Dan TasseandClaude Fable 5
944398d807
As in https://github.com/lancedb/lancedb/pull/3977, we're trying to reduce anything in the lancedb skill that duplicates other docs. So this shrinks the branch-ops logic down to a few lines that mostly just point the agent to fetch the branching docs from lancedb.github.io. Run stats (2 runs each): <img width="1001" height="232" alt="Screenshot 2026-08-20 at 5 02 34 PM" src="https://github.com/user-attachments/assets/a3f4d305-278e-4093-b153-07f0af57b251" /> This is out of order, rearranged: |condition|time (sec)|cost| |---|---|---| |No branch_ops.md|250|1.33| |No branch_ops.md|227|1.26| |Old branch_ops.md|116|0.83| |Old branch_ops.md|127|0.87| |New branch_ops.md|135|0.86| |New branch_ops.md|147|0.93| Averaged between each of the two runs: <img width="775" height="337" alt="Screenshot 2026-08-20 at 5 34 15 PM" src="https://github.com/user-attachments/assets/4f2fb2a8-3112-4614-87d9-8dbf807f3b75" /> It seems helpful to have *some* doc about branching; otherwise the model gets a little confused about our branch model and what methods to call. But it looks like the new one (in this PR; all just references to current docs) is basically as good as the old one (lots of duplicative text). --------- Co-authored-by: Claude Fable 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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