Bilal Atique
4e3ec7cc27
Closes #4300. The TypeScript `BlobFile` only had `size()`, `read()` and `readRange()`, while the Rust `BlobFile` behind it also has `seek`, `tell`, `read_up_to`, `read_ranges` and `close`. This exposes those on the Node binding: - `read(maxBytes?)` reads at most `maxBytes` from the cursor (Rust `read_up_to`). Without the argument it reads to the end, as before. - `readRanges([{ start, end }, ...])` reads several half-open ranges and returns one buffer per range, in order. Like `readRange`, it does not move the cursor. - `seek(position)` and `tell()` move and report the cursor. - `close()` releases the handle, and `isClosed()` reports it. Every read, `seek` and `tell` after `close()` fails with "blob file is already closed". A second `close()` does nothing. The binding keeps its own `closed` flag, the same way the Python `BlobFile` does. Offsets and lengths are `bigint`, like `readRange` and `size()`. `BlobRange` is exported as a type. The new tests in `table.test.ts` cover chunked reads with `read(maxBytes)`, `seek` and `tell`, a negative seek position, `readRanges` order and cursor, a `readRanges` end past the blob size, and reads after `close()`. The Node docs under `docs/src/js` are regenerated with `pnpm run docs`. I left the `ReadableStream`/async iterator idea from the issue out of this PR. The AbortSignal work in #4288 touches the same methods, so it can follow on top of this.
The Multimodal AI Lakehouse
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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.
- 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 |
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