vania-janet/multiturn-rag-retrieval-data huggingface dataset:
For example, if your console URL is localhost:3001/settings, you can view your logs by navigating to localhost:3001/api/v1/logs. Client and server logs are collected in separate files. The most recent logs are at the top of the page.





 Viewing your node logs 

The logs of your peers, ordering nodes, and Certificate Authorities (CAs) are collected by the IBM Kubernetes Service or Red Hat OpenShift. Use the steps below to view the logs of your nodes from the cluster where you deployed your IBM Blockchain Platform network based on the type of cluster you are using.



 Viewing nodes logs on an IBM Cloud Kubernetes Service cluster 

To more easily locate your node logs, it is recommended to filter on the namespace that was used when the nodes were deployed. To find the namespace, open any CA node in your console and click the Info and Usage icon. View the value of the API URL. For example: https://n2734d0-soorg10524.ibpv2-cluster.us-south.containers.appdomain.cloud:7054.

The namespace is the first part of the URL beginning with the letter n and followed by a random string of six alphanumeric characters. So in the example above the value of the namespace is n2734d0.



1. Open the [IBM Cloud dashboard](https://cloud.ibm.com/resources) and navigate to the overview screen of your IBM Kubernetes Service cluster.
2. Above the cluster overview screen, click Kubernetes Dashboard.
3. Inside the Kubernetes dashboard, use the left navigation Namespace drop-down list to change to the namespace for your IBM Blockchain Platform service instance that you discovered above.
4. On the left navigation, click Pods to view the list of node pods that you have deployed.
5. Click on a pod. Then click the View logs icon on the top menu to open the logs of your node. Above the logs, you can use the drop-down menu after Logs from to view the logs from the different containers within the pod.
This standard type of node is useful to capture questions about a certain topic and then in the root response ask a follow-up question that is addressed by the child nodes. For example, it might recognize a user question about discounts and ask a follow-up question about whether the user is a member of any associations with which the company has special discount arrangements. And the child nodes provide different responses based on the user's answer to the question about association membership.
* The second root node is a node with slots. It also conditions on an intent value. It defines a set of slots, one for each piece of information that you want to collect from the user. Each slot asks a question to elicit the answer from the user. It looks for a specific entity value in the user's reply to the prompt, which it then saves in a slot context variable.

This type of node is useful for collecting details you might need to perform a transaction on the user's behalf. For example, if the user's intent is to book a flight, the slots can collect the origin and destination location information, travel dates, and so on.







 Ready to get started? 

For more information, see [Building a dialog](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-dialog-overview).

Next topic:[Planning the dialog](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-dialog-plan)
Plan Dialog nodes per skill 

 Enterprise 100,000 
 Premium (legacy) 100,000 
 Plus 100,000 
 Trial 25,000 
 Lite 25,000 



The welcome and anything_else dialog nodes that are prepopulated in the tree do count toward the total.

Tree depth limit: The dialog supports 2,000 dialog node descendants; the dialog performs best with 20 or fewer.





 Finding a dialog node by its node ID 

You can search for a dialog node by its node ID. Enter the full node ID into the search field. You might want to find the dialog node that is associated with a known node ID for any of the following reasons:



* You are reviewing logs, and the log refers to a section of the dialog by its node ID.
* You want to map the node IDs listed in the nodes_visited property of the API message output to nodes that you can see in your dialog tree.
* A dialog runtime error message informs you about a syntax error, and uses a node ID to identify the node you need to fix.



Another way to discover a node based on its node ID is by following these steps:



1. From the Dialog page, select any node in your dialog tree.
2. Close the edit view if it is open for the current node.
3. In your web browser's location field, a URL should display that has syntax similar to the following:

https://{location}.assistant.watson.cloud.ibm.com/{location}/{instance-id}/skills/{skill-id}/build/dialognode={node-id}
4. Edit the URL by replacing the current {node-id} value with the ID of the node you want to find, and then submit the new URL.
5. If necessary, highlight the edited URL again, and resubmit it.



The page refreshes, and shifts focus to the dialog node with the node ID that you specified.
When you find important insights, you can take a snapshot of the view, and then add it to a report to share with others. For more information, see [Creating a report](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-cm-report).



 Start your analysis 

Use the content mining application to analyze documents in your collection based on the document text and any annotations or enrichments that are stored in the documents.

To start your analysis, complete the following steps:



1. Enter a search term, click a facet with which to filter the documents, or leave the search field blank to return all of your documents.
2. Click Search.



The guided mode view of the results shows suggested next steps that you can take to analyze your data further. If you don't want to see suggestions, you can switch to Expert mode. In Expert mode, the Documents view that lists the search results is returned whenever you submit a search.

Zoom

![Expert mode toggle](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/discovery-data/images/cm-expert-mode.png)

Figure 1. Mode toggle

The tasks in this topic describe how to use the application in guided mode.





 Look for relevant keywords 

To analyze keyword relevance, complete the following steps:



1. From the initial search page, submit a keyword search to filter the documents.
2. From the search results page in guided mode, click Analyze cause or characteristics.

After the characteristic words pane, a pane with relevancy information for each facet type is displayed.

Zoom

!
[checkmark icon](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/icons/checkmark-icon.svg) 







 Step 8: Not sure what you can build? 

For more information about the types of search solutions you can build, see [Start getting value from your data](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-product-overview).

You can access the product documentation at any time by selecting the Help icon ![Help icon](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/discovery-data/images/help.svg) from the page header of the product user interface. The help content is customized to provide information that is related to what you're doing in the product.

No matter what you build, step one is to create a project. Decide which project type best fits your needs.

If none of the existing types is quite right, you can choose None of the above to create a custom project instead.





 Project descriptions 



Project type use cases

 Need Goal Project type 

 Which document contains the answer to my question? Find meaningful information in sources that contain a mix of structured and unstructured data, and surface it in a stand-alone enterprise search application or in the search field of a business application. Document Retrieval 
 Where is the part of the contract that I need for my task? Quickly extract critical information from contracts. Document Retrieval for Contracts 
 I want the chatbot I'm building to use knowledge that I own. Give a virtual assistant quick access to technical information that is stored in various external data sources and document formats to answer customer questions. Conversational Search 
 I want to uncover insights I didn't know to ask about. Gain insights from pattern analysis or perform root cause analysis. Content Mining
Previewing your query results 

See the types of query results that are returned automatically and learn about how they are derived.

When a document is ingested, the text is extracted and indexed in thetext field. To return only the subset of information that is relevant to the query, Discovery returns passages from the text field. For more information about passages, see [How passages are derived](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-index-overviewquery-results-passages).

When you enter a query from the product user interface, the UI submits the text as a natural language query. For more information about how to query your data programmatically, see [Query API](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-query-concepts).

To query your data from the product user interface, complete the following steps:



1. From the navigation panel, open the Improve and customize page.
2. Take the appropriate next steps for your project type.



* [Document Retrieval](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-query-resultsquery-results-dr)
* [Conversational Search](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-query-resultsquery-results-chat)
* [Document Retrieval for Contracts](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-query-resultsquery-results-contract)
* [Content Mining](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-query-resultsquery-results-cm)







 Document Retrieval 



1. Do one of the following things:



* Click Run search for one of the keywords that Discovery calculated to have special meaning in your collection.
* Submit your own phrase or keyword from the search bar.



You can see that the query results that are returned consist of passages.
For more information about stopwords, see [Defining stopwords](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-search-settingsstopwords).





 Collection information 

This is not required, but it is a best practice to [retrieve the status](https://cloud.ibm.com/apidocs/discovery-datagetcollection) for each collection regularly and store the information locally. By retaining these statistics, you can later verify that your restoration processes were successful if needed.





 Smart Document Understanding models 

If you use Smart Document Understanding (SDU), you have models that are associated with your configuration. To avoid loss of this information, [export your models](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-configuring-fieldsimport), back them up, and store them locally. SDU models have the file extension of .sdumodel.





 Dictionary enrichments 



1. Open your project, and click Improve and customize.
2. On the Improvement tools panel, click Teach domain concepts and then Dictionaries.
3. Click the download icon next to your dictionary. Your dictionary then downloads as a .csv file.







 Regular expressions enrichments 

Back up your regular expressions as a .csv file, and store them locally. Note the regular expressions that you specified to create your enrichments so that you can re-create the enrichments from them. For more information, see [Regular expressions](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-domain-regex).





 Machine learning enrichments 

Back up your machine learning model .zip or .pear files, and store them locally. For more information, see [Machine learning enrichments and Watson Explorer Content Analytics Studio models](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-domain-ml).





 Pattern enrichments 



1. Open your project.
2. On the Improvement tools panel of the Improve and customize page, click Teach domain concepts and then Patterns (Beta).
Correlate documents from different sources 

Correlate content across documents by using the Python NLTK and IBM Data Science Experience.



* [Learn more ![External link icon](https://cloud.ibm.com/docs-content/v1/content/icons/launch-glyph.svg)](https://developer.ibm.com/patterns/watson-document-correlation/)
* [Get the Code ![External link icon](https://cloud.ibm.com/docs-content/v1/content/icons/launch-glyph.svg)](https://github.com/IBM/watson-document-co-relation?cm_sp=Developer-_-watson-document-correlation-_-Get-the-Code)
* [View the Demo ![External link icon](https://cloud.ibm.com/docs-content/v1/content/icons/launch-glyph.svg)](https://youtu.be/vDCaBPhAr64?cm_sp=Developer-_-watson-document-correlation-_-View-the-Demo)







 Discover hidden Facebook usage insights 

Harness the power of cognitive data analysis in a Jupyter Notebook with PixieDust.



* [Learn more ![External link icon](https://cloud.ibm.com/docs-content/v1/content/icons/launch-glyph.svg)](https://developer.ibm.com/patterns/discover-hidden-facebook-usage-insights/)
* [Get the Code ![External link icon](https://cloud.ibm.com/docs-content/v1/content/icons/launch-glyph.svg)](https://github.com/IBM/pixiedust-facebook-analysis?cm_sp=IBMCode-_-discover-hidden-facebook-usage-insights-_-Get-the-Code)
* [View the Demo !
The query term(enriched_text.concepts.text,count:10) displays under the Visual Query Builder.



3. Click More options, then enter 0 in the Number of documents to return field.
4. Click Run query. The top 10 concepts are displayed in both the Summary and JSON tabs.







 Step 6: Build a query in Watson Discovery News 

Discovery News, is a public data set that is pre-enriched with cognitive insights. It is included with Discovery. See [Watson Discovery News](https://cloud.ibm.com/docs/discovery?topic=discovery-watson-discovery-news) for more information about this collection.

You cannot adjust the Discovery News configuration, train, or add documents to Discovery News collection.

The following example query returns the top 10 articles in IBM Watson™ Discovery News about the Pittsburgh Steelers that have a positive sentiment.



1. Click on the Build queries icon ![Query icon](https://cloud.ibm.com/docs-content/v1/content/ded4adc3ea0bd2a81b113c579f2b1183926da211/discovery/images/icon_queryBuilder.png) to open the query page. Select the Discovery News collection and click Get started. (To query the Spanish, German, Korean, French, or Japanese Discovery News collections, you must first click the ![Manage Data](https://cloud.ibm.com/docs-content/v1/content/ded4adc3ea0bd2a81b113c579f2b1183926da211/discovery//images/icon_yourData.png) icon, then choose the appropriate language from the drop-down.)
2. Under Search for documents, click Use the Discovery Query Language, then:



* Click the Field drop-down and choose text, for the Operator choose contains, then enter the Value of Pittsburgh Steelers. Click Add rule, then click the Field drop-down and choose enriched_text.sentiment.document.label, for the Operator choose contains, then enter the Value of positive.
[checkbox](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/discovery-data/images/checkbox.png) Run test queries to assess the quality of the initial results. [Previewing the default query results](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-query-results) 
 ![checkbox](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/discovery-data/images/checkbox.png) Take actions to improve your results. For example, you can customize the search bar to enable autocompletion. [Improving your query results](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-improvements) 
 ![checkbox](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/discovery-data/images/checkbox.png) Deploy your search solution. [Deploying your project](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-deploy) 







 Extract meaning 

Use award-winning natural language processing technology to enrich your data and ensure that the right information is found when someone searches for answers.

Zoom

![Shows a custom machine learning facet.](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/discovery-data/images/nlu-search.png)

Figure 2. Machine learning facet for filtering search results with custom enrichment values
* [Creating network zones by using the CBR UI](https://cloud.ibm.com/docs/schematics?topic=schematics-access-control-cbrcbr-create-zone-ui)



* [Understanding network rules](https://cloud.ibm.com/docs/schematics?topic=schematics-access-control-cbrcbr-network-rules)



* [Create network rules by using the CBR API](https://cloud.ibm.com/docs/schematics?topic=schematics-access-control-cbrcbr-create-rules-api)
* [Creating network rules by using the CBR UI](https://cloud.ibm.com/docs/schematics?topic=schematics-access-control-cbrcbr-create-rules-ui)



* [Next steps](https://cloud.ibm.com/docs/schematics?topic=schematics-access-control-cbrcbr-next-steps)



[Data privacy and governance](https://cloud.ibm.com/docs/schematics?topic=schematics-data-privacy-and-governancedata-privacy-and-governance)

[General Data Protection Regulation (GDPR)](https://cloud.ibm.com/docs/schematics?topic=schematics-general-data-protection-regulation-gdprgeneral-data-protection-regulation-gdpr)



* [How do you audit access to Schematics?](https://cloud.ibm.com/docs/schematics?topic=schematics-general-data-protection-regulation-gdprhow-do-i-audit-access-to-ibm-schematics)
* [Supporting classifications of personal data](https://cloud.ibm.com/docs/schematics?topic=schematics-general-data-protection-regulation-gdprsupported-classifications-of-personal-data)
You can use the Discovery tooling or API to crawl various data sources, or do a web crawl. See [Connecting to data sources](https://cloud.ibm.com/docs/discovery?topic=discovery-sources) for more information.





 Adding enrichments 

Discovery enriches (adds cognitive metadata to) the text field of your ingested documents with semantic information collected by these four Watson functions - Entity Extraction, Sentiment Analysis, Category Classification, and Concept Tagging. (There are a total of nine Watson enrichments available; the others are Keyword Extraction, Relation Extraction, Emotion Analysis, Element Classification, and Semantic Role Extraction.)

Some Watson enrichments might be unavailable in certain plans or environments.

You can also integrate one or more custom models from IBM Watson™ Knowledge Studio with Discovery to provide custom entity and relations enrichments. See [Integrating with Watson Knowledge Studio](https://cloud.ibm.com/docs/discovery?topic=discovery-integrating-with-wks).

Only the first 50,000 characters of each JSON field selected for enrichment are enriched.

You can further augment your documents by adding more enrichments to the text field, or enriching other fields. To do so using Smart Document Understanding, open the Enrich Fields tab, choose the field(s) you'd like to enrich and select from the list of available Natural Language Understanding enrichments:



 Entity extraction 

Returns items such as persons, places, and organizations that are present in the input text. Entity extraction adds semantic knowledge to content to help understand the subject and context of the text that is being analyzed. The entity extraction techniques are based on sophisticated statistical algorithms and natural language processing technology, and are unique in the industry with their support for multilingual analysis and context-sensitive disambiguation.
6. In the Set result selectivity section, decide whether to be more selective with the answers that are returned. By increasing result selectivity, Search returns fewer but more accurate results. In most cases, Search is accurate enough that the default setting (off) is sufficient.
7. Click Preview to open the Preview pane for testing. Enter a test message to see the results that are returned when your configuration choices are applied to the search. Make adjustments as necessary.
8. Click Create.



If you want to change the configuration of the search result card later, open the search skill again, and make edits. You do not need to save changes as you make them; they are automatically applied. When you are happy with the search results, click Save to finish configuring the search skill.

If you decide you want to connect to a different Discovery service instance or data collection, then create a new search skill and configure it to connect to the other instance. You cannot change the service instance or data collection details for a search skill after you create it.





 Troubleshooting 

Review this information for help with performing common tasks.



* Creating a Web crawl data collection: Things to know when you create a web crawl data source:



* To increase the number of documents that are available to the data collection, click add a URL group where you can list the URLs for pages that you want to crawl but that are not linked to from the initial seed URL.
* To decrease the number of documents that are available to the data collection, specify a subdomain of the base URL. Or, in the web crawl settings, limit the number of hops that Watson can make from the original page. You can specify subdomains to explicitly exclude from the crawl also.
* If no documents are listed after a few minutes and a page refresh, then make sure that the content you want to ingest is available from the URL's page source. Some web page content is dynamically generated and therefore cannot be crawled.



* Configuring search results for uploaded documents: If you are using a collection of uploaded documents and cannot get the correct search results or the results are not concise enough, consider using Smart Document Understanding when you create the data collection.
See [Configuring the Jump to action](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-dialog-overviewdialog-overview-jump-to-config) for more details.



7. Optional: If you want this node to be considered when users are shown a set of node choices at run time, and asked to pick the one that best matches their goal, then add a short description of the user goal handled by this node to the external node name field. For example, Open an account.

See [Disambiguation](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-dialog-runtimedialog-runtime-disambiguation) for more details.
8. Optional: Name the node.

The dialog node name can contain letters (in Unicode), numbers, spaces, underscores, hyphens, and periods.

Naming the node makes it easier for you to remember its purpose and to locate the node when it is minimized. If you don't provide a name, the node condition is used as the name.
9. To add more nodes, select a node in the tree, and then click the More![More icon](https://cloud.ibm.com/docs-content/v1/content/a2b18c153837bce00a5b4de496e4b4ca64bbf56e/assistant-data/images/kabob.png) icon.



* To create a peer node that is checked next if the condition for the existing node is not met, select Add node below.
* To create a peer node that is checked before the condition for the existing node is checked, select Add node above.
* To create a child node to the selected node, select Add child node. A child node is processed after its parent node.
* To copy the current node, select Duplicate.