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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
How to Serialize Chat Messages to a JSON File in Python
Writes a list of chat message dicts to a JSON file with metadata like export time and message count.
import json
from pathlib import Path
from datetime import datetime
def serialize_messages(messages, output_path):
data = {
"exported_at": datetime.now().isoformat(),
"count": len(messages),
"messages": messages
}
Path(output_path).write_text(
json.dumps(data, indent=2, ensu…How to Stream Tokens from a Mock LLM in Python
Simulate real-time LLM streaming by yielding tokens one at a time with a delay, making it easy to test streaming UIs.
import time
from typing import Generator
def stream_tokens(text: str, delay: float = 0.05) -> Generator[str, None, None]:
"""Simulate an LLM streaming tokens word by word."""
for word in text.split():
yield word
time.sleep(delay)
if __name__ == "__main__":
sample = "Hello world! This is…How to Summarize Old Conversation Turns in Python
Compress old conversation turns into a brief summary while keeping recent turns intact for LLM context management.
from datetime import datetime, timedelta
def summarize_old_turns(conversation, max_turns=5):
"""Compress turns older than max_turns into a brief summary."""
if len(conversation) <= max_turns:
return conversation, ""
old_turns = conversation[:-max_turns]
recent_turns = conversation[-max_turns…How to Truncate Text to a Token Budget in Python
Truncate a string to a maximum token budget for LLM context using the tiktoken library and OpenAI's tokenizer.
import tiktoken
def truncate_to_token_budget(text, max_tokens, model="gpt-3.5-turbo"):
enc = tiktoken.encoding_for_model(model)
tokens = enc.encode(text)
if len(tokens) <= max_tokens:
return text
truncated_tokens = tokens[:max_tokens]
return enc.decode(truncated_tokens)
if __name__ == "__…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.
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 …How to Validate LLM Output in Python
A beginner-friendly DataValidator class that checks required fields and type constraints on LLM-generated or user JSON data.
import json
from typing import Any, Dict, List, Optional
class DataValidator:
"""Simple helper for validating LLM-generated or user data."""
def __init__(self, required_fields: List[str], schema: Optional[Dict[str, str]] = None):
self.required_fields = required_fields
self.schema = schema or…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.
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: …How to build a mock RAG pipeline in Python
Build a minimal Retrieval-Augmented Generation pipeline that retrieves the best-matching document by keyword overlap and generates a template-based answer.
def simple_rag_pipeline(question, documents):
"""
A minimal mock RAG pipeline: retrieve relevant context, then generate an answer.
"""
# Step 1: Retrieve — mock retrieval by simple keyword scoring
scores = []
for doc in documents:
doc_words = set(doc.lower().split())
question_wo…How to cache embeddings with a Python dict to avoid recomputation
Caches embeddings computed from text in a dictionary keyed by SHA-256 hash, returning cached results for repeated calls.
import hashlib
import time
class EmbeddingCache:
def __init__(self):
self.cache = {}
def _hash_text(self, text):
return hashlib.sha256(text.encode()).hexdigest()
def get_embedding(self, text, compute_func):
key = self._hash_text(text)
if key not in self.cache:
…How to compute ROUGE recall in Python
Compute ROUGE recall by counting token overlap between a reference and candidate summary with pure Python.
def rouge_recall(reference, candidate):
ref_tokens = reference.lower().split()
cand_tokens = candidate.lower().split()
ref_counts = {}
for token in ref_tokens:
ref_counts[token] = ref_counts.get(token, 0) + 1
cand_counts = {}
for token in cand_tokens:
cand_counts[token] = cand…How to compute exact match metric in Python
Computes the exact match (EM) metric for LLM outputs by normalizing text and comparing predictions against references.
def compute_exact_match(predictions, references):
def normalize(text):
import re
text = text.lower().strip()
text = re.sub(r'\b(a|an|the)\b', ' ', text)
text = re.sub(r'[^a-z0-9\s]', '', text)
text = ' '.join(text.split())
return text
matches = sum(1 for pred, r…How to hash a prompt with SHA-256 in Python
Create a SHA-256 hex fingerprint of a prompt string, with a short-prefix variant for quick references.
import hashlib
def prompt_hash_fingerprint(prompt: str) -> str:
"""Return the full SHA-256 hex digest of the prompt."""
return hashlib.sha256(prompt.encode("utf-8")).hexdigest()
def short_fingerprint(prompt: str, length: int = 12) -> str:
"""Return a short prefix of the SHA-256 digest for quick reference…How to implement exponential backoff for LLM API calls in Python
A decorator that retries flaky LLM API calls with exponential delay, using a mock client to demonstrate the pattern.
import time
import random
class MockLLM:
def call(self, prompt):
if random.random() < 0.7: # 70% chance of transient failure
raise ConnectionError("API unavailable")
return f"LLM response for: {prompt}"
def with_exponential_backoff(max_retries=5, base_delay=0.1):
def decorator(fu…How to parallel map embeddings with a thread pool in Python
Run embedding computations in parallel using ThreadPoolExecutor, collect results into a dict keyed by the original item.
import threading
from concurrent.futures import ThreadPoolExecutor
import time
def compute_embedding(item: int) -> tuple[int, int]:
time.sleep(0.05) # Simulate embedding work
return item, item * 10
def parallel_map_embed(items, max_workers=3):
results = {}
with ThreadPoolExecutor(max_workers=max_w…How to parse JSON in Python: A Beginner's Guide with Code Examples
This guide shows you how to parse JSON data in Python step by step, with practical code examples and expected outputs.
import json
from typing import Any, Dict, List, Optional
class DataHelper:
"""Beginner-friendly helper for common AI/LLM data tasks."""
def __init__(self, data: Optional[Dict[str, Any]] = None):
self.data = data or {}
def to_prompt(self, template: str) -> str:
"""Format a prompt…How to randomly assign a prompt variant to each key in Python
Randomly pick one variant from a list for each prompt key, useful for A/B testing message variations.
import random
def assign_prompt_variant(prompts: dict[str, list[str]]) -> dict[str, str]:
"""Assign a random prompt variant to each prompt key."""
return {key: random.choice(variants) for key, variants in prompts.items()}
if __name__ == "__main__":
prompt_bank = {
"greeting": ["Hello!", "Hi there…JSON Mode Prompt Schema Output in Python
Extract a user object to JSON with explicit schema keys, ready for LLM JSON-mode prompts.
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…Parse ReAct Logs into Thought Action Observation Steps in Python
Parse a ReAct agent's textual log into structured steps with thought, action, and observation using regex and named tuples.
import re
from collections import namedtuple
ReActStep = namedtuple("ReActStep", ["thought", "action", "observation"])
def parse_react_log(log: str) -> list[ReActStep]:
"""Parse a ReAct log into structured thought/action/observation steps."""
pattern = re.compile(
r"Thought:\s*(?P<thought>.+?)\s*"
…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.
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…Route Tool Call Name to Python Handler Dict
Routes a tool call name to the correct Python handler function using a dictionary lookup, returning an error for unknown tools.
def get_name():
return {"name": "Alice"}
def get_age():
return {"age": 30}
def get_email():
return {"email": "alice@example.com"}
handlers = {
"get_name": get_name,
"get_age": get_age,
"get_email": get_email,
}
def route(tool_call):
handler = handlers.get(tool_call["name"])
if handl…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.
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)
…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…Aggregate Log Errors Count by Hour in Python
Counts ERROR log lines per hour using regex and Counter, returning a sorted dictionary of hourly totals.
import re
from collections import Counter
from datetime import datetime
def aggregate_errors_by_hour(log_lines):
pattern = re.compile(r'^(\d{4}-\d{2}-\d{2} \d{2}):\d{2}:\d{2}.*ERROR')
hourly_counts = Counter()
for line in log_lines:
match = pattern.match(line)
if match:
ho…Automate Tweeting New Blog Posts in Python
A mock script that fetches new blog posts from a CMS and tweets them via a simulated Twitter API, outputting JSON results.
import json
import time
from datetime import datetime
def fetch_new_blog_posts():
"""Mock function to simulate fetching latest blog posts from a CMS."""
return [
{
"id": 1,
"title": "Getting Started with Python",
"url": "https://blog.example.com/python-start",
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Guide: free Python code samples library
Copy-ready Python snippets for learners and developers
PythonSkillset code samples are short, focused examples organised by topic and difficulty. Every snippet is server-rendered HTML — readable by search engines and easy to copy. Open any sample, read the notes, copy the code, then press Try in editor to run it in the browser with Pyodide.
How to use this library
- Pick a topic section — strings, lists, files, functions, and more
- Open a sample, read How it works, and copy the code block
- Run it in the IDE, tweak values, then take a related quiz or tutorial lesson
Samples vs tutorials and challenges
Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.