feat(agent): MemoryConfig::float16 stores half-precision embeddings

The setting was persisted in /meta and otherwise ignored: embeddings
were always written as f32. It now does what it says.

clawhdf5-format:
- `DatasetBuilder::with_f16_data` writes IEEE binary16 (numpy float16),
  rounding to nearest-even, and `make_f16_type`.
- `clawhdf5_format::float16` holds the f32 <-> f16 conversions, the one
  implementation the writer, the reader and the agent all use. Checked
  against the `half` crate on 16.7M f32 values and round-trips all 65536
  half values; the h5py interop tests confirm the rounding matches
  numpy's bit for bit (4020 values incl. ties, subnormals, overflow).
- Reading little-endian float16 as f32 has a fast path.

clawhdf5-agent:
- A float16 store writes /memory/embeddings as half precision, and
  `MemoryCache::half_precision` rounds each embedding as it enters the
  cache (save, update, WAL replay, and on load of a store still f32 on
  disk), so memory and file agree bit for bit and a store searches the
  same before and after a reopen (tested).
- Values beyond +-65504 are refused with the new
  `MemoryError::InvalidEntry` rather than stored as infinity, on every
  save path; batches are all or nothing, and a rejected ephemeral entry
  stays in the ephemeral tier. Breaking for exhaustive matches.
- CLI: `create --float16`. Off by default.

Measured on tank, 384-dim, six runs alternating order, medians
(search_harness --float16-study --full): at 100K the file goes from
154.0 to 80.8 MiB (-48%), checkpoint 752 -> 512 ms, open 300 -> 252 ms;
vector recall@10 against an exact scan and hybrid_search latency do not
change. At 10K open is 3 ms slower. Also a test that h5py opens a whole
agent store, f32 and float16, and decodes every dataset.

Docs: README, BENCHMARKS.md ("float16 embedding storage"), CHANGELOG
(including the h5py interop fixes in the previous commit), CLAUDE.md.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>

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

@@ -19,6 +19,7 @@
 //! cargo run --release -p clawhdf5-bench --bin search_harness -- --full  # + 100K
 //! cargo run --release -p clawhdf5-bench --bin search_harness -- --json out.json
 //! cargo run --release -p clawhdf5-bench --bin search_harness -- --ann-only --uniform
+//! cargo run --release -p clawhdf5-bench --bin search_harness -- --float16-study --full
 //! ```
 
 use std::time::{Duration, Instant};
@@ -88,6 +89,9 @@ static UNIFORM: std::sync::atomic::AtomicBool = std::sync::atomic::AtomicBool::n
 /// the memory) instead of f32, to price the recall it costs.
 static INT8: std::sync::atomic::AtomicBool = std::sync::atomic::AtomicBool::new(false);
 
+/// `--f16-first`: in `--float16-study`, run the float16 store first.
+static F16_FIRST: std::sync::atomic::AtomicBool = std::sync::atomic::AtomicBool::new(false);
+
 /// `--rerank`: re-score the candidate pool against the exact vectors before
 /// taking the top K.
 static RERANK: std::sync::atomic::AtomicBool = std::sync::atomic::AtomicBool::new(false);
@@ -483,6 +487,148 @@ fn bench_end_to_end(n: usize, json: &mut Vec<serde_json::Value>) {
     }));
 }
 
+// ---------------------------------------------------------------------------
+// float16 study: what does half-precision embedding storage cost?
+// ---------------------------------------------------------------------------
+
+/// `--float16-study`: the same data in an `f32` store and a `float16` store.
+/// Reports file size, checkpoint and open time, vector-search recall@10
+/// against an exact scan of the *original* f32 vectors, how often the two
+/// stores return the same top 10, and `hybrid_search` latency. Hebbian
+/// boosting is off, so every query sees the same store.
+fn float16_study(n: usize) {
+    let data = make_dataset(n, 0xF16 ^ n as u64);
+    let mut rng = Rng(11);
+    let query_texts: Vec<String> = data
+        .query_cluster
+        .iter()
+        .enumerate()
+        .map(|(i, c)| text_for(*c, i, &mut rng))
+        .collect();
+
+    // Exact top K by cosine (the vectors are unit length) on the f32 inputs.
+    let exact: Vec<Vec<usize>> = data
+        .queries
+        .iter()
+        .map(|q| {
+            let mut scored: Vec<(usize, f32)> = data
+                .vectors
+                .iter()
+                .enumerate()
+                .map(|(i, v)| (i, v.iter().zip(q).map(|(a, b)| a * b).sum()))
+                .collect();
+            scored.sort_by(|a, b| b.1.total_cmp(&a.1).then(a.0.cmp(&b.0)));
+            scored.into_iter().take(K).map(|(i, _)| i).collect()
+        })
+        .collect();
+
+    let dir = tempfile::tempdir().unwrap();
+    let mut per_variant: Vec<(bool, Vec<Vec<usize>>)> = Vec::new();
+    // `--f16-first` swaps the order, to check the numbers do not depend on
+    // which store runs first (page cache, allocator, CPU frequency).
+    let order = if F16_FIRST.load(std::sync::atomic::Ordering::Relaxed) {
+        [true, false]
+    } else {
+        [false, true]
+    };
+    for float16 in order {
+        let path = dir.path().join(format!("f16study_{float16}.h5"));
+        let mut rng = Rng(3);
+        let entries: Vec<MemoryEntry> = data
+            .vectors
+            .iter()
+            .enumerate()
+            .map(|(i, v)| MemoryEntry {
+                chunk: text_for(data.cluster_of[i], i, &mut rng),
+                embedding: v.clone(),
+                source_channel: "bench".into(),
+                timestamp: i as f64,
+                session_id: format!("s{}", i % 50),
+                tags: format!("t{i}"),
+            })
+            .collect();
+        let mut config = MemoryConfig::new(path.clone(), "bench", DIM);
+        config.float16 = float16;
+        config.hebbian_boost = 0.0;
+        let mut mem = HDF5Memory::create(config).unwrap();
+        mem.save_batch(entries).unwrap();
+        // Build the indexes, then time a checkpoint that writes everything.
+        std::hint::black_box(mem.hybrid_search(&data.queries[0], "", 1.0, 0.0, K));
+        let t = Instant::now();
+        mem.flush_wal().unwrap();
+        let checkpoint = t.elapsed();
+        drop(mem);
+        let file_bytes = std::fs::metadata(&path).unwrap().len();
+
+        // Median of three opens.
+        let mut opens: Vec<Duration> = (0..3)
+            .map(|_| {
+                let t = Instant::now();
+                let m = HDF5Memory::open(&path).unwrap();
+                let d = t.elapsed();
+                drop(m);
+                d
+            })
+            .collect();
+        opens.sort();
+        let mut mem = HDF5Memory::open(&path).unwrap();
+
+        // Vector-only search: empty text, all weight on the vector stage.
+        let results: Vec<Vec<usize>> = data
+            .queries
+            .iter()
+            .map(|q| {
+                mem.hybrid_search(q, "", 1.0, 0.0, K)
+                    .iter()
+                    .map(|r| r.index)
+                    .collect()
+            })
+            .collect();
+        let hits: usize = results
+            .iter()
+            .zip(&exact)
+            .map(|(got, want)| got.iter().filter(|i| want.contains(i)).count())
+            .sum();
+        let recall = hits as f64 / (K * data.queries.len()) as f64;
+
+        let latency = summarize(
+            (0..N_QUERIES)
+                .map(|i| {
+                    let t = Instant::now();
+                    std::hint::black_box(mem.hybrid_search(
+                        &data.queries[i],
+                        &query_texts[i],
+                        0.4,
+                        0.6,
+                        K,
+                    ));
+                    t.elapsed()
+                })
+                .collect(),
+        );
+        let overlap = match per_variant.first() {
+            Some((_, other)) => {
+                let same: usize = results
+                    .iter()
+                    .zip(other)
+                    .map(|(a, b)| a.iter().filter(|i| b.contains(i)).count())
+                    .sum();
+                format!("{:.4}", same as f64 / (K * data.queries.len()) as f64)
+            }
+            None => "—".into(),
+        };
+        println!(
+            "| {n} | {} | {:.1} | {:.0} | {:.1} | {recall:.4} | {overlap} | {:.3} |",
+            if float16 { "float16" } else { "f32" },
+            mib(file_bytes),
+            millis(checkpoint),
+            millis(opens[1]),
+            millis(latency.p50),
+        );
+        per_variant.push((float16, results));
+    }
+}
+
 // ---------------------------------------------------------------------------
 // Fusion study: does capping the keyword candidate pool change the ranking?
 // ---------------------------------------------------------------------------
@@ -642,6 +788,24 @@ fn main() {
         }
         return;
     }
+    if args.iter().any(|a| a == "--f16-first") {
+        F16_FIRST.store(true, std::sync::atomic::Ordering::Relaxed);
+    }
+    if args.iter().any(|a| a == "--float16-study") {
+        println!("## float16 embedding storage ({DIM}-dim, int8 index, Hebbian boost off)\n");
+        println!(
+            "| N | embeddings | file MiB | checkpoint ms | open ms | recall@10 | top-10 overlap with the other | hybrid p50 ms |"
+        );
+        println!("|---:|---|---:|---:|---:|---:|---:|---:|");
+        for &n in if full {
+            &[1_000, 10_000, 100_000][..]
+        } else {
+            &[1_000, 10_000][..]
+        } {
+            float16_study(n);
+        }
+        return;
+    }
     if args.iter().any(|a| a == "--int8") {
         INT8.store(true, std::sync::atomic::Ordering::Relaxed);
         println!("(int8-quantised index vectors)");