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26 lines
1.1 KiB
Python
26 lines
1.1 KiB
Python
from langchain.embeddings import OpenAIEmbeddings
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from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
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from langchain.retrievers.document_compressors import EmbeddingsFilter
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from langchain.vectorstores import Chroma
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def test_contextual_compression_retriever_get_relevant_docs() -> None:
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"""Test get_relevant_docs."""
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texts = [
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"This is a document about the Boston Celtics",
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"The Boston Celtics won the game by 20 points",
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"I simply love going to the movies",
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]
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embeddings = OpenAIEmbeddings()
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base_compressor = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.75)
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base_retriever = Chroma.from_texts(texts, embedding=embeddings).as_retriever(
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search_kwargs={"k": len(texts)}
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)
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retriever = ContextualCompressionRetriever(
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base_compressor=base_compressor, base_retriever=base_retriever
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)
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actual = retriever.get_relevant_documents("Tell me about the Celtics")
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assert len(actual) == 2
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assert texts[-1] not in [d.page_content for d in actual]
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