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Drew Gallardo
a548e59d49
Python bindings for blob v2 read on **local** tables. Rust read APIs landed in #3562. This PR wires `fetch_blob_files`, `fetch_blobs`, v2 query/`to_pandas(blob_mode="bytes")`, and hidden `_rowid` metadata so `fetch_*` works from query hits without exposing `_rowid` in the column list. **Cloud:** `RemoteTable.fetch_blobs` / `fetch_blob_files` raise `NotImplementedError` until Phalanx ships the server route (separate track; not blocking local merge). ### Primary path: lazy file handles ```python table = db.create_table("videos", schema=pa.schema([ pa.field("id", pa.int64()), lancedb.blob("video"), ])) table.add([{"id": 1, "video": open("clip.mp4", "rb").read()}]) hits = table.search().select(["id", "video"]).to_arrow() handle = table.fetch_blob_files("video", hits)[0] # seek + partial read — PyAV / decoders can use the handle handle.seek(frame_offset) chunk = handle.read_range(0, 65536) ``` `BlobFile` exposes `seek`, `read`, `read_range`, `read_up_to`, and works with `BufferedReader`. ### When you want full bytes ```python blobs = table.fetch_blobs("video", hits) # eager materialize, null-aligned df = table.to_pandas(blob_mode="bytes") # descriptors → bytes in pandas ``` ### `_rowid` (join key, not user `id`) Fetch needs Lance row ids. For v2 blob queries we auto-inject `_rowid`, stash it in Arrow schema metadata on `to_arrow()`, and drop the visible column unless you pass `.with_row_id(True)`. v1 legacy blobs (`lance-encoding:blob`) unchanged; fetch on v1 raises the migration error. ## Test plan - [x] `./scripts/test-blob.sh python` (105 passed in worktree) - [x] `fetch_blob_files` lazy read, seek, partial read, null alignment, cross-fragment dups - [x] hybrid query → `fetch_blobs` / `fetch_blob_files` - [ ] Will re-review after seek/`BlobFile` commit (`d77ab1a6`) --------- Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> Co-authored-by: Cursor <cursoragent@cursor.com>
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
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⭐ 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 |
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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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