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Jack Ye
f39a7a4dd9
`StreamingDataset`, `PermutationBuilder`, and `Permutation` now work
against a `RemoteTable` (LanceDB Cloud and Enterprise), which unblocks
benchmarking the loader against the enterprise cluster cache.
```python
db = lancedb.connect("db://my-db", api_key=..., host_override=...)
ds = StreamingDataset(db.open_table("training"), world_size=8, rank=r)
```
Rows are addressed by `_rowid` exactly as before —
`PermutationReader::load_batch` already built the same `_rowid IN (...)`
filter that `Table::take_row_ids` sends, so the loader's fetch was
always the take path. It just was never allowed to run.
### The guard
`PermutationBuilder.__init__` rejected anything without `_inner`, so a
`RemoteTable` raised `TypeError` before reaching the PyO3 layer — which
already unwraps one via `_table._inner`.
### A bounded schema lookup
`PermutationReader::output_schema` reads the schema off a query plan,
and building a plan on a remote table *executes* the query
(`create_plan` → `execute_query`). With no limit that is `k =
isize::MAX`, so asking a remote table for its output schema pulled the
whole table over HTTP and threw it away — once per assigned split, on
every epoch, since `StreamingDataset.__iter__` constructs a
`Permutation` per split.
One row rather than zero, deliberately: lance gates its limit node on
`self.limit.unwrap_or(0) > 0`, so `Some(0)` means *no limit*.
### Tables with an LSM write spec are refused
A permutation references rows by row id, and rows that have not been
flushed to the base table do not have one yet. The loader could read
around them, but they would then be missing from training with nothing
said about it, so the build refuses such a table up front instead of
half supporting it.
### Fallible identity construction
`PermutationReader::identity` resolved `inner_new` with `unwrap`. That
was near total against a local dataset, but construction counts the base
table — an HTTP round trip for a remote one — so a transient network or
auth failure became a panic across the PyO3 boundary.
### Tests
End-to-end `permutation_builder` and `StreamingDataset` runs against a
mock server, the former torch-free so it runs wherever the suite does,
plus a test that a build succeeds without an LSM write spec and is
refused once one is installed.
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.
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