Drew Gallardo
77208fd464
Remote half of the blob read path. #3578 did local Python. This makes `RemoteTable` hit the server. - `fetch_blobs(column, row_ids or hits)` → bytes over `POST /v1/table/{id}/fetch_blobs/` - `blob_columns()` from the cached schema (describe already has the metadata, no extra route) - search then `fetch_blobs` works. row identity rides inside the blob descriptor so you do not need a public `_rowid` - `fetch_blob_files` still `NotSupported` on remote. use `fetch_blobs` for full bytes for now. Range is a follow up Accepts Binary / LargeBinary / BinaryView on the way back. Empty `row_ids` short-circuits. Version + branch go in the request body same as other read calls. ### Example ```python db = lancedb.connect(uri="db://my-project", api_key=...) table = db.open_table("clips") hits = table.search(query_vec).select(["id", "video"]).limit(10).to_arrow() # hits is just id + video. row ids are stashed on the descriptor blobs = table.fetch_blobs("video", hits) # null-aligned, same length as hits ``` Or pass ids yourself: ```python blobs = table.fetch_blobs("video", [10, 20, 30]) ``` ### Testing - `cargo test -p lancedb --features remote --lib` - `cargo test -p lancedb --features remote --test blob_integration` - `pytest python/tests/test_remote_db.py -k remote_blob` - live e2e against a local 0.5.0 remote server (search → fetch, nulls, nested path, old server gate) --------- Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
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.
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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.
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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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