This PR adds support of multi-level namespace in a LanceDB database,
according to the Lance Namespace spec.
This allows users to create namespace inside a database connection,
perform create, drop, list, list_tables in a namespace. (other
operations like update, describe will be in a follow-up PR)
The 3 types of database connections behave like the following:
1 Local database connections will continue to have just a flat list of
tables for backwards compatibility.
2. Remote database connections will make REST API calls according to the
APIs in the Lance Namespace spec.
3. Lance Namespace connections will invoke the corresponding operations
against the specific namespace implementation which could have different
behaviors regarding these APIs.
All the table APIs now take identifier instead of name, for example
`/v1/table/{name}/create` is now `/v1/table/{id}/create`. If a table is
directly in the root namespace, the API call is identical. If the table
is in a namespace, then the full table ID should be used, with `$` as
the default delimiter (`.` is a special character and creates issues
with URL parsing so `$` is used), for example
`/v1/table/ns1$table1/create`. If a different parameter needs to be
passed in, user can configure the `id_delimiter` in client config and
that becomes a query parameter, for example
`/v1/table/ns1__table1/create?delimiter=__`
The Python and Typescript APIs are kept backwards compatible, but the
following Rust APIs are not:
1. `Connection::drop_table(&self, name: impl AsRef<str>) -> Result<()>`
is now `Connection::drop_table(&self, name: impl AsRef<str>, namespace:
&[String]) -> Result<()>`
2. `Connection::drop_all_tables(&self) -> Result<()>` is now
`Connection::drop_all_tables(&self, name: impl AsRef<str>) ->
Result<()>`
This shrinks the size of a local embedded build that can disable all the
default features. When combined with
https://github.com/lancedb/lance/pull/4362 and the dependencies are
updated to point to the fix, this resolves#2567 fully.
Verified by patching the workspace to redirect to my clone of lance with
the PR applied.
```
cargo tree -p lancedb -e no-build -e no-dev --no-default-features -i aws-config | less
```
The reason that lance itself needs to change too is that many
dependencies within that project depend on lance-io/default and lancedb
depends on them which transitively ends up enabling the cloud
regardless. The PR in lance removes the dependency on lance-io/default
from all sibling crates.
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Enables two new parameters when building indices:
* `name`: Allows explicitly setting a name on the index. Default is
`{col_name}_idx`.
* `train` (default `True`): When set to `False`, an empty index will be
immediately created.
The upgrade of Lance means there are also additional behaviors from
cd76a993b8:
* When a scalar index is created on a Table, it will be kept around even
if all rows are deleted or updated.
* Scalar indices can be created on empty tables. They will default to
`train=False` if the table is empty.
---------
Co-authored-by: Weston Pace <weston.pace@gmail.com>
Adds tests to ensure that users can paginate through simple scan, FTS,
and vector search results using `limit` and `offset`.
Tests upstream work: https://github.com/lancedb/lance/pull/4318Closes#2459
These operations have existed in lance for a long while and many users
need to drop down to lance for this capability. This PR adds the API and
implements it using filters (e.g. `_rowid IN (...)`) so that in doesn't
currently add any load to `BaseTable`. I'm not sure that is sustainable
as base table implementations may want to specialize how they handle
this method. However, I figure it is a good starting point.
In addition, unlike Lance, this API does not currently guarantee
anything about the order of the take results. This is necessary for the
fallback filter approach to work (SQL filters cannot guarantee result
order)
## Summary
- Enhanced vector column detection to use substring matching instead of
exact matching
- Now detects columns with names containing "vector" or "embedding"
(case-insensitive)
- Added integer vector support to Node.js implementation (matching
Python)
- Comprehensive test coverage for both float and integer vector types
## Changes
### Python (`python/python/lancedb/table.py`)
- Updated `_infer_target_schema()` to use substring matching with helper
function `_is_vector_column()`
- Preserved original field names instead of forcing "vector"
- Consolidated duplicate logic for better maintainability
### Node.js (`nodejs/lancedb/arrow.ts`)
- Enhanced type inference with `nameSuggestsVectorColumn()` helper
function
- Added `isAllIntegers()` function with performance optimization (checks
first 10 elements)
- Implemented integer vector support using `Uint8` type (matching
Python)
- Improved type safety by removing `any` usage
### Tests
- **Python**: Added
`test_infer_target_schema_with_vector_embedding_names()` in
`test_util.py`
- **Node.js**: Added comprehensive test case in `arrow.test.ts`
- Both test suites cover various naming patterns and integer/float
vector types
## Examples of newly supported column names:
- `user_vector`, `text_embedding`, `doc_embeddings`
- `my_vector_field`, `embedding_model`
- `VECTOR_COL`, `Vector_Mixed` (case-insensitive)
- Both float and integer arrays are properly converted to fixed-size
lists
## Test plan
- [x] All existing tests pass (backward compatibility maintained)
- [x] New tests pass for both Python and Node.js implementations
- [x] Integer vector detection works correctly in Node.js
- [x] Code passes linting and formatting checks
- [x] Performance optimized for large vector arrays
Fixes#2546🤖 Generated with [Claude Code](https://claude.ai/code)
---------
Co-authored-by: Claude <noreply@anthropic.com>
## Summary
- Adds an overall `timeout` parameter to `TimeoutConfig` that limits the
total time for the entire request
- Can be set via config or `LANCE_CLIENT_TIMEOUT` environment variable
- Exposed in Python and Node.js bindings
- Includes comprehensive tests
## Test plan
- [x] Unit tests for Rust TimeoutConfig
- [x] Integration tests for Python bindings
- [x] Integration tests for Node.js bindings
- [x] All existing tests pass
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Claude <noreply@anthropic.com>
Update license field from `Apache 2.0` to be `Apache-2.0` for all
Node.js packages.
This was causing GitHub's Dependency Review license check to fail with:
> The validity of the licenses of the dependencies below could not be
determined. Ensure that they are valid SPDX licenses
`nprobes` with a value greater than 20 fails with the minimum error:
```
self = <lancedb.query.AsyncVectorQuery object at 0x10b749720>, minimum_nprobes = 30
def minimum_nprobes(self, minimum_nprobes: int) -> Self:
"""Set the minimum number of probes to use.
See `nprobes` for more details.
These partitions will be searched on every indexed vector query and will
increase recall at the expense of latency.
"""
> self._inner.minimum_nprobes(minimum_nprobes)
E ValueError: Invalid input, minimum_nprobes must be less than or equal to maximum_nprobes
python/lancedb/query.py:2744: ValueError
```
Putting the max set before the min seems reasonable but it causes this
reasonable case to fail:
```
def test_nprobes_min_max_works_sync(table):
LanceVectorQueryBuilder(table, [0, 0], "vector").minimum_nprobes(2).maximum_nprobes(4).to_list()
```
with
```
self = <lancedb.query.AsyncVectorQuery object at 0x1203f1c90>, maximum_nprobes = 4
def maximum_nprobes(self, maximum_nprobes: int) -> Self:
"""Set the maximum number of probes to use.
See `nprobes` for more details.
If this value is greater than `minimum_nprobes` then the excess partitions
will be searched only if we have not found enough results.
This can be useful when there is a narrow filter to allow these queries to
spend more time searching and avoid potential false negatives.
If this value is 0 then no limit will be applied and all partitions could be
searched if needed to satisfy the limit.
"""
> self._inner.maximum_nprobes(maximum_nprobes)
E ValueError: Invalid input, maximum_nprobes must be greater than or equal to minimum_nprobes
python/lancedb/query.py:2761: ValueError
```.
The case I care about is where min == max, but this solution handles it
even if they're not. If both min and max exist, we set both to the
minimum and then set the max. This isn't 100% the same as the minimum
setter checks for 0 on the min and `.nprobes` does not do any sanity
checking at all. But I figured this was the most reasonable and general
solution without touching more of this code.
As part of this I noticed the error messages were a bit ambiguous so I
made them symmetric and clarified them while I was here.
## Summary
- Exposes `Session` in Python and Typescript so users can set the
`index_cache_size_bytes` and `metadata_cache_size_bytes`
* The `Session` is attached to the `Connection`, and thus shared across
all tables in that connection.
- Adds deprecation warnings for table-level cache configuration
🤖 Generated with [Claude Code](https://claude.ai/code)
---------
Co-authored-by: Claude <noreply@anthropic.com>
## Summary
Fixes#2515 by implementing comprehensive support for missing columns in
Arrow table inputs when using embedding functions.
### Problem
Previously, when an Arrow table was passed to `fromDataToBuffer` with
missing columns and a schema containing embedding functions, the system
would fail because `applyEmbeddingsFromMetadata` expected all columns to
be present in the table.
🤖 Generated with [Claude Code](https://claude.ai/code)
---------
Co-authored-by: Claude <noreply@anthropic.com>
Thanks for all your work.
The docstring for `OptimizeOptions ` seems to reference a non-existent
method on `Table`. I believe this is the correct example for
`cleanupOlderThan`.
This also appears in the generated docs, but I assume they live
downstream from this code?
- Enhanced error messages for schema inference failures to suggest
providing an explicit schema.
- Updated embedding application logic to check for existing destination
columns, allowing for filling embeddings in columns that are all null.
- Added comments for clarity on handling existing columns during
embedding application.
Fixes https://github.com/lancedb/lancedb/issues/2183
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Summary by CodeRabbit
- **Bug Fixes**
- Improved error messages for schema inference to enhance readability.
- Prevented redundant embedding application by skipping columns that
already contain data, avoiding unnecessary errors and computations.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
This exposes the maximum_nprobes and minimum_nprobes feature that was
added in https://github.com/lancedb/lance/pull/3903
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Added support for specifying minimum and maximum probe counts in
vector search queries, allowing finer control over search behavior.
- Users can now independently set minimum and maximum probes for vector
and hybrid queries via new methods and parameters in Python, Node.js,
and Rust APIs.
- **Bug Fixes**
- Improved parameter validation to ensure correct usage of minimum and
maximum probe values.
- **Tests**
- Expanded test coverage to validate correct handling, serialization,
and error cases for the new probe parameters.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
- operator for match query
- slop for phrase query
- boolean query
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Introduced support for boolean full-text search queries with AND/OR
logic and occurrence conditions.
- Added operator options for match and multi-match queries to control
term combination logic.
- Enabled phrase queries to specify proximity (slop) for flexible phrase
matching.
- Added new enumerations (`Operator`, `Occur`) and the `BooleanQuery`
class for enhanced query expressiveness.
- **Bug Fixes**
- Improved validation and error handling for invalid operator and
occurrence inputs in full-text queries.
- **Tests**
- Expanded test coverage with new cases for boolean queries and
operator-based full-text searches.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **Chores**
- Downgraded version numbers for the Node.js, Python, and Rust packages.
No other user-facing changes were made.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
this introduces some breaking changes in terms of rust API of creating
FTS index, and the default index params changed
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Updated default settings for full-text search (FTS) index creation:
stemming, stop word removal, and ASCII folding are now enabled by
default, while token position storage is disabled by default.
- **Refactor**
- Simplified and streamlined the configuration and handling of FTS index
parameters for improved maintainability and consistency across
interfaces.
- Enhanced serialization and request construction for FTS index
parameters to reduce manual handling and improve code clarity.
- Improved test coverage by explicitly enabling positional indexing in
FTS tests to support phrase queries.
- **Chores**
- Upgraded all internal dependencies related to FTS indexing to the
latest version for enhanced compatibility and performance.
- Updated package versions for Node.js, Python, and Rust components to
the latest beta releases.
- Improved CI workflows by adding Rust toolchain setup with formatting
and linting tools.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
Co-authored-by: Will Jones <willjones127@gmail.com>
