Wyatt Alt
6c8aa22704
`add_columns().computed()` accepted several columns in one call but bound each against the table's schema as it stood before the call, so `a` and `b = a + 1` had to be two commits. A server staging declarations behind other schema work has no atomic way to do that, and a caller reading the builder's plural signature reasonably expects the batch to be one. Each accepted column now joins the schema the next one resolves against, so the batch is planned and committed as one. Order is the dependency order; reading ahead is still an unknown column. `validate_declarations` exposes the schema-level checks -- the Function-binding guard and the planning -- without a commit, for callers that must reject before earlier work in the same request lands; LSM state is table state and stays a commit-time check. Refresh order matters for a dependent column: `b = coalesce(a, 0)` refreshed before `a` would bake zeros from `a`'s placeholder null, and the fill-once contract keeps them. Refresh now refuses, naming the input, while a computed input still has rows a refresh of it would fill -- the same probe refresh already uses to detect a no-op. Otherwise it is one snapshot and one commit, as before; a concurrent append is not in the commit and waits for the next refresh. Refreshing dependencies on the caller's behalf was considered and rejected: it is not how materialized views or our own backfill scheduler behave, and it needs multi-commit fencing that an explicit per-row fill marker would make unnecessary.
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
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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.
Contributors
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