mirror of
https://github.com/agentscope-ai/ReMe.git
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106 lines
4.1 KiB
Python
106 lines
4.1 KiB
Python
import json
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import os
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from concurrent.futures import ThreadPoolExecutor
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from typing import Dict, Any, List
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import fire
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from memory_scope.chat.base_memory_chat import BaseMemoryChat
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from memory_scope.chat.global_context import GLOBAL_CONTEXT
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from memory_scope.enumeration.language_enum import LanguageEnum
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from memory_scope.enumeration.model_enum import ModelEnum
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from memory_scope.utils.logger import Logger
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from memory_scope.utils.tool_functions import complete_config_name, init_instance_by_config
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class CliJob(object):
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def __init__(self, config_path: str):
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self.config_path: str = config_path
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self.config_base_dir: str = os.path.dirname(config_path)
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self.config: Dict[str, Any] = {}
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self.worker_chat_dict: Dict[str, List[str]] = {}
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self.logger: Logger = Logger.get_logger("memory_chat")
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def init_memory_chat(self):
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for chat_name in self.config["chat_list"]:
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memory_chat_config = self.config[chat_name]
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memory_chat: BaseMemoryChat = init_instance_by_config(memory_chat_config, chat_name=chat_name)
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GLOBAL_CONTEXT.memory_chat_dict[chat_name] = memory_chat
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for worker_name in memory_chat.memory_service.get_worker_list():
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if worker_name not in self.worker_chat_dict:
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self.worker_chat_dict[worker_name] = []
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self.worker_chat_dict[worker_name].append(chat_name)
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generation_model = memory_chat_config[ModelEnum.GENERATION_MODEL.value]
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self.init_model(generation_model)
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def init_model(self, model_name: str):
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if not model_name or model_name in GLOBAL_CONTEXT.model_dict:
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return
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with open(os.path.join(self.config_base_dir, "model", complete_config_name(model_name))) as f:
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model_config = json.load(f)
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GLOBAL_CONTEXT.model_dict[model_name] = init_instance_by_config(model_config)
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def init_workers(self):
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""" load worker config & init workers
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"""
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worker_config_name: str = self.config["workers"]
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with open(os.path.join(self.config_base_dir, complete_config_name(worker_config_name))) as f:
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worker_config_dict = json.load(f)
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for worker_name, worker_config in worker_config_dict.items():
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if worker_name not in self.worker_chat_dict:
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continue
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chat_name_list = self.worker_chat_dict[worker_name]
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for chat_name in chat_name_list:
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if chat_name not in GLOBAL_CONTEXT.worker_dict:
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GLOBAL_CONTEXT.worker_dict[chat_name] = {}
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GLOBAL_CONTEXT.worker_dict[chat_name][worker_name] = init_instance_by_config(
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worker_config,
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suffix_name="worker",
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**GLOBAL_CONTEXT.global_configs)
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self.init_model(worker_config.get(ModelEnum.EMBEDDING_MODEL.value))
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self.init_model(worker_config.get(ModelEnum.GENERATION_MODEL.value))
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self.init_model(worker_config.get(ModelEnum.RANK_MODEL.value))
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@staticmethod
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def set_global_config():
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""" TODO set global_configs & set apikey into env
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"""
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GLOBAL_CONTEXT.language = LanguageEnum(GLOBAL_CONTEXT.global_configs["language"])
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GLOBAL_CONTEXT.thread_pool = ThreadPoolExecutor(max_workers=int(GLOBAL_CONTEXT.global_configs["max_workers"]))
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def init_global_content_by_config(self):
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with open(complete_config_name(self.config_path)) as f:
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self.config = json.load(f)
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GLOBAL_CONTEXT.global_configs = self.config["global_configs"]
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self.set_global_config()
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self.init_memory_chat()
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self.init_workers()
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GLOBAL_CONTEXT.vector_store = init_instance_by_config(self.config["vector_store"])
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GLOBAL_CONTEXT.monitor = init_instance_by_config(self.config["monitor"])
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@staticmethod
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def run():
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with GLOBAL_CONTEXT.thread_pool:
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memory_chat = list(GLOBAL_CONTEXT.memory_chat_dict.values())[0]
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memory_chat.run()
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def main(config_path: str):
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job = CliJob(config_path=config_path)
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job.init_global_content_by_config()
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job.run()
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if __name__ == "__main__":
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fire.Fire(main)
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