224 lines
11 KiB
Python
224 lines
11 KiB
Python
from typing import List, Union
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import traceback
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import copy
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from loguru import logger
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from coagent.connector.schema import (
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Memory, Task, Env, Role, Message, ActionStatus, 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.agent_config import REACT_PROMPT_INPUT
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from coagent.llm_models import LLMConfig, EmbedConfig
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from .base_agent import BaseAgent
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from coagent.connector.memory_manager import LocalMemoryManager
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from coagent.connector.prompt_manager import PromptManager
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class ReactAgent(BaseAgent):
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def __init__(
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self,
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role: Role,
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prompt_config: [PromptField],
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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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#
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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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def step(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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for message in self.astep(query, history, background, memory_manager):
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pass
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return message
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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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step_nums = copy.deepcopy(self.chat_turn)
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react_memory = Memory(messages=[])
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# insert query
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output_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=query.input_query,
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step_content="",
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input_query=query.input_query,
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tools=query.tools,
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# parsed_output_list=[query.parsed_output],
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customed_kargs=query.customed_kargs
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)
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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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# if query.parsed_output:
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# query_c.parsed_output = {"Question": "\n".join([f"{v}" for k, v in query.parsed_output.items() if k not in ["Action Status"]])}
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# else:
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# query_c.parsed_output = {"Question": query.input_query}
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# react_memory.append(query_c)
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# self_memory = self.memory if self.do_use_self_memory else None
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idx = 0
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# start to react
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while step_nums > 0:
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output_message.role_content = output_message.step_content
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# prompt = self.create_prompt(query, self.memory, history, background, react_memory, memory_manager.current_memory)
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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=react_memory,
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memory_pool=memory_pool)
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try:
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content = self.llm.predict(prompt)
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except Exception as e:
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logger.error(f"error prompt: {prompt}")
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raise Exception(traceback.format_exc())
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output_message.role_content = "\n"+content
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output_message.step_content += "\n"+output_message.role_content
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yield output_message
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if LogVerboseEnum.ge(LogVerboseEnum.Log2Level, self.log_verbose):
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logger.debug(f"{self.role.role_name}, {idx} iteration 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}, {idx} iteration step_run: {output_message.role_content}")
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output_message = self.message_utils.parser(output_message)
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# when get finished signal can stop early
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if output_message.action_status == ActionStatus.FINISHED or output_message.action_status == ActionStatus.STOPPED:
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output_message.parsed_output_list.append(output_message.parsed_output)
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break
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# according the output to choose one action for code_content or tool_content
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output_message, observation_message = self.message_utils.step_router(output_message)
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output_message.parsed_output_list.append(output_message.parsed_output)
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react_message = copy.deepcopy(output_message)
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react_memory.append(react_message)
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if observation_message:
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react_memory.append(observation_message)
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output_message.parsed_output_list.append(observation_message.parsed_output)
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# logger.debug(f"{observation_message.role_name} content: {observation_message.role_content}")
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idx += 1
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step_nums -= 1
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yield output_message
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# react' self_memory saved at last
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self.append_history(output_message)
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output_message.input_query = query.input_query
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# end_action_step, BUG:it may cause slack some information
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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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yield output_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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react_memory = Memory(messages=[])
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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=react_memory,
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memory_pool=memory_manager.current_memory)
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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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# def create_prompt(
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# self, query: Message, memory: Memory =None, history: Memory = None, background: Memory = None, react_memory: Memory = None, memory_manager: BaseMemoryManager= None,
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# prompt_mamnger=None) -> str:
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# prompt_mamnger = PromptManager()
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# prompt_mamnger.register_standard_fields()
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# # input_keys = parse_section(self.role.role_prompt, 'Agent Profile')
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# data_dict = {
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# "agent_profile": extract_section(self.role.role_prompt, 'Agent Profile'),
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# "tool_information": query.tools,
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# "session_records": memory_manager,
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# "reference_documents": query,
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# "output_format": extract_section(self.role.role_prompt, 'Response Output Format'),
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# "response": "\n".join(["\n".join([f"**{k}:**\n{v}" for k,v in _dict.items()]) for _dict in react_memory.get_parserd_output()]),
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# }
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# # logger.debug(memory_pool)
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# return prompt_mamnger.generate_full_prompt(data_dict)
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def create_prompt(
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self, query: Message, memory: Memory =None, history: Memory = None, background: Memory = None, react_memory: Memory = None, memory_pool: Memory= None,
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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, _ = self.create_tools_prompt(query)
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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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#
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# extra_system_prompt = self.role.role_prompt
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prompt = self.role.role_prompt.format(**{"formatted_tools": formatted_tools, "tool_names": tool_names})
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# react 流程是自身迭代过程，另外二次触发的是需要作为历史对话信息
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# input_query = react_memory.to_tuple_messages(content_key="step_content")
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# # input_query = query.input_query + "\n" + "\n".join([f"{v}" for k, v in input_query if v])
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# input_query = "\n".join([f"{v}" for k, v in input_query if v])
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input_query = "\n".join(["\n".join([f"**{k}:**\n{v}" for k,v in _dict.items()]) for _dict in react_memory.get_parserd_output()])
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# logger.debug(f"input_query: {input_query}")
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prompt += "\n" + REACT_PROMPT_INPUT.format(**{"query": input_query})
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task = query.task or self.task
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# if task_prompt is not None:
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# prompt += "\n" + task.task_prompt
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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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# prompt += f"\n知识库信息: {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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# prompt += f"\n代码库信息: {code_infos}"
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# if background_prompt:
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# prompt += "\n" + background_prompt
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# if history_prompt:
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# prompt += "\n" + history_prompt
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# if selfmemory_prompt:
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# prompt += "\n" + selfmemory_prompt
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# logger.debug(f"{self.role.role_name} extra_system_prompt: {self.role.role_prompt}")
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# logger.debug(f"{self.role.role_name} input_query: {input_query}")
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# logger.debug(f"{self.role.role_name} doc_infos: {doc_infos}")
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# logger.debug(f"{self.role.role_name} tool_names: {tool_names}")
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# prompt += "\n" + REACT_PROMPT_INPUT.format(**{"query": input_query})
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# prompt = extra_system_prompt.format(**{"query": input_query, "doc_infos": doc_infos, "formatted_tools": formatted_tools, "tool_names": tool_names})
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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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return prompt
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