pinecone.data.dataclasses.search_query_vector 

 1from dataclasses import dataclass
 2from typing import Optional, List
 3
 4
 5@dataclass
 6class SearchQueryVector:
 7    """
 8    SearchQueryVector represents the vector values used to query.
 9    """
10
11    values: Optional[List[float]] = None
12    """
13    The vector data included in the search request.
14    Optional.
15    """
16
17    sparse_values: Optional[List[float]] = None
18    """
19    The sparse embedding values to search with.
20    Optional.
21    """
22
23    sparse_indices: Optional[List[int]] = None
24    """
25    The sparse embedding indices to search with.
26    Optional.
27    """
28
29    def as_dict(self) -> dict:
30        """
31        Returns the SearchQueryVector as a dictionary.
32        """
33        d = {
34            "values": self.values,
35            "sparse_values": self.sparse_values,
36            "sparse_indices": self.sparse_indices,
37        }
38        return {k: v for k, v in d.items() if v is not None}
@dataclass
class SearchQueryVector: 
 6@dataclass
 7class SearchQueryVector:
 8    """
 9    SearchQueryVector represents the vector values used to query.
10    """
11
12    values: Optional[List[float]] = None
13    """
14    The vector data included in the search request.
15    Optional.
16    """
17
18    sparse_values: Optional[List[float]] = None
19    """
20    The sparse embedding values to search with.
21    Optional.
22    """
23
24    sparse_indices: Optional[List[int]] = None
25    """
26    The sparse embedding indices to search with.
27    Optional.
28    """
29
30    def as_dict(self) -> dict:
31        """
32        Returns the SearchQueryVector as a dictionary.
33        """
34        d = {
35            "values": self.values,
36            "sparse_values": self.sparse_values,
37            "sparse_indices": self.sparse_indices,
38        }
39        return {k: v for k, v in d.items() if v is not None}

SearchQueryVector represents the vector values used to query.

SearchQueryVector( values: Optional[List[float]] = None, sparse_values: Optional[List[float]] = None, sparse_indices: Optional[List[int]] = None)
values: Optional[List[float]] = None

The vector data included in the search request. Optional.

sparse_values: Optional[List[float]] = None

The sparse embedding values to search with. Optional.

sparse_indices: Optional[List[int]] = None

The sparse embedding indices to search with. Optional.

def as_dict(self) -> dict: 
30    def as_dict(self) -> dict:
31        """
32        Returns the SearchQueryVector as a dictionary.
33        """
34        d = {
35            "values": self.values,
36            "sparse_values": self.sparse_values,
37            "sparse_indices": self.sparse_indices,
38        }
39        return {k: v for k, v in d.items() if v is not None}

Returns the SearchQueryVector as a dictionary.