# Progress Log — Semantic Search & AI Chat
# Branch: ralph/semantic-search
# Started: 2026-02-15

## Codebase Patterns
- `@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`
- Project uses `"type": "module"` in package.json
- Palette item IDs: `exp-{consultation.id}`, `skill-{skill.id}`, `proj-{investigation.id}`, `ach-{0-3}`, `edu-{0-3}`, `action-{0-3}`
- `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/embedding-model.ts` exports `initModel()`, `embedQuery(text)`, `isModelReady()` — check `isModelReady()` before calling `embedQuery()`
- `initModel()` is called fire-and-forget in `App.tsx` on mount — model loads during boot/ECG/login phases

---

## 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
- Typecheck passes
- Files changed: `package.json`, `package-lock.json`, `scripts/generate-embeddings.ts`
- **Learnings for future iterations:**
  - `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:
  - Consultations: role, org, duration, history narrative, examination bullets, coded entry descriptions
  - Skills: name, category, frequency, proficiency %, years of experience
  - Achievements: title, subtitle, full KPI explanation + story context + outcomes
  - Investigations: name, methodology, tech stack, results
  - Education: title, type, institution, duration, classification, research detail, notes (from documents.ts)
  - Quick Actions: title + subtitle
- IDs match PaletteItem IDs (e.g. `exp-{id}`, `skill-{id}`, `ach-{i}`, `proj-{id}`, `edu-{i}`, `action-{i}`)
- Typecheck and lint pass
- Files changed: `src/lib/search.ts`
- **Learnings for future iterations:**
  - 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
- Typecheck and lint pass
- Files changed: `scripts/generate-embeddings.ts`, `src/data/embeddings.json`
- **Learnings for future iterations:**
  - `import.meta.dirname` works in tsx/Node ESM scripts — use it instead of `__dirname` (which isn't available in ESM)
  - `@/` path alias works in `npx tsx` scripts because tsx resolves tsconfig paths automatically
  - The embeddings file is ~450KB with pretty-print; could be reduced with compact JSON but readability is preferred for now
  - Processing 42 items takes ~10-15 seconds on first run (model cached after first download)
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## 2026-02-15 - US-004
- Created `src/lib/embedding-model.ts` with three exports: `initModel()`, `embedQuery()`, `isModelReady()`
- Module-level `let extractor` pattern avoids React re-render issues
- `initModel()` uses `loading` guard to prevent duplicate pipeline loads
- `embedQuery()` uses same `pooling: 'mean'` and `normalize: true` as the build script
- `initModel()` called fire-and-forget in `App.tsx` `useEffect([], [])` — runs during boot phase
- Silent failure: try/catch swallows errors, `isModelReady()` stays false
- Typecheck, lint, and build all pass
- Files changed: `src/lib/embedding-model.ts` (new), `src/App.tsx`
- **Learnings for future iterations:**
  - `FeatureExtractionPipeline` type is exported from `@xenova/transformers` and can be used for the module-level variable
  - The `loading` boolean guard prevents race conditions if `initModel()` is called multiple times (e.g., React strict mode double-mount)
  - `initModel()` is intentionally not awaited — it's fire-and-forget so it doesn't block the boot animation
  - Consumers should check `isModelReady()` before calling `embedQuery()` — it throws if model isn't loaded
---