vania-janet/multiturn-rag-retrieval-data huggingface dataset:
* Classic: Provides an easy-to-use graphical user interface for defining stages and jobs that run on public shared infrastructure, with support for running individual stages on private workers.
* Tekton: Provides a dashboard that you can use to view the output of [Tekton pipeline](https://cloud.ibm.com/docs/services/ContinuousDelivery?topic=ContinuousDelivery-tekton-pipelines) runs on a defined Kubernetes cluster, with support for configuring pipeline definitions repos, the pipeline triggers, where the pipeline runs, and simple secrets.



5. If you plan to use your pipeline to deploy a user interface, and your pipeline is a Classic pipeline, select the Show apps in the View app menu checkbox. All of the apps that your pipeline creates are shown in the View App list on the toolchain's Overview page.
6. Click Create Integration to add the Delivery Pipeline to your toolchain.
7. From your toolchain, on the Delivery pipelines card, click the delivery pipeline to view and configure it. To learn the basics of configuring a pipeline, see [Building and deploying pipelines](https://cloud.ibm.com/docs/services/ContinuousDelivery?topic=ContinuousDelivery-deliverypipeline_build_deploy).

If you want the pipeline to automatically run when a commit is pushed to your GitHub or Git repository (repo), follow these steps:

a. Configure GitHub or Git Repos and Issue Tracking for your toolchain before you define the stages for your pipeline. The pipeline stages need the Git URLs for your repos. Each pipeline stage can refer to only one of the GitHub or Git repos that are associated with your toolchain. For instructions to configure GitHub, see the [GitHub](https://cloud.ibm.com/docs/ContinuousDelivery?topic=ContinuousDelivery-deliverypipelinegithub) section.
* Classic: Provides an easy-to-use graphical user interface for defining stages and jobs that run on public shared infrastructure, with support for running individual stages on private workers.
* Tekton: Provides a dashboard that you can use to view the output of [Tekton pipeline](https://cloud.ibm.com/docs/services/ContinuousDelivery?topic=ContinuousDelivery-tekton-pipelines) runs on a defined Kubernetes cluster, with support for configuring pipeline definitions repos, the pipeline triggers, where the pipeline runs, and simple secrets.



5. If you plan to use your pipeline to deploy a user interface, and your pipeline is a Classic pipeline, select the Show apps in the View app menu checkbox. All of the apps that your pipeline creates are shown in the View App list on the toolchain's Overview page.
6. Click Create Integration to add the Delivery Pipeline to your toolchain.
7. From your toolchain, on the Delivery pipelines card, click the delivery pipeline to view and configure it. To learn the basics of configuring a pipeline, see [Building and deploying pipelines](https://cloud.ibm.com/docs/services/ContinuousDelivery?topic=ContinuousDelivery-deliverypipeline_build_deploy).

If you want the pipeline to automatically run when a commit is pushed to your GitHub or Git repository (repo), follow these steps:

a. Configure GitHub or Git Repos and Issue Tracking for your toolchain before you define the stages for your pipeline. The pipeline stages need the Git URLs for your repos. Each pipeline stage can refer to only one of the GitHub or Git repos that are associated with your toolchain. For instructions to configure GitHub, see the [GitHub](https://cloud.ibm.com/docs/services/ContinuousDelivery?topic=ContinuousDelivery-deliverypipelinegithub) section.
The Discovery tooling does not yet use this API version (it currently uses 2018-12-03), so when you check document status in the Discovery tooling, the pending status is not returned.





 21 December 2018 

New option to connect and sync with Microsoft Sharepoint 2016 On-Premise : Added the option to connect to and sync with Microsoft SharePoint 2016 On-Premise. This data source is not available in Dedicated environments. See [SharePoint 2016 On-Premise](https://cloud.ibm.com/docs/discovery?topic=discovery-sourcesconnectsp_op) for more information.

New beta Web crawl connector : Added the beta version of the Web crawl connector, which can be used connect to, crawl, and sync with websites. This data source is not available in Dedicated environments. See [Web crawl](https://cloud.ibm.com/docs/discovery?topic=discovery-sourcesconnectwebcrawl) for more information. For a statement explaining beta features, see [Beta features](https://cloud.ibm.com/docs/discovery?topic=discovery-release-notesbeta-features).

New data source availability for Premium plan : Microsoft SharePoint Online, Salesforce, and Box data sources are now available in Premium environments. They are not available in Dedicated environments.





 17 December 2018 

New full support for Italian language : Added full support for Italian. For more information, see [Language support](https://cloud.ibm.com/docs/discovery?topic=discovery-language-support).





 14 December 2018 

New London location now available : You can now create Discovery instances that are hosted in the London data center without syndication. Like all locations, the IBM Cloud® London location (eu-gb) uses token-based Identity and Access Management (IAM) authentication. All new services instances that you create in this location use IAM authentication.

Update to version string : The Discovery tooling now uses the API version of 2018-12-03(it previously used 2018-08-01).





 12 December 2018 

New feature for Stopwords : Added the ability to define and upload a custom stopwords list. Custom stopwords are implemented with the Discovery API.
!/bin/bash
set -x
apt-get update
apt-get install -y nginx
indexhtml=/var/www/html/index.html

 Demonstrate the availability of internet repositories. If www.python.org is availble then other software internet software like
 npm, pip, docker, ... if isolated only the software from the ibm mirrors can be accessed
if curl -o /tmp/x -m 3 https://www.python.org/downloads/release/python-373/; then
echo INTERNET > $indexhtml
else
echo ISOLATED > $indexhtml
fi

In this script, upgrading the installed software and installing nginx and other packages using the operating system provided software installation tools demonstrates that even the isolated instances have access to the IBM provided mirrors. For Ubuntu, the apt-get commands will access mirrors.

The curl command accessing www.python.org demonstrates the attempt to access and potentially install software from the internet.

Based on whether the host has internet connectivity, the script modifies the index.html page served by nginx.





 Upload from the filesystem and execute on the instance 

There may be data and software that is available on the filesystem of your on-premise system or CI/CD pipeline that needs to be uploaded to the virtual server instance and then executed.

In such cases, you can use the SSH connection to the server to upload files with scp and then execute scripts on the server with ssh. The scripts could also retrieve software installers from the Internet, or from your on-premise systems assuming you have established a connection [such as a VPN](https://cloud.ibm.com/docs/solution-tutorials?topic=solution-tutorials-vpc-site2site-vpn) between your on-premise systems and the cloud.
The SDK uses the attribute values to determine if the specified entity satisfies the targeting rules, and returns the appropriate property value.







 How to access the payload secret data from the response 

//make sure this import statement is added
import (sm "github.com/IBM/secrets-manager-go-sdk/secretsmanagerv1")

secret := getSecretRes.Resources[0].(sm.SecretResource)
secretData := secret.SecretData.(map[string]interface{})
payload := secretData["payload"]

The GetCurrentValue will be sending the three objects as part of response.



* getSecretRes: this will give the meta data and payload.
* detailedResponse: this will give entire data which includes the http response header data, meta data and payload.
* err: this will give the error response if the request is invalid or failed for some reason.



secretData["payload"] will return interface{} so based on the data you need to do the type casting.





 Fetching the appConfigClient across other modules 

Once the SDK is initialized, the appConfigClient can be obtained across other modules as shown below:

// other modules

import (
AppConfiguration "github.com/IBM/appconfiguration-go-sdk/lib"
)

appConfigClient := AppConfiguration.GetInstance()
feature, err := appConfigClient.GetFeature("online-check-in")
if (err == nil) {
enabled := feature.IsEnabled()
featureValue := feature.GetCurrentValue(entityId, entityAttributes)
}









 Supported data types 

You can configure feature flags and properties with App Configuration, supporting the following data types: Boolean, Numeric, String, and SecretRef. The String data type can be a text string, JSON, or YAML.
<-- </section "id="section-deploy-a-node-that-uses-an-hsm" "> --><-- </section "id="section-ibp-v2-apis-custom" "> --><-- <section "id="section-ibp-v2-apis-import-with-apis" "> --> Import a network by using APIs You can also use the APIs to import IBM Blockchain components that are created by using the APIs or the IBM Blockchain Platform console into another service instance of the IBM Blockchain Platform.<-- <ol> -->1. Import a CA by calling POST /ak/api/v2/components/ca]POST _ak_api_v2_components_ca] .
Remember your input and the response, you will need them later.

You need to wait for the CA to start. It might take several minutes depending on environment. You can call GET /components]GET _components] to check the CA status. You will get repeated errors before you get a 200 status code to go to next step. Note that this API call times out in one minute.
2. Import an organization MSP definition by calling POST /ak/api/v2/components/msp]POST _ak_api_v2_components_msp 1] .3. Import an ordering service by calling POST /ak/api/v2/components/orderer]POST _ak_api_v2_components_orderer] .4. Import a peer by calling POST /ak/api/v2/components/peer]POST _ak_api_v2_components_peer] .5. If you plan to use the IBM Blockchain Platform console to operate your blockchain components, you must import your component administrator identities into your console wallet. For more information, see Importing an admin identity into the IBM Blockchain Platform console]] ! .6. After you deploy your network, you can use the Fabric SDKs, the Peer CLI, or the console UI to create channels and deploy smart contracts.
* Change the API endpoint to the endpoint mentioned in [API endpoints](https://cloud.ibm.com/apidocs/schematics?code=pythonapi-endpoints) according to the location that you want your Schematics workspace to reside, for example, schematics_service.set_service_url('https://us.schematics.cloud.ibm.com').



4. Inside the schematics_service.apply_workspace_command function, provide the following parameters:



* Provide the workspace ID that you generated in the [Creating a workspace](https://cloud.ibm.com/docs/hpc-slurm?topic=hpc-slurm-creating-workspace&interface=api) task, for example, us-south.workspace.Terraform-Schematics-Python-Workspace.b3bbc9f5.
* Run the following curl command to create a refresh token:



curl -X POST "https://iam.cloud.ibm.com/identity/token" -H "Content-Type: application/x-www-form-urlencoded" -d "grant_type=urn:ibm:params:oauth:grant-type:apikey&apikey=<ibmcloud-api-key>" -u bx:bx |jq
5. Run the Python script by using python3 <python-file-name> to apply a plan in the IBM Cloud.
6. You get an activity ID in response if the parameters passed as part of the request are valid. You should see the plan being applied in the Schematics workspace that you created in the IBM Cloud console. If you don’t get a successful response, the error response contains the errors that you need to resolve. Resolve those errors and run the script until you are able to get a valid response and apply a plan.
7.
Copy and paste the [Create a Schematics workspace using Python API](https://cloud.ibm.com/docs/hpc-slurm?topic=hpc-slurm-creating-workspace&interface=apiexample-request-create) example request to your Python file.
3. Change the following parameters as part of the request:



* Replace your IBM Cloud API key to the authenticator = IAMAuthenticator('<ibm-api-key>') variable.
* Change the API endpoint to the endpoint mentioned in [API endpoints](https://cloud.ibm.com/apidocs/schematics?code=pythonapi-endpoints) according to the location that you want your Schematics workspace to reside, for example, schematics_service.set_service_url('https://us.schematics.cloud.ibm.com').
* Provide environment values that you want to have in the form of a key and value pair, such as [{ 'Environment': 'hpc-dev-cluster' }]
* Change the folder location variable template_source_data_request_model['folder'] to the folder name inside your GitHub repository where your Terraform files reside. If the Terraform files exist in the base location of your repository, then set this variable to template_source_data_request_model['folder'] = ' '; otherwise, mention the folder name in the template_source_data_request_model['folder'] variable.
* Change the template_source_data_request_model['type'] variable to the Terraform version that you are using to create IBM Cloud resources such as terraform_v0.14. Ensure that your Terraform templates are compatible with the version that you are mentioning in this field.
* Provide your GitHub or GitLab Repository HTTPS URL where your Terraform files reside in the template_repo_request_model['url'] variable.
For details, see [Uploading resources from another workspace](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-exportimport).

Note: You must upload the type system before you can upload any other artifacts that you are moving from the backed up workspace.
2. Upload the dictionaries from the previously created dictionary backup. For details, see [Uploading resources from another workspace](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-exportimport).

If you used any read-only dictionaries in the previous version of the workspace, reupload them into this workspace from their original source.
3. For dictionary pre-annotators, associate the dictionaries with an entity type. Dictionaries that don't have mappings for entity types will not apply annotations when you pre-annotate documents.
4. Upload the documents that you downloaded from the previous version of the workspace into this version of the workspace. For details, see [Uploading resources from another workspace](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-exportimport).







 Restoring models 

At this point, all the artifacts that were used to train the model in the previous (backed up) version of the workspace are now available in this new instance.

To redeploy a machine learning model that you deployed in the previous instance, complete the following steps:



1. [Train the machine learning model](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-train-ml).
2. After creating the model, deploy it again. For details, see [Using the machine learning model](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-publish-ml).
Integrating SDKs 

The App Configuration client SDK is available for Android, JavaScript, and React, the server SDKs for Node, Python, Go, and Java, and the admin SDK for Go, to integrate with your web and mobile applications, microservices, and distributed environments.



 Client-side and Server-side SDKs 

Understand the differences between the various SDKs so that you can decide between SDK types for your use case.



 Types of SDKs 

SDKs supported by App Configuration include:



* Server-side SDK
* Client-side SDK
* Admin SDK



SDKs that help evaluate feature flag and property values are broadly classified as Server-side or Client-side - based on the deployment environment. These SDKs can be integrated into your application to assess the feature or property values by considering segment targeting rules, if any.

Evaluation SDKs fetch the latest configuration data from the App Configuration service and ensure that any change in the service configuration is made available to your application in real time.

Admin SDKs can be used to create and manage configurations for Environments, Collections, Feature flags, Properties, and Segments. As an option to IBM Cloud Dashboard or IBM Cloud CLI, Admin SDKs can be used to programmatically manage your service configuration from within your application.

The currently available Go language Admin SDK integrates with your Go application.

Differences between client-side and server-side SDKs:



Table 1. List of App Configuration server, client, and admin SDKs

 SDK type Details Links to SDKs and integration docs 

 Server side These SDKs are designed for multi-user systems and are intended to be used in a trusted environment, such as inside a corporate network or on a web server.
API Service SDK[IBM Analytics Engine v3 API](https://cloud.ibm.com/apidocs/ibm-analytics-engine/ibm-analytics-engine-v3)IBM Analytics Engine v3 API Service SDK[IBM Cloud Pak for Data](https://cloud.ibm.com/apidocs/cloud-pak-data/cloud-pak-data-4.7.0)IBM Cloud Pak for Data Software[Open Data for Industries](https://cloud.ibm.com/apidocs/open-data-industries)Open Data for Industries SoftwareBlockchain[IBM Blockchain Platform](https://cloud.ibm.com/apidocs/blockchain)IBM Blockchain Platform Service SDKCompute[IBM Cloud Foundry](https://cloud.ibm.com/apidocs/cloudfoundry)IBM Cloud Foundry Service Deprecated SDK[IBM Quantum Qiskit Runtime](https://cloud.ibm.com/apidocs/quantum-computing)IBM Quantum Qiskit Runtime Beta[Power Systems Virtual Server](https://cloud.ibm.com/apidocs/power-cloud)Power
* Change the API endpoint to the endpoint mentioned in [API endpoints](https://cloud.ibm.com/apidocs/schematics?code=pythonapi-endpoints) according to the location that you want your Schematics workspace to reside, for example, schematics_service.set_service_url('https://us.schematics.cloud.ibm.com').



4. Inside the schematics_service.apply_workspace_command function, provide the following parameters:



* Provide the workspace ID that you generated in the [Creating a workspace](https://cloud.ibm.com/docs/hpc-spectrum-symphony?topic=hpc-spectrum-symphony-creating-workspace) task, for example, us-south.workspace.Terraform-Schematics-Python-Workspace.b3bbc9f5.
* Export your IBM Cloud API key by using the following command:



export IBMCLOUD_API_KEY =”<ibm-cloud-api-key>”



* Run the following curl command to create a refresh token:



curl -X POST "https://iam.cloud.ibm.com/identity/token" -H "Content-Type: application/x-www-form-urlencoded" -d "grant_type=urn:ibm:params:oauth:grant-type:apikey&apikey=$IBMCLOUD_API_KEY" -u bx:bx
5. Run the Python script by using python3 <python-file-name> to apply a plan in the IBM Cloud.
6. You get an activity ID in response if the parameters passed as part of the request are valid. You should see the plan being applied in the Schematics workspace that you created in the IBM Cloud console. If you don’t get a successful response, the error response contains the errors that you need to resolve. Resolve those errors and run the script until you are able to get a valid response and apply a plan.
7.
See [Service endpoints](https://cloud.ibm.com/apidocs/ibm-analytics-engineservice-endpoints).





 Code samples using iaesdk 

Getting started with the Python SDK after you have installed it, involves sourcing credentials to the IBM Analytics Engine service, invoking the service and then issuing different cluster commands as shown in the following sample code snippets. The code examples are written for Python 3.7.

In addition to the sample code snippets in this topic, you can work with Python code samples from the [IBM Analytics Engine V3 API reference](https://cloud.ibm.com/apidocs/ibm-analytics-engine-v3?code=pythonintroduction).

The following code samples show you how to:



* Authenticate to the IBM Analytics Engine service and build a service client:

from iaesdk import IbmAnalyticsEngineApiV3
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
import json

 Constants for IBM Analytics Engine values
IAM_API_KEY = "{apikey}" eg "W00YiRnLW4a3fTjMB-odB-2ySfTrFBIQQWanc--P3byk"
IAE_ENDPOINT_URL = "{url}" Current list avaiable at https://cloud.ibm.com/apidocs/ibm-analytics-engineservice-endpoints
const API_AUTH_URL = "{api auth url}" "https://iam.cloud.ibm.com/identity/token"

 Create an IAM authenticator.
authenticator = IAMAuthenticator(apikey=IAM_API_KEY, url=API_AUTH_URL)

 Construct the service client.
ibm_analytics_engine_api_service = IbmAnalyticsEngineApiV3(authenticator=authenticator)

 Set our custom service URL
ibm_analytics_engine_api_service.set_service_url(IAE_ENDPOINT_URL)
For an overview of supported IBM Cloud resources and data sources, see the [IBM Cloud Provider plug-in reference](https://cloud.ibm.com/docs/ibm-cloud-provider-for-terraform?topic=ibm-cloud-provider-for-terraform-resources-datasource-list).





 22 June 2020 

Upload Terraform templates as TAR files
: You can now provide your Terraform template by uploading a tap archive file from your local machine. This feature is supported from the command-line or API. For more information, see the [ibmcloud schematics workspace upload](https://cloud.ibm.com/docs/schematics?topic=schematics-schematics-cli-referenceschematics-workspace-upload) command or [PUT /v1/workspaces/{id}/templates/{template_id}/template_repo_upload](https://cloud.ibm.com/apidocs/schematics/schematicsupload-a-tar-file-to-your-workspace) API.







 May 2020 

Review the release notes for May 2020.



 8 May 2020 

New EU API endpoint
: You can now choose to create your Schematics Workspaces in the US or Europe. Depending on the location that you choose, your Schematics Actions run in either the US (us-south or us-east) or in Europe (eu-de or eu-gb). The location that you choose for your workspace is independent from the location where you want to provision your resources. For more information, see [Locations and service endpoints](https://cloud.ibm.com/docs/schematics?topic=schematics-locations).







 April 2020 

Review the release notes for April 2020.



 17 April 2020 

Terraform v0.12 support
: You can now choose to run your infrastructure code with Terraform version 0.11 or 0.12. With Terraform version 0.12, the syntax for configuration files changed. Make sure that you use the syntax that is compatible with the Terraform version that you want to use.
For example, if a GitHub integration is selected and the JSON filter parameters.repo_url is specified, the value reflects the URL of the Git repo that is configured in the tool integration when the PipelineRun resource runs.



You can access these properties in your Tekton pipeline resources. For more information about these properties, see [Tekton Pipelines environment and resources](https://cloud.ibm.com/docs/ContinuousDelivery?topic=ContinuousDelivery-tekton_environment).
6. Click Save.









 Creating a Delivery Pipeline for Tekton with the API 



1. [Obtain an IAM bearer token](https://cloud.ibm.com/apidocs/tekton-pipelineauthentication). Alternatively, if you are using an SDK, [obtain an IAM API key](https://cloud.ibm.com/iam/apikeys) and set the client options by using environment variables.

export CD_TEKTON_PIPELINE_APIKEY={api_key}
2. [Determine the region and ID of the toolchain](https://cloud.ibm.com/docs/ContinuousDelivery?topic=ContinuousDelivery-toolchains_getting_started&interface=apiviewing-toolchain-api) to which you want to add the Delivery Pipeline tool integration.
3. Add the Delivery Pipeline tool integration to the toolchain.



* Curl
* Java
* Node
* Python
* Go



curl -X POST https://api.{region}.devops.cloud.ibm.com/toolchain/v2/toolchains/{toolchain_id}/tools -H 'Authorization: Bearer {iam_token}' -H 'Accept: application/json -H 'Content-Type: application/json' -d '{
"tool_type_id": "pipeline",
"parameters": {
"name": "{tool_integration_name}",
"type" : "tekton"
}
}'