Module markov.api.data.embedding

Classes

class BaseResponse (return_code: Optional[str], message: str)

BaseResponse(return_code: 'Optional[str]', message: 'str')

Subclasses

Class variables

var message : str
var return_code : Optional[str]

Static methods

def from_dict(kvs: Union[dict, list, str, int, float, bool, ForwardRef(None)], *, infer_missing=False) ‑> ~A
def from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) ‑> ~A
def schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]

Methods

def to_dict(self, encode_json=False) ‑> Dict[str, Union[dict, list, str, int, float, bool, ForwardRef(None)]]
def to_json(self, *, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Union[int, str, ForwardRef(None)] = None, separators: Optional[Tuple[str, str]] = None, default: Optional[Callable] = None, sort_keys: bool = False, **kw) ‑> str
class ClusterRawDataResponse (return_code: Optional[str], message: str, record_id_mapping: Dict[str, str], cols_used: List[str] = <factory>)

ClusterRawDataResponse(return_code: 'Optional[str]', message: 'str', record_id_mapping: 'Dict[str, str]', cols_used: 'List[str]' = )

Ancestors

Class variables

var cols_used : List[str]
var record_id_mapping : Dict[str, str]

Static methods

def from_dict(kvs: Union[dict, list, str, int, float, bool, ForwardRef(None)], *, infer_missing=False) ‑> ~A
def from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) ‑> ~A
def schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]

Methods

def to_dict(self, encode_json=False) ‑> Dict[str, Union[dict, list, str, int, float, bool, ForwardRef(None)]]
def to_json(self, *, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Union[int, str, ForwardRef(None)] = None, separators: Optional[Tuple[str, str]] = None, default: Optional[Callable] = None, sort_keys: bool = False, **kw) ‑> str
class ClusteringRawDataRequest (user_id: str, team_id: str, workspace_id: str, dataset_id: str, record_id_list: List[str] = <factory>, access_token: Optional[str] = None)

ClusteringRawDataRequest(user_id: 'str', team_id: 'str', workspace_id: 'str', dataset_id: 'str', record_id_list: 'List[str]' = , access_token: 'Optional[str]' = None)

Class variables

var access_token : Optional[str]
var dataset_id : str
var record_id_list : List[str]
var team_id : str
var user_id : str
var workspace_id : str

Static methods

def from_dict(kvs: Union[dict, list, str, int, float, bool, ForwardRef(None)], *, infer_missing=False) ‑> ~A
def from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) ‑> ~A
def schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]

Methods

def to_dict(self, encode_json=False) ‑> Dict[str, Union[dict, list, str, int, float, bool, ForwardRef(None)]]
def to_json(self, *, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Union[int, str, ForwardRef(None)] = None, separators: Optional[Tuple[str, str]] = None, default: Optional[Callable] = None, sort_keys: bool = False, **kw) ‑> str
class CreateSearchPodRequest (workspace_id: str, dataset_id: str)

CreateSearchPodRequest(workspace_id: 'str', dataset_id: 'str')

Class variables

var dataset_id : str
var workspace_id : str

Static methods

def from_dict(kvs: Union[dict, list, str, int, float, bool, ForwardRef(None)], *, infer_missing=False) ‑> ~A
def from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) ‑> ~A
def schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]

Methods

def to_dict(self, encode_json=False) ‑> Dict[str, Union[dict, list, str, int, float, bool, ForwardRef(None)]]
def to_json(self, *, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Union[int, str, ForwardRef(None)] = None, separators: Optional[Tuple[str, str]] = None, default: Optional[Callable] = None, sort_keys: bool = False, **kw) ‑> str
class CreateSearchPodResponse (return_code: Optional[str], message: str, ack: bool)

CreateSearchPodResponse(return_code: 'Optional[str]', message: 'str', ack: 'bool')

Ancestors

Class variables

var ack : bool

Static methods

def from_dict(kvs: Union[dict, list, str, int, float, bool, ForwardRef(None)], *, infer_missing=False) ‑> ~A
def from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) ‑> ~A
def schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]

Methods

def to_dict(self, encode_json=False) ‑> Dict[str, Union[dict, list, str, int, float, bool, ForwardRef(None)]]
def to_json(self, *, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Union[int, str, ForwardRef(None)] = None, separators: Optional[Tuple[str, str]] = None, default: Optional[Callable] = None, sort_keys: bool = False, **kw) ‑> str
class Embedding (embedding_prop: EmbeddingProperties, embedding_paths: List[DataSegmentPath], embedding_id: Optional[str] = '', analysis_status: str = '', cred_id: str = '')

Embedding(embedding_prop: 'EmbeddingProperties', embedding_paths: 'List[DataSegmentPath]', embedding_id: 'Optional[str]' = '', analysis_status: 'str' = '', cred_id: 'str' = '', _credentials: 'Optional[GenericCredential]' = None)

Class variables

var analysis_status : str
var cred_id : str
var embedding_id : Optional[str]
var embedding_paths : List[DataSegmentPath]
var embedding_prop : EmbeddingProperties

Static methods

def from_dict(kvs: Union[dict, list, str, int, float, bool, ForwardRef(None)], *, infer_missing=False) ‑> ~A
def from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) ‑> ~A
def schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]

Instance variables

prop ds_id
prop storage : str

Methods

def register(self, cred: Union[str, GenericCredential], analyze: bool = True)

Register the embedding with new credential_id and/or already registered credential id

Args

cred: analyze: Returns:

def to_dict(self, encode_json=False) ‑> Dict[str, Union[dict, list, str, int, float, bool, ForwardRef(None)]]
def to_json(self, *, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Union[int, str, ForwardRef(None)] = None, separators: Optional[Tuple[str, str]] = None, default: Optional[Callable] = None, sort_keys: bool = False, **kw) ‑> str
class EmbeddingProperties (name: str, notes: str, delimiter: str, ds_id: str, storage_type: str, x_indexes: Optional[List] = <factory>, embedding_index: int = -1, x_col_names: List[str] = <factory>, embedding_name: str = '', storage_format: str = StorageFormatType.CSV, info: dict = <factory>, source: str = '')

EmbeddingProperties(name: 'str', notes: 'str', delimiter: 'str', ds_id: 'str', storage_type: 'str', x_indexes: 'Optional[List]' = , embedding_index: 'int' = -1, x_col_names: 'List[str]' = , embedding_name: 'str' = '', storage_format: 'str' = , info: 'dict' = , source: 'str' = '')

Class variables

var delimiter : str
var ds_id : str
var embedding_index : int
var embedding_name : str
var info : dict
var name : str
var notes : str
var source : str
var storage_format : str
var storage_type : str
var x_col_names : List[str]
var x_indexes : Optional[List]

Static methods

def create_from_dict(json_dict: dict) ‑> EmbeddingProperties
def from_dict(kvs: Union[dict, list, str, int, float, bool, ForwardRef(None)], *, infer_missing=False) ‑> ~A
def from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) ‑> ~A
def schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]

Methods

def to_dict(self, encode_json=False) ‑> Dict[str, Union[dict, list, str, int, float, bool, ForwardRef(None)]]
def to_json(self, *, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Union[int, str, ForwardRef(None)] = None, separators: Optional[Tuple[str, str]] = None, default: Optional[Callable] = None, sort_keys: bool = False, **kw) ‑> str
class EmbeddingRegistrationRequest (embedding: Embedding, credential: GenericCredential, should_analyze: bool = True)

EmbeddingRegistrationRequest(embedding: 'Embedding', credential: 'GenericCredential', should_analyze: 'bool' = True)

Class variables

var credential : GenericCredential
var embedding : Embedding
var should_analyze : bool

Static methods

def create_from_dict(value: str) ‑> EmbeddingRegistrationRequest
def create_from_json(value: str) ‑> EmbeddingRegistrationRequest
def from_dict(kvs: Union[dict, list, str, int, float, bool, ForwardRef(None)], *, infer_missing=False) ‑> ~A
def from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) ‑> ~A
def schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]

Methods

def get_dict(self) ‑> Dict
def get_json(self) ‑> str
def to_dict(self, encode_json=False) ‑> Dict[str, Union[dict, list, str, int, float, bool, ForwardRef(None)]]
def to_json(self, *, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Union[int, str, ForwardRef(None)] = None, separators: Optional[Tuple[str, str]] = None, default: Optional[Callable] = None, sort_keys: bool = False, **kw) ‑> str
class EmbeddingRegistrationResponse (embedding_id: str, run_details: Optional[Dict], return_code: Optional[str], create_time: str, message: str)

EmbeddingRegistrationResponse(embedding_id: 'str', run_details: 'Optional[Dict]', return_code: 'Optional[str]', create_time: 'str', message: 'str')

Class variables

var create_time : str
var embedding_id : str
var message : str
var return_code : Optional[str]
var run_details : Optional[Dict]

Static methods

def create_from_dict(value: Dict) ‑> EmbeddingRegistrationResponse
def create_from_json(value: str) ‑> EmbeddingRegistrationResponse
def from_dict(kvs: Union[dict, list, str, int, float, bool, ForwardRef(None)], *, infer_missing=False) ‑> ~A
def from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) ‑> ~A
def schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]

Methods

def get_dict(self) ‑> str
def get_json(self) ‑> str
def is_being_analyzed(self) ‑> bool
def to_dict(self, encode_json=False) ‑> Dict[str, Union[dict, list, str, int, float, bool, ForwardRef(None)]]
def to_json(self, *, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Union[int, str, ForwardRef(None)] = None, separators: Optional[Tuple[str, str]] = None, default: Optional[Callable] = None, sort_keys: bool = False, **kw) ‑> str
class GetSearchPodStatusResponse (return_code: Optional[str], message: str, status: str)

GetSearchPodStatusResponse(return_code: 'Optional[str]', message: 'str', status: 'str')

Ancestors

Class variables

var status : str

Static methods

def from_dict(kvs: Union[dict, list, str, int, float, bool, ForwardRef(None)], *, infer_missing=False) ‑> ~A
def from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) ‑> ~A
def schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]

Methods

def to_dict(self, encode_json=False) ‑> Dict[str, Union[dict, list, str, int, float, bool, ForwardRef(None)]]
def to_json(self, *, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Union[int, str, ForwardRef(None)] = None, separators: Optional[Tuple[str, str]] = None, default: Optional[Callable] = None, sort_keys: bool = False, **kw) ‑> str
class SearchPodStatus (value, names=None, *, module=None, qualname=None, type=None, start=1)

An enumeration.

Ancestors

  • builtins.str
  • enum.Enum

Class variables

var FAILED
var PENDING
var RUNNING
class SearchPodStatusRequest (workspace_id: str, dataset_id: str)

SearchPodStatusRequest(workspace_id: 'str', dataset_id: 'str')

Class variables

var dataset_id : str
var workspace_id : str

Static methods

def from_dict(kvs: Union[dict, list, str, int, float, bool, ForwardRef(None)], *, infer_missing=False) ‑> ~A
def from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) ‑> ~A
def schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]

Methods

def to_dict(self, encode_json=False) ‑> Dict[str, Union[dict, list, str, int, float, bool, ForwardRef(None)]]
def to_json(self, *, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Union[int, str, ForwardRef(None)] = None, separators: Optional[Tuple[str, str]] = None, default: Optional[Callable] = None, sort_keys: bool = False, **kw) ‑> str
class SimilarPointsRequest (workspace_id: str, dataset_id: str, input_text_list: List[str])

SimilarPointsRequest(workspace_id: 'str', dataset_id: 'str', input_text_list: 'List[str]')

Class variables

var dataset_id : str
var input_text_list : List[str]
var workspace_id : str

Static methods

def from_dict(kvs: Union[dict, list, str, int, float, bool, ForwardRef(None)], *, infer_missing=False) ‑> ~A
def from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) ‑> ~A
def schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]

Methods

def to_dict(self, encode_json=False) ‑> Dict[str, Union[dict, list, str, int, float, bool, ForwardRef(None)]]
def to_json(self, *, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Union[int, str, ForwardRef(None)] = None, separators: Optional[Tuple[str, str]] = None, default: Optional[Callable] = None, sort_keys: bool = False, **kw) ‑> str
class SimilarPointsResponse (return_code: Optional[str], message: str, record_ids: List[str], workspace_id: str)

SimilarPointsResponse(return_code: 'Optional[str]', message: 'str', record_ids: 'List[str]', workspace_id: 'str')

Ancestors

Class variables

var record_ids : List[str]
var workspace_id : str

Static methods

def from_dict(kvs: Union[dict, list, str, int, float, bool, ForwardRef(None)], *, infer_missing=False) ‑> ~A
def from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) ‑> ~A
def schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]

Methods

def to_dict(self, encode_json=False) ‑> Dict[str, Union[dict, list, str, int, float, bool, ForwardRef(None)]]
def to_json(self, *, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Union[int, str, ForwardRef(None)] = None, separators: Optional[Tuple[str, str]] = None, default: Optional[Callable] = None, sort_keys: bool = False, **kw) ‑> str
class VectorStore (dataset_id, timeout, config)

To perform similarity operations on the embeddings

Static methods

def from_dict(kvs: Union[dict, list, str, int, float, bool, ForwardRef(None)], *, infer_missing=False) ‑> ~A
def from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) ‑> ~A
def schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) ‑> dataclasses_json.mm.SchemaF[~A]

Methods

def find_similar_points(self, input_points)
def to_dict(self, encode_json=False) ‑> Dict[str, Union[dict, list, str, int, float, bool, ForwardRef(None)]]
def to_json(self, *, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Union[int, str, ForwardRef(None)] = None, separators: Optional[Tuple[str, str]] = None, default: Optional[Callable] = None, sort_keys: bool = False, **kw) ‑> str