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33 lines
1.2 KiB
Python
33 lines
1.2 KiB
Python
from langchain.embeddings import OpenAIEmbeddings
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from langchain.retrievers.merger_retriever import MergerRetriever
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from langchain.vectorstores import Chroma
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def test_merger_retriever_get_relevant_docs() -> None:
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"""Test get_relevant_docs."""
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texts_group_a = [
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"This is a document about the Boston Celtics",
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"Fly me to the moon is one of my favourite songs."
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"I simply love going to the movies",
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]
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texts_group_b = [
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"This is a document about the Poenix Suns",
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"The Boston Celtics won the game by 20 points",
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"Real stupidity beats artificial intelligence every time. TP",
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]
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embeddings = OpenAIEmbeddings()
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retriever_a = Chroma.from_texts(texts_group_a, embedding=embeddings).as_retriever(
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search_kwargs={"k": 1}
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)
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retriever_b = Chroma.from_texts(texts_group_b, embedding=embeddings).as_retriever(
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search_kwargs={"k": 1}
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)
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# The Lord of the Retrievers.
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lotr = MergerRetriever([retriever_a, retriever_b])
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actual = lotr.get_relevant_documents("Tell me about the Celtics")
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assert len(actual) == 2
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assert texts_group_a[0] in [d.page_content for d in actual]
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assert texts_group_b[1] in [d.page_content for d in actual]
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