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jasonz-lance
f5e23ab080
Two independent fixes to 2-phase shuffled reads, found by the same gatekeeper review pass: - Packed checkpoint resume: the 2-phase branch of _iter_owned read the (empty, in packed mode) row-mode cursor maps instead of _pack_consumed, so restoring a packed checkpoint silently replayed already-consumed documents. Now mirrors the 1-phase branch's check. - Version pinning + remote (Cloud) table support: 2-phase previously called self._table.to_lance() fresh at iteration time, which (a) raises NotImplementedError on RemoteTable, since to_lance() only works against a local table, and (b) re-reads whatever version is live at that moment rather than the one the block plan was computed against, so a write to the table between construction and iteration (or between epochs) could shift which rows a block refers to. Now pins table.version (and rejects a table checked out to a non-default branch, which isn't supported yet) at construction, and reads through a private handle reopened via the same connection_factory/pickle-state machinery already used for worker reconnects, checked out to that pinned version. Reads go through Table's backend-agnostic query builder (search/select/take_offsets), the same interface RemoteTable implements, instead of pylance's to_lance(). take_offsets doesn't guarantee its output order, so filtered block reads always additionally select "_rowoffset" and sort locally by it to restore the requested order. Extends MockPermutationServer (utils.py) to answer the offset/limit scan and _rowoffset-filtered take request shapes 2-phase now sends, and adds tests for both fixes plus 2-phase over a (mocked) remote table, unfiltered and filtered+shuffled.
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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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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