lancedb-gatefixer[bot]
02ed5cf129
Remote handles at the latest version have no checkout version, so
`restore()` sent `{"version":null}` and surfaced a server
deserialization error. Validate the checkout version before constructing
the request and return `Error::InvalidInput` with the same message as
local tables. This shared Rust fix covers Python sync/async and
TypeScript callers.
Add a regression test proving that restore sends no request on a fresh
handle, after `checkout_latest()`, or after a successful restore clears
checkout state. It also verifies that a valid restore sends the
checked-out version. Update the schema-cache and branch restore tests to
check out a version before restoring and require success.
Validation:
- The new regression failed against the original implementation when the
mock received a null version.
- `cargo test --locked --profile ci --features remote -p lancedb --lib
restore` — 6 tests passed.
- `cargo fmt --all` and `git diff --check` passed.
- `cargo clippy --quiet --locked --profile ci --features remote -p
lancedb --tests --examples` passed.
Fixes #4381
<!-- lance-gatekeeper-fix:v1 agent=cc67544dfcbb46d631762752d55b39e3
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
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
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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.
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- 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 |
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