Files
lancedb/docs/src/cloud/index.md
Prashanth Rao 4d5d748acd docs: Updates and refactor (#683)
This PR makes incremental changes to the documentation.

* Closes #697
* Closes #698

- [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.

- [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>
2024-04-05 16:27:12 -07:00

1.7 KiB

About LanceDB Cloud

LanceDB Cloud is a SaaS (software-as-a-service) solution that runs serverless in the cloud, clearly separating storage from compute. It's designed to be highly scalable without breaking the bank. LanceDB Cloud is currently in private beta with general availability coming soon, but you can apply for early access with the private beta release by signing up below.

Try out LanceDB Cloud{ .md-button .md-button--primary }

Architecture

LanceDB Cloud provides the same underlying fast vector store that powers the OSS version, but without the need to maintain your own infrastructure. Because it's serverless, you only pay for the storage you use, and you can scale compute up and down as needed depending on the size of your data and its associated index.

Transitioning from the OSS to the Cloud version

The OSS version of LanceDB is designed to be embedded in your application, and it runs in-process. This makes it incredibly simple to self-host your own AI retrieval workflows for RAG and more and build and test out your concepts on your own infrastructure. The OSS version is forever free, and you can continue to build and integrate LanceDB into your existing backend applications without any added costs.

Should you decide that you need a managed deployment in production, it's possible to seamlessly transition from the OSS to the cloud version by changing the connection string to point to a remote database instead of a local one. With LanceDB Cloud, you can take your AI application from development to production without major code changes or infrastructure burden.