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Jack Ye
6d126a3601
Adds database-owned S3 storage integrations with Secret-style namespace
paths in a separate keyspace. Use a regular database under the cluster
root for bootstrap integrations and Secrets. `default` is an example
name, with no enforced or automatically provisioned database; `system`
remains read-only. Authentication uses either an IAM role or two
existing Secret references, including cross-database references;
configuration metadata never contains credential values.
### Python experience
```python
catalog = lancedb.connect_catalog("https://my-server.example", api_key="secret")
db = catalog.connect_database("default")
info = db.create_storage_integration(
"raw", "s3://customer-bucket/raw/",
aws_role_arn="arn:aws:iam::123456789012:role/lancedb-access",
aws_region="us-east-1",
)
print(info.configuration_instructions)
info = db.describe_storage_integration("raw")
print(list(db.list_storage_integrations()))
db.drop_storage_integration("raw")
```
For static credentials, replace `aws_role_arn` with
`aws_access_key_id_secret=SecretReference("s3_key_id",
database="default")` and
`aws_secret_access_key_secret=SecretReference("s3_secret_key",
database="default")`, imported from `lancedb.storage_integration`. Pass
`namespace_path` to address nested namespaces. Async database
connections expose the same APIs; listing is lazy. All four client
operations execute SQL through the existing SQL transport, with typed
Arrow result decoding and name-based listing cursors.
### SQL experience
```sql
CREATE STORAGE INTEGRATION default.public.raw
STORAGE_PROVIDER = 'S3'
STORAGE_PREFIX = 's3://customer-bucket/raw/'
STORAGE_AWS_ROLE_ARN = 'arn:aws:iam::123456789012:role/lancedb-access'
STORAGE_AWS_REGION = 'us-east-1';
DESCRIBE STORAGE INTEGRATION default.public.raw;
SHOW STORAGE INTEGRATIONS IN default.public;
DROP STORAGE INTEGRATION IF EXISTS default.public.raw;
CREATE STORAGE INTEGRATION default.public.raw
STORAGE_PROVIDER = 'S3'
STORAGE_PREFIX = 's3://customer-bucket/raw/'
STORAGE_AWS_ACCESS_KEY_ID_SECRET = default.public.s3_key_id
STORAGE_AWS_SECRET_ACCESS_KEY_SECRET = default.public.s3_secret_key;
```
CREATE and DESCRIBE return configuration and a single sentence of setup
guidance. IAM mode also returns the deployment role principal, stable
external ID, customer trust policy, and prefix-scoped permissions
policy. Registration does not verify access. Creation requires namespace
CREATE_STORAGE_INTEGRATION plus parent USAGE; Secret references require
source database, namespace, and Secret USAGE. Describe requires object
USAGE; drop requires OWNERSHIP. SQL supports GRANT/REVOKE for these
objects and their creation privilege.
Includes client documentation and Flight SQL regression coverage; no
dedicated storage-integration REST endpoints are used. Local
builds/tests are excluded by request.
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
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:
- LanceDB Embedded (open source): 100% open source, runs in your application's process; your data can live on local disk or in object storage. No vendor lock-in.
- LanceDB Enterprise: Production-scale distributed search, indexing, and maintenance. Plus, 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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