Adding unit tests for dspy.retrievers.Embeddings #8129
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Added three unit tests for the embeddings. 1) test_embeddings_basic_search: Verifies that the retriever returns the correct top k relevant passages and their indices for a single query. 2) test_embeddings_forward_batch: Ensures the retriever handles batch queries correctly, returning the top k relevant passages and indices for each query. 3) test_normalization: Confirms that the embeddings are correctly normalized to have a norm close to 1 after processing.
tests/retrievers/test_embeddings.py
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| from dspy.retrievers.embeddings import Embeddings | ||
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| @pytest.fixture |
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nit: we don't need fixture for the list since it's only used in this test
tests/retrievers/test_embeddings.py
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| assert isinstance(passage, str) | ||
| assert passage in dummy_corpus | ||
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| def test_embeddings_forward_batch(dummy_corpus, dummy_embedder): |
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We can remove the test and the one below, since they are testing the private methods.
1) Removed tests that were calling private functions. 2) Added a new test to check robustness on high concurrency. 3) Updated dummy embedder by keeping similar data close and different data far away.
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Hey @chenmoneygithub , can you take a look now? TIA. |
chenmoneygithub
approved these changes
May 3, 2025
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Added three unit tests for the embeddings.
test_embeddings_basic_search: Verifies that the retriever returns the correct top k relevant passages and their indices for a single query.
test_embeddings_forward_batch: Ensures the retriever handles batch queries correctly, returning the top k relevant passages and indices for each query.
test_normalization: Confirms that the embeddings are correctly normalized to have a norm close to 1 after processing.