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>
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
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](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.
**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>
### 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>
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
## Summary
This PR introduces a `HeaderProvider` which is called for all remote
HTTP calls to get the latest headers to inject. This is useful for
features like adding the latest auth tokens where the header provider
can auto-refresh tokens internally and each request always set the
refreshed token.
---------
Co-authored-by: Claude <noreply@anthropic.com>
This PR adds mTLS (mutual TLS) configuration support for the LanceDB
remote HTTP client, allowing users to authenticate with client
certificates and configure custom CA certificates for server
verification.
---------
Co-authored-by: Claude <noreply@anthropic.com>
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<()>`
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>
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
- 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>
Provides the ability to set a timeout for merge insert. The default
underlying timeout is however long the first attempt takes, or if there
are multiple attempts, 30 seconds. This has two use cases:
1. Make the timeout shorter, when you want to fail if it takes too long.
2. Allow taking more time to do retries.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Added support for specifying a timeout when performing merge insert
operations in Python, Node.js, and Rust APIs.
- Introduced a new option to control the maximum allowed execution time
for merge inserts, including retry timeout handling.
- **Documentation**
- Updated and added documentation to describe the new timeout option and
its usage in APIs.
- **Tests**
- Added and updated tests to verify correct timeout behavior during
merge insert operations.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
return version info for all write operations (add, update, merge_insert
and column modification operations)
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Table modification operations (add, update, delete, merge,
add/alter/drop columns) now return detailed result objects including
version numbers and operation statistics.
- Result objects provide clearer feedback such as rows affected and new
table version after each operation.
- **Documentation**
- Updated documentation to describe new result objects and their fields
for all relevant table operations.
- Added documentation for new result interfaces and updated method
return types in Node.js and Python APIs.
- **Tests**
- Enhanced test coverage to assert correctness of returned versioning
and operation metadata after table modifications.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Based on this comment:
https://github.com/lancedb/lancedb/issues/2228#issuecomment-2730463075
and https://github.com/lancedb/lance/pull/2357
Here is my attempt at implementing bindings for returning merge stats
from a `merge_insert.execute` call for lancedb.
Note: I have almost no idea what I am doing in Rust but tried to follow
existing code patterns and pay attention to compiler hints.
- The change in nodejs binding appeared to be necessary to get
compilation to work, presumably this could actual work properly by
returning some kind of NAPI JS object of the stats data?
- I am unsure of what to do with the remote/table.rs changes -
necessarily for compilation to work; I assume this is related to LanceDB
cloud, but unsure the best way to handle that at this point.
Proof of function:
```python
import pandas as pd
import lancedb
db = lancedb.connect("/tmp/test.db")
test_data = pd.DataFrame(
{
"title": ["Hello", "Test Document", "Example", "Data Sample", "Last One"],
"id": [1, 2, 3, 4, 5],
"content": [
"World",
"This is a test",
"Another example",
"More test data",
"Final entry",
],
}
)
table = db.create_table("documents", data=test_data, exist_ok=True, mode="overwrite")
update_data = pd.DataFrame(
{
"title": [
"Hello, World",
"Test Document, it's good",
"Example",
"Data Sample",
"Last One",
"New One",
],
"id": [1, 2, 3, 4, 5, 6],
"content": [
"World",
"This is a test",
"Another example",
"More test data",
"Final entry",
"New content",
],
}
)
stats = (
table.merge_insert(on="id")
.when_matched_update_all()
.when_not_matched_insert_all()
.execute(update_data)
)
print(stats)
```
returns
```
{'num_inserted_rows': 1, 'num_updated_rows': 5, 'num_deleted_rows': 0}
```
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Summary by CodeRabbit
- **New Features**
- Merge-insert operations now return detailed statistics, including
counts of inserted, updated, and deleted rows.
- **Bug Fixes**
- Tests updated to validate returned merge-insert statistics for
accuracy.
- **Documentation**
- Method documentation improved to reflect new return values and clarify
merge operation results.
- Added documentation for the new `MergeStats` interface detailing
operation statistics.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
metadata{filename=xyz} filename would be there structurally, but ALWAYS
null.
I didn't include this as a file but it may be useful for understanding
the problem for people searching on this issue so I'm including it here
as documentation. Before this patch any field that is more than 1 deep
is accepted but returns null values for subfields when queried.
```js
const lancedb = require('@lancedb/lancedb');
// Debug logger
function debug(message, data) {
console.log(`[TEST] ${message}`, data !== undefined ? data : '');
}
// Log when our unwrapArrowObject is called
const kParent = Symbol.for("parent");
const kRowIndex = Symbol.for("rowIndex");
// Override console.log for our test
const originalConsoleLog = console.log;
console.log = function() {
// Filter out noisy logs
if (arguments[0] && typeof arguments[0] === 'string' && arguments[0].includes('[INFO] [LanceDB]')) {
originalConsoleLog.apply(console, arguments);
}
originalConsoleLog.apply(console, arguments);
};
async function main() {
debug('Starting test...');
// Connect to the database
debug('Connecting to database...');
const db = await lancedb.connect('./.lancedb');
// Try to open an existing table, or create a new one if it doesn't exist
let table;
try {
table = await db.openTable('test_nested_fields');
debug('Opened existing table');
} catch (e) {
debug('Creating new table...');
// Create test data with nested metadata structure
const data = [
{
id: 'test1',
vector: [1, 2, 3],
metadata: {
filePath: "/path/to/file1.ts",
startLine: 10,
endLine: 20,
text: "function test() { return true; }"
}
},
{
id: 'test2',
vector: [4, 5, 6],
metadata: {
filePath: "/path/to/file2.ts",
startLine: 30,
endLine: 40,
text: "function test2() { return false; }"
}
}
];
debug('Data to be inserted:', JSON.stringify(data, null, 2));
// Create the table
table = await db.createTable('test_nested_fields', data);
debug('Table created successfully');
}
// Query the table and get results
debug('Querying table...');
const results = await table.search([1, 2, 3]).limit(10).toArray();
// Log the results
debug('Number of results:', results.length);
if (results.length > 0) {
const firstResult = results[0];
debug('First result properties:', Object.keys(firstResult));
// Check if metadata is accessible and what properties it has
if (firstResult.metadata) {
debug('Metadata properties:', Object.keys(firstResult.metadata));
debug('Metadata filePath:', firstResult.metadata.filePath);
debug('Metadata startLine:', firstResult.metadata.startLine);
// Destructure to see if that helps
const { filePath, startLine, endLine, text } = firstResult.metadata;
debug('Destructured values:', { filePath, startLine, endLine, text });
// Check if it's a proxy object
debug('Result is proxy?', Object.getPrototypeOf(firstResult) === Object.prototype ? false : true);
debug('Metadata is proxy?', Object.getPrototypeOf(firstResult.metadata) === Object.prototype ? false : true);
} else {
debug('Metadata is not accessible!');
}
}
// Close the database
await db.close();
}
main().catch(e => {
console.error('Error:', e);
});
```
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Summary by CodeRabbit
- **Bug Fixes**
- Improved handling of nested struct fields to ensure accurate
preservation of values during serialization and deserialization.
- Enhanced robustness when accessing nested object properties, reducing
errors with missing or null values.
- **Tests**
- Added tests to verify correct handling of nested struct fields through
serialization and deserialization.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
* Add a new "table stats" API to expose basic table and fragment
statistics with local and remote table implementations
### Questions
* This is using `calculate_data_stats` to determine total bytes in the
table. This seems like a potentially expensive operation - are there any
concerns about performance for large datasets?
### Notes
* bytes_on_disk seems to be stored at the column level but there does
not seem to be a way to easily calculate total bytes per fragment. This
may need to be added in lance before we can support fragment size
(bytes) statistics.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Added a method to retrieve comprehensive table statistics, including
total rows, index counts, storage size, and detailed fragment size
metrics such as minimum, maximum, mean, and percentiles.
- Enabled fetching of table statistics from remote sources through
asynchronous requests.
- Extended table interfaces across Python, Rust, and Node.js to support
synchronous and asynchronous retrieval of table statistics.
- **Tests**
- Introduced tests to verify the accuracy of the new table statistics
feature for both populated and empty tables.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
add the tag related API to list existing tags, attach tag to a version,
update the tag version, delete tag, get the version of the tag, and
checkout the version that the tag bounded to.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Introduced table version tagging, allowing users to create, update,
delete, and list human-readable tags for specific table versions.
- Enabled checking out a table by either version number or tag name.
- Added new interfaces for tag management in both Python and Node.js
APIs, supporting synchronous and asynchronous workflows.
- **Bug Fixes**
- None.
- **Documentation**
- Updated documentation to describe the new tagging features, including
usage examples.
- **Tests**
- Added comprehensive tests for tag creation, updating, deletion,
listing, and version checkout by tag in both Python and Node.js
environments.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
* Add new wait_for_index() table operation that polls until indices are
created/fully indexed
* Add an optional wait timeout parameter to all create_index operations
* Python and NodeJS interfaces
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Summary by CodeRabbit
- **New Features**
- Added optional waiting for index creation completion with configurable
timeout.
- Introduced methods to poll and wait for indices to be fully built
across sync and async tables.
- Extended index creation APIs to accept a wait timeout parameter.
- **Bug Fixes**
- Added a new timeout error variant for improved error reporting on
index operations.
- **Tests**
- Added tests covering successful index readiness waiting, timeout
scenarios, and missing index cases.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Added the ability to prewarm (load into memory) table indexes via new
methods in Python, Node.js, and Rust APIs, potentially reducing
cold-start query latency.
- **Bug Fixes**
- Ensured prewarming an index does not interfere with subsequent search
operations.
- **Tests**
- Introduced new test cases to verify full-text search index creation,
prewarming, and search functionalities in both Python and Node.js.
- **Chores**
- Updated dependencies for improved compatibility and performance.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: Lu Qiu <luqiujob@gmail.com>
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Enhanced full-text search capabilities with support for phrase
queries, fuzzy matching, boosting, and multi-column matching.
- Search methods now accept full-text query objects directly, improving
query flexibility and precision.
- Python and JavaScript SDKs updated to handle full-text queries
seamlessly, including async search support.
- **Tests**
- Added comprehensive tests covering fuzzy search, phrase search, and
boosted queries to ensure robust full-text search functionality.
- **Documentation**
- Updated query class documentation to reflect new constructor options
and removal of deprecated methods for clarity and simplicity.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
Closes#2287
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Added configurable timeout support for query executions. Users can now
specify maximum wait times for queries, enhancing control over
long-running operations across various integrations.
- **Tests**
- Expanded test coverage to validate timeout behavior in both
synchronous and asynchronous query flows, ensuring timely error
responses when query execution exceeds the specified limit.
- Introduced a new test suite to verify query operations when a timeout
is reached, checking for appropriate error handling.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **Chores**
- Updated dependency versions for improved performance and
compatibility.
- **New Features**
- Added support for structured full-text search with expanded query
types (e.g., match, phrase, boost, multi-match) and flexible input
formats.
- Introduced a new method to check server support for structural
full-text search features.
- Enhanced the query system with new classes and interfaces for handling
various full-text queries.
- Expanded the functionality of existing methods to accept more complex
query structures, including updates to method signatures.
- **Bug Fixes**
- Improved error handling and reporting for full-text search queries.
- **Refactor**
- Enhanced query processing with streamlined input handling and improved
error reporting, ensuring more robust and consistent search results
across platforms.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
Co-authored-by: BubbleCal <bubble-cal@outlook.com>
add analyze plan api to allow executing the queries and see runtime
metrics.
Which help identify the query IO overhead and help identify query
slowness
Previously, users could only specify new data types in `alterColumns` as
strings:
```ts
await tbl.alterColumns([
path: "price",
dataType: "float"
]);
```
But this has some problems:
1. It wasn't clear what were valid types
2. It was impossible to specify nested types, like lists and vector
columns.
This PR changes it to take an Arrow data type, similar to how the Python
API works. This allows casting vector types:
```ts
await tbl.alterColumns([
{
path: "vector",
dataType: new arrow.FixedSizeList(
2,
new arrow.Field("item", new arrow.Float16(), false),
),
},
]);
```
Closes#2185
BREAKING CHANGE: embedding function implementations in Node need to now
call `resolveVariables()` in their constructors and should **not**
implement `toJSON()`.
This tries to address the handling of secrets. In Node, they are
currently lost. In Python, they are currently leaked into the table
schema metadata.
This PR introduces an in-memory variable store on the function registry.
It also allows embedding function definitions to label certain config
values as "sensitive", and the preprocessing logic will raise an error
if users try to pass in hard-coded values.
Closes#2110Closes#521
---------
Co-authored-by: Weston Pace <weston.pace@gmail.com>
If we start supporting external catalogs then "drop database" may be
misleading (and not possible). We should be more clear that this is a
utility method to drop all tables. This is also a nice chance for some
consistency cleanup as it was `drop_db` in rust, `drop_database` in
python, and non-existent in typescript.
This PR also adds a public accessor to get the database trait from a
connection.
BREAKING CHANGE: the `drop_database` / `drop_db` methods are now
deprecated.
