mirror of
https://github.com/trustgraph-ai/trustgraph.git
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275 lines
No EOL
10 KiB
Python
275 lines
No EOL
10 KiB
Python
import pytest
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import asyncio
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from unittest.mock import AsyncMock, Mock, patch
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from trustgraph.schema import TextDocument, Chunk, Metadata
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from trustgraph.chunking.token.chunker import Processor as TokenChunker
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@pytest.fixture
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def mock_flow():
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output_mock = AsyncMock()
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flow_mock = Mock(return_value=output_mock)
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return flow_mock, output_mock
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@pytest.fixture
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def mock_consumer():
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consumer = Mock()
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consumer.id = "test-consumer"
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consumer.flow = "test-flow"
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return consumer
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@pytest.fixture
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def sample_document():
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metadata = Metadata(
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id="test-doc-1",
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metadata=[],
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user="test-user",
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collection="test-collection"
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)
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# Create text that will result in multiple token chunks
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text = "The quick brown fox jumps over the lazy dog. " * 50
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return TextDocument(
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metadata=metadata,
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text=text.encode("utf-8")
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)
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@pytest.fixture
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def short_document():
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metadata = Metadata(
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id="test-doc-2",
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metadata=[],
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user="test-user",
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collection="test-collection"
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)
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text = "Short text."
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return TextDocument(
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metadata=metadata,
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text=text.encode("utf-8")
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)
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class TestTokenChunker:
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def test_init_default_params(self, mock_async_processor_init):
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processor = TokenChunker()
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assert processor.text_splitter._chunk_size == 250
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assert processor.text_splitter._chunk_overlap == 15
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# Just verify the text splitter was created (encoding verification is complex)
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assert processor.text_splitter is not None
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assert hasattr(processor.text_splitter, 'split_text')
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def test_init_custom_params(self, mock_async_processor_init):
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processor = TokenChunker(chunk_size=100, chunk_overlap=10)
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assert processor.text_splitter._chunk_size == 100
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assert processor.text_splitter._chunk_overlap == 10
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def test_init_with_id(self, mock_async_processor_init):
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processor = TokenChunker(id="custom-token-chunker")
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assert processor.id == "custom-token-chunker"
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@pytest.mark.asyncio
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async def test_on_message_single_chunk(self, mock_async_processor_init, mock_flow, mock_consumer, short_document):
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flow_mock, output_mock = mock_flow
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processor = TokenChunker(chunk_size=250, chunk_overlap=15)
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msg = Mock()
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msg.value.return_value = short_document
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await processor.on_message(msg, mock_consumer, flow_mock)
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# Short text should produce exactly one chunk
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assert output_mock.send.call_count == 1
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# Verify the chunk was created correctly
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chunk_call = output_mock.send.call_args[0][0]
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assert isinstance(chunk_call, Chunk)
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assert chunk_call.metadata == short_document.metadata
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assert chunk_call.chunk.decode("utf-8") == short_document.text.decode("utf-8")
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@pytest.mark.asyncio
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async def test_on_message_multiple_chunks(self, mock_async_processor_init, mock_flow, mock_consumer, sample_document):
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flow_mock, output_mock = mock_flow
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processor = TokenChunker(chunk_size=50, chunk_overlap=5)
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msg = Mock()
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msg.value.return_value = sample_document
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await processor.on_message(msg, mock_consumer, flow_mock)
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# Should produce multiple chunks
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assert output_mock.send.call_count > 1
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# Verify all chunks have correct metadata
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for call in output_mock.send.call_args_list:
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chunk = call[0][0]
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assert isinstance(chunk, Chunk)
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assert chunk.metadata == sample_document.metadata
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assert len(chunk.chunk) > 0
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@pytest.mark.asyncio
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async def test_on_message_token_overlap(self, mock_async_processor_init, mock_flow, mock_consumer):
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flow_mock, output_mock = mock_flow
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processor = TokenChunker(chunk_size=20, chunk_overlap=5)
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# Create a document with repeated pattern
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metadata = Metadata(id="test", metadata=[], user="test-user", collection="test-collection")
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text = "one two three four five six seven eight nine ten " * 5
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document = TextDocument(metadata=metadata, text=text.encode("utf-8"))
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msg = Mock()
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msg.value.return_value = document
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await processor.on_message(msg, mock_consumer, flow_mock)
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# Collect all chunks
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chunks = []
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for call in output_mock.send.call_args_list:
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chunk_text = call[0][0].chunk.decode("utf-8")
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chunks.append(chunk_text)
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# Should have multiple chunks
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assert len(chunks) > 1
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# Verify chunks are not empty
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for chunk in chunks:
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assert len(chunk) > 0
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@pytest.mark.asyncio
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async def test_on_message_empty_document(self, mock_async_processor_init, mock_flow, mock_consumer):
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flow_mock, output_mock = mock_flow
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processor = TokenChunker()
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metadata = Metadata(id="empty", metadata=[], user="test-user", collection="test-collection")
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document = TextDocument(metadata=metadata, text=b"")
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msg = Mock()
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msg.value.return_value = document
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await processor.on_message(msg, mock_consumer, flow_mock)
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# Empty documents typically don't produce chunks with langchain splitters
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# This behavior is expected - no chunks should be produced
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assert output_mock.send.call_count == 0
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@pytest.mark.asyncio
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async def test_on_message_unicode_handling(self, mock_async_processor_init, mock_flow, mock_consumer):
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flow_mock, output_mock = mock_flow
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processor = TokenChunker(chunk_size=50)
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metadata = Metadata(id="unicode", metadata=[], user="test-user", collection="test-collection")
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# Test with various unicode characters
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text = "Hello 世界! 🌍 Test émojis café naïve résumé. Greek: αβγδε Hebrew: אבגדה"
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document = TextDocument(metadata=metadata, text=text.encode("utf-8"))
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msg = Mock()
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msg.value.return_value = document
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await processor.on_message(msg, mock_consumer, flow_mock)
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# Verify unicode is preserved correctly
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all_chunks = []
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for call in output_mock.send.call_args_list:
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chunk_text = call[0][0].chunk.decode("utf-8")
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all_chunks.append(chunk_text)
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# Reconstruct text
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reconstructed = "".join(all_chunks)
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assert "世界" in reconstructed
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assert "🌍" in reconstructed
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assert "émojis" in reconstructed
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assert "αβγδε" in reconstructed
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assert "אבגדה" in reconstructed
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@pytest.mark.asyncio
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async def test_on_message_token_boundary_preservation(self, mock_async_processor_init, mock_flow, mock_consumer):
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flow_mock, output_mock = mock_flow
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processor = TokenChunker(chunk_size=10, chunk_overlap=2)
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metadata = Metadata(id="boundary", metadata=[], user="test-user", collection="test-collection")
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# Text with clear word boundaries
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text = "This is a test of token boundaries and proper splitting."
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document = TextDocument(metadata=metadata, text=text.encode("utf-8"))
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msg = Mock()
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msg.value.return_value = document
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await processor.on_message(msg, mock_consumer, flow_mock)
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# Collect all chunks
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chunks = []
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for call in output_mock.send.call_args_list:
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chunk_text = call[0][0].chunk.decode("utf-8")
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chunks.append(chunk_text)
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# Token chunker should respect token boundaries
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for chunk in chunks:
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# Chunks should not start or end with partial words (in most cases)
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assert len(chunk.strip()) > 0
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@pytest.mark.asyncio
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async def test_metrics_recorded(self, mock_async_processor_init, mock_flow, mock_consumer, sample_document):
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flow_mock, output_mock = mock_flow
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processor = TokenChunker(chunk_size=50)
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msg = Mock()
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msg.value.return_value = sample_document
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# Mock the metric
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with patch.object(TokenChunker.chunk_metric, 'labels') as mock_labels:
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mock_observe = Mock()
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mock_labels.return_value.observe = mock_observe
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await processor.on_message(msg, mock_consumer, flow_mock)
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# Verify metrics were recorded
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mock_labels.assert_called_with(id="test-consumer", flow="test-flow")
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assert mock_observe.call_count > 0
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# Verify chunk sizes were observed
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for call in mock_observe.call_args_list:
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chunk_size = call[0][0]
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assert chunk_size > 0
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def test_add_args(self):
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parser = Mock()
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TokenChunker.add_args(parser)
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# Verify arguments were added
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calls = parser.add_argument.call_args_list
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arg_names = [call[0][0] for call in calls]
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assert '-z' in arg_names or '--chunk-size' in arg_names
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assert '-v' in arg_names or '--chunk-overlap' in arg_names
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@pytest.mark.asyncio
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async def test_encoding_specific_behavior(self, mock_async_processor_init, mock_flow, mock_consumer):
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flow_mock, output_mock = mock_flow
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processor = TokenChunker(chunk_size=10, chunk_overlap=0)
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metadata = Metadata(id="encoding", metadata=[], user="test-user", collection="test-collection")
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# Test text that might tokenize differently with cl100k_base encoding
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text = "GPT-4 is an AI model. It uses tokens."
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document = TextDocument(metadata=metadata, text=text.encode("utf-8"))
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msg = Mock()
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msg.value.return_value = document
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await processor.on_message(msg, mock_consumer, flow_mock)
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# Verify chunking happened
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assert output_mock.send.call_count >= 1
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# Collect all chunks
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chunks = []
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for call in output_mock.send.call_args_list:
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chunk_text = call[0][0].chunk.decode("utf-8")
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chunks.append(chunk_text)
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# Verify all text is preserved (allowing for overlap)
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all_text = " ".join(chunks)
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assert "GPT-4" in all_text
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assert "AI model" in all_text
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assert "tokens" in all_text |