mirror of
https://github.com/lancedb/lancedb.git
synced 2026-10-13 15:22:30 +00:00
Jack Ye
b21fb2a4dc
The pages describing this code lived in lancedb/docs, a repository with ten stars that neither contributing guide mentioned. A user who hit a wrong page while reading the source had to find a separate, near-invisible repository to fix it. These 102 pages now sit beside the code, and a documentation fix is a pull request in the repository the reader already had open. `docs/web` is a complete Mintlify site: `cd docs/web && mint dev` serves it with no other checkout. Enterprise pages stay private and are merged in when the published site is assembled, so links into them do not resolve here — every open-source page does. `docs/web-tests` holds the tests the code examples are extracted from. They ran against a published wheel before; here they can run against this repository's own SDK, so an example that stops compiling fails the pull request that broke it rather than being noticed later. Both directories sit beside `docs/src` and are invisible to it: mkdocs reads `docs_dir: src`, so the SDK reference builds and deploys exactly as before.
209 lines
6.7 KiB
Python
209 lines
6.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright The LanceDB Authors
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import lancedb
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import pytest
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def test_quickstart(db_path_factory):
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uri = db_path_factory("quickstart_db")
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db = lancedb.connect(uri)
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# --8<-- [start:quickstart_data]
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data = [
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{
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"id": "1",
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"name": "King Arthur",
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"role": "King",
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"description": "Leader of Camelot and wielder of Excalibur.",
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"stats": {"strength": 4, "magic": 1, "leadership": 5, "wisdom": 4},
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"vector": [0.7, 0.1, 0.9, 0.7],
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},
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{
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"id": "2",
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"name": "Merlin",
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"role": "Wizard",
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"description": "Advisor and prophet with deep magical knowledge.",
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"stats": {"strength": 2, "magic": 5, "leadership": 4, "wisdom": 5},
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"vector": [0.2, 0.9, 0.4, 0.9],
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},
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{
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"id": "3",
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"name": "Sir Lancelot",
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"role": "Knight",
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"description": "Legendary knight known for courage and combat skill.",
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"stats": {"strength": 5, "magic": 1, "leadership": 3, "wisdom": 3},
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"vector": [0.9, 0.1, 0.5, 0.4],
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},
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]
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# --8<-- [end:quickstart_data]
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# --8<-- [start:quickstart_create_table]
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table = db.create_table("characters", data=data, mode="overwrite")
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# --8<-- [end:quickstart_create_table]
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assert len(table) == 3
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# Drop the table to test create without overwrite
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db.drop_table("characters")
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# --8<-- [start:quickstart_create_table_no_overwrite]
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table = db.create_table("characters", data=data)
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# --8<-- [end:quickstart_create_table_no_overwrite]
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assert len(table) == 3
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# --8<-- [start:quickstart_vector_search_1]
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# Search for examples similar to a "wise magical advisor"
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query_vector = [0.2, 0.8, 0.4, 0.9]
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# Ensure you run `pip install polars` beforehand
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result = (
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table.search(query_vector)
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.select(["name", "role", "description", "_distance"])
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.limit(2)
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.to_polars()
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)
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print(result)
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# --8<-- [end:quickstart_vector_search_1]
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assert result.head(1)["name"][0] == "Merlin"
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# --8<-- [start:quickstart_curate_with_metadata]
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curated = (
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table.search(query_vector)
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.where("stats.magic >= 4")
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.select(["name", "role", "description", "_distance"])
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.limit(2)
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.to_polars()
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)
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print(curated)
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# --8<-- [end:quickstart_curate_with_metadata]
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assert curated.head(1)["name"][0] == "Merlin"
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# --8<-- [start:quickstart_output_pandas]
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# Ensure you run `pip install pandas` beforehand
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result = table.search(query_vector).limit(2).to_pandas()
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print(result)
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# --8<-- [end:quickstart_output_pandas]
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assert result.iloc[0]["name"] == "Merlin"
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# --8<-- [start:quickstart_add_feature]
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table.add_columns(
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{
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"power_score": "cast(((stats.strength + stats.magic + stats.leadership + stats.wisdom) / 4.0) as float)"
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}
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)
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# --8<-- [end:quickstart_add_feature]
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assert "power_score" in table.schema.names
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# --8<-- [start:quickstart_query_feature]
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features = table.search().select(["name", "role", "power_score"]).to_polars()
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print(features)
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# --8<-- [end:quickstart_query_feature]
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assert "power_score" in features.columns
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# --8<-- [start:quickstart_multimodal_bytes]
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from pathlib import Path
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image_path = Path("docs/static/assets/images/quickstart/sir-lancelot.jpg")
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image_bytes = image_path.read_bytes()
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multimodal_table = db.create_table(
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"character_images",
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data=[
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{
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"id": "lancelot",
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"description": "Portrait of Sir Lancelot",
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"image": image_bytes,
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"vector": [0.9, 0.1, 0.5, 0.4],
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}
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],
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mode="overwrite",
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)
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# --8<-- [end:quickstart_multimodal_bytes]
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assert len(multimodal_table) == 1
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# --8<-- [start:quickstart_open_table]
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table = db.open_table("characters")
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# --8<-- [end:quickstart_open_table]
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# --8<-- [start:quickstart_add_data]
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more_data = [
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{
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"id": "4",
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"name": "Morgana",
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"role": "Sorceress",
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"description": "Powerful sorceress of Avalon.",
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"stats": {"strength": 2, "magic": 5, "leadership": 4, "wisdom": 4},
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"vector": [0.3, 0.9, 0.6, 0.8],
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"power_score": 3.75,
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},
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]
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# Add data to table
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table.add(more_data)
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# --8<-- [end:quickstart_add_data]
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assert len(table) == 4
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# --8<-- [start:quickstart_vector_search_2]
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# Search for examples similar to a "powerful sorceress"
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query_vector = [0.3, 0.9, 0.6, 0.8]
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results = table.search(query_vector).limit(2).to_polars()
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print(results)
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# --8<-- [end:quickstart_vector_search_2]
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assert results.head(1)["name"][0] == "Morgana"
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@pytest.mark.asyncio
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async def test_quickstart_async_api(db_path_factory):
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db_uri = db_path_factory("quickstart_async_db")
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import lancedb
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async_db = await lancedb.connect_async(db_uri)
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# --8<-- [start:quickstart_data_async]
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data = [
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{
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"id": "1",
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"name": "King Arthur",
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"role": "King",
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"description": "Leader of Camelot and wielder of Excalibur.",
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"stats": {"strength": 4, "magic": 1, "leadership": 5, "wisdom": 4},
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"vector": [0.7, 0.1, 0.9, 0.7],
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},
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{
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"id": "2",
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"name": "Merlin",
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"role": "Wizard",
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"description": "Advisor and prophet with deep magical knowledge.",
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"stats": {"strength": 2, "magic": 5, "leadership": 4, "wisdom": 5},
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"vector": [0.2, 0.9, 0.4, 0.9],
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},
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{
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"id": "3",
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"name": "Sir Lancelot",
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"role": "Knight",
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"description": "Legendary knight known for courage and combat skill.",
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"stats": {"strength": 5, "magic": 1, "leadership": 3, "wisdom": 3},
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"vector": [0.9, 0.1, 0.5, 0.4],
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},
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]
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# --8<-- [end:quickstart_data_async]
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# --8<-- [start:quickstart_create_table_async]
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async_table = await async_db.create_table(
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"characters",
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data=data,
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mode="overwrite",
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)
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# --8<-- [end:quickstart_create_table_async]
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# --8<-- [start:quickstart_vector_search_1_async]
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# Search for examples similar to a "wise magical advisor"
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query_vector = [0.2, 0.8, 0.4, 0.9]
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# Ensure you run `pip install polars` beforehand
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async_result = await (
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await async_table.search(query_vector)
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).select(["name", "role", "description", "_distance"]).limit(2).to_polars()
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print(async_result)
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# --8<-- [end:quickstart_vector_search_1_async]
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assert async_result.head(1)["name"][0] == "Merlin"
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