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## Problem `LinearCombinationReranker.merge_results` has two related bugs that make it return **inverted relevance rankings** — the least relevant document ranks first (closes #3154). ### Bug 1 — `_combine_score` subtracts from 1, inverting the final ranking ```python def _combine_score(self, vector_score, fts_score): return 1 - (self.weight * vector_score + (1 - self.weight) * fts_score) ``` Both `vector_score` (already converted via `_invert_score`) and `fts_score` (BM25 relevance) are in **higher-is-better** space. Wrapping the weighted average in `1 - (...)` flips the direction: a perfectly matching document (`vector_score=1, fts_score=1`) gets `_relevance_score = 0.0`, while a non-matching document gets a high score. ### Bug 2 — Documents missing an FTS score are rewarded, not penalised ```python fts_score = result.get("_score", fill) # fill=1.0 by default ``` When a document has no FTS match, `fts_score = fill = 1.0`. In `_combine_score` (with the bug-1 formula), this large value becomes a **negative penalty** via `1 - (... + 0.3 * 1.0)`, counterintuitively *boosting* the document's score. By contrast, missing vector results correctly receive `_invert_score(fill) = 0.0` (penalised). ## Fix **Bug 1** — remove the `1 -` inversion from `_combine_score`: ```python def _combine_score(self, vector_score, fts_score): return self.weight * vector_score + (1 - self.weight) * fts_score ``` **Bug 2** — use `1 - fill` for missing FTS scores so both penalties are symmetric (mirror of what `_invert_score(fill)` already does for missing vector scores): ```python fts_score = result.get("_score", 1 - fill) # was: fill ``` With `fill=1.0` (default): `1 - 1.0 = 0.0` — missing-FTS entries contribute `0` to the FTS term, identical to how missing-vector entries contribute `0` to the vector term. ## Verification Concrete example from the issue. With `weight=0.7`, `fill=1.0`: | Document | `_distance` | `_score` | Old `_relevance_score` | New `_relevance_score` | |----------|-------------|----------|------------------------|------------------------| | `apple orange` | 0.0 (best) | 2.41 (only FTS) | 0.30 (**wrong: ranked 2nd**) | 1.42 (**correct: ranked 1st**) | | `banana grape` | 0.9999 (worst) | — | 0.70 (**wrong: ranked 1st**) | 0.00 (**correct: ranked last**) | ## Tests Two regression tests added to `python/python/tests/test_rerankers.py`: - `test_linear_combination_best_match_ranks_first` — the document with the smallest distance **and** an FTS match must have the highest `_relevance_score`. - `test_linear_combination_missing_fts_is_penalised` — a document with any FTS score must beat an otherwise-equal document with no FTS match. --------- Co-authored-by: Will Jones <willjones127@gmail.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.
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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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- 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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