langchain.retrievers.multi_vector.MultiVectorRetriever¶
class langchain.retrievers.multi_vector.MultiVectorRetriever[source]¶
Bases: BaseRetriever
Retrieve from a set of multiple embeddings for the same document.
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 docstore: langchain.schema.storage.BaseStore[str, langchain.schema.document.Document] [Required]¶
The storage layer for the parent documents
param id_key: str = 'doc_id'¶
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 search_kwargs: dict [Optional]¶
Keyword arguments to pass to the search function.
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 vectorstore: langchain.schema.vectorstore.VectorStore [Required]¶
The underlying vectorstore to use to store small chunks
and their embedding vectors
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.
Subclasses should override this method if they can batch more efficiently.