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Jack Ye
aab23eb39e
Supports fully nullable named Function outputs while preserving the
distinction between a valid all-null struct and a null/unassigned
result.
## Concrete example
This UDF contract is now valid:
```python
@udf(
input_schema=pa.schema([
pa.field("text", pa.string(), nullable=False),
]),
output_schema=pa.schema([
pa.field(
"embedding",
pa.list_(pa.float32(), list_size=1024),
nullable=True,
),
pa.field("embedding_failure_reason", pa.string(), nullable=True),
pa.field("embedding_failure_code", pa.int32(), nullable=True),
]),
)
def embed(text):
...
```
A successful row can return:
```text
embedding = [0.12, ...]
embedding_failure_reason = NULL
embedding_failure_code = NULL
```
If remote inference still fails after retries, it can return:
```text
embedding = NULL
embedding_failure_reason = "HTTP 429: rate limited"
embedding_failure_code = 429
```
An all-null but valid result struct is also assigned; it is not mistaken
for unfinished work.
## Binding shapes
- Mapping the result to one output column stores the `StructArray`
directly, including its parent validity bitmap.
- Flattening the result into top-level columns stores the parent
validity in a reserved internal nullable Boolean assignment column that
is not part of the UDF result mapping.
- An outer null struct remains unassigned/skipped. A valid struct
remains assigned regardless of which child fields are null.
- Scalar Function outputs remain non-nullable.
The contract is preserved through Python registration, Rust application
planning, persisted `FunctionBinding` metadata, schema revalidation, and
Enterprise execution.
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.
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