feat(agent): build the vector index in parallel by default

`parallel` joins the agent's default features, so the HNSW bulk build uses the
thread pool: cold index build at 10K records 1152 -> ~380 ms in a same-moment
A/B (the graph is identical either way). Nothing else on the measured paths
changes — ingest, checkpoint, open and steady-state query times are the same
with the feature on or off. Adds rayon to the default dependency set; opt out
with `--no-default-features --features float16,hnsw`.

Harness: `--e2e-only` runs the end-to-end section without the index
benchmarks. Note for anyone comparing numbers: this machine's absolute timings
drifted ~1.5x over a long session, so only same-moment A/B runs are
comparable.

Co-Authored-By: Claude Fable 5.1 <[email protected]>

CHANGELOG.md

@@ -9,7 +9,9 @@
   With the `parallel` feature, planning and pruning run on a thread pool (10K:
   388 ms, 100K: ~21 s -> 5.9 s on 16 cores). The graph is deterministic and
   identical with or without the feature. `clawhdf5-agent`'s `parallel` feature
-  now enables it for the agent's index.
+  enables it for the agent's index and is now **on by default** (adds `rayon`
+  to the default dependency set; build with `--no-default-features --features
+  float16,hnsw` to opt out).
 - `clawhdf5-ann`: `HnswIndex::search` returned fewer than `k` results — often
   none — when the records nearest the query had been deleted: it collected `ef`
   candidates, *then* dropped the deleted ones, *then* took `k`. Deleted nodes

CLAUDE.md

@@ -33,6 +33,8 @@ Cargo workspace with 16 crates under `crates/` (plus `libaec-sys`, an internal F
   the approximate `clawhdf5-ann` index for the vector stage (the index mirrors
   the cache and self-heals on drift). Build the agent with
   `--no-default-features --features float16` to force the exact linear cosine scan.
+  The agent's `parallel` feature (also default) builds the index on a thread
+  pool; the graph is identical with or without it.
   The index uses the HNSW paper's diversity heuristic for neighbour selection
   (plain closest-M capped recall on clustered data: 0.31 recall@10 at 100K). Its
   graph is saved to `<store>.h5.ann` at each checkpoint and reloaded by `open()`

crates/clawhdf5-agent/Cargo.toml

@@ -45,7 +45,7 @@ name = "memory_bench"
 harness = false
 
 [features]
-default = ["float16", "hnsw"]
+default = ["float16", "hnsw", "parallel"]
 float16 = ["half"]
 # Rayon-parallel brute-force search strategies, and a parallel bulk build of
 # the HNSW index (same graph, several times faster on a multi-core machine).

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

@@ -485,8 +485,12 @@ fn main() {
 
     let mut json = Vec::new();
     println!("## Search harness");
-    for &n in sizes {
+    // `--e2e-only` skips the index benchmarks, so the end-to-end section runs
-        bench_ann(n, &mut json);
+    // in a process that has not already spun up a thread pool.
+    if !args.iter().any(|a| a == "--e2e-only") {
+        for &n in sizes {
+            bench_ann(n, &mut json);
+        }
     }
 
     if ann_only {