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docs: add sync/async tabs to quickstart (#2087)

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Closes #2033
This commit is contained in:
Will Jones
2025-01-31 15:43:54 -08:00
committed by GitHub
parent dba85f4d6f
commit 2fc174f532
3 changed files with 174 additions and 114 deletions
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93
python/python/tests/docs/test_basic.py
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@@ -1,23 +1,22 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright The LanceDB Authors
import shutil
# --8<-- [start:imports]
import lancedb
import pandas as pd
import pyarrow as pa
# --8<-- [end:imports]
import pytest
from numpy.random import randint, random
shutil.rmtree("data/sample-lancedb", ignore_errors=True)
def test_quickstart():
# --8<-- [start:connect]
def test_quickstart(tmp_path):
# --8<-- [start:set_uri]
uri = "data/sample-lancedb"
# --8<-- [end:set_uri]
uri = tmp_path
# --8<-- [start:connect]
db = lancedb.connect(uri)
# --8<-- [end:connect]
@@ -27,7 +26,6 @@ def test_quickstart():
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
]
# Synchronous client
tbl = db.create_table("my_table", data=data)
# --8<-- [end:create_table]
@@ -38,24 +36,19 @@ def test_quickstart():
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
]
)
# Synchronous client
tbl = db.create_table("table_from_df", data=df)
# --8<-- [end:create_table_pandas]
# --8<-- [start:create_empty_table]
schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), list_size=2))])
# Synchronous client
tbl = db.create_table("empty_table", schema=schema)
# --8<-- [end:create_empty_table]
# --8<-- [start:open_table]
# Synchronous client
tbl = db.open_table("my_table")
# --8<-- [end:open_table]
# --8<-- [start:table_names]
# Synchronous client
print(db.table_names())
# --8<-- [end:table_names]
# Synchronous client
# --8<-- [start:add_data]
# Option 1: Add a list of dicts to a table
data = [
@@ -69,7 +62,6 @@ def test_quickstart():
tbl.add(data)
# --8<-- [end:add_data]
# --8<-- [start:vector_search]
# Synchronous client
tbl.search([100, 100]).limit(2).to_pandas()
# --8<-- [end:vector_search]
tbl.add(
@@ -95,42 +87,31 @@ def test_quickstart():
tbl.drop_columns(["dbl_price"])
# --8<-- [end:drop_columns]
# --8<-- [start:create_index]
# Synchronous client
tbl.create_index(num_sub_vectors=1)
# --8<-- [end:create_index]
# --8<-- [start:delete_rows]
# Synchronous client
tbl.delete('item = "fizz"')
# --8<-- [end:delete_rows]
# --8<-- [start:drop_table]
# Synchronous client
db.drop_table("my_table")
# --8<-- [end:drop_table]
@pytest.mark.asyncio
async def test_quickstart_async():
async def test_quickstart_async(tmp_path):
uri = tmp_path
# --8<-- [start:connect_async]
# LanceDb offers both a synchronous and an asynchronous client. There are still a
# few operations that are only supported by the synchronous client (e.g. embedding
# functions, full text search) but both APIs should soon be equivalent
# In this guide we will give examples of both clients. In other guides we will
# typically only provide examples with one client or the other.
uri = "data/sample-lancedb"
async_db = await lancedb.connect_async(uri)
db = await lancedb.connect_async(uri)
# --8<-- [end:connect_async]
# --8<-- [start:create_table_async]
data = [
{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
]
# --8<-- [start:create_table_async]
# Asynchronous client
async_tbl = await async_db.create_table("my_table_async", data=data)
tbl = await db.create_table("my_table_async", data=data)
# --8<-- [end:create_table_async]
# --8<-- [start:create_table_async_pandas]
df = pd.DataFrame(
[
{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
@@ -138,37 +119,41 @@ async def test_quickstart_async():
]
)
# --8<-- [start:create_table_async_pandas]
# Asynchronous client
async_tbl = await async_db.create_table("table_from_df_async", df)
tbl = await db.create_table("table_from_df_async", df)
# --8<-- [end:create_table_async_pandas]
schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), list_size=2))])
# --8<-- [start:create_empty_table_async]
# Asynchronous client
async_tbl = await async_db.create_table("empty_table_async", schema=schema)
schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), list_size=2))])
tbl = await db.create_table("empty_table_async", schema=schema)
# --8<-- [end:create_empty_table_async]
# --8<-- [start:open_table_async]
# Asynchronous client
async_tbl = await async_db.open_table("my_table_async")
tbl = await db.open_table("my_table_async")
# --8<-- [end:open_table_async]
# --8<-- [start:table_names_async]
# Asynchronous client
print(await async_db.table_names())
print(await db.table_names())
# --8<-- [end:table_names_async]
# --8<-- [start:add_data_async]
# Asynchronous client
await async_tbl.add(data)
# Option 1: Add a list of dicts to a table
data = [
{"vector": [1.3, 1.4], "item": "fizz", "price": 100.0},
{"vector": [9.5, 56.2], "item": "buzz", "price": 200.0},
]
await tbl.add(data)
# Option 2: Add a pandas DataFrame to a table
df = pd.DataFrame(data)
await tbl.add(data)
# --8<-- [end:add_data_async]
# Add sufficient data for training
data = [{"vector": [x, x], "item": "filler", "price": x * x} for x in range(1000)]
await async_tbl.add(data)
await tbl.add(data)
# --8<-- [start:vector_search_async]
await tbl.vector_search([100, 100]).limit(2).to_pandas()
# --8<-- [end:vector_search_async]
# --8<-- [start:add_columns_async]
await async_tbl.add_columns({"double_price": "cast((price * 2) as float)"})
await tbl.add_columns({"double_price": "cast((price * 2) as float)"})
# --8<-- [end:add_columns_async]
# --8<-- [start:alter_columns_async]
await async_tbl.alter_columns(
await tbl.alter_columns(
{
"path": "double_price",
"rename": "dbl_price",
@@ -178,20 +163,16 @@ async def test_quickstart_async():
)
# --8<-- [end:alter_columns_async]
# --8<-- [start:drop_columns_async]
await async_tbl.drop_columns(["dbl_price"])
await tbl.drop_columns(["dbl_price"])
# --8<-- [end:drop_columns_async]
# Asynchronous client
await async_tbl.vector_search([100, 100]).limit(2).to_pandas()
await tbl.vector_search([100, 100]).limit(2).to_pandas()
# --8<-- [end:vector_search_async]
# --8<-- [start:create_index_async]
# Asynchronous client (must specify column to index)
await async_tbl.create_index("vector")
await tbl.create_index("vector")
# --8<-- [end:create_index_async]
# --8<-- [start:delete_rows_async]
# Asynchronous client
await async_tbl.delete('item = "fizz"')
await tbl.delete('item = "fizz"')
# --8<-- [end:delete_rows_async]
# --8<-- [start:drop_table_async]
# Asynchronous client
await async_db.drop_table("my_table_async")
await db.drop_table("my_table_async")
# --8<-- [end:drop_table_async]