<!-- lance-gatekeeper-fix:v1 agent=5c80c44c083b3b8ad0da595419d468fc
generation=1 -->
## Root cause
The legacy synchronous Python table called `delete` on a shared, mutable
`lance.Dataset`. Concurrent table operations could hold a PyO3 borrow
while delete requested an exclusive borrow, producing `RuntimeError:
Already borrowed`. The current async-backed binding fixes this by
cloning its thread-safe Rust table handle before awaiting, but that
concurrency contract had no regression coverage.
## Fix
- Document why delete must clone the Rust table handle before entering
its async future.
- Add a barrier-synchronized regression test that deletes distinct rows
through one shared table from eight Python threads.
- Verify every delete commits exactly one row, every commit gets a
distinct version, and no rows remain.
## Validation
- `cargo check --quiet --features remote --tests --examples`
- `cargo fmt --all -- --check`
- `uv run --extra tests --extra dev ruff format --check
python/tests/test_table.py`
- `uv run --extra tests --extra dev ruff check
python/tests/test_table.py`
- `uv run --extra tests --extra dev pytest
python/tests/test_table.py::test_concurrent_deletes_are_thread_safe
python/tests/test_table.py::test_delete
python/tests/test_table.py::test_delete_expr
python/tests/test_table.py::test_delete_expr_async -q` (4 passed)
- Manual stress reproduction: 100 concurrent deletes on one table
completed at versions 2–101 with zero rows remaining.
Fixes#530
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- cache the immutable read consistency interval on synchronous
connection wrappers
- keep debugger property expansion from dispatching to the background
event loop
- cover direct connections and wrappers reconstructed from native
connections
## Root cause
The debugger expands connection variables by evaluating properties after
suspending all Python threads.
`LanceDBConnection.read_consistency_interval` dispatched a coroutine to
`LanceDBBackgroundEventLoop` and synchronously waited for it, but that
loop thread was also suspended, causing a deadlock.
## Validation
- `uv run --no-sync pytest python/tests/test_db.py -q` (48 passed)
- `ruff format --check python/python/lancedb/db.py
python/python/tests/test_db.py`
- `ruff check .`
- `git diff --check`
Fixes#3773
<!-- lance-gatekeeper-fix:v1 agent=e2e612236d722d926f64245d3f682bbc
generation=1 -->
---------
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Description
`Table::optimize()` compacts through
`lance::dataset::optimize::compact_files`
(`rust/lancedb/src/table/optimize.rs:155`). Until
lance-format/lance#7965 that rewrite corrupted blob columns holding null
or empty values, which is what #3744 reports:
- **storage 2.0** (legacy v1 `lance-encoding:blob` descriptors): every
payload following a null or empty row in the same fragment was rewritten
as `{position: 0, size: 0}`, so it read back as `b""` and the new
fragment no longer referenced the bytes — silent payload loss,
unrecoverable once the pre-optimize versions are pruned.
- **storage 2.2** (blob v2): a valid empty value was rewritten as null,
destroying the null-vs-empty distinction.
Both manifestations share one root cause: `is_inline_null_blob`
classified any inline blob with `position == 0 && size == 0` as null,
which is also exactly what a *valid empty value* looks like. Such rows
were dropped from `blob_read_addrs`, misaligning every payload that
followed.
The behaviour is already correct on `main`: the vendored lance crate
first carried the fix at `v10.0.0-beta.3` (#3710) and is now
`v10.1.0-beta.1` (#3757). What was missing is coverage — nothing in this
repo exercised a blob column containing a null or empty value through
`optimize()`, which is why this shipped unnoticed. This PR adds that
guard.
## Tests
Two tests in `rust/lancedb/tests/blob_integration.rs`, reusing the
file's existing 64 KiB dedicated-blob helpers and a delete-triggered
fragment rewrite. After `id IN (1, 4)` is deleted the surviving rows are
`2` (null), `3` (valid empty), `5` and `6` (payloads) — payloads sit
immediately after the null/empty, which is where the misalignment
landed.
- `optimize_preserves_v1_blob_payloads_with_null_and_empty` — storage
2.0; asserts the **payload bytes** are unchanged across
`OptimizeAction::All` (what the Python/Node `optimize()` bindings
invoke). Payloads are read through `lance::Dataset::take_blobs`, since
`Table::fetch_blobs` rejects legacy v1 columns. The before/after
descriptors are reported on failure but deliberately *not* asserted:
compaction repacks the blob file, so they shift legitimately (id 5
`(131072, 65536)` → `(0, 65536)`, id 6 `(196608, 65536)` → `(65536,
65536)`). Note that a post-compaction `position: 0` is both the
legitimate first-payload offset and the bug's signature, so asserting
descriptors would be actively misleading.
- `optimize_preserves_blob_v2_null_and_empty_distinction` — storage >=
2.2; asserts a null stays null and a valid empty value stays non-null
empty.
Both assert the pre-optimize state first, so a setup change that stops
producing the null/empty/payload mix fails loudly instead of passing
vacuously.
Both also assert the returned `CompactionMetrics` show a fragment was
actually rewritten. These tests depend on `delete("id IN (1, 4)")`
pushing the fragment past lance's `materialize_deletions_threshold` (0.1
by default; 2 of 6 rows here). That coupling is invisible and unasserted
otherwise: against a forced no-op (`materialize_deletions_threshold:
1.5`) the metrics come back all zeroes and *every payload assertion
still passes*. Since the whole point of these tests is to survive
dependency changes, they check that the rewrite happened rather than
trusting the planner to keep selecting the fragment.
Guard verified against a pre-fix lance: with the published
`lancedb==0.36.0` wheel (vendors lance 9.0.0), `Table.optimize()` on the
same data rewrites the descriptors of the two rows following the
null/empty from `(131072, 65536)` and `(196608, 65536)` to `(0, 0)`, and
the payloads read back empty. Against the pinned `v10.1.0-beta.1`, all
39 tests in the file pass, adding roughly 10–20 ms to the file's
runtime.
## Not addressed here
- **No released artifact has the fix yet.** PyPI `lancedb` 0.36.0
(2026-07-29) vendors lance 9.0.0; npm `@lancedb/lancedb` 0.37.1-beta.0
predates the bump. No 9.x lance tag carries the fix: `v10.0.0-beta.3` is
the first tag containing it, every `v9.1.0-beta.1`…`beta.8` is behind
it, and `v9.0.0` / `v9.0.1-rc.1` sit on a diverged branch without it. A
stable lancedb release needs a stable lance >= 10.
- **The version skew #3744 flagged is still live.**
`python/pyproject.toml` pins `pylance==9.0.0rc1` for the `tests` extra
against a vendored `10.1.0-beta.1`, so Python CI still cannot observe
this class of divergence.
- **Only the single-fragment rewrite shape is covered.** Both tests
rewrite one fragment by materializing deletions. lance's own
`test_compact_blob_v1/v2_preserves_null_empty_and_payload_order` cover
the multi-fragment merge shape (3 fragments → 1) at unit level, so this
PR is complementary rather than redundant — it covers the binding-level
path through `Table::optimize` — but it would not catch a regression
that only appears when *merging* fragments.
`multi_fragment_dedicated_blob_table` in the same file makes that a
cheap follow-up.
Closes#3744🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
Table::add_columns now takes no arguments and returns AddColumnsBuilder,
so calls become .add_columns().transform(t).execute().
read_columns was the second positional argument but reaches only one of
the five transform variants. In lance's add_columns_to_fragments only
BatchUDF receives the caller's value: SqlExpressions replaces it with
the columns its expressions reference, Stream and Reader pass None, and
AllNulls reads nothing. So it was mandatory on every call -- all
eighteen call sites here passed None -- and silently discarded four
times out of five. As a builder method it is optional, and setting it
where lance would discard it is now an error, which does reject a call
that previously succeeded while ignoring the argument.
Matches the builders add, update, and merge_insert already use.
## Summary
`LanceMergeInsertBuilder.when_not_matched_by_source_delete()` didn't
clear a previously-set condition when called again with no argument (or
a different condition type). Per the docstring, `condition=None` means
"delete all unmatched rows," but if the builder had already been
configured with a string/Expr condition, a later no-arg call left the
stale condition in place instead of widening the delete to
unconditional.
Fixes#3767
## Change
Each call now unconditionally sets both
`_when_not_matched_by_source_condition` and
`_when_not_matched_by_source_condition_expr` (one to the new value, the
other to `None`), so the latest call always wins — consistent with every
other setter on this builder (e.g.
`when_matched_update_all(where=...)`).
## Test plan
- [x] New regression test
`test_merge_insert_by_source_delete_reconfigure` in
`python/python/tests/test_table.py`
- [x] `uv run --extra tests pytest
python/tests/test_table.py::test_merge_insert_by_source_delete_reconfigure
python/tests/test_table.py::test_merge_insert_by_source_delete_expr
python/tests/test_table.py::test_merge_insert_by_source_delete_expr_async
-vv` — 3 passed
- [x] `uv run --extra dev ruff format` / `ruff check` — clean
Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
## Summary
`LanceHybridQueryBuilder._create_query_builders()` checked
`self._minimum_nprobes` for truthiness instead of `is not None` — the
very next line correctly checks `is not None` for
`self._maximum_nprobes`. Since `0` is falsy in Python,
`.minimum_nprobes(0)` on a hybrid query silently dropped the value
instead of forwarding it to the vector sub-query, where it would raise
the same `ValueError` a plain vector query raises for the same input
(`minimum_nprobes must be greater than 0`, validated in
`rust/lancedb/src/query.rs` and covered for the plain-query path by
`test_invalid_nprobes_sync`).
Fixes#3766
## Change
One-line fix: `if self._minimum_nprobes:` → `if self._minimum_nprobes is
not None:`, matching the existing `maximum_nprobes` check right below
it.
## Test plan
- [x] New regression test
`test_hybrid_query_minimum_nprobes_zero_raises` in
`python/python/tests/test_hybrid_query.py`
- [x] `uv run --extra tests pytest python/tests/test_hybrid_query.py
-vv` — 13 passed
- [x] `uv run --extra dev ruff format` / `ruff check` — clean
Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
## Summary
- Implements Cloud `fetch_blob_files`: returns real seekable `BlobFile`
handles over HTTP Range instead of `NotSupported`.
- Completes the second Cloud blob read verb after #3684 (`fetch_blobs` =
eager whole bytes; this = lazy / partial / sequential reads).
- Same public handle API as local (`read_range`, `read_up_to`, `seek`,
`tell`, `close`), so one code path works for local and Cloud.
Large blobs (video, audio, PDFs) should not require downloading the
whole object to inspect a header or stream a slice. After search,
callers open a handle and read only what they need:
```python
hits = table.search(vec).select(["id", "video"]).limit(5).to_arrow()
with table.fetch_blob_files("video", hits)[0] as f:
header = f.read_range(0, 256)
f.seek(keyframe_offset)
chunk = f.read_up_to(1 << 20)
```
### Behavior
- Handle creation probes size with `bytes=0-0` (bounded concurrency,
input order preserved).
- `204` → null (`None`); `416` with `bytes */0` → valid empty blob;
other `416` → error.
- `read_range` validates `Content-Range` and body length; OOB ranges
fail with `invalid_input` before the request (aligned with Lance).
- `read_up_to` reuses one open-ended Range response across sequential
reads; `seek` drops it.
- Servers older than 0.5.0 get a clear `NotSupported` (does not suggest
`fetch_blobs`, which they also lack).
## Testing
- `cargo test --features remote -p lancedb remote_blob`
- `cargo test --features remote -p lancedb test_blob`
- `cargo clippy --features remote --tests --examples` (no new warnings
from this change)
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
Bumps the pinned Rust toolchain from 1.95.0 to the latest stable
(1.97.0).
Rust 1.97's clippy adds `useless_borrows_in_formatting`, which flags a
redundant `&` in `format!`/`debug!` arguments in a few places. This PR
removes those to keep `cargo clippy` clean.
No behavior change; the MSRV (`rust-version = "1.91.0"`) is unchanged.
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Updates the Lance Rust workspace dependencies and Java lance-core
version to v10.1.0-beta.1.
Includes a compatibility fix for the Lance file writer API by using the
explicit V2_1 writer creation path for permutation shuffle spill files.
Triggered by
https://github.com/lance-format/lance/releases/tag/v10.1.0-beta.1
Adds job operations to the connection surface, building on the Job
handle from #3742: job(id), list_jobs, get_job, cancel_job, and
job_history, plus a non-blocking Job.status(). Implemented on the
Database trait (defaulting to NotSupported), the remote backend
(/v1/jobs), and the Python and Node bindings; job_history returns Arrow
batches.
errors() and progress() are not included.
Tested with mocked endpoints in all three languages.
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
IndexBuilder::execute now returns a Job with wait and cancel methods.
Local tables build the index synchronously and return an already-done
job. Remote tables read the job id the server returns from create_index
and track it through the /v1/jobs API: wait polls describe until the job
reaches a terminal state and cancel posts a cancellation. Servers that
return no job id yield a done job, so behavior against older servers is
unchanged. The job id is not exposed on the handle.
The Python and TypeScript bindings keep their current signatures and
discard the handle; exposing Job there is left to follow-ups.
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
`RemoteDBConnection.open_table` accepts `storage_options` and never uses
it:
```python
def open_table(
self,
name: str,
*,
namespace_path: Optional[List[str]] = None,
storage_options: Optional[Dict[str, str]] = None,
index_cache_size: Optional[int] = None,
...
) -> Table:
...
if index_cache_size is not None:
logging.info("index_cache_size is ignored in LanceDb Cloud ...")
table = LOOP.run(self._conn.open_table(name, namespace_path=namespace_path))
```
The value is never passed down and never mentioned. `index_cache_size`
is ignored on Cloud in the
same way, but it says so.
I checked this at runtime on 0.34.0, not just by reading it: swapping
the inner connection for a
recorder, `open_table("t", storage_options={...})` hands the layer below
`['namespace_path']` and
nothing else, no log record is emitted, and the same probe shows
`index_cache_size` producing its
message as expected.
This adds the matching log line, so the two ignored parameters behave
the same way. `ruff check` and
`ruff format --check` are clean on the file.
A note on severity. This is not a security hole and nothing is exposed.
Someone passing credentials
there gets silence instead of an error, and finds out later.
One thing I am unsure about, and it changes the fix. I have assumed
per-table storage options are
meaningless on Cloud, which is what the `index_cache_size` line next to
it implies about managed
storage. If they are supposed to work, then the right change is to pass
them through to
`self._conn.open_table` instead and this patch is the wrong one. Happy
to redo it that way.
I did not check whether `create_table` or the async connection have the
same gap.
`docs/src/python/python.md` is the whole Python API reference, but it is
maintained by hand and had drifted from the public API. Anything not
listed there simply doesn't get rendered, so a number of public,
documented, tested APIs were invisible to users — most notably branch
management, where `diff` and `merge` live.
I audited every public symbol reachable from `lancedb` and its
subpackages against the `:::` directives on the page. This adds the
missing ones:
- **Branching** — `Branches`, `AsyncBranches` (`list` / `create` /
`checkout` / `delete` / `diff` / `merge`)
- **Tables** — `TableStatistics` (returned by `Table.stats()`; the
fragment-level stats classes were already listed)
- **Full text queries** — `FullTextQuery`, `MatchQuery`, `PhraseQuery`,
`BoostQuery`, `MultiMatchQuery`, `BooleanQuery`, `FullTextOperator`,
`Occur`
- **Querying** — `LanceEmptyQueryBuilder`, `LanceTakeQueryBuilder`,
`AsyncTakeQuery`
- **Indices** — `Fm` (the FM-index for substring search), `IndexConfig`
- **Blobs** — `blob`, `BlobType`, `BlobFile`
- **Namespaces** — `connect_namespace`, `connect_namespace_async`, and
both namespace connection classes
- **Remote config** — `TlsConfig`, `HeaderProvider`, `OAuthConfig`,
`OAuthFlowType`
- **Rerankers** — the `Reranker` base class plus `JinaReranker`,
`RRFReranker`, `MRRReranker`, `AnswerdotaiRerankers`,
`VoyageAIReranker`, `WatsonxReranker` (5 of 12 were listed)
- **Embeddings** — `get_registry`, `register`, and the 14 embedding
functions that were missing (3 of 17 were listed)
- **PyTorch** — `StreamingDataset` and the permutation API it is built
on
- **Misc** — `Session`, `tokenize`, `FtsToken`, `pydantic.Vector`,
`pydantic.MultiVector`, `instrument_lancedb_metrics`, and the two
exception types
It also repairs cross-references in docstrings that no longer resolve:
links into guide pages that have since moved to lancedb.com
(`querying-an-ann-index`, `experimental-full-text-search`),
`lance.dataset` references with no inventory behind them, and the
relative targets `[Table](Table)` and `[PyArrow Table](pyarrow.Table)`.
Deliberately left out: concrete implementation classes reached through
their abstract base (`LanceTable`, `LanceDBConnection`,
`RemoteDBConnection`), query base classes already covered by
`inherited_members: true`, and internal plumbing such as
`FullTextSearchQuery` and `ColumnOrdering`.
## Testing
The docs job only runs on pushes to `main`, so I built the site locally
and compared against a build of `upstream/main`: every added entry
resolves, and no symbol that was rendered before stopped being rendered
when the four packages moved to automodule. `mkdocs build --strict`
exits 0 on this branch, against 61 warnings on `main`.
## Also in this PR
`lancedb.index`, `lancedb.embeddings`, `lancedb.remote` and
`lancedb.rerankers` are now rendered by a single mkdocstrings directive
each, driven by the module's `__all__`, rather than a hand-maintained
list. These four are where most of the drift was, and `__all__` is
harder to forget than a docs page. `lancedb.embeddings` had no
`__all__`; without one mkdocstrings renders no members at all for a
re-export package, so one is added. AGENTS.md gains a section on how the
page is wired up and how to build the docs locally.
Rendering all that code for the first time surfaced ~100 more build
warnings, which would have made #3707 (turning on `mkdocs build
--strict`) harder to land, so the warning backlog is cleared here too.
97 of the 158 warnings were one systematic false positive — griffe
cannot see the generated `__init__` of a pydantic dataclass, so every
documented parameter looks unknown — switched off via
`warn_unknown_params`. The remaining 61 came from 15 docstrings with
real bugs: prose trailing a `Parameters` section (we were rendering
parameters called `The`, `you` and `To`), types dropped because numpydoc
needs spaces around the colon, `num_partitions, default sqrt(num_rows)`
parsing as a list of names and inventing a `default` parameter, and one
parameter indented five spaces. `mkdocs build --strict` now exits 0.
---
#3747 (the coverage test that keeps this from happening again) is
stacked on this branch, so review it after this one.
---------
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
In the current LanceDB usage implementation, there is no way to check
whether a table or namespace already exists. This PR introduces the
namespace_exists and table_exists methods to determine the existence of
tables and namespaces.
useage like this:
```
# check table exists
db.table_exists(table_id=['xxx'])
# check namespace exists
db.namespace_exists(namespace_id=['xxx'])
```
fixes: #3419
---------
Signed-off-by: farmer <farmerchillax@outlook.com>
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Remote half of the blob read path. #3578 did local Python. This makes
`RemoteTable` hit the server.
- `fetch_blobs(column, row_ids or hits)` → bytes over `POST
/v1/table/{id}/fetch_blobs/`
- `blob_columns()` from the cached schema (describe already has the
metadata, no extra route)
- search then `fetch_blobs` works. row identity rides inside the blob
descriptor so you do not need a public `_rowid`
- `fetch_blob_files` still `NotSupported` on remote. use `fetch_blobs`
for full bytes for now. Range is a follow up
Accepts Binary / LargeBinary / BinaryView on the way back. Empty
`row_ids` short-circuits. Version + branch go in the request body same
as other read calls.
### Example
```python
db = lancedb.connect(uri="db://my-project", api_key=...)
table = db.open_table("clips")
hits = table.search(query_vec).select(["id", "video"]).limit(10).to_arrow()
# hits is just id + video. row ids are stashed on the descriptor
blobs = table.fetch_blobs("video", hits) # null-aligned, same length as hits
```
Or pass ids yourself:
```python
blobs = table.fetch_blobs("video", [10, 20, 30])
```
### Testing
- `cargo test -p lancedb --features remote --lib`
- `cargo test -p lancedb --features remote --test blob_integration`
- `pytest python/tests/test_remote_db.py -k remote_blob`
- live e2e against a local 0.5.0 remote server (search → fetch, nulls,
nested path, old server gate)
---------
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
`table_names()` lists any `*.lance` directory, but `open_table()` maps
every `DatasetNotFound` to `TableNotFound`, so a corrupt or
partially-written table looks identical to one that never existed
(#3127). This takes the issue's Option 2: on `DatasetNotFound`, check
the parent listing for the table's `.lance` entry — the same predicate
`table_names()` uses — and return a new `TableCorrupted` error when the
directory is present. The check runs only on the error path, and any
failure in the recheck falls back to the previous `TableNotFound`
behavior.
Tests cover the reporter's empty-dir repro, a deleted-manifest case,
true absence (still `TableNotFound`), and an end-to-end list-then-open
assertion; the three new corrupt-case tests fail without the src change.
`cargo test -p lancedb --lib` 732 passed, clippy/fmt clean, `cargo check
--workspace --all-targets` clean (both language bindings end in wildcard
error arms).
Two notes for review: `Error` isn't `#[non_exhaustive]`, so the new
variant is technically semver-breaking for exhaustive matchers (pre-1.0,
and the alternative — changing `TableNotFound`'s shape — breaks more);
and on the Python side corrupt tables now surface as `RuntimeError`
rather than `ValueError`, which is the intended distinction but worth a
maintainer's eye. `open_from_namespace` was left unchanged since
namespace listings come from a server-side registry, not directory
globbing.
Closes#3127
## Summary
Fixes#2339. `merge_insert()` on the remote client could mask the real
cause of a mid-stream input error, reporting only:
> stream error sent by user: unexpected internal error
## Root cause
There were two divergent streaming-write code paths in the remote
client:
- `add()` uses `RemoteInsertExec`, which streams the request body
through a `tokio::sync::oneshot` error side-channel and drains it before
reporting the HTTP result. If the input stream errors mid-body, the
original error is recovered.
- `merge_insert()` used a legacy path (`send_streaming` ->
`reader_as_body`) that piped arrow `Some(Err(e))` straight into the
HTTP2 request body. Hyper swallows body-stream errors under HTTP2 (see
hyperium/hyper#2547), so the original error was lost and only the
generic transport error surfaced.
## Fix
Consolidate both write paths onto the side-channel mechanism instead of
patching the legacy path:
- Generalize `RemoteInsertExec` into `RemoteWriteExec`, carrying a
`WriteOp` enum (`Insert { overwrite }` | `MergeInsert { query, timeout
}`) that selects the endpoint, query params, request-timeout header, and
response parsing. The executor returns a `WriteResult` enum (`Add` |
`Merge`) with typed accessors, and `with_new_children` still resets the
result so the rescannable retry loop is unaffected.
- Route `merge_insert()` through `RemoteWriteExec`. The public API only
accepts a `RecordBatchReader` (not rescannable), so the reader is
buffered into a `Vec<RecordBatch>` before the retry loop to preserve the
previous retry-on-retryable-status behaviour. This mirrors what the old
`send_streaming(with_retry=true)` path already did.
- Remove the now-unused `send_streaming` / `reader_as_body` /
`buffer_reader` / `make_reader` helpers. Multipart stays insert-only
(the server has no multipart merge_insert endpoint), so that hot path is
behaviorally unchanged.
## Testing
- Added `test_merge_insert_input_error_surfaces_original`, which drives
an erroring input through the single-request `merge_insert` path and
asserts the original error (`boom`) is surfaced rather than the masked
HTTP error. Confirmed it fails without the side-channel drain (it then
reports a masked `500 ... request or response body error`).
- Full suite green: `cargo test -p lancedb --lib --features remote` ->
694 passed, 0 failed. Includes the existing
`test_merge_insert_retries_on_409`, confirming retry behaviour is
preserved.
## Summary
- keep the existing synchronous `connect()` path unchanged
- make `LanceDBConnection.__repr__` and `LanceTable.__repr__`
side-effect-free
- add a regression test that verifies sync reprs do not call the Python
background loop
## Root cause
The freeze is caused by debugger rendering, not by `connect()` itself:
1. debugpy stops at a breakpoint and suspends all Python threads.
2. The debugger renders the new `db_connection` local by calling
`repr()`.
3. `LanceDBConnection.__repr__` reads `read_consistency_interval`.
4. That property calls `LOOP.run(...).result()`.
5. The `LanceDBBackgroundEventLoop` thread is suspended by the debugger,
so `repr()` waits for a thread that cannot run.
This explains why the symptom appears immediately after `connect()`: it
is the first point where a connection object exists in locals and is
automatically rendered. `LanceTable.__repr__` had the same problem
because it also read the connection's consistency interval.
This follows the same principle as #3411: `__repr__` must not trigger
async work or I/O that a debugger assumes is lightweight.
## Evidence
I reproduced the behavior with the real LanceDB classes and debugpy
1.8.21 using a DAP client:
- latest `main` (`ff6ff099`): the debugger reported `allThreadsStopped:
true`, and evaluating `repr(db_connection)` timed out
- this branch (`5755a5ba`): the same evaluation returned
`LanceDBConnection(uri='/tmp/lancedb-debug-repro')` immediately
- setting `PYDEVD_UNBLOCK_THREADS_TIMEOUT=0` also allowed the original
repr path to complete, independently confirming that it was waiting on a
suspended thread
The regression test creates a connection and table, replaces `LOOP.run`
with a function that fails, and verifies that both reprs still work.
## Validation
- `maturin develop --manifest-path python/Cargo.toml`
- `python -m pytest
python/python/tests/test_db.py::test_sync_repr_does_not_use_background_loop
python/python/tests/test_table.py::test_consistency -q` (`4 passed`)
- `ruff check .`
- `ruff format --check python/python/lancedb/db.py
python/python/lancedb/table.py python/python/tests/test_db.py
python/python/tests/test_table.py`
- `git diff --check`
Refs #3611.
`test_read_consistency_interval` asserted that a table opened with a
100ms `read_consistency_interval` still read stale data immediately
after a concurrent write. The cache timestamp is set when the table is
opened and reads within the interval do not refresh it, so that
assertion only held if the intervening open/count/commit/count sequence
finished within 100ms of real wall-clock time. On a loaded CI runner it
did not: the TTL expired, `count_rows` refreshed synchronously, and the
test failed with `left: 1, right: 0`. This broke the Rust workflow on
`main` at 0bc08160 (a Python-only commit).
This pins the `background_cache` mock clock once `table2` has seeded its
cache, and advances it explicitly in place of `tokio::time::sleep`, so
the test controls when the interval elapses. Same approach as #3547.
With the clock pinned there is no real sleep left to be imprecise, so
the `cfg(not(target_os = "windows"))` guard is dropped and the test now
runs on Windows too.
Verified by inserting a stall before the write: 120ms reproduces the
original failure deterministically, and with this change the test still
passes with a 500ms stall.
Fixes#3712
## What
Expose custom FTS stop-word lists in the Python and TypeScript public
APIs, including their standalone tokenize helpers and remote index
creation.
This PR supports concrete string lists only. It does not add file or
LanceDB-table stop-word sources.
## Why
Rust already exposes Lance's custom stop-word list option. The Python
and TypeScript APIs did not pass it through, and local index details did
not retain the full tokenizer parameters needed by index-backed
tokenization after reopening a table.
## How
- Add `custom_stop_words` / `customStopWords` to the Python and
TypeScript FTS and tokenize options.
- Preserve `None` / `undefined`, empty lists, and list contents without
normalization.
- Load the persisted FTS segment parameters when returning local index
details.
- Serialize the concrete list in remote create-index requests.
- Keep Python and TypeScript tests thin; behavior, persistence, query
tokenization, and remote JSON coverage live primarily in Rust.
## Validation
- `cargo check --quiet --features remote --tests --examples`
- `cargo clippy --quiet --features remote --tests --examples`
- `cargo test --quiet --features remote --tests`
- Python extension rebuild with `uv` and `maturin`
- Targeted Python tests: 4 passed
- Python `ruff format --check` and `ruff check`
- TypeScript build, typecheck, Biome lint, generated docs, and targeted
tests
---------
Co-authored-by: Yang Cen <yangcen@Yangs-Mac-mini.local>
## Summary
- add a keyword-only `transform_parallelism` option to
`StreamingDataset`
- preserve CPU auto-detection by default and fall back to one worker
when unavailable
- apply the configured limit to both the transform executor and
concurrency semaphore
- document and test explicit, default, fallback, and invalid values
## Testing
- `uv run --extra tests --with torch pytest
python/tests/test_elastic_dataloader.py -q` (`136 passed`)
- `uvx ruff check python/lancedb/streaming.py
python/tests/test_elastic_dataloader.py`
- `uvx ruff format --check python/lancedb/streaming.py
python/tests/test_elastic_dataloader.py`
- `git diff --check origin/main...HEAD`
Closes#3695
Co-authored-by: buduoqiu <yaodong-shen@users.noreply.github.com>
Standard GitHub-hosted runners are free on public repos, so all Actions
spend here is on the `*-8x-*` / `4x` larger runners. Measured over 30
days at current (post-Jan-2026) larger-runner rates, that is ~$1,400/mo,
and `npm-publish` is ~70% of it.
## Changes
**Fat LTO was forcing builds onto large runners.** `[profile.release]`
in `.cargo/config.toml` sets `lto = "fat"` with `codegen-units = 1`,
which is single-threaded and the peak-memory step. The macOS
`npm-publish` build was 111 of its 113 minutes in one `napi build` step,
making it the critical path of the whole publish pipeline. The ThinLTO
override already applied to Windows now covers macOS too, and both
Windows builds move from `windows-2025-8x-x64` to the free standard
`windows-2025`.
**The npm-publish cargo cache never existed.** There are zero caches
with its key prefix. The key was static, so `actions/cache` (which only
writes on a miss) could never refresh it, and a multi-GB release
`target/` per target could never fit the repo's 10 GB budget anyway. Now
caches only the crate registry, keyed on `Cargo.lock`. The docker builds
also mounted `.cargo/registry/*` while the cache saved `.cargo-cache`,
so containers re-downloaded the registry every run.
**Cache eviction thrash.** Repo cache usage is 10.4 GB against GitHub's
10 GB cap, so every PR run evicted main's warm entries. `rust.yml` and
`nodejs.yml` now restore everywhere but only save from `main`.
**npm-publish moves to nightly + tags** instead of every push to main
(~90/month). The cross-compiled targets do need watching, so
`report-failure` now fires on scheduled runs, and dedupes onto an
existing open issue rather than filing one per night.
**rust.yml aarch64-pc-windows-msvc** cross-compiled its tests and then
skipped them, paying full codegen and link cost for a compile check.
`windows-11-arm` is now GA and free on public repos, so it builds and
tests natively. Its test step also passes `--target` — without it cargo
used `target/ci/` rather than `target/<triple>/ci/` and rebuilt the
entire dependency graph a second time.
**pypi-publish.yml had no concurrency group**, so force-pushes left a
~74 minute Windows job running.
## What is cost vs. wall-clock
| Change | Cost | Wall-clock |
|---|---|---|
| Windows npm-publish → free runners | **−$570/mo** | slower per job
(8→4 cores) |
| npm-publish nightly | **−$125/mo** | — |
| pypi-publish concurrency | small | — |
| macOS ThinLTO | $0 (already free) | **−~50 min** per release |
| rust aarch64 Windows native | $0 (already free) | **−~25 min** |
| rust `--target` on test step | $0 | large, avoids a second full build
|
| rust-cache `save-if` | small | faster via real cache hits |
## Risks
- The two Windows builds now have 4 cores instead of 8 and ~14 GB of
free disk. If they fail, it is most likely disk rather than memory;
fallback is `windows-2025-4x-x64`, which still halves that line.
- `windows-11-arm` has a thinner toolset (choco/vcpkg/protoc under
emulation) and this enables a test step that has never run, so it may
surface real aarch64 failures. That is the point, but it is the change
most likely to need iteration.
- ThinLTO applies to published macOS and Windows binaries, typically
within a few percent of fat LTO. Linux release builds are untouched.
## Follow-ups
- `python.yml` `pydantic1x` (37 min) and `Doctest` (33 min) each rebuild
the extension from source via `pip install -e .` with no Rust cache;
they should consume the wheel the `linux` job already builds. Worth
~$235/mo and ~70 min of compute per run. Separate PR.
- The three `ubuntu-2404-8x-x64` npm-publish builds (~$420/mo at the old
cadence) are the remaining large-runner spend;
`aarch64-unknown-linux-gnu` could run natively on free
`ubuntu-24.04-arm`. Worth doing after this lands so the ThinLTO change
can be validated first.
- The wheel composite actions declare `python-minor-version` as required
but never use it, and every caller omits it (actionlint warns).
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
`list_versions()` against a remote table on a server that uses
lance-namespace was failing. The server was returning
`timestamp_millis`, while db-catalog deployments were returning
`timestamp`, and the client was only accepting `timestamp`. So, updated
the client to accept both. (assuming we're migrating over time;
eventually we can turn off the `timestamp` code path I suppose.)
## Summary
Lance can now plan multiple byte ranges for the same blob in one
`read_blob_ranges` operation, but LanceDB users currently cannot expose
a complete set of logical ranges to that planner.
This complements `BlobFile`: file-like consumers such as PyAV can
continue to discover ranges dynamically, while callers that already know
the ranges for a batch can submit them together.
## Motivating example
A training table may store a large video blob together with a small
application-level clip index:
```text
video: blob
clips: [{offset, length}, ...]
```
The caller can select the videos and clips for a batch, obtain their row
IDs from the query, and read all of the selected windows together:
```python
rows = (
table.search()
.select(["clips"])
.with_row_id(True)
.limit(64)
.to_arrow()
.to_pylist()
)
requests = []
for row in rows:
clip = sample_clip(row["clips"])
requests.append(
(row["_rowid"], clip["offset"], clip["length"])
)
chunks = table.fetch_blob_ranges("video", requests)
```
Here, `_rowid` comes from the LanceDB query, while `offset` and `length`
come from the application's clip index and are relative to that row's
video blob. The caller describes only the logical reads; Lance still
handles validation, source grouping, coalescing, scheduling, and byte
backpressure.
Lance v10.0.0-beta.5 returns one logical result per blob selector or
range request and explicitly distinguishes null blobs from valid empty
values. LanceDB consumes that aligned result contract directly and only
adds a cardinality check for unresolved row IDs.
This PR exposes batched blob-range reads on local Rust and Python
tables. Results preserve request identity, duplicates, null slots, and
valid empty ranges while allowing Lance to execute the physical reads
out of order. Scheduler buffer sizing remains an internal Lance concern,
so the LanceDB API does not expose `io_buffer_size`.
Cloud tables continue to report this operation as unsupported until
there is a corresponding remote API.
SELECT COUNT(*) FROM t WHERE <predicate> — and any query that plans an
empty-projection scan — panics the executing query task:
InvalidArgumentError("must either specify a row count or at least one
column")
Root cause
MetadataEraserExec wraps every LanceDB table scan to strip schema-level
metadata, rebuilding each batch in execute():
RecordBatch::try_new(schema.clone(), batch.columns().to_vec()).unwrap()
RecordBatch::try_new infers the row count from the columns. COUNT(*)
with a filter is planned with an empty projection, so the scan emits
zero-column batches — there are no columns to infer a length from,
try_new returns Err, and the .unwrap() panics.
(This is specific to the empty-projection case: COUNT(*) with no filter
is answered from statistics and never scans, and COUNT(<col>) projects a
column — both already work.)
Updates the Rust workspace Lance dependencies and Java lance-core
dependency to v10.0.0-beta.5. No compatibility fixes were required;
full-workspace Clippy passes with warnings denied. Lance tag:
https://github.com/lance-format/lance/releases/tag/v10.0.0-beta.5
## What
MemWAL LSM **read** support. When a table has an LSM write spec
(`set_lsm_write_spec`), `merge_insert` upserts live in the MemWAL
active/frozen memtables and flushed SSTables until an external
compaction merges them into the base table, so a normal scan returns
**stale** data. This routes reads through Lance's `LsmScanner` so
queries also surface that in-flight data, deduplicated by primary key
(newest generation wins).
## How
- Adds a **`use_lsm: Option<bool>`** query flag, symmetric with the
`merge_insert` flag:
- **unset** — auto-route through the LSM scanner when the table carries
a write spec
- **`use_lsm(true)`** — force the LSM path; error if there is no spec
- **`use_lsm(false)`** — read the base table only (the escape hatch)
- Plain scan, single-column full-text search, and single-vector ANN all
run through one `LsmScanner` (assembled from on-disk shard manifests
plus the cached writer's in-memory memtables), so a `where` predicate is
honored as a **prefilter** uniformly — including for vector search.
- **Compaction-aware snapshots:** an SSTable generation is dropped only
once it is both compacted into the base table and covered by the arm's
base-index catch-up (`index_catchup`); plain scans use the compaction
watermark alone.
- Query shapes the scanner cannot honor hard-error with guidance to set
`use_lsm(false)`: hybrid, multi/binary vectors, `with_row_id`,
reranking, `order_by`, dynamic/Substrait projection or filters,
`distance_range`, `use_index(false)`, postfilter, take-by-row-id/offset,
reads from a time-traveled version, and an unmaintained or ambiguous
(multiple) FTS/vector index. Namespace-pushdown queries fall back to
local execution when a spec is present; WAL-only writers are handled.
- Exposed across the Rust core and the Python (`use_lsm`) and TypeScript
(`useLsm`) bindings, including `TakeQuery`.
Rebased from Lance `7.2.0-beta.3` to `10.0.0-beta.3`.
Python was versioned and tagged separately from the Rust, Java, and
Node.js SDKs, and had drifted three minor versions ahead (0.36 vs 0.33).
Users had no way to tell which Python version corresponded to which Rust
or Node release, and the gap had no meaning behind it.
This unifies the two tracks so there is one version and one tag for all
four SDKs.
## Version
The shared version is set to `0.37.0-beta.0`. Python continues its own
sequence (highest published: 0.36 → 0.37) while Rust, Java, and Node.js
jump 0.33 → 0.37 to meet it. Picking Python's next minor means Python
users see no discontinuity at all, and only the other SDKs skip forward.
Note that `main` trails the `release/v0.32` branch on both lines (main
is at 0.32.0-beta.3 / 0.35.0-beta.3; the release branch carries
0.33.0-beta.0 / 0.36.0-beta.0), so 0.37 is chosen to clear the highest
tag on either branch. Every index stays monotonic:
| index | publishes | last published | next |
|---|---|---|---|
| PyPI | stable only | 0.34.0 | 0.37.0 |
| Fury | previews | 0.36.0b0 | 0.37.0-beta.1 |
| npm | both | 0.33.0-beta.0 | 0.37.0-beta.1 |
| crates.io | stable only | 0.31.0 | 0.37.0 |
| Maven | both | 0.33.0-beta.0 | 0.37.0-beta.1 |
A one-time jump for three SDKs, versus explaining the offset
indefinitely.
## Mechanism
* `python/.bumpversion.toml` is removed. `python/Cargo.toml` — the
source of the Python package version, since `pyproject.toml` declares
`dynamic = ["version"]` — becomes a tracked file of the root config. Its
`cargo update -p lancedb-python` pre-commit hook is dropped as
redundant: `ci/update_lockfiles.sh` already refreshes every workspace
member version in `Cargo.lock`.
* `pypi-publish.yml` triggers on `v*` instead of `python-v*`, so one tag
releases all four packages. `ci/bump_version.sh` and
`make-release-commit.yml` lose their now-dead tag-prefix and
per-language plumbing, including the `python` / `other` dispatch inputs.
* The two byte-identical GH release jobs in `npm-publish.yml` and
`pypi-publish.yml` are replaced by a single `gh-release.yml`. One
release per tag, named `LanceDB vX.Y.Z`, instead of separate "Python
LanceDB" and "Node/Rust LanceDB" releases for the same commit.
The trade-off: there is no longer a way to ship a Python-only patch
without also releasing crates.io, Maven, and npm. That is the cost of
making drift structurally impossible.
## Beta releases marked "Latest" (#3666)
Both GH release jobs used:
```yaml
prerelease: ${{ contains('beta', github.ref) }}
```
The arguments are reversed. `contains(search, item)` asks whether
*`search`* contains *`item`*, so this evaluated "does the literal string
`'beta'` contain `refs/tags/python-v0.35.0-beta.2`?" — always `false`.
Every beta was published as a full release, and GitHub awards "Latest"
to the newest non-prerelease.
The new workflow derives the flag from the parsed version rather than
the raw ref, and sets `make_latest` explicitly:
```yaml
prerelease: ${{ steps.extract_version.outputs.prerelease }}
make_latest: ${{ steps.extract_version.outputs.prerelease == 'false' }}
```
npm was never affected (`--tag preview` uses correct bash), and PyPI
already excludes pre-releases from resolution.
This only fixes releases published from here on. Already-published betas
need a one-time backfill:
```shell
gh api --paginate /repos/lancedb/lancedb/releases \
--jq '.[] | select(.prerelease == false) | select(.tag_name | test("beta")) | .id' \
| xargs -I{} gh api -X PATCH /repos/lancedb/lancedb/releases/{} -F prerelease=true
```
## Verification
Ran `ci/bump_version.sh` end-to-end against this branch with the release
tooling installed:
* `preview` → tags `v0.37.0-beta.1` (previous tag `v0.33.0-beta.0`
detected, `pre_n` bump)
* `stable` → tags `v0.37.0`
* Both paths update `.bumpversion.toml`, `rust/lancedb/Cargo.toml`,
`nodejs/Cargo.toml`, `python/Cargo.toml`, `nodejs/package.json`, the 7
`nodejs/npm/*/package.json` files, both Java poms, and
`docs/src/java/java.md` together
* `check_breaking_changes.py` resolves the last stable as `v0.31.0`, so
the minor-version gate passes
All five touched workflows parse as valid YAML and the pre-commit hooks
pass.
## Notes for review
* This targets `main` only, so it takes effect at the next
release-branch cut. The in-flight `release/v0.32` branch still carries
`v0.33.0-beta.0` / `python-v0.36.0-beta.0`; if we want the imminent
stable to be 0.37.0, this needs to be applied there too.
* Historical `python-v*` tags are left alone. The changelog builder
scans `^v`, which does not match them, so the first unified release's
notes will compute `fromTag` from the Rust/Node line only — a one-time
gap in the Python-side changelog.
* Pre-existing and not addressed here: `ci/update_lockfiles.sh --amend`
amends the commit that `bump-my-version` has already tagged, so the
lockfile update lands outside the tag on stable releases.
Fixes#3666
## What changed
- add `block_size` to Python FTS configuration and the deprecated
local/remote helpers
- add `blockSize` to the TypeScript FTS options and propagate it through
the NAPI binding
- serialize the value as `block_size` for remote index creation
- document the existing Rust builder API and generate the TypeScript API
reference
- add local, remote, metadata, search, and invalid-value regression
coverage
## Why
Lance supports configuring the number of documents per compressed FTS
posting block, but LanceDB's Python and TypeScript APIs did not expose
the setting. This made the experimental FTS V3 layout unavailable
through those clients and allowed the value to be dropped before index
creation.
## How it works
The default remains `128`. Supported values are `128` and `256`;
selecting `256` uses the experimental FTS V3 format. Invalid values are
rejected by the Lance builder and surfaced as Python or JavaScript
errors.
## Validation
- `cargo check --quiet --features remote --tests --examples`
- `cargo +1.94.0 clippy --quiet --features remote --tests --examples --
-D warnings`
- targeted Rust local and remote index tests
- Rust doctests: 34 passed
- Python Ruff checks, doctest, and targeted local/remote tests: 5 passed
- TypeScript build, Biome lint, generated docs, and targeted Jest tests:
9 passed
- `git diff --check`
## Limitations
The Java client remains unchanged because its external remote REST model
does not currently expose `block_size`.
Co-authored-by: Yang Cen <yangcen@Yangs-Mac-mini.local>
## 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>
## Summary
This updates the Java API reference to close the documentation gaps that
can be fixed from the current Java source and generated namespace API.
The patch adds an empty table example, shows how to wrap returned Arrow
IPC query bytes in a reusable `ArrowFileReader` helper, and documents
the Java index operations that are currently exposed by the namespace
client: vector indexes, scalar indexes, full text search indexes, and
listing indexes.
## Issue Links
Fixes https://github.com/lancedb/docs/issues/157
Fixes https://github.com/lancedb/docs/issues/160
Partially addresses https://github.com/lancedb/docs/issues/159 by
documenting the index parameters currently exposed by Java. The
requested `num_partitions` example is still blocked because
`CreateTableIndexRequest` does not expose IVF training parameters yet.
Not included: https://github.com/lancedb/docs/issues/158. The current
Java docs and source remain remote namespace oriented, so local DB
connection documentation should wait until the Java local DB API is
available and can be verified.
## Validation
- Built the Java core module with OpenJDK 17:
`./mvnw -pl lancedb-core -am -DskipTests compile`
- Checked the Markdown diff:
`git diff --check -- docs/src/java/java.md`
The Java build succeeds. It still reports pre-existing checkstyle
warnings in the namespace client builder, but the Maven build is green.
## Summary
- reconstruct foreign Arrow Map schemas from their single sanitized
entries field
- reject malformed Map types with anything other than one child
- preserve the complete Map schema and `keysSorted` value through
empty-table creation and IPC round trips across Arrow 15–18
## Testing
- `./node_modules/.bin/jest --runInBand __test__/arrow.test.ts
__test__/sanitize.test.ts`
- `pnpm lint`
- `pnpm build`
- `pnpm run docs`
Fixes#2337
## What
- Replace legacy model names in `WatsonxEmbeddings` with the current
supported set:
- `ibm/granite-embedding-278m-multilingual` (new default, 768-dim)
- `ibm/slate-125m-english-rtrvr-v2` (768-dim)
- `ibm/slate-30m-english-rtrvr-v2` (384-dim)
- `intfloat/multilingual-e5-large` (1024-dim)
- `sentence-transformers/all-minilm-l6-v2` (384-dim)
- Add `space_id` field — mutually exclusive with `project_id`, mirrors
the
existing pattern in `WatsonxReranker`
- `project_id` / `space_id` resolution now falls back to
`WATSONX_PROJECT_ID` /
`WATSONX_SPACE_ID` env vars; exactly one must be supplied
## Why
The previously hardcoded models (`ibm/slate-125m-english-rtrvr`,
`sentence-transformers/all-minilm-l12-v2`) are legacy and no longer
listed as
supported by the watsonx.ai platform. `space_id` scoping was already
supported
by `WatsonxReranker` but was missing from the embeddings counterpart.
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Updates the Rust workspace Lance dependencies and Java lance-core from
v9.1.0-beta.5 to v9.1.0-beta.7, including the generated Cargo lockfile.
No LanceDB compatibility changes were required for this release. See the
[Lance v9.1.0-beta.7
release](https://github.com/lance-format/lance/releases/tag/v9.1.0-beta.7).
This PR adds some support for `diff` / `merge` in the remote client as
for local tables we stay `NotSupported` until
https://github.com/lance-format/lance/issues/7263.
This wires the two review-and-land calls against the remote REST API:
- `POST /v1/table/{id}/branches/diff`
- `POST /v1/table/{id}/branches/merge`
Rust gets typed results (`BranchDiff`, `MergeBranchResult`). Python
returns the wire JSON, same shape as the REST response.
Merge here means promoting a branch's added columns onto `main`.
### Behavior
- Remote only. Local raises `NotSupported`.
- A rejected merge is not an exception. HTTP 409 still returns `Ok` / a
dict with `status="rejected"` and blockers in `diff.mergeBlockers`.
- Unknown blocker / status codes parse as `Unknown` so a newer server
does not break older clients.
- `MergePreview` tolerates missing fields for the same reason.
- Merge requests are not retried. 409 is final and carries the body you
need.
### Example
```python
table = db.open_table("images")
table.branches.create("exp")
exp = table.branches.checkout("exp")
exp.add_columns({"tag": "cast('draft' as string)"})
diff = table.branches.diff("exp")
preview = table.branches.merge("exp", dry_run=True)
result = table.branches.merge("exp", dry_run=False)
if result["status"] == "merged":
print("landed at", result["mainVersionAfter"])
elif result["status"] == "rejected":
print(result["diff"]["mergeBlockers"])
```
### Testing
cargo test -p lancedb --features remote diff_branch
cargo test -p lancedb --features remote merge_branch
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
## Problem
On the remote (LanceDB Cloud) write path, each write partition is
uploaded as a **single** `/insert?upload_id=...` request that stays open
until the whole partition has been streamed and the server has written
it to object storage. For large bulk ingests a partition can be many GB,
so a single request can run longer than the client read timeout (default
300s), surfacing as:
```
lancedb.remote.errors.HttpError: operation timed out
```
The server already supports staging **multiple** parts under one
`upload_id` (each `/insert` writes a separate transaction that
`complete` merges atomically), but the client never used that — it sent
one part per partition.
## Change
Split each partition into multiple parts of at most
`max_bytes_per_request` (Arrow IPC, LZ4-compressed) bytes, each uploaded
as its own `/insert?upload_id=...&upload_part_id=...` request. This
bounds how long any single request stays open, independent of total data
size or write parallelism.
Key properties:
- **Still streamed, not buffered.** Each part's body is driven through a
bounded channel while the request is in flight (`futures::join!` of a
producer + the send), so peak memory stays at a couple of batches per
partition regardless of the part size. Backpressure from a
slow/throttled server still propagates upstream.
- **Correct part accounting.** An empty partition still sends exactly
one (schema-only) part so `complete` has a transaction to commit; a size
cut landing exactly on the end of input does not emit a trailing empty
part.
- **Multipart only.** The single-request (non-multipart) path is
unchanged.
## Config
New `ClientConfig::max_bytes_per_request: Option<usize>`, also settable
via the `LANCE_CLIENT_MAX_BYTES_PER_REQUEST` environment variable.
**Default 1 GiB** (`Some(0)` disables splitting → one request per
partition). Python users pick up the default/env automatically through
the remote client.
## Tests
- `test_multipart_chunked_splits_into_parts`: a 1-byte budget puts each
batch in its own part → N requests, each carrying the shared `upload_id`
and a distinct `upload_part_id`.
- `test_multipart_single_part_when_under_budget`: a large budget keeps
the partition in a single request.
- Verified end-to-end against a live remote table: a forced-chunked
multipart add (many parts) assembles to the correct row count.
Related to ENT-1883.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
## Summary
Fixes#1653.
`infer_vector_column_name` in `util.py` could silently return `None`
when `query is None` and `query_type` is not `"fts"` or `"hybrid"`. This
`None` then propagated into downstream code, causing a cryptic
`TypeError: expected bytes, NoneType found` rather than a clear error
message.
## Changes
- **Removes the no-op `try/except Exception as e: raise e`** around
`inf_vector_column_query` (it was catching and immediately re-raising
without adding any value)
- - **Adds a `None` guard** after the inference block: if
`vector_column_name` is still `None` at this point, raise a clear
`ValueError` pointing the user to pass `vector_column_name` explicitly
## Before / After
**Before:** cryptic `TypeError: expected bytes, NoneType found` deep in
schema lookup code
**After:**
```
ValueError: No vector column found in the schema. Please specify the vector column name explicitly via the `vector_column_name` parameter.
```
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Some additions to our lancedb skill to enable agents to use the jobs
methods that we recently added. Eval tests (below, with and without
these additions to the skill) suggest that they're helping, mostly to
find the right method calls. These are a little unusual because they
require REST server connection, they're not yet implemented in the SDKs.
```
┌─────────────────────┬───────────┬────────────┬─────────────┬──────────┬───────────┬──────────┬───────────┐
│ eval │ grade w/o │ grade with │ improvement │ time w/o │ time with │ cost w/o │ cost with │
├─────────────────────┼───────────┼────────────┼─────────────┼──────────┼───────────┼──────────┼───────────┤
│ 8-list-running-jobs │ 2.5/3 │ 3/3 │ +0.5 │ 123s │ 29s │ $0.58 │ $0.18 │
├─────────────────────┼───────────┼────────────┼─────────────┼──────────┼───────────┼──────────┼───────────┤
│ 9-describe-job │ 1/5 │ 5/5 │ +4.0 │ 159s │ 52s │ $0.62 │ $0.25 │
├─────────────────────┼───────────┼────────────┼─────────────┼──────────┼───────────┼──────────┼───────────┤
│ 10-cancel-job │ 3/3 │ 3/3 │ +0.0 │ 99s │ 35s │ $0.55 │ $0.21 │
├─────────────────────┼───────────┼────────────┼─────────────┼──────────┼───────────┼──────────┼───────────┤
│ TOTAL │ 6.5/11 │ 11/11 │ +4.5 │ 381s │ 116s │ $1.75 │ $0.65 │
└─────────────────────┴───────────┴────────────┴─────────────┴──────────┴───────────┴──────────┴───────────┘
```
Failure reasons are because the agent didn't know the right method to
call, spent all its turns guessing REST calls, tried to inspect lancedb
code, but didn't find the answer in here.
## Problem
`table.add(dataset)` with a `pyarrow.dataset.Dataset` OOMs the client
during bulk ingestion of wide rows (e.g. embedding columns), even
against a remote table where the upload itself is streaming.
The cause is in `to_scannable`: a `Dataset` is scanned with pyarrow's
default scanner settings (`batch_size=131072` rows,
`batch_readahead=16`, `fragment_readahead=4`). pyarrow's internal
threads prefetch that read-ahead window independently of LanceDB's
backpressure, so for wide rows a large fraction of the dataset is held
in memory. On the remote path this is then multiplied across the
multipart write partitions (one in-flight batch per partition, up to
CPU-core count).
Reproduced on a 10 GB / 1.55M-row dataset with two 768-dim float32
embeddings: peak client RSS ~11.7 GB for the scan alone (6.8 GB after
consuming a *single* batch), ~15.4 GB for the full remote `add()`.
## Fix
`to_scannable` now sizes the scanner from an estimate of bytes-per-row
derived from the schema:
- **Narrow datasets keep pyarrow's defaults** (empty scanner kwargs) —
no throughput regression. The bound only engages above ~410 bytes/row.
- **Wide rows** get a smaller `batch_size` (~16 MiB/batch) and reduced
read-ahead (`batch_readahead=2`, `fragment_readahead=1`) so peak
in-flight memory stays near a ~1 GiB budget. Read-ahead (not just batch
size) has to drop, because pyarrow pins whole row-group buffers.
On the 10 GB dataset this drops peak client RSS to ~1.4 GB, and it stays
flat as the dataset grows. The `Dataset`/`LanceDataset` scannables
remain rescannable (retry-safe).
## Also: expose `write_parallelism` on `add()`
`AddDataBuilder::write_parallelism` already existed in Rust but was not
exposed in Python. This PR forwards it through the async, sync, and
remote `add()` methods, so users can cap the number of parallel write
partitions (each buffers data in flight) to trade throughput for memory
on large uploads.
## Tests
- `test_scannable.py`: bytes-per-row estimation; narrow → defaults; wide
→ bounded; `Dataset` reader streams bounded batches and stays
rescannable.
- `test_table.py`: `write_parallelism` on sync and async `add()`, and
that `write_parallelism=0` is rejected.
Fixes ENT-1883
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Moves the skill from .agents/skills/lancedb to
plugins/lancedb/skills/lancedb, as recommended by codex and claude.
Install path now is:
### Codex/ChatGPT app
Codex: Plugins -> "Create" arrow -> Add plugin marketplace
search for lancedb plugin and install
### Codex CLI
```
codex plugin marketplace add lancedb/lancedb
codex plugin install lancedb@lancedb # name@marketplace
```
### Claude app
Settings -> Plugins -> Add -> Add marketplace
search for lancedb plugin and install
### Claude CLI
```
claude plugin marketplace add lancedb/lancedb
claude plugin install lancedb@lancedb
```
Here's how it looks on ChatGPT/Codex app:
(the main icon has light and dark modes; the smaller one on the skill
doesn't so I made it gray 🤷 )
<img width="764" height="560" alt="Screenshot 2026-07-16 at 2 49 24 PM"
src="https://github.com/user-attachments/assets/b82cda16-3392-4740-ac47-b2f187cb2655"
/>
Hi, and thank you for LanceDB.
Small CI supply-chain hardening. In `make-release-commit.yml`, the
release job checks out with `LANCEDB_RELEASE_TOKEN` (a push-capable PAT)
and its final step pushes the version tag using a third-party action
pinned to a **mutable branch**:
```yaml
- name: Push new version tag
uses: ad-m/github-push-action@master
with:
github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
```
`@master` can move after review; whatever it points at then runs with
that release token in scope. This PR pins it to the commit behind the
current release (`v1.3.0` → `881a6320…`), keeping the version visible as
a comment. Behavior today is unchanged.
For transparency: I used AI assistance to spot and draft this; I
verified the workflow and resolved the SHA myself.
Tracks #3324. On x86_64 CPUs without AVX2 (Sandy Bridge / Ivy Bridge /
Westmere on Intel; Bulldozer / Piledriver / Steamroller on AMD), `import
lancedb` SIGILLs because the wheel bakes AVX2 + FMA into every compiled
function. Per [westonpace's
review](https://github.com/lancedb/lancedb/issues/3324#issuecomment-4328944354),
the default `lancedb` wheel stays fast; pre-Haswell users get a
separately-published `lancedb-compat` wheel.
## Summary
- Adds a `lancedb-compat` matrix entry to `pypi-publish.yml` that builds
with `RUSTFLAGS="-C target-cpu=x86-64-v2"` (Nehalem-class baseline).
Same Python API (`import lancedb` works) — files install to the same
namespace, so the two wheels conflict at install time and users pick
one. Same pattern as `psycopg2` / `psycopg2-binary` and `tensorflow` /
`tensorflow-cpu`.
- Generalizes `build_linux_wheel` and `upload_wheel` composites with
optional `package-name` and `rustflags` inputs (defaults preserve the
existing 4 `lancedb` matrix entries verbatim).
- Documents the choice in `python/README.md`: `pip install
lancedb-compat` for pre-Haswell hosts.
The default `.cargo/config.toml` baseline is unchanged.
## Sequencing
1. ~~lance-format/lance#6630 merges → runtime SIMD dispatch lands in
lance.~~ **Done — merged.**
2. lancedb's lance dep is bumped to a release that includes it (separate
PR / normal cadence).
3. This PR's `lancedb-compat` wheel build path starts producing a wheel
that runs on pre-Haswell hardware. **Maintainer setup**: register
`lancedb-compat` on PyPI and configure trusted publishing.
## Verified end-to-end on Sandy Bridge Xeon E5-2609
Verification was done locally against a fork-pinned lance dep that
includes the runtime dispatch implementation, using the same
`RUSTFLAGS="-C target-cpu=x86-64-v2"` flags this PR uses in CI:
```
$ RUSTFLAGS="-C target-cpu=x86-64-v2" maturin build --release
$ pip install ./target/wheels/lancedb-*.whl
$ python verify.py
PASS: import + simd dispatch + table create + vector search all work.
```
Pre-fix on the same CPU (default `pip install lancedb`): `Illegal
instruction (core dumped)`. Full reproducer (deps + clone + build +
verification):
https://gist.github.com/tobocop2/2e341358b55c143527416edfdb1e37df.
Fork-internal verification PR with the dep bump and full logs:
[`tobocop2/lancedb#2`](https://github.com/tobocop2/lancedb/pull/2).
## Benchmarks — no regressions on modern CPUs from the lance-side change
These are the numbers I ran for the lance PR, confirming the runtime
dispatch doesn't slow down the default (`target-cpu=haswell`) wheel that
existing users install. Criterion, one machine, one session, base → PR,
no `RUSTFLAGS` override. Full methodology, null experiments, and logs:
[lance-format/lance#6630 benchmark
comment](https://github.com/lance-format/lance/pull/6630#issuecomment-4933063394)
and the [logs
gist](https://gist.github.com/tobocop2/3c6d0f449cbd736aa2501f89a7fe56a2).
| benchmark | EPYC 7B13 (`avx2`, `fma`, no `avx512f`) | Xeon Cascade
Lake (`avx512f`) |
|---|---|---|
| `Cosine(f32, scalar)` *(control)* | +0.04% | +0.09% |
| `Cosine(f64, scalar)` | −0.34% | −1.94% |
| `Cosine(u8, SIMD)` | +2.30% | +3.63% |
| `Dot(f16, SIMD)` | −0.58% | +0.61% |
| `Dot(f32, SIMD)` | +0.34% | **−6.08%** |
| `Dot(f32, arrow_arity)` | +0.02% | −0.00% |
| `L2(f32, scalar)` | −0.10% | −0.02% |
| `L2(f32, simd)` (dim 1024) | +2.63% | −0.53% |
| **`L2(simd,f32x8)` (dim 8)** | **−45.9%** | **−25.1%** |
| `L2(u8, SIMD)` | +0.42% | −3.11% |
| `NormL2(f32, SIMD)` | −1.02% | −4.17% |
| `NormL2(f64, SIMD)` | +3.51% | −0.58% |
Nothing regresses beyond the noise floor. Dim 8 — the PQ sub-vector
width — improves 25–46%.
---
To be transparent: this isn't my domain of expertise and the lance-side
implementation is AI-generated. I verified it works end-to-end on the
failing hardware. Happy to roll in feedback.
Routes local sync child-namespace operations through the Rust-backed
connection instead of the Python namespace-client fallback.
Also keeps lazy namespace-client construction for table-to-Lance
conversion and preserves public namespace error mappings.
Validated locally with ruff format/check and targeted namespace pytest.
BREAKING CHANGE: splits generated by the permutation data loader will
not be the same, due to a change in hash function.
Updates the Lance dependencies and Java lance-core to
[v9.0.0-rc.1](https://github.com/lance-format/lance/releases/tag/v9.0.0-rc.1).
Includes the required DataFusion 54 and Lance file-reader compatibility
updates.
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Adds client-side support for analyze_plan distributed metrics modes
across Rust, Python, and TypeScript clients. Defaults to aggregate for
backward compatibility and sends the remote distributed_metrics
parameter only when a non-default mode is requested.
Fixes#3174
Also fixes#3645
Empty record batches now append correctly typed empty embedding arrays
without invoking embedding providers. This avoids OpenAI requests with
an invalid empty input while preserving source-column validation and
the non-empty execution paths.
As a small cleanup, the single- and multi-embedding code paths now share
a single upfront lookup of their source columns ("input_columns")
instead
of each path looking them up independently. Also moves `lance-testing`
from regular dependencies to dev-dependencies where it belongs.
Tests run:
- `cargo fmt --all -- --check`
- `cargo test --quiet -p lancedb --lib
empty_batch_skips_embedding_functions`
- `cargo test --quiet -p lancedb --lib
empty_batch_still_validates_source_column`
- `cargo test --quiet -p lancedb --lib
test_create_empty_table_with_embeddings`
- `cargo check --quiet -p lancedb --features remote --tests --examples`
- `cargo clippy --quiet -p lancedb --features remote --tests --examples`
- `cargo test --quiet -p lancedb --lib`
- `cargo test --quiet --features remote --tests`
## Summary
Fix `on_bad_vectors="fill"` so it replaces only invalid or missing
vector values instead of replacing the entire vector row.
Fixes#3026.
## Reasoning
The old Python sanitizer detected whether a vector row was bad at row
granularity. For `fill`, it then used that row-level flag to replace the
whole vector with `[fill_value] * dim`. That meant an input like `[1.0,
NaN, 3.0]` became `[0.0, 0.0, 0.0]`, even though the documented and more
useful behavior is to preserve valid values and fill only the bad
element.
I checked whether this should be a Rust-side fix so TypeScript users
would benefit too. Today, Rust core exposes `NaNVectorBehavior::{Error,
Keep}` for rejecting or keeping NaN vectors, while the Python
`on_bad_vectors` API (`error`, `drop`, `fill`, `null`) is implemented in
the Python ingestion sanitizer before data reaches Rust. TypeScript does
not expose the Python `on_bad_vectors="fill"` behavior today. Moving
this exact behavior to Rust would be a broader cross-language API
change, so this PR keeps the fix scoped to the currently affected Python
API.
## What changed
- Added a small helper that fills bad vector rows by preserving valid
elements, replacing NaN elements with `fill_value`, truncating vectors
longer than the expected dimension, and padding short vectors with
`fill_value`.
- Kept the existing fast path unchanged: the helper only runs after bad
vectors are detected and `on_bad_vectors="fill"` is selected.
- Updated sanitizer and table tests to assert element-wise NaN
replacement and short-vector padding for both `create_table` and `add`.
## Validation
- `uv run ruff format .`
- `uv run ruff check .`
- `cd python && uv run --no-sync pytest
python/tests/test_util.py::test_handle_bad_vectors_jagged
python/tests/test_util.py::test_handle_bad_vectors_nan
python/tests/test_table.py::test_create_with_nans
python/tests/test_table.py::test_add_with_nans -vv`
Targeted pytest result: `10 passed`.
## Why this fix is Python-side (and not Rust)
The problematic behavior lives in Python’s `on_bad_vectors` sanitizer,
before data is handed off to Rust. Rust currently only exposes
`NaNVectorBehavior::{Error, Keep}` for add operations, while Python has
the richer `on_bad_vectors={"error","drop","fill","null"}` API.
TypeScript does not currently expose the Python-style fill behavior, so
moving this exact fix into Rust would require designing a broader
cross-language bad-vector handling API.
This PR keeps the change scoped to the existing affected surface:
Python’s `on_bad_vectors="fill"` path. This way, Python users
immediately benefit.
## What the new agent skill covers
We want to help users _easily_ write LanceDB pipelines to bring their
data in from other places, no matter whether they use LanceDB OSS or
Enterprise.
The `lancedb` set of skills contains guidance for agents on the
following:
- Distinguishes local and remote table capabilities.
- Promotes bounded reads using `select()` and `limit()`.
- Prevents accidental full-table materialization.
- Documents correct Python sync/async scan APIs.
- Recommends validated Python schemas and batched ingestion.
- Provides indexing, query-tuning, diagnostics, and maintenance
guidance.
- Documents the Enterprise table-name cache issue: avoid immediately
reusing a dropped or overwritten table name; write to a fresh name and
rename after propagation.
- Adds Python and TypeScript API, pattern, and performance references.
- Adds a heuristic scanner for potentially unsafe Python and TypeScript
materialization patterns.
This change only adds agent documentation and tooling: no LanceDB
runtime code, Rust code, SDK APIs, dependencies, or CI configuration are
modified.
## Context
The LanceDB agent skill was accidentally pushed directly to `main` in
`8ea78e3fbcb26718112ab4ddec55a91804b869d3`, bypassing the normal review
workflow. That commit was reverted on `main` by `c12a6dce` so the
protected branch is back to its prior content.
## Summary
- add table-level FTS query tokenization returning token text and
position
- use the native index tokenizer for local tables and remote index
metadata for remote tables
- expose sync and async Python table wrappers with focused coverage
`Dataset::index_statistics()` loads index files and does meaningful CPU
work to serialize low-level info. Most fields
`NativeTable::index_stats()` needs are available from manifest metadata
via `Dataset::describe_indices()`, which is much cheaper.
`NativeTable::index_stats()` now:
- Calls `describe_indices()` filtered by name; returns `Ok(None)` if no
match.
- Parses `distance_type` from `description.details()` JSON (the
`VectorIndexDetails` proto stored in the manifest by recent Lance
versions).
- Falls back to `index_statistics()` only for vector indices where
`details()` returns no `distance_type` — this handles older Lance
datasets that didn't write `VectorIndexDetails`.
- `Unknown` index types (e.g. Lance's internal `FragReuseIndex`) are
explicitly filtered out of `list_indices` rather than erroring.
## Test plan
- [x] `test_create_scalar_index` — asserts `index_type`,
`distance_type`, and `num_unindexed_rows > 0` after adding rows
post-index
- [x] `test_create_fm_index`, `test_create_bitmap_index`,
`test_create_label_list_index` — added `index_stats` assertions
- [x] IvfPq, IvfHnswPq, IvfHnswSq, IvfHnswFlat tests assert
`distance_type == Some(L2)`
- [x] `test_list_indices_skip_frag_reuse` — FragReuseIndex is filtered
by the Unknown guard in `list_indices`
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
## Summary
- serialize sync phrase queries consistently for execution and query
plans
- restore the documented no-argument hybrid `phrase_query()` behavior
- keep reranker input as the original user text without mutating the
builder
Fixes#3653.
## Testing
- `python/.venv/bin/python -m pytest <8 focused test nodes> -q` (`8
passed`)
- `python/.venv/bin/python -m ruff format --check
python/python/lancedb/query.py python/python/tests/test_fts.py
python/python/tests/test_hybrid_query.py`
- `python/.venv/bin/python -m ruff check .`
- `git diff --check origin/main...HEAD`
The complete hybrid module and the real native FTS phrase test were not
completed
in the current PyO3 runtime environment: both stalled in the native
`lancedb.connect()` fixture and were interrupted without an assertion
failure.
The CODEOWNERS file added in #3312 automatically requests reviewers on
every PR — the `*` default owner routes all changes to two reviewers.
This is mostly noise for contributors, and we prefer a single requested
reviewer per PR.
Remove the file.
Reverts #3312.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Carrying over from #2915, this patch introduces:
* Single-API call batching support for Gemini embeddings (up to 100 at a
time, the API limit)
* A versioned user agent header for Gemini API calls
* Support for [variable embedding dimension
size](https://ai.google.dev/gemini-api/docs/embeddings#control-embedding-size)
(Gemini is MRL trained)
## Summary
- preserve explicit `0.0` distance bounds in synchronous hybrid search
- distinguish omitted `None` endpoints from zero-valued endpoints when
configuring the vector child query
- add a public end-to-end regression test for a zero upper bound
## Testing
- `cd python && uv run --extra tests pytest
python/tests/test_hybrid_query.py -q`
- `uv run --project python ruff format --check
python/python/lancedb/query.py python/python/tests/test_hybrid_query.py`
- `uv run --project python ruff check .`
Fixes#3651
description: Branch management for LanceDB tables via the REST API. Use this skill whenever someone wants to create, delete, list, or switch branches on a LanceDB table — or needs to make sure a write (metadata update, index build, etc.) lands on a specific branch instead of main. Invoke it even without the word "branch" if context makes clear they want an experimental copy of a table, want to isolate changes, or want to confirm a mutation didn't touch main. Covers: branches/list, branches/create, branches/delete, and passing "branch" in describe/update_field_metadata/create_index to target a non-main version.
---
## Goal
Manage branches on a LanceDB table: list what exists, create new ones, delete stale ones, and direct read/write operations at a specific branch without touching main.
## Step 0: Establish the connection
Use the `lancedb-connect` skill to resolve the base URL and auth headers (`x-api-key`, `x-lancedb-database`). Skip this only if the connection is already known from the current conversation.
All examples below use `{base_url}` — substitute the resolved endpoint and include the auth headers on every request.
## The branch model (important)
LanceDB branches are named snapshots that diverge from the table's current state at creation time. There is **no checkout command** — you never switch the whole table to a branch. Instead, you **pass `"branch": "<name>"` in the request body** of any operation to target that branch. Omitting the key (or sending an empty body) always targets main.
`branches/list` returns only non-main branches. Main always exists and is not listed.
description: Column metadata authoring for LanceDB tables via the REST API. This skill is required for tasks like writing field descriptions, setting tags on columns (field_type, model, project_id, version), classifying columns as embeddings vs labels vs eval metrics, or grouping versioned columns into logical families — because it has the API integration needed to read the schema and persist metadata back. Invoke whenever someone wants to document, annotate, tag, or classify what their table columns ARE. Trigger even without an explicit "LanceDB" mention, as long as the context is column-level documentation or tagging for an ML or vector database table.
metadata:
short-description: Write column descriptions, tags, and logical groupings to a LanceDB table
---
## Overview
This skill authors column-level metadata for a LanceDB table. It connects to a LanceDB deployment over its REST API, inspects the table schema, generates appropriate metadata, and writes it back.
## Step 0: Establish the connection
Use the `lancedb-connect` skill (invoke it via the Skill tool) to resolve the base URL and auth headers (`x-api-key`, `x-lancedb-database`) for whichever deployment the user is working against — enterprise/self-hosted or a local dev server. Skip it only if the connection details are already established in the conversation.
All examples below use `{base_url}` — substitute the resolved endpoint and include the resolved headers on every request.
## Metadata keys
All metadata uses namespaced keys:
| Key | Purpose | Example value |
|-----|---------|---------------|
| `lancedb:description` | Human-readable explanation of what the column contains | `"CLIP ViT-L/14 image embedding, L2-normalized (768-dim)"` |
| `lancedb:tag:<name>` | Flexible key-value tag; the suffix names the tag category | `lancedb:tag:field_type: "embedding"`, `lancedb:tag:model: "clip"`, `lancedb:tag:project_id: "foo"` |
| `lancedb:logical-column` | Logical group/family this column belongs to | `"clip_features"` |
Tags are open-ended — use whatever key suffix and value make sense given the user's intent. The tag suffix should describe *what is being classified* (e.g., `field_type`, `model`, `project_id`) and the value describes *how*.
## Step 1: Resolve the table identifier
You need:
- **Table name** (required) — e.g., `my_table` or `my_namespace.my_table`
- **Database name** — ask if not provided and not inferable from context; it goes in the `x-lancedb-database` header, never in the URL path
The table identifier in the URL path is typically `table_name` for a top-level table, or `namespace$table_name` if the table lives in a namespace. The API accepts a `delimiter` query parameter to parse compound identifiers (default `$`).
## Step 2: Describe the table
```http
POST{base_url}/v1/table/{table_id}/describe
Content-Type:application/json
{}
```
The response contains `schema.fields` — an array of field objects:
description: Resolve how to connect to a LanceDB deployment over the REST API — figure out the base URL, API key, and database header. Use this before making any REST requests to a LanceDB table, whenever the endpoint or auth setup is not already known. Also useful on its own when someone asks how to connect, authenticate, or curl their LanceDB instance.
metadata:
short-description: Resolve the base URL and auth headers for a LanceDB deployment
---
## Goal
Produce two things every REST request needs:
1.**Base URL** — the endpoint
2.**Headers** — `x-api-key`, and usually `x-lancedb-database`
## Resolution steps
1. If the user already gave a URL and API key (or said which environment they're working against), use that.
2. Otherwise, look for credentials already available in the environment:
- Env vars like `LANCEDB_URI` / `LANCEDB_HOST` / `LANCEDB_API_KEY`
- A LanceDB endpoint already running or port-forwarded locally (the REST default port is 2333, i.e. `http://localhost:2333`)
3. If you didn't find both pieces, ask the user directly: **"What's your LanceDB endpoint's URL, and what's your API key?"** Also ask which database to use if it isn't obvious. Don't guess or probe further — the user knows their deployment.
## Validating the connection
Make a cheap authenticated request and check the status:
"description":"Write, review, debug, and document LanceDB pipelines in Python and TypeScript that work across local LanceDB OSS tables and remote LanceDB Enterprise/Cloud tables.",
description:"Override [project] name in python/pyproject.toml (e.g. 'lancedb-compat'). Default keeps 'lancedb'."
required:false
default:"lancedb"
rustflags:
description:"RUSTFLAGS for the build container, as a single whitespace-free token (e.g. '-Ctarget-cpu=x86-64-v2'). Empty leaves RUSTFLAGS unset, keeping the defaults from .cargo/config.toml."
required:false
default:""
runs:
using:"composite"
steps:
@@ -27,6 +35,18 @@ runs:
ARM_BUILD:${{ inputs.arm-build }}
run:|
echo "ARM BUILD: $ARM_BUILD"
- name:Patch package name for variant build
if:${{ inputs.package-name != 'lancedb' }}
shell:bash
env:
PACKAGE_NAME:${{ inputs.package-name }}
run:|
# Swap the [project] name so this build produces e.g. lancedb-compat
# wheels. The package still installs files under the lancedb/
# namespace -- import lancedb still works after pip install.
sed -i.bak 's/^name = "lancedb"$/name = "'"$PACKAGE_NAME"'"/' python/pyproject.toml
"description":"Write, review, debug, and document LanceDB pipelines in Python and TypeScript that work across local LanceDB OSS tables and remote LanceDB Enterprise/Cloud tables, with idiomatic query/search patterns and performance defaults for ingestion, indexing, filtering, and diagnostics.",
"description":"Codex plugin for building LanceDB pipelines in Python and TypeScript.",
"author":{
"name":"LanceDB"
},
"keywords":[
"lancedb",
"vector-search",
"full-text-search",
"hybrid-search",
"python",
"typescript",
"pipelines"
],
"skills":"./skills/",
"interface":{
"displayName":"LanceDB",
"shortDescription":"Build LanceDB pipelines in Python and TypeScript.",
"longDescription":"Write, review, debug, and document LanceDB pipelines in Python and TypeScript that work across local LanceDB OSS tables and remote LanceDB Enterprise/Cloud tables, with idiomatic query/search patterns and performance defaults for ingestion, indexing, filtering, and diagnostics.",
"developerName":"LanceDB",
"websiteURL":"https://www.lancedb.com",
"category":"Developer Tools",
"capabilities":[
"Developer Tools"
],
"defaultPrompt":"Create a LanceDB table, embed sample text, and run a vector search.",
description: Use when writing, reviewing, debugging, or documenting LanceDB pipelines in Python or TypeScript, especially code that should work across local LanceDB OSS tables and remote LanceDB Enterprise/Cloud tables. Helps avoid non-portable full-table materialization, choose idiomatic query/search patterns, apply LanceDB performance defaults for ingestion, indexing, filtering, and diagnostics, and resolve connections to the remote server for Enterprise-only operations such as jobs.
---
# Building LanceDB Pipelines
Use this skill to produce LanceDB pipelines that are portable between local and remote tables (for LanceDB Enterprise/Cloud) and idiomatic for the selected SDK.
## LanceDB Table Modes
LanceDB has two common execution modes:
- **Local table**: embedded, open source, in-process LanceDB. The client opens data from a local path or object storage URI and executes queries in the application process.
- **Remote table**: LanceDB Enterprise/Cloud table opened through a `db://...` URI. The data may be very large, commonly backed by object storage, and queried through a remote service.
Do NOT assume local-only table helpers exist on remote tables. If the user asks for LanceDB Enterprise, Cloud, `db://...`, production remote access, or a remote table, focus on the remote table path: use `search()` / `query()`, keep reads bounded with `select()` and `limit()`, and avoid table-level full materialization APIs.
## Workflow
1. Identify the SDK: Python, TypeScript, or both.
2. Identify the table mode: local/embedded OSS, remote Enterprise/Cloud, or portable across both. If the user says "LanceDB Enterprise", choose the remote table path. If the task involves jobs in any way (listing, inspecting, creating, or canceling jobs), it is always the remote path and requires a remote server connection — see "Connecting to the LanceDB remote server" below before doing anything else.
3. Read the matching language branch before writing or changing code:
- Remote server connection resolution (jobs, raw REST): `references/remote_connect.md`
- Job operations REST API (list/describe/cancel/query_events): `references/remote_jobs.md`
4. Start with `patterns.md` for the selected SDK. Read `api_reference.md` when choosing method names or return collectors. Read `performance.md` when the task involves ingestion, indexing, filtering, query tuning, diagnostics, or large datasets. Read `column_metadata.md` when the task is documenting, tagging, classifying, or grouping table columns (field descriptions, `lancedb:tag:*` tags, logical column families). Read `branch_ops.md` when the task involves branch lifecycle (list/create/delete), writing to a non-main branch, or verifying a change stayed off main. Read `remote_connect.md` when the task involves jobs or direct REST access to an Enterprise deployment, and `remote_jobs.md` for the job REST methods themselves (list, describe, cancel, query_events).
5. For Python schemas, favor Pydantic models and validate records before writing. Use PyArrow schemas when Arrow-native, streaming, or highly dynamic data makes them materially better suited.
6. Prefer `search()` or `query()` builders with explicit `select()` and `limit()` for reads.
7. Avoid table-level full materialization in remote or portable code. This is the main local-vs-remote read pitfall.
8. After a successful embedded OSS ingestion, call `table.optimize()`. Do not call it for Enterprise/Cloud; remote maintenance is automatic.
9. For remote Enterprise/Cloud writes, never drop-then-reuse or `mode="overwrite"` the same table name — see "Enterprise: never drop-then-reuse the same table name" below. This is the main local-vs-remote write pitfall.
10. If reviewing an existing file or repo, run `scripts/check_materialization.py` on the relevant paths and inspect each finding before editing.
11. Cross-check unfamiliar or non-trivial API claims against the source tree instead of relying on memory.
## Core Portability Rule
Do not write code that assumes a local table API will exist on a remote table. Remote tables can be very large, so whole-table materialization helpers are intentionally unavailable or unsafe.
This does **not** mean result conversion is forbidden. Bounded query/search result collection is normal:
The unsafe pattern is table-level or unbounded collection, plus local-only dataset escape hatches in remote code:
- Python: `table.to_pandas()`, `table.to_arrow()`, `table.to_polars()`; `table.to_lance()` is local/OSS-only dataset access, not materialization
- TypeScript: `await table.toArrow()`, `await table.query().toArray()` without `limit()`
## Enterprise: never drop-then-reuse the same table name
LanceDB Enterprise/Cloud splits a **control plane** (DDL: create/drop/rename) from a **data plane** (query nodes that serve reads). Query nodes cache the resolved dataset for a table name for up to `table_cache_ttl` — **default 300 seconds (5 minutes)**. After you drop or overwrite a table, the control plane updates immediately but the data plane keeps serving the *old* dataset until that cache entry expires. During the window the two planes disagree.
The failure this causes: you `drop_table("t")` then immediately `create_table("t", ...)` (or `create_table("t", ..., mode="overwrite")`). The DDL returns success, but every query against `t` returns **`500 Internal Server Error`** (the query node resolves the stale/deleted dataset), and a fresh `describe` may still show the *old* schema/version. It looks like your write silently failed; it didn't — the name is cached.
**`mode="overwrite"` has the same problem** — it is a drop+create of the same name under the hood.
Rules for portable Enterprise ingestion:
1.**Never reuse a table name you just dropped/overwrote within the cache TTL.** Do not use `mode="overwrite"` to replace an existing Enterprise table in place.
2. To (re)load data, **write to a fresh table name** (e.g. `<table>_v2`, or a run-stamped suffix). A brand-new name has no cached data-plane entry, so writes and reads work immediately.
3. Before creating, `list_tables()` and **fail loudly if the name already exists** rather than overwriting — prompt for a new name.
4. To land on a specific final name that is currently occupied by an old table: drop the old table, **wait out the TTL (~5 min), then `rename_table(fresh_name, final_name)`**. Renaming onto a name whose old dataset is still cached hits the same race, so the wait is mandatory. `rename_table` is a supported control-plane op.
5. When you hand a table name back to a human, tell them which step still needs the propagation wait (usually: "the old `t` was dropped; run the rename in ~5 minutes").
This is Enterprise/Cloud-specific. Local/OSS tables have no separate data plane, so `mode="overwrite"` and immediate same-name reuse are fine there.
## Connecting to the LanceDB remote server
LanceDB Enterprise/Cloud deployments are served by a server implementing the lance-namespace OpenAPI spec (<https://github.com/lance-format/lance-namespace/blob/main/docs/src/spec.yaml>). Every remote (`db://...`) connection talks to such a server, and some operations exist only there. In particular, **all operations around jobs (listing, inspecting, creating, or canceling jobs) run server-side** — there is no local/OSS equivalent. Before any job work, or any direct REST call to an Enterprise deployment, read `references/remote_connect.md` to resolve the base URL, credentials, and database header and to validate the connection. Then use the four job REST methods documented in `references/remote_jobs.md` (list, describe, cancel, query_events).
## Script
Run the scanner when reviewing or modifying an existing codebase:
Manage branches on a LanceDB table: list what exists, create new ones, delete stale ones, and direct read/write operations at a specific branch without touching main. Use for branch lifecycle tasks, experimental/isolated table versions, targeting an operation at a non-main branch, or confirming a mutation did not affect main.
Works on local/OSS and remote Enterprise/Cloud tables, except merging a branch into main, which is Enterprise-only.
## The branch model (important)
Branches are isolated, writable lines of history forked from another branch (or a specific version). Writes on a branch never affect `main`.
There is **no global "switch branch" state** — you never repoint the whole table at a branch. Instead, **operations are scoped by which table handle you use**:
- The handle you got from `open_table(name)` / `openTable(name)` targets `main`.
-`branches.create(...)` and `branches.checkout(...)` return a **new table handle scoped to that branch**. Every read/write on that handle (add, update, `update_field_metadata`, `create_index`, search, …) lands on the branch.
- The original main handle is unaffected — keep it around to verify isolation.
`branches.list()` returns only non-main branches. Main always exists and is not listed.
## Python
`table.branches` is a property returning the branch manager; `table.current_branch()` tells you what a handle is scoped to (`None` = main).
```python
table=db.open_table("products")# scoped to main
# list — dict of name -> metadata (parent_branch, parent_version, ...); {} = only main
table.branches.list()
# create: forks from main by default and returns a handle scoped to the new branch
exp=table.branches.create("experiment-reindex")
exp=table.branches.create("exp2",from_ref="main",from_version=None)# optional fork point
# checkout an existing branch -> branch-scoped handle
wip=table.branches.checkout("wip-branch")
# with version= it pins to that version (read-only detached view); omit to track latest, writable
# operate on the branch simply by using its handle
assertb"lancedb:description"notin(table.schema.field("category").metadataor{})# main untouched
```
Two handles on the same branch see each other's writes (e.g. `table.branches.create("exp")` and `db.open_table(name, branch="exp")`); main stays isolated.
## Merging a branch into main (Enterprise only)
Merge is available through the SDKs (`table.branches.merge(...)`) on **Enterprise tables only** — it is not supported on Cloud or local/OSS tables, which raise `NotSupported`.
`merge` takes the branch to merge **from** and a `dry_run` flag. Both the SDK method and the underlying REST endpoint **actually merge by default** (`dry_run=False`); pass `dry_run=True` to only preview. A rejected merge is **not an exception** — it returns a result with `status="rejected"` rather than raising, so inspect the return value. Use `branches.diff(from_branch)` to inspect a branch's pending diff without attempting a merge.
```python
exp="experiment-reindex"
# preview only — returns status="ready" if it would merge cleanly
preview=table.branches.merge(exp,dry_run=True)
# actually merge (default)
result=table.branches.merge(exp)
ifresult["status"]=="merged":
print("landed at",result["mainVersionAfter"])
elifresult["status"]=="rejected":
print(result["diff"]["mergeBlockers"])# why it was refused
The result is the wire JSON, containing `status` (`ready` on a passing dry run, `merged` on success, `rejected` when refused — also `notImplemented`/`unknown`), the branch `diff` (including `mergeBlockers` explaining any rejection), a `preview` of the columns that would be promoted, and — after a real merge — `mainVersionAfter`.
### Merge preconditions
Merge only **promotes newly added columns** onto main; it does not replay arbitrary commits. Practically, a branch is mergeable only if it has **exactly one commit since it was created, and that commit added a column**. The merge is rejected (`status: "rejected"`, with `mergeBlockers` set) if:
- the branch was forked from another branch rather than directly from main
- main has advanced since the branch was forked
- the branch's rows changed since the fork (row counts must match main exactly)
- the branch removed columns or changed a column's type/nullability
- the branch added no columns (index-only changes are not merged)
### Adding a column in a single commit
Because the branch must contain just one column-adding commit, add the column with its values in one operation rather than add-then-backfill:
1.**SQL transformation** — `add_columns` with a SQL expression computed from existing columns, so the column lands populated in one commit.
2.**Precompute the values** — compute the column's values externally, then add the fully-populated column in a single operation (e.g. via `merge_insert`/`add_columns` with the data ready).
3.**Lance-format-level data evolution (pylance)** — use Lance's data evolution with backfill, documented at <https://lance.org/guide/data_evolution/#with-data-backfill>.
| Create from a fork point | `table.branches.create(name, from_ref=..., from_version=...)` | `await branches.create(name, fromRef, fromVersion)` |
| Get a branch handle | `table.branches.checkout(name)` or `db.open_table(t, branch=name)` | `await branches.checkout(name)` or `await db.openTable(t, { branch: name })` |
| Pin to a branch version (read-only) | `table.branches.checkout(name, version=v)` | `await branches.checkout(name, v)` |
Write column-level descriptions, tags, and logical groupings onto a LanceDB table's schema. Use this when the user wants to document, annotate, tag, or classify what their table columns ARE (embeddings vs labels vs eval metrics, model provenance, version families, etc.).
Works on local/OSS and remote Enterprise/Cloud tables alike — read the schema through the table handle, write through `update_field_metadata` (Python) / `updateFieldMetadata` (TypeScript).
## Metadata key conventions
All metadata uses namespaced keys:
| Key | Purpose | Example value |
|-----|---------|---------------|
| `lancedb:description` | Human-readable explanation of what the column contains | `"CLIP ViT-L/14 image embedding, L2-normalized (768-dim)"` |
| `lancedb:tag:<name>` | Flexible key-value tag; the suffix names the tag category | `lancedb:tag:field_type: "embedding"`, `lancedb:tag:model: "clip"`, `lancedb:tag:project_id: "foo"` |
| `lancedb:logical-column` | Logical group/family this column belongs to | `"clip_features"` |
Tags are open-ended — use whatever key suffix and value make sense given the user's intent. The tag suffix should describe *what is being classified* (e.g., `field_type`, `model`, `project_id`) and the value describes *how*. Multiple tags on the same column are fine — each is a separate key. All values are strings.
## Step 1: Read the schema and existing metadata
Read existing metadata before writing, to avoid redundant updates.
Python — `table.schema` (sync property; async: `await table.schema()`) returns a `pyarrow.Schema`. **Arrow field metadata is bytes-keyed in Python**:
```python
schema = table.schema
for field in schema:
meta = field.metadata or {} # dict[bytes, bytes], e.g. {b"lancedb:description": b"..."}
Quick method reference for Python LanceDB code. Cross-check source for non-trivial claims.
## Connect
If you're connecting to a remote database, use this:
```python
import lancedb
db = lancedb.connect("db://my-db", api_key=api_key, host_override=host_override) # remote
```
(values may be found in LANCEDB_API_KEY and LANCEDB_HOST_OVERRIDE, either in env vars or a .env file)
If you're connecting to a local table using OSS LanceDB, use this:
```python
db = lancedb.connect("./camelot-db") # local/OSS
```
If you're not sure which, or if you can't find the api_key or host_override params, ask the user.
**Place the local database directory next to the script/entrypoint that opens it** (i.e. resolve the path relative to the script, `Path(__file__).parent / "camelot-db"`), not buried under a shared `data/` folder. The Lance dataset is the database, not a data file — keeping it beside its code makes ownership obvious and paths stable regardless of the working directory the script is launched from.
**Do not name the directory `lancedb`** (e.g. `./lancedb`, `./data/lancedb`). It collides with the imported `lancedb` package name, which is confusing to read and easy to shadow in scripts. Give it a name derived from the repo or dataset with a clear prefix/suffix — for example `./<dataset>-db`, `./<repo>_lancedb`, or `./vectordb`.
| Full scan with filters/projection (sync) | `table.search().where(...).select(...).limit(...)` |
| Full scan with filters/projection (async) | `table.query().where(...).select(...).limit(...)` |
| Filter | `.where("col > 10")` |
| Projection | `.select(["id", "text"])` |
| Bound result count | `.limit(20)` |
| Collect bounded result as Python objects (default, no extra deps) | `.to_list()` on query/search result |
| Collect bounded result as Arrow (default, `pyarrow` always available) | `.to_arrow()` on query/search result |
| Collect bounded result as pandas (only if project uses pandas) | `.to_pandas()` on query/search result |
| Collect bounded result as Polars (only if project uses polars) | `.to_polars()` on query/search result |
## Sync vs Async Scan API
The plain-scan entry point differs between the sync and async clients. **Verified against `lancedb` 0.34.0** — re-check if the pinned version changes:
- **Sync** (`lancedb.connect(...)`): the table has **no `.query()` method**. Use `.search()` with no argument for a plain scan; it returns a query builder that supports `.where()`, `.select()`, `.limit()`, and the `.to_list()` / `.to_arrow()` / `.to_pandas()` / `.to_polars()` collectors.
Merges by default; a `None` value deletes that key; `"replace": True` swaps the whole map. Nested fields use dot-paths (`"a.b.c"`). `replace_field_metadata` is deprecated. See `references/column_metadata.md` for key conventions (`lancedb:description`, `lancedb:tag:<name>`, `lancedb:logical-column`) and the authoring workflow.
## Branches
```python
table.branches.list() # non-main branches; {} = only main
exp = table.branches.create("exp") # fork off main -> handle scoped to the branch
wip = db.open_table("t", branch="wip") # or open scoped directly
table.branches.delete("stale") # removes only the branch pointer
table.current_branch() # None = main
```
There is no global switch — scoping is per table handle: any read/write on a branch handle lands on that branch; the original handle keeps targeting main. See `references/branch_ops.md` for the model and isolation checks.
## Maintenance
```python
table.optimize()
```
Call this after every successful local/OSS ingestion. It handles compaction, cleanup of old versions according to retention, and index optimization. Do not add this for LanceDB Enterprise/Cloud remote tables; Enterprise handles compaction and cleanup automatically from cluster configuration.
Use these patterns when writing Python code with `lancedb`.
## Before Writing Code
Choose the output type from what the project actually depends on. **Do not assume `pandas` or `polars` is installed** — they are heavy dependencies that many LanceDB projects do not use. `pyarrow`, by contrast, ships as a LanceDB dependency and is always available, so it is a safe default to lean on.
Default output (after applying `select()` and `limit()`):
- **Python objects**: `.to_list()` — a list of dicts, no extra dependencies. Prefer this for scripts, examples, and agent-generated code unless there is a reason to do otherwise.
- **PyArrow**: `.to_arrow()` — a `pyarrow.Table`, when the surrounding code is Arrow-native or you need columnar/zero-copy handoff.
Only reach for a DataFrame when the project *already* declares that dependency:
- Pandas projects (pandas in `pyproject.toml`/requirements): `.to_pandas()`.
Use PyArrow schemas instead when the pipeline is already Arrow-native, needs
record-batch streaming, or has runtime schema requirements that would make a
Pydantic model harder to understand. Declare Pydantic as a direct project
dependency when application code imports it, even if LanceDB also depends on it.
## Recommended Patterns
### Bounded search or query
Use this for application reads, examples, notebooks, and agent-generated scripts:
```python
results = (
table.search(query_vector)
.where("status = 'ready'")
.select(["id", "text"])
.limit(20)
.to_list() # or .to_arrow(); .to_pandas()/.to_polars() only if the project uses them
)
```
Why: `search()` works across local and remote tables and on both the sync and async clients. `select()` avoids fetching unused columns. `limit()` prevents accidental full-table reads. `.to_list()` and `.to_arrow()` avoid assuming pandas/polars is installed (see "Before Writing Code").
For a **plain scan** (no query vector), the entry point differs by client:
```python
# Sync client: no .query() method — use .search() with no argument.
`table.query()` on a sync table raises `AttributeError` (verified on `lancedb` 0.34.0). See the "Sync vs Async Scan API" section in `api_reference.md`.
### Bounded query result conversion
It is fine to collect bounded query/search results:
df = table.search(query_vector).limit(10).to_pandas() # only if pandas is a project dep
```
### Local-only Lance dataset API
`table.to_lance()` does not itself materialize the full dataset. It returns the underlying `lance.LanceDataset`, making the table accessible through the PyLance dataset API. Use it when the task is explicitly local/OSS and needs Lance dataset methods not exposed by LanceDB:
```python
# Local/OSS only: RemoteTable does not expose table.to_lance().
ds = table.to_lance()
for batch in ds.to_batches(columns=["id", "text"], batch_size=10_000):
process(batch)
```
### Async Python
Keep the same shape and bound the result before collecting:
```python
results = await (
async_table.query()
.where("status = 'ready'")
.select(["id", "text"])
.limit(20)
.to_list() # or .to_arrow()
)
```
## Anti-Patterns
**Avoid the following anti-patterns in your code.**
### Table-level full materialization
Avoid whole-table collectors in portable or large-table code:
```python
df = table.to_pandas()
arrow_table = table.to_arrow()
polars_df = table.to_polars()
```
Why: local tables expose these whole-table collectors, but remote tables intentionally do not — a remote production table can be far larger than a local development table, so it is easy to accidentally pull the entire table into memory.
`table.to_lance()` is different: it is not a full materialization call, but it is still local/OSS-only and should not appear in code meant to run against remote Enterprise tables.
### Unbounded result collection
Avoid query/search collection without a meaningful limit:
Prefer `select(...).limit(...)` before collecting; for large reads, stream in batches instead.
### Per-row writes
Avoid loops that write one row per call:
```python
for row in rows:
table.add([row]) # one commit + fragment per row
```
Each `add()` creates a new version and fragment. Pass the whole batch in a single call, or chunk very large inputs:
```python
table.add(rows) # single commit
# for very large inputs, add batches of several thousand rows
```
After the final successful write to an embedded OSS table, call
`table.optimize()`. Skip this for Enterprise/Cloud tables because their
maintenance is automatic.
### Drop-then-reuse the same table name (Enterprise/Cloud)
Avoid dropping or overwriting a remote table and then reusing that name right away:
```python
db.drop_table("my_table")
table = db.create_table("my_table", data=rows) # reads 500 for ~5 min
table = db.create_table("my_table", data=rows, mode="overwrite") # same problem
```
Why: Enterprise/Cloud splits DDL (control plane) from query serving (data plane). The data plane caches the dataset behind a table name for up to `table_cache_ttl` (default 300s / 5 min), so after a drop/overwrite the DDL succeeds but queries against the reused name return `500 Internal Server Error` until the cache expires — and a fresh `describe` may still show the old schema. Instead, write to a **fresh name**, use `list_tables()` and fail if it already exists, then `rename_table(fresh, final)` onto the final name only after the old table's drop has propagated (~5 min). See the "Enterprise: never drop-then-reuse the same table name" section in `SKILL.md`. Local/OSS tables have no separate data plane — overwrite freely there.
### Guessing performance fixes
Avoid changing `nprobes`, `refine_factor`, or index types before checking the query plan and index stats. Diagnose first, then tune one knob at a time.
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