pinecone.data.sparse_vector_factory 

 1from collections.abc import Mapping
 2from typing import Union, Dict
 3
 4from ..utils import convert_to_list
 5
 6from .errors import (
 7    SparseValuesTypeError,
 8    SparseValuesMissingKeysError,
 9    SparseValuesDictionaryExpectedError,
10)
11
12from pinecone.core.openapi.data.models import SparseValues
13
14
15class SparseValuesFactory:
16    @staticmethod
17    def build(input: Union[Dict, SparseValues]) -> SparseValues:
18        if input is None:
19            return input
20        if isinstance(input, SparseValues):
21            return input
22        if not isinstance(input, Mapping):
23            raise SparseValuesDictionaryExpectedError(input)
24        if not {"indices", "values"}.issubset(input):
25            raise SparseValuesMissingKeysError(input)
26
27        indices = SparseValuesFactory._convert_to_list(input.get("indices"), int)
28        values = SparseValuesFactory._convert_to_list(input.get("values"), float)
29
30        if len(indices) != len(values):
31            raise ValueError("Sparse values indices and values must have the same length")
32
33        try:
34            return SparseValues(indices=indices, values=values)
35        except TypeError as e:
36            raise SparseValuesTypeError() from e
37
38    @staticmethod
39    def _convert_to_list(input, expected_type):
40        try:
41            converted = convert_to_list(input)
42        except TypeError as e:
43            raise SparseValuesTypeError() from e
44
45        SparseValuesFactory._validate_list_items_type(converted, expected_type)
46        return converted
47
48    @staticmethod
49    def _validate_list_items_type(input, expected_type):
50        if len(input) > 0 and not isinstance(input[0], expected_type):
51            raise SparseValuesTypeError()
class SparseValuesFactory: 
16class SparseValuesFactory:
17    @staticmethod
18    def build(input: Union[Dict, SparseValues]) -> SparseValues:
19        if input is None:
20            return input
21        if isinstance(input, SparseValues):
22            return input
23        if not isinstance(input, Mapping):
24            raise SparseValuesDictionaryExpectedError(input)
25        if not {"indices", "values"}.issubset(input):
26            raise SparseValuesMissingKeysError(input)
27
28        indices = SparseValuesFactory._convert_to_list(input.get("indices"), int)
29        values = SparseValuesFactory._convert_to_list(input.get("values"), float)
30
31        if len(indices) != len(values):
32            raise ValueError("Sparse values indices and values must have the same length")
33
34        try:
35            return SparseValues(indices=indices, values=values)
36        except TypeError as e:
37            raise SparseValuesTypeError() from e
38
39    @staticmethod
40    def _convert_to_list(input, expected_type):
41        try:
42            converted = convert_to_list(input)
43        except TypeError as e:
44            raise SparseValuesTypeError() from e
45
46        SparseValuesFactory._validate_list_items_type(converted, expected_type)
47        return converted
48
49    @staticmethod
50    def _validate_list_items_type(input, expected_type):
51        if len(input) > 0 and not isinstance(input[0], expected_type):
52            raise SparseValuesTypeError()
@staticmethod
def build( input: Union[Dict, pinecone.core.openapi.data.model.sparse_values.SparseValues]) -> pinecone.core.openapi.data.model.sparse_values.SparseValues: 
17    @staticmethod
18    def build(input: Union[Dict, SparseValues]) -> SparseValues:
19        if input is None:
20            return input
21        if isinstance(input, SparseValues):
22            return input
23        if not isinstance(input, Mapping):
24            raise SparseValuesDictionaryExpectedError(input)
25        if not {"indices", "values"}.issubset(input):
26            raise SparseValuesMissingKeysError(input)
27
28        indices = SparseValuesFactory._convert_to_list(input.get("indices"), int)
29        values = SparseValuesFactory._convert_to_list(input.get("values"), float)
30
31        if len(indices) != len(values):
32            raise ValueError("Sparse values indices and values must have the same length")
33
34        try:
35            return SparseValues(indices=indices, values=values)
36        except TypeError as e:
37            raise SparseValuesTypeError() from e