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Fix: Missing embedding_metadata in MatchNeighbor returned by find_neighbors in Vertex AI Matching Engine. #6120

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Fix: Missing embedding_metadata in MatchNeighbor returned by find_neighbors in Vertex AI Matching Engine. #6120

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8 changes: 6 additions & 2 deletions google/cloud/aiplatform/matching_engine/matching_engine_index_endpoint.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@
#

from dataclasses import dataclass, field
from typing import Dict, List, Optional, Sequence, Tuple, Union
from typing import Dict, List, Optional, Sequence, Tuple, Union, Any

from google.auth import credentials as auth_credentials
from google.cloud.aiplatform import base
Expand Down Expand Up @@ -208,7 +208,8 @@ class MatchNeighbor:
For example, values [1,2,3] with dimensions [4,5,6] means value 1 is
of the 4th dimension, value 2 is of the 4th dimension, and value 3 is
of the 6th dimension.

embedding_metadata (Dict[str,Any]):
Optional. The embedding metadata of the matching datapoint.
"""

id: str
Expand All @@ -220,6 +221,7 @@ class MatchNeighbor:
numeric_restricts: Optional[List[NumericNamespace]] = None
sparse_embedding_values: Optional[List[float]] = None
sparse_embedding_dimensions: Optional[List[int]] = None
embedding_metadata: Optional[Dict[str,Any]] = None

def from_index_datapoint(
self, index_datapoint: gca_index_v1beta1.IndexDatapoint
Expand Down Expand Up @@ -276,6 +278,8 @@ def from_index_datapoint(
self.sparse_embedding_dimensions = (
index_datapoint.sparse_embedding.dimensions
)
if index_datapoint.embedding_metadata is not None:
self.embedding_metadata = dict(index_datapoint.embedding_metadata)
return self

def from_embedding(self, embedding: match_service_pb2.Embedding) -> "MatchNeighbor":
Expand Down