Daniel RammerandClaude Opus 5
25176778a3
Asking `get_lsm_write_spec` up front to decide how to fuse taxed every hybrid query, on every table, to learn something almost no table needs -- and that endpoint is not cheap: it opens the table and does a `checkout_latest()` manifest reload so a just-set spec is visible. Ask for `_rowid` optimistically instead and fall back to the primary key when the server refuses. Only a MemWAL table refuses; the refusal is raised before the query is planned, so it costs nothing server-side; and it had to be handled regardless, because a write spec installed elsewhere can arrive between any two queries. The proactive probe was belt and braces over a mechanism that already had to exist. A base table now sends nothing beyond its two legs, covered by a test that counts request paths. A MemWAL table pays one refused attempt, then remembers -- `BaseTable::hybrid_pk_fusion_learned` / `note_hybrid_pk_fusion`, seeded by the refusal and never fetched, so reading it is free. Deletes the `lsm_enabled` accessor from `BaseTable`, `Table`, the PyO3 and Python tables, and the type stubs, along with the cache that backed it. A caller who explicitly asked for `_rowid` still gets the reason rather than a bare 400: the refusal is translated into the same message the anticipated path raises. `return_score="all"` turns row ids on for the reranker rather than the caller, so it falls back like any other query -- an e2e run caught both of these. Co-Authored-By: Claude Opus 5 <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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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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