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KarthikSandXuanwo
3dd579785e
Closes #4396. Calls written against `Table` can fail or bind different arguments when used with `RemoteTable`. Align the affected sync signatures, positional order, and defaults; preserve renamed remote keywords as deprecated aliases. Share legacy vector configuration and tokenizer inference, forward index tuning and timeout options, and accept `fast_search` consistently. Preserve current upstream conversion behavior: local `to_polars(batch_size=...)` returns a LazyFrame; remote tables accept the same argument signature but raise the documented unsupported-operation error and direct callers to `table.search().to_polars()`. The abstract `Table` method retains its unsupported-operation error, with the implementation confined to `LanceTable`. Regression coverage checks argument binding, deprecated aliases, option forwarding, native behavior through both wrappers, HTTP index requests, and remote conversion rejection. The merge preserves current tokenizer defaults, remote query defaults, HNSW-PQ support, and unsupported-operation handling. Runtime validation at `c4fb01f2` (Linux x86_64, uv-managed Python 3.12.15): - Focused Ruff lint and formatting passed for all four changed Python files. - Rebuilt and installed the current native extension successfully with the repository's `ci` Cargo profile. - Seven regression suites passed: `test_errors.py`, `test_table_signatures.py`, `test_remote_db.py`, `test_table.py`, `test_index.py`, `test_fts.py`, and `test_query.py`: **874 passed, 1 deselected**, with slow and S3 tests excluded. - CI caught an abstract-base conversion regression in the initial merge. The existing error-contract test reproduced it locally (1 failed, 7 passed); restoring the implementation to `LanceTable` fixes it while preserving signature parity. - Full-root Ruff reports 49 lint errors and 11 files needing formatting, all in unchanged upstream `docs/web-tests` files. The four changed Python files pass both checks. - Validated against upstream `197403e8`, using Lance 15.0.0-beta.1 and LanceDB 0.41.0-beta.14 bindings. Historical validation before this merge: 618 tests passed and one was deselected. That result does not establish correctness of the current merge. The original client-side reproduction produced nine argument-binding errors without contacting a server; the initial regression suite failed 32 cases before the fix and passed 39 after it. Documentation review follow-up: restored the `batch_size` parameter description on the abstract `Table.to_polars` method so it appears in the public API reference. This adds six docstring lines and changes no runtime behavior. After this edit, all 47 focused error-contract and signature tests pass; changed-file Ruff lint/format and `git diff --check` pass. Repository-wide Ruff still reports the same 49 findings and 11 formatting files in unchanged `docs/web-tests`. The full documentation site and seven-suite runtime run were not repeated for this documentation-only follow-up. The reported ARM64 production-server deployment has not been recreated. HTTP integration tests use the repository's local mock server. BREAKING CHANGE: Remote synchronous table methods now use the positional argument order and defaults documented by `Table`. Migrate calls using the former remote-only positional order to named arguments, particularly `create_index` and `search`; pass `index_type` by keyword to `create_scalar_index`. Remote scalar and vector index creation now default to `replace=True`; specify `replace=False` to retain rejection of an existing index. Renamed remote keywords remain supported as deprecated aliases, as documented above. --------- 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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