mirror of
https://github.com/lancedb/lancedb.git
synced 2026-05-18 20:40:41 +00:00
c0230f91d2
BREAKING CHANGE: Arbitrary `impl RecordBatchReader` is no longer accepted, it must be made into `Box<dyn RecordBatchReader>`. This PR replaces `IntoArrow` with a new trait `Scannable` to define input row data. This provides the following advantages: 1. **We can implement `Scannable` for more types than `IntoArrow`, such as `RecordBatch` and `Vec<RecordBatch>`.** The `IntoArrow` trait was implemented for arbitrary `T: RecordBatchReader`, and the Rust compiler would prevent us from implementing it for foreign types like `RecordBatch` because (theoretically) those types might implement `RecordBatchReader` in the future. That's why we implement `Scannable` for `Box<dyn RecordBatchReader>` instead; since it's a concrete type it doesn't block implementing for other foreign types. 2. **We can potentially replay `Scannable` values**. Previously, we had to choose between buffering all data in memory and supporting retries of writes. But because `Scannable` things can optionally support re-scanning, we now have a way of supporting retries while also streaming. 3. **`Scannable` can provide hints like `num_rows`, which can be used to schedule parallel writers.** Without knowing the total number of rows, it's difficult to know whether it's worth writing multiple files in parallel. We don't yet fully take advantage of (2) and (3) yet, but will in future PRs. For (2), in order to be ready to leverage this, we need to hook the `Scannable` implementation up to Python and NodeJS bindings. Right now they always pass down a stream, but we want to make sure they support retries when possible. And for (3), this will need to be hooked up to #2939 and to a pipeline for running pre-processing steps (like embedding generation). ## Other changes * Moved `create_table` and `add_data` into their own modules. I've created a follow up issue to split up `table.rs` further, as it's by far the largest file: https://github.com/lancedb/lancedb/issues/2949 * Eliminated the `HAS_DATA` generic for `CreateTableBuilder`. I didn't see any public-facing places where we differentiated methods, which is why I felt this simplification was okay. * Added an `Error::External` variant and integrated some conversions to allow certain errors to pass through transparently. This will fully work once we upgrade Lance and get to take advantage of changes in https://github.com/lance-format/lance/pull/5606 * Added LZ4 compression support for write requests to remote endpoints. I checked and this has been supported on the server for > 1 year. --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
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
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:
- 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 |
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
Stay in Touch With Us