Reference library

AI & LLM integration patterns

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

4 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 Create a Simple Data Helper in Python for LLM Projects

Create a beginner-friendly Python class that stores, filters, and serializes data records for AI/LLM workflows.

data-helper json llm

Python

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


class DataHelper:
    """Simple helper for beginners to manage data in AI/LLM projects."""

    def __init__(self, data: Optional[List[Dict[str, Any]]] = None) -> None:
        self.data: List[Dict[str, Any]] = data or []

    def add_item(self, item: Dict[str…

14 0 Open

AI & LLM integration patterns easy

How to build a function calling schema dict in Python

Build an OpenAI-compatible function calling schema dictionary with a helper function that takes name, description, parameters, and required fields.

llm-api function-calling schema

Python

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


def build_function_schema(
    name: str,
    description: str,
    parameters: Optional[Dict[str, Any]] = None,
    required: Optional[List[str]] = None
) -> Dict[str, Any]:
    """Build an OpenAI-compatible function calling schema dictionary."""
    schema: …

14 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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