type stringclasses 5
values | name stringlengths 1 55 | qualified_name stringlengths 5 130 | docstring stringlengths 15 3.11k ⌀ | filepath stringclasses 90
values | is_public bool 2
classes | is_private bool 2
classes | line_start int64 0 1.44k ⌀ | line_end int64 0 1.51k ⌀ | annotation stringclasses 2
values | returns stringclasses 82
values | value stringclasses 66
values | parameters listlengths 0 10 ⌀ | bases listlengths 0 2 ⌀ | parent_class stringclasses 193
values | api_element_summary stringlengths 199 3.43k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
function | extract | fenic.api.functions.semantic.extract | Extracts structured information from unstructured text using a provided 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.
Args:
colum... | null | true | false | 90 | 168 | null | Column | null | [
"column",
"schema",
"max_output_tokens",
"temperature",
"model_alias"
] | null | null | Type: function
Member Name: extract
Qualified Name: fenic.api.functions.semantic.extract
Docstring: Extracts structured information from unstructured text using a provided schema.
This function applies an instruction-driven extraction process to text columns, returning
structured data based on the fields and descripti... |
function | predicate | fenic.api.functions.semantic.predicate | Applies a natural language predicate to one or more string columns, returning a boolean result.
This is useful for filtering rows based on user-defined criteria expressed in natural language.
Args:
instruction: A string containing the semantic.predicate prompt.
The instruction must include placeholders in... | null | true | false | 171 | 229 | null | Column | null | [
"instruction",
"examples",
"model_alias",
"temperature"
] | null | null | Type: function
Member Name: predicate
Qualified Name: fenic.api.functions.semantic.predicate
Docstring: Applies a natural language predicate to one or more string columns, returning a boolean result.
This is useful for filtering rows based on user-defined criteria expressed in natural language.
Args:
instruction:... |
function | reduce | fenic.api.functions.semantic.reduce | Aggregate function: reduces a set of strings across columns into a single string using a natural language instruction.
Args:
instruction: A string containing the semantic.reduce prompt.
The instruction can include placeholders in curly braces that reference column names.
These placeholders will be ... | null | true | false | 232 | 269 | null | Column | null | [
"instruction",
"model_alias",
"temperature",
"max_output_tokens"
] | null | null | Type: function
Member Name: reduce
Qualified Name: fenic.api.functions.semantic.reduce
Docstring: Aggregate function: reduces a set of strings across columns into a single string using a natural language instruction.
Args:
instruction: A string containing the semantic.reduce prompt.
The instruction can inc... |
function | classify | fenic.api.functions.semantic.classify | Classifies a string column into one of the provided labels.
This is useful for tagging incoming documents with predefined categories.
Args:
column: Column or column name containing text to classify.
labels: List of category strings or an Enum defining the categories to classify the text into.
examples: ... | null | true | false | 272 | 333 | null | Column | null | [
"column",
"labels",
"examples",
"model_alias",
"temperature"
] | null | null | Type: function
Member Name: classify
Qualified Name: fenic.api.functions.semantic.classify
Docstring: Classifies a string column into one of the provided labels.
This is useful for tagging incoming documents with predefined categories.
Args:
column: Column or column name containing text to classify.
labels: ... |
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.
... | null | true | false | 336 | 366 | null | Column | null | [
"column",
"model_alias",
"temperature"
] | 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... | null | true | false | 369 | 395 | null | Column | null | [
"column",
"model_alias"
] | 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.
... |
module | embedding | fenic.api.functions.embedding | Embedding functions. | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.0.0-y6d85svd/fenic/src/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... | null | true | false | 17 | 51 | null | Column | null | [
"column"
] | 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
... | null | true | false | 54 | 142 | null | Column | null | [
"column",
"other",
"metric"
] | 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. | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.0.0-y6d85svd/fenic/src/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 | null | true | false | 16 | 29 | null | Column | null | [
"col_name"
] | 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 | 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:
ValueError: If the type of the value cannot be inferred | null | true | false | 32 | 49 | null | Column | null | [
"value"
] | 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:
ValueError: If the type of the value ... |
module | markdown | fenic.api.functions.markdown | Markdown functions. | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.0.0-y6d85svd/fenic/src/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... | null | true | false | 16 | 54 | null | Column | null | [
"column"
] | 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... | null | true | false | 56 | 92 | null | Column | null | [
"column",
"language_filter"
] | 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... | null | true | false | 95 | 132 | null | Column | null | [
"column",
"max_level"
] | 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... | null | true | false | 135 | 212 | null | Column | null | [
"column",
"header_level"
] | 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. | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.0.0-y6d85svd/fenic/src/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 fields from text using a template pattern.
Args:
template: Template string with fields marked as ``${field_name:format}``
column: Input text column to extract from
Returns:
Column: A struct column containing the extracted fields
Example: Basic field extraction
```python
# Extract name an... | null | true | false | 34 | 69 | null | Column | null | [
"column",
"template"
] | null | null | Type: function
Member Name: extract
Qualified Name: fenic.api.functions.text.extract
Docstring: Extracts fields from text using a template pattern.
Args:
template: Template string with fields marked as ``${field_name:format}``
column: Input text column to extract from
Returns:
Column: A struct column cont... |
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... | null | true | false | 71 | 130 | null | Column | null | [
"column",
"chunk_size",
"chunk_overlap_percentage",
"chunking_character_set_custom_characters"
] | 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... | null | true | false | 133 | 192 | null | Column | null | [
"column",
"chunk_size",
"chunk_overlap_percentage",
"chunking_character_set_custom_characters"
] | 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 ... | null | true | false | 195 | 254 | null | Column | null | [
"column",
"chunk_size",
"chunk_overlap_percentage",
"chunking_character_set_custom_characters"
] | 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 ... | null | true | false | 257 | 289 | null | Column | null | [
"column",
"chunk_size",
"chunk_overlap_percentage"
] | 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... | null | true | false | 292 | 324 | null | Column | null | [
"column",
"chunk_size",
"chunk_overlap_percentage"
] | 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... | null | true | false | 327 | 359 | null | Column | null | [
"column",
"chunk_size",
"chunk_overlap_percentage"
] | 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... | null | true | false | 362 | 382 | null | Column | null | [
"column"
] | 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... | null | true | false | 385 | 414 | null | Column | null | [
"cols"
] | 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, 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.
Args:
... | null | true | false | 418 | 449 | null | Column | null | [
"column",
"format"
] | 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, generic) to a standardized structure
with fields: index, speaker, start_time, end... |
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... | null | true | false | 452 | 484 | null | Column | null | [
"separator",
"cols"
] | 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(... | null | true | false | 487 | 509 | null | Column | null | [
"column",
"delimiter"
] | 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... | null | true | false | 512 | 551 | null | Column | null | [
"src",
"search",
"replace"
] | 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... | null | true | false | 554 | 605 | null | Column | null | [
"src",
"pattern",
"replacement"
] | 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... | null | true | false | 608 | 638 | null | Column | null | [
"src",
"pattern",
"limit"
] | 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... | null | true | false | 641 | 696 | null | Column | null | [
"src",
"delimiter",
"part_number"
] | 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... | null | true | false | 699 | 717 | null | Column | null | [
"column"
] | 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... | null | true | false | 720 | 738 | null | Column | null | [
"column"
] | 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... | null | true | false | 741 | 759 | null | Column | null | [
"column"
] | 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... | null | true | false | 762 | 783 | null | Column | null | [
"column"
] | 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:
... | null | true | false | 786 | 819 | null | Column | null | [
"col",
"trim"
] | 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: ... | null | true | false | 822 | 843 | null | Column | null | [
"col"
] | 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 ... | null | true | false | 846 | 867 | null | Column | null | [
"col"
] | 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... | null | true | false | 870 | 888 | null | Column | null | [
"column"
] | 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... | null | true | false | 891 | 909 | null | Column | null | [
"column"
] | 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 ... |
module | builtin | fenic.api.functions.builtin | Built-in functions for Fenic DataFrames. | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.0.0-y6d85svd/fenic/src/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 | null | true | false | 30 | 45 | null | Column | null | [
"column"
] | 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.
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 string | null | true | false | 48 | 63 | null | Column | null | [
"column"
] | 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.
Args:
column: Column or column name to compute the average of
Returns:
A Column expression representing the average aggregation
Rais... |
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 | null | true | false | 66 | 83 | null | Column | null | [
"column"
] | 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 | null | true | false | 86 | 101 | null | Column | null | [
"column"
] | 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 | null | true | false | 104 | 119 | null | Column | null | [
"column"
] | 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 | null | true | false | 122 | 139 | null | Column | null | [
"column"
] | 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 | null | true | false | 142 | 157 | null | Column | null | [
"column"
] | 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(). | null | true | false | 160 | 163 | null | Column | null | [
"column"
] | 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 | 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... | null | true | false | 166 | 195 | null | Column | null | [
"args"
] | 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... | null | true | false | 198 | 227 | null | Column | null | [
"args"
] | 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.
When applied, UDFs will:
- Access `StructType` columns as Python dictionaries (`dict[str, Any]`).
- Access `ArrayType` columns as Python lists (`list[Any]`).
- Access primitive types (e.g., `int`, `float`, `str`) ... | null | true | false | 230 | 277 | null | null | null | [
"f",
"return_type"
] | 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.
When applied, UDFs will:
- Access `StructType` columns as Python dictionaries (`dict[str, Any]`).
- Access `ArrayType` col... |
function | asc | fenic.api.functions.builtin.asc | Creates a Column expression representing an ascending sort order.
Args:
column: The column to apply the ascending ordering to.
Returns:
A Column expression representing the column and the ascending sort order.
Raises:
ValueError: If the type of the column cannot be inferred.
Error: If this expression... | null | true | false | 280 | 294 | null | Column | null | [
"column"
] | null | null | Type: function
Member Name: asc
Qualified Name: fenic.api.functions.builtin.asc
Docstring: Creates a Column expression representing an ascending sort order.
Args:
column: The column to apply the ascending ordering to.
Returns:
A Column expression representing the column and the ascending sort order.
Raises:
... |
function | asc_nulls_first | fenic.api.functions.builtin.asc_nulls_first | Creates a Column expression representing an ascending sort order with nulls first.
Args:
column: The column to apply the ascending ordering to.
Returns:
A Column expression representing the column and the ascending sort order with nulls first.
Raises:
ValueError: If the type of the column cannot be infer... | null | true | false | 297 | 311 | null | Column | null | [
"column"
] | null | null | Type: function
Member Name: asc_nulls_first
Qualified Name: fenic.api.functions.builtin.asc_nulls_first
Docstring: Creates a Column expression representing an ascending sort order with nulls first.
Args:
column: The column to apply the ascending ordering to.
Returns:
A Column expression representing the colum... |
function | asc_nulls_last | fenic.api.functions.builtin.asc_nulls_last | Creates a Column expression representing an 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.
Raises:
ValueError: If the type of the column cannot be inferre... | null | true | false | 314 | 328 | null | Column | null | [
"column"
] | null | null | Type: function
Member Name: asc_nulls_last
Qualified Name: fenic.api.functions.builtin.asc_nulls_last
Docstring: Creates a Column expression representing an ascending sort order with nulls last.
Args:
column: The column to apply the ascending ordering to.
Returns:
A Column expression representing the column a... |
function | desc | fenic.api.functions.builtin.desc | Creates a Column expression representing a descending sort order.
Args:
column: The column to apply the descending ordering to.
Returns:
A Column expression representing the column and the descending sort order.
Raises:
ValueError: If the type of the column cannot be inferred.
Error: If this expressi... | null | true | false | 331 | 345 | null | Column | null | [
"column"
] | null | null | Type: function
Member Name: desc
Qualified Name: fenic.api.functions.builtin.desc
Docstring: Creates a Column expression representing a descending sort order.
Args:
column: The column to apply the descending ordering to.
Returns:
A Column expression representing the column and the descending sort order.
Rais... |
function | desc_nulls_first | fenic.api.functions.builtin.desc_nulls_first | Creates a Column expression representing a descending sort order with nulls first.
Args:
column: The column to apply the descending ordering to.
Returns:
A Column expression representing the column and the descending sort order with nulls first.
Raises:
ValueError: If the type of the column cannot be inf... | null | true | false | 348 | 362 | null | Column | null | [
"column"
] | null | null | Type: function
Member Name: desc_nulls_first
Qualified Name: fenic.api.functions.builtin.desc_nulls_first
Docstring: Creates a Column expression representing a descending sort order with nulls first.
Args:
column: The column to apply the descending ordering to.
Returns:
A Column expression representing the co... |
function | desc_nulls_last | fenic.api.functions.builtin.desc_nulls_last | Creates a Column expression representing a descending sort order with nulls last.
Args:
column: The column to apply the descending ordering to.
Returns:
A Column expression representing the column and the descending sort order with nulls last.
Raises:
ValueError: If the type of the column cannot be infer... | null | true | false | 365 | 379 | null | Column | null | [
"column"
] | null | null | Type: function
Member Name: desc_nulls_last
Qualified Name: fenic.api.functions.builtin.desc_nulls_last
Docstring: Creates a Column expression representing a descending sort order with nulls last.
Args:
column: The column to apply the descending ordering to.
Returns:
A Column expression representing the colum... |
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:
... | null | true | false | 382 | 409 | null | Column | null | [
"column"
] | 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... | null | true | false | 412 | 453 | null | Column | null | [
"column",
"value"
] | 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.... | null | true | false | 456 | 486 | null | Column | null | [
"condition",
"value"
] | 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... | null | true | false | 489 | 531 | null | Column | null | [
"cols"
] | 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... |
module | json | fenic.api.functions.json | JSON functions. | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.0.0-y6d85svd/fenic/src/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... | null | true | false | 12 | 46 | null | Column | null | [
"column",
"query"
] | 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... | null | true | false | 49 | 73 | null | Column | null | [
"column"
] | 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... | null | true | false | 76 | 127 | null | Column | null | [
"column",
"value"
] | 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. | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.0.0-y6d85svd/fenic/src/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 | null | false | false | 15 | 25 | null | null | ['Session', 'SessionConfig', 'SemanticConfig', 'OpenAIModelConfig', 'AnthropicModelConfig', 'GoogleGLAModelConfig', 'ModelConfig', 'CloudConfig', 'CloudExecutorSize'] | null | null | null | Type: attribute
Member Name: __all__
Qualified Name: fenic.api.session.__all__
Docstring: none
Value: ['Session', 'SessionConfig', 'SemanticConfig', 'OpenAIModelConfig', 'AnthropicModelConfig', 'GoogleGLAModelConfig', 'ModelConfig', 'CloudConfig', 'CloudExecutorSize']
Annotation: none
is Public? : false
is Private? : f... |
module | config | fenic.api.session.config | Session configuration classes for Fenic. | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.0.0-y6d85svd/fenic/src/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 |
class | GoogleGLAModelConfig | fenic.api.session.config.GoogleGLAModelConfig | Configuration for Google GenerativeLAnguage (GLA) models.
This class defines the configuration settings for models available in Google Developer AI Studio,
including model selection and rate limiting parameters. These models are accessible using a GEMINI_API_KEY environment variable. | null | true | false | 32 | 41 | null | null | null | null | [
"BaseModel"
] | null | Type: class
Member Name: GoogleGLAModelConfig
Qualified Name: fenic.api.session.config.GoogleGLAModelConfig
Docstring: Configuration for Google GenerativeLAnguage (GLA) models.
This class defines the configuration settings for models available in Google Developer AI Studio,
including model selection and rate limiting ... |
class | OpenAIModelConfig | fenic.api.session.config.OpenAIModelConfig | Configuration for OpenAI models.
This class defines the configuration settings for OpenAI language and embedding 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 ... | null | true | false | 43 | 70 | null | null | null | null | [
"BaseModel"
] | null | Type: class
Member Name: OpenAIModelConfig
Qualified Name: fenic.api.session.config.OpenAIModelConfig
Docstring: Configuration for OpenAI models.
This class defines the configuration settings for OpenAI language and embedding models,
including model selection and rate limiting parameters.
Attributes:
model_name: ... |
class | AnthropicModelConfig | fenic.api.session.config.AnthropicModelConfig | Configuration for Anthropic 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; must be ... | null | true | false | 73 | 100 | null | null | null | null | [
"BaseModel"
] | null | Type: class
Member Name: AnthropicModelConfig
Qualified Name: fenic.api.session.config.AnthropicModelConfig
Docstring: Configuration for Anthropic models.
This class defines the configuration settings for Anthropic language models,
including model selection and separate rate limiting parameters for input and output to... |
attribute | ModelConfig | fenic.api.session.config.ModelConfig | null | null | true | false | 103 | 103 | null | null | Union[OpenAIModelConfig, AnthropicModelConfig, GoogleGLAModelConfig] | null | null | null | Type: attribute
Member Name: ModelConfig
Qualified Name: fenic.api.session.config.ModelConfig
Docstring: none
Value: Union[OpenAIModelConfig, AnthropicModelConfig, GoogleGLAModelConfig]
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
class | SemanticConfig | fenic.api.session.config.SemanticConfig | Configuration for semantic language and embedding models.
This class defines the configuration for both language models and optional
embedding models used in semantic operations. It ensures that all configured
models are valid and supported by their respective providers.
Attributes:
language_models: Mapping of mo... | null | true | false | 106 | 214 | null | null | null | null | [
"BaseModel"
] | null | Type: class
Member Name: SemanticConfig
Qualified Name: fenic.api.session.config.SemanticConfig
Docstring: Configuration for semantic language and embedding models.
This class defines the configuration for both language models and optional
embedding models used in semantic operations. It ensures that all configured
mo... |
method | model_post_init | fenic.api.session.config.SemanticConfig.model_post_init | Post initialization hook to set defaults.
This hook runs after the model is initialized and validated.
It sets the default language and embedding models if they are not set
and there is only one model available. | null | true | false | 129 | 141 | null | None | null | [
"self",
"__context"
] | null | SemanticConfig | Type: method
Member Name: model_post_init
Qualified Name: fenic.api.session.config.SemanticConfig.model_post_init
Docstring: Post initialization hook to set defaults.
This hook runs after the model is initialized and validated.
It sets the default language and embedding models if they are not set
and there is only one... |
method | validate_models | fenic.api.session.config.SemanticConfig.validate_models | Validates that the selected models are supported by the system.
This validator checks that both the language model and embedding model (if provided)
are valid and supported by their respective providers.
Returns:
The validated SemanticConfig instance.
Raises:
ConfigurationError: If any of the models are not ... | null | true | false | 143 | 214 | null | SemanticConfig | null | [
"self"
] | null | SemanticConfig | Type: method
Member Name: validate_models
Qualified Name: fenic.api.session.config.SemanticConfig.validate_models
Docstring: Validates that the selected models are supported by the system.
This validator checks that both the language model and embedding model (if provided)
are valid and supported by their respective p... |
class | CloudExecutorSize | fenic.api.session.config.CloudExecutorSize | Enum defining available cloud executor sizes.
This enum represents the different size options available for cloud-based
execution environments.
Attributes:
SMALL: Small instance size.
MEDIUM: Medium instance size.
LARGE: Large instance size.
XLARGE: Extra large instance size. | null | true | false | 217 | 232 | null | null | null | null | [
"str",
"Enum"
] | null | Type: class
Member Name: CloudExecutorSize
Qualified Name: fenic.api.session.config.CloudExecutorSize
Docstring: Enum defining available cloud executor sizes.
This enum represents the different size options available for cloud-based
execution environments.
Attributes:
SMALL: Small instance size.
MEDIUM: Mediu... |
class | CloudConfig | fenic.api.session.config.CloudConfig | Configuration for cloud-based execution.
This class defines settings for running operations in a cloud environment,
allowing for scalable and distributed processing of language model operations.
Attributes:
size: Size of the cloud executor instance.
If None, the default size will be used. | null | true | false | 235 | 245 | null | null | null | null | [
"BaseModel"
] | null | Type: class
Member Name: CloudConfig
Qualified Name: fenic.api.session.config.CloudConfig
Docstring: Configuration for cloud-based execution.
This class defines settings for running operations in a cloud environment,
allowing for scalable and distributed processing of language model operations.
Attributes:
size: ... |
class | SessionConfig | fenic.api.session.config.SessionConfig | Configuration for a user session.
This class defines the complete configuration for a user session, including
application settings, model configurations, and optional cloud settings.
It serves as the central configuration object for all language model operations.
Attributes:
app_name: Name of the application usin... | null | true | false | 248 | 325 | null | null | null | null | [
"BaseModel"
] | null | Type: class
Member Name: SessionConfig
Qualified Name: fenic.api.session.config.SessionConfig
Docstring: Configuration for a user session.
This class defines the complete configuration for a user session, including
application settings, model configurations, and optional cloud settings.
It serves as the central config... |
method | _to_resolved_config | fenic.api.session.config.SessionConfig._to_resolved_config | null | null | false | true | 271 | 325 | null | ResolvedSessionConfig | null | [
"self"
] | null | SessionConfig | Type: method
Member Name: _to_resolved_config
Qualified Name: fenic.api.session.config.SessionConfig._to_resolved_config
Docstring: none
Value: none
Annotation: none
is Public? : false
is Private? : true
Parameters: ["self"]
Returns: ResolvedSessionConfig
Parent Class: SessionConfig |
module | session | fenic.api.session.session | Main session class for interacting with the DataFrame API. | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.0.0-y6d85svd/fenic/src/fenic/api/session/session.py | true | false | null | null | null | null | null | null | null | null | Type: module
Member Name: session
Qualified Name: fenic.api.session.session
Docstring: Main session class for interacting with the DataFrame API.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
class | Session | fenic.api.session.session.Session | The entry point to programming with the DataFrame API. Similar to PySpark's SparkSession.
Example: Create a session with default configuration
```python
session = Session.get_or_create(SessionConfig(app_name="my_app"))
```
Example: Create a session with cloud configuration
```python
config = Sessi... | null | true | false | 30 | 314 | null | null | null | null | [] | null | Type: class
Member Name: Session
Qualified Name: fenic.api.session.session.Session
Docstring: The entry point to programming with the DataFrame API. Similar to PySpark's SparkSession.
Example: Create a session with default configuration
```python
session = Session.get_or_create(SessionConfig(app_name="my_app")... |
method | __new__ | fenic.api.session.session.Session.__new__ | Create a new Session instance. | null | true | false | 53 | 59 | null | null | null | [
"cls"
] | null | Session | Type: method
Member Name: __new__
Qualified Name: fenic.api.session.session.Session.__new__
Docstring: Create a new Session instance.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: ["cls"]
Returns: none
Parent Class: Session |
method | get_or_create | fenic.api.session.session.Session.get_or_create | Gets an existing Session or creates a new one with the configured settings.
Returns:
A Session instance configured with the provided settings | null | true | false | 61 | 89 | null | Session | null | [
"cls",
"config"
] | null | Session | Type: method
Member Name: get_or_create
Qualified Name: fenic.api.session.session.Session.get_or_create
Docstring: Gets an existing Session or creates a new one with the configured settings.
Returns:
A Session instance configured with the provided settings
Value: none
Annotation: none
is Public? : true
is Private?... |
method | _create_local_session | fenic.api.session.session.Session._create_local_session | Get or create a local session. | null | false | true | 91 | 101 | null | Session | null | [
"cls",
"session_state"
] | null | Session | Type: method
Member Name: _create_local_session
Qualified Name: fenic.api.session.session.Session._create_local_session
Docstring: Get or create a local session.
Value: none
Annotation: none
is Public? : false
is Private? : true
Parameters: ["cls", "session_state"]
Returns: Session
Parent Class: Session |
method | _create_cloud_session | fenic.api.session.session.Session._create_cloud_session | Create a cloud session. | null | false | true | 103 | 113 | null | Session | null | [
"cls",
"session_state"
] | null | Session | Type: method
Member Name: _create_cloud_session
Qualified Name: fenic.api.session.session.Session._create_cloud_session
Docstring: Create a cloud session.
Value: none
Annotation: none
is Public? : false
is Private? : true
Parameters: ["cls", "session_state"]
Returns: Session
Parent Class: Session |
method | create_dataframe | fenic.api.session.session.Session.create_dataframe | Create a DataFrame from a variety of Python-native data formats.
Args:
data: Input data. Must be one of:
- Polars DataFrame
- Pandas DataFrame
- dict of column_name -> list of values
- list of dicts (each dict representing a row)
- pyarrow Table
Returns:
A new DataFrame... | null | true | false | 132 | 219 | null | DataFrame | null | [
"self",
"data"
] | null | Session | Type: method
Member Name: create_dataframe
Qualified Name: fenic.api.session.session.Session.create_dataframe
Docstring: Create a DataFrame from a variety of Python-native data formats.
Args:
data: Input data. Must be one of:
- Polars DataFrame
- Pandas DataFrame
- dict of column_name -> li... |
method | table | fenic.api.session.session.Session.table | Returns the specified table as a DataFrame.
Args:
table_name: Name of the table
Returns:
Table as a DataFrame
Raises:
ValueError: If the table does not exist
Example: Load an existing table
```python
df = session.table("my_table")
``` | null | true | false | 221 | 242 | null | DataFrame | null | [
"self",
"table_name"
] | null | Session | Type: method
Member Name: table
Qualified Name: fenic.api.session.session.Session.table
Docstring: Returns the specified table as a DataFrame.
Args:
table_name: Name of the table
Returns:
Table as a DataFrame
Raises:
ValueError: If the table does not exist
Example: Load an existing table
```python
... |
method | sql | fenic.api.session.session.Session.sql | Execute a read-only SQL query against one or more DataFrames using named placeholders.
This allows you to execute ad hoc SQL queries using familiar syntax when it's more convenient than the DataFrame API.
Placeholders in the SQL string (e.g. `{df}`) should correspond to keyword arguments (e.g. `df=my_dataframe`).
For... | null | true | false | 244 | 310 | null | DataFrame | null | [
"self",
"query",
"tables"
] | null | Session | Type: method
Member Name: sql
Qualified Name: fenic.api.session.session.Session.sql
Docstring: Execute a read-only SQL query against one or more DataFrames using named placeholders.
This allows you to execute ad hoc SQL queries using familiar syntax when it's more convenient than the DataFrame API.
Placeholders in the... |
method | stop | fenic.api.session.session.Session.stop | Stops the session and closes all connections. | null | true | false | 312 | 314 | null | null | null | [
"self"
] | null | Session | Type: method
Member Name: stop
Qualified Name: fenic.api.session.session.Session.stop
Docstring: Stops the session and closes all connections.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: ["self"]
Returns: none
Parent Class: Session |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.