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Delete gmixai.py
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gmixai.py
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import json
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import uuid
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from typing import Any, Dict, List, Optional, Union
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from pydantic_core import PydanticCustomError
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from crewai.agents.cache import CacheHandler
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from crewai.tools.agent_tools import AgentTools
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from typing import Any, List, Optional
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from langchain.agents.format_scratchpad import format_log_to_str
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from langchain_openai import ChatOpenAI
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from langchain.memory import ConversationSummaryMemory
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from langchain.tools.render import render_text_description
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from langchain_core.runnables.config import RunnableConfig
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# Content from crew.py
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from pydantic import (
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UUID4,
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BaseModel,
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ConfigDict,
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Field,
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InstanceOf,
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Json,
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field_validator,
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model_validator,
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)
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from crewai.agents import (
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CacheHandler,
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CrewAgentExecutor,
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CrewAgentOutputParser,
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ToolsHandler,
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)
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class Gmix(BaseModel):
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"""Class that represents a group of agents, how they should work together and their tasks."""
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__hash__ = object.__hash__
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model_config = ConfigDict(arbitrary_types_allowed=True)
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tasks: List[Task] = Field(description="List of tasks", default_factory=list)
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agents: List[Agent] = Field(
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description="List of agents in this crew.", default_factory=list
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)
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process: Process = Field(
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description="Process that the crew will follow.", default=Process.sequential
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)
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verbose: Union[int, bool] = Field(
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description="Verbose mode for the Agent Execution", default=0
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)
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config: Optional[Union[Json, Dict[str, Any]]] = Field(
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description="Configuration of the crew.", default=None
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)
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cache_handler: Optional[InstanceOf[CacheHandler]] = Field(
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default=CacheHandler(), description="An instance of the CacheHandler class."
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)
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id: UUID4 = Field(
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default_factory=uuid.uuid4,
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frozen=True,
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description="Unique identifier for the object, not set by user.",
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)
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@field_validator("id", mode="before")
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@classmethod
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def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
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if v:
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raise PydanticCustomError(
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"may_not_set_field", "This field is not to be set by the user.", {}
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)
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@classmethod
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@field_validator("config", mode="before")
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def check_config_type(cls, v: Union[Json, Dict[str, Any]]):
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if isinstance(v, Json):
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return json.loads(v)
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return v
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@model_validator(mode="after")
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def check_config(self):
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if not self.config and not self.tasks and not self.agents:
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raise PydanticCustomError(
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"missing_keys", "Either agents and task need to be set or config.", {}
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)
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if self.config:
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if not self.config.get("agents") or not self.config.get("tasks"):
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raise PydanticCustomError(
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"missing_keys_in_config", "Config should have agents and tasks", {}
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)
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self.agents = [Agent(**agent) for agent in self.config["agents"]]
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tasks = []
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for task in self.config["tasks"]:
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task_agent = [agt for agt in self.agents if agt.role == task["agent"]][
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0
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]
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del task["agent"]
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tasks.append(Task(**task, agent=task_agent))
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self.tasks = tasks
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if self.agents:
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for agent in self.agents:
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agent.set_cache_handler(self.cache_handler)
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return self
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def kickoff(self) -> str:
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"""Kickoff the crew to work on its tasks.
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Returns:
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Output of the crew for each task.
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"""
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for agent in self.agents:
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agent.cache_handler = self.cache_handler
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if self.process == Process.sequential:
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return self.__sequential_loop()
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def __sequential_loop(self) -> str:
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"""Loop that executes the sequential process.
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Returns:
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Output of the crew.
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"""
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task_outcome = None
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for task in self.tasks:
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# Add delegation tools to the task if the agent allows it
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if task.agent.allow_delegation:
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tools = AgentTools(agents=self.agents).tools()
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task.tools += tools
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self.__log("debug", f"Working Agent: {task.agent.role}")
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self.__log("info", f"Starting Task: {task.description} ...")
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task_outcome = task.execute(task_outcome)
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self.__log("debug", f"Task output: {task_outcome}")
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return task_outcome
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def __log(self, level, message):
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"""Log a message"""
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level_map = {"debug": 1, "info": 2}
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verbose_level = (
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2 if isinstance(self.verbose, bool) and self.verbose else self.verbose
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)
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if verbose_level and level_map[level] <= verbose_level:
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print(message)
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class Agent(BaseModel):
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"""Represents an agent in a system.
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Each agent has a role, a goal, a backstory, and an optional language model (llm).
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The agent can also have memory, can operate in verbose mode, and can delegate tasks to other agents.
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Attributes:
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agent_executor: An instance of the CrewAgentExecutor class.
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role: The role of the agent.
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goal: The objective of the agent.
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backstory: The backstory of the agent.
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llm: The language model that will run the agent.
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memory: Whether the agent should have memory or not.
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verbose: Whether the agent execution should be in verbose mode.
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allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
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"""
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__hash__ = object.__hash__
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model_config = ConfigDict(arbitrary_types_allowed=True)
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id: UUID4 = Field(
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default_factory=uuid.uuid4,
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frozen=True,
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description="Unique identifier for the object, not set by user.",
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)
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role: str = Field(description="Role of the agent")
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goal: str = Field(description="Objective of the agent")
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backstory: str = Field(description="Backstory of the agent")
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llm: Optional[Any] = Field(
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default_factory=lambda: ChatOpenAI(
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temperature=0.7,
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model_name="gpt-4",
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),
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description="Language model that will run the agent.",
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)
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memory: bool = Field(
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default=True, description="Whether the agent should have memory or not"
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)
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verbose: bool = Field(
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default=False, description="Verbose mode for the Agent Execution"
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)
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allow_delegation: bool = Field(
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default=True, description="Allow delegation of tasks to agents"
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)
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tools: List[Any] = Field(
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default_factory=list, description="Tools at agents disposal"
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)
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agent_executor: Optional[InstanceOf[CrewAgentExecutor]] = Field(
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default=None, description="An instance of the CrewAgentExecutor class."
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)
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tools_handler: Optional[InstanceOf[ToolsHandler]] = Field(
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default=None, description="An instance of the ToolsHandler class."
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)
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cache_handler: Optional[InstanceOf[CacheHandler]] = Field(
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default=CacheHandler(), description="An instance of the CacheHandler class."
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)
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@field_validator("id", mode="before")
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@classmethod
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def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
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if v:
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raise PydanticCustomError(
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"may_not_set_field", "This field is not to be set by the user.", {}
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)
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@model_validator(mode="after")
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def check_agent_executor(self) -> "Agent":
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if not self.agent_executor:
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self.set_cache_handler(self.cache_handler)
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return self
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def execute_task(
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self, task: str, context: str = None, tools: List[Any] = None
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) -> str:
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"""Execute a task with the agent.
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Args:
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task: Task to execute.
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context: Context to execute the task in.
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tools: Tools to use for the task.
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Returns:
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Output of the agent
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"""
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if context:
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task = "\n".join(
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[task, "\nThis is the context you are working with:", context]
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)
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tools = tools or self.tools
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self.agent_executor.tools = tools
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return self.agent_executor.invoke(
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{
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"input": task,
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"tool_names": self.__tools_names(tools),
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"tools": render_text_description(tools),
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},
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RunnableConfig(callbacks=[self.tools_handler]),
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)["output"]
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def set_cache_handler(self, cache_handler) -> None:
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self.cache_handler = cache_handler
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self.tools_handler = ToolsHandler(cache=self.cache_handler)
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self.__create_agent_executor()
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def __create_agent_executor(self) -> CrewAgentExecutor:
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"""Create an agent executor for the agent.
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Returns:
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An instance of the CrewAgentExecutor class.
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"""
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agent_args = {
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"input": lambda x: x["input"],
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"tools": lambda x: x["tools"],
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"tool_names": lambda x: x["tool_names"],
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"agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]),
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}
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executor_args = {
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"tools": self.tools,
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"verbose": self.verbose,
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"handle_parsing_errors": True,
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}
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if self.memory:
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summary_memory = ConversationSummaryMemory(
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llm=self.llm, memory_key="chat_history", input_key="input"
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)
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executor_args["memory"] = summary_memory
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agent_args["chat_history"] = lambda x: x["chat_history"]
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prompt = Prompts.TASK_EXECUTION_WITH_MEMORY_PROMPT
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else:
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prompt = Prompts.TASK_EXECUTION_PROMPT
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execution_prompt = prompt.partial(
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goal=self.goal,
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role=self.role,
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backstory=self.backstory,
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)
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bind = self.llm.bind(stop=["\nObservation"])
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inner_agent = (
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agent_args
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| execution_prompt
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| bind
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| CrewAgentOutputParser(
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tools_handler=self.tools_handler, cache=self.cache_handler
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)
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)
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self.agent_executor = CrewAgentExecutor(agent=inner_agent, **executor_args)
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@staticmethod
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def __tools_names(tools) -> str:
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return ", ".join([t.name for t in tools])
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# Content from task.py
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class Task(BaseModel):
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"""Class that represent a task to be executed."""
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__hash__ = object.__hash__
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description: str = Field(description="Description of the actual task.")
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agent: Optional[Agent] = Field(
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description="Agent responsible for the task.", default=None
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)
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tools: List[Any] = Field(
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default_factory=list,
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description="Tools the agent are limited to use for this task.",
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)
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id: UUID4 = Field(
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default_factory=uuid.uuid4,
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frozen=True,
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description="Unique identifier for the object, not set by user.",
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)
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@field_validator("id", mode="before")
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@classmethod
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def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
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if v:
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raise PydanticCustomError(
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"may_not_set_field", "This field is not to be set by the user.", {}
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)
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@model_validator(mode="after")
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def check_tools(self):
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if not self.tools and (self.agent and self.agent.tools):
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self.tools.extend(self.agent.tools)
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return self
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def execute(self, context: str = None) -> str:
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"""Execute the task.
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Returns:
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Output of the task.
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"""
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if self.agent:
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return self.agent.execute_task(
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task=self.description, context=context, tools=self.tools
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)
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else:
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raise Exception(
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f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Gmix using a specific process that support that, either consensual or hierarchical."
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)
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# Content from process.py
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class Process(str, Enum):
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"""
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Class representing the different processes that can be used to tackle tasks
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"""
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sequential = "sequential"
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# TODO: consensual = 'consensual'
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# TODO: hierarchical = 'hierarchical'
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# Content from prompts.py
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"""Prompts for generic agent."""
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class Prompts(BaseModel):
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"""Prompts for generic agent."""
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TASK_SLICE: ClassVar[str] = dedent(
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"""\
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Begin! This is VERY important to you, your job depends on it!
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Current Task: {input}"""
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)
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SCRATCHPAD_SLICE: ClassVar[str] = "\n{agent_scratchpad}"
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MEMORY_SLICE: ClassVar[str] = dedent(
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"""\
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This is the summary of your work so far:
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{chat_history}"""
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)
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ROLE_PLAYING_SLICE: ClassVar[str] = dedent(
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"""\
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You are {role}.
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{backstory}
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Your personal goal is: {goal}"""
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)
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TOOLS_SLICE: ClassVar[str] = dedent(
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"""\
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TOOLS:
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------
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You have access to the following tools:
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{tools}
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To use a tool, please use the exact following format:
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```
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Thought: Do I need to use a tool? Yes
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Action: the action to take, should be one of [{tool_names}], just the name.
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Action Input: the input to the action
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Observation: the result of the action
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```
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When you have a response for your task, or if you do not need to use a tool, you MUST use the format:
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```
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Thought: Do I need to use a tool? No
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Final Answer: [your response here]
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```"""
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)
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VOTING_SLICE: ClassVar[str] = dedent(
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"""\
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You are working on a crew with your co-workers and need to decide who will execute the task.
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These are your format instructions:
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{format_instructions}
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These are your co-workers and their roles:
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{coworkers}"""
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)
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TASK_EXECUTION_WITH_MEMORY_PROMPT: ClassVar[str] = PromptTemplate.from_template(
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ROLE_PLAYING_SLICE + TOOLS_SLICE + MEMORY_SLICE + TASK_SLICE + SCRATCHPAD_SLICE
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)
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TASK_EXECUTION_PROMPT: ClassVar[str] = PromptTemplate.from_template(
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ROLE_PLAYING_SLICE + TOOLS_SLICE + TASK_SLICE + SCRATCHPAD_SLICE
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)
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CONSENSUNS_VOTING_PROMPT: ClassVar[str] = PromptTemplate.from_template(
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415 |
-
ROLE_PLAYING_SLICE + VOTING_SLICE + TASK_SLICE + SCRATCHPAD_SLICE
|
416 |
-
)
|
417 |
-
|
418 |
-
|
419 |
-
|
420 |
-
|
421 |
-
|
422 |
-
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