ReMe/reme4/steps/base_step.py

"""Base step class for LLM workflow execution."""

import copy
from abc import abstractmethod, ABC
from pathlib import Path
from typing import TypeVar, TYPE_CHECKING

from agentscope.formatter import FormatterBase
from agentscope.message import TextBlock
from agentscope.model import ChatModelBase
from agentscope.token import TokenCounterBase
from agentscope.tool import Toolkit, ToolResponse

from ..components.base_component import ComponentMixin
from ..components.embedding import BaseEmbeddingModel
from ..components.file_parser import BaseFileParser
from ..components.file_store import BaseFileStore
from ..components.prompt_handler import PromptHandler
from ..components.runtime_context import RuntimeContext
from ..enumeration import ComponentEnum
from ..schema import FileChunk, FileNode, Response

if TYPE_CHECKING:
    from ..components import ApplicationContext
    from ..components.job import BaseJob

T = TypeVar("T")

_UNSET = object()


class Ref:
    """Descriptor that lazily resolves a component dependency for Steps.

    Replaces the ``@property`` + ``_resolve()`` boilerplate with a single
    class-level declaration::

        as_llm = Ref(ChatModelBase, ComponentEnum.AS_LLM, "model")
        file_store = Ref(BaseFileStore, ComponentEnum.FILE_STORE)

    Resolution follows a 3-source fallback identical to the old ``_resolve``:
    ``kwargs`` -> ``context`` -> ``app_context`` component registry.
    The resolved value is cached on the instance for its lifetime
    (steps are rebuilt per job call via ``_build_steps``).
    """

    __slots__ = ("base_cls", "comp_enum", "attr", "optional", "key", "_cache_attr")

    def __init__(
        self,
        base_cls: type,
        comp_enum: ComponentEnum,
        attr: str | None = None,
        *,
        optional: bool = False,
    ) -> None:
        self.base_cls = base_cls
        self.comp_enum = comp_enum
        self.attr = attr
        self.optional = optional
        self.key: str = ""
        self._cache_attr: str = ""

    def __set_name__(self, owner: type, name: str) -> None:
        self.key = name
        self._cache_attr = f"_ref_{name}"

    def __get__(self, obj: "BaseStep | None", objtype: type | None = None):
        if obj is None:
            return self
        cached = obj.__dict__.get(self._cache_attr, _UNSET)
        if cached is not _UNSET:
            return cached
        value = self._resolve(obj)
        obj.__dict__[self._cache_attr] = value
        return value

    def __set__(self, obj: "BaseStep", value) -> None:
        obj.__dict__[self._cache_attr] = value

    def __delete__(self, obj: "BaseStep") -> None:
        obj.__dict__.pop(self._cache_attr, None)

    def _resolve(self, obj: "BaseStep"):
        for source in (obj.kwargs, obj.context or {}):
            value = source.get(self.key)
            if isinstance(value, self.base_cls):
                return value

        name = obj.kwargs.get(self.key, "default")
        if obj.app_context is None:
            if self.optional:
                return None
            raise RuntimeError(f"app_context is not set when resolving '{self.key}'")
        comp = obj.app_context.components[self.comp_enum].get(name)
        if comp is None:
            if self.optional:
                return None
            raise KeyError(f"Component '{name}' not found in {self.comp_enum.value}")
        return getattr(comp, self.attr) if self.attr else comp


class BaseStep(ComponentMixin, ABC):
    """Composable unit of an LLM workflow."""

    component_type = ComponentEnum.STEP

    def __new__(cls, *args, **kwargs):
        # Snapshot init args so copy() can rebuild an equivalent instance later.
        instance = object.__new__(cls)
        instance._init_args = copy.copy(args)
        instance._init_kwargs = copy.copy(kwargs)
        return instance

    def __init__(
        self,
        name: str | None = None,
        backend: str = "",
        app_context: "ApplicationContext | None" = None,
        language: str = "",
        prompt_dict: dict[str, str] | None = None,
        input_mapping: dict[str, str] | None = None,
        output_mapping: dict[str, str] | None = None,
        **kwargs,
    ):
        super().__init__(name=name, backend=backend, app_context=app_context, **kwargs)
        self.language: str = language
        self.input_mapping = input_mapping
        self.output_mapping = output_mapping
        self.context: RuntimeContext | None = None

        # Load class-level prompts first, then overlay caller-provided overrides.
        # Walk MRO in reverse so most-derived class wins; subclasses without their
        # own YAML inherit prompts from their parent (e.g. CronDreamer inherits
        # dreamer.yaml from Dreamer).
        self.prompt = PromptHandler(language=self.language)
        for cls in reversed(self.__class__.__mro__):
            self.prompt.load_prompt_by_class(cls)
        self.prompt.load_prompt_dict(prompt_dict)

    # ----- Component references (resolved lazily on first access) ----------

    as_llm: ChatModelBase = Ref(ChatModelBase, ComponentEnum.AS_LLM, "model")
    as_llm_formatter: FormatterBase = Ref(FormatterBase, ComponentEnum.AS_LLM_FORMATTER, "formatter")
    as_token_counter: TokenCounterBase = Ref(TokenCounterBase, ComponentEnum.AS_TOKEN_COUNTER, "token_counter")
    file_store: BaseFileStore = Ref(BaseFileStore, ComponentEnum.FILE_STORE)
    embedding: BaseEmbeddingModel = Ref(BaseEmbeddingModel, ComponentEnum.EMBEDDING_MODEL)

    @abstractmethod
    async def execute(self):
        """Run the step's logic against ``self.context``."""

    async def __call__(self, context: RuntimeContext | None = None, **kwargs):
        # Clear cached Ref values so context-supplied overrides take effect.
        for key in [k for k in self.__dict__ if k.startswith("_ref_")]:
            del self.__dict__[key]
        self.context = RuntimeContext.from_context(context, **kwargs)
        assert self.context is not None
        if self.input_mapping:
            self.context.apply_mapping(self.input_mapping)
        result = await self.execute()
        if self.output_mapping:
            self.context.apply_mapping(self.output_mapping)
        return result

    async def parse_file(self, path: str | Path) -> tuple[FileNode, list[FileChunk]]:
        """Parse ``path`` with the parser whose ``supported_extensions`` claims its suffix.

        First registered match wins (config insertion order). Falls back to the
        ``default`` parser (stat-only) when no parser claims the suffix — that's
        how attachments / binaries / unknown types still produce a FileNode.
        """
        if self.app_context is None:
            raise RuntimeError("app_context is not set when resolving file parser")
        file_parser_dict: dict[str, BaseFileParser] = self.app_context.components[ComponentEnum.FILE_PARSER]

        suffix = Path(path).suffix.lstrip(".").lower()

        parser: BaseFileParser | None = None
        if suffix:
            for candidate in file_parser_dict.values():
                if suffix in {ext.lower().lstrip(".") for ext in candidate.supported_extensions}:
                    parser = candidate
                    break

        if parser is None:
            parser = file_parser_dict.get("default")

        if parser is None:
            raise RuntimeError(
                f"No file parser supports {path} (suffix={suffix!r}) and no 'default' parser is configured",
            )

        return await parser.parse(path)

    def prompt_format(self, prompt_name: str, **kwargs) -> str:
        """Format a named prompt template with the given kwargs."""
        return self.prompt.prompt_format(prompt_name=prompt_name, **kwargs)

    def get_prompt(self, prompt_name: str) -> str:
        """Return a named prompt template as-is."""
        return self.prompt.get_prompt(prompt_name=prompt_name)

    def copy(self, **kwargs) -> "BaseStep":
        """Construct a new instance from the original init args, applying overrides."""
        return self.__class__(*self._init_args, **{**self._init_kwargs, **kwargs})

    def get_job(self, name: str) -> "BaseJob | None":
        """Return a job by name."""
        if self.app_context is None:
            raise RuntimeError("Cannot get job without an app context")
        return self.app_context.jobs.get(name)

    async def run_job(self, name: str, **kwargs) -> Response:
        """Execute a job by name and kwargs, return the final response."""
        job: "BaseJob | None" = self.get_job(name)
        if job is None:
            raise RuntimeError(f"Job {name} not found")
        return await job(**kwargs)

    def add_as_tool(self, toolkit: Toolkit, job_name: str, **kwargs) -> None:
        """Add the step as a tool to the toolkit."""
        job: "BaseJob | None" = self.get_job(job_name)
        if job is None:
            raise RuntimeError(f"Job {job_name} not found")

        async def run_job(**_kwargs) -> ToolResponse:
            response = await job(**{**_kwargs, **kwargs})
            return ToolResponse(content=[TextBlock(type="text", text=response.answer)])

        toolkit.register_tool_function(
            tool_func=run_job,
            func_name=job_name,
            func_description=job.description,
            json_schema={
                "type": "function",
                "function": {
                    "name": job_name,
                    "description": job.description,
                    "parameters": job.parameters,
                },
            },
        )