'''
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
import pandas as pd
from pm4py.objects.log.obj import EventLog, EventStream, Trace
from typing import Union, Optional, Dict, Tuple, List, Any
from pm4py.utils import get_properties, constants, check_is_pandas_dataframe
from pm4py.utils import __event_log_deprecation_warning
from pm4py.objects.ocel.obj import OCEL
from tempfile import NamedTemporaryFile
from copy import copy
from pm4py.objects.petri_net.obj import PetriNet, Marking
[docs]
def openai_query(
prompt: str,
api_key: Optional[str] = None,
openai_model: Optional[str] = None,
api_url: Optional[str] = None,
**kwargs
) -> str:
"""
Executes the provided prompt, obtaining the answer from the OpenAI APIs.
:param prompt: The prompt to be executed.
:param api_key: (Optional) OpenAI API key.
:param openai_model: (Optional) OpenAI model to be used (default: "gpt-3.5-turbo").
:param api_url: (Optional) OpenAI API URL.
:param **kwargs: Additional parameters to pass to the OpenAI API.
:return: The response from the OpenAI API as a string.
.. code-block:: python3
import pm4py
resp = pm4py.llm.openai_query('What is the result of 3+3?', api_key="sk-382393", openai_model="gpt-3.5-turbo")
print(resp)
"""
parameters = copy(kwargs) if kwargs is not None else {}
if api_url is not None:
parameters["api_url"] = api_url
if api_key is not None:
parameters["api_key"] = api_key
if openai_model is not None:
parameters["openai_model"] = openai_model
from pm4py.algo.querying.llm.connectors import openai as perform_query
return perform_query.apply(prompt, parameters=parameters)
[docs]
def google_query(
prompt: str,
api_key: Optional[str] = None,
model: Optional[str] = None,
**kwargs
) -> str:
"""
Executes the provided prompt, obtaining the answer from the Google APIs.
:param prompt: prompt that should be executed
:param api_key: API key
:param model: Model to be used (default: gemini-1.5-flash-002)
:rtype: ``str``
.. code-block:: python3
import pm4py
resp = pm4py.llm.google_query('what is the result of 3+3?', api_key="sk-382393", model="gemini-1.5-flash-002")
print(resp)
"""
parameters = copy(kwargs) if kwargs is not None else {}
if api_key is not None:
parameters["api_key"] = api_key
if model is not None:
parameters["google_model"] = model
from pm4py.algo.querying.llm.connectors import google as perform_query
return perform_query.apply(prompt, parameters=parameters)
[docs]
def anthropic_query(
prompt: str,
api_key: Optional[str] = None,
model: Optional[str] = None,
**kwargs
) -> str:
"""
Executes the provided prompt, obtaining the answer from the Google APIs.
:param prompt: prompt that should be executed
:param api_key: API key
:param model: Model to be used (default: claude-3-5-sonnet-20241022)
:rtype: ``str``
.. code-block:: python3
import pm4py
resp = pm4py.llm.anthropic_query('what is the result of 3+3?', api_key="sk-382393", model="claude-3-5-sonnet-20241022")
print(resp)
"""
parameters = copy(kwargs) if kwargs is not None else {}
if api_key is not None:
parameters["api_key"] = api_key
if model is not None:
parameters["anthropic_model"] = model
from pm4py.algo.querying.llm.connectors import anthropic as perform_query
return perform_query.apply(prompt, parameters=parameters)
[docs]
def abstract_dfg(
log_obj: Union[pd.DataFrame, EventLog, EventStream],
max_len: int = constants.OPENAI_MAX_LEN,
include_performance: bool = True,
relative_frequency: bool = False,
response_header: bool = True,
primary_performance_aggregation: str = "mean",
secondary_performance_aggregation: Optional[str] = None,
activity_key: str = "concept:name",
timestamp_key: str = "time:timestamp",
case_id_key: str = "case:concept:name",
) -> str:
"""
Obtains the DFG (Directly-Follows Graph) abstraction of a traditional event log.
:param log_obj: The log object to abstract.
:param max_len: Maximum length of the string abstraction (default: constants.OPENAI_MAX_LEN).
:param include_performance: Whether to include the performance of the paths in the abstraction.
:param relative_frequency: Whether to use relative instead of absolute frequency of the paths.
:param response_header: Whether to include a short header before the paths, describing the abstraction.
:param primary_performance_aggregation: Primary aggregation method for the arc's performance (default: "mean"). Other options: "median", "min", "max", "sum", "stdev".
:param secondary_performance_aggregation: (Optional) Secondary aggregation method for the arc's performance (default: None). Other options: "mean", "median", "min", "max", "sum", "stdev".
:param activity_key: The column name to be used as activity.
:param timestamp_key: The column name to be used as timestamp.
:param case_id_key: The column name to be used as case identifier.
:return: The DFG abstraction as a string.
.. code-block:: python3
import pm4py
log = pm4py.read_xes("tests/input_data/roadtraffic100traces.xes")
print(pm4py.llm.abstract_dfg(log))
"""
__event_log_deprecation_warning(log_obj)
parameters = get_properties(
log_obj,
activity_key=activity_key,
timestamp_key=timestamp_key,
case_id_key=case_id_key,
)
parameters["max_len"] = max_len
parameters["include_performance"] = include_performance
parameters["relative_frequency"] = relative_frequency
parameters["response_header"] = response_header
parameters["primary_performance_aggregation"] = (
primary_performance_aggregation
)
parameters["secondary_performance_aggregation"] = (
secondary_performance_aggregation
)
from pm4py.algo.querying.llm.abstractions import log_to_dfg_descr
return log_to_dfg_descr.apply(log_obj, parameters=parameters)
[docs]
def abstract_variants(
log_obj: Union[pd.DataFrame, EventLog, EventStream],
max_len: int = constants.OPENAI_MAX_LEN,
include_performance: bool = True,
relative_frequency: bool = False,
response_header: bool = True,
primary_performance_aggregation: str = "mean",
secondary_performance_aggregation: Optional[str] = None,
activity_key: str = "concept:name",
timestamp_key: str = "time:timestamp",
case_id_key: str = "case:concept:name",
) -> str:
"""
Obtains the variants abstraction of a traditional event log.
:param log_obj: The log object to abstract.
:param max_len: Maximum length of the string abstraction (default: constants.OPENAI_MAX_LEN).
:param include_performance: Whether to include the performance of the variants in the abstraction.
:param relative_frequency: Whether to use relative instead of absolute frequency of the variants.
:param response_header: Whether to include a short header before the variants, describing the abstraction.
:param primary_performance_aggregation: Primary aggregation method for the variants' performance (default: "mean"). Other options: "median", "min", "max", "sum", "stdev".
:param secondary_performance_aggregation: (Optional) Secondary aggregation method for the variants' performance (default: None). Other options: "mean", "median", "min", "max", "sum", "stdev".
:param activity_key: The column name to be used as activity.
:param timestamp_key: The column name to be used as timestamp.
:param case_id_key: The column name to be used as case identifier.
:return: The variants abstraction as a string.
.. code-block:: python3
import pm4py
log = pm4py.read_xes("tests/input_data/roadtraffic100traces.xes")
print(pm4py.llm.abstract_variants(log))
"""
__event_log_deprecation_warning(log_obj)
parameters = get_properties(
log_obj,
activity_key=activity_key,
timestamp_key=timestamp_key,
case_id_key=case_id_key,
)
parameters["max_len"] = max_len
parameters["include_performance"] = include_performance
parameters["relative_frequency"] = relative_frequency
parameters["response_header"] = response_header
parameters["primary_performance_aggregation"] = (
primary_performance_aggregation
)
parameters["secondary_performance_aggregation"] = (
secondary_performance_aggregation
)
from pm4py.algo.querying.llm.abstractions import log_to_variants_descr
return log_to_variants_descr.apply(log_obj, parameters=parameters)
[docs]
def abstract_ocel(ocel: OCEL, include_timestamps: bool = True) -> str:
"""
Obtains the abstraction of an object-centric event log, including the list of events and the objects of the OCEL.
:param ocel: The object-centric event log to abstract.
:param include_timestamps: Whether to include timestamp information in the abstraction.
:return: The OCEL abstraction as a string.
.. code-block:: python3
import pm4py
ocel = pm4py.read_ocel("tests/input_data/ocel/example_log.jsonocel")
print(pm4py.llm.abstract_ocel(ocel))
"""
parameters = {}
parameters["include_timestamps"] = include_timestamps
from pm4py.algo.transformation.ocel.description import (
algorithm as ocel_description,
)
return ocel_description.apply(ocel, parameters=parameters)
[docs]
def abstract_ocel_ocdfg(
ocel: OCEL,
include_header: bool = True,
include_timestamps: bool = True,
max_len: int = constants.OPENAI_MAX_LEN,
) -> str:
"""
Obtains the abstraction of an object-centric event log, representing the object-centric directly-follows graph in text.
:param ocel: The object-centric event log to abstract.
:param include_header: Whether to include a header in the abstraction.
:param include_timestamps: Whether to include timestamp information in the abstraction.
:param max_len: Maximum length of the abstraction (default: constants.OPENAI_MAX_LEN).
:return: The object-centric DFG abstraction as a string.
.. code-block:: python3
import pm4py
ocel = pm4py.read_ocel("tests/input_data/ocel/example_log.jsonocel")
print(pm4py.llm.abstract_ocel_ocdfg(ocel))
"""
parameters = {}
parameters["include_header"] = include_header
parameters["include_timestamps"] = include_timestamps
parameters["max_len"] = max_len
from pm4py.algo.querying.llm.abstractions import ocel_ocdfg_descr
return ocel_ocdfg_descr.apply(ocel, parameters=parameters)
[docs]
def abstract_ocel_features(
ocel: OCEL,
obj_type: str,
include_header: bool = True,
max_len: int = constants.OPENAI_MAX_LEN,
debug: bool = False,
enable_object_lifecycle_paths: bool = True,
) -> str:
"""
Obtains the abstraction of an object-centric event log, representing the features and their values in text.
:param ocel: The object-centric event log to abstract.
:param obj_type: The object type to consider in feature extraction.
:param include_header: Whether to include a header in the abstraction.
:param max_len: Maximum length of the abstraction (default: constants.OPENAI_MAX_LEN).
:param debug: Enables debugging mode, providing insights into feature extraction steps.
:param enable_object_lifecycle_paths: Enables the "lifecycle paths" feature in the abstraction.
:return: The OCEL features abstraction as a string.
.. code-block:: python3
import pm4py
ocel = pm4py.read_ocel("tests/input_data/ocel/example_log.jsonocel")
print(pm4py.llm.abstract_ocel_features(ocel, obj_type="Resource"))
"""
parameters = {}
parameters["include_header"] = include_header
parameters["max_len"] = max_len
parameters["debug"] = debug
parameters["enable_object_lifecycle_paths"] = enable_object_lifecycle_paths
from pm4py.algo.querying.llm.abstractions import ocel_fea_descr
return ocel_fea_descr.apply(ocel, obj_type, parameters=parameters)
[docs]
def abstract_event_stream(
log_obj: Union[pd.DataFrame, EventLog, EventStream],
max_len: int = constants.OPENAI_MAX_LEN,
response_header: bool = True,
activity_key: str = "concept:name",
timestamp_key: str = "time:timestamp",
case_id_key: str = "case:concept:name",
) -> str:
"""
Obtains the event stream abstraction of a traditional event log.
:param log_obj: The log object to abstract.
:param max_len: Maximum length of the string abstraction (default: constants.OPENAI_MAX_LEN).
:param response_header: Whether to include a short header before the event stream, describing the abstraction.
:param activity_key: The column name to be used as activity.
:param timestamp_key: The column name to be used as timestamp.
:param case_id_key: The column name to be used as case identifier.
:return: The event stream abstraction as a string.
.. code-block:: python3
import pm4py
log = pm4py.read_xes("tests/input_data/roadtraffic100traces.xes")
print(pm4py.llm.abstract_event_stream(log))
"""
__event_log_deprecation_warning(log_obj)
parameters = get_properties(
log_obj,
activity_key=activity_key,
timestamp_key=timestamp_key,
case_id_key=case_id_key,
)
parameters["max_len"] = max_len
parameters["response_header"] = response_header
from pm4py.algo.querying.llm.abstractions import stream_to_descr
return stream_to_descr.apply(log_obj, parameters=parameters)
[docs]
def abstract_petri_net(
net: PetriNet, im: Marking, fm: Marking, response_header: bool = True
) -> str:
"""
Obtains an abstraction of a Petri net.
:param net: The Petri net to abstract.
:param im: The initial marking of the Petri net.
:param fm: The final marking of the Petri net.
:param response_header: Whether to include a header in the abstraction.
:return: The Petri net abstraction as a string.
.. code-block:: python3
import pm4py
net, im, fm = pm4py.read_pnml('tests/input_data/running-example.pnml')
print(pm4py.llm.abstract_petri_net(net, im, fm))
"""
parameters = {}
parameters["response_header"] = response_header
from pm4py.algo.querying.llm.abstractions import net_to_descr
return net_to_descr.apply(net, im, fm, parameters=parameters)
[docs]
def abstract_log_attributes(
log_obj: Union[pd.DataFrame, EventLog, EventStream],
max_len: int = constants.OPENAI_MAX_LEN,
activity_key: str = "concept:name",
timestamp_key: str = "time:timestamp",
case_id_key: str = "case:concept:name",
) -> str:
"""
Abstracts the attributes of a log by reporting their names, types, and top values.
:param log_obj: The log object whose attributes are to be abstracted.
:param max_len: Maximum length of the string abstraction (default: constants.OPENAI_MAX_LEN).
:param activity_key: The column name to be used as activity.
:param timestamp_key: The column name to be used as timestamp.
:param case_id_key: The column name to be used as case identifier.
:return: The log attributes abstraction as a string.
.. code-block:: python3
import pm4py
log = pm4py.read_xes("tests/input_data/roadtraffic100traces.xes")
print(pm4py.llm.abstract_log_attributes(log))
"""
__event_log_deprecation_warning(log_obj)
parameters = get_properties(
log_obj,
activity_key=activity_key,
timestamp_key=timestamp_key,
case_id_key=case_id_key,
)
parameters["max_len"] = max_len
from pm4py.algo.querying.llm.abstractions import log_to_cols_descr
return log_to_cols_descr.apply(log_obj, parameters=parameters)
[docs]
def abstract_log_features(
log_obj: Union[pd.DataFrame, EventLog, EventStream],
max_len: int = constants.OPENAI_MAX_LEN,
include_header: bool = True,
activity_key: str = "concept:name",
timestamp_key: str = "time:timestamp",
case_id_key: str = "case:concept:name",
) -> str:
"""
Abstracts the machine learning features obtained from a log by reporting the top features until the desired length is achieved.
:param log_obj: The log object from which to extract features.
:param max_len: Maximum length of the string abstraction (default: constants.OPENAI_MAX_LEN).
:param include_header: Whether to include a header in the abstraction.
:param activity_key: The column name to be used as activity.
:param timestamp_key: The column name to be used as timestamp.
:param case_id_key: The column name to be used as case identifier.
:return: The log features abstraction as a string.
.. code-block:: python3
import pm4py
log = pm4py.read_xes("tests/input_data/roadtraffic100traces.xes")
print(pm4py.llm.abstract_log_features(log))
"""
__event_log_deprecation_warning(log_obj)
parameters = get_properties(
log_obj,
activity_key=activity_key,
timestamp_key=timestamp_key,
case_id_key=case_id_key,
)
parameters["max_len"] = max_len
parameters["include_header"] = include_header
from pm4py.algo.querying.llm.abstractions import log_to_fea_descr
return log_to_fea_descr.apply(log_obj, parameters=parameters)
[docs]
def abstract_temporal_profile(
temporal_profile: Dict[Tuple[str, str], Tuple[float, float]],
include_header: bool = True,
) -> str:
"""
Abstracts a temporal profile model into a descriptive string.
:param temporal_profile: The temporal profile model to abstract.
:param include_header: Whether to include a header in the abstraction describing the temporal profile.
:return: The temporal profile abstraction as a string.
.. code-block:: python3
import pm4py
log = pm4py.read_xes("tests/input_data/roadtraffic100traces.xes", return_legacy_log_object=True)
temporal_profile = pm4py.discover_temporal_profile(log)
text_abstr = pm4py.llm.abstract_temporal_profile(temporal_profile, include_header=True)
print(text_abstr)
"""
parameters = {}
parameters["include_header"] = include_header
from pm4py.algo.querying.llm.abstractions import tempprofile_to_descr
return tempprofile_to_descr.apply(temporal_profile, parameters=parameters)
[docs]
def abstract_case(
case: Trace,
include_case_attributes: bool = True,
include_event_attributes: bool = True,
include_timestamp: bool = True,
include_header: bool = True,
activity_key: str = "concept:name",
timestamp_key: str = "time:timestamp",
) -> str:
"""
Textually abstracts a single case from an event log.
:param case: The case object to abstract.
:param include_case_attributes: Whether to include attributes at the case level.
:param include_event_attributes: Whether to include attributes at the event level.
:param include_timestamp: Whether to include event timestamps in the abstraction.
:param include_header: Whether to include a header in the abstraction.
:param activity_key: The column name to be used as activity.
:param timestamp_key: The column name to be used as timestamp.
:return: The case abstraction as a string.
.. code-block:: python3
import pm4py
log = pm4py.read_xes("tests/input_data/roadtraffic100traces.xes", return_legacy_log_object=True)
print(pm4py.llm.abstract_case(log[0]))
"""
parameters = {}
parameters["include_case_attributes"] = include_case_attributes
parameters["include_event_attributes"] = include_event_attributes
parameters["include_timestamp"] = include_timestamp
parameters["include_header"] = include_header
parameters[constants.PARAMETER_CONSTANT_ACTIVITY_KEY] = activity_key
parameters[constants.PARAMETER_CONSTANT_TIMESTAMP_KEY] = timestamp_key
from pm4py.algo.querying.llm.abstractions import case_to_descr
return case_to_descr.apply(case, parameters=parameters)
[docs]
def abstract_declare(declare_model, include_header: bool = True) -> str:
"""
Textually abstracts a DECLARE model.
:param declare_model: The DECLARE model to abstract.
:param include_header: Whether to include a header in the abstraction.
:return: The DECLARE model abstraction as a string.
.. code-block:: python3
import pm4py
log = pm4py.read_xes("tests/input_data/roadtraffic100traces.xes", return_legacy_log_object=True)
log_ske = pm4py.discover_declare(log)
print(pm4py.llm.abstract_declare(log_ske))
"""
parameters = {}
parameters["include_header"] = include_header
from pm4py.algo.querying.llm.abstractions import declare_to_descr
return declare_to_descr.apply(declare_model, parameters=parameters)
[docs]
def abstract_log_skeleton(log_skeleton, include_header: bool = True) -> str:
"""
Textually abstracts a log skeleton process model.
:param log_skeleton: The log skeleton to abstract.
:param include_header: Whether to include a header in the abstraction.
:return: The log skeleton abstraction as a string.
.. code-block:: python3
import pm4py
log = pm4py.read_xes("tests/input_data/roadtraffic100traces.xes", return_legacy_log_object=True)
log_ske = pm4py.discover_log_skeleton(log)
print(pm4py.llm.abstract_log_skeleton(log_ske))
"""
parameters = {}
parameters["include_header"] = include_header
from pm4py.algo.querying.llm.abstractions import logske_to_descr
return logske_to_descr.apply(log_skeleton, parameters=parameters)
def __execute_prompt_to_db_query(
log: pd.DataFrame,
prompt: str,
executor=openai_query,
execute_query: bool = True,
**kwargs
) -> Union[str, pd.DataFrame]:
"""
Internal method to retrieve (and execute) a SQL query corresponding to a prompt
"""
import duckdb
if check_is_pandas_dataframe(log):
kwargs["table_name"] = "dataframe"
dataframe = log
response = executor(prompt, **kwargs)
sql_query = ""
if "```sql" in response:
sql_query = response.split("```sql")[1].split("```")[0]
sql_query = sql_query.strip()
if not sql_query:
raise Exception(
"The response does not contain a valid SQL query: " + response
)
if execute_query:
result = duckdb.query(sql_query).to_df()
if check_is_pandas_dataframe(log):
return result
return sql_query
[docs]
def nlp_to_log_query(
log: pd.DataFrame,
query: str,
executor=openai_query,
obtain_query: bool = True,
execute_query: bool = True,
**kwargs
) -> Union[str, pd.DataFrame]:
"""
Translates a natural language statement into a database (SQL) query executable against the event log.
:param log: event log object
:param query: query expressed in natural language
:param executor: the connector to the LLM (e.g., pm4py.llm.openai_query)
:param obtain_query: executes the prompt, to transform the natural statements into a database (SQL) query
:param execute_query: executes the database (SQL) query against the event data
:param kwargs: additional keyword arguments to the method
:rtype: ``Union[str, pd.DataFrame]``
.. code-block:: python3
import pm4py
log = pm4py.read_xes("tests/input_data/running-example.xes")
resp = pm4py.llm.nlp_to_log_query(log, "How many cases are contained in the event log?", api_key="sk-5HNn")
print(resp)
"""
from pm4py.algo.querying.llm.injection import (
algorithm as domain_knowledge_injector,
)
prompt = (
"Could you provide a database query for the following question?\n\n"
+ query
)
prompt += domain_knowledge_injector.apply(log, parameters=kwargs)
prompt += "\n\nPlease include the SQL query between the ```sql and the ``` tags.\n\n"
if obtain_query:
return __execute_prompt_to_db_query(
log,
prompt,
executor=executor,
execute_query=execute_query,
**kwargs
)
return prompt
[docs]
def nlp_to_log_filter(
log: pd.DataFrame,
filter_query: str,
executor=openai_query,
obtain_query: bool = True,
execute_query: bool = True,
**kwargs
) -> Union[str, pd.DataFrame]:
"""
Translates a filtering query expressed in natural language into a database (SQL) query that is used to filter the
event log.
:param log: event log object
:param filter_query: filtering query expressed in natural language
:param executor: the connector to the LLM (e.g., pm4py.llm.openai_query)
:param obtain_query: executes the prompt, to transform the natural statements into a database (SQL) query
:param execute_query: executes the database (SQL) query against the event data
:param kwargs: additional keyword arguments to the method
:rtype: ``Union[str, pd.DataFrame]``
.. code-block:: python3
import pm4py
log = pm4py.read_xes("tests/input_data/running-example.xes")
resp = pm4py.llm.nlp_to_log_filter(log, "There is an event with activity: pay compensation", api_key="sk-5HNn")
print(resp)
"""
from pm4py.algo.querying.llm.injection import (
algorithm as domain_knowledge_injector,
)
prompt = (
"Could you provide a database query to filter all the events of the cases for which at least an event (row) is satisfying the following filtering query?\n\n" +
filter_query)
prompt += domain_knowledge_injector.apply(log, parameters=kwargs)
prompt += "\n\nPlease include the SQL query between the ```sql and the ``` tags.\n\n"
if obtain_query:
return __execute_prompt_to_db_query(
log,
prompt,
executor=executor,
execute_query=execute_query,
**kwargs
)
return prompt
[docs]
def explain_visualization(
vis_saver, *args, connector=openai_query, **kwargs
) -> str:
"""
Explains a process mining visualization using LLMs by saving it as a .png image and providing the image to the Large Language Model along with a description.
:param vis_saver: The visualizer function used to save the visualization to disk.
:param args: Positional arguments required by the visualizer function.
:param connector: (Optional) The connector method to communicate with the large language model (default: openai_query).
:param **kwargs: Additional keyword arguments for the visualizer function or the connector (e.g., annotations, API key).
:return: The explanation of the visualization as a string.
.. code-block:: python3
import pm4py
log = pm4py.read_xes("tests/input_data/running-example.xes")
descr = pm4py.llm.explain_visualization(pm4py.save_vis_dotted_chart, log, api_key="sk-5HN", show_legend=False)
print(descr)
"""
F = NamedTemporaryFile(suffix=".png")
image_path = F.name
F.close()
description = vis_saver(*args, image_path, **kwargs)
parameters = copy(kwargs) if kwargs is not None else {}
parameters["image_path"] = image_path
return connector(
"Could you explain the included process mining visualization?\n\n"
+ description,
**parameters
)
