Taylor
003d5e522f
The Rust example downloads in the ingestion, search, reranking and SQL guides point to `/static/code/*.zip` URLs that return HTTP 404 on the published docs site. Replace all 15 references across 13 pages with public GitHub downloads pinned to commit `2af85bea8999c8cd23ec01ff62ba110c54652c10`. The three archives already exist in this repository. Pinning their URLs preserves the exact source, dependency manifests and lockfiles used by the existing `cargo run --locked` instructions. Only link destinations change. Validation: - All three public download URLs return HTTP 200 with ZIP content. Downloaded bytes match the repository archives and the deployed docs source archives exactly, and ZIP integrity checks pass. - Verified that the diff contains exactly 15 URL replacements in 13 pages. No example code, run instructions, manifests or lockfiles changed. - `git diff --check` passes. - `mint broken-links` reports the same 16 existing missing internal links on both the unchanged `main` baseline and this branch. - `mint validate` reports four existing missing snippet exports (`RsExampleBody` and `RsExampleTail` in the reranking and vector-search pages); the unchanged baseline has the same warnings. The combined docs build and internal-link check pass with the equivalent download-link fix.
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:
- LanceDB Embedded (open source): 100% open source, runs in your application's process; your data can live on local disk or in object storage. No vendor lock-in.
- LanceDB Enterprise: Production-scale distributed search, indexing, and maintenance. Plus, 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
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