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Sample

💡 TL;DR - GenAI leverages AI and Microservice Automation for 'hands-free' project creation

To create projects, the system (either WebGenAI or API Logic Server CLI - for more information, click here) performs the following:

  1. Uses ChatGPT APIs to submit prompts and obtain data model class responses

  2. It then uses these to create a database and project

This document presumes you have already created the project, and are using VSCode or GitHub to explore it. It illustrates how to run and customize the genai_demo project.

Note: if you not already created the project, proceed to the Appendix

 

Setup Codespaces

Coming soon.

 

What Just Happened?

genai processing is shown below (internal steps denoted in grey):

  1. You create your.prompt file, and invoke als genai --using=your.prompt. genai then creates your database and project as follows:

    a. Submits your prompt to the ChatGPT API

    b. Writes the response to file (system/genai/temp/response.json), so you can correct and retry if anything goes wrong

    c. Extracts system/genai/temp/create_db_models.py from the response

    d. Invokes als create-from-model, which creates the database and your project

  2. Your created project is opened in your IDE, ready to execute and customize

 

Microservice Automation

 

1. API/App Automation

API/App Automation means the created project is executable. To run:

  1. Press F5 to run
  2. Start your Browser (use the codespaces button, or locally) to view:
    • App Automation: the Admin App, and
    • API Automation: JSON:API, with Swagger
  3. Stop the server when you are done (red box on VSCode Debugger panel)

Microservice Automation

It's a modern, 3-tiered architecture, using standard Python libraries. For more information, click here - slide 2.

 

2. Customize: Rules and Python

The development environment is also standard: your IDE, standard languages, standard libraries, standard source control, etc. You customize API Logic Project in two ways, both performed in your IDE:

  • Logic Automation: declare spreadsheet-like rules to address multi-table derivations and constraints. These constitute nearly half of a typical database-oriented system. Declarative rules are 40X more concise than procedural code.

  • Standard Python: e.g, to create a new custom endpoint, and send a Kafka message

Note: projects are model-driven, resulting in a very small amount of code. This simlifies debugging and customizing your system.

Explore rules and Python customization below.

 

Logic Automation

To explore rules:

  1. Open logic/declare_logic.py

  2. View the Python-based rules created by GenAI

You can add more rules, with Python (code completion), and/or GenAI.

Add Rules

 

Standard Python, Libraries

To save time, issue the follow command to simulate changes you might make in your IDE, e.g., to create a new custom endpoint, and send a Kafka message.

Simulate IDE Customization
als genai-cust

Customize

Try it out

Set a breakpoint in the code above, and:

  1. Start the Server (F5)
  2. Use the Admin app to alter the first Customer, first Order, first Item, and change the quantity to 11111
    • Observe the error message, from the rules.
  3. To test the new endpoint, use Swagger (ServicesEndPoint > POST /ServicesEndPoint/OrderB2B).
    • Observe the swagger response - "Sending Order to Shipping sends:".

Note: Kafka is not activated in this example. To explore a running Tutorial for application integration with running Kafka, click here.

 

3. Deployment: Containers, Cloud

One of the best ways to de-risk projects is to verify the sponsors are in sync with what is happening. This is best addressed with working software, which often occurs late in project development. Surprises here can result in considerable rework... and frustrations.

GenAI Automation produces working software, now, so you can find misunderstandings before investing serious effort ("fail fast"). To expose the working software, it's often desirable to deploy to the cloud so business users can run it.

API Logic Server creates the devops directory, which scripts to containerize your project, and deploy it to Azure. For more information, see DevOps Automation.

 

Appendices

 

GenAI Restart Procedures

AI results are not consistent, so the created model file may need corrections. You can find it at system/genai/temp/model.py. You can correct the model file, and then run:

als create --project-name=genai_demo --from-model=system/genai/temp/model.py --db-url=sqlite

Or, correct the chatgpt response, and

als genai --using=genai_demo.prompt --gen-using-file=system/genai/temp/chatgpt_retry.txt

 

CLI Notes (effective as of release 11.00.22):

  • Projects are created in the your current working folder (typically the manager root directory). They were formerly created adjacent to the gen-using-file.

  • The project name is the last node of --using. This is a required argument, since it denotes the project directory name.

We have seen failures such as:

  • duplicate definition of DECIMAL (we hand-fix the response to fix this)
  • unclosed parentheses
  • data type errors in test data creation
  • wrong engine import: from logic_bank import Engine, constraint
  • bad test data creation: with Engine() as engine...
  • Bad load code (no session)
  • missing datetime import
  • relationship property errors ("mapper has no property...")

 

Diagnosis Tips

The system saves files used for creation:

Customize

Note there are multiple diagostic directories. recall GenAI results are not always predictable, so we make 3 attempts to get a successful result. This is often enough, but examining the failures can be useful.

A good technique is to:

  1. Open the response file in the Manager, and
  2. Use your IDE to run the file

It's usage create the sqlite database, but running it in this mode can provide more insight into causes.

If you are using Web/GenAI{:target="blank" rel="noopener"}, project files are always under /projects/gen$ID.

 

Creating genai_demo

Using WebGenAI

GenAI Automation

 

Using the ALS CLI

GenAI Automation

To create a microservice, identify an existing database, or provide a natural language "prompt" description.

You can explore genai_demo using the Manager. Optionally, you can sign-up for ChatGPT API and Copilot, or simulate the process as described below.

  1. If you have signed up for ChatGPT API and Copilot, this command will create and open a project called genai_demo from system/genai/examples/genai_demo/genai_demo.prompt:
als genai --using=system/genai/examples/genai_demo/genai_demo.prompt
  1. Or, if you have not signed up, you can simulate the process using a pre-installed response file:
als genai --using=genai_demo.prompt --gen-using-file=system/genai/temp/chatgpt_retry.txt

Here is the system/genai/examples/genai_demo/genai_demo.prompt file:

💡 TL;DR - GenAI Prompt

Create a system with customers, orders, items and products.

Include a notes field for orders.

Use LogicBank to enforce business logic.

Use case: Check Credit 1. The Customer's balance is less than the credit limit 2. The Customer's balance is the sum of the Order amount_total where date_shipped is null 3. The Order's amount_total is the sum of the Item amount 4. The Item amount is the quantity * unit_price 5. The Item unit_price is copied from the Product unit_price

Ensure each customer has a unique name.

Ensure each Item quantity is not null.

Ensure each order has a valid customer_id that exists in the Customer table.

 

GenAI Using Postgresql

The above examples use sqlite, since it requires no install. The GenAI process works for other database, such as Postgresql.

You can test this as follows:

  1. Use our docker image
  2. And:
als create --project-name=genai_demo_pg.prompt --db-url=postgresql://postgres:p@localhost/genai_demo

Provisos:

  • You have to create the database first