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__all__ = ['find', 'find_batched']
from typing import Any, Dict, List, NamedTuple, Optional, Type, Union, cast
from typing_inspect import is_union_type
from docarray.array.any_array import AnyDocArray
from docarray.array.doc_list.doc_list import DocList
from docarray.array.doc_vec.doc_vec import DocVec
from docarray.base_doc import BaseDoc
from docarray.helper import _get_field_type_by_access_path
from docarray.typing import AnyTensor
from docarray.typing.tensor.abstract_tensor import AbstractTensor
class FindResult(NamedTuple):
documents: DocList
scores: AnyTensor
class _FindResult(NamedTuple):
documents: Union[DocList, List[Dict[str, Any]]]
scores: AnyTensor
class FindResultBatched(NamedTuple):
documents: List[DocList]
scores: List[AnyTensor]
class _FindResultBatched(NamedTuple):
documents: Union[List[DocList], List[List[Dict[str, Any]]]]
scores: List[AnyTensor]
def find(
index: AnyDocArray,
query: Union[AnyTensor, BaseDoc],
search_field: str = '',
metric: str = 'cosine_sim',
limit: int = 10,
device: Optional[str] = None,
descending: Optional[bool] = None,
) -> FindResult:
"""
Find the closest Documents in the index to the query.
Supports PyTorch and NumPy embeddings.
!!! note
This is a simple implementation of exact search. If you need to do advance
search using approximate nearest neighbours search or hybrid search or
multi vector search please take a look at the [`BaseDoc`][docarray.base_doc.doc.BaseDoc].
---
```python
from docarray import DocList, BaseDoc
from docarray.typing import TorchTensor
from docarray.utils.find import find
import torch
class MyDocument(BaseDoc):
embedding: TorchTensor
index = DocList[MyDocument]([MyDocument(embedding=torch.rand(128)) for _ in range(100)])
# use Document as query
query = MyDocument(embedding=torch.rand(128))
top_matches, scores = find(
index=index,
query=query,
search_field='embedding',
metric='cosine_sim',
)
# use tensor as query
query = torch.rand(128)
top_matches, scores = find(
index=index,
query=query,
search_field='embedding',
metric='cosine_sim',
)
```
---
:param index: the index of Documents to search in
:param query: the query to search for
:param search_field: the tensor-like field in the index to use
for the similarity computation
:param metric: the distance metric to use for the similarity computation.
Can be one of the following strings:
'cosine_sim' for cosine similarity, 'euclidean_dist' for euclidean distance,
'sqeuclidean_dist' for squared euclidean distance
:param limit: return the top `limit` results
:param device: the computational device to use,
can be either `cpu` or a `cuda` device.
:param descending: sort the results in descending order.
Per default, this is chosen based on the `metric` argument.
:return: A named tuple of the form (DocList, AnyTensor),
where the first element contains the closes matches for the query,
and the second element contains the corresponding scores.
"""
query = _extract_embedding_single(query, search_field)
docs, scores = find_batched(
index=index,
query=query,
search_field=search_field,
metric=metric,
limit=limit,
device=device,
descending=descending,
)
return FindResult(documents=docs[0], scores=scores[0])
def find_batched(
index: AnyDocArray,
query: Union[AnyTensor, DocList],
search_field: str = '',
metric: str = 'cosine_sim',
limit: int = 10,
device: Optional[str] = None,
descending: Optional[bool] = None,
) -> FindResultBatched:
"""
Find the closest Documents in the index to the queries.
Supports PyTorch and NumPy embeddings.
!!! note
This is a simple implementation of exact search. If you need to do advance
search using approximate nearest neighbours search or hybrid search or
multi vector search please take a look at the [`BaseDoc`][docarray.base_doc.doc.BaseDoc]
---
```python
from docarray import DocList, BaseDoc
from docarray.typing import TorchTensor
from docarray.utils.find import find_batched
import torch
class MyDocument(BaseDoc):
embedding: TorchTensor
index = DocList[MyDocument]([MyDocument(embedding=torch.rand(128)) for _ in range(100)])
# use DocList as query
query = DocList[MyDocument]([MyDocument(embedding=torch.rand(128)) for _ in range(3)])
docs, scores = find_batched(
index=index,
query=query,
search_field='embedding',
metric='cosine_sim',
)
top_matches, scores = docs[0], scores[0]
# use tensor as query
query = torch.rand(3, 128)
docs, scores = find_batched(
index=index,
query=query,
search_field='embedding',
metric='cosine_sim',
)
top_matches, scores = docs[0], scores[0]
```
---
:param index: the index of Documents to search in
:param query: the query to search for
:param search_field: the tensor-like field in the index to use
for the similarity computation
:param metric: the distance metric to use for the similarity computation.
Can be one of the following strings:
'cosine_sim' for cosine similarity, 'euclidean_dist' for euclidean distance,
'sqeuclidean_dist' for squared euclidean distance
:param limit: return the top `limit` results
:param device: the computational device to use,
can be either `cpu` or a `cuda` device.
:param descending: sort the results in descending order.
Per default, this is chosen based on the `metric` argument.
:return: A named tuple of the form (DocList, AnyTensor),
where the first element contains the closest matches for each query,
and the second element contains the corresponding scores.
"""
if descending is None:
descending = metric.endswith('_sim') # similarity metrics are descending
embedding_type = _da_attr_type(index, search_field)
comp_backend = embedding_type.get_comp_backend()
# extract embeddings from query and index
index_embeddings = _extract_embeddings(index, search_field, embedding_type)
query_embeddings = _extract_embeddings(query, search_field, embedding_type)
# compute distances and return top results
metric_fn = getattr(comp_backend.Metrics, metric)
dists = metric_fn(query_embeddings, index_embeddings, device=device)
top_scores, top_indices = comp_backend.Retrieval.top_k(
dists, k=limit, device=device, descending=descending
)
batched_docs: List[DocList] = []
scores = []
for _, (indices_per_query, scores_per_query) in enumerate(
zip(top_indices, top_scores)
):
doc_type = cast(Type[BaseDoc], index.doc_type)
docs_per_query: DocList = DocList.__class_getitem__(doc_type)()
for idx in indices_per_query: # workaround until #930 is fixed
docs_per_query.append(index[int(idx)])
batched_docs.append(docs_per_query)
scores.append(scores_per_query)
return FindResultBatched(documents=batched_docs, scores=scores)
def _extract_embedding_single(
data: Union[DocList, BaseDoc, AnyTensor],
search_field: str,
) -> AnyTensor:
"""Extract the embeddings from a single query,
and return it in a batched representation.
:param data: the data
:param search_field: the embedding field
:param embedding_type: type of the embedding: torch.Tensor, numpy.ndarray etc.
:return: the embeddings
"""
if isinstance(data, BaseDoc):
emb = next(AnyDocArray._traverse(data, search_field))
else: # treat data as tensor
emb = data
if len(emb.shape) == 1:
# all currently supported frameworks provide `.reshape()`. Onc this is not true
# anymore, we need to add a `.reshape()` method to the computational backend
emb = emb.reshape(1, -1)
return emb
def _extract_embeddings(
data: Union[AnyDocArray, BaseDoc, AnyTensor],
search_field: str,
embedding_type: Type,
) -> AnyTensor:
"""Extract the embeddings from the data.
:param data: the data
:param search_field: the embedding field
:param embedding_type: type of the embedding: torch.Tensor, numpy.ndarray etc.
:return: the embeddings
"""
emb: AnyTensor
if isinstance(data, DocList):
emb_list = list(AnyDocArray._traverse(data, search_field))
emb = embedding_type._docarray_stack(emb_list)
elif isinstance(data, (DocVec, BaseDoc)):
emb = next(AnyDocArray._traverse(data, search_field))
else: # treat data as tensor
emb = cast(AnyTensor, data)
if len(emb.shape) == 1:
emb = emb.get_comp_backend().reshape(array=emb, shape=(1, -1))
return emb
def _da_attr_type(docs: AnyDocArray, access_path: str) -> Type[AnyTensor]:
"""Get the type of the attribute according to the Document type
(schema) of the DocList.
:param docs: the DocList
:param access_path: the "__"-separated access path
:return: the type of the attribute
"""
field_type: Optional[Type] = _get_field_type_by_access_path(
docs.doc_type, access_path
)
if field_type is None:
raise ValueError(f"Access path is not valid: {access_path}")
if is_union_type(field_type):
# determine type based on the fist element
field_type = type(next(AnyDocArray._traverse(docs[0], access_path)))
if not issubclass(field_type, AbstractTensor):
raise ValueError(
f'attribute {access_path} is not a tensor-like type, '
f'but {field_type.__class__.__name__}'
)
return cast(Type[AnyTensor], field_type)