From 63b2b6c9a2ec4143d50f85697d5fbaaadc202f1f Mon Sep 17 00:00:00 2001
From: ishaan-jaff <ishaanjaffer0324@gmail.com>
Date: Wed, 15 Nov 2023 17:30:47 -0800
Subject: [PATCH] (docs) remove bloat gpt cache

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 docs/my-website/docs/caching/gpt_cache.md | 127 ----------------------
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-# Using GPTCache with LiteLLM
-
-GPTCache is a Library for Creating Semantic Cache for LLM Queries
-
-GPTCache Docs: https://gptcache.readthedocs.io/en/latest/index.html#
-
-GPTCache Github: https://github.com/zilliztech/GPTCache
-
-In this document we cover:
-* Quick Start Usage
-* Advanced Usage - Set Custom Cache Keys
-
-
-## Quick Start Usage
-👉 Jump to [Colab Notebook Example](https://github.com/BerriAI/litellm/blob/main/cookbook/LiteLLM_GPTCache.ipynb)
-
-### Install GPTCache
-```
-pip install gptcache
-```
-
-### Using GPT Cache with Litellm Completion()
-
-#### Using GPTCache
-In order to use GPTCache the following lines are used to instantiate it
-```python
-from gptcache import cache
-# set API keys in .env / os.environ
-cache.init()
-cache.set_openai_key()
-```
-
-#### Full Code using GPTCache and LiteLLM
-By default GPT Cache uses the content in `messages` as the cache key
-
-```python
-from gptcache import cache
-from litellm.gpt_cache import completion # import completion from litellm.cache
-import time
-
-# Set your .env keys 
-os.environ['OPENAI_API_KEY'] = ""
-cache.init()
-cache.set_openai_key()
-
-question = "what's LiteLLM"
-for _ in range(2):
-    start_time = time.time()
-    response = completion(
-      model='gpt-3.5-turbo',
-      messages=[
-        {
-            'role': 'user',
-            'content': question
-        }
-      ],
-    )
-    print(f'Question: {question}')
-    print("Time consuming: {:.2f}s".format(time.time() - start_time))
-```
-
-## Advanced Usage - Set Custom Cache Keys
-
-By default gptcache uses the `messages` as the cache key
-
-GPTCache allows you to set custom cache keys by setting
-```python
-cache.init(pre_func=pre_cache_func)
-```
-
-In this code snippet below we define a `pre_func` that returns message content + model as key 
-
-### Defining a `pre_func` for GPTCache
-```python
-### using / setting up gpt cache
-from gptcache import cache
-from gptcache.processor.pre import last_content_without_prompt
-from typing import Dict, Any
-
-# use this function to set your cache keys -> gptcache
-# data are all the args passed to your completion call 
-def pre_cache_func(data: Dict[str, Any], **params: Dict[str, Any]) -> Any:
-        # use this to set cache key
-        print("in pre_cache_func")
-        last_content_without_prompt_val = last_content_without_prompt(data, **params)
-        print("last content without prompt", last_content_without_prompt_val)
-        print("model", data["model"])
-        cache_key = last_content_without_prompt_val + data["model"]
-        print("cache_key", cache_key)
-        return cache_key # using this as cache_key
-        
-```
-
-### Init Cache with `pre_func` to set custom keys
-
-```python
-# init GPT Cache with custom pre_func
-cache.init(pre_func=pre_cache_func)
-cache.set_openai_key()
-```
-
-## Using Cache 
-* Cache key is `message` + `model`
-
-We make 3 LLM API calls
-* 2 to OpenAI 
-* 1 to Cohere command nightly 
-
-```python
-messages = [{"role": "user", "content": "why should I use LiteLLM for completions()"}]
-response1 = completion(model="gpt-3.5-turbo", messages=messages)
-response2 = completion(model="gpt-3.5-turbo", messages=messages)
-response3 = completion(model="command-nightly", messages=messages) # calling cohere command nightly
-
-if response1["choices"] != response2["choices"]: # same models should cache 
-    print(f"Error occurred: Caching for same model+prompt failed")
-
-if response3["choices"] == response2["choices"]: # different models, don't cache 
-    # if models are different, it should not return cached response
-    print(f"Error occurred: Caching for different model+prompt failed")
-
-print("response1", response1)
-print("response2", response2)
-print("response3", response3)
-```
-
-