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# [WIP] Tree of Thought introducing a new ToTChain. This PR adds a new chain called ToTChain that implements the ["Large Language Model Guided Tree-of-Though"](https://arxiv.org/pdf/2305.08291.pdf) paper. There's a notebook example `docs/modules/chains/examples/tot.ipynb` that shows how to use it. Implements #4975 ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: - @hwchase17 - @vowelparrot --------- Co-authored-by: Vadim Gubergrits <vgubergrits@outbox.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
53 lines
1.4 KiB
Python
53 lines
1.4 KiB
Python
from abc import ABC, abstractmethod
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from typing import Any, Dict, List, Optional, Tuple
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from langchain.callbacks.manager import CallbackManagerForChainRun
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from langchain.chains.base import Chain
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from langchain_experimental.tot.thought import ThoughtValidity
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class ToTChecker(Chain, ABC):
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"""
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Tree of Thought (ToT) checker.
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This is an abstract ToT checker that must be implemented by the user. You
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can implement a simple rule-based checker or a more sophisticated
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neural network based classifier.
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"""
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output_key: str = "validity" #: :meta private:
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@property
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def input_keys(self) -> List[str]:
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"""The checker input keys.
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:meta private:
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"""
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return ["problem_description", "thoughts"]
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@property
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def output_keys(self) -> List[str]:
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"""The checker output keys.
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:meta private:
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"""
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return [self.output_key]
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@abstractmethod
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def evaluate(
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self,
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problem_description: str,
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thoughts: Tuple[str, ...] = (),
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) -> ThoughtValidity:
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"""
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Evaluate the response to the problem description and return the solution type.
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"""
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def _call(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, ThoughtValidity]:
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return {self.output_key: self.evaluate(**inputs)}
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