7ff72022dd
## Feature
### What is the new feature?
Adds Rust core API support for configuring vector query approximation
mode with `ApproxMode::{Fast, Normal, Accurate}`.
### Why do we need this feature?
Lance already exposes `lance_index::vector::ApproxMode` and scanner
support for controlling the speed/accuracy tradeoff for approximate
vector search. LanceDB Rust queries need to expose and pass this setting
through for local/native and remote vector searches.
### How does it work?
- Adds public `ApproxMode` in `rust/lancedb`, with lowercase serde,
`Default::Normal`, parse/display, and conversions to/from Lance's
`ApproxMode`.
- Adds `approx_mode: Option<ApproxMode>` to `VectorQueryRequest` and a
`VectorQuery::approx_mode(...)` builder.
- Applies the mode to native/local Lance scanners after `nearest(...)`
when explicitly set.
- Sends `approx_mode` in remote query JSON only when explicitly set;
default requests omit it.
## Validation
- `cargo fmt --all`
- `cargo test --quiet --features remote approx_mode`
- `cargo test --quiet --features remote
test_query_vector_default_values`
- `cargo check --quiet --features remote --tests --examples`
- `git diff --check`
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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⭐ 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
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