4c44587af0
Fixes #3183 ## Summary When `table.add(mode='overwrite')` is called, PyArrow infers input data types (e.g. `list<double>`) which differ from the original table schema (e.g. `fixed_size_list<float32>`). Previously, overwrite mode bypassed `cast_to_table_schema()` entirely, so the inferred types replaced the original schema, breaking vector search. This fix builds a merged target schema for overwrite: columns present in the existing table schema keep their original types, while columns unique to the input pass through as-is. This way `cast_to_table_schema()` is applied unconditionally, preserving vector column types without blocking schema evolution. ## Changes - `rust/lancedb/src/table/add_data.rs`: For overwrite mode, construct a target schema by matching input columns against the existing table schema, then cast. Non-overwrite (append) path is unchanged. - Added `test_add_overwrite_preserves_vector_type` test that creates a table with `fixed_size_list<float32>`, overwrites with `list<double>` input, and asserts the original type is preserved. ## Test Plan - `cargo test --features remote -p lancedb -- test_add_overwrite` — all 4 overwrite tests pass - Full suite: 454 passed, 2 failed (pre-existing `remote::retry` flakes unrelated to this change) --------- Signed-off-by: majiayu000 <1835304752@qq.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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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 |
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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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