Added a small bit of documentation for the `dim` feature which is
provided by the new `text-embedding-3` model series that allows users to
shorten an embedding.
Happy to discuss a bit on the phrasing but I struggled quite a bit with
getting it to work so wanted to help others who might want to use the
newer model too
Got some user feedback that the `implicit` / `explicit` distinction is
confusing.
Instead I was thinking we would just deprecate the `with_embeddings` API
and then organize working with embeddings into 3 buckets:
1. manually generate embeddings
2. use a provided embedding function
3. define your own custom embedding function
- Rename safe_import -> attempt_import_or_raise (closes
https://github.com/lancedb/lancedb/pull/923)
- Update docs
- Add Notebook example (@changhiskhan you can use it for the talk. Comes
with "open in colab" button)
- Latency benchmark & results comparison, sanity check on real-world
data
- Updates the default openai model to gpt-4
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>
Named it Gemini-text for now. Not sure how complicated it will be to
support both text and multimodal embeddings under the same class
"gemini"..But its not something to worry about for now I guess.
I found that it was quite incoherent to have to read through the
documentation and having to search which submodule that each class
should be imported from.
For example, it is cumbersome to have to navigate to another
documentation page to find out that `EmbeddingFunctionRegistry` is from
`lancedb.embeddings`
This PR adds an overview of embeddings docs:
- 2 ways to vectorize your data using lancedb - explicit & implicit
- explicit - manually vectorize your data using `wit_embedding` function
- Implicit - automatically vectorize your data as it comes by ingesting
your embedding function details as table metadata
- Multi-modal example w/ disappearing embedding function
