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from typing import List, Optional
from pydantic import BaseModel
class EmbedRequest(BaseModel):
"""
Request model for single text embedding.
Attributes:
text: The input text to embed
model_id: Identifier of the model to use
prompt: Optional prompt for instruction-based models
"""
text: str
model_id: str
prompt: Optional[str] = None
class BatchEmbedRequest(BaseModel):
"""
Request model for batch text embedding.
Attributes:
texts: List of input texts to embed
model_id: Identifier of the model to use
prompt: Optional prompt for instruction-based models
"""
texts: List[str]
model_id: str
prompt: Optional[str] = None
class EmbedResponse(BaseModel):
"""
Response model for single text embedding.
Attributes:
embedding: The generated embedding vector
dimension: Dimensionality of the embedding
model_id: Identifier of the model used
processing_time: Time taken to process the request
"""
embedding: List[float]
dimension: int
model_id: str
processing_time: float
class BatchEmbedResponse(BaseModel):
"""
Response model for batch text embedding.
Attributes:
embeddings: List of generated embedding vectors
dimension: Dimensionality of the embeddings
model_id: Identifier of the model used
processing_time: Time taken to process the request
"""
embeddings: List[List[float]]
dimension: int
model_id: str
processing_time: float
class SparseEmbedding(BaseModel):
"""
Sparse embedding model.
Attributes:
text: The input text that was embedded
indices: Indices of non-zero elements in the sparse vector
values: Values corresponding to the indices
"""
text: Optional[str] = None
indices: List[int]
values: List[float]
class SparseEmbedResponse(BaseModel):
"""
Sparse embedding response model.
Attributes:
sparse_embedding: The generated sparse embedding
model_id: Identifier of the model used
processing_time: Time taken to process the request
"""
sparse_embedding: SparseEmbedding
model_id: str
processing_time: float
class BatchSparseEmbedResponse(BaseModel):
"""
Batch sparse embedding response model.
Attributes:
embeddings: List of generated sparse embeddings
model_id: Identifier of the model used
"""
embeddings: List[SparseEmbedding]
model_id: str
processing_time: float
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