Kye
1 year ago
parent
4233e69c6a
commit
90398a4a58
@ -0,0 +1,134 @@
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from __future__ import annotations
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from typing import List, Optional
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from langchain.chains.llm import LLMChain
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from swarms.agents.memory.base import VectorStoreRetriever
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from swarms.agents.memory.base_memory import BaseChatMessageHistory, ChatMessageHistory
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from swarms.agents.memory.document import Document
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from swarms.agents.models.base import AbstractModel
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from swarms.agents.models.prompts.agent_prompt_auto import (
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MessageFormatter,
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PromptConstructor,
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)
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from swarms.agents.models.prompts.agent_prompt_generator import FINISH_NAME
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from swarms.agents.models.prompts.base import (
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AIMessage,
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HumanMessage,
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SystemMessage,
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)
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from swarms.agents.tools.base import BaseTool
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from swarms.agents.utils.Agent import AgentOutputParser
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from swarms.agents.utils.human_input import HumanInputRun
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class Agent:
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"""Base Agent class"""
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def __init__(
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self,
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ai_name: str,
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chain: LLMChain,
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memory: VectorStoreRetriever,
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output_parser: AgentOutputParser,
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tools: List[BaseTool],
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feedback_tool: Optional[HumanInputRun] = None,
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chat_history_memory: Optional[BaseChatMessageHistory] = None,
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):
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self.ai_name = ai_name
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self.chain = chain
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self.memory = memory
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self.next_action_count = 0
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self.output_parser = output_parser
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self.tools = tools
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self.feedback_tool = feedback_tool
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self.chat_history_memory = chat_history_memory or ChatMessageHistory()
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@classmethod
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def from_llm_and_tools(
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cls,
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ai_name: str,
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ai_role: str,
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memory: VectorStoreRetriever,
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tools: List[BaseTool],
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llm: AbstractModel,
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human_in_the_loop: bool = False,
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output_parser: Optional[AgentOutputParser] = None,
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chat_history_memory: Optional[BaseChatMessageHistory] = None,
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) -> Agent:
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prompt_constructor = PromptConstructor(ai_name=ai_name,
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ai_role=ai_role,
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tools=tools)
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message_formatter = MessageFormatter()
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human_feedback_tool = HumanInputRun() if human_in_the_loop else None
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chain = LLMChain(llm=llm, prompt_constructor=prompt_constructor, message_formatter=message_formatter)
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return cls(
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ai_name,
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memory,
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chain,
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output_parser or AgentOutputParser(),
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tools,
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feedback_tool=human_feedback_tool,
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chat_history_memory=chat_history_memory,
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)
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def run(self, goals: List[str]) -> str:
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user_input = (
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"Determine which next command to use, and respond using the format specified above:"
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)
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loop_count = 0
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while True:
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loop_count += 1
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# Send message to AI, get response
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assistant_reply = self.chain.run(
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goals=goals,
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messages=self.chat_history_memory.messages,
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memory=self.memory,
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user_input=user_input,
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)
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print(assistant_reply)
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self.chat_history_memory.add_message(HumanMessage(content=user_input))
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self.chat_history_memory.add_message(AIMessage(content=assistant_reply))
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# Get command name and arguments
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action = self.output_parser.parse(assistant_reply)
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tools = {t.name: t for t in self.tools}
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if action.name == FINISH_NAME:
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return action.args["response"]
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if action.name in tools:
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tool = tools[action.name]
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try:
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observation = tool.run(action.args)
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except Exception as error:
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observation = (
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f"Validation Error in args: {str(error)}, args: {action.args}"
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)
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except Exception as e:
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observation = (
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f"Error: {str(e)}, {type(e).__name__}, args: {action.args}"
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)
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result = f"Command {tool.name} returned: {observation}"
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elif action.name == "ERROR":
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result = f"Error: {action.args}. "
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else:
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result = (
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f"""Unknown command '{action.name}'.
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Please refer to the 'COMMANDS' list for available
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commands and only respond in the specified JSON format."""
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)
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memory_to_add = (
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f"Assistant Reply: {assistant_reply} " f"\nResult: {result} "
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)
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if self.feedback_tool is not None:
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feedback = f"\n{self.feedback_tool.run('Input: ')}"
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if feedback in {"q", "stop"}:
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print("EXITING")
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return "EXITING"
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memory_to_add += feedback
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self.memory.add_documents([Document(page_content=memory_to_add)])
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self.chat_history_memory.add_message(SystemMessage(content=result))
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@ -1,134 +1,25 @@
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from typing import List, Optional
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class AbstractAgent(ABC):
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#absrtact agent class
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from langchain.chains.llm import LLMChain
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from swarms.agents.memory.base import VectorStoreRetriever
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from swarms.agents.memory.base_memory import BaseChatMessageHistory, ChatMessageHistory
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from swarms.agents.memory.document import Document
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from swarms.agents.models.base import AbstractModel
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from swarms.agents.models.prompts.agent_prompt_auto import (
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MessageFormatter,
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PromptConstructor,
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)
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from swarms.agents.models.prompts.agent_prompt_generator import FINISH_NAME
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from swarms.agents.models.prompts.base import (
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AIMessage,
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HumanMessage,
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SystemMessage,
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)
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from swarms.agents.tools.base import BaseTool
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from swarms.agents.utils.Agent import AgentOutputParser
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from swarms.agents.utils.human_input import HumanInputRun
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class Agent:
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"""Base Agent class"""
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@classmethod
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def __init__(
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self,
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ai_name: str,
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chain: LLMChain,
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memory: VectorStoreRetriever,
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output_parser: AgentOutputParser,
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tools: List[BaseTool],
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feedback_tool: Optional[HumanInputRun] = None,
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chat_history_memory: Optional[BaseChatMessageHistory] = None,
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ai_name: str = None,
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ai_role: str = None,
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memory = None,
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tools = None,
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llm = None,
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human_in_the_loop=None,
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output_parser = None,
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chat_history_memory=None,
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*args,
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**kwargs
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):
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self.ai_name = ai_name
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self.chain = chain
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self.memory = memory
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self.next_action_count = 0
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self.output_parser = output_parser
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self.tools = tools
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self.feedback_tool = feedback_tool
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self.chat_history_memory = chat_history_memory or ChatMessageHistory()
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@classmethod
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def from_llm_and_tools(
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cls,
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ai_name: str,
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ai_role: str,
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memory: VectorStoreRetriever,
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tools: List[BaseTool],
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llm: AbstractModel,
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human_in_the_loop: bool = False,
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output_parser: Optional[AgentOutputParser] = None,
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chat_history_memory: Optional[BaseChatMessageHistory] = None,
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) -> Agent:
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prompt_constructor = PromptConstructor(ai_name=ai_name,
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ai_role=ai_role,
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tools=tools)
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message_formatter = MessageFormatter()
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human_feedback_tool = HumanInputRun() if human_in_the_loop else None
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chain = LLMChain(llm=llm, prompt_constructor=prompt_constructor, message_formatter=message_formatter)
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return cls(
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ai_name,
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memory,
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chain,
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output_parser or AgentOutputParser(),
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tools,
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feedback_tool=human_feedback_tool,
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chat_history_memory=chat_history_memory,
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)
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def run(self, goals: List[str]) -> str:
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user_input = (
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"Determine which next command to use, and respond using the format specified above:"
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)
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loop_count = 0
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while True:
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loop_count += 1
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# Send message to AI, get response
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assistant_reply = self.chain.run(
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goals=goals,
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messages=self.chat_history_memory.messages,
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memory=self.memory,
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user_input=user_input,
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)
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print(assistant_reply)
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self.chat_history_memory.add_message(HumanMessage(content=user_input))
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self.chat_history_memory.add_message(AIMessage(content=assistant_reply))
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# Get command name and arguments
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action = self.output_parser.parse(assistant_reply)
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tools = {t.name: t for t in self.tools}
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if action.name == FINISH_NAME:
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return action.args["response"]
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if action.name in tools:
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tool = tools[action.name]
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try:
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observation = tool.run(action.args)
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except Exception as error:
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observation = (
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f"Validation Error in args: {str(error)}, args: {action.args}"
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)
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except Exception as e:
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observation = (
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f"Error: {str(e)}, {type(e).__name__}, args: {action.args}"
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)
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result = f"Command {tool.name} returned: {observation}"
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elif action.name == "ERROR":
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result = f"Error: {action.args}. "
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else:
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result = (
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f"""Unknown command '{action.name}'.
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Please refer to the 'COMMANDS' list for available
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commands and only respond in the specified JSON format."""
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)
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memory_to_add = (
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f"Assistant Reply: {assistant_reply} " f"\nResult: {result} "
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)
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if self.feedback_tool is not None:
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feedback = f"\n{self.feedback_tool.run('Input: ')}"
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if feedback in {"q", "stop"}:
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print("EXITING")
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return "EXITING"
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memory_to_add += feedback
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self.memory.add_documents([Document(page_content=memory_to_add)])
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self.chat_history_memory.add_message(SystemMessage(content=result))
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pass
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@abstractmethod
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def run(self, goals=None):
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pass
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