Module markov.api.schemas.embedding_recording

Classes

class EmbeddingRecording (name: str, note: str = '', dataset_id: str = '')

The data model to store metadata for embeddings

Class variables

var dataset_id : str

var name : str

details about this embedding run that might be relevant for the user

var note : str

The dataset id used for this embedding

Static methods

def create_from_dict(value: dict) ‑> EmbeddingRecording

Create this object from the serialized (dictionary) representation of this object where the key's are attributes and values are attribute values.

Args

value : Dict

Dictionary serialized value of this object.

Returns

EmbeddingRecording

def create_from_json(value: str) ‑> EmbeddingRecording

Create EmbeddingRecording object from serialized JSON.

Args

value : str

JSON string that is serialized representation of this object.

Returns

EmbeddingRecording object

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