forked from Archives/langchain
32 lines
1.2 KiB
Python
32 lines
1.2 KiB
Python
"""Integration test for embedding-based redundant doc filtering."""
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from langchain.document_transformers import (
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EmbeddingsRedundantFilter,
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_DocumentWithState,
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)
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.schema import Document
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def test_embeddings_redundant_filter() -> None:
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texts = [
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"What happened to all of my cookies?",
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"Where did all of my cookies go?",
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"I wish there were better Italian restaurants in my neighborhood.",
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]
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docs = [Document(page_content=t) for t in texts]
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embeddings = OpenAIEmbeddings()
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redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)
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actual = redundant_filter.transform_documents(docs)
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assert len(actual) == 2
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assert set(texts[:2]).intersection([d.page_content for d in actual])
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def test_embeddings_redundant_filter_with_state() -> None:
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texts = ["What happened to all of my cookies?", "foo bar baz"]
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state = {"embedded_doc": [0.5] * 10}
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docs = [_DocumentWithState(page_content=t, state=state) for t in texts]
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embeddings = OpenAIEmbeddings()
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redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)
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actual = redundant_filter.transform_documents(docs)
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assert len(actual) == 1
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