`RemoteDBConnection.open_table` accepts `storage_options` and never uses
it:
```python
def open_table(
self,
name: str,
*,
namespace_path: Optional[List[str]] = None,
storage_options: Optional[Dict[str, str]] = None,
index_cache_size: Optional[int] = None,
...
) -> Table:
...
if index_cache_size is not None:
logging.info("index_cache_size is ignored in LanceDb Cloud ...")
table = LOOP.run(self._conn.open_table(name, namespace_path=namespace_path))
```
The value is never passed down and never mentioned. `index_cache_size`
is ignored on Cloud in the
same way, but it says so.
I checked this at runtime on 0.34.0, not just by reading it: swapping
the inner connection for a
recorder, `open_table("t", storage_options={...})` hands the layer below
`['namespace_path']` and
nothing else, no log record is emitted, and the same probe shows
`index_cache_size` producing its
message as expected.
This adds the matching log line, so the two ignored parameters behave
the same way. `ruff check` and
`ruff format --check` are clean on the file.
A note on severity. This is not a security hole and nothing is exposed.
Someone passing credentials
there gets silence instead of an error, and finds out later.
One thing I am unsure about, and it changes the fix. I have assumed
per-table storage options are
meaningless on Cloud, which is what the `index_cache_size` line next to
it implies about managed
storage. If they are supposed to work, then the right change is to pass
them through to
`self._conn.open_table` instead and this patch is the wrong one. Happy
to redo it that way.
I did not check whether `create_table` or the async connection have the
same gap.
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
Star LanceDB to get updates!
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.
- Cloud and Enterprise: Production-scale vector search with no servers to manage. Complete data sovereignty and security.
Ecosystem:
- Columnar Storage: Built on the Lance columnar format for efficient storage and analytics.
- Seamless Integration: Python, Node.js, Rust, and REST APIs for easy integration. Native Python and Javascript/Typescript support.
- 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/rest |
Join Us and Contribute
We welcome contributions from everyone! Whether you're a developer, researcher, or just someone who wants to help out.
If you have any suggestions or feature requests, please feel free to open an issue on GitHub or discuss it on our Discord server.
Check out the GitHub Issues if you would like to work on the features that are planned for the future. If you have any suggestions or feature requests, please feel free to open an issue on GitHub.
