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This PR makes incremental changes to the documentation. * Closes #697 * Closes #698 ## Chores - [x] Add dark mode - [x] Fix headers in navbar - [x] Add `extra.css` to customize navbar styles - [x] Customize fonts for prose/code blocks, navbar and admonitions - [x] Inspect all admonition boxes (remove redundant dropdowns) and improve clarity and readability - [x] Ensure that all images in the docs have white background (not transparent) to be viewable in dark mode - [x] Improve code formatting in code blocks to make them consistent with autoformatters (eslint/ruff) - [x] Add bolder weight to h1 headers - [x] Add diagram showing the difference between embedded (OSS) and serverless (Cloud) - [x] Fix [Creating an empty table](https://lancedb.github.io/lancedb/guides/tables/#creating-empty-table) section: right now, the subheaders are not clickable. - [x] In critical data ingestion methods like `table.add` (among others), the type signature often does not match the actual code - [x] Proof-read each documentation section and rewrite as necessary to provide more context, use cases, and explanations so it reads less like reference documentation. This is especially important for CRUD and search sections since those are so central to the user experience. ## Restructure/new content - [x] The section for [Adding data](https://lancedb.github.io/lancedb/guides/tables/#adding-to-a-table) only shows examples for pandas and iterables. We should include pydantic models, arrow tables, etc. - [x] Add conceptual tutorial for IVF-PQ index - [x] Clearly separate vector search, FTS and filtering sections so that these are easier to find - [x] Add docs on refine factor to explain its importance for recall. Closes #716 - [x] Add an FAQ page showing answers to commonly asked questions about LanceDB. Closes #746 - [x] Add simple polars example to the integrations section. Closes #756 and closes #153 - [ ] Add basic docs for the Rust API (more detailed API docs can come later). Closes #781 - [x] Add a section on the various storage options on local vs. cloud (S3, EBS, EFS, local disk, etc.) and the tradeoffs involved. Closes #782 - [x] Revamp filtering docs: add pre-filtering examples and redo headers and update content for SQL filters. Closes #783 and closes #784. - [x] Add docs for data management: compaction, cleaning up old versions and incremental indexing. Closes #785 - [ ] Add a benchmark section that also discusses some best practices. Closes #787 --------- Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com> Co-authored-by: Will Jones <willjones127@gmail.com>
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Integrations
LanceDB supports ingesting from and exporting to your favorite data formats across the Python and JavaScript ecosystems.
Tools
LanceDB is integrated with a lot of popular AI tools, with more coming soon. Get started using these examples and quick links.
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LlamaIndexLlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models. Llama index integrates with LanceDB as the serverless VectorDB.Lean More |
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LangchainLangchain allows building applications with LLMs through composabilityLean More |
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Langchain TSJavascript bindings for Langchain. It integrates with LanceDB's serverless vectordb allowing you to build powerful AI applications through composibility using only serverless functions.Learn More |
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Voxel51It is an open source toolkit that enables you to build better computer vision workflows by improving the quality of your datasets and delivering insights about your models.Learn More |
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PromptToolsOffers a set of free, open-source tools for testing and experimenting with models, prompts, and configurations. The core idea is to enable developers to evaluate prompts using familiar interfaces like code and notebooks. You can use it to experiment with different configurations of LanceDB, and test how LanceDB integrates with the LLM of your choice.Learn More |
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