"""Module contains utility functions for working with messages. Some examples of what you can do with these functions include: * Convert messages to strings (serialization) * Convert messages from dicts to Message objects (deserialization) * Filter messages from a list of messages based on name, type or id etc. """ from __future__ import annotations import base64 import inspect import json from collections.abc import Iterable, Sequence from functools import partial from typing import ( TYPE_CHECKING, Annotated, Any, Callable, Literal, Optional, Union, cast, overload, ) from pydantic import Discriminator, Field, Tag from langchain_core.exceptions import ErrorCode, create_message from langchain_core.messages.ai import AIMessage, AIMessageChunk from langchain_core.messages.base import BaseMessage, BaseMessageChunk 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.modifier import RemoveMessage from langchain_core.messages.system import SystemMessage, SystemMessageChunk from langchain_core.messages.tool import ToolCall, ToolMessage, ToolMessageChunk if TYPE_CHECKING: from langchain_text_splitters import TextSplitter from langchain_core.language_models import BaseLanguageModel from langchain_core.prompt_values import PromptValue from langchain_core.runnables.base import Runnable def _get_type(v: Any) -> str: """Get the type associated with the object for serialization purposes.""" if isinstance(v, dict) and "type" in v: return v["type"] elif hasattr(v, "type"): return v.type else: msg = ( f"Expected either a dictionary with a 'type' key or an object " f"with a 'type' attribute. Instead got type {type(v)}." ) raise TypeError(msg) AnyMessage = Annotated[ Union[ Annotated[AIMessage, Tag(tag="ai")], Annotated[HumanMessage, Tag(tag="human")], Annotated[ChatMessage, Tag(tag="chat")], Annotated[SystemMessage, Tag(tag="system")], Annotated[FunctionMessage, Tag(tag="function")], Annotated[ToolMessage, Tag(tag="tool")], Annotated[AIMessageChunk, Tag(tag="AIMessageChunk")], Annotated[HumanMessageChunk, Tag(tag="HumanMessageChunk")], Annotated[ChatMessageChunk, Tag(tag="ChatMessageChunk")], Annotated[SystemMessageChunk, Tag(tag="SystemMessageChunk")], Annotated[FunctionMessageChunk, Tag(tag="FunctionMessageChunk")], Annotated[ToolMessageChunk, Tag(tag="ToolMessageChunk")], ], Field(discriminator=Discriminator(_get_type)), ] 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. Default is "Human". ai_prefix: THe prefix to prepend to contents of AIMessages. Default is "AI". Returns: A single string concatenation of all input messages. Raises: ValueError: If an unsupported message type is encountered. 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: msg = f"Got unsupported message type: {m}" raise ValueError(msg) # noqa: TRY004 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 == "remove": return RemoveMessage(**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: msg = f"Got unexpected message type: {_type}" raise ValueError(msg) 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: """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. name: (str) the name of the message. Default is None. tool_call_id: (str) the tool call id. Default is None. tool_calls: (list[dict[str, Any]]) the tool calls. Default is None. id: (str) the id of the message. Default is None. additional_kwargs: (dict[str, Any]) additional keyword arguments. Returns: a message of the appropriate type. Raises: ValueError: if the message type is not one of "human", "user", "ai", "assistant", "function", "tool", "system", or "developer". """ 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: if response_metadata := additional_kwargs.pop("response_metadata", None): kwargs["response_metadata"] = response_metadata kwargs["additional_kwargs"] = additional_kwargs # type: ignore[assignment] additional_kwargs.update(additional_kwargs.pop("additional_kwargs", {})) if id is not None: kwargs["id"] = id if tool_calls is not None: kwargs["tool_calls"] = [] for tool_call in tool_calls: # Convert OpenAI-format tool call to LangChain format. if "function" in tool_call: args = tool_call["function"]["arguments"] if isinstance(args, str): args = json.loads(args, strict=False) kwargs["tool_calls"].append( { "name": tool_call["function"]["name"], "args": args, "id": tool_call["id"], "type": "tool_call", } ) else: kwargs["tool_calls"].append(tool_call) if message_type in ("human", "user"): if example := kwargs.get("additional_kwargs", {}).pop("example", False): kwargs["example"] = example message: BaseMessage = HumanMessage(content=content, **kwargs) elif message_type in ("ai", "assistant"): if example := kwargs.get("additional_kwargs", {}).pop("example", False): kwargs["example"] = example message = AIMessage(content=content, **kwargs) elif message_type in ("system", "developer"): if message_type == "developer": kwargs["additional_kwargs"] = kwargs.get("additional_kwargs") or {} kwargs["additional_kwargs"]["__openai_role__"] = "developer" message = SystemMessage(content=content, **kwargs) elif message_type == "function": message = FunctionMessage(content=content, **kwargs) elif message_type == "tool": artifact = kwargs.get("additional_kwargs", {}).pop("artifact", None) message = ToolMessage(content=content, artifact=artifact, **kwargs) elif message_type == "remove": message = RemoveMessage(**kwargs) else: msg = ( f"Unexpected message type: '{message_type}'. Use one of 'human'," f" 'user', 'ai', 'assistant', 'function', 'tool', 'system', or 'developer'." ) msg = create_message(message=msg, error_code=ErrorCode.MESSAGE_COERCION_FAILURE) raise ValueError(msg) 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. Raises: NotImplementedError: if the message type is not supported. ValueError: if the message dict does not contain the required keys. """ 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") # None msg content is not allowed msg_content = msg_kwargs.pop("content") or "" except KeyError as e: msg = f"Message dict must contain 'role' and 'content' keys, got {message}" msg = create_message( message=msg, error_code=ErrorCode.MESSAGE_COERCION_FAILURE ) raise ValueError(msg) from e _message = _create_message_from_message_type( msg_type, msg_content, **msg_kwargs ) else: msg = f"Unsupported message type: {type(message)}" msg = create_message(message=msg, error_code=ErrorCode.MESSAGE_COERCION_FAILURE) raise NotImplementedError(msg) return _message def convert_to_messages( messages: Union[Iterable[MessageLikeRepresentation], PromptValue], ) -> list[BaseMessage]: """Convert a sequence of messages to a list of messages. Args: messages: Sequence of messages to convert. Returns: list of messages (BaseMessages). """ # Import here to avoid circular imports from langchain_core.prompt_values import PromptValue if isinstance(messages, PromptValue): return messages.to_messages() return [_convert_to_message(m) for m in messages] def _runnable_support(func: Callable) -> Callable: @overload def wrapped( messages: Literal[None] = None, **kwargs: Any ) -> Runnable[Sequence[MessageLikeRepresentation], list[BaseMessage]]: ... @overload def wrapped( messages: Sequence[MessageLikeRepresentation], **kwargs: Any ) -> list[BaseMessage]: ... def wrapped( messages: Union[Sequence[MessageLikeRepresentation], None] = None, **kwargs: Any, ) -> Union[ list[BaseMessage], Runnable[Sequence[MessageLikeRepresentation], list[BaseMessage]], ]: from langchain_core.runnables.base import RunnableLambda if messages is not None: return func(messages, **kwargs) else: return RunnableLambda(partial(func, **kwargs), name=func.__name__) wrapped.__doc__ = func.__doc__ return wrapped @_runnable_support def filter_messages( messages: Union[Iterable[MessageLikeRepresentation], PromptValue], *, include_names: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[Union[str, type[BaseMessage]]]] = None, exclude_types: Optional[Sequence[Union[str, type[BaseMessage]]]] = None, include_ids: Optional[Sequence[str]] = None, exclude_ids: Optional[Sequence[str]] = None, ) -> list[BaseMessage]: """Filter messages based on name, type or id. Args: messages: Sequence Message-like objects to filter. include_names: Message names to include. Default is None. exclude_names: Messages names to exclude. Default is None. include_types: Message types to include. Can be specified as string names (e.g. "system", "human", "ai", ...) or as BaseMessage classes (e.g. SystemMessage, HumanMessage, AIMessage, ...). Default is None. exclude_types: Message types to exclude. Can be specified as string names (e.g. "system", "human", "ai", ...) or as BaseMessage classes (e.g. SystemMessage, HumanMessage, AIMessage, ...). Default is None. include_ids: Message IDs to include. Default is None. exclude_ids: Message IDs to exclude. Default is None. Returns: A list of Messages that meets at least one of the incl_* conditions and none of the excl_* conditions. If not incl_* conditions are specified then anything that is not explicitly excluded will be included. Raises: ValueError if two incompatible arguments are provided. Example: .. code-block:: python from langchain_core.messages import filter_messages, AIMessage, HumanMessage, SystemMessage messages = [ SystemMessage("you're a good assistant."), HumanMessage("what's your name", id="foo", name="example_user"), AIMessage("steve-o", id="bar", name="example_assistant"), HumanMessage("what's your favorite color", id="baz",), AIMessage("silicon blue", id="blah",), ] filter_messages( messages, incl_names=("example_user", "example_assistant"), incl_types=("system",), excl_ids=("bar",), ) .. code-block:: python [ SystemMessage("you're a good assistant."), HumanMessage("what's your name", id="foo", name="example_user"), ] """ # noqa: E501 messages = convert_to_messages(messages) filtered: list[BaseMessage] = [] for msg in messages: if ( (exclude_names and msg.name in exclude_names) or (exclude_types and _is_message_type(msg, exclude_types)) or (exclude_ids and msg.id in exclude_ids) ): continue else: pass # default to inclusion when no inclusion criteria given. if ( not (include_types or include_ids or include_names) or (include_names and msg.name in include_names) or (include_types and _is_message_type(msg, include_types)) or (include_ids and msg.id in include_ids) ): filtered.append(msg) else: pass return filtered @_runnable_support def merge_message_runs( messages: Union[Iterable[MessageLikeRepresentation], PromptValue], *, chunk_separator: str = "\n", ) -> list[BaseMessage]: """Merge consecutive Messages of the same type. **NOTE**: ToolMessages are not merged, as each has a distinct tool call id that can't be merged. Args: messages: Sequence Message-like objects to merge. chunk_separator: Specify the string to be inserted between message chunks. Default is "\n". Returns: list of BaseMessages with consecutive runs of message types merged into single messages. By default, if two messages being merged both have string contents, the merged content is a concatenation of the two strings with a new-line separator. The separator inserted between message chunks can be controlled by specifying any string with ``chunk_separator``. If at least one of the messages has a list of content blocks, the merged content is a list of content blocks. Example: .. code-block:: python from langchain_core.messages import ( merge_message_runs, AIMessage, HumanMessage, SystemMessage, ToolCall, ) messages = [ SystemMessage("you're a good assistant."), HumanMessage("what's your favorite color", id="foo",), HumanMessage("wait your favorite food", id="bar",), AIMessage( "my favorite colo", tool_calls=[ToolCall(name="blah_tool", args={"x": 2}, id="123", type="tool_call")], id="baz", ), AIMessage( [{"type": "text", "text": "my favorite dish is lasagna"}], tool_calls=[ToolCall(name="blah_tool", args={"x": -10}, id="456", type="tool_call")], id="blur", ), ] merge_message_runs(messages) .. code-block:: python [ SystemMessage("you're a good assistant."), HumanMessage("what's your favorite color\\nwait your favorite food", id="foo",), AIMessage( [ "my favorite colo", {"type": "text", "text": "my favorite dish is lasagna"} ], tool_calls=[ ToolCall({"name": "blah_tool", "args": {"x": 2}, "id": "123", "type": "tool_call"}), ToolCall({"name": "blah_tool", "args": {"x": -10}, "id": "456", "type": "tool_call"}) ] id="baz" ), ] """ # noqa: E501 if not messages: return [] messages = convert_to_messages(messages) merged: list[BaseMessage] = [] for msg in messages: last = merged.pop() if merged else None if not last: merged.append(msg) elif isinstance(msg, ToolMessage) or not isinstance(msg, last.__class__): merged.extend([last, msg]) else: last_chunk = _msg_to_chunk(last) curr_chunk = _msg_to_chunk(msg) if curr_chunk.response_metadata: curr_chunk.response_metadata.clear() if ( isinstance(last_chunk.content, str) and isinstance(curr_chunk.content, str) and last_chunk.content and curr_chunk.content ): last_chunk.content += chunk_separator merged.append(_chunk_to_msg(last_chunk + curr_chunk)) return merged # TODO: Update so validation errors (for token_counter, for example) are raised on # init not at runtime. @_runnable_support def trim_messages( messages: Union[Iterable[MessageLikeRepresentation], PromptValue], *, max_tokens: int, token_counter: Union[ Callable[[list[BaseMessage]], int], Callable[[BaseMessage], int], BaseLanguageModel, ], strategy: Literal["first", "last"] = "last", allow_partial: bool = False, end_on: Optional[ Union[str, type[BaseMessage], Sequence[Union[str, type[BaseMessage]]]] ] = None, start_on: Optional[ Union[str, type[BaseMessage], Sequence[Union[str, type[BaseMessage]]]] ] = None, include_system: bool = False, text_splitter: Optional[Union[Callable[[str], list[str]], TextSplitter]] = None, ) -> list[BaseMessage]: r"""Trim messages to be below a token count. trim_messages can be used to reduce the size of a chat history to a specified token count or specified message count. In either case, if passing the trimmed chat history back into a chat model directly, the resulting chat history should usually satisfy the following properties: 1. The resulting chat history should be valid. Most chat models expect that chat history starts with either (1) a `HumanMessage` or (2) a `SystemMessage` followed by a `HumanMessage`. To achieve this, set `start_on="human"`. In addition, generally a `ToolMessage` can only appear after an `AIMessage` that involved a tool call. Please see the following link for more information about messages: https://python.langchain.com/docs/concepts/#messages 2. It includes recent messages and drops old messages in the chat history. To achieve this set the `strategy="last"`. 3. Usually, the new chat history should include the `SystemMessage` if it was present in the original chat history since the `SystemMessage` includes special instructions to the chat model. The `SystemMessage` is almost always the first message in the history if present. To achieve this set the `include_system=True`. **Note** The examples below show how to configure `trim_messages` to achieve a behavior consistent with the above properties. Args: messages: Sequence of Message-like objects to trim. max_tokens: Max token count of trimmed messages. token_counter: Function or llm for counting tokens in a BaseMessage or a list of BaseMessage. If a BaseLanguageModel is passed in then BaseLanguageModel.get_num_tokens_from_messages() will be used. Set to `len` to count the number of **messages** in the chat history. strategy: Strategy for trimming. - "first": Keep the first <= n_count tokens of the messages. - "last": Keep the last <= n_count tokens of the messages. Default is "last". allow_partial: Whether to split a message if only part of the message can be included. If ``strategy="last"`` then the last partial contents of a message are included. If ``strategy="first"`` then the first partial contents of a message are included. Default is False. end_on: The message type to end on. If specified then every message after the last occurrence of this type is ignored. If ``strategy=="last"`` then this is done before we attempt to get the last ``max_tokens``. If ``strategy=="first"`` then this is done after we get the first ``max_tokens``. Can be specified as string names (e.g. "system", "human", "ai", ...) or as BaseMessage classes (e.g. SystemMessage, HumanMessage, AIMessage, ...). Can be a single type or a list of types. Default is None. start_on: The message type to start on. Should only be specified if ``strategy="last"``. If specified then every message before the first occurrence of this type is ignored. This is done after we trim the initial messages to the last ``max_tokens``. Does not apply to a SystemMessage at index 0 if ``include_system=True``. Can be specified as string names (e.g. "system", "human", "ai", ...) or as BaseMessage classes (e.g. SystemMessage, HumanMessage, AIMessage, ...). Can be a single type or a list of types. Default is None. include_system: Whether to keep the SystemMessage if there is one at index 0. Should only be specified if ``strategy="last"``. Default is False. text_splitter: Function or ``langchain_text_splitters.TextSplitter`` for splitting the string contents of a message. Only used if ``allow_partial=True``. If ``strategy="last"`` then the last split tokens from a partial message will be included. if ``strategy=="first"`` then the first split tokens from a partial message will be included. Token splitter assumes that separators are kept, so that split contents can be directly concatenated to recreate the original text. Defaults to splitting on newlines. Returns: list of trimmed BaseMessages. Raises: ValueError: if two incompatible arguments are specified or an unrecognized ``strategy`` is specified. Example: Trim chat history based on token count, keeping the SystemMessage if present, and ensuring that the chat history starts with a HumanMessage ( or a SystemMessage followed by a HumanMessage). .. code-block:: python from typing import list from langchain_core.messages import ( AIMessage, HumanMessage, BaseMessage, SystemMessage, trim_messages, ) messages = [ SystemMessage("you're a good assistant, you always respond with a joke."), HumanMessage("i wonder why it's called langchain"), AIMessage( 'Well, I guess they thought "WordRope" and "SentenceString" just didn\'t have the same ring to it!' ), HumanMessage("and who is harrison chasing anyways"), AIMessage( "Hmmm let me think.\n\nWhy, he's probably chasing after the last cup of coffee in the office!" ), HumanMessage("what do you call a speechless parrot"), ] trim_messages( messages, max_tokens=45, strategy="last", token_counter=ChatOpenAI(model="gpt-4o"), # Most chat models expect that chat history starts with either: # (1) a HumanMessage or # (2) a SystemMessage followed by a HumanMessage start_on="human", # Usually, we want to keep the SystemMessage # if it's present in the original history. # The SystemMessage has special instructions for the model. include_system=True, allow_partial=False, ) .. code-block:: python [ SystemMessage(content="you're a good assistant, you always respond with a joke."), HumanMessage(content='what do you call a speechless parrot'), ] Trim chat history based on the message count, keeping the SystemMessage if present, and ensuring that the chat history starts with a HumanMessage ( or a SystemMessage followed by a HumanMessage). trim_messages( messages, # When `len` is passed in as the token counter function, # max_tokens will count the number of messages in the chat history. max_tokens=4, strategy="last", # Passing in `len` as a token counter function will # count the number of messages in the chat history. token_counter=len, # Most chat models expect that chat history starts with either: # (1) a HumanMessage or # (2) a SystemMessage followed by a HumanMessage start_on="human", # Usually, we want to keep the SystemMessage # if it's present in the original history. # The SystemMessage has special instructions for the model. include_system=True, allow_partial=False, ) .. code-block:: python [ SystemMessage(content="you're a good assistant, you always respond with a joke."), HumanMessage(content='and who is harrison chasing anyways'), AIMessage(content="Hmmm let me think.\n\nWhy, he's probably chasing after the last cup of coffee in the office!"), HumanMessage(content='what do you call a speechless parrot'), ] Trim chat history using a custom token counter function that counts the number of tokens in each message. .. code-block:: python messages = [ SystemMessage("This is a 4 token text. The full message is 10 tokens."), HumanMessage("This is a 4 token text. The full message is 10 tokens.", id="first"), AIMessage( [ {"type": "text", "text": "This is the FIRST 4 token block."}, {"type": "text", "text": "This is the SECOND 4 token block."}, ], id="second", ), HumanMessage("This is a 4 token text. The full message is 10 tokens.", id="third"), AIMessage("This is a 4 token text. The full message is 10 tokens.", id="fourth"), ] def dummy_token_counter(messages: list[BaseMessage]) -> int: # treat each message like it adds 3 default tokens at the beginning # of the message and at the end of the message. 3 + 4 + 3 = 10 tokens # per message. default_content_len = 4 default_msg_prefix_len = 3 default_msg_suffix_len = 3 count = 0 for msg in messages: if isinstance(msg.content, str): count += default_msg_prefix_len + default_content_len + default_msg_suffix_len if isinstance(msg.content, list): count += default_msg_prefix_len + len(msg.content) * default_content_len + default_msg_suffix_len return count First 30 tokens, allowing partial messages: .. code-block:: python trim_messages( messages, max_tokens=30, token_counter=dummy_token_counter, strategy="first", allow_partial=True, ) .. code-block:: python [ SystemMessage("This is a 4 token text. The full message is 10 tokens."), HumanMessage("This is a 4 token text. The full message is 10 tokens.", id="first"), AIMessage( [{"type": "text", "text": "This is the FIRST 4 token block."}], id="second"), ] """ # noqa: E501 if start_on and strategy == "first": raise ValueError if include_system and strategy == "first": raise ValueError messages = convert_to_messages(messages) if hasattr(token_counter, "get_num_tokens_from_messages"): list_token_counter = token_counter.get_num_tokens_from_messages elif callable(token_counter): if ( list(inspect.signature(token_counter).parameters.values())[0].annotation is BaseMessage ): def list_token_counter(messages: Sequence[BaseMessage]) -> int: return sum(token_counter(msg) for msg in messages) # type: ignore[arg-type, misc] else: list_token_counter = token_counter # type: ignore[assignment] else: msg = ( f"'token_counter' expected to be a model that implements " f"'get_num_tokens_from_messages()' or a function. Received object of type " f"{type(token_counter)}." ) raise ValueError(msg) try: from langchain_text_splitters import TextSplitter except ImportError: text_splitter_fn: Optional[Callable] = cast(Optional[Callable], text_splitter) else: if isinstance(text_splitter, TextSplitter): text_splitter_fn = text_splitter.split_text else: text_splitter_fn = text_splitter text_splitter_fn = text_splitter_fn or _default_text_splitter if strategy == "first": return _first_max_tokens( messages, max_tokens=max_tokens, token_counter=list_token_counter, text_splitter=text_splitter_fn, partial_strategy="first" if allow_partial else None, end_on=end_on, ) elif strategy == "last": return _last_max_tokens( messages, max_tokens=max_tokens, token_counter=list_token_counter, allow_partial=allow_partial, include_system=include_system, start_on=start_on, end_on=end_on, text_splitter=text_splitter_fn, ) else: msg = f"Unrecognized {strategy=}. Supported strategies are 'last' and 'first'." raise ValueError(msg) def convert_to_openai_messages( messages: Union[MessageLikeRepresentation, Sequence[MessageLikeRepresentation]], *, text_format: Literal["string", "block"] = "string", ) -> Union[dict, list[dict]]: """Convert LangChain messages into OpenAI message dicts. Args: messages: Message-like object or iterable of objects whose contents are in OpenAI, Anthropic, Bedrock Converse, or VertexAI formats. text_format: How to format string or text block contents: - "string": If a message has a string content, this is left as a string. If a message has content blocks that are all of type 'text', these are joined with a newline to make a single string. If a message has content blocks and at least one isn't of type 'text', then all blocks are left as dicts. - "block": If a message has a string content, this is turned into a list with a single content block of type 'text'. If a message has content blocks these are left as is. Returns: The return type depends on the input type: - dict: If a single message-like object is passed in, a single OpenAI message dict is returned. - list[dict]: If a sequence of message-like objects are passed in, a list of OpenAI message dicts is returned. Example: .. code-block:: python from langchain_core.messages import ( convert_to_openai_messages, AIMessage, SystemMessage, ToolMessage, ) messages = [ SystemMessage([{"type": "text", "text": "foo"}]), {"role": "user", "content": [{"type": "text", "text": "whats in this"}, {"type": "image_url", "image_url": {"url": "data:image/png;base64,'/9j/4AAQSk'"}}]}, AIMessage("", tool_calls=[{"name": "analyze", "args": {"baz": "buz"}, "id": "1", "type": "tool_call"}]), ToolMessage("foobar", tool_call_id="1", name="bar"), {"role": "assistant", "content": "thats nice"}, ] oai_messages = convert_to_openai_messages(messages) # -> [ # {'role': 'system', 'content': 'foo'}, # {'role': 'user', 'content': [{'type': 'text', 'text': 'whats in this'}, {'type': 'image_url', 'image_url': {'url': "data:image/png;base64,'/9j/4AAQSk'"}}]}, # {'role': 'assistant', 'tool_calls': [{'type': 'function', 'id': '1','function': {'name': 'analyze', 'arguments': '{"baz": "buz"}'}}], 'content': ''}, # {'role': 'tool', 'name': 'bar', 'content': 'foobar'}, # {'role': 'assistant', 'content': 'thats nice'} # ] .. versionadded:: 0.3.11 """ # noqa: E501 if text_format not in ("string", "block"): err = f"Unrecognized {text_format=}, expected one of 'string' or 'block'." raise ValueError(err) oai_messages: list = [] if is_single := isinstance(messages, (BaseMessage, dict, str)): messages = [messages] messages = convert_to_messages(messages) for i, message in enumerate(messages): oai_msg: dict = {"role": _get_message_openai_role(message)} tool_messages: list = [] content: Union[str, list[dict]] if message.name: oai_msg["name"] = message.name if isinstance(message, AIMessage) and message.tool_calls: oai_msg["tool_calls"] = _convert_to_openai_tool_calls(message.tool_calls) if message.additional_kwargs.get("refusal"): oai_msg["refusal"] = message.additional_kwargs["refusal"] if isinstance(message, ToolMessage): oai_msg["tool_call_id"] = message.tool_call_id if not message.content: content = "" if text_format == "string" else [] elif isinstance(message.content, str): if text_format == "string": content = message.content else: content = [{"type": "text", "text": message.content}] else: if text_format == "string" and all( isinstance(block, str) or block.get("type") == "text" for block in message.content ): content = "\n".join( block if isinstance(block, str) else block["text"] for block in message.content ) else: content = [] for j, block in enumerate(message.content): # OpenAI format if isinstance(block, str): content.append({"type": "text", "text": block}) elif block.get("type") == "text": if missing := [k for k in ("text",) if k not in block]: err = ( f"Unrecognized content block at " f"messages[{i}].content[{j}] has 'type': 'text' " f"but is missing expected key(s) " f"{missing}. Full content block:\n\n{block}" ) raise ValueError(err) content.append({"type": block["type"], "text": block["text"]}) elif block.get("type") == "image_url": if missing := [k for k in ("image_url",) if k not in block]: err = ( f"Unrecognized content block at " f"messages[{i}].content[{j}] has 'type': 'image_url' " f"but is missing expected key(s) " f"{missing}. Full content block:\n\n{block}" ) raise ValueError(err) content.append( { "type": "image_url", "image_url": block["image_url"], } ) # Anthropic and Bedrock converse format elif (block.get("type") == "image") or "image" in block: # Anthropic if source := block.get("source"): if missing := [ k for k in ("media_type", "type", "data") if k not in source ]: err = ( f"Unrecognized content block at " f"messages[{i}].content[{j}] has 'type': 'image' " f"but 'source' is missing expected key(s) " f"{missing}. Full content block:\n\n{block}" ) raise ValueError(err) content.append( { "type": "image_url", "image_url": { "url": ( f"data:{source['media_type']};" f"{source['type']},{source['data']}" ) }, } ) # Bedrock converse elif image := block.get("image"): if missing := [ k for k in ("source", "format") if k not in image ]: err = ( f"Unrecognized content block at " f"messages[{i}].content[{j}] has key 'image', " f"but 'image' is missing expected key(s) " f"{missing}. Full content block:\n\n{block}" ) raise ValueError(err) b64_image = _bytes_to_b64_str(image["source"]["bytes"]) content.append( { "type": "image_url", "image_url": { "url": ( f"data:image/{image['format']};" f"base64,{b64_image}" ) }, } ) else: err = ( f"Unrecognized content block at " f"messages[{i}].content[{j}] has 'type': 'image' " f"but does not have a 'source' or 'image' key. Full " f"content block:\n\n{block}" ) raise ValueError(err) elif block.get("type") == "tool_use": if missing := [ k for k in ("id", "name", "input") if k not in block ]: err = ( f"Unrecognized content block at " f"messages[{i}].content[{j}] has 'type': " f"'tool_use', but is missing expected key(s) " f"{missing}. Full content block:\n\n{block}" ) raise ValueError(err) if not any( tool_call["id"] == block["id"] for tool_call in cast(AIMessage, message).tool_calls ): oai_msg["tool_calls"] = oai_msg.get("tool_calls", []) oai_msg["tool_calls"].append( { "type": "function", "id": block["id"], "function": { "name": block["name"], "arguments": json.dumps(block["input"]), }, } ) elif block.get("type") == "tool_result": if missing := [ k for k in ("content", "tool_use_id") if k not in block ]: msg = ( f"Unrecognized content block at " f"messages[{i}].content[{j}] has 'type': " f"'tool_result', but is missing expected key(s) " f"{missing}. Full content block:\n\n{block}" ) raise ValueError(msg) tool_message = ToolMessage( block["content"], tool_call_id=block["tool_use_id"], status="error" if block.get("is_error") else "success", ) # Recurse to make sure tool message contents are OpenAI format. tool_messages.extend( convert_to_openai_messages( [tool_message], text_format=text_format ) ) elif (block.get("type") == "json") or "json" in block: if "json" not in block: msg = ( f"Unrecognized content block at " f"messages[{i}].content[{j}] has 'type': 'json' " f"but does not have a 'json' key. Full " f"content block:\n\n{block}" ) raise ValueError(msg) content.append( { "type": "text", "text": json.dumps(block["json"]), } ) elif ( block.get("type") == "guard_content" ) or "guard_content" in block: if ( "guard_content" not in block or "text" not in block["guard_content"] ): msg = ( f"Unrecognized content block at " f"messages[{i}].content[{j}] has 'type': " f"'guard_content' but does not have a " f"messages[{i}].content[{j}]['guard_content']['text'] " f"key. Full content block:\n\n{block}" ) raise ValueError(msg) text = block["guard_content"]["text"] if isinstance(text, dict): text = text["text"] content.append({"type": "text", "text": text}) # VertexAI format elif block.get("type") == "media": if missing := [ k for k in ("mime_type", "data") if k not in block ]: err = ( f"Unrecognized content block at " f"messages[{i}].content[{j}] has 'type': " f"'media' but does not have key(s) {missing}. Full " f"content block:\n\n{block}" ) raise ValueError(err) if "image" not in block["mime_type"]: err = ( f"OpenAI messages can only support text and image data." f" Received content block with media of type:" f" {block['mime_type']}" ) raise ValueError(err) b64_image = _bytes_to_b64_str(block["data"]) content.append( { "type": "image_url", "image_url": { "url": ( f"data:{block['mime_type']};base64,{b64_image}" ) }, } ) else: err = ( f"Unrecognized content block at " f"messages[{i}].content[{j}] does not match OpenAI, " f"Anthropic, Bedrock Converse, or VertexAI format. Full " f"content block:\n\n{block}" ) raise ValueError(err) if text_format == "string" and not any( block["type"] != "text" for block in content ): content = "\n".join(block["text"] for block in content) oai_msg["content"] = content if message.content and not oai_msg["content"] and tool_messages: oai_messages.extend(tool_messages) else: oai_messages.extend([oai_msg, *tool_messages]) if is_single: return oai_messages[0] else: return oai_messages def _first_max_tokens( messages: Sequence[BaseMessage], *, max_tokens: int, token_counter: Callable[[list[BaseMessage]], int], text_splitter: Callable[[str], list[str]], partial_strategy: Optional[Literal["first", "last"]] = None, end_on: Optional[ Union[str, type[BaseMessage], Sequence[Union[str, type[BaseMessage]]]] ] = None, ) -> list[BaseMessage]: messages = list(messages) if not messages: return messages idx = 0 for i in range(len(messages)): if token_counter(messages[:-i] if i else messages) <= max_tokens: idx = len(messages) - i break if partial_strategy and (idx < len(messages) - 1 or idx == 0): included_partial = False if isinstance(messages[idx].content, list): excluded = messages[idx].model_copy(deep=True) num_block = len(excluded.content) if partial_strategy == "last": excluded.content = list(reversed(excluded.content)) for _ in range(1, num_block): excluded.content = excluded.content[:-1] if token_counter(messages[:idx] + [excluded]) <= max_tokens: messages = messages[:idx] + [excluded] idx += 1 included_partial = True break if included_partial and partial_strategy == "last": excluded.content = list(reversed(excluded.content)) if not included_partial: excluded = messages[idx].model_copy(deep=True) if isinstance(excluded.content, list) and any( isinstance(block, str) or block["type"] == "text" for block in messages[idx].content ): text_block = next( block for block in messages[idx].content if isinstance(block, str) or block["type"] == "text" ) text = ( text_block["text"] if isinstance(text_block, dict) else text_block ) elif isinstance(excluded.content, str): text = excluded.content else: text = None if text: split_texts = text_splitter(text) num_splits = len(split_texts) if partial_strategy == "last": split_texts = list(reversed(split_texts)) for _ in range(num_splits - 1): split_texts.pop() excluded.content = "".join(split_texts) if token_counter(messages[:idx] + [excluded]) <= max_tokens: if partial_strategy == "last": excluded.content = "".join(reversed(split_texts)) messages = messages[:idx] + [excluded] idx += 1 break if end_on: while idx > 0 and not _is_message_type(messages[idx - 1], end_on): idx -= 1 return messages[:idx] def _last_max_tokens( messages: Sequence[BaseMessage], *, max_tokens: int, token_counter: Callable[[list[BaseMessage]], int], text_splitter: Callable[[str], list[str]], allow_partial: bool = False, include_system: bool = False, start_on: Optional[ Union[str, type[BaseMessage], Sequence[Union[str, type[BaseMessage]]]] ] = None, end_on: Optional[ Union[str, type[BaseMessage], Sequence[Union[str, type[BaseMessage]]]] ] = None, ) -> list[BaseMessage]: messages = list(messages) if len(messages) == 0: return [] if end_on: while messages and not _is_message_type(messages[-1], end_on): messages.pop() swapped_system = include_system and isinstance(messages[0], SystemMessage) reversed_ = messages[:1] + messages[1:][::-1] if swapped_system else messages[::-1] reversed_ = _first_max_tokens( reversed_, max_tokens=max_tokens, token_counter=token_counter, text_splitter=text_splitter, partial_strategy="last" if allow_partial else None, end_on=start_on, ) if swapped_system: return reversed_[:1] + reversed_[1:][::-1] else: return reversed_[::-1] _MSG_CHUNK_MAP: dict[type[BaseMessage], type[BaseMessageChunk]] = { HumanMessage: HumanMessageChunk, AIMessage: AIMessageChunk, SystemMessage: SystemMessageChunk, ToolMessage: ToolMessageChunk, FunctionMessage: FunctionMessageChunk, ChatMessage: ChatMessageChunk, } _CHUNK_MSG_MAP = {v: k for k, v in _MSG_CHUNK_MAP.items()} def _msg_to_chunk(message: BaseMessage) -> BaseMessageChunk: if message.__class__ in _MSG_CHUNK_MAP: return _MSG_CHUNK_MAP[message.__class__](**message.model_dump(exclude={"type"})) for msg_cls, chunk_cls in _MSG_CHUNK_MAP.items(): if isinstance(message, msg_cls): return chunk_cls(**message.model_dump(exclude={"type"})) msg = ( f"Unrecognized message class {message.__class__}. Supported classes are " f"{list(_MSG_CHUNK_MAP.keys())}" ) msg = create_message(message=msg, error_code=ErrorCode.MESSAGE_COERCION_FAILURE) raise ValueError(msg) def _chunk_to_msg(chunk: BaseMessageChunk) -> BaseMessage: if chunk.__class__ in _CHUNK_MSG_MAP: return _CHUNK_MSG_MAP[chunk.__class__]( **chunk.model_dump(exclude={"type", "tool_call_chunks"}) ) for chunk_cls, msg_cls in _CHUNK_MSG_MAP.items(): if isinstance(chunk, chunk_cls): return msg_cls(**chunk.model_dump(exclude={"type", "tool_call_chunks"})) msg = ( f"Unrecognized message chunk class {chunk.__class__}. Supported classes are " f"{list(_CHUNK_MSG_MAP.keys())}" ) msg = create_message(message=msg, error_code=ErrorCode.MESSAGE_COERCION_FAILURE) raise ValueError(msg) def _default_text_splitter(text: str) -> list[str]: splits = text.split("\n") return [s + "\n" for s in splits[:-1]] + splits[-1:] def _is_message_type( message: BaseMessage, type_: Union[str, type[BaseMessage], Sequence[Union[str, type[BaseMessage]]]], ) -> bool: types = [type_] if isinstance(type_, (str, type)) else type_ types_str = [t for t in types if isinstance(t, str)] types_types = tuple(t for t in types if isinstance(t, type)) return message.type in types_str or isinstance(message, types_types) def _bytes_to_b64_str(bytes_: bytes) -> str: return base64.b64encode(bytes_).decode("utf-8") def _get_message_openai_role(message: BaseMessage) -> str: if isinstance(message, AIMessage): return "assistant" elif isinstance(message, HumanMessage): return "user" elif isinstance(message, ToolMessage): return "tool" elif isinstance(message, SystemMessage): return message.additional_kwargs.get("__openai_role__", "system") elif isinstance(message, FunctionMessage): return "function" elif isinstance(message, ChatMessage): return message.role else: msg = f"Unknown BaseMessage type {message.__class__}." raise ValueError(msg) # noqa: TRY004 def _convert_to_openai_tool_calls(tool_calls: list[ToolCall]) -> list[dict]: return [ { "type": "function", "id": tool_call["id"], "function": { "name": tool_call["name"], "arguments": json.dumps(tool_call["args"]), }, } for tool_call in tool_calls ]