feat(r42): improve KB grounding accuracy and add clear-history

R42 was missing knowledge-graph information (e.g. pension questions)
because retrieval and context-building dropped relevant facts:

- retrieval: exact-token TF-IDF could not match Dutch compound words,
  so a "pensioen" query scored 0 against "pensioenregeling" /
  "partnerpensioen" and never retrieved them. Add a compound-word
  fallback (shared >=6-char stem or containment, 0.4x weight) alongside
  exact matching.
- rag: deep article content was only injected for verbatim-mentioned
  topics; retrieved topics contributed just a 200-char description.
  Inject ~1000 chars of content for up to 5 topics (mentions first,
  then top-ranked retrieved) and widen the description snippet to 320.
- prompts: add a NAUWKEURIGHEID block (use all relevant facts, call
  lookup_topic before giving up) and relax the 4-sentence cap for
  detail/list answers so complete facts aren't summarised away.

Also add a clear-history control: a trash button in the chat header
(confirm dialog) wipes chat🧵{userId} and reseeds the greeting
via clearThread() in useChat.

Tests: compound-word matching + rag deep-content injection. Spec updated.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

docs/r42-spec.md

@@ -18,6 +18,8 @@ client-side and is grounded by local TF-IDF retrieval — **no vector database**
   context is truncated with a notice.
 - A greeting message seeds an empty thread.
 - Each turn calls `callLLM` (fast/standard Claude tier — low latency matters for chat).
+- The chat header has a **clear** button (trash icon). It confirms, then wipes
+  `chat:thread:{userId}` and reseeds the greeting via `clearThread` in `useChat.js`.
 
 ---
 
@@ -25,13 +27,21 @@ client-side and is grounded by local TF-IDF retrieval — **no vector database**
 
 `buildKbContext` in `rag.js`:
 1. Build / reuse the TF-IDF index over `topics` (`src/lib/retrieval.js`).
-2. Retrieve the top **10** topics for the user's message.
+2. Retrieve the top **10** topics for the user's message. Scoring is exact-token
+   TF-IDF **plus a compound-word fallback**: an unmatched query token (≥6 chars)
+   also matches a document term when they share a ≥6-char stem or one contains
+   the other, at a reduced weight. This recovers Dutch compounds — e.g. a
+   `pensioen` query matches `pensioenregeling` and `partnerpensioen`.
 3. Always include topics whose `id` or `label` appears verbatim in the message.
 4. Include relations only when **both** endpoints are in the retrieved set.
-5. For explicitly mentioned topics, inject up to ~1200 chars of their generated
-   content.
+5. Inject up to ~1000 chars of generated content for up to **5** topics —
+   verbatim-mentioned first, then the highest-ranked retrieved ones — so a query
+   that never names a topic exactly still gets rich content for what it matched.
 6. Append a short KB hash so the cached context busts when topics change.
 
+If the summarised context is still too thin, R42 can call the `lookup_topic`
+tool to pull a topic's full description and learning content on demand.
+
 The system prompt (`prompts.js`) is assembled as cacheable blocks: a stable
 preamble (role, tasks, style, "answer only from the KB"), the KB context block, and
 a per-turn tail with the user's name and admin/non-admin flag.