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Ambiguous gateway failures can occur after a write commits, so replaying the operation can append duplicate rows. Non-idempotent writes now retry only known precommit failures. Multipart retries reuse the same upload ID, part ID, and encoded bytes; ambiguous completion returns an unknown-outcome error without abort or replay. A definite 409/429 completion rejection triggers best-effort cleanup using the call's retry budget. Replayable parts and the shared byte-based partition target default to 64 MiB. Dictionary-bearing batches are compacted before size estimation and again when actual compressed IPC framing requires further splitting: keys are remapped without changing their types or schema, view backing buffers are collected, and ListView/LargeListView child ranges are compacted as an interval union so overlapping views retain sharing. Parts are prepared on demand; a single row and its schema can still exceed the target. Multipart progress starts at zero and counts each acknowledged IPC body once. Rust write builders and Python/TypeScript `add` expose per-call retry overrides. Atomic `create_table` pre-encodes to a temporary file and rejects compressed IPC over 256 MiB before sending any request; the schema-first import guidance now includes Python, TypeScript, and Rust examples. The shared partition target also affects local automatic partitioning. At `cb2a79473bd1b51b950c9fa3e9f247f929c8ac3b`, 70 targeted Rust cases pass on source verified against the committed four-file patch. With a 16 KiB target and 128 unique 1 KiB dictionary values, the original ordinary dictionary reproducer drops from 128 requests / 16,942,080 bytes to 16 / 143,488; ListView and LargeListView drop from 128 / 17,056,768 to 16 / 149,632. Encoded HTTP bodies are decoded to check values, nulls, slices, metadata, changing dictionaries, and overlapping views; additional cases cover framing-driven splitting, oversized rows, stable retry bytes/IDs, acknowledged progress, and atomic create. Real HTTP fault tests commit Blob payloads to a local Lance table and verify byte equality, no duplicate append, and zero abort/replay after ambiguous completion. The earlier 811 remote and 7 progress cases at `f36d217110a25f6b501c0255df9aaa60e3c8000a`, Linux Python remote database/options results (437 cases plus 8 fixture consumers), Node retry-binding evidence, and Rust/TypeScript documentation example checks remain historical validation. Python/Node extensions were not rebuilt for the dictionary follow-up; these results do not establish current binding integration or fresh cross-platform/minimum-dependency CI acceptance. A separate real Query Node lifecycle experiment used SDK `b8efe7d2d63647d4962789ca5fc042b59efe073d` and Sophon `58b7895c7c287810fff4e89f13226fc5fa60d9ef`. After the actual listener forced a connection closed, a surviving QN accepted a retry with identical upload ID, part ID, and IPC bytes. Ordinary insert and complete response-loss scenarios sent no replay or abort; fresh reads observed exactly one commit and exact Blob bytes in all three scenarios. This local experiment used a 1-second drain grace, two 32 KiB payloads, and one distinct staged part. It validates acknowledgement-loss recovery at those revisions, not concurrent overlapping attempts, large uploads, physical orphan cleanup, the later dictionary encoding change, or the original sixth-insert EOF. The part target is not a process memory limit: source/ancestor buffers and temporary compaction allocations may coexist. Struct/FixedSizeList parent nulls can retain hidden child keys; this is not complete logical-reachability GC for every Arrow combination, and retained hidden values may affect wire size. The current local Lance 15.0.0-beta.17 rejects ListView/LargeListView persistence; the tests establish remote client IPC encoding behavior, not native or Cloud persistence support. Live Cloud, full workspace/Clippy, the original ordinary-insert disconnect root cause, and Sophon's asynchronous transaction cleanup behavior are not established. Cancelling during staging retains the existing server-session cleanup limitation. Function reader delivery is separate.
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
Star LanceDB to get updates!
⭐ 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.
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