Reference library

AI & LLM integration patterns

Call LLM APIs, structure prompts, parse responses, and ship AI features safely.

3 matches

How to Build a System-User-Assistant Message List in Python

Use dataclasses to model a chat conversation and build the system/user/assistant message list expected by LLM APIs.

from dataclasses import dataclass, field
from typing import List


@dataclass
class Message:
    role: str
    content: str


@dataclass
class Conversation:
    messages: List[Message] = field(default_factory=list)

    def add_system(self, content: str) -> None:
        self.messages.append(Message(role="system", con…

12 0 Open

AI & LLM integration patterns easy

How to Mock an LLM Client in Python

Create a simple mock LLM client that returns a canned completion for testing or development without a real API.

llm mock testing

Python

from dataclasses import dataclass


@dataclass
class MockLLMClient:
    canned_response: str = "This is a canned completion."

    def complete(self, prompt: str) -> str:
        return f"{self.canned_response} [to: {prompt[:20]}]"


if __name__ == "__main__":
    client = MockLLMClient()
    result = client.complete(…

16 0 Open

AI & LLM integration patterns easy

Serialize and Format Data for LLM Prompts in Python

Use dataclasses and the json module to convert Python objects to JSON strings, parse them back, and format structured data into prompt-friendly text for LLM calls.

dataclasses json llm

Python

import json
from dataclasses import dataclass, asdict


@dataclass
class Recipe:
    """Simple data model to represent a recipe."""
    name: str
    cuisine: str
    prep_minutes: int


def to_json(recipe: Recipe) -> str:
    """Serialize a Recipe to a JSON string."""
    return json.dumps(asdict(recipe), indent=2)

…

14 0 Open

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