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
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](456198cd6f/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>
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.
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>
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.
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>
## 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)
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#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.
## 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>
## 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 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.
