from typing import List, Union, Dict, Tuple import os import json import importlib import copy from loguru import logger from coagent.connector.agents import BaseAgent from coagent.connector.chains import BaseChain from coagent.connector.schema import ( Memory, Task, Message, AgentConfig, ChainConfig, PhaseConfig, LogVerboseEnum, CompletePhaseConfig, load_chain_configs, load_phase_configs, load_role_configs ) from coagent.connector.memory_manager import BaseMemoryManager, LocalMemoryManager from coagent.connector.configs import AGETN_CONFIGS, CHAIN_CONFIGS, PHASE_CONFIGS from coagent.connector.message_process import MessageUtils from coagent.llm_models.llm_config import EmbedConfig, LLMConfig from coagent.base_configs.env_config import JUPYTER_WORK_PATH, KB_ROOT_PATH # from configs.model_config import JUPYTER_WORK_PATH, KB_ROOT_PATH # from configs.server_config import SANDBOX_SERVER role_configs = load_role_configs(AGETN_CONFIGS) chain_configs = load_chain_configs(CHAIN_CONFIGS) phase_configs = load_phase_configs(PHASE_CONFIGS) CUR_DIR = os.path.dirname(os.path.abspath(__file__)) class BasePhase: def __init__( self, phase_name: str, phase_config: CompletePhaseConfig = None, kb_root_path: str = KB_ROOT_PATH, jupyter_work_path: str = JUPYTER_WORK_PATH, sandbox_server: dict = {}, embed_config: EmbedConfig = EmbedConfig(), llm_config: LLMConfig = LLMConfig(), task: Task = None, base_phase_config: Union[dict, str] = PHASE_CONFIGS, base_chain_config: Union[dict, str] = CHAIN_CONFIGS, base_role_config: Union[dict, str] = AGETN_CONFIGS, log_verbose: str = "0" ) -> None: # self.phase_name = phase_name self.do_summary = False self.do_search = False self.do_code_retrieval = False self.do_doc_retrieval = False self.do_tool_retrieval = False # memory_pool dont have specific order # self.memory_pool = Memory(messages=[]) self.embed_config = embed_config self.llm_config = llm_config self.sandbox_server = sandbox_server self.jupyter_work_path = jupyter_work_path self.kb_root_path = kb_root_path self.log_verbose = max(os.environ.get("log_verbose", "0"), log_verbose) self.message_utils = MessageUtils(None, sandbox_server, jupyter_work_path, embed_config, llm_config, kb_root_path, log_verbose) self.global_memory = Memory(messages=[]) self.phase_memory: List[Memory] = [] # according phase name to init the phase contains self.chains: List[BaseChain] = self.init_chains( phase_name, phase_config, task=task, memory=None, base_phase_config = base_phase_config, base_chain_config = base_chain_config, base_role_config = base_role_config, ) self.memory_manager: BaseMemoryManager = LocalMemoryManager( unique_name=phase_name, do_init=True, kb_root_path = kb_root_path, embed_config=embed_config, llm_config=llm_config ) self.conv_summary_agent = BaseAgent( role=role_configs["conv_summary"].role, prompt_config=role_configs["conv_summary"].prompt_config, task = None, memory = None, llm_config=self.llm_config, embed_config=self.embed_config, sandbox_server=sandbox_server, jupyter_work_path=jupyter_work_path, kb_root_path=kb_root_path ) def astep(self, query: Message, history: Memory = None) -> Tuple[Message, Memory]: self.memory_manager.append(query) summary_message = None chain_message = Memory(messages=[]) local_phase_memory = Memory(messages=[]) # do_search、do_doc_search、do_code_search query = self.message_utils.get_extrainfo_step(query, self.do_search, self.do_doc_retrieval, self.do_code_retrieval, self.do_tool_retrieval) query.parsed_output = query.parsed_output if query.parsed_output else {"origin_query": query.input_query} query.parsed_output_list = query.parsed_output_list if query.parsed_output_list else [{"origin_query": query.input_query}] input_message = copy.deepcopy(query) self.global_memory.append(input_message) local_phase_memory.append(input_message) for chain in self.chains: # chain can supply background and query to next chain for output_message, local_chain_memory in chain.astep(input_message, history, background=chain_message, memory_manager=self.memory_manager): # logger.debug(f"local_memory: {local_phase_memory + local_chain_memory}") yield output_message, local_phase_memory + local_chain_memory output_message = self.message_utils.inherit_extrainfo(input_message, output_message) input_message = output_message # logger.info(f"{chain.chainConfig.chain_name} phase_step: {output_message.role_content}") # 这一段也有问题 self.global_memory.extend(local_chain_memory) local_phase_memory.extend(local_chain_memory) # whether to use summary_llm if self.do_summary: if LogVerboseEnum.ge(LogVerboseEnum.Log1Level, self.log_verbose): logger.info(f"{self.conv_summary_agent.role.role_name} input global memory: {local_phase_memory.to_str_messages(content_key='step_content')}") for summary_message in self.conv_summary_agent.astep(query, background=local_phase_memory, memory_manager=self.memory_manager): pass # summary_message = Message(**summary_message) summary_message.role_name = chain.chainConfig.chain_name summary_message = self.conv_summary_agent.message_utils.parser(summary_message) summary_message = self.message_utils.inherit_extrainfo(output_message, summary_message) chain_message.append(summary_message) message = summary_message or output_message yield message, local_phase_memory # 由于不会存在多轮chain执行，所以直接保留memory即可 for chain in self.chains: self.phase_memory.append(chain.global_memory) # TODO：local_memory缺少添加summary的过程 message = summary_message or output_message message.role_name = self.phase_name yield message, local_phase_memory def step(self, query: Message, history: Memory = None) -> Tuple[Message, Memory]: for message, local_phase_memory in self.astep(query, history=history): pass return message, local_phase_memory def pre_print(self, query, history: Memory = None) -> List[str]: chain_message = Memory(messages=[]) for chain in self.chains: chain.pre_print(query, history, background=chain_message, memory_manager=self.memory_manager) def init_chains(self, phase_name: str, phase_config: CompletePhaseConfig, base_phase_config, base_chain_config, base_role_config, task=None, memory=None) -> List[BaseChain]: # load config role_configs = load_role_configs(base_role_config) chain_configs = load_chain_configs(base_chain_config) phase_configs = load_phase_configs(base_phase_config) chains = [] self.chain_module = importlib.import_module("coagent.connector.chains") self.agent_module = importlib.import_module("coagent.connector.agents") phase: PhaseConfig = phase_configs.get(phase_name) # set phase self.do_summary = phase.do_summary self.do_search = phase.do_search self.do_code_retrieval = phase.do_code_retrieval self.do_doc_retrieval = phase.do_doc_retrieval self.do_tool_retrieval = phase.do_tool_retrieval logger.info(f"start to init the phase, the phase_name is {phase_name}, it contains these chains such as {phase.chains}") for chain_name in phase.chains: # logger.debug(f"{chain_configs.keys()}") chain_config: ChainConfig = chain_configs[chain_name] logger.info(f"start to init the chain, the chain_name is {chain_name}, it contains these agents such as {chain_config.agents}") agents = [] for agent_name in chain_config.agents: agent_config: AgentConfig = role_configs[agent_name] llm_config = copy.deepcopy(self.llm_config) llm_config.stop = agent_config.stop baseAgent: BaseAgent = getattr(self.agent_module, agent_config.role.agent_type) base_agent = baseAgent( role=agent_config.role, prompt_config = agent_config.prompt_config, prompt_manager_type=agent_config.prompt_manager_type, task = task, memory = memory, chat_turn=agent_config.chat_turn, focus_agents=agent_config.focus_agents, focus_message_keys=agent_config.focus_message_keys, llm_config=llm_config, embed_config=self.embed_config, sandbox_server=self.sandbox_server, jupyter_work_path=self.jupyter_work_path, kb_root_path=self.kb_root_path, log_verbose=self.log_verbose ) if agent_config.role.agent_type == "SelectorAgent": for group_agent_name in agent_config.group_agents: group_agent_config = role_configs[group_agent_name] llm_config = copy.deepcopy(self.llm_config) llm_config.stop = group_agent_config.stop baseAgent: BaseAgent = getattr(self.agent_module, group_agent_config.role.agent_type) group_base_agent = baseAgent( role=group_agent_config.role, prompt_config = group_agent_config.prompt_config, prompt_manager_type=agent_config.prompt_manager_type, task = task, memory = memory, chat_turn=group_agent_config.chat_turn, focus_agents=group_agent_config.focus_agents, focus_message_keys=group_agent_config.focus_message_keys, llm_config=llm_config, embed_config=self.embed_config, sandbox_server=self.sandbox_server, jupyter_work_path=self.jupyter_work_path, kb_root_path=self.kb_root_path, log_verbose=self.log_verbose ) base_agent.group_agents.append(group_base_agent) agents.append(base_agent) chain_instance = BaseChain( agents, chain_config.chat_turn, do_checker=chain_configs[chain_name].do_checker, jupyter_work_path=self.jupyter_work_path, sandbox_server=self.sandbox_server, embed_config=self.embed_config, llm_config=self.llm_config, kb_root_path=self.kb_root_path, log_verbose=self.log_verbose ) chains.append(chain_instance) return chains def update(self) -> Memory: pass def get_memory(self, ) -> Memory: return Memory.from_memory_list( [chain.get_memory() for chain in self.chains] ) def get_memory_str(self, do_all_memory=True, content_key="role_content") -> str: memory = self.global_memory if do_all_memory else self.phase_memory return "\n".join([": ".join(i) for i in memory.to_tuple_messages(content_key=content_key)]) def get_chains_memory(self, content_key="role_content") -> List[Tuple]: return [memory.to_tuple_messages(content_key=content_key) for memory in self.phase_memory] def get_chains_memory_str(self, content_key="role_content") -> str: return "************".join([f"{chain.chainConfig.chain_name}\n" + chain.get_memory_str(content_key=content_key) for chain in self.chains])