[ https://issues.apache.org/jira/browse/SOLR-8577?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Joel Bernstein updated SOLR-8577: --------------------------------- Description: The AlertStream will return the top N "new" documents for a query from a SolrCloud collection. The AlertStream will track the highest version numbers from each shard and use these as checkpoints to determine new content. The DaemonStream (SOLR-8550) can be used to create "live" alerts that run at intervals. Sample syntax: {code} daemon(alert(collection1, q="hello", n="20"), runInterval="2000") {code} The DaemonStream can be installed in a SolrCloud worker node where it can llive and send out alerts. *AI Models* The *AlertStream* will also accept an optional *ModelStream* which will apply a machine learning model to the alert. For example: {code} alert(collection1, q="hello", n="20", model(collection2, id="model1")) {code} The ModelStream will return a machine learning model saved in a SolrCloud collection. Function queries for different model types will be developed so the models can be applied in the re-ranker or as a sort. *Taking action* Custom decorator streams can be developed that *take actions based on the AI driven alerts*. For example the pseudo code below would notify an attending physician with the Tuples emitted by the AlertStream. {code} daemon(notifyAttending(alert(...)) {code} was: The AlertStream will return the top N "new" documents for a query from a SolrCloud collection. The AlertStream will track the highest version numbers from each shard and use these as checkpoints to determine new content. The DaemonStream (SOLR-8550) can be used to create "live" alerts that run at intervals. Sample syntax: {code} daemon(alert(collection1, q="hello", n="20"), runInterval="2000") {code} The DaemonStream can be installed in a SolrCloud worker node where it can llive and send out alerts. The *AlertStream* will also accept an optional *ModelStream* which will apply a machine learning model to the alert. For example: {code} alert(collection1, q="hello", n="20", model(collection2, id="model1")) {code} The ModelStream will return a machine learning model saved in a SolrCloud collection. Function queries for different model types will be developed so the models can be applied in the re-ranker or as a sort. > Add AlertStream and ModelStream to the Streaming API > ---------------------------------------------------- > > Key: SOLR-8577 > URL: https://issues.apache.org/jira/browse/SOLR-8577 > Project: Solr > Issue Type: New Feature > Reporter: Joel Bernstein > > The AlertStream will return the top N "new" documents for a query from a > SolrCloud collection. The AlertStream will track the highest version numbers > from each shard and use these as checkpoints to determine new content. > The DaemonStream (SOLR-8550) can be used to create "live" alerts that run at > intervals. Sample syntax: > {code} > daemon(alert(collection1, q="hello", n="20"), runInterval="2000") > {code} > The DaemonStream can be installed in a SolrCloud worker node where it can > llive and send out alerts. > *AI Models* > The *AlertStream* will also accept an optional *ModelStream* which will apply > a machine learning model to the alert. For example: > {code} > alert(collection1, q="hello", n="20", model(collection2, id="model1")) > {code} > The ModelStream will return a machine learning model saved in a SolrCloud > collection. Function queries for different model types will be developed so > the models can be applied in the re-ranker or as a sort. > *Taking action* > Custom decorator streams can be developed that *take actions based on the AI > driven alerts*. For example the pseudo code below would notify an attending > physician with the Tuples emitted by the AlertStream. > {code} > daemon(notifyAttending(alert(...)) > {code} -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org For additional commands, e-mail: dev-h...@lucene.apache.org