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https://issues.apache.org/jira/browse/SOLR-8577?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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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 run the function
*someAction* on the Tuples emitted by the AlertStream.
{code}
daemon(someAction(alert(...)))
{code}
*Learning*
While some SolrCloud worker collections are alerting and taking action, other
worker collections can be *learning models* which can be applied for alerting.
For example:
{code}
daemon(update(logit()))
{code}
The pseudo code above calls the LogitStream (SOLR-8492) which would learn a
Logistic Regression model and flow the model into a SolrCloud collection. The
model can then be used for alerting and taking action on new data as it enters
the system.
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.
*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}
> 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 run the function
> *someAction* on the Tuples emitted by the AlertStream.
> {code}
> daemon(someAction(alert(...)))
> {code}
> *Learning*
> While some SolrCloud worker collections are alerting and taking action, other
> worker collections can be *learning models* which can be applied for
> alerting. For example:
> {code}
> daemon(update(logit()))
> {code}
> The pseudo code above calls the LogitStream (SOLR-8492) which would learn a
> Logistic Regression model and flow the model into a SolrCloud collection. The
> model can then be used for alerting and taking action on new data as it
> enters the system.
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