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# LiteLLM - Caching
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## LiteLLM Caches `completion()` and `embedding()` calls when switched on
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## Caching `completion()` and `embedding()` calls when switched on
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liteLLM implements exact match caching and supports the following Caching:
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* In-Memory Caching [Default]
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* Redic Caching Hosted
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* GPTCache
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## Usage
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1. Caching - cache
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## Quick Start Usage - Completion
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Caching - cache
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Keys in the cache are `model`, the following example will lead to a cache hit
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```python
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import litellm
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# response1 == response2, response 1 is cached
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```
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## Using Redis Cache with LiteLLM
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### Pre-requisites
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Install redis
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```
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pip install redis
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```
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For the hosted version you can setup your own Redis DB here: https://app.redislabs.com/
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### Usage
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```python
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import litellm
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from litellm import completion
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from litellm.caching import Cache
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litellm.cache = Cache(type="redis", host=<host>, port=<port>, password=<password>)
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# Make completion calls
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response1 = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Tell me a joke."}])
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response2 = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Tell me a joke."}])
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# response1 == response2, response 1 is cached
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```
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## Caching with Streaming
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LiteLLM can cache your streamed responses for you
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### Usage
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```python
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import litellm
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from litellm import completion
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from litellm.caching import Cache
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litellm.cache = Cache()
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# Make completion calls
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response1 = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Tell me a joke."}], stream=True)
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for chunk in response1:
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print(chunk)
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response2 = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Tell me a joke."}], stream=True)
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for chunk in response2:
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print(chunk)
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```
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## Usage - Embedding()
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1. Caching - cache
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Keys in the cache are `model`, the following example will lead to a cache hit
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```python
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import time
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import litellm
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from litellm import completion
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from litellm.caching import Cache
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litellm.cache = Cache()
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start_time = time.time()
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embedding1 = embedding(model="text-embedding-ada-002", input=["hello from litellm"*5])
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end_time = time.time()
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print(f"Embedding 1 response time: {end_time - start_time} seconds")
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start_time = time.time()
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embedding2 = embedding(model="text-embedding-ada-002", input=["hello from litellm"*5])
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end_time = time.time()
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print(f"Embedding 2 response time: {end_time - start_time} seconds")
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```
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