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## Summary
Lance can now plan multiple byte ranges for the same blob in one
`read_blob_ranges` operation, but LanceDB users currently cannot expose
a complete set of logical ranges to that planner.
This complements `BlobFile`: file-like consumers such as PyAV can
continue to discover ranges dynamically, while callers that already know
the ranges for a batch can submit them together.
## Motivating example
A training table may store a large video blob together with a small
application-level clip index:
```text
video: blob
clips: [{offset, length}, ...]
```
The caller can select the videos and clips for a batch, obtain their row
IDs from the query, and read all of the selected windows together:
```python
rows = (
table.search()
.select(["clips"])
.with_row_id(True)
.limit(64)
.to_arrow()
.to_pylist()
)
requests = []
for row in rows:
clip = sample_clip(row["clips"])
requests.append(
(row["_rowid"], clip["offset"], clip["length"])
)
chunks = table.fetch_blob_ranges("video", requests)
```
Here, `_rowid` comes from the LanceDB query, while `offset` and `length`
come from the application's clip index and are relative to that row's
video blob. The caller describes only the logical reads; Lance still
handles validation, source grouping, coalescing, scheduling, and byte
backpressure.
Lance v10.0.0-beta.5 returns one logical result per blob selector or
range request and explicitly distinguishes null blobs from valid empty
values. LanceDB consumes that aligned result contract directly and only
adds a cardinality check for unresolved row IDs.
This PR exposes batched blob-range reads on local Rust and Python
tables. Results preserve request identity, duplicates, null slots, and
valid empty ranges while allowing Lance to execute the physical reads
out of order. Scheduler buffer sizing remains an internal Lance concern,
so the LanceDB API does not expose `io_buffer_size`.
Cloud tables continue to report this operation as unsupported until
there is a corresponding remote API.
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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