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Jonathan M HsiehandClaude Opus 5
7b29fb2f51
Adds the client half of database-scoped named Secrets: a Secret is a name and an opaque value stored by the service, and a Function binds one to the environment variable its library already reads. - `db.create_secret` / `alter_secret` / `list_secrets` / `describe_secret` / `drop_secret` on sync, async and remote connections, with the pyo3 binding and the Rust client behind them. There is no read API by construction rather than by policy: no code path returns a stored credential, and `describe_secret` answers with metadata only. - `EnvVarSecret(secret=..., env_variable=...)` pairs a Secret with the variable it arrives in. It is a pure local constructor -- it contacts no server, so it cannot fail on a Secret that does not exist -- and it exists so a bare string in that position, which would be a credential, is a TypeError rather than a plausible-looking mistake that reads identically in a diff. - `create_function(..., secrets=[...])` carries the bindings as `secret_bindings`, a map from variable name to Secret name. The value never travels: it is resolved by the service when the Function runs, which is what lets a rotation reach columns pinned to an older FunctionVersion. The UDF body is unchanged and stays portable -- it reads `OPENAI_API_KEY` the way it always did, and the binding is what puts a value there. Squashed: the original three commits were a first design plus a rewrite of it, so their sequence describes an interface that no longer exists. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01XE1UwYKsgbb3USBfkqCE6v
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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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.
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