This PR makes the following aesthetic and content updates to the docs.
- [x] Fix max width issue on mobile: Content should now render more
cleanly and be more readable on smaller devices
- [x] Improve image quality of flowchart in data management page
- [x] Fix syntax highlighting in text at the bottom of the IVF-PQ
concepts page
- [x] Add example of Polars LazyFrames to docs (Integrations)
- [x] Add example of adding data to tables using Polars (guides)
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>
This mimics CREATE TABLE IF NOT EXISTS behavior.
We add `db.create_table(..., exist_ok=True)` parameter.
By default it is set to False, so trying to create
a table with the same name will raise an exception.
If set to True, then it only opens the table if it
already exists. If you pass in a schema, it will
be checked against the existing table to make sure
you get what you want. If you pass in data, it will
NOT be added to the existing table.
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.
By default tantivy-py uses 128MB heapsize. We change the default to 1GB
and we allow the user to customize this
locally this makes `test_fts.py` run 10x faster
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`
If you add timezone information in the Field annotation for a datetime
then that will now be passed to the pyarrow data type.
I'm not sure how pyarrow enforces timezones, right now, it silently
coerces to the timezone given in the column regardless of whether the
input had the matching timezone or not. This is probably not the right
behavior. Though we could just make it so the user has to make the
pydantic model do the validation instead of doing that at the pyarrow
conversion layer.
Closes#721
fts will return results as a pyarrow table. Pyarrow tables has a
`filter` method but it does not take sql filter strings (only pyarrow
compute expressions). Instead, we do one of two things to support
`tbl.search("keywords").where("foo=5").limit(10).to_arrow()`:
Default path: If duckdb is available then use duckdb to execute the sql
filter string on the pyarrow table.
Backup path: Otherwise, write the pyarrow table to a lance dataset and
then do `to_table(filter=<filter>)`
Neither is ideal.
Default path has two issues:
1. requires installing an extra library (duckdb)
2. duckdb mangles some fields (like fixed size list => list)
Backup path incurs a latency penalty (~20ms on ssd) to write the
resultset to disk.
In the short term, once #676 is addressed, we can write the dataset to
"memory://" instead of disk, this makes the post filter evaluate much
quicker (ETA next week).
In the longer term, we'd like to be able to evaluate the filter string
on the pyarrow Table directly, one possibility being that we use
Substrait to generate pyarrow compute expressions from sql string. Or if
there's enough progress on pyarrow, it could support Substrait
expressions directly (no ETA)
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
Closes https://github.com/lancedb/lance/issues/1738
We add a `flatten` parameter to the signature of `to_pandas`. By default
this is None and does nothing.
If set to True or -1, then LanceDB will flatten structs before
converting to a pandas dataframe. All nested structs are also flattened.
If set to any positive integer, then LanceDB will flatten structs up to
the specified level of nesting.
---------
Co-authored-by: Weston Pace <weston.pace@gmail.com>
* Filename typo
* Remove rick_morty csv as users won't really be able to use it.. We can
create a an executable colab and download it from a bucket or smth.
Fix broken link to embedding functions
testing: broken link was verified after local docs build to have been
repaired
---------
Co-authored-by: Chang She <chang@lancedb.com>
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
Add `to_list` to return query results as list of python dict (so we're
not too pandas-centric). Closes#555
Add `to_pandas` API and add deprecation warning on `to_df`. Closes#545
Co-authored-by: Chang She <chang@lancedb.com>
I only modified those docs pages that are untouched in existing unmerged
PRs, so hopefully there are no merge conflicts!
1. The `tantivy-py` version specified in the docs doesn't work (pip
install fails), but with the latest version of pip and wheel installed
on my Mac, I was able to just `pip install tantivy` and FTS works great
for me. I updated the docs page to include this in
7ca4b757ce but can always modify to
another specific version in case this breaks any tests.
2. The `.add()` method for Python should take in a list of dicts as the
first option (to also align with the JS API), and additionally, users
can pass an existing pandas DataFrame to add to a table. Hope this makes
sense.
3. I've had multiple conversations with users who are unclear that the
terms "exhaustive", "flat" and "KNN" are all the same kind of search, so
I've updated the verbiage of this section to clarify this.
4. Fixed typos and improved clarity in the ANN indexes page.
