AI & LLM integration patterns
Call LLM APIs, structure prompts, parse responses, and ship AI features safely.
Cache LLM Completions by Hashing the Prompt in Python
A simple in-memory cache that stores LLM completions keyed by a SHA-256 hash of the prompt to avoid recomputing identical requests.
import hashlib
import json
class PromptCache:
def __init__(self):
self.cache = {}
def _hash_prompt(self, prompt: str) -> str:
return hashlib.sha256(prompt.encode("utf-8")).hexdigest()
def get(self, prompt: str) -> str | None:
key = self._hash_prompt(prompt)
return self.ca…Chain of Thought Prompting in Python: Step-by-Step Reasoning Demo
This demo shows how to structure a function that explains its own reasoning step-by-step, mimicking chain-of-thought prompting for AI systems.
def solve_math_step_by_step(expression: str) -> str:
"""Solves a simple expression, showing each reasoning step."""
# Step 1: Parse the expression (assume "a + b" or "a - b")
parts = expression.split()
a = int(parts[0])
op = parts[1]
b = int(parts[2])
steps = []
steps.append(f"Step…Circuit Breaker Pattern in Python for LLM API Calls
Implements a circuit breaker class that wraps LLM client calls to fail fast when the service is degrading, then recover automatically after a timeout.
import time
class CircuitBreaker:
def __init__(self, failure_threshold=3, recovery_timeout=5):
self.failure_threshold = failure_threshold
self.recovery_timeout = recovery_timeout
self.failure_count = 0
self.state = "closed"
self.last_failure_time = None
def call(self, …Demonstrate Prompt Injection Bypass in Python
Simulate why naive system prompt filters fail against prompt injection with casing and spacing variations.
# Demonstrate why system prompts can be bypassed by simulated user input
# This demo shows a naive filter being ignored via prompt injection
def process_user_message(message, system_rules):
"""Simulate an AI that follows system rules but gets tricked."""
# Claim to check system rules
for rule in system_ru…How to Batch Embed a List of Strings in Python
Batch embed a list of strings into deterministic pseudo-random vectors using a mock encoder class.
class MockEncoder:
def __init__(self, dim=8, seed=42):
self.dim = dim
self.seed = seed
def embed(self, text):
# Deterministic pseudo-random embedding based on text content
hash_val = hash(text)
import random
rng = random.Random(hash_val + self.seed)
retu…How to Build a Data Helper for LLM Prompts in Python
A beginner-friendly helper class that flattens nested dictionaries, formats prompt templates, and safely parses JSON for AI/LLM pipelines.
import json
from typing import Any, Dict, List, Optional
class DataHelper:
"""Simple helper class for working with data in AI/LLM pipelines."""
def __init__(self, data: Optional[Dict[str, Any]] = None) -> None:
self.data = data or {}
def flatten(self, prefix: str = "") -> Dict[str, Any]…How to Build a System-User-Assistant Message List in Python
Use dataclasses to model a chat conversation and build the system/user/assistant message list expected by LLM APIs.
from dataclasses import dataclass, field
from typing import List
@dataclass
class Message:
role: str
content: str
@dataclass
class Conversation:
messages: List[Message] = field(default_factory=list)
def add_system(self, content: str) -> None:
self.messages.append(Message(role="system", con…How to Build a Zero-Shot Classification Prompt in Python
Creates a prompt for zero-shot text classification by pairing input text with candidate labels and a hypothesis template.
from typing import Dict, List
def build_zero_shot_prompt(
text: str,
candidate_labels: List[str],
hypothesis_template: str = "This is about {}.",
) -> Dict[str, List[str]]:
"""Build a prompt ready for zero-shot classification."""
return {
"sequences": text,
"candidate_labels": can…How to Build an Agent Loop with Plan, Act, Observe in Python
Implements a simple plan-act-observe loop that an AI agent uses to iteratively complete a task in an environment while storing observations in memory.
class Agent:
def __init__(self, name):
self.name = name
self.memory = {}
def plan(self, task):
return f"Plan for {task}: step 1, step 2, step 3"
def act(self, plan, environment):
return f"Executing {plan} in {environment}"
def observe(self, action_result):
sel…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, …How to Convert Data to JSON and Back in Python
Convert a Python dict into a JSON string with indentation, then parse it back into a dict, demonstrating a common round-trip conversion for beginners.
import json
from datetime import datetime
def convert_data(data):
"""Convert a dict into a JSON string and back to dict."""
json_str = json.dumps(data, indent=2)
parsed = json.loads(json_str)
return json_str, parsed
def main():
sample_data = {
"user": "alice",
"message": "hello",
…How to Create a Mock LLM Judge Rubric Score in Python
Scores a response against a rubric by counting keyword matches, returning total, percentage, and per-criterion feedback.
def judge_score(response, rubric):
"""Mock LLM judge that scores a response against a rubric."""
total = 0
max_total = 0
feedback = []
for criterion, rubric_item in rubric.items():
max_points = rubric_item["max"]
description = rubric_item["description"]
# Simple mock scori…How to Create a Simple Data Helper in Python for LLM Projects
Create a beginner-friendly Python class that stores, filters, and serializes data records for AI/LLM workflows.
import json
from typing import Any, Dict, List, Optional
class DataHelper:
"""Simple helper for beginners to manage data in AI/LLM projects."""
def __init__(self, data: Optional[List[Dict[str, Any]]] = None) -> None:
self.data: List[Dict[str, Any]] = data or []
def add_item(self, item: Dict[str…How to Detect Prompt Injection in Python
Implements a regex-based heuristic in Python to flag common prompt injection attempts before sending input to an LLM.
import re
def contains_prompt_injection(user_input: str) -> bool:
# Directives to ignore previous instructions or act as system
ignore_patterns = [
r"\bignore\s+(all\s+)?previous\s+instructions\b",
r"\bdisregard\s+(all\s+)?previous\s+instructions\b",
r"\bdon'?t\s+follow\s+(any\s+)?inst…How to Estimate Token Count in Python
Estimates tokens in a text string using a whitespace and punctuation heuristic without external libraries.
def estimate_tokens(text: str) -> int:
"""Estimate token count using whitespace and punctuation heuristics."""
if not text:
return 0
words = text.split()
total_punctuation = sum(1 for char in text if char in ".,!?;:")
special_tokens = sum(1 for char in text if char in "\n\t")
# Rough …How to Filter Blocked Words in Python
Scans input text against a moderation blocklist, returning blocked terms and their counts.
MODERATION_BLOCKLIST = {"spam", "scam", "fraud", "phishing", "malware", "abuse"}
def scan_text(text: str) -> dict:
normalized = text.lower()
words = normalized.replace(".", " ").replace(",", " ").replace("!", " ").replace("?", " ").split()
found_terms = []
for word in words:
if word in MO…How to Filter Toxic Keywords in Python
Filter toxic keywords from text by replacing each occurrence with asterisks, useful as a basic guardrail for LLM inputs.
TOXIC_KEYWORDS = ["insult", "threat", "hate", "violence", "spam"]
def guardrails_filter(text: str, keywords: list[str] | None = None) -> str:
"""Filter out toxic keywords from the given text.
Args:
text: The input text to filter.
keywords: Optional keyword list. Defaults to TOXIC_KEYWORDS.
…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.
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…How to Mock OpenAI Tool Call Messages in Python
Create an assistant message with a function tool call in OpenAI's chat format, useful for testing and mocking.
from openai import OpenAI
def mock_tool_call(tool_name: str, arguments: dict) -> dict:
"""Simulate a tool call message in OpenAI style."""
return {
"role": "assistant",
"content": None,
"tool_calls": [
{
"id": "call_" + "a1b2c3d4e5f6",
"type…How to Parse Chat Completion JSON in Python
Parse a mock OpenAI chat completion JSON response into a clean dictionary with content, finish reason, and model.
import json
def parse_chat_response(raw: str) -> dict:
data = json.loads(raw)
choice = data["choices"][0]
return {
"content": choice["message"]["content"],
"finish_reason": choice["finish_reason"],
"model": data["model"],
}
if __name__ == "__main__":
mock_response = '''
…How to Parse an LLM Response in Python
This code parses a JSON string from an LLM response, stripping code fences and handling common issues like whitespace, returning a Python dictionary.
import json
from typing import Any, Dict, List
def parse_llm_response(response: str) -> Dict[str, Any]:
"""Parse a JSON string from an LLM response, handling common edge cases."""
# Remove code fences if present
cleaned = response.strip()
if cleaned.startswith("How to Redact Emails and Phones Before Sending to an LLM in Python
This code uses regular expressions to replace email addresses and US phone numbers with [EMAIL] and [PHONE] placeholders before any LLM processing.
import re
def redact_pii(text: str) -> str:
# Replace email addresses with [EMAIL]
text = re.sub(r'[\w.+-]+@[\w-]+\.[\w.-]+', '[EMAIL]', text)
# Replace phone numbers (US format) with [PHONE]
text = re.sub(r'\(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4}', '[PHONE]', text)
return text
if __name__ == "__main…How to Repair Malformed JSON Braces Heuristically in Python
Heuristically fix malformed JSON by balancing braces and quotes, using a stack-based approach to add missing closing characters.
import json
import re
def repair_json(text: str) -> str:
"""Heuristically repair malformed JSON by balancing braces and quotes."""
# Trim whitespace and handle leading/trailing garbage
text = text.strip()
# Remove common non-JSON decorations
text = re.sub(r'^(How to Retry LLM Calls on Rate Limit Errors in Python
Implement a retry mechanism with exponential backoff for LLM API calls that raises a custom RateLimitError, using a mock function to demonstrate the pattern.
import time
import random
def mock_llm_call():
"""Simulates an LLM API call that may raise a rate limit error."""
if random.random() < 0.4: # 40% chance of rate limit
raise RateLimitError("Rate limit exceeded. Try again later.")
return {"response": "Hello world from mock LLM"}
class RateLimitE…Browse by section
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AI & LLM integration patterns — Python code examples
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