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Wyatt Alt
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A computed column is a column defined by a SQL expression rather than by
values supplied at write time, so it is added through add_columns like any
other:
table.add_columns().computed("doubled", "x * 2").execute().await?
Declaring does not compute. The column is committed carrying its expression in
field metadata but no data, which makes declaration cost the same on a large
table as on an empty one and leaves a single code path that ever produces
values. A later refresh fills it.
The expression is the whole definition: both the result type and the input
columns are derived from it with lance-datafusion's planner, so a caller writes
neither, and the two can never disagree the way a hand-declared input list can.
Everything statically knowable is rejected at declare time rather than deferred:
an expression that does not parse, one referencing a column that does not
exist, a name already in use, and the same name declared twice in one call.
The binding lives in three field metadata keys: virtual_column marks the
column, virtual_column.expression holds it, and virtual_column.inputs holds the
parsed inputs as a JSON array. computed_columns() reads them back off a schema,
the way a SQL catalog reports a generation expression as another column of its
information schema, so introspection needs no round trip.
A transform and computed columns cannot be combined in one call, since they
commit through different transforms and could half-apply.
Only functions the query engine already knows can be named; resolving a
user-defined one needs a planner aware of the function registry, which does not
exist yet.
The Multimodal AI Lakehouse
How to Install ✦ Detailed Documentation ✦ Tutorials and Recipes ✦ Contributors
The ultimate multimodal data platform for AI/ML applications.
LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease. LanceDB is a central location where developers can build, train and analyze their AI workloads.
Demo: Multimodal Search by Keyword, Vector or with SQL
Star LanceDB to get updates!
⭐ Click here ⭐ to see how fast we're growing!
Key Features:
- Fast Vector Search: Search billions of vectors in milliseconds with state-of-the-art indexing.
- Comprehensive Search: Support for vector similarity search, full-text search and SQL.
- Multimodal Support: Store, query and filter vectors, metadata and multimodal data (text, images, videos, point clouds, and more).
- Advanced Features: Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index.
Products:
- Open Source & Local: 100% open source, runs locally or in your cloud. No vendor lock-in.
- Cloud and Enterprise: Production-scale vector search with no servers to manage. Complete data sovereignty and security.
Ecosystem:
- Columnar Storage: Built on the Lance columnar format for efficient storage and analytics.
- Seamless Integration: Python, Node.js, Rust, and REST APIs for easy integration. Native Python and Javascript/Typescript support.
- Rich Ecosystem: Integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
How to Install:
Follow the Quickstart doc to set up LanceDB locally.
API & SDK: We also support Python, Typescript and Rust SDKs
| Interface | Documentation |
|---|---|
| Python SDK | https://lancedb.github.io/lancedb/python/python/ |
| Typescript SDK | https://lancedb.github.io/lancedb/js/globals/ |
| Rust SDK | https://docs.rs/lancedb/latest/lancedb/index.html |
| REST API | https://docs.lancedb.com/api-reference/rest |
Join Us and Contribute
We welcome contributions from everyone! Whether you're a developer, researcher, or just someone who wants to help out.
If you have any suggestions or feature requests, please feel free to open an issue on GitHub or discuss it on our Discord server.
Check out the GitHub Issues if you would like to work on the features that are planned for the future. If you have any suggestions or feature requests, please feel free to open an issue on GitHub.
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