lancedb-gatefixer[bot]
061a3da8b9
<!-- lance-gatekeeper-fix:v1 agent=40e5cf476a59265c71653574eda834d2 generation=1 --> ## Summary - preserve incoming PyArrow `arrow.json` fields while schema sanitization aligns input to a stored `lance.json` schema - let Lance perform the required JSONB encoding instead of relabeling raw JSON bytes as encoded storage - cover both merge insert and the conditional add sanitization path with end-to-end regression tests ## Root cause Python schema sanitization aligns incoming data to the table schema before passing it to Lance. Merge insert always takes this path, while add takes it conditionally for preprocessing such as non-default bad-vector handling or embedding functions. For JSON columns, the cast changed logical `arrow.json` strings into the table's JSONB-backed `lance.json` storage type without encoding the bytes, so Lance treated raw JSON text as JSONB. ## Validation - `cd python && uv run --extra tests pytest python/tests/test_table.py -k 'merge_insert or add_sanitization_encodes_json' -q` - targeted schema-cast and JSON encoding tests - `ruff check .` - `ruff format --check python/python/lancedb/table.py python/python/tests/test_table.py` Fixes #3923 --------- 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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