Spaces:
Runtime error
Runtime error
File size: 1,421 Bytes
dc1b199 faa8fb3 dc1b199 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | """Abstract base class for all embedding clients."""
from abc import ABC, abstractmethod
class EmbeddingClient(ABC):
"""Interface for obtaining dense vector representations of text.
Implementors must provide both single and batched variants. The batched
variant should be preferred for throughput when indexing many chunks.
Example::
client = MockEmbeddingClient()
vector = client.get_embedding("hello world")
vectors = client.get_embeddings(["hello", "world"])
"""
@abstractmethod
def get_embedding(self, text: str) -> list[float]:
"""Return the embedding vector for a single text string.
Args:
text: Input text (should be ≤ model token limit).
Returns:
Dense float vector of the model's output dimensionality.
"""
...
@abstractmethod
def get_embeddings(self, texts: list[str]) -> list[list[float]]:
"""Return embedding vectors for a batch of text strings.
Args:
texts: List of input strings.
Returns:
List of dense float vectors in the same order as ``texts``.
"""
...
@property
@abstractmethod
def dimension(self) -> int:
"""The output dimensionality of the embedding model.
Returns:
Integer vector dimension (e.g. 1536 for text-embedding-3-small).
"""
...
|