feat: add desktop document search and verified native CPU embeddings

crates/searchi-semantic/tests/cpu_model.rs

@@ -0,0 +1,71 @@
+//! Opt-in real-artifact test. Default tests never acquire model/runtime assets.
+use searchi_semantic::{CpuEmbedder, DIMENSIONS};
+use std::path::PathBuf;
+
+#[test]
+#[ignore = "Requires explicit SEARCHI_MODEL_DIR and SEARCHI_ORT_LIBRARY local assets"]
+fn real_cpu_model_retrieves_english_and_german_fixture() {
+    let model =
+        PathBuf::from(std::env::var_os("SEARCHI_MODEL_DIR").expect("Set SEARCHI_MODEL_DIR"));
+    let runtime =
+        PathBuf::from(std::env::var_os("SEARCHI_ORT_LIBRARY").expect("Set SEARCHI_ORT_LIBRARY"));
+    let fixture: serde_json::Value =
+        serde_json::from_str(include_str!("../../../tools/semantic-proof/fixture.json")).unwrap();
+    let documents = fixture["documents"].as_array().unwrap();
+    let texts: Vec<_> = documents
+        .iter()
+        .map(|doc| doc["text"].as_str().unwrap())
+        .collect();
+    let mut embedder = CpuEmbedder::load(&model, &runtime, 1).unwrap();
+    let vectors = embedder.encode(&texts).unwrap();
+    assert!(vectors.iter().all(|vector| vector.len() == DIMENSIONS));
+    for vector in &vectors {
+        let norm = vector.iter().map(|v| v * v).sum::<f32>().sqrt();
+        assert!((norm - 1.0).abs() < 1e-5);
+    }
+    let mut correct = 0;
+    for query in fixture["queries"].as_array().unwrap() {
+        let vector = embedder
+            .encode(&[query["text"].as_str().unwrap()])
+            .unwrap()
+            .remove(0);
+        let mut scores: Vec<_> = vectors
+            .iter()
+            .enumerate()
+            .map(|(i, candidate)| {
+                (
+                    i,
+                    candidate
+                        .iter()
+                        .zip(&vector)
+                        .map(|(a, b)| a * b)
+                        .sum::<f32>(),
+                )
+            })
+            .collect();
+        scores.sort_by(|a, b| b.1.total_cmp(&a.1));
+        let rank = scores
+            .iter()
+            .position(|(i, _)| documents[*i]["id"] == query["expected"])
+            .unwrap()
+            + 1;
+        println!(
+            "{}: expected rank {rank}; top result {}",
+            query["language"], documents[scores[0].0]["id"]
+        );
+        assert!(
+            rank <= 3,
+            "Real model lost expected passage for {}",
+            query["text"]
+        );
+        correct += usize::from(rank == 1);
+    }
+    // This is the measured proof baseline, not the production quality gate.
+    // A known product-release query currently returns the expected result second.
+    assert!(
+        correct >= 5,
+        "Semantic proof regressed below measured 5/6 top-1 baseline"
+    );
+    assert!(embedder.encode(&[" "]).is_err());
+    assert!(embedder.encode(&["x"; 9]).is_err());
+}