mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-01-22 22:58:13 +00:00
Adding new tool usage and parsing logic
This commit is contained in:
João Moura
@@ -1,6 +1,6 @@
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from textwrap import dedent
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from typing import Any, List, Union
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import instructor
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from langchain.prompts import PromptTemplate
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from langchain_core.tools import BaseTool
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from langchain_openai import ChatOpenAI
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@@ -9,7 +9,9 @@ from crewai.agents.tools_handler import ToolsHandler
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from crewai.telemtry import Telemetry
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from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
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from crewai.tools.tool_output_parser import ToolOutputParser
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from crewai.utilities import I18N, Printer
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from crewai.utilities import I18N, Instructor, Printer
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OPENAI_BIGGER_MODELS = ["gpt-4"]
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class ToolUsageErrorException(Exception):
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@@ -31,6 +33,7 @@ class ToolUsage:
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tools_description: Description of the tools available for the agent.
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tools_names: Names of the tools available for the agent.
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llm: Language model to be used for the tool usage.
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function_calling_llm: Language model to be used for the tool usage.
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"""
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def __init__(
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@@ -47,18 +50,28 @@ class ToolUsage:
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self._printer: Printer = Printer()
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self._telemetry: Telemetry = Telemetry()
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self._run_attempts: int = 1
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self._max_parsing_attempts: int = 2
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self._max_parsing_attempts: int = 3
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self._remeber_format_after_usages: int = 3
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self.tools_description = tools_description
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self.tools_names = tools_names
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self.tools_handler = tools_handler
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self.tools = tools
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self.task = task
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self.llm = llm
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self.function_calling_llm = function_calling_llm
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self.llm = function_calling_llm or llm
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def use(self, tool_string: str):
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calling = self._tool_calling(tool_string)
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# Set the maximum parsing attempts for bigger models
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if (isinstance(self.llm, ChatOpenAI)) and (self.llm.openai_api_base == None):
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if self.llm.model_name in OPENAI_BIGGER_MODELS:
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self._max_parsing_attempts = 2
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self._remeber_format_after_usages = 4
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def parse(self, tool_string: str):
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"""Parse the tool string and return the tool calling."""
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return self._tool_calling(tool_string)
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def use(
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self, calling: Union[ToolCalling, InstructorToolCalling], tool_string: str
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) -> str:
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if isinstance(calling, ToolUsageErrorException):
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error = calling.message
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self._printer.print(content=f"\n\n{error}\n", color="red")
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@@ -69,7 +82,7 @@ class ToolUsage:
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error = getattr(e, "message", str(e))
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self._printer.print(content=f"\n\n{error}\n", color="red")
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return error
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return self._use(tool_string=tool_string, tool=tool, calling=calling)
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return f"{self._use(tool_string=tool_string, tool=tool, calling=calling)}\n{self._i18n.slice('final_answer_format')}"
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def _use(
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self,
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@@ -106,11 +119,11 @@ class ToolUsage:
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if self._run_attempts > self._max_parsing_attempts:
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self._telemetry.tool_usage_error(llm=self.llm)
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error = ToolUsageErrorException(
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self._i18n.errors("tool_usage_exception").format(error=e)
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f'{self._i18n.errors("tool_usage_exception").format(error=e)}.\n{self._i18n.slice("format").format(tool_names=self.tools_names)}'
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).message
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self._printer.print(content=f"\n\n{error}\n", color="red")
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return error
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return self.use(tool_string=tool_string)
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return self.use(calling=calling, tool_string=tool_string)
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self.tools_handler.on_tool_use(calling=calling, output=result)
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@@ -174,69 +187,55 @@ class ToolUsage:
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self, tool_string: str
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) -> Union[ToolCalling, InstructorToolCalling]:
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try:
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tool_string = tool_string.replace(
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"Thought: Do I need to use a tool? Yes", ""
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)
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tool_string = tool_string.replace("Action:", "Tool Name:")
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tool_string = tool_string.replace("Action Input:", "Tool Arguments:")
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if (isinstance(self.llm, ChatOpenAI)) and (
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self.llm.openai_api_base == None
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):
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instructor = Instructor(
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llm=self.llm,
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model=InstructorToolCalling,
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content=f"Tools available:\n\n{self._render()}\n\nReturn a valid schema for the tool, the tool name must be equal one of the options, use this text to inform a valid ouput schema:\n{tool_string}```",
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instructions=dedent(
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"""\
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The schema should have the following structure, only two keys:
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- tool_name: str
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- arguments: dict (with all arguments being passed)
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llm = self.function_calling_llm or self.llm
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if (isinstance(llm, ChatOpenAI)) and (llm.openai_api_base == None):
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client = instructor.patch(
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llm.client._client,
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mode=instructor.Mode.FUNCTIONS,
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)
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calling = client.chat.completions.create(
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model=llm.model_name,
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messages=[
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{
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"role": "system",
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"content": """
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The schema should have the following structure, only two key:
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- tool_name: str
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- arguments: dict (with all arguments being passed)
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Example:
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{"tool_name": "tool_name", "arguments": {"arg_name1": "value", "arg_name2": 2}}
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""",
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},
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{
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"role": "user",
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"content": f"Tools available:\n\n{self._render()}\n\nReturn a valid schema for the tool, use this text to inform a valid ouput schema:\n{tool_string}```",
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},
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],
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response_model=InstructorToolCalling,
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Example:
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{"tool_name": "tool_name", "arguments": {"arg_name1": "value", "arg_name2": 2}}
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"""
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),
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)
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calling = instructor.to_pydantic()
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else:
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parser = ToolOutputParser(pydantic_object=ToolCalling)
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prompt = PromptTemplate(
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template="Tools available:\n\n{available_tools}\n\nReturn a valid schema for the tool, use this text to inform a valid ouput schema:\n{tool_string}\n\n{format_instructions}\n```",
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template="Tools available:\n\n{available_tools}\n\nReturn a valid schema for the tool, the tool name must be equal one of the options, use this text to inform a valid ouput schema:\n{tool_string}\n\n{format_instructions}\n```",
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input_variables=["tool_string"],
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partial_variables={
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"available_tools": self._render(),
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"format_instructions": """
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The schema should have the following structure, only two key:
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"format_instructions": dedent(
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"""\
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The schema should have the following structure, only two keys:
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- tool_name: str
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- arguments: dict (with all arguments being passed)
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Example:
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{"tool_name": "tool_name", "arguments": {"arg_name1": "value", "arg_name2": 2}}
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""",
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"""
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),
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},
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)
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chain = prompt | llm | parser
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chain = prompt | self.llm | parser
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calling = chain.invoke({"tool_string": tool_string})
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except Exception as e:
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self._run_attempts += 1
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if self._run_attempts > self._max_parsing_attempts:
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self._telemetry.tool_usage_error(llm=llm)
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error = ToolUsageErrorException(
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self._i18n.errors("tool_usage_exception").format(error=e)
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).message
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self._printer.print(content=f"\n\n{error}\n", color="red")
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return error
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self._telemetry.tool_usage_error(llm=self.llm)
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self._printer.print(content=f"\n\n{e}\n", color="red")
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return ToolUsageErrorException(
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f'{self._i18n.errors("tool_usage_error")}.\n{self._i18n.slice("format").format(tool_names=self.tools_names)}'
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)
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return self._tool_calling(tool_string)
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return calling
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