How to populate the name attribute for AIMessage when using ChatOpenAI

[avinashkz]

I am trying to figure out if there is a way I can use langchain to populate the name attribute of an AIMessage when using an OpenAI model?

I tried setting the name field in ChatOpenAI but the name field in response message is still empty.

from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-4o-mini", name="chatbot")
response = model.invoke("Hi")
response.name
>  empty

I know I can just do the following but I would need to manually do this for every call

response.name = 'name'

Or potentially add chain a function to the ChatOpenAI function

def set_model_name(message):
    message.name = "chatbot"
    return message


model = ChatOpenAI(model="gpt-4o-mini", name="chatbot") | set_model_name

@dosu

[dosubot[bot]]

To automatically populate the name attribute of an AIMessage when using an OpenAI model with langchain, you can leverage the _convert_dict_to_message function, which handles the conversion of dictionaries to AIMessage objects and includes the name attribute. Here is an example of how you can achieve this:

def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
    """Convert a dictionary to a LangChain message.

    Args:
        _dict: The dictionary.
    Returns:
        The LangChain message.
    """
    role = _dict.get("role")
    name = _dict.get("name")
    id_ = _dict.get("id")
    if role == "user":
        return HumanMessage(content=_dict.get("content", ""), id=id_, name=name)
    elif role == "assistant":
        content = _dict.get("content", "") or ""
        additional_kwargs: Dict = {}
        if function_call := _dict.get("function_call"):
            additional_kwargs["function_call"] = dict(function_call)
        tool_calls = []
        invalid_tool_calls = []
        if raw_tool_calls := _dict.get("tool_calls"):
            additional_kwargs["tool_calls"] = raw_tool_calls
            for raw_tool_call in raw_tool_calls:
                try:
                    tool_calls.append(parse_tool_call(raw_tool_call, return_id=True))
                except Exception as e:
                    invalid_tool_calls.append(
                        make_invalid_tool_call(raw_tool_call, str(e))
                    )
        return AIMessage(
            content=content,
            additional_kwargs=additional_kwargs,
            name=name,
            id=id_,
            tool_calls=tool_calls,
            invalid_tool_calls=invalid_tool_calls,
        )
    elif role == "system":
        return SystemMessage(content=_dict.get("content", ""), name=name, id=id_)
    elif role == "function":
        return FunctionMessage(
            content=_dict.get("content", ""), name=cast(str, _dict.get("name")), id=id_
        )
    elif role == "tool":
        additional_kwargs = {}
        if "name" in _dict:
            additional_kwargs["name"] = _dict["name"]
        return ToolMessage(
            content=_dict.get("content", ""),
            tool_call_id=cast(str, _dict.get("tool_call_id")),
            additional_kwargs=additional_kwargs,
            name=name,
            id=id_,
        )
    else:
        return ChatMessage(content=_dict.get("content", ""), role=role, id=id_)

This function ensures that the name attribute is automatically populated without requiring manual intervention for every call ^[1][2].

To continue talking to Dosu, mention @dosu.

[avinashkz]

@dosu Can you provide an example of how to use this?

[dosubot[bot]]

Certainly! Here's an example of how you can use the _convert_dict_to_message function to automatically populate the name attribute for AIMessage when using ChatOpenAI:

First, ensure you have the _convert_dict_to_message function defined as follows:

from typing import Any, Dict, Mapping, Optional, Union
from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, SystemMessage, FunctionMessage, ToolMessage, ChatMessage
from langchain_core.output_parsers.openai_tools import parse_tool_call, make_invalid_tool_call

def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
    """Convert a dictionary to a LangChain message.

    Args:
        _dict: The dictionary.

    Returns:
        BaseMessage: The converted message with the name attribute populated.
    """
    role = _dict.get("role")
    name = _dict.get("name", "default_name")  # Default name if not provided
    id_ = _dict.get("id")

    if role == "user":
        return HumanMessage(content=_dict.get("content", ""), id=id_, name=name)
    elif role == "assistant":
        content = _dict.get("content", "") or ""
        additional_kwargs: Dict = {}
        if function_call := _dict.get("function_call"):
            additional_kwargs["function_call"] = dict(function_call)
        tool_calls = []
        invalid_tool_calls = []
        if raw_tool_calls := _dict.get("tool_calls"):
            additional_kwargs["tool_calls"] = raw_tool_calls
            for raw_tool_call in raw_tool_calls:
                try:
                    tool_calls.append(parse_tool_call(raw_tool_call, return_id=True))
                except Exception as e:
                    invalid_tool_calls.append(
                        make_invalid_tool_call(raw_tool_call, str(e))
                    )
        return AIMessage(
            content=content,
            additional_kwargs=additional_kwargs,
            name=name,
            id=id_,
            tool_calls=tool_calls,
            invalid_tool_calls=invalid_tool_calls,
        )
    elif role == "system":
        return SystemMessage(content=_dict.get("content", ""), name=name, id=id_)
    elif role == "function":
        return FunctionMessage(
            content=_dict.get("content", ""), name=name, id=id_
        )
    elif role == "tool":
        additional_kwargs = {}
        if "name" in _dict:
            additional_kwargs["name"] = _dict["name"]
        return ToolMessage(
            content=_dict.get("content", ""),
            tool_call_id=_dict.get("tool_call_id"),
            additional_kwargs=additional_kwargs,
            name=name,
            id=id_,
        )
    else:
        return ChatMessage(content=_dict.get("content", ""), role=role, id=id_)

Next, integrate this function into your ChatOpenAI usage:

from langchain_openai import ChatOpenAI

# Define a function to set the name attribute
def set_model_name(message: BaseMessage) -> BaseMessage:
    message.name = "chatbot"
    return message

# Initialize the ChatOpenAI model
model = ChatOpenAI(model="gpt-4o-mini")

# Invoke the model and convert the response to a message
response_dict = model.invoke("Hi")
message = _convert_dict_to_message(response_dict)

# Set the name attribute
message = set_model_name(message)

# Print the name attribute to verify
print(message.name)  # Output: chatbot

In this example:

1. The _convert_dict_to_message function is used to convert the response dictionary to a BaseMessage object.
2. The set_model_name function is used to set the name attribute of the message to "chatbot".

This ensures that the name attribute is automatically populated without requiring manual intervention for every call ^{[1][2][3][4][5]}.

To continue talking to Dosu, mention @dosu.