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>

docs/src/python/duckdb.md

@@ -1,14 +1,14 @@
 # DuckDB
 
-`LanceDB` works with `DuckDB` via [PyArrow integration](https://duckdb.org/docs/guides/python/sql_on_arrow).
+LanceDB is very well-integrated with [DuckDB](https://duckdb.org/), an in-process SQL OLAP database. This integration is done via [Arrow](https://duckdb.org/docs/guides/python/sql_on_arrow) .
 
-Let us start with installing `duckdb` and `lancedb`.
+We can demonstrate this by first installing `duckdb` and `lancedb`.
 
 ```shell
 pip install duckdb lancedb
 ```
 
-We will re-use [the dataset created previously](./arrow.md):
+We will re-use the dataset [created previously](./pandas_and_pyarrow.md):
 
 ```python
 import lancedb
@@ -22,7 +22,7 @@ table = db.create_table("pd_table", data=data)
 arrow_table = table.to_arrow()
 ```
 
-`DuckDB` can directly query the `arrow_table`:
+DuckDB can directly query the `pyarrow.Table` object:
 
 ```python
 import duckdb
@@ -40,6 +40,8 @@ duckdb.query("SELECT * FROM arrow_table")
 └─────────────┴─────────┴────────┘
 ```
 
+You can very easily run any other DuckDB SQL queries on your data.
+
 ```py
 duckdb.query("SELECT mean(price) FROM arrow_table")
 ```