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Dan RammerandClaude Opus 5
2216586023
Implementing a PK-based dedup to support WAL in hybrid search. Basically, the existing code just calls a vector query + FTS query and dedups locally using the row ID. Since row ID is unsupported in WAL (currently), we update the algorithm to use the tables PK (which is required in WAL, and uniquely linked to a PK on updates). To enable PK-dedup branch, we've chosen to fallback on error rather than fire an exploratory query. Specifically, we issue the regular hybrid query, using row IDs, and when that fails with "WAL does not support row IDs" we fallback to the PK-based variant, and then cache the fallback for future calls from the same client. The alternative is that we chec if WAL is enabled before issuing the query. This would require a `get_lsm_write_spec` call, slowing down all the existing non-WAL paths unnecessarily. The ultimate goal is to get row ID supported in WAL, but that is pending stable row ID GA and subsequent integration into the enterprise stack. Once that lands, we will rip out the PK-based dedup that is only for WAL-enabled tables and use the existing row ID code. This is the main reason for building this adaptively. I had spec-ed out a refactor so that rather than duplicating code between python and rust we build this in rust and have python call that through PyO3. However, that made the reverse path (ie. ) more difficult. So I think the tradeoff of maintaining duplicate code in the short term for the ease of reverting this in the medium term (hopefully 2-3 months). --------- Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
The Multimodal AI Lakehouse
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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 |
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