{"title":"Exercise: Object-Oriented Python and Dataclasses","description":null,"content":"# Exercise 1: Build a Conversation with Dataclasses\n\nModel a chat conversation with a `Message` dataclass, then serialize a list of messages into the exact format an LLM API expects: a list of dictionaries, each with a `role` and a `content` key.\n\nEvery chat request you send is a list of these message dictionaries. Using a dataclass to represent a message gives you type checking, sensible defaults, and a single place to define the structure, instead of passing around loose dictionaries that are easy to typo.\n\n### Requirements\n\n- Define a `Message` dataclass with `role` and `content` fields.\n- Build a short conversation: a system message, a user message, and an assistant message.\n- Write a function that converts a list of `Message` objects into a list of `{\"role\": ..., \"content\": ...}` dictionaries.\n- Print the serialized list.\n\n\n**main.py**\n\n```python\nfrom dataclasses import dataclass, asdict\n\n@dataclass\nclass Message:\n role: str\n content: str\n\nconversation = [\n Message(\"system\", \"You are a helpful assistant.\"),\n Message(\"user\", \"What is the capital of France?\"),\n Message(\"assistant\", \"The capital of France is Paris.\"),\n]\n\n# TODO: convert the list of Message objects into a list of dicts\n# with \"role\" and \"content\" keys\n\n# TODO: print the serialized list\n```\n\n\n### Expected Output\n\nThe serialized output is the message list you would pass straight to `client.chat.completions.create(messages=...)`.\n\n\n```plaintext\n[{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': 'What is the capital of France?'}, {'role': 'assistant', 'content': 'The capital of France is Paris.'}]\n```\n\n\n### Solution\n\n\n**main.py**\n\n```python\nfrom dataclasses import dataclass, asdict\n\n@dataclass\nclass Message:\n role: str\n content: str\n\nconversation = [\n Message(\"system\", \"You are a helpful assistant.\"),\n Message(\"user\", \"What is the capital of France?\"),\n Message(\"assistant\", \"The capital of France is Paris.\"),\n]\n\ndef to_api_format(messages: list[Message]) -> list[dict]:\n return [asdict(message) for message in messages]\n\nserialized = to_api_format(conversation)\nprint(serialized)\n```\n\n\n`asdict` from the `dataclasses` module turns a dataclass instance into a dictionary whose keys are the field names, in the order they were declared. That gives you the `{\"role\": ..., \"content\": ...}` shape for free, so the serialization function is a single comprehension. Because the structure lives in the `Message` definition, changing the message shape later means editing one class rather than every place that builds a dictionary by hand.\n\n---\n\n# Exercise 2: A Self-Trimming Conversation\n\nWrap the message list in a `Conversation` class that keeps itself within a maximum size. Each time you add a message, the class drops the oldest messages so the conversation never grows past `max_messages`.\n\nThis bundles state and behavior the way real chat clients do: the conversation owns its messages and its trimming rule, so callers just `add` and never worry about the window. It is the object-oriented version of the memory management you will see again with agents.\n\n### Requirements\n\n- Give `Conversation` a `max_messages` limit and a list of messages.\n- Implement `add(role, content)` to append a `Message` and then drop the oldest until the list fits `max_messages`.\n- Add five messages to a conversation with `max_messages=3` and print what remains.\n\n\n**main.py**\n\n```python\nfrom dataclasses import dataclass, field\n\n@dataclass\nclass Message:\n role: str\n content: str\n\n@dataclass\nclass Conversation:\n max_messages: int\n messages: list = field(default_factory=list)\n\n def add(self, role: str, content: str) -> None:\n # TODO: append a Message, then drop the oldest\n # messages while the list is longer than max_messages\n ...\n\nchat = Conversation(max_messages=3)\nfor i in range(5):\n chat.add(\"user\", f\"message {i}\")\n\n# TODO: print the role and content of each remaining message\n```\n\n\n### Expected Output\n\nOnly the three most recent messages survive.\n\n\n```plaintext\nuser: message 2\nuser: message 3\nuser: message 4\n```\n\n\n### Solution\n\n\n**main.py**\n\n```python\nfrom dataclasses import dataclass, field\n\n@dataclass\nclass Message:\n role: str\n content: str\n\n@dataclass\nclass Conversation:\n max_messages: int\n messages: list = field(default_factory=list)\n\n def add(self, role: str, content: str) -> None:\n self.messages.append(Message(role, content))\n while len(self.messages) > self.max_messages:\n self.messages.pop(0)\n\nchat = Conversation(max_messages=3)\nfor i in range(5):\n chat.add(\"user\", f\"message {i}\")\n\nfor message in chat.messages:\n print(f\"{message.role}: {message.content}\")\n```\n\n\nPutting the trimming logic inside `add` means the invariant (never more than `max_messages`) holds automatically after every call, so no caller can forget to enforce it. The dataclass gives you the message structure and a clean constructor for free. This is the same pin-and-window idea used for agent memory, expressed as a small object that manages its own state.","pageType":"ai-engineering"}

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