BREAKING CHANGE: embedding function implementations in Node need to now
call `resolveVariables()` in their constructors and should **not**
implement `toJSON()`.
This tries to address the handling of secrets. In Node, they are
currently lost. In Python, they are currently leaked into the table
schema metadata.
This PR introduces an in-memory variable store on the function registry.
It also allows embedding function definitions to label certain config
values as "sensitive", and the preprocessing logic will raise an error
if users try to pass in hard-coded values.
Closes#2110Closes#521
---------
Co-authored-by: Weston Pace <weston.pace@gmail.com>
The code to support VoyageAI embedding and rerank models was added in
the https://github.com/lancedb/lancedb/pull/1799 PR.
Some of the documentation changes was also made, here adding the
VoyageAI embedding doc link to the index page.
These are my first PRs in lancedb and while i checked the
documentation/code structure, i might missed something important. Please
let me know if any changes required!
This is done as setup for a PR that will fix the OpenAI dependency
issue.
* [x] FTS examples
* [x] Setup mock openai
* [x] Ran `npm audit fix`
* [x] sentences embeddings test
* [x] Double check formatting of docs examples
Resovles #1709. Adds `trust_remote_code` as a parameter to the
`TransformersEmbeddingFunction` class with a default of False. Updated
relevant documentation with the same.
Docs used `get_registry.get(...)` whereas what works is
`get_registry().get(...)`. Fixing the two instances I found. I tested
the open clip version by trying it locally in a Jupyter notebook.
### Fix markdown table rendering issue
This PR adds a missing whitespace before a markdown table in the
documentation. This issue causes the table to not render properly in
mkdocs, while it does render properly in GitHub's markdown viewer.
#### Change Details:
- Added a single line of whitespace before the markdown table to ensure
proper rendering in mkdocs.
#### Note:
- I wasn't able to test this fix in the mkdocs environment, but it
should be safe as it only involves adding whitespace which won't break
anything.
---
Cohere supports following input types:
| Input Type | Description |
|-------------------------|---------------------------------------|
| "`search_document`" | Used for embeddings stored in a vector|
| | database for search use-cases. |
| "`search_query`" | Used for embeddings of search queries |
| | run against a vector DB |
| "`semantic_similarity`" | Specifies the given text will be used |
| | for Semantic Textual Similarity (STS) |
| "`classification`" | Used for embeddings passed through a |
| | text classifier. |
| "`clustering`" | Used for the embeddings run through a |
| | clustering algorithm |
Usage Example:
* `table.add` requires `data` parameter on the docs page regarding use
of embedding models from HF
* also changed the name of example class from `TextModel` to `Words`
since that is what is used as parameter in the `db.create_table` call
* Per
https://lancedb.github.io/lancedb/python/python/#lancedb.table.Table.add
- Tried to address some onboarding feedbacks listed in
https://github.com/lancedb/lancedb/issues/1224
- Improve visibility of pydantic integration and embedding API. (Based
on onboarding feedback - Many ways of ingesting data, defining schema
but not sure what to use in a specific use-case)
- Add a guide that takes users through testing and improving retriever
performance using built-in utilities like hybrid-search and reranking
- Add some benchmarks for the above
- Add missing cohere docs
---------
Co-authored-by: Weston Pace <weston.pace@gmail.com>
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
