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af9e749edb
* Adding tool caching a loop execution prevention. This adds some guardrails, to both prevent the same tool to be used consecutively and also caching tool's results across the entire crew so it cuts down execution time and eventual LLM calls. This plays a huge role for smaller opensource models that usually fall into those behaviors patterns. It also includes some smaller improvements around the tool prompt and agent tools, all with the same intention of guiding models into better conform with agent instructions.
73 lines
2.7 KiB
Python
73 lines
2.7 KiB
Python
from textwrap import dedent
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from typing import List
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from langchain.tools import Tool
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from pydantic import BaseModel, Field
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from ..agent import Agent
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class AgentTools(BaseModel):
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"""Tools for generic agent."""
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agents: List[Agent] = Field(description="List of agents in this crew.")
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def tools(self):
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return [
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Tool.from_function(
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func=self.delegate_work,
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name="Delegate work to co-worker",
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description=dedent(
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f"""Useful to delegate a specific task to one of the
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following co-workers: [{', '.join([agent.role for agent in self.agents])}].
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The input to this tool should be a pipe (|) separated text of length
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three, representing the role you want to delegate it to, the task and
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information necessary. For example, `coworker|task|information`.
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"""
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),
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),
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Tool.from_function(
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func=self.ask_question,
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name="Ask question to co-worker",
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description=dedent(
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f"""Useful to ask a question, opinion or take from on
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of the following co-workers: [{', '.join([agent.role for agent in self.agents])}].
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The input to this tool should be a pipe (|) separated text of length
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three, representing the role you want to ask it to, the question and
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information necessary. For example, `coworker|question|information`.
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"""
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),
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),
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]
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def delegate_work(self, command):
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"""Useful to delegate a specific task to a coworker."""
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return self.__execute(command)
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def ask_question(self, command):
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"""Useful to ask a question, opinion or take from a coworker."""
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return self.__execute(command)
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def __execute(self, command):
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"""Execute the command."""
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try:
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agent, task, information = command.split("|")
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except ValueError:
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return "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|task|information`."
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if not agent or not task or not information:
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return "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|question|information`."
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agent = [
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available_agent
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for available_agent in self.agents
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if available_agent.role == agent
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]
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if len(agent) == 0:
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return f"\nError executing tool. Co-worker mentioned on the Action Input not found, it must to be one of the following options: {', '.join([agent.role for agent in self.agents])}."
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agent = agent[0]
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result = agent.execute_task(task, information)
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return result
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