Reference library

AI & LLM integration patterns

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

5 matches

How to Chunk a Long Document for RAG Retrieval in Python

Split text into overlapping chunks at sentence boundaries using a custom Python function suitable for RAG retrieval pipelines.

rag text-chunking nlp

Python

import re
from pathlib import Path

def chunk_document(text, chunk_size=500, overlap=100):
    """Split text into overlapping chunks suitable for RAG retrieval."""
    # Normalize whitespace
    text = re.sub(r'\s+', ' ', text).strip()
    
    chunks = []
    start = 0
    while start < len(text):
        end = min(s…

15 0 Open

AI & LLM integration patterns easy

How to Parse an LLM Response in Python

This code parses a JSON string from an LLM response, stripping code fences and handling common issues like whitespace, returning a Python dictionary.

llm json parsing

Python

import json
from typing import Any, Dict, List


def parse_llm_response(response: str) -> Dict[str, Any]:
    """Parse a JSON string from an LLM response, handling common edge cases."""
    # Remove code fences if present
    cleaned = response.strip()
    if cleaned.startswith("

13 0 Open

AI & LLM integration patterns easy

How to Stream Tokens from a Mock LLM in Python

Simulate real-time LLM streaming by yielding tokens one at a time with a delay, making it easy to test streaming UIs.

generator llm streaming

Python

import time
from typing import Generator


def stream_tokens(text: str, delay: float = 0.05) -> Generator[str, None, None]:
    """Simulate an LLM streaming tokens word by word."""
    for word in text.split():
        yield word
        time.sleep(delay)


if __name__ == "__main__":
    sample = "Hello world! This is…

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

Route Tool Call Name to Python Handler Dict

Routes a tool call name to the correct Python handler function using a dictionary lookup, returning an error for unknown tools.

tool-calls llm-integration dictionary-mapping

Python

def get_name():
    return {"name": "Alice"}

def get_age():
    return {"age": 30}

def get_email():
    return {"email": "alice@example.com"}

handlers = {
    "get_name": get_name,
    "get_age": get_age,
    "get_email": get_email,
}

def route(tool_call):
    handler = handlers.get(tool_call["name"])
    if handl…

11 0 Open

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