feat: Phase 3 — Brain Mode hybrid search (usearch + RRF)

Cargo.toml

@@ -59,6 +59,10 @@ chrono = { version = "0.4.44", features = ["serde"] }
 # Symbol index (v0.6 — Code Intelligence)
 rusqlite = { version = "0.31", features = ["bundled"] }
 
+# Vector search (Phase 3 — Brain Mode)
+usearch = "2"
+fs2 = "0.4"
+
 # Tree-sitter AST parsing (optional — enable with --features tree-sitter)
 tree-sitter = { version = "0.26", optional = true }
 tree-sitter-rust = { version = "0.24", optional = true }

src/embed/mod.rs

@@ -13,9 +13,7 @@
 //! Re-exports will be added to this module in Phase 3 when `cora brain`
 //! commands are wired up.
 
-// Phase 1: public API not yet consumed by any command.
-// Clippy dead_code warnings are expected and will resolve in Phase 3.
-#![allow(dead_code)]
+// Embedding engine — consumed by brain module (Phase 3).
 
 pub mod token_vocab;
 pub mod tokens;

src/embed/token_vocab.rs

@@ -1,3 +1,6 @@
+//! Pre-trained token vocabulary — reserved for Phase 5 (Voyage-4-Nano ONNX).
+#![allow(dead_code)]
+
 //! Pre-trained token vocabulary from nomic-embed-code.
 //!
 //! Provides real 768-dimensional code embeddings distilled from

src/embed/tokens.rs

@@ -51,6 +51,7 @@ impl TokenEmbedding {
     }
 
     /// Consumes self and returns the underlying vector.
+    #[allow(dead_code)]
     pub fn into_vec(self) -> Vec<f64> {
         self.vec
     }
@@ -278,6 +279,7 @@ pub fn embed_code(code: &str) -> TokenEmbedding {
 ///
 /// Because vectors are pre-normalised by [`embed`], this is just the dot
 /// product.  Returns a value in `[-1, 1]`.
+#[cfg(test)]
 pub fn cosine_similarity(a: &TokenEmbedding, b: &TokenEmbedding) -> f64 {
     a.vec.iter().zip(b.vec.iter()).map(|(x, y)| x * y).sum()
 }