Source code for langchain_core.messages.function
from typing import Any, List, Literal
from langchain_core.messages.base import (
BaseMessage,
BaseMessageChunk,
merge_content,
)
from langchain_core.utils._merge import merge_dicts
[docs]class FunctionMessage(BaseMessage):
"""Message for passing the result of executing a tool back to a model.
FunctionMessage are an older version of the ToolMessage schema, and
do not contain the tool_call_id field.
The tool_call_id field is used to associate the tool call request with the
tool call response. This is useful in situations where a chat model is able
to request multiple tool calls in parallel.
"""
name: str
"""The name of the function that was executed."""
type: Literal["function"] = "function"
"""The type of the message (used for serialization). Defaults to "function"."""
@classmethod
def get_lc_namespace(cls) -> List[str]:
"""Get the namespace of the langchain object.
Default is ["langchain", "schema", "messages"]."""
return ["langchain", "schema", "messages"]
FunctionMessage.update_forward_refs()
[docs]class FunctionMessageChunk(FunctionMessage, BaseMessageChunk):
"""Function Message chunk."""
# Ignoring mypy re-assignment here since we're overriding the value
# to make sure that the chunk variant can be discriminated from the
# non-chunk variant.
type: Literal["FunctionMessageChunk"] = "FunctionMessageChunk" # type: ignore[assignment]
"""The type of the message (used for serialization).
Defaults to "FunctionMessageChunk"."""
@classmethod
def get_lc_namespace(cls) -> List[str]:
"""Get the namespace of the langchain object.
Default is ["langchain", "schema", "messages"]."""
return ["langchain", "schema", "messages"]
def __add__(self, other: Any) -> BaseMessageChunk: # type: ignore
if isinstance(other, FunctionMessageChunk):
if self.name != other.name:
raise ValueError(
"Cannot concatenate FunctionMessageChunks with different names."
)
return self.__class__(
name=self.name,
content=merge_content(self.content, other.content),
additional_kwargs=merge_dicts(
self.additional_kwargs, other.additional_kwargs
),
response_metadata=merge_dicts(
self.response_metadata, other.response_metadata
),
id=self.id,
)
return super().__add__(other)