type stringclasses 5
values | name stringlengths 1 55 | qualified_name stringlengths 5 143 | docstring stringlengths 0 3.59k ⌀ | filepath stringclasses 180
values | is_public bool 2
classes | is_private bool 2
classes | line_start float64 0 1.54k ⌀ | line_end float64 0 1.56k ⌀ | annotation stringclasses 8
values | returns stringclasses 236
values | parameters listlengths 0 74 ⌀ | parent_class stringclasses 298
values | value stringclasses 112
values | bases listlengths 0 3 ⌀ | api_element_summary stringlengths 199 23k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
function | extract | fenic.api.functions.semantic.extract | Extracts structured information from unstructured text using a provided Pydantic model schema.
This function applies an instruction-driven extraction process to text columns, returning
structured data based on the fields and descriptions provided. Useful for pulling out key entities,
facts, or labels from documents.
... | site-packages/fenic/api/functions/semantic.py | true | false | 139 | 203 | null | Column | [
"column",
"response_format",
"max_output_tokens",
"temperature",
"model_alias"
] | null | null | null | Type: function
Member Name: extract
Qualified Name: fenic.api.functions.semantic.extract
Docstring: Extracts structured information from unstructured text using a provided Pydantic model schema.
This function applies an instruction-driven extraction process to text columns, returning
structured data based on the field... |
function | predicate | fenic.api.functions.semantic.predicate | Applies a boolean predicate to one or more columns, typically used for filtering.
Args:
predicate: A Jinja2 template containing a yes/no question or boolean claim.
Should reference column values using {{ column_name }} syntax. The model will
evaluate this condition for each row and return True or F... | site-packages/fenic/api/functions/semantic.py | true | false | 206 | 307 | null | Column | [
"predicate",
"strict",
"examples",
"model_alias",
"temperature",
"columns"
] | null | null | null | Type: function
Member Name: predicate
Qualified Name: fenic.api.functions.semantic.predicate
Docstring: Applies a boolean predicate to one or more columns, typically used for filtering.
Args:
predicate: A Jinja2 template containing a yes/no question or boolean claim.
Should reference column values using {{... |
function | reduce | fenic.api.functions.semantic.reduce | Aggregate function: reduces a set of strings in a column to a single string using a natural language instruction.
Args:
prompt: A string containing the semantic.reduce prompt.
The instruction can optionally include Jinja2 template variables (e.g., {{variable}}) that
reference columns from the group... | site-packages/fenic/api/functions/semantic.py | true | false | 310 | 401 | null | Column | [
"prompt",
"column",
"group_context",
"order_by",
"model_alias",
"temperature",
"max_output_tokens"
] | null | null | null | Type: function
Member Name: reduce
Qualified Name: fenic.api.functions.semantic.reduce
Docstring: Aggregate function: reduces a set of strings in a column to a single string using a natural language instruction.
Args:
prompt: A string containing the semantic.reduce prompt.
The instruction can optionally in... |
function | classify | fenic.api.functions.semantic.classify | Classifies a string column into one of the provided classes.
This is useful for tagging incoming documents with predefined categories.
Args:
column: Column or column name containing text to classify.
classes: List of class labels or ClassDefinition objects defining the available classes. Use ClassDefinition o... | site-packages/fenic/api/functions/semantic.py | true | false | 404 | 493 | null | Column | [
"column",
"classes",
"examples",
"model_alias",
"temperature"
] | null | null | null | Type: function
Member Name: classify
Qualified Name: fenic.api.functions.semantic.classify
Docstring: Classifies a string column into one of the provided classes.
This is useful for tagging incoming documents with predefined categories.
Args:
column: Column or column name containing text to classify.
classes:... |
function | analyze_sentiment | fenic.api.functions.semantic.analyze_sentiment | Analyzes the sentiment of a string column. Returns one of 'positive', 'negative', or 'neutral'.
Args:
column: Column or column name containing text for sentiment analysis.
model_alias: Optional alias for the language model to use for the mapping. If None, will use the language model configured as the default.
... | site-packages/fenic/api/functions/semantic.py | true | false | 496 | 527 | null | Column | [
"column",
"model_alias",
"temperature"
] | null | null | null | Type: function
Member Name: analyze_sentiment
Qualified Name: fenic.api.functions.semantic.analyze_sentiment
Docstring: Analyzes the sentiment of a string column. Returns one of 'positive', 'negative', or 'neutral'.
Args:
column: Column or column name containing text for sentiment analysis.
model_alias: Option... |
function | embed | fenic.api.functions.semantic.embed | Generate embeddings for the specified string column.
Args:
column: Column or column name containing the values to generate embeddings for.
model_alias: Optional alias for the embedding model to use for the mapping.
If None, will use the embedding model configured as the default.
Returns:
A Column... | site-packages/fenic/api/functions/semantic.py | true | false | 530 | 557 | null | Column | [
"column",
"model_alias"
] | null | null | null | Type: function
Member Name: embed
Qualified Name: fenic.api.functions.semantic.embed
Docstring: Generate embeddings for the specified string column.
Args:
column: Column or column name containing the values to generate embeddings for.
model_alias: Optional alias for the embedding model to use for the mapping.
... |
function | summarize | fenic.api.functions.semantic.summarize | Summarizes strings from a column.
Args:
column: Column or column name containing text for summarization
format: Format of the summary to generate. Can be either KeyPoints or Paragraph. If None, will default to Paragraph with a maximum of 120 words.
temperature: Optional temperature parameter for the langua... | site-packages/fenic/api/functions/semantic.py | true | false | 560 | 589 | null | Column | [
"column",
"format",
"temperature",
"model_alias"
] | null | null | null | Type: function
Member Name: summarize
Qualified Name: fenic.api.functions.semantic.summarize
Docstring: Summarizes strings from a column.
Args:
column: Column or column name containing text for summarization
format: Format of the summary to generate. Can be either KeyPoints or Paragraph. If None, will default ... |
module | embedding | fenic.api.functions.embedding | Embedding functions. | site-packages/fenic/api/functions/embedding.py | true | false | null | null | null | null | null | null | null | null | Type: module
Member Name: embedding
Qualified Name: fenic.api.functions.embedding
Docstring: Embedding functions.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
function | normalize | fenic.api.functions.embedding.normalize | Normalize embedding vectors to unit length.
Args:
column: Column containing embedding vectors.
Returns:
Column: A column of normalized embedding vectors with the same embedding type.
Notes:
- Normalizes each embedding vector to have unit length (L2 norm = 1)
- Preserves the original embedding model i... | site-packages/fenic/api/functions/embedding.py | true | false | 17 | 51 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: normalize
Qualified Name: fenic.api.functions.embedding.normalize
Docstring: Normalize embedding vectors to unit length.
Args:
column: Column containing embedding vectors.
Returns:
Column: A column of normalized embedding vectors with the same embedding type.
Notes:
- Normaliz... |
function | compute_similarity | fenic.api.functions.embedding.compute_similarity | Compute similarity between embedding vectors using specified metric.
Args:
column: Column containing embedding vectors.
other: Either:
- Another column containing embedding vectors for pairwise similarity
- A query vector (list of floats or numpy array) for similarity with each embedding
... | site-packages/fenic/api/functions/embedding.py | true | false | 54 | 142 | null | Column | [
"column",
"other",
"metric"
] | null | null | null | Type: function
Member Name: compute_similarity
Qualified Name: fenic.api.functions.embedding.compute_similarity
Docstring: Compute similarity between embedding vectors using specified metric.
Args:
column: Column containing embedding vectors.
other: Either:
- Another column containing embedding vecto... |
module | core | fenic.api.functions.core | Core functions for Fenic DataFrames. | site-packages/fenic/api/functions/core.py | true | false | null | null | null | null | null | null | null | null | Type: module
Member Name: core
Qualified Name: fenic.api.functions.core
Docstring: Core functions for Fenic DataFrames.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
function | col | fenic.api.functions.core.col | Creates a Column expression referencing a column in the DataFrame.
Args:
col_name: Name of the column to reference
Returns:
A Column expression for the specified column
Raises:
TypeError: If colName is not a string | site-packages/fenic/api/functions/core.py | true | false | 17 | 30 | null | Column | [
"col_name"
] | null | null | null | Type: function
Member Name: col
Qualified Name: fenic.api.functions.core.col
Docstring: Creates a Column expression referencing a column in the DataFrame.
Args:
col_name: Name of the column to reference
Returns:
A Column expression for the specified column
Raises:
TypeError: If colName is not a string
Va... |
function | null | fenic.api.functions.core.null | Creates a Column expression representing a null value of the specified data type.
Regardless of the data type, the column will contain a null (None) value.
This function is useful for creating columns with null values of a particular type.
Args:
data_type: The data type of the null value
Returns:
A Column ex... | site-packages/fenic/api/functions/core.py | true | false | 32 | 64 | null | Column | [
"data_type"
] | null | null | null | Type: function
Member Name: null
Qualified Name: fenic.api.functions.core.null
Docstring: Creates a Column expression representing a null value of the specified data type.
Regardless of the data type, the column will contain a null (None) value.
This function is useful for creating columns with null values of a partic... |
function | empty | fenic.api.functions.core.empty | Creates a Column expression representing an empty value of the given type.
- If the data type is `ArrayType(...)`, the empty value will be an empty array.
- If the data type is `StructType(...)`, the empty value will be an instance of the struct type with all fields set to `None`.
- For all other data types, the empty... | site-packages/fenic/api/functions/core.py | true | false | 66 | 106 | null | Column | [
"data_type"
] | null | null | null | Type: function
Member Name: empty
Qualified Name: fenic.api.functions.core.empty
Docstring: Creates a Column expression representing an empty value of the given type.
- If the data type is `ArrayType(...)`, the empty value will be an empty array.
- If the data type is `StructType(...)`, the empty value will be an inst... |
function | lit | fenic.api.functions.core.lit | Creates a Column expression representing a literal value.
Args:
value: The literal value to create a column for
Returns:
A Column expression representing the literal value
Raises:
ValidationError: If the type of the value cannot be inferred | site-packages/fenic/api/functions/core.py | true | false | 108 | 131 | null | Column | [
"value"
] | null | null | null | Type: function
Member Name: lit
Qualified Name: fenic.api.functions.core.lit
Docstring: Creates a Column expression representing a literal value.
Args:
value: The literal value to create a column for
Returns:
A Column expression representing the literal value
Raises:
ValidationError: If the type of the v... |
function | tool_param | fenic.api.functions.core.tool_param | Creates an unresolved literal placeholder column with a declared data type.
A placeholder argument for a DataFrame, representing a literal value to be provided at execution time.
If no value is supplied, it defaults to null. Enables parameterized views and macros over fenic DataFrames.
Notes:
Supports only Primi... | site-packages/fenic/api/functions/core.py | true | false | 135 | 187 | null | Column | [
"parameter_name",
"data_type"
] | null | null | null | Type: function
Member Name: tool_param
Qualified Name: fenic.api.functions.core.tool_param
Docstring: Creates an unresolved literal placeholder column with a declared data type.
A placeholder argument for a DataFrame, representing a literal value to be provided at execution time.
If no value is supplied, it defaults ... |
module | markdown | fenic.api.functions.markdown | Markdown functions. | site-packages/fenic/api/functions/markdown.py | true | false | null | null | null | null | null | null | null | null | Type: module
Member Name: markdown
Qualified Name: fenic.api.functions.markdown
Docstring: Markdown functions.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
function | to_json | fenic.api.functions.markdown.to_json | Converts a column of Markdown-formatted strings into a hierarchical JSON representation.
Args:
column (ColumnOrName): Input column containing Markdown strings.
Returns:
Column: A column of JSON-formatted strings representing the structured document tree.
Notes:
- This function parses Markdown into a stru... | site-packages/fenic/api/functions/markdown.py | true | false | 16 | 54 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: to_json
Qualified Name: fenic.api.functions.markdown.to_json
Docstring: Converts a column of Markdown-formatted strings into a hierarchical JSON representation.
Args:
column (ColumnOrName): Input column containing Markdown strings.
Returns:
Column: A column of JSON-formatted string... |
function | get_code_blocks | fenic.api.functions.markdown.get_code_blocks | Extracts all code blocks from a column of Markdown-formatted strings.
Args:
column (ColumnOrName): Input column containing Markdown strings.
language_filter (Optional[str]): Optional language filter to extract only code blocks with a specific language. By default, all code blocks are extracted.
Returns:
C... | site-packages/fenic/api/functions/markdown.py | true | false | 56 | 92 | null | Column | [
"column",
"language_filter"
] | null | null | null | Type: function
Member Name: get_code_blocks
Qualified Name: fenic.api.functions.markdown.get_code_blocks
Docstring: Extracts all code blocks from a column of Markdown-formatted strings.
Args:
column (ColumnOrName): Input column containing Markdown strings.
language_filter (Optional[str]): Optional language fil... |
function | generate_toc | fenic.api.functions.markdown.generate_toc | Generates a table of contents from markdown headings.
Args:
column (ColumnOrName): Input column containing Markdown strings.
max_level (Optional[int]): Maximum heading level to include in the TOC (1-6).
Defaults to 6 (all levels).
Returns:
Column: A column of Markdown-formatte... | site-packages/fenic/api/functions/markdown.py | true | false | 95 | 132 | null | Column | [
"column",
"max_level"
] | null | null | null | Type: function
Member Name: generate_toc
Qualified Name: fenic.api.functions.markdown.generate_toc
Docstring: Generates a table of contents from markdown headings.
Args:
column (ColumnOrName): Input column containing Markdown strings.
max_level (Optional[int]): Maximum heading level to include in the TOC (1-6)... |
function | extract_header_chunks | fenic.api.functions.markdown.extract_header_chunks | Splits markdown documents into logical chunks based on heading hierarchy.
Args:
column (ColumnOrName): Input column containing Markdown strings.
header_level (int): Heading level to split on (1-6). Creates a new chunk at every
heading of this level, including all nested content and subs... | site-packages/fenic/api/functions/markdown.py | true | false | 135 | 212 | null | Column | [
"column",
"header_level"
] | null | null | null | Type: function
Member Name: extract_header_chunks
Qualified Name: fenic.api.functions.markdown.extract_header_chunks
Docstring: Splits markdown documents into logical chunks based on heading hierarchy.
Args:
column (ColumnOrName): Input column containing Markdown strings.
header_level (int): Heading level to s... |
module | text | fenic.api.functions.text | Text manipulation functions for Fenic DataFrames. | site-packages/fenic/api/functions/text.py | true | false | null | null | null | null | null | null | null | null | Type: module
Member Name: text
Qualified Name: fenic.api.functions.text
Docstring: Text manipulation functions for Fenic DataFrames.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
function | extract | fenic.api.functions.text.extract | Extracts structured data from text using template-based pattern matching.
Matches each string in the input column against a template pattern with named
placeholders. Each placeholder can specify a format rule to handle different
data types within the text.
Args:
column: Input text column to extract from
templ... | site-packages/fenic/api/functions/text.py | true | false | 46 | 99 | null | Column | [
"column",
"template"
] | null | null | null | Type: function
Member Name: extract
Qualified Name: fenic.api.functions.text.extract
Docstring: Extracts structured data from text using template-based pattern matching.
Matches each string in the input column against a template pattern with named
placeholders. Each placeholder can specify a format rule to handle diff... |
function | recursive_character_chunk | fenic.api.functions.text.recursive_character_chunk | Chunks a string column into chunks of a specified size (in characters) with an optional overlap.
The chunking is performed recursively, attempting to preserve the underlying structure of the text
by splitting on natural boundaries (paragraph breaks, sentence breaks, etc.) to maintain context.
By default, these charact... | site-packages/fenic/api/functions/text.py | true | false | 101 | 160 | null | Column | [
"column",
"chunk_size",
"chunk_overlap_percentage",
"chunking_character_set_custom_characters"
] | null | null | null | Type: function
Member Name: recursive_character_chunk
Qualified Name: fenic.api.functions.text.recursive_character_chunk
Docstring: Chunks a string column into chunks of a specified size (in characters) with an optional overlap.
The chunking is performed recursively, attempting to preserve the underlying structure of ... |
function | recursive_word_chunk | fenic.api.functions.text.recursive_word_chunk | Chunks a string column into chunks of a specified size (in words) with an optional overlap.
The chunking is performed recursively, attempting to preserve the underlying structure of the text
by splitting on natural boundaries (paragraph breaks, sentence breaks, etc.) to maintain context.
By default, these characters a... | site-packages/fenic/api/functions/text.py | true | false | 163 | 222 | null | Column | [
"column",
"chunk_size",
"chunk_overlap_percentage",
"chunking_character_set_custom_characters"
] | null | null | null | Type: function
Member Name: recursive_word_chunk
Qualified Name: fenic.api.functions.text.recursive_word_chunk
Docstring: Chunks a string column into chunks of a specified size (in words) with an optional overlap.
The chunking is performed recursively, attempting to preserve the underlying structure of the text
by spl... |
function | recursive_token_chunk | fenic.api.functions.text.recursive_token_chunk | Chunks a string column into chunks of a specified size (in tokens) with an optional overlap.
The chunking is performed recursively, attempting to preserve the underlying structure of the text
by splitting on natural boundaries (paragraph breaks, sentence breaks, etc.) to maintain context.
By default, these characters ... | site-packages/fenic/api/functions/text.py | true | false | 225 | 284 | null | Column | [
"column",
"chunk_size",
"chunk_overlap_percentage",
"chunking_character_set_custom_characters"
] | null | null | null | Type: function
Member Name: recursive_token_chunk
Qualified Name: fenic.api.functions.text.recursive_token_chunk
Docstring: Chunks a string column into chunks of a specified size (in tokens) with an optional overlap.
The chunking is performed recursively, attempting to preserve the underlying structure of the text
by ... |
function | character_chunk | fenic.api.functions.text.character_chunk | Chunks a string column into chunks of a specified size (in characters) with an optional overlap.
The chunking is done by applying a simple sliding window across the text to create chunks of equal size.
This approach does not attempt to preserve the underlying structure of the text.
Args:
column: The input string ... | site-packages/fenic/api/functions/text.py | true | false | 287 | 319 | null | Column | [
"column",
"chunk_size",
"chunk_overlap_percentage"
] | null | null | null | Type: function
Member Name: character_chunk
Qualified Name: fenic.api.functions.text.character_chunk
Docstring: Chunks a string column into chunks of a specified size (in characters) with an optional overlap.
The chunking is done by applying a simple sliding window across the text to create chunks of equal size.
This ... |
function | word_chunk | fenic.api.functions.text.word_chunk | Chunks a string column into chunks of a specified size (in words) with an optional overlap.
The chunking is done by applying a simple sliding window across the text to create chunks of equal size.
This approach does not attempt to preserve the underlying structure of the text.
Args:
column: The input string colum... | site-packages/fenic/api/functions/text.py | true | false | 322 | 354 | null | Column | [
"column",
"chunk_size",
"chunk_overlap_percentage"
] | null | null | null | Type: function
Member Name: word_chunk
Qualified Name: fenic.api.functions.text.word_chunk
Docstring: Chunks a string column into chunks of a specified size (in words) with an optional overlap.
The chunking is done by applying a simple sliding window across the text to create chunks of equal size.
This approach does n... |
function | token_chunk | fenic.api.functions.text.token_chunk | Chunks a string column into chunks of a specified size (in tokens) with an optional overlap.
The chunking is done by applying a simple sliding window across the text to create chunks of equal size.
This approach does not attempt to preserve the underlying structure of the text.
Args:
column: The input string colu... | site-packages/fenic/api/functions/text.py | true | false | 357 | 389 | null | Column | [
"column",
"chunk_size",
"chunk_overlap_percentage"
] | null | null | null | Type: function
Member Name: token_chunk
Qualified Name: fenic.api.functions.text.token_chunk
Docstring: Chunks a string column into chunks of a specified size (in tokens) with an optional overlap.
The chunking is done by applying a simple sliding window across the text to create chunks of equal size.
This approach doe... |
function | count_tokens | fenic.api.functions.text.count_tokens | Returns the number of tokens in a string using OpenAI's cl100k_base encoding (tiktoken).
Args:
column: The input string column.
Returns:
Column: A column with the token counts for each input string.
Example: Count tokens in text
```python
# Count tokens in a text column
df.select(text.count_token... | site-packages/fenic/api/functions/text.py | true | false | 392 | 412 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: count_tokens
Qualified Name: fenic.api.functions.text.count_tokens
Docstring: Returns the number of tokens in a string using OpenAI's cl100k_base encoding (tiktoken).
Args:
column: The input string column.
Returns:
Column: A column with the token counts for each input string.
Exam... |
function | concat | fenic.api.functions.text.concat | Concatenates multiple columns or strings into a single string.
Args:
*cols: Columns or strings to concatenate
Returns:
Column: A column containing the concatenated strings
Example: Concatenate columns
```python
# Concatenate two columns with a space in between
df.select(text.concat(col("col1"), l... | site-packages/fenic/api/functions/text.py | true | false | 415 | 444 | null | Column | [
"cols"
] | null | null | null | Type: function
Member Name: concat
Qualified Name: fenic.api.functions.text.concat
Docstring: Concatenates multiple columns or strings into a single string.
Args:
*cols: Columns or strings to concatenate
Returns:
Column: A column containing the concatenated strings
Example: Concatenate columns
```python
... |
function | parse_transcript | fenic.api.functions.text.parse_transcript | Parses a transcript from text to a structured format with unified schema.
Converts transcript text in various formats (srt, webvtt, generic) to a standardized structure
with fields: index, speaker, start_time, end_time, duration, content, format.
All timestamps are returned as floating-point seconds from the start.
A... | site-packages/fenic/api/functions/text.py | true | false | 448 | 481 | null | Column | [
"column",
"format"
] | null | null | null | Type: function
Member Name: parse_transcript
Qualified Name: fenic.api.functions.text.parse_transcript
Docstring: Parses a transcript from text to a structured format with unified schema.
Converts transcript text in various formats (srt, webvtt, generic) to a standardized structure
with fields: index, speaker, start_t... |
function | concat_ws | fenic.api.functions.text.concat_ws | Concatenates multiple columns or strings into a single string with a separator.
Args:
separator: The separator to use
*cols: Columns or strings to concatenate
Returns:
Column: A column containing the concatenated strings
Example: Concatenate with comma separator
```python
# Concatenate columns wi... | site-packages/fenic/api/functions/text.py | true | false | 484 | 516 | null | Column | [
"separator",
"cols"
] | null | null | null | Type: function
Member Name: concat_ws
Qualified Name: fenic.api.functions.text.concat_ws
Docstring: Concatenates multiple columns or strings into a single string with a separator.
Args:
separator: The separator to use
*cols: Columns or strings to concatenate
Returns:
Column: A column containing the concat... |
function | array_join | fenic.api.functions.text.array_join | Joins an array of strings into a single string with a delimiter.
Args:
column: The column to join
delimiter: The delimiter to use
Returns:
Column: A column containing the joined strings
Example: Join array with comma
```python
# Join array elements with comma
df.select(text.array_join(col(... | site-packages/fenic/api/functions/text.py | true | false | 519 | 537 | null | Column | [
"column",
"delimiter"
] | null | null | null | Type: function
Member Name: array_join
Qualified Name: fenic.api.functions.text.array_join
Docstring: Joins an array of strings into a single string with a delimiter.
Args:
column: The column to join
delimiter: The delimiter to use
Returns:
Column: A column containing the joined strings
Example: Join ... |
function | replace | fenic.api.functions.text.replace | Replace all occurrences of a pattern with a new string, treating pattern as a literal string.
This method creates a new string column with all occurrences of the specified pattern
replaced with a new string. The pattern is treated as a literal string, not a regular expression.
If either search or replace is a column e... | site-packages/fenic/api/functions/text.py | true | false | 540 | 583 | null | Column | [
"src",
"search",
"replace"
] | null | null | null | Type: function
Member Name: replace
Qualified Name: fenic.api.functions.text.replace
Docstring: Replace all occurrences of a pattern with a new string, treating pattern as a literal string.
This method creates a new string column with all occurrences of the specified pattern
replaced with a new string. The pattern is ... |
function | regexp_replace | fenic.api.functions.text.regexp_replace | Replace all occurrences of a pattern with a new string, treating pattern as a regular expression.
This method creates a new string column with all occurrences of the specified pattern
replaced with a new string. The pattern is treated as a regular expression.
If either pattern or replacement is a column expression, th... | site-packages/fenic/api/functions/text.py | true | false | 586 | 640 | null | Column | [
"src",
"pattern",
"replacement"
] | null | null | null | Type: function
Member Name: regexp_replace
Qualified Name: fenic.api.functions.text.regexp_replace
Docstring: Replace all occurrences of a pattern with a new string, treating pattern as a regular expression.
This method creates a new string column with all occurrences of the specified pattern
replaced with a new strin... |
function | split | fenic.api.functions.text.split | Split a string column into an array using a regular expression pattern.
This method creates an array column by splitting each value in the input string column
at matches of the specified regular expression pattern.
Args:
src: The input string column or column name to split
pattern: The regular expression patt... | site-packages/fenic/api/functions/text.py | true | false | 643 | 673 | null | Column | [
"src",
"pattern",
"limit"
] | null | null | null | Type: function
Member Name: split
Qualified Name: fenic.api.functions.text.split
Docstring: Split a string column into an array using a regular expression pattern.
This method creates an array column by splitting each value in the input string column
at matches of the specified regular expression pattern.
Args:
s... |
function | split_part | fenic.api.functions.text.split_part | Split a string and return a specific part using 1-based indexing.
Splits each string by a delimiter and returns the specified part.
If the delimiter is a column expression, the split operation is performed dynamically
using the delimiter values from that column.
Behavior:
- If any input is null, returns null
- If par... | site-packages/fenic/api/functions/text.py | true | false | 676 | 737 | null | Column | [
"src",
"delimiter",
"part_number"
] | null | null | null | Type: function
Member Name: split_part
Qualified Name: fenic.api.functions.text.split_part
Docstring: Split a string and return a specific part using 1-based indexing.
Splits each string by a delimiter and returns the specified part.
If the delimiter is a column expression, the split operation is performed dynamically... |
function | upper | fenic.api.functions.text.upper | Convert all characters in a string column to uppercase.
Args:
column: The input string column to convert to uppercase
Returns:
Column: A column containing the uppercase strings
Example: Convert text to uppercase
```python
# Convert all text in the name column to uppercase
df.select(text.upper(col... | site-packages/fenic/api/functions/text.py | true | false | 740 | 758 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: upper
Qualified Name: fenic.api.functions.text.upper
Docstring: Convert all characters in a string column to uppercase.
Args:
column: The input string column to convert to uppercase
Returns:
Column: A column containing the uppercase strings
Example: Convert text to uppercase
`... |
function | lower | fenic.api.functions.text.lower | Convert all characters in a string column to lowercase.
Args:
column: The input string column to convert to lowercase
Returns:
Column: A column containing the lowercase strings
Example: Convert text to lowercase
```python
# Convert all text in the name column to lowercase
df.select(text.lower(col... | site-packages/fenic/api/functions/text.py | true | false | 761 | 779 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: lower
Qualified Name: fenic.api.functions.text.lower
Docstring: Convert all characters in a string column to lowercase.
Args:
column: The input string column to convert to lowercase
Returns:
Column: A column containing the lowercase strings
Example: Convert text to lowercase
`... |
function | title_case | fenic.api.functions.text.title_case | Convert the first character of each word in a string column to uppercase.
Args:
column: The input string column to convert to title case
Returns:
Column: A column containing the title case strings
Example: Convert text to title case
```python
# Convert text in the name column to title case
df.sel... | site-packages/fenic/api/functions/text.py | true | false | 782 | 800 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: title_case
Qualified Name: fenic.api.functions.text.title_case
Docstring: Convert the first character of each word in a string column to uppercase.
Args:
column: The input string column to convert to title case
Returns:
Column: A column containing the title case strings
Example: C... |
function | trim | fenic.api.functions.text.trim | Remove whitespace from both sides of strings in a column.
This function removes all whitespace characters (spaces, tabs, newlines) from
both the beginning and end of each string in the column.
Args:
column: The input string column or column name to trim
Returns:
Column: A column containing the trimmed string... | site-packages/fenic/api/functions/text.py | true | false | 803 | 824 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: trim
Qualified Name: fenic.api.functions.text.trim
Docstring: Remove whitespace from both sides of strings in a column.
This function removes all whitespace characters (spaces, tabs, newlines) from
both the beginning and end of each string in the column.
Args:
column: The input string ... |
function | btrim | fenic.api.functions.text.btrim | Remove specified characters from both sides of strings in a column.
This function removes all occurrences of the specified characters from
both the beginning and end of each string in the column.
If trim is a column expression, the characters to remove are determined dynamically
from the values in that column.
Args:
... | site-packages/fenic/api/functions/text.py | true | false | 827 | 864 | null | Column | [
"col",
"trim"
] | null | null | null | Type: function
Member Name: btrim
Qualified Name: fenic.api.functions.text.btrim
Docstring: Remove specified characters from both sides of strings in a column.
This function removes all occurrences of the specified characters from
both the beginning and end of each string in the column.
If trim is a column expression,... |
function | ltrim | fenic.api.functions.text.ltrim | Remove whitespace from the start of strings in a column.
This function removes all whitespace characters (spaces, tabs, newlines) from
the beginning of each string in the column.
Args:
col: The input string column or column name to trim
Returns:
Column: A column containing the left-trimmed strings
Example: ... | site-packages/fenic/api/functions/text.py | true | false | 867 | 888 | null | Column | [
"col"
] | null | null | null | Type: function
Member Name: ltrim
Qualified Name: fenic.api.functions.text.ltrim
Docstring: Remove whitespace from the start of strings in a column.
This function removes all whitespace characters (spaces, tabs, newlines) from
the beginning of each string in the column.
Args:
col: The input string column or colum... |
function | rtrim | fenic.api.functions.text.rtrim | Remove whitespace from the end of strings in a column.
This function removes all whitespace characters (spaces, tabs, newlines) from
the end of each string in the column.
Args:
col: The input string column or column name to trim
Returns:
Column: A column containing the right-trimmed strings
Example: Remove ... | site-packages/fenic/api/functions/text.py | true | false | 891 | 912 | null | Column | [
"col"
] | null | null | null | Type: function
Member Name: rtrim
Qualified Name: fenic.api.functions.text.rtrim
Docstring: Remove whitespace from the end of strings in a column.
This function removes all whitespace characters (spaces, tabs, newlines) from
the end of each string in the column.
Args:
col: The input string column or column name t... |
function | length | fenic.api.functions.text.length | Calculate the character length of each string in the column.
Args:
column: The input string column to calculate lengths for
Returns:
Column: A column containing the length of each string in characters
Example: Get string lengths
```python
# Get the length of each string in the name column
df.sele... | site-packages/fenic/api/functions/text.py | true | false | 915 | 933 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: length
Qualified Name: fenic.api.functions.text.length
Docstring: Calculate the character length of each string in the column.
Args:
column: The input string column to calculate lengths for
Returns:
Column: A column containing the length of each string in characters
Example: Get s... |
function | byte_length | fenic.api.functions.text.byte_length | Calculate the byte length of each string in the column.
Args:
column: The input string column to calculate byte lengths for
Returns:
Column: A column containing the byte length of each string
Example: Get byte lengths
```python
# Get the byte length of each string in the name column
df.select(tex... | site-packages/fenic/api/functions/text.py | true | false | 936 | 954 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: byte_length
Qualified Name: fenic.api.functions.text.byte_length
Docstring: Calculate the byte length of each string in the column.
Args:
column: The input string column to calculate byte lengths for
Returns:
Column: A column containing the byte length of each string
Example: Get ... |
function | jinja | fenic.api.functions.text.jinja | Render a Jinja template using values from the specified columns.
This function evaluates a Jinja2 template string for each row, using the provided
columns as template variables. Only a subset of Jinja2 features is supported.
Args:
jinja_template: A Jinja2 template string to render for each row.
... | site-packages/fenic/api/functions/text.py | true | false | 957 | 1,058 | null | Column | [
"jinja_template",
"strict",
"columns"
] | null | null | null | Type: function
Member Name: jinja
Qualified Name: fenic.api.functions.text.jinja
Docstring: Render a Jinja template using values from the specified columns.
This function evaluates a Jinja2 template string for each row, using the provided
columns as template variables. Only a subset of Jinja2 features is supported.
A... |
function | compute_fuzzy_ratio | fenic.api.functions.text.compute_fuzzy_ratio | Compute the similarity between two strings using a fuzzy string matching algorithm.
This function computes a fuzzy similarity score between two string columns (or a string column
and a literal string) for each row. It supports multiple well-known string similarity metrics,
including Levenshtein, Damerau-Levenshtein, J... | site-packages/fenic/api/functions/text.py | true | false | 1,060 | 1,107 | null | Column | [
"column",
"other",
"method"
] | null | null | null | Type: function
Member Name: compute_fuzzy_ratio
Qualified Name: fenic.api.functions.text.compute_fuzzy_ratio
Docstring: Compute the similarity between two strings using a fuzzy string matching algorithm.
This function computes a fuzzy similarity score between two string columns (or a string column
and a literal string... |
function | compute_fuzzy_token_sort_ratio | fenic.api.functions.text.compute_fuzzy_token_sort_ratio | Compute fuzzy similarity after sorting tokens in each string.
Tokenizes strings by whitespace, sorts tokens alphabetically, concatenates
them back into a string, then applies the specified similarity metric.
Useful for comparing strings where word order doesn't matter.
Based on https://rapidfuzz.github.io/RapidFuzz/U... | site-packages/fenic/api/functions/text.py | true | false | 1,109 | 1,140 | null | Column | [
"column",
"other",
"method"
] | null | null | null | Type: function
Member Name: compute_fuzzy_token_sort_ratio
Qualified Name: fenic.api.functions.text.compute_fuzzy_token_sort_ratio
Docstring: Compute fuzzy similarity after sorting tokens in each string.
Tokenizes strings by whitespace, sorts tokens alphabetically, concatenates
them back into a string, then applies th... |
function | compute_fuzzy_token_set_ratio | fenic.api.functions.text.compute_fuzzy_token_set_ratio | Compute fuzzy similarity using token set comparison.
Tokenizes strings by whitespace, creates sets of unique tokens, then
compares three combinations: diff1 vs diff2, intersection vs left set,
and intersection vs right set. Returns the maximum similarity score.
Useful for comparing strings where both word order and du... | site-packages/fenic/api/functions/text.py | true | false | 1,142 | 1,179 | null | Column | [
"column",
"other",
"method"
] | null | null | null | Type: function
Member Name: compute_fuzzy_token_set_ratio
Qualified Name: fenic.api.functions.text.compute_fuzzy_token_set_ratio
Docstring: Compute fuzzy similarity using token set comparison.
Tokenizes strings by whitespace, creates sets of unique tokens, then
compares three combinations: diff1 vs diff2, intersection... |
module | builtin | fenic.api.functions.builtin | Built-in functions for Fenic DataFrames. | site-packages/fenic/api/functions/builtin.py | true | false | null | null | null | null | null | null | null | null | Type: module
Member Name: builtin
Qualified Name: fenic.api.functions.builtin
Docstring: Built-in functions for Fenic DataFrames.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
function | sum | fenic.api.functions.builtin.sum | Aggregate function: returns the sum of all values in the specified column.
Args:
column: Column or column name to compute the sum of
Returns:
A Column expression representing the sum aggregation
Raises:
TypeError: If column is not a Column or string | site-packages/fenic/api/functions/builtin.py | true | false | 38 | 53 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: sum
Qualified Name: fenic.api.functions.builtin.sum
Docstring: Aggregate function: returns the sum of all values in the specified column.
Args:
column: Column or column name to compute the sum of
Returns:
A Column expression representing the sum aggregation
Raises:
TypeError: ... |
function | avg | fenic.api.functions.builtin.avg | Aggregate function: returns the average (mean) of all values in the specified column. Applies to numeric and embedding types.
Args:
column: Column or column name to compute the average of
Returns:
A Column expression representing the average aggregation
Raises:
TypeError: If column is not a Column or str... | site-packages/fenic/api/functions/builtin.py | true | false | 56 | 71 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: avg
Qualified Name: fenic.api.functions.builtin.avg
Docstring: Aggregate function: returns the average (mean) of all values in the specified column. Applies to numeric and embedding types.
Args:
column: Column or column name to compute the average of
Returns:
A Column expression re... |
function | mean | fenic.api.functions.builtin.mean | Aggregate function: returns the mean (average) of all values in the specified column.
Alias for avg().
Args:
column: Column or column name to compute the mean of
Returns:
A Column expression representing the mean aggregation
Raises:
TypeError: If column is not a Column or string | site-packages/fenic/api/functions/builtin.py | true | false | 74 | 91 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: mean
Qualified Name: fenic.api.functions.builtin.mean
Docstring: Aggregate function: returns the mean (average) of all values in the specified column.
Alias for avg().
Args:
column: Column or column name to compute the mean of
Returns:
A Column expression representing the mean agg... |
function | min | fenic.api.functions.builtin.min | Aggregate function: returns the minimum value in the specified column.
Args:
column: Column or column name to compute the minimum of
Returns:
A Column expression representing the minimum aggregation
Raises:
TypeError: If column is not a Column or string | site-packages/fenic/api/functions/builtin.py | true | false | 94 | 109 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: min
Qualified Name: fenic.api.functions.builtin.min
Docstring: Aggregate function: returns the minimum value in the specified column.
Args:
column: Column or column name to compute the minimum of
Returns:
A Column expression representing the minimum aggregation
Raises:
TypeErr... |
function | max | fenic.api.functions.builtin.max | Aggregate function: returns the maximum value in the specified column.
Args:
column: Column or column name to compute the maximum of
Returns:
A Column expression representing the maximum aggregation
Raises:
TypeError: If column is not a Column or string | site-packages/fenic/api/functions/builtin.py | true | false | 112 | 127 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: max
Qualified Name: fenic.api.functions.builtin.max
Docstring: Aggregate function: returns the maximum value in the specified column.
Args:
column: Column or column name to compute the maximum of
Returns:
A Column expression representing the maximum aggregation
Raises:
TypeErr... |
function | count | fenic.api.functions.builtin.count | Aggregate function: returns the count of non-null values in the specified column.
Args:
column: Column or column name to count values in
Returns:
A Column expression representing the count aggregation
Raises:
TypeError: If column is not a Column or string | site-packages/fenic/api/functions/builtin.py | true | false | 130 | 147 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: count
Qualified Name: fenic.api.functions.builtin.count
Docstring: Aggregate function: returns the count of non-null values in the specified column.
Args:
column: Column or column name to count values in
Returns:
A Column expression representing the count aggregation
Raises:
T... |
function | collect_list | fenic.api.functions.builtin.collect_list | Aggregate function: collects all values from the specified column into a list.
Args:
column: Column or column name to collect values from
Returns:
A Column expression representing the list aggregation
Raises:
TypeError: If column is not a Column or string | site-packages/fenic/api/functions/builtin.py | true | false | 150 | 165 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: collect_list
Qualified Name: fenic.api.functions.builtin.collect_list
Docstring: Aggregate function: collects all values from the specified column into a list.
Args:
column: Column or column name to collect values from
Returns:
A Column expression representing the list aggregation
... |
function | array_agg | fenic.api.functions.builtin.array_agg | Alias for collect_list(). | site-packages/fenic/api/functions/builtin.py | true | false | 167 | 170 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: array_agg
Qualified Name: fenic.api.functions.builtin.array_agg
Docstring: Alias for collect_list().
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: ["column"]
Returns: Column
Parent Class: none |
function | first | fenic.api.functions.builtin.first | Aggregate function: returns the first non-null value in the specified column.
Typically used in aggregations to select the first observed value per group.
Args:
column: Column or column name.
Returns:
Column expression for the first value. | site-packages/fenic/api/functions/builtin.py | true | false | 172 | 186 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: first
Qualified Name: fenic.api.functions.builtin.first
Docstring: Aggregate function: returns the first non-null value in the specified column.
Typically used in aggregations to select the first observed value per group.
Args:
column: Column or column name.
Returns:
Column expres... |
function | stddev | fenic.api.functions.builtin.stddev | Aggregate function: returns the sample standard deviation of the specified column.
Args:
column: Column or column name.
Returns:
Column expression for sample standard deviation. | site-packages/fenic/api/functions/builtin.py | true | false | 188 | 200 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: stddev
Qualified Name: fenic.api.functions.builtin.stddev
Docstring: Aggregate function: returns the sample standard deviation of the specified column.
Args:
column: Column or column name.
Returns:
Column expression for sample standard deviation.
Value: none
Annotation: none
is Pub... |
function | struct | fenic.api.functions.builtin.struct | Creates a new struct column from multiple input columns.
Args:
*args: Columns or column names to combine into a struct. Can be:
- Individual arguments
- Lists of columns/column names
- Tuples of columns/column names
Returns:
A Column expression representing a struct containing the inp... | site-packages/fenic/api/functions/builtin.py | true | false | 202 | 231 | null | Column | [
"args"
] | null | null | null | Type: function
Member Name: struct
Qualified Name: fenic.api.functions.builtin.struct
Docstring: Creates a new struct column from multiple input columns.
Args:
*args: Columns or column names to combine into a struct. Can be:
- Individual arguments
- Lists of columns/column names
- Tuples o... |
function | array | fenic.api.functions.builtin.array | Creates a new array column from multiple input columns.
Args:
*args: Columns or column names to combine into an array. Can be:
- Individual arguments
- Lists of columns/column names
- Tuples of columns/column names
Returns:
A Column expression representing an array containing values f... | site-packages/fenic/api/functions/builtin.py | true | false | 234 | 263 | null | Column | [
"args"
] | null | null | null | Type: function
Member Name: array
Qualified Name: fenic.api.functions.builtin.array
Docstring: Creates a new array column from multiple input columns.
Args:
*args: Columns or column names to combine into an array. Can be:
- Individual arguments
- Lists of columns/column names
- Tuples of c... |
function | udf | fenic.api.functions.builtin.udf | A decorator or function for creating user-defined functions (UDFs) that can be applied to DataFrame rows.
Warning:
UDFs cannot be serialized and are not supported in cloud execution.
User-defined functions contain arbitrary Python code that cannot be transmitted
to remote workers. For cloud compatibility, ... | site-packages/fenic/api/functions/builtin.py | true | false | 266 | 321 | null | null | [
"f",
"return_type"
] | null | null | null | Type: function
Member Name: udf
Qualified Name: fenic.api.functions.builtin.udf
Docstring: A decorator or function for creating user-defined functions (UDFs) that can be applied to DataFrame rows.
Warning:
UDFs cannot be serialized and are not supported in cloud execution.
User-defined functions contain arbitr... |
function | async_udf | fenic.api.functions.builtin.async_udf | A decorator for creating async user-defined functions (UDFs) with configurable concurrency and retries.
Async UDFs allow IO-bound operations (API calls, database queries, MCP tool calls)
to be executed concurrently while maintaining DataFrame semantics.
Args:
f: Async function to convert to UDF
return_type: E... | site-packages/fenic/api/functions/builtin.py | true | false | 323 | 419 | null | null | [
"f",
"return_type",
"max_concurrency",
"timeout_seconds",
"num_retries"
] | null | null | null | Type: function
Member Name: async_udf
Qualified Name: fenic.api.functions.builtin.async_udf
Docstring: A decorator for creating async user-defined functions (UDFs) with configurable concurrency and retries.
Async UDFs allow IO-bound operations (API calls, database queries, MCP tool calls)
to be executed concurrently w... |
function | asc | fenic.api.functions.builtin.asc | Mark this column for ascending sort order with nulls first.
Args:
column: The column to apply the ascending ordering to.
Returns:
A sort expression with ascending order and nulls first. | site-packages/fenic/api/functions/builtin.py | true | false | 422 | 432 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: asc
Qualified Name: fenic.api.functions.builtin.asc
Docstring: Mark this column for ascending sort order with nulls first.
Args:
column: The column to apply the ascending ordering to.
Returns:
A sort expression with ascending order and nulls first.
Value: none
Annotation: none
is P... |
function | asc_nulls_first | fenic.api.functions.builtin.asc_nulls_first | Alias for asc().
Args:
column: The column to apply the ascending ordering to.
Returns:
A sort expression with ascending order and nulls first. | site-packages/fenic/api/functions/builtin.py | true | false | 435 | 445 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: asc_nulls_first
Qualified Name: fenic.api.functions.builtin.asc_nulls_first
Docstring: Alias for asc().
Args:
column: The column to apply the ascending ordering to.
Returns:
A sort expression with ascending order and nulls first.
Value: none
Annotation: none
is Public? : true
is Pr... |
function | asc_nulls_last | fenic.api.functions.builtin.asc_nulls_last | Mark this column for ascending sort order with nulls last.
Args:
column: The column to apply the ascending ordering to.
Returns:
A Column expression representing the column and the ascending sort order with nulls last. | site-packages/fenic/api/functions/builtin.py | true | false | 448 | 458 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: asc_nulls_last
Qualified Name: fenic.api.functions.builtin.asc_nulls_last
Docstring: Mark this column for ascending sort order with nulls last.
Args:
column: The column to apply the ascending ordering to.
Returns:
A Column expression representing the column and the ascending sort o... |
function | desc | fenic.api.functions.builtin.desc | Mark this column for descending sort order with nulls first.
Args:
column: The column to apply the descending ordering to.
Returns:
A sort expression with descending order and nulls first. | site-packages/fenic/api/functions/builtin.py | true | false | 461 | 471 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: desc
Qualified Name: fenic.api.functions.builtin.desc
Docstring: Mark this column for descending sort order with nulls first.
Args:
column: The column to apply the descending ordering to.
Returns:
A sort expression with descending order and nulls first.
Value: none
Annotation: none... |
function | desc_nulls_first | fenic.api.functions.builtin.desc_nulls_first | Alias for desc().
Args:
column: The column to apply the descending ordering to.
Returns:
A sort expression with descending order and nulls first. | site-packages/fenic/api/functions/builtin.py | true | false | 474 | 484 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: desc_nulls_first
Qualified Name: fenic.api.functions.builtin.desc_nulls_first
Docstring: Alias for desc().
Args:
column: The column to apply the descending ordering to.
Returns:
A sort expression with descending order and nulls first.
Value: none
Annotation: none
is Public? : true
... |
function | desc_nulls_last | fenic.api.functions.builtin.desc_nulls_last | Mark this column for descending sort order with nulls last.
Args:
column: The column to apply the descending ordering to.
Returns:
A sort expression with descending order and nulls last. | site-packages/fenic/api/functions/builtin.py | true | false | 487 | 497 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: desc_nulls_last
Qualified Name: fenic.api.functions.builtin.desc_nulls_last
Docstring: Mark this column for descending sort order with nulls last.
Args:
column: The column to apply the descending ordering to.
Returns:
A sort expression with descending order and nulls last.
Value: n... |
function | array_size | fenic.api.functions.builtin.array_size | Returns the number of elements in an array column.
This function computes the length of arrays stored in the specified column.
Returns None for None arrays.
Args:
column: Column or column name containing arrays whose length to compute.
Returns:
A Column expression representing the array length.
Raises:
... | site-packages/fenic/api/functions/builtin.py | true | false | 500 | 527 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: array_size
Qualified Name: fenic.api.functions.builtin.array_size
Docstring: Returns the number of elements in an array column.
This function computes the length of arrays stored in the specified column.
Returns None for None arrays.
Args:
column: Column or column name containing array... |
function | array_contains | fenic.api.functions.builtin.array_contains | Checks if array column contains a specific value.
This function returns True if the array in the specified column contains the given value,
and False otherwise. Returns False if the array is None.
Args:
column: Column or column name containing the arrays to check.
value: Value to search for in the arrays. Ca... | site-packages/fenic/api/functions/builtin.py | true | false | 530 | 571 | null | Column | [
"column",
"value"
] | null | null | null | Type: function
Member Name: array_contains
Qualified Name: fenic.api.functions.builtin.array_contains
Docstring: Checks if array column contains a specific value.
This function returns True if the array in the specified column contains the given value,
and False otherwise. Returns False if the array is None.
Args:
... |
function | when | fenic.api.functions.builtin.when | Evaluates a condition and returns a value if true.
This function is used to create conditional expressions. If Column.otherwise() is not invoked,
None is returned for unmatched conditions.
Args:
condition: A boolean Column expression to evaluate.
value: A Column expression to return if the condition is true.... | site-packages/fenic/api/functions/builtin.py | true | false | 574 | 604 | null | Column | [
"condition",
"value"
] | null | null | null | Type: function
Member Name: when
Qualified Name: fenic.api.functions.builtin.when
Docstring: Evaluates a condition and returns a value if true.
This function is used to create conditional expressions. If Column.otherwise() is not invoked,
None is returned for unmatched conditions.
Args:
condition: A boolean Colum... |
function | coalesce | fenic.api.functions.builtin.coalesce | Returns the first non-null value from the given columns for each row.
This function mimics the behavior of SQL's COALESCE function. It evaluates the input columns
in order and returns the first non-null value encountered. If all values are null, returns null.
Args:
*cols: Column expressions or column names to eva... | site-packages/fenic/api/functions/builtin.py | true | false | 607 | 635 | null | Column | [
"cols"
] | null | null | null | Type: function
Member Name: coalesce
Qualified Name: fenic.api.functions.builtin.coalesce
Docstring: Returns the first non-null value from the given columns for each row.
This function mimics the behavior of SQL's COALESCE function. It evaluates the input columns
in order and returns the first non-null value encounter... |
function | greatest | fenic.api.functions.builtin.greatest | Returns the greatest value from the given columns for each row.
This function mimics the behavior of SQL's GREATEST function. It evaluates the input columns
in order and returns the greatest value encountered. If all values are null, returns null.
All arguments must be of the same primitive type (e.g., StringType, Bo... | site-packages/fenic/api/functions/builtin.py | true | false | 637 | 667 | null | Column | [
"cols"
] | null | null | null | Type: function
Member Name: greatest
Qualified Name: fenic.api.functions.builtin.greatest
Docstring: Returns the greatest value from the given columns for each row.
This function mimics the behavior of SQL's GREATEST function. It evaluates the input columns
in order and returns the greatest value encountered. If all v... |
function | least | fenic.api.functions.builtin.least | Returns the least value from the given columns for each row.
This function mimics the behavior of SQL's LEAST function. It evaluates the input columns
in order and returns the least value encountered. If all values are null, returns null.
All arguments must be of the same primitive type (e.g., StringType, BooleanType... | site-packages/fenic/api/functions/builtin.py | true | false | 670 | 700 | null | Column | [
"cols"
] | null | null | null | Type: function
Member Name: least
Qualified Name: fenic.api.functions.builtin.least
Docstring: Returns the least value from the given columns for each row.
This function mimics the behavior of SQL's LEAST function. It evaluates the input columns
in order and returns the least value encountered. If all values are null,... |
module | json | fenic.api.functions.json | JSON functions. | site-packages/fenic/api/functions/json.py | true | false | null | null | null | null | null | null | null | null | Type: module
Member Name: json
Qualified Name: fenic.api.functions.json
Docstring: JSON functions.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
function | jq | fenic.api.functions.json.jq | Applies a JQ query to a column containing JSON-formatted strings.
Args:
column (ColumnOrName): Input column of type `JsonType`.
query (str): A [JQ](https://jqlang.org/) expression used to extract or transform values.
Returns:
Column: A column containing the result of applying the JQ query to each row's JS... | site-packages/fenic/api/functions/json.py | true | false | 12 | 46 | null | Column | [
"column",
"query"
] | null | null | null | Type: function
Member Name: jq
Qualified Name: fenic.api.functions.json.jq
Docstring: Applies a JQ query to a column containing JSON-formatted strings.
Args:
column (ColumnOrName): Input column of type `JsonType`.
query (str): A [JQ](https://jqlang.org/) expression used to extract or transform values.
Returns... |
function | get_type | fenic.api.functions.json.get_type | Get the JSON type of each value.
Args:
column (ColumnOrName): Input column of type `JsonType`.
Returns:
Column: A column of strings indicating the JSON type
("string", "number", "boolean", "array", "object", "null").
Example: Get JSON types
```python
df.select(json.get_type(col("json_data... | site-packages/fenic/api/functions/json.py | true | false | 49 | 73 | null | Column | [
"column"
] | null | null | null | Type: function
Member Name: get_type
Qualified Name: fenic.api.functions.json.get_type
Docstring: Get the JSON type of each value.
Args:
column (ColumnOrName): Input column of type `JsonType`.
Returns:
Column: A column of strings indicating the JSON type
("string", "number", "boolean", "array", "o... |
function | contains | fenic.api.functions.json.contains | Check if a JSON value contains the specified value using recursive deep search.
Args:
column (ColumnOrName): Input column of type `JsonType`.
value (str): Valid JSON string to search for.
Returns:
Column: A column of booleans indicating whether the JSON contains the value.
Matching Rules:
- **Objects... | site-packages/fenic/api/functions/json.py | true | false | 76 | 127 | null | Column | [
"column",
"value"
] | null | null | null | Type: function
Member Name: contains
Qualified Name: fenic.api.functions.json.contains
Docstring: Check if a JSON value contains the specified value using recursive deep search.
Args:
column (ColumnOrName): Input column of type `JsonType`.
value (str): Valid JSON string to search for.
Returns:
Column: A c... |
module | session | fenic.api.session | Session module for managing query execution context and state. | site-packages/fenic/api/session/__init__.py | true | false | null | null | null | null | null | null | null | null | Type: module
Member Name: session
Qualified Name: fenic.api.session
Docstring: Session module for managing query execution context and state.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
attribute | __all__ | fenic.api.session.__all__ | null | site-packages/fenic/api/session/__init__.py | false | false | 20 | 35 | null | null | null | null | ['Session', 'SessionConfig', 'SemanticConfig', 'OpenAILanguageModel', 'OpenAIEmbeddingModel', 'AnthropicLanguageModel', 'GoogleDeveloperEmbeddingModel', 'GoogleDeveloperLanguageModel', 'GoogleVertexEmbeddingModel', 'GoogleVertexLanguageModel', 'ModelConfig', 'CloudConfig', 'CloudExecutorSize', 'CohereEmbeddingModel'] | null | Type: attribute
Member Name: __all__
Qualified Name: fenic.api.session.__all__
Docstring: none
Value: ['Session', 'SessionConfig', 'SemanticConfig', 'OpenAILanguageModel', 'OpenAIEmbeddingModel', 'AnthropicLanguageModel', 'GoogleDeveloperEmbeddingModel', 'GoogleDeveloperLanguageModel', 'GoogleVertexEmbeddingModel', 'Go... |
module | config | fenic.api.session.config | Session configuration classes for Fenic. | site-packages/fenic/api/session/config.py | true | false | null | null | null | null | null | null | null | null | Type: module
Member Name: config
Qualified Name: fenic.api.session.config
Docstring: Session configuration classes for Fenic.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
attribute | profiles_desc | fenic.api.session.config.profiles_desc | null | site-packages/fenic/api/session/config.py | true | false | 45 | 48 | null | null | null | null | '\n Allow the same model configuration to be used with different profiles, currently used to set thinking budget/reasoning effort\n for reasoning models. To use a profile of a given model alias in a semantic operator, reference the model as `ModelAlias(name="<model_alias>", profile="<profile_name>... | null | Type: attribute
Member Name: profiles_desc
Qualified Name: fenic.api.session.config.profiles_desc
Docstring: none
Value: '\n Allow the same model configuration to be used with different profiles, currently used to set thinking budget/reasoning effort\n for reasoning models. To use a profile of a g... |
attribute | default_profiles_desc | fenic.api.session.config.default_profiles_desc | null | site-packages/fenic/api/session/config.py | true | false | 50 | 52 | null | null | null | null | '\n If profiles are configured, which should be used by default?\n ' | null | Type: attribute
Member Name: default_profiles_desc
Qualified Name: fenic.api.session.config.default_profiles_desc
Docstring: none
Value: '\n If profiles are configured, which should be used by default?\n '
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Cla... |
attribute | GoogleEmbeddingTaskType | fenic.api.session.config.GoogleEmbeddingTaskType | null | site-packages/fenic/api/session/config.py | true | false | 54 | 63 | null | null | null | null | Literal['SEMANTIC_SIMILARITY', 'CLASSIFICATION', 'CLUSTERING', 'RETRIEVAL_DOCUMENT', 'RETRIEVAL_QUERY', 'CODE_RETRIEVAL_QUERY', 'QUESTION_ANSWERING', 'FACT_VERIFICATION'] | null | Type: attribute
Member Name: GoogleEmbeddingTaskType
Qualified Name: fenic.api.session.config.GoogleEmbeddingTaskType
Docstring: none
Value: Literal['SEMANTIC_SIMILARITY', 'CLASSIFICATION', 'CLUSTERING', 'RETRIEVAL_DOCUMENT', 'RETRIEVAL_QUERY', 'CODE_RETRIEVAL_QUERY', 'QUESTION_ANSWERING', 'FACT_VERIFICATION']
Annotati... |
class | GoogleDeveloperEmbeddingModel | fenic.api.session.config.GoogleDeveloperEmbeddingModel | Configuration for Google Developer embedding models.
This class defines the configuration settings for Google embedding models available in Google Developer AI Studio,
including model selection and rate limiting parameters. These models are accessible using a GOOGLE_API_KEY environment variable.
Attributes:
model... | site-packages/fenic/api/session/config.py | true | false | 65 | 138 | null | null | null | null | null | [
"BaseModel"
] | Type: class
Member Name: GoogleDeveloperEmbeddingModel
Qualified Name: fenic.api.session.config.GoogleDeveloperEmbeddingModel
Docstring: Configuration for Google Developer embedding models.
This class defines the configuration settings for Google embedding models available in Google Developer AI Studio,
including mode... |
class | GoogleDeveloperLanguageModel | fenic.api.session.config.GoogleDeveloperLanguageModel | Configuration for Gemini models accessible through Google Developer AI Studio.
This class defines the configuration settings for Google Gemini models available in Google Developer AI Studio,
including model selection and rate limiting parameters. These models are accessible using a GOOGLE_API_KEY environment variable.... | site-packages/fenic/api/session/config.py | true | false | 142 | 228 | null | null | null | null | null | [
"BaseModel"
] | Type: class
Member Name: GoogleDeveloperLanguageModel
Qualified Name: fenic.api.session.config.GoogleDeveloperLanguageModel
Docstring: Configuration for Gemini models accessible through Google Developer AI Studio.
This class defines the configuration settings for Google Gemini models available in Google Developer AI S... |
class | GoogleVertexEmbeddingModel | fenic.api.session.config.GoogleVertexEmbeddingModel | Configuration for Google Vertex AI embedding models.
This class defines the configuration settings for Google embedding models available in Google Vertex AI,
including model selection and rate limiting parameters. These models are accessible using Google Cloud credentials.
Attributes:
model_name: The name of the ... | site-packages/fenic/api/session/config.py | true | false | 230 | 304 | null | null | null | null | null | [
"BaseModel"
] | Type: class
Member Name: GoogleVertexEmbeddingModel
Qualified Name: fenic.api.session.config.GoogleVertexEmbeddingModel
Docstring: Configuration for Google Vertex AI embedding models.
This class defines the configuration settings for Google embedding models available in Google Vertex AI,
including model selection and ... |
class | GoogleVertexLanguageModel | fenic.api.session.config.GoogleVertexLanguageModel | Configuration for Google Vertex AI models.
This class defines the configuration settings for Google Gemini models available in Google Vertex AI,
including model selection and rate limiting parameters. These models are accessible using Google Cloud credentials.
Attributes:
model_name: The name of the Google Vertex... | site-packages/fenic/api/session/config.py | true | false | 306 | 392 | null | null | null | null | null | [
"BaseModel"
] | Type: class
Member Name: GoogleVertexLanguageModel
Qualified Name: fenic.api.session.config.GoogleVertexLanguageModel
Docstring: Configuration for Google Vertex AI models.
This class defines the configuration settings for Google Gemini models available in Google Vertex AI,
including model selection and rate limiting p... |
class | OpenAILanguageModel | fenic.api.session.config.OpenAILanguageModel | Configuration for OpenAI language models.
This class defines the configuration settings for OpenAI language models,
including model selection and rate limiting parameters.
Attributes:
model_name: The name of the OpenAI model to use.
rpm: Requests per minute limit; must be greater than 0.
tpm: Tokens per m... | site-packages/fenic/api/session/config.py | true | false | 394 | 502 | null | null | null | null | null | [
"BaseModel"
] | Type: class
Member Name: OpenAILanguageModel
Qualified Name: fenic.api.session.config.OpenAILanguageModel
Docstring: Configuration for OpenAI language models.
This class defines the configuration settings for OpenAI language models,
including model selection and rate limiting parameters.
Attributes:
model_name: T... |
class | OpenAIEmbeddingModel | fenic.api.session.config.OpenAIEmbeddingModel | Configuration for OpenAI embedding models.
This class defines the configuration settings for OpenAI embedding models,
including model selection and rate limiting parameters.
Attributes:
model_name: The name of the OpenAI embedding model to use.
rpm: Requests per minute limit; must be greater than 0.
tpm: ... | site-packages/fenic/api/session/config.py | true | false | 505 | 529 | null | null | null | null | null | [
"BaseModel"
] | Type: class
Member Name: OpenAIEmbeddingModel
Qualified Name: fenic.api.session.config.OpenAIEmbeddingModel
Docstring: Configuration for OpenAI embedding models.
This class defines the configuration settings for OpenAI embedding models,
including model selection and rate limiting parameters.
Attributes:
model_nam... |
class | AnthropicLanguageModel | fenic.api.session.config.AnthropicLanguageModel | Configuration for Anthropic language models.
This class defines the configuration settings for Anthropic language models,
including model selection and separate rate limiting parameters for input and output tokens.
Attributes:
model_name: The name of the Anthropic model to use.
rpm: Requests per minute limit;... | site-packages/fenic/api/session/config.py | true | false | 532 | 629 | null | null | null | null | null | [
"BaseModel"
] | Type: class
Member Name: AnthropicLanguageModel
Qualified Name: fenic.api.session.config.AnthropicLanguageModel
Docstring: Configuration for Anthropic language models.
This class defines the configuration settings for Anthropic language models,
including model selection and separate rate limiting parameters for input ... |
attribute | CohereEmbeddingTaskType | fenic.api.session.config.CohereEmbeddingTaskType | null | site-packages/fenic/api/session/config.py | true | false | 631 | 636 | null | null | null | null | Literal['search_document', 'search_query', 'classification', 'clustering'] | null | Type: attribute
Member Name: CohereEmbeddingTaskType
Qualified Name: fenic.api.session.config.CohereEmbeddingTaskType
Docstring: none
Value: Literal['search_document', 'search_query', 'classification', 'clustering']
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
class | CohereEmbeddingModel | fenic.api.session.config.CohereEmbeddingModel | Configuration for Cohere embedding models.
This class defines the configuration settings for Cohere embedding models,
including model selection and rate limiting parameters.
Attributes:
model_name: The name of the Cohere model to use.
rpm: Requests per minute limit for the model.
tpm: Tokens per minute li... | site-packages/fenic/api/session/config.py | true | false | 638 | 707 | null | null | null | null | null | [
"BaseModel"
] | Type: class
Member Name: CohereEmbeddingModel
Qualified Name: fenic.api.session.config.CohereEmbeddingModel
Docstring: Configuration for Cohere embedding models.
This class defines the configuration settings for Cohere embedding models,
including model selection and rate limiting parameters.
Attributes:
model_nam... |
attribute | EmbeddingModel | fenic.api.session.config.EmbeddingModel | null | site-packages/fenic/api/session/config.py | true | false | 709 | 709 | null | null | null | null | Union[OpenAIEmbeddingModel, GoogleVertexEmbeddingModel, GoogleDeveloperEmbeddingModel, CohereEmbeddingModel] | null | Type: attribute
Member Name: EmbeddingModel
Qualified Name: fenic.api.session.config.EmbeddingModel
Docstring: none
Value: Union[OpenAIEmbeddingModel, GoogleVertexEmbeddingModel, GoogleDeveloperEmbeddingModel, CohereEmbeddingModel]
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Pa... |
attribute | LanguageModel | fenic.api.session.config.LanguageModel | null | site-packages/fenic/api/session/config.py | true | false | 710 | 710 | null | null | null | null | Union[OpenAILanguageModel, AnthropicLanguageModel, GoogleDeveloperLanguageModel, GoogleVertexLanguageModel] | null | Type: attribute
Member Name: LanguageModel
Qualified Name: fenic.api.session.config.LanguageModel
Docstring: none
Value: Union[OpenAILanguageModel, AnthropicLanguageModel, GoogleDeveloperLanguageModel, GoogleVertexLanguageModel]
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Paren... |
attribute | ModelConfig | fenic.api.session.config.ModelConfig | null | site-packages/fenic/api/session/config.py | true | false | 711 | 711 | null | null | null | null | Union[EmbeddingModel, LanguageModel] | null | Type: attribute
Member Name: ModelConfig
Qualified Name: fenic.api.session.config.ModelConfig
Docstring: none
Value: Union[EmbeddingModel, LanguageModel]
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
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