103 lines
2.8 KiB
Python
103 lines
2.8 KiB
Python
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# This tests a very minimal of test_add in test_add.py as a example based test
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# instead of a property based test. We can use the delta to get the property
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# test working and then enable
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import random
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import time
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from chromadb.api import ClientAPI
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from chromadb.test.conftest import (
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reset,
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skip_if_not_cluster,
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)
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from chromadb.test.property import invariants
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from chromadb.test.utils.wait_for_version_increase import (
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wait_for_version_increase,
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get_collection_version,
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)
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import numpy as np
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@skip_if_not_cluster()
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def test_add(
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client: ClientAPI,
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) -> None:
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seed = time.time()
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random.seed(seed)
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print("Generating data with seed ", seed)
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reset(client)
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collection = client.create_collection(
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name="test",
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metadata={"hnsw:construction_ef": 128, "hnsw:search_ef": 128, "hnsw:M": 128},
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)
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# Add 1000 records, where each embedding has 3 dimensions randomly generated
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# between 0 and 1
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ids = []
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embeddings = []
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for i in range(1000):
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ids.append(str(i))
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embeddings.append(np.random.rand(1, 3)[0])
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collection.add(
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ids=[str(i)],
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embeddings=[embeddings[-1]],
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)
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random_query = np.random.rand(1, 3)[0]
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print("Generated data with seed ", seed)
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invariants.ann_accuracy(
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collection,
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{
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"ids": ids,
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"embeddings": embeddings,
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"metadatas": None,
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"documents": None,
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},
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10,
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query_embeddings=[random_query],
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)
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@skip_if_not_cluster()
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def test_add_include_all_with_compaction_delay(client: ClientAPI) -> None:
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seed = time.time()
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random.seed(seed)
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print("Generating data with seed ", seed)
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reset(client)
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collection = client.create_collection(
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name="test_add_include_all_with_compaction_delay",
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metadata={"hnsw:construction_ef": 128, "hnsw:search_ef": 128, "hnsw:M": 128},
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)
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initial_version = get_collection_version(client, collection.name)
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ids = []
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embeddings = []
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documents = []
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for i in range(1000):
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ids.append(str(i))
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embeddings.append(np.random.rand(1, 3)[0])
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documents.append(f"document_{i}")
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collection.add(
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ids=[str(i)],
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embeddings=[embeddings[-1]],
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documents=[documents[-1]],
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)
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wait_for_version_increase(client, collection.name, initial_version, 120)
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random_query_1 = np.random.rand(1, 3)[0]
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random_query_2 = np.random.rand(1, 3)[0]
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print("Generated data with seed ", seed)
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# Query the collection with a random query
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invariants.ann_accuracy(
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collection,
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{
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"ids": ids,
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"embeddings": embeddings,
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"metadatas": None,
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"documents": documents,
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},
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10,
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query_embeddings=[random_query_1, random_query_2],
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)
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