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##### Rasa Pro License

You'll need a license to get started with Rasa Pro. [Get it here](/content/rasa-pro-developer-edition-license-key-request/index.html)

##### New in 3.6

Process markers real-time from Rasa Assistants to track metrics like solution and abandonment rates, add metatags to conversations, or filter conversations for Conversation Driven Development.

In Rasa Pro, [Markers](https://legacy-docs-oss.rasa.com/docs/rasa/markers) are a powerful feature that allows you to track and extract custom conversational events. With the Analytics Data Pipeline, you can now process these markers in real-time, enabling you to gain valuable insights and enhance the performance of your Rasa Assistant. In this guide, we'll explore how to leverage real-time analysis of markers to track solution and abandonment rates in your conversations.

## Defining Markers

Please consult the [Markers](https://legacy-docs-oss.rasa.com/docs/rasa/markers/#defining-markers) section of Rasa documentation for details about defining markers.

## Enable Real-time Processing

To enable real-time analysis of markers in your Rasa project, you need to upload the markers to Analytics Data Pipeline with the `rasa markers upload` command. This command validates the marker YAML against the domain file to make sure all the slots, intents and actions referred in the file also exist in the domain and uploads the marker configuration YAML to the Analytics Data Pipeline (where they're persisted in a database) and exits. To get started, make sure you have the latest version of Rasa installed. Then, open your command line interface and navigate to your Rasa project directory. Run the following command:

```
rasa markers upload --config=<path-to-config-file> -d=<path-to-domain-file> -rasa-pro-services-url=<url>
```

By default, this command validates the marker configuration file against your bot's domain.yml file. To specify a different domain file, use the optional `-d` argument.

This command should be run whenever there is a change in the marker configuration file. The changes might include addition of new markers, changing an existing marker or removing an existing marker.

##### note

This command **uploads** the marker configurations to the data pipeline. The pipeline assumes the configuration file is the source of truth and only processes the markers defined in it. If you remove a marker from this file, and run this command, then the processing for that marker is stopped.

### Configuring the CLI command

Visit our [CLI page](https://legacy-docs-oss.rasa.com/docs/rasa/command-line-interface#rasa-marker-upload) for more information about the command line arguments available.

## How are Markers processed?

The markers YAML file describes the pattern of events for marker extraction. Once the YAML files are uploaded, the patterns to be used for marker extraction are stored in the `rasa_patterns` table. As the Kafka Consumer starts receiving events from the Rasa Assistant, it starts analyzing them for markers. The Pipeline processes all the events from the Kafka Event Broker and identifies points of interest in the conversation that match the marker. The extracted markers are then stored in the `rasa_marker` table.

The evaluation of Markers in the Pipeline is similar to the `rasa evaluate markers` command which can be used to process Markers from the conversations in Tracker Store. Read more about it [here](https://legacy-docs-oss.rasa.com/docs/rasa/markers/#extracting-markers)

Extracted markers are added to the `rasa_markers` table in the database immediately once they are processed. Each row in this table contains the foreign key identifiers for the pattern, session, sender and last event when marker was extracted along with `num_preceding_user_turns` which tracks the number of turns preceding the event at which the marker applied. Check out the [Data Structure Reference](https://legacy-docs-oss.rasa.com/docs/rasa/monitoring/analytics/data-structure-reference/#rasa-markers) page for the database schema of relevant tables.