LeonSGP
1e313d3c0d
Decimal literals such as Decimal("1E-7") and Decimal("1E+3") currently
raise ValueError while building an expression. Parse the coefficient and
exponent exactly, and normalize representable negative scales to zero so
both typed filters and predicate.to_sql() queries preserve the value.
Checked arithmetic bounds the Decimal128 coefficient to 38 digits,
preserves existing scale limits, and rejects nonfinite or overflowing
values. This does not add Decimal256 literal support.
Added 21 cases covering actual Arrow-backed table queries, SQL queries,
zero, signed exponents, precision boundaries, and errors. Both Python
3.11 and 3.13 expression suites pass 123 tests; the baseline fails 10 of
the new cases. Python binding Rust tests pass 13; official make doctest
passes 77 with 6 skips; Ruff, Rustfmt, hooks, Pyright and
workspace/tests/all-features Clippy with -D warnings pass. A fresh
environment restored from the original dependency freeze passes the full
required suite: 1528 passed, 37 skipped, 68 deselected (143.51s); while
optional Torch suites have the same baseline remote mock-server ordering
failure.
Tested against f4544628; observed official main d864e719 contains one
Catalog API commit without expr or Cargo.lock changes. AI assistance:
gpt-6.1-sol assisted implementation and local validation; all reported
checks were actually executed.
The Multimodal Lakehouse for AI
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