ComponentsRetrieversTF-IDFOn this pageTF-IDFTF-IDF means term-frequency times inverse document-frequency.This notebook goes over how to use a retriever that under the hood uses TF-IDF using scikit-learn package.For more information on the details of TF-IDF see this blog post.# !pip install scikit-learnfrom langchain.retrievers import TFIDFRetrieverCreate New Retriever with Texts​retriever = TFIDFRetriever.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 = TFIDFRetriever.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={})]Save and load​You can easily save and load this retriever, making it handy for local development!retriever.save_local("testing.pkl")retriever_copy = TFIDFRetriever.load_local("testing.pkl")retriever_copy.get_relevant_documents("foo")    [Document(page_content='foo', metadata={}),     Document(page_content='foo bar', metadata={}),     Document(page_content='hello', metadata={}),     Document(page_content='world', metadata={})]PreviousTavily Search APINextVespaCreate New Retriever with TextsCreate a New Retriever with DocumentsUse RetrieverSave and load