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Dan Rammer
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## What `LsmWriteSpec::maintained_indexes` becomes `Option<Vec<String>>`: | value | meaning | |---|---| | `None` (new default) | every index the MemWAL supports, resolved when the spec is installed | | `Some([])` | maintain nothing — a scan/filter-only WAL table | | `Some([..])` | exactly these, taken verbatim | `with_maintained_indexes` keeps its signature; `with_no_maintained_indexes()` is new. Surfaced through the remote path (null on the wire), Python, and Node. ## Why Callers had to state the maintained set by hand every time, which is both tedious and easy to get wrong — the common case is "maintain what I already built." Resolution filters on `IndexConfig::is_memwal_maintainable`, delegating to lance's `is_maintainable_index_type`. This is load-bearing rather than cosmetic: lance does **not** skip an index type its memtable cannot build, it errors when the shard writer opens, so sweeping up a bitmap index would fail every memtable claim and leave the table unwritable. The inferred set excludes those, and an explicit list naming one is now rejected at spec time instead of at claim time. ## Behavior change A freshly constructed spec used to maintain **nothing**; it now maintains **everything supported**. This flipped because napi collapses `undefined` and `null` to `None`, so TypeScript cannot express "absent means nothing, null means all" — any other choice makes the bindings disagree with the wire. The error direction also favors it: an unwanted maintained index costs memory, while a silently unmaintained one degrades FTS to an unscored scan. Three existing tests encoded the old default and are updated rather than worked around. ## Caveat The resolved set is a snapshot, not a subscription. An index created after the spec is installed is not maintained until the spec is unset and set again. `get_lsm_write_spec` therefore always reports a concrete list — `None` never round-trips. ## Dependency Needs a lance release carrying `is_maintainable_index_type` (lance-format/lance#8095) before this builds against the pinned tag. Draft until then. ## Testing 38 Rust LSM tests and 10 Python tests pass against a local lance build, including new coverage that a bitmap index is excluded from inference and rejected when named, and that `[]` stays distinguishable from null on the wire. 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
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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⭐ 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.
Contributors
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