Merge pull request #37 from RedisInsight/main

Updating the latest

src/ds/json/create.md

@@ -1,5 +1,3 @@
-## Create JSON documents
-
 Here's a query that creates a JSON document describing a single bike.
 
 ```redis:[run_confirmation=true] Create a JSON document

src/ds/ts/cross-key.md

@@ -1,7 +1,5 @@
-## Cross-key aggregations
-
 `TS.MRANGE` and `TS.REVMRANGE` can also take an optional `AGGREGATION` argument. This allows you to run queries across different keys using different filters. In the following example, the specified filter looks across time series keys that have a matching `region` label, calculates the daily average using the `AGGREGATION` clause, and groups the data by region and applying the `sum` reducer function.
 
 ```
 TS.MRANGE - + WITHLABELS FILTER region=(east,west) AGGREGATION avg 86400000 GROUPBY region REDUCE sum
-```
+```

src/ds/ts/retrieve.md

@@ -1,5 +1,3 @@
-## Basic data retrieval commands
-
 ### The `TS.GET` command
 
 `TS.GET` is used to get the sample with the highest timestamp from a given time series. It reports both the timestamp and the value.
@@ -124,4 +122,4 @@ The `GROUPBY` syntax is: `GROUPBY label REDUCE reducer`. The available reducer f
 
 ```redis TS.MRANGE with GROUPBY/REDUCE
 TS.MRANGE - + WITHLABELS COUNT 5 FILTER region=(east) GROUPBY region REDUCE sum
-```
+```

src/ds/ts/single-key.md

@@ -1,5 +1,3 @@
-## Single-key aggregations
-
 The previous tutorial showed that the `TS.RANGE` and `TS.REVRANGE` commands can take an optional `AGGREGATION` argument. Aggregations using the `RANGE` commands allow you to make ad hoc queries using, perhaps, a different aggregator than what you've used in your aggregation rules. In the following example, data are aggregated the `avg` function, calculating and reporting average daily sales for a bike shop.
 
 ```redis AGGREGATION in action

src/sq/geospatial.md

@@ -1,10 +1,3 @@
----
-title: "Geospatial queries"
-linkTitle: "Geospatial"
-description: Query based on geographic data
-weight: 4
----
-
 Redis Stack's geospatial feature allows you to query for data associated with geographic locations. You can either query for locations within a specific radius or based on geometric shapes, such as polygons. A polygon shape could, for instance, represent a lake or the layout of a building.
 
 The examples in this article use the following schema: