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The Python test jobs spend much of their pytest time waiting on the in-process mock HTTP servers. 1. Each server ran `serve_forever()` with the default 0.5 s poll interval, so every `server.shutdown()` blocked for up to 0.5 s. Around 300 tests use such a server (test_remote_db, test_authz/test_catalog, test_listing, the permutation helpers), about 150 s per test job on every platform. A shared `start_server_thread()` helper in `tests/utils.py` serves with a 0.01 s poll interval, so shutdown returns in about 10 ms. 2. The servers bind IPv4 only, but clients used `http://localhost:<port>`. The client tries ::1 first and falls back after the happy-eyeballs delay, about 0.3 s per connection on Windows. The mock handlers close the connection after each response, so one listing test took 9.5 s on Windows CI versus 0.6 s on Linux. Clients now use 127.0.0.1, as test_catalog, test_listing and test_namespace already do. Measured on Windows with the 0.41.0b6 wheel and `-m "not slow and not s3_test"`: full suite 546.7 s to 212.3 s; test_remote_db.py, test_listing.py and test_catalog.py together 284 s to 14 s on current main. Collected test ids are identical (1898) and pass/fail/skip results match. No assertion, test or marker 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:
- 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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