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abstract_concept_searcher

AbstractConceptSearcher

Bases: ABC

Abstract base class for concept searchers.

Source code in src/ariadne/vector_search/abstract_concept_searcher.py 
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class AbstractConceptSearcher(ABS):
    """Abstract base class for concept searchers."""

    @abstractmethod
    def search_term(self, term: str, limit: int = 25) -> Optional[pd.DataFrame]:
        """
        Searches for concepts matching the given term.

        Args:
            term: The clinical term to search for.
            limit: The maximum number of results to return.

        Returns:
            A DataFrame containing the matching concepts, or None if no matches are found.
        """
        pass

    @abstractmethod
    def search_terms(
        self,
        df: pd.DataFrame,
        term_column: str,
        matched_concept_id_column: str = "matched_concept_id",
        matched_concept_name_column: str = "matched_concept_name",
        match_score_column: str = "match_score",
        match_rank_column: str = "match_rank",
        limit: int = 25,
    ) -> pd.DataFrame:
        """
        Searches for concepts matching terms in a DataFrame column.

        Args:
            df: DataFrame containing the terms to search for.
            term_column: Name of the column with terms to search.
            matched_concept_id_column: Name of the column to store matched concept IDs.
            matched_concept_name_column: Name of the column to store matched concept names.
            match_score_column: Name of the column to store match scores.
            match_rank_column: Name of the column to store match ranks.
            limit: The maximum number of results to return for each term.

        Returns:
            A DataFrame containing the same columns as the input dataframe plus the matching concepts for each term. For
            each term in the input dataframe, multiple rows will be returned corresponding to each matching concept.

        """
        pass

search_term(term, limit=25) abstractmethod

Searches for concepts matching the given term.

Parameters:

Name Type Description Default
term str

The clinical term to search for.

 required
limit int

The maximum number of results to return.

 25

Returns:

Type Description
Optional[DataFrame]

A DataFrame containing the matching concepts, or None if no matches are found.
Source code in src/ariadne/vector_search/abstract_concept_searcher.py 
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@abstractmethod
def search_term(self, term: str, limit: int = 25) -> Optional[pd.DataFrame]:
    """
    Searches for concepts matching the given term.

    Args:
        term: The clinical term to search for.
        limit: The maximum number of results to return.

    Returns:
        A DataFrame containing the matching concepts, or None if no matches are found.
    """
    pass

search_terms(df, term_column, matched_concept_id_column='matched_concept_id', matched_concept_name_column='matched_concept_name', match_score_column='match_score', match_rank_column='match_rank', limit=25) abstractmethod

Searches for concepts matching terms in a DataFrame column.

Parameters:

Name Type Description Default
df DataFrame

DataFrame containing the terms to search for.

 required
term_column str

Name of the column with terms to search.

 required
matched_concept_id_column str

Name of the column to store matched concept IDs.

 'matched_concept_id'
matched_concept_name_column str

Name of the column to store matched concept names.

 'matched_concept_name'
match_score_column str

Name of the column to store match scores.

 'match_score'
match_rank_column str

Name of the column to store match ranks.

 'match_rank'
limit int

The maximum number of results to return for each term.

 25

Returns:

Type Description
DataFrame

A DataFrame containing the same columns as the input dataframe plus the matching concepts for each term. For
DataFrame

each term in the input dataframe, multiple rows will be returned corresponding to each matching concept.
Source code in src/ariadne/vector_search/abstract_concept_searcher.py 
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@abstractmethod
def search_terms(
    self,
    df: pd.DataFrame,
    term_column: str,
    matched_concept_id_column: str = "matched_concept_id",
    matched_concept_name_column: str = "matched_concept_name",
    match_score_column: str = "match_score",
    match_rank_column: str = "match_rank",
    limit: int = 25,
) -> pd.DataFrame:
    """
    Searches for concepts matching terms in a DataFrame column.

    Args:
        df: DataFrame containing the terms to search for.
        term_column: Name of the column with terms to search.
        matched_concept_id_column: Name of the column to store matched concept IDs.
        matched_concept_name_column: Name of the column to store matched concept names.
        match_score_column: Name of the column to store match scores.
        match_rank_column: Name of the column to store match ranks.
        limit: The maximum number of results to return for each term.

    Returns:
        A DataFrame containing the same columns as the input dataframe plus the matching concepts for each term. For
        each term in the input dataframe, multiple rows will be returned corresponding to each matching concept.

    """
    pass