Prashanth Rao
d6f9f8560e
## Summary This updates the Java API reference to close the documentation gaps that can be fixed from the current Java source and generated namespace API. The patch adds an empty table example, shows how to wrap returned Arrow IPC query bytes in a reusable `ArrowFileReader` helper, and documents the Java index operations that are currently exposed by the namespace client: vector indexes, scalar indexes, full text search indexes, and listing indexes. ## Issue Links Fixes https://github.com/lancedb/docs/issues/157 Fixes https://github.com/lancedb/docs/issues/160 Partially addresses https://github.com/lancedb/docs/issues/159 by documenting the index parameters currently exposed by Java. The requested `num_partitions` example is still blocked because `CreateTableIndexRequest` does not expose IVF training parameters yet. Not included: https://github.com/lancedb/docs/issues/158. The current Java docs and source remain remote namespace oriented, so local DB connection documentation should wait until the Java local DB API is available and can be verified. ## Validation - Built the Java core module with OpenJDK 17: `./mvnw -pl lancedb-core -am -DskipTests compile` - Checked the Markdown diff: `git diff --check -- docs/src/java/java.md` The Java build succeeds. It still reports pre-existing checkstyle warnings in the namespace client builder, but the Maven build is green.
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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