AI API calls are expensive and slow. Redis caching dramatically reduces both by storing AI responses for reuse. Here's a complete implementation guide.

Why Cache AI Responses?

Without Cache With Cache

Every request → AI API Cache hit → Return immediately

1-3s latency per request < 10ms for cache hits

Full API cost per request Pay only for cache misses

Rate limit pressure Rate limit relief

Semantic Caching vs Exact Match

`python

Exact match caching (simple)

cache_key = hash(messages) # Only matches identical prompts

Semantic caching (smart)

cachekey = generateembeddinghash(userprompt) # Matches similar prompts

`

This guide covers exact match caching first, then semantic.

Redis Setup

`python

import redis

import json

from typing import Optional

class AICache:

def init(self, redis_url: str = "redis://localhost:6379/0"):

self.redis = redis.fromurl(redisurl, decode_responses=True)

def makekey(self, messages: list[dict]) -> str:

"""Create cache key from messages."""

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