Reference library

AI & LLM integration patterns

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

4 matches

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.

circuit-breaker llm resilience

Python

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, …

15 0 Open

AI & LLM integration patterns easy

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.

embedding batch-processing mock-encoder

Python

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…

13 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 medium

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.

react regex llm

Python

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*"
…

13 0 Open

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