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from __future__ import annotations
from copy import deepcopy
from typing import Any, Dict
import hydra
from langchain.tools import BaseTool
from flows.base_flows import AtomicFlow
class LCToolFlow(AtomicFlow):
REQUIRED_KEYS_CONFIG = ["backend"]
SUPPORTS_CACHING: bool = False
backend: BaseTool
def __init__(self, backend: BaseTool, **kwargs) -> None:
super().__init__(**kwargs)
self.backend = backend
@classmethod
def _set_up_backend(cls, config: Dict[str, Any]) -> BaseTool:
if config["_target_"].startswith("."):
# assumption: cls is associated with relative data_transformation_configs
# for example, CF_Code and CF_Code.yaml should be in the same directory,
# and all _target_ in CF_Code.yaml should be relative
cls_parent_module = ".".join(cls.__module__.split(".")[:-1])
config["_target_"] = cls_parent_module + config["_target_"]
tool = hydra.utils.instantiate(config, _convert_="partial")
return tool
@classmethod
def instantiate_from_config(cls, config: Dict[str, Any]) -> LCToolFlow:
flow_config = deepcopy(config)
kwargs = {"flow_config": flow_config}
# ~~~ Set up LangChain backend ~~~
kwargs["backend"] = cls._set_up_backend(config["backend"])
# ~~~ Instantiate flow ~~~
return cls(**kwargs)
def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
observation = self.backend.run(tool_input=input_data)
return {"observation": observation}
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