Xuanwo 13e4cf7e58 feat: expose Function publication intervals in refresh options (#4928)
Python refresh APIs currently reject `publication_interval_rows`, so
callers cannot request incremental Function output publication through
the SDK. Add the optional typed parameter through the sync/async Python
APIs, PyO3, and Rust `RefreshColumnOptions`, and serialize it as a
top-level Enterprise refresh option.

An omitted value preserves the existing wire request and whole-fragment
default. Positive values set a publication threshold; zero and negative
Python values fail before a request is sent. Embedded refreshes reject
the Enterprise-only option. Documentation describes a threshold rather
than an exact number of rows per publication.

Validation on the designated development host:
- A matching baseline build reproduced the missing public keyword
argument in both sync and async submission tests.
- The updated PyO3 build passed 54 focused Python tests; Rust payload,
invalid-option, and default-wire tests each passed. Rust formatting and
Python Ruff checks passed.
- The single signed delivery commit differs from that tested
implementation only in a Rustdoc correction describing Embedded
rejection; formatting passed again. No dependency pins or lockfiles
changed.

The matching SDK is prepared for the separate server validation of
failure/cancellation retention and missing-only refill. Those end-to-end
results are still pending and are not implied by these SDK tests.

Refs [ENT-3733](https://linear.app/lancedb/issue/ENT-3733).
2026-11-10 15:22:56 +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

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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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Developer-friendly OSS embedded retrieval library for multimodal AI. Search More; Manage Less.
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