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## Problem The existing Function `column_status` surface cannot decode the Agent scheduler's `queued` state or the `failure_threshold_reached` and `binding_revision_missing` blocked reasons. It also preserves only the latest refresh job, so a later cancellation can hide an earlier actual failure. ## Change - Adds the `queued`, `failure_threshold_reached`, and `binding_revision_missing` wire states in Rust, Python, and Node. The wire change is additive, while Rust callers must update exhaustive enum matches and public struct literals. - Adds optional `last_failure_job` alongside `last_refresh_job`. The latest cancellation and the most recent actual failure retain separate meanings. - Reuses the canonical Rust `JobFailure` and existing Python/Node `JobFailureInfo` models for nested job failure details instead of introducing a parallel status error model. - Maps the existing public `sql_job_id` field through Node's `JobFailureInfo`. - Keeps `column_status`, `column_statuses`, and `wait_fresh` as the existing API surfaces. There is no new endpoint, row-error list, durable ledger, or unknown-enum fallback. - Documents `queued` versus `running` and the separate latest-attempt and latest-failure jobs in the existing hidden freshness release draft. The new status values are additive but cross-service: `queued`, the two new blocked reasons, and `last_failure_job` require matching Agent/QN and SDK deployments before clients can rely on them. This SDK follow-up does not change the normal `7cc3904a` dependency pin or claim whole ENT-3487 completion. ## Validation The tested private source is `b19c8adbdc936c5976def6c17be47f6d5e3faf3e`, based on product commit `c9bac25ecb8b73b1bd0256bbea65c2621bcf6a94`. The final product docs commit changes generated TypeDoc only. - Rust: 2 selected tests passed, 0 failed, exit 0. The same two Rust blobs are unchanged from their tested `788ef85b` source through `c9bac25e`. - Python: the PyO3 extension built successfully, and the selected native PyO3 SDK test against a mock HTTP REST transport passed 1/0, exit 0. The test requests pinned version 42. - Node: the addon and TypeScript builds passed, Biome passed, and the selected Jest transport test passed 1/0 with 57 filtered tests. Addon SHA-256: `c888e260b61d41e79689ed2f20dd0dc98586a2377ce34f21610b06111734d426`. - Python extension SHA-256: `36c961d8a0cbd2e102a7765f2e47efb4c5af6310baa6262c8e3120407463da2b`. - TypeDoc was generated from the native `native.d.ts` produced by that Node build. A second generation produced no remaining diff. The public diff contains only product, test, and generated API documentation changes. Native runner scripts and preserved compile-failure logs remain private validation evidence. Agent/QN production mapping, mixed-version rollout, and the remaining ENT-3487 server-side work are tracked separately.
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
Star LanceDB to get updates!
⭐ 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:
- 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.
Contributors
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