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## Summary by CodeRabbit
- **New Features**
- Added support for distance range filtering in hybrid vector queries,
allowing users to specify lower and upper bounds for search results.
- **Tests**
- Introduced new tests to validate distance range filtering and
reranking in both synchronous and asynchronous hybrid query scenarios.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
Fixes#2315.
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## Summary by CodeRabbit
- **Refactor**
- Enhanced query processing to maintain smooth functionality across
different dependency versions, ensuring improved stability and
performance.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Fixes#2344
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## Summary by CodeRabbit
- **Tests**
- Updated tests to use PyArrow Tables instead of pandas DataFrames where
possible, reducing reliance on pandas.
- Tests that require pandas are now automatically skipped if pandas is
not installed.
- **Chores**
- Improved workflow to uninstall both pylance and pandas in a specific
test step.
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* 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.
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This PR adds ColPali support with ColPaliEmbeddings class (tagged
"colpali") using ColQwen2.5 for multi-vector text/image embeddings. Also
added MultiVector Pydantic type to handle the vector lists.
I've added some integration test for the embedding model and some unit
test for the new Pydantic type. Could be a template for other ColPali
variants as well. or until transformers🤗 starts supporting it.
Still `TODO`:
- [ ] Documentation
- [ ] Add an example
_Could also allow Image as query, but didn't work well when testing it._
[ColPali-Engine](https://github.com/illuin-tech/colpali) version:
0.3.9.dev17+g3faee24
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## Summary by CodeRabbit
- **New Features**
- Introduced support for ColPali-based multimodal multi-vector
embeddings for both text and images.
- Added a new embedding class for generating multi-vector embeddings,
configurable for various model and processing options.
- Added a new Pydantic type for multi-vector embeddings, supporting
validation and schema generation for lists of fixed-dimension vectors.
- **Bug Fixes**
- Ensured proper asynchronous index creation in query tests for improved
reliability.
- **Tests**
- Added integration tests for ColPali embeddings, including
text-to-image search and validation of multi-vector fields.
- Added comprehensive tests for the new multi-vector Pydantic type,
covering schema, validation, and default value behavior.
- **Chores**
- Updated optional dependencies to include the ColPali engine.
- Added utility to check for availability of flash attention support.
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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.
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---------
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.
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---------
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
This change allows to centrally manage the plance depndency without
everybody needing to monitor for compatibility manually.
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## Summary by CodeRabbit
- **New Features**
- Introduced an optional dependency that enhances development support.
Users can now benefit from improved static analysis capabilities when
installing the recommended version (0.23.2 or later).
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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.
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---------
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
I found some edge cases while running experiments that - depending on
the base reranking libraries, some of them don't handle empty lists
well. This PR manually checks if the result set to be reranked is empty
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## Summary by CodeRabbit
- **Bug Fixes**
- Enhanced search result processing by ensuring that reordering only
occurs when valid, non-empty results are available, thereby preventing
unnecessary operations and potential errors.
- **Tests**
- Added automated tests to verify that empty search result sets are
handled correctly, ensuring consistent behavior across various
rerankers.
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<!-- This is an auto-generated comment: release notes by coderabbit.ai
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## Summary by CodeRabbit
- **Chores**
- Updated internal library dependencies to the latest beta version for
improved system stability.
- **Tests**
- Added automated tests to validate full-text search functionality on
list-based text fields.
- **Refactor**
- Enhanced the search processing logic to provide robust support for
list-type text data, ensuring more reliable results.
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---------
Signed-off-by: BubbleCal <bubble-cal@outlook.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 -->
missed to support it in `search()` API and there were some pydantic
errors
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## Summary by CodeRabbit
- **New Features**
- Enhanced full-text search capabilities by incorporating additional
parameters, enabling more flexible query definitions.
- Extended table search functionality to support full-text queries
alongside existing search types.
- **Tests**
- Introduced new tests that validate both structured and conditional
full-text search behaviors.
- Expanded test coverage for various query types, including MatchQuery,
BoostQuery, MultiMatchQuery, and PhraseQuery.
- **Bug Fixes**
- Fixed a logic issue in query processing to ensure correct handling of
full-text search queries.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
We recently allowed this for lance but there was a check in lancedb as
well that was preventing it
<!-- This is an auto-generated comment: release notes by coderabbit.ai
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## Summary by CodeRabbit
- **New Features**
- Added support for indexing fixed-size binary data using B-tree
structures for efficient data storage and retrieval.
- **Tests**
- Implemented automated tests to ensure the new binary indexing works
correctly and meets the expected configuration.
<!-- 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
- **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.
This fixes an issue for people wishing to use different kinds of
rerankers in lancedb via AnswerDotAI rerankers. Currently, the arguments
are passed sequentially, but they don't match the[Reranker class
implementation](d604a8c47d/rerankers/reranker.py (L179)):
the second argument is expected to be an optional "lang" for default
models, while model_type should be passed explicitly.
The one line changes in this PR fixes it and enables the use of other
methods (eg LLMs-as-rerankers)
This PR adds a `to_query_object` method to the various query builders
(except not hybrid queries yet). This makes it possible to inspect the
query that is built.
In addition this PR does some normalization between the sync and async
query paths. A few custom defaults were removed in favor of None (with
the default getting set once, in rust).
Also, the synchronous to_batches method will now actually stream results
Also, the remote API now defaults to prefiltering
- adds `loss` into the index stats for vector index
- now `optimize` can retrain the vector index
---------
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
`object_store` already hard codes `rustls` as the TLS implementation, so
we have been shipping a mix of `rustls` and `openssl`. For simplicity of
builds, we should consolidate to one, and that has to be `rustls`.
Closes#2114
Starting in #1965, we no longer pass the table schema into
`pa.Table.from_pylist()`. This means PyArrow is choosing the order of
the struct subfields, and apparently it does them in alphabetical order.
This is fine in theory, since in Lance we support providing fields in
any order. However, before we pass it to Lance, we call
`pa.Table.cast()` to align column types to the table types.
`pa.Table.cast()` is strict about field order, so we need to create a
cast target schema that aligns with the input data. We were doing this
at the top-level fields, but weren't doing this in nested fields. This
PR adds support to do this for nested ones.
Hello LanceDB team,
while developing using `lancedb` as a library I encountered a typing
problem affecting IDE hints and completions during development.
---
## Current Situation
Currently, the abstract base class `lancedb.query:LanceQueryBuilder`
uses method chaining to build up the search parameters, where the
methods have `LanceQueryBuilder` as a return type hint.
This leads to two issues:
1. Implementing subclasses of `LanceQueryBuilder` need to override
methods to modify the return type hint, even when they don't need to
change its implementation, just to ensure adequate IDE hints and
completions.
2. When using method chaining the first method directly inherited from
the abstract `LanceQueryBuilder` causes the inferred type to switch back
to `LanceQueryBuilder`. So even when the type starts from
`lancdb.table:LanceTable.search(query_type="vector", ...)` and therefor
correctly is inferred as `LanceVectorQueryBuilder`, after calling e.g.
`LanceVectorQueryBuilder.limit(...)` it is seen as the abstract
`LanceQueryBuilder` from that point on.
### Example of current situation
## Proposed changes
I propose to change the return type hints of the corresponding methods
(including classmethod `create()`) in the abstract base class
`LanceQueryBuilder` from `LanceQueryBuilder` to `Self`.
`Self` is already imported in the module:
```py
if sys.version_info >= (3, 11):
from typing import Self
else:
from typing_extensions import Self
```
### Further possible changes
Additionally, the implementing subclasses could also change the return
type hints to `Self` to potentially allow for further inheritance
easily.
> [!NOTE]
> **However this is not part of this pull request as of writing.**
### Example after proposed changes
---
Best regards
Martin
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
Hello LanceDB team,
---
I have fixed a discrepancy in the class docstring of
`lancedb.embeddings.base:EmbeddingFunction` and made consistency
alignments to that docstring.
### Changes made
1. The docstring referred to the abstract method
`get_source_embeddings()`.
This method does not exist in the repository at the current state.
I have changed the mention to refer to the actual abstract method
`compute_source_embeddings()`.
2. Also, I aligned the consistency within the ordered list which is
describing the methods to be implemented by concrete embedding
functions.
---
Thank you for developing this useful library. 👍
Best regards
Martin
