ComponentsRetrieversBM25On this pageBM25BM25 also known as the Okapi BM25, is a ranking function used in information retrieval systems to estimate the relevance of documents to a given search query.This notebook goes over how to use a retriever that under the hood uses BM25 using rank_bm25 package.# !pip install rank_bm25from langchain.retrievers import BM25Retriever    /workspaces/langchain/.venv/lib/python3.10/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.10) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.      warnings.warn(Create New Retriever with Texts​retriever = BM25Retriever.from_texts(["foo", "bar", "world", "hello", "foo bar"])Create a New Retriever with Documents​You can now create a new retriever with the documents you created.from langchain.schema import Documentretriever = BM25Retriever.from_documents(    [        Document(page_content="foo"),        Document(page_content="bar"),        Document(page_content="world"),        Document(page_content="hello"),        Document(page_content="foo bar"),    ])Use Retriever​We can now use the retriever!result = retriever.get_relevant_documents("foo")result    [Document(page_content='foo', metadata={}),     Document(page_content='foo bar', metadata={}),     Document(page_content='hello', metadata={}),     Document(page_content='world', metadata={})]PreviousAzure Cognitive SearchNextChaindeskCreate New Retriever with TextsCreate a New Retriever with DocumentsUse Retriever