason":"toolUse","timestamp":1774938590979}}
{"type":"message","id":"21e1effa","parentId":"b9381d8f","timestamp":"2026-03-31T06:29:54.270Z","message":{"role":"toolResult","toolCallId":"toolu_01JXbznahTvRmj7vj2nYAbxY","toolName":"bash","content":[{"type":"text","text":"204:class SearchResultItem(BaseModel):\n218:class SearchResponse(BaseModel):\n"}],"isError":false,"timestamp":1774938594267}}
{"type":"message","id":"7ca47c24","parentId":"21e1effa","timestamp":"2026-03-31T06:29:57.272Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01ERomNcscR89GTQeYMEjM7S","name":"read","arguments":{"limit":25,"offset":204,"path":"backend/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":89,"cacheRead":83751,"cacheWrite":259,"totalTokens":84100,"cost":{"input":0.000005,"output":0.002225,"cacheRead":0.041875499999999996,"cacheWrite":0.0016187500000000002,"total":0.04572425}},"stopReason":"toolUse","timestamp":1774938594268}}
{"type":"message","id":"d4b6139c","parentId":"7ca47c24","timestamp":"2026-03-31T06:29:57.278Z","message":{"role":"toolResult","toolCallId":"toolu_01ERomNcscR89GTQeYMEjM7S","toolName":"read","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\n\nclass SearchResponse(BaseModel):\n    \"\"\"Top-level search response with metadata.\"\"\"\n    items: list[SearchResultItem] = Field(default_factory=list)\n    total: int = 0\n    query: str = \"\"\n    fallback_used: bool = False\n\n\n# ── Technique Page Detail ────────────────────────────────────────────────────\n\nclass KeyMomentSummary(BaseModel):\n\n[181 more lines in file. Use offset=229 to continue.]"}],"isError":false,"timestamp":1774938597275}}
{"type":"message","id":"459a03b4","parentId":"d4b6139c","timestamp":"2026-03-31T06:30:03.404Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check models to see what data we can use for suggestions."},{"type":"toolCall","id":"toolu_01Ks9ET8r5e3exFNXur32wQs","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"class TechniquePage\\|class Creator\\|class KeyMoment\\|view_count\\|topic_tags\\|topic_category\" backend/models.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":130,"cacheRead":84010,"cacheWrite":325,"totalTokens":84466,"cost":{"input":0.000005,"output":0.0032500000000000003,"cacheRead":0.042005,"cacheWrite":0.00203125,"total":0.04729125}},"stopReason":"toolUse","timestamp":1774938597276}}
{"type":"message","id":"d0cd67ee","parentId":"459a03b4","timestamp":"2026-03-31T06:30:03.417Z","message":{"role":"toolResult","toolCallId":"toolu_01Ks9ET8r5e3exFNXur32wQs","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774938603415}}
{"type":"message","id":"1fc3dbde","parentId":"d0cd67ee","timestamp":"2026-03-31T06:30:08.843Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_011hWW315uz2Urnmt6fHY9xG","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"class TechniquePage\\|class Creator\\|class KeyMoment\\|view_count\\|topic_tags\\|topic_category\\b\" backend/models/ -g \"*.py\" 2>/dev/null; rg -rn \"class TechniquePage\\|view_count\\|topic_category\" backend/ -g \"*.py\" | grep -v __pycache__ | head -15"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":167,"cacheRead":84335,"cacheWrite":144,"totalTokens":84647,"cost":{"input":0.000005,"output":0.004175,"cacheRead":0.0421675,"cacheWrite":0.0009000000000000001,"total":0.0472475}},"stopReason":"toolUse","timestamp":1774938603416}}
{"type":"message","id":"7529fc1e","parentId":"1fc3dbde","timestamp":"2026-03-31T06:30:08.871Z","message":{"role":"toolResult","toolCallId":"toolu_011hWW315uz2Urnmt6fHY9xG","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774938608868}}
{"type":"message","id":"1151fee1","parentId":"7529fc1e","timestamp":"2026-03-31T06:30:12.477Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01JSQgapXWVQRq1UeVsS8mLy","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find backend -name \"models*\" | head -5; rg -l \"TechniquePage\" backend/ --include \"*.py\" 2>/dev/null || rg -l \"TechniquePage\" backend/ -g \"*.py\" | grep -v __pycache__"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":133,"cacheRead":84479,"cacheWrite":181,"totalTokens":84794,"cost":{"input":0.000005,"output":0.0033250000000000003,"cacheRead":0.0422395,"cacheWrite":0.00113125,"total":0.04670075}},"stopReason":"toolUse","timestamp":1774938608870}}
{"type":"message","id":"3f91f261","parentId":"1151fee1","timestamp":"2026-03-31T06:30:12.499Z","message":{"role":"toolResult","toolCallId":"toolu_01JSQgapXWVQRq1UeVsS8mLy","toolName":"bash","content":[{"type":"text","text":"backend/models.py\nbackend/__pycache__/models.cpython-312.pyc\nbackend/search_service.py\nbackend/schemas.py\nbackend/routers/topics.py\nbackend/routers/techniques.py\nbackend/routers/creators.py\nbackend/tests/test_public_api.py\nbackend/tests/test_search.py\nbackend/models.py\nbackend/tests/test_pipeline.py\nbackend/pipeline/stages.py\n"}],"isError":false,"timestamp":1774938612497}}
{"type":"message","id":"17e5c710","parentId":"3f91f261","timestamp":"2026-03-31T06:30:15.423Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01KL1tMWUvav6Cd2bho5RisA","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"class TechniquePage\\|view_count\\|topic_tags\\|topic_category\" backend/models.py | head -15"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":103,"cacheRead":84660,"cacheWrite":255,"totalTokens":85019,"cost":{"input":0.000005,"output":0.002575,"cacheRead":0.04233,"cacheWrite":0.0015937500000000001,"total":0.046503749999999996}},"stopReason":"toolUse","timestamp":1774938612498}}
{"type":"message","id":"b5053ccf","parentId":"17e5c710","timestamp":"2026-03-31T06:30:15.441Z","message":{"role":"toolResult","toolCallId":"toolu_01KL1tMWUvav6Cd2bho5RisA","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774938615439}}
{"type":"message","id":"15214bf9","parentId":"b5053ccf","timestamp":"2026-03-31T06:30:18.333Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_014Xk7JvHDGkD2eG7aSnWndU","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && grep -n \"class \\|view_count\\|topic_tags\\|topic_category\" backend/models.py | head -30"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":98,"cacheRead":84915,"cacheWrite":117,"totalTokens":85131,"cost":{"input":0.000005,"output":0.00245,"cacheRead":0.042457499999999995,"cacheWrite":0.00073125,"total":0.04564375}},"stopReason":"toolUse","timestamp":1774938615440}}
{"type":"message","id":"1d311f04","parentId":"15214bf9","timestamp":"2026-03-31T06:30:18.343Z","message":{"role":"toolResult","toolCallId":"toolu_014Xk7JvHDGkD2eG7aSnWndU","toolName":"bash","content":[{"type":"text","text":"33:class ContentType(str, enum.Enum):\n41:class ProcessingStatus(str, enum.Enum):\n54:class KeyMomentContentType(str, enum.Enum):\n62:class SourceQuality(str, enum.Enum):\n69:class RelationshipType(str, enum.Enum):\n98:class Creator(Base):\n106:    view_count: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\n120:class SourceVideo(Base):\n154:class TranscriptSegment(Base):\n171:class KeyMoment(Base):\n205:class TechniquePage(Base):\n214:    topic_category: Mapped[str] = mapped_column(String(255), nullable=False)\n215:    topic_tags: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n224:    view_count: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\n248:class RelatedTechniqueLink(Base):\n275:class TechniquePageVersion(Base):\n296:class Tag(Base):\n307:class ReportType(str, enum.Enum):\n316:class ReportStatus(str, enum.Enum):\n326:class ContentReport(Base):\n366:class PipelineEvent(Base):\n"}],"isError":false,"timestamp":1774938618341}}
{"type":"message","id":"8c90e0bd","parentId":"1d311f04","timestamp":"2026-03-31T06:30:22.256Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. Now I have a full picture. Let me check the SearchResults page for any autocomplete there too:"},{"type":"toolCall","id":"toolu_01642kYv734knBQaXgp38icp","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"onFocus\\|showDropdown\\|popular\\|suggestion\" frontend/src/pages/SearchResults.tsx"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":122,"cacheRead":85032,"cacheWrite":444,"totalTokens":85599,"cost":{"input":0.000005,"output":0.00305,"cacheRead":0.042516,"cacheWrite":0.002775,"total":0.048346}},"stopReason":"toolUse","timestamp":1774938618342}}
{"type":"message","id":"2b2615fe","parentId":"8c90e0bd","timestamp":"2026-03-31T06:30:22.274Z","message":{"role":"toolResult","toolCallId":"toolu_01642kYv734knBQaXgp38icp","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1774938622271}}
{"type":"message","id":"26cf8884","parentId":"2b2615fe","timestamp":"2026-03-31T06:31:01.826Z","message":{"role":"assistant","content":[{"type":"text","text":"SearchResults page has no autocomplete dropdown at all — just a text input that updates URL params. Now I have everything I need to write the research."},{"type":"toolCall","id":"toolu_01KcmP7BAMAdjXMNZCXHYfrn","name":"gsd_summary_save","arguments":{"artifact_type":"RESEARCH","content":"# S04 Research — Search Autocomplete & Suggestions\n\n## Summary\n\nThis is **light research**. The codebase already has a working typeahead dropdown on Home.tsx (debounced 300ms, shows top 5 results after 2+ chars). The slice adds two features: (1) show popular/suggested terms when the empty search box is focused, and (2) bring the same autocomplete experience to the SearchResults page. All infrastructure exists — this is UI wiring plus one lightweight backend endpoint.\n\n## Requirement Coverage\n\n- **R005 (Search-First Web UI)** — already validated. This slice polishes the search interaction.\n- **R015 (30-Second Retrieval Target)** — active. Autocomplete and popular suggestions directly reduce time-to-first-result.\n\n## Recommendation\n\nBuild a lightweight `/api/v1/search/suggestions` endpoint that returns popular search terms (derived from technique page titles, topic tags, and sub-topic names ranked by technique count). Frontend changes: extract the typeahead dropdown into a shared component, add popular-suggestions-on-focus to both Home.tsx and SearchResults.tsx.\n\n## Implementation Landscape\n\n### What Exists\n\n| Component | File | State |\n|---|---|---|\n| Search API endpoint | `backend/routers/search.py` | Working — `GET /search?q=&scope=&limit=` |\n| Search service (semantic + keyword) | `backend/search_service.py` | Working — embed → Qdrant → keyword fallback |\n| Search schemas | `backend/schemas.py` lines 204-224 | `SearchResultItem`, `SearchResponse` |\n| API client | `frontend/src/api/public-client.ts` | `searchApi()` function |\n| Home page typeahead | `frontend/src/pages/Home.tsx` | Debounced 300ms, 5 results after 2+ chars, dropdown with outside-click dismiss |\n| Popular topics on home | `frontend/src/pages/Home.tsx` | Already loads top-8 sub-topics by technique_count — displayed as pills |\n| SearchResults page | `frontend/src/pages/SearchResults.tsx` | Has search input but NO typeahead dropdown, no suggestions |\n| Typeahead CSS | `frontend/src/App.css` lines 1060-1135 | `.typeahead-dropdown`, `.typeahead-item`, `.typeahead-see-all` all styled |\n| Tests | `backend/tests/test_search.py` | Integration tests for search endpoint with mocked SearchService |\n\n### What's Missing\n\n1. **No suggestions endpoint** — nothing returns popular terms for an empty search box\n2. **No shared typeahead component** — Home.tsx has the dropdown inline, SearchResults.tsx lacks it entirely\n3. **No empty-focus behavior** — when input is focused but empty, dropdown doesn't appear\n\n### Backend: Suggestions Endpoint\n\nA new `GET /api/v1/search/suggestions` endpoint that returns a list of suggested search terms. These can be derived from:\n- Top technique page titles (by view_count or just most recent)\n- Popular sub-topic names (by technique_count — already computed in topics router)\n- Creator names\n\nNo Redis caching needed for now — a simple DB query hitting TechniquePage and the canonical tags YAML is fast enough. The response is a small list (8-12 items) that changes rarely.\n\nSchema: `{ suggestions: [{ text: string, type: \"topic\" | \"technique\" | \"creator\" }] }`\n\n### Frontend: Shared Autocomplete Component\n\nExtract the typeahead logic from Home.tsx into a reusable `SearchAutocomplete` component:\n- Props: `onSearch(query)`, `placeholder`, `heroSize?: boolean`\n- State: query, suggestions (search results), popularTerms, showDropdown\n- Behavior:\n  - **Focus + empty query** → show popular suggestions (fetched once on mount)\n  - **2+ chars** → show search results (existing debounced behavior)\n  - **Escape** → close\n  - **Outside click** → close\n  - **Enter** → navigate to `/search?q=...`\n  - **Arrow keys** → keyboard navigation through dropdown items (nice-to-have)\n\nThen use it in both `Home.tsx` and `SearchResults.tsx`.\n\n### CSS\n\nExisting typeahead CSS covers the dropdown styling. May need:\n- A `.typeahead-suggestions` section for popular terms (slightly different styling — maybe a header label \"Popular\" or \"Try searching for...\")\n- Keyboard-selected state (`.typeahead-item--active`) if arrow key nav is added\n\n### Natural Seams for Task Decomposition\n\n1. **Backend: suggestions endpoint** — new route in `backend/routers/search.py`, new schema in `schemas.py`, integration test. Independent of frontend.\n2. **Frontend: extract SearchAutocomplete component** — pull typeahead out of Home.tsx into `components/SearchAutocomplete.tsx`, wire it into both Home.tsx and SearchResults.tsx, add popular-suggestions-on-focus. This depends on task 1 (needs the API client function for suggestions).\n3. **Verification** — build check, manual browser verification on running app.\n\nTasks 1 and 2 are the core work. Task 2 is larger (component extraction + two page integrations + CSS additions).\n\n### Risks\n\n- **Low risk.** All patterns established. The typeahead UI exists — we're extracting it and adding an empty-state feature.\n- The only non-trivial part is making arrow-key navigation work cleanly in the dropdown. If it adds too much complexity, ship without it — mouse/touch works fine.\n\n### Verification Strategy\n\n- `npm run build` passes with zero TypeScript errors\n- Backend tests pass (existing + new suggestions endpoint test)\n- Browser: focus empty search on Home → popular suggestions appear in dropdown\n- Browser: type 2+ chars → search results appear\n- Browser: SearchResults page has the same autocomplete behavior\n","milestone_id":"M010","slice_id":"S04"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":1545,"cacheRead":85476,"cacheWrite":153,"totalTokens":87175,"cost":{"input":0.000005,"output":0.038625,"cacheRead":0.042738,"cacheWrite":0.0009562500000000001,"total":0.08232425}},"stopReason":"toolUse","timestamp":1774938622272}}
{"type":"message","id":"66ef5a2d","parentId":"26cf8884","timestamp":"2026-03-31T06:31:01.831Z","message":{"role":"toolResult","toolCallId":"toolu_01KcmP7BAMAdjXMNZCXHYfrn","toolName":"gsd_summary_save","content":[{"type":"text","text":"Saved RESEARCH artifact to milestones/M010/slices/S04/S04-RESEARCH.md"}],"details":{"operation":"save_summary","path":"milestones/M010/slices/S04/S04-RESEARCH.md","artifact_type":"RESEARCH"},"isError":false,"timestamp":1774938661829}}
{"type":"message","id":"8d681169","parentId":"66ef5a2d","timestamp":"2026-03-31T06:31:04.407Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S04 researched."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":11,"cacheRead":85629,"cacheWrite":1583,"totalTokens":87224,"cost":{"input":0.000005,"output":0.000275,"cacheRead":0.0428145,"cacheWrite":0.00989375,"total":0.05298825}},"stopReason":"stop","timestamp":1774938661830}}
