pinecone.data.dataclasses.vector 

 1from typing import List, Optional
 2from .sparse_values import SparseValues
 3from .utils import DictLike
 4from ..types import VectorTypedDict, VectorMetadataTypedDict
 5
 6from dataclasses import dataclass, field
 7
 8
 9@dataclass
10class Vector(DictLike):
11    id: str
12    values: List[float] = field(default_factory=list)
13    metadata: Optional[VectorMetadataTypedDict] = None
14    sparse_values: Optional[SparseValues] = None
15
16    def __post_init__(self):
17        if self.sparse_values is None and len(self.values) == 0:
18            raise ValueError("The values and sparse_values fields cannot both be empty")
19
20    def to_dict(self) -> VectorTypedDict:
21        vector_dict: VectorTypedDict = {"id": self.id, "values": self.values}
22        if self.metadata is not None:
23            vector_dict["metadata"] = self.metadata
24        if self.sparse_values is not None:
25            vector_dict["sparse_values"] = self.sparse_values.to_dict()
26        return vector_dict
27
28    @staticmethod
29    def from_dict(vector_dict: VectorTypedDict) -> "Vector":
30        passed_sparse_values = vector_dict.get("sparse_values")
31        if passed_sparse_values is not None:
32            parsed_sparse_values = SparseValues.from_dict(passed_sparse_values)
33        else:
34            parsed_sparse_values = None
35
36        return Vector(
37            id=vector_dict["id"],
38            values=vector_dict["values"],
39            metadata=vector_dict.get("metadata"),
40            sparse_values=parsed_sparse_values,
41        )
@dataclass
class Vector(pinecone.data.dataclasses.utils.DictLike): 
10@dataclass
11class Vector(DictLike):
12    id: str
13    values: List[float] = field(default_factory=list)
14    metadata: Optional[VectorMetadataTypedDict] = None
15    sparse_values: Optional[SparseValues] = None
16
17    def __post_init__(self):
18        if self.sparse_values is None and len(self.values) == 0:
19            raise ValueError("The values and sparse_values fields cannot both be empty")
20
21    def to_dict(self) -> VectorTypedDict:
22        vector_dict: VectorTypedDict = {"id": self.id, "values": self.values}
23        if self.metadata is not None:
24            vector_dict["metadata"] = self.metadata
25        if self.sparse_values is not None:
26            vector_dict["sparse_values"] = self.sparse_values.to_dict()
27        return vector_dict
28
29    @staticmethod
30    def from_dict(vector_dict: VectorTypedDict) -> "Vector":
31        passed_sparse_values = vector_dict.get("sparse_values")
32        if passed_sparse_values is not None:
33            parsed_sparse_values = SparseValues.from_dict(passed_sparse_values)
34        else:
35            parsed_sparse_values = None
36
37        return Vector(
38            id=vector_dict["id"],
39            values=vector_dict["values"],
40            metadata=vector_dict.get("metadata"),
41            sparse_values=parsed_sparse_values,
42        )
Vector( id: str, values: List[float] = <factory>, metadata: Optional[Dict[str, Union[str, int, float, List[str], List[int], List[float]]]] = None, sparse_values: Optional[pinecone.data.dataclasses.sparse_values.SparseValues] = None)
id: str
values: List[float]
metadata: Optional[Dict[str, Union[str, int, float, List[str], List[int], List[float]]]] = None
def to_dict(self) -> pinecone.data.types.vector_typed_dict.VectorTypedDict: 
21    def to_dict(self) -> VectorTypedDict:
22        vector_dict: VectorTypedDict = {"id": self.id, "values": self.values}
23        if self.metadata is not None:
24            vector_dict["metadata"] = self.metadata
25        if self.sparse_values is not None:
26            vector_dict["sparse_values"] = self.sparse_values.to_dict()
27        return vector_dict
@staticmethod
def from_dict( vector_dict: pinecone.data.types.vector_typed_dict.VectorTypedDict) -> Vector: 
29    @staticmethod
30    def from_dict(vector_dict: VectorTypedDict) -> "Vector":
31        passed_sparse_values = vector_dict.get("sparse_values")
32        if passed_sparse_values is not None:
33            parsed_sparse_values = SparseValues.from_dict(passed_sparse_values)
34        else:
35            parsed_sparse_values = None
36
37        return Vector(
38            id=vector_dict["id"],
39            values=vector_dict["values"],
40            metadata=vector_dict.get("metadata"),
41            sparse_values=parsed_sparse_values,
42        )