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`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.
The Multimodal AI Lakehouse
How to Install ✦ Detailed Documentation ✦ Tutorials and Recipes ✦ Contributors
The ultimate multimodal data platform for AI/ML applications.
LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease. LanceDB is a central location where developers can build, train and analyze their AI workloads.
Demo: Multimodal Search by Keyword, Vector or with SQL
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Key Features:
- Fast Vector Search: Search billions of vectors in milliseconds with state-of-the-art indexing.
- Comprehensive Search: Support for vector similarity search, full-text search and SQL.
- Multimodal Support: Store, query and filter vectors, metadata and multimodal data (text, images, videos, point clouds, and more).
- Advanced Features: Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index.
Products:
- Open Source & Local: 100% open source, runs locally or in your cloud. No vendor lock-in.
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- Rich Ecosystem: Integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
How to Install:
Follow the Quickstart doc to set up LanceDB locally.
API & SDK: We also support Python, Typescript and Rust SDKs
| Interface | Documentation |
|---|---|
| Python SDK | https://lancedb.github.io/lancedb/python/python/ |
| Typescript SDK | https://lancedb.github.io/lancedb/js/globals/ |
| Rust SDK | https://docs.rs/lancedb/latest/lancedb/index.html |
| REST API | https://docs.lancedb.com/api-reference/introduction |
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