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Wyatt AltandClaude Opus 5.5
fad3a1125a
Lets a registered-Function column read another computed column (SQL or Function). Resolves ENT-2780. Nothing records which rows of a computed column have been filled, so a refresh treats a null computed input as "not yet computed". It skips the row and leaves the outputs null until a later refresh, after the input is filled. Because the Function never receives a null from a computed input, that input binds a non-nullable parameter. The trade-off is that a computed input which is null by definition (a SQL expression that yields null, or a Function row skipped on error) leaves the downstream row unfilled. ## Changes - `plan_function_application` and `ensure_binding_matches_schema` now accept computed inputs and record them as non-nullable. They still refuse inputs that are: - unrecognized computed kinds, - incomplete declarations, - internal Function assignment columns. - A new public `function_input_is_computed` reports whether an input reads a computed column. A new public `computed_function_inputs` lists the inputs a server refresh must find non-null before it computes a row. - `ensure_supported_function_metadata` now refuses a schema whose computed columns read each other in a cycle. A declaration can only read columns that already exist, so only a schema supplied whole can contain one. - Dropping an upstream output while a downstream binding still reads it is refused by the existing binding guard. This is now covered by a test. The server-side refresh change (skipping rows that are not ready, clearing them on recompute) lives in sophon and follows this PR. ## Testing - `cargo test -p lancedb --features remote --lib -- computed refresh freshness schema_evolution function`: 255 passed. - `cargo clippy -p lancedb --features remote --tests -- -D warnings`: clean. 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
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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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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