125 lines
5.9 KiB
Python
125 lines
5.9 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 langchain.schema import BaseRetriever
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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.memory_manager import LocalMemoryManager
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from coagent.llm_models import LLMConfig, EmbedConfig
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from coagent.base_configs.env_config import JUPYTER_WORK_PATH, KB_ROOT_PATH
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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 = JUPYTER_WORK_PATH,
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kb_root_path: str = KB_ROOT_PATH,
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doc_retrieval: Union[BaseRetriever] = None,
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code_retrieval = None,
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search_retrieval = None,
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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, doc_retrieval, code_retrieval, search_retrieval, 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.get_memory_pool(query_c.user_name)
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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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# 把除了role以外的信息传给下一个agent
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query_c.parsed_output.update({k:v for k,v in select_message.parsed_output.items() if k!="Role"})
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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.spec_parsed_output.update(output_message.spec_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) |