From b7cc2028a20b71d544e9a913d5bfccb646397820 Mon Sep 17 00:00:00 2001
From: 7000pctAUTO <lukems3823@gmail.com>
Date: Sun, 22 Mar 2026 18:15:41 +0000
Subject: [PATCH] Initial upload: shell-history-semantic-search v0.1.0

---
 tests/test_embeddings.py | 90 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 90 insertions(+)
 create mode 100644 tests/test_embeddings.py

diff --git a/tests/test_embeddings.py b/tests/test_embeddings.py
new file mode 100644
index 0000000..31d705e
--- /dev/null
+++ b/tests/test_embeddings.py
@@ -0,0 +1,90 @@
+import pytest
+import numpy as np
+
+from shell_history_search.core import EmbeddingService
+
+
+class TestEmbeddingService:
+    def test_init_default(self):
+        service = EmbeddingService()
+        assert service.model_name == "all-MiniLM-L6-v2"
+        assert service.device == "cpu"
+
+    def test_init_custom_model(self, temp_cache_dir):
+        service = EmbeddingService(
+            model_name="all-MiniLM-L6-v2",
+            cache_dir=temp_cache_dir,
+        )
+        assert service.model_name == "all-MiniLM-L6-v2"
+        assert service.cache_dir == temp_cache_dir
+
+    def test_embedding_dim(self, temp_cache_dir):
+        service = EmbeddingService(cache_dir=temp_cache_dir)
+        assert service.embedding_dim == 384
+
+    def test_encode_single(self, temp_cache_dir):
+        service = EmbeddingService(cache_dir=temp_cache_dir)
+        embedding = service.encode_single("git commit")
+
+        assert isinstance(embedding, np.ndarray)
+        assert embedding.shape == (384,)
+        assert embedding.dtype == np.float32
+
+    def test_encode_batch(self, temp_cache_dir):
+        service = EmbeddingService(cache_dir=temp_cache_dir)
+        embeddings = service.encode(["git add .", "git commit", "git push"])
+
+        assert isinstance(embeddings, np.ndarray)
+        assert embeddings.shape == (3, 384)
+        assert embeddings.dtype == np.float32
+
+    def test_encode_empty_list(self, temp_cache_dir):
+        service = EmbeddingService(cache_dir=temp_cache_dir)
+        embeddings = service.encode([])
+
+        assert isinstance(embeddings, np.ndarray)
+        assert embeddings.shape == (0,)
+
+    def test_encode_returns_normalized(self, temp_cache_dir):
+        service = EmbeddingService(cache_dir=temp_cache_dir)
+        embedding = service.encode_single("test command")
+
+        norm = np.linalg.norm(embedding)
+        assert 0.99 < norm <= 1.01
+
+    def test_embedding_to_blob(self, temp_cache_dir):
+        service = EmbeddingService(cache_dir=temp_cache_dir)
+        embedding = service.encode_single("test")
+
+        blob = EmbeddingService.embedding_to_blob(embedding)
+        assert isinstance(blob, bytes)
+        assert len(blob) == 384 * 4
+
+    def test_blob_to_embedding(self, temp_cache_dir):
+        service = EmbeddingService(cache_dir=temp_cache_dir)
+        embedding = service.encode_single("test")
+        blob = EmbeddingService.embedding_to_blob(embedding)
+
+        recovered = EmbeddingService.blob_to_embedding(blob, 384)
+
+        assert np.allclose(embedding, recovered)
+
+    def test_cosine_similarity(self, temp_cache_dir):
+        service = EmbeddingService(cache_dir=temp_cache_dir)
+        e1 = service.encode_single("git commit")
+        e2 = service.encode_single("git add .")
+        e3 = service.encode_single("docker run")
+
+        sim_same = EmbeddingService.cosine_similarity(e1, e2)
+        sim_diff = EmbeddingService.cosine_similarity(e1, e3)
+
+        assert -1 <= sim_same <= 1
+        assert -1 <= sim_diff <= 1
+        assert sim_same > sim_diff
+
+    def test_cosine_similarity_perfect_match(self, temp_cache_dir):
+        service = EmbeddingService(cache_dir=temp_cache_dir)
+        e1 = service.encode_single("same command")
+
+        sim = EmbeddingService.cosine_similarity(e1, e1)
+        assert 0.9999 < sim <= 1.0001
\ No newline at end of file