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Brendan ClementandClaude Opus 5.5
ca343a5c06
## Summary Fixes the problems a page-by-page review of the docs found before launch, after checking each one against the current source and, where noted, the code. The matching fixes for the dataset and Geneva pages, which live in `lancedb/docs`, are in lancedb/docs#402. **Tables** - `integrations/embedding/cohere`: each input-type description was split across two table rows and wrapped in literal quotes (`"`search_document`"`). Now one row per type. - `integrations/embedding/gemini`: removes the same stray quotes from the task-type table. - `integrations/ai/langchain`: the MMR search table's `kwargs` row was missing a cell, so its description sat under Type. - Six reranker pages (answerdotai, cohere, colbert, jina, openai, voyageai): closes the backtick before `_relevance_score`. - `indexing/vector-index`: finishes a cell cut off at "with scalar". - `sql/dql/dedupe`: labels the empty header cell in the resolution table. **Corrections** - `curation/sampling`: sampling works in LanceDB Embedded through the Python SDK, so the page is available in both, with the SQL sections marked Enterprise. - `feature-engineering/index`, `add-computed-column`: automatic refresh is on by default for each computed column and takes effect once a deployment enables its scheduler. `refresh-column` already said so; these two implied it was off. - `indexing/scalar-index`, `indexing/index`: `LABEL_LIST` accelerates `array_has_all`/`array_has_any`; `array_contains_*` doesn't exist. Also merges scalar-index's two "nested fields" sections into one. - `integrations/embedding/ibm`: lists the four current models from `CURRENT_MODELS` (it listed a deprecated one) and explains that the default stays a legacy ID so existing tables reload with their model. - `integrations/reranking/openai`: the class is `OpenaiReranker`, not `OpenAI()`. - `integrations/ai/synthetic-data-kit`: the CLI is `synthetic-data-kit` throughout, each step runs one command, each step passes the file it writes to the next, and `save-as` uses `-f`/`-o`. - `integrations/data/dlt`: `secrets.toml` defines `api_token`, which the pipeline reads, and the run command runs the file the page creates. - `search/vector-search`: the multivector walkthrough connected to Enterprise in step 1 and a local path in step 2. It now runs locally throughout, with a note on connecting to Enterprise, and is renamed so it doesn't read as a duplicate of the short section above. **Structure and wording** - `integrations/embedding/nvidia`: adds headings, and drops test-suite details aimed at contributors. - `integrations/ai/genkit`: makes the retrieval test a real heading and describes the query step. - `integrations/data/voxel51`: replaces a raw MkDocs `!!! note` with a `<Note>`. - `sql/ddl/table`, `sql/ddl/job`: "Errors and limits" is a real heading, not bold text. - `curation/windowing`: sidebar title matches the curation overview ("Rank and Deduplicate"). - `deployment/storage`, `basics/agentic-skills`: fixes a garbled sentence and a stale reference to the quickstart. **Checked:** no new Vale alerts on the changed pages. Assembled together with lancedb/docs#402, the site passes `mint validate` and `mint broken-links --check-anchors --check-redirects`. Every changed page was reviewed in a local preview. 🤖 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:
- 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 |
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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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