- Machine Learning: other versions:
- What is Elastic Machine Learning?
- Setup and security
- Anomaly detection
- Finding anomalies
- Tutorial: Getting started with anomaly detection
- Advanced concepts
- API quick reference
- How-tos
- Generating alerts for anomaly detection jobs
- Aggregating data for faster performance
- Altering data in your datafeed with runtime fields
- Customizing detectors with custom rules
- Detecting anomalous categories of data
- Performing population analysis
- Reverting to a model snapshot
- Detecting anomalous locations in geographic data
- Mapping anomalies by location
- Adding custom URLs to machine learning results
- Anomaly detection jobs from visualizations
- Exporting and importing machine learning jobs
- Resources
- Function reference
- Supplied configurations
- Apache anomaly detection configurations
- APM anomaly detection configurations
- Auditbeat anomaly detection configurations
- Logs anomaly detection configurations
- Metricbeat anomaly detection configurations
- Metrics anomaly detection configurations
- Nginx anomaly detection configurations
- Security anomaly detection configurations
- Uptime anomaly detection configurations
- Data frame analytics
- Natural language processing
Limitations
editLimitations
editThe following limitations and known problems apply to the 9.0.0-beta1 release of the Elastic natural language processing trained models feature.
Document size limitations when using semantic_text
fields
editWhen using semantic text to ingest documents, chunking takes place automatically. The number of chunks is limited by the index.mapping.nested_objects.limit
cluster setting, which defaults to 10k. Documents that are too large will cause errors during ingestion. To avoid this issue, please split your documents into roughly 1MB parts before ingestion.
ELSER semantic search is limited to 512 tokens per field that inference is applied to
editWhen you use ELSER for semantic search, only the first 512 extracted tokens from each field of the ingested documents that ELSER is applied to are taken into account for the search process. If your data set contains long documents, divide them into smaller segments before ingestion if you need the full text to be searchable.
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