langchain.retrievers.tfidf.TFIDFRetriever¶
class langchain.retrievers.tfidf.TFIDFRetriever[source]¶
Bases: BaseRetriever
TF-IDF retriever.
Largely based on
https://github.com/asvskartheek/Text-Retrieval/blob/master/TF-IDF%20Search%20Engine%20(SKLEARN).ipynb
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param docs: List[langchain.schema.document.Document] [Required]¶
Documents.
param k: int = 4¶
Number of documents to return.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the retriever. Defaults to None
This metadata will be associated with each call to this retriever,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a retriever with its
use case.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the retriever. Defaults to None
These tags will be associated with each call to this retriever,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a retriever with its
use case.
param tfidf_array: Any = None¶
TF-IDF array.
param vectorizer: Any = None¶
TF-IDF vectorizer.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of abatch, which calls ainvoke N times.