vania-janet/multiturn-rag-retrieval-data huggingface dataset:
"version": "423792" // commit id representing the task version used to produce the evidence
}
"toolchain_crn": "crn:v1:bluemix:public:toolchain:us-south:a/190e0b4ce4cd013159917665213ddc51:7467a214-f404-4e23-acab-2e57e935d138::" // CRN of the toolchain
"pipeline_id": "b1cfb6a4-813d-4fec-bbf7-ce662e8ce4e0", // the id of the pipeline that generated this piece of evidence
"pipeline_run_id": 12345, // this is the id of the pipeline run that generated this piece of evidence
"artifact_name": "mybaseimage", //name of the dependent artifact
"checksum": "324334", // checksum or digital isgnature
"verified": "yes"|"no"], // checksum verified or not
"scan_status": "passed"|"failed"], // the scan of the status
"log": {
"url": "https://acme.org/log.txt", // the link to where the actual log of the test is
"hash": "abc123", // the hash of the log file
},
"checksum_issues": 134, 132, 342 ], // link(s) to the issues representing missing or not matching checksums
"scan_issues": 123, 342, 453], // link(s) to the issues found by the scan (X-Ray)
"expired_issues": 134, 132, 342], // link(s) to the issues for which the grace period expired
"required": "yes"|"no"] // yes: this is an auditable step; no: this is a non auditable step
}, {
This value is optional.

--quiet, -q
: Specify this option to reduce the output of the command. This value is optional. The default value is false.

--sort-by, -s
: Specifies the column by which to sort the list. Valid values are name and age. This value is optional. The default value is name.





 Example 

ibmcloud ce revision list





 Example output 

Code Engine retains only the latest inactive revision of your application in addition to your active app revision. Older revisions are not retained.

Listing all application revisions...
OK

Name Application Status URL Latest Tag Traffic Age Conditions Reason
myapp-hc3u8-2 myapp Ready 16d 3 OK / 4
myapp-hc3u8-3 myapp Ready true 100% 2d8h 3 OK / 4
newapp-mytest-00004 newapp-mytest Ready 4d20h 3 OK / 4
newapp-mytest-00005 newapp-mytest Ready true 100% 2d20h 3 OK / 4







 ibmcloud ce revision logs 

Display the logs of application revision instances.

ibmcloud ce revision logs (--instance REVISION_INSTANCE | --revision REVISION_NAME) [--all-containers] [--follow] [--output OUTPUT] [--quiet] [--tail TAIL] [--timestamps]



 Command Options 

--all-containers, --all
: Display the logs of all containers of the specified application revision instances. This value is optional. The default value is false.

--follow, -f
: Follow the logs of application revision instances. Use this option to stream logs of application revision instances. If you specify the --follow option, you must enter Ctrl+C to terminate this log command. This value is optional. The default value is false.

--instance, -i
: The name of a specific application revision instance. Use the revision get command to find the instance name. This value is required if --revision is not specified.

--output, -o
Tune the performance of your machine learning model by performing one or more of following steps, and then retraining your model:



1. Identify commonly occurring types with low accuracy.

When analyzing relations, look at both the F1 score of the relation type itself and at the F1 score of each of the two entities that participates in the relationship.
2. Identify commonly confused types. This information can be found by looking at the numbers that are off the diagonal in the confusion matrix.
3. Review errors where the machine learning model has high confidence.
4. Find patterns in the false negatives and false positives in the confusion matrix.
5. If certain types occur infrequently in the training data, add training data that contains those types.

You can determine the frequency of occurrence by checking the percentage statistics (there are three: % of annotations, % of corpus density, and % of documents) for the type.
6. If certain types have low F1 scores, review the clarity of the annotation guidelines that apply to those types.
7. Add a dictionary for types that occur infrequently in the training data.



![How to resolve low F1 scores.](https://cloud.ibm.com/docs-content/v1/content/148d3bd95f946aa1bb53ea1540475f522e6b61c9/watson-knowledge-studio-data/images/wks_lowf1.svg) Figure 1. How to resolve low F1 scores







 Analyzing low precision scores 

Tune the performance of your machine learning model to address low precision scores. At a high level, low precision indicates a need to improve annotation consistency.



 Symptoms 

A precision score reaches its best value at 1 and worst value at 0. A low precision score indicates that the machine learning model generated incorrect annotations.





 Causes 

Low precision scores can occur for many different reasons that depend on the domain, type system complexity, appropriateness of training documents, human annotator skills, and other factors.





 Resolving the problem 

Tune the performance of your machine learning model by performing one or more of following steps then retrain your model:
1.2.24 Ensure that the --audit-log-maxbackup option is set to 10 or as appropriate. Scored 1 [Fail](https://cloud.ibm.com/docs/openshift?topic=openshift-cis-benchmark-47cis-benchmark-remediations-47) Shared 
 1.2.25 Ensure that the --audit-log-maxsize option is set to 100 or as appropriate. Scored 1 [Fail](https://cloud.ibm.com/docs/openshift?topic=openshift-cis-benchmark-47cis-benchmark-remediations-47) Shared 
 1.2.26 Ensure that the --request-timeout option is set as appropriate. Scored 1 Pass IBM 
 1.2.27 Ensure that the --service-account-lookup option is set to true. Scored 1 Pass IBM 
 1.2.28 Ensure that the --service-account-key-file option is set as appropriate. Scored 1 Pass IBM 
 1.2.29 Ensure that the --etcd-certfile and --etcd-keyfile options are set as appropriate. Scored 1 Pass IBM 
 1.2.30 Ensure that the --tls-cert-file and --tls-private-key-file options are set as appropriate. Scored 1 Pass IBM 
 1.2.31 Ensure that the --client-ca-file option is set as appropriate. Scored 1 Pass IBM 
 1.2.32 Ensure that the --etcd-cafile option is set as appropriate. Scored 1 Pass IBM 
 1.2.33 Ensure that the --encryption-provider-config option is set as appropriate. Scored 1 [Fail](https://cloud.ibm.com/docs/openshift?topic=openshift-cis-benchmark-47cis-benchmark-remediations-47) Shared 
 1.2.34 Ensure that encryption providers are appropriately configured.
1.2.24 Ensure that the --audit-log-maxbackup option is set to 10 or as appropriate. Scored 1 [Fail](https://cloud.ibm.com/docs/openshift?topic=openshift-cis-benchmark-48cis-benchmark-remediations-48) Shared 
 1.2.25 Ensure that the --audit-log-maxsize option is set to 100 or as appropriate. Scored 1 [Fail](https://cloud.ibm.com/docs/openshift?topic=openshift-cis-benchmark-48cis-benchmark-remediations-48) Shared 
 1.2.26 Ensure that the --request-timeout option is set as appropriate. Scored 1 Pass IBM 
 1.2.27 Ensure that the --service-account-lookup option is set to true. Scored 1 Pass IBM 
 1.2.28 Ensure that the --service-account-key-file option is set as appropriate. Scored 1 Pass IBM 
 1.2.29 Ensure that the --etcd-certfile and --etcd-keyfile options are set as appropriate. Scored 1 Pass IBM 
 1.2.30 Ensure that the --tls-cert-file and --tls-private-key-file options are set as appropriate. Scored 1 Pass IBM 
 1.2.31 Ensure that the --client-ca-file option is set as appropriate. Scored 1 Pass IBM 
 1.2.32 Ensure that the --etcd-cafile option is set as appropriate. Scored 1 Pass IBM 
 1.2.33 Ensure that the --encryption-provider-config option is set as appropriate. Scored 1 [Fail](https://cloud.ibm.com/docs/openshift?topic=openshift-cis-benchmark-48cis-benchmark-remediations-48) Shared 
 1.2.34 Ensure that encryption providers are appropriately configured.
Section 5.1 RBAC and Service Accounts benchmark results

 Section Recommendation Scored/Not Scored Level Result Responsibility 

 5.1.1 Ensure that the cluster-admin role is only used where required. Not Scored 1 Pass Shared 
 5.1.2 Minimize access to secrets. Not Scored 1 [Fail](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-121cis-benchmark-remediations-121) Shared 
 5.1.3 Minimize wildcard use in Roles and ClusterRoles. Not Scored 1 [Fail](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-121cis-benchmark-remediations-121) Shared 
 5.1.4 Minimize access to create pods. Not Scored 1 Pass Shared 
 5.1.5 Ensure that default service accounts are not actively used. Scored 1 [Fail](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-121cis-benchmark-remediations-121) Shared 
 5.1.6 Ensure that Service Account Tokens are only mounted where necessary. Not Scored 1 [Fail](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-121cis-benchmark-remediations-121) Shared 







 5.2 Pod Security Policies 



Section 5.2 Pod Security Policies benchmark results

 Section Recommendation Scored/Not Scored Level Result Responsibility 

 5.2.1 Minimize the admission of privileged containers. Not Scored 1 [Pass](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-121cis-benchmark-remediations-121) Shared 
 5.2.2 Minimize the admission of containers wanting to share the host process ID namespace. Scored 1 [Pass](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-121cis-benchmark-remediations-121) Shared 
 5.2.3 Minimize the admission of containers wanting to share the host IPC namespace.
But the F1 score for other entity types might be low because differences in phrasing, and differences in how human annotators interpret the text or annotation guidelines, make it more difficult for the machine learning model to recognize the pattern and apply the correct annotation.
5. In the Confusion Matrix view for test data, specify the type of annotations that you want to see statistics for: entity types or relation types. For each entity type or relation type:



* Each row in the matrix shows ground truth (entity types and relation types added by a human annotator).
* Each column in the matrix shows the decoding results (annotation tokens added by the machine learning model).
* The numbers in each cell represent the number of annotation tokens per mention, not the occurrences of the mention.

Tokens are used to delineate text strings. They loosely correspond to words. A mention can contain more than one token. For example, Barack Obama is a mention that contains two tokens. In the PERSON row or column, a value of 2 would be used to represent the single mention of Barack Obama in a document. The token count only loosely corresponds to the word count because tokens are counted differently in some situations. For example, punctuation at the end of a sentence is counted as a token, and contractions are often expanded into two tokens.
* The column labeled O identifies tokens that should have been annotated by the machine learning model as the type that is identified in the intersecting row (the human annotation), but they are not annotated as any type.
* The value N/A is shown when there are no annotations for a given type in the document sets. For example, if there is no PERSON mention in the document sets that were annotated for ground truth or as test data, the scores for the PERSON entity type is N/A.



For example, the following confusion matrix example shows results of a machine learning model run on documents that deal with traffic incidents.

Table 1. Confusion matrix example



 Entity types MANUFACTURER MODEL O 

 MANUFACTURER 515 5 44 



You can learn the following things from this matrix:



* The model correctly recognized 515 tokens as mentions of the MANUFACTURER entity type.
4.1.6 Ensure that the kubelet.conf file ownership is set to root:root. Scored 1 Pass IBM 
 4.1.7 Ensure that the certificate authorities file permissions are set to 644 or more restrictive. Scored 1 Pass IBM 
 4.1.8 Ensure that the client certificate authorities file ownership is set to root:root. Scored 1 Pass IBM 
 4.1.9 Ensure that the kubelet configuration file has permissions set to 644 or more restrictive. Scored 1 Pass IBM 
 4.1.10 Ensure that the kubelet configuration file ownership is set to root:root. Scored 1 Pass IBM 







 4.2 Kubelet 



Section 4.2 Kubelet benchmark results

 Section Recommendation Scored/Not Scored Level Result Responsibility 

 4.2.1 Ensure that the --anonymous-auth option is set to false. Scored 1 Pass IBM 
 4.2.2 Ensure that the --authorization-mode option is not set to AlwaysAllow. Scored 1 Pass IBM 
 4.2.3 Ensure that the --client-ca-file option is set as appropriate. Scored 1 Pass IBM 
 4.2.4 Ensure that the --read-only-port option is set to 0. Scored 1 Pass IBM 
 4.2.5 Ensure that the --streaming-connection-idle-timeout option is not set to 0. Scored 1 Pass IBM 
 4.2.6 Ensure that the --protect-kernel-defaults option is set to true. Scored 1 [Fail](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-121cis-benchmark-remediations-121) IBM 
 4.2.7 Ensure that the --make-iptables-util-chains option is set to true. Scored 1 Pass IBM 
 4.2.8 Ensure that the --hostname-override option is not set. Not Scored 1 [Fail](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-121cis-benchmark-remediations-121) IBM
Section 5.1 RBAC and Service Accounts benchmark results

 Section Recommendation Scored/Not Scored Level Result Responsibility 

 5.1.1 Ensure that the cluster-admin role is only used where required. Not Scored 1 Pass Shared 
 5.1.2 Minimize access to secrets. Not Scored 1 [Fail](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-119cis-benchmark-remediations-119) Shared 
 5.1.3 Minimize wildcard use in Roles and ClusterRoles. Not Scored 1 [Fail](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-119cis-benchmark-remediations-119) Shared 
 5.1.4 Minimize access to create pods. Not Scored 1 Pass Shared 
 5.1.5 Ensure that default service accounts are not actively used. Scored 1 [Fail](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-119cis-benchmark-remediations-119) Shared 
 5.1.6 Ensure that Service Account Tokens are only mounted where necessary. Not Scored 1 [Fail](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-119cis-benchmark-remediations-119) Shared 







 5.2 Pod Security Policies 



Section 5.2 Pod Security Policies benchmark results

 Section Recommendation Scored/Not Scored Level Result Responsibility 

 5.2.1 Minimize the admission of privileged containers. Not Scored 1 [Pass](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-119cis-benchmark-remediations-119) Shared 
 5.2.2 Minimize the admission of containers wanting to share the host process ID namespace. Scored 1 [Pass](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-119cis-benchmark-remediations-119) Shared 
 5.2.3 Minimize the admission of containers wanting to share the host IPC namespace.
interim_results=false low_latency=false The service sends only final results. It returns a single JSON object that includes results for all utterances when transcription is complete. See [Example 1: Interim results and low latency are both false](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-interiminterim-low-latency-examples-one). 
 interim_results=false low_latency=true The service sends only final results. It returns a single JSON object that includes results for all utterances when transcription is complete. But because low_latency is true, the service returns the final results more quickly. See [Example 2: Interim results is false and low latency is true](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-interiminterim-low-latency-examples-two). 
 interim_results=true low_latency=false The service sends only final results. It returns multiple JSON objects for individual utterances as it performs transcription. The advantage of setting interim_results to true is that results for utterances arrive as they become complete. You do not need to wait for all utterances to be transcribed. See [Example 3: Interim results is true and low latency is false](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-interiminterim-low-latency-examples-three). 
 interim_results=true low_latency=true The service returns interim results as it develops transcription hypotheses, and it sends final results for utterances as they become complete. The service delivers one or more interim results for each final result. The quality of interim results is identical to what you receive with previous-generation models. But because low_latency is true, the service returns interim and final results more quickly.
4.1.6 Ensure that the kubelet.conf file ownership is set to root:root. Scored 1 Pass IBM 
 4.1.7 Ensure that the certificate authorities file permissions are set to 644 or more restrictive. Scored 1 Pass IBM 
 4.1.8 Ensure that the client certificate authorities file ownership is set to root:root. Scored 1 Pass IBM 
 4.1.9 Ensure that the kubelet configuration file has permissions set to 644 or more restrictive. Scored 1 Pass IBM 
 4.1.10 Ensure that the kubelet configuration file ownership is set to root:root. Scored 1 Pass IBM 







 4.2 Kubelet 



Section 4.2 Kubelet benchmark results

 Section Recommendation Scored/Not Scored Level Result Responsibility 

 4.2.1 Ensure that the --anonymous-auth option is set to false. Scored 1 Pass IBM 
 4.2.2 Ensure that the --authorization-mode option is not set to AlwaysAllow. Scored 1 Pass IBM 
 4.2.3 Ensure that the --client-ca-file option is set as appropriate. Scored 1 Pass IBM 
 4.2.4 Ensure that the --read-only-port option is set to 0. Scored 1 Pass IBM 
 4.2.5 Ensure that the --streaming-connection-idle-timeout option is not set to 0. Scored 1 Pass IBM 
 4.2.6 Ensure that the --protect-kernel-defaults option is set to true. Scored 1 [Fail](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-119cis-benchmark-remediations-119) IBM 
 4.2.7 Ensure that the --make-iptables-util-chains option is set to true. Scored 1 Pass IBM 
 4.2.8 Ensure that the --hostname-override option is not set. Not Scored 1 [Fail](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-119cis-benchmark-remediations-119) IBM
In the response, you get an empty result if the deletion was successful:

{}





 Validating a target using the API 

You can use the following curl command to validate a target by checking the credentials to write to the target.

curl -X POST <ENDPOINT>/api/v2/targets/<TARGET_ID>/validate -H "Authorization: $ACCESS_TOKEN" -H "content-type: application/json"

Where



* <ENDPOINT> is 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/atracker?topic=atracker-endpointsendpoints_api).
* <TARGET_ID> is the ID of the target.



For example, you can use the following curl request to validate a target in US-South with the ID 00000000-0000-0000-0000-000000000000:

curl -X POST https://private.us-south.atracker.cloud.ibm.com/api/v2/targets/<TARGETID>/validate -H "Authorization: $ACCESS_TOKEN" -H "content-type: application/json"

In the response, you get information in the section write_status, for example:

"write_status": {
"status": "success"
},





 Viewing a target using the API 

You can use the following curl command to view the configuration details of 1 target:

curl -X GET <ENDPOINT>/api/v2/targets/<TARGET_ID> -H "Authorization: $ACCESS_TOKEN" -H "content-type: application/json"

Where



* <ENDPOINT> is 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/atracker?topic=atracker-endpointsendpoints_api).
* <TARGET_ID> is the ID of the target.
The following three levels exist.

acks=0 (least reliable)
: The message is considered sent as soon as it was written to the network. There is no acknowledgment from the partition leader. As a result, messages can be lost if the partition leadership changes. This level of acknowledgment is fast, but comes with the possibility of message loss in some situations.

acks=1 (the default)
: The message is acknowledged to the producer as soon as the partition leader successfully wrote its record to the partition. Because the acknowledgment occurs before the record reached the in-sync replicas, the message might be lost if the leader fails, but the followers do not yet have the message. If partition leadership changes, the old leader informs the producer, which can handle the error and retry to send the message to the new leader. Because messages are acknowledged before their receipt was confirmed by all replicas, messages that were acknowledged but not yet fully replicated can be lost if the partition leadership changes.

acks=all (most reliable)
: The message is acknowledged to the producer when the partition leader successfully wrote its record and all in-sync replicas did the same. The message is not lost if the partition leadership changes provided that at least one in-sync replica is available.

Even if you do not wait for messages to be acknowledged to the producer, messages are still only available to be consumed when committed, and that means replication to the in-sync replicas is complete. In other words, the latency of sending the messages from the point of view of the producer is lower than the end-to-end latency measured from the producer sending a message to a consumer receiving the message.

If possible, avoid waiting for the acknowledgment of a message before you publish the next message. Waiting prevents the producer from being able to batch together messages and also reduces the rate that messages can be published to below the round-trip latency of the network.





 Batching, throttling, and compression 

For efficiency purposes, the producer collects batches of records together for sending to the servers. If you enable compression, the producer compresses each batch, which can improve performance by requiring less data to be transferred over the network.
See [Example 3: Interim results is true and low latency is false](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-interiminterim-low-latency-examples-three). 
 interim_results=true low_latency=true The service returns interim results as it develops transcription hypotheses, and it sends final results for utterances as they become complete. The service delivers one or more interim results for each final result. The quality of interim results is identical to what you receive with previous-generation models. But because low_latency is true, the service returns interim and final results more quickly. See [Example 4: Interim results and low latency are both true](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-interiminterim-low-latency-examples-four). 





 Interim results and low-latency examples 

The following abbreviated WebSocket code shows how to request interim results, low-latency results, or both with the WebSocket interface. It specifies the following parameters:



* The model query parameter of the /v1/recognize request passes the next-generation en-US_Telephony model, which supports low latency.
* The inactivity_timeout parameter of the JSON start message sets the inactivity timeout to -1 (infinity), which prevents the request from timing out.
* Both the interim_results and low_latency parameters are specified with the JSON start message of the request.



To show examples of results with all possible combinations of the parameters, the arguments for interim_results and low_latency are set to true or false in the examples in the following sections.

var access_token = {access_token};
var wsURI = '{ws_url}/v1/recognize'
+ '?access_token=' + access_token
+ '&model=en-US_Telephony';
var websocket = new WebSocket(wsURI);