## What
`AnswerdotaiRerankers(return_score="all").rerank_hybrid(...)` (and
`ColbertReranker`, which subclasses it without overriding
`rerank_hybrid`) raises:
```
pyarrow.lib.ArrowInvalid: Invalid sort key column: No match for FieldRef.Name(_relevance_score) in _rowid: int64 ...
```
## Why
```python
combined_results = self.merge_results(vector_results, fts_results)
combined_results = self._rerank(combined_results, query)
if self.score == "relevance":
combined_results = self._keep_relevance_score(combined_results)
elif self.score == "all":
combined_results = self._merge_and_keep_scores(vector_results, fts_results)
```
When `score == "all"`, `combined_results` is unconditionally overwritten
by `_merge_and_keep_scores(vector_results, fts_results)` **after**
`_rerank()` already computed and appended `_relevance_score` —
discarding it. The following `sort_by("_relevance_score", ...)` then has
nothing to sort on.
Every sibling reranker that supports `return_score="all"`
(`cross_encoder`, `openai`, `cohere`, `jinaai`, `voyageai`, `watsonx`)
instead calls `_merge_and_keep_scores()` **before** `_rerank()`. This
file is the one place the ordering got inverted when `"all"` support was
added (#2509) — a copy/paste inconsistency across the six files that PR
touched. Fix mirrors the pattern already used (and tested) by the other
five rerankers.
Also drops the now-stale `"Only 'relevance' is supported for now"`
docstring line on both classes, left over from before `"all"` support
existed.
## Testing
Added `test_answerdotai_reranker_return_all`, mirroring the existing
`test_cross_encoder_reranker_return_all`. Verified locally with the real
built Rust extension: red (reproduces the exact `ArrowInvalid` above) →
green, using the actual `rerank_hybrid`/`_rerank`/`base.py` code path
with the model call mocked out — my local environment's
`rerankers==0.10.0` fails to load the real ColBERT model against the
available `transformers` version (`AttributeError: 'ColBERTModel' object
has no attribute 'all_tied_weights_keys'`), which I confirmed also
breaks the **pre-existing**, unmodified
`test_colbert_reranker`/`test_answerdotai_reranker` baseline tests
identically — an unrelated local dependency-version issue, not a
regression from this change. `ruff check`/`ruff format` clean; full
`test_rerankers.py` run: 9 passed / 8 skipped / 3 failed (the 3 failures
are exactly those two pre-existing tests plus my new one, all failing at
model-loading time for the same unrelated reason before reaching the
changed code).
---
Disclosure: this PR was drafted with AI assistance (Claude); I reviewed,
tested, and take responsibility for the change.
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.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
Star LanceDB to get updates!
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.
