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…

13 0 Open

AI & LLM integration patterns easy

How to Parse Chat Completion JSON in Python

Parse a mock OpenAI chat completion JSON response into a clean dictionary with content, finish reason, and model.

json openai chat-completion

Python

import json

def parse_chat_response(raw: str) -> dict:
    data = json.loads(raw)
    choice = data["choices"][0]
    return {
        "content": choice["message"]["content"],
        "finish_reason": choice["finish_reason"],
        "model": data["model"],
    }

if __name__ == "__main__":
    mock_response = '''
  …

14 0 Open

AI & LLM integration patterns easy

How to Parse JSON from LLM Model Output Fence in Python

Extract and parse a JSON object from a language model's output that may be wrapped in triple-backtick fences with an optional language tag.

json llm parsing

Python

import json
import re

def parse_json_from_fence(text):
    """
    Extract JSON object from a model output that may be wrapped in
    triple-backtick fences with optional language tag.
    """
    # Match content inside

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