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Daniel RammerandClaude Opus 5
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Converge a table's LSM write path into its base table, and inspect it. `checkpoint_lsm` is `flush` then `compact`, repeated until the fresh tier is empty — and the loop runs **client-side**. Putting it on the server would mean a background task, which means a single-flight intent, an intent that leaks on panic, a bounded-iteration policy, an "is it done" observable, and a story for every way a client can vanish mid-operation. None of that exists in this shape: each request does a bounded unit of work and reports what is left, so completion is *carried in the responses* rather than inferred from a shared counter that cannot distinguish "converged" from "hasn't started yet". Best-effort by construction. Nothing is frozen, so `converged` means L0 was empty as of the last pass. It is idempotent, abandonable at any point with zero consequence, and safe to run on a cadence — an already- converged table costs one round trip and zero compaction passes, because `flush` reports `generations_remaining` and the loop is never entered. The failure taxonomy is the load-bearing part. Five distinct conditions used to arrive at a client as one 503, so `Error::LsmRoute` carries a classification read from the response body's namespace error code *at the point of receipt* — before any generic helper folds the body into a string and keeps only the status. Contention (429) retries; a draining node (503 / `InvalidTableState`) stops, because the drain gate is a one-way latch and retrying it spins until the deadline to report a failure that was knowable on the first response. Transport retry is disabled on these routes for the same reason: it treats every 503 alike. `get_lsm_stats` returns `Option<LsmStats>`, matching `get_lsm_write_spec` — `None` only when the table has no LSM write path, since a struct of zeros would read as measurements. Python bindings mirror all four, preserving the per-bucket detail rather than flattening to a table-level summary. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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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⭐ 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 |
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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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