The [`FieldLike` type in
arrow.ts](5ec12c9971/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.)
This PR adds support for namespace-backed databases through
lance-namespace integration, enabling centralized table management
through namespace APIs.
---------
Co-authored-by: Claude <noreply@anthropic.com>
### Bug Fix: Undefined Values in Nullable Fields
**Issue**: When inserting data with `undefined` values into nullable
fields, LanceDB was incorrectly coercing them to default values (`false`
for booleans, `NaN` for numbers, `""` for strings) instead of `null`.
**Fix**: Modified the `makeVector()` function in `arrow.ts` to properly
convert `undefined` values to `null` for nullable fields before passing
data to Apache Arrow.
fixes: #2645
**Result**: Now `{ text: undefined, number: undefined, bool: undefined
}` correctly becomes `{ text: null, number: null, bool: null }` when
fields are marked as nullable in the schema.
**Files Changed**:
- `nodejs/lancedb/arrow.ts` (core fix)
- `nodejs/__test__/arrow.test.ts` (test coverage)
- This ensures proper null handling for nullable fields as expected by
users.
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
### Solution
Added special handling in `makeVector` function for boolean arrays where
all values are null. The fix creates a proper null bitmap using
`makeData` and `arrowMakeVector` instead of relying on Apache Arrow's
`vectorFromArray` which doesn't handle this edge case correctly.
fixes: #2644
### Changes
- Added null value detection for boolean types in `makeVector` function
- Creates proper Arrow data structure with null bitmap when all boolean
values are null
- Preserves existing behavior for non-null boolean values and other data
types
- Fixes the boolean null value bug while maintaining backward
compatibility.
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
The basic idea of MRR is this -
https://www.evidentlyai.com/ranking-metrics/mean-reciprocal-rank-mrr
I've implemented a weighted version for allowing user to set weightage
between vector and fts.
The gist is something like this
### Scenario A: Document at rank 1 in one set, absent from another
```
# Assuming equal weights: weight_vector = 0.5, weight_fts = 0.5
vector_rr = 1.0 # rank 1 → 1/1 = 1.0
fts_rr = 0.0 # absent → 0.0
weighted_mrr = 0.5 × 1.0 + 0.5 × 0.0 = 0.5
```
### Scenario B: Document at rank 1 in one set, rank 2 in another
```
# Same weights: weight_vector = 0.5, weight_fts = 0.5
vector_rr = 1.0 # rank 1 → 1/1 = 1.0
fts_rr = 0.5 # rank 2 → 1/2 = 0.5
weighted_mrr = 0.5 × 1.0 + 0.5 × 0.5 = 0.5 + 0.25 = 0.75
```
And so with `return_score="all"` the result looks something like this
(this is from the reranker tests).
Because this is a weighted rank based reranker, some results might have
the same score
```
text vector _distance _rowid _score _relevance_score
0 I am your father [-0.010703234, 0.069315575, 0.030076642, 0.002... 8.149148e-13 8589934598 10.978719 1.000000
1 the ground beneath my feet [-0.09500901, 0.00092102867, 0.0755851, 0.0372... 1.376896e+00 8589934604 NaN 0.250000
2 I find your lack of faith disturbing [0.07525753, -0.0100010475, 0.09990541, 0.0209... NaN 8589934595 3.483394 0.250000
3 but I don't wanna die [0.033476487, -0.011235877, -0.057625435, -0.0... 1.538222e+00 8589934610 1.130355 0.238095
4 if you strike me down I shall become more powe... [0.00432201, 0.030120496, 5.3317923e-05, 0.033... 1.381086e+00 8589934594 0.715157 0.216667
5 I see a salty message written in the eves [-0.04213107, 0.0016004723, 0.061052393, -0.02... 1.638301e+00 8589934603 1.043785 0.133333
6 but his son was mortal [0.012462767, 0.049041674, -0.057339743, -0.04... 1.421566e+00 8589934620 NaN 0.125000
7 I've got a bad feeling about this [-0.06973199, -0.029960092, 0.02641632, -0.031... NaN 8589934596 1.043785 0.125000
8 now that's a name I haven't heard in a long time [-0.014374257, -0.013588792, -0.07487557, 0.03... 1.597573e+00 8589934593 0.848772 0.118056
9 he was a god [-0.0258895, 0.11925236, -0.029397793, 0.05888... 1.423147e+00 8589934618 NaN 0.100000
10 I wish they would make another one [-0.14737535, -0.015304729, 0.04318139, -0.061... NaN 8589934622 1.043785 0.100000
11 Kratos had a son [-0.057455737, 0.13734367, -0.03537109, -0.000... 1.488075e+00 8589934617 NaN 0.083333
12 I don't wanna live like this [-0.0028891307, 0.015214227, 0.025183653, 0.08... NaN 8589934609 1.043785 0.071429
13 I see a mansard roof through the trees [0.052383978, 0.087759204, 0.014739997, 0.0239... NaN 8589934602 1.043785 0.062500
14 great kid don't get cocky [-0.047043696, 0.054648954, -0.008509666, -0.0... 1.618125e+00 8589934592 NaN 0.055556
```
Support shallow cloning a dataset at a specific location to create a new
dataset, using the shallow_clone feature in Lance. Also introduce remote
`clone` API for remote tables for this functionality.
- Fixes issue where passing `{ vector: undefined }` with an embedding
function threw "Found field not in schema" error instead of calling the
embedding function like `null` or omitted fields.
**Changes:**
- Modified `rowPathsAndValues` to skip undefined values during schema
inference
- Added test case verifying undefined, null, and omitted vector fields
all work correctly
**Before:** `{ vector: undefined }` → Error
**After:** `{ vector: undefined }` → Calls embedding function
Closes#2647
We had previously prototyped a `Catalog` trait anticipating a
three-tiered Catalog-Database-Table structure. Now that we have
namespaces in the `Database` we can support any tiering scheme and the
`Catalog` trait is no longer needed.
