​
DeduplicationResult

class DeduplicationResult(BaseModel):

The result of deduplication.

Parameters:

  • original_texts (List[str]): The original texts.
  • unique_ids (List[int]): A list of ids that are unique (not duplicates).
  • unique_embeddings_dict (Dict[int, List[float]]): A mapping from the index of each unique text to its embedding.
  • duplicate_to_target_map (Dict[int, int]): A mapping from the index of the duplicate text to the index of the text it is considered a duplicate of.

​
deduplicate_internally

def deduplicate_internally(
    texts: List[str],
    threshold: float = 0.65,
    embedding_instance: Optional[BaseEmbedding[str]] = None,
    embeddings: Optional[List[List[float]]] = None,
    strategy: Literal['top1', 'llm-supervise'] = 'top1',
    batch_size: int = 1000
):

Deduplicate a list of strings based on their cosine similarity.

You can either:

  1. Provide a CAMEL BaseEmbedding instance via embedding_instance to let this function handle the embedding internally, OR
  2. Directly pass a list of pre-computed embeddings to embeddings.

If both embedding_instance and embeddings are provided, the function will raise a ValueError to avoid ambiguous usage.

strategy is used to specify different strategies, where ‘top1’ selects the one with highest similarity, and ‘llm-supervise’ uses LLM to determine if texts are duplicates (not yet implemented).

Parameters:

  • texts (List[str]): The list of texts to be deduplicated.
  • threshold (float, optional): The similarity threshold for considering two texts as duplicates. (default: :obj:0.65)
  • embedding_instance (Optional[BaseEmbedding[str]], optional): A CAMEL embedding instance for automatic embedding. (default: :obj:None)
  • embeddings (Optional[List[List[float]]], optional): Pre-computed embeddings of texts. Each element in the list corresponds to the embedding of the text in the same index of texts. (default: :obj:None)
  • strategy (Literal["top1", "llm-supervise"], optional): The strategy to use for deduplication. (default: :obj:"top1")
  • batch_size (int, optional): The size of the batch to use for calculating cosine similarities. (default: :obj:1000)

Returns:

DeduplicationResult: An object that contains:

  • original_texts: The original texts.
  • unique_ids: The unique ids after deduplication.
  • unique_embeddings_dict: A dict mapping from (unique) text id to its embedding.
  • duplicate_to_target_map: A dict mapping from the id of a duplicate text to the id of the text it is considered a duplicate of.

Raises:

  • NotImplementedError: If the strategy is not “top1”.
  • ValueError: If neither embeddings nor embedding_instance is provided,
  • ValueError: If the length of embeddings does not match the length of