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## Problem Function-backed columns need an exact `fresh`, `stale`, `never_computed`, and `failed` partition for the table version a caller is reading. The existing error listing is capped, and composing it with job and schema listings cannot prove which rows currently failed. ## Behavior - Adds a strict freshness reader beside the existing permissive planner. It distinguishes an absent certificate from legacy evidence that omits failures, and from a current certificate carrying failure coverage. - Extends the immutable freshness sidecar with per-fragment known and failed row bitmaps. Partial or `WHERE` publications cover only observed rows; untouched rows stay unknown. Deleted rows do not block classification. - Adds failure-aware whole-fragment and row-repair writers. A rejected older attempt cannot overwrite failure evidence already recorded for a newer input signature. - Adds `column_status`, `column_statuses`, and `wait_fresh` to Python and the corresponding Node methods. The client calls the server's exact status endpoint, validates the four-way partition and one-version guarantee, and preserves the complete Function version, last-job, and automatic-refresh metadata. - Adds an MDX-commented draft usage section to the Enterprise computed-column refresh guide, covering Python, Rust, and TypeScript and remaining hidden until the server integration ships. - Sends `column`, `branch`, and exact `version` selectors to the status route. A pinned handle rejects a nonempty response from any other table version; latest handles keep the existing read-freshness headers. - Retries `wait_fresh` only for HTTP 503 with `function_column_status_finalization_pending`. Existing failed rows remain visible and do not keep the wait pending; other errors propagate. - Models the shared scheduler projection, including `up_to_date`, real `blocked` states, their reason, failure streak, and effective cooldown. ## Validation This is the SDK side of ENT-3563. Sophon's failure-coverage publication and status endpoint are a separate integration; deployed end-to-end and whole-ticket acceptance remain pending. - Rust: 9 exact freshness/status tests passed, plus 1 exact latest-handle freshness-header test. The pinned transport test was extended with a wrong-version response and rerun at `ebd6649a` (1 passed). Coverage includes untouched partial rows, selected-row failure clearing, cross-generation evidence, typed and non-retried errors, `up_to_date`, blocked scheduler fields, and exact-version requests. - Python: 2 native PyO3 remote-transport tests passed together, including typed retry; one local facade test reaches the existing unsupported boundary. - Node: the NAPI addon built successfully, TypeScript and Biome passed, and the selected native remote-transport test passed 1/0, including exact-version selection and blocked scheduler metadata. - `cargo fmt`, `git diff --check`, Ruff format/lint, Python compile, Biome, and TypeScript checks passed. Validation is mapped by the relevant unchanged source rather than treated as one final-head run: the nine Rust tests ran at `e4d51fc8`, the latest-header and Python remote tests at `31e68b7d`, the local Python test and Node build at `6f213584`, and the final TypeScript/Jest delta at `4f2d5c27`. The final product tree excludes those five private EC2 runner files and adds six generated TypeDoc files plus one hidden draft usage section in the computed-column refresh guide. The Node delta corrected a private TypeScript command and reused the unchanged native addon.
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:
- LanceDB Embedded (open source): 100% open source, runs in your application's process; your data can live on local disk or in object storage. No vendor lock-in.
- LanceDB Enterprise: Production-scale distributed search, indexing, and maintenance. Plus, 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.
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