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swarms/swarms/prompts/chat_prompt.py

160 lines
3.7 KiB

from __future__ import annotations
from abc import abstractmethod
from typing import Sequence
class Message:
"""
The base abstract Message class.
Messages are the inputs and outputs of ChatModels.
"""
def __init__(
self, content: str, role: str, additional_kwargs: dict = None
):
self.content = content
self.role = role
self.additional_kwargs = (
additional_kwargs if additional_kwargs else {}
)
@abstractmethod
def get_type(self) -> str:
pass
class HumanMessage(Message):
"""
A Message from a human.
"""
def __init__(
self,
content: str,
role: str = "Human",
additional_kwargs: dict = None,
example: bool = False,
):
super().__init__(content, role, additional_kwargs)
self.example = example
def get_type(self) -> str:
return "human"
class AIMessage(Message):
"""
A Message from an AI.
"""
def __init__(
self,
content: str,
role: str = "AI",
additional_kwargs: dict = None,
example: bool = False,
):
super().__init__(content, role, additional_kwargs)
self.example = example
def get_type(self) -> str:
return "ai"
class SystemMessage(Message):
"""
A Message for priming AI behavior, usually passed in as the first of a sequence
of input messages.
"""
def __init__(
self,
content: str,
role: str = "System",
additional_kwargs: dict = None,
):
super().__init__(content, role, additional_kwargs)
def get_type(self) -> str:
return "system"
class FunctionMessage(Message):
"""
A Message for passing the result of executing a function back to a model.
"""
def __init__(
self,
content: str,
role: str = "Function",
name: str = None,
additional_kwargs: dict = None,
):
super().__init__(content, role, additional_kwargs)
self.name = name
def get_type(self) -> str:
return "function"
class ChatMessage(Message):
"""
A Message that can be assigned an arbitrary speaker (i.e. role).
"""
def __init__(
self, content: str, role: str, additional_kwargs: dict = None
):
super().__init__(content, role, additional_kwargs)
def get_type(self) -> str:
return "chat"
def get_buffer_string(
messages: Sequence[Message],
human_prefix: str = "Human",
ai_prefix: str = "AI",
) -> str:
string_messages = []
for m in messages:
message = f"{m.role}: {m.content}"
if (
isinstance(m, AIMessage)
and "function_call" in m.additional_kwargs
):
message += f"{m.additional_kwargs['function_call']}"
string_messages.append(message)
return "\n".join(string_messages)
def message_to_dict(message: Message) -> dict:
return {"type": message.get_type(), "data": message.__dict__}
def messages_to_dict(messages: Sequence[Message]) -> list[dict]:
return [message_to_dict(m) for m in messages]
def message_from_dict(message: dict) -> Message:
_type = message["type"]
if _type == "human":
return HumanMessage(**message["data"])
elif _type == "ai":
return AIMessage(**message["data"])
elif _type == "system":
return SystemMessage(**message["data"])
elif _type == "chat":
return ChatMessage(**message["data"])
elif _type == "function":
return FunctionMessage(**message["data"])
else:
raise ValueError(f"Got unexpected message type: {_type}")
def messages_from_dict(messages: list[dict]) -> list[Message]:
return [message_from_dict(m) for m in messages]