Source code for langchain_core.messages.utils

from typing import Any, Dict, List, Optional, Sequence, Tuple, Union

from import (
from langchain_core.messages.base import (
from 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

[docs]def get_buffer_string( messages: Sequence[BaseMessage], human_prefix: str = "Human", ai_prefix: str = "AI" ) -> str: """Convert a 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"]) elif _type == "ChatMessageChunk": return ChatMessageChunk(**message["data"]) else: raise ValueError(f"Got unexpected message type: {_type}")
[docs]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]
[docs]def message_chunk_to_message(chunk: BaseMessageChunk) -> BaseMessage: """Convert a message chunk to a message. Args: chunk: Message chunk to convert. Returns: Message. """ if not isinstance(chunk, BaseMessageChunk): return chunk # chunk classes always have the equivalent non-chunk class as their first parent ignore_keys = ["type"] if isinstance(chunk, AIMessageChunk): ignore_keys.append("tool_call_chunks") return chunk.__class__.__mro__[1]( **{k: v for k, v in chunk.__dict__.items() if k not in ignore_keys} )
MessageLikeRepresentation = Union[ BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any] ] def _create_message_from_message_type( message_type: str, content: str, name: Optional[str] = None, tool_call_id: Optional[str] = None, tool_calls: Optional[List[Dict[str, Any]]] = None, id: Optional[str] = None, **additional_kwargs: Any, ) -> BaseMessage: """Create a message from a message type and content string. Args: message_type: str the type of the message (e.g., "human", "ai", etc.) content: str the content string. Returns: a message of the appropriate type. """ kwargs: Dict[str, Any] = {} if name is not None: kwargs["name"] = name if tool_call_id is not None: kwargs["tool_call_id"] = tool_call_id if additional_kwargs: kwargs["additional_kwargs"] = additional_kwargs # type: ignore[assignment] if id is not None: kwargs["id"] = id if tool_calls is not None: kwargs["tool_calls"] = tool_calls if message_type in ("human", "user"): message: BaseMessage = HumanMessage(content=content, **kwargs) elif message_type in ("ai", "assistant"): message = AIMessage(content=content, **kwargs) elif message_type == "system": message = SystemMessage(content=content, **kwargs) elif message_type == "function": message = FunctionMessage(content=content, **kwargs) elif message_type == "tool": message = ToolMessage(content=content, **kwargs) else: raise ValueError( f"Unexpected message type: {message_type}. Use one of 'human'," f" 'user', 'ai', 'assistant', or 'system'." ) return message def _convert_to_message( message: MessageLikeRepresentation, ) -> BaseMessage: """Instantiate a message from a variety of message formats. The message format can be one of the following: - BaseMessagePromptTemplate - BaseMessage - 2-tuple of (role string, template); e.g., ("human", "{user_input}") - dict: a message dict with role and content keys - string: shorthand for ("human", template); e.g., "{user_input}" Args: message: a representation of a message in one of the supported formats Returns: an instance of a message or a message template """ if isinstance(message, BaseMessage): _message = message elif isinstance(message, str): _message = _create_message_from_message_type("human", message) elif isinstance(message, Sequence) and len(message) == 2: # mypy doesn't realise this can't be a string given the previous branch message_type_str, template = message # type: ignore[misc] _message = _create_message_from_message_type(message_type_str, template) elif isinstance(message, dict): msg_kwargs = message.copy() try: try: msg_type = msg_kwargs.pop("role") except KeyError: msg_type = msg_kwargs.pop("type") msg_content = msg_kwargs.pop("content") except KeyError: raise ValueError( f"Message dict must contain 'role' and 'content' keys, got {message}" ) _message = _create_message_from_message_type( msg_type, msg_content, **msg_kwargs ) else: raise NotImplementedError(f"Unsupported message type: {type(message)}") return _message
[docs]def convert_to_messages( messages: Sequence[MessageLikeRepresentation], ) -> List[BaseMessage]: """Convert a sequence of messages to a list of messages. Args: messages: Sequence of messages to convert. Returns: List of messages (BaseMessages). """ return [_convert_to_message(m) for m in messages]