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from __future__ import annotations
"""
An agent based upon Observation-Planning-Reflection architecture.
"""
from logging import getLogger
from abc import abstractmethod
from typing import List, Set, Union, NamedTuple, TYPE_CHECKING
from pydantic import BaseModel, Field, validator
from agentverse.llms import BaseLLM
from agentverse.memory import BaseMemory, ChatHistoryMemory
from agentverse.message import Message
from agentverse.output_parser import OutputParser
from agentverse.message import Message
from agentverse.agents.base import BaseAgent
from datetime import datetime as dt
import datetime
#from . import agent_registry
from string import Template
from agentverse.agents import agent_registry
from agentverse.agents.base import BaseAgent
logger = getLogger(__file__)
if TYPE_CHECKING:
from agentverse.environments.base import BaseEnvironment
@agent_registry.register("reflection")
class ReflectionAgent(BaseAgent):
async_mode: bool = (True,)
current_time: str = (None,)
environment: BaseEnvironment = None
step_cnt: int = 0
manipulated_memory: str = Field(
default="", description="one fragment used in prompt construction"
)
@validator("current_time")
def convert_str_to_dt(cls, current_time):
if not isinstance(current_time, str):
raise ValueError("current_time should be str")
return dt.strptime(current_time, "%Y-%m-%d %H:%M:%S")
def step(self, current_time: dt, env_description: str = "") -> Message:
"""
Call this method at each time frame
"""
self.current_time = current_time
self.manipulated_memory = self.memory_manipulator.manipulate_memory()
prompt = self._fill_prompt_template(env_description)
parsed_response, reaction, target = None, None, None
for i in range(self.max_retry):
try:
response = self.llm.agenerate_response(prompt)
parsed_response = self.output_parser.parse(response)
if "say(" in parsed_response.return_values["output"]:
reaction, target = eval(
"self._" + parsed_response.return_values["output"].strip()
)
elif "act(" in parsed_response.return_values["output"]:
reaction, target = eval(
"self._" + parsed_response.return_values["output"].strip()
)
elif "do_nothing(" in parsed_response.return_values["output"]:
reaction, target = None, None
else:
raise Exception(
f"no valid parsed_response detected, "
f"cur response {parsed_response.return_values['output']}"
)
break
except Exception as e:
logger.error(e)
logger.warn("Retrying...")
continue
if parsed_response is None:
logger.error(f"{self.name} failed to generate valid response.")
if reaction is None:
reaction = "Keep doing last action ..."
message = Message(
content="" if reaction is None else reaction,
sender=self.name,
receiver=self.get_receiver()
if target is None
else self.get_valid_receiver(target),
)
self.step_cnt += 1
return message
async def astep(self, current_time: dt, env_description: str = "") -> Message:
"""Asynchronous version of step"""
# use environment's time to update agent's time
self.current_time = current_time
# Before the agent step, we check current status,
# TODO add this func after
# self.check_status_passive()
self.manipulated_memory = self.memory_manipulator.manipulate_memory()
prompt = self._fill_prompt_template(env_description)
parsed_response, reaction, target = None, None, None
for i in range(self.max_retry):
try:
response = await self.llm.agenerate_response(prompt)
parsed_response = self.output_parser.parse(response)
if "say(" in parsed_response.return_values["output"]:
reaction, target = eval(
"self._" + parsed_response.return_values["output"].strip()
)
elif "act(" in parsed_response.return_values["output"]:
reaction, target = eval(
"self._" + parsed_response.return_values["output"].strip()
)
elif "do_nothing(" in parsed_response.return_values["output"]:
reaction, target = None, None
else:
raise Exception(
f"no valid parsed_response detected, "
f"cur response {parsed_response.return_values['output']}"
)
break
except Exception as e:
logger.error(e)
logger.warn("Retrying...")
continue
if parsed_response is None:
logger.error(f"{self.name} failed to generate valid response.")
if reaction is None:
reaction = "Keep doing last action ..."
message = Message(
content="" if reaction is None else reaction,
sender=self.name,
receiver=self.get_receiver()
if target is None
else self.get_valid_receiver(target),
)
self.step_cnt += 1
return message
def _act(self, description=None, target=None):
if description is None:
return ""
if target is None:
reaction_content = f"{self.name} performs action: '{description}'."
else:
reaction_content = (
f"{self.name} performs action to {target}: '{description}'."
)
# self.environment.broadcast_observations(self, target, reaction_content)
return reaction_content, target
def _say(self, description, target=None):
if description is None:
return ""
if target is None:
reaction_content = f"{self.name} says: '{description}'."
else:
reaction_content = f"{self.name} says to {target}: '{description}'."
# self.environment.broadcast_observations(self, target, reaction_content)
return reaction_content, target
def get_valid_receiver(self, target: str) -> set():
all_agents_name = []
for agent in self.environment.agents:
all_agents_name.append(agent.name)
if not (target in all_agents_name):
return {"all"}
else:
return {target}
def _fill_prompt_template(self, env_description: str = "") -> str:
"""Fill the placeholders in the prompt template
In the conversation agent, three placeholders are supported:
- ${agent_name}: the name of the agent
- ${env_description}: the description of the environment
- ${role_description}: the description of the role of the agent
- ${chat_history}: the chat history of the agent
"""
input_arguments = {
"agent_name": self.name,
"role_description": self.role_description,
"chat_history": self.memory.to_string(add_sender_prefix=True),
"current_time": self.current_time,
"env_description": env_description,
}
return Template(self.prompt_template).safe_substitute(input_arguments)
def add_message_to_memory(self, messages: List[Message]) -> None:
self.memory.add_message(messages)
def reset(self, environment: BaseEnvironment) -> None:
"""Reset the agent"""
self.environment = environment
self.memory.reset()
self.memory_manipulator.agent = self
self.memory_manipulator.memory = self.memory
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