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"""
Unit tests for embeddings functionality.
"""
import unittest
import numpy as np
from django.test import TestCase
from hue_portal.core.embeddings import (
get_embedding_model,
generate_embedding,
generate_embeddings_batch,
cosine_similarity,
get_embedding_dimension
)
from hue_portal.core.embedding_utils import (
save_embedding,
load_embedding,
has_embedding
)
class EmbeddingsTestCase(TestCase):
"""Test embedding generation and utilities."""
def test_get_embedding_model(self):
"""Test loading embedding model."""
model = get_embedding_model()
# Model might not be available in test environment
# Just check that function doesn't crash
self.assertIsNotNone(model or True)
def test_generate_embedding(self):
"""Test generating embedding for a single text."""
text = "Thủ tục đăng ký cư trú"
embedding = generate_embedding(text)
if embedding is not None:
self.assertIsInstance(embedding, np.ndarray)
self.assertGreater(len(embedding), 0)
def test_generate_embeddings_batch(self):
"""Test generating embeddings for multiple texts."""
texts = [
"Thủ tục đăng ký cư trú",
"Mức phạt vượt đèn đỏ",
"Địa chỉ công an phường"
]
embeddings = generate_embeddings_batch(texts, batch_size=2)
if embeddings and embeddings[0] is not None:
self.assertEqual(len(embeddings), len(texts))
self.assertIsInstance(embeddings[0], np.ndarray)
def test_cosine_similarity(self):
"""Test cosine similarity calculation."""
vec1 = np.array([1.0, 0.0, 0.0])
vec2 = np.array([1.0, 0.0, 0.0])
similarity = cosine_similarity(vec1, vec2)
self.assertAlmostEqual(similarity, 1.0, places=5)
vec3 = np.array([0.0, 1.0, 0.0])
similarity2 = cosine_similarity(vec1, vec3)
self.assertAlmostEqual(similarity2, 0.0, places=5)
def test_cosine_similarity_orthogonal(self):
"""Test cosine similarity for orthogonal vectors."""
vec1 = np.array([1.0, 0.0])
vec2 = np.array([0.0, 1.0])
similarity = cosine_similarity(vec1, vec2)
self.assertAlmostEqual(similarity, 0.0, places=5)
def test_get_embedding_dimension(self):
"""Test getting embedding dimension."""
dim = get_embedding_dimension()
# Dimension might be 0 if model not available
self.assertIsInstance(dim, int)
self.assertGreaterEqual(dim, 0)
def test_similar_texts_have_similar_embeddings(self):
"""Test that similar texts produce similar embeddings."""
text1 = "Thủ tục đăng ký cư trú"
text2 = "Đăng ký thủ tục cư trú"
text3 = "Mức phạt giao thông"
emb1 = generate_embedding(text1)
emb2 = generate_embedding(text2)
emb3 = generate_embedding(text3)
if emb1 is not None and emb2 is not None and emb3 is not None:
sim_similar = cosine_similarity(emb1, emb2)
sim_different = cosine_similarity(emb1, emb3)
# Similar texts should have higher similarity
self.assertGreater(sim_similar, sim_different)
class EmbeddingUtilsTestCase(TestCase):
"""Test embedding utility functions."""
def test_save_and_load_embedding(self):
"""Test saving and loading embeddings."""
from hue_portal.core.models import Procedure
# Create a test procedure
procedure = Procedure.objects.create(
title="Test Procedure",
domain="Test"
)
# Create a dummy embedding
dummy_embedding = np.random.rand(384).astype(np.float32)
# Save embedding
success = save_embedding(procedure, dummy_embedding)
self.assertTrue(success)
# Reload from database
procedure.refresh_from_db()
# Load embedding
loaded_embedding = load_embedding(procedure)
self.assertIsNotNone(loaded_embedding)
self.assertTrue(np.allclose(dummy_embedding, loaded_embedding))
def test_has_embedding(self):
"""Test checking if instance has embedding."""
from hue_portal.core.models import Procedure
procedure = Procedure.objects.create(
title="Test Procedure",
domain="Test"
)
# Initially no embedding
self.assertFalse(has_embedding(procedure))
# Add embedding
dummy_embedding = np.random.rand(384).astype(np.float32)
save_embedding(procedure, dummy_embedding)
# Refresh and check
procedure.refresh_from_db()
self.assertTrue(has_embedding(procedure))
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