93 lines
2.8 KiB
Python
93 lines
2.8 KiB
Python
"""
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Basic functionality tests for CI pipeline using pytest.
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"""
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import os
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import tempfile
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from pathlib import Path
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import pytest
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def test_imports():
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"""Test that all packages can be imported."""
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# Test C++ extensions
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@pytest.mark.skipif(
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os.environ.get("CI") == "true", reason="Skip model tests in CI to avoid MPS memory issues"
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)
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@pytest.mark.parametrize("backend_name", ["hnsw", "diskann"])
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def test_backend_basic(backend_name):
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"""Test basic functionality for each backend."""
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from leann.api import LeannBuilder, LeannSearcher, SearchResult
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# Create temporary directory for index
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with tempfile.TemporaryDirectory() as temp_dir:
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index_path = str(Path(temp_dir) / f"test.{backend_name}")
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# Test with small data
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texts = [f"This is document {i} about topic {i % 5}" for i in range(100)]
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# Configure builder based on backend
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if backend_name == "hnsw":
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builder = LeannBuilder(
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backend_name="hnsw",
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embedding_model="facebook/contriever",
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embedding_mode="sentence-transformers",
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M=16,
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efConstruction=200,
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)
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else: # diskann
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builder = LeannBuilder(
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backend_name="diskann",
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embedding_model="facebook/contriever",
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embedding_mode="sentence-transformers",
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num_neighbors=32,
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search_list_size=50,
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)
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# Add texts
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for text in texts:
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builder.add_text(text)
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# Build index
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builder.build_index(index_path)
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# Test search
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searcher = LeannSearcher(index_path)
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results = searcher.search("document about topic 2", top_k=5)
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# Verify results
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assert len(results) > 0
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assert isinstance(results[0], SearchResult)
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assert "topic 2" in results[0].text or "document" in results[0].text
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@pytest.mark.skipif(
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os.environ.get("CI") == "true", reason="Skip model tests in CI to avoid MPS memory issues"
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)
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def test_large_index():
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"""Test with larger dataset."""
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from leann.api import LeannBuilder, LeannSearcher
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with tempfile.TemporaryDirectory() as temp_dir:
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index_path = str(Path(temp_dir) / "test_large.hnsw")
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texts = [f"Document {i}: {' '.join([f'word{j}' for j in range(50)])}" for i in range(1000)]
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builder = LeannBuilder(
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backend_name="hnsw",
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embedding_model="facebook/contriever",
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embedding_mode="sentence-transformers",
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)
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for text in texts:
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builder.add_text(text)
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builder.build_index(index_path)
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searcher = LeannSearcher(index_path)
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results = searcher.search(["word10 word20"], top_k=10)
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assert len(results[0]) == 10
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