File size: 4,625 Bytes
3e0718c 1206897 37dc901 3e0718c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
import json
from copy import deepcopy
from typing import Any, Dict, List
from flow_modules.aiflows.ChatFlowModule import ChatAtomicFlow
from dataclasses import dataclass
@dataclass
class Command:
name: str
description: str
input_args: List[str]
class CodeWriterCtrlFlow(ChatAtomicFlow):
"""refer to https://huggingface.co/aiflows/JarvisFlowModule/blob/main/Controller_JarvisFlow.py
This class controls the execution of the CodeWriterFlow.
*Input Interface Non Initialized*:
- `goal`
*Input Interface Initialized*:
- `goal`
- `code`
- `feedback`
*Output Interface*:
- `command`
- `command_args`
*Config Parameters*:
- `backend`: the backend used to call the LLM.
- `commands`: a list of commands that the controller can use.
- `system_message_prompt_template`: the template of the system message prompt.
- `init_human_message_prompt_template`: the template of the init human (user) message prompt.
- `human_message_prompt_template`: the template of the human (user) message prompt.
- `previous_messages`: the sliding window of previous messages.
"""
def __init__(
self,
commands: List[Command],
**kwargs):
super().__init__(**kwargs)
self.system_message_prompt_template = self.system_message_prompt_template.partial(
commands=self._build_commands_manual(commands),
)
self.hint_for_model = """
Make sure your response is in the following format:
Response Format:
{
"command": "call code writer, the tester, or to finish",
"command_args": {
"arg name": "value"
}
}
"""
@staticmethod
def _build_commands_manual(commands: List[Command]) -> str:
ret = ""
for i, command in enumerate(commands):
command_input_json_schema = json.dumps(
{input_arg: f"YOUR_{input_arg.upper()}" for input_arg in command.input_args})
ret += f"{i + 1}. {command.name}: {command.description} Input arguments (given in the JSON schema): {command_input_json_schema}\n"
return ret
@classmethod
def instantiate_from_config(cls, config):
flow_config = deepcopy(config)
kwargs = {"flow_config": flow_config}
# ~~~ Set up prompts ~~~
kwargs.update(cls._set_up_prompts(flow_config))
# ~~~Set up backend ~~~
kwargs.update(cls._set_up_backend(flow_config))
# ~~~ Set up commands ~~~
commands = flow_config["commands"]
commands = [
Command(name, command_conf["description"], command_conf["input_args"]) for name, command_conf in
commands.items()
]
kwargs.update({"commands": commands})
# ~~~ Instantiate flow ~~~
return cls(**kwargs)
def _update_prompts_and_input(self, input_data: Dict[str, Any]):
if 'goal' in input_data:
input_data['goal'] += self.hint_for_model
if 'feedback' in input_data:
input_data['feedback'] += self.hint_for_model
def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
self._update_prompts_and_input(input_data)
# ~~~when conversation is initialized, append the updated system prompts to the chat history ~~~
if self._is_conversation_initialized():
updated_system_message_content = self._get_message(self.system_message_prompt_template, input_data)
self._state_update_add_chat_message(content=updated_system_message_content,
role=self.flow_config["system_name"])
while True:
api_output = super().run(input_data)["api_output"].strip()
try:
response = json.loads(api_output)
return response
except (json.decoder.JSONDecodeError, json.JSONDecodeError):
updated_system_message_content = self._get_message(self.system_message_prompt_template, input_data)
self._state_update_add_chat_message(content=updated_system_message_content,
role=self.flow_config["system_name"])
new_goal = "The previous respond cannot be parsed with json.loads. Next time, do not provide any comments or code blocks. Make sure your next response is purely json parsable."
new_input_data = input_data.copy()
new_input_data['feedback'] = new_goal
input_data = new_input_data
|