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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>
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Pandas and PyArrow
Because Lance is built on top of Apache Arrow, LanceDB is tightly integrated with the Python data ecosystem, including Pandas and PyArrow. The sequence of steps in a typical workflow is shown below.
Create dataset
First, we need to connect to a LanceDB database.
import lancedb
db = lancedb.connect("data/sample-lancedb")
We can load a Pandas DataFrame to LanceDB directly.
import pandas as pd
data = pd.DataFrame({
"vector": [[3.1, 4.1], [5.9, 26.5]],
"item": ["foo", "bar"],
"price": [10.0, 20.0]
})
table = db.create_table("pd_table", data=data)
Similar to the pyarrow.write_dataset() method, LanceDB's
db.create_table() accepts data in a variety of forms.
If you have a dataset that is larger than memory, you can create a table with Iterator[pyarrow.RecordBatch] to lazily load the data:
from typing import Iterable
import pyarrow as pa
def make_batches() -> Iterable[pa.RecordBatch]:
for i in range(5):
yield pa.RecordBatch.from_arrays(
[
pa.array([[3.1, 4.1], [5.9, 26.5]]),
pa.array(["foo", "bar"]),
pa.array([10.0, 20.0]),
],
["vector", "item", "price"])
schema=pa.schema([
pa.field("vector", pa.list_(pa.float32())),
pa.field("item", pa.utf8()),
pa.field("price", pa.float32()),
])
table = db.create_table("iterable_table", data=make_batches(), schema=schema)
You will find detailed instructions of creating a LanceDB dataset in Getting Started and API sections.
Vector search
We can now perform similarity search via the LanceDB Python API.
# Open the table previously created.
table = db.open_table("pd_table")
query_vector = [100, 100]
# Pandas DataFrame
df = table.search(query_vector).limit(1).to_pandas()
print(df)
vector item price _distance
0 [5.9, 26.5] bar 20.0 14257.05957
If you have a simple filter, it's faster to provide a where clause to LanceDB's search method.
For more complex filters or aggregations, you can always resort to using the underlying DataFrame methods after performing a search.
# Apply the filter via LanceDB
results = table.search([100, 100]).where("price < 15").to_pandas()
assert len(results) == 1
assert results["item"].iloc[0] == "foo"
# Apply the filter via Pandas
df = results = table.search([100, 100]).to_pandas()
results = df[df.price < 15]
assert len(results) == 1
assert results["item"].iloc[0] == "foo"