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Follows lance-format/lance#8680, which removes `FLAG_MEM_WAL_INDEX_CATCHUP`. With one set of semantics there is no mode to switch into. ## Removed `require_mem_wal_index_catchup` — the activation entry point — from the trait, from `Table`, and from the LSM merge module. ## The read path `exclusion_watermarks` loses its `catchup_required` argument and keeps the conservative branch: an index with no entry is not known to hold these rows, so every generation stays readable from its SSTable. Nothing is excluded until an index records that it covers those generations, so a table that has never recorded catch-up reads every row from its SSTables rather than assuming the base covers them. ## One guard needed a replacement, not deletion `refresh_column` and computed-column declaration refuse a table whose rows sit in un-compacted tiers, since refresh enumerates base fragments and would silently omit them. They keyed on the feature bit because `unset_lsm_write_spec` **drops the MemWAL index** — after an unset the write spec no longer describes such a table, and the bit was the only marker that outlived it. Two tests covered this, so deleting the term would have dropped a tested property. Both guards now check for MemWAL shard directories on storage, which outlive the index. That is strictly wider than the bit ever was: the bit only marked tables where activation had run. ## Two tests conflated two different things An index that is *caught up* and one that is *untracked* both fell back to the compaction watermark, because absence carried no information without the bit. Absence now means "not caught up", so untracked retains everything. `an_untracked_index_does_not_widen_a_lagging_sibling` becomes `an_untracked_index_retains_everything`, with the genuinely-caught-up case asserted separately. ## Testing 933 `lancedb` lib tests. `cargo fmt` clean. (The pre-existing `Error::Http` build failure in `job.rs` without the `remote` feature is unrelated and untouched.)
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 |
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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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