Release new docs

v2.2.x/site/en/getstarted/prerequisite-docker.md

@@ -34,7 +34,7 @@ Before you install Milvus, check your hardware and software to see if they meet
 
 ### Additional disk requirements
 
-Disk performance is critical to etcd. It is highly recommended that you use local NVMe SSDs. Slower disk reponse may cause frequent cluster elections that will eventually degrade the etcd service.
+Disk performance is critical to etcd. It is highly recommended that you use local NVMe SSDs. Slower disk response may cause frequent cluster elections that will eventually degrade the etcd service.
 
 To test if your disk is qualified, use [fio](https://github.com/axboe/fio).
 

v2.2.x/site/en/reference/schema.md

@@ -129,7 +129,7 @@ A collection schema is the logical definition of a collection. Usually you need
     <tr>
 		<td>enable_dynamic_field</td>
 		<td>Whether to enable dynamic schema or not</td>
-		<td>Data type: Boolean (<code>true</code> or <code>false</code>).<br/>Optional, defaults to <code>False</code><br/>For details on dynamic schema, refer to <a herf="dynamic_schema.md">Dynamic Schema</a> and the user guides for managing collections.</td>
+		<td>Data type: Boolean (<code>true</code> or <code>false</code>).<br/>Optional, defaults to <code>False</code>.<br/>For details on dynamic schema, refer to <a herf="dynamic_schema.md">Dynamic Schema</a> and the user guides for managing collections.</td>
 	</tr>
 	</tbody>
 </table>

v2.2.x/site/en/userGuide/bulk_insert.md

@@ -77,18 +77,22 @@ arr = numpy.array([json.dumps({"year": 2015, "price": 23.43}),
             json.dumps({"year": 2018, "price": 15.05}),
             json.dumps({"year": 2020, "price": 36.68}),
             json.dumps({"year": 2019, "price": 20.14}),
-            json.dumps({"year": 2021, "price": 9.36}))
+            json.dumps({"year": 2021, "price": 9.36})])
 numpy.save('book_props.npy', arr)
 ```
 
 </div>
 
 You can also add dynamic fields using NumPy files as follows. For details on dynamic fields, refer to [Dynamic Schema](dynamic_schema.md).
 
-```
+<div class="none-filter">
+
+```python
 numpy.save('$meta.py', numpy.array([ json.dumps({x: 2}), json.dumps({y: 8, z: 2}) ]))
 ```
 
+</div>
+
 <div class="alert note">
 
 - Use the field name of each column to name the NumPy file. Do not add files named after a field that does not exist in the target collection. There should be one NumPy file for each field.
@@ -209,6 +213,7 @@ In the flavor of PyMilvus, you can use [`get_bulk_insert_state()`](https://milvu
 </div>
 
 ```python
+from pymilvus import utility, BulkInsertState
 task = utility.get_bulk_insert_state(task_id=task_id)
 print("Task state:", task.state_name)
 print("Imported files:", task.files)
@@ -329,7 +334,7 @@ The following examples demonstrate how to create NumPy files for columns of data
 
   <div class="none-filter">
 
-    ```
+    ```python
     import numpy as np
     data = [True, False, True, False]
     dt = np.dtype('bool', (len(data)))
@@ -343,7 +348,7 @@ The following examples demonstrate how to create NumPy files for columns of data
 
   <div class="none-filter">
 
-  ```
+  ```python
   import numpy as np
   data = [1, 2, 3, 4]
   dt = np.dtype('int8', (len(data)))
@@ -357,7 +362,7 @@ The following examples demonstrate how to create NumPy files for columns of data
 
   <div class="none-filter">
 
-  ```
+  ```python
   import numpy as np
   data = [1, 2, 3, 4]
   dt = np.dtype('int16', (len(data)))
@@ -371,7 +376,7 @@ The following examples demonstrate how to create NumPy files for columns of data
 
   <div class="none-filter">
 
-  ```
+  ```python
   import numpy as np
   data = [1, 2, 3, 4]
   dt = np.dtype('int32', (len(data)))
@@ -385,7 +390,7 @@ The following examples demonstrate how to create NumPy files for columns of data
 
   <div class="none-filter">
 
-  ```
+  ```python
   import numpy as np
   data = [1, 2, 3, 4]
   dt = np.dtype('int64', (len(data)))
@@ -399,7 +404,7 @@ The following examples demonstrate how to create NumPy files for columns of data
 
   <div class="none-filter">
 
-  ```
+  ```python
   import numpy as np
   data = [0.1, 0.2, 0.3, 0.4]
   dt = np.dtype('float32', (len(data)))
@@ -413,7 +418,7 @@ The following examples demonstrate how to create NumPy files for columns of data
 
   <div class="none-filter">
 
-  ```
+  ```python
   import numpy as np
   data = [0.1, 0.2, 0.3, 0.4]
   dt = np.dtype('float64', (len(data)))
@@ -426,7 +431,8 @@ The following examples demonstrate how to create NumPy files for columns of data
 - Create a NumPy file from a VARCHAR array
   <div class="none-filter">
 
-  ```
+  ```python
+  import numpy as np
   data = ["a", "b", "c", "d"]
   arr = np.array(data)
   np.save(file_path, arr)
@@ -440,7 +446,8 @@ The following examples demonstrate how to create NumPy files for columns of data
 
   <div class="none-filter">
 
-  ```
+  ```python
+  import numpy as np
   data = [
       [43, 35, 124, 90],
       [65, 212, 12, 57],
@@ -462,7 +469,8 @@ The following examples demonstrate how to create NumPy files for columns of data
 
   <div class="none-filter">
 
-  ```
+  ```python
+  import numpy as np
   data = [
       [1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8],
       [2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8],
@@ -499,7 +507,7 @@ You can create multiple data-import tasks as follows
 
 <div class="none-filter">
 
-```
+```python
 task_1 = utility.do_bulk_insert(
     collection_name="book",
     files=["task_1/book_id.npy", "task_1/word_count.npy", "task_1/book_intro.npy", "task_1/book_props.npy"]
@@ -522,7 +530,7 @@ PyMilvus provides a utility method to wait for the index-building process to com
 
 <div class="none-filter">
 
-```
+```python
 utility.wait_for_index_building_complete(collection_name)
 ```
 

v2.2.x/site/en/userGuide/create_collection.md

@@ -196,7 +196,7 @@ Output:
 	</thead>
 	<tbody>
         <tr>
-            <td><code>FieldSchema</code></td>
+            <td><b><code>FieldSchema</code><b></td>
             <td>Schema of the fields within the collection to create. Refer to <a href="schema.md">Schema</a> for more information.</td>
             <td>N/A</td>
         </tr>
@@ -258,7 +258,7 @@ Output:
             <td>N/A</td>
         </tr>
         <tr>
-            <td><code>CollectionSchema</code></td>
+            <td><b><code>CollectionSchema</code><b></td>
         <td>Schema of the collection to create. Refer to <a href="schema.md">Schema</a> for more information.</td>
         <td>N/A</td>
         </tr>
@@ -272,6 +272,11 @@ Output:
             <td>Description of the collection to create.</td>
             <td>N/A</td>
         </tr>
+        <tr>
+		<td>enable_dynamic_field</td>
+		<td>Whether to enable dynamic schema or not</td>
+		<td>Data type: Boolean (<code>true</code> or <code>false</code>).<br/>Optional, defaults to <code>False</code>.<br/>For details on dynamic schema, refer to <a herf="dynamic_schema.md">Dynamic Schema</a> and the user guides for managing collections.</td>
+	    </tr>
         <tr>
             <td><code>collection_name</code></td>
             <td>Name of the collection to create.</td>

v2.2.x/site/en/userGuide/drop_index.md

@@ -6,7 +6,7 @@ summary: Learn how to drop an index in Milvus.
 
 # Drop an Index
 
-This topic describes how to drop an index in Milvus. 
+This topic describes how to drop an index in Milvus. Before dropping an index, make sure to release it first.
 
 <div class="alert caution">
 Dropping an index irreversibly removes all corresponding index files.

v2.2.x/site/en/userGuide/drop_partition.md

@@ -8,13 +8,15 @@ summary: Learn how to drop a partition in Milvus.
 
 This topic describes how to drop a partition in a specified collection.
 
-
 <div class="alert caution">
-- You have to release the partition before you drop it.
-- Dropping a partition irreversibly deletes all data within it.
+  <ul>
+    <li>&nbsp; You have to release the partition before you drop it.</li>
+    <li>&nbsp; Dropping a partition irreversibly deletes all data within it.</li>
+  </ul>
 </div>
 
 
+
 <div class="multipleCode">
   <a href="#python">Python </a>
   <a href="#java">Java</a>

v2.2.x/site/en/userGuide/load_partition.md

@@ -8,7 +8,7 @@ summary: Learn how to load a partition into memory for search or query in Milvus
 
 This topic describes how to load a partition to memory. Loading partitions instead of the whole collection to memory can significantly reduce the memory usage. All search and query operations within Milvus are executed in memory. 
 
-Milvus 2.1 allows users to load a partition as multiple replicas to utilize the CPU and memory resources of extra query nodes. This feature boost the overall QPS and throughput with extra hardware. It is supported on PyMilvus in current release.
+Milvus 2.1 or later allows users to load a partition as multiple replicas to utilize the CPU and memory resources of extra query nodes. This feature boost the overall QPS and throughput with extra hardware. It is supported on PyMilvus in current release.
 
 <div class="alert warning">
 <ul>