917aabd077
The [`FieldLike` type in
arrow.ts](5ec12c9971/nodejs/lancedb/arrow.ts (L71-L78))
can have a `type: string` property, but before this change, actually
trying to create a table that has a schema that specifies field types by
name results in an error:
```
Error: Expected a Type but object was null/undefined
```
This change adds support for mapping some type name strings to arrow
`DataType`s, so that passing `FieldLike`s with a `type: string` property
to `sanitizeField` does not throw an error.
The type names that can be passed are upper/lowercase variations of the
keys of the `constructorsByTypeName` object. This does not support
mapping types that need parameters, such as timestamps which need
timezones.
With this, it is possible to create empty tables from `SchemaLike`
objects without instantiating arrow types, e.g.:
```
import { SchemaLike } from "../lancedb/arrow"
// ...
const schemaLike = {
fields: [
{
name: "id",
type: "int64",
nullable: true,
},
{
name: "vector",
type: "float64",
nullable: true,
},
],
// ...
} satisfies SchemaLike;
const table = await con.createEmptyTable("test", schemaLike);
```
This change also makes `FieldLike.nullable` required since the `sanitizeField` function throws if it is undefined.
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
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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/introduction |
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
Stay in Touch With Us