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Jonathan M HsiehandClaude Opus 5
5b57991d69
`TABLE_NAME_REGEX`, `NAMESPACE_NAME_REGEX` and `SECRET_NAME_REGEX` admitted the same characters, spelled three ways. Separate and identical is worse than either having one or having a reason to differ: a reader had to compare them to find out which it was, and they could drift without anyone noticing -- as they briefly had, since the Secret one carried a length bound the other two never did. `OBJECT_NAME_REGEX` replaces all three, beside the other shared validators, and is what a table name, a namespace segment, a Secret name and a materialized view name are all checked against. The validators stay separate, because what they do with a failure differs: a table reports `InvalidTableName`, the others `InvalidInput`, and the Secret one names whether it was the name or a path segment. Only the set is shared. `.` and `..` remain a separate predicate rather than folding into the pattern. A character set says what a name may contain; the relative-segment rule says where a name may sit. A pattern that tried to express both would have to permit `a..b`, `..a` and `a..` while refusing the two exact values -- which is how the regex suggested in review came to allow `.` and reject `a..b`, exactly inverting the rule. Function names are still unchecked in this client, and materialized views borrow the table validator. Both are worth revisiting, but neither is a change this refactor makes. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UfmeJ533rQDnPBkMtjerV6
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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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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