Release new docs

v2.3.x/Variables.json

@@ -4,7 +4,7 @@
   "milvus_deb_name": "milvus_2.2.0-1_amd64",
   "milvus_rpm_name": "milvus-2.2.0-1.el7.x86_64",
   "milvus_python_sdk_version": "2.3.x",
-  "milvus_python_sdk_real_version": "2.3.5",
+  "milvus_python_sdk_real_version": "2.3.6",
   "milvus_node_sdk_version": "2.3.x",
   "milvus_node_sdk_real_version": "2.3.5",
   "milvus_go_sdk_version": "2.3.x",
@@ -14,7 +14,7 @@
   "milvus_csharp_sdk_version": "2.2.x",
   "milvus_csharp_sdk_real_version": "2.2.14", 
   "milvus_restful_sdk_version": "2.3.x",
-  "milvus_restful_sdk_real_version": "2.3.5",
+  "milvus_restful_sdk_real_version": "2.3.7",
   "milvus_operator_version": "0.9.3",
   "milvus_helm_chart_version": "4.1.16",
   "milvus_image": "2.3.7",

v2.3.x/site/en/adminGuide/chunk_cache.md

@@ -5,13 +5,13 @@ title: Configure Chunk Cache
 
 # Connfigure Chunk Cache
 
-The chunk cache mechanism enables Milvus to pre-load data into cache memory on the local hard disk of the query nodes before it is needed. This mechanism significantly improves vector retrieval performance by reducing the time it takes to load data from disk to memory.
+The chunk cache mechanism enables Milvus to pre-load data into cache on the local hard disk of the query nodes before it is needed. This mechanism significantly improves vector retrieval performance by reducing the time it takes to load data from disk to memory.
 
 ## Background
 
-Before conducting queries to retrieve vectors, you need to load the data from object storage to the memory cache on the local hard disk of the query nodes. This is a time-consuming process. Before all data is loaded, Milvus may respond to some vector retrieval requests with a delay.
+Before conducting queries to retrieve vectors, Milvus needs to load the data from object storage to the memory cache on the local hard disk of the query nodes. This is a time-consuming process. Before all data is loaded, Milvus may respond to some vector retrieval requests with a delay.
 
-To improve the query performance, Milvus provides a chunk cache mechanism to pre-load data from object storage into the cache memory on the local hard disk before it is needed. When a query request is received, the Segcore first checks if the data is in the cache memory, instead of the object storage. If the data is in the cache memory, Segcore can quickly retrieve it from the cache memory and return the result to the client.
+To improve the query performance, Milvus provides a chunk cache mechanism to pre-load data from object storage into the cache on the local hard disk before it is needed. When a query request is received, the Segcore first checks if the data is in the cache, instead of the object storage. If the data is in the cache, Segcore can quickly retrieve it from the cache and return the result to the client.
 
 ## Configure Chunk Cache
 
@@ -37,7 +37,7 @@ queryNode:
         warmup: async
 ```
 
-The `warmup` parameter determines whether Milvus pre-loads data from the object storage into the memory cache on the local hard disk of the query nodes before it is needed. This parameter defaults to `async`. Possible options are as follows:
+The `warmup` parameter determines whether Milvus pre-loads data from the object storage into the cache on the local hard disk of the query nodes before it is needed. This parameter defaults to `async`. Possible options are as follows:
 
 - `async`: Milvus pre-loads data asynchronously in the background, which does not affect the time it takes to load a collection. However, users may experience a delay when retrieving vectors for a short period of time after the load process is complete.  This is the default option.
 - `sync`: Milvus pre-loads data synchronously, which may affect the time it takes to load a collection. However, users can perform queries immediately after the load process is complete without any delay. 

v2.3.x/site/en/adminGuide/resource_group.md

@@ -18,7 +18,7 @@ Note that a Milvus instance maintains a default resource group to hold all the q
 
 <div class="alert note">
 
-All code samples on this page are in PyMilvus 2.3.5. Upgrade your PyMilvus installation before running them.
+All code samples on this page are in PyMilvus 2.3.6. Upgrade your PyMilvus installation before running them.
 
 </div>
 

v2.3.x/site/en/getstarted/example_code.md

@@ -19,7 +19,7 @@ Through running the example code we provided, you will have a primary understand
 
 - [Milvus 2.3.7](install_standalone-docker.md)
 - Python 3 (3.7.1 or later)
-- [PyMilvus 2.3.5](install-pymilvus.md)
+- [PyMilvus 2.3.6](install-pymilvus.md)
 
 
 ## Download example code

v2.3.x/site/en/getstarted/install_SDKs/install-pymilvus.md

@@ -26,7 +26,7 @@ It is recommended to install a PyMilvus version that matches the version of the
 </div>
 
 ```
-$ python3 -m pip install pymilvus==2.3.5
+$ python3 -m pip install pymilvus==2.3.6
 ```
 
 ## Verify installation

v2.3.x/site/en/release_notes.md

@@ -45,7 +45,7 @@ Milvus v2.3.7 marks a minor yet impactful update, concentrating on boosting over
 ### Critical Bug Fixes
 
 - Fixed a memory leak caused by incorrect usage of OpenTelemetry in the Segcore ([#30068](https://github.com/milvus-io/milvus/pull/30068)).
-- Addressed the issue of slow disk index loading by dynamically patching the index parameters ([#30016](https://github.com/milvus-io/milvus/pull/30016)).
+- Addressed the issue of slow disk index loading by dynamically patching the index parameters ([#30116](https://github.com/milvus-io/milvus/pull/30116)).
 - Resolved the problem of changes made through the "alter collection" command not being persisted ([#30156](https://github.com/milvus-io/milvus/pull/30156)).
 - Fixed the issue where read request rate limiting ultimately leads to the unavailability of the read service ([#30196](https://github.com/milvus-io/milvus/pull/30196)).
 - Resolve the deadlock issue when getting the configuration ([#30319](https://github.com/milvus-io/milvus/pull/30319))