lancedb-gatefixer[bot]
cc6b4aa4b4
Remote `merge_insert` buffered its reader into a `Vec<RecordBatch>`, losing the schema when the reader yielded no batches. Scanning that vector failed before the request could reach the server. Preserve the reader's schema in an empty record batch when buffering produces no batches. The source remains replayable for retries, and empty-source merges still reach the server so `when_not_matched_by_source_delete` can apply deletes. The regression tests decode the outgoing Arrow IPC and verify the source schema, zero rows, merge options, and result statistics on an initial request and a retry after HTTP 409. They cover readers with no batches and readers with an empty batch, in insert and delete modes, including an ID-only delete source. Validation: - Before the fix, both no-batch cases reproduced `Cannot scan an empty Vec<RecordBatch>`; the empty-batch cases passed. - `cargo test --quiet --profile ci --features remote -p lancedb --lib remote::table::tests::test_merge_insert` — 9 passed. - `cargo fmt --all` and `cargo fmt --all -- --check`. - `cargo clippy --quiet --profile ci --features remote -p lancedb --tests --examples`. Fixes #4380 <!-- lance-gatekeeper-fix:v1 agent=a48c9fc83772479990d76124f3a2b3be generation=1 --> Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.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
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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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