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https://github.com/outbackdingo/optimclaw.git
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[email protected]andClaude Opus 4.6
1e0494e72d
Instead of wrapping all ~80 Database trait methods in a 664-line decorator (lancedb_wrapper.rs), introduce a 4-method VectorStore trait that any vector backend can implement. Workspace composes FTS from the database with vector search from the external store via RRF fusion. - Add src/workspace/vector_store.rs with VectorStore trait - Rewrite lancedb_store.rs to implement VectorStore (not wrap Database) - Delete src/db/lancedb_wrapper.rs (664 lines removed) - Remove get_chunk_by_id from Database trait and all backends - Workspace gains with_vector_store() builder for optional composition - Fix LanceDB tests: bypass_vector_index() for brute-force search - Fix integration tests: use temp file DB (libSQL :memory: is per-connection) - Merge duplicate mod tests in config.rs Net: -724 lines. Adding a new vector backend requires 4 methods, not 80. Co-Authored-By: Claude Opus 4.6 <[email protected]>
124 lines
3.6 KiB
Rust
124 lines
3.6 KiB
Rust
//! Integration tests for LanceDB vector store with Workspace composition.
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//!
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//! Requires: cargo test --features "libsql,lancedb"
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//!
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//! Verifies that Workspace correctly composes FTS from libSQL with vector
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//! search from LanceDB via the VectorStore trait.
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#![cfg(all(feature = "libsql", feature = "lancedb"))]
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use std::sync::Arc;
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use ironclaw::db::Database;
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use ironclaw::db::libsql_backend::LibSqlBackend;
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use ironclaw::workspace::{LanceDbVectorStore, SearchConfig, Workspace};
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use tempfile::TempDir;
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const EMBEDDING_DIM: usize = 1536;
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fn make_embedding(seed: f32) -> Vec<f32> {
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(0..EMBEDDING_DIM)
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.map(|i| (seed * (i as f32 + 1.0)).sin())
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.collect()
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}
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/// Mock embedding provider that returns deterministic embeddings.
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struct FixedEmbeddings {
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embedding: Vec<f32>,
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}
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#[async_trait::async_trait]
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impl ironclaw::workspace::EmbeddingProvider for FixedEmbeddings {
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fn dimension(&self) -> usize {
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EMBEDDING_DIM
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}
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fn model_name(&self) -> &str {
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"fixed-test"
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}
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fn max_input_length(&self) -> usize {
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8192
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}
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async fn embed(
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&self,
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_text: &str,
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) -> Result<Vec<f32>, ironclaw::workspace::embeddings::EmbeddingError> {
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Ok(self.embedding.clone())
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}
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}
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async fn setup_workspace() -> (Workspace, TempDir, TempDir) {
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// Use a temp file (not :memory:) because libSQL in-memory DBs are connection-local
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let db_dir = TempDir::new().unwrap();
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let db_path = db_dir.path().join("test.db");
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let libsql = LibSqlBackend::new_local(&db_path).await.unwrap();
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libsql.run_migrations().await.unwrap();
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let lancedb_dir = TempDir::new().unwrap();
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let store = LanceDbVectorStore::new(lancedb_dir.path()).await.unwrap();
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let embedding = make_embedding(1.0);
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let ws = Workspace::new_with_db("test_user", Arc::new(libsql) as Arc<dyn Database>)
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.with_vector_store(Arc::new(store))
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.with_embeddings(Arc::new(FixedEmbeddings { embedding }));
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(ws, lancedb_dir, db_dir)
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}
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#[tokio::test]
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async fn test_workspace_hybrid_search_with_lancedb() {
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let (ws, _keep_lance, _keep_db) = setup_workspace().await;
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// Write a document — this triggers chunking + embedding + LanceDB sync
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ws.write(
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"context/rust.md",
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"Rust is a systems programming language focused on safety.",
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)
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.await
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.unwrap();
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// Hybrid search: FTS for "Rust" + vector from LanceDB
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let results = ws.search("Rust", 5).await.unwrap();
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assert!(!results.is_empty(), "hybrid search should return results");
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assert!(results[0].content.contains("Rust"));
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}
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#[tokio::test]
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async fn test_workspace_delete_removes_from_lancedb() {
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let (ws, _keep_lance, _keep_db) = setup_workspace().await;
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ws.write("notes/deleted.md", "Content to be deleted.")
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.await
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.unwrap();
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let before = ws.search("deleted", 5).await.unwrap();
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assert_eq!(before.len(), 1);
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ws.delete("notes/deleted.md").await.unwrap();
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let after = ws.search("deleted", 5).await.unwrap();
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assert!(after.is_empty());
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}
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#[tokio::test]
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async fn test_workspace_vector_only_search_uses_lancedb() {
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let (ws, _keep_lance, _keep_db) = setup_workspace().await;
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ws.write("sync/test.md", "Semantic content for vector search")
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.await
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.unwrap();
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// Vector-only search should find via LanceDB even with non-matching FTS query
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let config = SearchConfig::default().vector_only().with_limit(5);
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let results = ws
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.search_with_config("nonexistent_fts_term", config)
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.await
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.unwrap();
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assert_eq!(results.len(), 1);
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assert!(results[0].content.contains("Semantic content"));
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}
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