clawhdf5-migrate writes real agent stores; knowledge-graph fix; dated benchmark re-run #6

Merged
osobh merged 3 commits from feat/migrate-and-benchmarks into main 2026-09-25 14:52:36 +00:00
10 changed files with 1834 additions and 1131 deletions
Showing only changes of commit a8fb758489 - Show all commits
+115 -1
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@@ -73,7 +73,7 @@ pub use ephemeral::{EphemeralEntry, EphemeralStats};
use knowledge::KnowledgeCache; use knowledge::KnowledgeCache;
use memory_strategy::{Exchange, MemoryStrategy, StrategyOutput}; use memory_strategy::{Exchange, MemoryStrategy, StrategyOutput};
pub use search::SearchOptions; pub use search::SearchOptions;
use session::SessionCache; pub use session::{SessionCache, SessionEntry};
// --- Error type --- // --- Error type ---
@@ -1028,6 +1028,18 @@ impl HDF5Memory {
&self.config &self.config
} }
/// The sessions recorded in this store.
pub fn sessions(&self) -> &SessionCache {
&self.sessions
}
/// Mutable access to the sessions, e.g. to add many at once. Changes
/// reach the disk at the next checkpoint (any flushing call, such as
/// [`HDF5Memory::flush_wal`] or `save_batch`), not immediately.
pub fn sessions_mut(&mut self) -> &mut SessionCache {
&mut self.sessions
}
/// Get a reference to the knowledge cache. /// Get a reference to the knowledge cache.
pub fn knowledge(&self) -> &KnowledgeCache { pub fn knowledge(&self) -> &KnowledgeCache {
&self.knowledge &self.knowledge
@@ -1401,6 +1413,35 @@ impl HDF5Memory {
} }
impl HDF5Memory { impl HDF5Memory {
/// Delete many records with a single checkpoint, where
/// [`AgentMemory::delete`] checkpoints once per record.
///
/// All or nothing: if any id is out of range or already deleted (or
/// repeated), nothing is deleted and `MemoryError::NotFound` is returned.
/// Unlike `delete`, this never auto-compacts, so the records stay in the
/// store as tombstones (their indices unchanged) until [`AgentMemory::compact`]
/// is called — importers use it to carry over records that were already
/// deleted in the source.
pub fn delete_batch(&mut self, ids: &[usize]) -> Result<()> {
let mut seen = std::collections::HashSet::with_capacity(ids.len());
for &id in ids {
if self.cache.tombstones.get(id).copied() != Some(0) || !seen.insert(id) {
return Err(MemoryError::NotFound(format!(
"entry {id} not found or already deleted"
)));
}
}
if ids.is_empty() {
return Ok(());
}
for &id in ids {
self.cache.mark_deleted(id);
self.hnsw_on_delete(id);
self.bm25_on_delete(id);
}
self.flush()
}
pub fn tick_session(&mut self) -> Result<()> { pub fn tick_session(&mut self) -> Result<()> {
let d = self.config.decay_factor; let d = self.config.decay_factor;
for w in self.cache.activation_weights.iter_mut() { for w in self.cache.activation_weights.iter_mut() {
@@ -1599,6 +1640,79 @@ mod tests {
} }
} }
#[test]
fn delete_batch_tombstones_without_compacting() {
let dir = TempDir::new().unwrap();
let path = dir.path().join("test.h5");
let mut mem = HDF5Memory::create(make_config(&dir)).unwrap();
mem.save_batch(
(0..4)
.map(|i| make_entry(&format!("record {i}"), &[i as f32, 1.0, 0.0, 0.0]))
.collect(),
)
.unwrap();
// 3 of 4 is far past compact_threshold (0.3): delete() would compact.
mem.delete_batch(&[0, 1, 3]).unwrap();
assert_eq!(mem.count(), 4);
assert_eq!(mem.count_active(), 1);
drop(mem);
let mut mem = HDF5Memory::open(&path).unwrap();
assert_eq!(mem.cache.tombstones, vec![1, 1, 0, 1]);
let hits = mem.hybrid_search(&[0.0, 1.0, 0.0, 0.0], "record", 0.5, 0.5, 10);
assert!(
hits.iter().all(|r| r.index == 2),
"tombstoned record returned"
);
}
#[test]
fn delete_batch_is_all_or_nothing() {
let dir = TempDir::new().unwrap();
let mut mem = HDF5Memory::create(make_config(&dir)).unwrap();
mem.save_batch(vec![
make_entry("a", &[1.0, 0.0, 0.0, 0.0]),
make_entry("b", &[0.0, 1.0, 0.0, 0.0]),
])
.unwrap();
for bad in [&[0, 5][..], &[1, 1][..]] {
assert!(matches!(
mem.delete_batch(bad),
Err(MemoryError::NotFound(_))
));
assert_eq!(mem.count_active(), 2, "{bad:?} deleted something");
}
mem.delete_batch(&[]).unwrap();
assert_eq!(mem.count_active(), 2);
}
#[test]
fn sessions_mut_add_at_keeps_timestamp_across_reopen() {
let dir = TempDir::new().unwrap();
let path = dir.path().join("test.h5");
let mut mem = HDF5Memory::create(make_config(&dir)).unwrap();
mem.sessions_mut()
.add_at("s-old", 2, 7, "discord", "old summary", 1.7e15);
mem.flush_wal().unwrap();
drop(mem);
let mem = HDF5Memory::open_read_only(&path).unwrap();
let s = mem.sessions();
assert_eq!(s.len(), 1);
let e = &s.entries[0];
assert_eq!(
(
e.id.as_str(),
e.start_idx,
e.end_idx,
e.channel.as_str(),
e.ts
),
("s-old", 2, 7, "discord", 1.7e15)
);
assert_eq!(s.summaries[0], "old summary");
}
#[test] #[test]
fn create_new_file() { fn create_new_file() {
let dir = TempDir::new().unwrap(); let dir = TempDir::new().unwrap();
+16 -1
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@@ -33,7 +33,7 @@ impl SessionCache {
self.entries.is_empty() self.entries.is_empty()
} }
/// Add a new session with its summary. /// Add a new session with its summary, timestamped now.
pub fn add( pub fn add(
&mut self, &mut self,
id: &str, id: &str,
@@ -47,6 +47,21 @@ impl SessionCache {
.unwrap_or_default() .unwrap_or_default()
.as_secs_f64() .as_secs_f64()
* 1_000_000.0; // microseconds * 1_000_000.0; // microseconds
self.add_at(id, start_idx, end_idx, channel, summary, ts);
}
/// Add a session with an explicit timestamp (Unix **microseconds**, the
/// unit [`SessionEntry::ts`] uses) — for importers carrying sessions over
/// from another store, whose original time should be kept.
pub fn add_at(
&mut self,
id: &str,
start_idx: usize,
end_idx: usize,
channel: &str,
summary: &str,
ts: f64,
) {
self.entries.push(SessionEntry { self.entries.push(SessionEntry {
id: id.to_string(), id: id.to_string(),
start_idx: start_idx as u64, start_idx: start_idx as u64,
+1 -3
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@@ -3,7 +3,7 @@ name = "clawhdf5-migrate"
version = "2.7.0" version = "2.7.0"
edition = "2024" edition = "2024"
rust-version.workspace = true rust-version.workspace = true
description = "CLI to migrate SQLite agent memory databases to HDF5 format" description = "CLI to migrate SQLite agent memory databases to clawhdf5-agent stores"
license = "MIT" license = "MIT"
repository = "https://git.redclaw.dev/quantumclaw/clawhdf5" repository = "https://git.redclaw.dev/quantumclaw/clawhdf5"
readme = "README.md" readme = "README.md"
@@ -17,10 +17,8 @@ path = "src/main.rs"
[dependencies] [dependencies]
clawhdf5-agent = { path = "../clawhdf5-agent", version = "2.7.0" } clawhdf5-agent = { path = "../clawhdf5-agent", version = "2.7.0" }
clawhdf5-format = { path = "../clawhdf5-format", version = "2.7.0" } clawhdf5-format = { path = "../clawhdf5-format", version = "2.7.0" }
clawhdf5 = { path = "../clawhdf5", version = "2.7.0" }
rusqlite = { version = "0.31", features = ["bundled"] } rusqlite = { version = "0.31", features = ["bundled"] }
clap = { version = "4", features = ["derive"] } clap = { version = "4", features = ["derive"] }
half = { workspace = true }
[dev-dependencies] [dev-dependencies]
tempfile = { workspace = true } tempfile = { workspace = true }
+16 -3
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@@ -3,9 +3,12 @@
[![crates.io](https://img.shields.io/crates/v/clawhdf5-migrate.svg)](https://crates.io/crates/clawhdf5-migrate) [![crates.io](https://img.shields.io/crates/v/clawhdf5-migrate.svg)](https://crates.io/crates/clawhdf5-migrate)
[![docs.rs](https://img.shields.io/docsrs/clawhdf5-migrate)](https://docs.rs/clawhdf5-migrate) [![docs.rs](https://img.shields.io/docsrs/clawhdf5-migrate)](https://docs.rs/clawhdf5-migrate)
CLI tool to migrate SQLite agent memory databases to HDF5 format. CLI tool to migrate SQLite agent memory databases (the ZeroClaw layout) to a
[clawhdf5-agent](https://crates.io/crates/clawhdf5-agent) store.
Converts existing SQLite-based agent memory stores (embeddings, text chunks, metadata) into the HDF5 format used by [clawhdf5-agent](https://crates.io/crates/clawhdf5-agent). The output is written through `clawhdf5-agent`'s own API, so it opens with
`HDF5Memory::open` and is searchable immediately: memory records, sessions and
the knowledge graph (entities and relations) are carried over.
## Installation ## Installation
@@ -16,9 +19,19 @@ cargo install clawhdf5-migrate
## Usage ## Usage
```bash ```bash
clawhdf5-migrate --input agent.db --output agent.h5 clawhdf5-migrate --sqlite agent.db --hdf5 agent.h5 --agent-id my-agent
``` ```
Embeddings are stored as float16 (the library default for new stores); pass
`--f32` for full precision. Every embedding must have the same dimension
(the first row's, or `--embedding-dim`, which a source with no memory records
requires); rows are never truncated, and the whole source is checked before an
existing output store is replaced. `--incremental` adds only new rows to an
existing store of the same dimension and carries over changes to rows'
deleted flags, `--skip-deleted` leaves out tombstoned rows, and `--dry-run`
only counts.
See `clawhdf5-migrate --help` for every option.
## License ## License
MIT MIT
-163
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@@ -1,163 +0,0 @@
//! Read a migration HDF5 file back into the in-memory data model.
//!
//! Used to verify migrated content (real validation) and to merge new rows into
//! an existing output (incremental migration). Mirrors the layout produced by
//! [`crate::hdf5_writer`].
use clawhdf5::reader::{File, Group};
use clawhdf5_format::type_builders::AttrValue;
use crate::sqlite_reader::{Entity, MemoryChunk, Relation, Session, SqliteData};
type BoxErr = Box<dyn std::error::Error>;
fn read_strings(group: &Group<'_>, name: &str) -> Result<Vec<String>, BoxErr> {
Ok(group.dataset(name)?.read_string()?)
}
fn read_i64s(group: &Group<'_>, name: &str) -> Result<Vec<i64>, BoxErr> {
Ok(group.dataset(name)?.read_i64()?)
}
fn read_f64s(group: &Group<'_>, name: &str) -> Result<Vec<f64>, BoxErr> {
Ok(group.dataset(name)?.read_f64()?)
}
/// Read the embeddings dataset as a flat `Vec<f32>` of `n * dim` values,
/// handling both f32 and (lossy) f16 storage.
fn read_embeddings_flat(group: &Group<'_>) -> Result<Vec<f32>, BoxErr> {
Ok(group.dataset("embeddings")?.read_f32()?)
}
/// Read a migration HDF5 file into a [`SqliteData`].
pub fn read_hdf5(path: &str) -> Result<SqliteData, BoxErr> {
let file = File::open(path)?;
let embedding_dim = match file.root().attrs()?.get("embedding_dim") {
Some(AttrValue::I64(d)) => *d as usize,
_ => 0,
};
let chunks = read_chunks(&file, embedding_dim)?;
let sessions = read_sessions(&file)?;
let entities = read_entities(&file)?;
let relations = read_relations(&file)?;
Ok(SqliteData {
chunks,
sessions,
entities,
relations,
embedding_dim,
// Not a SQLite read — the caller (incremental migration) carries
// forward the current run's actual `source_path` from the fresh
// SQLite read instead of using this placeholder.
source_path: String::new(),
})
}
fn read_chunks(file: &File, dim: usize) -> Result<Vec<MemoryChunk>, BoxErr> {
let g = file.group("chunks")?;
let count = group_count(&g)?;
if count == 0 {
return Ok(Vec::new());
}
let ids = read_i64s(&g, "id")?;
let texts = read_strings(&g, "text")?;
let channels = read_strings(&g, "source_channel")?;
let timestamps = read_f64s(&g, "timestamp")?;
let session_ids = read_strings(&g, "session_id")?;
let tags = read_strings(&g, "tags")?;
let deleted = g.dataset("deleted")?.read_i32()?;
let emb_flat = read_embeddings_flat(&g)?;
let dim = dim.max(1);
let mut chunks = Vec::with_capacity(ids.len());
for (i, &id) in ids.iter().enumerate() {
let embedding = emb_flat
.get(i * dim..(i + 1) * dim)
.map(|s| s.to_vec())
.unwrap_or_default();
chunks.push(MemoryChunk {
id,
chunk: texts.get(i).cloned().unwrap_or_default(),
embedding,
source_channel: channels.get(i).cloned().unwrap_or_default(),
timestamp: timestamps.get(i).copied().unwrap_or(0.0),
session_id: session_ids.get(i).cloned().unwrap_or_default(),
tags: tags.get(i).cloned().unwrap_or_default(),
deleted: deleted.get(i).copied().unwrap_or(0),
});
}
Ok(chunks)
}
fn read_sessions(file: &File) -> Result<Vec<Session>, BoxErr> {
let g = file.group("sessions")?;
if group_count(&g)? == 0 {
return Ok(Vec::new());
}
let ids = read_strings(&g, "id")?;
let starts = read_i64s(&g, "start_idx")?;
let ends = read_i64s(&g, "end_idx")?;
let channels = read_strings(&g, "channel")?;
let timestamps = read_f64s(&g, "timestamp")?;
let summaries = read_strings(&g, "summary")?;
Ok((0..ids.len())
.map(|i| Session {
id: ids[i].clone(),
start_idx: starts.get(i).copied().unwrap_or(0),
end_idx: ends.get(i).copied().unwrap_or(0),
channel: channels.get(i).cloned().unwrap_or_default(),
timestamp: timestamps.get(i).copied().unwrap_or(0.0),
summary: summaries.get(i).cloned().unwrap_or_default(),
})
.collect())
}
fn read_entities(file: &File) -> Result<Vec<Entity>, BoxErr> {
let g = file.group("entities")?;
if group_count(&g)? == 0 {
return Ok(Vec::new());
}
let ids = read_i64s(&g, "id")?;
let names = read_strings(&g, "name")?;
let types = read_strings(&g, "type")?;
let emb_idxs = read_i64s(&g, "embedding_idx")?;
Ok((0..ids.len())
.map(|i| Entity {
id: ids[i],
name: names.get(i).cloned().unwrap_or_default(),
entity_type: types.get(i).cloned().unwrap_or_default(),
embedding_idx: emb_idxs.get(i).copied().unwrap_or(-1),
})
.collect())
}
fn read_relations(file: &File) -> Result<Vec<Relation>, BoxErr> {
let g = file.group("relations")?;
if group_count(&g)? == 0 {
return Ok(Vec::new());
}
let srcs = read_i64s(&g, "src")?;
let tgts = read_i64s(&g, "tgt")?;
let rels = read_strings(&g, "relation")?;
let weights = read_f64s(&g, "weight")?;
let timestamps = read_f64s(&g, "timestamp")?;
Ok((0..srcs.len())
.map(|i| Relation {
src: srcs[i],
tgt: tgts.get(i).copied().unwrap_or(0),
relation: rels.get(i).cloned().unwrap_or_default(),
weight: weights.get(i).copied().unwrap_or(1.0),
timestamp: timestamps.get(i).copied().unwrap_or(0.0),
})
.collect())
}
fn group_count(group: &Group<'_>) -> Result<u64, BoxErr> {
match group.attrs()?.get("count") {
Some(AttrValue::I64(n)) => Ok(*n as u64),
_ => Ok(0),
}
}
-366
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@@ -1,366 +0,0 @@
use clawhdf5::writer::FileBuilder;
use clawhdf5_format::datatype::{CharacterSet, Datatype, StringPadding};
use clawhdf5_format::type_builders::AttrValue;
use crate::sqlite_reader::SqliteData;
/// Options controlling HDF5 output.
pub struct WriteOptions {
pub agent_id: String,
pub embedder: String,
pub compression: bool,
pub compression_level: u32,
pub float16: bool,
}
/// Write SQLite data to an HDF5 file.
pub fn write_hdf5(
path: &str,
data: &SqliteData,
opts: &WriteOptions,
) -> Result<(), Box<dyn std::error::Error>> {
let mut builder = FileBuilder::new();
let timestamp = iso8601_now();
// Root-level metadata attributes
builder.set_attr("agent_id", AttrValue::String(opts.agent_id.clone()));
builder.set_attr("embedder", AttrValue::String(opts.embedder.clone()));
builder.set_attr("embedding_dim", AttrValue::I64(data.embedding_dim as i64));
builder.set_attr("source", AttrValue::String("sqlite-migration".into()));
builder.set_attr("version", AttrValue::I64(1));
// Lineage: which SQLite database this output was migrated from and when,
// plus the migrator tool version — so a chain of `--incremental` runs
// still has an audit trail instead of every run overwriting the same
// static attributes (see research/03_provenance.md, INT-03).
builder.set_attr("source_path", AttrValue::String(data.source_path.clone()));
builder.set_attr("migrated_at", AttrValue::String(timestamp.clone()));
builder.set_attr(
"migrator_version",
AttrValue::String(env!("CARGO_PKG_VERSION").to_owned()),
);
write_chunks_group(&mut builder, data, opts, &timestamp);
write_sessions_group(&mut builder, data);
write_entities_group(&mut builder, data);
write_relations_group(&mut builder, data);
builder.write(path)?;
Ok(())
}
/// Current UTC time formatted as an ISO-8601 / RFC-3339 timestamp
/// (`YYYY-MM-DDTHH:MM:SSZ`), with no external date/time dependency.
fn iso8601_now() -> String {
let secs = std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)
.unwrap_or_default()
.as_secs();
let days = (secs / 86_400) as i64;
let time_of_day = secs % 86_400;
let (h, m, s) = (
time_of_day / 3600,
(time_of_day % 3600) / 60,
time_of_day % 60,
);
let (y, mo, d) = civil_from_days(days);
format!("{y:04}-{mo:02}-{d:02}T{h:02}:{m:02}:{s:02}Z")
}
/// Days-since-epoch to (year, month, day), Howard Hinnant's `civil_from_days`
/// algorithm (proleptic Gregorian calendar, valid for the full `i64` range).
fn civil_from_days(z: i64) -> (i64, u32, u32) {
let z = z + 719_468;
let era = if z >= 0 { z } else { z - 146_096 } / 146_097;
let doe = (z - era * 146_097) as u64; // [0, 146096]
let yoe = (doe - doe / 1460 + doe / 36_524 - doe / 146_096) / 365; // [0, 399]
let y = yoe as i64 + era * 400;
let doy = doe - (365 * yoe + yoe / 4 - yoe / 100); // [0, 365]
let mp = (5 * doy + 2) / 153; // [0, 11]
let d = (doy - (153 * mp + 2) / 5 + 1) as u32; // [1, 31]
let m = (if mp < 10 { mp + 3 } else { mp - 9 }) as u32; // [1, 12]
let y = if m <= 2 { y + 1 } else { y };
(y, m, d)
}
/// Build a fixed-length string Datatype from the max byte length of the items.
fn string_dtype(max_len: usize) -> Datatype {
Datatype::String {
size: max_len.max(1) as u32,
padding: StringPadding::NullPad,
charset: CharacterSet::Utf8,
}
}
/// Pack a slice of strings into null-padded raw bytes of uniform width.
fn pack_strings(strings: &[String]) -> (Vec<u8>, usize) {
let max_len = strings.iter().map(|s| s.len()).max().unwrap_or(0).max(1);
let mut buf = vec![0u8; strings.len() * max_len];
for (i, s) in strings.iter().enumerate() {
let start = i * max_len;
let bytes = s.as_bytes();
let copy_len = bytes.len().min(max_len);
buf[start..start + copy_len].copy_from_slice(&bytes[..copy_len]);
}
(buf, max_len)
}
fn apply_compression(ds: &mut clawhdf5_format::type_builders::DatasetBuilder, opts: &WriteOptions) {
if opts.compression {
ds.with_deflate(opts.compression_level);
ds.with_shuffle();
}
}
fn write_chunks_group(
builder: &mut FileBuilder,
data: &SqliteData,
opts: &WriteOptions,
timestamp: &str,
) {
let mut group = builder.create_group("chunks");
let n = data.chunks.len() as u64;
if n == 0 {
group.set_attr("count", AttrValue::I64(0));
builder.add_group(group.finish());
return;
}
group.set_attr("count", AttrValue::I64(n as i64));
// Source attribution attached directly to the content-bearing datasets
// (SHA-256 of the raw bytes + creator/timestamp/source), so the chunk
// text and embeddings each carry their own verifiable provenance
// (see clawhdf5_format::provenance / `Dataset::verify_provenance`).
let source_opt = if data.source_path.is_empty() {
None
} else {
Some(data.source_path.as_str())
};
// ids
let ids: Vec<i64> = data.chunks.iter().map(|c| c.id).collect();
group.create_dataset("id").with_i64_data(&ids);
// text
let texts: Vec<String> = data.chunks.iter().map(|c| c.chunk.clone()).collect();
let (text_raw, text_len) = pack_strings(&texts);
group
.create_dataset("text")
.with_compound_data(string_dtype(text_len), text_raw, n)
.with_provenance("clawhdf5-migrate", timestamp, source_opt);
// embeddings - flatten to [N, dim]
let dim = data.embedding_dim;
if opts.float16 {
let f16_data: Vec<u16> = data
.chunks
.iter()
.flat_map(|c| {
c.embedding
.iter()
.map(|&v| half::f16::from_f32(v).to_bits())
})
.collect();
let raw: Vec<u8> = f16_data.iter().flat_map(|v| v.to_le_bytes()).collect();
let f16_dtype = Datatype::FloatingPoint {
size: 2,
byte_order: clawhdf5_format::datatype::DatatypeByteOrder::LittleEndian,
bit_offset: 0,
bit_precision: 16,
exponent_location: 10,
exponent_size: 5,
mantissa_location: 0,
mantissa_size: 10,
exponent_bias: 15,
};
let ds = group
.create_dataset("embeddings")
.with_compound_data(f16_dtype, raw, n)
.with_shape(&[n, dim as u64])
.with_provenance("clawhdf5-migrate", timestamp, source_opt);
apply_compression(ds, opts);
} else {
let flat: Vec<f32> = data
.chunks
.iter()
.flat_map(|c| c.embedding.iter().copied())
.collect();
let ds = group
.create_dataset("embeddings")
.with_f32_data(&flat)
.with_shape(&[n, dim as u64])
.with_provenance("clawhdf5-migrate", timestamp, source_opt);
apply_compression(ds, opts);
}
// source_channel
let channels: Vec<String> = data
.chunks
.iter()
.map(|c| c.source_channel.clone())
.collect();
let (ch_raw, ch_len) = pack_strings(&channels);
group
.create_dataset("source_channel")
.with_compound_data(string_dtype(ch_len), ch_raw, n);
// timestamp
let timestamps: Vec<f64> = data.chunks.iter().map(|c| c.timestamp).collect();
group.create_dataset("timestamp").with_f64_data(&timestamps);
// session_id
let sess_ids: Vec<String> = data.chunks.iter().map(|c| c.session_id.clone()).collect();
let (sid_raw, sid_len) = pack_strings(&sess_ids);
group
.create_dataset("session_id")
.with_compound_data(string_dtype(sid_len), sid_raw, n);
// tags
let tags: Vec<String> = data.chunks.iter().map(|c| c.tags.clone()).collect();
let (tag_raw, tag_len) = pack_strings(&tags);
group
.create_dataset("tags")
.with_compound_data(string_dtype(tag_len), tag_raw, n);
// deleted
let deleted: Vec<i32> = data.chunks.iter().map(|c| c.deleted).collect();
group.create_dataset("deleted").with_i32_data(&deleted);
builder.add_group(group.finish());
}
fn write_sessions_group(builder: &mut FileBuilder, data: &SqliteData) {
let mut group = builder.create_group("sessions");
let n = data.sessions.len() as u64;
group.set_attr("count", AttrValue::I64(n as i64));
if n == 0 {
builder.add_group(group.finish());
return;
}
let ids: Vec<String> = data.sessions.iter().map(|s| s.id.clone()).collect();
let (id_raw, id_len) = pack_strings(&ids);
group
.create_dataset("id")
.with_compound_data(string_dtype(id_len), id_raw, n);
let start_idxs: Vec<i64> = data.sessions.iter().map(|s| s.start_idx).collect();
group.create_dataset("start_idx").with_i64_data(&start_idxs);
let end_idxs: Vec<i64> = data.sessions.iter().map(|s| s.end_idx).collect();
group.create_dataset("end_idx").with_i64_data(&end_idxs);
let channels: Vec<String> = data.sessions.iter().map(|s| s.channel.clone()).collect();
let (ch_raw, ch_len) = pack_strings(&channels);
group
.create_dataset("channel")
.with_compound_data(string_dtype(ch_len), ch_raw, n);
let timestamps: Vec<f64> = data.sessions.iter().map(|s| s.timestamp).collect();
group.create_dataset("timestamp").with_f64_data(&timestamps);
let summaries: Vec<String> = data.sessions.iter().map(|s| s.summary.clone()).collect();
let (sum_raw, sum_len) = pack_strings(&summaries);
group
.create_dataset("summary")
.with_compound_data(string_dtype(sum_len), sum_raw, n);
builder.add_group(group.finish());
}
fn write_entities_group(builder: &mut FileBuilder, data: &SqliteData) {
let mut group = builder.create_group("entities");
let n = data.entities.len() as u64;
group.set_attr("count", AttrValue::I64(n as i64));
if n == 0 {
builder.add_group(group.finish());
return;
}
let ids: Vec<i64> = data.entities.iter().map(|e| e.id).collect();
group.create_dataset("id").with_i64_data(&ids);
let names: Vec<String> = data.entities.iter().map(|e| e.name.clone()).collect();
let (name_raw, name_len) = pack_strings(&names);
group
.create_dataset("name")
.with_compound_data(string_dtype(name_len), name_raw, n);
let types: Vec<String> = data
.entities
.iter()
.map(|e| e.entity_type.clone())
.collect();
let (type_raw, type_len) = pack_strings(&types);
group
.create_dataset("type")
.with_compound_data(string_dtype(type_len), type_raw, n);
let emb_idxs: Vec<i64> = data.entities.iter().map(|e| e.embedding_idx).collect();
group
.create_dataset("embedding_idx")
.with_i64_data(&emb_idxs);
builder.add_group(group.finish());
}
fn write_relations_group(builder: &mut FileBuilder, data: &SqliteData) {
let mut group = builder.create_group("relations");
let n = data.relations.len() as u64;
group.set_attr("count", AttrValue::I64(n as i64));
if n == 0 {
builder.add_group(group.finish());
return;
}
let srcs: Vec<i64> = data.relations.iter().map(|r| r.src).collect();
group.create_dataset("src").with_i64_data(&srcs);
let tgts: Vec<i64> = data.relations.iter().map(|r| r.tgt).collect();
group.create_dataset("tgt").with_i64_data(&tgts);
let rels: Vec<String> = data.relations.iter().map(|r| r.relation.clone()).collect();
let (rel_raw, rel_len) = pack_strings(&rels);
group
.create_dataset("relation")
.with_compound_data(string_dtype(rel_len), rel_raw, n);
let weights: Vec<f64> = data.relations.iter().map(|r| r.weight).collect();
group.create_dataset("weight").with_f64_data(&weights);
let timestamps: Vec<f64> = data.relations.iter().map(|r| r.timestamp).collect();
group.create_dataset("timestamp").with_f64_data(&timestamps);
builder.add_group(group.finish());
}
#[cfg(test)]
mod time_tests {
use super::civil_from_days;
#[test]
fn epoch_day_zero_is_1970_01_01() {
assert_eq!(civil_from_days(0), (1970, 1, 1));
}
#[test]
fn known_dates_roundtrip() {
// 2026-08-16 is 20,681 days after 1970-01-01.
assert_eq!(civil_from_days(20_681), (2026, 8, 16));
// 2000-02-29 (leap day itself) and 2000-03-01 (the day after).
assert_eq!(civil_from_days(11_016), (2000, 2, 29));
assert_eq!(civil_from_days(11_017), (2000, 3, 1));
}
#[test]
fn iso8601_now_has_expected_shape() {
let ts = super::iso8601_now();
assert_eq!(ts.len(), "2026-08-16T00:00:00Z".len());
assert!(ts.starts_with("20")); // sanity: 21st-century year
assert!(ts.ends_with('Z'));
}
}
File diff suppressed because it is too large Load Diff
+30 -39
View File
@@ -50,12 +50,8 @@ pub struct SqliteData {
pub sessions: Vec<Session>, pub sessions: Vec<Session>,
pub entities: Vec<Entity>, pub entities: Vec<Entity>,
pub relations: Vec<Relation>, pub relations: Vec<Relation>,
/// `--embedding-dim`, or the first row's; 0 when neither exists.
pub embedding_dim: usize, pub embedding_dim: usize,
/// Filesystem path of the SQLite database this data was read from, for
/// provenance attribution on the HDF5 output. Empty when the data did
/// not come directly from a SQLite read (e.g. re-read of a prior HDF5
/// migration output for an incremental merge).
pub source_path: String,
} }
/// A table name plus the ordered column names the reader maps by position. /// A table name plus the ordered column names the reader maps by position.
@@ -167,11 +163,13 @@ pub fn read_counts(
}) })
} }
/// Auto-detect embedding dimension from the first chunk's BLOB size. /// Auto-detect embedding dimension from the BLOB size of the first chunk (in
/// id order, deleted or not).
fn detect_embedding_dim(conn: &Connection, config: &SchemaConfig) -> SqlResult<Option<usize>> { fn detect_embedding_dim(conn: &Connection, config: &SchemaConfig) -> SqlResult<Option<usize>> {
let emb_col = config.chunks.columns.get(2).copied().unwrap_or("embedding"); let emb_col = config.chunks.columns.get(2).copied().unwrap_or("embedding");
let id_col = config.chunks.columns.first().copied().unwrap_or("id");
let mut stmt = conn.prepare(&format!( let mut stmt = conn.prepare(&format!(
"SELECT {emb_col} FROM {} LIMIT 1", "SELECT {emb_col} FROM {} ORDER BY {id_col} LIMIT 1",
config.chunks.table config.chunks.table
))?; ))?;
let mut rows = stmt.query([])?; let mut rows = stmt.query([])?;
@@ -195,24 +193,16 @@ fn blob_to_f32(blob: &[u8]) -> Vec<f32> {
/// Read all data from a ZeroClaw SQLite database. /// Read all data from a ZeroClaw SQLite database.
/// ///
/// If `skip_deleted` is true, rows with `deleted=1` are excluded from chunks. /// If `skip_deleted` is true, rows with `deleted=1` are excluded from chunks.
/// If `embedding_dim` is `None`, auto-detect from the first row. /// If `embedding_dim` is `None`, auto-detect from the first row (0 when there
/// are no rows). Embeddings are returned at their full stored length whatever
/// the dimension: checking that every row matches it is the writer's job
/// (`store_writer::write_store`), so a mismatch is an error, not silent
/// truncation.
pub fn read_sqlite( pub fn read_sqlite(
path: &str, path: &str,
skip_deleted: bool, skip_deleted: bool,
embedding_dim: Option<usize>, embedding_dim: Option<usize>,
config: &SchemaConfig, config: &SchemaConfig,
) -> Result<SqliteData, Box<dyn std::error::Error>> {
read_sqlite_filtered(path, skip_deleted, embedding_dim, config, 0)
}
/// Like [`read_sqlite`] but only reads chunks whose id is greater than
/// `min_chunk_id` (0 = all). Used for incremental migration.
pub fn read_sqlite_filtered(
path: &str,
skip_deleted: bool,
embedding_dim: Option<usize>,
config: &SchemaConfig,
min_chunk_id: i64,
) -> Result<SqliteData, Box<dyn std::error::Error>> { ) -> Result<SqliteData, Box<dyn std::error::Error>> {
let conn = Connection::open(path)?; let conn = Connection::open(path)?;
@@ -221,7 +211,7 @@ pub fn read_sqlite_filtered(
None => detect_embedding_dim(&conn, config)?.unwrap_or(0), None => detect_embedding_dim(&conn, config)?.unwrap_or(0),
}; };
let chunks = read_chunks(&conn, skip_deleted, dim, config, min_chunk_id)?; let chunks = read_chunks(&conn, skip_deleted, config)?;
let sessions = read_sessions(&conn, config)?; let sessions = read_sessions(&conn, config)?;
let entities = read_entities(&conn, config)?; let entities = read_entities(&conn, config)?;
let relations = read_relations(&conn, config)?; let relations = read_relations(&conn, config)?;
@@ -232,42 +222,43 @@ pub fn read_sqlite_filtered(
entities, entities,
relations, relations,
embedding_dim: dim, embedding_dim: dim,
source_path: path.to_owned(),
}) })
} }
fn read_chunks( fn read_chunks(
conn: &Connection, conn: &Connection,
skip_deleted: bool, skip_deleted: bool,
expected_dim: usize,
config: &SchemaConfig, config: &SchemaConfig,
min_chunk_id: i64,
) -> SqlResult<Vec<MemoryChunk>> { ) -> SqlResult<Vec<MemoryChunk>> {
let id_col = config.chunks.columns.first().copied().unwrap_or("id"); let id_col = config.chunks.columns.first().copied().unwrap_or("id");
let deleted_col = config.chunks.columns.get(7).copied().unwrap_or("deleted"); let deleted_col = config.chunks.columns.get(7).copied().unwrap_or("deleted");
let mut conds = Vec::new(); let mut where_clause = String::new();
if skip_deleted { if skip_deleted {
conds.push(format!("{deleted_col} = 0")); where_clause = format!(" WHERE {deleted_col} = 0");
} }
if min_chunk_id > 0 { // In id order, so the store's records follow the source's order.
conds.push(format!("{id_col} > {min_chunk_id}")); where_clause.push_str(&format!(" ORDER BY {id_col}"));
}
let where_clause = if conds.is_empty() {
String::new()
} else {
format!(" WHERE {}", conds.join(" AND "))
};
let sql = config.chunks.select(&where_clause); let sql = config.chunks.select(&where_clause);
let mut stmt = conn.prepare(&sql)?; let mut stmt = conn.prepare(&sql)?;
let rows = stmt.query_map([], |row| { let rows = stmt.query_map([], |row| {
let blob: Vec<u8> = row.get(2)?; let blob: Vec<u8> = row.get(2)?;
let mut embedding = blob_to_f32(&blob); if !blob.len().is_multiple_of(4) {
let id: i64 = row.get(0)?;
// Validate/truncate to expected dimension return Err(rusqlite::Error::FromSqlConversionFailure(
if expected_dim > 0 { 2,
embedding.truncate(expected_dim); rusqlite::types::Type::Blob,
format!(
"chunk id {id}: embedding BLOB is {} bytes, not a whole number of \
little-endian f32 values",
blob.len()
)
.into(),
));
} }
// Read at full length: rows of the wrong dimension are rejected by
// the writer, never truncated to fit.
let embedding = blob_to_f32(&blob);
Ok(MemoryChunk { Ok(MemoryChunk {
id: row.get(0)?, id: row.get(0)?,
+407
View File
@@ -0,0 +1,407 @@
//! Write migrated SQLite data into a clawhdf5-agent store.
//!
//! Everything goes through `clawhdf5-agent`'s own API — `HDF5Memory::create`
//! (or `open` for `--incremental`), `save_batch`, `delete_batch`, the session
//! cache and the knowledge graph — so the result is an ordinary agent store
//! that `HDF5Memory::open` accepts, not a second hand-built copy of its schema.
use std::collections::{HashMap, HashSet};
use std::path::Path;
use clawhdf5_agent::{AgentMemory, HDF5Memory, MemoryConfig, MemoryEntry};
use clawhdf5_format::float16::round_to_f16;
use crate::sqlite_reader::{MemoryChunk, SqliteData};
type BoxErr = Box<dyn std::error::Error>;
/// SQLite timestamps are Unix seconds; the agent's session and relation
/// timestamps are Unix microseconds (memory records stay in seconds).
pub const US_PER_SEC: f64 = 1_000_000.0;
/// Options controlling the output store.
#[derive(Debug, Clone)]
pub struct WriteOptions {
pub agent_id: String,
pub embedder: String,
pub compression: bool,
pub compression_level: u32,
/// Store full-precision `f32` embeddings instead of the library default
/// (half precision). Only applies to a newly created store: an existing
/// store keeps the precision it was created with.
pub f32: bool,
/// Add to the store at the output path if there is one, instead of
/// replacing it.
pub incremental: bool,
/// Leave out deleted source rows that are not in the store. (A deleted
/// row that matches an active store record still tombstones it, so pass
/// deleted rows in `data` for an incremental run.)
pub skip_deleted: bool,
}
/// What the migration wrote, and where each source row went, so validation
/// can compare the store with the source row by row.
#[derive(Debug, Default)]
pub struct Migration {
/// Whether the output store existed and was added to (`--incremental`).
pub appended_to_existing: bool,
/// The store's embedding precision.
pub float16: bool,
pub embedding_dim: usize,
/// Records in the store after the migration (including tombstones).
pub store_count: usize,
/// `(store index, source chunk index)` of every record written.
pub records: Vec<(usize, usize)>,
/// Source chunks already in the store (incremental), not written again.
pub chunks_present: usize,
/// `(store index, source chunk index)` of records that were active in
/// the store but whose source row is now deleted (incremental): they were
/// tombstoned by this run.
pub deleted_in_store: Vec<(usize, usize)>,
/// Source rows that were deleted in the store but are active in the
/// source (incremental): the agent has no un-delete, so each was written
/// again as a new record (counted in `records` too).
pub restored: usize,
/// Deleted source rows left out because of `skip_deleted`.
pub deleted_skipped: usize,
/// `(store session index, source session index)` of each session written.
pub sessions: Vec<(usize, usize)>,
pub sessions_present: usize,
/// `(store entity id, source entity index)` of each entity written.
pub entities: Vec<(u64, usize)>,
pub entities_present: usize,
/// SQLite entity id -> store entity id, for every source entity.
pub entity_ids: HashMap<i64, u64>,
/// `(store relation index, source relation index)` of each relation written.
pub relations: Vec<(usize, usize)>,
pub relations_present: usize,
/// Source relations naming an entity id that is not in the entities
/// table; the knowledge graph cannot hold them, so they are skipped.
pub dangling_relations: Vec<usize>,
/// Messages of the write-anomaly alerts the agent raised while importing
/// (informational; they never block a save — a bulk import typically
/// trips the write-rate check).
pub anomaly_alerts: Vec<String>,
}
/// Identity of a memory record for incremental de-duplication: every field
/// the agent stores except the embedding (whose stored form depends on the
/// store's precision).
type RecordKey = (String, String, String, String, u64);
fn record_key(
chunk: &str,
source_channel: &str,
session_id: &str,
tags: &str,
ts: f64,
) -> RecordKey {
(
chunk.to_owned(),
source_channel.to_owned(),
session_id.to_owned(),
tags.to_owned(),
ts.to_bits(),
)
}
/// Reject rows the agent would otherwise store differently from the source,
/// or not at all: an embedding of a different length from the store's
/// dimension (the agent pads/truncates silently), an empty embedding, or, in
/// a float16 store, a value beyond the half-precision range.
///
/// Every source row is checked, including ones that end up not being written
/// (already in the store, or deleted and skipped): the source must be
/// consistent as a whole, and the check runs before the store is touched.
fn check_chunks(chunks: &[MemoryChunk], dim: usize, float16: bool) -> Result<(), BoxErr> {
for c in chunks {
if c.embedding.is_empty() {
return Err(format!(
"chunk id {}: the embedding is empty; an agent store needs an embedding \
for every record",
c.id
)
.into());
}
if c.embedding.len() != dim {
return Err(format!(
"chunk id {}: embedding has {} values, expected {dim}; every row must have \
the store's dimension (detected from the first row unless --embedding-dim \
is given), and rows are never truncated or padded to fit",
c.id,
c.embedding.len()
)
.into());
}
if float16
&& let Some((k, v)) = c
.embedding
.iter()
.enumerate()
.find(|&(_, &v)| v.is_finite() && round_to_f16(v).is_infinite())
{
return Err(format!(
"chunk id {}: embedding[{k}] = {v} is outside the half-precision range \
(±65504) of a float16 store; migrate with --f32",
c.id
)
.into());
}
}
Ok(())
}
/// Migrate `data` into the agent store at `path`.
///
/// Without `opts.incremental` (or when nothing exists at `path`) a new store
/// is created, replacing any file there — but only once every source row has
/// passed [`check_chunks`], so a source that cannot be migrated leaves an
/// existing store untouched. With it, the existing store is opened and only
/// source rows it does not already hold are added: memory records are
/// matched on their content, sessions on their id, entities on name and
/// type, relations on (source, target, relation). A matched record then
/// takes the source row's deleted flag: see [`Migration::deleted_in_store`]
/// and [`Migration::restored`].
pub fn write_store(
path: &Path,
data: &SqliteData,
opts: &WriteOptions,
) -> Result<Migration, BoxErr> {
let existing = opts.incremental && path.exists();
let mut mem = if existing {
// `open` does not modify the store beyond what the agent itself does
// on open; the checks below run before anything is written.
let mem = HDF5Memory::open(path)?;
let dim = mem.config().embedding_dim;
// `data.embedding_dim` is 0 only for a source with no records and no
// --embedding-dim, which has no dimension to disagree with.
if data.embedding_dim != 0 && dim != data.embedding_dim {
let hint = if dim == 0 {
" (a store created from a source with no memory records; re-create it \
with --embedding-dim)"
} else {
""
};
return Err(format!(
"the store at {} has embedding_dim {dim}{hint}, the source {}; \
embeddings of a different dimension cannot be added to it",
path.display(),
data.embedding_dim
)
.into());
}
check_chunks(&data.chunks, dim, mem.config().float16)?;
mem
} else {
// (With records, a dimension of 0 means an empty first embedding,
// which `check_chunks` reports more precisely.)
if data.embedding_dim == 0 && data.chunks.is_empty() {
return Err(
"the source has no memory records to detect the embedding dimension \
from; pass --embedding-dim (the dimension of the agent's embedder), \
or the store could never hold a record"
.into(),
);
}
let mut config = MemoryConfig::new(path.to_path_buf(), &opts.agent_id, data.embedding_dim);
config.embedder = opts.embedder.clone();
config.compression = opts.compression;
config.compression_level = opts.compression_level;
// Only ever switch the library default off (as `clawhdf5-cli create`).
if opts.f32 {
config.float16 = false;
}
// Before `create`, which replaces whatever is at `path`.
check_chunks(&data.chunks, config.embedding_dim, config.float16)?;
HDF5Memory::create(config)?
};
let float16 = mem.config().float16;
let dim = mem.config().embedding_dim;
let mut m = Migration {
appended_to_existing: existing,
float16,
embedding_dim: dim,
..Migration::default()
};
// ---- Memory records --------------------------------------------------
// Store indices of every record the store already holds, by content, so
// a source row that appears twice is only treated as present as often
// as the store has it.
let mut present: HashMap<RecordKey, Vec<usize>> = HashMap::new();
if existing {
let c = &mem.cache;
for i in 0..c.len() {
let key = record_key(
&c.chunks[i],
&c.source_channels[i],
&c.session_ids[i],
&c.tags[i],
c.timestamps[i],
);
present.entry(key).or_default().push(i);
}
}
let key_of = |c: &MemoryChunk| {
record_key(
&c.chunk,
&c.source_channel,
&c.session_id,
&c.tags,
c.timestamp,
)
};
let tombstoned = |idx: usize| mem.cache.tombstones[idx] != 0;
// Pass 1: a store record in the same deleted state as the source row.
let mut unmatched: Vec<usize> = Vec::new();
for (i, c) in data.chunks.iter().enumerate() {
let src_deleted = c.deleted != 0;
let hit = present.get_mut(&key_of(c)).and_then(|idxs| {
let at = idxs.iter().position(|&x| tombstoned(x) == src_deleted)?;
Some(idxs.remove(at))
});
match hit {
Some(_) => m.chunks_present += 1,
None => unmatched.push(i),
}
}
// Pass 2: a store record whose deleted state differs — the source row
// was deleted or restored since the last migration. The source wins.
let mut new_chunks: Vec<usize> = Vec::with_capacity(unmatched.len());
let mut delete_in_store: Vec<usize> = Vec::new();
for i in unmatched {
let c = &data.chunks[i];
let hit = present
.get_mut(&key_of(c))
.and_then(|idxs| (!idxs.is_empty()).then(|| idxs.remove(0)));
match hit {
// Active in the store, deleted in the source: tombstone it.
Some(idx) if c.deleted != 0 => {
m.deleted_in_store.push((idx, i));
delete_in_store.push(idx);
}
// Deleted in the store, active in the source. The agent has no
// un-delete, so the row is written again as a new active record
// (the tombstone stays until the store is compacted).
Some(_) => {
m.restored += 1;
new_chunks.push(i);
}
None if c.deleted != 0 && opts.skip_deleted => m.deleted_skipped += 1,
None => new_chunks.push(i),
}
}
new_chunks.sort_unstable();
let to_write: Vec<&MemoryChunk> = new_chunks.iter().map(|&i| &data.chunks[i]).collect();
// ---- Sessions (in the cache; persisted by the save_batch checkpoint) ---
let known_sessions: HashSet<String> = mem
.sessions()
.entries
.iter()
.map(|e| e.id.clone())
.collect();
for (i, s) in data.sessions.iter().enumerate() {
if known_sessions.contains(&s.id) {
m.sessions_present += 1;
continue;
}
let sessions = mem.sessions_mut();
let at = sessions.len();
sessions.add_at(
&s.id,
s.start_idx.max(0) as usize,
s.end_idx.max(0) as usize,
&s.channel,
&s.summary,
s.timestamp * US_PER_SEC,
);
m.sessions.push((at, i));
}
// ---- Knowledge graph -------------------------------------------------
let kg = mem.knowledge_mut();
// Matched only against what the store held before this run: the source
// itself is copied as it is, duplicates included.
let by_name_type: HashMap<(String, String), u64> = kg
.entities
.iter()
.map(|e| ((e.name.clone(), e.entity_type.clone()), e.id))
.collect();
for (i, e) in data.entities.iter().enumerate() {
let key = (e.name.clone(), e.entity_type.clone());
let id = match by_name_type.get(&key) {
Some(&id) => {
m.entities_present += 1;
id
}
None => {
let id = kg.add_entity(&e.name, &e.entity_type, e.embedding_idx);
m.entities.push((id, i));
id
}
};
m.entity_ids.insert(e.id, id);
}
let known_relations: HashSet<(u64, u64, String)> = kg
.relations
.iter()
.map(|r| (r.src, r.tgt, r.relation.clone()))
.collect();
for (i, r) in data.relations.iter().enumerate() {
let (Some(&src), Some(&tgt)) = (m.entity_ids.get(&r.src), m.entity_ids.get(&r.tgt)) else {
m.dangling_relations.push(i);
continue;
};
if known_relations.contains(&(src, tgt, r.relation.clone())) {
m.relations_present += 1;
continue;
}
let at = kg.relations.len();
kg.add_relation(src, tgt, &r.relation, r.weight as f32);
kg.relations[at].ts = r.timestamp * US_PER_SEC;
m.relations.push((at, i));
}
// ---- Write: one checkpoint for records, sessions and graph -----------
let entries: Vec<MemoryEntry> = to_write
.iter()
.map(|c| MemoryEntry {
chunk: c.chunk.clone(),
embedding: c.embedding.clone(),
source_channel: c.source_channel.clone(),
timestamp: c.timestamp,
session_id: c.session_id.clone(),
tags: c.tags.clone(),
})
.collect();
let indices = mem.save_batch(entries)?;
m.records = indices
.iter()
.copied()
.zip(new_chunks.iter().copied())
.collect();
// Rows deleted in the source stay deleted: tombstones, as the agent's own
// `delete` leaves them (not compacted away).
// Records matched in the store whose source row has since been deleted
// are tombstoned too.
let tombstones: Vec<usize> = m
.records
.iter()
.filter(|&&(_, src)| data.chunks[src].deleted != 0)
.map(|&(idx, _)| idx)
.chain(delete_in_store)
.collect();
mem.delete_batch(&tombstones)?;
m.anomaly_alerts = mem
.take_anomaly_alerts()
.into_iter()
.map(|a| a.message)
.collect();
m.store_count = mem.count();
drop(mem); // release the single-writer lock before anyone re-opens it
Ok(m)
}
+208 -134
View File
@@ -1,192 +1,266 @@
use clawhdf5::reader::File as Hdf5File; //! Validate a migration by reading the store back the way an agent would:
use clawhdf5_format::provenance::VerifyResult; //! through `HDF5Memory::open_read_only`, comparing what it loads with the
//! SQLite source, and running a search for a migrated record.
use std::path::Path;
use clawhdf5_agent::{AgentMemory, HDF5Memory, SearchOptions};
use clawhdf5_format::float16::round_to_f16;
use crate::hdf5_reader::read_hdf5;
use crate::sqlite_reader::SqliteData; use crate::sqlite_reader::SqliteData;
use crate::store_writer::{Migration, US_PER_SEC};
type BoxErr = Box<dyn std::error::Error>; type BoxErr = Box<dyn std::error::Error>;
/// Summary of a migration validation. /// Summary of a migration validation.
#[derive(Debug)] #[derive(Debug)]
pub struct ValidationSummary { pub struct ValidationSummary {
pub chunks: u64, /// Records in the store (including tombstones).
pub sessions: u64, pub count: usize,
pub entities: u64, /// Records in the store that are not deleted.
pub relations: u64, pub active: usize,
pub embedding_dim: u64, pub sessions: usize,
/// Number of rows whose full content was compared against the source. pub entities: usize,
pub relations: usize,
pub embedding_dim: usize,
pub float16: bool,
/// Rows whose full content was compared against the source.
pub rows_checked: u64, pub rows_checked: u64,
/// Whether the `chunks/text` and `chunks/embeddings` SHINES provenance /// Whether a search for a migrated record found it (`false` when there
/// hashes (written via [`crate::hdf5_writer`]) were both present and /// was no active migrated record with an embedding to search for).
/// matched their recomputed SHA-256 on read-back. `false` when either pub search_checked: bool,
/// dataset has no provenance metadata (e.g. an older output file) or
/// there are zero chunks to check.
pub provenance_verified: bool,
} }
/// Validate a migrated HDF5 file against the source data. /// Validate the store at `path` against the source rows `migration` wrote.
/// ///
/// Reads the written file back and compares actual content — chunk text, /// Counts and the session / entity / relation rows are always checked in
/// embeddings, and every session/entity/relation field — to the source, not /// full. Memory records are content-checked on a representative sample, or
/// just the row counts. When `full` is false a representative sample of chunk /// all of them with `full`. Embeddings must match exactly: the source values
/// rows is content-checked (counts and all other groups are always checked in /// themselves in an `f32` store, their [`round_to_f16`] in a `float16` one.
/// full); when `full` is true every chunk row is compared too. `float16` widens pub fn validate_store(
/// the embedding tolerance to allow for half-precision quantization. path: &Path,
pub fn validate_hdf5(
path: &str,
source: &SqliteData, source: &SqliteData,
migration: &Migration,
full: bool, full: bool,
float16: bool,
) -> Result<ValidationSummary, BoxErr> { ) -> Result<ValidationSummary, BoxErr> {
let got = read_hdf5(path)?; let mut mem = HDF5Memory::open_read_only(path)?;
let provenance_verified = verify_chunk_provenance(path)?; let float16 = mem.config().float16;
let dim = mem.config().embedding_dim;
// ---- Counts ---- // ---- Counts ----
check_count("chunk", got.chunks.len(), source.chunks.len())?; check_count("record", mem.count(), migration.store_count)?;
check_count("session", got.sessions.len(), source.sessions.len())?; if float16 != migration.float16 {
check_count("entity", got.entities.len(), source.entities.len())?;
check_count("relation", got.relations.len(), source.relations.len())?;
if got.embedding_dim != source.embedding_dim {
return Err(format!( return Err(format!(
"embedding_dim mismatch: HDF5 has {}, source has {}", "float16 mismatch: store {float16}, expected {}",
got.embedding_dim, source.embedding_dim migration.float16
) )
.into()); .into());
} }
if dim != migration.embedding_dim {
return Err(format!(
"embedding_dim mismatch: store has {dim}, expected {}",
migration.embedding_dim
)
.into());
}
if !migration.appended_to_existing {
check_count("record", mem.count(), migration.records.len())?;
check_count("session", mem.sessions().len(), migration.sessions.len())?;
check_count(
"entity",
mem.knowledge().entities.len(),
migration.entities.len(),
)?;
check_count(
"relation",
mem.knowledge().relations.len(),
migration.relations.len(),
)?;
}
// ---- Chunk content (sampled or full) ---- // ---- Memory records (sampled or full) ----
let (emb_abs, emb_rel) = if float16 { (1e-2, 1e-2) } else { (1e-4, 0.0) };
let mut rows_checked = 0u64; let mut rows_checked = 0u64;
for i in sample_indices(source.chunks.len(), full) { let expected_value = |v: f32| if float16 { round_to_f16(v) } else { v };
let (s, g) = (&source.chunks[i], &got.chunks[i]); for k in sample_indices(migration.records.len(), full) {
if s.id != g.id { let (idx, src) = migration.records[k];
return Err(field_err("chunk", i, "id", s.id, g.id)); let s = &source.chunks[src];
let c = &mem.cache;
if idx >= c.len() {
return Err(
format!("record {idx} (chunk id {}) is missing from the store", s.id).into(),
);
} }
if s.chunk != g.chunk { let id = s.id;
if c.chunks[idx] != s.chunk {
return Err(format!( return Err(format!(
"chunk[{i}].text mismatch: source {:?}, HDF5 {:?}", "record {idx} (chunk id {id}) text mismatch: source {:?}, store {:?}",
truncate(&s.chunk), truncate(&s.chunk),
truncate(&g.chunk) truncate(&c.chunks[idx])
) )
.into()); .into());
} }
if s.session_id != g.session_id || s.source_channel != g.source_channel || s.tags != g.tags if c.source_channels[idx] != s.source_channel
|| c.session_ids[idx] != s.session_id
|| c.tags[idx] != s.tags
{ {
return Err(format!("chunk[{i}] string field mismatch").into()); return Err(format!("record {idx} (chunk id {id}) string field mismatch").into());
} }
if s.deleted != g.deleted { if c.timestamps[idx].to_bits() != s.timestamp.to_bits() {
return Err(field_err("chunk", i, "deleted", s.deleted, g.deleted));
}
if s.embedding.len() != g.embedding.len() {
return Err(format!( return Err(format!(
"chunk[{i}] embedding length mismatch: {} vs {}", "record {idx} (chunk id {id}) timestamp mismatch: source {}, store {}",
s.embedding.len(), s.timestamp, c.timestamps[idx]
g.embedding.len()
) )
.into()); .into());
} }
for (k, (&a, &b)) in s.embedding.iter().zip(g.embedding.iter()).enumerate() { let deleted = c.tombstones[idx] != 0;
if (a - b).abs() > emb_abs + emb_rel * a.abs() { if deleted != (s.deleted != 0) {
return Err( return Err(format!(
format!("chunk[{i}].embedding[{k}] mismatch: source {a}, HDF5 {b}").into(), "record {idx} (chunk id {id}) deleted mismatch: source {}, store {deleted}",
); s.deleted != 0
)
.into());
}
let got = c.embeddings.get(idx).unwrap_or(&[]);
if got.len() != s.embedding.len() {
return Err(format!(
"record {idx} (chunk id {id}) embedding length mismatch: source {}, store {}",
s.embedding.len(),
got.len()
)
.into());
}
for (j, (&a, &b)) in s.embedding.iter().zip(got).enumerate() {
let want = expected_value(a);
if want.to_bits() != b.to_bits() && !(want.is_nan() && b.is_nan()) {
return Err(format!(
"record {idx} (chunk id {id}) embedding[{j}] mismatch: source {a}, \
expected {want}, store {b}"
)
.into());
} }
} }
rows_checked += 1; rows_checked += 1;
} }
// ---- Other groups (always full — they are small) ---- // ---- Records tombstoned because their source row was deleted ----
for (i, (s, g)) in source.sessions.iter().zip(got.sessions.iter()).enumerate() { for &(idx, src) in &migration.deleted_in_store {
if s.id != g.id let s = &source.chunks[src];
|| s.start_idx != g.start_idx let c = &mem.cache;
|| s.end_idx != g.end_idx if idx >= c.len() || c.chunks[idx] != s.chunk || c.timestamps[idx] != s.timestamp {
|| s.channel != g.channel return Err(format!("record {idx} (chunk id {}) mismatch or missing", s.id).into());
|| s.summary != g.summary
{
return Err(format!("session[{i}] mismatch").into());
} }
rows_checked += 1; if c.tombstones[idx] == 0 {
} return Err(format!(
for (i, (s, g)) in source.entities.iter().zip(got.entities.iter()).enumerate() { "record {idx} (chunk id {}) is deleted in the source but active in the store",
if s.id != g.id s.id
|| s.name != g.name )
|| s.entity_type != g.entity_type .into());
|| s.embedding_idx != g.embedding_idx
{
return Err(format!("entity[{i}] mismatch").into());
}
rows_checked += 1;
}
for (i, (s, g)) in source
.relations
.iter()
.zip(got.relations.iter())
.enumerate()
{
if s.src != g.src || s.tgt != g.tgt || s.relation != g.relation {
return Err(format!("relation[{i}] mismatch").into());
} }
rows_checked += 1; rows_checked += 1;
} }
// ---- Sessions ----
let sessions = mem.sessions();
for &(at, src) in &migration.sessions {
let s = &source.sessions[src];
let (Some(e), Some(summary)) = (sessions.entries.get(at), sessions.summaries.get(at))
else {
return Err(format!("session {:?} is missing from the store", s.id).into());
};
if e.id != s.id
|| e.start_idx != s.start_idx.max(0) as u64
|| e.end_idx != s.end_idx.max(0) as u64
|| e.channel != s.channel
|| *summary != s.summary
|| e.ts != s.timestamp * US_PER_SEC
{
return Err(format!("session {:?} mismatch", s.id).into());
}
rows_checked += 1;
}
// ---- Knowledge graph ----
let kg = mem.knowledge();
for &(id, src) in &migration.entities {
let s = &source.entities[src];
let Some(e) = kg.get_entity(id) else {
return Err(format!(
"entity {:?} (id {}) is missing from the store",
s.name, s.id
)
.into());
};
if e.name != s.name || e.entity_type != s.entity_type || e.embedding_idx != s.embedding_idx
{
return Err(format!("entity {:?} (id {}) mismatch", s.name, s.id).into());
}
rows_checked += 1;
}
for &(at, src) in &migration.relations {
let s = &source.relations[src];
let r = kg.relations.get(at);
let ok = r.is_some_and(|r| {
Some(&r.src) == migration.entity_ids.get(&s.src)
&& Some(&r.tgt) == migration.entity_ids.get(&s.tgt)
&& r.relation == s.relation
&& r.weight == s.weight as f32
&& r.ts == s.timestamp * US_PER_SEC
});
if !ok {
return Err(format!(
"relation {} -[{}]-> {} mismatch or missing",
s.src, s.relation, s.tgt
)
.into());
}
rows_checked += 1;
}
// ---- A migrated record must be findable by search ----
let probe = migration
.records
.iter()
.copied()
.find(|&(idx, _)| dim > 0 && mem.cache.tombstones[idx] == 0);
let search_checked = match probe {
None => false,
Some((idx, _)) => {
let query = mem.cache.embeddings[idx].to_vec();
let text = mem.cache.chunks[idx].clone();
let hits = mem.search(&query, &text, &SearchOptions::new(10));
// A record with the same text is as good a hit: the source may
// hold duplicates, and they tie.
if !hits.iter().any(|h| h.index == idx || h.chunk == text) {
return Err(format!(
"search for migrated record {idx} ({:?}) did not return it",
truncate(&text)
)
.into());
}
true
}
};
Ok(ValidationSummary { Ok(ValidationSummary {
chunks: got.chunks.len() as u64, count: mem.count(),
sessions: got.sessions.len() as u64, active: mem.count_active(),
entities: got.entities.len() as u64, sessions: mem.sessions().len(),
relations: got.relations.len() as u64, entities: mem.knowledge().entities.len(),
embedding_dim: got.embedding_dim as u64, relations: mem.knowledge().relations.len(),
embedding_dim: dim,
float16,
rows_checked, rows_checked,
provenance_verified, search_checked,
}) })
} }
fn check_count(kind: &str, got: usize, expected: usize) -> Result<(), BoxErr> { fn check_count(kind: &str, got: usize, expected: usize) -> Result<(), BoxErr> {
if got != expected { if got != expected {
return Err(format!("{kind} count mismatch: HDF5 has {got}, source has {expected}").into()); return Err(format!("{kind} count mismatch: store has {got}, expected {expected}").into());
} }
Ok(()) Ok(())
} }
/// Re-verify the SHA-256 provenance hash of `chunks/text` and
/// `chunks/embeddings` against their actual stored bytes, catching
/// post-write corruption that a plain content comparison against the
/// in-memory source wouldn't (the source is compared against what
/// `read_hdf5` decoded, not against the raw bytes on disk).
///
/// Returns `Ok(true)` only if both datasets exist and both hashes match.
/// Returns `Ok(false)` (not an error) if a dataset has no provenance
/// attributes at all (e.g. a file written before this check existed) or
/// there are zero chunks. Returns an error only on an actual hash mismatch —
/// that indicates real corruption.
fn verify_chunk_provenance(path: &str) -> Result<bool, BoxErr> {
let file = Hdf5File::open(path)?;
let Ok(chunks) = file.group("chunks") else {
return Ok(false);
};
let mut all_present = true;
for name in ["text", "embeddings"] {
let Ok(ds) = chunks.dataset(name) else {
all_present = false;
continue;
};
match ds.verify_provenance()? {
VerifyResult::Ok => {}
VerifyResult::NoHash => all_present = false,
VerifyResult::Mismatch { stored, computed } => {
return Err(format!(
"provenance hash mismatch on chunks/{name}: stored {stored}, recomputed {computed} — data may be corrupted"
)
.into());
}
}
}
Ok(all_present)
}
fn field_err<T: std::fmt::Display>(kind: &str, i: usize, field: &str, s: T, g: T) -> BoxErr {
format!("{kind}[{i}].{field} mismatch: source {s}, HDF5 {g}").into()
}
fn truncate(s: &str) -> String { fn truncate(s: &str) -> String {
if s.len() <= 40 { if s.len() <= 40 {
s.to_string() s.to_string()
@@ -196,7 +270,7 @@ fn truncate(s: &str) -> String {
} }
} }
/// Indices of chunk rows to content-check. Full = all; otherwise a spread of /// Indices of records to content-check. Full = all; otherwise a spread of
/// representative rows (first/last and evenly-spaced interior samples). /// representative rows (first/last and evenly-spaced interior samples).
fn sample_indices(n: usize, full: bool) -> Vec<usize> { fn sample_indices(n: usize, full: bool) -> Vec<usize> {
if n == 0 { if n == 0 {