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135 lines
4.8 KiB
Python
135 lines
4.8 KiB
Python
"""Base interface that all chains should implement."""
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from abc import ABC, abstractmethod
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from typing import Any, Dict, List, Optional, Union
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from pydantic import BaseModel, Extra, Field
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import langchain
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class Memory(BaseModel, ABC):
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"""Base interface for memory in chains."""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@property
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@abstractmethod
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def memory_variables(self) -> List[str]:
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"""Input keys this memory class will load dynamically."""
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@abstractmethod
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def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
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"""Return key-value pairs given the text input to the chain."""
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@abstractmethod
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def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
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"""Save the context of this model run to memory."""
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@abstractmethod
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def clear(self) -> None:
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"""Clear memory contents."""
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def _get_verbosity() -> bool:
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return langchain.verbose
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class Chain(BaseModel, ABC):
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"""Base interface that all chains should implement."""
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memory: Optional[Memory] = None
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verbose: bool = Field(default_factory=_get_verbosity)
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"""Whether to print out response text."""
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@property
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@abstractmethod
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def input_keys(self) -> List[str]:
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"""Input keys this chain expects."""
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@property
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@abstractmethod
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def output_keys(self) -> List[str]:
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"""Output keys this chain expects."""
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def _validate_inputs(self, inputs: Dict[str, str]) -> None:
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"""Check that all inputs are present."""
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missing_keys = set(self.input_keys).difference(inputs)
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if missing_keys:
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raise ValueError(f"Missing some input keys: {missing_keys}")
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def _validate_outputs(self, outputs: Dict[str, str]) -> None:
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if set(outputs) != set(self.output_keys):
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raise ValueError(
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f"Did not get output keys that were expected. "
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f"Got: {set(outputs)}. Expected: {set(self.output_keys)}."
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)
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@abstractmethod
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def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
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"""Run the logic of this chain and return the output."""
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def __call__(
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self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False
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) -> Dict[str, Any]:
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"""Run the logic of this chain and add to output if desired.
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Args:
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inputs: Dictionary of inputs, or single input if chain expects
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only one param.
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return_only_outputs: boolean for whether to return only outputs in the
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response. If True, only new keys generated by this chain will be
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returned. If False, both input keys and new keys generated by this
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chain will be returned. Defaults to False.
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"""
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if not isinstance(inputs, dict):
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if len(self.input_keys) != 1:
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raise ValueError(
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f"A single string input was passed in, but this chain expects "
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f"multiple inputs ({self.input_keys}). When a chain expects "
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f"multiple inputs, please call it by passing in a dictionary, "
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"eg `chain({'foo': 1, 'bar': 2})`"
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)
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inputs = {self.input_keys[0]: inputs}
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if self.memory is not None:
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external_context = self.memory.load_memory_variables(inputs)
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inputs = dict(inputs, **external_context)
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self._validate_inputs(inputs)
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if self.verbose:
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print(
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f"\n\n\033[1m> Entering new {self.__class__.__name__} chain...\033[0m"
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)
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outputs = self._call(inputs)
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if self.verbose:
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print(f"\n\033[1m> Finished {self.__class__.__name__} chain.\033[0m")
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self._validate_outputs(outputs)
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if self.memory is not None:
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self.memory.save_context(inputs, outputs)
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if return_only_outputs:
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return outputs
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else:
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return {**inputs, **outputs}
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def apply(self, input_list: List[Dict[str, Any]]) -> List[Dict[str, str]]:
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"""Call the chain on all inputs in the list."""
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return [self(inputs) for inputs in input_list]
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def run(self, text: str) -> str:
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"""Run text in, text out (if applicable)."""
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if len(self.input_keys) != 1:
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raise ValueError(
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f"`run` not supported when there is not exactly "
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f"one input key, got {self.input_keys}."
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)
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if len(self.output_keys) != 1:
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raise ValueError(
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f"`run` not supported when there is not exactly "
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f"one output key, got {self.output_keys}."
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)
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return self({self.input_keys[0]: text})[self.output_keys[0]]
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