Jonathan HsiehandClaude Opus 5.5
ab2ae49200
Moves Function timestamps on the wire from RFC 3339 strings to epoch milliseconds. ## What - `FunctionVersion.created_at` (an RFC 3339 string) becomes `created_at_millis` (milliseconds since the epoch, as an integer). That's the shape Secrets, jobs and `FunctionErrorRecord` already use. - Rust: `FunctionVersion::created_at() -> &str` becomes `created_at_millis() -> i64`. - Python: `FunctionVersion.created_at: str` becomes `created_at_millis: int`. - Fixtures and the mock server now send `1787270400000`, the old `2026-08-21T00:00:00Z`. Node.js doesn't expose `FunctionVersion`, so it doesn't change. ## Compatibility Breaking, and it must ship together with the matching server change: a server that still sends `created_at` fails to deserialize on this client. Functions aren't deployed yet, so nothing is migrated. ## Tests - `cargo test --features remote --test first_class_function_slice1`: 10 passed - `cargo test --features remote --lib function`: 51 passed - `pytest python/tests/test_first_class_function*.py`: 100 passed - clippy, `cargo fmt --check`, ruff check and ruff format are clean 🤖 Generated with [Claude Code](https://claude.com/claude-code) https://claude.ai/code/session_01BY5xcEGEAa1E11VG55Kd1w Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
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
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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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