feat(agent): optional int8 vector index, re-scored against exact embeddings

`MemoryConfig::quantized_index` stores the HNSW index's own copy of the
embeddings as i8 rather than f32. At 100k x 384 that takes the index from
266 to 123 MiB and the whole reopened store from 399 to 256 MiB — 2.72x
to 1.74x the raw vectors, the largest remaining item in the footprint.

Quantised distances are approximate and `ef` cannot compensate, because
the loss is in the distances rather than in the graph: recall@10 tops out
at 0.967 against f32's 0.9995 and does not move between ef=128 and
ef=256. The store already holds the exact embeddings, though, so when the
index is quantised the query path re-scores the candidate pool against
them before fusion. That restores recall (0.9940 vs 0.9945 at ef=64) and
costs about 13% of QPS.

Off by default: it trades query speed for memory and which side is worth
more depends on the deployment. The flag is persisted in `/meta`, so a
reopened store does not silently revert to four times the index memory,
and the sidecar graph is rehydrated into the configured storage.

Also on the CLI as `create --quantized-index`.

Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>

crates/clawhdf5-bench/src/bin/search_harness.rs

@@ -563,7 +563,9 @@ fn bench_footprint(n: usize) {
         .collect();
     let after_entries = heap_bytes();
 
-    let mut mem = HDF5Memory::create(MemoryConfig::new(path, "bench", DIM)).unwrap();
+    let mut config = MemoryConfig::new(path, "bench", DIM);
+    config.quantized_index = INT8.load(std::sync::atomic::Ordering::Relaxed);
+    let mut mem = HDF5Memory::create(config).unwrap();
     mem.save_batch(entries).unwrap();
     let after_store = heap_bytes();