Jack Ye
eeb244fa57
`list_functions()` promises every published immutable revision. Request
`SHOW FUNCTIONS HISTORY` so the SQL server's latest-only default does
not hide older revisions, and preserve the server's numeric revision
ordering when constructing page cursors (`1`, `2`, `10`).
```python
[(fn.name, fn.version) for fn in db.list_functions(page_limit=2)]
# [("embed", "1"), ("embed", "2"), ("embed", "10")]
```
```sql
SHOW FUNCTIONS; -- latest visible revision per name
SHOW FUNCTIONS HISTORY WITH (limit = 3, include_definition = true);
SHOW FUNCTIONS HISTORY WITH (after_name = 'embed', after_version = '2', limit = 3);
```
Each listing page uses the existing SQL query and polling transport; the
continuation token stores the last returned name/revision and remains
bound to its database and namespace. Function mutations keep their
query-backed job handles: `Job.id` is the query's ID and `job_type` is
`query`, so waiting observes that same SQL execution. No registry job or
second query ID is added.
Update the SQL and Python documentation for exact-revision drops:
sibling revisions and grants survive, and only the last visible
revision's removal releases the name and schedules object cleanup.
Extend the existing Flight SQL regression to paginate revisions `1`,
`2`, and `10` and verify the explicit history statement. Formatting and
static Python checks pass; local builds and tests were intentionally not
run.
The Multimodal Lakehouse for AI
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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.
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Key Features:
- Fast Vector Search: Search billions of vectors in milliseconds with state-of-the-art indexing.
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- 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.
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Ecosystem:
- Columnar Storage: Built on the Lance columnar format for efficient storage and analytics.
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- 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 |
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