aiagent.AgentRetrievalQA

class AgentRetrievalQA(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, combine_documents_chain: BaseCombineDocumentsChain, input_key: str = 'query', output_key: str = 'result', return_source_documents: bool = False, retriever: BaseRetriever)

__init__(**kwargs: Any) → None

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.

Methods

__init__(**kwargs)	Create a new model by parsing and validating input data from keyword arguments.
acall(inputs[, return_only_outputs, ...])	Run the logic of this chain and add to output if desired.
apply(input_list[, callbacks])	Call the chain on all inputs in the list.
arun(*args[, callbacks, tags])	Run the chain as text in, text out or multiple variables, text out.
construct([_fields_set])	Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
copy(*[, include, exclude, update, deep])	Duplicate a model, optionally choose which fields to include, exclude and change.
dict(**kwargs)	Return dictionary representation of chain.
from_chain_type(llm[, chain_type, ...])	Load chain from chain type.
from_llm(llm[, prompt])	Initialize from LLM.
from_orm(obj)
json(*[, include, exclude, by_alias, ...])	Generate a JSON representation of the model, include and exclude arguments as per dict().
parse_file(path, *[, content_type, ...])
parse_obj(obj)
parse_raw(b, *[, content_type, encoding, ...])
prep_inputs(inputs)	Validate and prep inputs.
prep_outputs(inputs, outputs[, ...])	Validate and prep outputs.
raise_deprecation(values)	Raise deprecation warning if callback_manager is used.
run(*args[, callbacks, tags])	Run the chain as text in, text out or multiple variables, text out.
save(file_path)	Save the chain.
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
set_verbose(verbose)	If verbose is None, set it.
to_json()
to_json_not_implemented()
update_forward_refs(**localns)	Try to update ForwardRefs on fields based on this Model, globalns and localns.
validate(value)

Attributes

input_keys	Return the input keys.
lc_attributes	Return a list of attribute names that should be included in the serialized kwargs.
lc_namespace	Return the namespace of the langchain object.
lc_secrets	Return a map of constructor argument names to secret ids.
lc_serializable	Return whether or not the class is serializable.
output_keys	Return the output keys.
retriever