from __future__ import annotations

from typing import Any, cast

from crewai.tools import BaseTool
from pydantic import BaseModel, Field


class LlamaIndexTool(BaseTool):
    """Tool to wrap LlamaIndex tools/query engines."""

    llama_index_tool: Any

    def _run(
        self,
        *args: Any,
        **kwargs: Any,
    ) -> Any:
        """Run tool."""
        tool = self.llama_index_tool

        if self.result_as_answer:
            return tool(*args, **kwargs).content

        return tool(*args, **kwargs)

    @classmethod
    def from_tool(cls, tool: Any, **kwargs: Any) -> LlamaIndexTool:
        from llama_index.core.tools import (  # type: ignore[import-not-found]
            BaseTool as LlamaBaseTool,
        )

        if not isinstance(tool, LlamaBaseTool):
            raise ValueError(f"Expected a LlamaBaseTool, got {type(tool)}")

        if tool.metadata.fn_schema is None:
            raise ValueError(
                "The LlamaIndex tool does not have an fn_schema specified."
            )
        args_schema = cast(type[BaseModel], tool.metadata.fn_schema)

        return cls(
            name=tool.metadata.name,
            description=tool.metadata.description,
            args_schema=args_schema,
            llama_index_tool=tool,
            **kwargs,
        )

    @classmethod
    def from_query_engine(
        cls,
        query_engine: Any,
        name: str | None = None,
        description: str | None = None,
        return_direct: bool = False,
        **kwargs: Any,
    ) -> LlamaIndexTool:
        from llama_index.core.query_engine import (  # type: ignore[import-not-found]
            BaseQueryEngine,
        )
        from llama_index.core.tools import QueryEngineTool

        if not isinstance(query_engine, BaseQueryEngine):
            raise ValueError(f"Expected a BaseQueryEngine, got {type(query_engine)}")

        # NOTE: by default the schema expects an `input` variable. However this
        # confuses crewAI so we are renaming to `query`.
        class QueryToolSchema(BaseModel):
            """Schema for query tool."""

            query: str = Field(..., description="Search query for the query tool.")

        # NOTE: setting `resolve_input_errors` to True is important because the schema expects `input` but we are using `query`
        query_engine_tool = QueryEngineTool.from_defaults(
            query_engine,
            name=name,
            description=description,
            return_direct=return_direct,
            resolve_input_errors=True,
        )
        # HACK: we are replacing the schema with our custom schema
        query_engine_tool.metadata.fn_schema = QueryToolSchema

        return cls.from_tool(query_engine_tool, **kwargs)