Create a BM25Retriever from a list of Documents.
:param documents: A list of Documents to vectorize.
:param bm25_params: Parameters to pass to the BM25 vectorizer.
:param preprocess_func: A function to preprocess each text before vectorization.
:param **kwargs: Any other arguments to pass to the retriever.
Returns
A BM25Retriever instance.
classmethod from_orm(obj: Any) → Model¶
classmethod from_texts(texts: ~typing.Iterable[str], metadatas: ~typing.Optional[~typing.Iterable[dict]] = None, bm25_params: ~typing.Optional[~typing.Dict[str, ~typing.Any]] = None, preprocess_func: ~typing.Callable[[str], ~typing.List[str]] = <function default_preprocessing_func>, **kwargs: ~typing.Any) → BM25Retriever[source]¶
Create a BM25Retriever from a list of texts.
:param texts: A list of texts to vectorize.
:param metadatas: A list of metadata dicts to associate with each text.
:param bm25_params: Parameters to pass to the BM25 vectorizer.
:param preprocess_func: A function to preprocess each text before vectorization.
:param **kwargs: Any other arguments to pass to the retriever.
Returns
A BM25Retriever instance.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
get_relevant_documents(query: str, *, callbacks: Callbacks = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, **kwargs: Any) → List[Document]¶