AI & LLM integration patterns
Call LLM APIs, structure prompts, parse responses, and ship AI features safely.
How to Build an Entity Memory Dict to Store Facts in Python
Store and recall facts about entities using nested dictionaries with remember, recall, and forget functions in Python.
facts = {}
def remember(entity, attribute, value):
if entity not in facts:
facts[entity] = {}
facts[entity][attribute] = value
def recall(entity, attribute):
return facts.get(entity, {}).get(attribute, None)
def forget(entity, attribute=None):
if attribute is None:
facts.pop(entity, …Track GitHub Repository Growth in Python
A Python dashboard that fetches and displays GitHub repository statistics including stars, forks, creation date, and recent star activity using the GitHub API.
import requests
import json
from datetime import datetime, timedelta
def track_repo_growth(owner, repo):
url = f"https://api.github.com/repos/{owner}/{repo}"
headers = {"Accept": "application/vnd.github.v3+json"}
response = requests.get(url, headers=headers)
data = response.json()
name = data…Browse by section
Each section groups closely related Python snippets.
AI & LLM integration patterns — Python code examples
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This page collects ai & llm integration patterns snippets — short, copy-ready Python you can paste into our free online IDE and run without installing anything. Each sample includes a plain-English explanation and the full source code.
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