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from __future__ import annotations
from typing import List, Optional
from pydantic import ValidationError
from langchain.chains.llm import LLMChain
from langchain.chat_models.base import BaseChatModel
from langchain.experimental.autonomous_agents.autogpt.output_parser import (
AutoGPTOutputParser,
BaseAutoGPTOutputParser,
)
from .prompt import AutoGPTPrompt
from langchain.experimental.autonomous_agents.autogpt.prompt_generator import (
FINISH_NAME,
)
from langchain.schema import (
AIMessage,
BaseMessage,
Document,
HumanMessage,
SystemMessage,
)
from langchain.tools.base import BaseTool
from langchain.tools.human.tool import HumanInputRun
from langchain.vectorstores.base import VectorStoreRetriever
import json
class AutoGPT:
"""Agent class for interacting with Auto-GPT."""
def __init__(
self,
ai_name: str,
memory: VectorStoreRetriever,
chain: LLMChain,
output_parser: BaseAutoGPTOutputParser,
tools: List[BaseTool],
feedback_tool: Optional[HumanInputRun] = None,
):
self.ai_name = ai_name
self.memory = memory
self.full_message_history: List[BaseMessage] = []
self.next_action_count = 0
self.chain = chain
self.output_parser = output_parser
self.tools = tools
self.feedback_tool = feedback_tool
@classmethod
def from_llm_and_tools(
cls,
ai_name: str,
ai_role: str,
memory: VectorStoreRetriever,
tools: List[BaseTool],
llm: BaseChatModel,
human_in_the_loop: bool = False,
output_parser: Optional[BaseAutoGPTOutputParser] = None,
) -> AutoGPT:
prompt = AutoGPTPrompt(
ai_name=ai_name,
ai_role=ai_role,
tools=tools,
input_variables=["memory", "messages", "goals", "user_input"],
token_counter=llm.get_num_tokens,
)
human_feedback_tool = HumanInputRun() if human_in_the_loop else None
chain = LLMChain(llm=llm, prompt=prompt)
return cls(
ai_name,
memory,
chain,
output_parser or AutoGPTOutputParser(),
tools,
feedback_tool=human_feedback_tool,
)
def run(self, goals: List[str]) -> str:
user_input = (
"Determine which next command to use, "
"and respond using the format specified above:"
)
# Interaction Loop
loop_count = 0
while True:
# Discontinue if continuous limit is reached
loop_count += 1
# Send message to AI, get response
assistant_reply = self.chain.run(
goals=goals,
messages=self.full_message_history,
memory=self.memory,
user_input=user_input,
)
# Print Assistant thoughts
print(assistant_reply)
self.full_message_history.append(HumanMessage(content=user_input))
self.full_message_history.append(AIMessage(content=assistant_reply))
# Get command name and arguments
action = self.output_parser.parse(assistant_reply)
tools = {t.name: t for t in self.tools}
if action.name == FINISH_NAME:
return action.args["response"]
if action.name in tools:
tool = tools[action.name]
try:
# for tools in swarms.tools, the input should be string, while for default langchain toosl, the input is in json format, here we modify the following code
json_args = json.dumps(action.args)
observation = tool.run(json_args)
except ValidationError as e:
observation = f"Error in args: {str(e)}"
result = f"Command {tool.name} returned: {observation}"
elif action.name == "ERROR":
result = f"Error: {action.args}. "
else:
result = (
f"Unknown command '{action.name}'. "
f"Please refer to the 'COMMANDS' list for available "
f"commands and only respond in the specified JSON format."
)
memory_to_add = (
f"Assistant Reply: {assistant_reply} " f"\nResult: {result} "
)
if self.feedback_tool is not None:
feedback = f"\n{self.feedback_tool.run('Input: ')}"
if feedback in {"q", "stop"}:
print("EXITING")
return "EXITING"
memory_to_add += feedback
self.memory.add_documents([Document(page_content=memory_to_add)])
self.full_message_history.append(SystemMessage(content=result))
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