Reference library

AI & LLM integration patterns

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

3 matches

How to Parse JSON from LLM Model Output Fence in Python

Extract and parse a JSON object from a language model's output that may be wrapped in triple-backtick fences with an optional language tag.

json llm parsing

Python

import json
import re

def parse_json_from_fence(text):
    """
    Extract JSON object from a model output that may be wrapped in
    triple-backtick fences with optional language tag.
    """
    # Match content inside

12 0 Open

AI & LLM integration patterns easy

JSON Mode Prompt Schema Output in Python

Extract a user object to JSON with explicit schema keys, ready for LLM JSON-mode prompts.

json schema llm

Python

import json
from typing import Any, Dict


def extract_user_as_json(user: Dict[str, Any]) -> str:
    """Extract a user object and return it as JSON using explicit schema keys."""
    schema_fields = ("id", "name", "email", "is_active")
    user_subset = {key: user[key] for key in schema_fields if key in user}
    ret…

13 0 Open

AI & LLM integration patterns easy

Serialize and Format Data for LLM Prompts in Python

Use dataclasses and the json module to convert Python objects to JSON strings, parse them back, and format structured data into prompt-friendly text for LLM calls.

dataclasses json llm

Python

import json
from dataclasses import dataclass, asdict


@dataclass
class Recipe:
    """Simple data model to represent a recipe."""
    name: str
    cuisine: str
    prep_minutes: int


def to_json(recipe: Recipe) -> str:
    """Serialize a Recipe to a JSON string."""
    return json.dumps(asdict(recipe), indent=2)

…

14 0 Open

Browse by section

Each section groups closely related Python snippets.