crtx-store 0.1.0

//! Memory embedding repository operations (Phase 4.C foundation, D2-A slice).
//!
//! This module owns the durable side table that holds per-`(memory_id,
//! backend_id)` embedding vectors. It is the storage half of the foundation
//! slice; the pluggable [`Embedder`] trait, cosine similarity helper, and
//! re-export of [`EmbedRecord`] live in `cortex-retrieval::embedding`.
//!
//! Design notes:
//!
//!  - The on-disk encoding of `vector_blob` is raw little-endian f32 bytes.
//!    This is deliberately serializer-free: the foundation slice does not
//!    pull `bincode` or `serde_cbor` into the workspace (neither is a
//!    workspace dep today), and the encoding is fully described by the
//!    `(backend_id, dim)` contract a backend advertises. The reader fails
//!    closed when `vector_blob.len() != dim * 4`.
//!  - `encryption_kind` defaults to `"none"` on the row. A future at-rest
//!    encryption slice will introduce additional variants and an
//!    `encryption_key_id` reference; the foundation read path explicitly
//!    refuses any value other than `"none"` so we never silently decode
//!    encrypted bytes as plaintext f32.
//!  - The repo intentionally exposes a small CRUD-style surface
//!    (`write` / `read` / `list_by_backend` / `delete`). Embedding rows are
//!    derived state — they do not need the ADR 0026 policy lattice that
//!    gates the authoritative `memories` mutations, because each row is
//!    keyed to a memory that has itself been admitted by the gated path,
//!    and rebuilds are idempotent.
//!
//! Cross-link: see `docs/design/PHASE_4C_EMBEDDINGS_FOUNDATION.md` for the
//! design rationale and the D2-B / D2-C downstream commit shapes.
//!
//! [`Embedder`]: ../../../cortex_retrieval/embedding/trait.Embedder.html
//! [`EmbedRecord`]: struct.EmbedRecord.html

use chrono::{DateTime, Utc};
use cortex_core::MemoryId;
use rusqlite::{params, OptionalExtension, Row};

use crate::{Pool, StoreError, StoreResult};

/// Sentinel `encryption_kind` value indicating the vector blob is stored as
/// raw little-endian f32 bytes with no at-rest encryption.
///
/// The foundation slice writes this value unconditionally. A future
/// encryption slice will introduce additional variants and gate decryption
/// dispatch on this column.
pub const EMBEDDING_ENCRYPTION_KIND_NONE: &str = "none";

/// One persistable embedding row produced by an embedder backend.
///
/// This is the canonical struct shape: `cortex-retrieval::embedding`
/// re-exports it so a single name is reachable from both the storage and
/// retrieval sides of the foundation.
#[derive(Debug, Clone, PartialEq)]
pub struct EmbedRecord {
    /// Memory the vector was computed for.
    pub memory_id: MemoryId,
    /// Backend that produced the vector (matches `Embedder::backend_id`).
    pub backend_id: String,
    /// Vector dimensionality. Always equals `vector.len()`; carried
    /// explicitly so the durable row can fail closed on a corrupt blob
    /// without trusting the implicit length.
    pub dim: u32,
    /// The embedding vector. Length MUST equal `dim`.
    pub vector: Vec<f32>,
    /// Wall-clock time the embedding was computed.
    pub computed_at: DateTime<Utc>,
}

impl EmbedRecord {
    /// Construct a record after verifying that `vector.len()` fits in `u32`.
    ///
    /// The `dim` field is derived from `vector.len()` so every constructed
    /// record satisfies `dim as usize == vector.len()` by construction.
    pub fn new(
        memory_id: MemoryId,
        backend_id: impl Into<String>,
        vector: Vec<f32>,
        computed_at: DateTime<Utc>,
    ) -> StoreResult<Self> {
        let backend_id = backend_id.into();
        let dim_usize = vector.len();
        let dim = u32::try_from(dim_usize).map_err(|_| {
            StoreError::Validation(format!(
                "embedding vector length {dim_usize} does not fit in u32 dim column"
            ))
        })?;
        Ok(Self {
            memory_id,
            backend_id,
            dim,
            vector,
            computed_at,
        })
    }
}

/// Repository for `memory_embeddings` rows.
#[derive(Debug)]
pub struct EmbeddingRepo<'a> {
    pool: &'a Pool,
}

impl<'a> EmbeddingRepo<'a> {
    /// Construct a repository over an open SQLite connection.
    #[must_use]
    pub const fn new(pool: &'a Pool) -> Self {
        Self { pool }
    }

    /// Write (insert or replace) one embedding row.
    ///
    /// The composite primary key `(memory_id, backend_id)` makes this an
    /// upsert keyed to the producing backend: re-running a backend over a
    /// memory overwrites the prior row for that `(memory, backend)` pair
    /// without disturbing vectors produced by other backends.
    pub fn write(&self, record: &EmbedRecord) -> StoreResult<()> {
        validate_record_dim_matches_vector(record)?;
        validate_backend_id(&record.backend_id)?;

        let blob = encode_vector_blob(&record.vector);

        self.pool.execute(
            "INSERT OR REPLACE INTO memory_embeddings (
                memory_id,
                backend_id,
                dim,
                vector_blob,
                encryption_kind,
                encryption_key_id,
                computed_at
             ) VALUES (?1, ?2, ?3, ?4, ?5, NULL, ?6);",
            params![
                record.memory_id.to_string(),
                record.backend_id,
                record.dim,
                blob,
                EMBEDDING_ENCRYPTION_KIND_NONE,
                record.computed_at.to_rfc3339(),
            ],
        )?;
        Ok(())
    }

    /// Read the embedding row for `(memory_id, backend_id)`, if present.
    pub fn read(&self, memory_id: &MemoryId, backend_id: &str) -> StoreResult<Option<EmbedRecord>> {
        let row = self
            .pool
            .query_row(
                "SELECT memory_id, backend_id, dim, vector_blob, encryption_kind, computed_at
                   FROM memory_embeddings
                  WHERE memory_id = ?1 AND backend_id = ?2;",
                params![memory_id.to_string(), backend_id],
                embedding_row,
            )
            .optional()?;
        row.map(TryInto::try_into).transpose()
    }

    /// List every embedding row produced by `backend_id`.
    ///
    /// Returns rows in `memory_id` order for deterministic test fixtures and
    /// operator-readable listings.
    pub fn list_by_backend(&self, backend_id: &str) -> StoreResult<Vec<EmbedRecord>> {
        let mut stmt = self.pool.prepare(
            "SELECT memory_id, backend_id, dim, vector_blob, encryption_kind, computed_at
               FROM memory_embeddings
              WHERE backend_id = ?1
              ORDER BY memory_id;",
        )?;
        let rows = stmt
            .query_map(params![backend_id], embedding_row)?
            .collect::<Result<Vec<_>, _>>()?;
        rows.into_iter().map(EmbedRecord::try_from).collect()
    }

    /// Delete the embedding row for `(memory_id, backend_id)`.
    ///
    /// A delete with no matching row is a no-op: the caller's contract is
    /// "after this returns, the row is gone", and a missing row already
    /// satisfies that.
    pub fn delete(&self, memory_id: &MemoryId, backend_id: &str) -> StoreResult<()> {
        self.pool.execute(
            "DELETE FROM memory_embeddings WHERE memory_id = ?1 AND backend_id = ?2;",
            params![memory_id.to_string(), backend_id],
        )?;
        Ok(())
    }
}

#[derive(Debug)]
struct EmbeddingRow {
    memory_id: String,
    backend_id: String,
    dim: i64,
    vector_blob: Vec<u8>,
    encryption_kind: String,
    computed_at: String,
}

fn embedding_row(row: &Row<'_>) -> rusqlite::Result<EmbeddingRow> {
    Ok(EmbeddingRow {
        memory_id: row.get(0)?,
        backend_id: row.get(1)?,
        dim: row.get(2)?,
        vector_blob: row.get(3)?,
        encryption_kind: row.get(4)?,
        computed_at: row.get(5)?,
    })
}

impl TryFrom<EmbeddingRow> for EmbedRecord {
    type Error = StoreError;

    fn try_from(row: EmbeddingRow) -> StoreResult<Self> {
        if row.encryption_kind != EMBEDDING_ENCRYPTION_KIND_NONE {
            return Err(StoreError::Validation(format!(
                "memory_embeddings row carries encryption_kind {kind:?}; the Phase 4.C foundation \
                 only reads {expected:?} rows. A future at-rest encryption slice introduces \
                 additional decoders.",
                kind = row.encryption_kind,
                expected = EMBEDDING_ENCRYPTION_KIND_NONE,
            )));
        }

        let dim = u32::try_from(row.dim).map_err(|_| {
            StoreError::Validation(format!(
                "memory_embeddings.dim {} is not a valid u32 (CHECK dim > 0 enforced at write)",
                row.dim,
            ))
        })?;
        let expected_bytes = (dim as usize).checked_mul(4).ok_or_else(|| {
            StoreError::Validation(format!("memory_embeddings.dim {dim} * 4 overflows usize"))
        })?;
        if row.vector_blob.len() != expected_bytes {
            return Err(StoreError::Validation(format!(
                "memory_embeddings.vector_blob length {} does not match dim {dim} * 4 = {expected_bytes}",
                row.vector_blob.len(),
            )));
        }
        let vector = decode_vector_blob(&row.vector_blob);

        Ok(Self {
            memory_id: row.memory_id.parse()?,
            backend_id: row.backend_id,
            dim,
            vector,
            computed_at: DateTime::parse_from_rfc3339(&row.computed_at)?.with_timezone(&Utc),
        })
    }
}

fn encode_vector_blob(vector: &[f32]) -> Vec<u8> {
    let mut bytes = Vec::with_capacity(vector.len() * 4);
    for v in vector {
        bytes.extend_from_slice(&v.to_le_bytes());
    }
    bytes
}

fn decode_vector_blob(bytes: &[u8]) -> Vec<f32> {
    bytes
        .chunks_exact(4)
        .map(|chunk| {
            let arr = <[u8; 4]>::try_from(chunk).expect("chunks_exact yields four bytes");
            f32::from_le_bytes(arr)
        })
        .collect()
}

fn validate_record_dim_matches_vector(record: &EmbedRecord) -> StoreResult<()> {
    if record.dim as usize != record.vector.len() {
        return Err(StoreError::Validation(format!(
            "embedding record dim {} does not match vector length {} (backend `{}`)",
            record.dim,
            record.vector.len(),
            record.backend_id,
        )));
    }
    if record.dim == 0 {
        return Err(StoreError::Validation(
            "embedding record dim must be > 0 (CHECK constraint on memory_embeddings.dim)"
                .to_string(),
        ));
    }
    Ok(())
}

fn validate_backend_id(backend_id: &str) -> StoreResult<()> {
    if backend_id.trim().is_empty() {
        return Err(StoreError::Validation(
            "embedding record requires non-empty backend_id".to_string(),
        ));
    }
    Ok(())
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn encode_then_decode_roundtrips_known_values() {
        let vector = vec![0.0f32, 1.0, -1.5, 4.2_f32, f32::MIN_POSITIVE];
        let bytes = encode_vector_blob(&vector);
        assert_eq!(bytes.len(), vector.len() * 4);
        let decoded = decode_vector_blob(&bytes);
        assert_eq!(decoded, vector);
    }

    #[test]
    fn validate_record_rejects_dim_mismatch() {
        let record = EmbedRecord {
            memory_id: "mem_01ARZ3NDEKTSV4RRFFQ69G5FAV".parse().unwrap(),
            backend_id: "stub:v1".into(),
            dim: 4,
            vector: vec![0.0; 3],
            computed_at: Utc::now(),
        };
        assert!(validate_record_dim_matches_vector(&record).is_err());
    }

    #[test]
    fn validate_backend_id_rejects_blank() {
        assert!(validate_backend_id("").is_err());
        assert!(validate_backend_id("   ").is_err());
        assert!(validate_backend_id("stub:v1").is_ok());
    }
}