SHAIK SHIVAJI
1a037c0315
When `LinearCombinationReranker` uses `return_score="all"` and either the vector or full-text side has no rows, the unavailable score column is now a typed Arrow `float32` null column instead of a column of NaNs. Existing scores from the non-empty side and their descending `_relevance_score` ordering are preserved. Regression coverage exercises both missing sides through the hybrid query path. It checks the missing column's `float32` type, null count and row count, exact preservation of the original present-side values, and descending relevance ordering. The existing empty-result, score-column override, and `return_all` cases remain covered. The exact head `db33a068263cd69ee578f95a375f0060dcbb4e21` passed all 15 applicable check runs and all 4 applicable workflow runs, with no commit statuses. The hosted macOS Python run completed with 3019 passed, 126 skipped, and 70 deselected; the two new cases and nine adjacent regression cases passed. The retained CI collector reached the all-success snapshot but exited 1 while writing its final metadata because of a harness `TypeError`; it did not rerun or invalidate the product or CI results. BREAKING CHANGE: For `return_score="all"` with an empty vector or full-text side, the missing side's score column now contains typed nulls instead of NaN. Consumers that distinguish these values will observe different results.
The Multimodal Lakehouse for AI
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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⭐ Click here ⭐ to see how fast we're growing!
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:
- LanceDB Embedded (open source): 100% open source, runs in your application's process; your data can live on local disk or in object storage. No vendor lock-in.
- LanceDB Enterprise: Production-scale distributed search, indexing, and maintenance. Plus, 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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