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Yang Cen
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## Summary Native vector dedup currently materializes through the native MV refresh protocol. Add composable DataFusion pair and retained-row relations so a host can use its existing SQL job and MV writer instead. `VectorQueryTable` binds a retained snapshot, selects all/first/random nonempty IVF scopes before dispatch, and exposes `DuplicatePairsExec` as its physical child. Pair queries can include segment, partition and snapshot columns and fetch a scalar application key for each endpoint without reading source vectors for scoring. The default primitive schema remains unchanged. `VectorDedupExec` consumes the pair stream, reuses the spillable greedy direct-representative selection, and scans retained original rows from that snapshot. A–B–C without A–C retains A and C. This query path has no durable partition receipts or writes; publication and failure belong to the enclosing SQL sink. Existing native MV APIs remain available. `BaseTableAdapter::vector_query_snapshot(None)` captures latest without changing a shared table handle's pin; `Some(version)` selects a retained version. A host can retain unbound SQL and resolve latest again for each refresh. Single-segment random sampling matches Sophon's `ivf_partition` SplitMix64 policy; multi-segment sampling ranks canonical nonempty scopes. Host integration and SQL syntax: https://github.com/lancedb/sophon/pull/8135. The host authorizes sources, binds named arguments, distributes the pair child, and uses its existing SQL Job Registry and atomic MV replacement. ## Validation All manual validation ran on an AWS r8i.8xlarge VM, not the developer laptop. - Focused Rust integration suite: 2 passed, including multiple segments/partitions, empty results, threshold boundaries, 8,193 rows per segment, deterministic sampling, mapped application IDs, and native-oracle pair equality. - Additional compacted stable/nonstable row-ID and blob test: 1 passed, including application-key mapping, null vectors, snapshot deletions and explicit/latest version capture. - Rust formatting and focused Clippy with warnings denied passed. - Dependent Sophon SQL-job E2E passes with two worker PEs: pairs, sampling, direct representatives, latest/fixed snapshot refreshes, and blob payloads. Killing a SQL worker after partial staging preserves the old MV; full rerun commits the complete replacement. Performance measurement is in progress in the dependent PR; no performance claim is made here.
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 |
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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