type stringclasses 5
values | name stringlengths 1 55 | qualified_name stringlengths 5 147 | docstring stringlengths 15 4.52k ⌀ | filepath stringclasses 206
values | is_public bool 2
classes | is_private bool 2
classes | line_start int64 0 1.99k ⌀ | line_end int64 0 2.01k ⌀ | annotation stringclasses 14
values | returns stringclasses 308
values | value stringclasses 152
values | parameters listlengths 0 117 ⌀ | bases listlengths 0 3 ⌀ | parent_class stringclasses 376
values | api_element_summary stringlengths 199 33.8k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
function | validate_join_parameters | fenic.api.dataframe._join_utils.validate_join_parameters | Validate join parameter combinations. | null | true | false | 10 | 51 | null | None | null | [
"self",
"on",
"left_on",
"right_on",
"how"
] | null | null | Type: function
Member Name: validate_join_parameters
Qualified Name: fenic.api.dataframe._join_utils.validate_join_parameters
Docstring: Validate join parameter combinations.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: ["self", "on", "left_on", "right_on", "how"]
Returns: None
Parent ... |
function | build_join_conditions | fenic.api.dataframe._join_utils.build_join_conditions | Build left and right join condition lists. | null | true | false | 53 | 82 | null | Tuple[List, List] | null | [
"on",
"left_on",
"right_on"
] | null | null | Type: function
Member Name: build_join_conditions
Qualified Name: fenic.api.dataframe._join_utils.build_join_conditions
Docstring: Build left and right join condition lists.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: ["on", "left_on", "right_on"]
Returns: Tuple[List, List]
Parent Cla... |
function | _has_join_conditions | fenic.api.dataframe._join_utils._has_join_conditions | Check if any join conditions are specified. | null | false | true | 84 | 94 | null | bool | null | [
"on",
"left_on",
"right_on"
] | null | null | Type: function
Member Name: _has_join_conditions
Qualified Name: fenic.api.dataframe._join_utils._has_join_conditions
Docstring: Check if any join conditions are specified.
Value: none
Annotation: none
is Public? : false
is Private? : true
Parameters: ["on", "left_on", "right_on"]
Returns: bool
Parent Class: none |
function | _validate_join_condition_lengths | fenic.api.dataframe._join_utils._validate_join_condition_lengths | Validate that left_on and right_on have matching lengths. | null | false | true | 96 | 108 | null | None | null | [
"left_on",
"right_on"
] | null | null | Type: function
Member Name: _validate_join_condition_lengths
Qualified Name: fenic.api.dataframe._join_utils._validate_join_condition_lengths
Docstring: Validate that left_on and right_on have matching lengths.
Value: none
Annotation: none
is Public? : false
is Private? : true
Parameters: ["left_on", "right_on"]
Return... |
module | semantic_extensions | fenic.api.dataframe.semantic_extensions | Semantic extensions for DataFrames providing clustering and semantic join operations. | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.11.0-a2lcuovw/fenic/src/fenic/api/dataframe/semantic_extensions.py | true | false | null | null | null | null | null | null | null | null | Type: module
Member Name: semantic_extensions
Qualified Name: fenic.api.dataframe.semantic_extensions
Docstring: Semantic extensions for DataFrames providing clustering and semantic join operations.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
class | SemanticExtensions | fenic.api.dataframe.semantic_extensions.SemanticExtensions | A namespace for semantic dataframe operators. | null | true | false | 27 | 399 | null | null | null | null | [] | null | Type: class
Member Name: SemanticExtensions
Qualified Name: fenic.api.dataframe.semantic_extensions.SemanticExtensions
Docstring: A namespace for semantic dataframe operators.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
method | __init__ | fenic.api.dataframe.semantic_extensions.SemanticExtensions.__init__ | Initialize semantic extensions.
Args:
df: The DataFrame to extend with semantic operations. | null | true | false | 30 | 36 | null | null | null | [
"self",
"df"
] | null | SemanticExtensions | Type: method
Member Name: __init__
Qualified Name: fenic.api.dataframe.semantic_extensions.SemanticExtensions.__init__
Docstring: Initialize semantic extensions.
Args:
df: The DataFrame to extend with semantic operations.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: ["self", "df"]... |
method | with_cluster_labels | fenic.api.dataframe.semantic_extensions.SemanticExtensions.with_cluster_labels | Cluster rows using K-means and add cluster metadata columns.
This method clusters rows based on the given embedding column or expression using K-means.
It adds a new column with cluster assignments, and optionally includes the centroid embedding
for each assigned cluster.
Note:
Local execution requires the `clust... | null | true | false | 38 | 140 | null | DataFrame | null | [
"self",
"by",
"num_clusters",
"max_iter",
"num_init",
"label_column",
"centroid_column",
"request_timeout"
] | null | SemanticExtensions | Type: method
Member Name: with_cluster_labels
Qualified Name: fenic.api.dataframe.semantic_extensions.SemanticExtensions.with_cluster_labels
Docstring: Cluster rows using K-means and add cluster metadata columns.
This method clusters rows based on the given embedding column or expression using K-means.
It adds a new c... |
method | join | fenic.api.dataframe.semantic_extensions.SemanticExtensions.join | Performs a semantic join between two DataFrames using a natural language predicate.
This method evaluates a boolean predicate for each potential row pair between the two DataFrames,
including only those pairs where the predicate evaluates to True.
The join process:
1. For each row in the left DataFrame, evaluates the... | null | true | false | 142 | 267 | null | DataFrame | null | [
"self",
"other",
"predicate",
"left_on",
"right_on",
"strict",
"examples",
"model_alias",
"request_timeout"
] | null | SemanticExtensions | Type: method
Member Name: join
Qualified Name: fenic.api.dataframe.semantic_extensions.SemanticExtensions.join
Docstring: Performs a semantic join between two DataFrames using a natural language predicate.
This method evaluates a boolean predicate for each potential row pair between the two DataFrames,
including only ... |
method | sim_join | fenic.api.dataframe.semantic_extensions.SemanticExtensions.sim_join | Performs a semantic similarity join between two DataFrames using embedding expressions.
For each row in the left DataFrame, returns the top `k` most semantically similar rows
from the right DataFrame based on the specified similarity metric.
Note:
Local execution requires the `sim-join` extra: `pip install "fenic... | null | true | false | 269 | 396 | null | DataFrame | null | [
"self",
"other",
"left_on",
"right_on",
"k",
"similarity_metric",
"similarity_score_column",
"request_timeout"
] | null | SemanticExtensions | Type: method
Member Name: sim_join
Qualified Name: fenic.api.dataframe.semantic_extensions.SemanticExtensions.sim_join
Docstring: Performs a semantic similarity join between two DataFrames using embedding expressions.
For each row in the left DataFrame, returns the top `k` most semantically similar rows
from the right... |
module | io | fenic.api.io | IO module for reading and writing DataFrames to external storage. | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.11.0-a2lcuovw/fenic/src/fenic/api/io/__init__.py | true | false | null | null | null | null | null | null | null | null | Type: module
Member Name: io
Qualified Name: fenic.api.io
Docstring: IO module for reading and writing DataFrames to external storage.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
attribute | __all__ | fenic.api.io.__all__ | null | null | false | false | 6 | 6 | null | null | ['DataFrameReader', 'DataFrameWriter'] | null | null | null | Type: attribute
Member Name: __all__
Qualified Name: fenic.api.io.__all__
Docstring: none
Value: ['DataFrameReader', 'DataFrameWriter']
Annotation: none
is Public? : false
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
module | reader | fenic.api.io.reader | Reader interface for loading DataFrames from external storage systems. | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.11.0-a2lcuovw/fenic/src/fenic/api/io/reader.py | true | false | null | null | null | null | null | null | null | null | Type: module
Member Name: reader
Qualified Name: fenic.api.io.reader
Docstring: Reader interface for loading DataFrames from external storage systems.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
class | DataFrameReader | fenic.api.io.reader.DataFrameReader | Interface used to load a DataFrame from external storage systems.
Similar to PySpark's DataFrameReader.
Supported External Storage Schemes:
- Amazon S3 (s3://)
- Format: s3://{bucket_name}/{path_to_file}
- Notes:
- Uses boto3 to aquire AWS credentials.
- Examples:
- s3://my-bucket/data.c... | null | true | false | 22 | 380 | null | null | null | null | [] | null | Type: class
Member Name: DataFrameReader
Qualified Name: fenic.api.io.reader.DataFrameReader
Docstring: Interface used to load a DataFrame from external storage systems.
Similar to PySpark's DataFrameReader.
Supported External Storage Schemes:
- Amazon S3 (s3://)
- Format: s3://{bucket_name}/{path_to_file}
-... |
method | __init__ | fenic.api.io.reader.DataFrameReader.__init__ | Creates a DataFrameReader.
Args:
session_state: The session state to use for reading | null | true | false | 61 | 68 | null | null | null | [
"self",
"session_state"
] | null | DataFrameReader | Type: method
Member Name: __init__
Qualified Name: fenic.api.io.reader.DataFrameReader.__init__
Docstring: Creates a DataFrameReader.
Args:
session_state: The session state to use for reading
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: ["self", "session_state"]
Returns: none
Pare... |
method | csv | fenic.api.io.reader.DataFrameReader.csv | Load a DataFrame from one or more CSV files.
Args:
paths: A single file path, a glob pattern (e.g., "data/*.csv"), or a list of paths.
schema: (optional) A complete schema definition of column names and their types. Only primitive types are supported.
- For e.g.:
- Schema([ColumnField(name=... | null | true | false | 70 | 152 | null | DataFrame | null | [
"self",
"paths",
"schema",
"merge_schemas"
] | null | DataFrameReader | Type: method
Member Name: csv
Qualified Name: fenic.api.io.reader.DataFrameReader.csv
Docstring: Load a DataFrame from one or more CSV files.
Args:
paths: A single file path, a glob pattern (e.g., "data/*.csv"), or a list of paths.
schema: (optional) A complete schema definition of column names and their types... |
method | parquet | fenic.api.io.reader.DataFrameReader.parquet | Load a DataFrame from one or more Parquet files.
Args:
paths: A single file path, a glob pattern (e.g., "data/*.parquet"), or a list of paths.
merge_schemas: If True, infers and merges schemas across all files.
Missing columns are filled with nulls, and differing types are widened to a common supertype... | null | true | false | 154 | 203 | null | DataFrame | null | [
"self",
"paths",
"merge_schemas"
] | null | DataFrameReader | Type: method
Member Name: parquet
Qualified Name: fenic.api.io.reader.DataFrameReader.parquet
Docstring: Load a DataFrame from one or more Parquet files.
Args:
paths: A single file path, a glob pattern (e.g., "data/*.parquet"), or a list of paths.
merge_schemas: If True, infers and merges schemas across all fi... |
method | _read_file | fenic.api.io.reader.DataFrameReader._read_file | Internal helper method to read files of a specific format.
Args:
paths: Path(s) to the file(s). Can be a single path or a list of paths.
file_format: Format of the file (e.g., "csv", "parquet").
file_extension: Expected file extension (e.g., ".csv", ".parquet").
**options: Additional options to pass to... | null | false | true | 205 | 248 | null | DataFrame | null | [
"self",
"paths",
"file_format",
"file_extension",
"options"
] | null | DataFrameReader | Type: method
Member Name: _read_file
Qualified Name: fenic.api.io.reader.DataFrameReader._read_file
Docstring: Internal helper method to read files of a specific format.
Args:
paths: Path(s) to the file(s). Can be a single path or a list of paths.
file_format: Format of the file (e.g., "csv", "parquet").
f... |
method | docs | fenic.api.io.reader.DataFrameReader.docs | Load a DataFrame with the document contents of a list of paths (markdown or json).
Args:
paths: Glob pattern (or list of glob patterns) to the folder(s) to load.
content_type: Content type of the files. One of "markdown" or "json".
exclude: A regex pattern to exclude files.
If it is not provid... | null | true | false | 252 | 312 | null | DataFrame | null | [
"self",
"paths",
"content_type",
"exclude",
"recursive"
] | null | DataFrameReader | Type: method
Member Name: docs
Qualified Name: fenic.api.io.reader.DataFrameReader.docs
Docstring: Load a DataFrame with the document contents of a list of paths (markdown or json).
Args:
paths: Glob pattern (or list of glob patterns) to the folder(s) to load.
content_type: Content type of the files. One of "m... |
method | pdf_metadata | fenic.api.io.reader.DataFrameReader.pdf_metadata | Load a DataFrame with metadata of PDF files in a list of paths.
Note:
Local execution requires the `pdf` extra: `pip install "fenic[pdf]"`.
Args:
paths: Glob pattern (or list of glob patterns) to the folder(s) to load.
exclude: A regex pattern to exclude files.
If it is not provided no files ... | null | true | false | 314 | 380 | null | DataFrame | null | [
"self",
"paths",
"exclude",
"recursive"
] | null | DataFrameReader | Type: method
Member Name: pdf_metadata
Qualified Name: fenic.api.io.reader.DataFrameReader.pdf_metadata
Docstring: Load a DataFrame with metadata of PDF files in a list of paths.
Note:
Local execution requires the `pdf` extra: `pip install "fenic[pdf]"`.
Args:
paths: Glob pattern (or list of glob patterns) to... |
module | writer | fenic.api.io.writer | Writer interface for saving DataFrames to external storage systems. | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.11.0-a2lcuovw/fenic/src/fenic/api/io/writer.py | true | false | null | null | null | null | null | null | null | null | Type: module
Member Name: writer
Qualified Name: fenic.api.io.writer
Docstring: Writer interface for saving DataFrames to external storage systems.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
attribute | logger | fenic.api.io.writer.logger | null | null | true | false | 18 | 18 | null | null | logging.getLogger(__name__) | null | null | null | Type: attribute
Member Name: logger
Qualified Name: fenic.api.io.writer.logger
Docstring: none
Value: logging.getLogger(__name__)
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
class | DataFrameWriter | fenic.api.io.writer.DataFrameWriter | Interface used to write a DataFrame to external storage systems.
Similar to PySpark's DataFrameWriter.
Supported External Storage Schemes:
- Amazon S3 (s3://)
- Format: s3://{bucket_name}/{path_to_file}
- Notes:
- Uses boto3 to aquire AWS credentials.
- Examples:
- s3://my-bucket/data.cs... | null | true | false | 21 | 223 | null | null | null | null | [] | null | Type: class
Member Name: DataFrameWriter
Qualified Name: fenic.api.io.writer.DataFrameWriter
Docstring: Interface used to write a DataFrame to external storage systems.
Similar to PySpark's DataFrameWriter.
Supported External Storage Schemes:
- Amazon S3 (s3://)
- Format: s3://{bucket_name}/{path_to_file}
- ... |
method | __init__ | fenic.api.io.writer.DataFrameWriter.__init__ | Initialize a DataFrameWriter.
Args:
dataframe: The DataFrame to write. | null | true | false | 47 | 53 | null | null | null | [
"self",
"dataframe"
] | null | DataFrameWriter | Type: method
Member Name: __init__
Qualified Name: fenic.api.io.writer.DataFrameWriter.__init__
Docstring: Initialize a DataFrameWriter.
Args:
dataframe: The DataFrame to write.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: ["self", "dataframe"]
Returns: none
Parent Class: DataFram... |
method | save_as_table | fenic.api.io.writer.DataFrameWriter.save_as_table | Saves the content of the DataFrame as the specified table.
Args:
table_name: Name of the table to save to
mode: Write mode. Default is "error".
- error: Raises an error if table exists
- append: Appends data to table if it exists
- overwrite: Overwrites existing table
- igno... | null | true | false | 55 | 99 | null | QueryMetrics | null | [
"self",
"table_name",
"mode"
] | null | DataFrameWriter | Type: method
Member Name: save_as_table
Qualified Name: fenic.api.io.writer.DataFrameWriter.save_as_table
Docstring: Saves the content of the DataFrame as the specified table.
Args:
table_name: Name of the table to save to
mode: Write mode. Default is "error".
- error: Raises an error if table exists
... |
method | save_as_view | fenic.api.io.writer.DataFrameWriter.save_as_view | Saves the content of the DataFrame as a view.
Args:
view_name: Name of the view to save to
description: Optional human-readable view description to store in the catalog.
Returns:
None. | null | true | false | 101 | 117 | null | None | null | [
"self",
"view_name",
"description"
] | null | DataFrameWriter | Type: method
Member Name: save_as_view
Qualified Name: fenic.api.io.writer.DataFrameWriter.save_as_view
Docstring: Saves the content of the DataFrame as a view.
Args:
view_name: Name of the view to save to
description: Optional human-readable view description to store in the catalog.
Returns:
None.
Value:... |
method | csv | fenic.api.io.writer.DataFrameWriter.csv | Saves the content of the DataFrame as a single CSV file with comma as the delimiter and headers in the first row.
Args:
file_path: Path to save the CSV file to
mode: Write mode. Default is "overwrite".
- error: Raises an error if file exists
- overwrite: Overwrites the file if it exists
... | null | true | false | 119 | 170 | null | QueryMetrics | null | [
"self",
"file_path",
"mode"
] | null | DataFrameWriter | Type: method
Member Name: csv
Qualified Name: fenic.api.io.writer.DataFrameWriter.csv
Docstring: Saves the content of the DataFrame as a single CSV file with comma as the delimiter and headers in the first row.
Args:
file_path: Path to save the CSV file to
mode: Write mode. Default is "overwrite".
- e... |
method | parquet | fenic.api.io.writer.DataFrameWriter.parquet | Saves the content of the DataFrame as a single Parquet file.
Args:
file_path: Path to save the Parquet file to
mode: Write mode. Default is "overwrite".
- error: Raises an error if file exists
- overwrite: Overwrites the file if it exists
- ignore: Silently ignores operation if file ... | null | true | false | 172 | 223 | null | QueryMetrics | null | [
"self",
"file_path",
"mode"
] | null | DataFrameWriter | Type: method
Member Name: parquet
Qualified Name: fenic.api.io.writer.DataFrameWriter.parquet
Docstring: Saves the content of the DataFrame as a single Parquet file.
Args:
file_path: Path to save the Parquet file to
mode: Write mode. Default is "overwrite".
- error: Raises an error if file exists
... |
module | mcp | fenic.api.mcp | MCP Tool Creation/Server Management API. | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.11.0-a2lcuovw/fenic/src/fenic/api/mcp/__init__.py | true | false | null | null | null | null | null | null | null | null | Type: module
Member Name: mcp
Qualified Name: fenic.api.mcp
Docstring: MCP Tool Creation/Server Management API.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
attribute | __all__ | fenic.api.mcp.__all__ | null | null | false | false | 11 | 17 | null | null | ['create_mcp_server', 'run_mcp_server_sync', 'run_mcp_server_async', 'run_mcp_server_asgi', 'SystemToolConfig'] | null | null | null | Type: attribute
Member Name: __all__
Qualified Name: fenic.api.mcp.__all__
Docstring: none
Value: ['create_mcp_server', 'run_mcp_server_sync', 'run_mcp_server_async', 'run_mcp_server_asgi', 'SystemToolConfig']
Annotation: none
is Public? : false
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
module | server | fenic.api.mcp.server | Create MCP servers using Fenic DataFrames.
This module exposes helpers to:
- Build a Fenic-backed MCP server from datasets and tools
- Run the server synchronously or asynchronously | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.11.0-a2lcuovw/fenic/src/fenic/api/mcp/server.py | true | false | null | null | null | null | null | null | null | null | Type: module
Member Name: server
Qualified Name: fenic.api.mcp.server
Docstring: Create MCP servers using Fenic DataFrames.
This module exposes helpers to:
- Build a Fenic-backed MCP server from datasets and tools
- Run the server synchronously or asynchronously
Value: none
Annotation: none
is Public? : true
is Privat... |
function | create_mcp_server | fenic.api.mcp.server.create_mcp_server | Create an MCP server from datasets and tools.
Args:
session: Fenic session used to execute tools.
server_name: Name of the MCP server.
user_defined_tools: User defined tools to register with the MCP server.
system_tools: Configuration for automatically created system tools.
concurrency_limit: Maxim... | null | true | false | 21 | 52 | null | FenicMCPServer | null | [
"session",
"server_name",
"user_defined_tools",
"system_tools",
"concurrency_limit"
] | null | null | Type: function
Member Name: create_mcp_server
Qualified Name: fenic.api.mcp.server.create_mcp_server
Docstring: Create an MCP server from datasets and tools.
Args:
session: Fenic session used to execute tools.
server_name: Name of the MCP server.
user_defined_tools: User defined tools to register with the ... |
function | run_mcp_server_asgi | fenic.api.mcp.server.run_mcp_server_asgi | Run an MCP server as a Starlette ASGI app.
Returns a Starlette ASGI app that can be integrated into any ASGI server.
This is useful for running the MCP server in a production environment, or running the MCP server as part of a larger application.
Args:
server: MCP server to run.
stateless_http: If True, use s... | null | true | false | 54 | 79 | null | null | null | [
"server",
"stateless_http",
"transport",
"path",
"kwargs"
] | null | null | Type: function
Member Name: run_mcp_server_asgi
Qualified Name: fenic.api.mcp.server.run_mcp_server_asgi
Docstring: Run an MCP server as a Starlette ASGI app.
Returns a Starlette ASGI app that can be integrated into any ASGI server.
This is useful for running the MCP server in a production environment, or running the ... |
function | run_mcp_server_sync | fenic.api.mcp.server.run_mcp_server_sync | Run an MCP server synchronously.
Use this when calling from synchronous code. This creates a new event loop and runs the server in it.
Args:
server: MCP server to run.
transport: Transport protocol (http, stdio).
stateless_http: If True, use stateless HTTP.
port: Port to listen on.
host: Host to l... | null | true | false | 81 | 105 | null | null | null | [
"server",
"transport",
"stateless_http",
"port",
"host",
"path",
"kwargs"
] | null | null | Type: function
Member Name: run_mcp_server_sync
Qualified Name: fenic.api.mcp.server.run_mcp_server_sync
Docstring: Run an MCP server synchronously.
Use this when calling from synchronous code. This creates a new event loop and runs the server in it.
Args:
server: MCP server to run.
transport: Transport proto... |
function | run_mcp_server_async | fenic.api.mcp.server.run_mcp_server_async | Run an MCP server asynchronously.
Use this when calling from asynchronous code. This does not create a new event loop.
Args:
server: MCP server to run.
transport: Transport protocol (http, stdio).
stateless_http: If True, use stateless HTTP.
port: Port to listen on.
host: Host to listen on.
pa... | null | true | false | 108 | 132 | null | null | null | [
"server",
"transport",
"stateless_http",
"port",
"host",
"path",
"kwargs"
] | null | null | Type: function
Member Name: run_mcp_server_async
Qualified Name: fenic.api.mcp.server.run_mcp_server_async
Docstring: Run an MCP server asynchronously.
Use this when calling from asynchronous code. This does not create a new event loop.
Args:
server: MCP server to run.
transport: Transport protocol (http, std... |
module | _tool_generation_utils | fenic.api.mcp._tool_generation_utils | null | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.11.0-a2lcuovw/fenic/src/fenic/api/mcp/_tool_generation_utils.py | false | true | null | null | null | null | null | null | null | null | Type: module
Member Name: _tool_generation_utils
Qualified Name: fenic.api.mcp._tool_generation_utils
Docstring: none
Value: none
Annotation: none
is Public? : false
is Private? : true
Parameters: none
Returns: none
Parent Class: none |
attribute | LONG_TEXT_COLUMN_THRESHOLD_CHAR_LENGTH | fenic.api.mcp._tool_generation_utils.LONG_TEXT_COLUMN_THRESHOLD_CHAR_LENGTH | null | null | true | false | 27 | 27 | null | null | 1024 | null | null | null | Type: attribute
Member Name: LONG_TEXT_COLUMN_THRESHOLD_CHAR_LENGTH
Qualified Name: fenic.api.mcp._tool_generation_utils.LONG_TEXT_COLUMN_THRESHOLD_CHAR_LENGTH
Docstring: none
Value: 1024
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
attribute | PROFILE_MAX_SAMPLE_SIZE | fenic.api.mcp._tool_generation_utils.PROFILE_MAX_SAMPLE_SIZE | null | null | true | false | 29 | 29 | null | null | 10000 | null | null | null | Type: attribute
Member Name: PROFILE_MAX_SAMPLE_SIZE
Qualified Name: fenic.api.mcp._tool_generation_utils.PROFILE_MAX_SAMPLE_SIZE
Docstring: none
Value: 10000
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
attribute | SearchMode | fenic.api.mcp._tool_generation_utils.SearchMode | null | null | true | false | 31 | 31 | null | null | Literal['regex', 'literal'] | null | null | null | Type: attribute
Member Name: SearchMode
Qualified Name: fenic.api.mcp._tool_generation_utils.SearchMode
Docstring: none
Value: Literal['regex', 'literal']
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
class | ToolDataset | fenic.api.mcp._tool_generation_utils.ToolDataset | Specification for a dataset exposed to a tool.
Attributes:
table_name: name of the table registered in the catalog.
description: description of the table from the catalog. | null | true | false | 34 | 57 | null | null | null | null | [] | null | Type: class
Member Name: ToolDataset
Qualified Name: fenic.api.mcp._tool_generation_utils.ToolDataset
Docstring: Specification for a dataset exposed to a tool.
Attributes:
table_name: name of the table registered in the catalog.
description: description of the table from the catalog.
Value: none
Annotation: none
i... |
method | __init__ | fenic.api.mcp._tool_generation_utils.ToolDataset.__init__ | null | null | true | false | 45 | 51 | null | null | null | [
"self",
"table_name",
"description"
] | null | ToolDataset | Type: method
Member Name: __init__
Qualified Name: fenic.api.mcp._tool_generation_utils.ToolDataset.__init__
Docstring: none
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: ["self", "table_name", "description"]
Returns: none
Parent Class: ToolDataset |
method | df | fenic.api.mcp._tool_generation_utils.ToolDataset.df | null | null | true | false | 53 | 54 | null | DataFrame | null | [
"self",
"session"
] | null | ToolDataset | Type: method
Member Name: df
Qualified Name: fenic.api.mcp._tool_generation_utils.ToolDataset.df
Docstring: none
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: ["self", "session"]
Returns: DataFrame
Parent Class: ToolDataset |
method | schema | fenic.api.mcp._tool_generation_utils.ToolDataset.schema | null | null | true | false | 56 | 57 | null | Schema | null | [
"self",
"session"
] | null | ToolDataset | Type: method
Member Name: schema
Qualified Name: fenic.api.mcp._tool_generation_utils.ToolDataset.schema
Docstring: none
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: ["self", "session"]
Returns: Schema
Parent Class: ToolDataset |
function | auto_generate_system_tools_from_tables | fenic.api.mcp._tool_generation_utils.auto_generate_system_tools_from_tables | Generate Schema/Profile/Read/Search [Content/Summary]/Analyze tools from catalog tables.
Validates that each table exists and has a non-empty description in catalog metadata. | null | true | false | 60 | 81 | null | List[SystemTool] | null | [
"table_names",
"session",
"tool_namespace",
"max_result_limit"
] | null | null | Type: function
Member Name: auto_generate_system_tools_from_tables
Qualified Name: fenic.api.mcp._tool_generation_utils.auto_generate_system_tools_from_tables
Docstring: Generate Schema/Profile/Read/Search [Content/Summary]/Analyze tools from catalog tables.
Validates that each table exists and has a non-empty descrip... |
function | _auto_generate_system_tools | fenic.api.mcp._tool_generation_utils._auto_generate_system_tools | Generate core tools spanning all datasets: Schema, Profile, Analyze.
- Schema: list columns/types for any or all datasets
- Profile: dataset statistics for any or all datasets
- Read: read rows from a single dataset to sample the data
- Search Summary: literal or regex search across all datasets, with regex as the def... | null | false | true | 84 | 203 | null | List[SystemTool] | null | [
"datasets",
"session",
"tool_namespace",
"max_result_limit"
] | null | null | Type: function
Member Name: _auto_generate_system_tools
Qualified Name: fenic.api.mcp._tool_generation_utils._auto_generate_system_tools
Docstring: Generate core tools spanning all datasets: Schema, Profile, Analyze.
- Schema: list columns/types for any or all datasets
- Profile: dataset statistics for any or all data... |
function | _build_datasets_from_tables | fenic.api.mcp._tool_generation_utils._build_datasets_from_tables | Resolve catalog table names into DatasetSpec list with validated descriptions.
Raises ConfigurationError if any table is missing or lacks a non-empty description. | null | false | true | 206 | 238 | null | List[ToolDataset] | null | [
"table_names",
"session"
] | null | null | Type: function
Member Name: _build_datasets_from_tables
Qualified Name: fenic.api.mcp._tool_generation_utils._build_datasets_from_tables
Docstring: Resolve catalog table names into DatasetSpec list with validated descriptions.
Raises ConfigurationError if any table is missing or lacks a non-empty description.
Value: n... |
function | _auto_generate_read_tool | fenic.api.mcp._tool_generation_utils._auto_generate_read_tool | Create a read tool over one or many datasets. | null | false | true | 241 | 323 | null | SystemTool | null | [
"datasets",
"session",
"tool_name",
"tool_description",
"result_limit"
] | null | null | Type: function
Member Name: _auto_generate_read_tool
Qualified Name: fenic.api.mcp._tool_generation_utils._auto_generate_read_tool
Docstring: Create a read tool over one or many datasets.
Value: none
Annotation: none
is Public? : false
is Private? : true
Parameters: ["datasets", "session", "tool_name", "tool_descriptio... |
function | _auto_generate_search_summary_tool | fenic.api.mcp._tool_generation_utils._auto_generate_search_summary_tool | Create a grep-like summary tool over one or many datasets (string columns). | null | false | true | 326 | 375 | null | SystemTool | null | [
"datasets",
"session",
"tool_name",
"tool_description"
] | null | null | Type: function
Member Name: _auto_generate_search_summary_tool
Qualified Name: fenic.api.mcp._tool_generation_utils._auto_generate_search_summary_tool
Docstring: Create a grep-like summary tool over one or many datasets (string columns).
Value: none
Annotation: none
is Public? : false
is Private? : true
Parameters: ["d... |
function | _auto_generate_search_content_tool | fenic.api.mcp._tool_generation_utils._auto_generate_search_content_tool | Create a content search tool for a single dataset (string columns). | null | false | true | 378 | 475 | null | SystemTool | null | [
"datasets",
"session",
"tool_name",
"tool_description",
"result_limit"
] | null | null | Type: function
Member Name: _auto_generate_search_content_tool
Qualified Name: fenic.api.mcp._tool_generation_utils._auto_generate_search_content_tool
Docstring: Create a content search tool for a single dataset (string columns).
Value: none
Annotation: none
is Public? : false
is Private? : true
Parameters: ["datasets"... |
function | _search_predicate | fenic.api.mcp._tool_generation_utils._search_predicate | null | null | false | true | 478 | 484 | null | Column | null | [
"column_name",
"pattern",
"mode"
] | null | null | Type: function
Member Name: _search_predicate
Qualified Name: fenic.api.mcp._tool_generation_utils._search_predicate
Docstring: none
Value: none
Annotation: none
is Public? : false
is Private? : true
Parameters: ["column_name", "pattern", "mode"]
Returns: Column
Parent Class: none |
function | _validate_search_mode | fenic.api.mcp._tool_generation_utils._validate_search_mode | null | null | false | true | 487 | 489 | null | None | null | [
"mode"
] | null | null | Type: function
Member Name: _validate_search_mode
Qualified Name: fenic.api.mcp._tool_generation_utils._validate_search_mode
Docstring: none
Value: none
Annotation: none
is Public? : false
is Private? : true
Parameters: ["mode"]
Returns: None
Parent Class: none |
function | _parse_sort_ascending | fenic.api.mcp._tool_generation_utils._parse_sort_ascending | null | null | false | true | 492 | 502 | null | bool | None | null | [
"value"
] | null | null | Type: function
Member Name: _parse_sort_ascending
Qualified Name: fenic.api.mcp._tool_generation_utils._parse_sort_ascending
Docstring: none
Value: none
Annotation: none
is Public? : false
is Private? : true
Parameters: ["value"]
Returns: bool | None
Parent Class: none |
function | _auto_generate_schema_tool | fenic.api.mcp._tool_generation_utils._auto_generate_schema_tool | Create a schema tool over one or many datasets.
- Returns one row per dataset with a column `schema` containing a list of
{column, type} entries.
- If `df_name` is provided, returns only that dataset. | null | false | true | 505 | 566 | null | SystemTool | null | [
"datasets",
"session",
"tool_name",
"tool_description"
] | null | null | Type: function
Member Name: _auto_generate_schema_tool
Qualified Name: fenic.api.mcp._tool_generation_utils._auto_generate_schema_tool
Docstring: Create a schema tool over one or many datasets.
- Returns one row per dataset with a column `schema` containing a list of
{column, type} entries.
- If `df_name` is provide... |
function | _auto_generate_sql_tool | fenic.api.mcp._tool_generation_utils._auto_generate_sql_tool | Create an Analyze tool that executes DuckDB SELECT SQL across datasets.
- JOINs between the provided datasets are allowed.
- DDL/DML and multiple top-level queries are not allowed (enforced in `session.sql()`).
- The callable returns a LogicalPlan gathered later by the MCP server. | null | false | true | 569 | 625 | null | SystemTool | null | [
"datasets",
"session",
"tool_name",
"tool_description",
"result_limit"
] | null | null | Type: function
Member Name: _auto_generate_sql_tool
Qualified Name: fenic.api.mcp._tool_generation_utils._auto_generate_sql_tool
Docstring: Create an Analyze tool that executes DuckDB SELECT SQL across datasets.
- JOINs between the provided datasets are allowed.
- DDL/DML and multiple top-level queries are not allowed... |
function | _sanitize_name | fenic.api.mcp._tool_generation_utils._sanitize_name | null | null | false | true | 628 | 629 | null | str | null | [
"name"
] | null | null | Type: function
Member Name: _sanitize_name
Qualified Name: fenic.api.mcp._tool_generation_utils._sanitize_name
Docstring: none
Value: none
Annotation: none
is Public? : false
is Private? : true
Parameters: ["name"]
Returns: str
Parent Class: none |
function | _apply_paging | fenic.api.mcp._tool_generation_utils._apply_paging | Apply ordering, limit, and offset via a single SQL statement.
- If offset is provided, order_by must also be provided to ensure deterministic paging.
- Validates that all order_by columns exist.
- Builds: SELECT * FROM {src} [ORDER BY ...] [LIMIT N] [OFFSET M]
- When no ordering/limit/offset are provided, returns the ... | null | false | true | 632 | 677 | null | LogicalPlan | null | [
"df",
"session",
"limit",
"offset",
"order_by",
"sort_ascending"
] | null | null | Type: function
Member Name: _apply_paging
Qualified Name: fenic.api.mcp._tool_generation_utils._apply_paging
Docstring: Apply ordering, limit, and offset via a single SQL statement.
- If offset is provided, order_by must also be provided to ensure deterministic paging.
- Validates that all order_by columns exist.
- Bu... |
class | NumericStats | fenic.api.mcp._tool_generation_utils.NumericStats | null | null | true | false | 680 | 688 | null | null | null | null | [] | null | Type: class
Member Name: NumericStats
Qualified Name: fenic.api.mcp._tool_generation_utils.NumericStats
Docstring: none
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
method | __init__ | fenic.api.mcp._tool_generation_utils.NumericStats.__init__ | null | null | true | false | 0 | 0 | null | None | null | [
"self",
"min",
"max",
"mean",
"std_dev",
"median",
"quantile_25",
"quantile_75"
] | null | NumericStats | Type: method
Member Name: __init__
Qualified Name: fenic.api.mcp._tool_generation_utils.NumericStats.__init__
Docstring: none
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: ["self", "min", "max", "mean", "std_dev", "median", "quantile_25", "quantile_75"]
Returns: None
Parent Class: Numer... |
class | TopValues | fenic.api.mcp._tool_generation_utils.TopValues | null | null | true | false | 691 | 694 | null | null | null | null | [] | null | Type: class
Member Name: TopValues
Qualified Name: fenic.api.mcp._tool_generation_utils.TopValues
Docstring: none
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
method | __init__ | fenic.api.mcp._tool_generation_utils.TopValues.__init__ | null | null | true | false | 0 | 0 | null | None | null | [
"self",
"value",
"count"
] | null | TopValues | Type: method
Member Name: __init__
Qualified Name: fenic.api.mcp._tool_generation_utils.TopValues.__init__
Docstring: none
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: ["self", "value", "count"]
Returns: None
Parent Class: TopValues |
class | StringStats | fenic.api.mcp._tool_generation_utils.StringStats | null | null | true | false | 697 | 702 | null | null | null | null | [] | null | Type: class
Member Name: StringStats
Qualified Name: fenic.api.mcp._tool_generation_utils.StringStats
Docstring: none
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
method | __init__ | fenic.api.mcp._tool_generation_utils.StringStats.__init__ | null | null | true | false | 0 | 0 | null | None | null | [
"self",
"avg_length_chars",
"distinct_count",
"top_values",
"example_values"
] | null | StringStats | Type: method
Member Name: __init__
Qualified Name: fenic.api.mcp._tool_generation_utils.StringStats.__init__
Docstring: none
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: ["self", "avg_length_chars", "distinct_count", "top_values", "example_values"]
Returns: None
Parent Class: StringSta... |
class | BooleanStats | fenic.api.mcp._tool_generation_utils.BooleanStats | null | null | true | false | 705 | 708 | null | null | null | null | [] | null | Type: class
Member Name: BooleanStats
Qualified Name: fenic.api.mcp._tool_generation_utils.BooleanStats
Docstring: none
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
method | __init__ | fenic.api.mcp._tool_generation_utils.BooleanStats.__init__ | null | null | true | false | 0 | 0 | null | None | null | [
"self",
"true_rows",
"false_rows"
] | null | BooleanStats | Type: method
Member Name: __init__
Qualified Name: fenic.api.mcp._tool_generation_utils.BooleanStats.__init__
Docstring: none
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: ["self", "true_rows", "false_rows"]
Returns: None
Parent Class: BooleanStats |
class | ProfileRow | fenic.api.mcp._tool_generation_utils.ProfileRow | null | null | true | false | 711 | 729 | null | null | null | null | [] | null | Type: class
Member Name: ProfileRow
Qualified Name: fenic.api.mcp._tool_generation_utils.ProfileRow
Docstring: none
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
method | __init__ | fenic.api.mcp._tool_generation_utils.ProfileRow.__init__ | null | null | true | false | 0 | 0 | null | None | null | [
"self",
"dataset_name",
"column_name",
"data_type",
"total_rows",
"sample_size",
"sample_percentage_of_original",
"null_row_count",
"non_null_row_count",
"percent_rows_contains_null",
"semantic_type",
"cardinality",
"hints",
"numeric_stats",
"string_stats",
"boolean_stats"
] | null | ProfileRow | Type: method
Member Name: __init__
Qualified Name: fenic.api.mcp._tool_generation_utils.ProfileRow.__init__
Docstring: none
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: ["self", "dataset_name", "column_name", "data_type", "total_rows", "sample_size", "sample_percentage_of_original", "n... |
function | _auto_generate_profile_tool | fenic.api.mcp._tool_generation_utils._auto_generate_profile_tool | Create a cached Profile tool for one or many datasets.
Output columns include:
- dataset, column, type, row_count, non_null_count, null_count
- min, max, mean, std (for numerics)
- distinct_count, top_values (JSON) for strings
- true_count, false_count for booleans | null | false | true | 732 | 786 | null | SystemTool | null | [
"datasets",
"session",
"tool_name",
"tool_description",
"topk_distinct"
] | null | null | Type: function
Member Name: _auto_generate_profile_tool
Qualified Name: fenic.api.mcp._tool_generation_utils._auto_generate_profile_tool
Docstring: Create a cached Profile tool for one or many datasets.
Output columns include:
- dataset, column, type, row_count, non_null_count, null_count
- min, max, mean, std (fo... |
function | _compute_profile_for_dataset | fenic.api.mcp._tool_generation_utils._compute_profile_for_dataset | null | null | false | true | 789 | 841 | null | DataFrame | null | [
"session",
"spec",
"topk_distinct"
] | null | null | Type: function
Member Name: _compute_profile_for_dataset
Qualified Name: fenic.api.mcp._tool_generation_utils._compute_profile_for_dataset
Docstring: none
Value: none
Annotation: none
is Public? : false
is Private? : true
Parameters: ["session", "spec", "topk_distinct"]
Returns: DataFrame
Parent Class: none |
function | _compute_profile_rows | fenic.api.mcp._tool_generation_utils._compute_profile_rows | null | null | false | true | 844 | 1,107 | null | List[dict[str, Any]] | null | [
"df",
"dataset_name",
"topk_distinct"
] | null | null | Type: function
Member Name: _compute_profile_rows
Qualified Name: fenic.api.mcp._tool_generation_utils._compute_profile_rows
Docstring: none
Value: none
Annotation: none
is Public? : false
is Private? : true
Parameters: ["df", "dataset_name", "topk_distinct"]
Returns: List[dict[str, Any]]
Parent Class: none |
module | tools | fenic.api.mcp.tools | API-layer generators for automatic MCP tools from DataFrames.
These helpers generate System Tool Definitions for:
- Schema: dataset column names and types
- Profile: per-column statistics (counts, numeric summaries, simple string summaries)
- Analyze: DuckDB SQL across one or more datasets.
All generated tools return... | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.11.0-a2lcuovw/fenic/src/fenic/api/mcp/tools.py | true | false | null | null | null | null | null | null | null | null | Type: module
Member Name: tools
Qualified Name: fenic.api.mcp.tools
Docstring: API-layer generators for automatic MCP tools from DataFrames.
These helpers generate System Tool Definitions for:
- Schema: dataset column names and types
- Profile: per-column statistics (counts, numeric summaries, simple string summaries)... |
class | SystemToolConfig | fenic.api.mcp.tools.SystemToolConfig | Configuration for canonical system tools.
fenic can automatically generate a set of canonical tools for operating on one or more fenic tables.
- Schema: list columns/types for any or all tables
- Profile: column statistics (counts, basic numeric analysis [min, max, mean, etc.], contextual information for text columns... | null | true | false | 20 | 80 | null | null | null | null | [] | null | Type: class
Member Name: SystemToolConfig
Qualified Name: fenic.api.mcp.tools.SystemToolConfig
Docstring: Configuration for canonical system tools.
fenic can automatically generate a set of canonical tools for operating on one or more fenic tables.
- Schema: list columns/types for any or all tables
- Profile: column ... |
method | __init__ | fenic.api.mcp.tools.SystemToolConfig.__init__ | null | null | true | false | 0 | 0 | null | None | null | [
"self",
"table_names",
"tool_namespace",
"max_result_rows"
] | null | SystemToolConfig | Type: method
Member Name: __init__
Qualified Name: fenic.api.mcp.tools.SystemToolConfig.__init__
Docstring: none
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: ["self", "table_names", "tool_namespace", "max_result_rows"]
Returns: None
Parent Class: SystemToolConfig |
module | functions | fenic.api.functions | Functions for working with DataFrame columns.
Note: Array functions are available via fc.arr.* namespace (e.g., fc.arr.size()). | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.11.0-a2lcuovw/fenic/src/fenic/api/functions/__init__.py | true | false | null | null | null | null | null | null | null | null | Type: module
Member Name: functions
Qualified Name: fenic.api.functions
Docstring: Functions for working with DataFrame columns.
Note: Array functions are available via fc.arr.* namespace (e.g., fc.arr.size()).
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Cla... |
module | array | fenic.api.functions.array | Array functions for Fenic DataFrames.
This module provides array manipulation functions following PySpark conventions.
Functions are available via fc.arr.* namespace (e.g., fc.arr.size()). | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.11.0-a2lcuovw/fenic/src/fenic/api/functions/array.py | true | false | null | null | null | null | null | null | null | null | Type: module
Member Name: array
Qualified Name: fenic.api.functions.array
Docstring: Array functions for Fenic DataFrames.
This module provides array manipulation functions following PySpark conventions.
Functions are available via fc.arr.* namespace (e.g., fc.arr.size()).
Value: none
Annotation: none
is Public? : tru... |
function | size | fenic.api.functions.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 | 33 | 60 | null | Column | null | [
"column"
] | null | null | Type: function
Member Name: size
Qualified Name: fenic.api.functions.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 arrays whose length... |
function | distinct | fenic.api.functions.array.distinct | Removes duplicate values from an array column.
Args:
column: Column or column name containing arrays.
Returns:
A new column that is an array of unique values from the input column.
Notes:
- Will attempt to preserve order of first appearances, but order is not guaranteed.
Example:
```python
# cre... | null | true | false | 63 | 97 | null | Column | null | [
"column"
] | null | null | Type: function
Member Name: distinct
Qualified Name: fenic.api.functions.array.distinct
Docstring: Removes duplicate values from an array column.
Args:
column: Column or column name containing arrays.
Returns:
A new column that is an array of unique values from the input column.
Notes:
- Will attempt to ... |
function | contains | fenic.api.functions.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 | 100 | 141 | null | Column | null | [
"column",
"value"
] | null | null | Type: function
Member Name: contains
Qualified Name: fenic.api.functions.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:
column: Colu... |
function | max | fenic.api.functions.array.max | Returns the maximum value in an array.
Only works on arrays of comparable types (numeric, string, date, boolean).
Returns null if the array is null or empty.
Args:
column: Column or column name containing arrays of comparable types
(numeric, string, date, boolean). Does not work on arrays of structs.
Ret... | null | true | false | 144 | 194 | null | Column | null | [
"column"
] | null | null | Type: function
Member Name: max
Qualified Name: fenic.api.functions.array.max
Docstring: Returns the maximum value in an array.
Only works on arrays of comparable types (numeric, string, date, boolean).
Returns null if the array is null or empty.
Args:
column: Column or column name containing arrays of comparable... |
function | min | fenic.api.functions.array.min | Returns the minimum value in an array.
Only works on arrays of comparable types (numeric, string, date, boolean).
Returns null if the array is null or empty.
Args:
column: Column or column name containing arrays of comparable types
(numeric, string, date, boolean). Does not work on arrays of structs.
Ret... | null | true | false | 197 | 247 | null | Column | null | [
"column"
] | null | null | Type: function
Member Name: min
Qualified Name: fenic.api.functions.array.min
Docstring: Returns the minimum value in an array.
Only works on arrays of comparable types (numeric, string, date, boolean).
Returns null if the array is null or empty.
Args:
column: Column or column name containing arrays of comparable... |
function | sort | fenic.api.functions.array.sort | Sorts the array in ascending order.
Only works on arrays of comparable types (numeric, string, date, boolean).
Null values are placed at the end of the array.
Args:
column: Column or column name containing arrays of comparable types
(numeric, string, date, boolean). Does not work on arrays of structs.
Re... | null | true | false | 250 | 303 | null | Column | null | [
"column"
] | null | null | Type: function
Member Name: sort
Qualified Name: fenic.api.functions.array.sort
Docstring: Sorts the array in ascending order.
Only works on arrays of comparable types (numeric, string, date, boolean).
Null values are placed at the end of the array.
Args:
column: Column or column name containing arrays of compara... |
function | reverse | fenic.api.functions.array.reverse | Reverses the elements of an array.
Returns a new array with elements in reverse order. Returns null if the input
array is null.
Args:
column: Column or column name containing arrays.
Returns:
A Column with reversed arrays.
Example: Reversing arrays
```python
import fenic as fc
df = fc.Session.l... | null | true | false | 306 | 343 | null | Column | null | [
"column"
] | null | null | Type: function
Member Name: reverse
Qualified Name: fenic.api.functions.array.reverse
Docstring: Reverses the elements of an array.
Returns a new array with elements in reverse order. Returns null if the input
array is null.
Args:
column: Column or column name containing arrays.
Returns:
A Column with revers... |
function | remove | fenic.api.functions.array.remove | Removes all occurrences of an element from an array.
Returns a new array with all instances of the specified element removed.
Returns null if the input array is null.
Args:
column: Column or column name containing arrays.
element: Element to remove from the arrays. Can be a literal value or a Column expressio... | null | true | false | 346 | 399 | null | Column | null | [
"column",
"element"
] | null | null | Type: function
Member Name: remove
Qualified Name: fenic.api.functions.array.remove
Docstring: Removes all occurrences of an element from an array.
Returns a new array with all instances of the specified element removed.
Returns null if the input array is null.
Args:
column: Column or column name containing array... |
function | union | fenic.api.functions.array.union | Returns the union of two arrays without duplicates.
Returns all distinct elements from both arrays. The order of elements is not
guaranteed. Returns null if either input array is null.
Args:
col1: First array column or column name.
col2: Second array column or column name.
Returns:
A Column containing th... | null | true | false | 402 | 451 | null | Column | null | [
"col1",
"col2"
] | null | null | Type: function
Member Name: union
Qualified Name: fenic.api.functions.array.union
Docstring: Returns the union of two arrays without duplicates.
Returns all distinct elements from both arrays. The order of elements is not
guaranteed. Returns null if either input array is null.
Args:
col1: First array column or co... |
function | intersect | fenic.api.functions.array.intersect | Returns the intersection of two arrays.
Returns distinct elements that appear in both arrays. The order of elements
is not guaranteed. Returns null if either input array is null.
Args:
col1: First array column or column name.
col2: Second array column or column name.
Returns:
A Column containing distinct... | null | true | false | 454 | 503 | null | Column | null | [
"col1",
"col2"
] | null | null | Type: function
Member Name: intersect
Qualified Name: fenic.api.functions.array.intersect
Docstring: Returns the intersection of two arrays.
Returns distinct elements that appear in both arrays. The order of elements
is not guaranteed. Returns null if either input array is null.
Args:
col1: First array column or ... |
function | except_ | fenic.api.functions.array.except_ | Returns elements in the first array but not in the second.
Returns distinct elements from the first array that are not present in the
second array (set difference). Returns null if either input array is null.
Args:
col1: First array column or column name.
col2: Second array column or column name.
Returns:
... | null | true | false | 506 | 555 | null | Column | null | [
"col1",
"col2"
] | null | null | Type: function
Member Name: except_
Qualified Name: fenic.api.functions.array.except_
Docstring: Returns elements in the first array but not in the second.
Returns distinct elements from the first array that are not present in the
second array (set difference). Returns null if either input array is null.
Args:
co... |
function | compact | fenic.api.functions.array.compact | Removes null values from an array.
Returns a new array with all null values removed. Returns null if the input
array itself is null.
Args:
column: Column or column name containing arrays.
Returns:
A Column with arrays having null values removed.
Example: Removing nulls from arrays
```python
import f... | null | true | false | 558 | 602 | null | Column | null | [
"column"
] | null | null | Type: function
Member Name: compact
Qualified Name: fenic.api.functions.array.compact
Docstring: Removes null values from an array.
Returns a new array with all null values removed. Returns null if the input
array itself is null.
Args:
column: Column or column name containing arrays.
Returns:
A Column with a... |
function | repeat | fenic.api.functions.array.repeat | Creates an array containing the element repeated count times.
Returns a new array where the element is repeated the specified number of times.
Returns null if count is null or negative.
Args:
col: Column, column name, or literal value to repeat.
count: Number of times to repeat the element. Can be an integer ... | null | true | false | 605 | 659 | null | Column | null | [
"col",
"count"
] | null | null | Type: function
Member Name: repeat
Qualified Name: fenic.api.functions.array.repeat
Docstring: Creates an array containing the element repeated count times.
Returns a new array where the element is repeated the specified number of times.
Returns null if count is null or negative.
Args:
col: Column, column name, o... |
function | slice | fenic.api.functions.array.slice | Extracts a subarray from an array using 1-based indexing (PySpark compatible).
Extracts a contiguous subarray starting from the given position. Uses 1-based
indexing for compatibility with PySpark. Returns null if the input array is null.
Args:
column: Column or column name containing arrays.
start: Starting ... | null | true | false | 662 | 733 | null | Column | null | [
"column",
"start",
"length"
] | null | null | Type: function
Member Name: slice
Qualified Name: fenic.api.functions.array.slice
Docstring: Extracts a subarray from an array using 1-based indexing (PySpark compatible).
Extracts a contiguous subarray starting from the given position. Uses 1-based
indexing for compatibility with PySpark. Returns null if the input ar... |
function | element_at | fenic.api.functions.array.element_at | Returns the element at the given index in an array using 1-based indexing (PySpark compatible).
Uses 1-based indexing for compatibility with PySpark. Returns null if the
index is out of bounds or if the input array is null.
Args:
column: Column or column name containing arrays.
index: Index of the element (1-... | null | true | false | 736 | 809 | null | Column | null | [
"column",
"index"
] | null | null | Type: function
Member Name: element_at
Qualified Name: fenic.api.functions.array.element_at
Docstring: Returns the element at the given index in an array using 1-based indexing (PySpark compatible).
Uses 1-based indexing for compatibility with PySpark. Returns null if the
index is out of bounds or if the input array i... |
function | overlap | fenic.api.functions.array.overlap | Checks if two arrays have at least one common element.
Returns true if the two arrays share at least one common element, false if they
have no common elements. Returns null if either input array is null.
Args:
col1: First array column or column name.
col2: Second array column or column name.
Returns:
A b... | null | true | false | 812 | 874 | null | Column | null | [
"col1",
"col2"
] | null | null | Type: function
Member Name: overlap
Qualified Name: fenic.api.functions.array.overlap
Docstring: Checks if two arrays have at least one common element.
Returns true if the two arrays share at least one common element, false if they
have no common elements. Returns null if either input array is null.
Args:
col1: F... |
attribute | __all__ | fenic.api.functions.__all__ | null | null | false | false | 42 | 85 | null | null | ['semantic', 'text', 'embedding', 'arr', 'array', 'array_agg', 'array_contains', 'array_size', 'async_udf', 'avg', 'approx_count_distinct', 'collect_list', 'coalesce', 'count', 'count_distinct', 'json', 'markdown', 'max', 'mean', 'min', 'struct', 'sum_distinct', 'sum', 'udf', 'col', 'lit', 'asc', 'asc_nulls_first', 'as... | null | null | null | Type: attribute
Member Name: __all__
Qualified Name: fenic.api.functions.__all__
Docstring: none
Value: ['semantic', 'text', 'embedding', 'arr', 'array', 'array_agg', 'array_contains', 'array_size', 'async_udf', 'avg', 'approx_count_distinct', 'collect_list', 'coalesce', 'count', 'count_distinct', 'json', 'markdown', '... |
module | semantic | fenic.api.functions.semantic | Semantic functions for Fenic DataFrames - LLM-based operations. | /private/var/folders/w2/dyfkx_354cqghs4b74vb_x380000gn/T/fenic-clone-0.11.0-a2lcuovw/fenic/src/fenic/api/functions/semantic.py | true | false | null | null | null | null | null | null | null | null | Type: module
Member Name: semantic
Qualified Name: fenic.api.functions.semantic
Docstring: Semantic functions for Fenic DataFrames - LLM-based operations.
Value: none
Annotation: none
is Public? : true
is Private? : false
Parameters: none
Returns: none
Parent Class: none |
function | map | fenic.api.functions.semantic.map | Applies a generation prompt to one or more columns, enabling rich summarization and generation tasks.
Args:
prompt: A Jinja2 template for the generation prompt. References column
values using {{ column_name }} syntax. Each placeholder is replaced with the
corresponding value from the current row du... | null | true | false | 40 | 141 | null | Column | null | [
"prompt",
"strict",
"examples",
"response_format",
"model_alias",
"temperature",
"max_output_tokens",
"request_timeout",
"columns"
] | null | null | Type: function
Member Name: map
Qualified Name: fenic.api.functions.semantic.map
Docstring: Applies a generation prompt to one or more columns, enabling rich summarization and generation tasks.
Args:
prompt: A Jinja2 template for the generation prompt. References column
values using {{ column_name }} synta... |
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.
... | null | true | false | 144 | 211 | null | Column | null | [
"column",
"response_format",
"max_output_tokens",
"temperature",
"model_alias",
"request_timeout"
] | 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... | null | true | false | 214 | 318 | null | Column | null | [
"predicate",
"strict",
"examples",
"model_alias",
"temperature",
"request_timeout",
"columns"
] | 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... | null | true | false | 321 | 415 | null | Column | null | [
"prompt",
"column",
"group_context",
"order_by",
"model_alias",
"temperature",
"max_output_tokens",
"request_timeout"
] | 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... | null | true | false | 418 | 510 | null | Column | null | [
"column",
"classes",
"examples",
"model_alias",
"temperature",
"request_timeout"
] | 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.
... | null | true | false | 513 | 547 | null | Column | null | [
"column",
"model_alias",
"temperature",
"request_timeout"
] | 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 | 550 | 577 | 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.
... |
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... | null | true | false | 580 | 611 | null | Column | null | [
"column",
"format",
"temperature",
"model_alias",
"request_timeout"
] | 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 ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.