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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Desc : the implement of serialization and deserialization
# @From : https://github.com/geekan/MetaGPT/blob/main/metagpt/utils/serialize.py
import copy
from typing import Tuple, List, Type, Union, Dict
import pickle
from collections import defaultdict
from pydantic import create_model
from autoagents.system.schema import Message
from autoagents.actions.action import Action, ActionOutput
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
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