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## Bug Fix ### What is the bug? `QueryBuilder.to_pandas(blob_mode="descriptions")` could still fall back to `self.to_arrow()` for query outputs with blob columns. Custom query subclasses or wrappers can have `to_arrow()` behavior that is not compatible with pandas blob-description conversion, which can surface as low-level Arrow/list-batch conversion failures. ### What issues or incorrect behavior does the bug cause? Callers need to carry local `to_pandas` or plain-scan adapter special casing for blob descriptions, and scanner-only kwargs such as row addresses and fragment selection are not represented in LanceDB query state. ### How does this PR fix the problem? This PR routes blob-output query `to_pandas()` through the Lance scanner path for `lazy`, `bytes`, and `descriptions` modes when the query is a scanner-backed plain scan. For `blob_mode="descriptions"` with `flatten`, it collects scanner Arrow/table output, applies LanceDB `flatten_columns`, and converts to pandas from there. Non-plain blob query shapes now fail with a clear unsupported error instead of falling into subclass `to_arrow()` behavior. It also adds Python query state and builder methods for scanner-only plain-scan parameters: - `with_row_address()` for `_rowaddr` - `with_fragments(...)` for Lance fragment objects - `fragment_ids([...])` as a convenience wrapper that resolves IDs to Lance fragments ## Validation - `cd python && uv run --no-sync ruff format --check python/lancedb/query.py python/tests/test_query.py` - `cd python && uv run --no-sync ruff check python/lancedb/query.py python/tests/test_query.py` Targeted pytest was intentionally not run locally per maintainer request.
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.
Contributors
Stay in Touch With Us