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Caching

Cache LLM Responses

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

litellm_settings:
set_verbose: True
cache: # init cache
type: redis # tell litellm to use redis caching (Also: `pip install redis`)

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​

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
}'

Control caching per completion request​

Caching can be switched on/off per /chat/completions request

  • Caching on for completion - pass caching=True:
    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,
    "caching": true
    }'
  • Caching off for completion - pass caching=False:
    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,
    "caching": false
    }'