90 lines
3.2 KiB
Python
90 lines
3.2 KiB
Python
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
|