Jack Ye
4487b1108c
`execute_query` / `execute_query_async` can now bind values to `$1` /
`$name` placeholders instead of splicing them into the SQL text. The
values travel as Arrow, so a float32 keeps its exact bits and type and a
vector stays a compact `FixedSizeList` (3 KB for 768 dims rather than ~9
KB of decimal literals parsed back as Float64).
```rust
db.execute_query_async("SELECT id FROM docs ORDER BY distance(vector, $vector) LIMIT $k")
.parameters(batch) // one-row RecordBatch
.execute()
.await?;
```
```python
db.execute_query(sql, parameters={"vector": np.float32_vector, "k": 10})
```
- A statement with parameters is sent on one `DoExchange`: the first
message carries the `CommandStatementQuery` descriptor and the parameter
schema, the second the one-row batch, and the result rows stream back on
the same call. Statements without parameters keep `PollFlightInfo` +
`DoGet` unchanged.
- Column `i` binds `$<i+1>`; a column whose name is not a number also
binds `$<name>`. Python accepts a list (positional), a dict (named), or
a one-row `RecordBatch` / `Table`; 1-D numpy / pyarrow arrays become a
`FixedSizeList` of their element type.
- A parameterized query runs on its call: `cancel()`, dropping the
handle before reading, or dropping the reader ends the call and stops
the server-side statement. It is still registered for `describe_query`.
- `Database::execute_query_async` now takes an `ExecuteQueryRequest`
options struct.
Server side: lancedb/sophon#8256.
The Multimodal Lakehouse for AI
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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⭐ Click here ⭐ to see how fast we're growing!
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.
Contributors
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