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129 lines
3.8 KiB
Python
129 lines
3.8 KiB
Python
"""Generic agent."""
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from typing import List, Any, Optional
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from pydantic.v1 import BaseModel, PrivateAttr, Field, root_validator
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from langchain.agents import AgentExecutor
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from langchain.chat_models import ChatOpenAI
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from langchain.tools.render import render_text_description
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from langchain.agents.format_scratchpad import format_log_to_str
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from langchain.agents.output_parsers import ReActSingleInputOutputParser
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from langchain.memory import ConversationSummaryMemory
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from .prompts import Prompts
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class Agent(BaseModel):
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"""
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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 AgentExecutor 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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agent_executor: AgentExecutor = None
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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(description="LLM that will run the agent")
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memory: bool = Field(
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description="Whether the agent should have memory or not",
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default=True
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)
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verbose: bool = Field(
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description="Verbose mode for the Agent Execution",
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default=False
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)
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allow_delegation: bool = Field(
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description="Allow delegation of tasks to agents",
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default=True
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)
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tools: List[Any] = Field(
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description="Tools at agents disposal",
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default=[]
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)
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_task_calls: List[Any] = PrivateAttr()
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@root_validator(pre=True)
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def check_llm(_cls, values):
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if not values.get('llm'):
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values['llm'] = ChatOpenAI(
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temperature=0.7,
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model_name="gpt-4"
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)
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return values
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def __init__(self, **data):
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super().__init__(**data)
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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,
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memory_key='chat_history',
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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 = agent_args | execution_prompt | bind | ReActSingleInputOutputParser()
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self.agent_executor = AgentExecutor(
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agent=inner_agent,
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**executor_args
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)
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def execute_task(self, task: str, context: str = None, tools: List[Any] = None) -> str:
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"""
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Execute a task with the agent.
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Parameters:
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task (str): Task to execute
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Returns:
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output (str): 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,
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"\nThis is the context you are working with:",
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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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"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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})['output']
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def __tools_names(self, tools) -> str:
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return ", ".join([t.name for t in tools])
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