Fixes #4027 ## Summary `AsyncHybridQuery` (`table.query().nearest_to(...).nearest_to_text(...)`) paginates incorrectly when `.offset()` is used: the second page repeats rows from the first page and silently drops others. `offset()` on a hybrid query pushes the offset down into *both* sub-queries (`HybridQuery::offset` in `python/src/query.rs` forwards to `inner_vec` and `inner_fts`), so each sub-query independently skips its own first `offset` rows before the results are fused. `AsyncHybridQuery.to_batches` then called `_combine_hybrid_results(..., limit=self._inner.get_limit())` without an `offset`, so the reranked table was sliced starting at position 0 and the sub-query limits were never raised to cover the skipped prefix. On the 4-row fixture in `test_hybrid_query.py`, with `_rowid` ordering `[3, 0, 2, 1]`: | query | before | after | | --- | --- | --- | | `.limit(2)` | `[0, 3]` | `[0, 3]` | | `.offset(2).limit(2)` | `[3, 1]` | `[2, 1]` | Row `3` was returned on both pages and row `2` was never returned at all. This is the async counterpart of #3769 (`Fixes #3765`), which fixed the same bug in the synchronous `LanceHybridQueryBuilder`. #3765 explicitly deferred the async path; this PR closes that gap and reuses the `offset` parameter that #3769 already added to `_combine_hybrid_results`. The synchronous path is unaffected — it was fixed in #3769. ## Changes `python/python/lancedb/query.py`, `AsyncHybridQuery.to_batches`: - Each sub-query now fetches `limit + offset` rows and its own offset is reset to 0, so the fused result contains the full prefix the window is sliced out of. - The combined, reranked table is sliced with `offset=` instead of always starting at 0. Both halves are needed: raising the sub-query limits without the final slice still returns page 1, and slicing without raising the limits still misses rows. `nodejs` has no equivalent hybrid combine path, so there is no SDK parity gap here. ## Test plan - [x] New regression test `test_async_hybrid_query_offset` in `python/python/tests/test_hybrid_query.py`, mirroring the sync `test_hybrid_query_offset`. It asserts the offset window is a suffix of the un-offset result *and* that page 1 + page 2 together cover every row exactly once (a row-count-only assertion would pass even with duplicates). - [x] `pytest python/tests/test_hybrid_query.py` — 16 passed - [x] `pytest python/tests/test_rerankers.py` — 9 passed, 11 skipped - [x] `pytest python/tests/test_query.py` — 86 passed - [x] `pytest --doctest-modules python/lancedb/query.py` — 13 passed - [x] `ruff format --check` / `ruff check` — clean --- ## Scope, after review @lancedb-gatekeeper raised three points. Two were mine and are fixed in `04d07c2`; the third is deliberately left alone and I'd like a maintainer's call on it. **Fixed — effective limit was read from the FTS child only.** `HybridQuery::get_limit()` (`python/src/query.rs:1159`) returns `self.inner_fts.inner.current_request().limit`, so an FTS-first hybrid with no explicit `.limit()` yielded `None`, skipped the widening branch and passed `limit=None` to the combiner — returning the union of both candidate lists instead of the documented default of 10. The limit is now derived from both children with a `DEFAULT_HYBRID_LIMIT = 10` fallback, so construction order no longer matters. **Fixed — `explain_plan()` / `analyze_plan()` described a different query than the one that ran.** Both built their children straight from `self._inner`, bypassing the limit/offset rewrite in `to_batches`, and reported `skip=2, fetch=2` while execution used `skip=0, fetch=4`. Child preparation now lives in one `_create_child_queries()` helper used by all three. > **Visible change to `explain_plan()` output:** because the plan is now built from the real execution children, which carry `with_row_id()`, the two `ProjectionExec` lines gain a `_rowid` column. The doctest is updated to match. This is the diagnostic becoming truthful rather than the assertion being weakened — it is still an exact-match comparison. **Not fixed here — RRF candidate-pool invariance.** Widening each sub-query to `limit + offset` does change the candidate pool between page requests, so the fused ranking can shift and pagination can still repeat rows. That's a real problem, but it is exactly what the merged sync path does today: ```python # LanceHybridQueryBuilder (sync), merged in #3769 sub_query_limit = self._limit + (self._offset or 0) ``` Making the pool invariant means choosing a contract — a fixed candidate pool, or an explicit cursor — and that ought to apply to sync and async together rather than letting the two paths diverge. I've asked in the review thread which way you'd prefer, and I'm happy to do it here or in a follow-up covering both paths. So, to be precise about what this PR delivers: it makes `.offset()` take effect on the async hybrid path and makes the diagnostics honest. It does not make hybrid pagination stable across pages under reranking — that needs the contract decision above.
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.
