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0ccf2f0 55d584b 0ccf2f0 55d584b 0ccf2f0 55d584b 0ccf2f0 55d584b 0ccf2f0 55d584b 0ccf2f0 55d584b 0ccf2f0 55d584b 0ccf2f0 55d584b 0ccf2f0 55d584b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 | """Test suite for embedding providers.
Tests local provider, OpenAI provider, and SentenceTransformer provider.
"""
import sys
from pathlib import Path
import pytest
sys.path.insert(0, str(Path(__file__).parent.parent))
from warbler_cda.embeddings import (
EmbeddingProviderFactory,
LocalEmbeddingProvider,
EmbeddingProvider,
)
class TestEmbeddingProviderFactory:
"""Test embedding provider factory."""
def test_factory_creates_local_provider(self):
"""Test that factory can create local provider."""
provider = EmbeddingProviderFactory.create_provider("local", {"dimension": 64})
assert isinstance(provider, LocalEmbeddingProvider)
assert provider.get_dimension() == 64
def test_factory_list_available_providers(self):
"""Test that factory lists available providers."""
providers = EmbeddingProviderFactory.list_available_providers()
assert "local" in providers
assert "sentence_transformer" in providers
def test_factory_default_provider(self):
"""Test that factory can create default provider."""
try:
provider = EmbeddingProviderFactory.get_default_provider()
assert provider is not None
assert hasattr(provider, "embed_text")
assert hasattr(provider, "embed_batch")
except ImportError:
pytest.skip("SentenceTransformer not installed, " "testing with local fallback")
class TestLocalEmbeddingProvider:
"""Test local TF-IDF embedding provider."""
def setup_method(self):
"""Setup for each test."""
self.provider = LocalEmbeddingProvider({"dimension": 128})
def test_embed_single_text(self):
"""Test embedding a single text."""
text = "This is a test document about semantic embeddings"
embedding = self.provider.embed_text(text)
assert isinstance(embedding, list)
assert len(embedding) == 128
assert all(isinstance(x, float) for x in embedding)
def test_embed_batch(self):
"""Test batch embedding."""
texts = [
"First document about embeddings",
"Second document about semantics",
"Third document about search",
]
embeddings = self.provider.embed_batch(texts)
assert len(embeddings) == 3
assert all(len(emb) == 128 for emb in embeddings)
def test_similarity_calculation(self):
"""Test cosine similarity calculation."""
text1 = "performance optimization techniques"
text2 = "performance and optimization"
text3 = "completely unrelated weather report"
emb1 = self.provider.embed_text(text1)
emb2 = self.provider.embed_text(text2)
emb3 = self.provider.embed_text(text3)
sim_12 = self.provider.calculate_similarity(emb1, emb2)
sim_13 = self.provider.calculate_similarity(emb1, emb3)
assert 0.0 <= sim_12 <= 1.0
assert 0.0 <= sim_13 <= 1.0
assert sim_12 > sim_13, "Similar texts should have higher similarity"
def test_provider_info(self):
"""Test provider info method."""
info = self.provider.get_provider_info()
assert "provider_id" in info
assert "dimension" in info
assert info["dimension"] == 128
assert info["provider_id"] == "LocalEmbeddingProvider"
class TestSentenceTransformerProvider:
"""Test SentenceTransformer embedding provider."""
def setup_method(self):
"""Setup for each test."""
try:
from warbler_cda.embeddings.sentence_transformer_provider import (
SentenceTransformerEmbeddingProvider,
)
self.provider_class = SentenceTransformerEmbeddingProvider
self.skip = False
except ImportError:
self.skip = True
def test_provider_initialization(self):
"""Test SentenceTransformer provider initialization."""
if self.skip:
pytest.skip("SentenceTransformer not installed")
provider = self.provider_class()
assert provider.model is not None
assert provider.device in ["cpu", "cuda"]
assert provider.get_dimension() == 384
def test_embed_text_with_cache(self):
"""Test embedding with caching."""
if self.skip:
pytest.skip("SentenceTransformer not installed")
provider = self.provider_class()
text = "Cache test document for embeddings"
emb1 = provider.embed_text(text)
hits_before = provider.cache_stats["hits"]
emb2 = provider.embed_text(text)
hits_after = provider.cache_stats["hits"]
assert emb1 == emb2, "Same text should produce same embedding"
assert hits_after > hits_before, "Cache should register hit"
def test_batch_embedding(self):
"""Test batch embedding with SentenceTransformer."""
if self.skip:
pytest.skip("SentenceTransformer not installed")
provider = self.provider_class()
texts = [
"First test document",
"Second test document",
"Third test document",
]
embeddings = provider.embed_batch(texts)
assert len(embeddings) == 3
assert all(len(emb) == 384 for emb in embeddings)
def test_semantic_search(self):
"""Test semantic search functionality."""
if self.skip:
pytest.skip("SentenceTransformer not installed")
provider = self.provider_class()
documents = [
"The quick brown fox jumps over the lazy dog",
"Semantic embeddings enable efficient document retrieval",
"Machine learning models process text data",
"Neural networks learn from examples",
]
embeddings = [provider.embed_text(doc) for doc in documents]
query = "fast animal and jumping"
results = provider.semantic_search(query, embeddings, top_k=2)
assert len(results) == 2
assert all(isinstance(idx, int) and isinstance(score, float) for idx, score in results)
assert results[0][0] == 0, "First document should be most similar to jumping query"
def test_fractalstat_computation(self):
"""Test FractalStat coordinate computation from embedding."""
if self.skip:
pytest.skip("SentenceTransformer not installed")
provider = self.provider_class()
text = "Test document for FractalStat computation"
embedding = provider.embed_text(text)
fractalstat = provider.compute_fractalstat_from_embedding(embedding)
assert "lineage" in fractalstat
assert "adjacency" in fractalstat
assert "luminosity" in fractalstat
assert "polarity" in fractalstat
assert "dimensionality" in fractalstat
assert "horizon" in fractalstat
assert "realm" in fractalstat
# Verify expected ranges for different dimensions:
# lineage: unbounded positive (energy-based, generation/passage)
# adjacency: [-1, 1] (semantic connectivity)
# luminosity: [0, 100] (activity/coherence level)
# polarity: [-1, 1] (resonance balance)
# dimensionality: [1, 8] (complexity depth)
assert fractalstat["lineage"] >= 0.0, "lineage should be non-negative"
assert -1.0 <= fractalstat["adjacency"] <= 1.0, "adjacency should be between -1 and 1"
assert 0.0 <= fractalstat["luminosity"] <= 100.0, "luminosity should be between 0 and 100"
assert -1.0 <= fractalstat["polarity"] <= 1.0, "polarity should be between -1 and 1"
assert 1 <= fractalstat["dimensionality"] <= 8, "dimensionality should be between 1 and 8"
def test_provider_info(self):
"""Test provider info."""
if self.skip:
pytest.skip("SentenceTransformer not installed")
provider = self.provider_class()
info = provider.get_provider_info()
assert "provider_id" in info
assert "model_name" in info
assert "device" in info
assert "dimension" in info
assert info["dimension"] == 384
class TestEmbeddingProviderInterface:
"""Test the EmbeddingProvider abstract interface."""
def test_local_provider_implements_interface(self):
"""Test that LocalEmbeddingProvider implements full interface."""
provider = LocalEmbeddingProvider()
assert isinstance(provider, EmbeddingProvider)
assert hasattr(provider, "embed_text")
assert hasattr(provider, "embed_batch")
assert hasattr(provider, "calculate_similarity")
assert hasattr(provider, "get_dimension")
assert hasattr(provider, "get_provider_info")
def test_embedding_dimension_consistency(self):
"""Test that all embeddings have consistent dimension."""
provider = LocalEmbeddingProvider({"dimension": 128})
texts = ["First", "Second", "Third", "Fourth"]
embeddings = provider.embed_batch(texts)
expected_dim = provider.get_dimension()
for emb in embeddings:
assert len(emb) == expected_dim
def test_similarity_bounds(self):
"""Test that similarity scores are in valid range."""
provider = LocalEmbeddingProvider()
text1 = "Test document one"
text2 = "Test document two"
emb1 = provider.embed_text(text1)
emb2 = provider.embed_text(text2)
similarity = provider.calculate_similarity(emb1, emb2)
assert -1.0 <= similarity <= 1.0, "Similarity should be between -1 and 1"
if __name__ == "__main__":
pytest.main([__file__, "-v"])
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