vania-janet/multiturn-rag-retrieval-data huggingface dataset:
[Nexus](https://cloud.ibm.com/docs/services/ContinuousDelivery?topic=ContinuousDelivery-nexus) nexus 
 [Other Tool](https://cloud.ibm.com/docs/services/ContinuousDelivery?topic=ContinuousDelivery-othertool) customtool 
 [PagerDuty](https://cloud.ibm.com/docs/services/ContinuousDelivery?topic=ContinuousDelivery-pagerduty) pagerduty 
 [Rational Team Concert](https://cloud.ibm.com/docs/services/ContinuousDelivery?topic=ContinuousDelivery-rationalteamconcert) rationalteamconcert 
 [Sauce Labs](https://cloud.ibm.com/docs/services/ContinuousDelivery?topic=ContinuousDelivery-saucelabs) saucelabs 
 [Secrets Manager](https://cloud.ibm.com/docs/services/ContinuousDelivery?topic=ContinuousDelivery-secretsmanager) secretsmanager ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/562806c0b18119f4d21cb66f5ae6d051634b4a16/icons/checkmark-icon.svg) 
 [Security and Compliance Center](https://cloud.ibm.com/docs/services/ContinuousDelivery?topic=ContinuousDelivery-scc) security_compliance ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/562806c0b18119f4d21cb66f5ae6d051634b4a16/icons/checkmark-icon.svg) 
 [Slack](https://cloud.ibm.com/docs/services/ContinuousDelivery?topic=ContinuousDelivery-slack) slack 
 [SonarQube](https://cloud.ibm.com/docs/services/ContinuousDelivery?topic=ContinuousDelivery-sonarqube) sonarqube
4. Create a copy of the temnplate by clicking Create custom dashboard.

You can use the metric Topic bytes in per second to see how data is sent by Monitoring to Event Streams.

You can use the metric Topic bytes out per second to see how data is consumed by any application or service that is subscribed to Event Streams.
5. (Optional) Edit the panel Topic bytes in per second.

Zoom

![Edit panel](https://cloud.ibm.com/docs-content/v1/content/220cdcbf6f4421bd7fee83fd1028048dba3f16c8/monitoring/images/streaming-topic-metric.png)

Edit panel

Then, customize the metric to see data per topic.

Zoom

![Customize metric](https://cloud.ibm.com/docs-content/v1/content/220cdcbf6f4421bd7fee83fd1028048dba3f16c8/monitoring/images/streaming-topic-metric-1.png)

Customize metric

Check that the resolution is set to 10M.
For more information, see [IBM Cloud Metrics Routing v3 CLI](https://cloud.ibm.com/docs/metrics-router?topic=metrics-router-metrics-router-cli).





 API prerequisites 

Before you use the API to manage targets, complete the following steps:



1. Ensure you have the [correct IAM permissions to configure IBM Cloud Metrics Routing.](https://cloud.ibm.com/docs/metrics-router?topic=metrics-router-iam)
2. Get an IAM access token. For more information, see [Retrieving IAM access tokens](https://cloud.ibm.com/docs/metrics_router?topic=metrics_router-retrieve-iam-token).
3. Identify the API endpoint in the region where you plan to configure or manage a target. For more information, see [Endpoints](https://cloud.ibm.com/docs/metrics_router?topic=metrics_router-endpointsendpoints_api).







 API methods 

The following table lists the actions that you can run to manage targets:



Table 5. Target actions by using the IBM Cloud Metrics Routing REST API

 Action REST API Method API_URL 

 Create a target POST <ENDPOINT>/api/v3/targets 
 Update a target PATCH <ENDPOINT>/api/v3/targets/<TARGET_ID> 
 Delete a target DELETE <ENDPOINT>/api/v3/targets/<TARGET_ID> 
 Read a target GET <ENDPOINT>/api/v3/targets/<TARGET_ID> 
 List all targets GET <ENDPOINT>/api/v3/targets 



For more information, see [IBM Cloud Metrics Routing v3 API](https://cloud.ibm.com/apidocs/metrics-router).





 HTTP response codes 

When you use the IBM Cloud Metrics Routing REST API, you can get standard HTTP response codes to indicate whether a method completed successfully.
Table 46: Persistent message MQPUT count metric metadata

 Metadata Description 

 Metric Name ibm_mq_qmgr_persistent_message_mqput_total 
 Metric Type counter 
 Value Type none 
 Segment By Service instance, The queue manager name 







 Persistent message MQPUT1 count 

Total persistent message MQPUT1 count for a queue manager



Table 47: Persistent message MQPUT1 count metric metadata

 Metadata Description 

 Metric Name ibm_mq_qmgr_persistent_message_mqput1_total 
 Metric Type counter 
 Value Type none 
 Segment By Service instance, The queue manager name 







 Persistent message browse - byte count 

Total persistent message browse byte count for a queue manager



Table 48: Persistent message browse - byte count metric metadata

 Metadata Description 

 Metric Name ibm_mq_qmgr_persistent_message_browse_bytes_total 
 Metric Type counter 
 Value Type byte 
 Segment By Service instance, The queue manager name 







 Persistent message browse - count 

Total persistent message browse count for a queue manager



Table 49: Persistent message browse - count metric metadata

 Metadata Description 

 Metric Name ibm_mq_qmgr_persistent_message_browse_total 
 Metric Type counter 
 Value Type none 
 Segment By Service instance, The queue manager name 







 Persistent message destructive get - count 

Total persistent message destructive get count for a queue manager



Table 50: Persistent message destructive get - count metric metadata

 Metadata Description 

 Metric Name ibm_mq_qmgr_persistent_message_destructive_get_total 
 Metric Type counter 
 Value Type none 
 Segment By Service instance, The queue manager name 







 Published to subscribers - byte count 

Total published to subscribers byte count for a queue manager



Table 51: Published to subscribers - byte count metric metadata

 Metadata Description 

 Metric Name ibm_mq_qmgr_published_to_subscribers_bytes_total 
 Metric Type counter 
 Value Type byte 
 Segment By Service instance, The queue manager name 







 Published to subscribers - message count 

Total published to subscribers message count for a queue manager



Table 52: Published to subscribers - message count metric metadata

 Metadata Description
: Segment a time series whenever no observation occurs within the length of time, in timetick units, that is specified by the second parameter.

TS_SEGMENT_BY_SMOOTHED_SILENCE

[6]

(Double, Double, Long)
: Output: The type of the output segment time series corresponds to the type of the input.
: Segment a time series whenever no observation occurs within the length of time, in timetick units, that is calculated by the formula min(T×F, H) where:



* T = Interarrival time (that is, the time between two observations), smoothed by using the specified [exponential smoothing](https://en.wikipedia.org/wiki/Exponential_smoothing) factor alpha (second parameter)
* F = factor (third parameter)
* H = Threshold (fourth parameter)



TS_SEGMENT_BY_ANCHOR

[7]

(AnchorType, Long, Long)
: Output: The type of the output segment time series corresponds to the type of the input.
: Segment a time series based on the specified anchor. The specified Long values determine a segment that begins before (third parameter) and ends after (fourth parameter) each anchor point. For example:



* Specify 0,0 to return only those observations that correspond to the anchor points.
* Specify 5,3 to create a segment that includes, in addition to the observation at each anchor point, all observations that occur between five timeticks before and three timeticks after each anchor point.
* Specify 5,-3 to create a segment that includes all observations that occur between five and three timeticks before the anchor point.



TS_SEGMENT_BY_MARKER (ts: AnyTimeSeries, marker: BooleanExpressionType[Any], prevInclusive: Boolean, nextInclusive: Boolean, requiresStartAndEnd: Boolean)
: Output: Same as input
: Segment the time series by a marker point (Boolean expression) where each segment exists between markers.
Both of these scan types can find toolchains and associated evidence, as it is configured in the Name, Evidence repository name or URL, and Evidence namespace fields within the Security and Compliance Center tool integration.

The trigger validation scan after deploy field, and the other three trigger scan option fields work only with hybrid cloud scans.

Configure Security and Compliance Center to embed security checks into your workflows to monitor for security and compliance:



1. If you are configuring this tool integration as you are creating the toolchain, and a Security and Compliance Center tool integration exists within the template that you are configuring, click the Security and Compliance Center tab. Alternatively, in the More tools section, click Security and Compliance Center.
2. If you have a toolchain and are adding this tool integration to it, from the IBM Cloud console, click the menu icon ![hamburger icon](https://cloud.ibm.com/docs-content/v1/content/5eb88a2575e30e316dbb83829c7f4c8b1ddbc67c/devsecops/includes/ContinuousDelivery/includes/ContinuousDelivery/images/icon_hamburger.svg) and select DevOps. On the Toolchains page, click the toolchain to open its Overview page. Alternatively, on your app's Overview page, on the Continuous delivery card, click View toolchain. Then, click Overview.

a. Click Add tool.

b. In the Tool Integrations section, click Security and Compliance Center.
3. Type the name that you want to display for this tool integration on the Security and Compliance Center card in your toolchain. This name is used to identify the tool integration in your toolchain.
4. Specify the Evidence repository name or URL. This is the name or URL of the repo that stores the evidence locker for your toolchain. This repo must correspond to the Git tool integration you previously added to your toolchain to store your evidence. Currently, only GitHub, GitLab, and IBM-hosted Git are supported.
[Learn more](https://cloud.ibm.com/docs/overview?topic=overview-locationsmzr-table).







 Data availability 

The data that is managed by IBM Cloud Metrics Routing in a region is kept in the data centers near that region.

A multizone region (MZR) consists of 3 or more availability zones that are independent from each other to ensure that single failure metrics affect only a single zone.

By default, IBM Cloud Metrics Routing is deployed across 3 zones. Each zone is set up with active/active/active:



* Each zone is located in a different data center in the region.
* The data in each zone is automatically replicated to the other zones with low latency. You don't need to do anything to enable the replication.
* The service is designed to withstand a single zone failure with no interruption.



The MZR architecture offers automatic failover between zones within the region, and high availability for an instance withing a region.

IBM Cloud Metrics Routing data includes information on where and how to collect and store metrics in your account.



* A target defines a resource where you can collect metrics.
* A route defines the rules that determine where metrics are routed in your account or cross-account.



IBM Cloud Metrics Routing does regular backups of the data per region:



* The settings metadata locations indicate the region where data is backup.
* Regular backups are done daily.
* IBM Cloud Metrics Routing data is replicated across regions based on your account configuration for the primary and backup metadata locations.
[Schema used percentage](https://cloud.ibm.com/docs/EventStreams?topic=EventStreams-metricsibm_eventstreams_instance_schema_registry_schemas_used_percentage) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/e0c3acba1296ff980e76d5db456d53adefaabba4/icons/checkmark-icon.svg) 
 [Stable consumer groups](https://cloud.ibm.com/docs/EventStreams?topic=EventStreams-metricsibm_eventstreams_instance_stable_consumergroups) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/e0c3acba1296ff980e76d5db456d53adefaabba4/icons/checkmark-icon.svg) 
 [Topic bytes in per second](https://cloud.ibm.com/docs/EventStreams?topic=EventStreams-metricsibm_eventstreams_instance_topic_bytes_in_per_second) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/e0c3acba1296ff980e76d5db456d53adefaabba4/icons/checkmark-icon.svg) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/e0c3acba1296ff980e76d5db456d53adefaabba4/icons/checkmark-icon.svg) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/e0c3acba1296ff980e76d5db456d53adefaabba4/icons/checkmark-icon.svg)
6. Click Configure.
7. Your Hyper Protect Crypto Services instance is now set for platform metrics.









 Hyper Protect Crypto Services Metrics Details 

You can use the metrics in your Monitoring dashboard to measure the types of requests that are made to your service instance as well as the latency of the requests.



 API Hits 

The type and number of API requests that are made to your Hyper Protect Crypto Services instance. For example, you can track how many API requests that are made by an authorized user by setting an [alert](https://cloud.ibm.com/docs/hs-crypto?topic=hs-crypto-operational-metricsset-monitor-alerts). The alert triggers when your Monitoring instance notices a frequent number of 401 status codes that are returned from your Hyper Protect Crypto Services instance.



Table 1. Describes the API Hits metrics.

 Metadata Description 

 Metric Name ibm_hpcs_api_request_gauge 
 Metric Type Gauge 
 Value Type none 
 Segment By [Attributes for Segmentation](https://cloud.ibm.com/docs/hs-crypto?topic=hs-crypto-operational-metricsattributes-for-segmentation) 









 Latency 

The number of time it takes Hyper Protect Crypto Services to receive an API request and respond to it.

The latency is calculated by getting the average of all requests of the same type that occur within 60 seconds.



Table 2. Describes the Latency metrics.

 Metadata Description 

 Metric Name ibm_hpcs_api_latency_gauge 
 Metric Type Gauge 
 Value Type Milliseconds 
 Segment By [Attributes for Segmentation](https://cloud.ibm.com/docs/hs-crypto?topic=hs-crypto-operational-metricsattributes-for-segmentation) 







 Attributes for Segmentation 

You can filter your metrics by using the following attributes.



Table 3. Describes the attributes use for segmenting metrics.

 Attribute Name Description 

 ibm_resource_type Supported resource type is instance. 
 ibm_hpcs_response_code Response code for the Hyper Protect Crypto Services service API request.
fc","phase":-0.6916896718805451,"t0":1056},{"ch":"u15","name":"fc","phase":-3.141592653589793,"t0":1056},{"ch":"u20","name":"fc","phase":-3.141592653589793,"t0":0},{"ch":"u20","label":"CX_u20","name":"parametric_pulse","parameters":{"amp":0.3371692176291682,0.08892521109898281],"duration":896,"sigma":64,"width":640},"pulse_shape":"gaussian_square","t0":160},{"ch":"u20","name":"fc","phase":-0.04606788860377323,"t0":1056},{"ch":"u20","name":"fc","phase":-3.141592653589793,"t0":1056},{"ch":"u24","name":"fc","phase":-3.141592653589793,"t0":0},{"ch":"u24","name":"fc","phase":-0.6916896718805451,"t0":1056},{"ch":"u24","name":"fc","phase":
The service calculates the metrics at the requested interval, but it returns all metrics for the audio with the final transcription results.



The service also returns processing metrics for transcription events. If you request interim results with the WebSocket interface, you can receive metrics with greater frequency than the requested interval.

To receive processing metrics only for transcription events instead of at periodic intervals, set the processing interval to a large number. If the interval is larger than the duration of the audio, the service returns processing metrics only for transcription events.





 Understanding processing metrics 

The service returns processing metrics for transcription events in the processing_metrics field of the SpeechRecognitionResults object. For metrics generated at periodic intervals, not with transcription events, the object contains only the processing_metrics field, as shown in the following example.

{
"processing_metrics": {
"processed_audio": {
"received": float,
"seen_by_engine": float,
"transcription": float,
"speaker_labels": float
},
"wall_clock_since_first_byte_received": float,
"periodic": boolean
}
}

The processing_metrics field includes a ProcessingMetrics object that has the following fields:



* wall_clock_since_first_byte_received is the amount of real time in seconds that has passed since the service received the first byte of input audio. Values in this field are generally multiples of the specified metrics interval, with two differences:



* Values might not reflect exact intervals such as 0.25, 0.5, and so on. Actual values might, for example, be 0.27, 0.52, and so on, depending on when the service receives and processes audio.
* Values for transcription events are not related to the processing interval. The service returns event-driven results as they occur.



* periodic indicates whether the metrics apply to a periodic interval or to a transcription event:



* true means that the response was triggered by a processing interval. The information contains processing metrics only.
* false means that the response was triggered by a transcription event.
":-0.0021564674084263535,0.0874162373846442],"beta":-2.147473654798121,"duration":160,"sigma":40},"pulse_shape":"drag","t0":0},{"ch":"d3","name":"fc","phase":1.2802794119867433,"t0":1360},{"ch":"d3","name":"fc","phase":-3.141592653589793,"t0":1360},{"ch":"d3","label":"Y90p_d3","name":"parametric_pulse","parameters":{"amp":-0.0021564674084263535,0.0874162373846442],"beta":-2.147473654798121,"duration":160,"sigma":40},"pulse_shape":"drag","t0":1360},{"ch":"u10","name":"fc","phase":-3.141592653589793,"t0":0},{"ch":"u10","name":"fc","phase":1.2802794119867433,"t0":1360},{"ch":"u10","name":"fc","phase":-3.141592653589793,"t0":1360},{"ch":"u2","name":"fc","phase":-3.141592653589793
3. From the Facet analysis pane, select Topic.
4. Adjust the following values to suit your analysis:



* Number of results
* Date facet
* Time scale
* Date range



5. Choose a target facet or subfacet, and then click Analyze.

The resulting time series graph shows changes in the frequency of keyword mentions over time.

Zoom

![Topic analysis graph shows that Equipment has some orange bars in the summer months](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/discovery-data/images/cm-facet-topic.png)

Figure 5. Topic analysis time series view

Color coding is used to highlight when the number of mentions deviates from the expected frequency. The higher the deviation, the more intense the color, from yellow to orange to red. The average is calculated based on the frequency of occurrence of other keywords in the same time period.



The cyclical data is calculated from the current time zone setting of your collection. If you want to change the time zone that is used by the graph, see [Change the time zone](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-cm-edit-collectioncm-edit-collection-time-zone).





 Find significant terms 

Find characteristic words from your data set. The characteristic words view is a word cloud that shows terms that are mentioned frequently in the documents you are analyzing.

You can click a word from the word cloud to add it to the existing query and filter the current document set to include only documents that also mention the specified word.

To find significant terms, complete the following steps:



1. From the search results page in guided mode, click Analyze cause or characteristics.

The characteristic words view is displayed.

Zoom

!
":"drag","t0":1184},{"ch":"u23","name":"fc","phase":-3.141592653589793,"t0":0},{"ch":"u23","name":"fc","phase":-1.5707963267948966,"t0":1184},{"ch":"u25","name":"fc","phase":-1.5707963267948966,"t0":0},{"ch":"u28","name":"fc","phase":-3.141592653589793,"t0":0},{"ch":"u28","label":"CR90p_u28","name":"parametric_pulse","parameters":{"amp":-0.45756532344425743,-0.7274289089214204],"duration":432,"sigma":64,"width":176},"pulse_shape":"gaussian_square","t0":160},{"ch":"u28","label":"CR90m_u28","name":"parametric_pulse","parameters":{"amp":0.45756532344425754,0.7274289089214203],"duration":432,"sigma":64,"width":176},"pulse_shape":"gaussian_square","t0":752},{"
Monitoring Cloud Functions entities with IBM Cloud Monitoring 

Get insight into the performance of your actions that are deployed with IBM Cloud® Functions. Metrics can help you find bottlenecks or predict possible production problems based on action duration, results of action activations, or hitting action activation limits.

You can monitor your Cloud Functions actions through the IBM Cloud® Monitoring service. The Cloud Functions service forwards selected information about your actions to Monitoring so that you can monitor specific metrics such as status, duration, rate limits, and concurrency.



 Set up your IBM Cloud Monitoring service instance 

To set up your Cloud Functions customer metrics dashboards in Monitoring, you must create a service instance and then enable Platform Metrics in the same region as the Cloud Functions actions that you want to monitor. If you have deployments in more than one region, you must provision Monitoring and enable platform metrics for each region.

To set up Monitoring,



1. From the IBM Cloud navigation menu, select Observability.
2. Select Monitoring.
3. Either use an existing Monitoring service instance or create a new one.
4. After the instance is ready, enable platform metrics by clicking Configure platform metrics.
5. Select a region and then a Monitoring instance from that region. Note that if you have deployments in more than one region, you must provision Monitoring and enable platform metrics for each region.



Provisioning your monitor might take a few minutes and that you must run an action or trigger in the same region as the monitor before metrics are displayed.

You can also launch monitoring from your Cloud Functions dashboard by selecting Launch Monitoring.





 Accessing your IBM Cloud Monitoring metrics 

To see your Cloud Functions customer metrics dashboards in Monitoring:



1. From the IBM Cloud navigation menu, select Observability.
2. Select Monitoring.
3. Select View IBM Cloud Monitoring to open the dashboard.
4. From the navigation menu, select Dashboards->IBM->Cloud Functions - Customer Metrics Dashboard.



For more information, see [IBM Cloud Monitoring Getting started tutorial](https://cloud.ibm.com/docs/monitoring?topic=monitoring-getting-started).
+--------------------------------------+--------+

 Metrics Assessment 
> Most recently read values of experiment metrics for each version.
+--------------------------------------+--------+
 METRIC MY-APP 
+--------------------------------------+--------+
 iter8-system/request-count 40.000 
+--------------------------------------+--------+
 iter8-system/error-count 0.000 
+--------------------------------------+--------+
 iter8-system/mean-latency 66.060 
+--------------------------------------+--------+
 iter8-system/error-rate 0.000 
+--------------------------------------+--------+
 iter8-system/latency-95th-percentile 73.055 
+--------------------------------------+--------+
[Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/c5288bed34c3820e3a5251d820ee91adcd2591a3/icons/checkmark-icon.svg) + anti-affinity ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/c5288bed34c3820e3a5251d820ee91adcd2591a3/icons/checkmark-icon.svg) 
 CA replica sets ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/c5288bed34c3820e3a5251d820ee91adcd2591a3/icons/checkmark-icon.svg) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/c5288bed34c3820e3a5251d820ee91adcd2591a3/icons/checkmark-icon.svg) 
 Anti-affinity (CA replica sets) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/c5288bed34c3820e3a5251d820ee91adcd2591a3/icons/checkmark-icon.svg) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/c5288bed34c3820e3a5251d820ee91adcd2591a3/icons/checkmark-icon.svg) 
 Development or Test environment ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/c5288bed34c3820e3a5251d820ee91adcd2591a3/icons/checkmark-icon.svg) !