llm strategy下載llm strategy源代碼下載

llm strategy

其他源碼

2.1.1

下載

LLM-Strategy

使用LLMS實施策略模式。

另外，請參閱https://blog.blackhc.net/2022/12/llm_software_engineering/，以了解為什麼將來這可能很重要。

該軟件包添加了連接到LLM（例如OpenAI的GPT-3）的Decorator llm_strategy ，並使用LLM在接口類中“實現”抽象方法。它通過將請求轉發到LLM並使用Python的@dataclasses轉換回Python數據來實現此目的。

它使用DOC字符串，類型註釋和方法/功能名稱作為LLM的提示，並可以自動將結果轉換回Python類型（目前僅支持@dataclasses ）。它還可以提取數據模式以發送到LLM進行解釋。儘管llm-strategy軟件包仍然依賴一些Python代碼，但它有可能通過使用額外的，更便宜的LLMS自動化結構化數據的解析來減少對此代碼的需求。

github存儲庫：https：//github.com/blackhc/llm-strategy/
文檔https://blackhc.github.io/llm-strategy/

研究示例

最新版本還包括一個用於從LLM的超參數跟踪和收集痕蹟的軟件包。

例如，這允許進行元優化。有關使用仿製藥的簡單實現，請參見示例/研究。

You can find an example WandB trace at: https://wandb.ai/blackhc/blackboard-pagi/reports/Meta-Optimization-Example-Trace--Vmlldzo3MDMxODEz?accessToken=p9hubfskmq1z5yj1uz7wx1idh304diiernp7pjlrjrybpaozlwv3dnitjt7vni1j

使用仿製藥炫耀模式的提示很簡單：

 T_TaskParameters = TypeVar ( "T_TaskParameters" )
T_TaskResults = TypeVar ( "T_TaskResults" )
T_Hyperparameters = TypeVar ( "T_Hyperparameters" )


class TaskRun ( GenericModel , Generic [ T_TaskParameters , T_TaskResults , T_Hyperparameters ]):
    """
    The task run. This is the 'data' we use to optimize the hyperparameters.
    """

    task_parameters : T_TaskParameters = Field (..., description = "The task parameters." )
    hyperparameters : T_Hyperparameters = Field (
        ...,
        description = "The hyperparameters used for the task. We optimize these." ,
    )
    all_chat_chains : dict = Field (..., description = "The chat chains from the task execution." )
    return_value : T_TaskResults | None = Field (
        ..., description = "The results of the task. (None for exceptions/failure.)"
    )
    exception : list [ str ] | str | None = Field (..., description = "Exception that occurred during the task execution." )


class TaskReflection ( BaseModel ):
    """
    The reflections on the task.

    This contains the lessons we learn from each task run to come up with better
    hyperparameters to try.
    """

    feedback : str = Field (
        ...,
        description = (
            "Only look at the final results field. Does its content satisfy the "
            "task description and task parameters? Does it contain all the relevant "
            "information from the all_chains and all_prompts fields? What could be improved "
            "in the results?"
        ),
    )
    evaluation : str = Field (
        ...,
        description = (
            "The evaluation of the outputs given the task. Is the output satisfying? What is wrong? What is missing?"
        ),
    )
    hyperparameter_suggestion : str = Field (
        ...,
        description = "How we want to change the hyperparameters to improve the results. What could we try to change?" ,
    )
    hyperparameter_missing : str = Field (
        ...,
        description = (
            "What hyperparameters are missing to improve the results? What could "
            "be changed that is not exposed via hyperparameters?"
        ),
    )


class TaskInfo ( GenericModel , Generic [ T_TaskParameters , T_TaskResults , T_Hyperparameters ]):
    """
    The task run and the reflection on the experiment.
    """

    task_parameters : T_TaskParameters = Field (..., description = "The task parameters." )
    hyperparameters : T_Hyperparameters = Field (
        ...,
        description = "The hyperparameters used for the task. We optimize these." ,
    )
    reflection : TaskReflection = Field (..., description = "The reflection on the task." )


class OptimizationInfo ( GenericModel , Generic [ T_TaskParameters , T_TaskResults , T_Hyperparameters ]):
    """
    The optimization information. This is the data we use to optimize the
    hyperparameters.
    """

    older_task_summary : str | None = Field (
        None ,
        description = (
            "A summary of previous experiments and the proposed changes with "
            "the goal of avoiding trying the same changes repeatedly."
        ),
    )
    task_infos : list [ TaskInfo [ T_TaskParameters , T_TaskResults , T_Hyperparameters ]] = Field (
        ..., description = "The most recent tasks we have run and our reflections on them."
    )
    best_hyperparameters : T_Hyperparameters = Field (..., description = "The best hyperparameters we have found so far." )


class OptimizationStep ( GenericModel , Generic [ T_TaskParameters , T_TaskResults , T_Hyperparameters ]):
    """
    The next optimization steps. New hyperparameters we want to try experiments and new
    task parameters we want to evaluate on given the previous experiments.
    """

    best_hyperparameters : T_Hyperparameters = Field (
        ...,
        description = "The best hyperparameters we have found so far given task_infos and history." ,
    )
    suggestion : str = Field (
        ...,
        description = (
            "The suggestions for the next experiments. What could we try to "
            "change? We will try several tasks next and several sets of hyperparameters. "
            "Let's think step by step."
        ),
    )
    task_parameters_suggestions : list [ T_TaskParameters ] = Field (
        ...,
        description = "The task parameters we want to try next." ,
        hint_min_items = 1 ,
        hint_max_items = 4 ,
    )
    hyperparameter_suggestions : list [ T_Hyperparameters ] = Field (
        ...,
        description = "The hyperparameters we want to try next." ,
        hint_min_items = 1 ,
        hint_max_items = 2 ,
    )


class ImprovementProbability ( BaseModel ):
    considerations : list [ str ] = Field (..., description = "The considerations for potential improvements." )
    probability : float = Field (..., description = "The probability of improvement." )


class LLMOptimizer :
    @ llm_explicit_function
    @ staticmethod
    def reflect_on_task_run (
        language_model ,
        task_run : TaskRun [ T_TaskParameters , T_TaskResults , T_Hyperparameters ],
    ) -> TaskReflection :
        """
        Reflect on the results given the task parameters and hyperparameters.

        This contains the lessons we learn from each task run to come up with better
        hyperparameters to try.
        """
        raise NotImplementedError ()

    @ llm_explicit_function
    @ staticmethod
    def summarize_optimization_info (
        language_model ,
        optimization_info : OptimizationInfo [ T_TaskParameters , T_TaskResults , T_Hyperparameters ],
    ) -> str :
        """
        Summarize the optimization info. We want to preserve all relevant knowledge for
        improving the hyperparameters in the future. All information from previous
        experiments will be forgotten except for what this summary.
        """
        raise NotImplementedError ()

    @ llm_explicit_function
    @ staticmethod
    def suggest_next_optimization_step (
        language_model ,
        optimization_info : OptimizationInfo [ T_TaskParameters , T_TaskResults , T_Hyperparameters ],
    ) -> OptimizationStep [ T_TaskParameters , T_TaskResults , T_Hyperparameters ]:
        """
        Suggest the next optimization step.
        """
        raise NotImplementedError ()

    @ llm_explicit_function
    @ staticmethod
    def probability_for_improvement (
        language_model ,
        optimization_info : OptimizationInfo [ T_TaskParameters , T_TaskResults , T_Hyperparameters ],
    ) -> ImprovementProbability :
        """
        Return the probability for improvement (between 0 and 1).

        This is your confidence that your next optimization steps will improve the
        hyperparameters given the information provided. If you think that the
        information available is unlikely to lead to better hyperparameters, return 0.
        If you think that the information available is very likely to lead to better
        hyperparameters, return 1. Be concise.
        """
        raise NotImplementedError ()

應用示例

 from dataclasses import dataclass
from llm_strategy import llm_strategy
from langchain . llms import OpenAI


@ llm_strategy ( OpenAI ( max_tokens = 256 ))
@ dataclass
class Customer :
    key : str
    first_name : str
    last_name : str
    birthdate : str
    address : str

    @ property
    def age ( self ) -> int :
        """Return the current age of the customer.

        This is a computed property based on `birthdate` and the current year (2022).
        """

        raise NotImplementedError ()


@ dataclass
class CustomerDatabase :
    customers : list [ Customer ]

    def find_customer_key ( self , query : str ) -> list [ str ]:
        """Find the keys of the customers that match a natural language query best (sorted by closeness to the match).

        We support semantic queries instead of SQL, so we can search for things like
        "the customer that was born in 1990".

        Args:
            query: Natural language query

        Returns:
            The index of the best matching customer in the database.
        """
        raise NotImplementedError ()

    def load ( self ):
        """Load the customer database from a file."""
        raise NotImplementedError ()

    def store ( self ):
        """Store the customer database to a file."""
        raise NotImplementedError ()


@ llm_strategy ( OpenAI ( max_tokens = 1024 ))
@ dataclass
class MockCustomerDatabase ( CustomerDatabase ):
    def load ( self ):
        self . customers = self . create_mock_customers ( 10 )

    def store ( self ):
        pass

    @ staticmethod
    def create_mock_customers ( num_customers : int = 1 ) -> list [ Customer ]:
        """
        Create mock customers with believable data (our customers are world citizens).
        """
        raise NotImplementedError ()