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### **Summary** Closes #3212 Extends the Python `lit()` helper to natively support three additional types (`date`, `datetime`, and `Decimal`) and implements reflexive operators for the `Expr` class. This implementation specifically addresses the blocking feedback regarding precision loss, CI discovery, and query engine limitations: * **Logic Refactoring**: Simplified `lit()` by combining `date` and `datetime` normalization into ISO-8601 strings, ensuring stable SQL parsing across different engine locales. * **Precision Preservation**: `decimal.Decimal` objects are now passed as high-precision strings to the Rust bridge, bypassing intermediate float conversions and preserving full 128-bit decimal precision for DataFusion. * **Averted CI Failures**: Temporarily deferred `bytes` literal support to a future PR to resolve a known DataFusion `expr_to_sql` limitation that was crashing the `Doctest` runner. * **Reflexive Operators**: Added support for "literal-first" arithmetic and logical operations (e.g., `10 + col('a')` or `True & col('active')`). Redundant reflexive comparisons (e.g., `__rlt__`) were pruned as Python's data model handles them automatically. * **Integration Verification**: Added dedicated integration tests in the official test directory to ensure the query engine correctly handles the new types and preserves bit-perfect fidelity. ### **Changes** #### [python/python/lancedb/expr.py](file:///c:/Users/Laksh/Documents/lancedb/python/python/lancedb/expr.py) * Updated `lit()` to handle `date`, `datetime`, and `Decimal` natively. * Implemented reflexive operators (`__radd__`, `__rand__`, `__rmul__`, etc.) to support literals on the left-hand side. * Removed the problematic `bytes` doctest example and `lit()` type support to unblock CI. #### [python/src/expr.rs](file:///c:/Users/Laksh/Documents/lancedb/python/src/expr.rs) * Modified the Rust FFI bridge to extract `Decimal` objects as strings. * Ensured the `expr_lit` handler is ready to receive normalized temporal strings. * Consolidated imports and added missing operator documentation. #### [python/python/lancedb/_lancedb.pyi](file:///c:/Users/Laksh/Documents/lancedb/python/python/lancedb/_lancedb.pyi) * Updated type stubs for `expr_lit` to include `Any` (allowing for `Decimal`). ### **Testing** Added several new advanced test cases in [python/python/tests/test_expr.py](file:///c:/Users/Laksh/Documents/lancedb/python/python/tests/test_expr.py) covering: * **High-precision Decimal preservation**: Verified against 128-bit boundaries with a "one point off" test case (`1.234567890123456789 < 1.234567890123456790`). * **Reflexive operator positioning**: Verified successful query construction with literals on the left. * **Timezone-aware normalization**: Confirmed stable behavior for `datetime` objects. * **Integration Testing**: Confirmed Date32 and Decimal columns return the correct Python types and values from the engine during `.to_arrow()` calls. --------- Co-authored-by: Will Jones <willjones127@gmail.com> Co-authored-by: Claude Opus 4.8 <noreply@anthropic.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 |
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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.
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