docs: app sgg

Signed-off-by: jupyterjazz <saba.sturua@jina.ai>
docarray · JoanFM · Aug 1, 2023 · Jun 28, 2023 · Jul 6, 2023 · Jul 6, 2023
commit b4028024d386704c14df0bc5d2676125ea69a67f
diff --git a/docs/user_guide/storing/docindex.md b/docs/user_guide/storing/docindex.md
@@ -87,7 +87,7 @@ doc_index.index(docs)
 
 ### Perform a vector similarity search
 Now, let's perform a similarity search on the document embeddings. 
-As a result, we'll retrieve ten most similar documents and their corresponding similarity scores.
+As a result, we'll retrieve the ten most similar documents and their corresponding similarity scores.
 ```python
 query = np.ones(128)
 retrieved_docs, scores = doc_index.find(query, search_field='embedding', limit=10)

diff --git a/docs/user_guide/storing/index_elastic.md b/docs/user_guide/storing/index_elastic.md
@@ -37,7 +37,7 @@ but will also work for [ElasticV7DocIndex][docarray.index.backends.elasticv7.Ela
 ## Basic usage
 This snippet demonstrates the basic usage of [ElasticDocIndex][docarray.index.backends.elastic.ElasticDocIndex]. It defines a document schema with a title and an embedding, 
 creates ten dummy documents with random embeddings, initializes an instance of [ElasticDocIndex][docarray.index.backends.elastic.ElasticDocIndex] to index these documents, 
-and performs a vector similarity search to retrieve ten most similar documents to a given query vector.
+and performs a vector similarity search to retrieve the ten most similar documents to a given query vector.
 
 ```python
 from docarray import BaseDoc, DocList

diff --git a/docs/user_guide/storing/index_hnswlib.md b/docs/user_guide/storing/index_hnswlib.md
@@ -26,7 +26,7 @@ It stores vectors on disk in [hnswlib](https://github.com/nmslib/hnswlib), and s
 ## Basic usage
 This snippet demonstrates the basic usage of [HnswDocumentIndex][docarray.index.backends.hnswlib.HnswDocumentIndex]. It defines a document schema with a title and an embedding, 
 creates ten dummy documents with random embeddings, initializes an instance of [HnswDocumentIndex][docarray.index.backends.hnswlib.HnswDocumentIndex] to index these documents, 
-and performs a vector similarity search to retrieve ten most similar documents to a given query vector.
+and performs a vector similarity search to retrieve the ten most similar documents to a given query vector.
 
 ```python
 from docarray import BaseDoc, DocList

diff --git a/docs/user_guide/storing/index_in_memory.md b/docs/user_guide/storing/index_in_memory.md
@@ -4,7 +4,7 @@
 [InMemoryExactNNIndex][docarray.index.backends.in_memory.InMemoryExactNNIndex] stores all documents in memory using DocLists. 
 It is a great starting point for small datasets, where you may not want to launch a database server.
 
-For vector search and filtering the [InMemoryExactNNIndex][docarray.index.backends.in_memory.InMemoryExactNNIndex] 
+For vector search and filtering [InMemoryExactNNIndex][docarray.index.backends.in_memory.InMemoryExactNNIndex] 
 utilizes DocArray's [`find()`][docarray.utils.find.find] and [`filter_docs()`][docarray.utils.filter.filter_docs] functions.
 
 !!! note "Production readiness"
@@ -23,7 +23,7 @@ utilizes DocArray's [`find()`][docarray.utils.find.find] and [`filter_docs()`][d
 ## Basic usage
 This snippet demonstrates the basic usage of [InMemoryExactNNIndex][docarray.index.backends.in_memory.InMemoryExactNNIndex]. It defines a document schema with a title and an embedding, 
 creates ten dummy documents with random embeddings, initializes an instance of [InMemoryExactNNIndex][docarray.index.backends.in_memory.InMemoryExactNNIndex] to index these documents, 
-and performs a vector similarity search to retrieve ten most similar documents to a given query vector.
+and performs a vector similarity search to retrieve the ten most similar documents to a given query vector.
 
 ```python
 from docarray import BaseDoc, DocList

diff --git a/docs/user_guide/storing/index_milvus.md b/docs/user_guide/storing/index_milvus.md
@@ -14,7 +14,7 @@ focusing on special features and configurations of Milvus.
 ## Basic usage
 This snippet demonstrates the basic usage of [MilvusDocumentIndex][docarray.index.backends.milvus.MilvusDocumentIndex]. It defines a document schema with a title and an embedding, 
 creates ten dummy documents with random embeddings, initializes an instance of [MilvusDocumentIndex][docarray.index.backends.milvus.MilvusDocumentIndex] to index these documents, 
-and performs a vector similarity search to retrieve ten most similar documents to a given query vector.
+and performs a vector similarity search to retrieve the ten most similar documents to a given query vector.
 
 !!! note "Single Search Field Requirement"
     In order to utilize vector search, it's necessary to define 'is_embedding' for one field only. 

diff --git a/docs/user_guide/storing/index_qdrant.md b/docs/user_guide/storing/index_qdrant.md
@@ -14,7 +14,7 @@ based on the [Qdrant](https://qdrant.tech/) vector search engine.
 ## Basic usage
 This snippet demonstrates the basic usage of [QdrantDocumentIndex][docarray.index.backends.qdrant.QdrantDocumentIndex]. It defines a document schema with a title and an embedding, 
 creates ten dummy documents with random embeddings, initializes an instance of [QdrantDocumentIndex][docarray.index.backends.qdrant.QdrantDocumentIndex] to index these documents, 
-and performs a vector similarity search to retrieve ten most similar documents to a given query vector.
+and performs a vector similarity search to retrieve the ten most similar documents to a given query vector.
 
 ```python
 from docarray import BaseDoc, DocList

diff --git a/docs/user_guide/storing/index_redis.md b/docs/user_guide/storing/index_redis.md
@@ -14,7 +14,7 @@ focusing on special features and configurations of Redis.
 ## Basic usage
 This snippet demonstrates the basic usage of [RedisDocumentIndex][docarray.index.backends.redis.RedisDocumentIndex]. It defines a document schema with a title and an embedding, 
 creates ten dummy documents with random embeddings, initializes an instance of [RedisDocumentIndex][docarray.index.backends.redis.RedisDocumentIndex] to index these documents, 
-and performs a vector similarity search to retrieve ten most similar documents to a given query vector.
+and performs a vector similarity search to retrieve the ten most similar documents to a given query vector.
 
 ```python
 from docarray import BaseDoc, DocList

diff --git a/docs/user_guide/storing/index_weaviate.md b/docs/user_guide/storing/index_weaviate.md
@@ -14,7 +14,7 @@ focusing on special features and configurations of Weaviate.
 ## Basic usage
 This snippet demonstrates the basic usage of [WeaviateDocumentIndex][docarray.index.backends.weaviate.WeaviateDocumentIndex]. It defines a document schema with a title and an embedding, 
 creates ten dummy documents with random embeddings, initializes an instance of [WeaviateDocumentIndex][docarray.index.backends.weaviate.WeaviateDocumentIndex] to index these documents, 
-and performs a vector similarity search to retrieve ten most similar documents to a given query vector.
+and performs a vector similarity search to retrieve the ten most similar documents to a given query vector.
 
 !!! note "Single Search Field Requirement"
     In order to utilize vector search, it's necessary to define 'is_embedding' for one field only. 

diff --git a/docs/user_guide/storing/nested_data.md b/docs/user_guide/storing/nested_data.md
@@ -153,7 +153,8 @@ doc_index.index(index_docs)
 
 ### Search
 
-You can perform search on any level by using `find_subindex()` method and the dunder operator `'root__subindex'` to specify the index to search on.
+You can perform search on any level by using [`find_subindex()`][docarray.index.abstract.BaseDocIndex.find_subindex] method 
+and the dunder operator `'root__subindex'` to specify the index to search on:
 
 ```python
 # find by the `VideoDoc` tensor