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https://github.com/lancedb/lancedb.git
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docs: add async examples to doc (#1941)
- added sync and async tabs for python examples - moved python code to tests/docs --------- Co-authored-by: Will Jones <willjones127@gmail.com>
This commit is contained in:
QianZhu
GitHub
576
python/python/tests/docs/test_guide_tables.py
Normal file
576
python/python/tests/docs/test_guide_tables.py
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@@ -0,0 +1,576 @@
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# --8<-- [start:import-lancedb]
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import lancedb
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# --8<-- [end:import-lancedb]
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# --8<-- [start:import-pandas]
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import pandas as pd
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# --8<-- [end:import-pandas]
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# --8<-- [start:import-pyarrow]
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import pyarrow as pa
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# --8<-- [end:import-pyarrow]
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# --8<-- [start:import-polars]
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import polars as pl
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# --8<-- [end:import-polars]
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# --8<-- [start:import-numpy]
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import numpy as np
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# --8<-- [end:import-numpy]
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# --8<-- [start:import-lancedb-pydantic]
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from lancedb.pydantic import Vector, LanceModel
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# --8<-- [end:import-lancedb-pydantic]
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# --8<-- [start:import-datetime]
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from datetime import timedelta
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# --8<-- [end:import-datetime]
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# --8<-- [start:import-embeddings]
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from lancedb.embeddings import get_registry
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# --8<-- [end:import-embeddings]
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# --8<-- [start:import-pydantic-basemodel]
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from pydantic import BaseModel
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# --8<-- [end:import-pydantic-basemodel]
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import pytest
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# --8<-- [start:class-Content]
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class Content(LanceModel):
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movie_id: int
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vector: Vector(128)
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genres: str
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title: str
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imdb_id: int
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@property
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def imdb_url(self) -> str:
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return f"https://www.imdb.com/title/tt{self.imdb_id}"
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# --8<-- [end:class-Content]
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# --8<-- [start:class-Document]
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class Document(BaseModel):
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content: str
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source: str
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# --8<-- [end:class-Document]
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# --8<-- [start:class-NestedSchema]
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class NestedSchema(LanceModel):
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id: str
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vector: Vector(1536)
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document: Document
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# --8<-- [end:class-NestedSchema]
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# --8<-- [start:class-Item]
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class Item(LanceModel):
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vector: Vector(2)
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item: str
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price: float
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# --8<-- [end:class-Item]
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# --8<-- [start:make_batches]
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def make_batches():
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for i in range(5):
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yield pa.RecordBatch.from_arrays(
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[
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pa.array(
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[[3.1, 4.1, 5.1, 6.1], [5.9, 26.5, 4.7, 32.8]],
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pa.list_(pa.float32(), 4),
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),
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pa.array(["foo", "bar"]),
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pa.array([10.0, 20.0]),
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],
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["vector", "item", "price"],
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)
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# --8<-- [end:make_batches]
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# --8<-- [start:make_batches_for_add]
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def make_batches_for_add():
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for i in range(5):
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yield [
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{"vector": [3.1, 4.1], "item": "peach", "price": 6.0},
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{"vector": [5.9, 26.5], "item": "pear", "price": 5.0},
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]
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# --8<-- [end:make_batches_for_add]
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def test_table():
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# --8<-- [start:connect]
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uri = "data/sample-lancedb"
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db = lancedb.connect(uri)
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# --8<-- [end:connect]
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# --8<-- [start:create_table]
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data = [
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{"vector": [1.1, 1.2], "lat": 45.5, "long": -122.7},
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{"vector": [0.2, 1.8], "lat": 40.1, "long": -74.1},
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]
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db.create_table("test_table", data)
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db["test_table"].head()
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# --8<-- [end:create_table]
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# --8<-- [start:create_table_exist_ok]
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db.create_table("test_table", data, exist_ok=True)
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# --8<-- [end:create_table_exist_ok]
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# --8<-- [start:create_table_overwrite]
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db.create_table("test_table", data, mode="overwrite")
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# --8<-- [end:create_table_overwrite]
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# --8<-- [start:create_table_from_pandas]
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data = pd.DataFrame(
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{
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"vector": [[1.1, 1.2, 1.3, 1.4], [0.2, 1.8, 0.4, 3.6]],
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"lat": [45.5, 40.1],
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"long": [-122.7, -74.1],
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}
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)
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db.create_table("my_table_pandas", data)
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db["my_table_pandas"].head()
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# --8<-- [end:create_table_from_pandas]
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# --8<-- [start:create_table_custom_schema]
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custom_schema = pa.schema(
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[
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pa.field("vector", pa.list_(pa.float32(), 4)),
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pa.field("lat", pa.float32()),
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pa.field("long", pa.float32()),
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]
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)
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tbl = db.create_table("my_table_custom_schema", data, schema=custom_schema)
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# --8<-- [end:create_table_custom_schema]
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# --8<-- [start:create_table_from_polars]
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data = pl.DataFrame(
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{
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"vector": [[3.1, 4.1], [5.9, 26.5]],
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"item": ["foo", "bar"],
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"price": [10.0, 20.0],
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}
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)
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tbl = db.create_table("my_table_pl", data)
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# --8<-- [end:create_table_from_polars]
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# --8<-- [start:create_table_from_arrow_table]
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dim = 16
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total = 2
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schema = pa.schema(
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[pa.field("vector", pa.list_(pa.float16(), dim)), pa.field("text", pa.string())]
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)
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data = pa.Table.from_arrays(
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[
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pa.array(
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[np.random.randn(dim).astype(np.float16) for _ in range(total)],
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pa.list_(pa.float16(), dim),
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),
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pa.array(["foo", "bar"]),
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],
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["vector", "text"],
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)
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tbl = db.create_table("f16_tbl", data, schema=schema)
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# --8<-- [end:create_table_from_arrow_table]
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# --8<-- [start:create_table_from_pydantic]
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tbl = db.create_table("movielens_small", schema=Content)
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# --8<-- [end:create_table_from_pydantic]
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# --8<-- [start:create_table_nested_schema]
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tbl = db.create_table("nested_table", schema=NestedSchema)
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# --8<-- [end:create_table_nested_schema]
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# --8<-- [start:create_table_from_batch]
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schema = pa.schema(
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[
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pa.field("vector", pa.list_(pa.float32(), 4)),
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pa.field("item", pa.utf8()),
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pa.field("price", pa.float32()),
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]
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)
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db.create_table("batched_tale", make_batches(), schema=schema)
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# --8<-- [end:create_table_from_batch]
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# --8<-- [start:list_tables]
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print(db.table_names())
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# --8<-- [end:list_tables]
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# --8<-- [start:open_table]
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tbl = db.open_table("test_table")
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# --8<-- [end:open_table]
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# --8<-- [start:create_empty_table]
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schema = pa.schema(
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[
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pa.field("vector", pa.list_(pa.float32(), 2)),
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pa.field("item", pa.string()),
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pa.field("price", pa.float32()),
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]
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)
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tbl = db.create_table("test_empty_table", schema=schema)
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# --8<-- [end:create_empty_table]
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# --8<-- [start:create_empty_table_pydantic]
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tbl = db.create_table("test_empty_table_new", schema=Item.to_arrow_schema())
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# --8<-- [end:create_empty_table_pydantic]
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# --8<-- [start:add_table_from_pandas]
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df = pd.DataFrame(
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{
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"vector": [[1.3, 1.4], [9.5, 56.2]],
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"item": ["banana", "apple"],
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"price": [5.0, 7.0],
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}
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)
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tbl.add(df)
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# --8<-- [end:add_table_from_pandas]
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# --8<-- [start:add_table_from_polars]
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df = pl.DataFrame(
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{
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"vector": [[1.3, 1.4], [9.5, 56.2]],
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"item": ["banana", "apple"],
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"price": [5.0, 7.0],
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}
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)
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tbl.add(df)
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# --8<-- [end:add_table_from_polars]
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# --8<-- [start:add_table_from_batch]
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tbl.add(make_batches_for_add())
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# --8<-- [end:add_table_from_batch]
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# --8<-- [start:add_table_from_pyarrow]
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pa_table = pa.Table.from_arrays(
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[
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pa.array([[9.1, 6.7], [9.9, 31.2]], pa.list_(pa.float32(), 2)),
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pa.array(["mango", "orange"]),
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pa.array([7.0, 4.0]),
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],
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["vector", "item", "price"],
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)
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tbl.add(pa_table)
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# --8<-- [end:add_table_from_pyarrow]
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# --8<-- [start:add_table_from_pydantic]
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pydantic_model_items = [
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Item(vector=[8.1, 4.7], item="pineapple", price=10.0),
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Item(vector=[6.9, 9.3], item="avocado", price=9.0),
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]
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tbl.add(pydantic_model_items)
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# --8<-- [end:add_table_from_pydantic]
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# --8<-- [start:delete_row]
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tbl.delete('item = "fizz"')
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# --8<-- [end:delete_row]
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# --8<-- [start:delete_specific_row]
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data = [
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{"x": 1, "vector": [1, 2]},
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{"x": 2, "vector": [3, 4]},
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{"x": 3, "vector": [5, 6]},
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]
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# Synchronous client
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tbl = db.create_table("delete_row", data)
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tbl.to_pandas()
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# x vector
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# 0 1 [1.0, 2.0]
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# 1 2 [3.0, 4.0]
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# 2 3 [5.0, 6.0]
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tbl.delete("x = 2")
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tbl.to_pandas()
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# x vector
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# 0 1 [1.0, 2.0]
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# 1 3 [5.0, 6.0]
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# --8<-- [end:delete_specific_row]
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# --8<-- [start:delete_list_values]
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to_remove = [1, 5]
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to_remove = ", ".join(str(v) for v in to_remove)
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tbl.delete(f"x IN ({to_remove})")
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tbl.to_pandas()
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# x vector
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# 0 3 [5.0, 6.0]
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# --8<-- [end:delete_list_values]
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# --8<-- [start:update_table]
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# Create a table from a pandas DataFrame
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data = pd.DataFrame({"x": [1, 2, 3], "vector": [[1, 2], [3, 4], [5, 6]]})
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tbl = db.create_table("test_table", data, mode="overwrite")
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# Update the table where x = 2
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tbl.update(where="x = 2", values={"vector": [10, 10]})
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# Get the updated table as a pandas DataFrame
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df = tbl.to_pandas()
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print(df)
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# --8<-- [end:update_table]
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# --8<-- [start:update_table_sql]
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# Update the table where x = 2
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tbl.update(values_sql={"x": "x + 1"})
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print(tbl.to_pandas())
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# --8<-- [end:update_table_sql]
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# --8<-- [start:table_strong_consistency]
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uri = "data/sample-lancedb"
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db = lancedb.connect(uri, read_consistency_interval=timedelta(0))
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tbl = db.open_table("test_table")
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# --8<-- [end:table_strong_consistency]
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# --8<-- [start:table_eventual_consistency]
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uri = "data/sample-lancedb"
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db = lancedb.connect(uri, read_consistency_interval=timedelta(seconds=5))
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tbl = db.open_table("test_table")
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# --8<-- [end:table_eventual_consistency]
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# --8<-- [start:table_checkout_latest]
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tbl = db.open_table("test_table")
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# (Other writes happen to my_table from another process)
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# Check for updates
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tbl.checkout_latest()
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# --8<-- [end:table_checkout_latest]
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@pytest.mark.skip
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def test_table_with_embedding():
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db = lancedb.connect("data/sample-lancedb")
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# --8<-- [start:create_table_with_embedding]
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embed_fcn = get_registry().get("huggingface").create(name="BAAI/bge-small-en-v1.5")
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class Schema(LanceModel):
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text: str = embed_fcn.SourceField()
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vector: Vector(embed_fcn.ndims()) = embed_fcn.VectorField(default=None)
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tbl = db.create_table("my_table_with_embedding", schema=Schema, mode="overwrite")
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models = [Schema(text="hello"), Schema(text="world")]
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tbl.add(models)
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# --8<-- [end:create_table_with_embedding]
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@pytest.mark.skip
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async def test_table_with_embedding_async():
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async_db = await lancedb.connect_async("data/sample-lancedb")
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# --8<-- [start:create_table_async_with_embedding]
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embed_fcn = get_registry().get("huggingface").create(name="BAAI/bge-small-en-v1.5")
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class Schema(LanceModel):
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text: str = embed_fcn.SourceField()
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vector: Vector(embed_fcn.ndims()) = embed_fcn.VectorField(default=None)
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async_tbl = await async_db.create_table(
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"my_table_async_with_embedding", schema=Schema, mode="overwrite"
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)
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models = [Schema(text="hello"), Schema(text="world")]
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await async_tbl.add(models)
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# --8<-- [end:create_table_async_with_embedding]
|
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|
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@pytest.mark.asyncio
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async def test_table_async():
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# --8<-- [start:connect_async]
|
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uri = "data/sample-lancedb"
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async_db = await lancedb.connect_async(uri)
|
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# --8<-- [end:connect_async]
|
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# --8<-- [start:create_table_async]
|
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data = [
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{"vector": [1.1, 1.2], "lat": 45.5, "long": -122.7},
|
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{"vector": [0.2, 1.8], "lat": 40.1, "long": -74.1},
|
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]
|
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async_tbl = await async_db.create_table("test_table_async", data)
|
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await async_tbl.head()
|
||||
# --8<-- [end:create_table_async]
|
||||
# --8<-- [start:create_table_async_exist_ok]
|
||||
await async_db.create_table("test_table_async", data, exist_ok=True)
|
||||
# --8<-- [end:create_table_async_exist_ok]
|
||||
# --8<-- [start:create_table_async_overwrite]
|
||||
await async_db.create_table("test_table_async", data, mode="overwrite")
|
||||
# --8<-- [end:create_table_async_overwrite]
|
||||
# --8<-- [start:create_table_async_from_pandas]
|
||||
data = pd.DataFrame(
|
||||
{
|
||||
"vector": [[1.1, 1.2, 1.3, 1.4], [0.2, 1.8, 0.4, 3.6]],
|
||||
"lat": [45.5, 40.1],
|
||||
"long": [-122.7, -74.1],
|
||||
}
|
||||
)
|
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async_tbl = await async_db.create_table("my_table_async_pd", data)
|
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await async_tbl.head()
|
||||
# --8<-- [end:create_table_async_from_pandas]
|
||||
# --8<-- [start:create_table_async_custom_schema]
|
||||
custom_schema = pa.schema(
|
||||
[
|
||||
pa.field("vector", pa.list_(pa.float32(), 4)),
|
||||
pa.field("lat", pa.float32()),
|
||||
pa.field("long", pa.float32()),
|
||||
]
|
||||
)
|
||||
async_tbl = await async_db.create_table(
|
||||
"my_table_async_custom_schema", data, schema=custom_schema
|
||||
)
|
||||
# --8<-- [end:create_table_async_custom_schema]
|
||||
# --8<-- [start:create_table_async_from_polars]
|
||||
data = pl.DataFrame(
|
||||
{
|
||||
"vector": [[3.1, 4.1], [5.9, 26.5]],
|
||||
"item": ["foo", "bar"],
|
||||
"price": [10.0, 20.0],
|
||||
}
|
||||
)
|
||||
async_tbl = await async_db.create_table("my_table_async_pl", data)
|
||||
# --8<-- [end:create_table_async_from_polars]
|
||||
# --8<-- [start:create_table_async_from_arrow_table]
|
||||
dim = 16
|
||||
total = 2
|
||||
schema = pa.schema(
|
||||
[pa.field("vector", pa.list_(pa.float16(), dim)), pa.field("text", pa.string())]
|
||||
)
|
||||
data = pa.Table.from_arrays(
|
||||
[
|
||||
pa.array(
|
||||
[np.random.randn(dim).astype(np.float16) for _ in range(total)],
|
||||
pa.list_(pa.float16(), dim),
|
||||
),
|
||||
pa.array(["foo", "bar"]),
|
||||
],
|
||||
["vector", "text"],
|
||||
)
|
||||
async_tbl = await async_db.create_table("f16_tbl_async", data, schema=schema)
|
||||
# --8<-- [end:create_table_async_from_arrow_table]
|
||||
# --8<-- [start:create_table_async_from_pydantic]
|
||||
async_tbl = await async_db.create_table("movielens_small_async", schema=Content)
|
||||
# --8<-- [end:create_table_async_from_pydantic]
|
||||
# --8<-- [start:create_table_async_nested_schema]
|
||||
async_tbl = await async_db.create_table("nested_table_async", schema=NestedSchema)
|
||||
# --8<-- [end:create_table_async_nested_schema]
|
||||
# --8<-- [start:create_table_async_from_batch]
|
||||
schema = pa.schema(
|
||||
[
|
||||
pa.field("vector", pa.list_(pa.float32(), 4)),
|
||||
pa.field("item", pa.utf8()),
|
||||
pa.field("price", pa.float32()),
|
||||
]
|
||||
)
|
||||
await async_db.create_table("batched_table", make_batches(), schema=schema)
|
||||
# --8<-- [end:create_table_async_from_batch]
|
||||
# --8<-- [start:list_tables_async]
|
||||
print(await async_db.table_names())
|
||||
# --8<-- [end:list_tables_async]
|
||||
# --8<-- [start:open_table_async]
|
||||
async_tbl = await async_db.open_table("test_table_async")
|
||||
# --8<-- [end:open_table_async]
|
||||
# --8<-- [start:create_empty_table_async]
|
||||
schema = pa.schema(
|
||||
[
|
||||
pa.field("vector", pa.list_(pa.float32(), 2)),
|
||||
pa.field("item", pa.string()),
|
||||
pa.field("price", pa.float32()),
|
||||
]
|
||||
)
|
||||
async_tbl = await async_db.create_table("test_empty_table_async", schema=schema)
|
||||
# --8<-- [end:create_empty_table_async]
|
||||
# --8<-- [start:create_empty_table_async_pydantic]
|
||||
async_tbl = await async_db.create_table(
|
||||
"test_empty_table_async_new", schema=Item.to_arrow_schema()
|
||||
)
|
||||
# --8<-- [end:create_empty_table_async_pydantic]
|
||||
# --8<-- [start:add_table_async_from_pandas]
|
||||
df = pd.DataFrame(
|
||||
{
|
||||
"vector": [[1.3, 1.4], [9.5, 56.2]],
|
||||
"item": ["banana", "apple"],
|
||||
"price": [5.0, 7.0],
|
||||
}
|
||||
)
|
||||
await async_tbl.add(df)
|
||||
# --8<-- [end:add_table_async_from_pandas]
|
||||
# --8<-- [start:add_table_async_from_polars]
|
||||
df = pl.DataFrame(
|
||||
{
|
||||
"vector": [[1.3, 1.4], [9.5, 56.2]],
|
||||
"item": ["banana", "apple"],
|
||||
"price": [5.0, 7.0],
|
||||
}
|
||||
)
|
||||
await async_tbl.add(df)
|
||||
# --8<-- [end:add_table_async_from_polars]
|
||||
# --8<-- [start:add_table_async_from_batch]
|
||||
await async_tbl.add(make_batches_for_add())
|
||||
# --8<-- [end:add_table_async_from_batch]
|
||||
# --8<-- [start:add_table_async_from_pyarrow]
|
||||
pa_table = pa.Table.from_arrays(
|
||||
[
|
||||
pa.array([[9.1, 6.7], [9.9, 31.2]], pa.list_(pa.float32(), 2)),
|
||||
pa.array(["mango", "orange"]),
|
||||
pa.array([7.0, 4.0]),
|
||||
],
|
||||
["vector", "item", "price"],
|
||||
)
|
||||
await async_tbl.add(pa_table)
|
||||
# --8<-- [end:add_table_async_from_pyarrow]
|
||||
# --8<-- [start:add_table_async_from_pydantic]
|
||||
pydantic_model_items = [
|
||||
Item(vector=[8.1, 4.7], item="pineapple", price=10.0),
|
||||
Item(vector=[6.9, 9.3], item="avocado", price=9.0),
|
||||
]
|
||||
await async_tbl.add(pydantic_model_items)
|
||||
# --8<-- [end:add_table_async_from_pydantic]
|
||||
# --8<-- [start:delete_row_async]
|
||||
await async_tbl.delete('item = "fizz"')
|
||||
# --8<-- [end:delete_row_async]
|
||||
# --8<-- [start:delete_specific_row_async]
|
||||
data = [
|
||||
{"x": 1, "vector": [1, 2]},
|
||||
{"x": 2, "vector": [3, 4]},
|
||||
{"x": 3, "vector": [5, 6]},
|
||||
]
|
||||
async_db = await lancedb.connect_async(uri)
|
||||
async_tbl = await async_db.create_table("delete_row_async", data)
|
||||
await async_tbl.to_pandas()
|
||||
# x vector
|
||||
# 0 1 [1.0, 2.0]
|
||||
# 1 2 [3.0, 4.0]
|
||||
# 2 3 [5.0, 6.0]
|
||||
|
||||
await async_tbl.delete("x = 2")
|
||||
await async_tbl.to_pandas()
|
||||
# x vector
|
||||
# 0 1 [1.0, 2.0]
|
||||
# 1 3 [5.0, 6.0]
|
||||
# --8<-- [end:delete_specific_row_async]
|
||||
# --8<-- [start:delete_list_values_async]
|
||||
to_remove = [1, 5]
|
||||
to_remove = ", ".join(str(v) for v in to_remove)
|
||||
|
||||
await async_tbl.delete(f"x IN ({to_remove})")
|
||||
await async_tbl.to_pandas()
|
||||
# x vector
|
||||
# 0 3 [5.0, 6.0]
|
||||
# --8<-- [end:delete_list_values_async]
|
||||
# --8<-- [start:update_table_async]
|
||||
# Create a table from a pandas DataFrame
|
||||
data = pd.DataFrame({"x": [1, 2, 3], "vector": [[1, 2], [3, 4], [5, 6]]})
|
||||
|
||||
async_tbl = await async_db.create_table("update_table_async", data)
|
||||
# Update the table where x = 2
|
||||
await async_tbl.update({"vector": [10, 10]}, where="x = 2")
|
||||
# Get the updated table as a pandas DataFrame
|
||||
df = await async_tbl.to_pandas()
|
||||
# Print the DataFrame
|
||||
print(df)
|
||||
# --8<-- [end:update_table_async]
|
||||
# --8<-- [start:update_table_sql_async]
|
||||
# Update the table where x = 2
|
||||
await async_tbl.update(updates_sql={"x": "x + 1"})
|
||||
print(await async_tbl.to_pandas())
|
||||
# --8<-- [end:update_table_sql_async]
|
||||
# --8<-- [start:table_async_strong_consistency]
|
||||
uri = "data/sample-lancedb"
|
||||
async_db = await lancedb.connect_async(uri, read_consistency_interval=timedelta(0))
|
||||
async_tbl = await async_db.open_table("test_table_async")
|
||||
# --8<-- [end:table_async_strong_consistency]
|
||||
# --8<-- [start:table_async_ventual_consistency]
|
||||
uri = "data/sample-lancedb"
|
||||
async_db = await lancedb.connect_async(
|
||||
uri, read_consistency_interval=timedelta(seconds=5)
|
||||
)
|
||||
async_tbl = await async_db.open_table("test_table_async")
|
||||
# --8<-- [end:table_async_eventual_consistency]
|
||||
# --8<-- [start:table_async_checkout_latest]
|
||||
async_tbl = await async_db.open_table("test_table_async")
|
||||
|
||||
# (Other writes happen to test_table_async from another process)
|
||||
|
||||
# Check for updates
|
||||
await async_tbl.checkout_latest()
|
||||
# --8<-- [end:table_async_checkout_latest]
|
||||
Reference in New Issue
Block a user