Some Rust jobs (such as
[Rust/linux](https://github.com/lancedb/lancedb/actions/runs/13019232960/job/36315830779))
take almost minutes. This can be a bit of a bottleneck.
* Two fixes to make caches more effective
* Check in `Cargo.lock` so that dependencies don't change much between
runs
* Added a new CI job to validate we can build without a lockfile
* Altered build commands so they don't have contradictory features and
therefore don't trigger multiple builds
Sadly, I don't think there's much to be done for windows-arm64, as much
of the compile time is because the base image is so bare we need to
install the build tools ourselves.
Todo:
- [x] add proper documentation
- [x] add unit tests
- [x] better handling of the registry**1
- [x] allow user defined registry**2
**1 The python implementation just uses a global registry so it makes
things a bit easier. I attached it to the db/connection to prevent
future conflicts if running multiple connections/databases. I mostly
modeled the registry & pattern off of datafusion's
[FunctionRegistry](https://docs.rs/datafusion/latest/datafusion/execution/trait.FunctionRegistry.html).
**2 Ideally, the user should be able to provide it's own registry
entirely, but currently it just uses an in memory registry by default
(_which isn't configurable_)
`rust/lancedb/examples/embedding_registry.rs` provides a thorough
example of expected usage.
---
Some additional notes:
This does not provide any of the out of box functionality that the
python registry does.
_i.e there are no built-in embedding functions._
You can think of this as the ground work for adding those built in
functions, So while this is part of
https://github.com/lancedb/lancedb/issues/994, it does not yet offer
feature parity.
This changes `lancedb` from a "pure python" setuptools project to a
maturin project and adds a rust lancedb dependency.
The async python client is extremely minimal (only `connect` and
`Connection.table_names` are supported). The purpose of this PR is to
get the infrastructure in place for building out the rest of the async
client.
Although this is not technically a breaking change (no APIs are
changing) it is still a considerable change in the way the wheels are
built because they now include the native shared library.
Sometimes LangChain would insert a single `[np.nan]` as a placeholder if
the embedding function failed. This causes a problem for Lance format
because then the array can't be stored as a FixedSizedListArray.
Instead:
1. By default we remove rows with embedding lengths less than the
maximum length in the batch
2. If `strict=True` kwargs is set to True, then a `ValueError` is raised
if the embeddings aren't all the same length
---------
Co-authored-by: Chang She <chang@lancedb.com>
* Refactors the Node module to load the shared library from a separate
package. When a user does `npm install vectordb`, the correct optional
dependency is automatically downloaded by npm.
* Add scripts and instructions to build Linux and MacOS node artifacts
locally.
* Add instructions for publishing the npm module and crates.
Co-authored-by: Will Jones <willjones127@gmail.com>
Changes:
* Refactors the Node module to load the shared library from a separate
package. When a user does `npm install vectordb`, the correct optional
dependency is automatically downloaded by npm.
* Brings Rust and Node versions in alignment at 0.1.2.
* Add scripts and instructions to build Linux and MacOS node artifacts
locally.
* Add instructions for publishing the npm module and crates.
- Core rust library
- ffi bridge that exposes rust functionality to javascript
- npm package that provides a TypeScript / JavaScript library
- limitations: it only supports reading for now
