Reference library

AI & LLM integration patterns

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

4 matches

How to Build a Simple Semantic Cache for Similar Prompts in Python

Mock a semantic cache that finds the closest matching prompt using word-overlap similarity and returns cached results above a threshold.

semantic cache prompt matching llm

Python

prompt_cache = [
    "What is the capital of France?",
    "How does recursion work?",
    "Best practices for Python logging?",
    "Explain binary search in one line.",
    "How to reverse a string in Python?"
]

def normalize(text):
    return " ".join(text.lower().split())

def similarity(a, b):
    a_words = set(…

14 0 Open

AI & LLM integration patterns easy

How to Log Prompts and Completions as JSONL Audit Files in Python

Read a JSONL file of LLM prompt–completion pairs, compute totals and averages, then write an audit summary with timestamps.

jsonl audit llm

Python

import json
from pathlib import Path
from datetime import datetime


def audit_jsonl(filepath):
    logs = []
    with open(filepath, encoding="utf-8") as f:
        for line in f:
            line = line.strip()
            if not line:
                continue
            entry = json.loads(line)
            logs.ap…

15 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)

…

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