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Unit tests for the open_notebook.utils.embedding module.
Tests embedding generation and mean pooling functionality.
"""
import pytest
from open_notebook.utils.chunking import CHUNK_SIZE
from open_notebook.utils.embedding import (
generate_embedding,
generate_embeddings,
mean_pool_embeddings,
)
from open_notebook.utils.token_utils import token_count
def _build_text_exceeding_tokens(fragment: str, threshold_tokens: int) -> str:
"""Build text that exceeds a token threshold."""
text = fragment
while token_count(text) <= threshold_tokens:
text += fragment
return text
# ============================================================================
# TEST SUITE 1: Mean Pooling
# ============================================================================
class TestMeanPoolEmbeddings:
"""Test suite for mean pooling functionality."""
@pytest.mark.asyncio
async def test_single_embedding(self):
"""Test mean pooling with single embedding returns normalized version."""
embedding = [1.0, 0.0, 0.0]
result = await mean_pool_embeddings([embedding])
assert len(result) == 3
# Should be normalized (already unit length)
assert abs(result[0] - 1.0) < 0.001
assert abs(result[1]) < 0.001
assert abs(result[2]) < 0.001
@pytest.mark.asyncio
async def test_two_embeddings(self):
"""Test mean pooling with two embeddings."""
embeddings = [
[1.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
]
result = await mean_pool_embeddings(embeddings)
assert len(result) == 3
# Mean of normalized vectors, then normalized
# Result should be roughly [0.707, 0.707, 0]
assert abs(result[0] - result[1]) < 0.001 # x and y should be equal
assert abs(result[2]) < 0.001 # z should be ~0
@pytest.mark.asyncio
async def test_identical_embeddings(self):
"""Test mean pooling with identical embeddings."""
embedding = [0.5, 0.5, 0.5, 0.5]
embeddings = [embedding, embedding, embedding]
result = await mean_pool_embeddings(embeddings)
assert len(result) == 4
# Result should be same direction, just normalized
# Original is already normalized if we normalize it
import numpy as np
orig_norm = np.linalg.norm(embedding)
expected = [v / orig_norm for v in embedding]
for i in range(4):
assert abs(result[i] - expected[i]) < 0.001
@pytest.mark.asyncio
async def test_empty_list_raises(self):
"""Test that empty list raises ValueError."""
with pytest.raises(ValueError, match="empty"):
await mean_pool_embeddings([])
@pytest.mark.asyncio
async def test_normalization(self):
"""Test that result is normalized to unit length."""
embeddings = [
[3.0, 4.0, 0.0], # Not unit length
[0.0, 5.0, 0.0], # Not unit length
]
result = await mean_pool_embeddings(embeddings)
# Check result is unit length
import numpy as np
norm = np.linalg.norm(result)
assert abs(norm - 1.0) < 0.001
@pytest.mark.asyncio
async def test_high_dimensional(self):
"""Test mean pooling with high-dimensional embeddings."""
import numpy as np
# Create random embeddings of dimension 768 (typical embedding size)
np.random.seed(42)
embeddings = [
np.random.randn(768).tolist(),
np.random.randn(768).tolist(),
np.random.randn(768).tolist(),
]
result = await mean_pool_embeddings(embeddings)
assert len(result) == 768
# Check result is normalized
norm = np.linalg.norm(result)
assert abs(norm - 1.0) < 0.001
# ============================================================================
# TEST SUITE 2: Generate Embeddings (requires mocking)
# ============================================================================
class TestGenerateEmbeddings:
"""Test suite for batch embedding generation."""
@pytest.mark.asyncio
async def test_empty_list(self):
"""Test that empty list returns empty list."""
result = await generate_embeddings([])
assert result == []
@pytest.mark.asyncio
async def test_no_model_raises(self):
"""Test that missing model raises ValueError."""
from unittest.mock import AsyncMock, patch
with patch(
"open_notebook.ai.models.model_manager.get_embedding_model",
new_callable=AsyncMock,
return_value=None,
):
with pytest.raises(ValueError, match="No embedding model configured"):
await generate_embeddings(["test text"])
@pytest.mark.asyncio
async def test_successful_embedding(self):
"""Test successful embedding generation with mocked model."""
from unittest.mock import AsyncMock, MagicMock, patch
mock_model = MagicMock()
mock_model.aembed = AsyncMock(return_value=[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]])
with patch(
"open_notebook.ai.models.model_manager.get_embedding_model",
new_callable=AsyncMock,
return_value=mock_model,
):
result = await generate_embeddings(["text1", "text2"])
assert len(result) == 2
assert result[0] == [0.1, 0.2, 0.3]
assert result[1] == [0.4, 0.5, 0.6]
mock_model.aembed.assert_called_once_with(["text1", "text2"])
# ============================================================================
# TEST SUITE 3: Generate Single Embedding (requires mocking)
# ============================================================================
class TestGenerateEmbedding:
"""Test suite for single embedding generation."""
@pytest.mark.asyncio
async def test_empty_text_raises(self):
"""Test that empty text raises ValueError."""
with pytest.raises(ValueError, match="empty"):
await generate_embedding("")
with pytest.raises(ValueError, match="empty"):
await generate_embedding(" ")
@pytest.mark.asyncio
async def test_short_text_direct_embedding(self):
"""Test that short text is embedded directly without chunking."""
from unittest.mock import AsyncMock, MagicMock, patch
mock_model = MagicMock()
mock_model.aembed = AsyncMock(return_value=[[0.1, 0.2, 0.3]])
with patch(
"open_notebook.ai.models.model_manager.get_embedding_model",
new_callable=AsyncMock,
return_value=mock_model,
):
result = await generate_embedding("Short text")
assert result == [0.1, 0.2, 0.3]
# Should be called with single text
mock_model.aembed.assert_called_once_with(["Short text"])
@pytest.mark.asyncio
async def test_long_text_chunked_and_pooled(self):
"""Test that long text is chunked and mean pooled."""
from unittest.mock import AsyncMock, MagicMock, patch
long_text = _build_text_exceeding_tokens("This is a sentence. ", CHUNK_SIZE)
mock_model = MagicMock()
# Return multiple embeddings (one per chunk)
mock_model.aembed = AsyncMock(
return_value=[
[1.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
]
)
with patch(
"open_notebook.ai.models.model_manager.get_embedding_model",
new_callable=AsyncMock,
return_value=mock_model,
):
result = await generate_embedding(long_text)
# Should return mean pooled result
assert len(result) == 3
# Model should have been called with multiple chunks
assert mock_model.aembed.called
@pytest.mark.asyncio
async def test_content_type_parameter(self):
"""Test that content type parameter is passed through."""
from unittest.mock import AsyncMock, MagicMock, patch
from open_notebook.utils.chunking import ContentType
mock_model = MagicMock()
mock_model.aembed = AsyncMock(return_value=[[0.1, 0.2, 0.3]])
with patch(
"open_notebook.ai.models.model_manager.get_embedding_model",
new_callable=AsyncMock,
return_value=mock_model,
):
result = await generate_embedding(
"# Markdown Header\n\nContent",
content_type=ContentType.MARKDOWN,
)
assert len(result) == 3
@pytest.mark.asyncio
async def test_batching(self):
"""Test that large input is split into batches of EMBEDDING_BATCH_SIZE."""
from unittest.mock import AsyncMock, MagicMock, call, patch
from open_notebook.utils.embedding import EMBEDDING_BATCH_SIZE
num_texts = 120
texts = [f"text_{i}" for i in range(num_texts)]
mock_model = MagicMock()
mock_model.model_name = "test-model"
def make_embeddings(batch):
return [[float(i)] * 3 for i in range(len(batch))]
mock_model.aembed = AsyncMock(side_effect=lambda batch: make_embeddings(batch))
with patch(
"open_notebook.ai.models.model_manager.get_embedding_model",
new_callable=AsyncMock,
return_value=mock_model,
):
result = await generate_embeddings(texts)
assert len(result) == num_texts
# 120 texts / 50 batch size = 3 batches (50, 50, 20)
assert mock_model.aembed.call_count == 3
assert len(mock_model.aembed.call_args_list[0][0][0]) == EMBEDDING_BATCH_SIZE
assert len(mock_model.aembed.call_args_list[1][0][0]) == EMBEDDING_BATCH_SIZE
assert len(mock_model.aembed.call_args_list[2][0][0]) == 20
@pytest.mark.asyncio
async def test_batch_retry_on_transient_failure(self):
"""Test that a transient failure is retried and succeeds."""
from unittest.mock import AsyncMock, MagicMock, patch
texts = ["text_a", "text_b"]
mock_model = MagicMock()
mock_model.model_name = "test-model"
# Fail once, then succeed
mock_model.aembed = AsyncMock(
side_effect=[
RuntimeError("transient error"),
[[0.1, 0.2], [0.3, 0.4]],
]
)
with (
patch(
"open_notebook.ai.models.model_manager.get_embedding_model",
new_callable=AsyncMock,
return_value=mock_model,
),
patch("open_notebook.utils.embedding.EMBEDDING_RETRY_DELAY", 0),
):
result = await generate_embeddings(texts)
assert result == [[0.1, 0.2], [0.3, 0.4]]
assert mock_model.aembed.call_count == 2
@pytest.mark.asyncio
async def test_batch_retry_exhaustion(self):
"""Test that RuntimeError is raised after all retries are exhausted."""
from unittest.mock import AsyncMock, MagicMock, patch
from open_notebook.utils.embedding import EMBEDDING_MAX_RETRIES
texts = ["text_a"]
mock_model = MagicMock()
mock_model.model_name = "test-model"
mock_model.aembed = AsyncMock(side_effect=RuntimeError("persistent error"))
with (
patch(
"open_notebook.ai.models.model_manager.get_embedding_model",
new_callable=AsyncMock,
return_value=mock_model,
),
patch("open_notebook.utils.embedding.EMBEDDING_RETRY_DELAY", 0),
):
with pytest.raises(RuntimeError, match="Failed to generate embeddings"):
await generate_embeddings(texts)
assert mock_model.aembed.call_count == EMBEDDING_MAX_RETRIES
# ============================================================================
# TEST SUITE 4: Error Classification for 413
# ============================================================================
class TestErrorClassifier413:
"""Test that 413 payload-too-large errors are classified correctly."""
def test_413_status_code(self):
from open_notebook.exceptions import ExternalServiceError
from open_notebook.utils.error_classifier import classify_error
exc = Exception("HTTP 413: Payload Too Large")
exc_class, message = classify_error(exc)
assert exc_class is ExternalServiceError
assert "payload is too large" in message
def test_request_entity_too_large(self):
from open_notebook.exceptions import ExternalServiceError
from open_notebook.utils.error_classifier import classify_error
exc = Exception("Request Entity Too Large")
exc_class, message = classify_error(exc)
assert exc_class is ExternalServiceError
assert "payload is too large" in message
if __name__ == "__main__":
pytest.main([__file__, "-v"])
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