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Wyatt Alt
0e65123bd8
Registering a real (embedding) Function failed on the client for three
reasons:
- `_package_source` treated `inspect.getclosurevars().unbound` as
"unresolved globals"; CPython puts attribute names there, so any body
with `np.linalg.norm(...)` or `body.split()` was rejected. Module-scope
references now come from Python's own scope analysis (`symtable`) over
the function source, recursively, and each is resolved the way the
interpreter would: the function's globals first (a module global may
shadow a builtin), then builtins. Free variables of nested scopes stay
lexical; postponed annotations are not runtime loads. A genuinely
missing global still fails.
- `_canonical_arrow_type` emitted spellings the server's frozen grammar
rejects (`fixed_size_list<T>[n]`, `timestamp[us]`, `struct<...>`,
zero-sized lists). It now emits exactly the grammar, with the server's
`fixed_size_list<item, size>` form, and the Rust declaration planner
parses that form too.
A shared golden
(`tests/fixtures/first_class_functions/v1/arrow_types.json`)
enumerates every grammar type, nested forms and rejected spellings; the
Python emitter and Rust parser are tested against it, and the same file
is under test in sophon. Packaging tests execute the shipped artifact in
a fresh namespace.
Contract changes (hence `breaking-change`):
- `@udf` now rejects namespace acquisition structurally
(`globals()`/`eval`/... by name, plus `import
sys`/`builtins`/`importlib`/`inspect` inside the body), requires the
function's captured `__builtins__` to be the standard mapping itself
(identity, so neither lookups nor implicit hooks such as `__import__`
can differ), rejects module globals that are namespace-bearing modules
(`builtins`, `sys`, ...), and treats the function's own name as
recursion only when the module binds it to the function or to the exact
`UdfDefinition` the decorator produced; it resolves module globals
through the function's real namespace (a module global may shadow a
builtin) and ships importable classes/functions as imports.
- List outputs must declare a non-nullable, metadata-free child named
`item` (`pa.list_(pa.field("item", t, nullable=False))`); that is what
the grammar means, and pyarrow's default nullable child was being
silently collapsed into it.
Contract, stated in the `udf` docstring: the artifact is a snapshot of
the function source plus exactly the module names it references.
Reaching the module namespace by another route is rejected where a
static packager can see it and is otherwise unsupported; there is no
dynamic-access detection beyond that.
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!
⭐ 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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