Add reranking, RRF fusion, bench harness, tag contexts, and data ingestion

Implements five of the six enhancements from docs/kb-enhancements-proposal.htm,
closing the retrieval-quality gap identified in the qmd review.

- Cross-encoder reranking: new kb/reranker.py loads an optional reranking
  model at startup (KB_RERANK_ENABLED, KB_RERANKER_MODEL,
  KB_RERANK_CANDIDATES). Search degrades gracefully to plain hybrid
  retrieval when the model is absent. Exposed via a "rerank" block in
  /status, a rerank flag on search, and --no-rerank in the CLI.
- RRF rank fusion: FTS and vector lists now merge by reciprocal rank
  fusion with a top-rank bonus, replacing the old score blend. Scores are
  comparable across queries.
- Bench harness and explain traces: kb bench runs a query fixture against
  each backend and reports precision@k, recall and MRR. --explain returns a
  per-result score breakdown.
- Tag context descriptions: tags carry an optional one-line description
  (kb tag-describe), returned as tag_contexts with search results. Adds a
  tags.description column migration.
- Structured data ingestion: .json/.yaml/.toml files ingest as text via the
  new "data" doc type, pretty-printing minified JSON before chunking.

Query expansion (proposal item 5) is deliberately left out pending bench
results. Requires engine v3.3.0.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

engine/compose.nvidia.yaml

@@ -21,6 +21,9 @@ services:
       - KB_INGEST_DEVICE=${KB_INGEST_DEVICE:-auto}
       - KB_API_KEY=${KB_API_KEY:-}
       - KB_SEARCH_THRESHOLD=${KB_SEARCH_THRESHOLD:-0.01}
+      - KB_RERANK_ENABLED=${KB_RERANK_ENABLED:-true}
+      - KB_RERANKER_MODEL=${KB_RERANKER_MODEL:-BAAI/bge-reranker-v2-m3}
+      - KB_RERANK_CANDIDATES=${KB_RERANK_CANDIDATES:-40}
       - HF_HUB_OFFLINE=${HF_HUB_OFFLINE:-}
     restart: unless-stopped