langchain.retrievers.multi_query.MultiQueryRetriever¶
class langchain.retrievers.multi_query.MultiQueryRetriever[source]¶
Bases: BaseRetriever
Given a user query, use an LLM to write a set of queries.
Retrieve docs for each query. Rake the unique union of all retrieved docs.
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 llm_chain: langchain.chains.llm.LLMChain [Required]¶
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 parser_key: str = 'lines'¶
param retriever: langchain.schema.retriever.BaseRetriever [Required]¶
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 verbose: bool = True¶
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output]¶