Skip to main content

Caching

Cache LLM Responses

LiteLLM supports:

  • In Memory Cache
  • Redis Cache
  • Redis Semantic Cache
  • s3 Bucket Cache

Quick Start - Redis, s3 Cache, Semantic Cache

Caching can be enabled by adding the cache key in the config.yaml

Step 1: Add cache to the config.yaml

model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: gpt-3.5-turbo
- model_name: text-embedding-ada-002
litellm_params:
model: text-embedding-ada-002

litellm_settings:
set_verbose: True
cache: True # set cache responses to True, litellm defaults to using a redis cache

Step 2: Add Redis Credentials to .env

Set either REDIS_URL or the REDIS_HOST in your os environment, to enable caching.

REDIS_URL = ""        # REDIS_URL='redis://username:password@hostname:port/database'
## OR ##
REDIS_HOST = "" # REDIS_HOST='redis-18841.c274.us-east-1-3.ec2.cloud.redislabs.com'
REDIS_PORT = "" # REDIS_PORT='18841'
REDIS_PASSWORD = "" # REDIS_PASSWORD='liteLlmIsAmazing'

Additional kwargs
You can pass in any additional redis.Redis arg, by storing the variable + value in your os environment, like this:

REDIS_<redis-kwarg-name> = ""

See how it's read from the environment

Step 3: Run proxy with config

$ litellm --config /path/to/config.yaml

Using Caching - /chat/completions

Send the same request twice:

curl http://0.0.0.0:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [{"role": "user", "content": "write a poem about litellm!"}],
"temperature": 0.7
}'

curl http://0.0.0.0:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [{"role": "user", "content": "write a poem about litellm!"}],
"temperature": 0.7
}'

Advanced

Set Cache Params on config.yaml

model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: gpt-3.5-turbo
- model_name: text-embedding-ada-002
litellm_params:
model: text-embedding-ada-002

litellm_settings:
set_verbose: True
cache: True # set cache responses to True, litellm defaults to using a redis cache
cache_params: # cache_params are optional
type: "redis" # The type of cache to initialize. Can be "local" or "redis". Defaults to "local".
host: "localhost" # The host address for the Redis cache. Required if type is "redis".
port: 6379 # The port number for the Redis cache. Required if type is "redis".
password: "your_password" # The password for the Redis cache. Required if type is "redis".

# Optional configurations
supported_call_types: ["acompletion", "completion", "embedding", "aembedding"] # defaults to all litellm call types

Turn on / off caching per request.

The proxy support 3 cache-controls:

  • ttl: Optional(int) - Will cache the response for the user-defined amount of time (in seconds).
  • s-maxage: Optional(int) Will only accept cached responses that are within user-defined range (in seconds).
  • no-cache: Optional(bool) Will not return a cached response, but instead call the actual endpoint.
  • no-store: Optional(bool) Will not cache the response.

Let us know if you need more

Turn off caching

import os
from openai import OpenAI

client = OpenAI(
# This is the default and can be omitted
api_key=os.environ.get("OPENAI_API_KEY"),
base_url="http://0.0.0.0:8000"
)

chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": "Say this is a test",
}
],
model="gpt-3.5-turbo",
extra_body = { # OpenAI python accepts extra args in extra_body
cache: {
"no-cache": True # will not return a cached response
}
}
)

Turn on caching

import os
from openai import OpenAI

client = OpenAI(
# This is the default and can be omitted
api_key=os.environ.get("OPENAI_API_KEY"),
base_url="http://0.0.0.0:8000"
)

chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": "Say this is a test",
}
],
model="gpt-3.5-turbo",
extra_body = { # OpenAI python accepts extra args in extra_body
cache: {
"ttl": 600 # caches response for 10 minutes
}
}
)
import os
from openai import OpenAI

client = OpenAI(
# This is the default and can be omitted
api_key=os.environ.get("OPENAI_API_KEY"),
base_url="http://0.0.0.0:8000"
)

chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": "Say this is a test",
}
],
model="gpt-3.5-turbo",
extra_body = { # OpenAI python accepts extra args in extra_body
cache: {
"s-maxage": 600 # only get responses cached within last 10 minutes
}
}
)

Supported cache_params on proxy config.yaml

cache_params:
# Type of cache (options: "local", "redis", "s3")
type: s3

# List of litellm call types to cache for
# Options: "completion", "acompletion", "embedding", "aembedding"
supported_call_types:
- completion
- acompletion
- embedding
- aembedding

# Redis cache parameters
host: localhost # Redis server hostname or IP address
port: "6379" # Redis server port (as a string)
password: secret_password # Redis server password

# S3 cache parameters
s3_bucket_name: your_s3_bucket_name # Name of the S3 bucket
s3_region_name: us-west-2 # AWS region of the S3 bucket
s3_api_version: 2006-03-01 # AWS S3 API version
s3_use_ssl: true # Use SSL for S3 connections (options: true, false)
s3_verify: true # SSL certificate verification for S3 connections (options: true, false)
s3_endpoint_url: https://s3.amazonaws.com # S3 endpoint URL
s3_aws_access_key_id: your_access_key # AWS Access Key ID for S3
s3_aws_secret_access_key: your_secret_key # AWS Secret Access Key for S3
s3_aws_session_token: your_session_token # AWS Session Token for temporary credentials