mirror of
https://github.com/Mintplex-Labs/langchain-python.git
synced 2026-07-21 00:35:23 -04:00
Samantha Whitmore
a408ed3ea3
Add MemoryChain and ConversationChain as chains that take a docstore in addition to the prompt, and use the docstore to stuff context into the prompt. This can be used to have an ongoing conversation with a chatbot. Probably needs a bit of refactoring for code quality Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
113 lines
3.9 KiB
Python
113 lines
3.9 KiB
Python
"""Base interface that all chains should implement."""
|
|
from abc import ABC, abstractmethod
|
|
from typing import Any, Dict, List, Optional
|
|
|
|
from pydantic import BaseModel, Extra
|
|
|
|
|
|
class Memory(BaseModel, ABC):
|
|
"""Base interface for memory in chains."""
|
|
|
|
class Config:
|
|
"""Configuration for this pydantic object."""
|
|
|
|
extra = Extra.forbid
|
|
arbitrary_types_allowed = True
|
|
|
|
@property
|
|
@abstractmethod
|
|
def dynamic_keys(self) -> List[str]:
|
|
"""Input keys this memory class will load dynamically."""
|
|
|
|
@abstractmethod
|
|
def _load_dynamic_keys(self, inputs: Dict[str, Any]) -> Dict[str, str]:
|
|
"""Return key-value pairs given the text input to the chain."""
|
|
|
|
@abstractmethod
|
|
def _save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
|
|
"""Save the context of this model run to memory."""
|
|
|
|
|
|
class Chain(BaseModel, ABC):
|
|
"""Base interface that all chains should implement."""
|
|
|
|
memory: Optional[Memory] = None
|
|
|
|
verbose: bool = False
|
|
"""Whether to print out response text."""
|
|
|
|
@property
|
|
@abstractmethod
|
|
def input_keys(self) -> List[str]:
|
|
"""Input keys this chain expects."""
|
|
|
|
@property
|
|
@abstractmethod
|
|
def output_keys(self) -> List[str]:
|
|
"""Output keys this chain expects."""
|
|
|
|
def _validate_inputs(self, inputs: Dict[str, str]) -> None:
|
|
"""Check that all inputs are present."""
|
|
missing_keys = set(self.input_keys).difference(inputs)
|
|
if missing_keys:
|
|
raise ValueError(f"Missing some input keys: {missing_keys}")
|
|
|
|
def _validate_outputs(self, outputs: Dict[str, str]) -> None:
|
|
if set(outputs) != set(self.output_keys):
|
|
raise ValueError(
|
|
f"Did not get output keys that were expected. "
|
|
f"Got: {set(outputs)}. Expected: {set(self.output_keys)}."
|
|
)
|
|
|
|
@abstractmethod
|
|
def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
|
|
"""Run the logic of this chain and return the output."""
|
|
|
|
def __call__(
|
|
self, inputs: Dict[str, Any], return_only_outputs: bool = False
|
|
) -> Dict[str, str]:
|
|
"""Run the logic of this chain and add to output if desired.
|
|
|
|
Args:
|
|
inputs: Dictionary of inputs.
|
|
return_only_outputs: boolean for whether to return only outputs in the
|
|
response. If True, only new keys generated by this chain will be
|
|
returned. If False, both input keys and new keys generated by this
|
|
chain will be returned. Defaults to False.
|
|
|
|
"""
|
|
if self.memory is not None:
|
|
external_context = self.memory._load_dynamic_keys(inputs)
|
|
inputs = dict(inputs, **external_context)
|
|
self._validate_inputs(inputs)
|
|
if self.verbose:
|
|
print("\n\n\033[1m> Entering new chain...\033[0m")
|
|
outputs = self._call(inputs)
|
|
if self.verbose:
|
|
print("\n\033[1m> Finished chain.\033[0m")
|
|
self._validate_outputs(outputs)
|
|
if self.memory is not None:
|
|
self.memory._save_context(inputs, outputs)
|
|
if return_only_outputs:
|
|
return outputs
|
|
else:
|
|
return {**inputs, **outputs}
|
|
|
|
def apply(self, input_list: List[Dict[str, Any]]) -> List[Dict[str, str]]:
|
|
"""Call the chain on all inputs in the list."""
|
|
return [self(inputs) for inputs in input_list]
|
|
|
|
def run(self, text: str) -> str:
|
|
"""Run text in, text out (if applicable)."""
|
|
if len(self.input_keys) != 1:
|
|
raise ValueError(
|
|
f"`run` not supported when there is not exactly "
|
|
f"one input key, got {self.input_keys}."
|
|
)
|
|
if len(self.output_keys) != 1:
|
|
raise ValueError(
|
|
f"`run` not supported when there is not exactly "
|
|
f"one output key, got {self.output_keys}."
|
|
)
|
|
return self({self.input_keys[0]: text})[self.output_keys[0]]
|