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A simple dataclass that represents a message for Large Language Model (LLM) interactions, containing only the message content as a string.

Purpose

LLMMessage serves as a lightweight data container for storing message content in LLM-based applications. It provides a structured way to represent messages that will be sent to or received from language models like OpenAI's GPT, Azure OpenAI, or Anthropic's Claude. The dataclass decorator automatically generates __init__, __repr__, __eq__, and other special methods, making it easy to create, compare, and display message instances.

Source Code

class LLMMessage:
    content: str

Parameters

Parameter Details

content: A string containing the actual message text. This is the primary data field that holds the content of the message to be processed by or received from an LLM. No constraints on length or format are enforced at the class level.

Return Value

Instantiating LLMMessage returns an instance of the class with the content attribute set to the provided string value. The dataclass decorator ensures the instance has standard comparison and representation methods.

Class Interface

Methods

Attributes

Dependencies

• dataclasses
• typing
• logging
• config
• openai
• langchain_anthropic

Required Imports

from dataclasses import dataclass

Conditional/Optional Imports

These imports are only needed under specific conditions:

from typing import Optional

import logging

import config

from openai import OpenAI

from openai import AzureOpenAI

from langchain_anthropic import ChatAnthropic

Usage Example

from dataclasses import dataclass

@dataclass
class LLMMessage:
    content: str

# Create a message instance
message = LLMMessage(content="Hello, how can I help you today?")

# Access the content
print(message.content)  # Output: Hello, how can I help you today?

# Create another message
response = LLMMessage(content="I need help with Python programming.")

# Compare messages
print(message == response)  # Output: False

# String representation
print(message)  # Output: LLMMessage(content='Hello, how can I help you today?')

Best Practices

• LLMMessage is a simple data container with no methods or complex logic, making it straightforward to use.
• Since it's a dataclass, instances are mutable by default. Consider using frozen=True in the decorator if immutability is desired: @dataclass(frozen=True).
• The class has no validation logic, so any string (including empty strings) can be passed as content.
• This class is typically used as part of a larger conversation or message history system for LLM interactions.
• Consider extending this class with additional fields like role (user/assistant/system), timestamp, or metadata if needed for more complex use cases.
• The dataclass decorator automatically provides __init__, __repr__, __eq__, and __hash__ (if frozen) methods.
• No special cleanup or resource management is needed; instances can be created and discarded freely.
• For type safety, always pass a string to the content parameter during instantiation.

Tags

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