Jack Ye
1fb2dd1551
Adds GCP inputs to the existing database-owned storage integration
methods. GCP takes one Secret reference; the Secret stores the complete
application credentials JSON as an opaque string. The same methods and
SQL transport handle create, describe, list and drop.
```python
info = db.create_storage_integration(
"gcs", "gs://customer-data/raw/", storage_provider="GCP",
gcp_credentials_json_secret=SecretReference(
"gcp_json", database="shared", namespace_path=("credentials",),
),
)
print(info.configuration_instructions)
info = db.describe_storage_integration("gcs")
names = list(db.list_storage_integrations())
db.drop_storage_integration("gcs")
```
```sql
CREATE STORAGE INTEGRATION gcs
STORAGE_PROVIDER = 'GCP'
STORAGE_PREFIX = 'gs://customer-data/raw/'
GCP_CREDENTIALS_JSON_SECRET = shared.credentials.gcp_json;
DESCRIBE STORAGE INTEGRATION gcs;
SHOW STORAGE INTEGRATIONS;
DROP STORAGE INTEGRATION gcs;
```
```mermaid
flowchart LR
Python[Sync or async Python] --> SQL[Existing Flight SQL transport]
SQL --> Config[Configuration and one Secret reference]
```
Adds Rust request construction, SQL rendering and typed metadata
decoding, plus sync/async Flight SQL regressions across the three Arrow
string encodings. Rejects missing or mixed-cloud credential options.
Local builds/tests are excluded by request; validation runs in CI.
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
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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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