mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-09-19 00:01:33 +00:00
42a3343cb5
- Introduce Application class for managing application lifecycle - Add base component classes for LLM formatters and token counters - Implement embedding model base with caching and batching support - Create file watcher base with watchfiles integration - Add job and step base components for workflow execution - Update base component with async locks and improved lifecycle management - Register new component types in component registry - Add application context and runtime context for dependency injection
103 lines
4 KiB
Python
103 lines
4 KiB
Python
"""Base step class for LLM workflow execution."""
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import copy
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from abc import abstractmethod
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from agentscope.formatter import FormatterBase
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from agentscope.model import ChatModelBase
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from agentscope.token import TokenCounterBase
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from .base_component import BaseComponent
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from .embedding import BaseEmbeddingModel
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from .file_store import BaseFileStore
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from .prompt_handler import PromptHandler
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from .runtime_context import RuntimeContext
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from ..enumeration import ComponentEnum
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class BaseStep(BaseComponent):
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"""Base step for LLM workflow execution and composition."""
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component_type = ComponentEnum.STEP
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def __new__(cls, *args, **kwargs):
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instance = super().__new__(cls)
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instance._init_args = copy.copy(args)
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instance._init_kwargs = copy.copy(kwargs)
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return instance
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def __init__(
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self,
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language: str = "",
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prompt_dict: dict[str, str] | None = None,
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input_mapping: dict[str, str] | None = None,
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output_mapping: dict[str, str] | None = None,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.language = language
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self.prompt = PromptHandler(language=self.language)
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self.prompt.load_prompt_by_class(self.__class__).load_prompt_dict(prompt_dict)
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self.input_mapping = input_mapping
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self.output_mapping = output_mapping
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self.context: RuntimeContext | None = None
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@abstractmethod
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async def execute(self):
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"""Execute the step logic."""
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async def __call__(self, context: RuntimeContext | None = None, **kwargs):
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self.context = RuntimeContext.from_context(context, **kwargs)
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assert self.context is not None
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if self.input_mapping:
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self.context.apply_mapping(self.input_mapping)
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result = await self.execute()
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if self.output_mapping:
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self.context.apply_mapping(self.output_mapping)
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return result
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def _get_component(self, key: ComponentEnum, name: str, attr: str | None = None):
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assert self.app_context is not None
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comp = self.app_context.components[key][name]
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return getattr(comp, attr) if attr else comp
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@property
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def as_llm(self) -> ChatModelBase:
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name = self.kwargs.get("as_llm", "default")
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return name if isinstance(name, ChatModelBase) else self._get_component(ComponentEnum.AS_LLM, name, "model")
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@property
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def as_llm_formatter(self) -> FormatterBase:
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name = self.kwargs.get("as_llm_formatter", "default")
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return name if isinstance(name, FormatterBase) else self._get_component(ComponentEnum.AS_LLM_FORMATTER, name,
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"formatter")
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@property
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def as_token_counter(self) -> TokenCounterBase:
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name = self.kwargs.get("as_token_counter", "default")
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return name if isinstance(name, TokenCounterBase) else self._get_component(ComponentEnum.AS_TOKEN_COUNTER, name,
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"token_counter")
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@property
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def file_store(self) -> BaseFileStore:
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name = self.kwargs.get("file_store", "default")
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return name if isinstance(name, BaseFileStore) else self._get_component(ComponentEnum.FILE_STORE, name)
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@property
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def embedding(self) -> BaseEmbeddingModel:
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name = self.kwargs.get("embedding", "default")
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return name if isinstance(name, BaseEmbeddingModel) else self._get_component(ComponentEnum.EMBEDDING_MODEL,
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name)
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def prompt_format(self, prompt_name: str, **kwargs) -> str:
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return self.prompt.prompt_format(prompt_name=prompt_name, **kwargs)
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def get_prompt(self, prompt_name: str) -> str:
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return self.prompt.get_prompt(prompt_name=prompt_name)
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def copy(self, **kwargs) -> "BaseStep":
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return self.__class__(*self._init_args, **{**self._init_kwargs, **kwargs})
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