Prashanth Rao
137eac9b50
## What the new agent skill covers We want to help users _easily_ write LanceDB pipelines to bring their data in from other places, no matter whether they use LanceDB OSS or Enterprise. The `lancedb` set of skills contains guidance for agents on the following: - Distinguishes local and remote table capabilities. - Promotes bounded reads using `select()` and `limit()`. - Prevents accidental full-table materialization. - Documents correct Python sync/async scan APIs. - Recommends validated Python schemas and batched ingestion. - Provides indexing, query-tuning, diagnostics, and maintenance guidance. - Documents the Enterprise table-name cache issue: avoid immediately reusing a dropped or overwritten table name; write to a fresh name and rename after propagation. - Adds Python and TypeScript API, pattern, and performance references. - Adds a heuristic scanner for potentially unsafe Python and TypeScript materialization patterns. This change only adds agent documentation and tooling: no LanceDB runtime code, Rust code, SDK APIs, dependencies, or CI configuration are modified. ## Context The LanceDB agent skill was accidentally pushed directly to `main` in `8ea78e3fbcb26718112ab4ddec55a91804b869d3`, bypassing the normal review workflow. That commit was reverted on `main` by `c12a6dce` so the protected branch is back to its prior content.
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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