Reference library

AI & LLM integration patterns

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

4 matches

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.

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…

13 0 Open

AI & LLM integration patterns easy

How to Truncate Text to a Token Budget in Python

Truncate a string to a maximum token budget for LLM context using the tiktoken library and OpenAI's tokenizer.

tiktoken llm tokens

Python

import tiktoken

def truncate_to_token_budget(text, max_tokens, model="gpt-3.5-turbo"):
    enc = tiktoken.encoding_for_model(model)
    tokens = enc.encode(text)
    if len(tokens) <= max_tokens:
        return text
    truncated_tokens = tokens[:max_tokens]
    return enc.decode(truncated_tokens)

if __name__ == "__…

16 0 Open

AI & LLM integration patterns easy

Prepare LLM prompt data with a Python helper class

A beginner-friendly Python class that collects records, converts them to JSON, and produces a quick summary for building LLM prompt context.

llm json prompt-engineering

Python

import json
from typing import Any, Dict, List

class DataHelper:
    """Simple helper to prepare data for LLM prompts."""
    
    def __init__(self):
        self.data = []
    
    def add(self, item: Dict[str, Any]) -> "DataHelper":
        self.data.append(item)
        return self
    
    def to_json(self) -> s…

16 0 Open

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