Module `markov.api.schemas.embedding_recording`

Classes

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

The data model to store metadata for embeddings

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_json(s: 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

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

Methods

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