- clippy --all-targets plus a clawhdf5-format feature matrix (parallel, lz4, zstd, pcodec, fast-checksum); fix the accumulated lint backlog in test, bench and feature-gated code (no behaviour changes). - Install python3 + h5py/numpy/netCDF4/xarray in the CI container and set CLAWHDF5_REQUIRE_INTEROP=1, which makes a missing interop dependency a test failure. Every h5py/netCDF4 interop test used to skip silently in CI. Run the #[ignore]d writer_h5py_tests suite explicitly. - cargo bench --no-run so benches can't rot; fix bench.rs and memory_bench.rs, which no longer compiled against the current strategy/consolidation APIs. - Optional fuzz smoke run via CLAWHDF5_FUZZ_SECONDS. - CHANGELOG and docs/known-issues.md updated. Co-Authored-By: Claude Fable 5.1 <[email protected]>
328 lines
9.8 KiB
Rust
328 lines
9.8 KiB
Rust
use rusqlite::{Connection, Result as SqlResult};
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/// A memory chunk read from SQLite.
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#[derive(Debug, Clone)]
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pub struct MemoryChunk {
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pub id: i64,
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pub chunk: String,
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pub embedding: Vec<f32>,
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pub source_channel: String,
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pub timestamp: f64,
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pub session_id: String,
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pub tags: String,
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pub deleted: i32,
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}
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/// A session read from SQLite.
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#[derive(Debug, Clone)]
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pub struct Session {
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pub id: String,
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pub start_idx: i64,
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pub end_idx: i64,
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pub channel: String,
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pub timestamp: f64,
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pub summary: String,
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}
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/// An entity read from SQLite.
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#[derive(Debug, Clone)]
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pub struct Entity {
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pub id: i64,
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pub name: String,
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pub entity_type: String,
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pub embedding_idx: i64,
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}
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/// A relation read from SQLite.
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#[derive(Debug, Clone)]
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pub struct Relation {
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pub src: i64,
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pub tgt: i64,
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pub relation: String,
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pub weight: f64,
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pub timestamp: f64,
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}
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/// All data read from a ZeroClaw SQLite database.
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#[derive(Debug)]
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pub struct SqliteData {
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pub chunks: Vec<MemoryChunk>,
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pub sessions: Vec<Session>,
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pub entities: Vec<Entity>,
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pub relations: Vec<Relation>,
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pub embedding_dim: usize,
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/// Filesystem path of the SQLite database this data was read from, for
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/// provenance attribution on the HDF5 output. Empty when the data did
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/// not come directly from a SQLite read (e.g. re-read of a prior HDF5
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/// migration output for an incremental merge).
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pub source_path: String,
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}
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/// A table name plus the ordered column names the reader maps by position.
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#[derive(Debug, Clone)]
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pub struct TableSchema {
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pub table: String,
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pub columns: Vec<&'static str>,
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}
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/// Configurable mapping from a SQLite layout to the migration's data model.
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///
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/// Defaults to the ZeroClaw schema; the CLI can override the table names so the
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/// tool can migrate databases whose tables are named differently. Column names
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/// (and order) are part of the config too, so a library caller can remap them.
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#[derive(Debug, Clone)]
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pub struct SchemaConfig {
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pub chunks: TableSchema,
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pub sessions: TableSchema,
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pub entities: TableSchema,
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pub relations: TableSchema,
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}
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impl Default for SchemaConfig {
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fn default() -> Self {
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SchemaConfig {
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chunks: TableSchema {
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table: "memory_chunks".into(),
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columns: vec![
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"id",
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"chunk",
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"embedding",
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"source_channel",
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"timestamp",
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"session_id",
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"tags",
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"deleted",
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],
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},
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sessions: TableSchema {
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table: "sessions".into(),
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columns: vec![
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"id",
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"start_idx",
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"end_idx",
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"channel",
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"timestamp",
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"summary",
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],
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},
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entities: TableSchema {
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table: "entities".into(),
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columns: vec!["id", "name", "type", "embedding_idx"],
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},
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relations: TableSchema {
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table: "relations".into(),
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columns: vec!["src", "tgt", "relation", "weight", "timestamp"],
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},
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}
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}
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}
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impl TableSchema {
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fn select(&self, where_clause: &str) -> String {
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format!(
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"SELECT {} FROM {}{}",
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self.columns.join(", "),
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self.table,
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where_clause
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)
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}
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}
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/// Row counts for each table — a fast pass that does not load row contents.
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/// Used for `--dry-run` and progress without buffering the whole database.
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#[derive(Debug, Default, Clone, Copy)]
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pub struct RowCounts {
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pub chunks: u64,
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pub sessions: u64,
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pub entities: u64,
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pub relations: u64,
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}
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fn count_rows(conn: &Connection, table: &str, where_clause: &str) -> SqlResult<u64> {
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conn.query_row(
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&format!("SELECT COUNT(*) FROM {table}{where_clause}"),
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[],
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|r| r.get(0),
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)
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}
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/// Count rows in each table without reading their contents.
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pub fn read_counts(
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path: &str,
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skip_deleted: bool,
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config: &SchemaConfig,
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) -> Result<RowCounts, Box<dyn std::error::Error>> {
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let conn = Connection::open(path)?;
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let deleted_col = config.chunks.columns.get(7).copied().unwrap_or("deleted");
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let chunk_where = if skip_deleted {
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format!(" WHERE {deleted_col} = 0")
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} else {
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String::new()
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};
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Ok(RowCounts {
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chunks: count_rows(&conn, &config.chunks.table, &chunk_where)?,
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sessions: count_rows(&conn, &config.sessions.table, "")?,
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entities: count_rows(&conn, &config.entities.table, "")?,
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relations: count_rows(&conn, &config.relations.table, "")?,
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})
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}
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/// Auto-detect embedding dimension from the first chunk's BLOB size.
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fn detect_embedding_dim(conn: &Connection, config: &SchemaConfig) -> SqlResult<Option<usize>> {
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let emb_col = config.chunks.columns.get(2).copied().unwrap_or("embedding");
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let mut stmt = conn.prepare(&format!(
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"SELECT {emb_col} FROM {} LIMIT 1",
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config.chunks.table
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))?;
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let mut rows = stmt.query([])?;
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if let Some(row) = rows.next()? {
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let blob: Vec<u8> = row.get(0)?;
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Ok(Some(blob.len() / 4)) // f32 = 4 bytes
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} else {
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Ok(None)
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}
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}
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/// Parse a raw byte BLOB into a Vec<f32>.
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fn blob_to_f32(blob: &[u8]) -> Vec<f32> {
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blob.as_chunks::<4>()
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.0
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.iter()
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.map(|b| f32::from_le_bytes(*b))
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.collect()
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}
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/// Read all data from a ZeroClaw SQLite database.
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///
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/// If `skip_deleted` is true, rows with `deleted=1` are excluded from chunks.
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/// If `embedding_dim` is `None`, auto-detect from the first row.
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pub fn read_sqlite(
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path: &str,
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skip_deleted: bool,
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embedding_dim: Option<usize>,
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config: &SchemaConfig,
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) -> Result<SqliteData, Box<dyn std::error::Error>> {
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read_sqlite_filtered(path, skip_deleted, embedding_dim, config, 0)
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}
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/// Like [`read_sqlite`] but only reads chunks whose id is greater than
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/// `min_chunk_id` (0 = all). Used for incremental migration.
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pub fn read_sqlite_filtered(
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path: &str,
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skip_deleted: bool,
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embedding_dim: Option<usize>,
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config: &SchemaConfig,
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min_chunk_id: i64,
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) -> Result<SqliteData, Box<dyn std::error::Error>> {
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let conn = Connection::open(path)?;
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let dim = match embedding_dim {
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Some(d) => d,
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None => detect_embedding_dim(&conn, config)?.unwrap_or(0),
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};
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let chunks = read_chunks(&conn, skip_deleted, dim, config, min_chunk_id)?;
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let sessions = read_sessions(&conn, config)?;
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let entities = read_entities(&conn, config)?;
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let relations = read_relations(&conn, config)?;
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Ok(SqliteData {
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chunks,
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sessions,
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entities,
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relations,
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embedding_dim: dim,
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source_path: path.to_owned(),
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})
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}
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fn read_chunks(
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conn: &Connection,
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skip_deleted: bool,
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expected_dim: usize,
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config: &SchemaConfig,
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min_chunk_id: i64,
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) -> SqlResult<Vec<MemoryChunk>> {
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let id_col = config.chunks.columns.first().copied().unwrap_or("id");
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let deleted_col = config.chunks.columns.get(7).copied().unwrap_or("deleted");
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let mut conds = Vec::new();
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if skip_deleted {
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conds.push(format!("{deleted_col} = 0"));
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}
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if min_chunk_id > 0 {
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conds.push(format!("{id_col} > {min_chunk_id}"));
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}
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let where_clause = if conds.is_empty() {
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String::new()
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} else {
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format!(" WHERE {}", conds.join(" AND "))
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};
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let sql = config.chunks.select(&where_clause);
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let mut stmt = conn.prepare(&sql)?;
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let rows = stmt.query_map([], |row| {
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let blob: Vec<u8> = row.get(2)?;
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let mut embedding = blob_to_f32(&blob);
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// Validate/truncate to expected dimension
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if expected_dim > 0 {
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embedding.truncate(expected_dim);
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}
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Ok(MemoryChunk {
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id: row.get(0)?,
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chunk: row.get(1)?,
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embedding,
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source_channel: row.get::<_, Option<String>>(3)?.unwrap_or_default(),
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timestamp: row.get(4)?,
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session_id: row.get::<_, Option<String>>(5)?.unwrap_or_default(),
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tags: row.get::<_, Option<String>>(6)?.unwrap_or_default(),
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deleted: row.get(7)?,
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})
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})?;
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rows.collect()
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}
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fn read_sessions(conn: &Connection, config: &SchemaConfig) -> SqlResult<Vec<Session>> {
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let mut stmt = conn.prepare(&config.sessions.select(""))?;
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let rows = stmt.query_map([], |row| {
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Ok(Session {
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id: row.get(0)?,
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start_idx: row.get::<_, Option<i64>>(1)?.unwrap_or(0),
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end_idx: row.get::<_, Option<i64>>(2)?.unwrap_or(0),
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channel: row.get::<_, Option<String>>(3)?.unwrap_or_default(),
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timestamp: row.get::<_, Option<f64>>(4)?.unwrap_or(0.0),
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summary: row.get::<_, Option<String>>(5)?.unwrap_or_default(),
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})
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})?;
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rows.collect()
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}
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fn read_entities(conn: &Connection, config: &SchemaConfig) -> SqlResult<Vec<Entity>> {
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let mut stmt = conn.prepare(&config.entities.select(""))?;
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let rows = stmt.query_map([], |row| {
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Ok(Entity {
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id: row.get(0)?,
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name: row.get(1)?,
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entity_type: row.get(2)?,
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embedding_idx: row.get::<_, Option<i64>>(3)?.unwrap_or(-1),
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})
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})?;
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rows.collect()
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}
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fn read_relations(conn: &Connection, config: &SchemaConfig) -> SqlResult<Vec<Relation>> {
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let mut stmt = conn.prepare(&config.relations.select(""))?;
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let rows = stmt.query_map([], |row| {
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Ok(Relation {
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src: row.get(0)?,
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tgt: row.get(1)?,
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relation: row.get(2)?,
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weight: row.get::<_, Option<f64>>(3)?.unwrap_or(1.0),
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timestamp: row.get::<_, Option<f64>>(4)?.unwrap_or(0.0),
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})
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})?;
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rows.collect()
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}
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