## Summary
- Let local plain queries over row-addressable legacy v1 and v2 blob
columns use `blob_mode="bytes"` without importing `pylance` through
pandas, batch, or Arrow conversion.
- Preserve aliases, filters, ordering, limits, null and empty payloads,
empty result schemas, and the caller's `_rowid` choice.
- Build the hidden row-id query separately so reusing an async builder
for lazy reads or non-blob projections cannot retain an automatic
`_rowid`.
- Keep byte reads closed for blobs inside lists, remote or namespace
pushdown, and query shapes whose result rows cannot be matched back to
the source. Blob v2 lazy auto-row-id behavior is unchanged.
Fixes#4645
## Validation
- 26 focused Python tests passed with the test subprocess blocking all
`lance` imports. The persisted fixture writes and reopens a storage-v2.1
table, then covers sync and async pandas, batches, and Arrow conversion
plus async builder reuse.
- The existing unsupported vector and hybrid reranker regressions now
assert the current eligibility diagnostic, and the legacy table-level
Arrow regression now asserts successful bytes, schema, values, and
hidden row IDs. Both focused tests passed.
- The runtime receipt records no installed `pylance` distribution and no
importable `lance` module. The Python-only change was verified with an
existing compatible 0.42.0b9 extension; the relevant query and fetch
APIs are unchanged between that source and this 0.42.0b10 base.
- Ruff format and lint pass for every changed Python file.
Downstream workspaces adopting Lance 15.0.0-beta.11 currently also
resolve beta.10 because the SDK pins every Lance crate exactly. This
produces incompatible Lance types across the SDK and the consumer.
Update the workspace pins and lockfile to the published
[v15.0.0-beta.11](https://github.com/lance-format/lance/releases/tag/v15.0.0-beta.11).
This release includes payload-free list length reads and preserves those
read modes across serialized plans (lance-format/lance#10759,
lance-format/lance#10766). Existing feature flags and unrelated locked
packages are unchanged.
The release also includes the metric/k-dependent search-effort
calibration from lance-format/lance#10695. Update the Node and Python
execution tests to its exact probe budgets (for example, effort 0.25
with L2, k=8 and Auto=6 now probes 4 partitions), and cover the 0.1
tier. Recall, caller bounds and probe-precedence assertions remain in
place.
Validation:
- `cargo fmt --all --check`
- `cargo clippy --quiet --features remote --tests --examples`
- Downstream metadata fixture with SDK source and the released Lance
beta.11 path overrides resolves exactly one Lance version.
- `cargo test --quiet --features remote --tests`: 2,271 passed, 0
failed, 79 existing ignored tests.
- Node native/TypeScript build, lint and API documentation generation
passed; 41 query tests passed.
- Python native binding build and package Ruff checks passed; 208 query
tests passed, 1 slow test deselected, 5 existing deprecation warnings.
- Rust results remain applicable: the follow-up changes only binding
tests, with product source and dependency graph unchanged from the
tested dependency update.
Repository-root Ruff currently reports 59 lint findings and 6 formatting
failures in untouched documentation snippets. Their blobs match the PR
base; the changed Python test and the entire Python package pass
lint/format checks.
`take_row_ids` used an `IN` filter, so the backend could reorder rows
and collapse duplicate IDs. That can silently misalign training rows
with labels or sample weights.
Restore the requested order and duplicate occurrences in the shared Rust
take path for native and remote tables, and retain that information
through Python query serialization. Missing or deleted IDs are omitted.
Explicit Rust `order_by` overrides request order, and pagination applies
after restoring occurrences. Empty projections and `_rowid` projection
aliases retain their output schema.
RTE tables reject unsupported takes with the existing base-table
guidance before checking the internal row-ID projection used for
ordering. An explicit `use_rte(False)` reads the base table with the
same requested order and occurrences.
A pre-existing fixture defect also blocked CI:
`test_table_add_in_threadpool` responded without reading uploads,
allowing a connection reset during response decoding. This PR includes a
fix confined to that test's handler: consume each request body before
responding and verify one create row plus ten inserted rows. This was
not a regression in the take implementation. Production write retries,
concurrency and HTTP client code are unchanged.
The selective sampling guide scopes the row-ID guarantees to SDK
releases containing this fix, preserves the distinct offset-take
semantics, and explains that ordered takes collect and combine matching
rows before output batching. It recommends smaller ID requests and
narrow projections because `max_batch_length` does not cap working
memory. This uses the existing REST query protocol and requires a new
SDK release; no deployed service was changed.
Validation:
- Docs follow-up: the changed page compiles with the Mint 4.2.888 MDX
compiler; Typos 1.26.0 passes and Vale 3.24.0 has no findings on changed
lines. Full-site Mint validation reports four existing snippet-import
warnings on the reranking/vector-search pages, identical with the
original page restored in a baseline comparison.
- The applied threadpool test passed locally after the fixture change.
The identical candidate previously passed 10 trials with the ordinary
reader and 10 with an unbuffered reader; each trial verifies all
uploaded rows. Those checks used macOS arm64/Python 3.14/default
features, not the full failing CI environment. The normal new-head macOS
Python 3.13/experimental-functions job remains the required platform
check.
- Python package Ruff format/check passed. Repository-root Ruff still
reports 59 existing errors and six formatting differences in unchanged
`docs/web-tests` files; none are in changed files.
- At `a569d3f0`, the RTE diagnostic regression was reproduced and fixed;
5 targeted cases and strict workspace Clippy passed. The fixture-only
follow-up changes no production Rust/Python code, so that evidence is
reused.
- Earlier validation at `6bcc0878`: 439 Rust query/remote-table tests
and 24 Python take/getitems tests passed; the focused 49-test suite also
covered offset dataloaders. A local 9,216-row, four-fragment
reproduction returned 200 seeded random batches of 64 rows with zero
ordering or label-alignment mismatches. The ordering and projection
implementation is unchanged by the follow-ups.
- No live Enterprise run was performed.
Fixes [ENT-3469](https://linear.app/lancedb/issue/ENT-3469).
`ApproxMode` landed in the Rust core in #3549, but the Python and Node.js bindings were never plumbed, so `approx_mode` is unreachable from either SDK. Both were built mirroring the existing `ef` plumbing.
Co-authored-by: Leo <leo@insight.co>
Plain local blob v2 scans tried to open a pylance dataset before
reaching LanceDB's byte-fetch path, so `to_pandas(blob_mode="bytes")`
failed when the optional dependency was absent. Batch readers also had
no `blob_mode` option.
Route supported blob v2 pandas conversions through the native Arrow
path. Add `blob_mode` to sync and async batch readers, convert payloads
during iteration, and expose the converted schema without fetching the
first batch. Apply conversion after reranking and retain the default
descriptor behavior. Clean up scanner error punctuation and remove the
misleading plain-scan suggestion.
Bytes mode retains the existing `to_arrow` restrictions for legacy/list
blobs, remote tables, and namespace query pushdown. Lazy pandas handles
and legacy pandas scans still require pylance; sync FTS/hybrid batching
remains unsupported.
Validation:
- UV-managed editable Python development build completed successfully.
- The absent-pylance regression tests fail against the original query
implementation and pass with this change.
- Blob and query suites: 403 passed, 1 skipped, 1 deselected.
- Remote query suite: 190 passed.
- Python-tree Ruff lint/format checks and the async reader Pyright check
pass. Repository-wide Ruff reports existing errors outside the Python
tree.
Fixes#4645
<!-- lance-gatekeeper-fix:v1 agent=fd85998f14bb0b3c92b2d050df93a615
generation=1 -->
---------
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
Co-authored-by: Xuanwo <github@xuanwo.io>
Async `to_query_object()` dropped enabled `fast_search` on every query
type and `postfilter` on plain and full-text queries. Re-executing a
serialized fast search could therefore return unindexed rows.
Copy enabled `fast_search` into the Python `Query` and serialize enabled
plain/FTS postfilter from the Rust request's prefilter setting. Preserve
the existing unset defaults: `None` for these options, with the vector
query's existing `False` postfilter default.
Validation:
- Added 12 serialization cases across plain, FTS, and vector queries,
covering both enabled and unset options, plus two FTS execution
round-trip cases. Before the fix, nine cases failed, including returning
an unindexed row after serialization.
- Rebuilt the Python extension using the uv-managed environment.
- Query tests and existing FTS fast-search tests: 192 passed, 1 slow
test deselected.
- Rust formatting and Python package lint/format checks pass.
Repository-wide `ruff check .` reports 49 pre-existing errors in
unchanged `docs/web-tests` files.
Fixes#4658
<!-- lance-gatekeeper-fix:v1 agent=f828b24871a2dc928668be2de4f85078
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
Co-authored-by: Xuanwo <github@xuanwo.io>
`Table.take_offsets` and `Table.take_row_ids` declare a keyword-only
`with_row_id: bool = False`, but none of the three implementations
accept it — `LanceTable`, `AsyncTable` and `RemoteTable` all raise
`TypeError: ... got an unexpected keyword argument 'with_row_id'` on the
documented call. The builders they return already support it
(`LanceTakeQueryBuilder.with_row_id()` /
`AsyncTakeQuery.with_row_id()`), so this is a signature that never
reached the implementations. The base docstrings also don't mention the
parameter and still say `take_offsets` returns a `pa.RecordBatch` (it
returns a `LanceTakeQueryBuilder`), so those are corrected too.
Each implementation now accepts the kwarg and applies `.with_row_id()`
to the builder it returns. Tests: sync and async assert `_rowid` appears
in the result columns; the remote test uses the suite's
request-capturing fixture and asserts `with_row_id: true` reaches the
wire (and is absent by default). All three fail on `main` with the
`TypeError`. ruff format/check clean.
A related mismatch I left alone: the deprecated
`LanceTable.create_scalar_index`/`create_fts_index` also drop a
`wait_timeout` the base declares — deprecated since 0.25, so not worth
touching here.
Co-authored-by: Xuanwo <github@xuanwo.io>
## Summary
- add a Python regression for filtered scans over deeply nested
list/struct values
- verify the public query path returns every nested sibling field
without an Arrow schema mismatch
## Root cause
Older Lance scanner code split filter projections into partial nested
arrays and did not recursively merge List<Struct> children, so a sibling
field could be omitted from the returned array while remaining in the
declared schema. That merge was fixed upstream in
lance-format/lance#4838, and the Lance revision currently pinned by
LanceDB contains that fix. This test makes the repair durable at the
LanceDB Python API boundary.
## Validation
- cd python && uv run --extra tests pytest
python/tests/test_query.py::test_filter_preserves_deeply_nested_struct_fields
-q
- cd python && uv run --extra tests pytest python/tests/test_query.py -q
- uv run --project python --extra tests --extra dev ruff check .
- uv run --project python --extra tests --extra dev ruff format --check
python/python/tests/test_query.py
Fixes#2217
<!-- lance-gatekeeper-fix:v1 agent=9ddc0efd1f13888b2a7a896881c6e68a
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
Lance supports `search_effort`, but LanceDB cannot pass it through its
query APIs. Expose the parameter in Rust, Python sync/async and
TypeScript, including remote requests and hybrid query conversion, using
the [Lance
v14.0.0-beta.13](https://github.com/lance-format/lance/releases/tag/v14.0.0-beta.13)
dependency already on main.
The default is `None` (Auto, internally 0.5). Any explicit effort,
including 0.5, overrides fixed `nprobes` in either setter order;
explicit minimum/maximum bounds remain effective. Invalid values are
rejected in the Rust core. Hybrid conversion now retains the vector
probe parameters and handles an inferred FTS column without panicking.
Namespace queries with explicit effort execute locally because the
namespace wire model cannot carry it. MemWAL LSM queries reject it
explicitly because Lance's LSM scanner does not yet expose this option.
Maturin is included in the dev extra so the documented build command
works in the project environment.
BREAKING CHANGE: The public Rust `VectorQueryRequest` struct now
includes `search_effort: Option<f64>`. Downstream code that initializes
every field explicitly must add `search_effort: None` or use
`..Default::default()`. Exhaustive destructuring patterns must include
the new field or `..`. Query-builder calls and struct literals that
already use `..Default::default()` require no source changes. The new
effort setters in Python and TypeScript are additive. This PR does not
change the persisted file format.
Using explicit effort with remote tables requires server support for
`search_effort` and its precedence over `nprobes`; the client wire tests
do not establish that a deployed server supports it. Server-side support
is tracked in lancedb/sophon#8607.
Validation on AWS after merging current main (Lance beta.13 / SDK
beta.5):
- 14 Rust regressions and the API doctest pass.
- 60 parameter regressions, the full 47-test namespace suite, and three
API doctests pass. The parameter selection includes some namespace
cases.
- 40 TypeScript query tests and 54 remote tests pass; build, lint and
generated docs pass.
- Rust Clippy, Python package lint/format, Pyright and lockfile checks
pass. Repository-wide Ruff reports 49 lint errors and 11 formatting
failures in unchanged `docs/web-tests` files; those files are
byte-identical to main and outside this change.
All 1,103 Git-tracked files match the AWS validation checkout. No
benchmark scripts or result artifacts are included, and no performance
improvement is claimed.
Uses lance-format/lance#9798. Server-side support is in
lancedb/sophon#8607.
Keep IVF probe fields unset by default so Lance supplies adaptive
behavior. Preserve `nprobes`, `minimum_nprobes`, and `maximum_nprobes`
independently through Rust, Python, namespace, and remote request
construction, with compatibility fields serialized only when explicitly
configured.
The final Rust namespace and LSM propagation update depends on the Lance
release containing lance-format/lance#9184.
Empty list and NumPy vector queries were validated too late: local
search inferred a vector column first, producing misleading schema
errors, while remote search indexed `vector[0]` and raised `IndexError`.
Add a shared empty-vector check before column inference and query
creation, and validate direct vector-builder inputs. Local and remote
sync search now raise `ValueError: Query vector must not be empty` for
these inputs.
Validation:
- All 26 new regression cases failed on the original code and pass with
the fix, covering list/NumPy inputs, automatic/explicit vector search,
inferred/explicit column names, one/two local vector columns, and remote
search before query submission.
- After merging current `main`, all 32 focused cases passed, covering
these regressions and upstream async searches without a query.
- The query, table, remote, utility, hybrid-query, and database suites
passed in the rebuilt uv environment: 541 passed, 1 skipped, 1 slow test
deselected.
- `ruff check .`, `ruff format .`, and `cargo fmt --all` passed from the
repository root.
Fixes#4389
<!-- lance-gatekeeper-fix:v1 agent=f1f4875090ef391bb1aa912dd54026dc
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
`nprobes(0)` bypassed the validation used by the minimum and maximum
probe setters, allowing zero into query requests and producing
inconsistent local and remote results. Sync Python could additionally
interpret the zero maximum as an unbounded search.
Reject zero in the Rust vector-query builder and propagate its input
error through the Python vector/hybrid and Node bindings. Validate
nonpositive counts in the sync Python vector and hybrid builders so
every SDK and backend reports `nprobes must be greater than 0` before
execution. Update the Rust examples and document the positive-count
requirement.
Regression tests cover local and remote vector/hybrid builders,
rejection before remote query requests, promised TypeScript query
vectors, and positive counts replacing both probe bounds.
Validation:
- Rust probe regression tests: 2 passed.
- Focused Python query, remote, hybrid, and serialization tests: 22
passed. The four sync regression cases fail with the original setters.
- Focused TypeScript indexed local and mocked remote tests: 2 passed.
- Workspace `cargo check --features remote --tests --examples` and
Clippy passed.
- Rust formatting, Python formatting/lint, and TypeScript
build/lint/docs passed. Repository-wide `ruff check .` reports 29
existing errors in unchanged CI scripts; `ruff check python` passes.
Fixes#4388
BREAKING CHANGE: Rust `VectorQuery::nprobes` now returns `Result<Self>`
instead of `Self`; callers must handle the result, as with the existing
minimum/maximum probe setters.
<!-- lance-gatekeeper-fix:v1 agent=e92fda5a212c7f9e80fa55e7b4e4634d
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
`AsyncTable.search()` and `search(None)` reached vector search without
assigning `vector_query`, raising `UnboundLocalError` on both local and
remote tables. Return a plain `AsyncQuery` before inferring the search
type, equivalent to `table.query()`, and update the overloads and
documentation to describe that return value.
Add six regression cases for omitted and explicit `None` queries on
local tables with and without vectors and on a mocked remote endpoint.
They verify scans, limits, filters, column projection, and the remote
request body.
Validation: all six regressions reproduced the original
`UnboundLocalError`; all 11 focused query tests pass with the fix,
including existing vector, full-text, hybrid, and sync scan coverage.
The uv development bootstrap, locked Ruff formatting/lint checks, and
focused Pyright return-type assertions also pass.
Fixes#4385
<!-- lance-gatekeeper-fix:v1 agent=bb9a7509f6dadde030d8113b172abc9a
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
Updates the Rust workspace Lance dependencies, Cargo lockfile, and Java
lance-core from v12.0.0-beta.14 to
[v12.0.0-beta.15](https://github.com/lance-format/lance/releases/tag/v12.0.0-beta.15).
No compatibility fixes were required; `cargo clippy --quiet --workspace
--tests --all-features -- -D warnings`, `cargo fmt --all --quiet`, and
`git diff --check` passed.
---------
Co-authored-by: Jack Ye <yezhaoqin@gmail.com>
## Summary
- preserve repeated table offsets without adding a public ordering
guarantee
- retain exact requested ordering in identity and persisted permutations
- cover local, projected, multi-batch, and mocked-remote query paths
## Root cause
Take queries lowered offsets to a set-like IN predicate and discarded
repeated occurrences. Persisted permutation loading also compared the
distinct base-table result count with the requested occurrence count,
rejecting repeated row IDs before its existing reordering step could
expand them.
## Fix
The shared take-query path now deduplicates the predicate for efficient
lookup, requests row-offset metadata internally, and expands each
matching row to the requested multiplicity in backend result order. An
internal opt-in keeps exact requested order for identity
PermutationReader reads, while persisted permutations continue using
their existing ordering map.
## Validation
- cargo test --quiet --features remote --tests
- cargo check --quiet --features remote --tests --examples
- cargo clippy --quiet --features remote --tests --examples
- targeted Python local and mocked-remote regression tests
- exact issue reproduction
Fixes#2820
<!-- lance-gatekeeper-fix:v1 agent=75acf840afa6f4be4bff98b567b504bd
generation=1 -->
---------
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
Co-authored-by: Xuanwo <github@xuanwo.io>
## Summary
- add Python regression coverage for integer and double arithmetic
against the generated _distance column
- merge the current main base containing Lance v11.0.0-beta.3 from #3896
- verify both expressions retain the generated scoring field Float32
type and compute the expected values
## Root cause
Lance parsed dynamic projection expressions before vector search added
its generated Float32 _distance field. Without a typed provisional
field, expression discovery rejected mixed numeric arithmetic. Lance
upstream fixed discovery and final-schema replanning in
lance-format/lance#8163, and the current base consumes that fix through
Lance v11.0.0-beta.3.
## Validation
- uv run --extra tests pytest
python/tests/test_query.py::test_select_arithmetic_with_distance -vv
--maxfail=2 — 2 passed
- python/.venv/bin/ruff format --check python/python/tests/test_query.py
- python/.venv/bin/ruff check .
Fixes#2618
<!-- lance-gatekeeper-fix:v1 agent=816a060090517471edfb73652bb5c9fe
generation=1 -->
---------
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
Co-authored-by: Xuanwo <github@xuanwo.io>
<!-- lance-gatekeeper-fix:v1 agent=d30696bc46eb32f04c9927b0792e35d3
generation=1 -->
## Summary
- use the Lance native batch KNN path so fixed-size batch vector
searches share one flat table scan
- validate consistent query-vector dimensions and retain the per-vector
plan when offsets require its existing semantics
- add Rust and Python regressions and update Rust, Python, and
TypeScript API documentation
## Root cause
LanceDB expanded every vector in a batch into a separate scan plan and
joined the plans with `UnionExec`. For unindexed tables on S3, a batch
of ten vectors therefore ran ten concurrent full scans, amplifying CPU
and retained data enough to produce the reported memory spike.
The native Lance batch KNN path performs bounded-memory selection for
all query vectors over one flat scan. LanceDB now supplies the vectors
as a batch and avoids applying a global scanner limit to the combined
per-query results. Batch queries with a nonzero offset keep the previous
plan because the native batch API does not support per-query offsets.
## Validation
- targeted Rust batch-query plan and execution tests
- `cargo check --quiet --features remote --tests --examples`
- `cargo clippy --quiet --features remote --tests --examples`
- `cargo fmt --all -- --check`
- targeted Python batch-vector regression after rebuilding the extension
- Ruff formatting/checks for the touched Python files
- Node.js build, lint, docs generation, and targeted batch-vector Jest
test
- `git diff --check`
Fixes#2468
---------
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
Co-authored-by: Xuanwo <github@xuanwo.io>
## Summary
- add regression coverage for multiple query vectors in the local
synchronous Python API
- verify that each query vector receives its own limited
nearest-neighbor result and `query_index`
## Root cause
In LanceDB v0.16, the local synchronous scanner passed a nested vector
array as one query, unlike the async and remote implementations. The
subsequent sync-to-async table migration supplied the correct shared
runtime path, but this local sync behavior was never regression-tested
and issue #1857 remained open.
## Validation
- `uv run --extra tests pytest
python/tests/test_query.py::test_query_multiple_vectors -q`
- `uv run --project python --extra tests --extra dev ruff format --check
python/python/tests/test_query.py`
- `uv run --project python --extra tests --extra dev ruff check .`
Fixes#1857
<!-- lance-gatekeeper-fix:v1 agent=6b25bc529d76813c3db7627c8be947ef
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
BREAKING CHANGE: When passing multiple where clauses to a query, they
now stack instead of replacing the previous filter.
Previously, calling `where`/`only_if` more than once on a query silently
replaced the previous filter, so only the last filter was applied. This
was
surprising and could return rows that an earlier filter should have
excluded.
This implements the alternative suggested in
https://github.com/lancedb/lancedb/pull/3514#issuecomment-4664901580:
instead of
rejecting a second filter, repeated filters are combined with a logical
AND
(`(previous) AND (new)`).
The combination happens in the Rust core (`QueryBase::only_if` and
`only_if_expr`), so it applies to all SDKs at once (Rust, Python async,
and
TypeScript). The Python sync query builder keeps its own filter state,
so it
combines filters in the binding layer as well.
SQL string and expression filters are combined within their own
representation.
When the two representations are mixed, the expression is lowered to SQL
(via
`expr_to_sql_string`) and the filters are combined as SQL strings, so
chaining
`where` works regardless of which form each filter takes.
Fixes#2649
## Tests
- Rust: `cargo test --features remote -p lancedb --lib query`
- Python: `uv run --extra tests pytest python/tests/test_query.py`
- TypeScript: `pnpm test __test__/query.test.ts`
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
## Bug Fix
### What is the bug?
`QueryBuilder.to_pandas(blob_mode="descriptions")` could still fall back
to `self.to_arrow()` for query outputs with blob columns. Custom query
subclasses or wrappers can have `to_arrow()` behavior that is not
compatible with pandas blob-description conversion, which can surface as
low-level Arrow/list-batch conversion failures.
### What issues or incorrect behavior does the bug cause?
Callers need to carry local `to_pandas` or plain-scan adapter special
casing for blob descriptions, and scanner-only kwargs such as row
addresses and fragment selection are not represented in LanceDB query
state.
### How does this PR fix the problem?
This PR routes blob-output query `to_pandas()` through the Lance scanner
path for `lazy`, `bytes`, and `descriptions` modes when the query is a
scanner-backed plain scan. For `blob_mode="descriptions"` with
`flatten`, it collects scanner Arrow/table output, applies LanceDB
`flatten_columns`, and converts to pandas from there. Non-plain blob
query shapes now fail with a clear unsupported error instead of falling
into subclass `to_arrow()` behavior.
It also adds Python query state and builder methods for scanner-only
plain-scan parameters:
- `with_row_address()` for `_rowaddr`
- `with_fragments(...)` for Lance fragment objects
- `fragment_ids([...])` as a convenience wrapper that resolves IDs to
Lance fragments
## Validation
- `cd python && uv run --no-sync ruff format --check
python/lancedb/query.py python/tests/test_query.py`
- `cd python && uv run --no-sync ruff check python/lancedb/query.py
python/tests/test_query.py`
Targeted pytest was intentionally not run locally per maintainer
request.
## Feature
- What is the new feature?
- Adds `blob_mode` support to sync and async Python query `to_pandas()`
APIs.
- Enables plain scan queries to return blob columns as lazy `BlobFile`
objects, raw bytes, or blob descriptions.
- Lets namespace-backed local tables use Lance native blob-aware pandas
conversion for lazy blobs.
- Why do we need this feature?
- Table and Lance dataset/scanner APIs already support blob-aware pandas
conversion, but LanceDB query builders did not expose that capability.
- Geneva and other callers should be able to use query-level
`to_pandas(blob_mode=...)` without manually constructing Lance scanners.
- How does it work?
- Plain scan queries route through Lance scanner native
`to_pandas(blob_mode=...)`, preserving filter, projection, limit,
offset, row id, and alias/expression projection behavior.
- Non-native query shapes keep existing Arrow fallback semantics and
raise a clear error when they return blob columns with
`blob_mode="lazy"` or `blob_mode="bytes"`.
- Focused tests cover table/query blob modes,
filter/select/limit/offset/alias query cases, async query behavior,
vector-query error boundaries, and namespace-backed lazy blobs.
## Validation
- `cd python && .venv/bin/maturin develop --uv --extras tests,dev
--profile dev`
- `cd python && uv run --frozen --no-sync pytest
python/tests/test_table.py::test_table_to_pandas_blob_modes
python/tests/test_table.py::test_async_table_to_pandas_blob_bytes
python/tests/test_query.py::test_plain_scan_query_to_pandas_blob_modes
python/tests/test_query.py::test_plain_scan_query_to_pandas_blob_projection
python/tests/test_query.py::test_async_plain_scan_query_to_pandas_blob_projection
python/tests/test_query.py::test_vector_query_to_pandas_blob_mode_requires_native_path
python/tests/test_namespace.py::TestNamespaceConnection::test_table_to_pandas_blob_lazy_through_namespace
-q`
- `cd python && uv run --frozen --no-sync ruff format --check .`
- `cd python && uv run --frozen --no-sync ruff check .`
- `git diff --check`
## Summary
Fixes#3424.
`LanceTakeQueryBuilder.to_batches()` raised `AttributeError:
'AsyncTakeQuery' object has no attribute 'execute'`. The inherited
`BaseQueryBuilder.to_batches` called `self._inner.execute(...)`, but
`self._inner` is an `AsyncQueryBase` (Python wrapper) — only its native
inner exposes `execute`. Every other sync builder overrides
`to_batches`, so the bug only surfaced on take-query builders, which
inherit the base unchanged. `take_offsets(...).to_batches()` is broken
for the same reason.
Route the sync wrapper through the async `to_batches` on the background
event loop, so the native `execute` is invoked from inside an awaiting
context (matching how the async path works correctly).
## Repro
```python
import lancedb, pyarrow as pa, tempfile
db = lancedb.connect(tempfile.mkdtemp())
tbl = db.create_table("t", data=pa.table({"a": list(range(100))}))
tbl.take_row_ids([0, 1, 2]).to_arrow() # works
tbl.search().to_batches() # works
list(tbl.take_row_ids([0, 1, 2]).to_batches()) # AttributeError (before)
```
## Test plan
- [x] New regression test `test_take_queries_to_batches` covers
`take_offsets(...).to_batches()`, `take_row_ids(...).to_batches()`, and
the `select(...)` projection — all fail on `main` with the patch
reverted, all pass with the fix.
- [x] `test_take_queries`, `test_query_builder_batches`, and
`test_query_schema` still pass.
- [x] `ruff format --check` and `ruff check` clean on changed files.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
## Feature
This PR aligns LanceDB Python `to_pandas()` APIs with Lance pandas
conversion capabilities while keeping LanceDB query-specific semantics
intact.
- Adds `blob_mode` and pandas `**kwargs` support to local table
`to_pandas()`.
- Delegates local `LanceTable.to_pandas()` to Lance dataset
`to_pandas(blob_mode=..., **kwargs)`.
- Keeps remote table `to_pandas()` unsupported with
`NotImplementedError`.
- Allows sync and async query `to_pandas()` to forward pandas kwargs
after LanceDB `flatten` and `timeout` handling.
Why we need this feature:
Users can access Lance blob-aware pandas conversion from LanceDB local
tables and can pass PyArrow pandas conversion options through
table/query APIs without losing existing `flatten` or `timeout`
behavior.
How it works:
The table API exposes a `BlobMode` literal type for `lazy`, `bytes`, and
`descriptions`. Local tables call through to the backing Lance dataset.
Query APIs do not add `blob_mode`; they materialize Arrow results, apply
LanceDB flattening when requested, and then call `to_pandas(**kwargs)`.
## Validation
- `uv run --frozen --extra tests pytest
python/tests/test_table.py::test_table_to_pandas_default_matches_arrow
python/tests/test_table.py::test_table_to_pandas_blob_bytes
python/tests/test_table.py::test_table_to_pandas_kwargs
python/tests/test_query.py::test_query_to_pandas_kwargs
python/tests/test_query.py::test_query_timeout
python/tests/test_remote_db.py::test_table_to_pandas_not_supported`
- `uv run --frozen --extra dev ruff check python/lancedb/table.py
python/lancedb/query.py python/lancedb/remote/table.py
python/tests/test_table.py python/tests/test_query.py
python/tests/test_remote_db.py`
- `uv run --frozen --extra tests pytest python/tests/test_table.py
python/tests/test_query.py python/tests/test_remote_db.py`
Note: `python/uv.lock` was intentionally not committed in this branch.
This follows the Rust-side Tantivy removal by deleting the remaining
Python Tantivy runtime, tests, and packaging references.
It also turns the legacy Python-only Tantivy parameters into explicit
errors and stops reading legacy `_indices/fts` directories so Python FTS
is fully native-only.
Fixes#2612
This PR exposes the private _fast_search attribute via a public
fast_search() method in the synchronous LanceVectorQueryBuilder.
Previously, enabling fast search in the sync API required accessing a
private member (query._fast_search = True). This change aligns the
synchronous API with the Async and Remote APIs, allowing for cleaner,
more Pythonic method chaining.
Changes:
Added fast_search() method to LanceVectorQueryBuilder in
python/python/lancedb/query.py.
Added a unit test verifying the flag works with high-dimensional data
(2560 dims) and chaining.
Example Usage:
Before:
```
query = table.search(vector)
query._fast_search = True # Private attribute usage
results = query.limit(10).to_pandas()
```
After:
```
results = (
table.search(vector)
.fast_search()
.limit(10)
.to_pandas()
)
```
Verification:
I have added a test case (test_fast_search_high_dimension) that
replicates the scenario described in the issue (2560 dimensions, cosine
distance) to ensure the pipeline constructs the query correctly without
errors.
Checklist:
- [ ] I have added tests to cover my changes.
- [ ] All new and existing tests passed.
- [ ] Documentation has been updated (inline docstrings).
Signed-off-by: Rashidul Islam <rasidulislam71@gmail.com>
These operations have existed in lance for a long while and many users
need to drop down to lance for this capability. This PR adds the API and
implements it using filters (e.g. `_rowid IN (...)`) so that in doesn't
currently add any load to `BaseTable`. I'm not sure that is sustainable
as base table implementations may want to specialize how they handle
this method. However, I figure it is a good starting point.
In addition, unlike Lance, this API does not currently guarantee
anything about the order of the take results. This is necessary for the
fallback filter approach to work (SQL filters cannot guarantee result
order)
`nprobes` with a value greater than 20 fails with the minimum error:
```
self = <lancedb.query.AsyncVectorQuery object at 0x10b749720>, minimum_nprobes = 30
def minimum_nprobes(self, minimum_nprobes: int) -> Self:
"""Set the minimum number of probes to use.
See `nprobes` for more details.
These partitions will be searched on every indexed vector query and will
increase recall at the expense of latency.
"""
> self._inner.minimum_nprobes(minimum_nprobes)
E ValueError: Invalid input, minimum_nprobes must be less than or equal to maximum_nprobes
python/lancedb/query.py:2744: ValueError
```
Putting the max set before the min seems reasonable but it causes this
reasonable case to fail:
```
def test_nprobes_min_max_works_sync(table):
LanceVectorQueryBuilder(table, [0, 0], "vector").minimum_nprobes(2).maximum_nprobes(4).to_list()
```
with
```
self = <lancedb.query.AsyncVectorQuery object at 0x1203f1c90>, maximum_nprobes = 4
def maximum_nprobes(self, maximum_nprobes: int) -> Self:
"""Set the maximum number of probes to use.
See `nprobes` for more details.
If this value is greater than `minimum_nprobes` then the excess partitions
will be searched only if we have not found enough results.
This can be useful when there is a narrow filter to allow these queries to
spend more time searching and avoid potential false negatives.
If this value is 0 then no limit will be applied and all partitions could be
searched if needed to satisfy the limit.
"""
> self._inner.maximum_nprobes(maximum_nprobes)
E ValueError: Invalid input, maximum_nprobes must be greater than or equal to minimum_nprobes
python/lancedb/query.py:2761: ValueError
```.
The case I care about is where min == max, but this solution handles it
even if they're not. If both min and max exist, we set both to the
minimum and then set the max. This isn't 100% the same as the minimum
setter checks for 0 on the min and `.nprobes` does not do any sanity
checking at all. But I figured this was the most reasonable and general
solution without touching more of this code.
As part of this I noticed the error messages were a bit ambiguous so I
made them symmetric and clarified them while I was here.
## Summary
Fixes IndexError when creating tables with empty list data and a
provided schema. Previously, `_into_pyarrow_reader()` would attempt to
access `data[0]` on empty lists, causing an IndexError. Now properly
handles empty lists by using the provided schema.
Also adds regression tests for GitHub issues #1968 and #303 to prevent
future regressions with empty table scenarios.
## Changes
- Fix IndexError in `_into_pyarrow_reader()` for empty list + schema
case
- Add Optional[pa.Schema] parameter to handle empty data gracefully
- Add `test_create_table_empty_list_with_schema` for the IndexError fix
- Add `test_create_empty_then_add_data` for issue #1968
- Add `test_search_empty_table` for issue #303
## Test plan
- [x] All new regression tests pass
- [x] Existing tests continue to pass
- [x] Code formatted with `make format`
## Summary
- Add `create_import_stub()` helper to `embeddings/utils.py` for
handling optional dependencies
- Fix MLX doctest collection failures by using import stubs in
`gte_mlx_model.py`
- Module now imports successfully for doctest collection even when MLX
is not installed
## Changes
- **New utility function**: `create_import_stub()` creates placeholder
objects that allow class inheritance but raise helpful errors when used
- **Updated MLX model**: Uses import stubs instead of direct imports
that fail immediately
- **Graceful degradation**: Clear error messages when MLX functionality
is accessed without MLX installed
## Test Results
- ✅ `pytest --doctest-modules python/lancedb` now passes (with and
without MLX installed)
- ✅ All existing tests continue to pass
- ✅ MLX functionality works normally when MLX is installed
- ✅ Helpful error messages when MLX functionality is used without MLX
installed
Fixes#2538
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
this test adds a new vector and then performs vector search with
distance range.
this may fail if the new vector becomes the closest one to the query
vector
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
## Summary
Fixes issue #2465 where FTS explain plans only showed basic `LanceScan`
instead of detailed execution plans with FTS query details, limits, and
offsets.
## Root Cause
The `FTSQuery::explain_plan()` and `analyze_plan()` methods were missing
the `.full_text_search()` call before calling explain/analyze plan,
causing them to operate on the base query without FTS context.
## Changes
- **Fixed** `explain_plan()` and `analyze_plan()` in `src/query.rs` to
call `.full_text_search()`
- **Added comprehensive test coverage** for FTS explain plans with
limits, offsets, and filters
- **Updated existing tests** to expect correct behavior instead of buggy
behavior
## Before/After
**Before (broken):**
```
LanceScan: uri=..., projection=[...], row_id=false, row_addr=false, ordered=true
```
**After (fixed):**
```
ProjectionExec: expr=[id@2 as id, text@3 as text, _score@1 as _score]
Take: columns="_rowid, _score, (id), (text)"
CoalesceBatchesExec: target_batch_size=1024
GlobalLimitExec: skip=2, fetch=4
MatchQuery: query=test
```
## Test Plan
- [x] All new FTS explain plan tests pass
- [x] Existing tests continue to pass
- [x] FTS queries now show proper execution plans with MatchQuery,
limits, filters
Closes#2465🤖 Generated with [Claude Code](https://claude.ai/code)
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Tests**
* Added new test cases to verify explain plan output for full-text
search, vector queries with pagination, and queries with filters.
* **Bug Fixes**
* Improved the accuracy of explain plan and analysis output for
full-text search queries, ensuring the correct query details are
reflected.
* **Refactor**
* Enhanced the formatting and hierarchical structure of execution plans
for hybrid queries, providing clearer and more detailed plan
representations.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: Claude <noreply@anthropic.com>
This exposes the maximum_nprobes and minimum_nprobes feature that was
added in https://github.com/lancedb/lance/pull/3903
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Added support for specifying minimum and maximum probe counts in
vector search queries, allowing finer control over search behavior.
- Users can now independently set minimum and maximum probes for vector
and hybrid queries via new methods and parameters in Python, Node.js,
and Rust APIs.
- **Bug Fixes**
- Improved parameter validation to ensure correct usage of minimum and
maximum probe values.
- **Tests**
- Expanded test coverage to validate correct handling, serialization,
and error cases for the new probe parameters.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
- AND operator
- phrase query slop param
- boolean query
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Added support for combining full-text search queries using AND/OR
operators, enabling more flexible query composition.
- Introduced new query types and parameters, including boolean queries,
operator selection, occurrence constraints, and phrase slop for advanced
search scenarios.
- Enhanced asynchronous search to accept rich full-text query objects
directly.
- **Bug Fixes**
- Improved handling and validation of full-text search queries in both
synchronous and asynchronous search operations.
- **Tests**
- Updated and expanded tests to cover new full-text query types and
their usage in search functions.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
This PR adds ColPali support with ColPaliEmbeddings class (tagged
"colpali") using ColQwen2.5 for multi-vector text/image embeddings. Also
added MultiVector Pydantic type to handle the vector lists.
I've added some integration test for the embedding model and some unit
test for the new Pydantic type. Could be a template for other ColPali
variants as well. or until transformers🤗 starts supporting it.
Still `TODO`:
- [ ] Documentation
- [ ] Add an example
_Could also allow Image as query, but didn't work well when testing it._
[ColPali-Engine](https://github.com/illuin-tech/colpali) version:
0.3.9.dev17+g3faee24
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Introduced support for ColPali-based multimodal multi-vector
embeddings for both text and images.
- Added a new embedding class for generating multi-vector embeddings,
configurable for various model and processing options.
- Added a new Pydantic type for multi-vector embeddings, supporting
validation and schema generation for lists of fixed-dimension vectors.
- **Bug Fixes**
- Ensured proper asynchronous index creation in query tests for improved
reliability.
- **Tests**
- Added integration tests for ColPali embeddings, including
text-to-image search and validation of multi-vector fields.
- Added comprehensive tests for the new multi-vector Pydantic type,
covering schema, validation, and default value behavior.
- **Chores**
- Updated optional dependencies to include the ColPali engine.
- Added utility to check for availability of flash attention support.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Closes#2287
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Added configurable timeout support for query executions. Users can now
specify maximum wait times for queries, enhancing control over
long-running operations across various integrations.
- **Tests**
- Expanded test coverage to validate timeout behavior in both
synchronous and asynchronous query flows, ensuring timely error
responses when query execution exceeds the specified limit.
- Introduced a new test suite to verify query operations when a timeout
is reached, checking for appropriate error handling.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
add analyze plan api to allow executing the queries and see runtime
metrics.
Which help identify the query IO overhead and help identify query
slowness
This PR adds a `to_query_object` method to the various query builders
(except not hybrid queries yet). This makes it possible to inspect the
query that is built.
In addition this PR does some normalization between the sync and async
query paths. A few custom defaults were removed in favor of None (with
the default getting set once, in rust).
Also, the synchronous to_batches method will now actually stream results
Also, the remote API now defaults to prefiltering
@wjones127 is there a standard way you guys setup your virtualenv? I can
either relist all the dependencies in the pyright precommit section, or
specify a venv, or the user has to be in the virtual environment when
they run git commit. If the venv location was standardized or a python
manager like `uv` was used it would be easier to avoid duplicating the
pyright dependency list.
Per your suggestion, in `pyproject.toml` I added in all the passing
files to the `includes` section.
For ruff I upgraded the version and removed "TCH" which doesn't exist as
an option.
I added a `pyright_report.csv` which contains a list of all files sorted
by pyright errors ascending as a todo list to work on.
I fixed about 30 issues in `table.py` stemming from str's being passed
into methods that required a string within a set of string Literals by
extracting them into `types.py`
Can you verify in the rust bridge that the schema should be a property
and not a method here? If it's a method, then there's another place in
the code where `inner.schema` should be `inner.schema()`
``` python
class RecordBatchStream:
@property
def schema(self) -> pa.Schema: ...
```
Also unless the `_lancedb.pyi` file is wrong, then there is no
`__anext__` here for `__inner` when it's not an `AsyncGenerator` and
only `next` is defined:
``` python
async def __anext__(self) -> pa.RecordBatch:
return await self._inner.__anext__()
if isinstance(self._inner, AsyncGenerator):
batch = await self._inner.__anext__()
else:
batch = await self._inner.next()
if batch is None:
raise StopAsyncIteration
return batch
```
in the else statement, `_inner` is a `RecordBatchStream`
```python
class RecordBatchStream:
@property
def schema(self) -> pa.Schema: ...
async def next(self) -> Optional[pa.RecordBatch]: ...
```
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Reviving #1966.
Closes#1938
The `search()` method can apply embeddings for the user. This simplifies
hybrid search, so instead of writing:
```python
vector_query = embeddings.compute_query_embeddings("flower moon")[0]
await (
async_tbl.query()
.nearest_to(vector_query)
.nearest_to_text("flower moon")
.to_pandas()
)
```
You can write:
```python
await (await async_tbl.search("flower moon", query_type="hybrid")).to_pandas()
```
Unfortunately, we had to do a double-await here because `search()` needs
to be async. This is because it often needs to do IO to retrieve and run
an embedding function.
we found a bug that flat KNN plan node's stats is not in right order as
fields in schema, it would cause an error if querying with distance
range and new unindexed rows.
we've fixed this in lance so add this test for verifying it works
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
Xuanwo
Yang Cen
LeoandLeo
lancedb-gatefixer[bot]andXuanwo
Joaquin Hui GomezandXuanwo
Drew
Jack Ye
LanceDB RobotandJack Ye
Will JonesandClaude Opus 4.8
Justin MillerandClaude Opus 4.7
Xin Sun
Dan Tasse
Rashid Ul Islam
Colin Patrick McCabe
Weston Pace
Mark McCaskey
Tristan Zajonc
Tristan ZajoncandWill Jones
Magnus
LuQQiu
Gagan BhullarandWill Jones
msu-reevoandWill Jones