Load the necessary chains.
pydantic model langchain.chains.SequentialChain[source]#
Chain where the outputs of one chain feed directly into next.
Validators
raise_deprecation » all fields
set_verbose » verbose
validate_chains » all fields
field chains: List[langchain.chains.base.Chain] [Required]#
field input_variables: List[str] [Required]#
field return_all: bool = False#
pydantic model langchain.chains.SimpleSequentialChain[source]#
Simple chain where the outputs of one step feed directly into next.
Validators
raise_deprecation » all fields
set_verbose » verbose
validate_chains » all fields
field chains: List[langchain.chains.base.Chain] [Required]#
field strip_outputs: bool = False#
pydantic model langchain.chains.TransformChain[source]#
Chain transform chain output.
Example
from langchain import TransformChain
transform_chain = TransformChain(input_variables=["text"],
 output_variables["entities"], transform=func())
Validators
raise_deprecation » all fields
set_verbose » verbose
field input_variables: List[str] [Required]#
field output_variables: List[str] [Required]#
field transform: Callable[[Dict[str, str]], Dict[str, str]] [Required]#
pydantic model langchain.chains.VectorDBQA[source]#
Chain for question-answering against a vector database.
Validators
raise_deprecation » all fields
set_verbose » verbose
validate_search_type » all fields
field k: int = 4#
Number of documents to query for.
field search_kwargs: Dict[str, Any] [Optional]#
Extra search args.
field search_type: str = 'similarity'#
Search type to use over vectorstore. similarity or mmr.
field vectorstore: VectorStore [Required]#
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html