adenzhou1350
9c161869e1
## Summary `tokenizer` is a documented option, but initialization always passed the model name to `AutoTokenizer.from_pretrained`. Preserve the resolved tokenizer option, fall back to the model name when it is omitted, and identify the selected tokenizer in loading errors. Add five initialization regressions covering default model/tokenizer selection, an explicit tokenizer, registry variables, and tokenizer loading errors. They run in native Node child processes so the real ESM import works without changing Jest/CI flags. Model loaders are mocked and local/remote model access is disabled; these tests do not download models or test inference. ## Validation - Before: three regressions fail and two default-selection controls pass. After: all five pass. - New tests plus the existing registry and embedding-registry suites: 48 passed. - TypeScript compilation, changed-file Biome checks, and `pnpm run docs` pass; generated tracked docs are unchanged. - Checked with Node 24.14.1 / pnpm 11.1.1 on Linux, both with default Jest flags and the Linux CI ESM flag. The native binding and declarations came from the official `0.40.0-beta.13` packages, with registry integrity verified. Rebuilding Rust from source timed out during Cargo metadata resolution, before compilation; no Rust code is changed.
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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⭐ 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