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docs: fix docstring example of find_batched #1308

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samsja merged 1 commit into from
Mar 29, 2023
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docs: fix docstring example of find_batched #1308

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66 changes: 33 additions & 33 deletions docarray/utils/find.py
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Original file line number Diff line number Diff line change
Expand Up @@ -130,39 +130,39 @@ def find_batched(
---

```python
# from docarray import DocArray, BaseDoc
# from docarray.typing import TorchTensor
# from docarray.utils.find import find
# import torch
#
#
# class MyDocument(BaseDoc):
# embedding: TorchTensor
#
#
# index = DocArray[MyDocument](
# [MyDocument(embedding=torch.rand(128)) for _ in range(100)]
# )
#
# # use DocArray as query
# query = DocArray[MyDocument]([MyDocument(embedding=torch.rand(128)) for _ in range(3)])
# results = find(
# index=index,
# query=query,
# embedding_field='embedding',
# metric='cosine_sim',
# )
# top_matches, scores = results[0]
#
# # use tensor as query
# query = torch.rand(3, 128)
# results, scores = find(
# index=index,
# query=query,
# embedding_field='embedding',
# metric='cosine_sim',
# )
# top_matches, scores = results[0]
from docarray import DocArray, BaseDoc
from docarray.typing import TorchTensor
from docarray.utils.find import find_batched
import torch


class MyDocument(BaseDoc):
embedding: TorchTensor


index = DocArray[MyDocument](
[MyDocument(embedding=torch.rand(128)) for _ in range(100)]
)

# use DocArray as query
query = DocArray[MyDocument]([MyDocument(embedding=torch.rand(128)) for _ in range(3)])
results = find_batched(
index=index,
query=query,
embedding_field='embedding',
metric='cosine_sim',
)
top_matches, scores = results[0]

# use tensor as query
query = torch.rand(3, 128)
results = find_batched(
index=index,
query=query,
embedding_field='embedding',
metric='cosine_sim',
)
top_matches, scores = results[0]
```

---
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