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Fixes #2934 ## Problem Passing a `RemoteTable` to `permutation_builder()` raises a cryptic `AttributeError`: ``` AttributeError: 'RemoteTable' object has no attribute '_inner' ``` This leaves users confused about what went wrong and why. ## Root Cause `PermutationBuilder.__init__()` calls `async_permutation_builder(table)` which accesses `table._inner` — the underlying Rust Lance table object. `RemoteTable` connects to LanceDB Cloud/Enterprise and does not have a local `_inner` attribute, making permutations fundamentally unsupported on remote tables. ## Solution Added an early check in `PermutationBuilder.__init__()` that verifies the table has `_inner` before calling the Rust function, raising a clear `TypeError` with an explanation of why permutations don't work on remote tables. ## Verification - Syntax validated with `ast.parse()` - Structural verification: single call site (`permutation_builder()`), guard placed before Rust FFI call - Error message tested with mock: `MockRemoteTable()` correctly triggers `TypeError` ## Changelog | Date | Change | Author | |------|--------|--------| | 2026-06-28 | Added remote table guard in PermutationBuilder.__init__ | rtmalikian | ### Files Changed - python/python/lancedb/permutation.py — Added `hasattr(table, "_inner")` check with clear error --- **About the Author:** Raphael Malikian — Clinical AI Solutions Architect. I specialise in building and fixing AI/ML systems for healthcare, including vector databases, RAG pipelines, and clinical NLP. If you need help with your project or think I can add value to your organisation, feel free to reach out — I'd love to connect. 📧 rtmalikian@gmail.com 🔗 GitHub: https://github.com/rtmalikian 🔗 LinkedIn: http://www.linkedin.com/in/raphael-t-malikian-mbbs-bsc-hons-71075436a --- **Disclosure:** This code was developed with assistance from deepseek-v4-pro (DeepSeek) via Hermes Agent (Nous Research). All changes were reviewed, tested against the actual codebase, and verified for correctness. Signed-off-by: rtmalikian <rtmalikian@gmail.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.
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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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