Generate workflows (for flowcharts or low code) in DOT format, from natural language, via an LLM.
Also perform the inverse: describe a given workflow (given in DOT format) in natural language.
The DOT graph format (as used by tools like graphviz) is a simple way to represent a flow chart. A flow chart is a good approximation of a workflow.
Because DOT graphs are a common public format, large LLMs such as Open AI gpt-3.5-turbo have included them in their training corpus. So, such LLMs are already capable of both generating DOT files and summarizing them back to natural language. We can use such LLMs both to generate workflows from natural language, and the inverse (generating a summary), by using DOT as an intermediate format.
The DOT script generated by the LLM can be further processed, for example by generating a flow chart image OR by populating some kind of workflow system inside an application.
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| Workflow to decide on a mortgage application | Workflow to decide on a job interview candidate |
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| Workflow to decide what is this animal | Workflow to add an item to a list |
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| Workflow to combine two lists | Workflow to conditionally add an item to a list |
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| Iiterate over Job Applications in a list. For each Job Application, call another flow that checks if the application should proceed to interview stage. | The step-by-step process of reading the Orders, iterating over them, calculating the total amount, and writing it back to storage. |
>> Create a flow that makes a series of decisions about whether to approve a mortgage application
Writing png to '.\temp\dot_graph_1.png'
digraph G {
// start
start [shape=ellipse, label="Start"];
// decision_credit_score
start -> decision_credit_score;
decision_credit_score [shape=Mdiamond, label="Credit Score > 700?"];
// decision_income
decision_credit_score -> decision_income;
decision_income [shape=Mdiamond, label="Income > $50,000?"];
// decision_employment
decision_income -> decision_employment;
decision_employment [shape=Mdiamond, label="Employment > 2 years?"];
// decision_down_payment
decision_employment -> decision_down_payment;
decision_down_payment [shape=Mdiamond, label="Down Payment > 20%?"];
// approve
decision_down_payment -> approve;
approve [shape=box, label="Approve"];
// reject
decision_credit_score -> reject;
reject [shape=box, label="Reject"];
decision_income -> reject;
decision_employment -> reject;
decision_down_payment -> reject;
}
>> Create a flow that makes a series of decisions about whether to recommend a job interview candidate.
Writing png to '.\temp\dot_graph_2.png'
digraph G {
// start
start [shape=ellipse, label="Start"];
// decision_experience
start -> decision_experience;
decision_experience [shape=Mdiamond, label="Has relevant experience?"];
// decision_education
decision_experience -> decision_education;
decision_education [shape=Mdiamond, label="Has required education?"];
// decision_skills
decision_education -> decision_skills;
decision_skills [shape=Mdiamond, label="Has necessary skills?"];
// decision_references
decision_skills -> decision_references;
decision_references [shape=Mdiamond, label="Has positive references?"];
// recommend
decision_references -> recommend;
recommend [shape=box, label="Recommend for interview"];
// reject
decision_experience -> reject;
reject [shape=box, label="Reject"];
decision_education -> reject;
decision_skills -> reject;
decision_references -> reject;
}
>> Create a flow that makes a series of decisions about an animal, to decide what kind of animal is it
Writing png to '.\temp\dot_graph_3.png'
digraph G {
// start
start [shape=ellipse, label="Start"];
// decision_has_feathers
start -> decision_has_feathers;
decision_has_feathers [shape=Mdiamond, label="Has feathers?"];
// decision_can_fly
decision_has_feathers -> decision_can_fly;
decision_can_fly [shape=Mdiamond, label="Can fly?"];
// decision_has_fins
decision_has_feathers -> decision_has_fins;
decision_has_fins [shape=Mdiamond, label="Has fins?"];
// Hawk
decision_can_fly -> Hawk;
Hawk [shape=box, label="Hawk"];
// Penguin
decision_can_fly -> Penguin;
Penguin [shape=box, label="Penguin"];
// Dolphin
decision_has_fins -> Dolphin;
Dolphin [shape=box, label="Dolphin"];
// Bear
decision_has_fins -> Bear;
Bear [shape=box, label="Bear"];
}
>> Create a flow that takes a list and an object. Call another flow to get a boolean result. If the boolean is true, then add the item to the list.
Writing png to '.\temp\dot_graph_6.png'
digraph G {
// start
start [shape=ellipse, label="Start"];
// call_flow
call_flow [shape=box, label="Call Flow"];
// decision_boolean
decision_boolean [shape=diamond, label="Boolean Result?"];
// add_item
add_item [shape=box, label="Add Item"];
// end
end [shape=ellipse, label="End"];
// start -> call_flow
start -> call_flow;
// call_flow -> decision_boolean
call_flow -> decision_boolean;
// decision_boolean -> add_item [label="true"];
decision_boolean -> add_item [label="true"];
// decision_boolean -> end [label="false"];
decision_boolean -> end [label="false"];
// add_item -> end
add_item -> end;
call_flow [shape=box, label="Call Flow"];
}
>> Create a flow that iterates over Job Applications in a list. For each Job Application, call another flow that checks if the application should proceed to interview stage
digraph G {
// start
start [shape=ellipse, label="Start"];
// initialize
start -> initialize;
initialize [shape=box, label="Initialize list and counter"];
// decision_has_next
initialize -> decision_has_next;
decision_has_next [shape=Mdiamond, label="Has next job application?"];
// action_call_flow
decision_has_next -> action_call_flow;
action_call_flow [shape=box, label="Call flow to check application"];
// increment_counter
action_call_flow -> increment_counter;
increment_counter [shape=box, label="Increment counter"];
// decision_has_next
increment_counter -> decision_has_next;
// end
decision_has_next -> end;
end [shape=ellipse, label="End"];
initialize -> decision_has_next;
}
>> Descibe this workflow:
digraph G {
// start
start [shape=ellipse, label="Start"];
// decision_credit_score
start -> decision_credit_score;
decision_credit_score [shape=Mdiamond, label="Credit Score > 700?"];
// decision_income
decision_credit_score -> decision_income;
decision_income [shape=Mdiamond, label="Income > $50,000?"];
// decision_employment
decision_income -> decision_employment;
decision_employment [shape=Mdiamond, label="Employment > 2 years?"];
// decision_down_payment
decision_employment -> decision_down_payment;
decision_down_payment [shape=Mdiamond, label="Down Payment > 20%?"];
// approve
decision_down_payment -> approve;
approve [shape=box, label="Approve"];
// reject
decision_credit_score -> reject;
reject [shape=box, label="Reject"];
decision_income -> reject;
decision_employment -> reject;
decision_down_payment -> reject;
}
This flow chart represents a decision-making process for approving or rejecting a loan application.
The process starts at the "Start" node and then moves to the "Credit Score > 700?" decision node. If the credit score is greater than 700, the flow moves to the "Income > $50,000?" decision node. If the income is greater than $50,000, the flow moves to the "Employment > 2 years?" decision node. If the employment is greater than 2 years, the flow moves to the "Down Payment > 20%?" decision node. If the down payment is greater than 20%, the flow reaches the "Approve" node, indicating that the loan application should be approved.
However, if at any point in the process a condition is not met, the flow moves to the "Reject" node, indicating that the loan application should be rejected.
Overall, this flow chart helps guide the decision-making process for loan approval based on various criteria such as credit score, income, employment, and down payment.
>> Descibe this workflow:
digraph G {
// start
start [shape=ellipse, label="Start"];
// decision_has_feathers
start -> decision_has_feathers;
decision_has_feathers [shape=Mdiamond, label="Has feathers?"];
// decision_can_fly
decision_has_feathers -> decision_can_fly;
decision_can_fly [shape=Mdiamond, label="Can fly?"];
// decision_has_fins
decision_has_feathers -> decision_has_fins;
decision_has_fins [shape=Mdiamond, label="Has fins?"];
// Hawk
decision_can_fly -> Hawk;
Hawk [shape=box, label="Hawk"];
// Penguin
decision_can_fly -> Penguin;
Penguin [shape=box, label="Penguin"];
// Dolphin
decision_has_fins -> Dolphin;
Dolphin [shape=box, label="Dolphin"];
// Bear
decision_has_fins -> Bear;
Bear [shape=box, label="Bear"];
}
This flow chart represents a decision-making process to determine the characteristics of different animals based on whether they have feathers, can fly, or have fins.
The process starts at the "Start" node and then moves to the "Has feathers?" decision node. If the animal has feathers, the flow moves to the "Can fly?" decision node. If the animal can fly, the flow reaches the "Hawk" node, indicating that the animal is a hawk. However, if the animal cannot fly, the flow reaches the "Penguin" node, indicating that the animal is a penguin.
If the animal does not have feathers, the flow moves to the "Has fins?" decision node. If the animal has fins, the flow reaches the "Dolphin" node, indicating that the animal is a dolphin. However, if the animal does not have fins, the flow reaches the "Bear" node, indicating that the animal is a bear.
Overall, this flow chart helps classify animals based on their characteristics, specifically whether they have feathers, can fly, or have fins
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>> what is 2 + 5 divided by 10 ?
I'm sorry, but I can only assist with questions related to creating a flow chart.
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>> Who won the battle of Agincourt, and why was it fought?
I'm sorry, but I can only assist with questions related to creating a flow chart.
---
>> What is my favourite color?
I'm sorry, but I don't have access to personal information.
- Requires an LLM - by default, uses OpenAI's ChatGPT.
- Python 3
- graphviz
To use as a CLI (Command Line Interface) REPL (Read-Eval-Print Loop) prompt:
go.sh
or to use as a web server:
go_web.sh
For the web server, you need to pass the user prompt as GET query parameter 'p'.
Example:
So, another application can use the web server to send in natural language prompts from the user, and receive response in the graphviz DOT format.
The other application can then generate an image or some kind of workflow, from the DOT script.
pip3 install --upgrade openai pydot
Set environment variable with your OpenAI key:
export OPENAI_API_KEY="xxx"
Add that to your shell initializing script (~/.zprofile or similar)
Load in current terminal:
source ~/.zprofile
test.sh
or
python test.py
See Training README about training a custom LLM for gpt-workflow.