pinecone.data.sparse_vector_factory 

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