From ae15ccf961b0c42eb0956b10f272945bdcdbae5b Mon Sep 17 00:00:00 2001
From: Andy Charlwood <andrew.charlwood@gmail.com>
Date: Sun, 15 Feb 2026 17:59:11 +0000
Subject: [PATCH] chore: mark US-004 complete, update progress log

---
 Ralph/prd.json     |  2 +-
 Ralph/progress.txt | 18 ++++++++++++++++++
 2 files changed, 19 insertions(+), 1 deletion(-)

diff --git a/Ralph/prd.json b/Ralph/prd.json
index 0276a32..c7549ee 100644
--- a/Ralph/prd.json
+++ b/Ralph/prd.json
@@ -72,7 +72,7 @@
         "Typecheck passes"
       ],
       "priority": 4,
-      "passes": false,
+      "passes": true,
       "notes": "Use pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2') which auto-downloads and caches the ONNX model. The module-level pattern (let pipelineInstance = null) avoids React re-render issues. embedQuery should mean-pool the tensor output the same way as the build script. Wrap initModel() in a try/catch that silently swallows errors."
     },
     {
diff --git a/Ralph/progress.txt b/Ralph/progress.txt
index 881027b..b2d9467 100644
--- a/Ralph/progress.txt
+++ b/Ralph/progress.txt
@@ -10,6 +10,8 @@
 - 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
 
 ---
 
@@ -62,3 +64,19 @@
   - 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)
 ---
+
+## 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
+---