src/agents/SOP.py

# coding=utf-8
# Copyright 2023  The AIWaves Inc. team.

#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""standard operation procedure of an LLM Autonomous agent"""
import random
from LLM.base_LLM import *
from State import State
from utils import extract, get_relevant_history
from Memory import Memory
from Prompt import *
import json
import os

class SOP:
    """
    Responsible for managing the operational processes of all agents
    """

    # SOP should have args : "states" "relations" "root"

    def __init__(self, **kwargs):
        self.controller_dict = {}
        self.LLM = init_LLM("logs/god",**kwargs)

        self.states = {}
        self.init_states(kwargs["states"])
        self.init_relation(kwargs["relations"])
        for state_name, states_dict in kwargs["states"].items():
            if state_name != "end_state" and "controller" in states_dict:
                self.controller_dict[state_name] = states_dict["controller"]

        self.user_names = kwargs["user_names"] if "user_names" in kwargs else []
        self.root = self.states[kwargs["root"]]
        self.current_state = self.root
        self.finish_state_name = (
            kwargs["finish_state_name"]
            if "finish_state_name" in kwargs
            else "end_state"
        )
        self.roles_to_names = None
        self.names_to_roles = None
        self.finished = False

    @classmethod
    def from_config(cls, config_path):
        with open(config_path) as f:
            config = json.load(f)
        os.environ.clear()
        for key,value in config["config"].items():
            if key == "API_BASE":
                if value == "":
                    pass
            else:
                os.environ[key] = value
        # assert "API_KEY" in os.environ and os.environ["API_KEY"] != "API_KEY","Please go to config.json to set API_KEY"
        
        sop = SOP(**config)
        return sop

    def init_states(self, states_dict):
        for state_name, state_dict in states_dict.items():
            state_dict["name"] = state_name
            self.states[state_name] = State(**state_dict)

    def init_relation(self, relations):
        for state_name, state_relation in relations.items():
            for idx, next_state_name in state_relation.items():
                self.states[state_name].next_states[idx] = self.states[next_state_name]

    def transit(self, chat_history, **kwargs):
        """
        Determine the next state based on the current situation
        Return : 
        next_state(State) : the next state
        """
        # 如果是单一循环节点，则一直循环即可
        # If it is a single loop node, just keep looping
        if len(self.current_state.next_states) == 1:
            next_state = "0"
            
        # 否则则需要controller去判断进入哪一节点
        # Otherwise, the controller needs to determine which node to enter.   
        else:
            current_state = self.current_state
            controller_dict = self.controller_dict[current_state.name]
            relevant_history = kwargs["relevant_history"]
            
            max_chat_nums = controller_dict["max_chat_nums"] if "max_chat_nums" in controller_dict else 1000
            if current_state.chat_nums>=max_chat_nums:
                return self.current_state.next_states["1"]
            

            # 否则则让controller判断是否结束
            # Otherwise, let the controller judge whether to end
            judge_system_prompt = controller_dict["judge_system_prompt"]
            environment_prompt = eval(Get_environment_prompt) if current_state.environment_prompt else ""
            transit_system_prompt = eval(Transit_system_prompt)
            
            judge_last_prompt = controller_dict["judge_last_prompt"]
            transit_last_prompt = eval(Transit_last_prompt)
            

            
            environment = kwargs["environment"]
            environment_summary = environment.shared_memory["short_term_memory"]
            chat_history_message = Memory.get_chat_history(chat_history)
            query = chat_history[-1].get_query()
            
            chat_messages = [
                {
                    "role": "user",
                    "content": eval(Transit_message)
                }
            ]
            
            extract_words = controller_dict["judge_extract_words"] if "judge_extract_words" in controller_dict else "end"


            response = self.LLM.get_response(
                chat_messages, transit_system_prompt, transit_last_prompt, stream=False, **kwargs
            )
            next_state = (
                response if response.isdigit() else extract(response, extract_words)
            )
            
            # 如果没有parse出来则继续循环
            # If no parse comes out, continue looping
            if not next_state.isdigit():
                next_state = "0"
            
        next_state = self.current_state.next_states[next_state]
        return next_state


    def route(self, chat_history, **kwargs):
        """
        Determine the role that needs action based on the current situation
        Return : 
        current_agent(Agent) : the next act agent
        """
        
        agents = kwargs["agents"]
        
        # 知道进入哪一状态后开始分配角色，如果该状态下只有一个角色则直接分配给他
        # Start assigning roles after knowing which state you have entered. If there is only one role in that state, assign it directly to him.
        if len(self.current_state.roles) == 1:
            next_role = self.current_state.roles[0]
        
        
        
        # 否则controller进行分配
        # Otherwise the controller determines
        else:
            relevant_history = kwargs["relevant_history"]
            controller_type = (
                self.controller_dict[self.current_state.name]["controller_type"]
                if "controller_type" in self.controller_dict[self.current_state.name]
                else "order"
            )


            # 如果是rule 控制器，则交由LLM进行分配角色
            # If  controller type is rule, it is left to LLM to assign roles.
            if controller_type == "rule":
                controller_dict = self.controller_dict[self.current_state.name]
                
                call_last_prompt = controller_dict["call_last_prompt"] if "call_last_prompt" in controller_dict else ""
                
                allocate_prompt = ""
                roles = list(set(self.current_state.roles))
                for role in roles:
                    allocate_prompt += eval(Allocate_component)
                    
                call_system_prompt = controller_dict["call_system_prompt"]  if "call_system_prompt" in controller_dict else ""
                environment_prompt = eval(Get_environment_prompt) if self.current_state.environment_prompt else ""    
                # call_system_prompt + environment + allocate_prompt 
                call_system_prompt = eval(Call_system_prompt)
                
                query = chat_history[-1].get_query()
                last_name = chat_history[-1].send_name
                # last_prompt: note + last_prompt + query
                call_last_prompt =eval(Call_last_prompt)
                
                
                chat_history_message = Memory.get_chat_history(chat_history)
                # Intermediate historical conversation records
                chat_messages = [
                    {
                        "role": "user",
                        "content": eval(Call_message),
                    }
                ]

                extract_words = controller_dict["call_extract_words"] if "call_extract_words" in controller_dict else "end"

                response = self.LLM.get_response(
                    chat_messages, call_system_prompt, call_last_prompt, stream=False, **kwargs
                )

                # get next role
                next_role = extract(response, extract_words)

            # Speak in order
            elif controller_type == "order":
                # If there is no begin role, it will be given directly to the first person.
                if not self.current_state.current_role:
                    next_role = self.current_state.roles[0]
                # otherwise first
                else:
                    self.current_state.index += 1
                    self.current_state.index =  (self.current_state.index) % len(self.current_state.roles)
                    next_role = self.current_state.roles[self.current_state.index]
            # random speak
            elif controller_type == "random":
                next_role = random.choice(self.current_state.roles)
            
        # 如果下一角色不在，则随机挑选一个
        # If the next character is not available, pick one at random    
        if next_role not in self.current_state.roles:
            next_role = random.choice(self.current_state.roles)
            
        self.current_state.current_role = next_role 
        
        next_agent = agents[self.roles_to_names[self.current_state.name][next_role]]
        
        return next_agent
    
    def next(self, environment, agents):
        """
        Determine the next state and the agent that needs action based on the current situation
        """
        
        # 如果是第一次进入该状态
        # If it is the first time to enter this state
        
        if self.current_state.is_begin:
            agent_name = self.roles_to_names[self.current_state.name][self.current_state.begin_role]
            agent = agents[agent_name]
            return self.current_state,agent
    
    
        # get relevant history
        query = environment.shared_memory["long_term_memory"][-1].content
        relevant_history = get_relevant_history(
            query,
            environment.shared_memory["long_term_memory"][:-1],
            environment.shared_memory["chat_embeddings"][:-1],
        )
        relevant_history = Memory.get_chat_history(relevant_history)
        
        
        
        next_state = self.transit(
            chat_history=environment.shared_memory["long_term_memory"][
                environment.current_chat_history_idx :
            ],
            relevant_history=relevant_history,
            environment=environment,
        )
        # 如果进入终止节点，则直接终止
        # If you enter the termination node, terminate directly
        if next_state.name == self.finish_state_name:
            self.finished = True
            return None, None

        self.current_state = next_state
        
        # 如果是首次进入该节点且有开场白，则直接分配给开场角色
        # If it is the first time to enter the state and there is a begin query, it will be directly assigned to the begin role.
        if self.current_state.is_begin and self.current_state.begin_role:
            agent_name = self.roles_to_names[self.current_state.name][self.current_state.begin_role]
            agent = agents[agent_name]
            return self.current_state,agent
           

        next_agent = self.route(
            chat_history=environment.shared_memory["long_term_memory"][
                environment.current_chat_history_idx :
            ],
            agents = agents,
            relevant_history=relevant_history,
        )

        return self.current_state, next_agent