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Update README.md

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Ishaan Jaff 2023-10-02 12:14:27 -07:00 • committed by GitHub
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@ -78,21 +78,14 @@ LiteLLM supports caching `completion()` and `embedding()` calls for all LLMs
```python ```python
import litellm import litellm
from litellm.caching import Cache from litellm.caching import Cache
litellm.cache = Cache(type="hosted") # init cache to use api.litellm.ai litellm.cache = Cache() # init cache to use api.litellm.ai
# Make completion calls # stores this response in cache
response1 = litellm.completion( response1 = litellm.completion(
model="gpt-3.5-turbo", model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Tell me a joke."}] messages=[{"role": "user", "content": "Tell me a joke."}]
caching=True caching=True
) )
response2 = litellm.completion(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Tell me a joke."}],
caching=True
)
# response1 == response2, response 1 is cached
``` ```
## OpenAI Proxy Server ([Docs](https://docs.litellm.ai/docs/proxy_server)) ## OpenAI Proxy Server ([Docs](https://docs.litellm.ai/docs/proxy_server))