Add describe-workflow feature

Author: mrseanryan

README.md

@@ -1,5 +1,7 @@
 # gpt-workflow
-Generate workflows (for flowcharts or low code) via LLM.
+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.
 
 ## Approach: generate DOT notation as a simple format to represent a workflow
 
@@ -23,8 +25,6 @@ The DOT script generated by the LLM can be further processed, for example by gen
 ## Example Execution - generating flows in DOT format from natural language input
 
 ```
-[[[TEST Simple workflow to model a tree of decisions]]]
----
 >> 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 {
@@ -60,7 +60,9 @@ digraph G {
   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 {
@@ -96,7 +98,9 @@ digraph G {
   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 {
@@ -132,52 +136,9 @@ digraph G {
   decision_has_fins -> Bear;
   Bear [shape=box, label="Bear"];
 }
-[[[TEST Simple workflow adding an item to a list]]]
----
->> Create a flow that takes a list and adds an item of the same type
-Writing png to '.\temp\dot_graph_4.png'
-digraph G {
-
-  // start
-  start [shape=ellipse, label="Start"];
-
-  // add_item
-  add_item [shape=box, label="Add Item"];
-
-  // end
-  end [shape=ellipse, label="End"];
-
-  // start -> add_item
-  start -> add_item;
-
-  // add_item -> end
-  add_item -> end;
-
-  add_item [shape=box, label="Add Item"];
-}
----
->> Create a flow that takes two lists and concatenates them
-Writing png to '.\temp\dot_graph_5.png'
-digraph G {
-
-  // start
-  start [shape=ellipse, label="Start"];
-
-  // concatenate_lists
-  concatenate_lists [shape=box, label="Concatenate Lists"];
-
-  // end
-  end [shape=ellipse, label="End"];
-
-  // start -> concatenate_lists
-  start -> concatenate_lists;
-
-  // concatenate_lists -> end
-  concatenate_lists -> end;
+```
 
-  concatenate_lists [shape=box, label="Concatenate Lists"];
-}
----
+```
 >> 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 {
@@ -214,7 +175,11 @@ digraph G {
 
   call_flow [shape=box, label="Call Flow"];
 }
-[[[TEST Irrelevant prompts]]]
+```
+
+## Example Execution - Handling irrelevant prompts
+
+```
 ---
 >> what is 2 + 5 divided by 10 ?
 I'm sorry, but I can only assist with questions related to creating a flow chart.
@@ -226,6 +191,96 @@ I'm sorry, but I can only assist with questions related to creating a flow chart
 I'm sorry, but I don't have access to personal information.
 ```
 
+## Example Execution - Describing a given 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.
+```
+
+```
+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
+```
+
 ## Dependencies
 
 - Requires an LLM - by default, uses OpenAI's ChatGPT.

config__database_schema_creator.py

@@ -1,4 +1,4 @@
-from config__database_schema_creator import EXPERT_COMMANDS
+from prompts__dot_graph_creator import EXPERT_COMMANDS
 import core
 
 command_messages = core.create_command_messages(EXPERT_COMMANDS)

main_cli.py

@@ -3,7 +3,7 @@
 from urllib.parse import urlparse, parse_qs
 import threading, socket
 
-from config__database_schema_creator import EXPERT_COMMANDS
+from prompts__dot_graph_creator import EXPERT_COMMANDS
 import core
 import config
 import config_web

main_web_service.py

@@ -0,0 +1,94 @@
+from command import Command
+
+# note: Instead of this steps-in-single-prompt approach, we could have an inter-dependent chain, to collect info about the app, THEN try to generate.
+# BUT the step-by-step approach works really well, at least with Chat-GPT3.5 Turbo.
+
+create_dot_flowchart__expert_template = """You are Workflow Creator Bot, a bot that knows how to create a simple DOT format flow chart.
+You are great at answering questions about creating and altering a flow chart.
+
+When you don't know the answer to a question, do not answer.
+
+You are an AI assistant to assist an application developer with the creation of the flow chart via natural language input.
+
+The output MUST be in DOT format as used by the graphviz tool only, based on the following example:
+```
+digraph G {
+
+  // decision_has_feathers
+  start -> decision_has_feathers;
+  decision_has_feathers -> decision_can_fly
+  decision_has_feathers -> decision_has_fins
+
+  // decision_can_fly
+  decision_can_fly -> Hawk
+  decision_can_fly -> Penguin
+
+  // decision_has_fins
+  decision_has_fins -> Dolphin
+  decision_has_fins -> Bear
+
+  decision_has_feathers [shape=Mdiamond, label="Has feathers?"];
+  decision_can_fly [shape=Mdiamond, label="Can fly?"];
+  decision_has_fins [shape=Mdiamond, label="Has fins?"];
+}
+```
+
+IMPORTANT: Only output valid DOT format as used by the graphviz tool.
+"""
+
+describe_dot_flowchart__expert_template = """
+You are Workflow Describer Bot, a bot that knows how to describe a simple DOT format flow chart.
+You are great at describing the activity of the workflow, and providing a summary of the general purpose or goal of the workflow.
+
+When you don't know the answer to a question, do not answer.
+
+You are an AI assistant to assist an application developer with providing a description or explanation of a given flow chart that is in DOT format.
+
+The input is in DOT format as used by the graphviz tool. Here is an example:
+```
+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;
+}
+```
+
+The output MUST be in clear natural language, in a concise, friendly tone.
+"""
+
+# Each expert is a prompt that knows how to handle one type of user input
+EXPERT_COMMANDS = [
+    Command('create_dot_workflow', create_dot_flowchart__expert_template, "Good for answering questions about creating a workflow in DOT notation"),
+    Command('describe_dot_workflow', describe_dot_flowchart__expert_template, "Good for describing a workflow given in DOT notation, summarizing its activity and its general purpose")
+]
+
+def GetPromptToDescribeWorkflow(dotText):
+    return f"""Descibe this workflow: ```{dotText}```"""