> Latest Results KPI tile now uses a dedicated responsive grid class: mobile defaults to 1 column and md+ forces 4 columns; coachmark/pulse behavior removed from PatientSummaryTile and related CSS.
Reviewed Ralph objective in Ralph/PROMPT.md. No existing runtime tasks or memories for this scope. Plan: implement a single atomic UI task in src/components/tiles/PatientSummaryTile.tsx to remove coachmark/pulse behavior, move helper instructional copy into the Latest Results header row (right side), and enforce responsive KPI grid behavior (mobile 1 column, md+ 4 columns) while tightening card spacing without changing KPI content or interactions. After edit, run typecheck/lint/build, commit atomically, close task, and record a memory for this pattern.
## 2026-02-16 Iteration Result
Implemented task task-1771238094-7dc9 and committed as ab80d65. Removed Latest Results coachmark/pulse and localStorage dismissal logic; moved instructional helper copy into the header row area; switched KPI layout to mobile 1-column and md+ 4-column grid; tightened KPI card internal spacing while preserving KPI labels/values/subtext and click/keyboard open-panel behavior. Verification: npm run typecheck (pass), npm run lint (pass with 2 pre-existing warnings in context files), npm run build (pass with existing bundle-size warning).
{"id":"task-1771238094-7dc9","title":"Compact Latest Results KPI section","description":"Remove coachmark/pulse, move instruction text to heading row right area, enforce 1x4 mobile and 4x1 md+ KPI layout, reduce KPI card whitespace in PatientSummaryTile while preserving content/interactions.","status":"closed","priority":1,"blocked_by":[],"loop_id":"primary-20260216-103430","created":"2026-02-16T10:34:54.490955020+00:00","closed":"2026-02-16T10:36:37.836478822+00:00"}
- `@xenova/transformers` pipeline with `pooling: 'mean'` and `normalize: true` returns a Tensor; use `Array.from(output.data as Float32Array)` to extract the 384-d vector
- Scripts live in `scripts/` and run via `npx tsx` (tsx is not a project dep, npx fetches it)
- tsconfig `include` only covers `src/` — scripts are type-checked by tsx at runtime, not by `tsc --noEmit`
- `buildEmbeddingTexts()` in `src/lib/search.ts` returns `Array<{ id: string, text: string }>` with IDs matching PaletteItem IDs — use this for both embedding generation and chat context
- `src/data/embeddings.json` is an array of `{ id: string, embedding: number[] }` — 42 items, 384-d vectors, IDs match PaletteItem IDs. Vite imports JSON natively.
- `src/lib/semantic-search.ts` exports `semanticSearch(queryEmbedding, embeddings, threshold?)` and `loadEmbeddings()` — embeddings are normalized so cosine similarity is dot(a,b)/(mag(a)*mag(b))
- CommandPalette uses `semanticResults` state + debounced `useEffect` for async semantic search, falling back to Fuse.js when `isModelReady()` returns false or on any error
- `loadEmbeddings()` and `paletteMap` (Map<id, PaletteItem>) are precomputed via `useMemo` — no re-computation on each search
- ChatWidget is mounted in DashboardLayout alongside CommandPalette and DetailPanel — z-index 90 (below command palette z-1000)
- `prefersReducedMotion` pattern: read `window.matchMedia` at module level, use in framer-motion variants to skip animation
- ChatWidget stores messages as `Array<{ role: 'user' | 'assistant', content: string }>` — same shape as LLM message format, ready for Gemini integration
- ChatWidget `isOpen` state controls both panel visibility and button icon (MessageCircle ↔ X) — panel rendering handled by AnimatePresence
- Gemini API uses SSE streaming: POST to `:streamGenerateContent?alt=sse&key=KEY`, parse `data:` lines as JSON, extract `candidates[0].content.parts[0].text`
- System prompt built from `buildEmbeddingTexts()` — instructs model to end responses with `[ITEMS: id1, id2, id3]` for portfolio item linking
- `isGeminiAvailable()` checks `import.meta.env.VITE_GEMINI_API_KEY` — when missing, chat panel shows "unavailable" message but button remains visible
- Assistant messages store item IDs as `<!--ITEMS:id1,id2-->` HTML comment suffix for US-010 to parse — `getDisplayText()` strips this before rendering
- Conversation history capped at 10 messages (`MAX_HISTORY`), metadata stripped before sending to API
- Icon/color mappings (`iconByType`, `iconColorStyles`) live in `src/lib/palette-icons.ts` — shared between CommandPalette and ChatWidget
- ChatWidget accepts optional `onAction?: (action: PaletteAction) => void` prop — same pattern as CommandPalette's `onAction`
- `DashboardLayout` passes `handlePaletteAction` to both CommandPalette and ChatWidget for unified action routing
- TopBar is `z-index: 100` (fixed), nav is `z-index: 99` (sticky) — mobile full-screen overlays need `z-index > 100` to appear above them
- Inline `style={{ display: 'flex' }}` overrides Tailwind's `hidden` class — use `!important` modifier (`max-md:!hidden`) or move display to Tailwind classes to allow responsive hiding
- ChatWidget mobile breakpoint is `md` (768px) — below this, panel is full-screen; above, it's 380px anchored bottom-right
- `handleSubmit(overrideText?)` accepts optional text param — use this when programmatically sending messages (e.g., suggested question chips) to avoid stale `inputValue` state
- `SUGGESTED_QUESTIONS` const array at top of ChatWidget — edit here to change welcome screen chip text
- System prompt prefixes each CV entry with `[item-id]` so the model can directly reference IDs in its `[ITEMS: ...]` suffix — more reliable than expecting pattern inference
---
## 2026-02-15 - US-001
- Installed `@xenova/transformers` (^2.17.2)
- Created `scripts/generate-embeddings.ts` with main() that loads `Xenova/all-MiniLM-L6-v2` and embeds a test string
- Added `"generate-embeddings"` npm script
- Verified: outputs vector length 384 and exits cleanly
- `pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2')` auto-downloads and caches the ONNX model (~23MB)
- First run takes a few seconds for model download; subsequent runs are near-instant from cache
- The pipeline's `pooling: 'mean'` and `normalize: true` options handle mean-pooling and L2 normalization in one step — no manual tensor manipulation needed
- `output.data` is a `Float32Array`; wrap in `Array.from()` for a plain number array
---
## 2026-02-15 - US-002
- Added `buildEmbeddingTexts()` function to `src/lib/search.ts`
- Imports all raw data files (consultations, skills, kpis, investigations, documents)
- Generates natural-language paragraphs for each palette item type:
- Education items in `buildPaletteData()` are hardcoded arrays (not iterated from `documents`), with ids `edu-0` through `edu-3`. The mapping to `documents.ts` entries is: edu-0→doc-mary-seacole, edu-1→doc-mpharm, edu-2→doc-alevels, edu-3→doc-gphc
- Achievement items are similarly hardcoded with ids `ach-0` through `ach-3`, each linked to a KPI id
- Quick action items are `action-0` through `action-3`
- `documents.ts` is imported but wasn't previously used in `search.ts` — now used for education embedding text
---
## 2026-02-15 - US-003
- Updated `scripts/generate-embeddings.ts` to import `buildEmbeddingTexts()` and generate full embeddings
- Script embeds all 42 palette items sequentially using `Xenova/all-MiniLM-L6-v2`
- Outputs `src/data/embeddings.json` as `Array<{ id: string, embedding: number[] }>`
- Each embedding is a 384-dimensional float array
- File is ~453KB (42 items × 384 floats with pretty-printed JSON)
- `npm run generate-embeddings` regenerates the file successfully
- Vite handles JSON imports natively — `import data from '@/data/embeddings.json'` just works, no dynamic import needed
- Since embeddings are already L2-normalized (from pipeline's `normalize: true`), cosine similarity simplifies to just the dot product. However, the full formula is kept for correctness in case non-normalized vectors are ever used
- With only ~42 items and 384-d vectors, brute-force cosine similarity is fast enough — no need for approximate nearest neighbor libraries
---
## 2026-02-15 - US-006
- Integrated semantic search into CommandPalette with Fuse.js fallback
- When `isModelReady()` is true: debounces query by 200ms, calls `embedQuery()`, runs `semanticSearch()` against preloaded embeddings, maps result IDs back to PaletteItems via O(1) Map lookup
- When model is NOT ready: uses existing Fuse.js search (behavior preserved exactly)
- Results maintain `groupBySection()` grouping and section ordering
- Existing keyboard navigation, action routing, and UI unchanged
- Semantic results state is cleared when palette opens/closes and when query is empty
- Error handling: any failure in embedQuery/semanticSearch silently falls back to Fuse.js
- Typecheck, lint, and build all pass
- Browser verified: Fuse.js fallback works correctly; ONNX model loads asynchronously during boot and activates semantic search when ready
- Semantic search is async so it can't live in a `useMemo` — use `useState` + debounced `useEffect` pattern instead
- The `useRef + setTimeout` debounce pattern works well here: set `debounceRef.current = setTimeout(...)`, clear it in the cleanup function, and in early-return paths
- `isModelReady()` is a synchronous check — call it before setting up the debounce timeout to avoid unnecessary delays when model isn't loaded
- The ONNX model takes several seconds to load in the browser (downloads ~23MB first time, then cached in IndexedDB), so initial searches will always use Fuse.js fallback
- `loadEmbeddings()` is cheap (just returns the already-imported JSON) — safe to call in `useMemo` without performance concern
---
## 2026-02-15 - US-007
- Created `src/components/ChatWidget.tsx` — floating chat button with toggle state
- 48px circular button (40px on mobile <640px), fixed bottom-right, teal accent background, white MessageCircle icon
- Entrance animation: fade + translateY(8px→0), 1s delay after mount, via framer-motion variants
- Responsive sizing via Tailwind classes (`h-10 w-10 sm:h-12 sm:w-12`) works well with inline style for non-Tailwind properties (boxShadow, border-radius)
- `AnimatePresence` is already imported and ready for the panel animation in US-008
- The `isOpen` state lives in ChatWidget — US-008 will add the panel UI inside the same component
- Hover effects use `onMouseEnter/Leave` with direct style mutation (same pattern as other dashboard components)
---
## 2026-02-15 - US-008
- Built chat panel UI inside `ChatWidget.tsx` with header, message area, and input
- Panel opens above the floating button with scale+opacity entrance/exit animation via framer-motion `AnimatePresence`
- Messages stored as `Array<{ role: 'user' | 'assistant', content: string }>` in component state
- User messages right-aligned in teal-tinted bubbles (`var(--accent-light)` bg, `var(--accent-border)` border)
- Respects `prefers-reduced-motion` — skips all animation
- Close button in header triggers `setIsOpen(false)` (same as floating button toggle)
- Submitting appends both user message and placeholder assistant response to state
- Typecheck, lint (0 errors), and build all pass
- Browser verified: panel opens/closes correctly, messages display, input works, Enter submits, close button works
- Files changed: `src/components/ChatWidget.tsx`
- **Learnings for future iterations:**
- `AnimatePresence` with `key` prop on the panel div is needed for exit animations to work
- Panel uses `transformOrigin: 'bottom right'` for natural scale animation from the button corner
- CSS-in-JS `<style>` tag with `data-chat-panel` attribute handles responsive width/height (Tailwind can't express max-height conditionally based on viewport width easily)
- `textarea` with `rows={1}` and `maxHeight: 80px` gives auto-growing feel; `resize: none` prevents manual resize
- The `ChatMessage` interface (`{ role, content }`) is ready to be extended for US-009 Gemini integration — same shape as typical LLM message format
- `onFocus/onBlur` border color transitions on the textarea give a polished input interaction
---
## 2026-02-15 - US-009
- Created `src/lib/gemini.ts` — Gemini Flash streaming integration module
- `sendChatMessage(messages)` async generator that streams SSE tokens from Gemini 2.0 Flash
- `isGeminiAvailable()` checks for `VITE_GEMINI_API_KEY` env var
- `parseItemIds(text)` extracts `[ITEMS: id1, id2]` from response text
- `stripItemsSuffix(text)` removes the `[ITEMS: ...]` line for clean display
- System prompt built from `buildEmbeddingTexts()` output — full CV context (~42 items)
- Model instructed to answer concisely and append relevant palette item IDs
- Rewired `ChatWidget.tsx` to use real Gemini API instead of placeholder responses
- Streaming: tokens progressively appear in assistant message bubble
- Typing indicator (Loader2 spinner + "Thinking...") shown while waiting for first token
- Input disabled during streaming, send button grayed out
- Error handling: API failures show "Sorry, I couldn't process that. Please try again."
- Missing API key: panel shows "Chat is currently unavailable", input area hidden
- Conversation history capped at 10 messages before sending to API
- Assistant messages store parsed item IDs as `<!--ITEMS:id1,id2-->` HTML comment (for US-010)
- Messages sent to API have metadata stripped to keep context clean
- Extracting shared constants to `src/lib/` is the right pattern — both `CommandPalette` and `ChatWidget` now use the same icon mappings without duplication
- `buildPaletteData()` is pure (no side effects) and idempotent — safe to call in `useMemo` with empty deps
- The `<!--ITEMS:-->` HTML comment regex `<!--ITEMS:([^>]*)-->` works reliably; `[^>]*` captures everything between the colons and closing
- Item card buttons use `fontFamily: 'inherit'` to pick up the panel's `font-ui` — without this, browser defaults apply
- The `overflow: 'hidden'` on the message bubble container is needed so the item cards section (with its own border-top) stays visually contained within the bubble's border-radius
---
## 2026-02-15 - US-011
- Updated ChatWidget mobile breakpoint from `sm` (640px) to `md` (768px)
- Changed mobile panel from 85vh bottom-sheet to full-screen overlay using `position: fixed; inset: 0` with `100dvh` height
- Panel z-index on mobile bumped to 101 (`max-md:z-[101]`) to render above TopBar (z-100) and nav (z-99)
- Floating chat button hidden on mobile when panel is open via `max-md:!hidden` Tailwind class
- Fixed specificity issue: inline `style={{ display: 'flex' }}` was overriding Tailwind's `hidden` — moved flex/centering to Tailwind classes (`flex items-center justify-center`)
- Safe area insets applied via `env(safe-area-inset-*)` CSS on the `[data-chat-panel]` element for notched devices
- Input area stays pinned to bottom via existing flex layout (flex-col container + flex-1 message area + flex-shrink-0 input)
- Panel open/close animations still respect `prefers-reduced-motion`
- Typecheck, lint (0 errors), and build all pass
- Browser verified at 375×812 (mobile) and 1280×800 (desktop): full-screen overlay works, button hides/shows correctly, close button works
- Files changed: `src/components/ChatWidget.tsx`
- **Learnings for future iterations:**
- Inline `style` properties always override CSS classes — to allow Tailwind responsive utilities (like `max-md:hidden`) to work, move conflicting properties (especially `display`) to Tailwind classes instead
- Use `!important` modifier (`max-md:!hidden`) when competing with framer-motion's inline styles that can't be easily removed
- TopBar (`z-100`) and nav (`z-99`) sit above the chat panel's default `z-90` — mobile full-screen panels need `z-101+` to overlay properly
- `100dvh` (dynamic viewport height) is essential for mobile full-screen panels — it accounts for browser chrome (address bar, toolbar) unlike `100vh`
- The `[data-chat-panel]` CSS selector in the `<style>` block is the right place for responsive size rules since Tailwind can't conditionally set max-height based on viewport width
---
## 2026-02-15 - US-012
- Replaced empty-state centered text with welcome bubble + suggested question chips
- Clicking a chip calls `handleSubmit(questionText)` — same codepath as typing + Enter
- Refactored `handleSubmit` to accept optional `overrideText` parameter (avoids stale state issue with `setInputValue` + immediate submit)
- Wrapped send button `onClick` in arrow function to prevent passing MouseEvent as text argument
- Welcome/chips visible when `messages.length === 0`, replaced by conversation once any message is sent
- Typecheck passes (0 errors), lint passes (0 new errors/warnings)
- Browser verified: welcome bubble displays correctly, chips render, clicking chip sends message and replaces welcome state
- Files changed: `src/components/ChatWidget.tsx`
- **Learnings for future iterations:**
- When refactoring a callback to accept optional parameters, wrap `onClick={handler}` as `onClick={() => handler()}` to prevent React from passing the SyntheticEvent as the first argument
- `SUGGESTED_QUESTIONS` as a module-level const is the simplest approach — easily editable, no data file needed for 3 items
- The `handleSubmit(overrideText?)` pattern avoids the stale-state problem: `setInputValue(text)` followed by immediate `handleSubmit()` would read the old `inputValue` since React batches state updates
---
## 2026-02-15 - US-013
- Downloaded all-MiniLM-L6-v2 model files to `public/models/Xenova/all-MiniLM-L6-v2/`:
- Build script (`npm run generate-embeddings`) still works with local model files
- Typecheck passes (0 errors), lint passes (0 new errors/warnings)
- Files changed: `src/lib/embedding-model.ts`, `scripts/generate-embeddings.ts`, `public/models/Xenova/all-MiniLM-L6-v2/` (new directory with 4 files)
- **Learnings for future iterations:**
- `@xenova/transformers` env configuration: `env.localModelPath` sets the base path, `env.allowRemoteModels = false` prevents CDN fallback, `env.useBrowserCache = false` bypasses Browser Cache API
- The library constructs paths as `{localModelPath}/{modelId}/{filename}` — so `/models/` + `Xenova/all-MiniLM-L6-v2` + `/onnx/model_quantized.onnx`
- Browser Cache API can retain stale entries from previous HF CDN loads — setting `useBrowserCache = false` forces fresh fetches from the configured local path
- For Node.js scripts, use an absolute filesystem path for `localModelPath` (not a URL)
- The quantized ONNX model (`model_quantized.onnx`) is ~22MB — acceptable for a static asset since it's cached after first load
---
## 2026-02-15 - US-014
- Reviewed and tightened system prompt in `src/lib/gemini.ts` for Gemini 3 Flash Preview
- Prefixed each CV entry with its item ID (`[exp-nhs-nwicb] ...`) so the model can directly map entries to IDs for the ITEMS suffix
- Replaced numbered rules with cleaner bullet-point format, added rule against fabricating URLs/contacts
- Provided concrete example in ITEMS instruction (`[ITEMS: exp-nhs-nwicb, skill-python]`) instead of generic placeholders
- Verified model constant (`GEMINI_MODEL = 'gemini-3-flash-preview'`), display name, API URL, and header indicator were already in place from previous iteration
- Confirmed `gemini-3-flash-preview` is the correct model ID via Google AI docs
- Typecheck (0 errors), lint (0 new warnings), and production build all pass
- Files changed: `src/lib/gemini.ts`
- **Learnings for future iterations:**
- Prefixing CV data with `[item-id]` in the system prompt makes ID references more reliable — model can directly see and copy IDs rather than inferring from patterns
- Concrete examples in format instructions (e.g., `[ITEMS: exp-nhs-nwicb, skill-python]`) are more reliable than generic placeholders (`[ITEMS: id1, id2]`)
- The `GEMINI_MODEL` and `GEMINI_DISPLAY_NAME` constants in `gemini.ts` are already exported and used by `ChatWidget.tsx` — single source of truth for model identity
---
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