Yang Cen 34e8b8087b fix: preserve function freshness across input-preserving writes (#4948)
Updating an unrelated column or merging rows with unchanged Function
inputs rewrites fragment files, causing already computed outputs to be
treated as stale. Preserve their freshness from the successful write
transaction when the inputs and outputs are provably unchanged; keep
changed or uncertain inputs eligible for recomputation.

Mixed update/insert writes keep a separate pending-fill proof so
retained outputs can be reused while inserted NULL outputs still need
computation. Partial refreshes under a new Function definition discard
the previous definition’s completed and pending proofs. Preservation
uses a version-fenced metadata commit, and an optional preservation
failure does not turn a successful data write into an error.

Validation: 67 freshness tests pass, including the new
partial-definition regression (confirmed failing before its fix),
unrelated updates, identical/mixed merges, NULL/nested/blob inputs,
dirty-state preservation, concurrency fences and sidecar retention.
`cargo fmt --all` and `cargo clippy --quiet --locked --features remote
--tests --examples` pass.

Compatibility: `StalenessPlan` adds a public `pending_fill` field, so
direct Rust struct literals must be updated. Refresh consumers must
handle pending-fill admission and publication before adopting this
behavior; a dependency bump alone does not complete consumer
integration. End-to-end consumer validation and deployment remain
separate. No latency claim is made for this SDK-only change.
2026-11-11 16:11:47 +08:00
2023-03-17 18:15:19 -07:00
2025-03-10 09:01:23 -07:00

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LanceDB

The Multimodal Lakehouse for AI

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

LanceDB Multimodal Search

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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:

  • LanceDB Embedded (open source): 100% open source, runs in your application's process; your data can live on local disk or in object storage. No vendor lock-in.
  • LanceDB Enterprise: Production-scale distributed search, indexing, and maintenance. Plus, 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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Developer-friendly OSS embedded retrieval library for multimodal AI. Search More; Manage Less.
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