Source code for airflow.providers.common.ai.durable.caching_toolset

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"""Caching toolset wrapper for durable execution."""

from __future__ import annotations

from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any

import structlog
from pydantic_ai.toolsets.wrapper import WrapperToolset

if TYPE_CHECKING:
    from pydantic_ai.toolsets.abstract import ToolsetTool

    from airflow.providers.common.ai.durable.step_counter import DurableStepCounter
    from airflow.providers.common.ai.durable.storage import DurableStorage


[docs]
log = structlog.get_logger(logger_name="task")



@dataclass

[docs]
class CachingToolset(WrapperToolset[Any]):
    """
    Wraps a toolset to cache tool call results in ObjectStorage for durable execution.

    On each ``call_tool()`` invocation, checks if a cached result exists for
    the current step index. If so, returns the cached result without executing
    the tool. Otherwise, executes the tool and caches the result.

    The step index is grabbed before the first ``await``, so parallel tool
    calls via ``asyncio.gather`` get deterministic indices (tasks start
    executing their synchronous preamble in creation order).
    """


[docs]
    storage: DurableStorage = field(repr=False)


[docs]
    counter: DurableStepCounter = field(repr=False)



[docs]
    async def call_tool(
        self,
        name: str,
        tool_args: dict[str, Any],
        ctx: Any,
        tool: ToolsetTool[Any],
    ) -> Any:
        # Grab step index BEFORE any await -- ensures deterministic ordering
        # even when multiple tool calls run concurrently via asyncio.gather.
        step = self.counter.next_step()
        key = f"tool_step_{step}"

        found, cached = self.storage.load_tool_result(key)
        if found:
            self.counter.replayed_tool += 1
            log.debug("Durable: replayed cached tool result", step=step, tool=name)
            return cached

        result = await self.wrapped.call_tool(name, tool_args, ctx, tool)
        self.storage.save_tool_result(key, result)
        self.counter.cached_tool += 1
        log.debug("Durable: cached tool result", step=step, tool=name)
        return result