📝 fix grammar

Signed-off-by: vankichi <kyukawa315@gmail.com>

docs/user-guides/search-config.md

@@ -4,37 +4,37 @@
 
 <!-- Describe vald search service: search, searchbyid, linearsearch, lineasearchbyid ... -->
 
-Vald provides the two types of search service.
+Vald provides the two types of search services.
 
 1. Search
 
    - `Search` is just the `ANN(Approximate Nearest Neighbor)` search with query vector.
-     It is the fast search even the vector consists large dimension.
-     The search duration is fast, but less accurate than `LinearSearch`.
-     Search algorithm depends on each core algorithm.
+     It is a fast search even though the vector consists large dimension.
+     The search duration is quick but less accurate than `LinearSearch`.
+     The search algorithm depends on each core algorithm.
 
 1. SearchById
 
-   - `SearchById` is just the `ANN(Approximate Nearest Neighbor)` search with vector's id.
-     The id should be already indexed before search process.
-     The sarch algorithm is the same as `Search`.
+   - `SearchById` is just the `ANN(Approximate Nearest Neighbor)` search with the vector's id.
+     The id should already exist in the NGT indexes before the search process.
+     The search algorithm is the same as `Search`.
 
 1. LinearSearch
 
-   - `LinearSearch` is the basic search algorithm with query vector.
+   - `LinearSearch` is the primary search algorithm with a query vector.
      It searches all indexed vectors and calculates the distance between the query.
      Its accuracy is exact, but the search time requires more than `Search` (ANN search) and increases the amount of indexed vector.
 
 1. LinearSearchById
-   - `LinearSearchById` is the basic search algorithm with vector's id.
-     The id should be already indexed before search process.
-     The sarch algorithm is the same as `LinearSearch`.
+   - `LinearSearchById` is the primary search algorithm with the vector's id.
+     The id should already exist in the NGT indexes before the search process.
+     The search algorithm is the same as `LinearSearch`.
 
 <div class="notice">
 Linear Search service is available from Vald v1.4 or later.
 </div>
 
-For more details, please refer to [Search API document](../api/search.md)
+For more details, please refer to [the Search API document](../api/search.md).
 
 ## Configuration
 
@@ -67,50 +67,51 @@ message Config {
 
 ### request_id
 
-`request_id` is unique request ID.
+`request_id` is a unique request ID.
 It is **NOT** indexed vector's id.
-It can be used for e.g., error handling process.
+Users can use it for, e.g., the error handling process.
 
 ### num
 
-`num` is the maximum number of search result which you'd like to get.
-`num` should be positive integer.
+`num` is the maximum number of search results you'd like to get.
+`num` should be a positive integer.
 
 ### radius
 
 `radius`, the specific parameter for NGT, specifies the search range centered on the query vector in terms of the radius of a circle.
 The number of search target vectors increases along with the radius is large.
-There is the trade-off between accuracy and search speed.
-In many cases, it is hard to set it due to depending on dataset.
+There is a trade-off between accuracy and search speed.
+In many cases, it is hard to set it depending on dataset.
 
-The default value is infinite circle.
-When setting negative number as `radius`, `NGT` applies the radius as infinite circle.
+The default value is the infinite circle.
+When setting a negative number as `radius`, `NGT` applies the radius as an infinite circle.
 
 <div class="notice">
-NGT will self-update radius during search process.
+NGT will self-update the radius during the search process.
 </div>
 
 ### epsilon
 
-`epsilon`, the specific parameter for NGT, specifies the magnification coefficient (epsilon) of the search range.
+`epsilon`, the specific parameter for NGT, specifies the search range's magnification coefficient (epsilon).
 NGT will use `(1+epsilon)*radius` as the search range.
-The number of search target vectors increases along with the epsilon is large.
+The number of search target vectors increases along with the epsilon being large.
 
-The default value (recommend value) is `0.1`.
-While it is desirable to adjust this value within the range of 0 - 0.3, a negative value (over than `-1`) may also be specified.
+
+The default value (recommend value) is 0.1.
+While it is desirable to adjust this value within 0 - 0.3, it can also set a negative value (over than -1).
 
 ### ingress_filters
 
-`ingress_filters` is required when using ingress filter component.
-It requires ingress filter component's hostname and port.
+`ingress_filters` is required when using the ingress filter component.
+It requires the ingress filter component's hostname and port.
 
 ### egress_filters
 
-`egress_filters` is required when using egress filter component.
-It requires egress filter component's hostname and port.
+`egress_filters` is required when using the egress filter component.
+It requires the egress filter component's hostname and port.
 
 ### min_num
 
-`min_num` is the minimum number of search result which you'd like to get at least.
-It helps you to avoid getting the timeout error when the search process requires more time.
-`min_num` should be positive integer and smaller than `num`.
+`min_num` is the minimum number of search result you'd like to get at least.
+It helps you avoid getting the timeout error when the search process requires more time.
+`min_num` should be a positive integer and smaller than `num`.