Embedding data model

export type GenaiEmbedding = {
	model_id?: string;
	content?: [];
	enconding_format?: 'FLOAT' | 'UINT8' | 'INT8' | 'BINARY' | 'UBINARY' | 'BASE64';
	type?: string; // 'SEARCH_DOC' | 'SEARCH_QUERY' | 'CLUSTERING' | 'CLASSIFICATION';
	dimension?: number; // 256, 512, 1024, and 1536
	max_tokens?: number;
	embeddings?: string; // JSON string based off of the format
	tokens_used?: number;
	raw?: any;
	id?: string;
} 

import { z } from 'zod';

export const ZodGenaiEmbedding = z.object({
	model_id: z.string().optional(),
	content: z.array().optional(),
	enconding_format: z.enum("FLOAT", "UINT8", "INT8", "BINARY", "UBINARY", "BASE64").optional(),
	type: z.string().optional().describe("'SEARCH_DOC' | 'SEARCH_QUERY' | 'CLUSTERING' | 'CLASSIFICATION';"),
	dimension: z.number().optional().describe("256, 512, 1024, and 1536"),
	max_tokens: z.number().optional(),
	embeddings: z.string().optional().describe("JSON string based off of the format"),
	tokens_used: z.number().optional(),
,
	id: z.string().optional()
});

import dataclasses

@dataclasses.dataclass
class GenaiEmbeddingEnconding_format(str, Enum):
	FLOAT = 'FLOAT'
	UINT8 = 'UINT8'
	INT8 = 'INT8'
	BINARY = 'BINARY'
	UBINARY = 'UBINARY'
	BASE64 = 'BASE64'

@dataclasses.dataclass
class GenaiEmbedding:
	model_id: Optional[str]
	content: Optional[PropertyGenaiEmbeddingContent]
	enconding_format: Optional[GenaiEmbeddingEnconding_format]
	"""'SEARCH_DOC' | 'SEARCH_QUERY' | 'CLUSTERING' | 'CLASSIFICATION';"""
	type: Optional[str]
	"""256, 512, 1024, and 1536"""
	dimension: Optional[int]
	max_tokens: Optional[int]
	"""JSON string based off of the format"""
	embeddings: Optional[str]
	tokens_used: Optional[int]

	id: Optional[str]