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 Keep Last K Turns in a Memory Buffer in Python

A TurnBuffer class using deque with maxlen to keep only the most recent k conversation turns in memory for LLM context.

deque llm-context memory-buffer

Python

from collections import deque

class TurnBuffer:
    def __init__(self, k):
        self.k = k
        self.turns = deque(maxlen=k)

    def add(self, turn):
        self.turns.append(turn)

    def last_k(self):
        return list(self.turns)


if __name__ == "__main__":
    buffer = TurnBuffer(3)
    buffer.add("tu…

14 0 Open

AI & LLM integration patterns easy

How to Summarize Old Conversation Turns in Python

Compress old conversation turns into a brief summary while keeping recent turns intact for LLM context management.

llm context compression

Python

from datetime import datetime, timedelta


def summarize_old_turns(conversation, max_turns=5):
    """Compress turns older than max_turns into a brief summary."""
    if len(conversation) <= max_turns:
        return conversation, ""

    old_turns = conversation[:-max_turns]
    recent_turns = conversation[-max_turns…

14 0 Open

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