## 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>
## 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
The `build - aarch64-pc-windows-msvc` node build job (and, marginally,
the x86_64 one) had started hitting `rustc-LLVM ERROR: out of memory`
while linking the `lancedb-nodejs` cdylib — most recently surfaced by
#3526, which adds the goosefs backend (and its tonic/prost gRPC subtree)
to the default node binary.
The peak-memory step is the fat-LTO codegen (`lto=fat`,
`codegen-units=1` from `.cargo/config.toml`), which merges the whole
crate graph into a single LLVM module and runs single-threaded. It
therefore neither parallelizes across cores nor fits in the 16 GB of the
standard `windows-latest` runner as the dependency graph grows.
This PR:
- Moves both `*-pc-windows-msvc` node build jobs to
`windows-2025-8x-x64` (more memory + cores).
- Overrides the release profile to ThinLTO for just these jobs, via
`CARGO_PROFILE_RELEASE_LTO=thin` /
`CARGO_PROFILE_RELEASE_CODEGEN_UNITS=16` in `pre_build`. ThinLTO
parallelizes the cross-module optimization across the runner's cores and
keeps peak memory well under the limit. Scoped so Python wheels and Rust
release builds keep fat LTO.
The larger runner alone would clear the OOM but waste the added cores on
the single-threaded fat-LTO tail; ThinLTO is what makes the extra cores
actually reduce wall-clock and gives durable memory headroom for future
dependency growth.
Tradeoff: ThinLTO can leave a small runtime-perf gap vs fat LTO for the
node native binary, but it recovers most of it and is a common release
configuration.
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Bridges Lance's internal `metrics`-crate instrumentation (object store
request counts, bytes, latency, errors, and throttles) into
OpenTelemetry, in both the Python and Node bindings, with a shared
adapter in the Rust core. This is the LanceDB counterpart to
lance-format/lance#7537.
## Rust core (`rust/lancedb`)
Two new, **off-by-default** features:
- `metrics` — re-exports the [`metrics`](https://docs.rs/metrics) crate
as `lancedb::metrics` and turns on Lance's object-store instrumentation.
Install any `metrics`-compatible recorder to collect them.
- `metrics-otel` — adds `lancedb::metrics_otel`, a pull-based adapter
that installs a process-global recorder aggregating into lock-free
cumulative storage and exposes a snapshot/catalog API
(`register_metrics_recorder`, `metrics_catalog`, `snapshot_metrics`,
`MetricPoint`/`MetricValue`/`MetricKind`/`MetricDescription`). Both
bindings build on this.
## Python
`lancedb.otel.instrument_lancedb_metrics()` registers each metric as an
OpenTelemetry observable instrument on the given (or global)
`MeterProvider`. Available via the `otel` extra (`pip install
lancedb[otel]`), which pulls in only `opentelemetry-api` — the
application supplies and configures the SDK.
## Node
`instrumentLanceDbMetrics()` provides the equivalent wiring against
`@opentelemetry/api`. This is the only public entry point; the
underlying recorder/catalog/snapshot functions stay internal.
Because OpenTelemetry has no asynchronous histogram instrument,
histograms are exported Prometheus-style as `<name>_bucket` (with an
`le` attribute), `<name>_count`, and `<name>_sum`. Only `_sum` carries
the histogram's unit; `_bucket` and `_count` observe cumulative counts
and are unitless. The adapter is enabled by default in the Python and
Node builds, and off by default in the Rust crate.
## Notes
- Requires Lance ≥ `v9.0.0-beta.19`, which ships the object-store
metrics APIs (upstream lance-format/lance#7537, now merged). `main` is
already on beta.19, so this is a single feature commit with no
dependency bump.
- Tests: 8 Rust unit tests, 3 Python tests, 2 Node tests, all covering
the end-to-end object-store-metrics → OpenTelemetry path.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Bumps Lance to v9.0.0-beta.19, which includes lance-format/lance#7687
for side-effect-free DirectoryNamespace read paths.
This fixes root-level read-only table opens that previously could
trigger `__manifest` creation through directory namespace construction,
including Hugging Face bucket reads with read-only tokens. A LanceDB
regression test now covers root listing operations without creating
`__manifest`.
Fixes#3633.
### Summary
`flatten_columns` raises `ValueError` when called with `flatten=False`,
even though `False` should mean "do not flatten". This is reachable from
the public API — `Query.to_pandas(flatten=...)` and
`to_batches(flatten=...)` type their `flatten` param as
`Optional[Union[int, bool]]` and pass it straight to `flatten_columns`.
### Cause
`bool` is a subclass of `int`, so `isinstance(False, int)` is `True`.
`flatten=False` skips the `flatten is True` check, falls into the
integer branch, and `False <= 0` evaluates to `True`, raising:
```
ValueError: Please specify a positive integer for flatten or the boolean value `True`
```
### Reproduction
```python
import lancedb
db = lancedb.connect("/tmp/db")
t = db.create_table("t", data=[{"id": 1, "vector": [0.1, 0.2]}])
t.search([0.1, 0.2]).to_pandas(flatten=False) # -> ValueError
```
### Fix
Guard the integer branch with `not isinstance(flatten, bool)` so that
`flatten=False` (and `None`) mean "do not flatten". Behavior is
otherwise unchanged:
- `flatten=True` → flatten all nested levels
- positive `int` → flatten to that depth
- non-positive `int` (e.g. `0`) → still rejected with `ValueError`
Added a regression test in `tests/test_util.py` covering `None`,
`False`, `True`, a positive depth, and `0`.
This PR fixes a serialization error when using Ollama embeddings in
`create_table`.
The use of `@cached_property` for the Ollama client was causing issues
during serialization/pickling, which is required by certain LanceDB
operations (like when using multiprocessing or certain storage
backends). Switching to a standard `@property` ensures the client is
instantiated when needed without being stored in a way that breaks
serialization.
Verified with the following script:
```python
import lancedb
from lancedb.embeddings import get_registry
import pickle
registry = get_registry().get(\"ollama\")
model = registry(name=\"llama3\")
# This would fail before the fix
pickled = pickle.dumps(model)
unpickled = pickle.loads(pickled)
```
Fixes#2629 (or similar serialization issues reported).
---------
Co-authored-by: Unmilan Mukherjee <Missing-Identity@users.noreply.github.com>
## Summary
Closes#3525
This PR wires up two new optional object-store backends at the LanceDB
layer, exposing capabilities that already exist upstream in `lance` /
`lance-io`:
| Backend | Cargo feature | Default in Rust crate | Default in Python
wheel | Default in Node binding |
| --- | --- | --- | --- | --- |
| **Tencent COS** | `cos` | ❌ off | ✅ on | ❌ off |
| **GooseFS** | `goosefs` | ❌ off | ✅ on | ✅ on |
Both backends are additive and do not affect existing users who don't
opt in.
## Motivation
- **Tencent COS** is the dominant object storage in the China region.
Tencent Cloud users currently need an S3-compatible proxy or a private
fork to use LanceDB against COS buckets.
- **GooseFS** is Tencent Cloud's distributed cache acceleration layer
that sits in front of COS/S3, a common pattern for vector search / AI
training where the same hot dataset is read repeatedly.
- This brings COS / GooseFS to feature parity with the existing
first-class backends (`aws`, `gcs`, `azure`, `oss`, `huggingface`).
See the linked issue #3525 for the full discussion.
## Changes
### `rust/lancedb/Cargo.toml`
Add two new optional features that pull through the corresponding
upstream feature flags:
```toml
cos = ["lance/tencent", "lance-io/tencent"]
goosefs = [
"lance/goosefs",
"lance-io/goosefs",
"lance-namespace-impls/dir-goosefs",
]
```
### `python/Cargo.toml`
Enable both `cos` and `goosefs` by default for the Python wheels, so
`pip install lancedb` works against COS / GooseFS out of the box
(consistent with how `aws` / `gcs` / `azure` / `oss` are bundled today):
```diff
-default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface"]
+default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface", "lancedb/cos", "lancedb/goosefs"]
```
### `nodejs/Cargo.toml`
Enable `goosefs` by default for the Node binding (COS kept opt-in to
limit the default native binary size; can be revisited based on demand):
```diff
-default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface"]
+default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface", "lancedb/goosefs"]
```
### `Cargo.lock`
Regenerated to reflect the transitive dependencies brought in by the new
upstream features. No manual edits.
## Example Usage
### Rust
```toml
# Cargo.toml
lancedb = { version = "0.30", features = ["cos", "goosefs"] }
```
```rust
// Tencent COS
let db = lancedb::connect("cos://my-bucket/my-db").execute().await?;
// GooseFS
let db = lancedb::connect("goosefs://my-namespace/my-db").execute().await?;
```
### Python
```python
import lancedb
db = lancedb.connect(
"cos://my-bucket/my-db",
storage_options={
"secret_id": "...",
"secret_key": "...",
"region": "ap-guangzhou",
},
)
```
## Backwards Compatibility
- All new features are **opt-in** at the Rust crate level (`default =
[]` for `lancedb` itself is unchanged).
- The Python wheel gains both backends by default, increasing wheel size
slightly but matching the existing pattern of bundling all major cloud
backends.
- Node binding only adds `goosefs` to defaults; existing users see no
behavior change.
## Testing
- `cargo check --all-features` ✅
- `cargo check -p lancedb --features cos` ✅
- `cargo check -p lancedb --features goosefs` ✅
- End-to-end COS / GooseFS smoke tests require Tencent Cloud credentials
and are intentionally not added to CI in this PR (same approach used for
`s3-test`). Happy to add a gated test feature in a follow-up if
reviewers prefer.
## Checklist
- [x] Added `cos` and `goosefs` features to `rust/lancedb/Cargo.toml`
- [x] Updated `python/Cargo.toml` default features
- [x] Updated `nodejs/Cargo.toml` default features
- [x] Regenerated `Cargo.lock`
- [x] Verified build with `--all-features`
- [ ] Documentation update (can be done in a follow-up PR once API
stabilizes)
## Related
- Issue: #3525
- Upstream support:
[`lance/tencent`](https://github.com/lance-format/lance),
[`lance/goosefs`](https://github.com/lance-format/lance)
## Summary
Adds `Table::get_lsm_write_spec` returning `Option<LsmWriteSpec>` — the
read counterpart to the existing `set_lsm_write_spec` /
`unset_lsm_write_spec`. Returns `None` when the MemWAL LSM write path is
not enabled; otherwise reconstructs the spec (mode, shard column,
`num_buckets`, `maintained_indexes`, `writer_config_defaults`) exactly
as installed.
## Changes
- **Rust core (`NativeTable`)** — reconstructs the spec from
`mem_wal_index_details()`, resolving the shard column from its Lance
field id via the dataset schema. This is a raw metadata read, so it is
unaffected by `describe_indices` system-index filtering.
- **Remote (`RemoteTable`)** — reads the `__lance_mem_wal` system index
through `index/list` with `include_system: true` (so the curated
`list_indices` surface stays unchanged), then parses the index `details`
JSON. It matches the index by name and ignores `index_type`, so no
client `IndexType` variant is needed. It uses the **server-resolved
`column` name** from the details (Lance field ids do not travel to the
remote client).
- **Python + TypeScript bindings** — sync and async, mirroring
`set`/`unset`, with round-trip tests (bucket / identity / unsharded,
plus `None` when unset).
## Tests
- Rust: native round-trip unit test + remote mock-endpoint tests
(present + absent). All green (`cargo test --features remote -p
lancedb`).
- Python/TS: round-trip tests added; binding-runtime execution runs in
CI.
## Dependencies for the remote path
The remote path is complete on the client side but depends on two
out-of-repo pieces to work end-to-end:
1. **lance** — emit the server-resolved shard **`column`** name in the
MemWAL index `details` JSON (field ids can't reach the client). See
lance-format/lance#7667.
2. **server** — honor `include_system` on `index/list` so the
`__lance_mem_wal` entry is returned for this read.
Against an older server (no `include_system`), the remote getter
degrades gracefully to `Ok(None)` rather than erroring.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Updates LanceDB's Lance dependencies to v9.0.0-beta.18.\n\nThis
refreshes the Rust workspace lockfile and Java lance-core version using
the repository update script. Triggering Lance tag:
https://github.com/lancedb/lance/releases/tag/v9.0.0-beta.18
Closes#3245.
> **BREAKING CHANGE:** `with_format("torch")` no longer returns a list
of stacked row tensors. It now returns per-row dicts so PyTorch's
default `DataLoader` collate stacks them into `{col: tensor(B,)}`.
Switch to `with_format("torch_row")` to keep the old shape.
### What changed
`"torch"` now returns a list of per-row dicts (`[{col: tensor}, ...]`)
at every indexed access path. The default `DataLoader` collate stacks
them into a column-keyed batched dict, no custom `collate_fn` needed.
The old shape is preserved under a new `"torch_row"` literal.
`"torch_col"` is unchanged.
The unbatching lives inside the transform (`batch_to_tensor_dict`), not
`__getitems__`, so the shape survives pickling and works under
`DataLoader(num_workers>0, multiprocessing_context="spawn")`.
### Format comparison
| Format | `iter(batch_size=N)` | `__getitems__([0,1,2])` | `DataLoader`
default collate |
|---|---|---|---|
| `"torch"` (new) | `list[{col: tensor}]` length N | `list[{col:
tensor}]` length 3 | `{col: tensor(B,)}` |
| `"torch_row"` (old `"torch"` behavior) | `list[tensor(n_cols,)]`
length N | `list[tensor(n_cols,)]` length 3 | `tensor(B, n_cols)` |
| `"torch_col"` (unchanged) | `tensor(n_cols, N)` | `tensor(n_cols, 3)`
| needs `collate_fn=lambda x: x` |
Output matches HuggingFace `Dataset.set_format("torch")` on container
shape, keys, and values at every access path. The only divergence:
HuggingFace downcasts `float64` to `torch.float32` by default, LanceDB
preserves dtype. Verified by `scripts/verify_torch_format.py`.
### Migration
```python
# Old default — column names lost, shape was tensor(B, n_cols)
DataLoader(Permutation.identity(table).with_format("torch"))
# New default — column names preserved
DataLoader(Permutation.identity(table).with_format("torch")) # {col: tensor(B,)}
# Keep old behavior
DataLoader(Permutation.identity(table).with_format("torch_row")) # tensor(B, n_cols)
```
Fixes#3296
## Problem
The repository has no `CODEOWNERS` file, so there is no enforced review
routing for sensitive areas such as release workflows, auth code, and
FFI boundaries. This means changes to critical paths can be merged
without an explicit codeowner review.
## Solution
Add `.github/CODEOWNERS` covering:
- `/.github/workflows/` — release/publish workflows (supply chain risk)
- `/rust/lancedb/src/remote/` — remote client & auth code
- `/python/src/` and `/nodejs/src/` — FFI language boundaries
The listed owners (`@jackye1995`, `@wjones127`, `@Xuanwo`, `@AyushExel`)
are based on recent merge activity. Feel free to adjust to match the
actual team structure or replace with GitHub team handles if preferred.
## Testing
No code change — only adds a metadata file. GitHub will start routing
review requests automatically once this is merged and branch protection
is configured to require codeowner approval.
Co-authored-by: octo-patch <octo-patch@github.com>
### **Summary**
Closes#3212
Extends the Python `lit()` helper to natively support three additional
types (`date`, `datetime`, and `Decimal`) and implements reflexive
operators for the `Expr` class.
This implementation specifically addresses the blocking feedback
regarding precision loss, CI discovery, and query engine limitations:
* **Logic Refactoring**: Simplified `lit()` by combining `date` and
`datetime` normalization into ISO-8601 strings, ensuring stable SQL
parsing across different engine locales.
* **Precision Preservation**: `decimal.Decimal` objects are now passed
as high-precision strings to the Rust bridge, bypassing intermediate
float conversions and preserving full 128-bit decimal precision for
DataFusion.
* **Averted CI Failures**: Temporarily deferred `bytes` literal support
to a future PR to resolve a known DataFusion `expr_to_sql` limitation
that was crashing the `Doctest` runner.
* **Reflexive Operators**: Added support for "literal-first" arithmetic
and logical operations (e.g., `10 + col('a')` or `True &
col('active')`). Redundant reflexive comparisons (e.g., `__rlt__`) were
pruned as Python's data model handles them automatically.
* **Integration Verification**: Added dedicated integration tests in the
official test directory to ensure the query engine correctly handles the
new types and preserves bit-perfect fidelity.
### **Changes**
####
[python/python/lancedb/expr.py](file:///c:/Users/Laksh/Documents/lancedb/python/python/lancedb/expr.py)
* Updated `lit()` to handle `date`, `datetime`, and `Decimal` natively.
* Implemented reflexive operators (`__radd__`, `__rand__`, `__rmul__`,
etc.) to support literals on the left-hand side.
* Removed the problematic `bytes` doctest example and `lit()` type
support to unblock CI.
####
[python/src/expr.rs](file:///c:/Users/Laksh/Documents/lancedb/python/src/expr.rs)
* Modified the Rust FFI bridge to extract `Decimal` objects as strings.
* Ensured the `expr_lit` handler is ready to receive normalized temporal
strings.
* Consolidated imports and added missing operator documentation.
####
[python/python/lancedb/_lancedb.pyi](file:///c:/Users/Laksh/Documents/lancedb/python/python/lancedb/_lancedb.pyi)
* Updated type stubs for `expr_lit` to include `Any` (allowing for
`Decimal`).
### **Testing**
Added several new advanced test cases in
[python/python/tests/test_expr.py](file:///c:/Users/Laksh/Documents/lancedb/python/python/tests/test_expr.py)
covering:
* **High-precision Decimal preservation**: Verified against 128-bit
boundaries with a "one point off" test case (`1.234567890123456789 <
1.234567890123456790`).
* **Reflexive operator positioning**: Verified successful query
construction with literals on the left.
* **Timezone-aware normalization**: Confirmed stable behavior for
`datetime` objects.
* **Integration Testing**: Confirmed Date32 and Decimal columns return
the correct Python types and values from the engine during `.to_arrow()`
calls.
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
# Elastic Streaming Dataloader
## Motivation
Training large models on LanceDB tables today requires loading the
entire dataset
into memory or writing bespoke batching logic. This PR introduces
`StreamingDataset`, a PyTorch `IterableDataset` that streams directly
from a
LanceDB table with two hard guarantees that are difficult to achieve
together:
**elastic determinism** and **resumability**.
## Goals
### Elastic determinism
The dataset partitions the table into a fixed number of *splits*
(controlled by
`num_splits`, `shuffle_seed`, and `epoch`). Samples are yielded by
round-robining
over splits one sample per split per cycle. Because the split structure
is fixed,
the set of samples that makes up each global training step is identical
regardless
of `world_size` or `num_workers`. You can scale your cluster up or down
between
runs and the model sees the same data in the same order — no
re-sharding, no
gradient variance from topology changes.
### Resumability
`state_dict()` / `load_state_dict()` capture how many samples each split
has
consumed. Because all splits are the same size and the round-robin
design keeps
them in lockstep, the state reduces to a single scalar
(`samples_consumed_per_split`)
that is topology-independent. A checkpoint saved with 8 GPUs can resume
correctly
on 4 GPUs or 16 GPUs without any adjustment.
### PyTorch `IterableDataset` / streaming
`StreamingDataset` implements the standard PyTorch `IterableDataset`
interface, so
it drops into any existing `DataLoader` pipeline without modification.
Data is
fetched lazily from Lance in chunks — only the rows needed for the
current batch are
ever in memory.
Compared to the map dataset this takes more work from pytorch and puts
it into the dataset itself (e.g. shuffling, filtering, etc.). We do this
because we cannot achieve things like elastic determinism or
prefiltering otherwise.
### Multi-worker support
DataLoader workers are automatically assigned contiguous sub-blocks of
splits (the
rank's splits are divided evenly across workers). Each worker is
independent:
no shared state, no inter-process coordination. The only constraint is
that
`num_splits` must be divisible by `world_size * num_workers`.
That being said, multi-worker is highly discouraged as it relies on
multiprocessing which is inefficient. Still, we want to support it.
### Filters as prefilters
Filters are applied at *permutation-build time* via
`PermutationBuilder.filter()`,
not re-evaluated on every fetch. The filtered row IDs are stored in the
permutation
table so that subsequent reads see only the matching rows. This allows
us to avoid loading rows that don't match the filter (which is the
default pytorch behavior)
### Prefetching
Two parameters control the I/O pipeline:
- `read_batch_size` (default 64) — number of rows fetched per
`take_offsets` call.
Larger values amortise per-request overhead, which is critical on object
storage
where a single round-trip can cost ~100 ms.
- `prefetch_batches` (default 4) — number of batches prefetched in
parallel per
split via a `ThreadPoolExecutor`. While the model processes the current
batch,
the next several batches are already in flight, hiding storage latency
behind
compute.
If set correctly then you can get good performance even with
num_workers=0 (unless you are bottlenecked on transform).
### Transform parallelism
The underlying `Permutation` API supports a `with_transform()` callback
for
decoding, augmentation, and format conversion. Unfortunately, this is
not parallelized. Pytorch typically parallelizes this with num_workers
which is multiprocessing which is highly inefficient. For simple
transforms we should be able to utilize multithreading and Rust based
UDFs. For complex python UDFs we could have a dedicated multiprocessing
pipeline for just the transform. Or we could just utilize
multithreading. In both cases we would exclude the I/O stage from the
multiprocessing because that ends up being very memory hungry and
inefficient.
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
quick-xml < 0.41.0 has two DoS advisories (quadratic attribute-name
check and unbounded namespace allocation in NsReader). All three
versions in our lockfile (0.26.0, 0.38.4, 0.39.4) are below the patched
threshold.
These are pulled in transitively by inferno (dev-only flame-graph dep),
lance-namespace-impls (git dep from lance), and opendal/reqsign (cloud
storage XML parsing). None of these paths expose attacker- controlled
XML; clearing them requires upstream to upgrade to quick-xml >= 0.41.0.
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
## Summary
- When an embedding function returns an empty list (e.g. `[]`) for an
input row — as can happen when a model produces no output for a blank
string — `_append_vector_columns` crashed with `ArrowInvalid: Length of
item not correct: expected N but got array of size 0` because PyArrow
cannot fit a zero-length value into a fixed-size list element.
- The fix adds a validation step in `gen()`, inside
`_append_vector_columns`, that replaces any vector whose length does not
match the expected `ndims` (including empty lists and `None`) with
`None` before `pa.array()` is called.
- `None` is a valid null in a PyArrow fixed-size list array, so the bad
entry flows into `_handle_bad_vectors` and is handled according to the
caller-supplied `on_bad_vectors` policy (`error` / `drop` / `fill` /
`null`) instead of causing an unconditional crash.
## Test plan
- [ ] Added `test_embedding_with_empty_output_vectors` in
`python/python/tests/test_embeddings.py` that uses an embedding function
returning `[]` for empty-string inputs, calls `table.add(...,
on_bad_vectors="drop")`, and asserts no crash and that bad rows are
correctly dropped.
- [ ] Existing `test_embedding_with_bad_results` continues to pass (NaN
vectors still handled correctly).
- [ ] Verified manually that `pa.array([[1.,2.,3.,4.], []],
type=pa.list_(pa.float32(), 4))` raises `ArrowInvalid` without the fix,
and succeeds with `None` in place of `[]`.
Fixes#1672
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
## What
`MRRReranker.rerank_multivector` averages each document's reciprocal
ranks over the wrong denominator. It divides by the number of rankings
the document *happens to appear in*, instead of the total number of
rankings being fused.
```python
# python/python/lancedb/rerankers/mrr.py
for result_id, reciprocal_ranks in mrr_score_map.items():
mean_rr = np.mean(reciprocal_ranks) # divides by len(present systems)
```
`mrr_score_map[doc]` only accumulates a reciprocal rank for the systems
in which the document was returned, so `np.mean` never accounts for the
systems that missed it.
## Why it's wrong
Mean Reciprocal Rank fusion treats a system that didn't return a
document as a reciprocal rank of `0` and averages across **all**
systems. That's the exact mechanism by which it rewards cross-system
consensus. Dividing by the appearance count removes that, so a document
liked by a single ranking can beat one ranked highly by every ranking.
Concretely, fusing 3 vector rankings:
| Doc | Ranks | Current score | Correct score |
|-----|-------|---------------|---------------|
| A | #1 in 1 system only | `mean([1.0]) = 1.000` | `1.0 / 3 = 0.333` |
| B | #1, #1, #2 across all 3 | `mean([1, 1, .5]) = 0.833` | `2.5 / 3 =
0.833` |
The current code ranks **A above B** - a document two of three rankings
ignored outranks one all three ranked at or near the top.
This also makes `rerank_multivector` inconsistent with `rerank_hybrid`
in the same file, which already treats a missing system as `0`
(`vector_rr = 0.0` / `fts_rr = 0.0`), and with the class docstring
("average of reciprocal ranks across different search results").
## Fix
Divide the summed reciprocal ranks by the total number of rankings:
```python
num_systems = len(vector_results)
...
mean_rr = float(np.sum(reciprocal_ranks)) / num_systems
```
## Tests
Adds `test_mrr_multivector_rewards_consensus`, which asserts the exact
MRR scores and that the consensus document ranks first. It fails on
`main` and passes with this change. Existing reranker tests are
unaffected.
BREAKING CHANGE: When passing multiple where clauses to a query, they
now stack instead of replacing the previous filter.
Previously, calling `where`/`only_if` more than once on a query silently
replaced the previous filter, so only the last filter was applied. This
was
surprising and could return rows that an earlier filter should have
excluded.
This implements the alternative suggested in
https://github.com/lancedb/lancedb/pull/3514#issuecomment-4664901580:
instead of
rejecting a second filter, repeated filters are combined with a logical
AND
(`(previous) AND (new)`).
The combination happens in the Rust core (`QueryBase::only_if` and
`only_if_expr`), so it applies to all SDKs at once (Rust, Python async,
and
TypeScript). The Python sync query builder keeps its own filter state,
so it
combines filters in the binding layer as well.
SQL string and expression filters are combined within their own
representation.
When the two representations are mixed, the expression is lowered to SQL
(via
`expr_to_sql_string`) and the filters are combined as SQL strings, so
chaining
`where` works regardless of which form each filter takes.
Fixes#2649
## Tests
- Rust: `cargo test --features remote -p lancedb --lib query`
- Python: `uv run --extra tests pytest python/tests/test_query.py`
- TypeScript: `pnpm test __test__/query.test.ts`
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
lancedb's public API forces downstream crates to construct foreign types
— `RecordBatch`/arrays/builders for `Table::add(...)` (arrow), and
`datafusion_expr::Expr` for `only_if_expr`/`expr_projection`/merge
filters. The required version must exactly match lancedb's internal
arrow/datafusion line, but nothing on the API surface makes that
visible. Drift surfaces only as confusing trait/type errors:
```text
error[E0277]: the trait bound `RecordBatch: Scannable` is not satisfied
= note: there are multiple different versions of crate `arrow_array` in the dependency graph
```
This re-exports the crates lancedb already pins, so consumers can rely
on a single, guaranteed-matching line via a discoverable import path
instead of declaring their own (potentially mismatched) direct
dependency.
- `lancedb::arrow::{arrow, arrow_array, arrow_buffer, arrow_cast,
arrow_data, arrow_ipc, arrow_ord, arrow_schema, arrow_select}` —
previously only `arrow_schema` was re-exported. `arrow-buffer` is
promoted from a transitive to a direct dependency.
- `lancedb::datafusion` — `Expr` is a first-class part of the query and
merge APIs (`only_if_expr`, `expr_projection`,
`QueryFilter::Datafusion`, `when_matched_update_all_expr`), and
`ExecutionPlan` is returned from `create_plan`.
This follows DataFusion's own precedent of re-exporting `arrow`. The
coupling already exists via the trait/impl bounds — this surfaces it
rather than hiding it behind an `E0277`.
Closes#3575🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Summary:
- Route built-in async namespace-backed connections through the Rust
namespace connector.
- Delegate async namespace/table management methods to the inner
AsyncConnection while keeping the custom implementation Python-client
fallback.
- Add regressions for the native async dir path and lazy
namespace_client() construction.
Validated locally with targeted namespace/db/table pytest, full
test_namespace.py, ruff, cargo fmt/check/clippy, and cargo test -p
lancedb-python.
Summary:
- Route built-in sync namespace connections through the Rust namespace
connector.
- Keep custom namespace clients on the existing Python fallback.
- Preserve namespace-backed to_lance compatibility with lazy Python
client construction and add regressions.
## Summary
Adds per-session monotonic reads for remote (LanceDB Cloud/Enterprise)
tables, preventing successive reads on a handle from moving *backward*
in dataset version when a load balancer routes them to query nodes with
differently-cached views.
Each `RemoteTable` handle tracks the highest dataset version it has
observed in a read response — surfaced by the server via a new
`x-lancedb-version` response header — and sends it back as
`x-lancedb-min-read-version` on subsequent reads (`count_rows`,
`query`). A query node whose cache is behind that version refreshes
before serving; a node already at/beyond it serves from cache at no
extra cost.
The watermark is sourced only from reads (always committed dataset
versions), so unlike the retired `x-lancedb-min-version` it is
unaffected by WAL writes returning WAL entry ids. It is reset on
`checkout_latest()`. Both headers are optional and ignored by older
peers.
Server-side enforcement lives in LanceDB Enterprise. Targets the
`codex/update-lance-9-0-0-beta-8` integration branch to match the
Enterprise submodule pin.
This PR is part cleanup, part feature, part example.
It removes `IntoArrow` and `IntoArrowStream`. There was only one
redundant call site between the two. Once we moved everything to
`Scannable` these traits no longer serve any purpose.
It adds a `Scannable` impl for a polars DataFrame. We used to have this
at one point for `IntoArrow` so this is more like a regression fix than
anything.
It adds an example (and unit test) which ensures we can ingest from a
Polars DataFrame and export to one. LazyFrame support would be a
follow-up (though a pretty straightforward one) but we've never had
proper LazyFrame support before.
Agents seemed to have trouble finding the right calls to work with
branches (create, list, delete) and passing the right params to get it
to work. We probably don't need a big skill to get it on the right track
but a little nudge seems helpful. Doing a couple simple tasks, it saved
about half the time and tokens, so feels worthwhile. Created with the
Claude skills creator, hence the "skill.md in a bare folder"
organization - happy to move it if that's not the standard anymore.
```
Benchmark results (3 evals, with-skill vs baseline):
┌────────────────┬────────────┬────────────────────┐
│ Metric │ With skill │ Without skill │
├────────────────┼────────────┼────────────────────┤
│ Pass rate │ 3/3 (100%) │ 3/3 (100%) │
├────────────────┼────────────┼────────────────────┤
│ Avg time │ 51s │ 142s (2.8× slower) │
├────────────────┼────────────┼────────────────────┤
│ Avg tokens │ 19,305 │ 36,513 (47% more) │
├────────────────┼────────────┼────────────────────┤
│ Avg tool calls │ 5.7 │ 26 (4.5× more) │
└────────────────┴────────────┴────────────────────┘
```
Expose the merged Rust OAuth header provider through the Node/TypeScript
connection path.
Includes:
- Native OAuthConfig conversion for napi-rs
- ConnectionOptions.oauthConfig plumbing
- Public TypeScript OAuthConfig and OAuthFlowType exports
- Generated TypeScript API docs for the new config surface
- input-validation and debug-redaction coverage in the Rust binding
layer
Local validation: cargo fmt --all; git diff --check.
Fixes#3589
## Problem
Multiple `warnings.warn()` calls across the Python client are missing
the `stacklevel=2` parameter. This causes warning messages to point to
lancedb internal code instead of the user's code that triggered the
warning, making debugging difficult.
## Solution
Add `stacklevel=2` to 7 `warnings.warn()` calls across 4 files:
| File | Warnings Fixed |
|------|---------------|
| `remote/db.py` | `request_thread_pool`, `connection_timeout`,
`read_timeout` deprecation warnings |
| `remote/table.py` | `cleanup_old_versions`, `compact_files`,
`optimize` no-op warnings |
| `table.py` | `data_storage_version`, `enable_v2_manifest_paths`,
`retrain` deprecation warnings |
| `embeddings/colpali.py` | `use_token_pooling` deprecation warning |
## Verification
- All 4 modified files pass `ast.parse()` syntax check
- Only `stacklevel=2` added — no other changes
## Changelog
| Date | Change | Author |
|------|--------|--------|
| 2026-06-27 | Add missing stacklevel=2 to warnings.warn() calls |
rtmalikian |
### Files Changed
- `python/python/lancedb/remote/db.py` — Add stacklevel=2 to 3
deprecation warnings
- `python/python/lancedb/remote/table.py` — Add stacklevel=2 to 3 no-op
warnings
- `python/python/lancedb/table.py` — Add stacklevel=2 to 3 deprecation
warnings
- `python/python/lancedb/embeddings/colpali.py` — Add stacklevel=2 to 1
deprecation warning
### Verification
- Syntax check passed on all modified files
---
**About the Author:** Raphael Malikian — Clinical AI Solutions
Architect. I specialise in building and fixing AI/ML systems for
healthcare, including vector databases, RAG pipelines, and clinical NLP.
If you need help with your project or think I can add value to your
organisation, feel free to reach out — I'd love to connect.
📧rtmalikian@gmail.com🔗 GitHub: https://github.com/rtmalikian🔗 LinkedIn:
http://www.linkedin.com/in/raphael-t-malikian-mbbs-bsc-hons-71075436a
---
**Disclosure:** This code was developed with assistance from
DeepSeek-V4-Pro (DeepSeek) via Hermes Agent (Nous Research). All changes
were reviewed, tested against the actual codebase, and verified for
correctness.
Signed-off-by: rtmalikian <rtmalikian@gmail.com>
Fixes#2934
## Problem
Passing a `RemoteTable` to `permutation_builder()` raises a cryptic
`AttributeError`:
```
AttributeError: 'RemoteTable' object has no attribute '_inner'
```
This leaves users confused about what went wrong and why.
## Root Cause
`PermutationBuilder.__init__()` calls `async_permutation_builder(table)`
which accesses `table._inner` — the underlying Rust Lance table object.
`RemoteTable` connects to LanceDB Cloud/Enterprise and does not have a
local `_inner` attribute, making permutations fundamentally unsupported
on remote tables.
## Solution
Added an early check in `PermutationBuilder.__init__()` that verifies
the table has `_inner` before calling the Rust function, raising a clear
`TypeError` with an explanation of why permutations don't work on remote
tables.
## Verification
- Syntax validated with `ast.parse()`
- Structural verification: single call site (`permutation_builder()`),
guard placed before Rust FFI call
- Error message tested with mock: `MockRemoteTable()` correctly triggers
`TypeError`
## Changelog
| Date | Change | Author |
|------|--------|--------|
| 2026-06-28 | Added remote table guard in PermutationBuilder.__init__ |
rtmalikian |
### Files Changed
- python/python/lancedb/permutation.py — Added `hasattr(table,
"_inner")` check with clear error
---
**About the Author:** Raphael Malikian — Clinical AI Solutions
Architect. I specialise in building and fixing AI/ML systems for
healthcare, including vector databases, RAG pipelines, and clinical NLP.
If you need help with your project or think I can add value to your
organisation, feel free to reach out — I'd love to connect.
📧rtmalikian@gmail.com🔗 GitHub: https://github.com/rtmalikian🔗 LinkedIn:
http://www.linkedin.com/in/raphael-t-malikian-mbbs-bsc-hons-71075436a
---
**Disclosure:** This code was developed with assistance from
deepseek-v4-pro (DeepSeek) via Hermes Agent (Nous Research). All changes
were reviewed, tested against the actual codebase, and verified for
correctness.
Signed-off-by: rtmalikian <rtmalikian@gmail.com>
Updates Lance Rust workspace dependencies and Java lance-core to
v9.0.0-beta.10.
No compatibility code changes were required; clippy and rustfmt passed
after installing the missing runner components.
Lance tag:
https://github.com/lance-format/lance/releases/tag/v9.0.0-beta.10
Expose the merged Rust OAuth header provider through the Python async
connection path.
Includes:
- Python OAuthConfig and OAuthFlowType public config objects
- PyO3 conversion into the Rust OAuthConfig
- connect_async(oauth_config=...) plumbing
- repr redaction coverage for client_secret
Local validation: cargo fmt --all; ruff format/check on touched Python
files.
## Summary
Add the Rust OAuth header provider for remote LanceDB connections.
This supports client credentials and Azure managed identity flows,
handles token caching and refresh, redacts secrets in Debug output, and
wires `ConnectBuilder::oauth_config()` into the remote client while
rejecting ambiguous API-key/header-provider combinations.
By default the read freshness provider was not included in the namespace
client, preventing the read freshness headers from being included in the
request. This prevents checkout_latest() from working as expected when
using the namespace client.
This fix ensures the provided is built into the client when the
namespace impl and properties are provided.
## Summary
Skip inserting the x-api-key header when the configured API key is
empty.
This lets bearer-token or other dynamic-header authentication avoid
sending an empty static API key header alongside the real auth header.
Fixes#3563
## Summary
- Add `stacklevel=2` to 10 `warnings.warn()` calls across 4 files
- Fix broken message concatenation in `table.py` where the second string
was incorrectly passed as the `category` parameter
## Problem
Multiple `warnings.warn()` calls in the `python/lancedb/` codebase were
missing the `stacklevel` parameter. Without `stacklevel=2`, warnings
point to library internals instead of the caller's code, making it
impossible for users to identify which of their function calls triggered
the warning.
Additionally, two calls in `table.py` (lines 3411 and 3420) had a more
serious bug: the deprecation message was split across two separate
string arguments, causing the second string to be passed as the
`category` parameter instead of being concatenated with the first
string. This would cause `TypeError` when the warning was triggered.
## Changes
| File | Fixes | Description |
|------|-------|-------------|
| `embeddings/colpali.py` | 1 | Add `stacklevel=2` to
`use_token_pooling` deprecation warning |
| `remote/db.py` | 3 | Add `stacklevel=2` to `request_thread_pool`,
`connection_timeout`, `read_timeout` deprecation warnings |
| `remote/table.py` | 3 | Add `stacklevel=2` to `cleanup_old_versions`,
`compact_files`, `optimize` no-op warnings |
| `table.py` | 3 | Fix broken message concatenation for
`data_storage_version` and `enable_v2_manifest_paths` deprecation
warnings + add `stacklevel=2` to `retrain` deprecation warning |
## Verification
```python
# All warnings.warn() calls now have stacklevel
python3 -c "import ast, os; ..."
# Result: All warnings.warn() calls now have stacklevel!
```
## Changelog
| Date | Change | Author |
|------|--------|--------|
| 2026-06-20 | Fix missing stacklevel=2 in 10 warnings.warn() calls +
fix broken message concatenation | rtmalikian |
### Files Changed
- `python/python/lancedb/embeddings/colpali.py` — Add stacklevel=2
- `python/python/lancedb/remote/db.py` — Add stacklevel=2 to 3
deprecation warnings
- `python/python/lancedb/remote/table.py` — Add stacklevel=2 to 3 no-op
warnings
- `python/python/lancedb/table.py` — Fix broken message concatenation +
add stacklevel=2
### Verification
- AST-based audit confirms all `warnings.warn()` calls now include
`stacklevel=2`
- Syntax check passes for all 4 modified files
---
**About the Author:** Raphael Malikian — Clinical AI Solutions
Architect. I specialise in building and fixing AI/ML systems for
healthcare, including vector databases, RAG pipelines, and clinical NLP.
If you need help with your project or think I can add value to your
organisation, feel free to reach out — I'd love to connect.
📧rtmalikian@gmail.com🔗 GitHub: https://github.com/rtmalikian🔗 LinkedIn:
http://www.linkedin.com/in/raphael-t-malikian-mbbs-bsc-hons-71075436a
---
**Disclosure:** This code was developed with assistance from **Hermes
Agent** (Nous Research). All changes were reviewed, tested against the
actual codebase, and verified for correctness.
Signed-off-by: rtmalikian <rtmalikian@gmail.com>
Updates LanceDB's Lance dependencies to v9.0.0-beta.2 across the Rust
workspace and Java lance-core dependency.\n\nNo compatibility fixes were
required; clippy and formatting pass after installing the missing
toolchain components on the runner. Triggering Lance tag:
https://github.com/lance-format/lance/releases/tag/v9.0.0-beta.2
This PR is for the Read path against blob v2. #3528 handles declare +
write, and this this adds materialization on local tables.
- blob_columns()
- fetch_blobs(column, row_ids) → bytes
- fetch_blob_files(column, row_ids) → lazy handles
- Pass _rowid from query().with_row_id(). Remote returns NotSupported.
(for now)
### Use cases
search, grab row ids, materialize images:
```rust
let row_ids = /* _rowid from hits */;
let images = table.fetch_blobs("image", &row_ids).await?;
```
Large blobs: open handles, read only what you need:
```rust
let handles = table.fetch_blob_files("image", &row_ids).await?;
let bytes = handles[0].as_ref().unwrap().read().await?;
```
Filter then batch fetch: collect ids from a filter, one call.
Multiple blob columns: image and thumbnail independently.
Row ids from before compact: still resolve.
### Alignment note
Lance `read_blobs` drops null rows. We descriptor-take first, read
non-null ids, re-expand to match input order. Null and zero-length blobs
come back null/None. Bytes path sets `preserve_order(true)`. So I added:
```
TODO(lance): expose selection_index or an aligned execute so we can drop the pre-read.
```
### Tests
`cargo test -p lancedb --test blob_integration`
- 30 tests covering nulls, reorder, dups, cross-fragment bytes + files,
compact, delete, legacy v1 errors.
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The server now serializes an index's `created_at` as an RFC 3339 string
(e.g. `"2026-06-18T21:37:36.637Z"`), but the client deserializer only
accepted a unix timestamp in milliseconds. This caused `list_indices` to
fail with:
```
Failed to parse list_indices response: invalid type: string "2026-06-18T21:37:36.637Z", expected a unix timestamp in milliseconds
```
This PR replaces the fixed millisecond deserializer with a custom one
that accepts both an RFC 3339 string (current server) and a
unix-millisecond integer (legacy deployments), so the client works
against any server version.
It also improves the `IndexConfig` repr in the Python bindings.
Previously it printed only three fields (`Index(FTS, columns=["text"],
name="text_idx")`), hiding the metadata that `list_indices` returns. It
now renders every populated field, omitting any that are `None`. Each
value is valid Python — integer counts use `_` thousands separators and
`created_at` uses the `datetime` repr — so values round-trip. The real
repr is a single line; it's wrapped here for readability:
```python
>>> table.list_indices()
[IndexConfig(
name="text_idx",
index_type="FTS",
columns=["text"],
index_uuid="aefd3e00-2f95-4bdc-92ac-06de84442bf1",
type_url="/lance.table.InvertedIndexDetails",
created_at=datetime.datetime(2026, 6, 18, 21, 37, 36, 637000, tzinfo=datetime.timezone.utc),
num_indexed_rows=2,
size_bytes=3_669,
num_segments=1,
index_version=1,
index_details={
'lance_tokenizer': None,
'base_tokenizer': 'simple',
'language': 'English',
'with_position': False,
'max_token_length': 40,
'lower_case': True,
'stem': True,
'remove_stop_words': True,
'custom_stop_words': None,
'ascii_folding': True,
'min_ngram_length': 3,
'max_ngram_length': 3,
'prefix_only': False,
},
)]
```
Fixes#3556🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Updates LanceDB's Lance dependencies from v8.0.0-beta.17 to
v8.0.0-beta.19.
This includes the Rust workspace Lance crates, Cargo.lock refresh, and
Java lance-core version bump. Triggering Lance tag:
https://github.com/lance-format/lance/releases/tag/v8.0.0-beta.19
Updates the Lance Rust workspace dependencies and Java lance-core
dependency to v8.0.0-beta.17.
No LanceDB compatibility code changes were required; validation passed
with cargo clippy and cargo fmt. Triggering Lance tag:
https://github.com/lance-format/lance/releases/tag/v8.0.0-beta.17
The "Create release commit" workflow (`make-release-commit.yml`) has
failed on its last two runs; no release tags have been created since
June 4. Since this workflow creates the tag that the cargo/npm/pypi/java
publish workflows trigger off of, all recent releases are effectively
blocked.
The workflow installs `bump-my-version` unpinned. Version `1.4.0` added
a check that refuses to run `pre_commit_hooks` containing shell syntax
(pipes, `&&`, `if`, variable expansion) unless `allow_shell_hooks =
true` is set. Both bumpversion configs use such hooks:
- `python/.bumpversion.toml` — updates `Cargo.lock` after the bump
(fails first)
- `.bumpversion.toml` — runs `mvn versions:set` for the Java packages
The job dies at the version-bump step with:
> Hook '…' contains shell syntax (pipes, redirects, or variable
expansion). Set `allow_shell_hooks = true` in your configuration to
enable shell execution…
This sets `allow_shell_hooks = true` in both configs to restore the
previous behavior.
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Closes#3506
## Problem
The Bedrock embedding compute path
(`rust/lancedb/src/embeddings/bedrock.rs`) panics instead of returning a
typed error in several places:
- `serde_json::to_vec(&request_body).unwrap()`: request serialization.
- `block_in_place(...).unwrap()`: the AWS `invoke_model` send result;
any API error terminates the worker instead of propagating.
- `v.as_f64().unwrap() as f32`: panics on non-numeric values in the
returned embedding array.
- `Handle::current()` + `block_in_place` assume a multi-threaded Tokio
runtime and panic when that assumption does not hold (no runtime, or a
current-thread runtime).
Malformed payloads, non-numeric embedding values, or an incompatible
runtime should surface as typed errors and never panic.
## Fix
- Serialize the request body before the blocking section so a
serialization failure returns `Error::Runtime` via `?`.
- Map the `invoke_model` send error to `Error::Runtime` instead of
`unwrap`.
- Add a `json_array_to_f32` helper that converts the response array to
`Vec<f32>`, returning `Error::Runtime` for a missing/non-array field or
a non-numeric element (used by both the Titan and Cohere paths).
- Add `current_multi_thread_handle()` (`Handle::try_current()` + a
`RuntimeFlavor::CurrentThread` guard) so an absent or incompatible
runtime returns a typed error rather than panicking in `block_in_place`.
Scope note: the sibling `openai.rs` provider uses the same
`block_in_place` + `block_on` bridge, so the bridge pattern itself is
kept; this change only removes the panic paths that are specific to the
Bedrock provider.
## Testing
Added 6 unit tests (no AWS credentials required):
- `json_array_to_f32`: valid numbers, non-array payload, and non-numeric
element.
- `current_multi_thread_handle`: errors with no runtime, errors on a
current-thread runtime, and succeeds on a multi-threaded runtime.
All pass; `cargo fmt` and `cargo clippy` clean. Build/test with
`--features bedrock,lance/protoc`.
## Summary
- clarify the Python error for passing a single dictionary to table
creation/add paths
- add a regression test for `create_table(..., data=dict)` so it points
users to a list of dictionaries
Fixes#409
## Testing
- `python -m pytest python/tests/test_table.py -q`
- `python -m ruff format python/lancedb/table.py
python/lancedb/scannable.py python/tests/test_table.py`
- `python -m ruff check python/lancedb/table.py
python/lancedb/scannable.py python/tests/test_table.py`
### Bug
`value_to_sql({...})` builds a DataFusion `named_struct(...)` literal
but interpolates the struct field names directly as `f"'{k}'"`. A field
name that contains a single quote therefore produces invalid SQL:
```python
>>> from lancedb.util import value_to_sql
>>> value_to_sql({"it's": 1})
"named_struct('it's', 1)" # invalid SQL — the quote terminates the literal
```
String *values* are already escaped (single quotes doubled) by the `str`
branch of `value_to_sql`, so keys and values were handled
inconsistently. This affects `Table.update(values={...})` /
`merge_insert` when a struct column has a field name containing `'`.
### Fix
Render the key through `value_to_sql(str(k))` so field names are escaped
exactly like string values:
```python
>>> value_to_sql({"it's": 1})
"named_struct('it''s', 1)"
```
Keys without special characters are unchanged (`'a'` stays `'a'`), so
existing behavior is preserved.
### Verification
```
$ pytest python/tests/test_util.py -k value_to_sql_dict
```
The new `test_value_to_sql_dict_key_escaping` covers quoted keys (incl.
nested structs) and fails on `main` (`named_struct('it's', 1)`), passes
with this change; the existing `test_value_to_sql_dict` still passes.
Co-authored-by: JSap0914 <JSap0914@users.noreply.github.com>
Fixes#3360.
This updates native table writes so local write progress uses Lance
writer byte stats instead of Arrow in-memory batch size once write bytes
are available. The change wires the existing `WriteProgressTracker` into
`InsertExec` for native `add` writes, installs a Lance `WriteProgressFn`
only when no lower-level callback is already configured, and keeps the
existing public `InsertExec::new` signature unchanged.
Validation:
- `cargo test -p lancedb --features remote
table::write_progress::tests::test_progress_uses_lance_write_bytes_for_local_tables
-- --nocapture` passed: 1 passed, 0 failed.
- `cargo test -p lancedb --features remote table::write_progress::tests
-- --nocapture` passed: 7 passed, 0 failed.
- `cargo check --quiet --features remote --tests --examples` passed.
- `cargo fmt --all --check` passed.
- `git diff --check` passed.
- `git diff | gitleaks stdin --no-banner --redact --timeout 30` passed:
no leaks found.
I did not run the full `cargo test --quiet --features remote --tests`
suite.
Co-authored-by: Ghxst <200635707+GHX5T-SOL@users.noreply.github.com>
Closes#3502
## Problem
A bare, unparameterised `typing.List` / `typing.Tuple` field crashes
`to_arrow_schema` with an opaque `AttributeError: __args__`:
```python
from typing import Tuple
from lancedb.pydantic import LanceModel
class Doc(LanceModel):
items: Tuple
Doc.to_arrow_schema() # AttributeError: __args__
```
In `_py_type_to_arrow_type`, the branch `elif getattr(py_type,
"__origin__", None) in (list, tuple)` is taken for a bare generic (its
`__origin__` is `list / tuple`), but the next line reads
`py_type.__args__[0]`, and a bare generic has no `__args__`. Other
unsupported types (e.g. `Dict[str, int]`) correctly raise a clear
`TypeError`, so this case is inconsistent.
Fix
Guard the element-type lookup with `getattr(py_type, "__args__", None)`
and raise a clear `TypeError` when it is missing, matching the existing
behavior for other unsupported types. Bare builtin list / tuple are
unaffected (their `__origin__` is `None`, so they already fall through
to the existing `TypeError`).
Testing
- Added `test_bare_generic_raises_type_error` covering both `List` and
`Tuple`.
- ruff format and ruff check clean.
This PR fixes a flaky test I hit on Windows test in #3528.
Looks like `test_eventual_consistency_background_refresh` was failing
with `v_cached` expected 1, got 2. There was a pr which swapped
`tokio::time::sleep(300ms)` for `clock::advance_by(300ms)`, which is
pretty much fine but the test necer pinned the clock so the first
`get()` locks the `cached_at` on wall time. Therefore, if our CI is
taking long enough the ttl expires before the value assertion in the
test.
So now we can add a `pin()` and call it first `get()`. After that we can
advance the clock manually with no problems.
Also, it's worth noting that I tried pinning in `BackgroundCache::new()`
first. That broke another test `test_reload_resets_consistency_timer`,
which uses real `tokio::time::sleep` and needs wall clock after
`clear_mock()`. So the pin stays in this test only. And this should
unblock us.
Failing instances:
-
https://github.com/lancedb/lancedb/actions/runs/27567527236/job/81495265474?pr=3528
-
https://github.com/lancedb/lancedb/actions/runs/27560366489/job/81470414928
### Description
`db://`-style connections that use the lance-namespace path
(`LanceNamespaceDatabase` → `NativeTable` + the lance-namespace REST
client) never sent a read-freshness signal. Against a server configured
to serve cached table metadata up to some staleness window, this allows
stale-read-after-write across handles and processes. The remote table
path already solved this (#3439). This brings the namespace path to
parity.
The namespace REST client doesn't let callers attach headers directly,
but it forwards a `DynamicContextProvider`'s `headers.*` context entries
as HTTP headers per request. So:
- A shared per-table baseline map is created before the namespace
client. I built and installed on the `ConnectBuilder` via a context
provider.
- On read operations the provider emits ·x-lancedb-min-timestamp =
max(baseline, now − read_consistency_interval)`
(RFC3339), keyed by the operation's `object_id`.
- Each table handle bumps its baseline (monotonically) on
`checkout_latest()`, `restore()`, and every data/schema write.
`checkout_latest()` is the primary hook: consumers refresh a handle
there after writing elsewhere, then poll.
Read operations that carry the floor: `describe_table`,
`list_table_versions`, `query_table`, `list_tables`.
`list_table_versions` is what resolves "latest" for managed-versioning
tables (`get_latest_version`), so it's the op that makes
`checkout_latest()` actually observe a prior write.
`describe_table_version` is excluded (pinned to an immutable version).
This mirrors #3439 (timestamp baseline, `max(baseline, now − interval)`,
monotonic); no `min_version` and no body channel, since the namespace
path has no version-returning write responses.
### Testing
- Unit tests for `compute_min_timestamp` / `next_freshness_baseline` and
the provider (header at/after a bumped baseline; nothing for an empty
baseline + no interval; interval floor applies; non-read ops emit
nothing; `list_tables` uses only the interval floor).
- Verified end-to-end against a local server that honors the header:
reads carry `x-lancedb-min-timestamp`, writes don't, and read-your-write
holds.
## Feature
### What is the new feature?
Adds Rust core API support for configuring vector query approximation
mode with `ApproxMode::{Fast, Normal, Accurate}`.
### Why do we need this feature?
Lance already exposes `lance_index::vector::ApproxMode` and scanner
support for controlling the speed/accuracy tradeoff for approximate
vector search. LanceDB Rust queries need to expose and pass this setting
through for local/native and remote vector searches.
### How does it work?
- Adds public `ApproxMode` in `rust/lancedb`, with lowercase serde,
`Default::Normal`, parse/display, and conversions to/from Lance's
`ApproxMode`.
- Adds `approx_mode: Option<ApproxMode>` to `VectorQueryRequest` and a
`VectorQuery::approx_mode(...)` builder.
- Applies the mode to native/local Lance scanners after `nearest(...)`
when explicitly set.
- Sends `approx_mode` in remote query JSON only when explicitly set;
default requests omit it.
## Validation
- `cargo fmt --all`
- `cargo test --quiet --features remote approx_mode`
- `cargo test --quiet --features remote
test_query_vector_default_values`
- `cargo check --quiet --features remote --tests --examples`
- `git diff --check`
## Feature
### What is the new feature?
Adds Rust core API support for configuring vector query approximation
mode with `ApproxMode::{Fast, Normal, Accurate}`.
### Why do we need this feature?
Lance already exposes `lance_index::vector::ApproxMode` and scanner
support for controlling the speed/accuracy tradeoff for approximate
vector search. LanceDB Rust queries need to expose and pass this setting
through for local/native and remote vector searches.
### How does it work?
- Adds public `ApproxMode` in `rust/lancedb`, with lowercase serde,
`Default::Normal`, parse/display, and conversions to/from Lance's
`ApproxMode`.
- Adds `approx_mode: Option<ApproxMode>` to `VectorQueryRequest` and a
`VectorQuery::approx_mode(...)` builder.
- Applies the mode to native/local Lance scanners after `nearest(...)`
when explicitly set.
- Sends `approx_mode` in remote query JSON only when explicitly set;
default requests omit it.
## Validation
- `cargo fmt --all`
- `cargo test --quiet --features remote approx_mode`
- `cargo test --quiet --features remote
test_query_vector_default_values`
- `cargo check --quiet --features remote --tests --examples`
- `git diff --check`
### Description
Adding branch support for RemoteTable by threading a branch selector
onto every operation the data plane accepts it on. Exposes the
currentBranch to nodejs and python through the bindings.
Matching the server handlers, the branch rides as:
- a `?branch=` query parameter for Arrow-body and query-only ops
(insert, merge_insert, multipart_*, version/list, drop_index)
- a `branch` field in the JSON body for everything else (count_rows,
query, update, delete, create_index, column ops, index list/stats,
stats, restore, describe, tags create/update)
A main-branch handle (`branch == None`) produces byte-identical requests
to before: no `branch` field and no `?branch=`
- Handle-per-branch: `create_branch` / `checkout_branch` return a new
handle with fresh caches and reset version/freshness state, mirroring
`NativeTable`.
- `create_branch` maps 409 to already-exists, 400 to invalid, and 404 to
not-found with source context, and sends without retry so the 409 stays
observable.
- `Ref` translation covers version, version-number (relative to the
handle's branch), and tag (resolved via the tags endpoint); `"main"` and
empty normalize to the main branch.
- Python branch handles persist their branch (and pinned version) across
pickle/fork, so a forked or pickled handle reopens on its branch rather
than silently reverting to main.
### Tests
- Rust mock tests per op category (query-param and body mechanisms,
branch CRUD, error paths, backward-compat).
- Python sync branch CRUD, `open_table(branch=)`, and a pickle
round-trip regression test.
Updates LanceDB's Lance dependencies to v8.0.0-beta.14.\n\nThis
refreshes the Rust workspace lockfile and Java lance-core version; no
compatibility code changes were required. Triggering Lance tag:
https://github.com/lance-format/lance/releases/tag/v8.0.0-beta.14
`RemoteTable::list_indices` currently makes one `/index/list/` call plus
one `/index/{name}/stats/` call per index just to recover `index_type`.
When the server returns `index_type` directly in the `/index/list/`
response, all enriched fields are used and the per-index stats fan-out
is skipped entirely. When `index_type` is absent (legacy servers), the
existing stats fallback runs as before. This is content-based: no
version header required.
## Changes
- `RemoteTable::parse_index_list_response` replaces the old split
between enriched and legacy parsers. A single struct deserializes both
old and new response shapes, with all fields except `index_name` and
`columns` optional. `index_type` acts as the sentinel: present → use
enriched fields directly; absent → call `/index/{name}/stats/`.
## Tests
Added `test_list_indices_enriched` covering:
- All enriched fields populated correctly when `index_type` is in the
list response
- Optional fields absent from the response deserialize as `None`
- Stats endpoint is **not** called (panics if hit), verifying the
fan-out is eliminated
Existing `test_list_indices` and `test_list_indices_nested_field_paths`
exercise the legacy path unchanged.
## Depends on
- #3497 (expand `IndexConfig`) — already merged
- Server-side enriched response support
Closes#3494
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Two skills to help people connect and manage their column metadata using
a server that implements the [REST
API](https://lance.org/format/catalog/rest/)
lancedb-column-metadata was built using the [Claude skill
creator](https://claude.com/plugins/skill-creator); without the skill it
was usually calling at least one method that didn't exist and usually
not setting "replace": "false". So, while the base case is already
pretty good, adding this skill improves things somewhat.
lancedb-connect should help with most agentic workflows, because
"finding all the things you need to connect to your server" can be the
hardest part.
## Summary
- Extracts the `create_index` code cluster from `table.rs` into a new
`rust/lancedb/src/table/create_index.rs` submodule, continuing the work
from #2949.
- Moves 8 `NativeTable` inherent methods (`load_indices`,
`validate_index_type`, `build_ivf_params`, `get_num_sub_vectors`,
`get_vector_dimension`, `resolve_index_field`, `make_index_params`,
`get_index_type_for_field`) and 11 associated tests into the new module.
- Reduces `table.rs` from ~5009 to ~3804 lines (-1205 lines) with no
behavioral changes.
## Test plan
UT
## Summary
Surfaces the rich per-index metadata added in #3497 to the Python and
Node.js language bindings. Closes#3495.
New optional fields exposed on `IndexConfig` in both bindings:
- `index_uuid` / `indexUuid` — UUID of the first index segment
- `type_url` / `typeUrl` — protobuf type URL for the index
- `created_at` / `createdAt` — creation timestamp (milliseconds since
Unix epoch)
- `num_indexed_rows` / `numIndexedRows` — rows covered by the index
- `num_unindexed_rows` / `numUnindexedRows` — rows not yet indexed
- `size_bytes` / `sizeBytes` — total index file size in bytes
- `num_segments` / `numSegments` — number of index segments
- `index_version` / `indexVersion` — on-disk format version
- `index_details` / `indexDetails` — type-specific JSON details string
All fields are `None`/`undefined` for remote tables (which don't yet
surface this metadata through the server response).
## Changes
- `python/src/index.rs`: extend `IndexConfig` pyclass; update `From`
impl; update `__getitem__`
- `python/python/lancedb/_lancedb.pyi`: add type hints for new fields
- `python/python/tests/test_table.py`: new `test_index_config_fields`
test
- `nodejs/src/table.rs`: extend `IndexConfig` napi struct; update `From`
impl
- `nodejs/__test__/table.test.ts`: new test; update existing `toEqual`
assertions to `expect.objectContaining` to accommodate new fields
## Test plan
- [x] Python: `uv run --extra tests pytest
python/tests/test_table.py::test_index_config_fields`
- [x] Node.js: `pnpm test __test__/table.test.ts`
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
## Summary
Closes#3412
Implements `rename_table` for `LanceNamespaceDatabase` (sync and async
Python) and the Rust `NamespaceDatabase` backend. Previously these
raised `NotImplementedError`; this PR delegates to the
`LanceNamespace.rename_table` method which is part of the
lance-namespace spec.
### Changes
- **`rust/lancedb/src/database/namespace.rs`**: Remove the
`NotImplementedError` stub for `rename_table`. Build a
`RenameTableRequest` (with `id`, `new_table_name`, and optionally
`new_namespace_id`) and call `self.namespace.rename_table(...)`,
mirroring the existing `drop_table` pattern.
- **`python/python/lancedb/namespace.py`**: Import `RenameTableRequest`
from `lance_namespace`. Replace the `raise NotImplementedError` in both
`LanceNamespaceDatabase.rename_table` (sync) and
`AsyncLanceNamespaceDatabase.rename_table` (async) with a call to
`self._namespace_client.rename_table(request)`.
- **`python/python/tests/test_namespace.py`**: Replace the
`test_rename_table_not_supported` test (which checked for
`NotImplementedError`) with `test_rename_table`, which:
1. Creates a table in a namespace
2. Calls `rename_table` with `cur_namespace_path` and
`new_namespace_path`
3. Asserts the old name is gone from `table_names()`
4. Asserts the new name appears in `table_names()`
5. Verifies the renamed table can be opened
## Test plan
- [ ] Existing namespace tests pass in CI (all rely on
`lance.namespace.DirectoryNamespace` which requires the full lance
package)
- [ ] `test_rename_table` exercises the full rename path: create →
rename → verify old gone → verify new present → open
- [ ] Rust build passes with the updated `namespace.rs` (requires Rust
toolchain in CI)
## Summary
Closes#3406
Add a regression matrix in `python/python/tests/test_nested_fields.py`
that exercises the full nested field index lifecycle for both the sync
and async Python table APIs. The tests will fail if any implementation
regresses to leaf-only field names in `list_indices`, `index_stats`,
search, or filter results.
## Test scenarios covered
**Index types:** BTree scalar, IvfPq vector, FTS
**Field-name edge cases (per acceptance criteria):**
- `rowId` — camelCase top-level field
- `` `row-id` `` — hyphenated top-level field (escaped)
- `parent.`\``leaf.name`\`` ` — struct leaf whose name contains a
literal dot
- `MetaData.userId` — mixed-case nested path
- `` `meta-data`.`user-id` `` — hyphenated struct with hyphenated leaf
**Lifecycle operations per index type:**
- `create_index` / `create_scalar_index` / `create_fts_index`
- `list_indices` → verify canonical full dotted path (not leaf name)
- `index_stats` → verify row count and index type
- Filtered scan (`WHERE nested.field = value`)
- Vector search via nested embedding column
- FTS search via nested text column
- `add` (append) then re-check index listing
- `optimize` then re-check index listing
**Both sync and async APIs** are covered in parallel test classes.
## Notes
Lance forbids top-level field names that contain a literal `.`, so the
`` `a.b` `` acceptance-criterion variant is exercised as a *struct leaf*
field (`parent.`\``leaf.name`\``) rather than a top-level column.
Another little pain point as I was working to integrate with
paperless-ngx. The read path of table.search() or table.query() already
accepted an Expr, but write paths Table.delete and
merge_insert(...).when_not_matched_by_source_delete did not. This PR
attempts to close that gap, so writes and reads can both use Expr,
instead of one side needing to build a string.
Updates Lance dependencies to v8.0.0-beta.11 and refreshes the Rust and
Java lock/config files. This also adapts namespace external manifest
store call sites to the new table-root-aware constructor required by
Lance. Triggering tag:
https://github.com/lancedb/lance/releases/tag/v8.0.0-beta.11
`IndexConfig` (returned by `Table::list_indices`) previously exposed
only `name`, `index_type`, and `columns`. Lance's `describe_indices`
provides richer per-index info cheaply (reads manifest metadata, often
cached), so this surfaces it.
Adds these `Option<T>` fields to `lancedb::index::IndexConfig`,
populated in `NativeTable::list_indices` from the `IndexDescription`:
- `index_uuid`: uuid of the first segment
- `type_url`: protobuf type URL (`IndexDescription::type_url`)
- `created_at`: minimum creation time across segments
- `num_indexed_rows`: approximate rows indexed across segments
- `num_unindexed_rows`: table row count minus `num_indexed_rows`
- `size_bytes`: total size of index files across segments
- `num_segments`: number of segments making up the index
- `index_version`: on-disk index format version (first segment)
- `index_details`: index-type-specific details as JSON
This field set mirrors the lance-namespace `IndexContent` contract
(lance-format/lance-namespace#348) so client and server agree on the
same shape. Note these are populated **locally** via `describe_indices`
— `NativeTable::list_indices` reads the dataset directly and does not
depend on the namespace spec change.
`RemoteTable` leaves the new fields `None` until a follow-up wires them
through the server response (#3494). Bindings exposure will also be a
follow up: #3495
Existing `list_indices` tests in `rust/lancedb/src/table.rs` are
extended to assert the new fields.
Fixes#3492🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
The `Expr` build already includes a lot of useful filtering options,
`eq, ne, gt/gte, lt/lte, and_, or_, contains, cast`, but is was missing
a membership like `isin`. This PR adds that support, as minimally as
possible, allowing easy filtering for membership in a list, without
needing to be a series of `where` expressions.
I didn't see anything in CONTRIBUTING.md about needing a feature request
or issue first, so I just made the change. My apologies if I missed that
somewhere.
Thanks for the vector store, we're using it now in paperless-ngx.
Adds an FM-Index — a scalar index over string and binary columns that
accelerates substring search (`contains(col, 'needle')`), distinct from
the tokenized `FTS` index — across the Rust core and the Python and
TypeScript bindings.
## Rust
- `Index::Fm(FmIndexBuilder)` and `IndexType::Fm`.
- `make_index_params` maps `Index::Fm` to Lance's
`ScalarIndexParams::for_builtin(BuiltinIndexType::Fm)`.
- `supported_fm_data_type` validates
`Utf8`/`LargeUtf8`/`Binary`/`LargeBinary` columns.
- `list_indices` round-trips the type (`"Fm"` → `IndexType::Fm`); the
remote wire type is `"FM"`.
## Python
Adds `lancedb.index.Fm`, accepted by `create_index`:
```python
from lancedb.index import Fm
await tbl.create_index("text", config=Fm())
```
## TypeScript
Adds the `Index.fm()` factory:
```ts
await tbl.createIndex("text", { config: Index.fm() });
```
## Summary
This PR extends nested-field regression coverage across Rust
local/remote, Python sync/async, and Node so canonical escaped paths
stay consistent across scalar, vector, and FTS index lifecycle behavior.
It also aligns LanceDB's LabelList type gate with Lance by accepting
`LargeList<primitive>` columns while keeping `List<Struct<...>>`
unsupported until Lance defines stable membership semantics for struct
labels.
Part of #3406.
## Summary
Fixes the `NAPI_RS_FORCE_WASI=false` issue by upgrading `@napi-rs/cli`
from `3.5.1` to `3.7.0`.
Closes#3267
## Root Cause
In the `native.js` loader generated by `napi build`, the check was:
```js
if (!nativeBinding || process.env.NAPI_RS_FORCE_WASI) {
```
In JavaScript, any non-empty string is truthy, so
`NAPI_RS_FORCE_WASI=false` (a non-empty string) inadvertently triggered
the WASI fallback path. This caused an `ENOENT` error when
`lancedb.wasi.cjs` was not present.
## Fix
`@napi-rs/cli@3.7.0`
([napi-rs/napi-rs#3236](https://github.com/napi-rs/napi-rs/pull/3236))
introduced a tri-state check in the template that generates `native.js`:
**Before (generated by @napi-rs/cli@3.5.1):**
```js
if (!nativeBinding || process.env.NAPI_RS_FORCE_WASI) {
```
**After (generated by @napi-rs/cli@3.7.0):**
```js
const forceWasi =
process.env.NAPI_RS_FORCE_WASI === 'true' || process.env.NAPI_RS_FORCE_WASI === 'error'
if (!nativeBinding || forceWasi) {
```
Only the literal string `'true'` (or `'error'` for strict mode) now
activates the WASI path. All other values, including `'false'`, `'0'`,
or an unset variable, behave as if WASI is not forced.
## Changes
- `nodejs/package.json`: bump `@napi-rs/cli` from `3.5.1` to `3.7.0`
- `nodejs/package-lock.json` / `nodejs/pnpm-lock.yaml`: update lock
files to match
The fix is in the upstream napi-rs tool; the generated `native.js` is
not committed to this repository and is produced at build time by `napi
build`.
## What's broken
`Table.update(values={...})` raises `NotImplementedError: SQL conversion
is not implemented for this type` when a value is a numpy scalar such as
`np.int64`, `np.int32`, `np.float32`, or `np.bool_`. These arise
naturally from indexing an ndarray or a pandas int/bool column.
`np.float64` happens to work (it subclasses `float`), which makes the
failure inconsistent and surprising.
```python
df = pd.DataFrame({"id": np.array([10, 20], dtype="int32")})
t.update(where="id = 1", values={"id": df["id"].iloc[0]}) # np.int32
# -> NotImplementedError: SQL conversion is not implemented for this type
```
## Why it happens
`value_to_sql` is a `singledispatch` with handlers only for native
Python types and `np.ndarray`; numpy `integer`/`floating`/`bool_`
scalars aren't Python subclasses, so they fall through to the
`NotImplementedError` base.
## Fix
Register handlers for `np.bool_`, `np.integer`, and `np.floating` that
delegate to the existing native handlers.
## Test
`value_to_sql` on `np.int32/int64/float32/float64/bool_` all convert;
`np.int32` raised before.
Co-authored-by: Ishaan Samantray <ishaansamantray@Ishaans-MacBook-Pro.local>
### Description
Stacked on #3490. Adds an optional version to branch checkout across the
Rust core and the Python and TypeScript SDKs, so you can open a specific
version on a branch ("version V of branch B"), not just the branch's
latest version
Rust
```rust
// Open version 3 of branch "exp" (a read-only view): check out from an
// existing table, or open it directly from the connection.
let exp_v3 = table.checkout_branch("exp", Some(3)).await?;
let exp_v3 = db.open_table("items").branch("exp").version(3).execute().await?;
// checkout_latest re-attaches to the branch's writable HEAD.
exp_v3.checkout_latest().await?;
// With no branch, a version opens main at that version.
let main_v3 = db.open_table("items").version(3).execute().await?;
```
Python
```python
# Open version 3 of branch "exp" (a read-only view): check out from an
# existing table, or open it directly from the connection.
branch_v3 = await table.branches.checkout("exp", version=3)
branch_v3 = await db.open_table("items", branch="exp", version=3)
# checkout_latest re-attaches to the branch's writable HEAD.
await branch_v3.checkout_latest()
# With no branch, a version opens main at that version.
main_v3 = await db.open_table("items", version=3)
```
TypeScript
```typescript
// Open version 3 of branch "exp" (a read-only view): check out from an
// existing table, or open it directly from the connection.
const branchV3 = await (await table.branches()).checkout("exp", 3);
const opened = await db.openTable("items", undefined, { branch: "exp", version: 3 });
// checkoutLatest re-attaches to the branch's writable HEAD.
await branchV3.checkoutLatest();
// With no branch, a version opens main at that version.
const mainV3 = await db.openTable("items", undefined, { version: 3 });
```
### Testing
- Added unit tests (Rust, Python sync + async, TypeScript):
branch-scoped resolution at a version number shared with `main` and with
another branch, read-only enforcement on a pinned handle,
`checkout_latest` recovery to the branch's HEAD, fork-point reads, and
the nonexistent-version/branch error paths.
- Ran smoke tests against the Python and TypeScript SDKs on local
machine.
### Description
Adds first-class support for table branches across the Rust core and the
Python and TypeScript SDKs.
Rust
```rust
use lance::dataset::refs::Ref;
// Create a branch from main and write to it — main is untouched.
let exp = table.create_branch("exp", Ref::Version(None, None)).await?;
exp.add(batches).await?;
// Reopen the branch later: check out from a table, or open it directly.
let exp = table.checkout_branch("exp").await?;
let exp = db.open_table("items").branch("exp").execute().await?;
let branches = table.list_branches().await?;
table.delete_branch("exp").await?;
```
Python
```python
# Create a branch from main and write to it
branch = await table.branches.create("exp", from_ref="main")
await branch.add(data)
# Reopen the branch later: check out from a table, or open it directly.
branch = await table.branches.checkout("exp")
branch = await db.open_table("items", branch="exp")
await table.branches.list()
await table.branches.delete("exp")
```
TypeScript
```typescript
const branches = await table.branches();
// Create a branch from main and write to it
const branch = await branches.create("exp");
await branch.add(data);
// Reopen the branch later: check out from a table, or open it directly.
const checkedOut = await branches.checkout("exp");
const opened = await db.openTable("items", undefined, { branch: "exp" });
await branches.list();
await branches.delete("exp");
```
### Testing
- Added unit tests
- ran smoke tests against python and typescript sdks on local machine
### Next steps
- Add RemoteTable support
- Add Branch Comparison support
- Merge Branching support
## Bug Fix
### What is the bug?
Namespace-backed `LanceTable.to_arrow()` full-table reads bypassed the
existing `QueryTable` server-side query path and called the lower-level
table `to_arrow()` implementation directly. In Geneva/Sophon this could
fail while parsing the Arrow IPC response for
`hist.get_table().to_arrow()` / `to_pandas()`, even though
`hist.get_table().search().to_arrow()` worked.
### What issues or incorrect behavior does the bug cause?
Full-table reads on namespace-backed tables with `QueryTable` pushdown
could fail with Arrow IPC parse errors, while query/search reads on the
same table succeeded. Since `to_pandas()` delegates through `to_arrow()`
for non-blob/native cases, pandas export was affected too.
### How does this PR fix the problem?
When `QueryTable` pushdown is enabled, sync and async table `to_arrow()`
now construct a plain no-filter, no-limit, all-columns query and execute
it through the table-level `_execute_query()` path. `AsyncTable` now
preserves namespace context from async namespace connections so async
full reads can make the same pushdown decision. Non-namespace tables and
namespace tables without `QueryTable` pushdown keep their existing
behavior.
### Tests
- `uv run --extra tests --extra dev --no-sync ruff check
python/lancedb/table.py python/lancedb/namespace.py
python/tests/test_namespace.py`
- `uv run --extra tests --extra dev --no-sync ruff format
python/lancedb/table.py python/lancedb/namespace.py
python/tests/test_namespace.py`
- `uv run --extra tests --extra dev --no-sync pytest
python/tests/test_namespace.py::TestPushdownOperations::test_lance_table_to_arrow_uses_query_pushdown
python/tests/test_namespace.py::TestAsyncPushdownOperations::test_async_table_to_arrow_uses_query_pushdown
python/tests/test_namespace.py::test_local_table_to_arrow_and_to_pandas_are_unchanged
-q`
- `uv run --extra tests --extra dev --no-sync pytest
python/tests/test_namespace.py -q`
Updates LanceDB Lance dependencies from v8.0.0-beta.5 to v8.0.0-beta.6
and refreshes Cargo metadata.
No compatibility fixes were required; Java lance-core was bumped to
8.0.0-beta.6 as well.
Lance tag:
https://github.com/lance-format/lance/releases/tag/v8.0.0-beta.6
Updates Lance dependencies from v8.0.0-beta.4 to v8.0.0-beta.5 across
the Rust workspace and Java lance-core version.
No compatibility code changes were required; clippy and rustfmt pass
after installing the missing runner components.
Lance tag:
https://github.com/lance-format/lance/releases/tag/v8.0.0-beta.5
BREAKING CHANGE: direct Rust users lose the `IndexStatistics::loss`
field. Python and Node.js consumers are unaffected in practice for
remote tables (the value was always `None`/absent), but the attribute is
gone for local tables too.
`IndexStatistics::loss` was local-only — LanceDB Cloud never returned
it, so
`RemoteTable::index_stats` always set `loss: None`. It's vestigial; this
removes it.
- Remove `loss` from `IndexStatistics` and the internal `IndexMetadata`
in `rust/lancedb/src/index.rs`, plus the summing logic in
`NativeTable::index_stats`.
- Drop `loss` from the Python and Node.js bindings (and their
tests/docs).
Fixes#3493🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
## Summary
`AsyncTable.search()` computes the query embedding with
`loop.run_in_executor(None, ...)`, which uses asyncio's **default**
`ThreadPoolExecutor`. That pool is shared with all other
`run_in_executor(None, ...)` work, so a slow embedding call — a heavy
local model or an HTTP request to an embeddings API — ties up those
threads and starves unrelated async I/O under concurrent load.
This moves the (potentially blocking) embedding call onto a **dedicated
executor**, isolating it from the default pool.
Closes#3310.
## Problem
`python/lancedb/table.py`, `AsyncTable.search()`:
```python
return (
await loop.run_in_executor(
None, # asyncio's default executor, shared with other blocking I/O
embedding.function.compute_query_embeddings_with_retry,
query,
)
)[0]
```
Under load, concurrent searches whose embeddings block (or any other
code using the default executor) contend for the same small thread pool.
## Change
- Add a dedicated
`ThreadPoolExecutor(thread_name_prefix="lancedb-embedding")` in
`background_loop.py`, exposed via `embedding_executor()`.
- Use it in `AsyncTable.search()`'s `make_embedding` instead of the
default executor.
- Reset the executor in the existing `_reset_after_fork` hook — its
worker threads don't survive `fork()`, same as the background event
loop. It's recreated lazily, so this is cheap.
## Design notes
The issue asked whether maintainers preferred a configurable executor, a
dedicated internal one, or another approach (no response in the thread).
I went with a **dedicated internal executor**: it fixes the starvation
with no public API change and stays consistent with the existing `LOOP`
singleton. Making the pool size configurable would be an easy follow-up
if preferred.
Scope is limited to `search()`. The broader "embedding functions need
real async support" (including `add()`) is tracked separately in #3268.
## Testing
- Added `test_async_search_runs_embedding_on_dedicated_executor`:
patches the embedding function to record the executing thread during an
async search and asserts it runs on a `lancedb-embedding` thread.
Verified it **fails** against the previous `run_in_executor(None, ...)`
and passes with the fix.
- `ruff format`, `ruff check`, and `pyright` pass on the changed files.
## Summary
Wires `RemoteTable::set_lsm_write_spec` / `unset_lsm_write_spec` to the
sophon REST endpoints added in
[lancedb/sophon#6181](https://github.com/lancedb/sophon/pull/6181),
replacing the previous `NotSupported` stubs.
- `set_lsm_write_spec` maps the `LsmWriteSpec` onto sophon's request DTO
— mode-tagged `sharding` (`unsharded` / `bucket` / `identity`),
`maintained_indexes`, and `writer_config_defaults` — and POSTs to
`/v1/table/{name}/set_lsm_write_spec/`.
- `unset_lsm_write_spec` POSTs to
`/v1/table/{name}/unset_lsm_write_spec/`.
- Both call `check_mutable` first, matching the other remote mutations.
- `maintained_indexes` is sent verbatim (an empty list means "no
maintained indexes", matching native semantics).
## Testing
- Added mocked-endpoint unit tests for unsharded / bucket / identity set
and for unset.
- `cargo check --features remote --tests` passes.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Updates Lance dependencies to v8.0.0-beta.2 across the Rust workspace
and Java lance-core metadata.
The update was generated with ci/update_lance_dependency.py and required
no compatibility code changes.
Lance tag:
https://github.com/lance-format/lance/releases/tag/v8.0.0-beta.2
## ⛔ Merge blocker: legal review required
This bump pulls in a new transitive **dev/profiling** dependency chain
`inferno v0.11.21` → `pprof v0.15.0` → `lance-testing`, and `inferno` is
licensed **CDDL-1.0** (copyleft). To get `cargo-deny` green, `CDDL-1.0`
was added to the `deny.toml` allow list.
**Do not merge until legal has reviewed and signed off on allowing
CDDL-1.0.** The dependency is dev/test-only and not distributed, but the
allow-list addition still requires legal approval per our policy.
---------
Co-authored-by: Daniel Rammer <hamersaw@protonmail.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
## Summary
- Add `__reduce__` methods to `LanceDBClientError` and `RetryError` so
that instances can be pickled and unpickled correctly
- `HttpError` inherits the fix from `LanceDBClientError` since it has no
additional `__init__` parameters
- Add tests verifying pickle roundtrip for all three exception classes
Fixes#3447
## Test plan
- [x] Verified pickle roundtrip for `LanceDBClientError` with and
without `status_code`
- [x] Verified pickle roundtrip for `HttpError` (subclass, no extra init
params)
- [x] Verified pickle roundtrip for `RetryError` (subclass with many
extra params)
- [ ] CI tests pass
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Will Jones <willjones127@gmail.com>
## Bug Fix
### What is the bug?
`QueryBuilder.to_pandas(blob_mode="descriptions")` could still fall back
to `self.to_arrow()` for query outputs with blob columns. Custom query
subclasses or wrappers can have `to_arrow()` behavior that is not
compatible with pandas blob-description conversion, which can surface as
low-level Arrow/list-batch conversion failures.
### What issues or incorrect behavior does the bug cause?
Callers need to carry local `to_pandas` or plain-scan adapter special
casing for blob descriptions, and scanner-only kwargs such as row
addresses and fragment selection are not represented in LanceDB query
state.
### How does this PR fix the problem?
This PR routes blob-output query `to_pandas()` through the Lance scanner
path for `lazy`, `bytes`, and `descriptions` modes when the query is a
scanner-backed plain scan. For `blob_mode="descriptions"` with
`flatten`, it collects scanner Arrow/table output, applies LanceDB
`flatten_columns`, and converts to pandas from there. Non-plain blob
query shapes now fail with a clear unsupported error instead of falling
into subclass `to_arrow()` behavior.
It also adds Python query state and builder methods for scanner-only
plain-scan parameters:
- `with_row_address()` for `_rowaddr`
- `with_fragments(...)` for Lance fragment objects
- `fragment_ids([...])` as a convenience wrapper that resolves IDs to
Lance fragments
## Validation
- `cd python && uv run --no-sync ruff format --check
python/lancedb/query.py python/tests/test_query.py`
- `cd python && uv run --no-sync ruff check python/lancedb/query.py
python/tests/test_query.py`
Targeted pytest was intentionally not run locally per maintainer
request.
### Description
This PR exposes native DataFusion expression support in the Rust SDK's
`MergeInsertBuilder` via two new builder methods:
`when_matched_update_all_expr` and
`when_not_matched_by_source_delete_expr`.
For remote LanceDB tables (where operations are serialized over
HTTP/JSON to the SaaS backend), native DataFusion expression trees
cannot be executed directly. The SDK handles this gracefully by
returning a `NotSupported` error.
### Key Changes
- **`MergeFilter` Enum**: Introduced a helper enum to store either a SQL
string or a native `datafusion_expr::Expr`.
- **`MergeInsertBuilder`**: Updated `when_matched_update_all_filt` and
`when_not_matched_by_source_delete_filt` fields to store the new enum,
and added `when_matched_update_all_expr` and
`when_not_matched_by_source_delete_expr` builder methods.
- **Execution & Remote Dispatch**: Dispatched the filter variants during
local execution, and rejected expression filters with a clean
`NotSupported` error in remote table request conversion.
- **Testing**: Added a `test_merge_insert_expr` unit test covering
conditional updates and deletes with programmatically built DataFusion
expressions.
### Verification
- Added integration test `test_merge_insert_expr` which successfully
compiles and passes.
- Formatted and linted the code.
Closes#3416
## Summary
Regression test for [issue
#2654](https://github.com/lancedb/lancedb/issues/2654) — a nullable
struct column whose first batch contains only `None` values crashed in
`_align_field_types` with `AttributeError: 'pyarrow.lib.DataType' object
has no attribute 'fields'`.
The actual fix landed in #3394, but no test was added. This PR adds the
reproducer from the issue as a test.
## Test plan
- `test_add_nullable_struct_with_none`: creates a table with a nullable
struct column, adds a row with a non-null struct value, then a row with
`None` for the struct field. Verifies both rows land correctly.
- Uses Lance file format v2.1 (`new_table_data_storage_version="2.1"`)
because nullable structs aren't supported on v2.0.
## Related
- #3028 (the original fix attempt, now superseded)
Adds a REVIEW.md at the repo root with cross-SDK parity guidance for
automated code review. The Claude Code review feature automatically
loads `REVIEW.md` as review-only context.
This is intentionally a semantic nudge, not a deterministic check, it
relies on the reviewer reading the sibling SDK, so it will catch most
gaps.
## What's broken
Calling `RRFReranker().rerank_multivector([])` crashes with `IndexError:
list index out of range` because the method accesses `vector_results[0]`
for the type-homogeneity check before verifying the list is non-empty.
The `all()` call passes vacuously on an empty iterable so the crash hits
the next lines.
```python
from lancedb.rerankers import RRFReranker
RRFReranker().rerank_multivector([])
# IndexError: list index out of range
```
## Why it happens
The type check uses `vector_results[0]` as the reference type but never
guards against an empty list. `all(...)` short-circuits to `True` when
the iterable is empty, so the bad index access on the lines that follow
is never reached by the existing guard logic.
## Fix
Add an explicit empty-list check before any indexing.
## What's broken
`MRRReranker.rerank_multivector([])` raises `IndexError: list index out
of range`. The crash happens on line 128 (the `all()` type-homogeneity
check passes vacuously on an empty iterable) and on line 134 which
accesses `vector_results[0]` unconditionally, with no prior guard for an
empty list.
## Why it happens
`all()` over an empty iterable returns `True`, so the type check
silently passes and execution falls through to `vector_results[0]` which
crashes.
## Fix
Added a two-line guard at the top of `rerank_multivector` that raises a
clear `ValueError("vector_results must not be empty")` before any
indexing occurs.
## Test
Added `test_mrr_reranker_empty_input` in `test_rerankers.py` which calls
`rerank_multivector([])` and asserts that a `ValueError` with the
message "must not be empty" is raised.
Fixes#3468
Co-authored-by: Aegis Dev <aegis@devteamaegis.com>
## Feature
- What is the new feature?
- Adds `blob_mode` support to sync and async Python query `to_pandas()`
APIs.
- Enables plain scan queries to return blob columns as lazy `BlobFile`
objects, raw bytes, or blob descriptions.
- Lets namespace-backed local tables use Lance native blob-aware pandas
conversion for lazy blobs.
- Why do we need this feature?
- Table and Lance dataset/scanner APIs already support blob-aware pandas
conversion, but LanceDB query builders did not expose that capability.
- Geneva and other callers should be able to use query-level
`to_pandas(blob_mode=...)` without manually constructing Lance scanners.
- How does it work?
- Plain scan queries route through Lance scanner native
`to_pandas(blob_mode=...)`, preserving filter, projection, limit,
offset, row id, and alias/expression projection behavior.
- Non-native query shapes keep existing Arrow fallback semantics and
raise a clear error when they return blob columns with
`blob_mode="lazy"` or `blob_mode="bytes"`.
- Focused tests cover table/query blob modes,
filter/select/limit/offset/alias query cases, async query behavior,
vector-query error boundaries, and namespace-backed lazy blobs.
## Validation
- `cd python && .venv/bin/maturin develop --uv --extras tests,dev
--profile dev`
- `cd python && uv run --frozen --no-sync pytest
python/tests/test_table.py::test_table_to_pandas_blob_modes
python/tests/test_table.py::test_async_table_to_pandas_blob_bytes
python/tests/test_query.py::test_plain_scan_query_to_pandas_blob_modes
python/tests/test_query.py::test_plain_scan_query_to_pandas_blob_projection
python/tests/test_query.py::test_async_plain_scan_query_to_pandas_blob_projection
python/tests/test_query.py::test_vector_query_to_pandas_blob_mode_requires_native_path
python/tests/test_namespace.py::TestNamespaceConnection::test_table_to_pandas_blob_lazy_through_namespace
-q`
- `cd python && uv run --frozen --no-sync ruff format --check .`
- `cd python && uv run --frozen --no-sync ruff check .`
- `git diff --check`
### Summary
Deprecates the Python replace_field_metadata (on Table and AsyncTable)
in favor of update_field_metadata. Mirrors Lance, which already
deprecated Dataset.replace_field_metadata for update_field_metadata.
Stacked on top of #3482 as this was a follow-up task after adding
update_field_metadata
### Summary
Adds update_field_metadata to the client SDK (Rust core, Python, and
TypeScript) so clients can edit per-field (column) Arrow metadata
(schema.fields[].metadata)
### Testing
- added unit tests
- ran E2E against a local server on both local and remote tables (set →
merge → delete), across Python sync/async and TypeScript
### Next steps
- deprecate replace_field_metadata in the python lancedb favor of this
(typescript didn't have replace_field_metadata method). This matches
Lance's API direction (Lance already deprecated replace_field_metadata
for update_field_metadata)
Moves the Lance dependency bump process into an in-repository skill so
local agents and GitHub Actions share the same workflow definition.
The update workflow is now an explicit, optional-tag entrypoint;
latest-release resolution, duplicate PR handling, Java/Rust dependency
updates, and Sophon follow-up are documented in the skill and backed by
a small deterministic helper.
This PR makes remote LanceDB tables usable from PyTorch multiprocessing
workers. Remote tables now carry enough safe JSON connection state to
reopen themselves after pickle/spawn or fork, and permutations lazily
rebuild their reader from restored tables instead of trying to reuse
process-local handles.
This addresses the remote-table gap in the PyTorch dataset path while
preserving the explicit connection factory escape hatch for custom
worker-side credential loading or non-serializable header providers.
Validated with targeted remote table, permutation, and PyTorch
DataLoader tests.
This updates the workspace Lance dependencies from `v7.1.0-beta.4` to
`v7.2.0-beta.3` and refreshes `Cargo.lock`.
The lockfile now points at Lance commit
`7c070f760fa8e24c8015cb2afbd22c5e6b7898e8` and includes the transitive
dependency updates required by the new beta.
Update the Rust workspace Lance git dependencies and Java lance-core
dependency to v7.2.0-beta.1.
This keeps LanceDB aligned with the latest Lance beta release and
refreshes the Cargo lockfile for the new Lance dependency graph.
## Summary
When an `LsmWriteSpec` is installed on a table (#3396), `merge_insert`
upsert
calls are dispatched through Lance's MemWAL `ShardWriter` (LSM-style
append)
instead of the standard merge path.
- **`use_lsm_write`** — a `merge_insert` builder option, default `true`;
set it
`false` to use the standard path for a call even when a spec is set.
- **`assume_pre_sharded`** — a `merge_insert` builder option, default
`false`;
skips the per-row shard check and routes by the first row only.
- **`close_lsm_writers`** — drains and closes the table's cached MemWAL
shard
writers.
- The `merge_insert` **`on`** columns default to, and are validated
against,
the table's unenforced primary key.
- Shard writers are cached alongside the dataset (in
`DatasetConsistencyWrapper`) and reused for the session.
- `MergeResult` gains **`num_rows`** — on the LSM path the insert/update
breakdown is unknown until compaction, so only the total is reported.
Routing covers all three sharding strategies — bucket (murmur3,
Iceberg-compatible), identity, and unsharded. Each `merge_insert` call
targets
a single shard; the whole input is collected and validated before a
single
atomic `ShardWriter::put`, so a validation failure leaves the MemWAL
untouched.
Bindings: Python (`merge_insert(...).use_lsm_write(...)` /
`.assume_pre_sharded(...)`, `Table.close_lsm_writers`) and TypeScript
(`mergeInsert(...).useLsmWrite(...)` / `.assumePreSharded(...)`,
`Table.closeLsmWriters`).
## Context
Reconstructed from the original #3354 branch onto current `main`: the
branch
predated the #3394 (unenforced primary key) / #3396 (`LsmWriteSpec`)
split and
has been rebuilt on that merged foundation. Depends on Lance
`v7.0.0-beta.13`.
The MemWAL read path (reading un-flushed shard data back into queries)
and
remote (LanceDB Cloud) LSM support are follow-ups.
---------
Co-authored-by: Jack Ye <yezhaoqin@gmail.com>
Bumps the pinned toolchain in `rust-toolchain.toml` from 1.94.0 to
1.95.0.
Fixes new lints surfaced by clippy on 1.95.0:
- `manual_checked_ops` — fragment size mean in `table.rs` uses
`checked_div`
- `explicit_counter_loop` — shuffle test loop in `shuffle.rs`
No rustc warnings were introduced.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The latest main Python workflow fails across multiple matrix jobs
because `test_remote_create_index_new_api` opens a remote table whose
mocked schema only exposes `id`, while the new `create_index(...,
config=...)` path validates the requested indexed columns.
This updates the remote-table fixture to include the indexed columns
used by the smoke test and checks the emitted column payloads, keeping
the test aligned with the schema-aware API path.
## Summary
- Transitions `LanceTable` and `RemoteTable` to use the unified
`create_index()` API matching `AsyncTable`
- Deprecates `create_scalar_index()` and `create_fts_index()` with
deprecation warnings
- Adds detection logic to distinguish legacy vs new API calls
- Adds `@overload` decorators for type checker compatibility
- Adds `accelerator` parameter to IVF config classes for GPU support
**New API:**
```python
table.create_index("vec", config=IvfPq(distance_type="l2"))
table.create_index("col", config=BTree())
table.create_index("text_col", config=FTS(with_position=True))
```
**Legacy API (deprecated):**
```python
table.create_index("l2", vector_column_name="vec") # emits DeprecationWarning
table.create_scalar_index("col", index_type="BTREE") # deprecated
table.create_fts_index("text_col") # deprecated
```
Fixes#2879🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
manylinux2_17 reached EOL in 2024 and pyarrow stopped publishing 2_17
wheels long ago. We already build manylinux2_28 wheels, so drop the 2_17
matrix entries.
Fixes#3452
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The python-v0.32.0 publish run failed on every build matrix entry. Three
independent issues:
1. **Mac and Windows**: `pypa/gh-action-pypi-publish` only runs on
Linux, but was being called inline from each build job.
2. **Linux (all arches)**: `pypa/gh-action-pypi-publish` derives its
docker image name from `github.action_repository`, which is empty when
the action is invoked from inside a composite action
(actions/runner#2473 — pypa's own `action.yml` references this bug). It
falls back to `github.repository`, generating
`docker://ghcr.io/lancedb/lancedb:<tag>`, which doesn't exist →
`denied`. Only the ARM matrix entry surfaced this because it failed
first and cancel-cascaded the rest.
3. **Windows**: `upload-artifact` in `build_windows_wheel` pointed at
`python\target\wheels`, but maturin writes to the workspace-root
`target/wheels`. The artifact was always empty. Also, `pypi-publish.yml`
passed a `vcpkg_token` input that the composite doesn't declare.
## Changes
- Build jobs (linux/mac/windows) now upload their wheels as
`actions/upload-artifact` artifacts.
- New Linux `publish` job downloads all wheel artifacts and runs the
Fury or PyPA publish step directly (not via a composite), so
`github.action_repository` resolves correctly.
- Delete the unused `upload_wheel` composite action.
- Drop the broken upload-artifact step inside `build_windows_wheel`.
- Remove the bogus `vcpkg_token` input.
- Fury upload now loops over all wheels instead of just the first.
- Bump `actions/checkout`, `actions/upload-artifact`,
`actions/download-artifact` to current major versions (Node 24) to clear
deprecation warnings.
- Bump Windows job timeout 60 → 90 minutes; previous run was
cancel-timing-out on a 60m cap.
- Use `rust-lld` as the Windows MSVC linker via
`CARGO_TARGET_X86_64_PC_WINDOWS_MSVC_LINKER`. `link.exe` is
single-threaded and the long pole on Windows builds.
Fixes#3445
## Test plan
- [x] Open this PR — `paths` filter triggers a dry-run build on all
three platforms.
- [x] Verify all three builds produce wheels.
- [x] Confirm the `pypa/gh-action-pypi-publish` container actually
starts (the actions/runner#2473 bug) via the `publish-dry-run` job
pointed at TestPyPI.
- [x] **REMOVE BEFORE MERGE**: drop the `publish-dry-run` job and the
now-redundant `actions/upload-artifact` runs on PRs (currently always-on
so the dry-run has wheels to publish).
- [ ] After merge, cherry-pick onto `python-v0.32.0` and force-push the
tag to re-trigger the publish.
When a table is created with `pa.json_()` (PyArrow's JSON extension
type),
it is stored internally as `lance.json` (LargeBinary with `lance.json`
extension metadata). Calling `table.add()` with `pa.json_()` data failed
with:
```
RuntimeError: lance error: Append with different schema:
`data` should have type json but type was large_binary
```
`build_field_exprs` in `rust/lancedb/src/table/datafusion/cast.rs` saw
that
the input field (`Utf8` with `arrow.json` metadata) differed from the
table
field (`LargeBinary` with `lance.json` metadata). Since
`can_cast_types(Utf8, LargeBinary)` is true, it inserted a DataFusion
`Utf8 → LargeBinary` cast. That cast preserved the input field's
`arrow.json`
extension metadata instead of adopting the table's `lance.json`
metadata, so
lance-core detected a schema mismatch and rejected the append.
This adds a special case in `build_field_exprs`: when the input is
`arrow.json` and the table field is `lance.json`, the expression is
passed
through unchanged. Lance-core's write path already handles the
`arrow.json → lance.json` conversion (including JSONB encoding), so no
DataFusion cast is needed.
Fixes#3144
Continues #3291 from a fork (the original author's branch could not be
pushed to). The original commits are preserved; an additional commit
fixes
the CI failures on that PR — formatting, a missing trait import, and
read-back assertions that assumed binary storage when a lance.json
column
is read back as `Utf8`.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: yunju.lly <yunju.lly@antgroup.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
## Summary
- Bump lance dependency from `v7.0.0-beta.13` to `v7.0.0-rc.1`
- Remove PK constraint from `LsmWriteSpec::Bucket` docs and
`Table::set_lsm_write_spec` docs
- Remove test assertions that expected rejection when no PK is set or
when bucket column != PK
Closes https://github.com/lance-format/lance/issues/6917
## Problem
`LinearCombinationReranker.merge_results` has two related bugs that make
it return **inverted relevance rankings** — the least relevant document
ranks first (closes#3154).
### Bug 1 — `_combine_score` subtracts from 1, inverting the final
ranking
```python
def _combine_score(self, vector_score, fts_score):
return 1 - (self.weight * vector_score + (1 - self.weight) * fts_score)
```
Both `vector_score` (already converted via `_invert_score`) and
`fts_score` (BM25 relevance) are in **higher-is-better** space. Wrapping
the weighted average in `1 - (...)` flips the direction: a perfectly
matching document (`vector_score=1, fts_score=1`) gets `_relevance_score
= 0.0`, while a non-matching document gets a high score.
### Bug 2 — Documents missing an FTS score are rewarded, not penalised
```python
fts_score = result.get("_score", fill) # fill=1.0 by default
```
When a document has no FTS match, `fts_score = fill = 1.0`. In
`_combine_score` (with the bug-1 formula), this large value becomes a
**negative penalty** via `1 - (... + 0.3 * 1.0)`, counterintuitively
*boosting* the document's score. By contrast, missing vector results
correctly receive `_invert_score(fill) = 0.0` (penalised).
## Fix
**Bug 1** — remove the `1 -` inversion from `_combine_score`:
```python
def _combine_score(self, vector_score, fts_score):
return self.weight * vector_score + (1 - self.weight) * fts_score
```
**Bug 2** — use `1 - fill` for missing FTS scores so both penalties are
symmetric (mirror of what `_invert_score(fill)` already does for missing
vector scores):
```python
fts_score = result.get("_score", 1 - fill) # was: fill
```
With `fill=1.0` (default): `1 - 1.0 = 0.0` — missing-FTS entries
contribute `0` to the FTS term, identical to how missing-vector entries
contribute `0` to the vector term.
## Verification
Concrete example from the issue. With `weight=0.7`, `fill=1.0`:
| Document | `_distance` | `_score` | Old `_relevance_score` | New
`_relevance_score` |
|----------|-------------|----------|------------------------|------------------------|
| `apple orange` | 0.0 (best) | 2.41 (only FTS) | 0.30 (**wrong: ranked
2nd**) | 1.42 (**correct: ranked 1st**) |
| `banana grape` | 0.9999 (worst) | — | 0.70 (**wrong: ranked 1st**) |
0.00 (**correct: ranked last**) |
## Tests
Two regression tests added to `python/python/tests/test_rerankers.py`:
- `test_linear_combination_best_match_ranks_first` — the document with
the smallest distance **and** an FTS match must have the highest
`_relevance_score`.
- `test_linear_combination_missing_fts_is_penalised` — a document with
any FTS score must beat an otherwise-equal document with no FTS match.
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Closes client side work of #3370
### Summary
- Plumbs `read_consistency_interval` from `ConnectBuilder` through
`RestfulLanceDbClient` so remote reads attach an
`x-lancedb-min-timestamp` freshness header. None = no header (default),
zero = "now", positive = `now - interval`.
- Adds per-table `FreshnessState` on `RemoteTable`: write responses
(`update`, `delete`, `merge_insert`, `add_columns`, `alter_columns`,
`drop_columns`) track the committed version, and the next read sends
`x-lancedb-min-version` so the server's cache honors read-your-write.
- `checkout(v)` / `checkout_tag(t)` / `checkout_latest()` / `restore()`
reset the freshness state appropriately; the validating `/describe/` and
tag-resolve requests are sent without freshness headers so they don't
carry stale state.
- Updates Rust, Python, and Node docstrings and calls out that stronger
consistency raises per-read latency and cost.
### Testing
- Unit tests cover default behavior, interval=0, positive interval,
checkout_latest baseline, min_version-after-write, checkout clears
state, and the two no-stale-header invariants on `checkout(v)` and
`checkout_tag(t)`.
- Ran smoke tests against local remote table to verify functionality
Modified the parameter of delete to a Predicate that could be
constructed from either datafusion Expr, from str (to support SQL
predicate), or from String to support python and javascript bindings.
When a datafusion Expr is used, it avoids the overhead of serializing to
SQL and re-parsing when callers already have an Expr (e.g. from query
planning).
The native implementation uses lance's `DeleteBuilder::from_expr`. The
remote implementation converts the Expr to SQL via `expr_to_sql_string`
before sending to the server, consistent with the existing query and
count_rows paths.
Closes#3204
Signed-off-by: Yuval Lifshitz <ylifshit@ibm.com>
Co-authored-by: Claude Code <noreply@anthropic.com>
Previously `check_lance_release.py` used `git/refs/tags` with
`--paginate --jq`, which drops the last page in some `gh` versions. The
7.x Lance tags all landed on the final (partial) page, causing the
script to report `v6.0.1` as the latest and never triggering an update.
Switch to the releases API with `per_page=20`, which returns the 20 most
recent releases sorted newest-first — one API call, no pagination
needed.
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
## Problem
Issue #2247 notes that the Python docs do not show how to use LanceDB's
in-memory backend via `connect("memory://")`.
## Solution
Add `memory://` examples to the sync and async `connect` docstrings, and
call out that in-memory databases are intended for tests/temporary data
and are not persisted.
## Validation
- `python3 -m py_compile python/python/lancedb/__init__.py`
- `git diff --check`
## Confidence
82/100 — docs-only update, directly tied to the documented missing
`memory://` usage. It changes API documentation only and was syntax/diff
validated.
Closes#2247.
The `test_resolve_user_id_*` tests in `remote/client.rs` mutate the
process-global `LANCEDB_USER_ID` and `LANCEDB_USER_ID_ENV_KEY`
environment variables. cargo runs tests in a binary across multiple
threads, so one test's `remove_var` can race another's `set_var` between
when it's set and when `resolve_user_id()` reads it.
This surfaced as an intermittent failure of
`test_resolve_user_id_from_env_key` on Windows CI:
```
assertion `left == right` failed
left: None
right: Some("custom-env-user-id")
```
Annotates the five env-mutating tests with `serial_test`'s
`#[serial(user_id_env)]` so they run serially with respect to each
other.
Should be backported to `release/v0.28` (CI for #3421 hit this same
flake).
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Fixes#3407.
Remote tables now resolve create-index field paths against the table
schema before sending requests, so nested, escaped, and case-insensitive
inputs use the same canonical path contract as local tables. Remote
`list_indices()` also canonicalizes returned columns against the current
schema, and the remote query tests lock explicit nested vector and FTS
request payloads.
## Summary
Fixes#3424.
`LanceTakeQueryBuilder.to_batches()` raised `AttributeError:
'AsyncTakeQuery' object has no attribute 'execute'`. The inherited
`BaseQueryBuilder.to_batches` called `self._inner.execute(...)`, but
`self._inner` is an `AsyncQueryBase` (Python wrapper) — only its native
inner exposes `execute`. Every other sync builder overrides
`to_batches`, so the bug only surfaced on take-query builders, which
inherit the base unchanged. `take_offsets(...).to_batches()` is broken
for the same reason.
Route the sync wrapper through the async `to_batches` on the background
event loop, so the native `execute` is invoked from inside an awaiting
context (matching how the async path works correctly).
## Repro
```python
import lancedb, pyarrow as pa, tempfile
db = lancedb.connect(tempfile.mkdtemp())
tbl = db.create_table("t", data=pa.table({"a": list(range(100))}))
tbl.take_row_ids([0, 1, 2]).to_arrow() # works
tbl.search().to_batches() # works
list(tbl.take_row_ids([0, 1, 2]).to_batches()) # AttributeError (before)
```
## Test plan
- [x] New regression test `test_take_queries_to_batches` covers
`take_offsets(...).to_batches()`, `take_row_ids(...).to_batches()`, and
the `select(...)` projection — all fail on `main` with the patch
reverted, all pass with the fix.
- [x] `test_take_queries`, `test_query_builder_batches`, and
`test_query_schema` still pass.
- [x] `ruff format --check` and `ruff check` clean on changed files.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
LanceDB default vector column discovery only considered top-level
fields, so tables with a single nested vector leaf still required users
to pass an explicit field path. This updates Rust and Python discovery
to recurse into struct fields, return canonical field paths, and
preserve actionable errors when no default or multiple defaults exist.
The explicit nested path flow for index creation and search remains
supported across Rust, Python, and Node, with regression coverage for
single nested vector leaves, multiple candidate leaves, and schemas
without vector leaves.
Closes#3405.
There have been a couple of reports of this function freezing debuggers
because it triggers a network round-trip but is assumed to be extremely
light-weight: https://github.com/lancedb/lancedb/discussions/2853. We'll
just cache the last version we see.
I considered digging into see if we could assume or get the version at
create time or after other operations, but that could be a bit of a
rabbit hole as I'm a bit unfamiliar with this. Claude was having a hard
time of it too 😅 I propose we see how the currently implementation goes
and improve it if people find "unknown" or stale values coming up
disruptively often before improving this further.
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
## Summary
- Fix `name in db` and `len(db)` for local Python connections with more
than one page of tables.
- Use `list_tables()` pagination instead of deprecated `table_names()`
with its default 10-item page.
- Add regression coverage with 20 tables so later pages are included.
Fixes#2727.
## Validation
- `python3 -m py_compile python/python/lancedb/db.py
python/python/tests/test_db.py`
- No-build Python harness that extracts and executes the edited
`LanceDBConnection` pagination methods: passed
- `uvx ruff check python/python/lancedb/db.py
python/python/tests/test_db.py`
- `uvx ruff format --check python/python/lancedb/db.py
python/python/tests/test_db.py`
Note: `uv run pytest
python/tests/test_db.py::test_db_contains_and_len_include_all_table_name_pages
-q` was attempted first, but it stayed in the broad Rust/PyO3 native
extension build and was stopped before pytest started.
Adds regression coverage for Python FTS APIs targeting nested text
leaves, including sync and async match, phrase, and hybrid query paths.
This also locks in the intended error boundary: nested text leaf paths
are valid, while struct containers, non-text leaves, and missing paths
remain rejected.
Fixes#3404.
Index metadata APIs now resolve stored field ids back to Lance canonical
field paths instead of leaf names, so nested indexes such as
`metadata.user_id` and escaped literal-dot fields round-trip through
`list_indices()`. Native index creation also canonicalizes the input
path before handing it to Lance, keeping local metadata consistent with
the field-path contract while remote responses continue to expose
server-provided canonical columns.
Fixes#3403.
Weekly dependabot refresh of `Cargo.lock`.
Dependabot's original PR also raised the lower-bound version
requirements
in `Cargo.toml` (arrow, tokio, aws-sdk-*, etc.) to match the new
lockfile
versions. That forces our library's consumers onto newer minimum
versions and broke the MSRV check, which downgrades aws-sdk-* crates to
verify they still build on Rust 1.91.
Changes from the original:
- Reverted all `Cargo.toml` requirement changes; `Cargo.lock`
regenerated
with `cargo update` within the existing ranges. The lockfile (and the
binaries we ship) stays current on security fixes without bumping our
public minimum versions.
- Set `versioning-strategy: lockfile-only` in `.github/dependabot.yml`
so
future cargo dependabot PRs only touch `Cargo.lock`.
Note: `aws-lc-rs` stays at 1.16.3 — `nodejs/Cargo.toml` pins it with
`=`,
which `lockfile-only` cannot move; bumping it needs a manual change.
---------
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Will Jones <will.jones127@gmail.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Co-authored-by: Will Jones <willjones127@gmail.com>
Native index creation was resolving requested columns through top-level
Arrow schema lookup before handing the request to Lance, which rejected
nested paths and could collapse a nested field to its leaf name. This PR
resolves index targets with Lance field-path semantics, passes the
canonical path through to Lance, and reports indexed columns from field
ids as canonical full paths.
This also removes the Python native FTS guard that rejected dotted paths
so scalar, vector, and FTS index creation share the same nested-field
contract. Related to #3402.
### Summary
- Add an optional `progress` callback to `Table.add(data, { progress
})`. Callback fires once per batch written and once more with `done:
true` when the write completes.
- Errors thrown from the user's callback are logged with `console.warn`
and swallowed
### Testing
- npm test
- ran smoke test script to verify functionality
Adds three index configuration classes to the Python API Reference that
were missing from the documentation:
- `IvfSq` - IVF Scalar Quantization index
- `IvfRq` - IVF RabitQ Quantization index
- `HnswFlat` - HNSW without quantization (stores raw vectors)
These classes are exported in `lancedb.index.__all__` and have complete
docstrings in the source, but weren't showing up in the rendered docs at
https://lancedb.github.io/lancedb/python/python/#indices-asynchronous.
Closes#1855
Fixes#3136.
## Summary
- `WithEmbeddingsScannable::scan_as_stream` matched columns positionally
against the table schema, so a `CastError` was raised whenever the
computed batch order differed from the table schema order.
- The mismatch surfaced when `add_columns` added a new physical column
**after** an embedding column: the table schema became
`[..., embedding, extra]`, but `compute_embeddings_for_batch` always
appends embeddings at the end, producing `[..., extra, embedding]`.
Position 2 then tried to cast e.g. `score: Float64` →
`embedding: FixedSizeList` and failed.
- Now we look each output column up by name in the result batch, which
is order-independent. If a non-embedding column required by the table
schema is missing from the input, we return a clear `InvalidInput`
error instead of a confusing cast error.
## Reproduction (from the issue)
```text
Table created with: [id, text, text_vec(embedding)]
add_columns("score") → schema: [id, text, text_vec, score]
table.add([id, text, score]) → BEFORE: CastError on position 2
AFTER: succeeds, embedding is computed
```
## Tests
-
`data::scannable.rs::test_with_embeddings_scannable_column_added_after_embedding`
— unit test exercising the exact column-order mismatch via
`WithEmbeddingsScannable::with_schema`.
-
`data::scannable.rs::test_with_embeddings_scannable_missing_required_column`
— covers the new "missing column" error path.
- `table::add_data.rs::test_add_with_embeddings_after_add_columns`
— end-to-end regression test mirroring the reproduction in the issue
(create table with embedding → `add_columns` → `table.add`).
## Test plan
- [x] `cargo check --quiet --features remote --tests --examples`
- [x] `cargo clippy --quiet --features remote --tests --examples`
- [x] `cargo fmt --all`
- [x] `cargo test --quiet --features remote -p lancedb embedding` — 18
tests pass
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This updates the agent instructions to codify the CI gates that failed
before code review started: PR titles must satisfy Conventional Commits,
and Python changes need root-level ruff format/lint checks.
It also makes the touched-language preflight explicit so mixed Rust,
Python, and TypeScript changes run the checks CI expects before opening
a PR.
## Feature
This PR aligns LanceDB Python `to_pandas()` APIs with Lance pandas
conversion capabilities while keeping LanceDB query-specific semantics
intact.
- Adds `blob_mode` and pandas `**kwargs` support to local table
`to_pandas()`.
- Delegates local `LanceTable.to_pandas()` to Lance dataset
`to_pandas(blob_mode=..., **kwargs)`.
- Keeps remote table `to_pandas()` unsupported with
`NotImplementedError`.
- Allows sync and async query `to_pandas()` to forward pandas kwargs
after LanceDB `flatten` and `timeout` handling.
Why we need this feature:
Users can access Lance blob-aware pandas conversion from LanceDB local
tables and can pass PyArrow pandas conversion options through
table/query APIs without losing existing `flatten` or `timeout`
behavior.
How it works:
The table API exposes a `BlobMode` literal type for `lazy`, `bytes`, and
`descriptions`. Local tables call through to the backing Lance dataset.
Query APIs do not add `blob_mode`; they materialize Arrow results, apply
LanceDB flattening when requested, and then call `to_pandas(**kwargs)`.
## Validation
- `uv run --frozen --extra tests pytest
python/tests/test_table.py::test_table_to_pandas_default_matches_arrow
python/tests/test_table.py::test_table_to_pandas_blob_bytes
python/tests/test_table.py::test_table_to_pandas_kwargs
python/tests/test_query.py::test_query_to_pandas_kwargs
python/tests/test_query.py::test_query_timeout
python/tests/test_remote_db.py::test_table_to_pandas_not_supported`
- `uv run --frozen --extra dev ruff check python/lancedb/table.py
python/lancedb/query.py python/lancedb/remote/table.py
python/tests/test_table.py python/tests/test_query.py
python/tests/test_remote_db.py`
- `uv run --frozen --extra tests pytest python/tests/test_table.py
python/tests/test_query.py python/tests/test_remote_db.py`
Note: `python/uv.lock` was intentionally not committed in this branch.
description: Update LanceDB to a specific Lance release or tag. Use when bumping Lance dependencies in the lancedb repository, including Rust workspace Lance crates, Java lance-core, validation, branch creation, commit, push, and PR creation when requested.
---
# LanceDB Update Lance Dependency
## Scope
Use this skill in the `lancedb/lancedb` repository when updating the Lance dependency to a specific Lance version or tag.
Inputs can be a version (`7.2.0-beta.1`), a tag (`v7.2.0-beta.1`), a tag ref (`refs/tags/v7.2.0-beta.1`), or `latest`.
## Workflow
1. Confirm the worktree status with `git status --short`.
2. Resolve the target Lance version:
- If the input is `latest`, empty, or omitted, run:
```bash
python3 ci/check_lance_release.py
```
Parse the JSON output. If `needs_update` is not `true`, stop without creating a PR. Otherwise use `latest_tag`.
- If the input is explicit, use it directly.
3. Compute update metadata without changing files:
This updates the Rust workspace Lance dependencies through `ci/set_lance_version.py`, updates `java/pom.xml`, refreshes Cargo metadata, and prints JSON metadata containing `branch_name`, `commit_message`, and `pr_title`.
Fix real diagnostics and rerun clippy until it succeeds. Do not skip warnings.
6. Inspect `git status --short` and `git diff` to ensure only the Lance dependency update and required compatibility fixes are present.
7. If the task only asks to prepare local changes, stop here and report the changed files and validation result.
8. If the task asks to publish the update, create a branch using the printed `branch_name`, stage all relevant files, and commit using the printed `commit_message`. Do not amend or rewrite existing commits.
9. Push to `origin`. Before creating the PR, check that the current token has push permission:
```bash
gh api repos/lancedb/lancedb --jq .permissions.push
```
If the remote branch already exists for the same generated branch name, delete the remote ref with `gh api -X DELETE repos/lancedb/lancedb/git/refs/heads/$BRANCH_NAME`, then push. Do not force-push.
10. Create a PR targeting `main` with the printed `pr_title`. If there is no PR template, keep the body to two or three concise sentences: state the Lance dependency bump, note any required compatibility fixes, and link the triggering Lance tag or release.
11. Read back the remote PR title after creation. If it is not a Conventional Commit title, fix it immediately.
12. When running in GitHub Actions after creating the LanceDB PR, trigger the Sophon dependency update:
```bash
gh workflow run codex-bump-lancedb-lance.yml \
--repo lancedb/sophon \
-f lance_ref="$LANCE_TAG" \
-f lancedb_ref="$BRANCH_NAME"
gh run list --repo lancedb/sophon --workflow codex-bump-lancedb-lance.yml --limit 1 --json databaseId,url,displayTitle
```
Use the emitted metadata `tag` value as `LANCE_TAG`. Do this only after a new LanceDB PR has been created. If the update was skipped because no update is needed or an open PR already exists, do not trigger Sophon.
## GitHub Actions
When this skill is used from GitHub Actions, `TAG`, `GH_TOKEN`, and `GITHUB_TOKEN` may already be set. Resolve `latest` first when `TAG` is empty. Once an explicit tag or version is known, use:
"description":"Write, review, debug, and document LanceDB pipelines in Python and TypeScript that work across local LanceDB OSS tables and remote LanceDB Enterprise/Cloud tables.",
description:"Override [project] name in python/pyproject.toml (e.g. 'lancedb-compat'). Default keeps 'lancedb'."
required:false
default:"lancedb"
rustflags:
description:"RUSTFLAGS for the build container, as a single whitespace-free token (e.g. '-Ctarget-cpu=x86-64-v2'). Empty leaves RUSTFLAGS unset, keeping the defaults from .cargo/config.toml."
required:false
default:""
runs:
using:"composite"
steps:
@@ -27,6 +35,18 @@ runs:
ARM_BUILD:${{ 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 = "'"$PACKAGE_NAME"'"/' python/pyproject.toml
# Use "chore" for beta/rc versions, "feat" for stable releases
if [[ "${VERSION}" == *beta* ]] || [[ "${VERSION}" == *rc* ]]; then
COMMIT_TYPE="chore"
else
COMMIT_TYPE="feat"
fi
TARGET_TAG="${TAG:-latest}"
cat <<EOF >/tmp/codex-prompt.txt
You are running inside the lancedb repository on a GitHub Actions runner. Update the Lance dependency to version ${VERSION} and prepare a pull request for maintainers to review.
You are running inside the lancedb repository on a GitHub Actions runner.
Follow these steps exactly:
1. Use script "ci/set_lance_version.py" to update Lance Rust dependencies. The script already refreshes Cargo metadata, so allow it to finish even if it takes time.
2. Update the Java lance-core dependency version in "java/pom.xml": change the "<lance-core.version>...</lance-core.version>" property to "${VERSION}".
3. Run "cargo clippy --workspace --tests --all-features -- -D warnings". If diagnostics appear, fix them yourself and rerun clippy until it exits cleanly. Do not skip any warnings.
4. After clippy succeeds, run "cargo fmt --all" to format the workspace.
5. Ensure the repository is clean except for intentional changes. Inspect "git status --short" and "git diff" to confirm the dependency update and any required fixes.
6. Create and switch to a new branch named "${BRANCH_NAME}" (replace any duplicated hyphens if necessary).
7. Stage all relevant files with "git add -A". Commit using the message "${COMMIT_TYPE}: update lance dependency to v${VERSION}".
8. Push the branch to origin. If the remote branch already exists, delete it first with "gh api -X DELETE repos/lancedb/lancedb/git/refs/heads/${BRANCH_NAME}" then push with "git push origin ${BRANCH_NAME}". Do NOT use "git push --force" or "git push -f".
9. env "GH_TOKEN" is available, use "gh" tools for github related operations like creating pull request.
10. Create a pull request targeting "main" with title "${COMMIT_TYPE}: update lance dependency to v${VERSION}". First, write the PR body to /tmp/pr-body.md using a heredoc (cat <<'EOF' > /tmp/pr-body.md). The body should summarize the dependency bump, clippy/fmt verification, and link the triggering tag (${TAG}). Then run "gh pr create --body-file /tmp/pr-body.md".
11. After creating the PR, display the PR URL, "git status --short", and a concise summary of the commands run and their results.
Use \$lancedb-update-lance-dependency with target "${TARGET_TAG}".
Constraints:
- Use bash commands; avoid modifying GitHub workflow files other than through the scripted task above.
- Do not merge the PR.
- If any command fails, diagnose and fix the issue instead of aborting.
- Use env "GH_TOKEN" for GitHub operations.
- Do not merge the pull request.
- Do not force-push.
- Do not create a duplicate pull request if an open PR already exists for the target Lance version.
- If any command fails, diagnose and fix the root cause instead of aborting.
- After creating the PR, display the PR URL, "git status --short", and a concise summary of the commands run and their results.
* Set `dryRun` to `true` to preview the merge. A rejected merge resolves
* with `status: "rejected"` instead of throwing.
*
* @param fromBranch Branch to merge from.
* @param dryRun When true, only preview the merge. Defaults to false.
*/
asyncmerge(
fromBranch: string,
dryRun: boolean=false,
):Promise<MergeBranchResult>{
return(awaitthis.#inner.merge(
fromBranch,
dryRun,
))asunknownasMergeBranchResult;
}
}
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