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fix: fix search in memory with AnyEmbedding #1696

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Merged
JoanFM merged 6 commits into from
Jul 12, 2023
Merged

fix: fix search in memory with AnyEmbedding #1696

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a2ccb08
fix: fix inmemory search with anyembedding
Jul 10, 2023
bc0e40d
fix: allow find in in memory index with AnyEmbedding
Jul 10, 2023
e7315e8
test: add tests for torch and tf
Jul 11, 2023
2babe51
fix: try to pass all mypy checks
Jul 11, 2023
bab8099
test: undo changes in docker compose
Jul 11, 2023
5a05a17
Merge branch 'main' into anytensor-in-memory-search-fix
Jul 11, 2023
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test: add tests for torch and tf 
Signed-off-by: Joan Fontanals Martinez <[email protected]>
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Joan Fontanals Martinez committed Jul 11, 2023
commit e7315e8bb5e19d0abcb9b1156e9f62805f06faf3
43 changes: 41 additions & 2 deletions tests/index/in_memory/test_in_memory.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,13 @@
from docarray.index.backends.in_memory import InMemoryExactNNIndex
from docarray.typing import NdArray, TorchTensor

from docarray.utils._internal.misc import is_tf_available

tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
from docarray.typing import TensorFlowTensor


class SchemaDoc(BaseDoc):
text: str
Expand Down Expand Up @@ -113,11 +120,43 @@ class MyDoc(BaseDoc):
assert len(scores) == 0


def test_with_text_doc():
def test_with_text_doc_ndarray():
index = InMemoryExactNNIndex[TextDoc]()

docs = DocList[TextDoc](
[TextDoc(text='hey', embedding=np.random.rand(128)) for _ in range(200)]
)
index.index(docs)
res = index.find_batched(docs[0:10], search_field='embedding')
assert len(res.documents) == 10
for r in res.documents:
assert len(r) == 5


@pytest.mark.tensorflow
def test_with_text_doc_tensorflow():
index = InMemoryExactNNIndex[TextDoc]()

docs = DocList[TextDoc](
[
TextDoc(text='hey', embedding=tf.random.uniform(shape=[128]))
for _ in range(200)
]
)
index.index(docs)
res = index.find_batched(docs[0:10], search_field='embedding')
assert len(res.documents) == 10
for r in res.documents:
assert len(r) == 5


def test_with_text_doc_torch():
import torch

index = InMemoryExactNNIndex[TextDoc]()

docs = DocList[TextDoc](
[TextDoc(text='hey', embedding=np.random.rand(128)) for i in range(200)]
[TextDoc(text='hey', embedding=torch.rand(128)) for _ in range(200)]
)
index.index(docs)
res = index.find_batched(docs[0:10], search_field='embedding')
Expand Down