Adds example for querying a dataset with SQL
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## Summary by CodeRabbit
- **Documentation**
- Added new guides on querying LanceDB tables using SQL with DuckDB and
Apache Datafusion.
- Included detailed instructions for integrating LanceDB with Datafusion
in Python.
- Updated navigation to include Datafusion and SQL querying
documentation.
- Improved formatting in TypeScript and vectordb update examples for
consistency.
- **Tests**
- Added a new test demonstrating SQL querying on Lance tables via
DataFusion integration.
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---------
Co-authored-by: Weston Pace <weston.pace@gmail.com>
Added a new readme for better navigation, updated language and more
detail
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## Summary by CodeRabbit
- **Documentation**
- Updated the README with a modernized header, improved structure, and
clearer descriptions of features and architecture.
- Expanded and reorganized key features and product offerings for better
clarity.
- Simplified installation instructions and added a table of SDK
interfaces with documentation links.
- Enhanced community and contributor sections with new visuals and links
to social and support channels.
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## Summary by CodeRabbit
- **Documentation**
- Added an integration banner image to the beginning of the
Genkitx-LanceDB documentation.
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## Summary by CodeRabbit
- **Documentation**
- Added a comprehensive guide for integrating LanceDB with Genkit,
including installation instructions, setup, indexing, retrieval, and
building a custom RAG pipeline with example code and screenshots.
- Updated the documentation navigation to include the new Genkit
integration, making it accessible from the site menu.
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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.
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## 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.
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return version info for all write operations (add, update, merge_insert
and column modification operations)
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## 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}
```
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## 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>
* 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.
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## 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.
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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.
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## 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 -->
The docs in the Guide here do not match the [API reference]
(https://lancedb.github.io/lancedb/js/classes/Table/#updateopts) for the
nodejs client.
I am writing an Elixir wrapper over the typescript library (Rust
forthcoming!) and confirmed in testing that the API reference is correct
vs the Guide.
Following the Guide docs, the error I got was:
"lance error: Invalid user input: Schema error: No field named bar.
Valid fields are foo. For a query of:
await table.update({foo: "buzz"}, { where: "foo = 'bar'"});
Over a table with a schema of just {foo: Utf8}.
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## Summary by CodeRabbit
- **Documentation**
- Reformatted a code snippet in the guide to enhance readability by
splitting it into multiple lines for improved clarity.
<!-- 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
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## 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
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## 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>
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## 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>
This reverts commit a547c523c2 or #2281
The current implementation can cause panics and performance degradation.
I will bring this back with more testing in
https://github.com/lancedb/lancedb/pull/2311
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## Summary by CodeRabbit
- **Documentation**
- Enhanced clarity on read consistency settings with updated
descriptions and default behavior.
- Removed outdated warnings about eventual consistency from the
troubleshooting guide.
- **Refactor**
- Streamlined the handling of the read consistency interval across
integrations, now defaulting to "None" for improved performance.
- Simplified internal logic to offer a more consistent experience.
- **Tests**
- Updated test expectations to reflect the new default representation
for the read consistency interval.
- Removed redundant tests related to "no consistency" settings for
streamlined testing.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
Closes#2287
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## 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, when we loaded the next version of the table, we would block
all reads with a write lock. Now, we only do that if
`read_consistency_interval=0`. Otherwise, we load the next version
asynchronously in the background. This should mean that
`read_consistency_interval > 0` won't have a meaningful impact on
latency.
Along with this change, I felt it was safe to change the default
consistency interval to 5 seconds. The current default is `None`, which
means we will **never** check for a new version by default. I think that
default is contrary to most users expectations.
* direct users to cloud.lancedb.com since LanceDB Cloud is in public
beta
* removed the `cast vector dimension` from alter columns as we don't
support it
This PR fixes build issues associated with `aws-lc-rs`, while
simplifying the build process. Previously, we used custom scripts for
the musl and Windows ARM builds. These were complicated and prone to
breaking. This PR switches to a setup that mirrors
https://github.com/napi-rs/package-template/blob/main/.github/workflows/CI.yml.
* linux glibc and musl builds now use the Docker images provided by the
napi project
* Windows ARM build now just cross compiles from Windows x64, which
turns out to work quite well.
- adds `loss` into the index stats for vector index
- now `optimize` can retrain the vector index
---------
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
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>
Reviving #1966.
Closes#1938
The `search()` method can apply embeddings for the user. This simplifies
hybrid search, so instead of writing:
```python
vector_query = embeddings.compute_query_embeddings("flower moon")[0]
await (
async_tbl.query()
.nearest_to(vector_query)
.nearest_to_text("flower moon")
.to_pandas()
)
```
You can write:
```python
await (await async_tbl.search("flower moon", query_type="hybrid")).to_pandas()
```
Unfortunately, we had to do a double-await here because `search()` needs
to be async. This is because it often needs to do IO to retrieve and run
an embedding function.
Address usage mistakes in
https://github.com/lancedb/lancedb/issues/2135.
* Add example of how to use `LanceModel` and `Vector` decorator
* Add test for pydantic doc
* Fix the example to directly use LanceModel instead of calling
`MyModel.to_arrow_schema()` in the example.
* Add cross-reference link to pydantic doc site
* Configure mkdocs to watch code changes in python directory.
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.
Closes#1106
Unfortunately, these need to be set at the connection level. I
investigated whether if we let users provide a callback they could use
`AsyncLocalStorage` to access their context. However, it doesn't seem
like NAPI supports this right now. I filed an issue:
https://github.com/napi-rs/napi-rs/issues/2456
This opens up the door for more custom database implementations than the
two we have today. The biggest change should be inivisble:
`ConnectionInternal` has been renamed to `Database`, made public, and
refactored
However, there are a few breaking changes. `data_storage_version` and
`enable_v2_manifest_paths` have been moved from options on
`create_table` to options for the database which are now set via
`storage_options`.
Before:
```
db = connect(uri)
tbl = db.create_table("my_table", data, data_storage_version="legacy", enable_v2_manifest_paths=True)
```
After:
```
db = connect(uri, storage_options={
"new_table_enable_v2_manifest_paths": "true",
"new_table_data_storage_version": "legacy"
})
tbl = db.create_table("my_table", data)
```
BREAKING CHANGE: the data_storage_version, enable_v2_manifest_paths
options have moved from options to create_table to storage_options.
BREAKING CHANGE: the use_legacy_format option has been removed,
data_storage_version has replaced it for some time now
* Make `npm run docs` fail if there are any warnings. This will catch
items missing from the API reference.
* Add a check in our CI to make sure `npm run dos` runs without warnings
and doesn't generate any new files (indicating it might be out-of-date.
* Hide constructors that aren't user facing.
* Remove unused enum `WriteMode`.
Closes#2068
This includes several improvements and fixes to the Python Async query
builders:
1. The API reference docs show all the methods for each builder
2. The hybrid query builder now has all the same setter methods as the
vector search one, so you can now set things like `.distance_type()` on
a hybrid query.
3. Re-rankers are now properly hooked up and tested for FTS and vector
search. Previously the re-rankers were accidentally bypassed in unit
tests, because the builders overrode `.to_arrow()`, but the unit test
called `.to_batches()` which was only defined in the base class. Now all
builders implement `.to_batches()` and leave `.to_arrow()` to the base
class.
4. The `AsyncQueryBase` and `AsyncVectoryQueryBase` setter methods now
return `Self`, which provides the appropriate subclass as the type hint
return value. Previously, `AsyncQueryBase` had them all hard-coded to
`AsyncQuery`, which was unfortunate. (This required bringing in
`typing-extensions` for older Python version, but I think it's worth
it.)
