Enterprise-only When working with multimodal data at scale, LanceDB Enterprise makes it easy to define, extract, and transform raw data into useful information and features for your AI applications. LanceDB Enterprise’s Multimodal Feature Engineering package is designed to improve the productivity of AI engineers operating at immense scale. With an API designed to leverage LanceDB’s optimized data storage and retrieval, it streamlines prototyping extraction and transformation tasks, performing experiments, exploring your data, scaling up execution, and moving to production.Documentation Index
Fetch the complete documentation index at: https://docs.lancedb.com/llms.txt
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Feature Engineering and the
geneva Python package are currently only available as part of
LanceDB Enterprise. Please contact us if you’re interested
in scaling up your feature engineering workloads for your AI and multimodal use cases.geneva package uses Python User Defined Functions (UDFs) to define features
as columns in a Lance dataset. Adding a feature is straightforward:
Wrap the function with a small UDF decorator (see UDFs).
(Optional) Configure where the UDF will run: locally, on a Ray cluster, or on a Kubernetes cluster with KubeRay (see Contexts).
Trigger a
backfill operation (see Backfilling).Continue learning
Visit the following pages to learn more about featuring engineering in LanceDB Enterprise:- Overview: What is Feature Engineering? · End-to-end example
- UDFs: Using UDFs · Blob helpers · Error handling · Advanced configuration
- Jobs: Backfilling · Startup optimizations · Materialized views · Execution contexts · Geneva console · Performance
- Deployment: Deployment overview · Helm deployment · Troubleshooting
API Reference
geneva.connect()— connect to a Geneva database- Connection — manage tables, views, jobs, clusters, and manifests
- Table — add columns, backfill, search, and manage table data
- UDF — define user-defined functions for feature computation