Dynamic Process Generation for Collaborative Development of Multi-Agent System

Quick Start with terminal 💻

To get started, follow these steps:

1. Setup Python Environment Ensure you have a version 3.9 or higher Python environment. You can create and activate this environment using the following commands, replacing ToP_env with your preferred environment name:

   conda create -n ToP_env python=3.9 -y
   conda activate ToP_env
   

2. Install Dependencies: Move into the ChatDev directory and install the necessary dependencies by running:

   cd ChatDev
   pip3 install -r requirements.txt
   

3. Set OpenAI API Key: Export your OpenAI API key as an environment variable. Replace "your_OpenAI_API_key" with your actual API key. Remember that this environment variable is session-specific, so you need to set it again if you open a new terminal session.（ Notice：OpenAI Baes Url is the same operation if is necessary) On Unix/Linux:

   export OPENAI_API_KEY="your_OpenAI_API_key" or export OPENAI_BASE_URL="your_OpenAI_Base_Url"
   

   On Windows:

   $env:OPENAI_API_KEY="your_OpenAI_API_key" or $env:OPENAI_BASE_URL="your_OpenAI_Base_Url"
   

4. Build Your Software: Use the following command to initiate the building of your software, replacing [idea] with your idea's description and [name] with your desired project name: On Unix/Linux:

   python3 run.py --task "[idea]" --name "[name]"
   

   On Windows:

   python run.py --task "[idea]" --name "[name]"
   

5. Check instance and Run Your Software: Once over generated, you can find your dynamic instance in traces_data.txt and parameters in traces_hyperparameters.txt, and you can find your software in the WareHouse directory under a specific project folder, such as name_DefaultOrganization_timestamp. Run your software using the following command within that directory: On Unix/Linux:

   cd WareHouse/project_name_DefaultOrganization_timestamp
   python3 main.py
   

   On Windows:

   cd WareHouse/project_name_DefaultOrganization_timestamp
   python main.py
   

6. NLDD Different software development tasks are included in the dataset which contains 5 categories, You can choose any software development task in it.

BPMN transfer to process textual description 📃 and LLM enhancement 💪

1. Use the desired SR to filter the instances in traces_data.txt.
2. If you don't install pm4py, following the below instructions:

pip install pm4py
from pm4py.objects.conversion.log import converter as log_converter
from pm4py.algo.discovery.alpha import algorithm as alpha_miner
from pm4py.visualization.petri_net import visualizer as pn_visualizer
from pm4py.objects.bpmn.exporter import exporter as bpmn_exporter

dataframe = pd.read_csv('/content/traces_eventlog.csv')
event_log = pm4py.convert_to_event_log(dataframe)
process_model = pm4py.discover_bpmn_inductive(event_log)
pm4py.view_bpmn(process_model)

# save the bpmn model to pdf
pm4py.save_vis_bpmn(process_model, "bpmn.pdf")

3. Use the ''' https://github.com/woped/P2T.git ''' to clone tool used for process description transfering.
4. Put process description as prompt to enchance the LLM.
5. Compare the SR of the generated instances of the same task before and after LLM enhancement.