Reference library

AI & LLM integration patterns

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

3 matches

How to Build a Data Helper for LLM Prompts in Python

A beginner-friendly helper class that flattens nested dictionaries, formats prompt templates, and safely parses JSON for AI/LLM pipelines.

llm prompt-engineering data-prep

Python

import json
from typing import Any, Dict, List, Optional


class DataHelper:
    """Simple helper class for working with data in AI/LLM pipelines."""
    
    def __init__(self, data: Optional[Dict[str, Any]] = None) -> None:
        self.data = data or {}
    
    def flatten(self, prefix: str = "") -> Dict[str, Any]…

17 0 Open

AI & LLM integration patterns easy

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 build a mock RAG pipeline in Python

Build a minimal Retrieval-Augmented Generation pipeline that retrieves the best-matching document by keyword overlap and generates a template-based answer.

rag llm retrieval

Python

def simple_rag_pipeline(question, documents):
    """
    A minimal mock RAG pipeline: retrieve relevant context, then generate an answer.
    """
    # Step 1: Retrieve — mock retrieval by simple keyword scoring
    scores = []
    for doc in documents:
        doc_words = set(doc.lower().split())
        question_wo…

14 0 Open

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