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Mohammad HijjawiandXuanwo
a0946282ca
## Summary With `rerank(..., normalize="rank")`, a hybrid query returns ranks (1.0, 2.0, 3.0, ...) in the `_distance` and `_score` columns instead of the real vector distances and BM25 scores. `_combine_hybrid_results` converted both result sets to ranks first, and only then captured `original_distances` / `original_scores`. So the step that restores the original values after reranking put the ranks back. With `normalize="score"`, the real values are returned as expected. Repro (RRFReranker(return_score="all"), vector distances 0 / 25 / 100): | normalize | `_distance` returned | `_score` returned | | --- | --- | --- | | `"score"` | 0.0, 25.0, 100.0 | 0.47, 0.67, None | | `"rank"` (before) | 1.0, 2.0, 3.0 | 1.0, 2.0, None | | `"rank"` (after) | 0.0, 25.0, 100.0 | 0.47, 0.67, None | ## Changes - Capture the original `_distance` / `_score` columns before converting them to ranks, then normalize the ranked column. The reranker input is unchanged; only the values restored afterwards are fixed. `AsyncHybridQuery` uses the same function, so both sync and async are covered. ## Test plan - [x] New `test_hybrid_return_all_restores_original_scores` in `test_rerankers.py`, parametrized over `score` / `rank`: the `rank` case fails before the fix and passes after - [x] `pytest python/tests/test_rerankers.py python/tests/test_hybrid_query.py` - [x] `ruff format --check` / `ruff check` I have no local Rust toolchain, so I ran the tests against the released 0.39.0 native module with the changed Python files overlaid. The changed functions are identical between 0.39.0 and main. Co-authored-by: Xuanwo <github@xuanwo.io>
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.
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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.
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Ecosystem:
- Columnar Storage: Built on the Lance columnar format for efficient storage and analytics.
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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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