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langchain/libs/core/langchain_core/messages/__init__.py

143 lines
4.5 KiB
Python

from typing import List, Sequence, Union
from langchain_core.messages.ai import AIMessage, AIMessageChunk
from langchain_core.messages.base import (
BaseMessage,
BaseMessageChunk,
merge_content,
message_to_dict,
messages_to_dict,
)
from langchain_core.messages.chat import ChatMessage, ChatMessageChunk
from langchain_core.messages.function import FunctionMessage, FunctionMessageChunk
from langchain_core.messages.human import HumanMessage, HumanMessageChunk
from langchain_core.messages.system import SystemMessage, SystemMessageChunk
from langchain_core.messages.tool import ToolMessage, ToolMessageChunk
AnyMessage = Union[
AIMessage, HumanMessage, ChatMessage, SystemMessage, FunctionMessage, ToolMessage
]
def get_buffer_string(
messages: Sequence[BaseMessage], human_prefix: str = "Human", ai_prefix: str = "AI"
) -> str:
"""Convert sequence of Messages to strings and concatenate them into one string.
Args:
messages: Messages to be converted to strings.
human_prefix: The prefix to prepend to contents of HumanMessages.
ai_prefix: THe prefix to prepend to contents of AIMessages.
Returns:
A single string concatenation of all input messages.
Example:
.. code-block:: python
from langchain_core import AIMessage, HumanMessage
messages = [
HumanMessage(content="Hi, how are you?"),
AIMessage(content="Good, how are you?"),
]
get_buffer_string(messages)
# -> "Human: Hi, how are you?\nAI: Good, how are you?"
"""
string_messages = []
for m in messages:
if isinstance(m, HumanMessage):
role = human_prefix
elif isinstance(m, AIMessage):
role = ai_prefix
elif isinstance(m, SystemMessage):
role = "System"
elif isinstance(m, FunctionMessage):
role = "Function"
elif isinstance(m, ToolMessage):
role = "Tool"
elif isinstance(m, ChatMessage):
role = m.role
else:
raise ValueError(f"Got unsupported message type: {m}")
message = f"{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_from_dict(message: dict) -> BaseMessage:
_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"])
elif _type == "tool":
return ToolMessage(**message["data"])
elif _type == "AIMessageChunk":
return AIMessageChunk(**message["data"])
elif _type == "HumanMessageChunk":
return HumanMessageChunk(**message["data"])
elif _type == "FunctionMessageChunk":
return FunctionMessageChunk(**message["data"])
elif _type == "ToolMessageChunk":
return ToolMessageChunk(**message["data"])
elif _type == "SystemMessageChunk":
return SystemMessageChunk(**message["data"])
else:
raise ValueError(f"Got unexpected message type: {_type}")
def messages_from_dict(messages: Sequence[dict]) -> List[BaseMessage]:
"""Convert a sequence of messages from dicts to Message objects.
Args:
messages: Sequence of messages (as dicts) to convert.
Returns:
List of messages (BaseMessages).
"""
return [_message_from_dict(m) for m in messages]
def message_chunk_to_message(chunk: BaseMessageChunk) -> BaseMessage:
if not isinstance(chunk, BaseMessageChunk):
return chunk
# chunk classes always have the equivalent non-chunk class as their first parent
return chunk.__class__.__mro__[1](
**{k: v for k, v in chunk.__dict__.items() if k != "type"}
)
__all__ = [
"AIMessage",
"AIMessageChunk",
"AnyMessage",
"BaseMessage",
"BaseMessageChunk",
"ChatMessage",
"ChatMessageChunk",
"FunctionMessage",
"FunctionMessageChunk",
"HumanMessage",
"HumanMessageChunk",
"SystemMessage",
"SystemMessageChunk",
"ToolMessage",
"ToolMessageChunk",
"get_buffer_string",
"message_chunk_to_message",
"messages_from_dict",
"messages_to_dict",
"message_to_dict",
"merge_content",
]