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Joaquin Hui GomezandXuanwo
256db55a51
`RemoteDBConnection.create_table` orders its parameters `(name, data,
schema, on_bad_vectors, fill_value, mode, exist_ok, ...)`, while
`DBConnection`, `LanceDBConnection` and `LanceNamespaceDBConnection` all
use `(name, data, schema, mode, exist_ok, on_bad_vectors, fill_value,
...)`. Since `RemoteDBConnection` subclasses `DBConnection`, identical
positional calls bind to different parameters: switching a working
script from `lancedb.connect(path)` to `lancedb.connect("db://...")`
silently turns `mode="overwrite"` into `on_bad_vectors="overwrite"` and
`exist_ok=True` into `fill_value=True`, and nothing client-side
validates either. `inspect.signature(...).bind(None, "t", data, None,
"overwrite", True)` shows it deterministically, no cloud needed.
This reorders the remote signature to match the other three; every
parameter keeps its own existing default (including the remote's
`mode=None`, which its body relies on), and the body already forwards by
keyword so nothing else changes. The new test asserts positional parity
with `DBConnection` through `embedding_functions` and fails on `main`.
Blast radius, stated plainly: only remote callers passing four or more
positional arguments are affected — positions 4–7 move; positions 1–3
and 8 are unchanged and keyword callers are unaffected. No in-repo
caller passes more than two positionals. Anyone relying on the current
order was relying on behaviour that contradicts the documented base
contract and diverges from local connections. Not added here: the
`storage_options`/`data_storage_version`/`enable_v2_manifest_paths`
parameters the remote signature still lacks.
---------
Co-authored-by: Xuanwo <github@xuanwo.io>
The Multimodal Lakehouse for AI
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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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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