[Backport 2.6] Propose fix some typos and ignore emacs backup files (#3131) (#3145)

Backport of #3131 to `2.6`.

Signed-off-by: John E <[email protected]>
Signed-off-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: John Eismeier <[email protected]>
Co-authored-by: XuanYang-cn <[email protected]>

Author: 3 people

.gitignore

@@ -4,6 +4,8 @@
 
 .vscode
 
+*~
+
 # Python files
 *.pyc
 dist

docs/source/api/connections.rst

@@ -15,7 +15,7 @@ Constructor
 +-----------------------------------------+---------------------------------------------------------------------------------+
 | Constructor                             | Description                                                                     |
 +=========================================+=================================================================================+
-| `Connections() <#pymilvus.Connection>`_ | A singleton class used to manage connections and correspoinding configurations. |
+| `Connections() <#pymilvus.Connection>`_ | A singleton class used to manage connections and corresponding configurations. |
 +-----------------------------------------+---------------------------------------------------------------------------------+
 
 

docs/source/api/partition.rst

@@ -5,7 +5,7 @@ Partition
 A partition is a group of entities in one collection with the same label. Entities inserted without a label
 will be tagged a default label by milvus.
 
-Partition is managable, which means managing a group of entities with the same label in one collection.
+Partition is manageable, which means managing a group of entities with the same label in one collection.
 
 Constructor
 -----------
@@ -64,4 +64,3 @@ API Refereences
 .. autoclass:: pymilvus.Partition
    :member-order: bysource
    :members: description, name, is_empty, num_entities, drop, load, release, insert, search, query, delete
-

docs/source/tutorial.rst

@@ -143,7 +143,7 @@ Search Entities by Vector Similarity
 
 You can get entities by vector similarity. Assuming there's a ``embedding_A``, and you want to get top 2 entities that are most similar with it.
 
-In the example below, you can perfrom search based on vector similarity.
+In the example below, you can perform search based on vector similarity.
 
 .. note:
     Before searching, you need to load this collection into memory.
@@ -210,4 +210,3 @@ Finally, you can drop an entire collection.
    Once you drop a collection, all the data in this collection will be dropped too.
 
 >>> utility.drop_collection(tutorial.name)
-

examples/customize_schema.py

@@ -55,13 +55,13 @@
 
 print(fmt.format("Start query by specifying filtering expression"))
 query_results = milvus_client.query(collection_name, filter= "f == 600 or title == 't2'")
-for ret in query_results: 
+for ret in query_results:
     print(ret)
 
 rng = np.random.default_rng(seed=19530)
 vectors_to_search = rng.random((1, dim))
 
-print(fmt.format(f"Start search with retrieve serveral fields."))
+print(fmt.format(f"Start search with retrieve several fields."))
 result = milvus_client.search(collection_name, vectors_to_search, limit=3, output_fields=["pk", "a", "b"])
 for hits in result:
     for hit in hits:

examples/customize_schema_auto_id.py

@@ -55,13 +55,13 @@
 
 print(fmt.format("Start query by specifying filtering expression"))
 query_results = milvus_client.query(collection_name, filter= "f == 600 or title == 't2'")
-for ret in query_results: 
+for ret in query_results:
     print(ret)
 
 rng = np.random.default_rng(seed=19530)
 vectors_to_search = rng.random((1, dim))
 
-print(fmt.format(f"Start search with retrieve serveral fields."))
+print(fmt.format(f"Start search with retrieve several fields."))
 result = milvus_client.search(collection_name, vectors_to_search, limit=3, output_fields=["pk", "a", "b"])
 for hits in result:
     for hit in hits:

examples/customize_schema_id.py

@@ -53,7 +53,7 @@
 rng = np.random.default_rng(seed=19530)
 vectors_to_search = rng.random((1, dim))
 
-print(fmt.format(f"Start search with retrieve serveral fields."))
+print(fmt.format(f"Start search with retrieve several fields."))
 result = milvus_client.search(collection_name, vectors_to_search, limit=3, output_fields=["id"])
 for hits in result:
     for hit in hits:

examples/index.py

@@ -60,11 +60,11 @@
 
 print(fmt.format("Start query by specifying filtering expression"))
 query_results = milvus_client.query(collection_name, filter= "f == 600 or title == 't2'")
-for ret in query_results: 
+for ret in query_results:
     print(ret)
 
 vectors_to_search = rng.random((1, dim))
-print(fmt.format(f"Start search with retrieve serveral fields."))
+print(fmt.format(f"Start search with retrieve several fields."))
 result = milvus_client.search(collection_name, vectors_to_search, limit=3, output_fields=["title"])
 for hits in result:
     for hit in hits:

examples/partition.py

@@ -76,10 +76,10 @@
 
 print(fmt.format("Start query by specifying filtering expression"))
 query_results = milvus_client.query(collection_name, filter= "f == 500")
-for ret in query_results: 
+for ret in query_results:
     print(ret)
 
-print(fmt.format(f"Start search with retrieve serveral fields."))
+print(fmt.format(f"Start search with retrieve several fields."))
 result = milvus_client.search(collection_name, vectors_to_search, limit=3, output_fields=["pk", "a", "b"])
 for hits in result:
     for hit in hits:

examples/simple.py

@@ -49,7 +49,7 @@
 
 print(fmt.format("Start query by specifying filtering expression"))
 query_results = milvus_client.query(collection_name, filter= "f == 600")
-for ret in query_results: 
+for ret in query_results:
     print(ret)
 
 
@@ -64,7 +64,7 @@
 rng = np.random.default_rng(seed=19530)
 vectors_to_search = rng.random((1, dim))
 
-print(fmt.format(f"Start search with retrieve serveral fields."))
+print(fmt.format(f"Start search with retrieve several fields."))
 result = milvus_client.search(collection_name, vectors_to_search, limit=3, output_fields=["pk", "a", "b"])
 for hits in result:
     for hit in hits: