litellm/docs/my-website/docs/caching/caching_api.md

Hosted Cache - api.litellm.ai

Use api.litellm.ai for caching completion() and embedding() responses

Quick Start Usage - Completion

import litellm
from litellm import completion
from litellm.caching import Cache
litellm.cache = Cache(type="hosted") # init cache to use api.litellm.ai

# Make completion calls
response1 = completion(
    model="gpt-3.5-turbo", 
    messages=[{"role": "user", "content": "Tell me a joke."}]
    caching=True
)

response2 = completion(
    model="gpt-3.5-turbo", 
    messages=[{"role": "user", "content": "Tell me a joke."}],
    caching=True
)
# response1 == response2, response 1 is cached

Usage - Embedding()

import time
import litellm
from litellm import completion, embedding
from litellm.caching import Cache
litellm.cache = Cache(type="hosted")

start_time = time.time()
embedding1 = embedding(model="text-embedding-ada-002", input=["hello from litellm"*5], caching=True)
end_time = time.time()
print(f"Embedding 1 response time: {end_time - start_time} seconds")

start_time = time.time()
embedding2 = embedding(model="text-embedding-ada-002", input=["hello from litellm"*5], caching=True)
end_time = time.time()
print(f"Embedding 2 response time: {end_time - start_time} seconds")

Caching with Streaming

LiteLLM can cache your streamed responses for you

Usage

import litellm
from litellm import completion
from litellm.caching import Cache
litellm.cache = Cache(type="hosted")

# Make completion calls
response1 = completion(
    model="gpt-3.5-turbo", 
    messages=[{"role": "user", "content": "Tell me a joke."}], 
    stream=True,
    caching=True)
for chunk in response1:
    print(chunk)


response2 = completion(
    model="gpt-3.5-turbo", 
    messages=[{"role": "user", "content": "Tell me a joke."}], 
    stream=True,
    caching=True)
for chunk in response2:
    print(chunk)