#!/usr/bin/env python # -*- coding: utf-8 -*- # @Desc : the implement of serialization and deserialization import copy import pickle from typing import Dict, List, Tuple from metagpt.actions.action_output import ActionOutput from metagpt.schema import Message def actionoutout_schema_to_mapping(schema: Dict) -> Dict: """ directly traverse the `properties` in the first level. schema structure likes ``` { "title":"prd", "type":"object", "properties":{ "Original Requirements":{ "title":"Original Requirements", "type":"string" }, }, "required":[ "Original Requirements", ] } ``` """ mapping = dict() for field, property in schema["properties"].items(): if property["type"] == "string": mapping[field] = (str, ...) elif property["type"] == "array" and property["items"]["type"] == "string": mapping[field] = (List[str], ...) elif property["type"] == "array" and property["items"]["type"] == "array": # here only consider the `Tuple[str, str]` situation mapping[field] = (List[Tuple[str, str]], ...) return mapping def serialize_message(message: Message): message_cp = copy.deepcopy(message) # avoid `instruct_content` value update by reference ic = message_cp.instruct_content if ic: # model create by pydantic create_model like `pydantic.main.prd`, can't pickle.dump directly schema = ic.schema() mapping = actionoutout_schema_to_mapping(schema) message_cp.instruct_content = {"class": schema["title"], "mapping": mapping, "value": ic.dict()} msg_ser = pickle.dumps(message_cp) return msg_ser def deserialize_message(message_ser: str) -> Message: message = pickle.loads(message_ser) if message.instruct_content: ic = message.instruct_content ic_obj = ActionOutput.create_model_class(class_name=ic["class"], mapping=ic["mapping"]) ic_new = ic_obj(**ic["value"]) message.instruct_content = ic_new return message