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AI & LLM integration patterns

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

3 matches

How to Validate JSON Output Against a Dict Schema in Python

Validate JSON-like data against a simple dict schema with type checking and descriptive error messages using only the Python standard library.

json validation schema

Python

from typing import Dict, Any, List, Union

def validate_json(data: Any, schema: Dict[str, str]) -> List[str]:
    """
    Validate JSON-like data against a simple dict schema.
    Schema format: {field_name: expected_type} where type is one of:
    'str', 'int', 'float', 'bool', 'list', 'dict', 'any'
    Returns list …

13 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

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…

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