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-agent/tests/h5py_interop.rs

@@ -0,0 +1,94 @@
+//! An agent store is a standard HDF5 file: h5py can open it and read every
+//! dataset.
+//!
+//! It could not: the float datatype's sign-bit position was hard-coded for
+//! f64, so every f32 dataset (embeddings, norms, activation weights) made
+//! libhdf5 refuse the file with "sign bit position out of bounds".
+
+use std::process::Command;
+
+use clawhdf5_agent::{AgentMemory, HDF5Memory, MemoryConfig, MemoryEntry};
+
+fn python() -> String {
+    std::env::var("CLAWHDF5_PYTHON").unwrap_or_else(|_| "python3".to_string())
+}
+
+fn h5py_available() -> bool {
+    Command::new(python())
+        .args(["-c", "import h5py"])
+        .output()
+        .map(|o| o.status.success())
+        .unwrap_or(false)
+}
+
+#[test]
+fn h5py_reads_every_dataset_of_an_agent_store() {
+    if !h5py_available() {
+        assert!(
+            std::env::var("CLAWHDF5_REQUIRE_INTEROP").as_deref() != Ok("1"),
+            "CLAWHDF5_REQUIRE_INTEROP=1 but python3 with h5py is not available"
+        );
+        eprintln!("SKIP: python3 with h5py not available");
+        return;
+    }
+    let dir = tempfile::tempdir().unwrap();
+    for float16 in [false, true] {
+        let path = dir.path().join(format!("store_{float16}.h5"));
+        let mut cfg = MemoryConfig::new(path.clone(), "agent", 8);
+        cfg.float16 = float16;
+        let mut m = HDF5Memory::create(cfg).unwrap();
+        // save_batch checkpoints, so the records are in the .h5, not the WAL.
+        m.save_batch(
+            (0..20)
+                .map(|i| MemoryEntry {
+                    chunk: format!("memory {i}"),
+                    embedding: (0..8).map(|j| ((i * 8 + j) as f32).sin()).collect(),
+                    source_channel: "test".into(),
+                    timestamp: i as f64,
+                    session_id: "s".into(),
+                    tags: String::new(),
+                })
+                .collect(),
+        )
+        .unwrap();
+        drop(m);
+
+        // Exact expected values, as bits: numpy's sin need not match Rust's
+        // to the last place.
+        let bits = (0..160)
+            .map(|k| (k as f32).sin().to_bits().to_string())
+            .collect::<Vec<_>>()
+            .join(",");
+        let script = format!(
+            r#"
+import h5py, numpy as np
+want = np.float16 if {py_bool} else np.float32
+with h5py.File("{path}", "r") as f:
+    names = []
+    f.visititems(lambda n, o: names.append(n) if isinstance(o, h5py.Dataset) else None)
+    for n in names:
+        f[n][()]  # every dataset must decode
+    e = f["memory/embeddings"]
+    assert e.dtype == want, e.dtype
+    assert e.shape == (20, 8), e.shape
+    ref = np.array([{bits}], dtype=np.uint32).view(np.float32).astype(want).reshape(20, 8)
+    assert (e[()] == ref).all()
+    assert f["memory/norms"].dtype == np.float32
+print(len(names))
+"#,
+            py_bool = if float16 { "True" } else { "False" },
+            path = path.display()
+        );
+        let out = Command::new(python())
+            .args(["-c", &script])
+            .output()
+            .unwrap();
+        assert!(
+            out.status.success(),
+            "float16={float16}: {}",
+            String::from_utf8_lossy(&out.stderr)
+        );
+        let n: usize = String::from_utf8_lossy(&out.stdout).trim().parse().unwrap();
+        assert!(n >= 10, "only {n} datasets");
+    }
+}