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## Feature - What is the new feature? - Adds `blob_mode` support to sync and async Python query `to_pandas()` APIs. - Enables plain scan queries to return blob columns as lazy `BlobFile` objects, raw bytes, or blob descriptions. - Lets namespace-backed local tables use Lance native blob-aware pandas conversion for lazy blobs. - Why do we need this feature? - Table and Lance dataset/scanner APIs already support blob-aware pandas conversion, but LanceDB query builders did not expose that capability. - Geneva and other callers should be able to use query-level `to_pandas(blob_mode=...)` without manually constructing Lance scanners. - How does it work? - Plain scan queries route through Lance scanner native `to_pandas(blob_mode=...)`, preserving filter, projection, limit, offset, row id, and alias/expression projection behavior. - Non-native query shapes keep existing Arrow fallback semantics and raise a clear error when they return blob columns with `blob_mode="lazy"` or `blob_mode="bytes"`. - Focused tests cover table/query blob modes, filter/select/limit/offset/alias query cases, async query behavior, vector-query error boundaries, and namespace-backed lazy blobs. ## Validation - `cd python && .venv/bin/maturin develop --uv --extras tests,dev --profile dev` - `cd python && uv run --frozen --no-sync pytest python/tests/test_table.py::test_table_to_pandas_blob_modes python/tests/test_table.py::test_async_table_to_pandas_blob_bytes python/tests/test_query.py::test_plain_scan_query_to_pandas_blob_modes python/tests/test_query.py::test_plain_scan_query_to_pandas_blob_projection python/tests/test_query.py::test_async_plain_scan_query_to_pandas_blob_projection python/tests/test_query.py::test_vector_query_to_pandas_blob_mode_requires_native_path python/tests/test_namespace.py::TestNamespaceConnection::test_table_to_pandas_blob_lazy_through_namespace -q` - `cd python && uv run --frozen --no-sync ruff format --check .` - `cd python && uv run --frozen --no-sync ruff check .` - `git diff --check`
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 |
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.
Contributors
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