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>

README.md

@@ -143,8 +143,9 @@ kb bulk-set-tags --tags "old-scheme" --set "new-scheme" --yes
 
 ## How it works
 
-- **Ingestion**: Files are uploaded to the engine and queued for async processing. The engine chunks documents (PDFs via Docling, markdown by headers, code by AST/functions, notes as whole text), generates embeddings on GPU, and stores everything in SQLite.
-- **Search**: Hybrid retrieval combining BM25 keyword scoring (FTS5) and vector similarity (sqlite-vec), merged via Reciprocal Rank Fusion. Sub-100ms with a warm model.
+- **Ingestion**: Files are uploaded to the engine and queued for async processing. The engine chunks documents (PDFs via Docling, markdown by headers, code by AST/functions, notes as whole text, JSON/YAML/TOML as pretty-printed text), generates embeddings on GPU, and stores everything in SQLite.
+- **Search**: Hybrid retrieval combining BM25 keyword scoring (FTS5) and vector similarity (sqlite-vec), merged via Reciprocal Rank Fusion with a top-rank bonus. Optionally reranked by a local cross-encoder (`KB_RERANK_ENABLED`). Sub-100ms with a warm model (without reranking). Add `--explain` to any search for a per-result score breakdown.
+- **Quality measurement**: `kb bench fixture.json` runs a fixture of queries with known-relevant documents and reports precision@k / recall / MRR per backend (fts, vec, hybrid, hybrid+rerank). See `docs/bench-example.json`.
 - **Output**: JSON (for scripts/LLM tool use) or human-readable terminal format. Use `--format json` on any command.
 
 ## Engine configuration
@@ -159,6 +160,9 @@ The engine is configured via environment variables (set in the compose file or v
 | `KB_INGEST_DEVICE` | `auto` | Docling layout detection device: `auto`, `cpu`, or `cuda` |
 | `KB_API_KEY` | (none) | Optional Bearer token for API authentication |
 | `KB_SEARCH_THRESHOLD` | `0.01` | Minimum score for search results (filters noise) |
+| `KB_RERANK_ENABLED` | `false` (`true` in nvidia compose) | Load a cross-encoder and rerank hybrid results server-side |
+| `KB_RERANKER_MODEL` | `BAAI/bge-reranker-v2-m3` | Cross-encoder model for reranking |
+| `KB_RERANK_CANDIDATES` | `40` | Hybrid candidates scored by the reranker per query |
 | `KB_BULK_SAFETY_PERCENT` | `70` | Bulk operations affecting more than this % of documents are rejected unless `force` is set (0 disables) |
 | `KB_PORT` | `8000` | Port to expose |
 | `KB_HOST` | `0.0.0.0` | Host to bind to |