5338aeb006
Fixes #3299 ## Problem Two security issues exist in `.github/workflows/java-publish.yml`: 1. **`gpg-passphrase` input is misused**: `actions/setup-java`'s `gpg-passphrase` input expects the **name** of an environment variable (default: `GPG_PASSPHRASE`), not the secret value itself. The previous value `${{ secrets.GPG_PASSPHRASE }}` was setting the env var name to the actual secret, which is incorrect. 2. **Passphrase visible on the command line**: `-Dgpg.passphrase=${{ secrets.GPG_PASSPHRASE }}` passes the GPG passphrase as a Maven system property argument, making it visible in process listings and potentially echoed in debug logs — a supply-chain security risk for release workflows. ## Solution - Fix `gpg-passphrase: MAVEN_GPG_PASSPHRASE` — use the correct env var name so `actions/setup-java` generates a proper Maven `settings.xml` entry that reads from `MAVEN_GPG_PASSPHRASE`. - Remove `-Dgpg.passphrase=...` from the Maven CLI invocation. - Add `MAVEN_GPG_PASSPHRASE: ${{ secrets.GPG_PASSPHRASE }}` to the `env:` block of the Publish step, so the passphrase is available as an environment variable rather than a CLI argument. ## Testing The Java publish workflow only runs on tag pushes, so this cannot be exercised in a PR build. The logic change is straightforward: `actions/setup-java` is documented to write a `settings.xml` that reads `<gpg.passphrase>` from the named env var, and `maven-gpg-plugin` picks it up from there without any CLI argument. Co-authored-by: octo-patch <octo-patch@github.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 |
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.
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