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6b054651a7
* Refactoring task cache to be a tool The previous implementation of the task caching system was early exiting the agent executor due to the fact it was returning an AgentFinish object. This now refactors it to use a cache specific tool that is dynamically added and forced into the agent in case of a task execution that was already executed with the same input.
29 lines
831 B
Python
29 lines
831 B
Python
from langchain.tools import Tool
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from pydantic import BaseModel, ConfigDict, Field
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from crewai.agents import CacheHandler
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class CacheTools(BaseModel):
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"""Default tools to hit the cache."""
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model_config = ConfigDict(arbitrary_types_allowed=True)
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name: str = "Hit Cache"
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cache_handler: CacheHandler = Field(
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description="Cache Handler for the crew",
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default=CacheHandler(),
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)
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def tool(self):
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return Tool.from_function(
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func=self.hit_cache,
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name=self.name,
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description="Reads directly from the cache",
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)
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def hit_cache(self, key):
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split = key.split("tool:")
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tool = split[1].split("|input:")[0].strip()
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tool_input = split[1].split("|input:")[1].strip()
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return self.cache_handler.read(tool, tool_input)
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