Daniel RammerandClaude Opus 5
959645f1c7
Hybrid search fuses its two legs on `_rowid`, so both sub-queries set `with_row_id` -- which a MemWAL table rejects before it plans, because the fresh tier has no stable row id. Every hybrid shape and every reranker therefore 400s on a WAL-backed table. Join on the primary key instead. Every WAL-managed table declares an unenforced primary key and the WAL union already dedups each leg by it, so the two legs share a key space by construction -- and because they are separate round trips that can land on different snapshots, joining on the key is strictly more correct than joining on row ids, which would emit the same logical row twice at two versions. The key is dictionary-encoded into a dense u64 written under the `_rowid` name. The fusion only ever compares that column for equality -- dedup, grouping, score restoration -- so any injective mapping serves, and writing it under that name is what lets every reranker, third-party implementations included, run unmodified. It never reaches the caller. Routing is decided by `lsm_enabled()`, added to `BaseTable` with a `false` default and implemented on the remote table over the existing `get_lsm_write_spec` endpoint, cached beside the schema. No server change. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UozJQ9kQxntWarST7hC971
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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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.
Contributors
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