mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 16:07:46 +00:00
Xu Yang
a126c84c92
* refine CI script * refine all the code to higher quality * refine the script to factor extraction and implementation * add task loader interface * add a task loader interface && move pdf analysis to pdf task loader * change the name to global variables --------- Co-authored-by: xuyang1 <xuyang1@microsoft.com>
102 lines
2.3 KiB
Python
102 lines
2.3 KiB
Python
from __future__ import annotations
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import copy
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from abc import ABC, abstractmethod
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from dataclasses import dataclass
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from typing import Any
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class Feedback:
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pass
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class Knowledge:
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pass
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class QueriedKnowledge:
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pass
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class KnowledgeBase(ABC):
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@abstractmethod
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def query(
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self,
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) -> QueriedKnowledge | None:
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raise NotImplementedError
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class EvolvableSubjects:
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"""The target object to be evolved"""
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def clone(self) -> EvolvableSubjects:
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return copy.deepcopy(self)
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class QlibEvolvableSubjects(EvolvableSubjects): ...
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@dataclass
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class EvoStep:
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"""At a specific step,
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based on
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- previous trace
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- newly RAG kownledge `QueriedKnowledge`
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the EvolvableSubjects is evolved to a new one `EvolvableSubjects`.
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(optional) After evaluation, we get feedback `feedback`.
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"""
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evolvable_subjects: EvolvableSubjects
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queried_knowledge: QueriedKnowledge | None = None
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feedback: Feedback | None = None
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class EvolvingStrategy(ABC):
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@abstractmethod
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def evolve(
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self,
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*evo: EvolvableSubjects,
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evolving_trace: list[EvoStep] | None = None,
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queried_knowledge: QueriedKnowledge | None = None,
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**kwargs: Any,
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) -> EvolvableSubjects:
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"""The evolving trace is a list of (evolvable_subjects, feedback) ordered
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according to the time.
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The reason why the parameter is important for the evolving.
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- evolving_trace: the historical feedback is important.
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- queried_knowledge: queried knowledge
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"""
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class RAGStrategy(ABC):
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"""Retrival Augmentation Generation Strategy"""
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def __init__(self, knowledgebase: KnowledgeBase) -> None:
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self.knowledgebase = knowledgebase
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@abstractmethod
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def query(
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self,
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evo: EvolvableSubjects,
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evolving_trace: list[EvoStep],
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**kwargs: Any,
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) -> QueriedKnowledge | None:
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pass
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@abstractmethod
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def generate_knowledge(
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self,
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evolving_trace: list[EvoStep],
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*,
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return_knowledge: bool = False,
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**kwargs: Any,
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) -> Knowledge | None:
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"""Generating new knowledge based on the evolving trace.
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- It is encouraged to query related knowledge before generating new knowledge.
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RAGStrategy should maintain the new knowledge all by itself.
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"""
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