Merge pull request #2393 from qdrant/szabosteve/initial-docs-review

Fix typos in docs

qdrant-landing/content/documentation/manage-data/payload.md

@@ -367,6 +367,6 @@ The response will contain the counts for each unique value in the field:
 The results are sorted by the count in descending order, then by the value in ascending order.
 Only values with non-zero counts will be returned.
 
-By default, the way Qdrant the counts for each value is approximate to achieve fast results. This should accurate enough for most cases, but if you need to debug your storage, you can use the `exact` parameter to get exact counts.
+By default, the way Qdrant calculates the counts for each value is approximate to achieve fast results. This should accurate enough for most cases, but if you need to debug your storage, you can use the `exact` parameter to get exact counts.
 
 {{< code-snippet path="/documentation/headless/snippets/facet-counts/exact/" >}}

qdrant-landing/content/documentation/manage-data/points.md

@@ -78,11 +78,11 @@ Read more about vector types, how they are stored and optimized in the [vectors]
 
 ## Upload points
 
-To optimize performance, Qdrant supports batch loading of points. I.e., you can load several points into the service in one API call.
+To optimize performance, Qdrant supports batch loading of points. This means you can load several points into the service in one API call.
 Batching allows you to minimize the overhead of creating a network connection.
 
-The Qdrant API supports two ways of creating batches - record-oriented and column-oriented.
-Internally, these options do not differ and are made only for the convenience of interaction.
+The Qdrant API supports two ways of creating batches: record-oriented and column-oriented.
+Internally, these options are equivalent and only provided for convenience.
 
 Create points with batch:
 

qdrant-landing/content/documentation/manage-data/vectors.md

@@ -23,7 +23,7 @@ Usually, this algorithm is a neural network that converts the object into a fixe
 
 The neural network is usually [trained](/articles/metric-learning-tips/) on a pairs or [triplets](/articles/triplet-loss/) of similar and dissimilar objects, so it learns to recognize a specific type of similarity.
 
-By using this property of vectors, you can explore your data in a number of ways; e.g. by searching for similar objects, clustering objects, and more.
+By using this property of vectors, you can explore your data in a number of ways; for example, by searching for similar objects, clustering objects, and more.
 
 
 ## Vector Types
@@ -150,7 +150,7 @@ To search with multivector (available in `query` API):
 
 ## Named Vectors
 
-In Qdrant, you can store multiple vectors of different sizes and [types](#vector-types) in the same data [point](/documentation/manage-data/points/). This is useful when you need to define your data with multiple embeddings to represent different features or modalities (e.g., image, text or video). 
+In Qdrant, you can store multiple vectors of different sizes and [types](#vector-types) in the same data [point](/documentation/manage-data/points/). This is useful when you need to define your data with multiple embeddings to represent different features or modalities (for example, image, text, or video).
 
 To store different vectors for each point, you need to create separate named vector spaces in the [collection](/documentation/manage-data/collections/). You can define these vector spaces during collection creation or [add them later](#adding-and-removing-named-vectors) and manage them independently.
 

qdrant-landing/content/documentation/overview/_index.md

@@ -114,7 +114,7 @@ Vertical scaling has natural limits \- eventually, you'll hit the maximum capaci
 
 Qdrant uses sharding to split collections across multiple nodes, where each shard is an independent store of points. A common recommendation is to start with 12 shards, which provides flexibility to scale from 1 node up to 2, 3, 6, or 12 nodes without resharding. However, this approach can limit throughput on small clusters since each node manages multiple shards.
 
-For optimal throughput, set `shard_number` equal to your node count (read more here). If you want to have better control over sharding, Qdrant supports [custom shards](/documentation/distributed_deployment/#user-defined-sharding).
+For optimal throughput, set `shard_number` equal to your node count (read more [here](/documentation/distributed_deployment/#sharding)). If you want to have better control over sharding, Qdrant supports [custom shards](/documentation/distributed_deployment/#user-defined-sharding).
 
 #### Replication {#replication}
 
@@ -132,10 +132,10 @@ In Qdrant Cloud, replication factor changes are applied automatically, and shard
 
 ### Safety {#safety}
 
-Some of the collection-level operations may degrade performance of the Qdrant cluster. Qdrant's [strict mode](/documentation/ops-configuration/administration/#strict-mode) prevents inefficient usage patterns through multiple controls: it may block filtering and updates on non-indexed payload fields, limit query result sizes and timeout durations, restrict the complexity and number of filter conditions, cap payload index counts, constrain batch upsert sizes, enforce maximum collection storage limits (for vectors, payloads, and point counts), and implement rate limiting for read and write operations to prevent system overload. 
+Some of the collection-level operations may degrade performance of the Qdrant cluster. Qdrant's [strict mode](/documentation/ops-configuration/administration/#strict-mode) prevents inefficient usage patterns through multiple controls: it may block filtering and updates on non-indexed payload fields, limit query result sizes and timeout durations, restrict the complexity and number of filter conditions, cap payload index counts, constrain batch upsert sizes, enforce maximum collection storage limits (for vectors, payloads, and point counts), and implement rate limiting for read and write operations to prevent system overload.
 
 <aside role="status">
-Qdrant Cloud disables filtering and updating by a non-indexed payload attribute by default, and also restricts the maximum number of payload indexes to 100\. You may consider disabling it temporarily if you want to execute some one-time queries on unindexed payload attributes, but in general you should need to do that.
+Qdrant Cloud disables filtering and updating by a non-indexed payload attribute by default, and also restricts the maximum number of payload indexes to 100. You may consider disabling it temporarily if you want to execute some one-time queries on unindexed payload attributes, but in general you should need to do that.
 </aside>
 
 The OSS version does not enforce anything, but please consider enabling and configuring strict mode settings according to the application needs. Otherwise, some of the API calls may impact the performance of your cluster by using Qdrant in a suboptimal way.

qdrant-landing/content/documentation/overview/vector-search.md

@@ -42,7 +42,7 @@ Every once in a while, when we discover new problems with inverted indexes, we c
 
 ## The Representation Revolution
 
-The latest research in Machine Learning for NLP is heavily focused on training Deep Language Models. In this process, the neural network takes a large corpus of text as input and creates a mathematical representation of the words in the form of vectors. These vectors are created in such a way that words with similar meanings and occurring in similar contexts are grouped together and represented by similar vectors. And we can also take, for example, an average of all the word vectors to create the vector for a whole text (e.g query, sentence, or paragraph).
+The latest research in Machine Learning for NLP is heavily focused on training Deep Language Models. In this process, the neural network takes a large corpus of text as input and creates a mathematical representation of the words in the form of vectors. These vectors are created in such a way that words with similar meanings and occurring in similar contexts are grouped together and represented by similar vectors. And we can also take, for example, an average of all the word vectors to create the vector for a whole text (such as query, sentence, or paragraph).
 
 ![deep neural](/docs/gettingstarted/deep-neural.png)
 
@@ -70,7 +70,7 @@ Vector search is an exciting alternative to sparse methods. It solves the issues
 Despite its complicated background, vectors search is extraordinarily simple to set up. With Qdrant, you can have a search engine up-and-running in five minutes. Our [Complete Beginners tutorial](/documentation/tutorials-basics/search-beginners/) will show you how.
 
 [**Tutorial 2 - Question and Answer System**](/articles/qa-with-cohere-and-qdrant/)
-However, you can also choose SaaS tools to generate them and avoid building your model. Setting up a vector search project with Qdrant Cloud and Cohere co.embed API is fairly easy if you follow the [Question and Answer system tutorial](/articles/qa-with-cohere-and-qdrant/).
+However, you can also choose SaaS tools to generate them and avoid building your model. Setting up a vector search project with Qdrant Cloud and Cohere co.embed API is fairly easy if you follow the [Question and Answer System tutorial](/articles/qa-with-cohere-and-qdrant/).
 
 There is another exciting thing about vector search. You can search for any kind of data as long as there is a neural network that would vectorize your data type. Do you think about a reverse image search? That’s also possible with vector embeddings.
 

qdrant-landing/content/documentation/overview/what-is-qdrant.md

@@ -22,9 +22,9 @@ learn about one of the most popular and fastest growing vector databases in the
 
 ## What is Qdrant?
 
-[Qdrant](https://github.com/qdrant/qdrant) "is a vector similarity search engine that provides a production-ready 
+[Qdrant](https://github.com/qdrant/qdrant) is a vector similarity search engine that provides a production-ready 
 service with a convenient API to store, search, and manage points (i.e. vectors) with an additional 
-payload." You can think of the payloads as additional pieces of information that can help you 
+payload. You can think of the payloads as additional pieces of information that can help you 
 hone in on your search and also receive useful information that you can give to your users.
 
 You can get started using Qdrant with the Python `qdrant-client`, by pulling the latest docker