mirror of
https://github.com/NicolasBohn/NexQuant.git
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Linlang
c7cfd397ca
* fix isort & black & toml-sort & sphinx error * fix ci error * fix ci error * add comments * Update Makefile * change sphinx build command * add auto-lint * add black args * format with black * Auto Linting document * fix ci error --------- Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> Co-authored-by: Young <afe.young@gmail.com>
105 lines
3.6 KiB
Python
105 lines
3.6 KiB
Python
from abc import abstractmethod
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from pathlib import Path
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from typing import Tuple
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from jinja2 import Environment, StrictUndefined
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from rdagent.components.coder.model_coder.model import ModelExperiment
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from rdagent.core.prompts import Prompts
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from rdagent.core.proposal import (
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Hypothesis,
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Hypothesis2Experiment,
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HypothesisGen,
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Scenario,
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Trace,
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)
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from rdagent.oai.llm_utils import APIBackend
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ModelHypothesis = Hypothesis
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prompt_dict = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
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class ModelHypothesisGen(HypothesisGen):
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prompts: Prompts = prompt_dict
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# The following methods are scenario related so they should be implemented in the subclass
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@abstractmethod
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def prepare_context(self, trace: Trace) -> Tuple[dict, bool]:
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...
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@abstractmethod
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def convert_response(self, response: str) -> ModelHypothesis:
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...
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def gen(self, trace: Trace) -> ModelHypothesis:
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context_dict, json_flag = self.prepare_context(trace)
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system_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(ModelHypothesisGen.prompts["hypothesis_gen"]["system_prompt"])
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.render(
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targets="model",
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scenario=self.scen.get_scenario_all_desc(),
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hypothesis_output_format=context_dict["hypothesis_output_format"],
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hypothesis_specification=context_dict["hypothesis_specification"],
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)
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)
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user_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(ModelHypothesisGen.prompts["hypothesis_gen"]["user_prompt"])
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.render(
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targets="model",
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hypothesis_and_feedback=context_dict["hypothesis_and_feedback"],
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RAG=context_dict["RAG"],
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)
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)
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resp = APIBackend().build_messages_and_create_chat_completion(user_prompt, system_prompt, json_mode=json_flag)
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hypothesis = self.convert_response(resp)
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return hypothesis
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class ModelHypothesis2Experiment(Hypothesis2Experiment[ModelExperiment]):
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prompts: Prompts = prompt_dict
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def __init__(self) -> None:
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super().__init__()
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@abstractmethod
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def prepare_context(self, hypothesis: Hypothesis, trace: Trace) -> Tuple[dict, bool]:
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...
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@abstractmethod
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def convert_response(self, response: str, trace: Trace) -> ModelExperiment:
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...
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def convert(self, hypothesis: Hypothesis, trace: Trace) -> ModelExperiment:
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context, json_flag = self.prepare_context(hypothesis, trace)
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system_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(ModelHypothesis2Experiment.prompts["hypothesis2experiment"]["system_prompt"])
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.render(
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targets="model",
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scenario=trace.scen.get_scenario_all_desc(),
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experiment_output_format=context["experiment_output_format"],
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)
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)
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user_prompt = (
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Environment(undefined=StrictUndefined)
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.from_string(ModelHypothesis2Experiment.prompts["hypothesis2experiment"]["user_prompt"])
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.render(
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targets="model",
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target_hypothesis=context["target_hypothesis"],
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hypothesis_and_feedback=context["hypothesis_and_feedback"],
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target_list=context["target_list"],
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RAG=context["RAG"],
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)
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)
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resp = APIBackend().build_messages_and_create_chat_completion(user_prompt, system_prompt, json_mode=json_flag)
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return self.convert_response(resp, trace)
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