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Embedding Function Base Classes

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EmbeddingFunction

Protocol for embedding functions. To implement a new embedding function, you need to implement the following methods:
  • init
  • call
  • name
  • build_from_config
  • get_config
Additionally, you should register the embedding function so it will automatically be used by the Chroma client. 
@register_embedding_function
class MyEmbeddingFunction(EmbeddingFunction[Documents]):
    ...
Methods __init__(), build_from_config(), default_space(), embed_query(), embed_with_retries(), get_config(), is_legacy(), name(), supported_spaces(), validate_config(), validate_config_update()

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SparseEmbeddingFunction

Protocol for sparse embedding functions. To implement a new sparse embedding function, you need to implement the following methods:
  • call
  • init
  • name
  • build_from_config
  • get_config
Methods __init__(), build_from_config(), embed_query(), embed_with_retries(), get_config(), name(), validate_config(), validate_config_update()

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Registration

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register_embedding_function

Register a custom embedding function. Can be used as a decorator: 
@register_embedding_function
class MyEmbedding(EmbeddingFunction):
    @classmethod
    def name(cls): return "my_embedding"
Or directly: 
register_embedding_function(MyEmbedding)
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ef_class
Any
The embedding function class to register.

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register_sparse_embedding_function

Register a custom sparse embedding function. Can be used as a decorator: 
@register_sparse_embedding_function
class MySparseEmbeddingFunction(SparseEmbeddingFunction):
    @classmethod
    def name(cls): return "my_sparse_embedding"
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ef_class
Any

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Types

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Embedding

Embedding[Tuple[Any, Ellipsis], dtype[Union[int32, float32]]]

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SparseVector

Sparse vector using parallel indices and values arrays. Properties
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indices
List[int]
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values
List[float]
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labels
Optional[IDs]
Methods __init__(), from_dict(), to_dict()