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use anyhow::Result;
use rusqlite::Connection;
/// Load sqlite-vec extension into the connection.
/// For now, we use a placeholder implementation.
/// TODO: Integrate actual sqlite-vec when ready for production.
pub fn load_vec_extension(_conn: &Connection) -> Result<()> {
// Placeholder: sqlite-vec integration requires either:
// 1. Compiling the extension and loading it dynamically
// 2. Using a bundled version
// For Phase 1, we skip this and focus on the LLM extraction pipeline.
Ok(())
}
/// Create vector table for observations embeddings.
/// Using 768 dimensions (typical for sentence transformers like all-MiniLM-L6-v2).
pub fn ensure_vec_table(_conn: &Connection) -> Result<()> {
// Placeholder: will be implemented when sqlite-vec is properly integrated
Ok(())
}
/// Insert or update observation embedding.
pub fn upsert_embedding(_conn: &Connection, _obs_id: i64, embedding: &[f32]) -> Result<()> {
if embedding.len() != 768 {
anyhow::bail!("embedding must be 768 dimensions, got {}", embedding.len());
}
// Placeholder: will be implemented when sqlite-vec is properly integrated
Ok(())
}
/// Vector similarity search: find top K most similar observations.
/// Returns (observation_id, distance) pairs sorted by distance (lower = more similar).
pub fn vector_search(
_conn: &Connection,
query_embedding: &[f32],
_limit: usize,
) -> Result<Vec<(i64, f32)>> {
if query_embedding.len() != 768 {
anyhow::bail!(
"query embedding must be 768 dimensions, got {}",
query_embedding.len()
);
}
// Placeholder: will be implemented when sqlite-vec is properly integrated
Ok(vec![])
}
/// Find observations with cosine similarity > threshold.
/// Used for deduplication (threshold typically 0.95).
pub fn find_similar_observations(
conn: &Connection,
query_embedding: &[f32],
threshold: f32,
limit: usize,
) -> Result<Vec<i64>> {
let candidates = vector_search(conn, query_embedding, limit)?;
// Filter by threshold (distance < 1 - threshold for cosine similarity)
let distance_threshold = 1.0 - threshold;
let similar: Vec<i64> = candidates
.into_iter()
.filter(|(_, dist)| *dist < distance_threshold)
.map(|(id, _)| id)
.collect();
Ok(similar)
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_vec_extension_loads() -> Result<()> {
let conn = rusqlite::Connection::open_in_memory()?;
load_vec_extension(&conn)?;
ensure_vec_table(&conn)?;
Ok(())
}
}