pm4py.llm#

PM4Py – A Process Mining Library for Python

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

Functions

`abstract_case`(case[, ...])	Textually abstracts a single case from an event log.
`abstract_declare`(declare_model[, include_header])	Textually abstracts a DECLARE model.
`abstract_dfg`(log_obj[, max_len, ...])	Obtains the DFG (Directly-Follows Graph) abstraction of a traditional event log.
`abstract_event_stream`(log_obj[, max_len, ...])	Obtains the event stream abstraction of a traditional event log.
`abstract_log_attributes`(log_obj[, max_len, ...])	Abstracts the attributes of a log by reporting their names, types, and top values.
`abstract_log_features`(log_obj[, max_len, ...])	Abstracts the machine learning features obtained from a log by reporting the top features until the desired length is achieved.
`abstract_log_skeleton`(log_skeleton[, ...])	Textually abstracts a log skeleton process model.
`abstract_ocel`(ocel[, include_timestamps])	Obtains the abstraction of an object-centric event log, including the list of events and the objects of the OCEL.
`abstract_ocel_features`(ocel, obj_type[, ...])	Obtains the abstraction of an object-centric event log, representing the features and their values in text.
`abstract_ocel_ocdfg`(ocel[, include_header, ...])	Obtains the abstraction of an object-centric event log, representing the object-centric directly-follows graph in text.
`abstract_petri_net`(net, im, fm[, ...])	Obtains an abstraction of a Petri net.
`abstract_temporal_profile`(temporal_profile)	Abstracts a temporal profile model into a descriptive string.
`abstract_variants`(log_obj[, max_len, ...])	Obtains the variants abstraction of a traditional event log.
`explain_visualization`(vis_saver, *args[, ...])	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.
`openai_query`(prompt[, api_key, ...])	Executes the provided prompt, obtaining the answer from the OpenAI APIs.