docarray · samsja · Dec 12, 2022 · Dec 6, 2022 · Dec 6, 2022 · Dec 6, 2022
diff --git a/docs/advanced/document-store/annlite.md b/docs/advanced/document-store/annlite.md
@@ -1,7 +1,7 @@
 (annlite)=
 # Annlite
 
-One can use [Annlite](https://github.com/jina-ai/annlite) as the document store for DocumentArray. It is useful when one wants to have faster Document retrieval on embeddings, i.e. `.match()`, `.find()`.
+You can use [Annlite](https://github.com/jina-ai/annlite) as a document store for DocumentArray. It's suitable for faster Document retrieval on embeddings, i.e. `.match()`, `.find()`.
 
 ````{tip}
 This feature requires `annlite`. You can install it via `pip install "docarray[annlite]".` 
@@ -10,7 +10,7 @@ This feature requires `annlite`. You can install it via `pip install "docarray[a
 
 ## Usage
 
-One can instantiate a DocumentArray with Annlite storage like so:
+You can instantiate a DocumentArray with Annlite storage like so:
 
 ```python
 from docarray import DocumentArray
@@ -20,7 +20,7 @@ da = DocumentArray(storage='annlite', config={'n_dim': 10})
 
 The usage would be the same as the ordinary DocumentArray.
 
-To access a DocumentArray formerly persisted, one can specify the `data_path` in `config`. 
+To access a DocumentArray formerly persisted, you can specify the `data_path` in `config`. 
 
 ```python
 from docarray import DocumentArray

diff --git a/docs/advanced/document-store/elasticsearch.md b/docs/advanced/document-store/elasticsearch.md
@@ -2,7 +2,7 @@
 
 # Elasticsearch
 
-One can use [Elasticsearch](https://www.elastic.co) as the document store for DocumentArray. It is useful when one wants to have faster Document retrieval on embeddings, i.e. `.match()`, `.find()`.
+You can use [Elasticsearch](https://www.elastic.co) as a document store for DocumentArray. It's suitable for faster Document retrieval on embeddings, i.e. `.match()`, `.find()`.
 
 ````{tip}
 This feature requires `elasticsearch`. You can install it via `pip install "docarray[elasticsearch]".`
@@ -41,7 +41,7 @@ docker-compose up
 
 ### Create DocumentArray with Elasticsearch backend
 
-Assuming service is started using the default configuration (i.e. server address is `http://localhost:9200`), one can instantiate a DocumentArray with Elasticsearch storage as such:
+Assuming service is started using the default configuration (i.e. server address is `http://localhost:9200`), you can instantiate a DocumentArray with Elasticsearch storage as such:
 
 ```python
 from docarray import DocumentArray
@@ -70,7 +70,7 @@ da = DocumentArray(
 
 Here is [the official Documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/docker.html#elasticsearch-security-certificates) for you to get certificate, password etc.
 
-To access a DocumentArray formerly persisted, one can specify `index_name` and the hosts.
+To access a DocumentArray formerly persisted, you can specify `index_name` and the hosts.
 
 The following example will build a DocumentArray with previously stored data from `old_stuff` on `http://localhost:9200`:
 
@@ -160,7 +160,7 @@ You can read more about parallel bulk config and their default values [here](htt
 
 ### Vector search with filter query
 
-One can perform Approximate Nearest Neighbor Search and pre-filter results using a filter query that follows [ElasticSearch's DSL](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html).
+You can perform Approximate Nearest Neighbor Search and pre-filter results using a filter query that follows [ElasticSearch's DSL](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html).
 
 Consider Documents with embeddings `[0,0,0]` up to `[9,9,9]` where the document with embedding `[i,i,i]`
 has as tag `price` with value `i`. We can create such example with the following code:
@@ -238,7 +238,7 @@ You can read more about approximate kNN tuning [here](https://www.elastic.co/gui
 
 ### Search by filter query
 
-One can search with user-defined query filters using the `.find` method. Such queries can be constructed following the
+You can search with user-defined query filters using the `.find` method. Such queries can be constructed following the
 guidelines in [ElasticSearch's Documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html).
 
 Consider you store Documents with a certain tag `price` into ElasticSearch and you want to retrieve all Documents

diff --git a/docs/advanced/document-store/extend.md b/docs/advanced/document-store/extend.md
@@ -25,7 +25,7 @@ Let's get started!
 
 ## Step 1: create the folder
 
-Go to `docarray/array/storage` folder, create a sub-folder for your document store. Let's call it `mydocstore`. You will need to create four empty files in that folder:
+Go to `docarray/array/storage` folder, create a sub-folder for your document store. Let's call it `mydocstore`. You need to create four empty files in that folder:
 
 ```{code-block} 
 ---
@@ -80,7 +80,7 @@ class GetSetDelMixin(BaseGetSetDelMixin):
         ...
 ```
 
-You will need to implement the above five functions, which correspond to the logics of get/set/delete items via a string `.id`. They are essential to ensure DocumentArray works.
+You need to implement the above five functions, which correspond to the logics of get/set/delete items via a string `.id`. They are essential to ensure DocumentArray works.
 
 Note that DocumentArray maintains an `offset2ids` mapping to allow a list-like behaviour. This mapping is 
 inherited from the `BaseGetSetDelMixin`. Therefore, you need to implement methods to persist this mapping, in case you 
@@ -111,9 +111,9 @@ upper level. Also, make sure that `_set_doc_by_id` performs an **upsert operatio
 ```{tip}
 Let's call the above five functions as **the essentials**.
 
-If you aim for high performance, it is recommeneded to implement other methods *without* leveraging your essentials. They are: `_get_docs_by_ids`, `_del_docs_by_ids`, `_clear_storage`, `_set_doc_value_pairs`, `_set_doc_value_pairs_nested`, `_set_docs_by_ids`. One can get their full signatures from {class}`~docarray.array.storage.base.getsetdel.BaseGetSetDelMixin`. These functions define more fine-grained get/set/delete logics that are frequently used in DocumentArray. 
+If you aim for high performance, it is recommeneded to implement other methods *without* leveraging your essentials. They are: `_get_docs_by_ids`, `_del_docs_by_ids`, `_clear_storage`, `_set_doc_value_pairs`, `_set_doc_value_pairs_nested`, `_set_docs_by_ids`. You can get their full signatures from {class}`~docarray.array.storage.base.getsetdel.BaseGetSetDelMixin`. These functions define more fine-grained get/set/delete logics that are frequently used in DocumentArray. 
 
-Implementing them is fully optional, and you can only implement some of them not all of them. If you are not implementing them, those methods will use a generic-but-slow version that is based on your five essentials.
+Implementing them is fully optional, and you can only implement some of them not all of them. If you are not implementing them, those methods use a generic-but-slow version based on your five essentials.
 ```
 
 ```{seealso}
@@ -149,7 +149,7 @@ class SequenceLikeMixin(BaseSequenceLikeMixin):
         ...
 
     def insert(self, index: int, value: 'Document'):
-        # Optional. By default, this will add a new item and update offset2id
+        # Optional. By default, this adds a new item and update offset2id
         # if you want to customize this, make sure to handle offset2id
         ...
 
@@ -162,7 +162,7 @@ class SequenceLikeMixin(BaseSequenceLikeMixin):
         ...
 
     def __iter__(self) -> Iterator['Document']:
-        # Optional. By default, this will rely on offset2id to iterate
+        # Optional. By default, this relies on offset2id to iterate
         ...
 ```
 
@@ -244,7 +244,7 @@ By default, this should be set to `True`.
 Further, you have to store the value of this flag in `self._list_like`. Some methods that are handled outside of your control will take the value form there and use it appropriately.
 
 `_init_storage` is a very important function to be called during the DocumentArray construction.
-You will need to handle different construction & copy behaviors in this function.
+You need to handle different construction and copy behaviors in this function.
 
 `_ensure_unique_config` is needed to support DocArray's subindex feature.
 A subindex inherits its configuration from the root index, unless a field of the configuration is explicitly provided to the subindex.
@@ -308,7 +308,7 @@ class StorageMixins(BackendMixin, GetSetDelMixin, SequenceLikeMixin, ABC):
     ...
 ```
 
-Just copy-paste it will do the work.
+Just copying and pasting it should work.
 
 If you have implemented a `find.py` module, make sure to also inherit the `FindMixin`:
 ```python
@@ -391,7 +391,7 @@ Done! Now you should be able to use it like `DocumentArrayMyDocStore`!
 
 ## On pull request: add tests and type-hint
 
-Welcome to contribute your extension back to DocArray. You will need to include `DocumentArrayMyDocStore` in at least the following tests:
+You are welcome to contribute your extension back to DocArray. You need to include `DocumentArrayMyDocStore` in at least the following tests:
 
 ```text
 tests/unit/array/test_advance_indexing.py