`list_tables` asked for `/v2` unconditionally, which 404s against every server
that is not a recent Phalanx -- including `RestAdapter`, the namespace spec's
own reference implementation.
The connection now asks `/v1/version` once, before its first listing, and keeps
the one bit it needs: whether this server serves the `/v2` listing. Servers
that do not keep the listing they have always served, which is correct and
merely slower.
The answer has to be known before the first page rather than learned from it.
The two routes resume from different things, so a walk that started on one
cannot finish on the other, and a walk that learned from its own first page
would hand `/v2` a token `/v1` minted.
What is cached is that bit, not the `ServerVersion` it came from. A server that
sends no version header is indistinguishable from one running the oldest
version we know of, so caching the version would let a stripped header switch
off multivector, structural FTS, multipart write and blobs for the life of the
connection. A missing header can only cost a listing its pushdown.
`table_names` is untouched: it stays on `/v1`, where `page_token` is a table
name to resume after that its callers build themselves.
`ListingDatabase::list_tables` listed every table directory under the database
prefix before applying `limit` and `page_token`. The cost of a request was set by
the size of the database rather than the size of the page, so listing one table
out of ten thousand took ten S3 round trips instead of one.
List through `ObjectStore::read_dir_page`, which pushes the resume position and
the page size into the store's list request. Stores with no paginated list API
list the level in full and page it locally, which is what every store did before.
Children that are not tables leave a page short of its limit, and one page is one
request, so the listing asks again until the page is full or the database runs
out.
Two behaviour changes come with it:
- `page_token` is opaque. It was a table name; it is now whatever resumes the
store the database sits on, which for S3, GCS and Azure is a continuation
token. Callers hand it back and do not construct or interpret one. Nothing
validates it, so a token a caller invents resumes from the wrong place rather
than failing.
- Tables are reported in the order the store lists directories, which differs
from sorting by name only between a name and one that extends it:
`users-archive` now precedes `users`, because the `-` of `users-archive.lance`
sorts below the `.` of `users.lance`. Pagination cannot report an order other
than the one it resumes in.
`table_names` is left on the full listing it has today: its `start_after` is a
table name, which cannot be pushed into a store that resumes from a continuation
token, and it is deprecated.
A pushed-down listing does not pass through `WrappingObjectStore::wrap`, so every
wrapper here says whether the pushdown survives it: the mirroring wrapper keeps
it, since only writes are mirrored, and the test IO tracker gives it up rather
than let a listing go around the counter.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
`Connection::list_tables` took a `lance_namespace::models::ListTablesRequest`
directly, so its generated shape -- including `identity`, `context` and
`include_declared`, none of which lancedb reads -- was part of the public API,
and Node had no binding at all.
Replaces it with a `ListTablesBuilder` carrying `page_token`, `limit` and
`namespace`, matching every other operation on `Connection`. This is a breaking
change for Rust callers. Node gains `listTables` with `ListTablesOptions` and
`ListTablesResponse`; Python's public API is unchanged, since it already had
`list_tables` everywhere.
`table_names` and `TableNamesBuilder` are deprecated. Its `start_after` takes a
table name rather than an opaque token, which cannot be pushed down into a store
that resumes from a continuation token.
Also fixes the page boundary in `ListingDatabase::list_tables`: the token was the
first name of the next page while resuming skips names at or before the token, so
one table was dropped per boundary. Walking `[a, b, c, d, e]` with a limit of 2
returned `[a, b, d, e]`.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
> Stacked on #3780. Blocked only on #3922 (`lance` → `v11.0.0-beta.6`),
so CI
> stays red until that lands.
## Missing coverage must mean "not known to be covered"
#3780 caps the SSTable exclusion watermark at an index's recorded
catch-up when
there is one, and silently ignores the case where there is none. On a
table that
requires catch-up, an absent entry means the index is *not* known to
hold the
compacted rows — and the LSM base arm reads base through the index
(`fast_search`, no brute-force tail), so dropping that SSTable loses
those rows
for that query.
```rust
Some(caught_up) => watermark = watermark.min(caught_up),
None if catchup_required => watermark = 0, // retain everything
None => {}
```
`catchup_required` reads the manifest feature bit directly, and requires
both
words: a half-set manifest is treated as legacy, which is the
conservative side.
Without the bit the field is not maintained at all, so absence carries
no
information and behaviour is unchanged.
## Activation, as a table-level entry point
`Table::require_mem_wal_index_catchup()` performs the one-way switch,
separate
from `set_lsm_write_spec`: a table carrying the bit retains every
generation
until something records catch-up, so it has to follow the deployment of
whatever
repairs coverage, not the creation of the table.
This is a convenience, not the only path — a writer holding the dataset
calls
the equivalent on `DatasetMemWalExt`, which is what the WAL pod does.
Lance
enforces the preconditions either way: the MemWAL index must exist, and
the
table must not already carry `compacted_sstables` from before this
protocol,
since those numbers cannot be validated.
## Still correct after the Lance rework
lance-format/lance#8481 replaced the transmitted `IndexCatchupAdvance`
with a
position derived at commit time from the version a transaction read.
That
changed how a writer earns coverage; it did not change what a reader may
conclude from its absence. The rule here, and the field it reads, are
unchanged.
## Tests
Existing `exclusion_watermarks` unit tests carry the new argument.
Coverage
against a real dataset follows once #3922 lands and this can build.
Updates the Rust workspace Lance dependencies and Java lance-core
dependency to v11.0.0-beta.10. No compatibility fixes were required;
workspace clippy with all features and Rust formatting pass.
Lance tag:
https://github.com/lance-format/lance/releases/tag/v11.0.0-beta.10
## Summary
- add `drop_table_async` and return a job handle while preserving
`drop_table`
- consume remote 202 responses with cleanup job IDs and retain
older-server compatibility
- expose the API through Python and TypeScript connection wrappers
Say what resolving "latest" actually does: pick the newest release,
preferring stable over pre-release, and skip the run if it is not newer
than the version pinned in Cargo.toml.
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
`exclusion_watermarks` resolved a single index and capped SSTable
exclusion at that index's catch-up watermark. It now takes every index
the query relies on and retains to the **lowest** of them, and the
resolver collects arms together rather than returning at the first
match.
This is groundwork, not a fix for a reachable bug: `reject_unsupported`
refuses hybrid search, so the vector and full-text arms are mutually
exclusive and the list never holds more than one entry today. The
generalisation is what the remaining work below plugs into.
Unchanged: a plain scan uses the compaction watermark alone, an index
with no catch-up entry contributes no cap, and a caught-up index falls
back to the compaction watermark. Taking a minimum over more indexes can
only lower a watermark, so the failure direction is "read an SSTable
unnecessarily", never "miss rows".
## Tests
Three in `lsm`: the existing lagging-index test updated for the new
signature;
`exclusion_watermark_takes_the_minimum_across_every_index_used` (two
indexes at 7 and 4 against compaction at 9 — each alone stops at its own
watermark, together the lower governs, order-independent); and
`an_untracked_index_does_not_widen_a_lagging_sibling`.
`cargo test -p lancedb --lib` — 45 lsm tests, 484 in the crate. `cargo
fmt --check` clean.
## Follow-ups
This crate pins lance to a released tag, so anything needing unreleased
Lance symbols waits for a bump.
1. **Select legacy versus strict semantics from the feature bit.** On a
table with `FLAG_MEM_WAL_INDEX_CATCHUP` set, a *missing* entry must mean
"not caught up" and retain the SSTables, instead of leaving the
compaction watermark unchanged. Needs the bit from
lance-format/lance#8263. **This must land before any table is
activated** — otherwise the bit is set while queries still read
permissively.
2. **Collect scalar and bitmap-family prefilter indexes.** The genuinely
multi-index query is a vector search with a scalar prefilter, and it is
gated on the vector index alone today. Identifying the others needs the
planner's chosen indexes, not the columns the filter names, so it needs
a Lance-side helper.
3. **Verify a retained SSTable can actually answer.** Both base and
SSTable arms use `fast_search`; a source without a compatible index
contributes nothing, so retention alone does not guarantee its rows are
returned. Needs a flat-search fallback or an explicit error in Lance's
`LsmScanner`.
4. **Planner-level integration tests.** Current tests exercise the
watermark arithmetic directly. End-to-end coverage over real queries —
prefilter forms, legacy versus activated, missing index and missing
shard entries — depends on 1–3.
## What is the bug?
#3731 tries to distinguish a missing table from a corrupt table after
Lance returns `DatasetNotFound`. It does that by listing the database
parent and treating a physical `<name>.lance` entry as evidence that the
table exists.
That premise is not sound for a listing database. Table creation writes
data before atomically committing the first manifest, so the same
physical prefix can represent a live concurrent create, abandoned
uncommitted data, or an old empty directory. It is not evidence of a
committed table. The parent listing also makes every missing-table open,
including the create-on-miss path, perform work proportional to the
number of sibling tables. Cloud `list_with_delimiter` exhausts all pages
before returning.
## How does this PR fix the problem?
This PR makes the committed Lance manifest the sole table-existence
authority for listing-database opens:
- `DatasetNotFound` maps directly to `TableNotFound`; no parent or
target storage probe runs.
- Other Lance load errors continue to propagate unchanged.
- A physical directory, object prefix, or uncommitted data file alone
does not block `Create`.
- Concurrent `Create` requests are arbitrated by the conditional
version-1 manifest commit: one succeeds and the loser receives
`TableAlreadyExists`.
- `table_names` is documented as physical discovery, not an atomic
table-existence check. Its snapshot can contain an entry that is still
being created, has only uncommitted storage, or is concurrently dropped.
This removes the need for a new Lance object-store capability. LanceDB
remains on the official Lance `v11.0.0-beta.6` dependency from `main`;
the merge commit for lance-format/lance#7722 is an ancestor of that tag,
so the ambiguous-GCS-500 corruption-prevention fix is retained.
## Performance evidence
Lower is better. The benchmark uses real `.lance` directories with
marker objects on the local filesystem; fixture creation and teardown
are outside the timed region. Baseline is `origin/main` at `6fb976cf`,
candidate is `e1240751`. Both were built from the same lockfile on the
same macOS arm64 machine with the repository's `release` profile (fat
LTO), then executed in alternating baseline/candidate order for three
pairs. Each run used 10 warmups and 100 distinct missing-table opens per
scale. The table reports the median of the three run-level percentiles.
| Scenario / metric | Baseline | This PR | Benefit |
| --- | ---: | ---: | ---: |
| 1,000 real sibling directories, p50 | 11.905 ms | 21.042 us | 566x
speedup |
| 10,000 real sibling directories, p50 | 143.630 ms | 18.375 us | 7,817x
speedup |
| 100,000 real sibling directories, p50 | 1.991 s | 19.917 us | 99,984x
speedup |
| 100,000 real sibling directories, p95 | 2.346 s | 25.792 us | 90,965x
speedup |
These results validate removal of the sibling-cardinality dependency in
this local-filesystem workload; they are not an extrapolation to
production GCS latency. A structural object-store regression test
separately asserts that opening one missing table performs zero
parent-scoped `list`, `list_with_offset`, or `list_with_delimiter`
calls.
Run with:
```bash
BENCH_SIBLINGS=1000,10000,100000 BENCH_WARMUPS=10 BENCH_TRIALS=100 \
cargo run --locked --release --quiet -p lancedb --example bench_open_missing_table
```
## Correctness and compatibility boundaries
- An empty `.lance` directory or orphan data without a committed
manifest now opens as `TableNotFound` and may be replaced by a
successful `Create`.
- Two synchronized creators sharing one object store deterministically
produce one success and one conditional-manifest conflict mapped to
`TableAlreadyExists`.
- A readable manifest remains authoritative; non-`DatasetNotFound`
corruption, external-manifest, authorization, and object-store errors
are not folded into `TableNotFound`.
- `TableCorrupted` remains in the public error enum for compatibility,
but this listing-database fallback no longer synthesizes it from an
ambiguous physical footprint.
- Reliably distinguishing `Missing`, `Creating`, and `Corrupt` would
require explicit authoritative lifecycle/catalog metadata (for example a
leased creation record). It cannot be inferred from a directory or
prefix, and is outside this incident fix.
## Validation
- `cargo fmt --all -- --check`
- `cargo check --quiet --locked -p lancedb --features remote --tests
--examples`
- `cargo clippy --quiet --locked -p lancedb --features remote --tests
--examples -- -D warnings`
- `cargo test --quiet --locked -p lancedb --features remote --tests`
- library: 843 passed, 1 ignored
- integration groups: 39 passed, 6 passed, 5 passed
- focused coverage for empty directories, orphan data, physical listing
snapshots, zero parent listings, and concurrent manifest arbitration
Closes#3704
## Problem
Transforms can fail on bad data (e.g. nulls/NaNs from incomplete user
surveys). Today any transform exception aborts iteration, and there is
no way to skip invalid rows during loading.
## Solution
New `on_transform_error` parameter on `StreamingDataset`:
- `"raise"` (default, matches current behavior and the convention in
tf.data / WebDataset / Ray Data)
- `"skip"` — drop the failing rows and continue
- `"warn"` — like skip, plus a logged warning per failing batch
- a WebDataset-style callable `handler(exc) -> bool`, so users can skip
only expected error types
Key design points:
- **Row-granular skipping**: when a batch fails, the transform is re-run
on single-row slices so only the rows that actually fail are dropped
(avoids Ray-style whole-block loss). Skips are counted in a new
`rows_skipped` property.
- **No crash on uneven skips**: the round-robin loop now ends the epoch
at the last cycle where every split still has a row, instead of hitting
`IndexError` when a split runs dry early.
- **Exact resumability under skips**: checkpoints are now
position-based. `state_dict` gains `positions_consumed_per_split` (exact
for owned splits), and a new `merge_state_dicts` static method combines
per-rank states via elementwise max for elastic resume across topology
changes. Old checkpoints without the new key still load. Positions equal
sample counts when nothing is skipped, so existing behavior is
unchanged.
- **Guardrail**: transforms returning the wrong number of rows now raise
a clear `ValueError` instead of silently corrupting split accounting.
### Answers to the issue's open questions
- *Can we do this?* Yes — all transforms funnel through one guarded call
in the Stage 2 pipeline.
- *What do other libraries do?* tf.data `ignore_errors()`, WebDataset
`handler=`, Ray `max_errored_blocks`; MosaicML StreamingDataset offers
nothing (skipping conflicts with its determinism model). This design
follows the common conventions: raise by default, opt-in skipping,
count/log drops.
- *Error handling or pre-filtering?* Both: the existing `filter=`
remains the recommended tool for predictable bad data (splits are built
post-filter, so all guarantees hold — now documented);
`on_transform_error` covers failures not expressible as a predicate.
- *Impact on splits / elastic determinism?* Per-split sample sequences
stay deterministic (skips are data-dependent, not topology-dependent).
With unequal bad-row counts across splits the last few global steps of
an epoch can differ across topologies (bounded by the skew), which is
documented on the parameter. With equal counts per split, full
determinism is preserved — covered by a test.
## Testing
15 new tests in `test_elastic_dataloader.py` covering: default raise,
invalid values, uniform and uneven skips (including epoch-end
truncation), warn logging, selective callable handlers, wrong-row-count
guardrail, determinism across runs and across world sizes (1/2/3/4) with
skips, exact mid-epoch resume with skips on the same topology, elastic
resume via `merge_state_dicts` (ws=2 → ws=1), merge validation, and
backward-compat loading of old checkpoints.
Note: relying on CI for the test run — my local machine OOMs during the
final link of the native extension. The change itself is pure Python.
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
PyO3 defaults native extension classes to `builtins`, so
mkdocstrings/Griffe could not resolve the newly documented
`lancedb.Session` alias and `Deploy docs to Pages` failed on `main`.
Declare the extension module for the public native types referenced by
the Python API docs so Griffe resolves them through `lancedb._lancedb`
and Pages can build again.
Validated with the docs toolchain used by CI (`griffe==0.49.0`,
`mkdocstrings==0.25.2`, and `mkdocs==1.6.1`); `PYTHONPATH=. mkdocs
build` succeeds.
## Summary
`opendal 0.58.1` (the version pulled in transitively via Lance) already
ships
`goosefs-sdk 0.1.9`, which includes the upstream fix for the 0.1.6
compile
break. The explicit version pin that lancedb has been carrying since the
GooseFS feature was introduced is therefore no longer necessary and is
now
redundant work to maintain.
## Changes
- Remove the direct `goosefs-sdk` dependency from
`rust/lancedb/Cargo.toml`
(it was pinned to `=0.1.9` with a comment referencing the 0.1.6 compile
break).
- Remove the `dep:goosefs-sdk` entry from the `goosefs` cargo feature,
since
no source file in lancedb imports the crate directly.
- Refresh `Cargo.lock`; `goosefs-sdk 0.1.9` now resolves transitively
through
`lance` → `opendal 0.58.1`.
## Verification
- `cargo fmt --all` — clean
- `cargo check --features remote,goosefs --tests --examples` — passes
- `Cargo.lock` confirms `goosefs-sdk 0.1.9` is still resolved (now
transitively), so the `goosefs` feature continues to enable the same set
of
Lance/IOPaths as before.
## Backwards compatibility
No public API changes. The `goosefs` cargo feature still activates
`lance/goosefs`, `lance-io/goosefs`, and
`lance-namespace-impls/dir-goosefs`,
and the same `goosefs-sdk 0.1.9` version is selected by the resolver.
<!-- lance-gatekeeper-fix:v1 agent=613a074d606e626c5169d601373a32d8
generation=1 -->
## Root cause
When LanceDB accepted an Arrow table created by a different installed
Arrow package, its compatibility sanitizer rebuilt each Data node
without converting the foreign type or preserving nested children. It
also dropped the separate dictionary vector payload and did not preserve
identity shared by dictionary schema types, vector wrappers, or growing
dictionary chunks.
## Fix
Recursively sanitize nested Arrow data types and child data. Use one
table-scoped sanitization context to rebuild and memoize source type
objects, dictionary vectors, and Data nodes in the local Arrow realm,
preserving all identities required by Arrow IPC.
Add Arrow 15 through 18 regressions for list serialization, ordinary
dictionaries, dictionaries shared across fields and batches, growing
dictionaries, and IPC round trips.
## Validation
- pnpm test __test__/arrow.test.ts --runInBand (188 passed)
- pnpm lint
- pnpm build
- pnpm test --runInBand (706 passed, 5 skipped)
- pnpm run docs
Fixes#2256
---------
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
Some issues:
- file_size_bytes is optional in the manifest, so if it's not there (old
writer I guess) it'll under-report the table size.
- it changes results a little bit from the old way by including per-file
footers and metadata (probably not a big difference at real scale)
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
## What
`JinaEmbeddings._generate_image_input_dict()` crashes with
`AttributeError: 'function' object has no attribute 'urlparse'` on any
image given as a URL string, local path string, or `pathlib.Path` — i.e.
every documented `jina-clip-v1` image-embedding use case except raw
`bytes`.
## Why
```python
from urllib.parse import urlparse
...
parsed = urlparse.urlparse(image)
```
`urlparse` is imported as a function, then called as if it were the
`urllib.parse` module (`urlparse.urlparse(...)`). The module-level
`is_valid_url()` a few lines above does it correctly (`urlparse(text)`),
which is why this reads as a typo rather than intentional. Fixed to
`urlparse(str(image))` — `str()` is needed because `urlparse()` only
accepts `str`/`bytes` and raises a different `AttributeError` on a raw
`Path`.
## Testing
Added `test_jina_generate_image_input_dict_local_path`, which fails with
the original `AttributeError` before the fix and passes after, covering
both a `str` path and a `pathlib.Path`. Verified locally (built the Rust
extension, ran red→green, then the full `test_embeddings.py` file: 15
passed / 8 skipped, no regressions) and with `ruff check`/`ruff format`.
---
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>
## What
`LsmWriteSpec::maintained_indexes` becomes `Option<Vec<String>>`:
| value | meaning |
|---|---|
| `None` (new default) | every index the MemWAL supports, resolved when
the spec is installed |
| `Some([])` | maintain nothing — a scan/filter-only WAL table |
| `Some([..])` | exactly these, taken verbatim |
`with_maintained_indexes` keeps its signature;
`with_no_maintained_indexes()` is new. Surfaced through the remote path
(null on the wire), Python, and Node.
## Why
Callers had to state the maintained set by hand every time, which is
both tedious and easy to get wrong — the common case is "maintain what I
already built."
Resolution filters on `IndexConfig::is_memwal_maintainable`, delegating
to lance's `is_maintainable_index_type`. This is load-bearing rather
than cosmetic: lance does **not** skip an index type its memtable cannot
build, it errors when the shard writer opens, so sweeping up a bitmap
index would fail every memtable claim and leave the table unwritable.
The inferred set excludes those, and an explicit list naming one is now
rejected at spec time instead of at claim time.
## Behavior change
A freshly constructed spec used to maintain **nothing**; it now
maintains **everything supported**. This flipped because napi collapses
`undefined` and `null` to `None`, so TypeScript cannot express "absent
means nothing, null means all" — any other choice makes the bindings
disagree with the wire. The error direction also favors it: an unwanted
maintained index costs memory, while a silently unmaintained one
degrades FTS to an unscored scan.
Three existing tests encoded the old default and are updated rather than
worked around.
## Caveat
The resolved set is a snapshot, not a subscription. An index created
after the spec is installed is not maintained until the spec is unset
and set again. `get_lsm_write_spec` therefore always reports a concrete
list — `None` never round-trips.
## Dependency
Needs a lance release carrying `is_maintainable_index_type`
(lance-format/lance#8095) before this builds against the pinned tag.
Draft until then.
## Testing
38 Rust LSM tests and 10 Python tests pass against a local lance build,
including new coverage that a bitmap index is excluded from inference
and rejected when named, and that `[]` stays distinguishable from null
on the wire.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
Updates the Rust workspace Lance dependencies and Java lance-core
dependency to v11.0.0-beta.3. No compatibility fixes were required;
all-features clippy and Rust formatting pass. Triggering tag:
https://github.com/lance-format/lance/releases/tag/v11.0.0-beta.3
Converge a table's LSM write path into its base table, and inspect it.
`checkpoint_lsm` is `flush` then `compact`, repeated until the fresh
tier is empty — and the loop runs **client-side**. Putting it on the
server would mean a background task, which means a single-flight intent,
an intent that leaks on panic, a bounded-iteration policy, an "is it
done" observable, and a story for every way a client can vanish
mid-operation. None of that exists in this shape: each request does a
bounded unit of work and reports what is left, so completion is *carried
in the responses* rather than inferred from a shared counter that cannot
distinguish "converged" from "hasn't started yet".
Best-effort by construction. Nothing is frozen, so `converged` means L0
was empty as of the last pass. It is idempotent, abandonable at any
point with zero consequence, and safe to run on a cadence — an
already-converged table costs one round trip and zero compaction passes,
because `flush` reports `generations_remaining` and the loop is never
entered.
## The failure taxonomy is the load-bearing part
Five distinct conditions used to arrive at a client as one 503.
`Error::LsmRoute` carries a classification read from the response body's
namespace error code **at the point of receipt** — before any generic
helper folds the body into a string and keeps only the status.
| condition | wire | client action |
|---|---|---|
| contention (latch held / pool saturated) | 429, code 21 | retry with
backoff |
| owning node draining | 503, code 19 `InvalidTableState` | **stop** |
| fenced / no slot / transport | 503, code 17 | retry with backoff |
| registry entry vanished | 404 | re-issue from `flush` (capped) |
| table being dropped / not WAL-backed | 409 / 400 | stop |
Draining is terminal because the drain gate is a one-way latch —
retrying spins until the deadline to report a failure that was knowable
on the first response. Transport retry is disabled on these routes for
the same reason: it treats every 503 alike and would burn its budget
before the classifier ever saw the body.
`get_lsm_stats` returns `Option<LsmStats>`, matching
`get_lsm_write_spec` — `None` only when the table has no LSM write path,
since a struct of zeros would read as measurements.
Python bindings mirror all four, preserving per-bucket detail rather
than flattening to a table-level summary.
## Testing
Six new unit tests against the mocked endpoint, plus the taxonomy
round-trip:
- flush into an empty L0 issues **zero** compact calls (asserts the call
count — `generations_consumed: 0` is also true of a loop that ran a
pointless pass)
- the loop drives compact until the server reports zero remaining
- **contention is not draining**: a 429 retries and converges; asserts
the retry count
- a draining node stops after **exactly one** request, no retries
- stats round-trips fully populated; `include_generation_rows` off by
default
- every `(status, code)` pair classifies correctly, including
unparseable 503 bodies falling back to *retryable* rather than terminal
`cargo test -p lancedb --features remote --lib`: 723 passed.
## Notes for review
- Depends on the sibling lance change returning `SealedGeneration` from
`force_seal_active` only at the *server* level — no lance API is used
here.
- The branch is based on `codex/update-lance-10-0-0-beta-5`, so it
carries one extra commit (`chore: update lance dependency to
v10.0.0-beta.5`) that is not part of this change.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: lancedb automation <robot@lancedb.com>
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
## Summary
- cover Hugging Face cache layouts where both manifests and Lance data
files are relative symlinks into a blob directory
- reconnect with a fresh session before opening so the test exercises
filesystem discovery instead of cached manifest metadata
- scan the reopened table to verify both manifest recovery and data-file
reads
## Root cause
Lance 3.0.1 recorded Unix symlink metadata as the known manifest size,
so the short link length caused a file size is too small error. The
current Lance v11.0.0-beta.2 dependency repairs this by detecting an
invalid footer from a stale known size and retrying with the target file
metadata. This regression test locks that behavior into the LanceDB
open-table path used by Node.
## Validation
- cargo fmt --all
- cargo test --quiet --features remote -p lancedb --lib
test_open_table_follows_hugging_face_symlinks -- --nocapture
- cargo test --quiet --features remote -p lancedb --lib
database::listing::tests
- cargo clippy --quiet --features remote -p lancedb --lib --tests -- -D
warnings
- cargo check --quiet --features remote --tests --examples
Fixes#3197
<!-- lance-gatekeeper-fix:v1 agent=4aadcf04e9ac93b97d499d7448b67e19
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- add Node regression coverage for vector-search offset pagination
- add equivalent coverage for full-text search
- compare later pages with the corresponding complete-result slice and
assert page sizes
## Root cause
The historical query path requested only the user limit from
nearest-neighbor or full-text search before applying the offset, so a
page became empty when its offset reached that limit. The production
query path on current main already incorporates the later fix from
#2592; this change adds the missing Node binding coverage for the
still-open report and protects both affected APIs from regression.
## Validation
- corepack pnpm build
- corepack pnpm test -- query.test.ts --runInBand
--testNamePattern="Search pagination"
- corepack pnpm lint-ci
- corepack pnpm tsc
- corepack pnpm run docs
Fixes#2229
<!-- lance-gatekeeper-fix:v1 agent=8ba8b18a18260a68a3e605d1bbfa518e
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- classify unsupported local-filesystem operations from Lance as a
NotSupported error
- explain that object-storage mounts cannot provide the safe commit
operations Lance requires and direct users to native object-store URIs
- preserve existing error behavior for other local I/O failures and
non-local backends
## Root cause
Mountpoint for Amazon S3 exposes an S3 bucket as a local path but does
not implement atomic rename. Lance uses atomic rename for safe local
commits, and the resulting unsupported I/O error was previously passed
through as a generic Lance error, leaving Python users with an opaque
low-level failure. Transparent support for such mounts is not safe;
direct s3:// access remains the supported path.
## Validation
- cargo test --quiet --features remote -p lancedb error::tests
- cargo test --quiet --features remote -p lancedb --lib (807 passed, 1
ignored)
- cargo check --quiet --features remote --tests --examples
- cargo clippy --quiet --features remote --tests --examples
- cargo fmt --all -- --check
Fixes#2016
<!-- lance-gatekeeper-fix:v1 agent=d53283c18fdb00a3a1b69448b1f40529
generation=1 -->
---------
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- Add an issue-specific regression for appending generated embeddings to
an empty table with a non-nullable vector field.
- Verify the custom embedding function produces the declared Float64
vectors and both appended rows are readable.
## Root cause
In v0.4.19, records without a vector value were materialized against the
explicit schema before embeddings were inserted. Apache Arrow inferred
the generated batch vector field as nullable while the table retained
the user-provided non-nullable field, then rejected the mismatched
schemas.
The current conversion path excludes the generated field from the
initial record conversion and realigns the completed batch to the stored
schema after embedding, but the reported empty-table append sequence
lacked permanent regression coverage.
## Validation
- `pnpm exec biome format --write __test__/embedding.test.ts`
- `pnpm lint-ci`
- `pnpm test -- --runInBand __test__/embedding.test.ts` (12 passed, 1
skipped integration test)
- `pnpm build`
- `pnpm run docs`
Fixes#1281
<!-- lance-gatekeeper-fix:v1 agent=6b7270aeb92e6b6c6f5b45022fa83f6a
generation=1 -->
---------
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- add an end-to-end regression for indexed vector search after merging a
pandas column
- verify unmatched rows retain a null merged value instead of failing
Arrow batch assembly
## Root cause
Historical Lance readers could assemble schema-evolved columns in
physical data-file order. Indexed row-ID reads after a merge could
therefore omit or misorder the newly merged column for unmatched rows.
The currently pinned Lance release contains the reader correction, but
LanceDB did not cover the reported merge-then-search path.
## Validation
- uv run --extra tests pytest python/tests/test_table.py::test_merge
python/tests/test_table.py::test_search_after_merge -q
- uv run --project python --extra dev ruff check .
- uv run --project python --extra dev ruff format --check
python/python/tests/test_table.py
Fixes#599
<!-- lance-gatekeeper-fix:v1 agent=4e17331e0542c132eae31e86da508629
generation=1 -->
---------
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Root cause
`Table.to_polars()` disabled PyArrow predicate pushdown by selecting the
non-PyArrow Polars scan callback. Polars 1.32.3 invokes that callback
with `batch_size` both positionally and through its partial, so
collecting the returned lazy frame raises `TypeError:
_scan_pyarrow_dataset_impl() got multiple values for argument
batch_size`.
## Fix
- Keep the compatible PyArrow callback path.
- Add an identity `map_batches` barrier so predicates stay in Polars
instead of reaching the LanceDB adapter as unsupported PyArrow
expressions.
- Extend the tested Polars range through 1.32.3 and retain lazy-frame
regression coverage.
## Validation
- `python/tests/test_table.py::test_polars` with Polars 1.32.3
- `python/tests/test_table.py::test_polars` with the locked Polars 1.3.0
baseline
- `ruff format --check` on the changed Python files
- `ruff check .`
- `uv lock --check`
Fixes#2619
<!-- lance-gatekeeper-fix:v1 agent=0d42bcda944ac42765b25f2c19ff729f
generation=1 -->
---------
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- exercise float16 sanitization through the reported direct Arrow-data
table creation path
- assert that the inferred fixed-size vector schema remains float16
- retain end-to-end index creation and vector search coverage
## Root cause and fix
PyArrow 16 does not provide an is_nan kernel for half-float arrays, so
passing float16 vector values directly to that kernel raises
ArrowNotImplementedError. LanceDB's sanitizer already carries the
compatibility fix from #837: it casts float16 values to float32 only for
NaN detection while preserving the stored vector type.
The existing end-to-end regression created an empty schema-defined table
and added data afterward. This change aligns that regression with the
issue reproduction by creating a table directly from a
FixedSizeList<float16> Arrow table and verifying the persisted schema.
## Validation
- uv run --extra tests pytest
python/tests/test_table.py::test_create_f16_table_from_arrow_data -q
- direct 1,000-row by 128-dimension float16 Arrow-table reproduction
- PyArrow 16.1 half-float is_nan kernel reproduction
- uvx ruff@0.15.20 format --check python/python/tests/test_table.py
- uvx ruff@0.15.20 check .
Fixes#835
<!-- lance-gatekeeper-fix:v1 agent=dd0a32a959f691f49de958d4333fb29d
generation=1 -->
---------
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- require Node.js 18-compatible type declarations when TypeScript
consumers install them
- keep the type peer optional for JavaScript-only consumers
- add a regression test tying the Node type peer range to the supported
runtime
## Root cause
LanceDB requires Node.js 18 or newer, and its public types expose Apache
Arrow declarations that import built-ins through the node: scheme. The
package did not declare a matching @types/node peer requirement, so npm
accepted projects pinned to Node 12 declarations and TypeScript then
reported that node:stream and node:fs/promises did not exist.
## Validation
- pnpm lint
- pnpm build
- pnpm run docs
- pnpm test --runInBand (678 passed, 5 skipped)
- packed-package consumer probe rejects @types/node 12.20.55 and
installs with @types/node 18.19.130
Fixes#1713
<!-- lance-gatekeeper-fix:v1 agent=7a2b68f3daad20bed9e46cb8892d6e6c
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- publish the PEP 561 `py.typed` marker so downstream type checkers
consume the inline public annotations
- add a Pyright contract test that distinguishes synchronous `connect`
from awaited `connect_async`
- verify the marker is present in the installed package
## Root cause
The public Python module already annotated `lancedb.connect` as
synchronous and `lancedb.connect_async` as asynchronous. The private
native `_lancedb.connect` stub is intentionally awaitable because it
backs `connect_async`. However, the distribution did not include a PEP
561 marker, so downstream tools such as mypy could ignore the public
inline annotations and expose misleading or incomplete type information.
## Validation
- `python/.venv/bin/ruff format --check python/python/tests/test_db.py
python/python/type_tests/connect.py`
- `python/.venv/bin/ruff check .`
- `cd python && .venv/bin/pytest
python/tests/test_db.py::test_package_includes_pep_561_marker -q`
- `cd python && .venv/bin/pyright --pythonpath .venv/bin/python`
- downstream mypy contract check for both public connection functions
Fixes#2159
<!-- lance-gatekeeper-fix:v1 agent=b07901451487187fc03f61890d3aa6bb
generation=1 -->
Co-authored-by: lancedb-gatefixer[bot] <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- raise a clear `TypeError` when `Vector` is used without a dimension
- preserve normal `Vector(dim)` behavior across Pydantic v1 and v2
- add a regression test that defines a model without importing PyArrow
## Root cause
Pydantic interpreted the bare `Vector` factory as a callable field type
and inspected its postponed annotations in the user model's namespace.
Because that namespace did not define LanceDB's internal `pa` alias,
model construction failed with the misleading `NameError: name 'pa' is
not defined` instead of explaining that `Vector` must be parameterized.
The factory now exposes Pydantic's v1 and v2 schema hooks and rejects
bare use before signature introspection with guidance to use
`Vector(dim)`.
## Validation
- `uvx --from 'ruff==0.15.20' ruff check .`
- `uvx --from 'ruff==0.15.20' ruff format --check
python/python/lancedb/pydantic.py python/python/tests/test_pydantic.py`
- `cd python && uv run --extra tests pytest
python/tests/test_pydantic.py::test_bare_vector_raises_clear_error -q`
- `cd python && uv run --extra tests pytest
python/tests/test_pydantic.py -q`
- compatibility checks with Pydantic 1.10.22, 2.11.4, and 2.13.4
Fixes#2384
<!-- lance-gatekeeper-fix:v1 agent=71e7473e18c91db5137a3c0d3bb73640
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- add regression coverage for adding dictionary rows with a nullable
fixed-size-list column
- verify ordinary list columns remain aligned alongside the null
fixed-size-list value
## Root cause
PyArrow infers an all-`None` dictionary column as the generic `null`
type. The original schema-alignment path treated the target
fixed-size-list type as proof that the inferred source was also
list-like and unconditionally accessed `value_field`, which raised
`AttributeError`. Current alignment logic correctly falls back to the
target type when the source is not list-like; this test locks in that
repair for the reported ingestion path.
## Validation
- `uv run --extra tests pytest
python/tests/test_table.py::test_add_with_empty_fixed_size_list_drops_bad_rows
python/tests/test_table.py::test_add_nullable_fixed_size_list_with_none
python/tests/test_table.py::test_add_nullable_struct_with_none -q`
- `uv run --with pyarrow==19.0.1 --extra tests pytest
python/tests/test_table.py::test_add_nullable_fixed_size_list_with_none
-q`
- `uv run --project python --extra dev ruff format --check
python/python/tests/test_table.py`
- `uv run --project python --extra dev ruff check .`
Fixes#2340
<!-- lance-gatekeeper-fix:v1 agent=cb0475e85e764f79bd03b35eb8955ec4
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Root cause
The former listing-database table URI builder used OS-native
`Path::join` for object-store URIs. On Windows this inserted backslashes
into `az://` table paths, so `table_names` found slash-delimited objects
while `open_table` addressed a different key. The production path now
builds URI paths with forward slashes after the equivalent S3 report was
fixed in #2575, but #1072 remained open without Azure-specific
regression coverage.
## Fix
- Add Azure URI regression assertions at the Rust table URI construction
boundary.
- Cover connection bases both with and without a trailing slash,
matching the behavior reported in #1072.
- Verify the resulting table URI always uses forward slashes on every
platform.
## Validation
- `cargo fmt --all -- --check`
- `cargo test --quiet -p lancedb --lib
database::listing::tests::test_table_uri`
- `cargo check --quiet --features remote --tests --examples`
- `cargo clippy --quiet --features remote --tests --examples`
- `cargo test --quiet --features remote --tests` (866 passed, 1 ignored)
Fixes#1072
<!-- lance-gatekeeper-fix:v1 agent=7d385255a072ed89ddc3ff4d08f82218
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- add regression coverage for multiple query vectors in the local
synchronous Python API
- verify that each query vector receives its own limited
nearest-neighbor result and `query_index`
## Root cause
In LanceDB v0.16, the local synchronous scanner passed a nested vector
array as one query, unlike the async and remote implementations. The
subsequent sync-to-async table migration supplied the correct shared
runtime path, but this local sync behavior was never regression-tested
and issue #1857 remained open.
## Validation
- `uv run --extra tests pytest
python/tests/test_query.py::test_query_multiple_vectors -q`
- `uv run --project python --extra tests --extra dev ruff format --check
python/python/tests/test_query.py`
- `uv run --project python --extra tests --extra dev ruff check .`
Fixes#1857
<!-- lance-gatekeeper-fix:v1 agent=6b25bc529d76813c3db7627c8be947ef
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
Lancedb does not work with any other version of `futures`.
With futures 0.1 it fails like this:
```console
error[E0432]: unresolved imports `futures::StreamExt`, `futures::TryStreamExt`
--> rust/lancedb/src/arrow.rs:21:23
|
21 | use futures::{Stream, StreamExt, TryStreamExt};
| ^^^^^^^^^ ^^^^^^^^^^^^ no `TryStreamExt` in the root
| |
| no `StreamExt` in the root
|
error[E0432]: unresolved import `futures::StreamExt`
--> rust/lancedb/src/data/scannable.rs:24:5
|
24 | use futures::StreamExt;
| ^^^^^^^^^^^^^^^^^^ no `StreamExt` in the root
|
error[E0432]: unresolved import `futures::TryStreamExt`
--> rust/lancedb/src/dataloader/permutation/builder.rs:9:5
|
9 | use futures::TryStreamExt;
| ^^^^^^^^^^^^^^^^^^^^^ no `TryStreamExt` in the root
error[E0432]: unresolved imports `futures::StreamExt`, `futures::TryStreamExt`
--> rust/lancedb/src/dataloader/permutation/reader.rs:25:15
|
25 | use futures::{StreamExt, TryStreamExt};
| ^^^^^^^^^ ^^^^^^^^^^^^ no `TryStreamExt` in the root
| |
| no `StreamExt` in the root
|
error[E0432]: unresolved imports `futures::StreamExt`, `futures::TryStreamExt`
--> rust/lancedb/src/dataloader/permutation/shuffle.rs:8:15
|
8 | use futures::{StreamExt, TryStreamExt};
| ^^^^^^^^^ ^^^^^^^^^^^^ no `TryStreamExt` in the root
| |
| no `StreamExt` in the root
|
error[E0432]: unresolved imports `futures::StreamExt`, `futures::TryStreamExt`
--> rust/lancedb/src/dataloader/permutation/split.rs:12:15
|
12 | use futures::{StreamExt, TryStreamExt};
| ^^^^^^^^^ ^^^^^^^^^^^^ no `TryStreamExt` in the root
| |
| no `StreamExt` in the root
|
error[E0432]: unresolved import `futures::TryStreamExt`
--> rust/lancedb/src/dataloader/permutation/util.rs:9:5
|
9 | use futures::TryStreamExt;
| ^^^^^^^^^^^^^^^^^^^^^ no `TryStreamExt` in the root
error[E0432]: unresolved imports `futures::StreamExt`, `futures::TryFutureExt`
--> rust/lancedb/src/io/object_store.rs:8:15
|
8 | use futures::{StreamExt, TryFutureExt, stream::BoxStream};
| ^^^^^^^^^ ^^^^^^^^^^^^ no `TryFutureExt` in the root
| |
| no `StreamExt` in the root
|
error[E0432]: unresolved imports `futures::FutureExt`, `futures::TryFutureExt`, `futures::TryStreamExt`, `futures::try_join`
--> rust/lancedb/src/query.rs:12:15
|
12 | use futures::{FutureExt, TryFutureExt, TryStreamExt, stream, try_join};
| ^^^^^^^^^ ^^^^^^^^^^^^ ^^^^^^^^^^^^ ^^^^^^^^ no `try_join` in the root
| | | |
| | | no `TryStreamExt` in the root
| | no `TryFutureExt` in the root
| no `FutureExt` in the root
|
error[E0432]: unresolved imports `futures::StreamExt`, `futures::TryStreamExt`
--> rust/lancedb/src/remote/table/blobs.rs:13:15
|
13 | use futures::{StreamExt, TryStreamExt};
| ^^^^^^^^^ ^^^^^^^^^^^^ no `TryStreamExt` in the root
| |
| no `StreamExt` in the root
|
error[E0432]: unresolved imports `futures::SinkExt`, `futures::StreamExt`
--> rust/lancedb/src/remote/table/insert.rs:20:15
|
20 | use futures::{SinkExt, StreamExt};
| ^^^^^^^ ^^^^^^^^^ no `StreamExt` in the root
| |
| no `SinkExt` in the root
|
error[E0432]: unresolved imports `futures::StreamExt`, `futures::TryStreamExt`
--> rust/lancedb/src/remote/table.rs:58:15
|
58 | use futures::{StreamExt, TryStreamExt};
| ^^^^^^^^^ ^^^^^^^^^^^^ no `TryStreamExt` in the root
| |
| no `StreamExt` in the root
|
error[E0432]: unresolved import `futures::StreamExt`
--> rust/lancedb/src/remote/util.rs:5:23
|
5 | use futures::{Stream, StreamExt};
| ^^^^^^^^^ no `StreamExt` in the root
|
error[E0432]: unresolved import `futures::StreamExt`
--> rust/lancedb/src/table.rs:14:5
|
14 | use futures::StreamExt;
| ^^^^^^^^^^^^^^^^^^ no `StreamExt` in the root
|
error[E0432]: unresolved import `futures::TryStreamExt`
--> rust/lancedb/src/table/datafusion/insert.rs:20:5
|
20 | use futures::TryStreamExt;
| ^^^^^^^^^^^^^^^^^^^^^ no `TryStreamExt` in the root
error[E0432]: unresolved import `futures::TryStreamExt`
--> rust/lancedb/src/table/datafusion/scannable_exec.rs:14:5
|
14 | use futures::TryStreamExt;
| ^^^^^^^^^^^^^^^^^^^^^ no `TryStreamExt` in the root
error[E0432]: unresolved imports `futures::TryFutureExt`, `futures::TryStreamExt`
--> rust/lancedb/src/table/datafusion.rs:25:15
|
25 | use futures::{TryFutureExt, TryStreamExt};
| ^^^^^^^^^^^^ ^^^^^^^^^^^^ no `TryStreamExt` in the root
| |
| no `TryFutureExt` in the root
error[E0432]: unresolved import `futures::FutureExt`
--> rust/lancedb/src/table/delete.rs:3:5
|
3 | use futures::FutureExt;
| ^^^^^^^^^^^^^^^^^^ no `FutureExt` in the root
|
error[E0432]: unresolved imports `futures::FutureExt`, `futures::TryFutureExt`
--> rust/lancedb/src/table/merge.rs:9:15
|
9 | use futures::{FutureExt, TryFutureExt};
| ^^^^^^^^^ ^^^^^^^^^^^^ no `TryFutureExt` in the root
| |
| no `FutureExt` in the root
|
error[E0432]: unresolved import `futures::future::try_join_all`
--> rust/lancedb/src/table/query.rs:24:5
|
24 | use futures::future::try_join_all;
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ no `try_join_all` in `future`
|
error[E0432]: unresolved import `futures::FutureExt`
--> rust/lancedb/src/utils/background_cache.rs:12:5
|
12 | use futures::FutureExt;
| ^^^^^^^^^^^^^^^^^^ no `FutureExt` in the root
|
error[E0432]: unresolved import `futures::FutureExt`
--> rust/lancedb/src/utils/mod.rs:12:15
|
12 | use futures::{FutureExt, Stream};
| ^^^^^^^^^ no `FutureExt` in the root
|
error[E0433]: cannot find `join` in `futures`
--> rust/lancedb/src/remote/table/insert.rs:504:55
|
504 | let (producer_result, send_result) = futures::join!(producer, send);
| ^^^^ could not find `join` in `futures`
error[E0407]: method `poll_next` is not a member of trait `Stream`
--> rust/lancedb/src/arrow.rs:108:5
|
108 | / fn poll_next(
109 | | self: Pin<&mut Self>,
110 | | cx: &mut std::task::Context<'_>,
111 | | ) -> std::task::Poll<Option<Self::Item>> {
112 | | let this = self.project();
113 | | this.stream.poll_next(cx)
114 | | }
| |_____^ not a member of trait `Stream`
error[E0407]: method `poll_next` is not a member of trait `Stream`
--> rust/lancedb/src/utils/mod.rs:362:5
|
362 | / fn poll_next(
363 | | mut self: std::pin::Pin<&mut Self>,
364 | | cx: &mut std::task::Context<'_>,
365 | | ) -> std::task::Poll<Option<Self::Item>> {
... |
391 | | }
| |_____^ not a member of trait `Stream`
error[E0407]: method `poll_next` is not a member of trait `Stream`
--> rust/lancedb/src/utils/mod.rs:433:5
|
433 | / fn poll_next(
434 | | mut self: Pin<&mut Self>,
435 | | cx: &mut std::task::Context<'_>,
436 | | ) -> std::task::Poll<Option<Self::Item>> {
... |
470 | | }
| |_____^ not a member of trait `Stream`
error[E0425]: cannot find function `try_unfold` in module `futures::stream`
--> rust/lancedb/src/remote/table/insert.rs:230:39
|
230 | let stream = futures::stream::try_unfold(
| ^^^^^^^^^^ not found in `futures::stream`
error[E0433]: cannot find `channel` in `futures`
--> rust/lancedb/src/remote/table/insert.rs:418:22
|
418 | futures::channel::mpsc::channel::<Result<Vec<u8>, std::io::Error>>(2);
| ^^^^^^^ could not find `channel` in `futures`
|
error[E0425]: cannot find function `try_join_all` in module `futures::future`
--> rust/lancedb/src/remote/table.rs:1062:40
|
1062 | let streams = futures::future::try_join_all(futures);
| ^^^^^^^^^^^^
|
::: $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/future/join_all.rs:76:1
|
76 | / pub fn join_all<I>(i: I) -> JoinAll<I>
77 | | where I: IntoIterator,
78 | | I::Item: IntoFuture,
| |______________________________- similarly named function `join_all` defined here
|
error[E0425]: cannot find function `try_join_all` in module `futures::future`
--> rust/lancedb/src/remote/table.rs:1660:40
|
1660 | let results = futures::future::try_join_all(futures).await?;
| ^^^^^^^^^^^^
|
::: $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/future/join_all.rs:76:1
|
76 | / pub fn join_all<I>(i: I) -> JoinAll<I>
77 | | where I: IntoIterator,
78 | | I::Item: IntoFuture,
| |______________________________- similarly named function `join_all` defined here
|
error[E0425]: cannot find function `try_join_all` in module `futures::future`
--> rust/lancedb/src/remote/table.rs:2243:43
|
2243 | let plan_texts = futures::future::try_join_all(futures).await?;
| ^^^^^^^^^^^^
|
::: $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/future/join_all.rs:76:1
|
76 | / pub fn join_all<I>(i: I) -> JoinAll<I>
77 | | where I: IntoIterator,
78 | | I::Item: IntoFuture,
| |______________________________- similarly named function `join_all` defined here
|
error[E0425]: cannot find function `try_join_all` in module `futures::future`
--> rust/lancedb/src/remote/table.rs:2290:53
|
2290 | let analyze_result_texts = futures::future::try_join_all(futures).await?;
| ^^^^^^^^^^^^
|
::: $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/future/join_all.rs:76:1
|
76 | / pub fn join_all<I>(i: I) -> JoinAll<I>
77 | | where I: IntoIterator,
78 | | I::Item: IntoFuture,
| |______________________________- similarly named function `join_all` defined here
|
error[E0425]: cannot find function `try_unfold` in module `futures::stream`
--> rust/lancedb/src/remote/util.rs:21:35
|
21 | let stream = futures::stream::try_unfold(
| ^^^^^^^^^^ not found in `futures::stream`
error[E0191]: the value of the associated type `Error` in `futures::Stream` must be specified
--> rust/lancedb/src/arrow.rs:70:50
|
70 | pub type SendableRecordBatchStream = Pin<Box<dyn RecordBatchStream + Send>>;
| ^^^^^^^^^^^^^^^^^
|
help: specify the associated type
|
70 | pub type SendableRecordBatchStream = Pin<Box<dyn RecordBatchStream<Error = /* Type */> + Send>>;
| ++++++++++++++++++++
error[E0107]: type alias takes 0 lifetime arguments but 1 lifetime argument was supplied
--> rust/lancedb/src/utils/background_cache.rs:15:31
|
15 | type SharedFut<V, E> = Shared<BoxFuture<'static, Result<V, Arc<E>>>>;
| ^^^^^^^^^ ------- help: remove the lifetime argument
| |
| expected 0 lifetime arguments
|
note: type alias defined here, with 0 lifetime parameters
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/future/mod.rs:106:14
|
106 | pub type BoxFuture<T, E> = ::std::boxed::Box<Future<Item = T, Error = E> + Send>;
| ^^^^^^^^^
error[E0107]: type alias takes 2 generic arguments but 1 generic argument was supplied
--> rust/lancedb/src/utils/background_cache.rs:15:31
|
15 | type SharedFut<V, E> = Shared<BoxFuture<'static, Result<V, Arc<E>>>>;
| ^^^^^^^^^ ----------------- supplied 1 generic argument
| |
| expected 2 generic arguments
|
note: type alias defined here, with 2 generic parameters: `T`, `E`
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/future/mod.rs:106:14
|
106 | pub type BoxFuture<T, E> = ::std::boxed::Box<Future<Item = T, Error = E> + Send>;
| ^^^^^^^^^ - -
help: add missing generic argument
|
15 | type SharedFut<V, E> = Shared<BoxFuture<'static, Result<V, Arc<E>>, E>>;
| +++
error[E0046]: not all trait items implemented, missing: `Error`, `poll`
--> rust/lancedb/src/arrow.rs:105:1
|
105 | impl<S: Stream<Item = Result<arrow_array::RecordBatch>>> Stream for SimpleRecordBatchStream<S> {
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ missing `Error`, `poll` in implementation
|
= help: implement the missing item: `type Error = /* Type */;`
= help: implement the missing item: `fn poll(&mut self) -> std::result::Result<Async<std::option::Option<<Self as futures::Stream>::Item>>, <Self as futures::Stream>::Error> { todo!() }`
error[E0107]: type alias takes 0 lifetime arguments but 1 lifetime argument was supplied
--> rust/lancedb/src/io/object_store.rs:97:46
|
97 | fn list(&self, prefix: Option<&Path>) -> BoxStream<'static, Result<ObjectMeta>> {
| ^^^^^^^^^ ------- help: remove the lifetime argument
| |
| expected 0 lifetime arguments
|
note: type alias defined here, with 0 lifetime parameters
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/stream/mod.rs:132:14
|
132 | pub type BoxStream<T, E> = ::std::boxed::Box<Stream<Item = T, Error = E> + Send>;
| ^^^^^^^^^
error[E0107]: type alias takes 2 generic arguments but 1 generic argument was supplied
--> rust/lancedb/src/io/object_store.rs:97:46
|
97 | fn list(&self, prefix: Option<&Path>) -> BoxStream<'static, Result<ObjectMeta>> {
| ^^^^^^^^^ ------------------ supplied 1 generic argument
| |
| expected 2 generic arguments
|
note: type alias defined here, with 2 generic parameters: `T`, `E`
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/stream/mod.rs:132:14
|
132 | pub type BoxStream<T, E> = ::std::boxed::Box<Stream<Item = T, Error = E> + Send>;
| ^^^^^^^^^ - -
help: add missing generic argument
|
97 | fn list(&self, prefix: Option<&Path>) -> BoxStream<'static, Result<ObjectMeta>, E> {
| +++
error[E0107]: type alias takes 0 lifetime arguments but 1 lifetime argument was supplied
--> rust/lancedb/src/io/object_store.rs:107:20
|
107 | locations: BoxStream<'static, Result<Path>>,
| ^^^^^^^^^ ------- help: remove the lifetime argument
| |
| expected 0 lifetime arguments
|
note: type alias defined here, with 0 lifetime parameters
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/stream/mod.rs:132:14
|
132 | pub type BoxStream<T, E> = ::std::boxed::Box<Stream<Item = T, Error = E> + Send>;
| ^^^^^^^^^
error[E0107]: type alias takes 2 generic arguments but 1 generic argument was supplied
--> rust/lancedb/src/io/object_store.rs:107:20
|
107 | locations: BoxStream<'static, Result<Path>>,
| ^^^^^^^^^ ------------ supplied 1 generic argument
| |
| expected 2 generic arguments
|
note: type alias defined here, with 2 generic parameters: `T`, `E`
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/stream/mod.rs:132:14
|
132 | pub type BoxStream<T, E> = ::std::boxed::Box<Stream<Item = T, Error = E> + Send>;
| ^^^^^^^^^ - -
help: add missing generic argument
|
107 | locations: BoxStream<'static, Result<Path>, E>,
| +++
error[E0107]: type alias takes 0 lifetime arguments but 1 lifetime argument was supplied
--> rust/lancedb/src/io/object_store.rs:108:10
|
108 | ) -> BoxStream<'static, Result<Path>> {
| ^^^^^^^^^ ------- help: remove the lifetime argument
| |
| expected 0 lifetime arguments
|
note: type alias defined here, with 0 lifetime parameters
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/stream/mod.rs:132:14
|
132 | pub type BoxStream<T, E> = ::std::boxed::Box<Stream<Item = T, Error = E> + Send>;
| ^^^^^^^^^
error[E0107]: type alias takes 2 generic arguments but 1 generic argument was supplied
--> rust/lancedb/src/io/object_store.rs:108:10
|
108 | ) -> BoxStream<'static, Result<Path>> {
| ^^^^^^^^^ ------------ supplied 1 generic argument
| |
| expected 2 generic arguments
|
note: type alias defined here, with 2 generic parameters: `T`, `E`
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/stream/mod.rs:132:14
|
132 | pub type BoxStream<T, E> = ::std::boxed::Box<Stream<Item = T, Error = E> + Send>;
| ^^^^^^^^^ - -
help: add missing generic argument
|
108 | ) -> BoxStream<'static, Result<Path>, E> {
| +++
error[E0599]: no method named `map_err` found for struct `Pin<Box<dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send>>` in the current scope
--> rust/lancedb/src/dataloader/permutation/builder.rs:208:32
|
208 | let stream = df_stream.map_err(|e| Error::Other {
| ----------^^^^^^^ method not found in `Pin<Box<dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send>>`
|
::: $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-util-0.3.32/src/stream/try_stream/mod.rs:248:8
|
248 | fn map_err<E, F>(self, f: F) -> MapErr<Self, F>
| ------- the method is available for `Pin<Box<dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send>>` here
|
error[E0599]: no method named `try_collect` found for struct `DatasetRecordBatchStream` in the current scope
--> rust/lancedb/src/dataloader/permutation/reader.rs:220:28
|
220 | let batches = data.try_collect::<Vec<_>>().await?;
| ^^^^^^^^^^^
|
error[E0599]: no method named `map_err` found for struct `DatasetRecordBatchStream` in the current scope
--> rust/lancedb/src/dataloader/permutation/reader.rs:287:14
|
286 | let mut stream = row_ids
| __________________________-
287 | | .map_err(Error::from)
| | -^^^^^^^ method not found in `DatasetRecordBatchStream`
| |_____________|
|
error[E0599]: the method `chain` exists for struct `futures::stream::Once<_, _>`, but its trait bounds were not satisfied
--> rust/lancedb/src/dataloader/permutation/reader.rs:307:81
|
307 | let stream = futures::stream::once(std::future::ready(Ok(first_batch))).chain(stream);
| ^^^^^ method cannot be called on `futures::stream::Once<_, _>` due to unsatisfied trait bounds
error[E0308]: mismatched types
--> rust/lancedb/src/dataloader/permutation/shuffle.rs:120:35
|
120 | futures::stream::once(async move { Ok(shuffled) }),
| --------------------- ^^^^^^^^^^^^^^^^^^^^^^^^^^^ expected `Result<_, _>`, found `async` block
| |
| arguments to this function are incorrect
|
= note: expected enum `std::result::Result<_, _>`
found `async` block `{async block@rust/lancedb/src/dataloader/permutation/shuffle.rs:120:35: 120:45}`
note: function defined here
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/stream/once.rs:20:8
|
20 | pub fn once<T, E>(item: Result<T, E>) -> Once<T, E> {
| ^^^^
help: try wrapping the expression in a variant of `std::result::Result`
|
120 | futures::stream::once(Ok(async move { Ok(shuffled) })),
| +++ +
120 | futures::stream::once(Err(async move { Ok(shuffled) })),
| ++++ +
error[E0271]: type mismatch resolving `<Range<u64> as IntoIterator>::Item == Result<_, _>`
--> rust/lancedb/src/dataloader/permutation/shuffle.rs:228:44
|
228 | let stream = futures::stream::iter(0..num_files)
| --------------------- ^^^^^^^^^^^^ expected `Result<_, _>`, found `u64`
| |
| required by a bound introduced by this call
|
= note: expected enum `std::result::Result<_, _>`
found type `u64`
note: required by a bound in `futures::stream::iter`
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/stream/iter.rs:31:27
|
30 | pub fn iter<J, T, E>(i: J) -> Iter<J::IntoIter>
| ---- required by a bound in this function
31 | where J: IntoIterator<Item=Result<T, E>>,
| ^^^^^^^^^^^^^^^^^ required by this bound in `iter`
error[E0599]: no method named `then` found for struct `IterStream<I>` in the current scope
--> rust/lancedb/src/dataloader/permutation/shuffle.rs:229:14
|
228 | let stream = futures::stream::iter(0..num_files)
| ______________________-
229 | | .then(move |file_index| {
| | -^^^^ method not found in `IterStream<std::ops::Range<u64>>`
| |_____________|
|
error[E0599]: no method named `try_collect` found for struct `Pin<Box<dyn lance::io::RecordBatchStream>>` in the current scope
--> rust/lancedb/src/dataloader/permutation/shuffle.rs:258:26
|
250 | let batches = reader
| ___________________________________-
251 | | .read_stream(
252 | | ReadBatchParams::RangeFull,
253 | | reader.num_rows() as u32,
... |
257 | | .await?
258 | | .try_collect::<Vec<_>>()
| |_________________________-^^^^^^^^^^^
error[E0599]: no method named `and_then` found for associated type `impl Future<Output = Result<Arc<...>, ...>> + Send` in the current scope
--> rust/lancedb/src/query.rs:766:14
|
765 | / self.create_plan(QueryExecutionOptions::default())
766 | | .and_then(|plan| std::future::ready(Ok(plan.schema())))
| |_____________-^^^^^^^^
|
::: $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-util-0.3.32/src/future/try_future/mod.rs:395:8
|
395 | fn and_then<Fut, F>(self, f: F) -> AndThen<Self, Fut, F>
| -------- the method is available for `impl std::future::Future<Output = std::result::Result<Arc<(dyn ExecutionPlan + 'static)>, error::Error>> + std::marker::Send` here
error[E0599]: no method named `boxed` found for `async` block `{async block@rust/lancedb/src/query.rs:1492:33: 1492:43}` in the current scope
--> rust/lancedb/src/query.rs:1493:18
|
1492 | let hybrid_result = async move { self.execute_hybrid(options).await }
| _________________________________-
1493 | | .boxed()
| | -^^^^^ method not found in `{async block@rust/lancedb/src/query.rs:1492:33: 1492:43}`
| |_________________|
error[E0271]: expected `{closure@blobs.rs:181:58}` to return `Result<_, _>`, but it returns `impl Future<Output = Result<Bytes, Error>>`
--> rust/lancedb/src/remote/table/blobs.rs:181:66
|
181 | futures::stream::iter(ranges.iter().cloned().map(|range| self.read_range(range)))
| --------------------- ------- ^^^^^^^^^^^^^^^^^^^^^^ expected `Result<_, _>`, found future
| | |
| | this closure
| required by a bound introduced by this call
error[E0599]: no method named `buffered` found for struct `IterStream<I>` in the current scope
--> rust/lancedb/src/remote/table/blobs.rs:182:14
|
181 | / futures::stream::iter(ranges.iter().cloned().map(|range| self.read_range(range)))
182 | | .buffered(BLOB_REQUEST_CONCURRENCY)
| | -^^^^^^^^ method not found in `Iter<Map<Cloned<Iter<'_, Range<u64>>>, {closure@...}>>`
| |_____________|
error[E0599]: no method named `try_next` found for struct `Pin<Box<dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send>>` in the current scope
--> rust/lancedb/src/remote/table/blobs.rs:379:40
|
379 | while let Some(batch) = stream.try_next().await? {
| ^^^^^^^^ method not found in `Pin<Box<dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send>>`
error[E0271]: type mismatch resolving `<Vec<...> as IntoIterator>::Item == Result<_, _>`
--> rust/lancedb/src/remote/table/blobs.rs:481:27
|
481 | futures::stream::iter(probe_futures)
| --------------------- ^^^^^^^^^^^^^ expected `Result<_, _>`, found future
| |
| required by a bound introduced by this call
error[E0599]: no method named `buffered` found for struct `IterStream<I>` in the current scope
--> rust/lancedb/src/remote/table/blobs.rs:482:10
|
481 | / futures::stream::iter(probe_futures)
482 | | .buffered(BLOB_REQUEST_CONCURRENCY)
| | -^^^^^^^^ method not found in `Iter<IntoIter<impl Future<Output = Result<..., ...>>>>`
| |_________|
error[E0599]: no method named `next` found for struct `Pin<Box<dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send>>` in the current scope
--> rust/lancedb/src/remote/table/insert.rs:324:37
|
324 | let mut first = match input.next().await {
| ^^^^ method not found in `Pin<Box<dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send>>`
error[E0599]: no method named `next` found for struct `Pin<Box<dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send>>` in the current scope
--> rust/lancedb/src/remote/table/insert.rs:345:33
|
345 | first = match input.next().await {
| ^^^^ method not found in `Pin<Box<dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send>>`
error[E0599]: the method `next` exists for mutable reference `&mut Pin<Box<dyn RecordBatchStream + Send>>`, but its trait bounds were not satisfied
--> rust/lancedb/src/remote/table/insert.rs:446:41
|
446 | None => match input.next().await {
| ^^^^ method cannot be called on `&mut Pin<Box<dyn RecordBatchStream + Send>>` due to unsatisfied trait bounds
|
= note: the following trait bounds were not satisfied:
`Pin<Box<(dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send + 'static)>>: Iterator`
which is required by `&mut Pin<Box<(dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send + 'static)>>: Iterator`
error[E0599]: no method named `map_err` found for struct `IterStream<I>` in the current scope
--> rust/lancedb/src/remote/table.rs:688:53
|
688 | let stream = futures::stream::iter(batches).map_err(DataFusionError::from);
| ^^^^^^^ method not found in `Iter<Box<dyn Iterator<Item = Result<..., ...>> + Send>>`
error[E0599]: no method named `try_collect` found for struct `Pin<Box<dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send>>` in the current scope
--> rust/lancedb/src/remote/table.rs:1378:49
|
1378 | let result: Result<Vec<_>> = stream.try_collect().await.map_err(Error::from);
| ^^^^^^^^^^^
error[E0599]: no method named `next` found for struct `Pin<Box<dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send>>` in the current scope
--> rust/lancedb/src/remote/table.rs:1509:48
|
1509 | while let Some(batch) = stream.next().await {
| ^^^^ method not found in `Pin<Box<dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send>>`
error[E0599]: no method named `boxed` found for opaque type `impl Future<Output = Result<DeleteResult, Error>>` in the current scope
--> rust/lancedb/src/table/delete.rs:35:51
|
35 | let delete_result = dataset.delete(s).boxed().await?;
| ^^^^^ method not found in `impl Future<Output = Result<DeleteResult, Error>>`
error[E0599]: no variant, associated function, or constant named `Left` found for enum `Either<A, B>` in the current scope
--> rust/lancedb/src/table/merge.rs:292:17
|
292 | Either::Left(tokio::time::timeout(timeout, future).map(|res| match res {
| ^^^^ variant, associated function, or constant not found in `Either<_, _>`
error[E0599]: `Timeout<impl Future<Output = Result<(Arc<...>, ...), ...>>>` is not an iterator
--> rust/lancedb/src/table/merge.rs:292:60
|
292 | Either::Left(tokio::time::timeout(timeout, future).map(|res| match res {
| --------------------------------------^^^ `Timeout<impl Future<Output = Result<(Arc<...>, ...), ...>>>` is not an iterator
|
::: $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/pin-project-lite-0.2.17/src/lib.rs:745:9
|
745 | / $vis struct $ident $($def_generics)*
746 | | $(where
747 | | $($where_clause)*)?
... |
751 | | ),+
752 | | }
| |_________- doesn't satisfy `_: Iterator`
|
= note: the following trait bounds were not satisfied:
`tokio::time::Timeout<impl std::future::Future<Output = std::result::Result<(Arc<lance::Dataset>, MergeStats), lance::Error>>>: Iterator`
which is required by `&mut tokio::time::Timeout<impl std::future::Future<Output = std::result::Result<(Arc<lance::Dataset>, MergeStats), lance::Error>>>: Iterator`
error[E0599]: no variant, associated function, or constant named `Right` found for enum `Either<A, B>` in the current scope
--> rust/lancedb/src/table/merge.rs:301:17
|
301 | Either::Right(job.execute_reader(new_data).map_err(|e| e.into()))
| ^^^^^ variant, associated function, or constant not found in `Either<_, _>`
error[E0599]: no method named `map_err` found for opaque type `impl Future<Output = Result<(Arc<Dataset>, ...), ...>>` in the current scope
--> rust/lancedb/src/table/merge.rs:301:52
|
301 | Either::Right(job.execute_reader(new_data).map_err(|e| e.into()))
| ^^^^^^^ method not found in `impl Future<Output = Result<(Arc<Dataset>, ...), ...>>`
error[E0277]: the trait bound `Iter<Map<IntoIter<RecordBatch>, ...>>: Stream` is not satisfied
--> rust/lancedb/src/table/query.rs:681:38
|
681 | Ok(DatasetRecordBatchStream::new(record_batch_stream))
| ^^^^^^^^^^^^^^^^^^^ the trait `futures_core::stream::Stream` is not implemented for `Iter<Map<IntoIter<RecordBatch>, ...>>`
error[E0277]: the trait bound `TimeoutStream: futures_core::stream::Stream` is not satisfied
--> rust/lancedb/src/utils/mod.rs:353:28
|
353 | impl RecordBatchStream for TimeoutStream {
| ^^^^^^^^^^^^^ unsatisfied trait bound
error[E0046]: not all trait items implemented, missing: `Error`, `poll`
--> rust/lancedb/src/utils/mod.rs:359:1
|
359 | impl Stream for TimeoutStream {
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ missing `Error`, `poll` in implementation
|
= help: implement the missing item: `type Error = /* Type */;`
= help: implement the missing item: `fn poll(&mut self) -> std::result::Result<Async<std::option::Option<<Self as futures::Stream>::Item>>, <Self as futures::Stream>::Error> { todo!() }`
error[E0277]: the trait bound `MaxBatchLengthStream: futures_core::stream::Stream` is not satisfied
--> rust/lancedb/src/utils/mod.rs:424:28
|
424 | impl RecordBatchStream for MaxBatchLengthStream {
| ^^^^^^^^^^^^^^^^^^^^ unsatisfied trait bound
error[E0046]: not all trait items implemented, missing: `Error`, `poll`
--> rust/lancedb/src/utils/mod.rs:430:1
|
430 | impl Stream for MaxBatchLengthStream {
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ missing `Error`, `poll` in implementation
|
= help: implement the missing item: `type Error = /* Type */;`
= help: implement the missing item: `fn poll(&mut self) -> std::result::Result<Async<std::option::Option<<Self as futures::Stream>::Item>>, <Self as futures::Stream>::Error> { todo!() }`
error[E0599]: no method named `map` found for type parameter `I` in the current scope
--> rust/lancedb/src/arrow.rs:75:45
|
72 | impl<I: lance::io::RecordBatchStream + 'static> From<I> for SendableRecordBatchStream {
| - method `map` not found for this type parameter
...
75 | let mapped_stream = Box::pin(stream.map(|r| r.map_err(Into::into)));
| ^^^
error[E0599]: no method named `poll_next` found for struct `Pin<&mut S>` in the current scope
--> rust/lancedb/src/arrow.rs:113:21
|
113 | this.stream.poll_next(cx)
| ^^^^^^^^^
|
= help: items from traits can only be used if the trait is implemented and in scope
= note: the following traits define an item `poll_next`, perhaps you need to implement one of them:
candidate #1: `futures_core::stream::Stream`
candidate #2: `sorts::stream::PartitionedStream`
help: there is a method `collect` with a similar name, but with different arguments
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/stream/mod.rs:563:5
|
563 | / fn collect(self) -> Collect<Self>
564 | | where Self: Sized
| |_________________________^
error[E0599]: the method `map_err` exists for struct `Pin<Box<dyn Stream<Item = Result<..., ...>> + Send>>`, but its trait bounds were not satisfied
--> rust/lancedb/src/arrow.rs:150:29
|
150 | let stream = stream.map_err(|err| Error::Arrow { source: err });
| ^^^^^^^ method cannot be called due to unsatisfied trait bounds
error[E0308]: mismatched types
--> rust/lancedb/src/data/scannable.rs:80:26
|
80 | stream: once(async move { Ok(batch) }),
| ---- ^^^^^^^^^^^^^^^^^^^^^^^^ expected `Result<_, _>`, found `async` block
| |
| arguments to this function are incorrect
|
= note: expected enum `std::result::Result<_, _>`
found `async` block `{async block@rust/lancedb/src/data/scannable.rs:80:26: 80:36}`
note: function defined here
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/stream/once.rs:20:8
|
20 | pub fn once<T, E>(item: Result<T, E>) -> Once<T, E> {
| ^^^^
help: try wrapping the expression in a variant of `std::result::Result`
|
80 | stream: once(Ok(async move { Ok(batch) })),
| +++ +
80 | stream: once(Err(async move { Ok(batch) })),
| ++++ +
error[E0308]: mismatched types
--> rust/lancedb/src/data/scannable.rs:107:30
|
107 | stream: once(async {
| _________________________----_^
| | |
| | arguments to this function are incorrect
108 | | Err(Error::InvalidInput {
109 | | message: "Cannot scan an empty Vec<RecordBatch>".to_string(),
110 | | })
111 | | }),
| |_________________^ expected `Result<_, _>`, found `async` block
|
= note: expected enum `std::result::Result<_, _>`
found `async` block `{async block@rust/lancedb/src/data/scannable.rs:107:30: 107:35}`
note: function defined here
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/stream/once.rs:20:8
|
20 | pub fn once<T, E>(item: Result<T, E>) -> Once<T, E> {
| ^^^^
help: try wrapping the expression in a variant of `std::result::Result`
|
107 ~ stream: once(Ok(async {
108 | Err(Error::InvalidInput {
109 | message: "Cannot scan an empty Vec<RecordBatch>".to_string(),
110 | })
111 ~ })),
|
107 ~ stream: once(Err(async {
108 | Err(Error::InvalidInput {
109 | message: "Cannot scan an empty Vec<RecordBatch>".to_string(),
110 | })
111 ~ })),
|
error[E0271]: expected `Ok` to return `Result<Result<RecordBatch, Error>, _>`, but it returns `Result<RecordBatch, _>`
--> rust/lancedb/src/data/scannable.rs:117:52
|
117 | Box::pin(SimpleRecordBatchStream { schema, stream })
| ^^^^^^ expected `Result<Result<RecordBatch, Error>, _>`, found `Result<RecordBatch, _>`
error[E0308]: mismatched types
--> rust/lancedb/src/data/scannable.rs:158:59
|
158 | let stream = futures::stream::unfold(rx, |mut rx| async move {
| ___________________________________________________________^
159 | | rx.recv().await.map(|batch| (batch, rx))
160 | | })
| |_________^ expected `Option<_>`, found `async` block
|
= note: expected enum `std::option::Option<_>`
found `async` block `{async block@rust/lancedb/src/data/scannable.rs:158:59: 158:69}`
help: try wrapping the expression in `Some`
|
158 ~ let stream = futures::stream::unfold(rx, |mut rx| Some(async move {
159 | rx.recv().await.map(|batch| (batch, rx))
160 ~ }))
|
error[E0599]: the method `fuse` exists for struct `Unfold<Receiver<Result<RecordBatch, Error>>, ..., _>`, but its trait bounds were not satisfied
--> rust/lancedb/src/data/scannable.rs:161:10
|
158 | let stream = futures::stream::unfold(rx, |mut rx| async move {
| ______________________-
159 | | rx.recv().await.map(|batch| (batch, rx))
160 | | })
161 | | .fuse();
| | -^^^^ method cannot be called due to unsatisfied trait bounds
| |_________|
error[E0308]: mismatched types
--> rust/lancedb/src/data/scannable.rs:178:26
|
178 | stream: once(async {
| _____________________----_^
| | |
| | arguments to this function are incorrect
179 | | Err(Error::InvalidInput {
180 | | message: "Stream has already been consumed".to_string(),
181 | | })
182 | | }),
| |_____________^ expected `Result<_, _>`, found `async` block
|
= note: expected enum `std::result::Result<_, _>`
found `async` block `{async block@rust/lancedb/src/data/scannable.rs:178:26: 178:31}`
note: function defined here
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/stream/once.rs:20:8
|
20 | pub fn once<T, E>(item: Result<T, E>) -> Once<T, E> {
| ^^^^
help: try wrapping the expression in a variant of `std::result::Result`
|
178 ~ stream: once(Ok(async {
179 | Err(Error::InvalidInput {
180 | message: "Stream has already been consumed".to_string(),
181 | })
182 ~ })),
|
178 ~ stream: once(Err(async {
179 | Err(Error::InvalidInput {
180 | message: "Stream has already been consumed".to_string(),
181 | })
182 ~ })),
|
error[E0308]: mismatched types
--> rust/lancedb/src/data/scannable.rs:474:53
|
474 | let prepend = futures::stream::once(std::future::ready(Ok(batch)));
| --------------------- ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ expected `Result<_, _>`, found `Ready<Result<RecordBatch, _>>`
| |
| arguments to this function are incorrect
|
= note: expected enum `std::result::Result<_, _>`
found struct `std::future::Ready<std::result::Result<arrow_array::RecordBatch, _>>`
note: function defined here
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/stream/once.rs:20:8
|
20 | pub fn once<T, E>(item: Result<T, E>) -> Once<T, E> {
| ^^^^
help: try wrapping the expression in a variant of `std::result::Result`
|
474 | let prepend = futures::stream::once(Ok(std::future::ready(Ok(batch))));
| +++ +
474 | let prepend = futures::stream::once(Err(std::future::ready(Ok(batch))));
| ++++ +
error[E0599]: the method `chain` exists for struct `futures::stream::Once<_, _>`, but its trait bounds were not satisfied
--> rust/lancedb/src/data/scannable.rs:477:37
|
477 | stream: prepend.chain(rest),
| ^^^^^ method cannot be called on `futures::stream::Once<_, _>` due to unsatisfied trait bounds
error[E0308]: mismatched types
--> rust/lancedb/src/data/scannable.rs:482:47
|
482 | stream: futures::stream::once(std::future::ready(Ok(batch))),
| --------------------- ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ expected `Result<_, _>`, found `Ready<Result<RecordBatch, _>>`
| |
| arguments to this function are incorrect
|
= note: expected enum `std::result::Result<_, _>`
found struct `std::future::Ready<std::result::Result<arrow_array::RecordBatch, _>>`
note: function defined here
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/stream/once.rs:20:8
|
20 | pub fn once<T, E>(item: Result<T, E>) -> Once<T, E> {
| ^^^^
help: try wrapping the expression in a variant of `std::result::Result`
|
482 | stream: futures::stream::once(Ok(std::future::ready(Ok(batch)))),
| +++ +
482 | stream: futures::stream::once(Err(std::future::ready(Ok(batch)))),
| ++++ +
error[E0308]: mismatched types
--> rust/lancedb/src/data/scannable.rs:486:56
|
486 | let stream = futures::stream::once(std::future::ready(err));
| --------------------- ^^^^^^^^^^^^^^^^^^^^^^^ expected `Result<_, _>`, found `Ready<Result<_, Error>>`
| |
| arguments to this function are incorrect
|
= note: expected enum `std::result::Result<_, _>`
found struct `std::future::Ready<std::result::Result<_, error::Error>>`
note: function defined here
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/stream/once.rs:20:8
|
20 | pub fn once<T, E>(item: Result<T, E>) -> Once<T, E> {
| ^^^^
help: try wrapping the expression in a variant of `std::result::Result`
|
486 | let stream = futures::stream::once(Ok(std::future::ready(err)));
| +++ +
486 | let stream = futures::stream::once(Err(std::future::ready(err)));
| ++++ +
error[E0599]: no method named `and_then` found for struct `Pin<Box<dyn Future<Output = Result<(), Error>> + Send>>` in the current scope
--> rust/lancedb/src/io/object_store.rs:153:32
|
153 | Box::pin(put_secondary.and_then(|_| put_primary))
| ^^^^^^^^
error[E0271]: expected `IntoIter<Result<RecordBatch, _>, 1>` to be an iterator that yields `Result<Result<RecordBatch, Error>, _>`, but it yields `Result<RecordBatch, _>`
--> rust/lancedb/src/query.rs:1465:25
|
1465 | return Box::pin(SimpleRecordBatchStream::new(
| ^^^^^^^^^^^^^^^^^^^^^^^ expected `Result<Result<RecordBatch, Error>, _>`, found `Result<RecordBatch, _>`
error[E0271]: expected `IntoIter<Result<RecordBatch, _>>` to be an iterator that yields `Result<Result<RecordBatch, Error>, _>`, but it yields `Result<RecordBatch, _>`
--> rust/lancedb/src/query.rs:1478:14
|
1478 | Box::pin(SimpleRecordBatchStream::new(stream::iter(batches), schema))
| ^^^^^^^^^^^^^^^^^^^^^^^ expected `Result<Result<RecordBatch, Error>, _>`, found `Result<RecordBatch, _>`
error[E0308]: mismatched types
--> rust/lancedb/src/remote/table/insert.rs:626:44
|
626 | let stream = futures::stream::once(async move {
| ______________________---------------------_^
| | |
| | arguments to this function are incorrect
... |
791 | | Ok::<_, DataFusionError>(batch)
792 | | });
| |_________^ expected `Result<_, _>`, found `async` block
|
= note: expected enum `std::result::Result<_, _>`
found `async` block `{async block@rust/lancedb/src/remote/table/insert.rs:626:44: 626:54}`
note: function defined here
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/stream/once.rs:20:8
|
20 | pub fn once<T, E>(item: Result<T, E>) -> Once<T, E> {
| ^^^^
help: try wrapping the expression in a variant of `std::result::Result`
|
626 ~ let stream = futures::stream::once(Ok(async move {
627 | // Multipart writes with a byte budget split the partition into
...
791 | Ok::<_, DataFusionError>(batch)
792 ~ }));
|
626 ~ let stream = futures::stream::once(Err(async move {
627 | // Multipart writes with a byte budget split the partition into
...
791 | Ok::<_, DataFusionError>(batch)
792 ~ }));
|
error[E0277]: the trait bound `futures::stream::Once<_, _>: futures_core::stream::Stream` is not satisfied
--> rust/lancedb/src/remote/table/insert.rs:794:12
|
794 | Ok(Box::pin(RecordBatchStreamAdapter::new(
| ____________^
795 | | COUNT_SCHEMA.clone(),
796 | | stream,
797 | | )))
| |__________^ the trait `futures_core::stream::Stream` is not implemented for `futures::stream::Once<_, _>`
error[E0599]: no method named `try_collect` found for struct `Pin<Box<dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send>>` in the current scope
--> rust/lancedb/src/remote/table.rs:2442:49
|
2442 | let result: Result<Vec<_>> = stream.try_collect().await.map_err(Error::from);
| ^^^^^^^^^^^
error[E0277]: the trait bound `impl Stream<Item = Result<Bytes, Error>>: TryStream` is not satisfied
--> rust/lancedb/src/remote/util.rs:47:35
|
47 | Ok(reqwest::Body::wrap_stream(stream))
| -------------------------- ^^^^^^ unsatisfied trait bound
| |
| required by a bound introduced by this call
error[E0599]: no method named `map_ok` found for struct `Pin<Box<dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send>>` in the current scope
--> rust/lancedb/src/table/datafusion/insert.rs:200:30
|
200 | input_stream.map_ok(move |batch| {
| -------------^^^^^^ method not found in `Pin<Box<dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send>>`
error[E0308]: mismatched types
--> rust/lancedb/src/table/datafusion/insert.rs:208:44
|
208 | let stream = futures::stream::once(async move {
| ______________________---------------------_^
| | |
| | arguments to this function are incorrect
209 | | if let Some(tracker) = tracker
210 | | && write_params.write_progress.is_none()
... |
255 | | )?)
256 | | });
| |_________^ expected `Result<_, _>`, found `async` block
|
= note: expected enum `std::result::Result<_, _>`
found `async` block `{async block@rust/lancedb/src/table/datafusion/insert.rs:208:44: 208:54}`
note: function defined here
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/stream/once.rs:20:8
|
20 | pub fn once<T, E>(item: Result<T, E>) -> Once<T, E> {
| ^^^^
help: try wrapping the expression in a variant of `std::result::Result`
|
208 ~ let stream = futures::stream::once(Ok(async move {
209 | if let Some(tracker) = tracker
...
255 | )?)
256 ~ }));
|
208 ~ let stream = futures::stream::once(Err(async move {
209 | if let Some(tracker) = tracker
...
255 | )?)
256 ~ }));
|
error[E0277]: the trait bound `futures::stream::Once<_, _>: futures_core::stream::Stream` is not satisfied
--> rust/lancedb/src/table/datafusion/insert.rs:258:12
|
258 | Ok(Box::pin(RecordBatchStreamAdapter::new(
| ____________^
259 | | COUNT_SCHEMA.clone(),
260 | | stream,
261 | | )))
| |__________^ the trait `futures_core::stream::Stream` is not implemented for `futures::stream::Once<_, _>`
error[E0599]: no method named `map_ok` found for struct `Pin<Box<dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send>>` in the current scope
--> rust/lancedb/src/table/datafusion.rs:128:29
|
128 | let stream = stream.map_ok(move |batch| {
| -------^^^^^^ method not found in `Pin<Box<dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send>>`
error[E0599]: no method named `map_err` found for struct `Pin<Box<dyn Future<Output = Result<Arc<...>, ...>> + Send>>` in the current scope
--> rust/lancedb/src/table/datafusion.rs:245:14
|
242 | let plan = self
| ____________________-
243 | | .table
244 | | .create_plan(&AnyQuery::Query(query), options)
245 | | .map_err(|err| DataFusionError::External(err.into()))
| | -^^^^^^^ method not found in `Pin<Box<dyn Future<Output = Result<Arc<...>, ...>> + Send>>`
| |_____________|
error[E0599]: no method named `next` found for struct `Pin<Box<dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send>>` in the current scope
--> rust/lancedb/src/table.rs:3048:48
|
3048 | while let Some(batch) = stream.next().await {
| ^^^^ method not found in `Pin<Box<dyn datafusion_physical_plan::RecordBatchStream + std::marker::Send>>`
error[E0277]: the trait bound `JoinHandle<Result<(), Error>>: Future` is not satisfied
--> rust/lancedb/src/table.rs:3038:23
|
3038 | let handles = FuturesUnordered::new();
| ^^^^^^^^^^^^^^^^^^^^^^^ the trait `futures::Future` is not implemented for `tokio::task::JoinHandle<std::result::Result<(), error::Error>>`
error[E0277]: `FuturesUnordered<JoinHandle<Result<(), Error>>>` is not an iterator
--> rust/lancedb/src/table.rs:3054:23
|
3054 | for handle in handles {
| ^^^^^^^ `FuturesUnordered<JoinHandle<Result<(), Error>>>` is not an iterator
error[E0277]: the trait bound `{async block@rust/lancedb/src/table.rs:3452:29: 3452:39}: futures::IntoFuture` is not satisfied
--> rust/lancedb/src/table.rs:3450:13
|
3449 | let mut sorted_sizes = join_all(
| -------- required by a bound introduced by this call
3450 | / frags
3451 | | .iter()
3452 | | .map(|frag| async move { frag.physical_rows().await.unwrap_or(0) }),
| |___________________________________________________________________________________^ the trait `futures::Future` is not implemented for `{async block@rust/lancedb/src/table.rs:3452:29: 3452:39}`
|
= note: `{async block@rust/lancedb/src/table.rs:3452:29: 3452:39}` implements similarly named trait `std::future::Future`, but not `futures::Future`
= help: the following other types implement trait `futures::Future`:
&'a mut F
AssertUnwindSafe<F>
BiLockAcquire<T>
Box<F>
Concat2<S>
Either<A, B>
Finished<T, E>
Fold<S, F, Fut, T>
and 43 others
= note: required for `{async block@rust/lancedb/src/table.rs:3452:29: 3452:39}` to implement `futures::IntoFuture`
note: required by a bound in `join_all`
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/future/join_all.rs:78:20
|
76 | pub fn join_all<I>(i: I) -> JoinAll<I>
| -------- required by a bound in this function
77 | where I: IntoIterator,
78 | I::Item: IntoFuture,
| ^^^^^^^^^^ required by this bound in `join_all`
error[E0277]: the trait bound `{async block@rust/lancedb/src/table.rs:3452:29: 3452:39}: futures::Future` is not satisfied
--> rust/lancedb/src/table.rs:3449:32
|
3449 | let mut sorted_sizes = join_all(
| ________________________________^
3450 | | frags
3451 | | .iter()
3452 | | .map(|frag| async move { frag.physical_rows().await.unwrap_or(0) }),
3453 | | )
| |_________^ the trait `futures::Future` is not implemented for `{async block@rust/lancedb/src/table.rs:3452:29: 3452:39}`
|
= note: `{async block@rust/lancedb/src/table.rs:3452:29: 3452:39}` implements similarly named trait `std::future::Future`, but not `futures::Future`
= help: the following other types implement trait `futures::Future`:
&'a mut F
AssertUnwindSafe<F>
BiLockAcquire<T>
Box<F>
Concat2<S>
Either<A, B>
Finished<T, E>
Fold<S, F, Fut, T>
and 43 others
= note: required for `{async block@rust/lancedb/src/table.rs:3452:29: 3452:39}` to implement `futures::IntoFuture`
note: required by a bound in `JoinAll`
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/future/join_all.rs:24:20
|
22 | pub struct JoinAll<I>
| ------- required by a bound in this struct
23 | where I: IntoIterator,
24 | I::Item: IntoFuture,
| ^^^^^^^^^^ required by this bound in `JoinAll`
error[E0277]: `JoinAll<Map<Iter<'_, FileFragment>, {closure@...}>>` is not a future
--> rust/lancedb/src/table.rs:3454:10
|
3449 | let mut sorted_sizes = join_all(
| ________________________________-
3450 | | frags
3451 | | .iter()
3452 | | .map(|frag| async move { frag.physical_rows().await.unwrap_or(0) }),
3453 | | )
| |_________- this call returns `JoinAll<std::iter::Map<std::slice::Iter<'_, FileFragment>, {closure@rust/lancedb/src/table.rs:3452:22: 3452:28}>>`
3454 | .await;
| ^^^^^ `JoinAll<Map<Iter<'_, FileFragment>, {closure@...}>>` is not a future
error[E0277]: the trait bound `{async block@rust/lancedb/src/table.rs:3452:29: 3452:39}: futures::Future` is not satisfied
--> rust/lancedb/src/table.rs:3454:10
|
3454 | .await;
| ^^^^^ the trait `futures::Future` is not implemented for `{async block@rust/lancedb/src/table.rs:3452:29: 3452:39}`
|
= note: `{async block@rust/lancedb/src/table.rs:3452:29: 3452:39}` implements similarly named trait `std::future::Future`, but not `futures::Future`
= help: the following other types implement trait `futures::Future`:
&'a mut F
AssertUnwindSafe<F>
BiLockAcquire<T>
Box<F>
Concat2<S>
Either<A, B>
Finished<T, E>
Fold<S, F, Fut, T>
and 43 others
= note: required for `{async block@rust/lancedb/src/table.rs:3452:29: 3452:39}` to implement `futures::IntoFuture`
note: required by a bound in `JoinAll`
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/future/join_all.rs:24:20
|
22 | pub struct JoinAll<I>
| ------- required by a bound in this struct
23 | where I: IntoIterator,
24 | I::Item: IntoFuture,
| ^^^^^^^^^^ required by this bound in `JoinAll`
error[E0282]: type annotations needed
--> rust/lancedb/src/utils/background_cache.rs:119:40
|
119 | inner: Arc::new(Mutex::new(CacheInner {
| ________________________________________^
120 | | state: State::Empty,
121 | | generation: 0,
122 | | })),
| |_____________^ cannot infer type of the type parameter `E` declared on the struct `CacheInner`
|
help: consider specifying the generic arguments
|
119 | inner: Arc::new(Mutex::new(CacheInner::<V, E> {
| ++++++++
error[E0282]: type annotations needed
--> rust/lancedb/src/utils/background_cache.rs:134:9
|
134 | cache.state.fresh_value(self.ttl, self.refresh_window)
| ^^^^^^^^^^^ cannot infer type for type parameter `E`
error[E0282]: type annotations needed
--> rust/lancedb/src/utils/background_cache.rs:173:23
|
173 | cache.state = State::Current(value, clock::now());
| ^^^^^^^^^^^^^^ cannot infer type of the type parameter `E` declared on the enum `State`
|
help: consider specifying the generic arguments
|
173 | cache.state = State::<V, E>::Current(value, clock::now());
| ++++++++
error[E0282]: type annotations needed
--> rust/lancedb/src/utils/background_cache.rs:182:23
|
182 | cache.state = State::Empty;
| ^^^^^^^^^^^^ cannot infer type of the type parameter `E` declared on the enum `State`
|
help: consider specifying the generic arguments
|
182 | cache.state = State::<V, E>::Empty;
| ++++++++
error[E0599]: no method named `boxed` found for `async` block `{async block@rust/lancedb/src/utils/background_cache.rs:269:22: 269:32}` in the current scope
--> rust/lancedb/src/utils/background_cache.rs:270:14
|
269 | let shared = async move { (fetch)().await.map_err(Arc::new) }
| ______________________-
270 | | .boxed()
| | -^^^^^ method not found in `{async block@rust/lancedb/src/utils/background_cache.rs:269:22: 269:32}`
| |_____________|
error[E0277]: the trait bound `TimeoutStream: futures_core::stream::Stream` is not satisfied
--> rust/lancedb/src/utils/mod.rs:345:9
|
345 | Box::pin(Self::new(inner, timeout))
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ unsatisfied trait bound
error[E0599]: no method named `poll_next` found for struct `Pin<&mut TimeoutStream>` in the current scope
--> rust/lancedb/src/utils/mod.rs:376:22
|
376 | self.poll_next(cx)
| ^^^^^^^^^
|
= help: items from traits can only be used if the trait is implemented and in scope
= note: the following traits define an item `poll_next`, perhaps you need to implement one of them:
candidate #1: `futures_core::stream::Stream`
candidate #2: `sorts::stream::PartitionedStream`
help: there is a method `collect` with a similar name, but with different arguments
--> $CARGO_HOME/registry/src/index.crates.io-1949cf8c6b5b557f/futures-0.1.31/src/stream/mod.rs:563:5
|
563 | / fn collect(self) -> Collect<Self>
564 | | where Self: Sized
| |_________________________^
error[E0599]: no method named `poll_unpin` found for mutable reference `&mut Pin<Box<Sleep>>` in the current scope
--> rust/lancedb/src/utils/mod.rs:378:75
|
378 | TimeoutState::Started { deadline, timeout } => match deadline.poll_unpin(cx) {
| ^^^^^^^^^^ method not found in `&mut Pin<Box<Sleep>>`
error[E0599]: no method named `poll_next` found for struct `Pin<&mut Pin<Box<dyn RecordBatchStream + Send>>>` in the current scope
--> rust/lancedb/src/utils/mod.rs:386:27
|
386 | inner.poll_next(cx)
| ^^^^^^^^^
error[E0277]: the trait bound `MaxBatchLengthStream: futures_core::stream::Stream` is not satisfied
--> rust/lancedb/src/utils/mod.rs:419:13
|
419 | Box::pin(Self::new(inner, max_batch_length))
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ unsatisfied trait bound
error[E0599]: no method named `poll_next` found for struct `Pin<&mut Pin<Box<dyn RecordBatchStream + Send>>>` in the current scope
--> rust/lancedb/src/utils/mod.rs:439:50
|
439 | return Pin::new(&mut self.inner).poll_next(cx);
| ^^^^^^^^^
error[E0599]: no method named `poll_next` found for struct `Pin<&mut Pin<Box<dyn RecordBatchStream + Send>>>` in the current scope
--> rust/lancedb/src/utils/mod.rs:459:45
|
459 | match Pin::new(&mut self.inner).poll_next(cx) {
| ^^^^^^^^^
Some errors have detailed explanations: E0046, E0107, E0191, E0271, E0277, E0282, E0308, E0407, E0425...
For more information about an error, try `rustc --explain E0046`.
error: could not compile `lancedb` (lib) due to 118 previous errors
```
The docs have no link checking at all, so external links rot silently: a
trial run already found `docs/src/python/python.md` pointing at
`lancedb.github.io/lance-namespace`, which returns 404 since the
repository moved to the lance-format org.
Checking external links on the blocking path would be the wrong trade:
third-party hosts rate-limit automated clients, reject non-browser user
agents, and go down temporarily, so any of them having a bad minute
would turn unrelated PRs red. Following lance-format/lance#8315, this
adds a daily `lychee` run that reports broken links into a single
tracking issue, rewritten in place on each run and closed automatically
once every link resolves. The scan job runs the downloaded lychee binary
with a read-only token; everything that writes lives in a separate
report job, and a non-verdict lychee exit fails the run instead of
publishing a bogus report.
The check is restricted to http(s) links because much of `docs/src` is
generated API reference (the `js/` tree comes from `npm run docs`) and
the hand-written pages use mkdocstrings cross-references and
nav-relative paths that only resolve in the site mkdocs builds, so
relative links would be reported as broken on every run.
The one broken link the trial run surfaced is fixed here; after the fix,
a local run over all 154 files reports 0 errors across 216 unique links.
## Summary
- add regression coverage for repeated table opens through one database
connection
- assert that each open reuses the connection object-store client
without another registry miss
- exercise the table after every open so the test covers the complete
dataset-loading path
## Root cause
At the commit reported in #1600, opening a table constructed a separate
object-store client rather than reusing the client that had already
connected to the database. On S3 this repeated credential discovery,
which could fail intermittently in AWS Lambda and surface as
TableNotFound. The connection-owned Session reuse added later fixed the
runtime path, but no focused test protected the open-table invariant.
## Fix
Add a regression test backed by ObjectStoreRegistry statistics. Three
successive opens must add cache hits while leaving the miss count
unchanged, proving that open_table uses the connection Session and its
authenticated object-store client.
## Validation
- cargo fmt --all
- cargo test --quiet --features remote -p lancedb
database::listing::tests::test_open_table_reuses_connection_object_store
- cargo check --quiet --features remote --tests --examples
- cargo clippy --quiet --features remote --tests --examples
- cargo test --quiet --features remote --tests
Fixes#1600
<!-- lance-gatekeeper-fix:v1 agent=974491978c3e42840f32dbc35492d856
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- convert PyArrow scalar values through their Python representation
before SQL literal rendering
- add an end-to-end regression for updating a fixed-size-list vector
from a queried FixedSizeListScalar
## Root cause
Python update literal conversion used single dispatch for native Python
and NumPy values but had no PyArrow Scalar registration. A
FixedSizeListScalar returned by a query therefore reached the
unsupported generic conversion instead of the existing recursive list
converter.
## Validation
- uv run --extra tests pytest python/tests/test_table.py::test_update
python/tests/test_table.py::test_update_with_arrow_scalar
python/tests/test_table.py::test_update_types -q
- uv run --extra tests pytest python/tests/test_util.py -q
- uv run --project python --extra tests --extra dev ruff format --check
python/python/lancedb/util.py python/python/tests/test_table.py
- uv run --project python --extra tests --extra dev ruff check .
Fixes#1228
<!-- lance-gatekeeper-fix:v1 agent=950dd892194e53b61c203d5e3715cac7
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary\n\n- add a create-table regression for a named database\n-
assert that the derived table URI uses URL separators\n- restore the
four query tests that were moved to temporary files for #1051\n\n## Root
cause\n\n historically joined table names with . On Windows this
inserted a backslash into , so Lance interpreted the URI as an invalid
local filename. The production URI builder now preserves forward slashes
for URI schemes; this change restores the issue-specific tests and adds
direct regression coverage for table creation and the derived URI.\n\n##
Validation\n\n- \n- \n- (passes with four pre-existing warnings in
unrelated remote-table code)\n-
running 814 tests
.......................................................................................
87/814
.....................................i.................................................
174/814
.......................................................................................
261/814
.......................................................................................
348/814
.......................................................................................
435/814
.......................................................................................
522/814
.......................................................................................
609/814
.......................................................................................
696/814
.......................................................................................
783/814
...............................
test result: ok. 813 passed; 0 failed; 1 ignored; 0 measured; 0 filtered
out; finished in 7.76s
running 39 tests
.......................................
test result: ok. 39 passed; 0 failed; 0 ignored; 0 measured; 0 filtered
out; finished in 0.23s
running 6 tests
......
test result: ok. 6 passed; 0 failed; 0 ignored; 0 measured; 0 filtered
out; finished in 0.03s
running 5 tests
.....
test result: ok. 5 passed; 0 failed; 0 ignored; 0 measured; 0 filtered
out; finished in 0.10s
running 0 tests
test result: ok. 0 passed; 0 failed; 0 ignored; 0 measured; 0 filtered
out; finished in 0.00s
running 2 tests
..
test result: ok. 2 passed; 0 failed; 0 ignored; 0 measured; 0 filtered
out; finished in 0.00s
running 2 tests
..
test result: ok. 2 passed; 0 failed; 0 ignored; 0 measured; 0 filtered
out; finished in 0.00s (867 passed, 1 ignored)\n- focused named-memory
create and restored query tests\n\nFixes #1051\n\n<!--
lance-gatekeeper-fix:v1 agent=5ddf7a9520292b4cbaa58b9ea5a1fe76
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
Nothing validates index names, so a `/` in one is reachable, and the
remote client interpolates it straight into the URL, splitting the path
so the router 404s. The index then reads back as missing and cannot be
dropped, while `create_index` keeps succeeding because it sends the name
in the body.
Encode at the three affected sites, mirroring `fetch_blob_files`. The
shared Rust client covers all bindings.
## Summary
- add a public Node API regression test for JSON server errors from
remote table operations
- verify countRows reports the server message instead of an ArrayBuffer
decoding TypeError
## Root cause and fix
The former TypeScript remote HTTP client passed an Axios-decoded JSON
error object to TextDecoder, which masked the server response with an
ArrayBuffer TypeError. The current Rust-backed remote client consumes
non-success response bodies as text and propagates them through the Node
error chain. This test exercises that corrected path through countRows
and prevents the original failure from regressing.
## Validation
- pnpm build
- pnpm lint-ci
- pnpm test --runInBand __test__/remote.test.ts
- pnpm run docs
Fixes#825
<!-- lance-gatekeeper-fix:v1 agent=91591c3d6b065796e6166664ef638aa7
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- treat `NotFound` from the mirrored secondary copy as a cache miss
while preserving every other secondary error
- perform the durable primary copy after either a successful secondary
copy or a secondary cache miss
- cover both an initially missing secondary manifest and eviction
immediately before the secondary copy
## Root cause
Readers can use process-local secondary stores that do not contain a
staging manifest written by another process, or that evict it before
finalization. `MirroringObjectStore::copy_opts` propagated that
secondary `NotFound`, so older object_store versions could loop
indefinitely and the locked version aborted before performing the
durable primary copy.
## Validation
- `cargo fmt --all -- --check`
- `cargo test --quiet --features remote -p lancedb
io::object_store::test::test_copy_when -- --nocapture`
- `cargo check --quiet --features remote --tests --examples`
- `cargo clippy --quiet --features remote --tests --examples`
- `cargo test --quiet --features remote --tests`
Fixes#1176
<!-- lance-gatekeeper-fix:v1 agent=636210af9dcd25b6dceadebd2fcafc6f
generation=1 -->
---------
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- add an end-to-end regression for schemas created by a different Apache
Arrow package instance
- cover seeded table creation, filtered scanning, and Float64 vector
search across Arrow 15–18
## Root cause
Apache Arrow's runtime identity checks historically rejected schemas
created by another installed Arrow instance, producing the constructor
failures reported in the issue. LanceDB's peer dependency and
foreign-schema sanitization now handle that boundary, but the complete
reported workflow was only covered by separate unit tests. This
regression keeps the repaired behavior protected end to end.
## Validation
- `pnpm exec jest --runInBand __test__/table.test.ts` (281 passed)
- `pnpm lint-ci`
- `pnpm build`
- `pnpm run docs`
Fixes#882
<!-- lance-gatekeeper-fix:v1 agent=43b19dea581cfbc83ee1e9ed21a335a6
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- validate the generated LanceDB Cloud hostname during connection setup
- return a clear invalid-input error for empty, overlong, or oversized
DNS names before network resolution
- add Rust and Python regression coverage for malformed `db://`
authorities
## Root cause
The `db://` authority and region were interpolated into the Cloud API
hostname without DNS length validation. Empty or overlong labels
therefore reached the resolver and surfaced as an opaque IDNA
`UnicodeError` instead of a useful connection error.
## Validation
- `cargo test --quiet --features remote -p lancedb
test_rejects_invalid_cloud_dns_hostname --lib`
- `cargo check --quiet --features remote --tests --examples`
- `uv run --no-sync --extra tests pytest
python/tests/test_remote_db.py::test_async_remote_db
python/tests/test_remote_db.py::test_connect_rejects_invalid_cloud_dns_hostname
-q`
- `cargo fmt --all -- --check`
- `ruff check .`
- `ruff format --check python/python/tests/test_remote_db.py`
Fixes#799
<!-- lance-gatekeeper-fix:v1 agent=4d1597b3d244b58f0603ed40a8a59cf9
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- Adds a LanceDB regression for stable row IDs, scattered deletes,
IVF_RQ, and default index optimization.
- Verifies optimization completes and preserves the expected live-row
count.
## Root cause
Lance 3.0.1 built the stable-row-ID address list by dropping deleted IDs
while retaining the original ID list. The subsequent positional zip
misaligned IDs and addresses, so vector partition joins requested
deleted rows and failed with batch.num_rows() != chunk.len(). Lance PR
https://github.com/lance-format/lance/pull/7704 corrected the generic
filter, and the LanceDB dependency currently pinned on main contains
that correction.
## Fix
Add regression coverage at the Rust Table optimize surface using the
IVF_RQ configuration from the report. This locks the upstream correction
into the LanceDB workflow that originally crashed.
## Validation
- cargo fmt --all -- --check
- cargo test --quiet --features remote -p lancedb table::optimize::tests
(14 passed)
- cargo check --quiet --features remote --tests --examples
- cargo clippy --quiet --features remote --tests --examples -p lancedb
Fixes#3330
<!-- lance-gatekeeper-fix:v1 agent=4c2c25373942aab9ba9f7444977de7e3
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- add a Python regression test for two partial-schema merge inserts
against the same BTree-indexed rows
- verify repeated updates retain one copy of every row and the final
update values
## Root cause
Lance 4.0, used by LanceDB 0.30.2, removed a rewritten fragment from the
index bitmap while stale BTree entries for that fragment remained
searchable. The next merge found each target through both the stale
index and the unindexed-fragment scan, producing the ambiguous-match
error. Lance fixed the root cause in lance-format/lance#6563 by applying
the fragment-bitmap allow-list to index results, and the Lance release
pinned by current LanceDB includes that fix. This test preserves the
corrected behavior through the Python API.
## Validation
- `cd python && uv run --extra tests pytest python/tests/test_table.py
-k merge_insert -q` (9 passed)
- `cd python && uv run --extra tests --extra dev ruff format --check
python/tests/test_table.py`
- `cd python && uv run --extra tests --extra dev ruff check
python/tests/test_table.py`
Repository-wide Ruff also reports 20 pre-existing violations in
untouched CI and plugin scripts.
Fixes#3280
<!-- lance-gatekeeper-fix:v1 agent=ee6b9565f9780712026076930566f116
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- add a minimized regression for mostly-null `list<float32>` data at the
v2.2 structural page boundary
- verify scans preserve all 64,885 rows, including 64,668 null list
values
## Root cause
Lance 3.0.0 sliced repetition/definition state using top-level row
offsets in the complex all-null decoder. At this page boundary, the list
and validity children were materialized at different lengths. The
current Lance dependency contains the upstream decoder repair; this test
locks that behavior into the LanceDB Python suite without duplicating
decoder logic.
## Validation
- reproduced the attached 1,892,466-row case on `lancedb==0.30.0` with
`expected 1024 got 285`
- verified the full attachment reads on the current branch
- `python/.venv/bin/ruff format --check
python/python/tests/test_table.py`
- `python/.venv/bin/ruff check .`
- `cd python && uv run --extra tests pytest
python/tests/test_table.py::test_read_mostly_null_list_v2_2_page_boundary
-q`
- `cd python && uv run --extra tests pytest python/tests/test_table.py
-q` (137 passed)
Fixes#3194
<!-- lance-gatekeeper-fix:v1 agent=0445adc5303a3302152cea3d2110bed1
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- add a LanceDB regression for `merge_insert` with a non-nullable
`FixedSizeBinary` column
- exercise matched updates, unmatched inserts, and source-missing
deletes
- assert the exact merge statistics and final row count
## Root cause
The Arrow `take` kernel previously ignored nulls in the index array for
`FixedSizeBinary`. DataFusion uses that kernel while constructing
outer-join results, so the join behind
`when_not_matched_by_source_delete` could place invalid values into
non-nullable columns. The current Arrow dependency contains the upstream
fix; this test locks the corrected behavior at the LanceDB API boundary.
## Validation
- `cargo fmt --all -- --check`
- `cargo test --quiet --features remote --tests`
- `cargo check --quiet --features remote --tests --examples`
- `cargo clippy --quiet --features remote --tests --examples`
Fixes#2869
<!-- lance-gatekeeper-fix:v1 agent=e275446044185ef4e8cf88da6af3e70b
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- add deterministic regression coverage that `Table.add()` releases
backing Arrow buffers without cyclic garbage collection
- track the foreign buffer owner rather than RSS, separating live input
retention from allocator high-water behavior
- preserve the bounded-lifetime behavior of the Scannable writer that
superseded the historical preprocessing path
## Root cause
The historical Python preprocessing/write path produced a high allocator
RSS while ingesting very wide IPC batches. The current Scannable writer
releases each input buffer when `Table.add()` completes; remaining RSS
is allocator high-water rather than a live Arrow reference. The resolved
behavior had no regression coverage, so a future native lifetime
regression could silently reintroduce the original failure mode.
## Validation
- `uv run --extra tests --extra dev maturin develop`
- `uv run --project python --extra tests pytest
python/python/tests/test_table.py::test_add
python/python/tests/test_table.py::test_add_releases_arrow_buffers_without_gc
-q`
- `uv run --project python --extra dev ruff format --check
python/python/tests/test_table.py`
- `uv run --project python --extra dev ruff check .`
Fixes#2512
<!-- lance-gatekeeper-fix:v1 agent=29226408a8d07da592daf341d5384e37
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- add an end-to-end Python regression for pandas DataFrame inputs merged
into a table created from a Pydantic model
- verify reordered, nullable Arrow source fields can update and insert
into a non-nullable target schema when the values contain no nulls
## Root cause
Lance merge_insert previously compared source schema nullability with
the target, unlike add. The upstream fix now pinned by LanceDB ignores
declared nullability during schema compatibility and validates actual
null values at write time. LanceDB lacked regression coverage for the
full pandas-to-Pydantic path, so this test locks in the correct behavior
without falsifying the input schema nullability.
## Validation
- 5 focused merge-insert tests passed
- Ruff lint passed for the repository
- Ruff format check passed for the changed file
- git diff --check passed
Fixes#2366
<!-- lance-gatekeeper-fix:v1 agent=f897fccfa206620c8a2acdc3bcd1c21f
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- capture the stale-index state behind the reported fixed-size-binary
panic: the vector and FTS indices cover newer fragments while the BTree
prefilter does not
- verify vector, FTS, and hybrid searches return matches from both
scalar-indexed and unindexed fragments without panicking
- preserve binding-level coverage for the Lance fix in
https://github.com/lance-format/lance/pull/3768, which restricts
incomplete scalar prefilters when search indices are further ahead
The production root cause is in Lance and the current LanceDB dependency
already contains that fix, so this change adds the missing LanceDB
Python regression coverage.
## Validation
- `cd python && uv run --no-sync pytest
python/tests/test_hybrid_query.py::test_hybrid_query_with_stale_fixed_size_binary_prefilter
-q`
- `cd python && uv run --no-sync pytest
python/tests/test_hybrid_query.py -q`
- `python/.venv/bin/ruff check .`
- `python/.venv/bin/ruff format --check
python/python/tests/test_hybrid_query.py`
Fixes#2370
<!-- lance-gatekeeper-fix:v1 agent=5d16e59b9e513fd9247e0698732fa283
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- cover explicit FixedSizeList schemas populated from Float32Array
values
- verify the original vector.0 failure stays fixed across Arrow 15, 16,
17, and 18
## Root cause and fix
In v0.16, schema subset inference treated typed-array vectors as nested
objects and looked up numeric paths such as vector.0, which do not exist
in a FixedSizeList schema. Current typed-array handling correctly
recognizes ArrayBuffer views as vector values instead of traversing
their elements. This change adds the missing regression coverage for the
reported explicit-schema path so that behavior cannot regress unnoticed.
## Validation
- pnpm test __test__/arrow.test.ts --runInBand
- pnpm lint
- pnpm build
- pnpm run docs
- pnpm test --runInBand (681 passed, 5 skipped)
Fixes#2134
<!-- lance-gatekeeper-fix:v1 agent=1d548cb70f6df110ce0a5b119395b52a
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- add fast regression coverage for VoyageAI `voyage-3` source embeddings
- verify table text uses `client.embed` and never
`client.multimodal_embed`
## Root cause
The original VoyageAI source-embedding path treated table source values
as images and always invoked the multimodal API. Production routing was
corrected by later merged changes, but the table regression was covered
only by API-gated slow tests. This test locks the corrected text routing
into the regular unit suite.
## Validation
- `cd python && uv run --extra tests pytest
python/tests/test_voyageai_embeddings.py -q`
- `uv run --project python --extra tests --extra dev ruff format --check
python/python/tests/test_voyageai_embeddings.py`
- `uv run --project python --extra tests --extra dev ruff check .`
Fixes#2059
<!-- lance-gatekeeper-fix:v1 agent=49b9e2daeed95a78ce827e2bf90abda0
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- pass an Instructor-compatible `[instruction, text]` pair when
detecting embedding dimensions
- add a regression test that verifies the dimension probe uses the
configured source instruction
## Root cause
`InstructorEmbeddingFunction.ndims()` encoded a bare string even though
Instructor models require instruction/text pairs. With affected
`sentence-transformers` versions, the bare input omitted
`instruction_mask` and raised `KeyError` while defining the LanceDB
schema.
## Validation
- `uv run --extra tests pytest python/tests/test_embeddings.py -q` (`14
passed, 9 skipped`)
- `uv run --project python --extra tests --extra dev ruff format --check
python/python/lancedb/embeddings/instructor.py
python/python/tests/test_embeddings.py`
- `uv run --project python --extra tests --extra dev ruff check .`
Fixes#2041
<!-- lance-gatekeeper-fix:v1 agent=4b05e0d9f3eef17bccfb446e788294f4
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- add a Python regression for vector search over a sliced Arrow table
with nullable scalar columns
- verify the nearest row retains its non-null score values after the
table is written
## Root cause
Lance 0.19.2 deep-copied a validity bitmap without preserving its
non-zero bit offset. For a sliced nullable table, scalar values and
vectors began at the slice while the copied validity bitmap began at the
parent table's first row. That made valid score values appear null even
though the corresponding vector stayed intact. The upstream Lance repair
is already present in the current dependency; this adds a LanceDB-level
guard for the reported create/search path.
## Validation
- reproduced on Python 3.12 with LanceDB 0.16.0, pylance 0.19.2, PyArrow
18.0.0, and Polars 1.14.0
- `uv run --project python --extra dev ruff format --check
python/python/tests/test_table.py`
- `uv run --project python --extra dev ruff check .`
- `cd python && uv run --extra tests pytest
python/tests/test_table.py::test_search_preserves_nulls_from_sliced_arrow_table
-q`
Fixes#1879
<!-- lance-gatekeeper-fix:v1 agent=bfa0551793f8e3cf3980cf64ad89908a
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- add a LanceDB core regression for compaction overlapping appends
through separate table handles
- verify concurrent commits preserve fragment ID order on an indexed
table
- run the follow-up compaction that exposed the original row-ID ordering
failure and verify all rows remain
## Root cause
Older Lance versions could reserve fragment IDs for compaction, allow
concurrent appends to commit later IDs, and then commit the reserved
compaction fragments at the end of the manifest. A later compaction
could consequently receive row IDs out of order. Current Lance sorts
fragments at the transaction boundary; this adds the missing
LanceDB-level regression coverage for the Node-visible concurrency
contract.
## Validation
- `cargo fmt --all`
- focused regression passed once with output and 20 repeated runs
- `cargo test --quiet --features remote -p lancedb
table::optimize::tests` (14 passed)
- `cargo check --quiet --features remote --tests --examples`
- `cargo clippy --quiet --features remote --tests --examples`
- `cargo test --quiet --features remote --tests` (867 passed, 1 ignored)
Fixes#1498
<!-- lance-gatekeeper-fix:v1 agent=93aaefb15507dca52d064e15388773d7
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- make the existing #1968 regression explicitly assert that schema-only
table creation succeeds
- verify the new table has zero rows and preserves the requested
fixed-size vector schema before accepting subsequent data
## Root cause
In v0.16.0, schema-only table creation sent an empty table through
vector sanitization, which calculated a remainder using `len(data)` and
raised `ZeroDivisionError`. Later refactors removed that runtime path,
but the issue-specific regression only asserted the final row count
after a subsequent add. This change makes the reported operation and its
expected empty-table state explicit so the original defect remains
directly covered.
## Validation
- `uv run --extra tests pytest
python/tests/test_table.py::test_create_table_without_data_with_vector_schema
-q`
- `uv --project python run --extra tests --extra dev ruff format --check
python/python/tests/test_table.py`
- `uv --project python run --extra tests --extra dev ruff check .`
- `git diff --check`
Fixes#1968
<!-- lance-gatekeeper-fix:v1 agent=b8ec6f40f4bba2f9beeaaae12233e5c4
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- select rustls with native certificate roots explicitly for LanceDB's
remote HTTP client
- add a Linux regression test that rejects `libssl` or `libcrypto`
dependencies in the built Python extension
## Root cause
The Python remote client originally enabled reqwest's native TLS
backend. During manylinux wheel repair, that caused OpenSSL 1.1
libraries to be bundled into the wheel. Loading those libraries on RHEL
9 with FIPS enabled aborts during the OpenSSL self-test before `import
lancedb` can complete.
LanceDB has since moved away from native TLS, but its own reqwest
dependency relied on transitive rustls feature selection and the built
extension had no regression guard. This change makes rustls selection
explicit and tests the produced Linux native module's dynamic
dependencies.
## Validation
- `uv run --no-sync pytest python/tests/test_import.py -q`
- `ruff format --check python`
- `ruff check .`
- `cargo fmt --all -- --check`
- `cargo check --quiet --features remote --tests --examples`
- `ldd python/lancedb/_lancedb.abi3.so` (no `libssl` or `libcrypto`
dependency)
- verified the resolved Python Rust dependency graph contains rustls and
no `openssl-sys` or `native-tls`
Fixes#1884
<!-- lance-gatekeeper-fix:v1 agent=31f916c7ac5c072bbbd54f3539d24f71
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- align the PyO3 runtime and build ABI floor with the declared Python
3.10 minimum
- add a regression test that keeps both ABI features synchronized with
`requires-python`
## Root cause
The Python 3.10 support-floor update originally changed PyO3 to
`abi3-py310`, but a later dependency update reverted both PyO3 features
to `abi3-py39`. Published Windows wheels were consequently tagged
`cp39-abi3` while importing `PyCMethod_New`, a stable-ABI procedure
absent from CPython 3.9.0 and 3.9.1. Windows reports that mismatch as
“The specified procedure could not be found” while loading `_lancedb`.
Restoring `abi3-py310` makes the wheel tag and native imports agree with
the package metadata and prevents future wheels from advertising
unsupported Python 3.9 compatibility.
## Validation
- `uv run --extra tests pytest python/tests/test_package_metadata.py -q`
- `uv run --extra tests --extra dev ruff format --check .`
- `uv run --extra tests --extra dev ruff check .`
- `cargo fmt --all`
- `cargo check --quiet -p lancedb-python`
- `uvx --from maturin==1.12.4 maturin build --profile ci` (built
`lancedb-0.37.1b0-cp310-abi3-manylinux_2_34_x86_64.whl`)
Fixes#2051
<!-- lance-gatekeeper-fix:v1 agent=d66c984498190d2207d1c5126cba5047
generation=1 -->
---------
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- expand the synchronous debugger regression to enumerate every exposed
connection attribute while the Python background loop is unavailable
- retain direct representation checks for connections and tables
## Root cause
VS Code debugpy suspends Python threads at a breakpoint and inspects
local variables. Connection representation and property access
previously dispatched asynchronous work to LanceDBBackgroundEventLoop
and waited for the suspended loop thread, deadlocking the debugger. The
production safeguards landed in #3620 and #3788; this regression
exercises debugger-style whole-object expansion so a newly exposed
property cannot reintroduce the original failure.
## Validation
- uv run --no-sync pytest
python/tests/test_db.py::test_sync_debugger_inspection_does_not_use_background_loop
python/tests/test_db.py::test_read_consistency_interval_does_not_use_background_loop
-q (2 passed)
- uv run --no-sync pytest python/tests/test_db.py -q (48 passed)
- python/.venv/bin/ruff format --check python/python/tests/test_db.py
- python/.venv/bin/ruff check .
- git diff --check
Fixes#3611
<!-- lance-gatekeeper-fix:v1 agent=cdf4b39b2ce2ccb3eb5fe501acae77bb
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- replace the synthetic registry-variable metadata test with the OpenAI
embedding function reported in #2387
- verify the resolved API key survives table metadata reconstruction
- assert the OpenAI client receives the resolved key while serialized
metadata retains the variable reference
## Root cause
LanceDB 0.22.0 reconstructed embedding functions from table metadata
with the model constructor, bypassing EmbeddingFunction.create and
leaving the literal $var:api_key placeholder in OpenAI configuration.
The production path was corrected for duplicate #2181 by #2640; this
change gives that fix direct, network-free OpenAI regression coverage
for #2387.
## Validation
- uv run --extra tests pytest python/tests/test_embeddings.py -q (13
passed, 9 skipped)
- uv run --project python --extra dev ruff check .
- uv run --project python --extra dev ruff format --check
python/python/tests/test_embeddings.py
- git diff --check
Fixes#2387
<!-- lance-gatekeeper-fix:v1 agent=d453b1b9b2a298a776f2e4ea1b1449b5
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- add Python regression coverage for an IVF build that cannot form all
requested non-empty partitions
- verify hierarchical k-means returns an actionable RuntimeError instead
of panicking or silently creating a degenerate index
- exercise the current Lance v10.1.0-beta.1 dependency, which contains
the upstream error-return fix
## Root cause
Hierarchical k-means previously guarded a shortfall in generated
clusters with only a debug assertion. Debug builds panicked, while
release builds could silently publish an index with many empty
partitions. The upstream Lance fix now returns a descriptive error and
is already included in the dependency pinned on main; this test locks in
propagation through the LanceDB Python API.
## Validation
- uv run --extra tests pytest python/tests/test_index.py -q (24 passed)
- uv run --extra tests pytest
python/tests/test_index.py::test_create_ivf_index_reports_unsplittable_partitions
-q (1 passed)
- python/.venv/bin/ruff format python/python/tests/test_index.py
- python/.venv/bin/ruff check .
- git diff --check
Fixes#3649
<!-- lance-gatekeeper-fix:v1 agent=a4d34448a9d350a3e2e659f33f5db6f2
generation=1 -->
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
<!-- lance-gatekeeper-fix:v1 agent=5c80c44c083b3b8ad0da595419d468fc
generation=1 -->
## Root cause
The legacy synchronous Python table called `delete` on a shared, mutable
`lance.Dataset`. Concurrent table operations could hold a PyO3 borrow
while delete requested an exclusive borrow, producing `RuntimeError:
Already borrowed`. The current async-backed binding fixes this by
cloning its thread-safe Rust table handle before awaiting, but that
concurrency contract had no regression coverage.
## Fix
- Document why delete must clone the Rust table handle before entering
its async future.
- Add a barrier-synchronized regression test that deletes distinct rows
through one shared table from eight Python threads.
- Verify every delete commits exactly one row, every commit gets a
distinct version, and no rows remain.
## Validation
- `cargo check --quiet --features remote --tests --examples`
- `cargo fmt --all -- --check`
- `uv run --extra tests --extra dev ruff format --check
python/tests/test_table.py`
- `uv run --extra tests --extra dev ruff check
python/tests/test_table.py`
- `uv run --extra tests --extra dev pytest
python/tests/test_table.py::test_concurrent_deletes_are_thread_safe
python/tests/test_table.py::test_delete
python/tests/test_table.py::test_delete_expr
python/tests/test_table.py::test_delete_expr_async -q` (4 passed)
- Manual stress reproduction: 100 concurrent deletes on one table
completed at versions 2–101 with zero rows remaining.
Fixes#530
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Summary
- cache the immutable read consistency interval on synchronous
connection wrappers
- keep debugger property expansion from dispatching to the background
event loop
- cover direct connections and wrappers reconstructed from native
connections
## Root cause
The debugger expands connection variables by evaluating properties after
suspending all Python threads.
`LanceDBConnection.read_consistency_interval` dispatched a coroutine to
`LanceDBBackgroundEventLoop` and synchronously waited for it, but that
loop thread was also suspended, causing a deadlock.
## Validation
- `uv run --no-sync pytest python/tests/test_db.py -q` (48 passed)
- `ruff format --check python/python/lancedb/db.py
python/python/tests/test_db.py`
- `ruff check .`
- `git diff --check`
Fixes#3773
<!-- lance-gatekeeper-fix:v1 agent=e2e612236d722d926f64245d3f682bbc
generation=1 -->
---------
Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
## Description
`Table::optimize()` compacts through
`lance::dataset::optimize::compact_files`
(`rust/lancedb/src/table/optimize.rs:155`). Until
lance-format/lance#7965 that rewrite corrupted blob columns holding null
or empty values, which is what #3744 reports:
- **storage 2.0** (legacy v1 `lance-encoding:blob` descriptors): every
payload following a null or empty row in the same fragment was rewritten
as `{position: 0, size: 0}`, so it read back as `b""` and the new
fragment no longer referenced the bytes — silent payload loss,
unrecoverable once the pre-optimize versions are pruned.
- **storage 2.2** (blob v2): a valid empty value was rewritten as null,
destroying the null-vs-empty distinction.
Both manifestations share one root cause: `is_inline_null_blob`
classified any inline blob with `position == 0 && size == 0` as null,
which is also exactly what a *valid empty value* looks like. Such rows
were dropped from `blob_read_addrs`, misaligning every payload that
followed.
The behaviour is already correct on `main`: the vendored lance crate
first carried the fix at `v10.0.0-beta.3` (#3710) and is now
`v10.1.0-beta.1` (#3757). What was missing is coverage — nothing in this
repo exercised a blob column containing a null or empty value through
`optimize()`, which is why this shipped unnoticed. This PR adds that
guard.
## Tests
Two tests in `rust/lancedb/tests/blob_integration.rs`, reusing the
file's existing 64 KiB dedicated-blob helpers and a delete-triggered
fragment rewrite. After `id IN (1, 4)` is deleted the surviving rows are
`2` (null), `3` (valid empty), `5` and `6` (payloads) — payloads sit
immediately after the null/empty, which is where the misalignment
landed.
- `optimize_preserves_v1_blob_payloads_with_null_and_empty` — storage
2.0; asserts the **payload bytes** are unchanged across
`OptimizeAction::All` (what the Python/Node `optimize()` bindings
invoke). Payloads are read through `lance::Dataset::take_blobs`, since
`Table::fetch_blobs` rejects legacy v1 columns. The before/after
descriptors are reported on failure but deliberately *not* asserted:
compaction repacks the blob file, so they shift legitimately (id 5
`(131072, 65536)` → `(0, 65536)`, id 6 `(196608, 65536)` → `(65536,
65536)`). Note that a post-compaction `position: 0` is both the
legitimate first-payload offset and the bug's signature, so asserting
descriptors would be actively misleading.
- `optimize_preserves_blob_v2_null_and_empty_distinction` — storage >=
2.2; asserts a null stays null and a valid empty value stays non-null
empty.
Both assert the pre-optimize state first, so a setup change that stops
producing the null/empty/payload mix fails loudly instead of passing
vacuously.
Both also assert the returned `CompactionMetrics` show a fragment was
actually rewritten. These tests depend on `delete("id IN (1, 4)")`
pushing the fragment past lance's `materialize_deletions_threshold` (0.1
by default; 2 of 6 rows here). That coupling is invisible and unasserted
otherwise: against a forced no-op (`materialize_deletions_threshold:
1.5`) the metrics come back all zeroes and *every payload assertion
still passes*. Since the whole point of these tests is to survive
dependency changes, they check that the rewrite happened rather than
trusting the planner to keep selecting the fragment.
Guard verified against a pre-fix lance: with the published
`lancedb==0.36.0` wheel (vendors lance 9.0.0), `Table.optimize()` on the
same data rewrites the descriptors of the two rows following the
null/empty from `(131072, 65536)` and `(196608, 65536)` to `(0, 0)`, and
the payloads read back empty. Against the pinned `v10.1.0-beta.1`, all
39 tests in the file pass, adding roughly 10–20 ms to the file's
runtime.
## Not addressed here
- **No released artifact has the fix yet.** PyPI `lancedb` 0.36.0
(2026-07-29) vendors lance 9.0.0; npm `@lancedb/lancedb` 0.37.1-beta.0
predates the bump. No 9.x lance tag carries the fix: `v10.0.0-beta.3` is
the first tag containing it, every `v9.1.0-beta.1`…`beta.8` is behind
it, and `v9.0.0` / `v9.0.1-rc.1` sit on a diverged branch without it. A
stable lancedb release needs a stable lance >= 10.
- **The version skew #3744 flagged is still live.**
`python/pyproject.toml` pins `pylance==9.0.0rc1` for the `tests` extra
against a vendored `10.1.0-beta.1`, so Python CI still cannot observe
this class of divergence.
- **Only the single-fragment rewrite shape is covered.** Both tests
rewrite one fragment by materializing deletions. lance's own
`test_compact_blob_v1/v2_preserves_null_empty_and_payload_order` cover
the multi-fragment merge shape (3 fragments → 1) at unit level, so this
PR is complementary rather than redundant — it covers the binding-level
path through `Table::optimize` — but it would not catch a regression
that only appears when *merging* fragments.
`multi_fragment_dedicated_blob_table` in the same file makes that a
cheap follow-up.
Closes#3744🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
Table::add_columns now takes no arguments and returns AddColumnsBuilder,
so calls become .add_columns().transform(t).execute().
read_columns was the second positional argument but reaches only one of
the five transform variants. In lance's add_columns_to_fragments only
BatchUDF receives the caller's value: SqlExpressions replaces it with
the columns its expressions reference, Stream and Reader pass None, and
AllNulls reads nothing. So it was mandatory on every call -- all
eighteen call sites here passed None -- and silently discarded four
times out of five. As a builder method it is optional, and setting it
where lance would discard it is now an error, which does reject a call
that previously succeeded while ignoring the argument.
Matches the builders add, update, and merge_insert already use.
## Summary
`LanceMergeInsertBuilder.when_not_matched_by_source_delete()` didn't
clear a previously-set condition when called again with no argument (or
a different condition type). Per the docstring, `condition=None` means
"delete all unmatched rows," but if the builder had already been
configured with a string/Expr condition, a later no-arg call left the
stale condition in place instead of widening the delete to
unconditional.
Fixes#3767
## Change
Each call now unconditionally sets both
`_when_not_matched_by_source_condition` and
`_when_not_matched_by_source_condition_expr` (one to the new value, the
other to `None`), so the latest call always wins — consistent with every
other setter on this builder (e.g.
`when_matched_update_all(where=...)`).
## Test plan
- [x] New regression test
`test_merge_insert_by_source_delete_reconfigure` in
`python/python/tests/test_table.py`
- [x] `uv run --extra tests pytest
python/tests/test_table.py::test_merge_insert_by_source_delete_reconfigure
python/tests/test_table.py::test_merge_insert_by_source_delete_expr
python/tests/test_table.py::test_merge_insert_by_source_delete_expr_async
-vv` — 3 passed
- [x] `uv run --extra dev ruff format` / `ruff check` — clean
Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
## Summary
`LanceHybridQueryBuilder._create_query_builders()` checked
`self._minimum_nprobes` for truthiness instead of `is not None` — the
very next line correctly checks `is not None` for
`self._maximum_nprobes`. Since `0` is falsy in Python,
`.minimum_nprobes(0)` on a hybrid query silently dropped the value
instead of forwarding it to the vector sub-query, where it would raise
the same `ValueError` a plain vector query raises for the same input
(`minimum_nprobes must be greater than 0`, validated in
`rust/lancedb/src/query.rs` and covered for the plain-query path by
`test_invalid_nprobes_sync`).
Fixes#3766
## Change
One-line fix: `if self._minimum_nprobes:` → `if self._minimum_nprobes is
not None:`, matching the existing `maximum_nprobes` check right below
it.
## Test plan
- [x] New regression test
`test_hybrid_query_minimum_nprobes_zero_raises` in
`python/python/tests/test_hybrid_query.py`
- [x] `uv run --extra tests pytest python/tests/test_hybrid_query.py
-vv` — 13 passed
- [x] `uv run --extra dev ruff format` / `ruff check` — clean
Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
## Summary
- Implements Cloud `fetch_blob_files`: returns real seekable `BlobFile`
handles over HTTP Range instead of `NotSupported`.
- Completes the second Cloud blob read verb after #3684 (`fetch_blobs` =
eager whole bytes; this = lazy / partial / sequential reads).
- Same public handle API as local (`read_range`, `read_up_to`, `seek`,
`tell`, `close`), so one code path works for local and Cloud.
Large blobs (video, audio, PDFs) should not require downloading the
whole object to inspect a header or stream a slice. After search,
callers open a handle and read only what they need:
```python
hits = table.search(vec).select(["id", "video"]).limit(5).to_arrow()
with table.fetch_blob_files("video", hits)[0] as f:
header = f.read_range(0, 256)
f.seek(keyframe_offset)
chunk = f.read_up_to(1 << 20)
```
### Behavior
- Handle creation probes size with `bytes=0-0` (bounded concurrency,
input order preserved).
- `204` → null (`None`); `416` with `bytes */0` → valid empty blob;
other `416` → error.
- `read_range` validates `Content-Range` and body length; OOB ranges
fail with `invalid_input` before the request (aligned with Lance).
- `read_up_to` reuses one open-ended Range response across sequential
reads; `seek` drops it.
- Servers older than 0.5.0 get a clear `NotSupported` (does not suggest
`fetch_blobs`, which they also lack).
## Testing
- `cargo test --features remote -p lancedb remote_blob`
- `cargo test --features remote -p lancedb test_blob`
- `cargo clippy --features remote --tests --examples` (no new warnings
from this change)
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
Bumps the pinned Rust toolchain from 1.95.0 to the latest stable
(1.97.0).
Rust 1.97's clippy adds `useless_borrows_in_formatting`, which flags a
redundant `&` in `format!`/`debug!` arguments in a few places. This PR
removes those to keep `cargo clippy` clean.
No behavior change; the MSRV (`rust-version = "1.91.0"`) is unchanged.
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Updates the Lance Rust workspace dependencies and Java lance-core
version to v10.1.0-beta.1.
Includes a compatibility fix for the Lance file writer API by using the
explicit V2_1 writer creation path for permutation shuffle spill files.
Triggered by
https://github.com/lance-format/lance/releases/tag/v10.1.0-beta.1
Adds job operations to the connection surface, building on the Job
handle from #3742: job(id), list_jobs, get_job, cancel_job, and
job_history, plus a non-blocking Job.status(). Implemented on the
Database trait (defaulting to NotSupported), the remote backend
(/v1/jobs), and the Python and Node bindings; job_history returns Arrow
batches.
errors() and progress() are not included.
Tested with mocked endpoints in all three languages.
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
IndexBuilder::execute now returns a Job with wait and cancel methods.
Local tables build the index synchronously and return an already-done
job. Remote tables read the job id the server returns from create_index
and track it through the /v1/jobs API: wait polls describe until the job
reaches a terminal state and cancel posts a cancellation. Servers that
return no job id yield a done job, so behavior against older servers is
unchanged. The job id is not exposed on the handle.
The Python and TypeScript bindings keep their current signatures and
discard the handle; exposing Job there is left to follow-ups.
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
`RemoteDBConnection.open_table` accepts `storage_options` and never uses
it:
```python
def open_table(
self,
name: str,
*,
namespace_path: Optional[List[str]] = None,
storage_options: Optional[Dict[str, str]] = None,
index_cache_size: Optional[int] = None,
...
) -> Table:
...
if index_cache_size is not None:
logging.info("index_cache_size is ignored in LanceDb Cloud ...")
table = LOOP.run(self._conn.open_table(name, namespace_path=namespace_path))
```
The value is never passed down and never mentioned. `index_cache_size`
is ignored on Cloud in the
same way, but it says so.
I checked this at runtime on 0.34.0, not just by reading it: swapping
the inner connection for a
recorder, `open_table("t", storage_options={...})` hands the layer below
`['namespace_path']` and
nothing else, no log record is emitted, and the same probe shows
`index_cache_size` producing its
message as expected.
This adds the matching log line, so the two ignored parameters behave
the same way. `ruff check` and
`ruff format --check` are clean on the file.
A note on severity. This is not a security hole and nothing is exposed.
Someone passing credentials
there gets silence instead of an error, and finds out later.
One thing I am unsure about, and it changes the fix. I have assumed
per-table storage options are
meaningless on Cloud, which is what the `index_cache_size` line next to
it implies about managed
storage. If they are supposed to work, then the right change is to pass
them through to
`self._conn.open_table` instead and this patch is the wrong one. Happy
to redo it that way.
I did not check whether `create_table` or the async connection have the
same gap.
`docs/src/python/python.md` is the whole Python API reference, but it is
maintained by hand and had drifted from the public API. Anything not
listed there simply doesn't get rendered, so a number of public,
documented, tested APIs were invisible to users — most notably branch
management, where `diff` and `merge` live.
I audited every public symbol reachable from `lancedb` and its
subpackages against the `:::` directives on the page. This adds the
missing ones:
- **Branching** — `Branches`, `AsyncBranches` (`list` / `create` /
`checkout` / `delete` / `diff` / `merge`)
- **Tables** — `TableStatistics` (returned by `Table.stats()`; the
fragment-level stats classes were already listed)
- **Full text queries** — `FullTextQuery`, `MatchQuery`, `PhraseQuery`,
`BoostQuery`, `MultiMatchQuery`, `BooleanQuery`, `FullTextOperator`,
`Occur`
- **Querying** — `LanceEmptyQueryBuilder`, `LanceTakeQueryBuilder`,
`AsyncTakeQuery`
- **Indices** — `Fm` (the FM-index for substring search), `IndexConfig`
- **Blobs** — `blob`, `BlobType`, `BlobFile`
- **Namespaces** — `connect_namespace`, `connect_namespace_async`, and
both namespace connection classes
- **Remote config** — `TlsConfig`, `HeaderProvider`, `OAuthConfig`,
`OAuthFlowType`
- **Rerankers** — the `Reranker` base class plus `JinaReranker`,
`RRFReranker`, `MRRReranker`, `AnswerdotaiRerankers`,
`VoyageAIReranker`, `WatsonxReranker` (5 of 12 were listed)
- **Embeddings** — `get_registry`, `register`, and the 14 embedding
functions that were missing (3 of 17 were listed)
- **PyTorch** — `StreamingDataset` and the permutation API it is built
on
- **Misc** — `Session`, `tokenize`, `FtsToken`, `pydantic.Vector`,
`pydantic.MultiVector`, `instrument_lancedb_metrics`, and the two
exception types
It also repairs cross-references in docstrings that no longer resolve:
links into guide pages that have since moved to lancedb.com
(`querying-an-ann-index`, `experimental-full-text-search`),
`lance.dataset` references with no inventory behind them, and the
relative targets `[Table](Table)` and `[PyArrow Table](pyarrow.Table)`.
Deliberately left out: concrete implementation classes reached through
their abstract base (`LanceTable`, `LanceDBConnection`,
`RemoteDBConnection`), query base classes already covered by
`inherited_members: true`, and internal plumbing such as
`FullTextSearchQuery` and `ColumnOrdering`.
## Testing
The docs job only runs on pushes to `main`, so I built the site locally
and compared against a build of `upstream/main`: every added entry
resolves, and no symbol that was rendered before stopped being rendered
when the four packages moved to automodule. `mkdocs build --strict`
exits 0 on this branch, against 61 warnings on `main`.
## Also in this PR
`lancedb.index`, `lancedb.embeddings`, `lancedb.remote` and
`lancedb.rerankers` are now rendered by a single mkdocstrings directive
each, driven by the module's `__all__`, rather than a hand-maintained
list. These four are where most of the drift was, and `__all__` is
harder to forget than a docs page. `lancedb.embeddings` had no
`__all__`; without one mkdocstrings renders no members at all for a
re-export package, so one is added. AGENTS.md gains a section on how the
page is wired up and how to build the docs locally.
Rendering all that code for the first time surfaced ~100 more build
warnings, which would have made #3707 (turning on `mkdocs build
--strict`) harder to land, so the warning backlog is cleared here too.
97 of the 158 warnings were one systematic false positive — griffe
cannot see the generated `__init__` of a pydantic dataclass, so every
documented parameter looks unknown — switched off via
`warn_unknown_params`. The remaining 61 came from 15 docstrings with
real bugs: prose trailing a `Parameters` section (we were rendering
parameters called `The`, `you` and `To`), types dropped because numpydoc
needs spaces around the colon, `num_partitions, default sqrt(num_rows)`
parsing as a list of names and inventing a `default` parameter, and one
parameter indented five spaces. `mkdocs build --strict` now exits 0.
---
#3747 (the coverage test that keeps this from happening again) is
stacked on this branch, so review it after this one.
---------
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
In the current LanceDB usage implementation, there is no way to check
whether a table or namespace already exists. This PR introduces the
namespace_exists and table_exists methods to determine the existence of
tables and namespaces.
useage like this:
```
# check table exists
db.table_exists(table_id=['xxx'])
# check namespace exists
db.namespace_exists(namespace_id=['xxx'])
```
fixes: #3419
---------
Signed-off-by: farmer <farmerchillax@outlook.com>
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Remote half of the blob read path. #3578 did local Python. This makes
`RemoteTable` hit the server.
- `fetch_blobs(column, row_ids or hits)` → bytes over `POST
/v1/table/{id}/fetch_blobs/`
- `blob_columns()` from the cached schema (describe already has the
metadata, no extra route)
- search then `fetch_blobs` works. row identity rides inside the blob
descriptor so you do not need a public `_rowid`
- `fetch_blob_files` still `NotSupported` on remote. use `fetch_blobs`
for full bytes for now. Range is a follow up
Accepts Binary / LargeBinary / BinaryView on the way back. Empty
`row_ids` short-circuits. Version + branch go in the request body same
as other read calls.
### Example
```python
db = lancedb.connect(uri="db://my-project", api_key=...)
table = db.open_table("clips")
hits = table.search(query_vec).select(["id", "video"]).limit(10).to_arrow()
# hits is just id + video. row ids are stashed on the descriptor
blobs = table.fetch_blobs("video", hits) # null-aligned, same length as hits
```
Or pass ids yourself:
```python
blobs = table.fetch_blobs("video", [10, 20, 30])
```
### Testing
- `cargo test -p lancedb --features remote --lib`
- `cargo test -p lancedb --features remote --test blob_integration`
- `pytest python/tests/test_remote_db.py -k remote_blob`
- live e2e against a local 0.5.0 remote server (search → fetch, nulls,
nested path, old server gate)
---------
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
`table_names()` lists any `*.lance` directory, but `open_table()` maps
every `DatasetNotFound` to `TableNotFound`, so a corrupt or
partially-written table looks identical to one that never existed
(#3127). This takes the issue's Option 2: on `DatasetNotFound`, check
the parent listing for the table's `.lance` entry — the same predicate
`table_names()` uses — and return a new `TableCorrupted` error when the
directory is present. The check runs only on the error path, and any
failure in the recheck falls back to the previous `TableNotFound`
behavior.
Tests cover the reporter's empty-dir repro, a deleted-manifest case,
true absence (still `TableNotFound`), and an end-to-end list-then-open
assertion; the three new corrupt-case tests fail without the src change.
`cargo test -p lancedb --lib` 732 passed, clippy/fmt clean, `cargo check
--workspace --all-targets` clean (both language bindings end in wildcard
error arms).
Two notes for review: `Error` isn't `#[non_exhaustive]`, so the new
variant is technically semver-breaking for exhaustive matchers (pre-1.0,
and the alternative — changing `TableNotFound`'s shape — breaks more);
and on the Python side corrupt tables now surface as `RuntimeError`
rather than `ValueError`, which is the intended distinction but worth a
maintainer's eye. `open_from_namespace` was left unchanged since
namespace listings come from a server-side registry, not directory
globbing.
Closes#3127
## Summary
Fixes#2339. `merge_insert()` on the remote client could mask the real
cause of a mid-stream input error, reporting only:
> stream error sent by user: unexpected internal error
## Root cause
There were two divergent streaming-write code paths in the remote
client:
- `add()` uses `RemoteInsertExec`, which streams the request body
through a `tokio::sync::oneshot` error side-channel and drains it before
reporting the HTTP result. If the input stream errors mid-body, the
original error is recovered.
- `merge_insert()` used a legacy path (`send_streaming` ->
`reader_as_body`) that piped arrow `Some(Err(e))` straight into the
HTTP2 request body. Hyper swallows body-stream errors under HTTP2 (see
hyperium/hyper#2547), so the original error was lost and only the
generic transport error surfaced.
## Fix
Consolidate both write paths onto the side-channel mechanism instead of
patching the legacy path:
- Generalize `RemoteInsertExec` into `RemoteWriteExec`, carrying a
`WriteOp` enum (`Insert { overwrite }` | `MergeInsert { query, timeout
}`) that selects the endpoint, query params, request-timeout header, and
response parsing. The executor returns a `WriteResult` enum (`Add` |
`Merge`) with typed accessors, and `with_new_children` still resets the
result so the rescannable retry loop is unaffected.
- Route `merge_insert()` through `RemoteWriteExec`. The public API only
accepts a `RecordBatchReader` (not rescannable), so the reader is
buffered into a `Vec<RecordBatch>` before the retry loop to preserve the
previous retry-on-retryable-status behaviour. This mirrors what the old
`send_streaming(with_retry=true)` path already did.
- Remove the now-unused `send_streaming` / `reader_as_body` /
`buffer_reader` / `make_reader` helpers. Multipart stays insert-only
(the server has no multipart merge_insert endpoint), so that hot path is
behaviorally unchanged.
## Testing
- Added `test_merge_insert_input_error_surfaces_original`, which drives
an erroring input through the single-request `merge_insert` path and
asserts the original error (`boom`) is surfaced rather than the masked
HTTP error. Confirmed it fails without the side-channel drain (it then
reports a masked `500 ... request or response body error`).
- Full suite green: `cargo test -p lancedb --lib --features remote` ->
694 passed, 0 failed. Includes the existing
`test_merge_insert_retries_on_409`, confirming retry behaviour is
preserved.
## Summary
- keep the existing synchronous `connect()` path unchanged
- make `LanceDBConnection.__repr__` and `LanceTable.__repr__`
side-effect-free
- add a regression test that verifies sync reprs do not call the Python
background loop
## Root cause
The freeze is caused by debugger rendering, not by `connect()` itself:
1. debugpy stops at a breakpoint and suspends all Python threads.
2. The debugger renders the new `db_connection` local by calling
`repr()`.
3. `LanceDBConnection.__repr__` reads `read_consistency_interval`.
4. That property calls `LOOP.run(...).result()`.
5. The `LanceDBBackgroundEventLoop` thread is suspended by the debugger,
so `repr()` waits for a thread that cannot run.
This explains why the symptom appears immediately after `connect()`: it
is the first point where a connection object exists in locals and is
automatically rendered. `LanceTable.__repr__` had the same problem
because it also read the connection's consistency interval.
This follows the same principle as #3411: `__repr__` must not trigger
async work or I/O that a debugger assumes is lightweight.
## Evidence
I reproduced the behavior with the real LanceDB classes and debugpy
1.8.21 using a DAP client:
- latest `main` (`ff6ff099`): the debugger reported `allThreadsStopped:
true`, and evaluating `repr(db_connection)` timed out
- this branch (`5755a5ba`): the same evaluation returned
`LanceDBConnection(uri='/tmp/lancedb-debug-repro')` immediately
- setting `PYDEVD_UNBLOCK_THREADS_TIMEOUT=0` also allowed the original
repr path to complete, independently confirming that it was waiting on a
suspended thread
The regression test creates a connection and table, replaces `LOOP.run`
with a function that fails, and verifies that both reprs still work.
## Validation
- `maturin develop --manifest-path python/Cargo.toml`
- `python -m pytest
python/python/tests/test_db.py::test_sync_repr_does_not_use_background_loop
python/python/tests/test_table.py::test_consistency -q` (`4 passed`)
- `ruff check .`
- `ruff format --check python/python/lancedb/db.py
python/python/lancedb/table.py python/python/tests/test_db.py
python/python/tests/test_table.py`
- `git diff --check`
Refs #3611.
`test_read_consistency_interval` asserted that a table opened with a
100ms `read_consistency_interval` still read stale data immediately
after a concurrent write. The cache timestamp is set when the table is
opened and reads within the interval do not refresh it, so that
assertion only held if the intervening open/count/commit/count sequence
finished within 100ms of real wall-clock time. On a loaded CI runner it
did not: the TTL expired, `count_rows` refreshed synchronously, and the
test failed with `left: 1, right: 0`. This broke the Rust workflow on
`main` at 0bc08160 (a Python-only commit).
This pins the `background_cache` mock clock once `table2` has seeded its
cache, and advances it explicitly in place of `tokio::time::sleep`, so
the test controls when the interval elapses. Same approach as #3547.
With the clock pinned there is no real sleep left to be imprecise, so
the `cfg(not(target_os = "windows"))` guard is dropped and the test now
runs on Windows too.
Verified by inserting a stall before the write: 120ms reproduces the
original failure deterministically, and with this change the test still
passes with a 500ms stall.
Fixes#3712
## What
Expose custom FTS stop-word lists in the Python and TypeScript public
APIs, including their standalone tokenize helpers and remote index
creation.
This PR supports concrete string lists only. It does not add file or
LanceDB-table stop-word sources.
## Why
Rust already exposes Lance's custom stop-word list option. The Python
and TypeScript APIs did not pass it through, and local index details did
not retain the full tokenizer parameters needed by index-backed
tokenization after reopening a table.
## How
- Add `custom_stop_words` / `customStopWords` to the Python and
TypeScript FTS and tokenize options.
- Preserve `None` / `undefined`, empty lists, and list contents without
normalization.
- Load the persisted FTS segment parameters when returning local index
details.
- Serialize the concrete list in remote create-index requests.
- Keep Python and TypeScript tests thin; behavior, persistence, query
tokenization, and remote JSON coverage live primarily in Rust.
## Validation
- `cargo check --quiet --features remote --tests --examples`
- `cargo clippy --quiet --features remote --tests --examples`
- `cargo test --quiet --features remote --tests`
- Python extension rebuild with `uv` and `maturin`
- Targeted Python tests: 4 passed
- Python `ruff format --check` and `ruff check`
- TypeScript build, typecheck, Biome lint, generated docs, and targeted
tests
---------
Co-authored-by: Yang Cen <yangcen@Yangs-Mac-mini.local>
## Summary
- add a keyword-only `transform_parallelism` option to
`StreamingDataset`
- preserve CPU auto-detection by default and fall back to one worker
when unavailable
- apply the configured limit to both the transform executor and
concurrency semaphore
- document and test explicit, default, fallback, and invalid values
## Testing
- `uv run --extra tests --with torch pytest
python/tests/test_elastic_dataloader.py -q` (`136 passed`)
- `uvx ruff check python/lancedb/streaming.py
python/tests/test_elastic_dataloader.py`
- `uvx ruff format --check python/lancedb/streaming.py
python/tests/test_elastic_dataloader.py`
- `git diff --check origin/main...HEAD`
Closes#3695
Co-authored-by: buduoqiu <yaodong-shen@users.noreply.github.com>
Standard GitHub-hosted runners are free on public repos, so all Actions
spend here is on the `*-8x-*` / `4x` larger runners. Measured over 30
days at current (post-Jan-2026) larger-runner rates, that is ~$1,400/mo,
and `npm-publish` is ~70% of it.
## Changes
**Fat LTO was forcing builds onto large runners.** `[profile.release]`
in `.cargo/config.toml` sets `lto = "fat"` with `codegen-units = 1`,
which is single-threaded and the peak-memory step. The macOS
`npm-publish` build was 111 of its 113 minutes in one `napi build` step,
making it the critical path of the whole publish pipeline. The ThinLTO
override already applied to Windows now covers macOS too, and both
Windows builds move from `windows-2025-8x-x64` to the free standard
`windows-2025`.
**The npm-publish cargo cache never existed.** There are zero caches
with its key prefix. The key was static, so `actions/cache` (which only
writes on a miss) could never refresh it, and a multi-GB release
`target/` per target could never fit the repo's 10 GB budget anyway. Now
caches only the crate registry, keyed on `Cargo.lock`. The docker builds
also mounted `.cargo/registry/*` while the cache saved `.cargo-cache`,
so containers re-downloaded the registry every run.
**Cache eviction thrash.** Repo cache usage is 10.4 GB against GitHub's
10 GB cap, so every PR run evicted main's warm entries. `rust.yml` and
`nodejs.yml` now restore everywhere but only save from `main`.
**npm-publish moves to nightly + tags** instead of every push to main
(~90/month). The cross-compiled targets do need watching, so
`report-failure` now fires on scheduled runs, and dedupes onto an
existing open issue rather than filing one per night.
**rust.yml aarch64-pc-windows-msvc** cross-compiled its tests and then
skipped them, paying full codegen and link cost for a compile check.
`windows-11-arm` is now GA and free on public repos, so it builds and
tests natively. Its test step also passes `--target` — without it cargo
used `target/ci/` rather than `target/<triple>/ci/` and rebuilt the
entire dependency graph a second time.
**pypi-publish.yml had no concurrency group**, so force-pushes left a
~74 minute Windows job running.
## What is cost vs. wall-clock
| Change | Cost | Wall-clock |
|---|---|---|
| Windows npm-publish → free runners | **−$570/mo** | slower per job
(8→4 cores) |
| npm-publish nightly | **−$125/mo** | — |
| pypi-publish concurrency | small | — |
| macOS ThinLTO | $0 (already free) | **−~50 min** per release |
| rust aarch64 Windows native | $0 (already free) | **−~25 min** |
| rust `--target` on test step | $0 | large, avoids a second full build
|
| rust-cache `save-if` | small | faster via real cache hits |
## Risks
- The two Windows builds now have 4 cores instead of 8 and ~14 GB of
free disk. If they fail, it is most likely disk rather than memory;
fallback is `windows-2025-4x-x64`, which still halves that line.
- `windows-11-arm` has a thinner toolset (choco/vcpkg/protoc under
emulation) and this enables a test step that has never run, so it may
surface real aarch64 failures. That is the point, but it is the change
most likely to need iteration.
- ThinLTO applies to published macOS and Windows binaries, typically
within a few percent of fat LTO. Linux release builds are untouched.
## Follow-ups
- `python.yml` `pydantic1x` (37 min) and `Doctest` (33 min) each rebuild
the extension from source via `pip install -e .` with no Rust cache;
they should consume the wheel the `linux` job already builds. Worth
~$235/mo and ~70 min of compute per run. Separate PR.
- The three `ubuntu-2404-8x-x64` npm-publish builds (~$420/mo at the old
cadence) are the remaining large-runner spend;
`aarch64-unknown-linux-gnu` could run natively on free
`ubuntu-24.04-arm`. Worth doing after this lands so the ThinLTO change
can be validated first.
- The wheel composite actions declare `python-minor-version` as required
but never use it, and every caller omits it (actionlint warns).
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
`list_versions()` against a remote table on a server that uses
lance-namespace was failing. The server was returning
`timestamp_millis`, while db-catalog deployments were returning
`timestamp`, and the client was only accepting `timestamp`. So, updated
the client to accept both. (assuming we're migrating over time;
eventually we can turn off the `timestamp` code path I suppose.)
## Summary
Lance can now plan multiple byte ranges for the same blob in one
`read_blob_ranges` operation, but LanceDB users currently cannot expose
a complete set of logical ranges to that planner.
This complements `BlobFile`: file-like consumers such as PyAV can
continue to discover ranges dynamically, while callers that already know
the ranges for a batch can submit them together.
## Motivating example
A training table may store a large video blob together with a small
application-level clip index:
```text
video: blob
clips: [{offset, length}, ...]
```
The caller can select the videos and clips for a batch, obtain their row
IDs from the query, and read all of the selected windows together:
```python
rows = (
table.search()
.select(["clips"])
.with_row_id(True)
.limit(64)
.to_arrow()
.to_pylist()
)
requests = []
for row in rows:
clip = sample_clip(row["clips"])
requests.append(
(row["_rowid"], clip["offset"], clip["length"])
)
chunks = table.fetch_blob_ranges("video", requests)
```
Here, `_rowid` comes from the LanceDB query, while `offset` and `length`
come from the application's clip index and are relative to that row's
video blob. The caller describes only the logical reads; Lance still
handles validation, source grouping, coalescing, scheduling, and byte
backpressure.
Lance v10.0.0-beta.5 returns one logical result per blob selector or
range request and explicitly distinguishes null blobs from valid empty
values. LanceDB consumes that aligned result contract directly and only
adds a cardinality check for unresolved row IDs.
This PR exposes batched blob-range reads on local Rust and Python
tables. Results preserve request identity, duplicates, null slots, and
valid empty ranges while allowing Lance to execute the physical reads
out of order. Scheduler buffer sizing remains an internal Lance concern,
so the LanceDB API does not expose `io_buffer_size`.
Cloud tables continue to report this operation as unsupported until
there is a corresponding remote API.
SELECT COUNT(*) FROM t WHERE <predicate> — and any query that plans an
empty-projection scan — panics the executing query task:
InvalidArgumentError("must either specify a row count or at least one
column")
Root cause
MetadataEraserExec wraps every LanceDB table scan to strip schema-level
metadata, rebuilding each batch in execute():
RecordBatch::try_new(schema.clone(), batch.columns().to_vec()).unwrap()
RecordBatch::try_new infers the row count from the columns. COUNT(*)
with a filter is planned with an empty projection, so the scan emits
zero-column batches — there are no columns to infer a length from,
try_new returns Err, and the .unwrap() panics.
(This is specific to the empty-projection case: COUNT(*) with no filter
is answered from statistics and never scans, and COUNT(<col>) projects a
column — both already work.)
Updates the Rust workspace Lance dependencies and Java lance-core
dependency to v10.0.0-beta.5. No compatibility fixes were required;
full-workspace Clippy passes with warnings denied. Lance tag:
https://github.com/lance-format/lance/releases/tag/v10.0.0-beta.5
## What
MemWAL LSM **read** support. When a table has an LSM write spec
(`set_lsm_write_spec`), `merge_insert` upserts live in the MemWAL
active/frozen memtables and flushed SSTables until an external
compaction merges them into the base table, so a normal scan returns
**stale** data. This routes reads through Lance's `LsmScanner` so
queries also surface that in-flight data, deduplicated by primary key
(newest generation wins).
## How
- Adds a **`use_lsm: Option<bool>`** query flag, symmetric with the
`merge_insert` flag:
- **unset** — auto-route through the LSM scanner when the table carries
a write spec
- **`use_lsm(true)`** — force the LSM path; error if there is no spec
- **`use_lsm(false)`** — read the base table only (the escape hatch)
- Plain scan, single-column full-text search, and single-vector ANN all
run through one `LsmScanner` (assembled from on-disk shard manifests
plus the cached writer's in-memory memtables), so a `where` predicate is
honored as a **prefilter** uniformly — including for vector search.
- **Compaction-aware snapshots:** an SSTable generation is dropped only
once it is both compacted into the base table and covered by the arm's
base-index catch-up (`index_catchup`); plain scans use the compaction
watermark alone.
- Query shapes the scanner cannot honor hard-error with guidance to set
`use_lsm(false)`: hybrid, multi/binary vectors, `with_row_id`,
reranking, `order_by`, dynamic/Substrait projection or filters,
`distance_range`, `use_index(false)`, postfilter, take-by-row-id/offset,
reads from a time-traveled version, and an unmaintained or ambiguous
(multiple) FTS/vector index. Namespace-pushdown queries fall back to
local execution when a spec is present; WAL-only writers are handled.
- Exposed across the Rust core and the Python (`use_lsm`) and TypeScript
(`useLsm`) bindings, including `TakeQuery`.
Rebased from Lance `7.2.0-beta.3` to `10.0.0-beta.3`.
Python was versioned and tagged separately from the Rust, Java, and
Node.js SDKs, and had drifted three minor versions ahead (0.36 vs 0.33).
Users had no way to tell which Python version corresponded to which Rust
or Node release, and the gap had no meaning behind it.
This unifies the two tracks so there is one version and one tag for all
four SDKs.
## Version
The shared version is set to `0.37.0-beta.0`. Python continues its own
sequence (highest published: 0.36 → 0.37) while Rust, Java, and Node.js
jump 0.33 → 0.37 to meet it. Picking Python's next minor means Python
users see no discontinuity at all, and only the other SDKs skip forward.
Note that `main` trails the `release/v0.32` branch on both lines (main
is at 0.32.0-beta.3 / 0.35.0-beta.3; the release branch carries
0.33.0-beta.0 / 0.36.0-beta.0), so 0.37 is chosen to clear the highest
tag on either branch. Every index stays monotonic:
| index | publishes | last published | next |
|---|---|---|---|
| PyPI | stable only | 0.34.0 | 0.37.0 |
| Fury | previews | 0.36.0b0 | 0.37.0-beta.1 |
| npm | both | 0.33.0-beta.0 | 0.37.0-beta.1 |
| crates.io | stable only | 0.31.0 | 0.37.0 |
| Maven | both | 0.33.0-beta.0 | 0.37.0-beta.1 |
A one-time jump for three SDKs, versus explaining the offset
indefinitely.
## Mechanism
* `python/.bumpversion.toml` is removed. `python/Cargo.toml` — the
source of the Python package version, since `pyproject.toml` declares
`dynamic = ["version"]` — becomes a tracked file of the root config. Its
`cargo update -p lancedb-python` pre-commit hook is dropped as
redundant: `ci/update_lockfiles.sh` already refreshes every workspace
member version in `Cargo.lock`.
* `pypi-publish.yml` triggers on `v*` instead of `python-v*`, so one tag
releases all four packages. `ci/bump_version.sh` and
`make-release-commit.yml` lose their now-dead tag-prefix and
per-language plumbing, including the `python` / `other` dispatch inputs.
* The two byte-identical GH release jobs in `npm-publish.yml` and
`pypi-publish.yml` are replaced by a single `gh-release.yml`. One
release per tag, named `LanceDB vX.Y.Z`, instead of separate "Python
LanceDB" and "Node/Rust LanceDB" releases for the same commit.
The trade-off: there is no longer a way to ship a Python-only patch
without also releasing crates.io, Maven, and npm. That is the cost of
making drift structurally impossible.
## Beta releases marked "Latest" (#3666)
Both GH release jobs used:
```yaml
prerelease: ${{ contains('beta', github.ref) }}
```
The arguments are reversed. `contains(search, item)` asks whether
*`search`* contains *`item`*, so this evaluated "does the literal string
`'beta'` contain `refs/tags/python-v0.35.0-beta.2`?" — always `false`.
Every beta was published as a full release, and GitHub awards "Latest"
to the newest non-prerelease.
The new workflow derives the flag from the parsed version rather than
the raw ref, and sets `make_latest` explicitly:
```yaml
prerelease: ${{ steps.extract_version.outputs.prerelease }}
make_latest: ${{ steps.extract_version.outputs.prerelease == 'false' }}
```
npm was never affected (`--tag preview` uses correct bash), and PyPI
already excludes pre-releases from resolution.
This only fixes releases published from here on. Already-published betas
need a one-time backfill:
```shell
gh api --paginate /repos/lancedb/lancedb/releases \
--jq '.[] | select(.prerelease == false) | select(.tag_name | test("beta")) | .id' \
| xargs -I{} gh api -X PATCH /repos/lancedb/lancedb/releases/{} -F prerelease=true
```
## Verification
Ran `ci/bump_version.sh` end-to-end against this branch with the release
tooling installed:
* `preview` → tags `v0.37.0-beta.1` (previous tag `v0.33.0-beta.0`
detected, `pre_n` bump)
* `stable` → tags `v0.37.0`
* Both paths update `.bumpversion.toml`, `rust/lancedb/Cargo.toml`,
`nodejs/Cargo.toml`, `python/Cargo.toml`, `nodejs/package.json`, the 7
`nodejs/npm/*/package.json` files, both Java poms, and
`docs/src/java/java.md` together
* `check_breaking_changes.py` resolves the last stable as `v0.31.0`, so
the minor-version gate passes
All five touched workflows parse as valid YAML and the pre-commit hooks
pass.
## Notes for review
* This targets `main` only, so it takes effect at the next
release-branch cut. The in-flight `release/v0.32` branch still carries
`v0.33.0-beta.0` / `python-v0.36.0-beta.0`; if we want the imminent
stable to be 0.37.0, this needs to be applied there too.
* Historical `python-v*` tags are left alone. The changelog builder
scans `^v`, which does not match them, so the first unified release's
notes will compute `fromTag` from the Rust/Node line only — a one-time
gap in the Python-side changelog.
* Pre-existing and not addressed here: `ci/update_lockfiles.sh --amend`
amends the commit that `bump-my-version` has already tagged, so the
lockfile update lands outside the tag on stable releases.
Fixes#3666
## What changed
- add `block_size` to Python FTS configuration and the deprecated
local/remote helpers
- add `blockSize` to the TypeScript FTS options and propagate it through
the NAPI binding
- serialize the value as `block_size` for remote index creation
- document the existing Rust builder API and generate the TypeScript API
reference
- add local, remote, metadata, search, and invalid-value regression
coverage
## Why
Lance supports configuring the number of documents per compressed FTS
posting block, but LanceDB's Python and TypeScript APIs did not expose
the setting. This made the experimental FTS V3 layout unavailable
through those clients and allowed the value to be dropped before index
creation.
## How it works
The default remains `128`. Supported values are `128` and `256`;
selecting `256` uses the experimental FTS V3 format. Invalid values are
rejected by the Lance builder and surfaced as Python or JavaScript
errors.
## Validation
- `cargo check --quiet --features remote --tests --examples`
- `cargo +1.94.0 clippy --quiet --features remote --tests --examples --
-D warnings`
- targeted Rust local and remote index tests
- Rust doctests: 34 passed
- Python Ruff checks, doctest, and targeted local/remote tests: 5 passed
- TypeScript build, Biome lint, generated docs, and targeted Jest tests:
9 passed
- `git diff --check`
## Limitations
The Java client remains unchanged because its external remote REST model
does not currently expose `block_size`.
Co-authored-by: Yang Cen <yangcen@Yangs-Mac-mini.local>
## What
`AnswerdotaiRerankers(return_score="all").rerank_hybrid(...)` (and
`ColbertReranker`, which subclasses it without overriding
`rerank_hybrid`) raises:
```
pyarrow.lib.ArrowInvalid: Invalid sort key column: No match for FieldRef.Name(_relevance_score) in _rowid: int64 ...
```
## Why
```python
combined_results = self.merge_results(vector_results, fts_results)
combined_results = self._rerank(combined_results, query)
if self.score == "relevance":
combined_results = self._keep_relevance_score(combined_results)
elif self.score == "all":
combined_results = self._merge_and_keep_scores(vector_results, fts_results)
```
When `score == "all"`, `combined_results` is unconditionally overwritten
by `_merge_and_keep_scores(vector_results, fts_results)` **after**
`_rerank()` already computed and appended `_relevance_score` —
discarding it. The following `sort_by("_relevance_score", ...)` then has
nothing to sort on.
Every sibling reranker that supports `return_score="all"`
(`cross_encoder`, `openai`, `cohere`, `jinaai`, `voyageai`, `watsonx`)
instead calls `_merge_and_keep_scores()` **before** `_rerank()`. This
file is the one place the ordering got inverted when `"all"` support was
added (#2509) — a copy/paste inconsistency across the six files that PR
touched. Fix mirrors the pattern already used (and tested) by the other
five rerankers.
Also drops the now-stale `"Only 'relevance' is supported for now"`
docstring line on both classes, left over from before `"all"` support
existed.
## Testing
Added `test_answerdotai_reranker_return_all`, mirroring the existing
`test_cross_encoder_reranker_return_all`. Verified locally with the real
built Rust extension: red (reproduces the exact `ArrowInvalid` above) →
green, using the actual `rerank_hybrid`/`_rerank`/`base.py` code path
with the model call mocked out — my local environment's
`rerankers==0.10.0` fails to load the real ColBERT model against the
available `transformers` version (`AttributeError: 'ColBERTModel' object
has no attribute 'all_tied_weights_keys'`), which I confirmed also
breaks the **pre-existing**, unmodified
`test_colbert_reranker`/`test_answerdotai_reranker` baseline tests
identically — an unrelated local dependency-version issue, not a
regression from this change. `ruff check`/`ruff format` clean; full
`test_rerankers.py` run: 9 passed / 8 skipped / 3 failed (the 3 failures
are exactly those two pre-existing tests plus my new one, all failing at
model-loading time for the same unrelated reason before reaching the
changed code).
---
Disclosure: this PR was drafted with AI assistance (Claude); I reviewed,
tested, and take responsibility for the change.
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
## Summary
This updates the Java API reference to close the documentation gaps that
can be fixed from the current Java source and generated namespace API.
The patch adds an empty table example, shows how to wrap returned Arrow
IPC query bytes in a reusable `ArrowFileReader` helper, and documents
the Java index operations that are currently exposed by the namespace
client: vector indexes, scalar indexes, full text search indexes, and
listing indexes.
## Issue Links
Fixes https://github.com/lancedb/docs/issues/157
Fixes https://github.com/lancedb/docs/issues/160
Partially addresses https://github.com/lancedb/docs/issues/159 by
documenting the index parameters currently exposed by Java. The
requested `num_partitions` example is still blocked because
`CreateTableIndexRequest` does not expose IVF training parameters yet.
Not included: https://github.com/lancedb/docs/issues/158. The current
Java docs and source remain remote namespace oriented, so local DB
connection documentation should wait until the Java local DB API is
available and can be verified.
## Validation
- Built the Java core module with OpenJDK 17:
`./mvnw -pl lancedb-core -am -DskipTests compile`
- Checked the Markdown diff:
`git diff --check -- docs/src/java/java.md`
The Java build succeeds. It still reports pre-existing checkstyle
warnings in the namespace client builder, but the Maven build is green.
## Summary
- reconstruct foreign Arrow Map schemas from their single sanitized
entries field
- reject malformed Map types with anything other than one child
- preserve the complete Map schema and `keysSorted` value through
empty-table creation and IPC round trips across Arrow 15–18
## Testing
- `./node_modules/.bin/jest --runInBand __test__/arrow.test.ts
__test__/sanitize.test.ts`
- `pnpm lint`
- `pnpm build`
- `pnpm run docs`
Fixes#2337
## What
- Replace legacy model names in `WatsonxEmbeddings` with the current
supported set:
- `ibm/granite-embedding-278m-multilingual` (new default, 768-dim)
- `ibm/slate-125m-english-rtrvr-v2` (768-dim)
- `ibm/slate-30m-english-rtrvr-v2` (384-dim)
- `intfloat/multilingual-e5-large` (1024-dim)
- `sentence-transformers/all-minilm-l6-v2` (384-dim)
- Add `space_id` field — mutually exclusive with `project_id`, mirrors
the
existing pattern in `WatsonxReranker`
- `project_id` / `space_id` resolution now falls back to
`WATSONX_PROJECT_ID` /
`WATSONX_SPACE_ID` env vars; exactly one must be supplied
## Why
The previously hardcoded models (`ibm/slate-125m-english-rtrvr`,
`sentence-transformers/all-minilm-l12-v2`) are legacy and no longer
listed as
supported by the watsonx.ai platform. `space_id` scoping was already
supported
by `WatsonxReranker` but was missing from the embeddings counterpart.
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Updates the Rust workspace Lance dependencies and Java lance-core from
v9.1.0-beta.5 to v9.1.0-beta.7, including the generated Cargo lockfile.
No LanceDB compatibility changes were required for this release. See the
[Lance v9.1.0-beta.7
release](https://github.com/lance-format/lance/releases/tag/v9.1.0-beta.7).
This PR adds some support for `diff` / `merge` in the remote client as
for local tables we stay `NotSupported` until
https://github.com/lance-format/lance/issues/7263.
This wires the two review-and-land calls against the remote REST API:
- `POST /v1/table/{id}/branches/diff`
- `POST /v1/table/{id}/branches/merge`
Rust gets typed results (`BranchDiff`, `MergeBranchResult`). Python
returns the wire JSON, same shape as the REST response.
Merge here means promoting a branch's added columns onto `main`.
### Behavior
- Remote only. Local raises `NotSupported`.
- A rejected merge is not an exception. HTTP 409 still returns `Ok` / a
dict with `status="rejected"` and blockers in `diff.mergeBlockers`.
- Unknown blocker / status codes parse as `Unknown` so a newer server
does not break older clients.
- `MergePreview` tolerates missing fields for the same reason.
- Merge requests are not retried. 409 is final and carries the body you
need.
### Example
```python
table = db.open_table("images")
table.branches.create("exp")
exp = table.branches.checkout("exp")
exp.add_columns({"tag": "cast('draft' as string)"})
diff = table.branches.diff("exp")
preview = table.branches.merge("exp", dry_run=True)
result = table.branches.merge("exp", dry_run=False)
if result["status"] == "merged":
print("landed at", result["mainVersionAfter"])
elif result["status"] == "rejected":
print(result["diff"]["mergeBlockers"])
```
### Testing
cargo test -p lancedb --features remote diff_branch
cargo test -p lancedb --features remote merge_branch
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
## Problem
On the remote (LanceDB Cloud) write path, each write partition is
uploaded as a **single** `/insert?upload_id=...` request that stays open
until the whole partition has been streamed and the server has written
it to object storage. For large bulk ingests a partition can be many GB,
so a single request can run longer than the client read timeout (default
300s), surfacing as:
```
lancedb.remote.errors.HttpError: operation timed out
```
The server already supports staging **multiple** parts under one
`upload_id` (each `/insert` writes a separate transaction that
`complete` merges atomically), but the client never used that — it sent
one part per partition.
## Change
Split each partition into multiple parts of at most
`max_bytes_per_request` (Arrow IPC, LZ4-compressed) bytes, each uploaded
as its own `/insert?upload_id=...&upload_part_id=...` request. This
bounds how long any single request stays open, independent of total data
size or write parallelism.
Key properties:
- **Still streamed, not buffered.** Each part's body is driven through a
bounded channel while the request is in flight (`futures::join!` of a
producer + the send), so peak memory stays at a couple of batches per
partition regardless of the part size. Backpressure from a
slow/throttled server still propagates upstream.
- **Correct part accounting.** An empty partition still sends exactly
one (schema-only) part so `complete` has a transaction to commit; a size
cut landing exactly on the end of input does not emit a trailing empty
part.
- **Multipart only.** The single-request (non-multipart) path is
unchanged.
## Config
New `ClientConfig::max_bytes_per_request: Option<usize>`, also settable
via the `LANCE_CLIENT_MAX_BYTES_PER_REQUEST` environment variable.
**Default 1 GiB** (`Some(0)` disables splitting → one request per
partition). Python users pick up the default/env automatically through
the remote client.
## Tests
- `test_multipart_chunked_splits_into_parts`: a 1-byte budget puts each
batch in its own part → N requests, each carrying the shared `upload_id`
and a distinct `upload_part_id`.
- `test_multipart_single_part_when_under_budget`: a large budget keeps
the partition in a single request.
- Verified end-to-end against a live remote table: a forced-chunked
multipart add (many parts) assembles to the correct row count.
Related to ENT-1883.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
## Summary
Fixes#1653.
`infer_vector_column_name` in `util.py` could silently return `None`
when `query is None` and `query_type` is not `"fts"` or `"hybrid"`. This
`None` then propagated into downstream code, causing a cryptic
`TypeError: expected bytes, NoneType found` rather than a clear error
message.
## Changes
- **Removes the no-op `try/except Exception as e: raise e`** around
`inf_vector_column_query` (it was catching and immediately re-raising
without adding any value)
- - **Adds a `None` guard** after the inference block: if
`vector_column_name` is still `None` at this point, raise a clear
`ValueError` pointing the user to pass `vector_column_name` explicitly
## Before / After
**Before:** cryptic `TypeError: expected bytes, NoneType found` deep in
schema lookup code
**After:**
```
ValueError: No vector column found in the schema. Please specify the vector column name explicitly via the `vector_column_name` parameter.
```
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Some additions to our lancedb skill to enable agents to use the jobs
methods that we recently added. Eval tests (below, with and without
these additions to the skill) suggest that they're helping, mostly to
find the right method calls. These are a little unusual because they
require REST server connection, they're not yet implemented in the SDKs.
```
┌─────────────────────┬───────────┬────────────┬─────────────┬──────────┬───────────┬──────────┬───────────┐
│ eval │ grade w/o │ grade with │ improvement │ time w/o │ time with │ cost w/o │ cost with │
├─────────────────────┼───────────┼────────────┼─────────────┼──────────┼───────────┼──────────┼───────────┤
│ 8-list-running-jobs │ 2.5/3 │ 3/3 │ +0.5 │ 123s │ 29s │ $0.58 │ $0.18 │
├─────────────────────┼───────────┼────────────┼─────────────┼──────────┼───────────┼──────────┼───────────┤
│ 9-describe-job │ 1/5 │ 5/5 │ +4.0 │ 159s │ 52s │ $0.62 │ $0.25 │
├─────────────────────┼───────────┼────────────┼─────────────┼──────────┼───────────┼──────────┼───────────┤
│ 10-cancel-job │ 3/3 │ 3/3 │ +0.0 │ 99s │ 35s │ $0.55 │ $0.21 │
├─────────────────────┼───────────┼────────────┼─────────────┼──────────┼───────────┼──────────┼───────────┤
│ TOTAL │ 6.5/11 │ 11/11 │ +4.5 │ 381s │ 116s │ $1.75 │ $0.65 │
└─────────────────────┴───────────┴────────────┴─────────────┴──────────┴───────────┴──────────┴───────────┘
```
Failure reasons are because the agent didn't know the right method to
call, spent all its turns guessing REST calls, tried to inspect lancedb
code, but didn't find the answer in here.
## Problem
`table.add(dataset)` with a `pyarrow.dataset.Dataset` OOMs the client
during bulk ingestion of wide rows (e.g. embedding columns), even
against a remote table where the upload itself is streaming.
The cause is in `to_scannable`: a `Dataset` is scanned with pyarrow's
default scanner settings (`batch_size=131072` rows,
`batch_readahead=16`, `fragment_readahead=4`). pyarrow's internal
threads prefetch that read-ahead window independently of LanceDB's
backpressure, so for wide rows a large fraction of the dataset is held
in memory. On the remote path this is then multiplied across the
multipart write partitions (one in-flight batch per partition, up to
CPU-core count).
Reproduced on a 10 GB / 1.55M-row dataset with two 768-dim float32
embeddings: peak client RSS ~11.7 GB for the scan alone (6.8 GB after
consuming a *single* batch), ~15.4 GB for the full remote `add()`.
## Fix
`to_scannable` now sizes the scanner from an estimate of bytes-per-row
derived from the schema:
- **Narrow datasets keep pyarrow's defaults** (empty scanner kwargs) —
no throughput regression. The bound only engages above ~410 bytes/row.
- **Wide rows** get a smaller `batch_size` (~16 MiB/batch) and reduced
read-ahead (`batch_readahead=2`, `fragment_readahead=1`) so peak
in-flight memory stays near a ~1 GiB budget. Read-ahead (not just batch
size) has to drop, because pyarrow pins whole row-group buffers.
On the 10 GB dataset this drops peak client RSS to ~1.4 GB, and it stays
flat as the dataset grows. The `Dataset`/`LanceDataset` scannables
remain rescannable (retry-safe).
## Also: expose `write_parallelism` on `add()`
`AddDataBuilder::write_parallelism` already existed in Rust but was not
exposed in Python. This PR forwards it through the async, sync, and
remote `add()` methods, so users can cap the number of parallel write
partitions (each buffers data in flight) to trade throughput for memory
on large uploads.
## Tests
- `test_scannable.py`: bytes-per-row estimation; narrow → defaults; wide
→ bounded; `Dataset` reader streams bounded batches and stays
rescannable.
- `test_table.py`: `write_parallelism` on sync and async `add()`, and
that `write_parallelism=0` is rejected.
Fixes ENT-1883
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Moves the skill from .agents/skills/lancedb to
plugins/lancedb/skills/lancedb, as recommended by codex and claude.
Install path now is:
### Codex/ChatGPT app
Codex: Plugins -> "Create" arrow -> Add plugin marketplace
search for lancedb plugin and install
### Codex CLI
```
codex plugin marketplace add lancedb/lancedb
codex plugin install lancedb@lancedb # name@marketplace
```
### Claude app
Settings -> Plugins -> Add -> Add marketplace
search for lancedb plugin and install
### Claude CLI
```
claude plugin marketplace add lancedb/lancedb
claude plugin install lancedb@lancedb
```
Here's how it looks on ChatGPT/Codex app:
(the main icon has light and dark modes; the smaller one on the skill
doesn't so I made it gray 🤷 )
<img width="764" height="560" alt="Screenshot 2026-07-16 at 2 49 24 PM"
src="https://github.com/user-attachments/assets/b82cda16-3392-4740-ac47-b2f187cb2655"
/>
Hi, and thank you for LanceDB.
Small CI supply-chain hardening. In `make-release-commit.yml`, the
release job checks out with `LANCEDB_RELEASE_TOKEN` (a push-capable PAT)
and its final step pushes the version tag using a third-party action
pinned to a **mutable branch**:
```yaml
- name: Push new version tag
uses: ad-m/github-push-action@master
with:
github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
```
`@master` can move after review; whatever it points at then runs with
that release token in scope. This PR pins it to the commit behind the
current release (`v1.3.0` → `881a6320…`), keeping the version visible as
a comment. Behavior today is unchanged.
For transparency: I used AI assistance to spot and draft this; I
verified the workflow and resolved the SHA myself.
Tracks #3324. On x86_64 CPUs without AVX2 (Sandy Bridge / Ivy Bridge /
Westmere on Intel; Bulldozer / Piledriver / Steamroller on AMD), `import
lancedb` SIGILLs because the wheel bakes AVX2 + FMA into every compiled
function. Per [westonpace's
review](https://github.com/lancedb/lancedb/issues/3324#issuecomment-4328944354),
the default `lancedb` wheel stays fast; pre-Haswell users get a
separately-published `lancedb-compat` wheel.
## Summary
- Adds a `lancedb-compat` matrix entry to `pypi-publish.yml` that builds
with `RUSTFLAGS="-C target-cpu=x86-64-v2"` (Nehalem-class baseline).
Same Python API (`import lancedb` works) — files install to the same
namespace, so the two wheels conflict at install time and users pick
one. Same pattern as `psycopg2` / `psycopg2-binary` and `tensorflow` /
`tensorflow-cpu`.
- Generalizes `build_linux_wheel` and `upload_wheel` composites with
optional `package-name` and `rustflags` inputs (defaults preserve the
existing 4 `lancedb` matrix entries verbatim).
- Documents the choice in `python/README.md`: `pip install
lancedb-compat` for pre-Haswell hosts.
The default `.cargo/config.toml` baseline is unchanged.
## Sequencing
1. ~~lance-format/lance#6630 merges → runtime SIMD dispatch lands in
lance.~~ **Done — merged.**
2. lancedb's lance dep is bumped to a release that includes it (separate
PR / normal cadence).
3. This PR's `lancedb-compat` wheel build path starts producing a wheel
that runs on pre-Haswell hardware. **Maintainer setup**: register
`lancedb-compat` on PyPI and configure trusted publishing.
## Verified end-to-end on Sandy Bridge Xeon E5-2609
Verification was done locally against a fork-pinned lance dep that
includes the runtime dispatch implementation, using the same
`RUSTFLAGS="-C target-cpu=x86-64-v2"` flags this PR uses in CI:
```
$ RUSTFLAGS="-C target-cpu=x86-64-v2" maturin build --release
$ pip install ./target/wheels/lancedb-*.whl
$ python verify.py
PASS: import + simd dispatch + table create + vector search all work.
```
Pre-fix on the same CPU (default `pip install lancedb`): `Illegal
instruction (core dumped)`. Full reproducer (deps + clone + build +
verification):
https://gist.github.com/tobocop2/2e341358b55c143527416edfdb1e37df.
Fork-internal verification PR with the dep bump and full logs:
[`tobocop2/lancedb#2`](https://github.com/tobocop2/lancedb/pull/2).
## Benchmarks — no regressions on modern CPUs from the lance-side change
These are the numbers I ran for the lance PR, confirming the runtime
dispatch doesn't slow down the default (`target-cpu=haswell`) wheel that
existing users install. Criterion, one machine, one session, base → PR,
no `RUSTFLAGS` override. Full methodology, null experiments, and logs:
[lance-format/lance#6630 benchmark
comment](https://github.com/lance-format/lance/pull/6630#issuecomment-4933063394)
and the [logs
gist](https://gist.github.com/tobocop2/3c6d0f449cbd736aa2501f89a7fe56a2).
| benchmark | EPYC 7B13 (`avx2`, `fma`, no `avx512f`) | Xeon Cascade
Lake (`avx512f`) |
|---|---|---|
| `Cosine(f32, scalar)` *(control)* | +0.04% | +0.09% |
| `Cosine(f64, scalar)` | −0.34% | −1.94% |
| `Cosine(u8, SIMD)` | +2.30% | +3.63% |
| `Dot(f16, SIMD)` | −0.58% | +0.61% |
| `Dot(f32, SIMD)` | +0.34% | **−6.08%** |
| `Dot(f32, arrow_arity)` | +0.02% | −0.00% |
| `L2(f32, scalar)` | −0.10% | −0.02% |
| `L2(f32, simd)` (dim 1024) | +2.63% | −0.53% |
| **`L2(simd,f32x8)` (dim 8)** | **−45.9%** | **−25.1%** |
| `L2(u8, SIMD)` | +0.42% | −3.11% |
| `NormL2(f32, SIMD)` | −1.02% | −4.17% |
| `NormL2(f64, SIMD)` | +3.51% | −0.58% |
Nothing regresses beyond the noise floor. Dim 8 — the PQ sub-vector
width — improves 25–46%.
---
To be transparent: this isn't my domain of expertise and the lance-side
implementation is AI-generated. I verified it works end-to-end on the
failing hardware. Happy to roll in feedback.
Routes local sync child-namespace operations through the Rust-backed
connection instead of the Python namespace-client fallback.
Also keeps lazy namespace-client construction for table-to-Lance
conversion and preserves public namespace error mappings.
Validated locally with ruff format/check and targeted namespace pytest.
BREAKING CHANGE: splits generated by the permutation data loader will
not be the same, due to a change in hash function.
Updates the Lance dependencies and Java lance-core to
[v9.0.0-rc.1](https://github.com/lance-format/lance/releases/tag/v9.0.0-rc.1).
Includes the required DataFusion 54 and Lance file-reader compatibility
updates.
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Adds client-side support for analyze_plan distributed metrics modes
across Rust, Python, and TypeScript clients. Defaults to aggregate for
backward compatibility and sends the remote distributed_metrics
parameter only when a non-default mode is requested.
Fixes#3174
Also fixes#3645
Empty record batches now append correctly typed empty embedding arrays
without invoking embedding providers. This avoids OpenAI requests with
an invalid empty input while preserving source-column validation and
the non-empty execution paths.
As a small cleanup, the single- and multi-embedding code paths now share
a single upfront lookup of their source columns ("input_columns")
instead
of each path looking them up independently. Also moves `lance-testing`
from regular dependencies to dev-dependencies where it belongs.
Tests run:
- `cargo fmt --all -- --check`
- `cargo test --quiet -p lancedb --lib
empty_batch_skips_embedding_functions`
- `cargo test --quiet -p lancedb --lib
empty_batch_still_validates_source_column`
- `cargo test --quiet -p lancedb --lib
test_create_empty_table_with_embeddings`
- `cargo check --quiet -p lancedb --features remote --tests --examples`
- `cargo clippy --quiet -p lancedb --features remote --tests --examples`
- `cargo test --quiet -p lancedb --lib`
- `cargo test --quiet --features remote --tests`
## Summary
Fix `on_bad_vectors="fill"` so it replaces only invalid or missing
vector values instead of replacing the entire vector row.
Fixes#3026.
## Reasoning
The old Python sanitizer detected whether a vector row was bad at row
granularity. For `fill`, it then used that row-level flag to replace the
whole vector with `[fill_value] * dim`. That meant an input like `[1.0,
NaN, 3.0]` became `[0.0, 0.0, 0.0]`, even though the documented and more
useful behavior is to preserve valid values and fill only the bad
element.
I checked whether this should be a Rust-side fix so TypeScript users
would benefit too. Today, Rust core exposes `NaNVectorBehavior::{Error,
Keep}` for rejecting or keeping NaN vectors, while the Python
`on_bad_vectors` API (`error`, `drop`, `fill`, `null`) is implemented in
the Python ingestion sanitizer before data reaches Rust. TypeScript does
not expose the Python `on_bad_vectors="fill"` behavior today. Moving
this exact behavior to Rust would be a broader cross-language API
change, so this PR keeps the fix scoped to the currently affected Python
API.
## What changed
- Added a small helper that fills bad vector rows by preserving valid
elements, replacing NaN elements with `fill_value`, truncating vectors
longer than the expected dimension, and padding short vectors with
`fill_value`.
- Kept the existing fast path unchanged: the helper only runs after bad
vectors are detected and `on_bad_vectors="fill"` is selected.
- Updated sanitizer and table tests to assert element-wise NaN
replacement and short-vector padding for both `create_table` and `add`.
## Validation
- `uv run ruff format .`
- `uv run ruff check .`
- `cd python && uv run --no-sync pytest
python/tests/test_util.py::test_handle_bad_vectors_jagged
python/tests/test_util.py::test_handle_bad_vectors_nan
python/tests/test_table.py::test_create_with_nans
python/tests/test_table.py::test_add_with_nans -vv`
Targeted pytest result: `10 passed`.
## Why this fix is Python-side (and not Rust)
The problematic behavior lives in Python’s `on_bad_vectors` sanitizer,
before data is handed off to Rust. Rust currently only exposes
`NaNVectorBehavior::{Error, Keep}` for add operations, while Python has
the richer `on_bad_vectors={"error","drop","fill","null"}` API.
TypeScript does not currently expose the Python-style fill behavior, so
moving this exact fix into Rust would require designing a broader
cross-language bad-vector handling API.
This PR keeps the change scoped to the existing affected surface:
Python’s `on_bad_vectors="fill"` path. This way, Python users
immediately benefit.
## What the new agent skill covers
We want to help users _easily_ write LanceDB pipelines to bring their
data in from other places, no matter whether they use LanceDB OSS or
Enterprise.
The `lancedb` set of skills contains guidance for agents on the
following:
- Distinguishes local and remote table capabilities.
- Promotes bounded reads using `select()` and `limit()`.
- Prevents accidental full-table materialization.
- Documents correct Python sync/async scan APIs.
- Recommends validated Python schemas and batched ingestion.
- Provides indexing, query-tuning, diagnostics, and maintenance
guidance.
- Documents the Enterprise table-name cache issue: avoid immediately
reusing a dropped or overwritten table name; write to a fresh name and
rename after propagation.
- Adds Python and TypeScript API, pattern, and performance references.
- Adds a heuristic scanner for potentially unsafe Python and TypeScript
materialization patterns.
This change only adds agent documentation and tooling: no LanceDB
runtime code, Rust code, SDK APIs, dependencies, or CI configuration are
modified.
## Context
The LanceDB agent skill was accidentally pushed directly to `main` in
`8ea78e3fbcb26718112ab4ddec55a91804b869d3`, bypassing the normal review
workflow. That commit was reverted on `main` by `c12a6dce` so the
protected branch is back to its prior content.
## Summary
- add table-level FTS query tokenization returning token text and
position
- use the native index tokenizer for local tables and remote index
metadata for remote tables
- expose sync and async Python table wrappers with focused coverage
`Dataset::index_statistics()` loads index files and does meaningful CPU
work to serialize low-level info. Most fields
`NativeTable::index_stats()` needs are available from manifest metadata
via `Dataset::describe_indices()`, which is much cheaper.
`NativeTable::index_stats()` now:
- Calls `describe_indices()` filtered by name; returns `Ok(None)` if no
match.
- Parses `distance_type` from `description.details()` JSON (the
`VectorIndexDetails` proto stored in the manifest by recent Lance
versions).
- Falls back to `index_statistics()` only for vector indices where
`details()` returns no `distance_type` — this handles older Lance
datasets that didn't write `VectorIndexDetails`.
- `Unknown` index types (e.g. Lance's internal `FragReuseIndex`) are
explicitly filtered out of `list_indices` rather than erroring.
## Test plan
- [x] `test_create_scalar_index` — asserts `index_type`,
`distance_type`, and `num_unindexed_rows > 0` after adding rows
post-index
- [x] `test_create_fm_index`, `test_create_bitmap_index`,
`test_create_label_list_index` — added `index_stats` assertions
- [x] IvfPq, IvfHnswPq, IvfHnswSq, IvfHnswFlat tests assert
`distance_type == Some(L2)`
- [x] `test_list_indices_skip_frag_reuse` — FragReuseIndex is filtered
by the Unknown guard in `list_indices`
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
## Summary
- serialize sync phrase queries consistently for execution and query
plans
- restore the documented no-argument hybrid `phrase_query()` behavior
- keep reranker input as the original user text without mutating the
builder
Fixes#3653.
## Testing
- `python/.venv/bin/python -m pytest <8 focused test nodes> -q` (`8
passed`)
- `python/.venv/bin/python -m ruff format --check
python/python/lancedb/query.py python/python/tests/test_fts.py
python/python/tests/test_hybrid_query.py`
- `python/.venv/bin/python -m ruff check .`
- `git diff --check origin/main...HEAD`
The complete hybrid module and the real native FTS phrase test were not
completed
in the current PyO3 runtime environment: both stalled in the native
`lancedb.connect()` fixture and were interrupted without an assertion
failure.
The CODEOWNERS file added in #3312 automatically requests reviewers on
every PR — the `*` default owner routes all changes to two reviewers.
This is mostly noise for contributors, and we prefer a single requested
reviewer per PR.
Remove the file.
Reverts #3312.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Carrying over from #2915, this patch introduces:
* Single-API call batching support for Gemini embeddings (up to 100 at a
time, the API limit)
* A versioned user agent header for Gemini API calls
* Support for [variable embedding dimension
size](https://ai.google.dev/gemini-api/docs/embeddings#control-embedding-size)
(Gemini is MRL trained)
## Summary
- preserve explicit `0.0` distance bounds in synchronous hybrid search
- distinguish omitted `None` endpoints from zero-valued endpoints when
configuring the vector child query
- add a public end-to-end regression test for a zero upper bound
## Testing
- `cd python && uv run --extra tests pytest
python/tests/test_hybrid_query.py -q`
- `uv run --project python ruff format --check
python/python/lancedb/query.py python/python/tests/test_hybrid_query.py`
- `uv run --project python ruff check .`
Fixes#3651
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.
Closes#3243.
This PR exposes a new public api `Permutation.take_offsets(offsets:
list[int])`, since users initially had to call __getitems__ directly to
batch-fetch rows by position.
Currently, the name matches the existing `Table.take_offsets` pattern,
and now the dunder `__getitem__` and `__getitems__` now delegate to it.
Also, fixes a parse error when `PermutationReader::take_offsets` gets an
empty list. Now returns an empty `RecordBatch` with the correct schema
instead. Bundled this because without the fix the new public API blows
up on a perfectly reasonable input.
`__getitems__` is preserved since PyTorch's batched DataLoader requires
it.
### Testing
- Added 3 new Rust tests for empty offsets including permutation table
with Select::All, Select::Columns, and identity path
- Added 3 new Python tests for the public API including a happy case,
and empty input on both identity and permutation
clippy, format, check all clean!
cc: @westonpace
The 0.29 release happened on a branch because the main line had already
moved past the 6.0.0 stable lance release. As a result the version bump
commits ended up on the branch. This merges those commits back into
main.
---------
Co-authored-by: Lance Release <lance-dev@lancedb.com>
## Summary
Split out from #3354
Adds `LsmWriteSpec` and `Table::set_lsm_write_spec` /
`unset_lsm_write_spec` to
install and clear the spec that selects Lance's MemWAL LSM-style write
path for
`merge_insert`.
`LsmWriteSpec` offers three sharding strategies, all built on Lance's
`InitializeMemWalBuilder`:
- `LsmWriteSpec::bucket(column, num_buckets)` — hash-bucket sharding by
the
single-column unenforced primary key.
- `LsmWriteSpec::identity(column)` — identity sharding by the raw value
of a
scalar column.
- `LsmWriteSpec::unsharded()` — a single MemWAL shard.
Each can be refined with `with_maintained_indexes(...)` (indexes the
MemWAL
keeps up to date as rows are appended) and
`with_writer_config_defaults(...)`
(default `ShardWriter` configuration recorded in the MemWAL index, so
every
writer starts from the same defaults). All variants require the table to
have
an unenforced primary key.
- `set_lsm_write_spec` installs the spec by initializing the MemWAL
index;
`unset_lsm_write_spec` removes it (dropping the MemWAL index), reverting
to
the standard `merge_insert` path. `unset` is idempotent.
- Bindings: Python (`LsmWriteSpec.bucket` / `.identity` / `.unsharded`,
`set_lsm_write_spec` / `unset_lsm_write_spec`) and TypeScript
(`setLsmWriteSpec` with `specType` `"bucket"` / `"identity"` /
`"unsharded"`). `RemoteTable` returns `NotSupported`.
The actual `merge_insert` LSM dispatch and `ShardWriter` write path are
a
follow-up — this PR only installs and clears the spec.
## Summary
Adds `Table::set_unenforced_primary_key` — records a single column as
the
table's unenforced primary key in Lance schema field metadata.
"Unenforced"
means LanceDB does not check uniqueness on write; the key is metadata
that
`merge_insert` consumes.
- Single-column only; the column must exist and have a supported dtype
(Int32, Int64, Utf8, LargeUtf8, Binary, LargeBinary, FixedSizeBinary).
The
API accepts an iterable for binding ergonomics but requires exactly one
column — compound keys are rejected.
- The primary key is immutable: calling this on a table that already has
an
unenforced primary key is rejected. Concurrent writers racing to set the
key
fail at commit time rather than silently overriding it.
- `RemoteTable` returns `NotSupported`.
- Bindings: Python (`AsyncTable`, `LanceTable`, `RemoteTable`) and
TypeScript
(`Table.setUnenforcedPrimaryKey`).
## Context
Split out from #3354 per review feedback, so the unenforced primary key
and the
`merge_insert` sharding spec land as separate reviewable PRs.
No Lance dependency bump — `main` is already on v7.0.0-beta.10, which
includes
the field-metadata round-trip fix the API relies on. Enforcing
primary-key
immutability at the Lance commit layer (so the cross-column concurrent
race is
also rejected) is a companion Lance change: lance-format/lance#6810.
### Summary
- Expose Connection.renameTable in the Node.js bindings and align it
with existing namespace-aware connection APIs.
### Changes
- Add napi-rs rename_table on Connection, delegating to Rust
Connection::rename_table.
- Add renameTable(oldName, newName, namespacePath?) on abstract
Connection and implement on LocalConnection.
- Add a connection test that renames a table and checks names / open
behavior.
#### Testing
- cd nodejs && npm run build
- cd nodejs && npm test __test__/connection.test.ts
fix : #3364
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Closes#3261.
## Summary
Adds `bytes` to the accepted types of `lancedb.expr.lit()` so that
binary scalars can be used in filter / projection expressions. The
previous attempt in #3235 had to be reverted because DataFusion's SQL
unparser does not support `Binary` / `LargeBinary` scalars, so any
expression containing such a literal would fail in both `to_sql()` and
`__repr__`.
## How
`expr_to_sql_string` now has two paths:
- **Fast path** (no binary literals): delegate to DataFusion's unparser
unchanged.
- **Slow path**: rewrite each `Binary(Some(bytes))` literal in the tree
to a unique string-literal placeholder, run the unparser, then
substitute `'<placeholder>'` with `X'<HEX>'` in the resulting SQL.
`Binary(None)` / `LargeBinary(None)` are rewritten to
`ScalarValue::Null` so the unparser emits plain `NULL`.
This keeps DataFusion as the single source of truth for operator and
function serialization, so binary literals work in every expression node
type the unparser already supports — including nested cases like
`contains(col("data"), lit(b"\xff"))`, `NOT (col == lit(b"..."))`, and
`col.cast(...) == lit(b"...")`.
## Changes
- `rust/lancedb/src/expr/sql.rs`: placeholder-substitution
implementation.
- `rust/lancedb/src/expr.rs`: 4 new unit tests covering binary literals
in equality, compound predicates, scalar function calls, negation, and
`NULL` binary literals.
- `python/src/expr.rs`: `expr_lit` accepts `PyBytes` and produces
`ScalarValue::Binary`.
- `python/Cargo.toml` + `Cargo.lock`: pull in `datafusion-common` for
`ScalarValue`.
- `python/python/lancedb/expr.py`: extend `ExprLike` and `lit()` type
annotations / docstrings with `bytes`.
- `python/python/lancedb/_lancedb.pyi`: update `expr_lit` stub.
- `python/tests/test_expr.py`: unit tests for `to_sql` / `repr` of
binary literals and an integration test against a real `pa.binary()`
column for equality / inequality / compound filters.
## Example
```python
from lancedb.expr import col, lit, func
# Equality against a binary column
col("payload") == lit(b"\xca\xfe")
# Expr((payload = X'CAFE'))
# Nested inside a function call (previously failed)
func("contains", col("data"), lit(b"\xff"))
# Expr(contains(data, X'FF'))
# repr() no longer crashes
repr(lit(b"\xde\xad\xbe\xef"))
# "Expr(X'DEADBEEF')"
```
## Verification
- [x] `cargo test -p lancedb --lib expr::` — 12/12 pass (was 9; +3 new
tests)
- [x] `cargo check --features remote --tests --examples` — clean
- [x] `cargo clippy --features remote --tests --examples` — no warnings
- [x] `cargo fmt --all -- --check` — clean
- [x] `pytest python/tests/test_expr.py` — 76/76 pass (was 74; +2 new
tests)
- [x] `ruff check python` / `ruff format --check python` — clean
## Follow-ups (not in this PR)
Issue #3261 also raises the possibility of a *truncated* `__repr__` for
very large binary literals. This PR keeps `__repr__` exact (it forwards
to `to_sql()`), since truncating display output would diverge from the
SQL that actually gets executed. A display-only truncation could be
added in a follow-up by giving `__repr__` its own renderer.
Made with [Cursor](https://cursor.com)
Co-authored-by: Cursor <cursoragent@cursor.com>
## Summary
`LanceNamespaceDatabase::open_table` and `create_table` were squashing
`NamespaceError::TableNotFound` and `TableAlreadyExists` into generic
`Error::Runtime`, so callers couldn't distinguish a missing-table or
duplicate-table error from any other internal failure. Downstream this
surfaced to geneva-style code as HTTP 500 / "internal server error" on
operations that should have been 400/404 — see
[ENT-1235](https://linear.app/lancedb/issue/ENT-1235/fix-ns-errors-for-create-tableopen-table).
This PR walks the boxed-error chain from `lance::Error::Namespace` down
to the inner `NamespaceError` and maps its `ErrorCode` onto the proper
`lancedb::Error` variant:
- `NamespaceError::TableNotFound` → `Error::TableNotFound { name, source
}`
- `NamespaceError::TableAlreadyExists` → `Error::TableAlreadyExists {
name }`
- everything else → `Error::Runtime` (unchanged behavior for the long
tail)
It also replaces the existing `e.to_string().contains("already exists")`
string match in `LanceNamespaceDatabase::create_table` with a downcast
on the `NamespaceError` code. That string-match happened to work for the
`dir` backend but isn't guaranteed to match the REST namespace backend's
error format; the downcast works for both.
The chain-walk is needed because `DatasetBuilder::from_namespace`
re-wraps the inner namespace error in a fresh `lance::Error::Namespace`,
so a single top-level downcast misses it.
## How this helps geneva
Geneva's workaround (linked in the parent issue) currently has to use
`except Exception:` with a `# todo: this is too broad` comment, plus
`str(e).lower().contains("already exists")` string matching, because the
namespace-impl path raised a generic `RuntimeError`. After this PR:
- `db.open_table("missing")` raises `ValueError("Table 'missing' was not
found")` (via the existing Python binding mapping of `TableNotFound` →
`PyValueError`) — geneva can catch `ValueError` cleanly.
- `db.create_table("dup")` raises `ValueError("Table 'dup' already
exists")` reliably across both `dir` and REST backends, so the existing
string match becomes deterministic.
In phalanx (the sophon REST server), `LanceDBError::TableNotFound` and
`LanceDBError::TableAlreadyExists` already map directly to HTTP 404 and
HTTP 400 respectively — see
[phalanx/src/error.rs:77-94](https://github.com/lancedb/sophon/blob/main/src/rust/phalanx/src/error.rs#L77).
No phalanx code change is needed for the bug fix; the previous 500 came
from phalanx's string-match fallback not finding `"namespace"` AND `"not
found"` in the `Runtime` error's debug-formatted message.
## Follow-up
[ENT-1246](https://linear.app/lancedb/issue/ENT-1246/remove-dead-namespace-error-string-matching-in-phalanx)
— after this lands and phalanx picks up the new lancedb, the
string-matching fallback for table errors in
`src/rust/phalanx/src/error.rs` (lines 99-168, 236-256, 502-514) and
`src/rust/phalanx/src/rest/table/create_table.rs` (lines 224-241)
becomes dead code and can be removed. The `// TODO: Refactor for better
namespace error handling` comment at phalanx/src/error.rs:96-98 is
exactly what this PR addresses on the lancedb side; ENT-1246 finishes
the cleanup on the sophon side.
## Test plan
- [x] `cargo test --quiet --features remote -p lancedb --lib` — all 495
lib tests pass, including 4 new tests in `database::namespace::tests`:
- `test_namespace_table_not_found` — extended to assert
`Error::TableNotFound` (was just `is_err()`)
- `test_namespace_open_table_not_found_at_root` — covers the
root-namespace path
- `test_namespace_create_table_already_exists` — covers child namespace
- `test_namespace_create_table_already_exists_at_root` — covers root
namespace
- [x] `cargo clippy --quiet --features remote --tests` — clean
- [x] `cargo fmt --all` — clean
- [x] Manually confirmed (via test failures before the fix) that the two
`open_table` tests were returning `Error::Runtime { message: "Failed to
get table info from namespace: Namespace { source: TableNotFound { ... }
}" }` prior to this change.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Follow-up to #3371 , which added runtime validation for namespace `mode`
and `behavior` parameters in the NodeJS SDK. Bringing the same fix to
Python for cross-SDK consistency.
**Before:** unrecognized values were silently dropped to `None`, so
`db.create_namespace(["x"], mode="foobar")` would quietly fall through
to the server's default mode and hide caller typos.
**After:** raises `ValueError` listing the valid values.
## **Summary**
This PR adds a **Scannable primitive** to the Node.js bindings, bringing
parity with Python's `PyScannable`.
A `Scannable` wraps a schema, an optional row count hint, a rescannable
flag, and a batch producing callback. On the Rust side it implements
`lancedb::data::scannable::Scannable`. The goal is to give consumers
such as `Table.add`, `createTable`, and `mergeInsert` a way to stream
data without materializing the full dataset in JS memory.
This PR introduces only the primitive. Migrating existing consumers to
use it will come in follow up work.
---
## **Design**
### **Transport**
The transport uses the **Arrow IPC Stream format, one batch at a time**.
The JS side encodes each `RecordBatch` into a self contained IPC Stream
message containing schema, batch, and end of stream. The message is
returned as a `Buffer` through a napi `ThreadsafeFunction`. The Rust
side decodes it using `arrow_ipc::reader::StreamReader`.
Only one batch is active at a time, so JS memory stays bounded by the
batch size. The Node `Buffer` size limit of about 4 GiB therefore does
not constrain the stream as a whole.
I initially evaluated the Arrow C Data Interface, which is the approach
used in Python. I dropped that path after confirming that the
`apache-arrow` npm package does not expose a C Data Interface export in
any supported version from 15 to 18. JavaScript is not listed in Arrow's
C Data Interface implementation table, and the upstream tracking issue
remains open with no scheduled work.
Third party FFI shims would introduce additional dependency risk without
solving the core maintenance problem. Using IPC adds one encode and
decode step per batch, but the cost is predictable and typically
dominated by Lance's write path.
---
### **API**
```ts
class Scannable {
readonly schema: Schema
readonly numRows: number | null
readonly rescannable: boolean
static fromFactory(schema, factory, opts?)
static fromTable(table, opts?)
static fromIterable(schema, iter, opts?)
static fromRecordBatchReader(reader, opts?)
}
```
The FFI boundary consists of a single callback:
`getNextBatch(isStart: boolean): Promise<Buffer | null>`
`isStart` is `true` on the first call of each new scan and `false` for
every call after it. The JS side uses it to drop any cached iterator and
re-invoke the factory at scan boundaries. This is what makes a
rescannable source restart at batch 0 on every `scan_as_stream` call,
even when a previous scan ended mid stream, for example a retried write
after a network error. Without this signal a retry would resume a stale
iterator and silently skip already emitted batches.
In addition, a schema only IPC buffer is transferred once during
construction.
---
## **Changes**
* `nodejs/src/scannable.rs`
Adds `NapiScannable` and the `LanceScannable` implementation. Implements
`schema()`, `num_rows()`, `rescannable()`, and `scan_as_stream()`.
Includes per batch schema validation against the declared schema, one
shot enforcement for non rescannable sources, and a scan boundary reset
signal (`isStart`) so rescannable sources restart from batch 0 on every
`scan_as_stream` call rather than resuming a stale iterator.
* `nodejs/src/lib.rs`
Module registration.
* `nodejs/lancedb/scannable.ts`
Defines the `Scannable` class and the four constructors listed above.
Each constructor rejects option combinations it cannot honor, for
example a `rescannable: true` request on a one shot iterable or reader,
and a `numRows` that disagrees with an in memory table's row count.
* `nodejs/lancedb/index.ts`
Exports the new primitive.
* `nodejs/__test__/scannable.test.ts`
Test suite for the primitive.
---
## **Validation**
Before implementing the bridge, I ran an end to end harness with a JS
producer feeding a standalone Rust consumer built against the same
`arrow-ipc` version used in the bridge.
The harness covered the following scenarios:
* happy path
* empty stream
* 1,000 small batches
* 10 large batches
* mixed primitive types with nullables
* nested `List<Struct<>>`
* truncated stream error handling
* declared schema mismatch validation
* a 6 GB stress test through the pipe
All scenarios completed with bounded memory usage. The goal of this
harness was to confirm that the IPC Stream transport works correctly end
to end and that Node's `Buffer` size limit does not constrain the
overall stream.
Separately, the rescannable restart contract was verified with a focused
harness. A rescannable source is consumed partially and the scan is
dropped mid stream, then re-scanned. The re-scan replays from batch 0
rather than resuming the stale iterator. The same harness was run with
the `isStart` reset path disabled and the mid stream restart case failed
as expected, confirming the test exercises the real regression.
These harnesses are not meant to replace the full test suite, which is
described below.
---
## **Tests**
`__test__/scannable.test.ts` covers construction, metadata reflection,
per constructor defaults and overrides, construction time validation,
the native handle surface, and schema variety across empty tables,
nested types, `FixedSizeList`, and wide schemas.
Runtime scan behavior including `scan_as_stream`, one shot enforcement
on non rescannable sources, schema mismatch detection, IPC decode
failures, and rescannable restart semantics is not exercised here. There
is no in tree JS consumer of `NapiScannable` yet. This mirrors Python's
`PyScannable`, which has no dedicated test file and is covered
transitively through the consumers that accept a Scannable.
Runtime coverage will follow in the consumer migration work.
---
## **Status**
Ready for review.
Closes#3223
---
## Summary
Switch the nodejs bindings and examples package from npm to pnpm 11 to
pick up its stronger supply-chain defaults:
- `minimumReleaseAge` defaults to 1 day, so newly-published (potentially
compromised) versions aren't resolved into installs for at least 24h.
- Install lifecycle scripts (`preinstall`/`install`/`postinstall`) are
no longer run for arbitrary transitive deps; only an explicit allowlist
may run them, and unapproved scripts cause install to fail
(`strictDepBuilds: true`).
- Audit uses GHSA IDs and `--fix=update` to add patched versions to
`minimumReleaseAgeExclude`.
This is the same class of protection that would have blunted the recent
TanStack/`@uipath`/etc. compromise discussed in the [Aikido
write-up](https://www.aikido.dev/blog/mini-shai-hulud-is-back-tanstack-compromised).
## Changes
- Replace `nodejs/package-lock.json` and
`nodejs/examples/package-lock.json` with `pnpm-lock.yaml`.
- Pin pnpm via `packageManager: pnpm@11.1.1` in both `package.json`s.
- Add `pnpm-workspace.yaml` with the four build-script packages we
actually need: `@biomejs/biome`, `onnxruntime-node`, `protobufjs`,
`sharp`. Everything else is blocked from running install scripts.
- Update package.json scripts (`npm run X` → `pnpm X`).
- Update workflows: `.github/workflows/nodejs.yml`,
`.github/workflows/npm-publish.yml`, and
`.github/workflows/codex-fix-ci.yml` — install pnpm via
`pnpm/action-setup@v4` and switch `setup-node` caches to
`pnpm-lock.yaml`.
- Refresh `nodejs/AGENTS.md`, `nodejs/CLAUDE.md`, and
`nodejs/CONTRIBUTING.md`.
`docs/package-lock.json` is **not** touched — out of scope for this PR.
## Test plan
- [ ] `Lint` job (lint Rust/TS + examples lint) passes on CI.
- [ ] `Linux (NodeJS 18/20)` build+test passes, including the examples
test step.
- [ ] `macos` build+test passes.
- [ ] `NPM Publish` workflow's PR dry-run completes (build matrix + test
matrix + dry `npm publish`).
- [ ] No new install-script approvals are required at install time.
## Follow-ups
- `update_package_lock_run_nodejs.yml` references a composite action
path that doesn't exist
(`./.github/workflows/update_package_lock_nodejs`); it was already
broken pre-PR. We may want to either delete this workflow or rewrite it
for pnpm in a follow-up.
- Consider migrating `docs/` to pnpm in a separate PR.
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
### Summary
- Closes#3362
- Adds `prewarmData(columns?: string[])` to the Node bindings, mirroring
the Rust and Python implementations
### Testing
- [x] `npm run build` (regenerates the napi `.node` module + TS
declarations)
- [x] `npm run lint`
- [x] `npm test
- [ ] live test against remote table - just waiting for my dev stack to
get created
### Documentation
- updated docs
## Summary
Wire up `IVF_HNSW_FLAT` in the Rust core and Python SDK. The index was
documented at https://docs.lancedb.com/indexing/vector-index but
`lancedb.Table.create_index(index_type="IVF_HNSW_FLAT")` raised
`ValueError: Unknown index type IVF_HNSW_FLAT` — the underlying
`pylance` already accepted it, only the LanceDB wrapper was missing the
wiring.
**Rust core (`rust/lancedb`):**
- Add `Index::IvfHnswFlat` / `IndexType::IvfHnswFlat` variants and the
`IvfHnswFlatIndexBuilder` (modelled on `IvfHnswSqIndexBuilder`).
- Build Lance params via the existing `VectorIndexParams::ivf_hnsw(...)`
helper, keeping symmetry with the other `IVF_HNSW_*` variants.
- Forward the variant in `RemoteTable::create_index` and add two
parametrised tests (default + customised config) for the JSON
serialisation.
- New `NativeTable` integration test
(`test_create_index_ivf_hnsw_flat`).
**Python binding (`python/`):**
- New `HnswFlat` dataclass + backwards-compat `IvfHnswFlat` alias.
- PyO3 `extract_index_params` recognises the `HnswFlat` config.
- `LanceTable.create_index(index_type="IVF_HNSW_FLAT", …)` and the sync
`RemoteTable.create_index` both dispatch to the new config.
- `IndexStatistics.index_type` `Literal` and `_lancedb.pyi` stubs cover
the new type so `pyright`/`make check` stays clean.
- Async integration tests (`HnswFlat` + `IvfHnswFlat` alias) and a sync
dispatcher test, mirroring the existing `IVF_HNSW_SQ` coverage.
- Existing `test_index_statistics_index_type_lists_all_supported_values`
updated to include `IVF_HNSW_FLAT`.
A matching Node.js / TypeScript binding is in a follow-up PR.
Closes#3331
## Test plan
- [ ] \`cargo check --quiet --features remote --tests --examples\`
- [ ] \`cargo test --quiet --features remote -p lancedb\` (covers the
new \`test_create_index_ivf_hnsw_flat\` and the two new parametrised
\`RemoteTable::create_index\` cases)
- [ ] \`cargo fmt --all\` / \`cargo clippy --quiet --features remote
--tests --examples\`
- [ ] \`cd python && make develop && make check && make test\` (covers
the two new async tests, the alias test, the dispatcher test, and the
updated \`test_index_statistics_index_type_lists_all_supported_values\`
assertion)
This wires Lance's existing `jieba/*` and `lindera/*` native FTS
tokenizers through the Python SDK instead of leaving them behind
disabled features and narrow public typing. It also documents the
`LANCE_LANGUAGE_MODEL_HOME` model layout and adds Python coverage for
successful CJK indexing plus missing-model error guidance.
Closes#2168.
## Summary
`url::Url::query_pairs_mut()` leaves the URL with `query=Some("")` after
`.clear()` even when the input had no query string. The listing-database
connect path then captured that empty query into
`ListingDatabase::query_string`, and `table_uri()` blindly appended
`?<query>` to every per-table URI — producing URIs like
`s3://bucket/prefix/foo.lance?`.
The trailing `?` is benign for normal table operations, but it breaks
any caller that constructs a sub-path from the table URI. In particular,
MemWAL flushes write to `<table_uri>/_mem_wal/<shard>/<rand>_gen_<n>`,
which `url::Url::parse` then re-parses as `path=<base table>` +
`query=/_mem_wal/...`. `Dataset::write` resolves the base table dataset,
finds it already exists, and fails with `Dataset already exists:
…_gen_1` on the very first MemTable flush (observed deterministically
against S3 across all merge_insert LSM modes; tracked in
[lance-format/lance#6713](https://github.com/lance-format/lance/pull/6715)).
## Fix
Treat `Some("")` query the same as no query when capturing
`query_string`. A real `?foo=bar` query is still propagated unchanged.
Adds a regression test covering both the empty-query and non-empty-query
paths.
## Verification
- `url::Url::parse("s3://bucket/prefix/").query()` → `None`, but after
`query_pairs_mut().clear()` → `Some("")`. Confirmed in a standalone
repro.
- Without this fix, every `table_uri()` for an `s3://`-style connection
ends with `?`, breaking MemWAL and any future sub-path consumer in the
same way.
- New unit test `test_table_uri_url_path_has_no_trailing_question_mark`
exercises both code paths.
Fixes#3299
## Problem
Two security issues exist in `.github/workflows/java-publish.yml`:
1. **`gpg-passphrase` input is misused**: `actions/setup-java`'s
`gpg-passphrase` input expects the **name** of an environment variable
(default: `GPG_PASSPHRASE`), not the secret value itself. The previous
value `${{ secrets.GPG_PASSPHRASE }}` was setting the env var name to
the actual secret, which is incorrect.
2. **Passphrase visible on the command line**: `-Dgpg.passphrase=${{
secrets.GPG_PASSPHRASE }}` passes the GPG passphrase as a Maven system
property argument, making it visible in process listings and potentially
echoed in debug logs — a supply-chain security risk for release
workflows.
## Solution
- Fix `gpg-passphrase: MAVEN_GPG_PASSPHRASE` — use the correct env var
name so `actions/setup-java` generates a proper Maven `settings.xml`
entry that reads from `MAVEN_GPG_PASSPHRASE`.
- Remove `-Dgpg.passphrase=...` from the Maven CLI invocation.
- Add `MAVEN_GPG_PASSPHRASE: ${{ secrets.GPG_PASSPHRASE }}` to the
`env:` block of the Publish step, so the passphrase is available as an
environment variable rather than a CLI argument.
## Testing
The Java publish workflow only runs on tag pushes, so this cannot be
exercised in a PR build. The logic change is straightforward:
`actions/setup-java` is documented to write a `settings.xml` that reads
`<gpg.passphrase>` from the named env var, and `maven-gpg-plugin` picks
it up from there without any CLI argument.
Co-authored-by: octo-patch <octo-patch@github.com>
## Summary
PyTorch's `DataLoader` uses fork-based multiprocessing by default on
Linux, but threads do not survive `fork()`. LanceDB's Python bindings
drive async work through two threaded layers, both of which become inert
in a forked child:
- `BackgroundEventLoop` runs an asyncio loop on a Python
`threading.Thread`.
- `pyo3-async-runtimes::tokio` holds a global multi-threaded tokio
runtime whose worker threads also die on fork — and its runtime lives in
a `OnceLock` that cannot be replaced after first use.
As a result, any `Permutation` (or other async API) used inside a
fork-based `DataLoader` worker hangs indefinitely. This PR makes both
layers fork-safe so `Permutation` works as a `torch.utils.data.Dataset`
with `num_workers > 0`.
## Approach
### Rust — new `python/src/runtime.rs`
Mirrors the pattern used in [Lance's Python
bindings](https://github.com/lance-format/lance/blob/456198cd6f42be07f99617a6d7e39d6209cdf3cc/python/src/lib.rs#L139),
adapted for the async-bridge use case.
- `LanceRuntime` implements `pyo3_async_runtimes::generic::Runtime +
ContextExt`, backed by an `AtomicPtr<tokio::runtime::Runtime>` we own
(sidestepping `pyo3-async-runtimes`'s frozen `OnceLock` global).
- A `pthread_atfork(after_in_child)` handler nulls the pointer; the next
`spawn` rebuilds the runtime in the child. The previous runtime is
intentionally **leaked** — calling `Drop` would try to join now-dead
worker threads and hang.
- `runtime::future_into_py` is a drop-in for
`pyo3_async_runtimes::tokio::future_into_py`. All ~80 call sites in
`arrow.rs` / `connection.rs` / `permutation.rs` / `query.rs` /
`table.rs` are updated to route through it.
- `python/Cargo.toml` adds `libc = "0.2"` and the tokio
`rt-multi-thread` feature.
### Python — `lancedb/background_loop.py`
- Refactors `BackgroundEventLoop.__init__` to a reusable `_start()`
method.
- An `os.register_at_fork(after_in_child=…)` hook calls `LOOP._start()`
to give the singleton a fresh asyncio loop and thread **in place**. This
matters because the rest of the codebase imports `LOOP` via `from
.background_loop import LOOP` — rebinding the module attribute would
leave those references holding the dead loop.
### Python — `lancedb/__init__.py`
Removes the `__warn_on_fork` pre-fork warning (and the now-unused
`import warnings`). Fork is supported.
## Test plan
- [x] New `test_permutation_dataloader_fork_workers` in
`python/tests/test_torch.py`: runs a `Permutation` through
`torch.utils.data.DataLoader(num_workers=2,
multiprocessing_context="fork")` inside a spawn-isolated child with a
30s hang detector. **Pre-fix**: timed out at 36s. **Post-fix**: passes
in ~3.6s.
- [x] New `test_remote_connection_after_fork` in
`python/tests/test_remote_db.py`: forks a child that creates a fresh
`lancedb.connect(...)` against a mock HTTP server and calls
`table_names()`; passes in <1s, validates the runtime reset is
sufficient for fresh remote clients.
- [x] All 62 tests in `test_torch.py` + `test_permutation.py` pass.
- [x] All 35 tests in `test_remote_db.py` pass.
- [x] `test_table.py` (87) + `test_db.py` + `test_query.py` (157, minus
one unrelated `sentence_transformers` import skip) — 244 passing.
- [x] `cargo clippy -p lancedb-python --tests` clean.
- [x] `cargo fmt`, `ruff check`, `ruff format` all clean.
## Known limitation (follow-up)
This PR makes a **freshly-built** `lancedb.connect(...)` work in a
forked child. An **inherited** `Connection` from the parent still
carries an inherited `reqwest::Client` whose hyper connection pool
references socket FDs and TCP/TLS state shared with the parent — using
it from the child after fork is unsafe (especially with HTTP/1.1
keep-alive). The recommended pattern for fork-based `DataLoader` workers
that hit a remote DB is to construct a new connection inside the worker.
Auto-clearing inherited HTTP client pools on fork would require tracking
live `Connection` instances in `lancedb` core and is left for a
follow-up PR.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
## Summary
When pytorch is used with multiprocessing and the mp mode is spawn then
the Permutation needs to be pickled. It could not be pickled because
`Table` and `Connection` are not serializable. This PR adds pickle
support to Permutation without adding general pickle support to `Table`
or `Connection`. To add general support we probably need to start by
adding serialization in the namespace client.
In the meantime this PR enable pickling by adding special cases for:
* In-memory tables (just serialize as Arrow IPC)
* Native tables (serialize the URI)
If a user is not using one of the above cases (e.g. using a remote
connection) then they will need to provide a connection factory that can
be pickled.
## Breaking change
`PermutationBuilder.persist(...)` is removed from the Python bindings;
the permutation table is now always in-memory. The underlying Rust
`PermutationBuilder::persist` API is untouched and can be re-exposed
later if needed. It probably won't make sense to do that until we have a
way to serialize `Table` and `Connection`.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The `build:release` command already outputs the `*.node` files directly
to the `dist/` directory via the `--output-dir dist` flag.
Therefore, the `postbuild:release` script, which attempts to copy
`*.node` files from the `lancedb/` source directory, fails with a "no
such file or directory" error because the source files do not exist
there.
This commit removes the redundant `postbuild:release` script to resolve
the build failure.
fix#3284
Signed-off-by: qingfeng-occ <qing.feng@zte.com.cn>
Hi, the hybrid query error message looks like it can use a space, just
added it.
```python
def _validate_query(self, query, vector=None, text=None):
if query is not None and (vector is not None or text is not None):
raise ValueError(
"You can either provide a string query in search() method"
"or set `vector()` and `text()` explicitly for hybrid search."
"But not both."
)
```
Adds manifest_enabled for local/native connections so directory
namespace manifests can be the source of truth, including migration from
directory listing and Azure credential vending feature wiring. Also
exposes the option through Rust, Python, and Node bindings with focused
validation.
## Summary
- Update `rustls-webpki` 0.103.10 → 0.103.13 to fix RUSTSEC-2026-0104
(reachable panic in CRL parsing)
- Add advisory ignore for the legacy `rustls-webpki` 0.101.7 copy pinned
to the aws-smithy/rustls 0.21 chain (same chain already exempted for
RUSTSEC-2026-0098/0099)
Fixes the `deny` CI job failure seen in #3325.
## Test plan
- [x] `cargo deny check advisories` passes locally
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Adds a `deny.toml` at the workspace root and a `deny` CI job that runs
`cargo deny check` on every PR. Catches yanked crates, license drift,
banned or wildcard dependencies, unapproved sources, and new RUSTSEC
advisories.
As part of wiring this up:
- Updated `aws-lc-rs` 1.13.0 → 1.16.3 / `aws-lc-sys` 0.28.0 → 0.40.0 to
clear four 2026 AWS-LC advisories (timing side-channel, PKCS7 bypass,
CRL scope). Removed the `=0.28.0` workaround pin; the original build
failure no longer reproduces.
- Updated `bytes`, `zlib-rs`, `rand`, `rustls-webpki`, `lz4_flex` to
clear their current advisories.
- Marked `lancedb-nodejs` and `lancedb-python` as `publish = false` and
pinned `lzma-sys` from `*` to `0.1` so `bans.wildcards = "deny"` can
be enforced.
10 remaining advisories have no safe upgrade available (transitive via
opendal, lance, datafusion, async-openai, aws-sdk on the legacy rustls
0.21 chain). Each is ignored in `deny.toml` with a per-entry rationale
and a link to the RUSTSEC advisory. New advisories still fail CI.
Fixes#3297
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
## Summary
- Replaces `LANCEDB_PYPI_API_TOKEN` (long-lived token) with OIDC trusted
publishing via `pypa/gh-action-pypi-publish`
- Adds `id-token: write` permission to linux/mac/windows jobs
- Removes `twine`-based upload and the `pypi_token` input from
`upload_wheel` composite action
- Enables PEP 740 Sigstore attestations on published wheels as a bonus
After merging, rotate/revoke the `LANCEDB_PYPI_API_TOKEN` secret.
Closes#3294🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds `.github/dependabot.yml` enabling weekly cargo update PRs for the
root workspace, which produces the Rust binaries we ship: the Node.js
and Python native extensions. The `rust/lancedb` library crate shares
the same lockfile — its consumers pick versions themselves, but bumping
transitive deps here keeps the shipped binaries current.
Also removes the misleading `exclude = ["python"]` line from the root
`Cargo.toml`: `python` is listed in `members`, and `cargo metadata`
confirms it's a workspace member, so the exclude was dead code that
implied the opposite.
Minor/patch updates are grouped to reduce PR noise.
Part of #3292. Only covers the cargo ecosystem; pip, npm, and
github-actions can follow.
## Summary
- make `TlsConfig::default()` enable hostname verification by default
- align the Rust default with the documented Python and Node behavior
- update the Rust unit test to lock in the safe default
This follows the Rust-side Tantivy removal by deleting the remaining
Python Tantivy runtime, tests, and packaging references.
It also turns the legacy Python-only Tantivy parameters into explicit
errors and stops reading legacy `_indices/fts` directories so Python FTS
is fully native-only.
Adds `permissions: contents: read` to the 10 workflows that had no
top-level permissions block. Workflows that already declared
permissions, or individual jobs that need elevated permissions (`issues:
write`, `pull-requests: write`, `contents: write`), are left unchanged.
Affected workflows: `dev.yml`, `java-publish.yml`, `java.yml`,
`license-header-check.yml`, `nodejs.yml`, `pypi-publish.yml`,
`python.yml`, `rust.yml`, `update_package_lock_run.yml`,
`update_package_lock_run_nodejs.yml`
Fixes#3269.
## What I observed
Using a reranker in a hybrid query could keep the Node.js process alive
even after `table.close()` and `db.close()`.
## Root cause
The reranker callback bridge used a `ThreadsafeFunction` in referenced
mode, which can keep the event loop alive longer than intended.
## Minimal fix
- In `nodejs/src/rerankers.rs`, create the reranker callback TSFN in
weak mode (`.weak::<true>()`).
- Add a regression test in `nodejs/__test__/rerankers.test.ts` that
spawns a child process, runs a rerank query, and asserts the process
exits naturally.
## Validation
- Built Node bindings successfully.
- Ran targeted tests: `rerankers.test.ts` passes (including new
regression test).
- Pre-commit checks for changed files were run and clean.
So far, I have been using a hacky approach that creates and opens
namespace-backed table, by getting its location and use a temporary
lancedb connection to create or open it. This was working for features
like credentials vending but is no longer fully working for the managed
versioning feature, recently geneva tests have been failing here and
there and various patches are not addressing the root cause. This PR
fully fixes this and implements proper rust binding for it.
Specifically:
- build a real Rust namespace-backed connection from the Python
namespace client
- route namespace table create/open through that connection instead of
resolved-location temp connections
- keep namespace client naming consistent in the Rust bridge and
preserve federated namespace + DuckDB behavior
## Summary
- pass `namespace_client` through the Python create-table path
- ensure schema-only namespace table creation uses the namespace-aware
empty-table flow
- fix reopening namespace tables created without initial data
## Summary
- delegate child-namespace `ListingDatabase` operations through an
eagerly initialized `LanceNamespaceDatabase`
- support nested namespace create/open/list/drop flows without requiring
callers to inject explicit locations
- add `namespace_client_properties` plumbing for local and namespace
connections so directory namespace settings like
`table_version_tracking_enabled` can be configured
- add regression tests for nested namespace ops and namespace client
property propagation
## Summary
Add connection serialization and child namespace support to
`LanceDBConnection`.
- `DBConnection.serialize()` / `lancedb.deserialize()` for connection
reconstruction in remote workers
- Cache `namespace_client()` in `LanceDBConnection` to avoid repeated
DirectoryNamespace builds
- `LanceDBConnection` transparently delegates child namespace operations
(open_table, create_table, list_tables, drop_table, create_namespace,
etc.) to `LanceNamespaceDBConnection` via `_namespace_conn()`
- Root namespace operations still go through the original Rust path
- Generic worker property override mechanism: any
`namespace_client_properties` key prefixed with `_lancedb_worker_` has
the prefix stripped and overrides the corresponding property when
`deserialize(data, for_worker=True)`
- `LanceNamespaceDBConnection` stores
`namespace_client_impl`/`namespace_client_properties` for serialization
roundtrip
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Bumps the Rust toolchain to 1.94.0 (latest installed) to unblock CI
failures caused by the AWS SDK's MSRV requirement. No lint fixes were
needed.
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
## Summary
- migrate gemini-text embedding provider from deprecated
google.generativeai to google.genai
- update Python embedding extra dependency to google-genai
- update default model name to gemini-embedding-001
- adapt embed calls to Client().models.embed_content(...)
- apply lint fixes from CI
## Related
- Closes#3191
`.get(b"split_names", None).decode()` was called unconditionally in both
Permutations.__init__ and Permutation.from_tables(), crashing with
AttributeError when schema metadata existed but lacked the split_names
key. Guard the decode behind a None check and add regression tests.
## Problem
`on_bad_vectors="drop"` is supposed to remove invalid vector rows before
write, but for some schema-defined vector columns it can still fail
later during Arrow cast instead of dropping the bad row.
Repro:
```python
class MySchema(LanceModel):
text: str
embedding: Vector(16)
table = db.create_table("test", schema=MySchema)
table.add(
[
{"text": "hello", "embedding": []},
{"text": "bar", "embedding": [0.1] * 16},
],
on_bad_vectors="drop",
)
```
Before:
```
RuntimeError
Arrow error: C Data interface error: Invalid: ListType can only be casted to FixedSizeListType if the lists are all the expected size.
```
After:
```
rows 1
texts ['bar']
```
## Solution
Make bad-vector sanitization use schema dimensions before cast, while
keeping the handling scoped to vector columns identified by schema
metadata or existing vector-name heuristics.
This also preserves existing integer vector inputs and avoids applying
on_bad_vectors to unrelated fixed-size float columns.
Fixes#1670
Signed-off-by: yaommen <myanstu@163.com>
## Summary
- Add a `user_id` field to `ClientConfig` that allows users to identify
themselves to LanceDB Cloud/Enterprise
- The user_id is sent as the `x-lancedb-user-id` HTTP header in all
requests
- Supports three configuration methods:
- Direct assignment via `ClientConfig.user_id`
- Environment variable `LANCEDB_USER_ID`
- Indirect env var lookup via `LANCEDB_USER_ID_ENV_KEY`
Closes#3230🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
## Summary
Fixes#1846.
Python `Enum` fields raised `TypeError: Converting Pydantic type to
Arrow Type: unsupported type <enum 'SomethingTypes'>` when converting a
Pydantic model to an Arrow schema.
The fix adds Enum detection in `_pydantic_type_to_arrow_type`. When an
Enum subclass is encountered, the value type of its members is inspected
and mapped to the appropriate Arrow type:
- `str`-valued enums (e.g. `class Status(str, Enum)`) → `pa.utf8()`
- `int`-valued enums (e.g. `class Priority(int, Enum)`) → `pa.int64()`
- Other homogeneous value types → the Arrow type for that Python type
- Mixed-value or empty enums → `pa.utf8()` (safe fallback)
This covers the common `(str, Enum)` and `(int, Enum)` mixin patterns
used in practice.
## Changes
- `python/python/lancedb/pydantic.py`: add Enum branch in
`_pydantic_type_to_arrow_type`
- `python/python/tests/test_pydantic.py`: add `test_enum_types` covering
`str`, `int`, and `Optional` Enum fields
## Note on #2395
PR #2395 handles `StrEnum` (Python 3.11+) specifically, using a
dictionary-encoded type. This PR handles the broader `(str, Enum)` /
`(int, Enum)` mixin pattern that works across all Python versions and
stores values as their natural Arrow type.
AI assistance was used in developing this fix.
1. Refactored every client (Rust core, Python, Node/TypeScript) so
“namespace” usage is explicit: code now keeps namespace paths
(namespace_path) separate from namespace clients (namespace_client).
Connections propagate the client, table creation routes through it, and
managed versioning defaults are resolved from namespace metadata. Python
gained LanceNamespaceDBConnection/async counterparts, and the
namespace-focused tests were rewritten to match the clarified API
surface.
2. Synchronized the workspace with Lance 5.0.0-beta.3 (see
https://github.com/lance-format/lance/pull/6186 for the upstream
namespace refactor), updating Cargo/uv lockfiles and ensuring all
bindings align with the new namespace semantics.
3. Added a namespace-backed code path to lancedb.connect() via new
keyword arguments (namespace_client_impl, namespace_client_properties,
plus the existing pushdown-ops flag). When those kwargs are supplied,
connect() delegates to connect_namespace, so users can opt into
namespace clients without changing APIs. (The async helper will gain
parity in a later change)
Bumps all lance-* workspace dependencies from `4.0.0-rc.3` (git source)
to the stable `4.0.0` release on crates.io, removing the `git`/`tag`
overrides.
No code changes were required — compiles and passes clippy cleanly.
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Fixes#1540
I could not reproduce this on current `main` from Python, but I could
still reproduce it from the Rust SDK.
Python no longer reproduces because the current Python vector/hybrid
query paths re-chunk results into a `pyarrow.Table` before returning
batches. Rust still reproduced because `max_batch_length` was passed
into planning/scanning, but vector search could still emit larger
`RecordBatch`es later in execution (for example after KNN / TopK), so it
was not enforced on the final Rust output stream.
This PR enforces `max_batch_length` on the final Rust query output
stream and adds Rust regression coverage.
Before the fix, the Rust repro produced:
`num_batches=2, max_batch=8192, min_batch=1808, all_le_100=false`
After the fix, the same repro produces batches `<= 100`.
## Runnable Rust repro
Before this fix, current `main` could still return batches like `[8192,
1808]` here even with `max_batch_length = 100`:
```rust
use std::sync::Arc;
use arrow_array::{
types::Float32Type, FixedSizeListArray, RecordBatch, RecordBatchReader, StringArray,
};
use arrow_schema::{DataType, Field, Schema};
use futures::TryStreamExt;
use lancedb::query::{ExecutableQuery, QueryBase, QueryExecutionOptions};
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let tmp = tempfile::tempdir()?;
let uri = tmp.path().to_str().unwrap();
let rows = 10_000;
let schema = Arc::new(Schema::new(vec![
Field::new("id", DataType::Utf8, false),
Field::new(
"vector",
DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4),
false,
),
]));
let ids = StringArray::from_iter_values((0..rows).map(|i| format!("row-{i}")));
let vectors = FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
(0..rows).map(|i| Some(vec![Some(i as f32), Some(1.0), Some(2.0), Some(3.0)])),
4,
);
let batch = RecordBatch::try_new(schema.clone(), vec![Arc::new(ids), Arc::new(vectors)])?;
let reader: Box<dyn RecordBatchReader + Send> = Box::new(
arrow_array::RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema),
);
let db = lancedb::connect(uri).execute().await?;
let table = db.create_table("test", reader).execute().await?;
let mut opts = QueryExecutionOptions::default();
opts.max_batch_length = 100;
let mut stream = table
.query()
.nearest_to(vec![0.0, 1.0, 2.0, 3.0])?
.limit(rows)
.execute_with_options(opts)
.await?;
let mut sizes = Vec::new();
while let Some(batch) = stream.try_next().await? {
sizes.push(batch.num_rows());
}
println!("{sizes:?}");
Ok(())
}
```
Signed-off-by: yaommen <myanstu@163.com>
The test added in #3190 unconditionally imports `PIL`, which is an
optional dependency. This causes CI failures in environments where
Pillow isn't installed (`ModuleNotFoundError: No module named 'PIL'`).
Use `pytest.importorskip` to skip gracefully when Pillow is unavailable.
Fixes CI failure on main.
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
## Summary
- Namespace tests expected `RuntimeError` for table-not-found and
namespace-not-empty cases, but `lance_namespace` raises
`TableNotFoundError` and `NamespaceNotEmptyError` which inherit from
`Exception`, not `RuntimeError`.
- Updated `pytest.raises` to use the correct exception types.
## Test plan
- [x] CI passes on `test_namespace.py`
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
url_retrieve() calls urllib.request.urlopen() but only urllib.error was
imported, causing AttributeError for any HTTP URL input. This affects
open-clip, siglip, and jinaai embedding functions when processing image
URLs.
The bug has existed since the embeddings API refactor (#580) but was
masked because most users pass local file paths or bytes rather than
HTTP URLs.
Fixes#2716
## Summary
Add support for querying with Float16Array, Float64Array, and Uint8Array
vectors in the Node.js SDK, eliminating precision loss from the previous
\Float32Array.from()\ conversion.
## Implementation
Follows @wjones127's [5-step
plan](https://github.com/lancedb/lancedb/issues/2716#issuecomment-3447750543):
### Rust (\
odejs/src/query.rs\)
1. \ytes_to_arrow_array(data: Uint8Array, dtype: String)\ helper that:
- Creates an Arrow \Buffer\ from the raw bytes
- Wraps it in a typed \ScalarBuffer<T>\ based on the dtype enum
- Constructs a \PrimitiveArray\ and returns \Arc<dyn Array>\
2. \
earest_to_raw(data, dtype)\ and \dd_query_vector_raw(data, dtype)\ NAPI
methods that pass the type-erased array to the core \
earest_to\/\dd_query_vector\ which already accept \impl
IntoQueryVector\ for \Arc<dyn Array>\
### TypeScript (\
odejs/lancedb/query.ts\, \rrow.ts\)
3. Extended \IntoVector\ type to include \Uint8Array\ (and
\Float16Array\ via runtime check for Node 22+)
4. \xtractVectorBuffer()\ helper detects non-Float32 typed arrays and
extracts their underlying byte buffer + dtype string
5. \
earestTo()\ and \ddQueryVector()\ route through the raw NAPI path when
the input is Float16/Float64/Uint8
### Backward compatibility
Existing \Float32Array\ and \
umber[]\ inputs are unchanged -- they still use the original \
earest_to(Float32Array)\ NAPI method. The new raw path is only used when
a non-Float32 typed array is detected.
## Usage
\\\ ypescript
// Float16Array (Node 22+) -- no precision loss
const f16vec = new Float16Array([0.1, 0.2, 0.3]);
const results = await
table.query().nearestTo(f16vec).limit(10).toArray();
// Float64Array -- no precision loss
const f64vec = new Float64Array([0.1, 0.2, 0.3]);
const results = await
table.query().nearestTo(f64vec).limit(10).toArray();
// Uint8Array (binary embeddings)
const u8vec = new Uint8Array([1, 0, 1, 1, 0]);
const results = await
table.query().nearestTo(u8vec).limit(10).toArray();
// Existing usage unchanged
const results = await table.query().nearestTo([0.1, 0.2,
0.3]).limit(10).toArray();
\\\
## Note on dependencies
The Rust side uses \rrow_array\, \rrow_buffer\, and \half\ crates.
These should already be in the dependency tree via \lancedb\ core, but
\Cargo.toml\ may need explicit entries for \half\ and the arrow
sub-crates in the nodejs workspace.
---------
Signed-off-by: Vedant Madane <6527493+VedantMadane@users.noreply.github.com>
Co-authored-by: Will Jones <willjones127@gmail.com>
Fixes#3183
## Summary
When `table.add(mode='overwrite')` is called, PyArrow infers input data
types (e.g. `list<double>`) which differ from the original table schema
(e.g. `fixed_size_list<float32>`). Previously, overwrite mode bypassed
`cast_to_table_schema()` entirely, so the inferred types replaced the
original schema, breaking vector search.
This fix builds a merged target schema for overwrite: columns present in
the existing table schema keep their original types, while columns
unique to the input pass through as-is. This way
`cast_to_table_schema()` is applied unconditionally, preserving vector
column types without blocking schema evolution.
## Changes
- `rust/lancedb/src/table/add_data.rs`: For overwrite mode, construct a
target schema by matching input columns against the existing table
schema, then cast. Non-overwrite (append) path is unchanged.
- Added `test_add_overwrite_preserves_vector_type` test that creates a
table with `fixed_size_list<float32>`, overwrites with `list<double>`
input, and asserts the original type is preserved.
## Test Plan
- `cargo test --features remote -p lancedb -- test_add_overwrite` — all
4 overwrite tests pass
- Full suite: 454 passed, 2 failed (pre-existing `remote::retry` flakes
unrelated to this change)
---------
Signed-off-by: majiayu000 <1835304752@qq.com>
dict.update() mutates in place and returns None. Assigning its result
caused with_metadata(None) to strip all schema metadata when embedding
metadata was merged during create_table with embedding_functions.
This patch mitigates template injection vulnerabilities in GitHub
Workflows by replacing direct references with an environment variable.
Aikido used AI to generate this PR.
High confidence: Aikido has a robust set of benchmarks for similar
fixes, and they are proven to be effective.
Co-authored-by: aikido-autofix[bot] <119856028+aikido-autofix[bot]@users.noreply.github.com>
Replace ~30 production `lock().unwrap()` calls that would cascade-panic
on a poisoned Mutex. Functions returning `Result` now propagate the
poison as an error via `?` (leveraging the existing `From<PoisonError>`
impl). Functions without a `Result` return recover via
`unwrap_or_else(|e| e.into_inner())`, which is safe because the guarded
data (counters, caches, RNG state) remains logically valid after a
panic.
## Summary
Adds progress reporting for `table.add()` so users can track large write
operations. The progress callback is available in Rust, Python (sync and
async), and through the PyO3 bindings.
### Usage
Pass `progress=True` to get an automatic tqdm bar:
```python
table.add(data, progress=True)
# 100%|██████████| 1000000/1000000 [00:12<00:00, 82345 rows/s, 45.2 MB/s | 4/4 workers]
```
Or pass a tqdm bar for more control:
```python
from tqdm import tqdm
with tqdm(unit=" rows") as pbar:
table.add(data, progress=pbar)
```
Or use a callback for custom progress handling:
```python
def on_progress(p):
print(f"{p['output_rows']}/{p['total_rows']} rows, "
f"{p['active_tasks']}/{p['total_tasks']} workers, "
f"done={p['done']}")
table.add(data, progress=on_progress)
```
In Rust:
```rust
table.add(data)
.progress(|p| println!("{}/{:?} rows", p.output_rows(), p.total_rows()))
.execute()
.await?;
```
### Details
- `WriteProgress` struct in Rust with getters for `elapsed`,
`output_rows`, `output_bytes`, `total_rows`, `active_tasks`,
`total_tasks`, and `done`. Fields are private behind getters so new
fields can be added without breaking changes.
- `WriteProgressTracker` tracks progress across parallel write tasks
using a mutex for row/byte counts and atomics for active task counts.
- Active task tracking uses an RAII guard pattern (`ActiveTaskGuard`)
that increments on creation and decrements on drop.
- For remote writes, `output_bytes` reflects IPC wire bytes rather than
in-memory Arrow size. For local writes it uses in-memory Arrow size as a
proxy (see TODO below).
- tqdm postfix displays throughput (MB/s) and worker utilization
(active/total).
- The `done` callback always fires, even on error (via `FinishOnDrop`),
so progress bars are always finalized.
### TODO
- Track actual bytes written to disk for local tables. This requires
Lance to expose a progress callback from its write path. See
lance-format/lance#6247.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Lance v4.1.0-beta requires the default-https-client feature on
aws-sdk-dynamodb and aws-sdk-s3, which was introduced in the March
2025 AWS SDK release. Update all AWS SDK pins to versions from the
same AWS SDK release to maintain internal dependency compatibility.
Co-authored-by: Esteban Gutierrez <esteban@lancedb.com>
Similar to https://github.com/lancedb/lancedb/pull/3062, we can write in
parallel to remote tables if the input data source is large enough.
We take advantage of new endpoints coming in server version 0.4.0, which
allow writing data in multiple requests, and the committing at the end
in a single request.
To make testing easier, I also introduce a `write_parallelism`
parameter. In the future, we can expose that in Python and NodeJS so
users can manually specify the parallelism they get.
Closes#2861
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
## Problem
The generated Python API docs for
`lancedb.table.IndexStatistics.index_type` were misleading because
mkdocstrings renders that field’s type annotation directly, and the
existing `Literal[...]` listed only a subset of the actual canonical SDK
index type strings.
Current (missing index types):
<img width="823" height="83" alt="image"
src="https://github.com/user-attachments/assets/f6f29fe3-4c16-4d00-a4e9-28a7cd6e19ec"
/>
## Fix
- Update the `IndexStatistics.index_type` annotation in
`python/python/lancedb/table.py` to include the full supported set of
canonical values, so the generated docs show all valid index_type
strings inline.
- Add a small regression test in `python/python/tests/test_index.py` to
ensure the docs-facing annotation does not drift silently again in case
we add a new index/quantization type in the future.
- Bumps mkdocs and material theme versions to mkdocs 1.6 to allow access
to more features like hooks
After fix (all index types are included and tested for in the
annotations):
<img width="1017" height="93" alt="image"
src="https://github.com/user-attachments/assets/66c74d5c-34b3-4b44-8173-3ee23e3648ac"
/>
When Lance 3.0.0 released the check_lance_release.py script did not make
a PR for it because it was a pre-release. This change may not be perfect
but it always ranks stable releases above non-stable releases.
When using hybrid search with a where filter, the prefilter argument is
silently inverted. Passing prefilter=True actually performs
post-filtering, and prefilter=False actually performs pre-filtering.
## Summary
- Update all 14 lance crates from `3.0.0-rc.3` (git source) to `3.0.0`
(crates.io release)
- Remove git/tag source references since 3.0.0 is published on crates.io
## Test plan
- [x] `cargo check --features remote --tests --examples` passes
- [x] `cargo clippy --features remote --tests --examples` passes
- [ ] CI passes
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
## Summary
- Upgrade LocalStack from 3.3 to 4.0 in `docker-compose.yml` to fix S3
integration test failures in CI
- Version 3.3 has compatibility issues with newer Python 3.13 and
updated boto3 dependencies
- Matches the LocalStack version used successfully in the lance
repository
## Test plan
- [ ] Verify `docker compose up --detach --wait` completes successfully
in CI
- [ ] All tests in `test_s3.py` pass (5 tests)
- [ ] All `@pytest.mark.s3_test` tests in
`test_namespace_integration.py` pass (7 tests)
- [ ] No regressions in non-integration test jobs (Mac, Windows)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* Move away from buildjet, which is shutting down runners for GHA [^1]
* Add `Cargo.lock` to build jobs, so when we upgrade locked dependencies
we check the builds actually pass. CI started failing because
dependencies were changed in #3116 without running all build jobs.
* Add fixes for aws-lc-rs build in NodeJS.
[^1]: https://buildjet.com/for-github-actions/blog/we-are-shutting-down
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Add support for passing field/data type information into add_columns()
method, bringing parity with Python bindings. The method now accepts:
- AddColumnsSql[] - SQL expressions (existing functionality)
- Field - single Arrow field with explicit data type
- Field[] - array of Arrow fields with explicit data types
- Schema - Arrow schema with explicit data types
New columns added via Field/Schema are initialized with null values. All
field-based columns must be nullable due to null initialization.
Resolves#3107
---------
Signed-off-by: Pratik <pratikrocks.dey11@gmail.com>
Co-authored-by: Claude <noreply@anthropic.com>
## Summary
- Removes the "Experimental API" section from `optimize` method
documentation across Rust, Python, and TypeScript
- Adds a warning to `delete_unverified` documentation in all bindings:
this should only be set to true if you can guarantee no other process is
working on the dataset, otherwise it could be corrupted
- Fixes a typo ("shoudl" → "should")
Closes#3125🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
## Summary
- Implement `RemoteTable.prewarm_data(columns)` calling `POST
/v1/table/{id}/page_cache/prewarm/`
- Implement `RemoteTable.prewarm_index(name)` calling `POST
/v1/table/{id}/index/{name}/prewarm/` (previously returned
`NotSupported`)
- Add `BaseTable::prewarm_data(columns)` trait method and `Table` public
API in Rust core
- Add PyO3 bindings and Python API (`AsyncTable`, `LanceTable`,
`RemoteTable`) for `prewarm_data`
- Add type stubs for `prewarm_index` and `prewarm_data` in
`_lancedb.pyi`
- Upgrade Lance to 3.0.0-rc.3 with breaking change fixes
Co-authored-by: Will Jones <willjones127@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
## Summary
- Update dependencies across Rust, Python, Node.js, Java, Docker, and
docs
- Pin unpinned dependency lower bounds to prevent silent downgrades
- Bump CI actions to current major versions
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
When we create tables without using Arrow by parsing JS records we
always infer to float64. Many times embeddings are not float64 and it
would be nice to be able to use the native type without requiring users
to pull in Arrow. We can utilize JS's builtin Float32Array to do this.
This PR also adds support for UInt8/16/32 and Int8/16/32 arrays as well.
Closes#3115
Without this fix, if user directly use the native table to do operations
like `add_columns`, even if it is configured to use namespace db
connection, it is not really propagated through.
The fix is to bring lancedb's python binding up to date and do a similar
implementation as https://github.com/lance-format/lance/pull/5968, and
make sure the namespace is fully propagated through all the related
calls.
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
When we write data with `add()`, we can input data to the table's
schema. However, we were using "safe" mode, which propagates errors as
nulls. For example, if you pass `u64::max` into a field that is a `u32`,
it will just write null instead of giving overflow error. Now it
propagates the overflow. This is the same behavior as other systems like
DuckDB.
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Prior to this commit we supported passing the azure storage account name
to the lancedb remote SDK through headers. This adds support for client
ID and tenant ID as well.
This PR migrates all Rust crates in the workspace to Rust 2024 edition
and addresses the resulting compatibility updates. It also fixes all
clippy warnings surfaced by the workspace checks so the codebase remains
warning-free under the current lint configuration.
Context:
- Scope: workspace edition bump (`2021` -> `2024`) plus follow-up
refactors required by new edition and clippy rules.
- Validation: `cargo fmt --all` and `cargo clippy --quiet --features
remote --tests --examples -- -D warnings` both pass.
## Summary
- Add `@value_to_sql.register(dict)` handler that converts Python dicts
to DataFusion's `named_struct()` SQL syntax
- Enables updating struct-typed columns via `table.update(values={"col":
{"field_a": 1, "field_b": "hello"}})`
- Recursively handles nested structs, lists, nulls, and all existing
scalar types
Closes#1363
## Details
The `named_struct` function was introduced in DataFusion 38 and is now
available (LanceDB uses DataFusion 52.1). The implementation follows the
existing `singledispatch` pattern in `util.py`.
**Example conversion:**
```python
value_to_sql({"field_a": 1, "field_b": "hello"})
# => "named_struct('field_a', 1, 'field_b', 'hello')"
```
## Test plan
- [x] Unit tests for flat struct, nested struct, list inside struct,
mixed types, null values, and empty dict
- [ ] CI integration tests with actual table.update() on struct columns
🔗 [DataFusion named_struct
docs](https://datafusion.apache.org/user-guide/sql/scalar_functions.html#named-struct)
This PR fixes the npm publish dry-run failure for prerelease versions
without changing the existing workflow trigger behavior. The publish
step now detects prerelease versions from `nodejs/package.json` and
always appends `--tag preview` when needed.
Context:
- On `main` pushes, the workflow still runs `npm publish --dry-run` by
design.
- Recent failures were caused by prerelease versions (for example
`0.27.0-beta.3`) running without `--tag`, which npm rejects.
- The previous `refs/tags/v...-beta...` check did not apply on branch
pushes, so dry-run could fail even though release tags worked.
We don't necessarily need to do this, but one user was confused having
used `fast_search=True` as a keyword argument for vector searches, but
being unable to do so for FTS, even after the most recent changes. I
think this is the only discrepancy in where that is possible.
## Summary
Adds a Rust expression builder API as a type-safe alternative to SQL
strings for query filters.
## Motivation
Filtering with raw SQL strings can be awkward when using variables and
special types:
Closes #3038
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
When input data is sufficiently large, we automatically split up into
parallel writes using a round-robin exchange operator. We sample the
first batch to determine data width, and target size of 1 million rows
or 2GB, whichever is smaller.
This hooks up a new writer implementation for the `add()` method. The
main immediate benefit is it allows streaming requests to remote tables,
and at the same time allowing retries for most inputs.
In NodeJS, we always convert the data to `Vec<RecordBatch>`, so it's
always retry-able.
For Python, all are retry-able, except `Iterator` and
`pa.RecordBatchReader`, which can only be consumed once. Some, like
`pa.datasets.Dataset` are retry-able *and* streaming.
A lot of the changes here are to make the new DataFusion write pipeline
maintain the same behavior as the existing Python-based preprocessing,
such as:
* casting input data to target schema
* rejecting NaN values if `on_bad_vectors="error"`
* applying embedding functions.
In future PRs, we'll enhance these by moving the embedding calls into
DataFusion and making sure we parallelize them. See:
https://github.com/lancedb/lancedb/issues/3048
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
## Summary
- Upgrades `@napi-rs/cli` from v2 to v3, `napi`/`napi-derive` Rust
crates to 3.x
- Fixes a bug
([napi-rs#1170](https://github.com/napi-rs/napi-rs/issues/1170)) where
the CLI failed to locate the built `.node` binary when a custom Cargo
target directory is set (via `config.toml`)
## Changes
**package.json / CLI**:
- `napi.name` → `napi.binaryName`, `napi.triples` → `napi.targets`
- Removed `--no-const-enum` flag and fixed output dir arg
- `napi universal` → `napi universalize`
**Rust API migration**:
- `#[napi::module_init]` → `#[napi_derive::module_init]`
- `napi::JsObject` → `Object`, `.get::<_, T>()` → `.get::<T>()`
- `ErrorStrategy` removed; `ThreadsafeFunction` now takes an explicit
`Return` type with `CalleeHandled = false` const generic
- `JsFunction` + `create_threadsafe_function` replaced by typed
`Function<Args, Return>` + `build_threadsafe_function().build()`
- `RerankerCallbacks` struct removed (`Function<'env,...>` can't be
stored in structs); `VectorQuery::rerank` now accepts the function
directly
- `ClassInstance::clone()` now returns `ClassInstance`, fixed with
explicit deref
- `Vec<u8>` in `#[napi(object)]` now maps to `Array<number>` in v3;
changed to `Buffer` to preserve the TypeScript `Buffer` type
**TypeScript**:
- `inner.rerank({ rerankHybrid: async (_, args) => ... })` →
`inner.rerank(async (args) => ...)`
- Header provider callback wrapped in `async` to match stricter typed
constructor signature
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
## Summary
`DatasetConsistencyWrapper::update()` only stored datasets with a
strictly newer
version. This caused `migrate_manifest_paths_v2` to silently drop its
update since
the migration renames files without bumping the dataset version. The
subsequent
`uses_v2_manifest_paths()` call would then return the stale cached
dataset.
Changed the version check from `>` to `>=` so same-version updates are
accepted.
## Test plan
- [x] Existing `test_create_table_v2_manifest_paths_async` Python test
should pass
- [x] Existing `should be able to migrate tables to the V2 manifest
paths` NodeJS test should pass
- [x] All dataset wrapper unit tests pass locally
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
This updates `DatasetConsistencyWrapper` to block less:
1. `DatasetConsistencyWrapper::get()` just returns `Arc<Dataset>` now,
instead of a guard that blocks writes.
`DatasetConsistencyWrapper::get_mut()` is gone; now write methods just
use `get()` and then later call `update()` with the new version. This
means a given table handle can do concurrent reads **and** writes.
2. In weak consistency mode, will check for dataset updates in the
background, instead of blocking calls to `get()`.
---------
Co-authored-by: Claude Sonnet 4.5 <noreply@anthropic.com>
## Summary
Fixes#1679
This PR prevents the OpenAI embedding function from retrying when
receiving a 401 Unauthorized error. Authentication errors are permanent
failures that won't be fixed by retrying, yet the current implementation
retries all exceptions up to 7 times by default.
## Changes
- Modified `retry_with_exponential_backoff` in `utils.py` to check for
non-retryable errors before retrying
- Added `_is_non_retryable_error` helper function that detects:
- Exceptions with name `AuthenticationError` (OpenAI's 401 error)
- Exceptions with `status_code` attribute of 401 or 403
- Enhanced OpenAI embeddings to explicitly catch and re-raise
`AuthenticationError` with better logging
- Added unit test `test_openai_no_retry_on_401` to verify authentication
errors don't trigger retries
## Test Plan
- Added test that verifies:
1. A function raising `AuthenticationError` is only called once
2. No retry delays occur (sleep is never called)
- Existing tests continue to pass
- Formatting applied via `make format`
## Example Behavior
**Before**: With an invalid API key, users would see 7 retry attempts
over ~2 minutes:
```
WARNING:root:Error occurred: Error code: 401 - {'error': {'message': 'Incorrect API key provided...'}}
Retrying in 3.97 seconds (retry 1 of 7)
WARNING:root:Error occurred: Error code: 401...
Retrying in 7.94 seconds (retry 2 of 7)
...
```
**After**: With an invalid API key, the error is raised immediately:
```
ERROR:root:Authentication failed: Invalid API key provided
AuthenticationError: Error code: 401 - {'error': {'message': 'Incorrect API key provided...'}}
```
This provides better UX and prevents unnecessary API calls that would
fail anyway.
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
There are old and outdated files in our embedding registry that can
confuse coding agents. This PR deprecates the following files that have
newer, more modern methods to generate such embeddings.
- Deprecate `embeddings/siglip.py`
- Deprecate `embeddings/gte.py`
## Why this change?
Per a discussion with @AyushExel, the [embedding registry directory
](https://github.com/lancedb/lancedb/tree/1840aa7edcdbf0c3f7d3a303000c8f75842a2f55/python/python/lancedb/embeddings)
in the LanceDB repo has a number of outdated files that need to be
deprecated.
See https://github.com/lancedb/docs/issues/85 for the docs gaps that
identified this.
- Add note in `openclip` docs that it can be used for SigLip embeddings,
which it now supports
- Add note in the `sentence-transformers` page that ALL text embedding
models on Hugging Face can be used
There were two issues:
1. The python code needs to get access to the underlying rust table to
setup the permutation reader and the attributes involved in this differ
between the python local table and remote table objects.
~~2. The remote table was sending projection dictionaries as arrays of
tuples and (on LanceDB cloud at least) it does not appear this is how
rest servers are setup to receive them.~~ (this is now fixed as #3023)
~~Leaving as draft as this is built on
https://github.com/lancedb/lancedb/pull/3016~~
## Problem
When applying hard filters that result in zero matches, hybrid search
crashes with `IndexError: list index out of range` during reranking.
This happens because empty result tables are passed through the full
reranker pipeline, which expects at least one result.
Traceback from the issue:
```
lancedb/query.py: in _combine_hybrid_results
results = reranker.rerank_hybrid(fts_query, vector_results, fts_results)
lancedb/rerankers/answerdotai.py: in rerank_hybrid
combined_results = self._rerank(combined_results, query)
...
IndexError: list index out of range
```
## Fix
Added an early return in `_combine_hybrid_results` when both vector and
FTS results are empty. Instead of passing empty tables through
normalization, reranking, and score restoration (which can fail in
various ways), we now build a properly-typed empty result table with the
`_relevance_score` column and return it directly.
## Test
Added `test_empty_hybrid_result_reranker` that exercises
`_combine_hybrid_results` directly with empty vector and FTS tables,
verifying:
- Returns empty table with correct schema
- Includes `_relevance_score` column
- Respects `with_row_ids` flag
Closes#2425
Completes the **merge_insert.rs** checklist item from #2949.
## Changes
- Moved `MergeResult` struct from `table.rs` to `table/merge.rs`
- Moved the `NativeTable::merge_insert` implementation into
`merge::execute_merge_insert()`, with the trait impl now delegating to
it (same pattern as `delete.rs`)
- Moved `test_merge_insert` and `test_merge_insert_use_index` tests into
`table/merge.rs`
- Improved moved tests to use `memory://` URIs instead of temporary
directories
- Cleaned up unused imports from `table.rs` (`FutureExt`,
`TryFutureExt`, `Either`, `WhenMatched`, `WhenNotMatchedBySource`,
`LanceMergeInsertBuilder`)
- `MergeResult` is re-exported from `table.rs` so the public API is
unchanged
## Testing
`cargo build -p lancedb` compiles cleanly with no warnings.
References #2949 Moved query logic and helpers from table.rs to
query.rs. Refactored tests using guidelines and added coverage for multi
vector plan structure.
When a table has a read consistency interval, queries within the
interval skip the version check. Once the interval expires, a list call
checks for new versions. If the version hasn't changed, the timer should
reset so the next interval begins, but it didn't. The timer stayed
expired, so every query after that triggered a list call, even though
nothing changed.
This affects all read operations (queries, schema lookups, searches) on
tables with read_consistency_interval set. Each operation adds a
list("_versions/") call to object storage, adding latency proportional
to the store's list performance. For high-QPS workloads, this can
saturate object store list throughput and significantly degrade query
latency.
Bug flow:
1. Every read operation (query, schema, search) calls
ensure_up_to_date()
2. ensure_up_to_date() calls is_up_to_date(), which compares
last_consistency_check.elapsed() against
read_consistency_interval
3. If the interval has expired, it calls reload()
4. reload() calls need_reload(), which calls latest_version_id() — this
is the list IOP
(list("_versions/"))
5. If no new version, reload() returns early without resetting
last_consistency_check
6. On the next query, step 2 sees the stale timer again → step 3 → step
4 → another list IOP
7. This repeats on every query forever
Caches the schema of remote tables and invalidates the cache when:
1. After 30 second TTL
2. When we do an operation that changes schema (e.g. add_columns) or
checks out a different version (e.g. checkout_version)
3. When we get a 400, 404, or 500 reponse
If the schema is retrieved close to the TTL, we optimistically fetch the
schema in the background. This means a continuous stream of queries will
never have the schema fetch on the critical path.
Closes#3014
---------
Co-authored-by: Claude Sonnet 4.5 <noreply@anthropic.com>
BREAKING CHANGE: Arbitrary `impl RecordBatchReader` is no longer
accepted, it must be made into `Box<dyn RecordBatchReader>`.
This PR replaces `IntoArrow` with a new trait `Scannable` to define
input row data. This provides the following advantages:
1. **We can implement `Scannable` for more types than `IntoArrow`, such
as `RecordBatch` and `Vec<RecordBatch>`.** The `IntoArrow` trait was
implemented for arbitrary `T: RecordBatchReader`, and the Rust compiler
would prevent us from implementing it for foreign types like
`RecordBatch` because (theoretically) those types might implement
`RecordBatchReader` in the future. That's why we implement `Scannable`
for `Box<dyn RecordBatchReader>` instead; since it's a concrete type it
doesn't block implementing for other foreign types.
2. **We can potentially replay `Scannable` values**. Previously, we had
to choose between buffering all data in memory and supporting retries of
writes. But because `Scannable` things can optionally support
re-scanning, we now have a way of supporting retries while also
streaming.
3. **`Scannable` can provide hints like `num_rows`, which can be used to
schedule parallel writers.** Without knowing the total number of rows,
it's difficult to know whether it's worth writing multiple files in
parallel.
We don't yet fully take advantage of (2) and (3) yet, but will in future
PRs. For (2), in order to be ready to leverage this, we need to hook the
`Scannable` implementation up to Python and NodeJS bindings. Right now
they always pass down a stream, but we want to make sure they support
retries when possible. And for (3), this will need to be hooked up to
#2939 and to a pipeline for running pre-processing steps (like embedding
generation).
## Other changes
* Moved `create_table` and `add_data` into their own modules. I've
created a follow up issue to split up `table.rs` further, as it's by far
the largest file: https://github.com/lancedb/lancedb/issues/2949
* Eliminated the `HAS_DATA` generic for `CreateTableBuilder`. I didn't
see any public-facing places where we differentiated methods, which is
why I felt this simplification was okay.
* Added an `Error::External` variant and integrated some conversions to
allow certain errors to pass through transparently. This will fully work
once we upgrade Lance and get to take advantage of changes in
https://github.com/lance-format/lance/pull/5606
* Added LZ4 compression support for write requests to remote endpoints.
I checked and this has been supported on the server for > 1 year.
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
This changes around the output format of `Permutation` in some breaking
ways but I think the API is still new enough to be considered
experimental.
1. In order to align with both huggingface's dataset and torch's
expectations the default output format is now a list of dicts
(row-major) instead of a dict of lists (column-major). I've added a
python_col option which will return the dict of lists.
2. In order to align with pytorch's expectation the `torch` format is
now a list of tensors (row-major) instead of a 2D tensor (column-major).
I've added a torch_col option which will return the 2D tensor instead.
Added tests for torch integration with Permutation
~~Leaving draft until https://github.com/lancedb/lancedb/pull/3013
merges as this is built on top of that~~
Closes#3000
The hybrid search `explain_plan` now shows the reranker as the top-level
node with
the vector and FTS sub-plans indented underneath, instead of just
listing them
separately with no reranker context.
**Before:**
```
Vector Search Plan:
ProjectionExec: ...
FTS Search Plan:
ProjectionExec: ...
```
**After:**
```
RRFReranker(K=60)
Vector Search Plan:
ProjectionExec: ...
FTS Search Plan:
ProjectionExec: ...
```
Other rerankers display similarly ; e.g.
`LinearCombinationReranker(weight=0.7, fill=1.0)`,
`MRRReranker(weight_vector=0.5, weight_fts=0.5)`,
`CohereReranker(model_name=name)`.
---------
Signed-off-by: dask-58 <googldhruv@gmail.com>
Co-authored-by: Will Jones <willjones127@gmail.com>
Fixes#2999
The error message previously said `"field_names must be a string when
use_tantivy=False"` implying they should use the to be deprecated
tantivy backend #2998.
Updated the error message and docstring to instead guide users to create
a separate FTS index for each field
Signed-off-by: dask-58 <googldhruv@gmail.com>
## Summary
Continues the modularization effort of table operations as outlined in
#2949.
- Extracts optimization operations (`OptimizeAction`, `OptimizeStats`,
`execute_optimize`, `compact_files_impl`, `cleanup_old_versions`,
`optimize_indices`) from
`table.rs` into `table/optimize.rs`
- Public API remains unchanged via re-exports
- Adds comprehensive tests including error cases with message assertions
## Test plan
- [x] All new optimization tests pass
- [x] All existing tests pass
- [x] `cargo clippy` passes with no warnings
- [x] `cargo fmt --check` passes
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
## Summary
- Added `repository` field to all nodejs package.json files (main
package + 7 platform-specific packages)
- This fixes the npm publish E422 error where sigstore provenance
verification fails because the repository.url was empty
## Root Cause
Failing CI:
https://github.com/lancedb/lancedb/actions/runs/21770794768/job/62821570260
npm's sigstore provenance verification requires the `repository.url`
field in package.json to match the GitHub repository URL from the
provenance bundle. The platform-specific packages
(`@lancedb/lancedb-darwin-arm64`, etc.) were missing this field
entirely, causing the publish to fail with:
```
npm error 422 Unprocessable Entity - Error verifying sigstore provenance bundle:
Failed to validate repository information: package.json: "repository.url" is "",
expected to match "https://github.com/lancedb/lancedb" from provenance
```
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Continues the modularization effort of schema evolution operations as
outlined in #2949
## Summary
- Extracts schema evolution operations (add_columns, alter_columns,
drop_columns) from `table.rs` into `table/schema_evolution.rs`
- Public API remains unchanged via re-exports
## Test plan
- [x] All new schema evolution tests pass
- [x] All existing tests pass
- [x] `cargo clippy` passes with no warnings
- [x] `cargo fmt --check` passes
## Summary
- Codex CLI v0.95.0 ([PR
#10258](https://github.com/openai/codex/pull/10258)) hardened git
command safety so force push (`git push -f`, `--force`,
`--force-with-lease`, `+refspec`) now requires approval, which blocks it
in non-interactive `exec` mode.
- This broke the
[codex-update-lance-dependency](https://github.com/lancedb/lancedb/actions/runs/21727536000/job/62673436482)
workflow — the job succeeded but failed to push the branch or create the
PR.
- Replace force push with `gh api` branch deletion followed by regular
`git push`.
- Also update the script to bump Java lance-core version which was
missing previously
## Test plan
- [x] Re-run the `Codex Update Lance Dependency` workflow with a test
tag to verify the push and PR creation succeed:
https://github.com/lancedb/lancedb/pull/2983🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Regenerate TypeScript docs to include the new initialStorageOptions()
and latestStorageOptions() methods added in #2966.
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
## Summary
The `report-failure` jobs in npm, cargo, and pypi publish workflows
checked for
`release` or `workflow_dispatch` events, but these workflows are
triggered by tag
pushes where `github.event_name` is `push`. The condition was never
true, so failure
notifications were silently skipped.
- Use `startsWith(github.ref, 'refs/tags/...')` to match actual tag
triggers
- Add `failure()` to only notify on actual failures
This matches the pattern already used by `java-publish.yml`.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Expose `initial_storage_options()` and `latest_storage_options()` in
lance Dataset, in lancedb rust, python and typescript SDKs.
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Implements `InsertExec` and `RemoteInsertExec` to support running
inserts in DataFusion.
## Context
In https://github.com/lancedb/lancedb/pull/2929, I've prototyped moving
the insert pipeline into DataFusion. This will enable parallelism at two
levels:
1. Running preprocessing, such as casting the input schema or computing
embeddings
2. Writing out files
This PR is just the first part of running the actual writes. In the end,
the plans might look like:
```
InsertExec
RepartitionExec num_partitions=<write_parallelism>
ProjectionExec vector=compute_embedding()
RepartitionExec num_partitions=<num_cpus>
DataSourceExec
```
where `num_cpus` is used to take advantage of all cores, while
`write_parallelism` might be less than `num_cpus` if there are too few
rows to want to split writes across `num_cpus` files.
Later PRs will move the preprocessing steps into DataFusion, and then
hook this up to the `Table::add()` implementations.
## Relation to future SQL work
We eventually plan on having the Remote SDK go through a FlightSQL
endpoint. Then for most queries we will send just the SQL string to the
server, and not run any sort of DataFusion plan on the client.
However, I think writes will be a little special, especially bulk writes
where we need to upload large streams of data and likely want
parallelism. So we'll have different code paths for writes, and I think
using DataFusion makes sense, especially as long as we are doing the
pre-processing on the client side still.
## Summary
This PR changes takeRowIds to accept bigint[] instead of
number[], matching the type of _rowid returned by withRowId().
## Problem
When retrieving row IDs using \withRowId()\ and querying them back with
takeRowIds(), users get an error because:
1. _rowid values are returned as JavaScript bigint
2. takeRowIds() expected number[]
3. NAPI failed to convert: Error: Failed to convert napi value BigInt
into rust type i64
## Reproduction
\\\js
import lancedb from '@lancedb/lancedb';
const db = await lancedb.connect('memory://');
const table = await db.createTable('test', [{ id: 1, vector: [1.0, 2.0]
}]);
const results = await table.query().withRowId().toArray();
const rowIds = results.map(row => row._rowid);
console.log('types:', rowIds.map(id => typeof id)); // ['bigint']
await table.takeRowIds(rowIds).toArray(); // ⌠Error before fix
\\\
## Solution
- Updated TypeScript signature from takeRowIds(rowIds: number[]) to
takeRowIds(rowIds: bigint[])
- Updated Rust NAPI binding to accept Vec<BigInt> and convert using
get_u64()
Fixes#2722
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
References #2949 Part 2 of table.rs refactor. Moved UpdateResult,
UpdateBuilder, and execution logic to src/table/update.rs. No functional
changes API remains identical.
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Fixes#2612
This PR exposes the private _fast_search attribute via a public
fast_search() method in the synchronous LanceVectorQueryBuilder.
Previously, enabling fast search in the sync API required accessing a
private member (query._fast_search = True). This change aligns the
synchronous API with the Async and Remote APIs, allowing for cleaner,
more Pythonic method chaining.
Changes:
Added fast_search() method to LanceVectorQueryBuilder in
python/python/lancedb/query.py.
Added a unit test verifying the flag works with high-dimensional data
(2560 dims) and chaining.
Example Usage:
Before:
```
query = table.search(vector)
query._fast_search = True # Private attribute usage
results = query.limit(10).to_pandas()
```
After:
```
results = (
table.search(vector)
.fast_search()
.limit(10)
.to_pandas()
)
```
Verification:
I have added a test case (test_fast_search_high_dimension) that
replicates the scenario described in the issue (2560 dimensions, cosine
distance) to ensure the pipeline constructs the query correctly without
errors.
Checklist:
- [ ] I have added tests to cover my changes.
- [ ] All new and existing tests passed.
- [ ] Documentation has been updated (inline docstrings).
Signed-off-by: Rashidul Islam <rasidulislam71@gmail.com>
## Summary
- PR #2957 changed the permutation builder to only select `_rowid` from
the base table, but `Splitter::project()` for hash and calculated splits
replaced the selection entirely, dropping `_rowid`.
- Include `_rowid` in the column selections for hash and calculated
split projections.
- Fix a Python test that queried the permutation table for base table
columns no longer materialized.
Fixes the `test_split_hash`, `test_split_hash_with_discard`,
`test_split_calculated`, `test_shuffle_combined_with_splits`, and
`test_filter_with_splits` failures in `test_permutation.py`.
## Test plan
- [x] `cargo test -p lancedb -- permutation` (22 passed)
- [x] `pytest python/tests/test_permutation.py` (46 passed)
- [x] `npm test __test__/permutation.test.ts` (20 passed)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
References #2949 Moved DeleteResult and delete() implementation to
src/table/delete.rs. No functional changes. Added a test delete which
works. Will work on refactoring update next.
Fixes#2898
Problem:
Sync API cancellations didn’t stop remote query coroutines, so requests
could continue after interrupt.
Changes:
- Cancel run_coroutine_threadsafe futures on any BaseException in the
sync background loop
- Update cancellation test to avoid starting a real background thread
and cover GeneratorExit
Fixes the Rust SDK's `create_empty_table` to properly support embedding
column definitions, bringing it to parity with the Python SDK.
## Problem
The Rust SDK's `Connection::create_empty_table` did not support setting
embedding columns. When using `.add_embedding()` on the builder, the
embedding column definitions were lost because
`TableDefinition::new_from_schema(schema)` marks all columns as physical
only, without embedding metadata.
The Python SDK worked around this by creating an empty record batch with
proper schema metadata rather than using `create_empty_table` directly.
## Solution
Modified `CreateTableBuilder<false>` to handle embeddings
Closes#2759
Importing `PIL` alone does not guarantee that the `Image` submodule is
loaded. In a clean environment where no other code has imported
`PIL.Image` before, `PIL.Image` does not exist on the `PIL` package,
which leads to the AttributeError.
The permutation table was always intended to be a small table of row id
pointers (and split id). However, it was accidentally doing a full
materialization of the base table 🤦
This PR changes the permutation builder to only store row id and split
id.
Realized our MSRV check was inert because `rust-toolchain.toml` was
overriding the Rust version. We set the `RUSTUP_TOOLCHAIN` environment
variable, which overrides that.
Also needed to update to MSRV 1.88 (due to dependencies like Lance and
DataFusion) and fix some clippy warnings.
Unlike in Amazon S3, in Azure bucket names are not globally unique.
Instead, the combination of (storage_account_name, bucket_name) is
unique.
Therefore, when using Azure blob store, we always need a way to
configure the storage account name. One way is to use the
storage_options hash map and set azure_storage_account_name. Another way
is to set an environment variable, AZURE_STORAGE_ACCOUNT_NAME.
Prior to this PR, the second way (environment variable) did not work
with remote connections. This is because the existing code that checks
for these environment variables happens inside the Azure object store
implementation itself, which does not run locally when using remote
connections.
This PR addresses that situation by adding a check of the environment
variable. This functions as a default if the relevant storage option is
not set in the storage_options hash map.
Pandas 3.0+ string now converts to Arrow large_utf8. This PR mainly
makes sure our test accounts for the difference across the pandas
versions when constructing schema.
BREAKING CHANGE: removes `aws`, `dynamodb`, `azure`, `gcs`, `oss`,
`huggingface` from default Rust features. They can be enabled by users
as needed.
They are still enabled for Python and NodeJS, since those users don't
control the compilation of artifacts.
Closes#2911
RemoteDBConnection should support passing exist_ok to create_table, just
like LanceDBConnection (the non-remote form) does. It can support this
by passing 'exist_ok' as the mode parameter.
Implement parallel execution of multiple embedding functions using
std::thread::scope to improve performance when a table has multiple
embedding columns.
Key changes:
- Add compute_embeddings_parallel() helper method to WithEmbeddings
- Use fast path for single embeddings (no threading overhead)
- Use scoped threads for parallel execution of multiple embeddings
- Add comprehensive tests including parallelization timing verification
- Update WithEmbeddings documentation
Performance improvements:
- I/O-bound embeddings (OpenAI, Bedrock): High benefit from concurrent
API calls
- CPU-bound embeddings (sentence-transformers): Medium benefit from core
utilization
- Single embedding: No overhead (fast path)
Closes TODO on line 266 in rust/lancedb/src/embeddings.rs
Aesthetic and styling fixes to the SDK reference docs:
- [x] Improve readability of LanceDB in the header
- [x] Make header more compact, and consistent in gradient color with
the main website/docs
- [x] Updated favicon to match with the docs page
- [x] Enable permalink display to allow users to get anchor links to
each function/method
- [x] Point readers to the main docs at
[docs.lancedb.com](https://docs.lancedb.com)
Convert test_table_names to test both remote and local connections.
This PR also includes some miscellaneous improvements in
src/test_utils/connection.rs. It starts a thread to drain stdout from
the server process. It adds the
PRINT_LANCEDB_TEST_CONNECTION_SCRIPT_OUTPUT environment variable, which
optionally displays server stdout.
Fix a bash conditional in run_with_test_connection.sh.
The page_token and limit parameters for table_names() are supported by
both local storage and LanceDB Cloud, not just Cloud as the docstring
incorrectly stated.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Adds `Table.to_lance()` and `Table.to_polars()` methods (non-abstract
methods, defaulting to `NotImplementedError`) so type checkers like
mypy, pyright and ty don’t flag them as unknown attributes on `Table`.
Not making these abstract methods should keep existing remote/other
`Table` implementations instantiable.
This is non-breaking change to existing functionality and is purely for
the purpose of pleasing static type-checkers like mypy, ty and pyright.
<img width="626" height="134" alt="image"
src="https://github.com/user-attachments/assets/f4619bca-a882-432b-bd23-ae8f189ff9e3"
/>
Issues found during integration tests:
1. describe_namespace should use POST
2. service needs to access the underlying namespace to be able to do
operations like create_empty_table directly, or get credentials in
isolated paths like a remote take
## Summary
- update all lance crates to v1.0.0 using the helper script (fallbacks
to the v1.0.0 tag)
- refresh Cargo.lock to pull the new release
- add script fallback to retry with the git tag when a crates.io release
is unavailable
## Testing
- cargo clippy --workspace --tests --all-features -- -D warnings
- cargo fmt --all
Tag: https://github.com/lance-format/lance/releases/tag/v1.0.0
---------
Co-authored-by: Jack Ye <yezhaoqin@gmail.com>
## Summary
- infer integer vector columns as float32 when any value exceeds uint8
range or is negative
- keep uint8 for integer vectors within range and nulls only
- add sync/async tests covering large integer vector inference
## Testing
- ./.venv/bin/pytest python/python/tests/test_table.py -k
"large_int_vectors"
In #2845 we ported the lancedb integration in lance-namespace to
lancedb. But that is too specific to RestNamespace. We can improve the
user entry point so that we can put local mode and future version of the
Flight SQL-based LanceDB server all behind this single
`LanceDbNamespaceClientBuilder` API.
Also I renamed `namespace` to `namesapceClient` to avoid confusion with
the namespace path.
After the refactoring on both client and server side, we should have the
ability to fully use lance REST namespace to call into LanceDB cloud and
enterprise. We can avoid having a JNI implementation (which today does
not really do anything except for vending a connection object), and just
use lance-core's RestNamespace.
We will at this moment have a LanceDbRestNamespaceBuilder to allow users
to more easily build the RestNamespace to talk to LanceDB Cloud or
Enterprise endpoint.
In the future, we could extend this further to also support the local
mode through DirectoryNamespace. That will be a separated PR.
Adds IVF_SQ index config through Rust core and Python bindings, plus
alias names IvfHnswSq/Pq for backward compatibility. Updates
remote/table helpers and types to accept the new index type. Includes
tests covering IVF SQ creation and alias usage.
1. Use generated models in lance-namespace for request response models
to avoid multiple layers of conversions
2. Make sure the API is consistent with the namespace spec
3. Deprecate the table_names API in favor of the list_tables API in
namespace that allows full pagination support without the need to have
sorted table names
4. Add describe_namespace API which was a miss in the original
implementation
Currently a table in a namespace is still backed with a `NativeTable`,
which means after getting the location of the table and optional storage
options override from `namespace.describe_table`, all things work like a
normal local table. However, namespace also supports `query_table`,
which is exactly the same API as remote table. This PR adds a
`server_side_query` capability, when enabled, it runs the query by
calling `namespace.query_table`. For namespace that implements the
operation (e.g. REST namespace), this could hit a backend server that
could execute the query faster (e.g. using a distributed engine).
We have very low download stats for mac x86, and also latest github
runners for mac are all arm, so it makes sense at this point to
deprecate x86 support in general.
Add support for enabling stable row IDs when creating tables via the
`new_table_enable_stable_row_ids` storage option.
Stable row IDs ensure that row identifiers remain constant after
compaction, update, delete, and merge operations. This is useful for
materialized views and other use cases that need to track source rows
across these operations.
The option can be set at two levels:
- Connection level: applies to all tables created with that connection
- Table level: per-table override via create_table storage_options
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude <noreply@anthropic.com>
## Summary
- bump all Lance crates to v1.0.0-beta.16 via ci/set_lance_version.py
- refresh Cargo.lock (reqwest/opendal/etc.) to satisfy the new release
## Verification
- cargo clippy --workspace --tests --all-features -- -D warnings
- cargo fmt --all
Triggered by
[refs/tags/v1.0.0-beta.16](https://github.com/lance-format/lance/releases/tag/v1.0.0-beta.16)
---------
Co-authored-by: Jack Ye <yezhaoqin@gmail.com>
## Summary
- bump all Lance crates to 1.0.0-beta.14 via ci/set_lance_version.py
- refresh Cargo.lock to capture new transitive requirements
- verified `cargo clippy --workspace --tests --all-features -- -D
warnings` and `cargo fmt --all`
Triggered by refs/tags/v1.0.0-beta.14
---------
Co-authored-by: Jack Ye <yezhaoqin@gmail.com>
This tries to fix#2771. It is not a complete fix because
`lance-namespace-impls` uses `lance` which has its default features
enabled. Thus, to close#2771, the lance repo also needs an update.
The `dir-*` features are enabled by the respective remote feature
(`aws`, `gcp`, `azure`, `oss`).
The `rest` feature is enabled via `remote`.
clippy::string_to_string has been replaced by implicit_clone, so lancedb
will raise a build error in Rust 1.91. This PR suppresses it.
---
**This PR was primarily authored with Codex using GPT-5-Codex and then
hand-reviewed by me. I AM responsible for every change made in this PR.
I aimed to keep it aligned with our goals, though I may have missed
minor issues. Please flag anything that feels off, I'll fix it
quickly.**
Signed-off-by: Xuanwo <github@xuanwo.io>
This PR will migrate macos CI runners.
---
**This PR was primarily authored with Codex using GPT-5-Codex and then
hand-reviewed by me. I AM responsible for every change made in this PR.
I aimed to keep it aligned with our goals, though I may have missed
minor issues. Please flag anything that feels off, I'll fix it
quickly.**
Signed-off-by: Xuanwo <github@xuanwo.io>
This PR improves the docstring for `IVF_RQ` (RabitQ) in Python. The
earlier version referred to it as "residual quantization", which is
confusing to future readers of the code.
In contrast, the TypeScript and Rust codebases defined `IVF_RQ` as
RabitQ. So now the three languages use comments that are consistent with
one another.
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Fixes the breaking CI for nodejs, related to the documentation of the
new Permutation API in typescript.
- Expanded the generated typings in `nodejs/lancedb/native.d.ts` to
include `SplitCalculatedOptions`, `splitNames` fields, and the
persist/options-based `splitCalculated` methods so the permutation
exports match the native API.
- The previous block comment block had an inconsistency.
`splitCalculated` takes an options object (`SplitCalculatedOptions`) in
our bindings, not a bare string. The previous example showed
`builder.splitCalculated("user_id % 3");`, which doesn’t match the
actual signature and would fail TS typecheck. I updated the comment to
`builder.splitCalculated({ calculation: "user_id % 3" });` so the
example is now correct.
- Updated the `splitCalculated` example in
`nodejs/lancedb/permutation.ts` to use the options object.
- Ran `npm docs` to ensure docs build correctly.
> [!NOTE]
> **Disclaimer**: I used GPT-5.1-Codex-Max to make these updates, but I
have read the code and run `npm run docs` to verify that they work and
are correct to the best of my knowledge.
Did a full scan of all URLs that used to point to the old mkdocs pages,
and now links to the appropriate pages on lancedb.com/docs or lance.org
docs.
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **Documentation**
- Expanded Python API reference with new entries for table metadata,
tagging, remote client configuration, and index statistics.
- Added documentation for new classes and modules in both synchronous
and asynchronous sections, including `FragmentStatistics`,
`FragmentSummaryStats`, `Tags`, `AsyncTags`, `IndexStatistics`, and
remote configuration options.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
The `self._query` value was not set when wrapping its copy `query` with
quotation marks.
The test for phrase queries has been updated to test the
`.phrase_query()` method as well, which will catch this bug.
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
This request improves support for `pydantic` integration by adding
`to_pydantic` method to asynchronous queries and handling models that
use `alias` in field definitions. Fixes#2436 and closes#2437 .
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Added support for converting asynchronous query results to Pydantic
models.
- **Bug Fixes**
- Simplified conversion of query results to Pydantic models for improved
reliability.
- Improved handling of field aliases and computed fields when mapping
query results to Pydantic models.
- **Tests**
- Added tests to verify correct mapping of aliased and computed fields
in both synchronous and asynchronous scenarios.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
This pipes the num_attempts field from lance's merge insert result
through lancedb. This allows callers of merge_insert to get a better
idea of whether transaction conflicts are occurring.
This PR will add a timely lance release check for lancedb which will
auto bump lance while new tags released.
---
**This PR was primarily authored with Codex using GPT-5-Codex and then
hand-reviewed by me. I AM responsible for every change made in this PR.
I aimed to keep it aligned with our goals, though I may have missed
minor issues. Please flag anything that feels off, I'll fix it
quickly.**
---------
Signed-off-by: Xuanwo <github@xuanwo.io>
* Add `ci` profile for smaller build caches. This had a meaningful
impact in Lance, and I expect a similar impact here.
https://github.com/lancedb/lance/pull/5236
* Get caching working in Rust. Previously was not working due to
`workspaces: rust`.
* Get caching working in NodeJs lint job. Previously wasn't working
because we installed the toolchain **after** we called `- uses:
Swatinem/rust-cache@v2`, which invalidates the cache locally.
* Fix broken pytest from async io transition
(`pytest.PytestRemovedIn9Warning`)
* Altered `get_num_sub_vectors` to handle bug in case of 4-bit PQ. This
was cause of `rust future panicked: unknown error`. Raised an issue
upstream to change panic to error:
https://github.com/lancedb/lance/issues/5257
* Call `npm run docs` to fix doc issue.
* Disable flakey Windows test for consistency. It's just an OS-specific
timer issue, not our fault.
* Fix Windows absolute path handling in namespaces. Was causing CI
failure `OSError: [WinError 123] The filename, directory name, or volume
label syntax is incorrect: `
Fixes pydantic validation errors when creating materialized views with
namespace.
```
> return JsonArrowSchema(fields=fields, metadata=schema.metadata)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E pydantic_core._pydantic_core.ValidationError: 4 validation errors for JsonArrowSchema
E metadata.b'geneva::view::query'
E Input should be a valid string [type=string_type, input_value=b'{"base":{"vector_column...t-image:latest\\"}"}}]}', input_type=bytes]
E For further information visit https://errors.pydantic.dev/2.12/v/string_type
```
Based on https://github.com/lancedb/lance/pull/4984
1. Bump to 1.0.0-beta.2
2. Use DirectoryNamespace in lance to perform all testing in python and
rust for much better coverage
3. Refactor `ListingDatabase` to be able to accept location and
namespace. This is because we have to leverage listing database (local
lancedb connection) for using namespace, namespace only resolves the
location and storage options but we don't want to bind all the way to
rust since user will plug-in namespace from python side. And thus
`ListingDatabase` needs to be able to accept location and namespace that
are created from namespace connection.
4. For credentials vending, we also pass storage options provider all
the way to rust layer, and the rust layer calls back to the python
function to fetch next storage option. This is exactly the same thing we
did in pylance.
This PR does the following:
- Pare down the docs to only what's needed (Python, JS/TS API docs and a
pointer to Rust docs)
- Styling changes to be more in line with the main website theme
The relative URLs remain unchanged, so assuming CI passes, there should
be no breaking changes from the main docs site that points back here.
This will make it easier to see if there are underlying problems. We
should see the actual object store HTTP request error within the error
chain after this.
Support FTS feature parity in SQL to match current Python API
capability.
Add `.to_json()` method to FTS query classes to enable usage with SQL
`fts()` UDTF.
Related: https://github.com/lancedb/blog-lancedb/pull/147
query = MatchQuery("puppy", "text", fuzziness=2)
result = client.execute(f"SELECT * FROM fts('table',
'{query.to_json()}')")
---------
Co-authored-by: Claude <noreply@anthropic.com>
JS native Async Generator, more efficient asynchronous iteration, fewer
synthetic promises, and the ability to handle `catch` or `break` of
parent loop in `finally` block
## Summary
- Updated all Lance dependencies from v0.38.3-beta.9 to v0.38.3-beta.11
- Migrated `lance-namespace-impls` to use new granular cloud provider
features (`dir-aws`, `dir-gcp`, `dir-azure`, `dir-oss`) instead of
deprecated `dir` feature
- Updated namespace connection API to use `ConnectBuilder` instead of
deprecated `connect()` function
## API Changes
The Lance team refactored the `lance-namespace-impls` package in
v0.38.3-beta.11:
1. **Feature flags**: The single `dir` feature was split into cloud
provider-specific features:
- `dir-aws` for AWS S3 support
- `dir-gcp` for Google Cloud Storage support
- `dir-azure` for Azure Blob Storage support
- `dir-oss` for Alibaba Cloud OSS support
2. **Connection API**: The `connect()` function was replaced with a
`ConnectBuilder` pattern for more flexibility
## Testing
- ✅ Ran `cargo clippy --workspace --tests --all-features -- -D warnings`
- no warnings
- ✅ Ran `cargo fmt --all` - code formatted
- ✅ All changes verified and committed
## Related
This update was triggered by the Lance release:
https://github.com/lancedb/lance/releases/tag/v0.38.3-beta.11🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude <noreply@anthropic.com>
This PR will add an action that allow codex to auto upgrade lance.
---
**This PR was primarily authored with Codex using GPT-5-Codex and then
hand-reviewed by me. I AM responsible for every change made in this PR.
I aimed to keep it aligned with our goals, though I may have missed
minor issues. Please flag anything that feels off, I'll fix it
quickly.**
Signed-off-by: Xuanwo <github@xuanwo.io>
I'm working on a lancedb version of pytorch data loading (and hopefully
addressing https://github.com/lancedb/lance/issues/3727).
However, rather than rely on pytorch for everything I'm moving some of
the things that pytorch does into rust. This gives us more control over
data loading (e.g. using shards or a hash-based split) and it allows
permutations to be persistent. In particular I hope to be able to:
* Create a persistent permutation
* This permutation can handle splits, filtering, shuffling, and sharding
* Create a rust data loader that can read a permutation (one or more
splits), or a subset of a permutation (for DDP)
* Create a python data loader that delegates to the rust data loader
Eventually create integrations for other data loading libraries,
including rust & node
The [`FieldLike` type in
arrow.ts](https://github.com/lancedb/lancedb/blob/5ec12c99713e28a8fa550547cc9e7536da063f6e/nodejs/lancedb/arrow.ts#L71-L78)
can have a `type: string` property, but before this change, actually
trying to create a table that has a schema that specifies field types by
name results in an error:
```
Error: Expected a Type but object was null/undefined
```
This change adds support for mapping some type name strings to arrow
`DataType`s, so that passing `FieldLike`s with a `type: string` property
to `sanitizeField` does not throw an error.
The type names that can be passed are upper/lowercase variations of the
keys of the `constructorsByTypeName` object. This does not support
mapping types that need parameters, such as timestamps which need
timezones.
With this, it is possible to create empty tables from `SchemaLike`
objects without instantiating arrow types, e.g.:
```
import { SchemaLike } from "../lancedb/arrow"
// ...
const schemaLike = {
fields: [
{
name: "id",
type: "int64",
nullable: true,
},
{
name: "vector",
type: "float64",
nullable: true,
},
],
// ...
} satisfies SchemaLike;
const table = await con.createEmptyTable("test", schemaLike);
```
This change also makes `FieldLike.nullable` required since the `sanitizeField` function throws if it is undefined.
Fixes error that when converting a federated database operation to a
listing database operation, the namespace parameter is no longer correct
and should be dropped.
Note that with the testing infra we have today, we don't have a good way
to test these changes. I will do a quick follow up on
https://github.com/lancedb/lancedb/issues/2701 but would be great to get
this in first to resolve the related issues.
## Description of changes
Fixes#2698
This PR uses
[`typing.override`](https://docs.python.org/3/library/typing.html#typing.override)
in favor of the [`overrides`](https://pypi.org/project/overrides/)
dependency when possible. As of Python 3.12, the standard library offers
`typing.override` to perform a static check on overridden methods.
### Motivation
Currently, `overrides` is incompatible with Python 3.14. As a result,
any package that attempts to import `overrides` using Python 3.14+ will
raise an `AttributeError`. An
[issue](https://github.com/mkorpela/overrides/issues/127) has been
raised and a [pull
request](https://github.com/mkorpela/overrides/pull/133) has been
submitted to the GitHub repo for the `overrides` project. But the
maintainer has been unresponsive.
To ensure readiness for Python 3.14, this package (and any other package
directly depending on `overrides`) should consider using
`typing.override` instead.
### Impact
The standard library added `typing.override` as of 3.12. As a result,
this change will affect only users of Python 3.12+. Previous versions
will continue to rely on `overrides`. Notably, the standard library
implementation is slightly different than that of `overrides`. A
thorough discussion of those differences is shown in [PEP
698](https://peps.python.org/pep-0698/), and it is also summarized
nicely by the maintainer of `overrides`
[here](https://github.com/mkorpela/overrides/issues/126#issuecomment-2401327116).
There are 2 main ways that switching from `overrides` to
`typing.override` will have an impact on developers of this repo.
1. `typing.override` does not implement any runtime checking. Instead,
it provides information to type checkers.
2. The stdlib does not provide a mixin class to enforce override
decorators on child classes. (Their reasoning for this is explained in
[the PEP](https://peps.python.org/pep-0698/).) This PR disables that
behavior entirely by replacing the `EnforceOverrides`.
**Problem**: When a vector field is marked as nullable, users should be
able to omit it or pass `undefined`, but this was throwing an error:
"Table has embeddings: 'vector', but no embedding function was provided"
fixes: #2646
**Solution**: Modified `validateSchemaEmbeddings` to check
`field.nullable` before treating `undefined` values as missing embedding
fields.
**Changes**:
- Fixed validation logic in `nodejs/lancedb/arrow.ts`
- Enabled previously skipped test for nullable fields
- Added reproduction test case
**Behavior**:
- ✅ `{ vector: undefined }` now works for nullable fields
- ✅ `{}` (omitted field) now works for nullable fields
- ✅ `{ vector: null }` still works (unchanged)
- ✅ Non-nullable fields still properly throw errors (unchanged)
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Co-authored-by: neha <neha@posthog.com>
## Summary
- Created custom GitHub Action that creates issues when workflow jobs
fail
- Added report-failure jobs to cargo-publish.yml, java-publish.yml,
npm-publish.yml, and pypi-publish.yml
- Issues are created automatically with workflow name, failed job names,
and run URL
## Test plan
- Workflows will only create issues on actual release or
workflow_dispatch events
- Can be tested by triggering workflow_dispatch on a publish workflow
Based on lancedb/lance#4873🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude <noreply@anthropic.com>
Fixed the issue on lance-namespace side to avoid pinning to a specific
lance version. This should fix the issue of the increased release
artifact size and build time.
Add a new test feature which allows for running the lancedb tests
against a remote server. Convert over a few tests in src/connection.rs
as a proof of concept.
To make local development easier, the remote tests can be run locally
from a Makefile. This file can also be used to run the feature tests,
with a single invocation of 'make'. (The feature tests require bringing
up a docker compose environment.)
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="$BRANCH_NAME"
gh run list --repo lancedb/sophon --workflow codex-bump-lancedb-lance.yml --limit 1 --json databaseId,url,displayTitle
```
Use the emitted metadata `tag` value as `LANCE_TAG`. Do this only after a new LanceDB PR has been created. If the update was skipped because no update is needed or an open PR already exists, do not trigger Sophon.
## GitHub Actions
When this skill is used from GitHub Actions, `TAG`, `GH_TOKEN`, and `GITHUB_TOKEN` may already be set. Resolve `latest` first when `TAG` is empty. Once an explicit tag or version is known, use:
"description":"Write, review, debug, and document LanceDB pipelines in Python and TypeScript that work across local LanceDB OSS tables and remote LanceDB Enterprise/Cloud tables.",
description:"Override [project] name in python/pyproject.toml (e.g. 'lancedb-compat'). Default keeps 'lancedb'."
required:false
default:"lancedb"
rustflags:
description:"RUSTFLAGS for the build container, as a single whitespace-free token (e.g. '-Ctarget-cpu=x86-64-v2'). Empty leaves RUSTFLAGS unset, keeping the defaults from .cargo/config.toml."
required:false
default:""
runs:
using:"composite"
steps:
- name:CONFIRM ARM BUILD
shell:bash
env:
ARM_BUILD:${{ inputs.arm-build }}
run:|
echo "ARM BUILD: ${{ inputs.arm-build }}"
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
1. Extract the run ID from the workflow URL. The URL format is https://github.com/lancedb/lancedb/actions/runs/<run_id>.
2. Use "gh run view <run_id> --json jobs,conclusion,name" to get information about the failed run.
3. Identify which jobs failed. For each failed job, use "gh run view <run_id> --job <job_id> --log-failed" to get the failure logs.
4. Analyze the failure logs to understand what went wrong. Common failures include:
- Compilation errors
- Test failures
- Clippy warnings treated as errors
- Formatting issues
- Dependency issues
5. Based on the analysis, fix the issues in the codebase:
- For compilation errors: Fix the code that doesn't compile
- For test failures: Fix the failing tests or the code they test
- For clippy warnings: Apply the suggested fixes
- For formatting issues: Run "cargo fmt --all"
- For other issues: Apply appropriate fixes
6. After making fixes, verify them locally:
- Run "cargo fmt --all" to ensure formatting is correct
- Run "cargo clippy --workspace --tests --all-features -- -D warnings" to check for issues
- Run ONLY the specific failing tests to confirm they pass now:
- For Rust test failures: Run the specific test with "cargo test -p <crate> <test_name>"
- For Python test failures: Build with "cd python && maturin develop" then run "pytest <specific_test_file>::<test_name>"
- For Java test failures: Run "cd java && mvn test -Dtest=<TestClass>#<testMethod>"
- For TypeScript test failures: Run "cd nodejs && pnpm build && pnpm test -- --testNamePattern='<test_name>'"
- Do NOT run the full test suite - only run the tests that were failing
7. If the additional guidelines are provided, follow them as well.
8. Inspect "git status --short" and "git diff" to review your changes.
9. Create a fix branch: "git checkout -b codex/fix-ci-<run_id>".
10. Stage all changes with "git add -A" and commit with message "fix: resolve CI failures from run <run_id>".
11. Push the branch: "git push origin codex/fix-ci-<run_id>". If the remote branch exists, delete it first with "gh api -X DELETE repos/lancedb/lancedb/git/refs/heads/codex/fix-ci-<run_id>" then push. Do NOT use "git push --force" or "git push -f".
12. Create a pull request targeting "${BRANCH}":
- Title: "ci: <short summary describing the fix>" (e.g., "ci: fix clippy warnings in lancedb" or "ci: resolve test flakiness in vector search")
- First, write the PR body to /tmp/pr-body.md using a heredoc (cat <<'PREOF' > /tmp/pr-body.md). The body should include:
- Link to the failing workflow run
- Summary of what failed
- Description of the fixes applied
- Then run "gh pr create --base ${BRANCH} --body-file /tmp/pr-body.md".
13. Display the new PR URL, "git status --short", and a summary of what was fixed.
Constraints:
- Use bash commands for all operations.
- Do not merge the PR.
- Do not modify GitHub workflow files unless they are the cause of the failure.
- If any command fails, diagnose and attempt to fix the issue instead of aborting immediately.
- If you cannot fix the issue automatically, create the PR anyway with a clear explanation of what you tried and what remains to be fixed.
- env "GH_TOKEN" is available, use "gh" tools for GitHub-related operations.
description:"Tag name from Lance (e.g. `v7.2.0-beta.1`). If omitted, the newest release is resolved automatically — stable releases are preferred over pre-releases — and the run is skipped if it is not newer than the version currently pinned in Cargo.toml."
required:false
default:""
type:string
workflow_dispatch:
inputs:
tag:
description:"Tag name from Lance (e.g. `v7.2.0-beta.1`). Leave empty to resolve the newest release automatically — stable releases are preferred over pre-releases — and skip the run if it is not newer than the version currently pinned in Cargo.toml."
echo "Broken documentation links found by [\`$GITHUB_WORKFLOW\`]($run_url)."
echo
echo "This issue is rewritten by every scheduled run and closed automatically once all links resolve."
echo
echo "Entries can be false positives: some sites rate-limit or block automated clients while working fine in a browser. Confirm before editing the docs, and add persistent offenders to \`--exclude\` in \`.github/workflows/docs-link-check.yml\`."
echo
# Timeouts are reported alongside errors: entries land in
# timeout_map with a status text instead of an HTTP code.
jq -r '
"\(.errors) of \(.total) links failed, \(.timeouts) timed out.",
📚 Starting June 1st, 2025, please use <ahref="https://lancedb.github.io/documentation"target="_blank"rel="noopener noreferrer">lancedb.github.io/documentation</a> for the latest docs.
IVF_PQ index parameters are more fully defined in the [crate docs](https://docs.rs/lancedb/latest/lancedb/index/vector/struct.IvfPqIndexBuilder.html).
The following IVF_PQ paramters can be specified:
- **distance_type**: The distance metric to use. By default it uses euclidean distance "`l2`".
We also support "cosine" and "dot" distance as well.
- **num_partitions**: The number of partitions in the index. The default is the square root
of the number of rows.
!!! note
In the synchronous python SDK and node's `vectordb` the default is 256. This default has
changed in the asynchronous python SDK and node's `lancedb`.
- **num_sub_vectors**: The number of sub-vectors (M) that will be created during Product Quantization (PQ).
For D dimensional vector, it will be divided into `M` subvectors with dimension `D/M`, each of which is replaced by
a single PQ code. The default is the dimension of the vector divided by 16.
- **num_bits**: The number of bits used to encode each sub-vector. Only 4 and 8 are supported. The higher the number of bits, the higher the accuracy of the index, also the slower search. The default is 8.
!!! note
In the synchronous python SDK and node's `vectordb` the default is currently 96. This default has
changed in the asynchronous python SDK and node's `lancedb`.
<figure markdown>
<figcaption>IVF_PQ index with <code>num_partitions=2, num_sub_vectors=4</code></figcaption>
</figure>
### Use GPU to build vector index
Lance Python SDK has experimental GPU support for creating IVF index.
Using GPU for index creation requires [PyTorch>2.0](https://pytorch.org/) being installed.
You can specify the GPU device to train IVF partitions via
- **accelerator**: Specify to `cuda` or `mps` (on Apple Silicon) to enable GPU training.
=== "Linux"
<!-- skip-test -->
``` { .python .copy }
# Create index using CUDA on Nvidia GPUs.
tbl.create_index(
num_partitions=256,
num_sub_vectors=96,
accelerator="cuda"
)
```
=== "MacOS"
<!-- skip-test -->
```python
# Create index using MPS on Apple Silicon.
tbl.create_index(
num_partitions=256,
num_sub_vectors=96,
accelerator="mps"
)
```
!!! note
GPU based indexing is not yet supported with our asynchronous client.
Troubleshooting:
If you see `AssertionError: Torch not compiled with CUDA enabled`, you need to [install
PyTorch with CUDA support](https://pytorch.org/get-started/locally/).
## Querying an ANN Index
Querying vector indexes is done via the [search](https://lancedb.github.io/lancedb/python/#lancedb.table.LanceTable.search) function.
There are a couple of parameters that can be used to fine-tune the search:
- **limit** (default: 10): The amount of results that will be returned
- **nprobes** (default: 20): The number of probes used. A higher number makes search more accurate but also slower.<br/>
Most of the time, setting nprobes to cover 5-15% of the dataset should achieve high recall with low latency.<br/>
- _For example_, For a dataset of 1 million vectors divided into 256 partitions, `nprobes` should be set to ~20-40. This value can be adjusted to achieve the optimal balance between search latency and search quality. <br/>
- **refine_factor** (default: None): Refine the results by reading extra elements and re-ranking them in memory.<br/>
A higher number makes search more accurate but also slower. If you find the recall is less than ideal, try refine_factor=10 to start.<br/>
- _For example_, For a dataset of 1 million vectors divided into 256 partitions, setting the `refine_factor` to 200 will initially retrieve the top 4,000 candidates (top k * refine_factor) from all searched partitions. These candidates are then reranked to determine the final top 20 results.<br/>
!!! note
Both `nprobes` and `refine_factor` are only applicable if an ANN index is present. If specified on a table without an ANN index, those parameters are ignored.
Currently, LanceDB does _not_ automatically create the ANN index.
LanceDB is well-optimized for kNN (exhaustive search) via a disk-based index. For many use-cases,
datasets of the order of ~100K vectors don't require index creation. If you can live with up to
100ms latency, skipping index creation is a simpler workflow while guaranteeing 100% recall.
### When is it necessary to create an ANN vector index?
`LanceDB` comes out-of-the-box with highly optimized SIMD code for computing vector similarity.
In our benchmarks, computing distances for 100K pairs of 1K dimension vectors takes **less than 20ms**.
We observe that for small datasets (~100K rows) or for applications that can accept 100ms latency,
vector indices are usually not necessary.
For large-scale or higher dimension vectors, it can beneficial to create vector index for performance.
### How big is my index, and how many memory will it take?
In LanceDB, all vector indices are **disk-based**, meaning that when responding to a vector query, only the relevant pages from the index file are loaded from disk and cached in memory. Additionally, each sub-vector is usually encoded into 1 byte PQ code.
For example, with a 1024-dimension dataset, if we choose `num_sub_vectors=64`, each sub-vector has `1024 / 64 = 16` float32 numbers.
Product quantization can lead to approximately `16 * sizeof(float32) / 1 = 64` times of space reduction.
### How to choose `num_partitions` and `num_sub_vectors` for `IVF_PQ` index?
`num_partitions` is used to decide how many partitions the first level `IVF` index uses.
Higher number of partitions could lead to more efficient I/O during queries and better accuracy, but it takes much more time to train.
On `SIFT-1M` dataset, our benchmark shows that keeping each partition 4K-8K rows lead to a good latency / recall.
`num_sub_vectors` specifies how many Product Quantization (PQ) short codes to generate on each vector. The number should be a factor of the vector dimension. Because
PQ is a lossy compression of the original vector, a higher `num_sub_vectors` usually results in
less space distortion, and thus yields better accuracy. However, a higher `num_sub_vectors` also causes heavier I/O and more PQ computation, and thus, higher latency. `dimension / num_sub_vectors` should be a multiple of 8 for optimum SIMD efficiency.
!!! note
if `num_sub_vectors` is set to be greater than the vector dimension, you will see errors like `attempt to divide by zero`
### How to choose `m` and `ef_construction` for `IVF_HNSW_*` index?
`m` determines the number of connections a new node establishes with its closest neighbors upon entering the graph. Typically, `m` falls within the range of 5 to 48. Lower `m` values are suitable for low-dimensional data or scenarios where recall is less critical. Conversely, higher `m` values are beneficial for high-dimensional data or when high recall is required. In essence, a larger `m` results in a denser graph with increased connectivity, but at the expense of higher memory consumption.
`ef_construction` balances build speed and accuracy. Higher values increase accuracy but slow down the build process. A typical range is 150 to 300. For good search results, a minimum value of 100 is recommended. In most cases, setting this value above 500 offers no additional benefit. Ensure that `ef_construction` is always set to a value equal to or greater than `ef` in the search phase
!!! info "LanceDB can be run in a number of ways:"
* Embedded within an existing backend (like your Django, Flask, Node.js or FastAPI application)
* Directly from a client application like a Jupyter notebook for analytical workloads
* Deployed as a remote serverless database
## Installation
=== "Python"
```shell
pip install lancedb
```
=== "Typescript[^1]"
=== "@lancedb/lancedb"
```shell
npm install @lancedb/lancedb
```
!!! note "Bundling `@lancedb/lancedb` apps with Webpack"
Since LanceDB contains a prebuilt Node binary, you must configure `next.config.js` to exclude it from webpack. This is required for both using Next.js and deploying a LanceDB app on Vercel.
Unlike other package managers, Yarn does not automatically resolve peer dependencies. If you are using Yarn, you will need to manually install 'apache-arrow':
```shell
yarn add apache-arrow
```
=== "vectordb (deprecated)"
```shell
npm install vectordb
```
!!! note "Bundling `vectordb` apps with Webpack"
Since LanceDB contains a prebuilt Node binary, you must configure `next.config.js` to exclude it from webpack. This is required for both using Next.js and deploying a LanceDB app on Vercel.
```javascript
/** @type {import('next').NextConfig} */
module.exports = ({
webpack(config) {
config.externals.push({ vectordb: 'vectordb' })
return config;
}
})
```
!!! note "Yarn users"
Unlike other package managers, Yarn does not automatically resolve peer dependencies. If you are using Yarn, you will need to manually install 'apache-arrow':
```shell
yarn add apache-arrow
```
=== "Rust"
```shell
cargo add lancedb
```
!!! info "To use the lancedb create, you first need to install protobuf."
=== "macOS"
```shell
brew install protobuf
```
=== "Ubuntu/Debian"
```shell
sudo apt install -y protobuf-compiler libssl-dev
```
!!! info "Please also make sure you're using the same version of Arrow as in the [lancedb crate](https://github.com/lancedb/lancedb/blob/main/Cargo.toml)"
### Preview releases
Stable releases are created about every 2 weeks. For the latest features and bug
fixes, you can install the preview release. These releases receive the same
level of testing as stable releases, but are not guaranteed to be available for
more than 6 months after they are released. Once your application is stable, we
Support for additional formats (such as serde or polars) is on the roadmap.
!!! info "Under the hood, LanceDB reads in the Apache Arrow data and persists it to disk using the [Lance format](https://www.github.com/lancedb/lance)."
!!! info "Automatic embedding generation with Embedding API"
When working with embedding models, it is recommended to use the LanceDB embedding API to automatically create vector representation of the data and queries in the background. See the [quickstart example](#using-the-embedding-api) or the embedding API [guide](./embeddings/)
### Create an empty table
Sometimes you may not have the data to insert into the table at creation time.
In this case, you can create an empty table and specify the schema, so that you can add
data to the table at a later time (as long as it conforms to the schema). This is
LanceDB comes with Pydantic support, which allows you to define the schema of your data using Pydantic models. This makes it easy to work with LanceDB tables and data. Learn more about all supported types in [tables guide](./guides/tables.md).
You can use the embedding API when working with embedding models. It automatically vectorizes the data at ingestion and query time and comes with built-in integrations with popular embedding models like Openai, Hugging Face, Sentence Transformers, CLIP and more.
Learn about using the existing integrations and creating custom embedding functions in the [embedding API guide](./embeddings/index.md).
## What's next
This section covered the very basics of using LanceDB. If you're learning about vector databases for the first time, you may want to read the page on [indexing](concepts/index_ivfpq.md) to get familiar with the concepts.
If you've already worked with other vector databases, you may want to read the [guides](guides/tables.md) to learn how to work with LanceDB in more detail.
[^1]: The `vectordb` package is a legacy package that is deprecated in favor of `@lancedb/lancedb`. The `vectordb` package will continue to receive bug fixes and security updates until September 2024. We recommend all new projects use `@lancedb/lancedb`. See the [migration guide](migration.md) for more information.
This section provides answers to the most common questions asked about LanceDB Cloud. By following these guidelines, you can ensure a smooth, performant experience with LanceDB Cloud.
### Should I reuse the database connection?
Yes! It is recommended to establish a single database connection and maintain it throughout your interaction with the tables within.
LanceDB uses HTTP connections to communicate with the servers. By re-using the Connection object, you avoid the overhead of repeatedly establishing HTTP connections, significantly improving efficiency.
### Should I re-use the `Table` object?
`table = db.open_table()` should be called once and used for all subsequent table operations. If there are changes to the opened table, `table` always reflect the **latest version** of the data.
### What should I do if I need to search for rows by `id`?
LanceDB Cloud currently does not support an ID or primary key column. You are recommended to add a
user-defined ID column. To significantly improve the query performance with SQL causes, a scalar BITMAP/BTREE index should be created on this column.
### What are the vector indexing types supported by LanceDB Cloud?
We support `IVF_PQ` and `IVF_HNSW_SQ` as the `index_type` which is passed to `create_index`. LanceDB Cloud tunes the indexing parameters automatically to achieve the best tradeoff between query latency and query quality.
### When I add new rows to a table, do I need to manually update the index?
No! LanceDB Cloud triggers an asynchronous background job to index the new vectors.
Even though indexing is asynchronous, your vectors will still be immediately searchable. LanceDB uses brute-force search to search over unindexed rows. This makes you new data is immediately available, but does increase latency temporarily. To disable the brute-force part of search, set the `fast_search` flag in your query to `true`.
### Do I need to reindex the whole dataset if only a small portion of the data is deleted or updated?
No! Similar to adding data to the table, LanceDB Cloud triggers an asynchronous background job to update the existing indices. Therefore, no action is needed from users and there is absolutely no
downtime expected.
### How do I know whether an index has been created?
While index creation in LanceDB Cloud is generally fast, querying immediately after a `create_index` call may result in errors. It's recommended to use `list_indices` to verify index creation before querying.
### Why is my query latency higher than expected?
Multiple factors can impact query latency. To reduce query latency, consider the following:
- Send pre-warm queries: send a few queries to warm up the cache before an actual user query.
- Check network latency: LanceDB Cloud is hosted in AWS `us-east-1` region. It is recommended to run queries from an EC2 instance that is in the same region.
- Create scalar indices: If you are filtering on metadata, it is recommended to create scalar indices on those columns. This will speedup searches with metadata filtering. See [here](../guides/scalar_index.md) for more details on creating a scalar index.
LanceDB Cloud is a SaaS (software-as-a-service) solution that runs serverless in the cloud, clearly separating storage from compute. It's designed to be highly scalable without breaking the bank. LanceDB Cloud is currently in private beta with general availability coming soon, but you can apply for early access with the private beta release by signing up below.
[Try out LanceDB Cloud (Public Beta)](https://cloud.lancedb.com){ .md-button .md-button--primary }
## Architecture
LanceDB Cloud provides the same underlying fast vector store that powers the OSS version, but without the need to maintain your own infrastructure. Because it's serverless, you only pay for the storage you use, and you can scale compute up and down as needed depending on the size of your data and its associated index.
## Transitioning from the OSS to the Cloud version
The OSS version of LanceDB is designed to be embedded in your application, and it runs in-process. This makes it incredibly simple to self-host your own AI retrieval workflows for RAG and more and build and test out your concepts on your own infrastructure. The OSS version is forever free, and you can continue to build and integrate LanceDB into your existing backend applications without any added costs.
Should you decide that you need a managed deployment in production, it's possible to seamlessly transition from the OSS to the cloud version by changing the connection string to point to a remote database instead of a local one. With LanceDB Cloud, you can take your AI application from development to production without major code changes or infrastructure burden.
This section covers concepts related to managing your data over time in LanceDB.
## A primer on Lance
Because LanceDB is built on top of the [Lance](https://lancedb.github.io/lance/) data format, it helps to understand some of its core ideas. Just like Apache Arrow, Lance is a fast columnar data format, but it has the added benefit of being versionable, query and train ML models on. Lance is designed to be used with simple and complex data types, like tabular data, images, videos audio, 3D point clouds (which are deeply nested) and more.
The following concepts are important to keep in mind:
- Data storage is columnar and is interoperable with other columnar formats (such as Parquet) via Arrow
- Data is divided into fragments that represent a subset of the data
- Data is versioned, with each insert operation creating a new version of the dataset and an update to the manifest that tracks versions via metadata
!!! note
1. First, each version contains metadata and just the new/updated data in your transaction. So if you have 100 versions, they aren't 100 duplicates of the same data. However, they do have 100x the metadata overhead of a single version, which can result in slower queries.
2. Second, these versions exist to keep LanceDB scalable and consistent. We do not immediately blow away old versions when creating new ones because other clients might be in the middle of querying the old version. It's important to retain older versions for as long as they might be queried.
## What are fragments?
Fragments are chunks of data in a Lance dataset. Each fragment includes multiple files that contain several columns in the chunk of data that it represents.
## Compaction
As you insert more data, your dataset will grow and you'll need to perform *compaction* to maintain query throughput (i.e., keep latencies down to a minimum). Compaction is the process of merging fragments together to reduce the amount of metadata that needs to be managed, and to reduce the number of files that need to be opened while scanning the dataset.
### How does compaction improve performance?
Compaction performs the following tasks in the background:
- Removes deleted rows from fragments
- Removes dropped columns from fragments
- Merges small fragments into larger ones
Depending on the use case and dataset, optimal compaction will have different requirements. As a rule of thumb:
- It’s always better to use *batch* inserts rather than adding 1 row at a time (to avoid too small fragments). If single-row inserts are unavoidable, run compaction on a regular basis to merge them into larger fragments.
- Keep the number of fragments under 100, which is suitable for most use cases (for *really* large datasets of >500M rows, more fragments might be needed)
!!! note
LanceDB Cloud/Enterprise supports [auto-compaction](https://docs.lancedb.com/enterprise/architecture/architecture#write-path) which automatically optimizes fragments in the background as data changes.
## Deletion
Although Lance allows you to delete rows from a dataset, it does not actually delete the data immediately. It simply marks the row as deleted in the `DataFile` that represents a fragment. For a given version of the dataset, each fragment can have up to one deletion file (if no rows were ever deleted from that fragment, it will not have a deletion file). This is important to keep in mind because it means that the data is still there, and can be recovered if needed, as long as that version still exists based on your backup policy.
## Reindexing
Reindexing is the process of updating the index to account for new data, keeping good performance for queries. This applies to either a full-text search (FTS) index or a vector index. For ANN search, new data will always be included in query results, but queries on tables with unindexed data will fallback to slower search methods for the new parts of the table. This is another important operation to run periodically as your data grows, as it also improves performance. This is especially important if you're appending large amounts of data to an existing dataset.
!!! tip
When adding new data to a dataset that has an existing index (either FTS or vector), LanceDB doesn't immediately update the index until a reindex operation is complete.
Both LanceDB OSS and Cloud support reindexing, but the process (at least for now) is different for each, depending on the type of index.
In LanceDB OSS, re-indexing happens synchronously when you call either `create_index` or `optimize` on a table. In LanceDB Cloud, re-indexing happens asynchronously as you add and update data in your table.
By default, queries will search new data even if it has yet to be indexed. This is done using brute-force methods, such as kNN for vector search, and combined with the fast index search results. This is done to ensure that you're always searching over all your data, but it does come at a performance cost. Without reindexing, adding more data to a table will make queries slower and more expensive. This behavior can be disabled by setting the [fast_search](https://lancedb.github.io/lancedb/python/python/#lancedb.query.AsyncQuery.fast_search) parameter which will instruct the query to ignore un-indexed data.
* LanceDB Cloud/Enterprise supports [automatic incremental reindexing](https://docs.lancedb.com/core#vector-index) for vector, scalar, and FTS indices, where a background process will trigger a new index build for you automatically when new data is added or modified in a dataset
* LanceDB OSS requires you to manually trigger a reindex operation -- we are working on adding incremental reindexing to LanceDB OSS as well
Approximate Nearest Neighbor (ANN) search is a method for finding data points near a given point in a dataset, though not always the exact nearest one. HNSW is one of the most accurate and fastest Approximate Nearest Neighbour search algorithms, It’s beneficial in high-dimensional spaces where finding the same nearest neighbor would be too slow and costly
[Jump to usage](#usage)
There are three main types of ANN search algorithms:
* **Tree-based search algorithms**: Use a tree structure to organize and store data points.
* **Hash-based search algorithms**: Use a specialized geometric hash table to store and manage data points. These algorithms typically focus on theoretical guarantees, and don't usually perform as well as the other approaches in practice.
* **Graph-based search algorithms**: Use a graph structure to store data points, which can be a bit complex.
HNSW is a graph-based algorithm. All graph-based search algorithms rely on the idea of a k-nearest neighbor (or k-approximate nearest neighbor) graph, which we outline below.
HNSW also combines this with the ideas behind a classic 1-dimensional search data structure: the skip list.
## k-Nearest Neighbor Graphs and k-approximate Nearest neighbor Graphs
The k-nearest neighbor graph actually predates its use for ANN search. Its construction is quite simple:
* Each vector in the dataset is given an associated vertex.
* Each vertex has outgoing edges to its k nearest neighbors. That is, the k closest other vertices by Euclidean distance between the two corresponding vectors. This can be thought of as a "friend list" for the vertex.
* For some applications (including nearest-neighbor search), the incoming edges are also added.
Eventually, it was realized that the following greedy search method over such a graph typically results in good approximate nearest neighbors:
* Given a query vector, start at some fixed "entry point" vertex (e.g. the approximate center node).
* Look at that vertex's neighbors. If any of them are closer to the query vector than the current vertex, then move to that vertex.
* Repeat until a local optimum is found.
The above algorithm also generalizes to e.g. top 10 approximate nearest neighbors.
Computing a k-nearest neighbor graph is actually quite slow, taking quadratic time in the dataset size. It was quickly realized that near-identical performance can be achieved using a k-approximate nearest neighbor graph. That is, instead of obtaining the k-nearest neighbors for each vertex, an approximate nearest neighbor search data structure is used to build much faster.
In fact, another data structure is not needed: This can be done "incrementally".
That is, if you start with a k-ANN graph for n-1 vertices, you can extend it to a k-ANN graph for n vertices as well by using the graph to obtain the k-ANN for the new vertex.
One downside of k-NN and k-ANN graphs alone is that one must typically build them with a large value of k to get decent results, resulting in a large index.
## HNSW: Hierarchical Navigable Small Worlds
HNSW builds on k-ANN in two main ways:
* Instead of getting the k-approximate nearest neighbors for a large value of k, it sparsifies the k-ANN graph using a carefully chosen "edge pruning" heuristic, allowing for the number of edges per vertex to be limited to a relatively small constant.
* The "entry point" vertex is chosen dynamically using a recursively constructed data structure on a subset of the data, similarly to a skip list.
This recursive structure can be thought of as separating into layers:
* At the bottom-most layer, an k-ANN graph on the whole dataset is present.
* At the second layer, a k-ANN graph on a fraction of the dataset (e.g. 10%) is present.
* At the Lth layer, a k-ANN graph is present. It is over a (constant) fraction (e.g. 10%) of the vectors/vertices present in the L-1th layer.
Then the greedy search routine operates as follows:
* At the top layer (using an arbitrary vertex as an entry point), use the greedy local search routine on the k-ANN graph to get an approximate nearest neighbor at that layer.
* Using the approximate nearest neighbor found in the previous layer as an entry point, find an approximate nearest neighbor in the next layer with the same method.
* Repeat until the bottom-most layer is reached. Then use the entry point to find multiple nearest neighbors (e.g. top 10).
## Usage
There are three key parameters to set when constructing an HNSW index:
*`metric`: Use an `l2` euclidean distance metric. We also support `dot` and `cosine` distance.
*`m`: The number of neighbors to select for each vector in the HNSW graph.
*`ef_construction`: The number of candidates to evaluate during the construction of the HNSW graph.
We can combine the above concepts to understand how to build and query an HNSW index in LanceDB.
An ANN (Approximate Nearest Neighbors) index is a data structure that represents data in a way that makes it more efficient to search and retrieve. Using an ANN index is faster, but less accurate than kNN or brute force search because, in essence, the index is a lossy representation of the data.
LanceDB is fundamentally different from other vector databases in that it is built on top of [Lance](https://github.com/lancedb/lance), an open-source columnar data format designed for performant ML workloads and fast random access. Due to the design of Lance, LanceDB's indexing philosophy adopts a primarily *disk-based* indexing philosophy.
## IVF-PQ
IVF-PQ is a composite index that combines inverted file index (IVF) and product quantization (PQ). The implementation in LanceDB provides several parameters to fine-tune the index's size, query throughput, latency and recall, which are described later in this section.
### Product quantization
Quantization is a compression technique used to reduce the dimensionality of an embedding to speed up search.
Product quantization (PQ) works by dividing a large, high-dimensional vector of size into equally sized subvectors. Each subvector is assigned a "reproduction value" that maps to the nearest centroid of points for that subvector. The reproduction values are then assigned to a codebook using unique IDs, which can be used to reconstruct the original vector.
It's important to remember that quantization is a *lossy process*, i.e., the reconstructed vector is not identical to the original vector. This results in a trade-off between the size of the index and the accuracy of the search results.
As an example, consider starting with 128-dimensional vector consisting of 32-bit floats. Quantizing it to an 8-bit integer vector with 4 dimensions as in the image above, we can significantly reduce memory requirements.
!!! example "Effect of quantization"
Original: `128 × 32 = 4096` bits
Quantized: `4 × 8 = 32` bits
Quantization results in a **128x** reduction in memory requirements for each vector in the index, which is substantial.
### Inverted file index
While PQ helps with reducing the size of the index, IVF primarily addresses search performance. The primary purpose of an inverted file index is to facilitate rapid and effective nearest neighbor search by narrowing down the search space.
In IVF, the PQ vector space is divided into *Voronoi cells*, which are essentially partitions that consist of all the points in the space that are within a threshold distance of the given region's seed point. These seed points are initialized by running K-means over the stored vectors. The centroids of K-means turn into the seed points which then each define a region. These regions are then are used to create an inverted index that correlates each centroid with a list of vectors in the space, allowing a search to be restricted to just a subset of vectors in the index.
During query time, depending on where the query lands in vector space, it may be close to the border of multiple Voronoi cells, which could make the top-k results ambiguous and span across multiple cells. To address this, the IVF-PQ introduces the `nprobe` parameter, which controls the number of Voronoi cells to search during a query. The higher the `nprobe`, the more accurate the results, but the slower the query.
## Putting it all together
We can combine the above concepts to understand how to build and query an IVF-PQ index in LanceDB.
### Construct index
There are three key parameters to set when constructing an IVF-PQ index:
*`metric`: Use an `l2` euclidean distance metric. We also support `dot` and `cosine` distance.
*`num_partitions`: The number of partitions in the IVF portion of the index.
*`num_sub_vectors`: The number of sub-vectors that will be created during Product Quantization (PQ).
In Python, the index can be created as follows:
```python
# Create and train the index for a 1536-dimensional vector
# Make sure you have enough data in the table for an effective training step
`num_partitions`=256 and `num_sub_vectors`=96 does not work for every dataset. Those values needs to be adjusted for your particular dataset.
The `num_partitions` is usually chosen to target a particular number of vectors per partition. `num_sub_vectors` is typically chosen based on the desired recall and the dimensionality of the vector. See [here](../ann_indexes.md/#how-to-choose-num_partitions-and-num_sub_vectors-for-ivf_pq-index) for best practices on choosing these parameters.
### Query the index
```python
# Search using a random 1536-dimensional embedding
tbl.search(np.random.random((1536))) \
.limit(2) \
.nprobes(20) \
.refine_factor(10) \
.to_pandas()
```
The above query will perform a search on the table `tbl` using the given query vector, with the following parameters:
*`limit`: The number of results to return
*`nprobes`: The number of probes determines the distribution of vector space. While a higher number enhances search accuracy, it also results in slower performance. Typically, setting `nprobes` to cover 5–10% of the dataset proves effective in achieving high recall with minimal latency.
*`refine_factor`: Refine the results by reading extra elements and re-ranking them in memory. A higher number makes the search more accurate but also slower (see the [FAQ](../faq.md#do-i-need-to-set-a-refine-factor-when-using-an-index) page for more details on this).
*`to_pandas()`: Convert the results to a pandas DataFrame
And there you have it! You now understand what an IVF-PQ index is, and how to create and query it in LanceDB.
To see how to create an IVF-PQ index in LanceDB, take a look at the [ANN indexes](../ann_indexes.md) section.
LanceDB is among the only vector databases built on top of multiple modular components designed from the ground-up to be efficient on disk. This gives it the unique benefit of being flexible enough to support multiple storage backends, including local NVMe, EBS, EFS and many other third-party APIs that connect to the cloud.
It is important to understand the tradeoffs between cost and latency for your specific application and use case. This section will help you understand the tradeoffs between the different storage backends.
## Storage options
We've prepared a simple diagram to showcase the thought process that goes into choosing a storage backend when using LanceDB OSS, Cloud or Enterprise.
When architecting your system, you'd typically ask yourself the following questions to decide on a storage option:
1.**Latency**: How fast do I need results? What do the p50 and also p95 look like?
2.**Scalability**: Can I scale up the amount of data and QPS easily?
3.**Cost**: To serve my application, what’s the all-in cost of *both* storage and serving infra?
4.**Reliability/Availability**: How does replication work? Is disaster recovery addressed?
## Tradeoffs
This section reviews the characteristics of each storage option in four dimensions: latency, scalability, cost and reliability.
**We begin with the lowest cost option, and end with the lowest latency option.**
### 1. S3 / GCS / Azure Blob Storage
!!! tip "Lowest cost, highest latency"
- **Latency** ⇒ Has the highest latency. p95 latency is also substantially worse than p50. In general you get results in the order of several hundred milliseconds
- **Scalability** ⇒ Infinite on storage, however, QPS will be limited by S3 concurrency limits
- **Cost** ⇒ Lowest (order of magnitude cheaper than other options)
- **Reliability/Availability** ⇒ Highly available, as blob storage like S3 are critical infrastructure that form the backbone of the internet.
Another important point to note is that LanceDB is designed to separate storage from compute, and the underlying Lance format stores the data in numerous immutable fragments. Due to these factors, LanceDB is a great storage option that addresses the _N + 1_ query problem. i.e., when a high query throughput is required, query processes can run in a stateless manner and be scaled up and down as needed.
### 2. EFS / GCS Filestore / Azure File Storage
!!! info "Moderately low cost, moderately low latency (<100ms)"
- **Latency** ⇒ Much better than object/blob storage but not as good as EBS/Local disk; < 100ms p95 achievable
- **Scalability** ⇒ High, but the bottleneck will be the IOPs limit, but when scaling you can provision multiple EFS volumes
- **Cost** ⇒ Significantly more expensive than S3 but still very cost effective compared to in-memory dbs. Inactive data in EFS is also automatically tiered to S3-level costs.
- **Reliability/Availability** ⇒ Highly available, as query nodes can go down without affecting EFS. However, EFS does not provide replication / backup - this must be managed manually.
A recommended best practice is to keep a copy of the data on S3 for disaster recovery scenarios. If any downtime is unacceptable, then you would need another EFS with a copy of the data. This is still much cheaper than EC2 instances holding multiple copies of the data.
### 3. Third-party storage solutions
Solutions like [MinIO](https://blog.min.io/lancedb-trusted-steed-against-data-complexity/), WekaFS, etc. that deliver S3 compatible API with much better performance than S3.
!!! info "Moderately low cost, moderately low latency (<100ms)"
- **Latency** ⇒ Should be similar latency to EFS, better than S3 (<100ms)
- **Scalability** ⇒ Up to the solutions architect, who can add as many nodes to their MinIO or other third-party provider's cluster as needed
- **Cost** ⇒ Definitely higher than S3. The cost can be marginally higher than EFS until you get to maybe >10TB scale with high utilization
- **Reliability/Availability** ⇒ These are all shareable by lots of nodes, quality/cost of replication/backup depends on the vendor
### 4. EBS / GCP Persistent Disk / Azure Managed Disk
!!! info "Very low latency (<30ms), higher cost"
- **Latency** ⇒ Very good, pretty close to local disk. You’re looking at <30ms latency in most cases
- **Scalability** ⇒ EBS is not shareable between instances. If deployed via k8s, it can be shared between pods that live on the same instance, but beyond that you would need to shard data or make an additional copy
- **Cost** ⇒ Higher than EFS. There are some hidden costs to EBS as well if you’re paying for IO.
- **Reliability/Availability** ⇒ Not shareable between instances but can be shared between pods on the same instance. Survives instance termination. No automatic backups.
Just like EFS, an EBS or persistent disk setup requires more manual work to manage data sharding, backups and capacity.
### 5. Local disk (SSD/NVMe)
!!! danger "Lowest latency (<10ms), highest cost"
- **Latency** ⇒ Lowest latency with modern NVMe drives, <10ms p95
- **Scalability** ⇒ Difficult to scale on cloud. Also need additional copies / sharding if QPS needs to be higher
- **Cost** ⇒ Highest cost; the main issue with keeping your application and storage tightly integrated is that it’s just not really possible to scale this up in cloud environments
- **Reliability/Availability** ⇒ If the instance goes down, so does your data. You have to be _very_ diligent about backing up your data
As a rule of thumb, local disk should be your storage option if you require absolutely *crazy low* latency and you’re willing to do a bunch of data management work to make it happen.
Vector search is a technique used to search for similar items based on their vector representations, called embeddings. It is also known as similarity search, nearest neighbor search, or approximate nearest neighbor search.
Raw data (e.g. text, images, audio, etc.) is converted into embeddings via an embedding model, which are then stored in a vector database like LanceDB. To perform similarity search at scale, an index is created on the stored embeddings, which can then used to perform fast lookups.
## Embeddings
Modern machine learning models can be trained to convert raw data into embeddings, represented as arrays (or vectors) of floating point numbers of fixed dimensionality. What makes embeddings useful in practice is that the position of an embedding in vector space captures some of the semantics of the data, depending on the type of model and how it was trained. Points that are close to each other in vector space are considered similar (or appear in similar contexts), and points that are far away are considered dissimilar.
Large datasets of multi-modal data (text, audio, images, etc.) can be converted into embeddings with the appropriate model. Projecting the vectors' principal components in 2D space results in groups of vectors that represent similar concepts clustering together, as shown below.
## Indexes
Embeddings for a given dataset are made searchable via an **index**. The index is constructed by using data structures that store the embeddings such that it's very efficient to perform scans and lookups on them. A key distinguishing feature of LanceDB is it uses a disk-based index: IVF-PQ, which is a variant of the Inverted File Index (IVF) that uses Product Quantization (PQ) to compress the embeddings.
See the [IVF-PQ](./index_ivfpq.md) page for more details on how it works.
## Brute force search
The simplest way to perform vector search is to perform a brute force search, without an index, where the distance between the query vector and all the vectors in the database are computed, with the top-k closest vectors returned. This is equivalent to a k-nearest neighbours (kNN) search in vector space.
As you can imagine, the brute force approach is not scalable for datasets larger than a few hundred thousand vectors, as the latency of the search grows linearly with the size of the dataset. This is where approximate nearest neighbour (ANN) algorithms come in.
## Approximate nearest neighbour (ANN) search
Instead of performing an exhaustive search on the entire database for each and every query, approximate nearest neighbour (ANN) algorithms use an index to narrow down the search space, which significantly reduces query latency. The trade-off is that the results are not guaranteed to be the true nearest neighbors of the query, but are usually "good enough" for most use cases.
We have support for [imagebind](https://github.com/facebookresearch/ImageBind) model embeddings. You can download our version of the packaged model via - `pip install imagebind-packaged==0.1.2`.
This function is registered as `imagebind` and supports Audio, Video and Text modalities(extending to Thermal,Depth,IMU data):
| Parameter | Type | Default Value | Description |
|---|---|---|---|
| `name` | `str` | `"imagebind_huge"` | Name of the model. |
| `device` | `str` | `"cpu"` | The device to run the model on. Can be `"cpu"` or `"gpu"`. |
| `normalize` | `bool` | `False` | set to `True` to normalize your inputs before model ingestion. |
Below is an example demonstrating how the API works:
If you have any questions about the embeddings API, supported models, or see a relevant model missing, please raise an issue [on GitHub](https://github.com/lancedb/lancedb/issues).
We support CLIP model embeddings using the open source alternative, [open-clip](https://github.com/mlfoundations/open_clip) which supports various customizations. It is registered as `open-clip` and supports the following customizations:
| Parameter | Type | Default Value | Description |
|---|---|---|---|
| `name` | `str` | `"ViT-B-32"` | The name of the model. |
| `pretrained` | `str` | `"laion2b_s34b_b79k"` | The name of the pretrained model to load. |
| `device` | `str` | `"cpu"` | The device to run the model on. Can be `"cpu"` or `"gpu"`. |
| `batch_size` | `int` | `64` | The number of images to process in a batch. |
| `normalize` | `bool` | `True` | Whether to normalize the input images before feeding them to the model. |
This embedding function supports ingesting images as both bytes and urls. You can query them using both test and other images.
!!! info
LanceDB supports ingesting images directly from accessible links.
```python
importlancedb
fromlancedb.pydanticimportLanceModel,Vector
fromlancedb.embeddingsimportget_registry
db=lancedb.connect(tmp_path)
func=get_registry().get("open-clip").create()
classImages(LanceModel):
label:str
image_uri:str=func.SourceField()# image uri as the source
image_bytes:bytes=func.SourceField()# image bytes as the source
AWS Bedrock supports multiple base models for generating text embeddings. You need to setup the AWS credentials to use this embedding function.
You can do so by using `awscli` and also add your session_token:
```shell
aws configure
aws configure set aws_session_token "<your_session_token>"
```
to ensure that the credentials are set up correctly, you can run the following command:
```shell
aws sts get-caller-identity
```
Supported Embedding modelIDs are:
*`amazon.titan-embed-text-v1`
*`cohere.embed-english-v3`
*`cohere.embed-multilingual-v3`
Supported parameters (to be passed in `create` method) are:
| Parameter | Type | Default Value | Description |
|---|---|---|---|
| **name** | str | "amazon.titan-embed-text-v1" | The model ID of the bedrock model to use. Supported base models for Text Embeddings: amazon.titan-embed-text-v1, cohere.embed-english-v3, cohere.embed-multilingual-v3 |
| **region** | str | "us-east-1" | Optional name of the AWS Region in which the service should be called (e.g., "us-east-1"). |
| **profile_name** | str | None | Optional name of the AWS profile to use for calling the Bedrock service. If not specified, the default profile will be used. |
| **assumed_role** | str | None | Optional ARN of an AWS IAM role to assume for calling the Bedrock service. If not specified, the current active credentials will be used. |
| **role_session_name** | str | "lancedb-embeddings" | Optional name of the AWS IAM role session to use for calling the Bedrock service. If not specified, a "lancedb-embeddings" name will be used. |
| **runtime** | bool | True | Optional choice of getting different client to perform operations with the Amazon Bedrock service. |
| **max_retries** | int | 7 | Optional number of retries to perform when a request fails. |
Using cohere API requires cohere package, which can be installed using `pip install cohere`. Cohere embeddings are used to generate embeddings for text data. The embeddings can be used for various tasks like semantic search, clustering, and classification.
You also need to set the `COHERE_API_KEY` environment variable to use the Cohere API.
Supported models are:
- embed-english-v3.0
- embed-multilingual-v3.0
- embed-english-light-v3.0
- embed-multilingual-light-v3.0
- embed-english-v2.0
- embed-english-light-v2.0
- embed-multilingual-v2.0
Supported parameters (to be passed in `create` method) are:
| Parameter | Type | Default Value | Description |
|---|---|--------|---------|
| `name` | `str` | `"embed-english-v2.0"` | The model ID of the cohere model to use. Supported base models for Text Embeddings: embed-english-v3.0, embed-multilingual-v3.0, embed-english-light-v3.0, embed-multilingual-light-v3.0, embed-english-v2.0, embed-english-light-v2.0, embed-multilingual-v2.0 |
| `source_input_type` | `str` | `"search_document"` | The type of input data to be used for the source column. |
| `query_input_type` | `str` | `"search_query"` | The type of input data to be used for the query. |
With Google's Gemini, you can represent text (words, sentences, and blocks of text) in a vectorized form, making it easier to compare and contrast embeddings. For example, two texts that share a similar subject matter or sentiment should have similar embeddings, which can be identified through mathematical comparison techniques such as cosine similarity. For more on how and why you should use embeddings, refer to the Embeddings guide.
The Gemini Embedding Model API supports various task types:
| "`retrieval_query`" | Specifies the given text is a query in a search/retrieval setting. |
| "`retrieval_document`" | Specifies the given text is a document in a search/retrieval setting. Using this task type requires a title but is automatically proided by Embeddings API |
| "`semantic_similarity`" | Specifies the given text will be used for Semantic Textual Similarity (STS). |
| "`classification`" | Specifies that the embeddings will be used for classification. |
| "`clusering`" | Specifies that the embeddings will be used for clustering. |
We offer support for all Hugging Face models (which can be loaded via [transformers](https://huggingface.co/docs/transformers/en/index) library). The default model is `colbert-ir/colbertv2.0` which also has its own special callout - `registry.get("colbert")`. Some Hugging Face models might require custom models defined on the HuggingFace Hub in their own modeling files. You may enable this by setting `trust_remote_code=True`. This option should only be set to True for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
Generate text embeddings using IBM's watsonx.ai platform.
## Supported Models
You can find a list of supported models at [IBM watsonx.ai Documentation](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-embed.html?context=wx). The currently supported model names are:
-`ibm/slate-125m-english-rtrvr`
-`ibm/slate-30m-english-rtrvr`
-`sentence-transformers/all-minilm-l12-v2`
-`intfloat/multilingual-e5-large`
## Parameters
The following parameters can be passed to the `create` method:
| Parameter | Type | Default Value | Description |
[Instructor](https://instructor-embedding.github.io/) is an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g. classification, retrieval, clustering, text evaluation, etc.) and domains (e.g. science, finance, etc.) by simply providing the task instruction, without any finetuning.
If you want to calculate customized embeddings for specific sentences, you can follow the unified template to write instructions.
!!! info
Represent the `domain``text_type` for `task_objective`:
*`domain` is optional, and it specifies the domain of the text, e.g. science, finance, medicine, etc.
*`text_type` is required, and it specifies the encoding unit, e.g. sentence, document, paragraph, etc.
*`task_objective` is optional, and it specifies the objective of embedding, e.g. retrieve a document, classify the sentence, etc.
More information about the model can be found at the [source URL](https://github.com/xlang-ai/instructor-embedding).
| Argument | Type | Default | Description |
|---|---|---|---|
| `name` | `str` | "hkunlp/instructor-base" | The name of the model to use |
| `batch_size` | `int` | `32` | The batch size to use when generating embeddings |
| `device` | `str` | `"cpu"` | The device to use when generating embeddings |
| `show_progress_bar` | `bool` | `True` | Whether to show a progress bar when generating embeddings |
| `normalize_embeddings` | `bool` | `True` | Whether to normalize the embeddings |
| `quantize` | `bool` | `False` | Whether to quantize the model |
| `source_instruction` | `str` | `"represent the docuement for retreival"` | The instruction for the source column |
| `query_instruction` | `str` | `"represent the document for retreiving the most similar documents"` | The instruction for the query |
LanceDB registers the OpenAI embeddings function in the registry by default, as `openai`. Below are the parameters that you can customize when creating the instances:
| Parameter | Type | Default Value | Description |
|---|---|---|---|
| `name` | `str` | `"text-embedding-ada-002"` | The name of the model. |
| `dim` | `int` | Model default | For OpenAI's newer text-embedding-3 model, we can specify a dimensionality that is smaller than the 1536 size. This feature supports it |
| `use_azure` | bool | `False` | Set true to use Azure OpenAPI SDK |
You can also load many other model architectures from the library. For example models from sources such as BAAI, nomic, salesforce research, etc.
See this HF hub page for all [supported models](https://huggingface.co/models?library=sentence-transformers).
!!! note "BAAI Embeddings example"
Here is an example that uses BAAI embedding model from the HuggingFace Hub [supported models](https://huggingface.co/models?library=sentence-transformers)
```python
import lancedb
from lancedb.pydantic import LanceModel, Vector
from lancedb.embeddings import get_registry
db = lancedb.connect("/tmp/db")
model = get_registry().get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", device="cpu")
- voyage-4-lite (1024 dims, optimized for latency and cost, 1M batch tokens)
- voyage-4-large (1024 dims, best retrieval quality, 120K batch tokens)
**Voyage-3 Series**
- voyage-3
- voyage-3-lite
**Domain-Specific Models**
- voyage-finance-2
- voyage-multilingual-2
- voyage-law-2
@@ -20,7 +31,7 @@ Supported parameters (to be passed in `create` method) are:
| Parameter | Type | Default Value | Description |
|---|---|--------|---------|
| `name` | `str` | `None` | The model ID of the model to use. Supported base models for Text Embeddings: voyage-3, voyage-3-lite, voyage-finance-2, voyage-multilingual-2, voyage-law-2, voyage-code-2 |
| `name` | `str` | `None` | The model ID of the model to use. Supported base models for Text Embeddings: voyage-4, voyage-4-lite, voyage-4-large, voyage-3, voyage-3-lite, voyage-finance-2, voyage-multilingual-2, voyage-law-2, voyage-code-2 |
| `input_type` | `str` | `None` | Type of the input text. Default to None. Other options: query, document. |
| `truncation` | `bool` | `True` | Whether to truncate the input texts to fit within the context length. |
To use your own custom embedding function, you can follow these 2 simple steps:
1. Create your embedding function by implementing the `EmbeddingFunction` interface
2. Register your embedding function in the global `EmbeddingFunctionRegistry`.
Let us see how this looks like in action.
`EmbeddingFunction` and `EmbeddingFunctionRegistry` handle low-level details for serializing schema and model information as metadata. To build a custom embedding function, you don't have to worry about the finer details - simply focus on setting up the model and leave the rest to LanceDB.
## `TextEmbeddingFunction` interface
There is another optional layer of abstraction available: `TextEmbeddingFunction`. You can use this abstraction if your model isn't multi-modal in nature and only needs to operate on text. In such cases, both the source and vector fields will have the same work for vectorization, so you simply just need to setup the model and rest is handled by `TextEmbeddingFunction`. You can read more about the class and its attributes in the class reference.
Let's implement `SentenceTransformerEmbeddings` class. All you need to do is implement the `generate_embeddings()` and `ndims` function to handle the input types you expect and register the class in the global `EmbeddingFunctionRegistry`
=== "Python"
```python
from lancedb.embeddings import register
from lancedb.util import attempt_import_or_raise
@register("sentence-transformers")
class SentenceTransformerEmbeddings(TextEmbeddingFunction):
name: str = "all-MiniLM-L6-v2"
# set more default instance vars like device, etc.
method. This prevents users from accidentally instantiating the embedding
function with hard-coded secrets.
Now you can use this embedding function to create your table schema and that's it! you can then ingest data and run queries without manually vectorizing the inputs.
You can always implement the `EmbeddingFunction` interface directly if you want or need to, `TextEmbeddingFunction` just makes it much simpler and faster for you to do so, by setting up the boiler plat for text-specific use case
## Multi-modal embedding function example
You can also use the `EmbeddingFunction` interface to implement more complex workflows such as multi-modal embedding function support.
=== "Python"
LanceDB implements `OpenClipEmeddingFunction` class that suppports multi-modal seach. Here's the implementation that you can use as a reference to build your own multi-modal embedding functions.
```python
@register("open-clip")
class OpenClipEmbeddings(EmbeddingFunction):
name: str = "ViT-B-32"
pretrained: str = "laion2b_s34b_b79k"
device: str = "cpu"
batch_size: int = 64
normalize: bool = True
_model = PrivateAttr()
_preprocess = PrivateAttr()
_tokenizer = PrivateAttr()
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
open_clip = attempt_import_or_raise("open_clip", "open-clip") # EmbeddingFunction util to import external libs and raise if not found
There are various embedding functions available out of the box with LanceDB to manage your embeddings implicitly. We're actively working on adding other popular embedding APIs and models. 🚀
Before jumping on the list of available models, let's understand how to get an embedding model initialized and configured to use in our code:
!!! example "Example usage"
```python
model = get_registry()
.get("openai")
.create(name="text-embedding-ada-002")
```
Now let's understand the above syntax:
```python
model = get_registry().get("model_id").create(...params)
```
**This👆 line effectively creates a configured instance of an `embedding function` with `model` of choice that is ready for use.**
- `get_registry()` : This function call returns an instance of a `EmbeddingFunctionRegistry` object. This registry manages the registration and retrieval of embedding functions.
- `.get("model_id")` : This method call on the registry object and retrieves the **embedding models functions** associated with the `"model_id"` (1) .
{ .annotate }
1. Hover over the names in table below to find out the `model_id` of different embedding functions.
- `.create(...params)` : This method call is on the object returned by the `get` method. It instantiates an embedding model function using the **specified parameters**.
??? question "What parameters does the `.create(...params)` method accepts?"
**Checkout the documentation of specific embedding models (links in the table below👇) to know what parameters it takes**.
!!! tip "Moving on"
Now that we know how to get the **desired embedding model** and use it in our code, let's explore the comprehensive **list** of embedding models **supported by LanceDB**, in the tables below.
## Text Embedding Functions 📝
These functions are registered by default to handle text embeddings.
- 🔄 **Embedding functions** have an inbuilt rate limit handler wrapper for source and query embedding function calls that retry with **exponential backoff**.
- 🌕 Each `EmbeddingFunction` implementation automatically takes `max_retries` as an argument which has the default value of 7.
🌟 **Available Text Embeddings**
| **Embedding** :material-information-outline:{ title="Hover over the name to find out the model_id" } | **Description** | **Documentation** |
|-----------|-------------|---------------|
| [**Sentence Transformers**](available_embedding_models/text_embedding_functions/sentence_transformers.md "sentence-transformers") | 🧠 **SentenceTransformers** is a Python framework for state-of-the-art sentence, text, and image embeddings. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/sbert_2.png" alt="Sentence Transformers Icon" width="90" height="35">](available_embedding_models/text_embedding_functions/sentence_transformers.md)|
| [**Huggingface Models**](available_embedding_models/text_embedding_functions/huggingface_embedding.md "huggingface") |🤗 We offer support for all **Huggingface** models. The default model is `colbert-ir/colbertv2.0`. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/hugging_face.png" alt="Huggingface Icon" width="130" height="35">](available_embedding_models/text_embedding_functions/huggingface_embedding.md) |
| [**Ollama Embeddings**](available_embedding_models/text_embedding_functions/ollama_embedding.md "ollama") | 🔍 Generate embeddings via the **Ollama** python library. Ollama supports embedding models, making it possible to build RAG apps. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/Ollama.png" alt="Ollama Icon" width="110" height="35">](available_embedding_models/text_embedding_functions/ollama_embedding.md)|
| [**OpenAI Embeddings**](available_embedding_models/text_embedding_functions/openai_embedding.md "openai")| 🔑 **OpenAI’s** text embeddings measure the relatedness of text strings. **LanceDB** supports state-of-the-art embeddings from OpenAI. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/openai.png" alt="OpenAI Icon" width="100" height="35">](available_embedding_models/text_embedding_functions/openai_embedding.md)|
| [**Instructor Embeddings**](available_embedding_models/text_embedding_functions/instructor_embedding.md "instructor") | 📚 **Instructor**: An instruction-finetuned text embedding model that can generate text embeddings tailored to any task and domains by simply providing the task instruction, without any finetuning. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/instructor_embedding.png" alt="Instructor Embedding Icon" width="140" height="35">](available_embedding_models/text_embedding_functions/instructor_embedding.md) |
| [**Gemini Embeddings**](available_embedding_models/text_embedding_functions/gemini_embedding.md "gemini-text") | 🌌 Google’s Gemini API generates state-of-the-art embeddings for words, phrases, and sentences. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/gemini.png" alt="Gemini Icon" width="95" height="35">](available_embedding_models/text_embedding_functions/gemini_embedding.md) |
| [**Cohere Embeddings**](available_embedding_models/text_embedding_functions/cohere_embedding.md "cohere") | 💬 This will help you get started with **Cohere** embedding models using LanceDB. Using cohere API requires cohere package. Install it via `pip`. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/cohere.png" alt="Cohere Icon" width="140" height="35">](available_embedding_models/text_embedding_functions/cohere_embedding.md) |
| [**Jina Embeddings**](available_embedding_models/text_embedding_functions/jina_embedding.md "jina") | 🔗 World-class embedding models to improve your search and RAG systems. You will need **jina api key**. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/jina.png" alt="Jina Icon" width="90" height="35">](available_embedding_models/text_embedding_functions/jina_embedding.md) |
| [ **AWS Bedrock Functions**](available_embedding_models/text_embedding_functions/aws_bedrock_embedding.md "bedrock-text") | ☁️ AWS Bedrock supports multiple base models for generating text embeddings. You need to setup the AWS credentials to use this embedding function. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/aws_bedrock.png" alt="AWS Bedrock Icon" width="120" height="35">](available_embedding_models/text_embedding_functions/aws_bedrock_embedding.md) |
| [**IBM Watsonx.ai**](available_embedding_models/text_embedding_functions/ibm_watsonx_ai_embedding.md "watsonx") | 💡 Generate text embeddings using IBM's watsonx.ai platform. **Note**: watsonx.ai library is an optional dependency. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/watsonx.png" alt="Watsonx Icon" width="140" height="35">](available_embedding_models/text_embedding_functions/ibm_watsonx_ai_embedding.md) |
| [**VoyageAI Embeddings**](available_embedding_models/text_embedding_functions/voyageai_embedding.md "voyageai") | 🌕 Voyage AI provides cutting-edge embedding and rerankers. This will help you get started with **VoyageAI** embedding models using LanceDB. Using voyageai API requires voyageai package. Install it via `pip`. | [<img src="https://www.voyageai.com/logo.svg" alt="VoyageAI Icon" width="140" height="35">](available_embedding_models/text_embedding_functions/voyageai_embedding.md) |
[st-key]: "sentence-transformers"
[hf-key]: "huggingface"
[ollama-key]: "ollama"
[openai-key]: "openai"
[instructor-key]: "instructor"
[gemini-key]: "gemini-text"
[cohere-key]: "cohere"
[jina-key]: "jina"
[aws-key]: "bedrock-text"
[watsonx-key]: "watsonx"
[voyageai-key]: "voyageai"
## Multi-modal Embedding Functions🖼️
Multi-modal embedding functions allow you to query your table using both images and text. 💬🖼️
🌐 **Available Multi-modal Embeddings**
| Embedding :material-information-outline:{ title="Hover over the name to find out the model_id" } | Description | Documentation |
|-----------|-------------|---------------|
| [**OpenClip Embeddings**](available_embedding_models/multimodal_embedding_functions/openclip_embedding.md "open-clip") | 🎨 We support CLIP model embeddings using the open source alternative, **open-clip** which supports various customizations. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/openclip_github.png" alt="openclip Icon" width="150" height="35">](available_embedding_models/multimodal_embedding_functions/openclip_embedding.md) |
| [**Imagebind Embeddings**](available_embedding_models/multimodal_embedding_functions/imagebind_embedding.md "imageind") | 🌌 We have support for **imagebind model embeddings**. You can download our version of the packaged model via - `pip install imagebind-packaged==0.1.2`. | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/imagebind_meta.png" alt="imagebind Icon" width="150" height="35">](available_embedding_models/multimodal_embedding_functions/imagebind_embedding.md)|
| [**Jina Multi-modal Embeddings**](available_embedding_models/multimodal_embedding_functions/jina_multimodal_embedding.md "jina") | 🔗 **Jina embeddings** can also be used to embed both **text** and **image** data, only some of the models support image data and you can check the detailed documentation. 👉 | [<img src="https://raw.githubusercontent.com/lancedb/assets/main/docs/assets/logos/jina.png" alt="jina Icon" width="90" height="35">](available_embedding_models/multimodal_embedding_functions/jina_multimodal_embedding.md) |
!!! note
If you'd like to request support for additional **embedding functions**, please feel free to open an issue on our LanceDB [GitHub issue page](https://github.com/lancedb/lancedb/issues).
Representing multi-modal data as vector embeddings is becoming a standard practice. Embedding functions can themselves be thought of as key part of the data processing pipeline that each request has to be passed through. The assumption here is: after initial setup, these components and the underlying methodology are not expected to change for a particular project.
For this purpose, LanceDB introduces an **embedding functions API**, that allow you simply set up once, during the configuration stage of your project. After this, the table remembers it, effectively making the embedding functions *disappear in the background* so you don't have to worry about manually passing callables, and instead, simply focus on the rest of your data engineering pipeline.
!!! Note "Embedding functions on LanceDB cloud"
When using embedding functions with LanceDB cloud, the embeddings will be generated on the source device and sent to the cloud. This means that the source device must have the necessary resources to generate the embeddings.
!!! warning
Using the embedding function registry means that you don't have to explicitly generate the embeddings yourself.
However, if your embedding function changes, you'll have to re-configure your table with the new embedding function
and regenerate the embeddings. In the future, we plan to support the ability to change the embedding function via
table metadata and have LanceDB automatically take care of regenerating the embeddings.
## 1. Define the embedding function
=== "Python"
In the LanceDB python SDK, we define a global embedding function registry with
many different embedding models and even more coming soon.
Here's let's an implementation of CLIP as example.
```python
from lancedb.embeddings import get_registry
registry = get_registry()
clip = registry.get("open-clip").create()
```
You can also define your own embedding function by implementing the `EmbeddingFunction`
abstract base interface. It subclasses Pydantic Model which can be utilized to write complex schemas simply as we'll see next!
=== "TypeScript"
In the TypeScript SDK, the choices are more limited. For now, only the OpenAI
embedding function is available.
```javascript
import * as lancedb from '@lancedb/lancedb'
import { getRegistry } from '@lancedb/lancedb/embeddings'
// You need to provide an OpenAI API key
const apiKey = "sk-..."
// The embedding function will create embeddings for the 'text' column
The embedding function defined above abstracts away all the details about the models and dimensions required to define the schema. You can simply set a field as **source** or **vector** column. Here's how:
```python
class Pets(LanceModel):
vector: Vector(clip.ndims()) = clip.VectorField()
image_uri: str = clip.SourceField()
```
`VectorField` tells LanceDB to use the clip embedding function to generate query embeddings for the `vector` column and `SourceField` ensures that when adding data, we automatically use the specified embedding function to encode `image_uri`.
=== "TypeScript"
For the TypeScript SDK, a schema can be inferred from input data, or an explicit
Arrow schema can be provided.
## 3. Create table and add data
Now that we have chosen/defined our embedding function and the schema,
we can create the table and ingest data without needing to explicitly generate
Not only can you forget about the embeddings during ingestion, you also don't
need to worry about it when you query the table:
=== "Python"
Our OpenCLIP query embedding function supports querying via both text and images:
```python
results = (
table.search("dog")
.limit(10)
.to_pandas()
)
```
Or we can search using an image:
```python
p = Path("path/to/images/samoyed_100.jpg")
query_image = Image.open(p)
results = (
table.search(query_image)
.limit(10)
.to_pandas()
)
```
Both of the above snippet returns a pandas DataFrame with the 10 closest vectors to the query.
=== "TypeScript"
=== "@lancedb/lancedb"
```ts
const results = await table.search("What's the best pizza topping?")
.limit(10)
.toArray()
```
=== "vectordb (deprecated)"
```ts
const results = await table
.search("What's the best pizza topping?")
.limit(10)
.execute()
```
The above snippet returns an array of records with the top 10 nearest neighbors to the query.
---
## Rate limit Handling
`EmbeddingFunction` class wraps the calls for source and query embedding generation inside a rate limit handler that retries the requests with exponential backoff after successive failures. By default, the maximum retires is set to 7. You can tune it by setting it to a different number, or disable it by setting it to 0.
An example of how to do this is shown below:
```python
clip = registry.get("open-clip").create() # Defaults to 7 max retries
clip = registry.get("open-clip").create(max_retries=10) # Increase max retries to 10
Embedding functions can also fail due to other errors that have nothing to do with rate limits.
This is why the error is also logged.
## Some fun with Pydantic
LanceDB is integrated with Pydantic, which was used in the example above to define the schema in Python. It's also used behind the scenes by the embedding function API to ingest useful information as table metadata.
You can also use the integration for adding utility operations in the schema. For example, in our multi-modal example, you can search images using text or another image. Let's define a utility function to plot the image.
```python
class Pets(LanceModel):
vector: Vector(clip.ndims()) = clip.VectorField()
image_uri: str = clip.SourceField()
@property
def image(self):
return Image.open(self.image_uri)
```
Now, you can covert your search results to a Pydantic model and use this property.
Due to the nature of vector embeddings, they can be used to represent any kind of data, from text to images to audio.
This makes them a very powerful tool for machine learning practitioners.
However, there's no one-size-fits-all solution for generating embeddings - there are many different libraries and APIs
(both commercial and open source) that can be used to generate embeddings from structured/unstructured data.
LanceDB supports 3 methods of working with embeddings.
1. You can manually generate embeddings for the data and queries. This is done outside of LanceDB.
2. You can use the built-in [embedding functions](./embedding_functions.md) to embed the data and queries in the background.
3. You can define your own [custom embedding function](./custom_embedding_function.md)
that extends the default embedding functions.
For python users, there is also a legacy [with_embeddings API](./legacy.md).
It is retained for compatibility and will be removed in a future version.
## Quickstart
To get started with embeddings, you can use the built-in embedding functions.
### OpenAI Embedding function
LanceDB registers the OpenAI embeddings function in the registry as `openai`. You can pass any supported model name to the `create`. By default it uses `"text-embedding-ada-002"`.
LanceDB registers the Sentence Transformers embeddings function in the registry as `sentence-transformers`. You can pass any supported model name to the `create`. By default it uses `"sentence-transformers/paraphrase-MiniLM-L6-v2"`.
=== "Python"
```python
import lancedb
from lancedb.pydantic import LanceModel, Vector
from lancedb.embeddings import get_registry
db = lancedb.connect("/tmp/db")
model = get_registry().get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", device="cpu")
actual = table.search(query).limit(1).to_pydantic(Words)[0]
print(actual.text)
```
=== "TypeScript"
Coming Soon!
=== "Rust"
Coming Soon!
### Embedding function with LanceDB cloud
Embedding functions are now supported on LanceDB cloud. The embeddings will be generated on the source device and sent to the cloud. This means that the source device must have the necessary resources to generate the embeddings. Here's an example using the OpenAI embedding function:
```python
import os
import lancedb
from lancedb.pydantic import LanceModel, Vector
from lancedb.embeddings import get_registry
os.environ['OPENAI_API_KEY'] = "..."
db = lancedb.connect(
uri="db://....",
api_key="sk_...",
region="us-east-1"
)
func = get_registry().get("openai").create()
class Words(LanceModel):
text: str = func.SourceField()
vector: Vector(func.ndims()) = func.VectorField()
table = db.create_table("words", schema=Words)
table.add([
{"text": "hello world"},
{"text": "goodbye world"}
])
query = "greetings"
actual = table.search(query).limit(1).to_pydantic(Words)[0]
Another popular alternative is to use an external API like OpenAI's [embeddings API](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings).
```python
importopenai
importos
# Configuring the environment variable OPENAI_API_KEY
The term **dimension** is a synonym for the number of elements in a feature vector. Each feature can be thought of as a different axis in a geometric space.
High-dimensional data means there are many features(or attributes) in the data.
!!! example
1. An image is a data point and it might have thousands of dimensions because each pixel could be considered as a feature.
2. Text data, when represented by each word or character, can also lead to high dimensions, especially when considering all possible words in a language.
Embedding captures **meaning and relationships** within data by mapping high-dimensional data into a lower-dimensional space. It captures it by placing inputs that are more **similar in meaning** closer together in the **embedding space**.
## What are Vector Embeddings?
Vector embeddings is a way to convert complex data, like text, images, or audio into numerical coordinates (called vectors) that can be plotted in an n-dimensional space(embedding space).
The closer these data points are related in the real world, the closer their corresponding numerical coordinates (vectors) will be to each other in the embedding space. This proximity in the embedding space reflects their semantic similarities, allowing machines to intuitively understand and process the data in a way that mirrors human perception of relationships and meaning.
In a way, it captures the most important aspects of the data while ignoring the less important ones. As a result, tasks like searching for related content or identifying patterns become more efficient and accurate, as the embeddings make it possible to quantify how **closely related** different **data points** are and **reduce** the **computational complexity**.
??? question "Are vectors and embeddings the same thing?"
When we say “vectors” we mean - **list of numbers** that **represents the data**.
When we say “embeddings” we mean - **list of numbers** that **capture important details and relationships**.
Although the terms are often used interchangeably, “embeddings” highlight how the data is represented with meaning and structure, while “vector” simply refers to the numerical form of that representation.
## Embedding vs Indexing
We already saw that creating **embeddings** on data is a method of creating **vectors** for a **n-dimensional embedding space** that captures the meaning and relationships inherent in the data.
Once we have these **vectors**, indexing comes into play. Indexing is a method of organizing these vector embeddings, that allows us to quickly and efficiently locate and retrieve them from the entire dataset of vector embeddings.
## What types of data/objects can be embedded?
The following are common types of data that can be embedded:
1.**Text**: Text data includes sentences, paragraphs, documents, or any written content.
2.**Images**: Image data encompasses photographs, illustrations, or any visual content.
3.**Audio**: Audio data includes sounds, music, speech, or any auditory content.
4.**Video**: Video data consists of moving images and sound, which can convey complex information.
Large datasets of multi-modal data (text, audio, images, etc.) can be converted into embeddings with the appropriate model.
!!! tip "LanceDB vs Other traditional Vector DBs"
While many vector databases primarily focus on the storage and retrieval of vector embeddings, **LanceDB** uses **Lance file format** (operates on a disk-based architecture), which allows for the storage and management of not just embeddings but also **raw file data (bytes)**. This capability means that users can integrate various types of data, including images and text, alongside their vector embeddings in a unified system.
With the ability to store both vectors and associated file data, LanceDB enhances the querying process. Users can perform semantic searches that not only retrieve similar embeddings but also access related files and metadata, thus streamlining the workflow.
## How does embedding works?
As mentioned, after creating embedding, each data point is represented as a vector in a n-dimensional space (embedding space). The dimensionality of this space can vary depending on the complexity of the data and the specific embedding technique used.
Points that are close to each other in vector space are considered similar (or appear in similar contexts), and points that are far away are considered dissimilar. To quantify this closeness, we use distance as a metric which can be measured in the following way -
1.**Euclidean Distance (l2)**: It calculates the straight-line distance between two points (vectors) in a multidimensional space.
2.**Cosine Similarity**: It measures the cosine of the angle between two vectors, providing a normalized measure of similarity based on their direction.
3.**Dot product**: It is calculated as the sum of the products of their corresponding components. To measure relatedness it considers both the magnitude and direction of the vectors.
## How do you create and store vector embeddings for your data?
1.**Creating embeddings**: Choose an embedding model, it can be a pre-trained model (open-source or commercial) or you can train a custom embedding model for your scenario. Then feed your preprocessed data into the chosen model to obtain embeddings.
??? question "Popular choices for embedding models"
For text data, popular choices are OpenAI’s text-embedding models, Google Gemini text-embedding models, Cohere’s Embed models, and SentenceTransformers, etc.
For image data, popular choices are CLIP (Contrastive Language–Image Pretraining), Imagebind embeddings by meta (supports audio, video, and image), and Jina multi-modal embeddings, etc.
2.**Storing vector embeddings**: This effectively requires **specialized databases** that can handle the complexity of vector data, as traditional databases often struggle with this task. Vector databases are designed specifically for storing and querying vector embeddings. They optimize for efficient nearest-neighbor searches and provide built-in indexing mechanisms.
!!! tip "Why LanceDB"
LanceDB **automates** the entire process of creating and storing embeddings for your data. LanceDB allows you to define and use **embedding functions**, which can be **pre-trained models** or **custom models**.
This enables you to **generate** embeddings tailored to the nature of your data (e.g., text, images) and **store** both the **original data** and **embeddings** in a **structured schema** thus providing efficient querying capabilities for similarity searches.
Let's quickly [get started](./index.md) and learn how to manage embeddings in LanceDB.
## Bonus: As a developer, what you can create using embeddings?
As a developer, you can create a variety of innovative applications using vector embeddings. Check out the following -
<div class="grid cards" markdown>
-__Chatbots__
---
Develop chatbots that utilize embeddings to retrieve relevant context and generate coherent, contextually aware responses to user queries.
[:octicons-arrow-right-24: Check out examples](../examples/python_examples/chatbot.md)
-__Recommendation Systems__
---
Develop systems that recommend content (such as articles, movies, or products) based on the similarity of keywords and descriptions, enhancing user experience.
[:octicons-arrow-right-24: Check out examples](../examples/python_examples/recommendersystem.md)
-__Vector Search__
---
Build powerful applications that harness the full potential of semantic search, enabling them to retrieve relevant data quickly and effectively.
[:octicons-arrow-right-24: Check out examples](../examples/python_examples/vector_search.md)
-__RAG Applications__
---
Combine the strengths of large language models (LLMs) with retrieval-based approaches to create more useful applications.
[:octicons-arrow-right-24: Check out examples](../examples/python_examples/rag.md)
-__Many more examples__
---
Explore applied examples available as Colab notebooks or Python scripts to integrate into your applications.
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