190 lines
9.2 KiB
Python
190 lines
9.2 KiB
Python
from typing import List, Union
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import copy
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import random
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from loguru import logger
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from coagent.connector.schema import (
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Memory, Task, Role, Message, PromptField, LogVerboseEnum
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)
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from coagent.connector.memory_manager import BaseMemoryManager
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from coagent.connector.configs.prompts import BEGIN_PROMPT_INPUT
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from coagent.connector.memory_manager import LocalMemoryManager
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from coagent.llm_models import LLMConfig, EmbedConfig
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from .base_agent import BaseAgent
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class SelectorAgent(BaseAgent):
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def __init__(
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self,
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role: Role,
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prompt_config: List[PromptField] = None,
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prompt_manager_type: str = "PromptManager",
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task: Task = None,
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memory: Memory = None,
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chat_turn: int = 1,
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focus_agents: List[str] = [],
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focus_message_keys: List[str] = [],
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group_agents: List[BaseAgent] = [],
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#
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llm_config: LLMConfig = None,
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embed_config: EmbedConfig = None,
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sandbox_server: dict = {},
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jupyter_work_path: str = "",
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kb_root_path: str = "",
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log_verbose: str = "0"
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):
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super().__init__(role, prompt_config, prompt_manager_type, task, memory, chat_turn,
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focus_agents, focus_message_keys, llm_config, embed_config, sandbox_server,
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jupyter_work_path, kb_root_path, log_verbose
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)
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self.group_agents = group_agents
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def astep(self, query: Message, history: Memory = None, background: Memory = None, memory_manager: BaseMemoryManager=None) -> Message:
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'''agent reponse from multi-message'''
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# insert query into memory
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query_c = copy.deepcopy(query)
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query_c = self.start_action_step(query_c)
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# create your llm prompt
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if memory_manager is None:
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memory_manager = LocalMemoryManager(
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unique_name=self.role.role_name,
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do_init=True,
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kb_root_path = self.kb_root_path,
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embed_config=self.embed_config,
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llm_config=self.embed_config
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)
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memory_manager.append(query)
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memory_pool = memory_manager.current_memory
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else:
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memory_pool = memory_manager.current_memory
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prompt = self.prompt_manager.generate_full_prompt(
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previous_agent_message=query_c, agent_long_term_memory=self.memory, ui_history=history, chain_summary_messages=background, react_memory=None,
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memory_pool=memory_pool, agents=self.group_agents)
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content = self.llm.predict(prompt)
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if LogVerboseEnum.ge(LogVerboseEnum.Log2Level, self.log_verbose):
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logger.debug(f"{self.role.role_name} prompt: {prompt}")
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if LogVerboseEnum.ge(LogVerboseEnum.Log1Level, self.log_verbose):
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logger.info(f"{self.role.role_name} content: {content}")
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# select agent
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select_message = Message(
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role_name=self.role.role_name,
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role_type="assistant", #self.role.role_type,
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role_content=content,
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step_content=content,
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input_query=query_c.input_query,
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tools=query_c.tools,
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# parsed_output_list=[query_c.parsed_output]
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customed_kargs=query.customed_kargs
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)
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# common parse llm' content to message
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select_message = self.message_utils.parser(select_message)
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select_message.parsed_output_list.append(select_message.parsed_output)
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output_message = None
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if select_message.parsed_output.get("Role", "") in [agent.role.role_name for agent in self.group_agents]:
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for agent in self.group_agents:
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if agent.role.role_name == select_message.parsed_output.get("Role", ""):
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break
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for output_message in agent.astep(query_c, history, background=background, memory_manager=memory_manager):
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yield output_message or select_message
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# update self_memory
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self.append_history(query_c)
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self.append_history(output_message)
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output_message.input_query = output_message.role_content
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# output_message.parsed_output_list.append(output_message.parsed_output)
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#
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output_message = self.end_action_step(output_message)
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# update memory pool
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memory_manager.append(output_message)
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select_message.parsed_output = output_message.parsed_output
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select_message.parsed_output_list.extend(output_message.parsed_output_list)
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yield select_message
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def pre_print(self, query: Message, history: Memory = None, background: Memory = None, memory_manager: BaseMemoryManager=None):
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prompt = self.prompt_manager.pre_print(
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previous_agent_message=query, agent_long_term_memory=self.memory, ui_history=history, chain_summary_messages=background, react_memory=None,
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memory_pool=memory_manager.current_memory, agents=self.group_agents)
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title = f"<<<<{self.role.role_name}'s prompt>>>>"
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print("#"*len(title) + f"\n{title}\n"+ "#"*len(title)+ f"\n\n{prompt}\n\n")
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for agent in self.group_agents:
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agent.pre_print(query=query, history=history, background=background, memory_manager=memory_manager)
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# def create_prompt(
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# self, query: Message, memory: Memory =None, history: Memory = None, background: Memory = None, memory_manager: BaseMemoryManager=None, prompt_mamnger=None) -> str:
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# '''
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# role\task\tools\docs\memory
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# '''
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# #
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# doc_infos = self.create_doc_prompt(query)
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# code_infos = self.create_codedoc_prompt(query)
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# #
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# formatted_tools, tool_names, tools_descs = self.create_tools_prompt(query)
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# agent_names, agents = self.create_agent_names()
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# task_prompt = self.create_task_prompt(query)
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# background_prompt = self.create_background_prompt(background)
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# history_prompt = self.create_history_prompt(history)
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# selfmemory_prompt = self.create_selfmemory_prompt(memory, control_key="step_content")
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# DocInfos = ""
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# if doc_infos is not None and doc_infos!="" and doc_infos!="不存在知识库辅助信息":
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# DocInfos += f"\nDocument Information: {doc_infos}"
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# if code_infos is not None and code_infos!="" and code_infos!="不存在代码库辅助信息":
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# DocInfos += f"\nCodeBase Infomation: {code_infos}"
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# input_query = query.input_query
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# logger.debug(f"{self.role.role_name} input_query: {input_query}")
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# prompt = self.role.role_prompt.format(**{"agent_names": agent_names, "agents": agents, "formatted_tools": tools_descs, "tool_names": tool_names})
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# #
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# memory_pool_select_by_agent_key = self.select_memory_by_agent_key(memory_manager.current_memory)
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# memory_pool_select_by_agent_key_context = '\n\n'.join([f"*{k}*\n{v}" for parsed_output in memory_pool_select_by_agent_key.get_parserd_output_list() for k, v in parsed_output.items() if k not in ['Action Status']])
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# input_keys = parse_section(self.role.role_prompt, 'Input Format')
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# #
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# prompt += "\n" + BEGIN_PROMPT_INPUT
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# for input_key in input_keys:
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# if input_key == "Origin Query":
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# prompt += "\n**Origin Query:**\n" + query.origin_query
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# elif input_key == "Context":
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# context = "\n".join([f"*{k}*\n{v}" for i in query.parsed_output_list for k,v in i.items() if "Action Status" !=k])
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# if history:
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# context = history_prompt + "\n" + context
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# if not context:
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# context = "there is no context"
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# if self.focus_agents and memory_pool_select_by_agent_key_context:
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# context = memory_pool_select_by_agent_key_context
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# prompt += "\n**Context:**\n" + context + "\n" + input_query
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# elif input_key == "DocInfos":
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# prompt += "\n**DocInfos:**\n" + DocInfos
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# elif input_key == "Question":
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# prompt += "\n**Question:**\n" + input_query
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# while "{{" in prompt or "}}" in prompt:
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# prompt = prompt.replace("{{", "{")
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# prompt = prompt.replace("}}", "}")
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# # logger.debug(f"{self.role.role_name} prompt: {prompt}")
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# return prompt
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# def create_agent_names(self):
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# random.shuffle(self.group_agents)
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# agent_names = ", ".join([f'{agent.role.role_name}' for agent in self.group_agents])
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# agent_descs = []
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# for agent in self.group_agents:
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# role_desc = agent.role.role_prompt.split("####")[1]
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# while "\n\n" in role_desc:
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# role_desc = role_desc.replace("\n\n", "\n")
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# role_desc = role_desc.replace("\n", ",")
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# agent_descs.append(f'"role name: {agent.role.role_name}\nrole description: {role_desc}"')
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# return agent_names, "\n".join(agent_descs) |