(refactor) caching use LLMCachingHandler for async_get_cache and set_cache (#6208)

* use folder for caching

* fix importing caching

* fix clickhouse pyright

* fix linting

* fix correctly pass kwargs and args

* fix test case for embedding

* fix linting

* fix embedding caching logic

* fix refactor handle utils.py

* fix test_embedding_caching_azure_individual_items_reordered

docs/my-website/docs/caching/all_caches.md

@@ -3,7 +3,7 @@ import TabItem from '@theme/TabItem';
 
 # Caching - In-Memory, Redis, s3, Redis Semantic Cache, Disk
 
-[**See Code**](https://github.com/BerriAI/litellm/blob/main/litellm/caching.py)
+[**See Code**](https://github.com/BerriAI/litellm/blob/main/litellm.caching.caching.py)
 
 :::info
 
@@ -31,7 +31,7 @@ For the hosted version you can setup your own Redis DB here: https://app.redisla
 ```python
 import litellm
 from litellm import completion
-from litellm.caching import Cache
+from litellm.caching.caching import Cache
 
 litellm.cache = Cache(type="redis", host=<host>, port=<port>, password=<password>)
 
@@ -68,7 +68,7 @@ AWS_SECRET_ACCESS_KEY = "WOl*****"
 ```python
 import litellm
 from litellm import completion
-from litellm.caching import Cache
+from litellm.caching.caching import Cache
 
 # pass s3-bucket name
 litellm.cache = Cache(type="s3", s3_bucket_name="cache-bucket-litellm", s3_region_name="us-west-2")
@@ -101,7 +101,7 @@ For the hosted version you can setup your own Redis DB here: https://app.redisla
 ```python
 import litellm
 from litellm import completion
-from litellm.caching import Cache
+from litellm.caching.caching import Cache
 
 random_number = random.randint(
     1, 100000
@@ -155,7 +155,7 @@ To set up a Qdrant cluster locally follow: https://qdrant.tech/documentation/qui
 ```python
 import litellm
 from litellm import completion
-from litellm.caching import Cache
+from litellm.caching.caching import Cache
 
 random_number = random.randint(
     1, 100000
@@ -210,7 +210,7 @@ assert response1.id == response2.id
 ```python
 import litellm
 from litellm import completion
-from litellm.caching import Cache
+from litellm.caching.caching import Cache
 litellm.cache = Cache()
 
 # Make completion calls
@@ -246,7 +246,7 @@ Then you can use the disk cache as follows.
 ```python
 import litellm
 from litellm import completion
-from litellm.caching import Cache
+from litellm.caching.caching import Cache
 litellm.cache = Cache(type="disk")
 
 # Make completion calls
@@ -422,7 +422,7 @@ def custom_get_cache_key(*args, **kwargs):
 
 Set your function as litellm.cache.get_cache_key
 ```python
-from litellm.caching import Cache
+from litellm.caching.caching import Cache
 
 cache = Cache(type="redis", host=os.environ['REDIS_HOST'], port=os.environ['REDIS_PORT'], password=os.environ['REDIS_PASSWORD'])
 
@@ -434,7 +434,7 @@ litellm.cache = cache # set litellm.cache to your cache
 ## How to write custom add/get cache functions 
 ### 1. Init Cache 
 ```python
-from litellm.caching import Cache
+from litellm.caching.caching import Cache
 cache = Cache()
 ``` 
 

docs/my-website/docs/caching/caching_api.md

@@ -6,7 +6,7 @@ Use api.litellm.ai for caching `completion()` and `embedding()` responses
 ```python
 import litellm
 from litellm import completion
-from litellm.caching import Cache
+from litellm.caching.caching import Cache
 litellm.cache = Cache(type="hosted") # init cache to use api.litellm.ai
 
 # Make completion calls
@@ -31,7 +31,7 @@ response2 = completion(
 import time
 import litellm
 from litellm import completion, embedding
-from litellm.caching import Cache
+from litellm.caching.caching import Cache
 litellm.cache = Cache(type="hosted")
 
 start_time = time.time()
@@ -53,7 +53,7 @@ LiteLLM can cache your streamed responses for you
 import litellm
 import time
 from litellm import completion
-from litellm.caching import Cache
+from litellm.caching.caching import Cache
 
 litellm.cache = Cache(type="hosted")
 

docs/my-website/docs/caching/local_caching.md

@@ -13,7 +13,7 @@ Keys in the cache are `model`, the following example will lead to a cache hit
 ```python
 import litellm
 from litellm import completion
-from litellm.caching import Cache
+from litellm.caching.caching import Cache
 litellm.cache = Cache()
 
 # Make completion calls
@@ -35,7 +35,7 @@ response2 = completion(
 Add custom key-value pairs to your cache. 
 
 ```python 
-from litellm.caching import Cache
+from litellm.caching.caching import Cache
 cache = Cache()
 
 cache.add_cache(cache_key="test-key", result="1234")
@@ -50,7 +50,7 @@ LiteLLM can cache your streamed responses for you
 ```python
 import litellm
 from litellm import completion
-from litellm.caching import Cache
+from litellm.caching.caching import Cache
 litellm.cache = Cache()
 
 # Make completion calls
@@ -77,7 +77,7 @@ Keys in the cache are `model`, the following example will lead to a cache hit
 import time
 import litellm
 from litellm import embedding
-from litellm.caching import Cache
+from litellm.caching.caching import Cache
 litellm.cache = Cache()
 
 start_time = time.time()

docs/my-website/docs/proxy/caching.md

@@ -49,13 +49,13 @@ litellm_settings:
   cache: true 
   cache_params:        # set cache params for redis
     type: redis
-    namespace: "litellm_caching"
+    namespace: "litellm.caching.caching"
 ```
 
 and keys will be stored like:
 
 ```
-litellm_caching:<hash>
+litellm.caching.caching:<hash>
 ```
 
 #### Redis Cluster 

docs/my-website/docs/proxy/configs.md

@@ -645,7 +645,7 @@ litellm_settings:
     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".
-    namespace: "litellm_caching" # namespace for redis cache
+    namespace: "litellm.caching.caching" # namespace for redis cache
   
     # Optional - Redis Cluster Settings
     redis_startup_nodes: [{"host": "127.0.0.1", "port": "7001"}] 

docs/my-website/docs/proxy/guardrails/custom_guardrail.md

@@ -25,7 +25,7 @@ from typing import Any, Dict, List, Literal, Optional, Union
 
 import litellm
 from litellm._logging import verbose_proxy_logger
-from litellm.caching import DualCache
+from litellm.caching.caching import DualCache
 from litellm.integrations.custom_guardrail import CustomGuardrail
 from litellm.proxy._types import UserAPIKeyAuth
 from litellm.proxy.guardrails.guardrail_helpers import should_proceed_based_on_metadata