Shengan Zhang 650f173236 feat(python): add IVF_HNSW_FLAT vector index support (#3366)
## Summary

Wire up `IVF_HNSW_FLAT` in the Rust core and Python SDK. The index was
documented at https://docs.lancedb.com/indexing/vector-index but
`lancedb.Table.create_index(index_type="IVF_HNSW_FLAT")` raised
`ValueError: Unknown index type IVF_HNSW_FLAT` — the underlying
`pylance` already accepted it, only the LanceDB wrapper was missing the
wiring.

**Rust core (`rust/lancedb`):**
- Add `Index::IvfHnswFlat` / `IndexType::IvfHnswFlat` variants and the
`IvfHnswFlatIndexBuilder` (modelled on `IvfHnswSqIndexBuilder`).
- Build Lance params via the existing `VectorIndexParams::ivf_hnsw(...)`
helper, keeping symmetry with the other `IVF_HNSW_*` variants.
- Forward the variant in `RemoteTable::create_index` and add two
parametrised tests (default + customised config) for the JSON
serialisation.
- New `NativeTable` integration test
(`test_create_index_ivf_hnsw_flat`).

**Python binding (`python/`):**
- New `HnswFlat` dataclass + backwards-compat `IvfHnswFlat` alias.
- PyO3 `extract_index_params` recognises the `HnswFlat` config.
- `LanceTable.create_index(index_type="IVF_HNSW_FLAT", …)` and the sync
`RemoteTable.create_index` both dispatch to the new config.
- `IndexStatistics.index_type` `Literal` and `_lancedb.pyi` stubs cover
the new type so `pyright`/`make check` stays clean.
- Async integration tests (`HnswFlat` + `IvfHnswFlat` alias) and a sync
dispatcher test, mirroring the existing `IVF_HNSW_SQ` coverage.
- Existing `test_index_statistics_index_type_lists_all_supported_values`
updated to include `IVF_HNSW_FLAT`.

A matching Node.js / TypeScript binding is in a follow-up PR.

Closes #3331

## Test plan

- [ ] \`cargo check --quiet --features remote --tests --examples\`
- [ ] \`cargo test --quiet --features remote -p lancedb\` (covers the
new \`test_create_index_ivf_hnsw_flat\` and the two new parametrised
\`RemoteTable::create_index\` cases)
- [ ] \`cargo fmt --all\` / \`cargo clippy --quiet --features remote
--tests --examples\`
- [ ] \`cd python && make develop && make check && make test\` (covers
the two new async tests, the alias test, the dispatcher test, and the
updated \`test_index_statistics_index_type_lists_all_supported_values\`
assertion)
2026-05-11 15:08:32 -07:00
2023-03-17 18:15:19 -07:00
2025-03-10 09:01:23 -07:00

LanceDB Cloud Public Beta

LanceDB Website Blog Discord Twitter LinkedIn

LanceDB

The Multimodal AI Lakehouse

How to Install Detailed DocumentationTutorials and RecipesContributors

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

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


Website Blog Discord Twitter LinkedIn

Description
Languages
HTML 39.8%
Rust 28.9%
Python 23.1%
TypeScript 7.7%
Shell 0.3%
Other 0.1%