docs: move keyconcepts to separate dir (#6855)

Moving key-concepts-related docs to a separate dir should make it easier
to navigate in `docs/` folder and helps to avoid adding prefixes to
image assets.

-----------

The change shouldn't have any visual changes or changes to the links.

Signed-off-by: hagen1778 <roman@victoriametrics.com>

docs/keyConcepts/keyConcepts.md

@@ -135,7 +135,7 @@ So, the `counter` metric shows the number of observed events since the service s
 
 In programming, `counter` is a variable that you **increment** each time something happens.
 
-![counter](keyConcepts_counter.webp)
+![counter](counter.webp)
 
 `vm_http_requests_total` is a typical example of a counter. The interpretation of a graph
 above is that time series `vm_http_requests_total{instance="localhost:8428", job="victoriametrics", path="api/v1/query_range"}`
@@ -161,7 +161,7 @@ by humans from other metric types.
 
 Gauge is used for measuring a value that can go up and down:
 
-![gauge](keyConcepts_gauge.webp)
+![gauge](gauge.webp)
 
 The metric `process_resident_memory_anon_bytes` on the graph shows the memory usage of the application at every given time.
 It is changing frequently, going up and down showing how the process allocates and frees the memory.
@@ -259,7 +259,7 @@ Such a combination of `counter` metrics allows
 plotting [Heatmaps in Grafana](https://grafana.com/docs/grafana/latest/visualizations/heatmap/)
 and calculating [quantiles](https://prometheus.io/docs/practices/histograms/#quantiles):
 
-![histogram](keyConcepts_histogram.webp)
+![histogram](histogram.webp)
 
 Grafana doesn't understand buckets with `vmrange` labels, so the [prometheus_buckets](https://docs.victoriametrics.com/metricsql/#prometheus_buckets)
 function must be used for converting buckets with `vmrange` labels to buckets with `le` labels before building heatmaps in Grafana.
@@ -301,7 +301,7 @@ go_gc_duration_seconds_count 83
 
 The visualization of summaries is pretty straightforward:
 
-![summary](keyConcepts_summary.webp)
+![summary](summary.webp)
 
 Such an approach makes summaries easier to use but also puts significant limitations compared to [histograms](#histogram):
 
@@ -372,7 +372,7 @@ VictoriaMetrics supports both models used in modern monitoring applications: [pu
 
 Client regularly sends the collected metrics to the server in the push model:
 
-![push model](keyConcepts_push_model.webp)
+![push model](push_model.webp)
 
 The client (application) decides when and where to send its metrics. VictoriaMetrics supports many protocols
 for data ingestion (aka `push protocols`) - see [the full list here](https://docs.victoriametrics.com/#how-to-import-time-series-data).
@@ -417,7 +417,7 @@ The cons of push protocol:
 Pull model is an approach popularized by [Prometheus](https://prometheus.io/), where the monitoring system decides when
 and where to pull metrics from:
 
-![pull model](keyConcepts_pull_model.webp)
+![pull model](pull_model.webp)
 
 In pull model, the monitoring system needs to be aware of all the applications it needs to monitor. The metrics are
 scraped (pulled) from the known applications (aka `scrape targets`) via HTTP protocol on a regular basis (aka `scrape_interval`).
@@ -448,7 +448,7 @@ models for data collection. Many installations use exclusively one of these mode
 
 The most common approach for data collection is using both models:
 
-![data collection](keyConcepts_data_collection.webp)
+![data collection](data_collection.webp)
 
 In this approach the additional component is used - [vmagent](https://docs.victoriametrics.com/vmagent/). Vmagent is
 a lightweight agent whose main purpose is to collect, filter, relabel and deliver metrics to VictoriaMetrics.
@@ -463,7 +463,7 @@ installation for querying collected data.
 
 VictoriaMetrics components allow building more advanced topologies. For example, vmagents can push metrics from separate datacenters to the central VictoriaMetrics:
 
-![two dcs](keyConcepts_two_dcs.webp)
+![two dcs](two_dcs.webp)
 
 VictoriaMetrics in this example may be either [single-node VictoriaMetrics](https://docs.victoriametrics.com/single-server-victoriametrics/)
 or [VictoriaMetrics Cluster](https://docs.victoriametrics.com/cluster-victoriametrics/). Vmagent also allows
@@ -527,7 +527,7 @@ foo_bar 4.00 1652170560000 # 2022-05-10 10:16:00
 The data above contains a list of samples for the `foo_bar` time series with time intervals between samples
 ranging from 1m to 3m. If we plot this data sample on the graph, it will have the following form:
 
-![data samples](keyConcepts_data_samples.webp)
+![data samples](data_samples.webp)
 {width="500"}
     
 To get the value of the `foo_bar` series at some specific moment of time, for example `2022-05-10 10:03:00`, in
@@ -562,7 +562,7 @@ In response, VictoriaMetrics returns a single sample-timestamp pair with a value
 we'll see that there is no raw sample at `2022-05-10 10:03`. When there is no raw sample at the
 requested timestamp, VictoriaMetrics will try to locate the closest sample before the requested timestamp:
 
-![instant query](keyConcepts_instant_query.webp)
+![instant query](instant_query.webp)
 {width="500"}
 
 The time range in which VictoriaMetrics will try to locate a replacement for a missing data sample is equal to `5m`
@@ -703,7 +703,7 @@ see that it contains only 13 raw samples. What happens here is that the range qu
 an [instant query](#instant-query) executed `1 + (start-end)/step` times on the time range from `start` to `end`. If we plot
 this request in VictoriaMetrics the graph will be shown as the following:
 
-![range query](keyConcepts_range_query.webp)
+![range query](range_query.webp)
 {width="500"}
 
 The blue dotted lines in the figure are the moments when the instant query was executed. Since the instant query retains the
@@ -745,13 +745,13 @@ This flag prevents from non-consistent results due to the fact that only part of
 
 Here is an illustration of a potential problem when `-search.latencyOffset` is set to zero:
 
-![without latency offset](keyConcepts_without_latencyOffset.webp)
+![without latency offset](without_latencyOffset.webp)
 {width="1000"}
 
 When this flag is set, the VM will return the last metric value collected before the `-search.latencyOffset`
 duration throughout the `-search.latencyOffset` duration:
 
-![with latency offset](keyConcepts_with_latencyOffset.webp)
+![with latency offset](with_latencyOffset.webp)
 {width="1000"}
 
 It can be overridden on per-query basis via `latency_offset` query arg.
@@ -950,7 +950,7 @@ VictoriaMetrics has a built-in graphical User Interface for querying and visuali
 [VMUI](https://docs.victoriametrics.com/single-server-victoriametrics/#vmui).
 Open `http://victoriametrics:8428/vmui` page, type the query and see the results:
 
-![vmui](keyConcepts_vmui.webp)
+![vmui](vmui.webp)
 
 VictoriaMetrics supports [Prometheus HTTP API](https://docs.victoriametrics.com/single-server-victoriametrics/#prometheus-querying-api-usage)
 which makes it possible to [query it with Grafana](https://docs.victoriametrics.com/single-server-victoriametrics/#grafana-setup)