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Yang Cen
c16216e1c4
A synchronous remote table opened in a namespace is displayed as if it
were in the database root. For example, `db.open_table("test",
namespace_path=["analytics", "features"])` shows
`RemoteTable(dev.test)`.
Include the cached namespace path using the existing `$`-delimited table
identifier: `RemoteTable(dev.analytics$features$test)`. Root output and
the leaf `table.name` stay the same. Using cached identity also keeps
repr available after pickle without reopening the native handle or
making a request.
Addresses the repr portion of ENT-3559. The native formatter already
carries the qualified identifier used by Python async and TypeScript;
this is a Python sync wrapper fix with no new API or naming rules.
Validation:
- Before the fix, three namespaced sync cases failed while root and
async controls passed. All six cases now pass through the real SDK and
mock HTTP endpoint.
- Covers default/explicit root, nested paths, valid
dotted/mixed-case/dashed names, and repr after pickle with the server
stopped. Requests remain limited to the original table open.
- Rebuilt current editable bindings in the repository's uv environment
with Python 3.12 and pinned Rust 1.97.0 through mbx.
- Ruff check and format check pass for the Python package (178 files).
- Required repository-wide Ruff check reports 48 pre-existing
diagnostics in six `docs/web-tests` files; the repository-wide format
check reports nine unchanged documentation files. Their source matches
the base commit. No lint suppression or documentation cleanup is
included.
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
Star LanceDB to get updates!
⭐ 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:
- 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.
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