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fenic.core.types.enums

Enums used in the DataFrame API.

Classes:

Attributes:

  • BranchSide –

    Type alias representing the side of a branch in a lineage graph.

  • DateTimeUnit –

    Type alias representing the supported date part or time unit to be used in date functions.

  • DocContentType –

    Type alias representing the type of document content.

  • FuzzySimilarityMethod –

    Type alias representing the supported fuzzy string similarity algorithms.

  • JoinType –

    Type alias representing supported join types.

  • SemanticSimilarityMetric –

    Type alias representing supported semantic similarity metrics.

  • StringCasingType –

    Type alias representing the type of string casing.

  • StripCharsSide –

    Type alias representing the side of a string to strip characters from.

  • TranscriptFormatType –

    Type alias representing supported transcript formats.

BranchSide module-attribute 

BranchSide = Literal['left', 'right']

Type alias representing the side of a branch in a lineage graph.

Valid values:

  • "left": The left branch of a join.
  • "right": The right branch of a join.

DateTimeUnit module-attribute 

DateTimeUnit = Literal['year', 'month', 'day', 'hour', 'minute', 'second', 'millisecond']

Type alias representing the supported date part or time unit to be used in date functions.

Valid values:

  • "year": Year.
  • "month": Month.
  • "day": Day.
  • "hour": Hour.
  • "minute": Minute.
  • "second": Second.
  • "millisecond": Millisecond.

DocContentType module-attribute 

DocContentType = Literal['markdown', 'json', 'pdf']

Type alias representing the type of document content.

Valid values:

  • "markdown": Markdown format.
  • "json": JSON format.
  • "pdf": PDF format.

FuzzySimilarityMethod module-attribute 

FuzzySimilarityMethod = Literal['indel', 'levenshtein', 'damerau_levenshtein', 'jaro_winkler', 'jaro', 'hamming']

Type alias representing the supported fuzzy string similarity algorithms.

These algorithms quantify the similarity or difference between two strings using various distance or similarity metrics:

  • "indel": Computes the Indel (Insertion-Deletion) distance, which counts only insertions and deletions needed to transform one string into another, excluding substitutions. This is equivalent to the Longest Common Subsequence (LCS) problem. Useful when character substitutions should not be considered as valid operations (e.g., DNA sequence alignment where only insertions/deletions occur).
  • "levenshtein": Computes the Levenshtein distance, which is the minimum number of single-character edits (insertions, deletions, or substitutions) required to transform one string into another. Suitable for general-purpose fuzzy matching where transpositions do not matter.
  • "damerau_levenshtein": An extension of Levenshtein distance that also accounts for transpositions of adjacent characters (e.g., "ab" → "ba"). This metric is more accurate for real-world typos and keyboard errors.
  • "jaro": Measures similarity based on the number and order of common characters between two strings. It is particularly effective for short strings such as names. Returns a normalized score between 0 (no similarity) and 1 (exact match).
  • "jaro_winkler": A variant of the Jaro distance that gives more weight to common prefixes. Designed to improve accuracy on strings with shared beginnings (e.g., first names, surnames).
  • "hamming": Measures the number of differing characters between two strings of equal length. Only valid when both strings are the same length. It does not support insertions or deletions—only substitutions.

Choose the method based on the type of expected variation (e.g., typos, transpositions, or structural changes).

JoinType module-attribute 

JoinType = Literal['inner', 'full', 'left', 'right', 'cross']

Type alias representing supported join types.

Valid values:

  • "inner": Inner join, returns only rows that have matching values in both tables.
  • "outer": Outer join, returns all rows from both tables, filling missing values with nulls.
  • "left": Left join, returns all rows from the left table and matching rows from the right table.
  • "right": Right join, returns all rows from the right table and matching rows from the left table.
  • "cross": Cross join, returns the Cartesian product of the two tables.

SemanticSimilarityMetric module-attribute 

SemanticSimilarityMetric = Literal['cosine', 'l2', 'dot']

Type alias representing supported semantic similarity metrics.

Valid values:

  • "cosine": Cosine similarity, measures the cosine of the angle between two vectors.
  • "l2": Euclidean (L2) distance, measures the straight-line distance between two vectors.
  • "dot": Dot product similarity, the raw inner product of two vectors.

These metrics are commonly used for comparing embedding vectors in semantic search and other similarity-based applications.

StringCasingType module-attribute 

StringCasingType = Literal['lower', 'upper', 'title']

Type alias representing the type of string casing.

Valid values:

  • "lower": Convert to lowercase.
  • "upper": Convert to uppercase.
  • "title": Convert to title case.

StripCharsSide module-attribute 

StripCharsSide = Literal['left', 'right', 'both']

Type alias representing the side of a string to strip characters from.

Valid values:

  • "left": Strip characters from the left side.
  • "right": Strip characters from the right side.
  • "both": Strip characters from both sides.

TranscriptFormatType module-attribute 

TranscriptFormatType = Literal['srt', 'generic', 'webvtt']

Type alias representing supported transcript formats.

Valid values:

  • "srt": SubRip Subtitle format with indexed entries and timestamp ranges
  • "generic": Conversation transcript format with speaker names and timestamps
  • "webvtt": Web Video Text Tracks format with speaker names and timestamps

All formats are parsed into a unified schema with fields: index, speaker, start_time, end_time, duration, content, format.

CacheBackend

Bases: str, Enum

Cache backend implementations.

Attributes:

  • LOCAL –

    Local persistent cache backend.