What is GenAI-Powered Microservice Automation?

Microservice Automation means you can create microservices from a database with 1 command. You get an Admin App and a JSON:API with Swagger. Then, use your IDE to declare logic using spreadsheet-like rules (40X more concise than code), and Python as necessary. It's fast - you are up in seconds - and flexible using your IDE and familiar tools.

Power it with GenAI to make it even simpler. Just describe the system in business terms, and the system is created. Then, declare rules in natural language.

Let's have a look.

 

1. Simplicity of AI

To create system, provide a prompt in business terms:

Create an AI Prompt file named genai_demo.prompt

Create an AI Prompt file named genai_demo.prompt

Use SQLAlchemy to create a sqlite database named system/genai/temp/model.sqlite,
with customers, orders, items and product

Hints: use autonum keys, allow nulls, Decimal types, foreign keys, no check constraints.

Include a notes field for orders.

Create a few rows of only customer and product data.

Enforce the Check Credit requirement (do not generate check constraints):
1. Customer.balance <= credit_limit
2. Customer.balance = Sum(Order.amount_total where date_shipped is null)
3. Order.amount_total = Sum(Item.mount)
4. Item.amount = quantity * unit_price
5. Store the Item.unit_price as a copy from Product.unit_price

 

That's all it takes to create a system. Note:

• This is a high-level, business-oriented description of the system - not just the database, but the multi-table business logic too.
• It is not "techno-babble" of low-level framework details (routes...). You don't need to provide. or even learn such details. That would be a failure to communicate.
• The key take-way: Natural Language must be enhanced with microservice automation to deliver real simplicity and speed. API Logic Server provides this automation, with:
  • API and App Automation (see Step 2)
  • Logic Automation (step 3), enabling us to execute the check credit requirement above

 

2. Speed of Low Code

Then, 1 command creates the project (you can also create from an existing database):

Create the project

als genai --using=genai_demo.prompt

App and API are ready to run

The system creates the database, and an executable project providing API and App Automation. The microservice is ready to run, instantly:

 

3. Flexibility of a Framework

You can then customize the project with Python in your IDE. Create executable spreadsheet-like rules from our prompt - 40X more concise than code. Use Python to extend the rules (e.g, to send a Kafka message), and use the framework to create a custom endpoint:

Customize the Logic and API

 

Fully Delivering on Simplicity, Speed, Flexibility

Technology Promises	Limited By...	Deliver the Promise With API Logic Server
Framework Promises Flexibility	- No API, Logic or App Automation - Months of complex development	- Full Microservice Automation - Preserves Full framework access
Low Code Promises Speed	- No Logic Automation (nearly half the effort) - Proprietary IDE, Framework	- Logic Automation - Open IDE, Tools, Libraries
GenAI Promises Simplicity	- No API, Logic or App Automation - Only Database Automation	- API Logic Server uses GenAI, adding... - Full Microservice Automation

API Logic Server differentiates from traditional approaches:

• Unlike Frameworks, API Logic Server creates complete microservices (APIs and App in a customizable project), including logic automation.. You're ready to run. With frameworks, you're ready to code -- weeks-to-months of complex development.

• Unlike Low Code, API Logic Server provides logic automation, and preserves the framework flexibility. With Low Code, you have to code your logic, quite likely in a non-standard IDE.

  • For systems providing update, logic automation is critical. It's nearly half the effort. The promise of Low Code requires logic automation.

• Unlike basic GenAI, API Logic Server creates complete microservices -- including logic automation. You have working software. With GenAI, you have a database.

For more information, see the FAQs.

Does GenAI require Microservice Automation?

GenAI brings well-known value to app development. It's great for generating code snippets, including code snippets for driving other sub-systems, such as sql (e.g., "create a database..."). API Logic Server leverages both of these strengths.

While GenAI is great for driving sub-systems (like sql), it's not appropriate for creating sub-systems. For example, you would not want to generate a DBMS using GenAI.

But what about microservices, and their logic? It is like code snippets, or more like a sub-system? We investigated GenAI logic creation, and here's what we found...

In most cases, GenAI responses simply ignores the logic requirement. Attempts to address it fall in 3 categories: triggers, Logic Bank code with no context, and Logic Bank code with context.

 

Triggers

The trigger solution GenAI response typically looks something like this:

-- Note: The enforcement of the Check Credit requirement is complex and might be better handled in application logic.
-- However, you can create a stored procedure or use triggers to enforce these rules, keeping in mind the performance implications.

-- Example of a trigger to enforce Customer.balance <= credit_limit (simplified version)
DELIMITER //
CREATE TRIGGER CheckCreditBeforeCustomerUpdate
BEFORE UPDATE ON Customers
FOR EACH ROW
BEGIN
    IF NEW.Balance > NEW.Credit_Limit THEN
        SIGNAL SQLSTATE '45000' SET MESSAGE_TEXT = 'Customer balance exceeds credit limit.';
    END IF;
END;
//
DELIMITER ;

This is simply not a solution: it does not address the triggering event - some change to the order. By contrast, our logic automates about a dozen Use Cases, including:

• The customer balance is adjusted (and credit is checked) when an order is inserted, deleted, shipped, or re-assigned to a different customer. Importantly, the logic (and its overhead) is pruned on other changes, such as changing the shipping address.

• This dependency management chains: the order is adjusted when items are inserted, deleted, or updated with new quantities or different products.

Addressing this level of logic is why rules are 40X more concise than code.

 

Logic Bank Code, without context

If we engineer our prompt to suggest using Logic Bank (a component of API Logic Server), we get a response like this if there is no project context:

def declare_logic():
    LogicBank.activate(session=session, activator=logic_bank_util.register_rules)

    @LogicBank.rule("order", "before_flush")
    def order_amount_total(row: LogicRow):
        row.amount_total = sum([item.quantity * item.unit_price for item in row.items])

    @LogicBank.rule("customer", "before_flush")
    def customer_balance(row: LogicRow):
        row.balance = sum([order.amount_total for order in row.orders if order.date_shipped is None])

    @LogicBank.rule("item", "before_flush")
    def item_amount(row: LogicRow):
        row.amount = row.quantity * row.unit_price

    @LogicBank.rule("item", "before_flush")
    def copy_unit_price_from_product(row: LogicRow):
        row.unit_price = row.product.unit_price

    @LogicBank.rule("customer", "before_flush")
    def check_credit_limit(row: LogicRow):
        if row.balance > row.credit_limit:
            raise Exception(f"Customer {row.name}'s balance exceeds their credit limit.")

There are no existing Logic Bank APIs remotely like those above. This code does not even compile, much less run. It is, as they say, an hallucination.

 

Logic Bank Code, With Context

Excellent results are obtained when the prompt has available context. Copilot turns our Natural Language requirements into Logic Bank code, requiring only minor adjustments.

And this is *far preferable to generating logic code -- it's much better to understand and maintain the 5 rules than the 200 lines of generated code.

 

Conclusion

As perhaps expected, large scale sub-system creation from GenAI is not practical. However, it is a great driver for engines, and for creating code snippets. API Logic Server leverages these strengths, and provides the missing microservice logic automation.

Of course, the Logic Bank engine (part of API Logic Server) is required for actual execution. Watch it in the video below.

 

Video - 1 minute

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In Depth

Simplicity of AI, Speed of Low Code, Flexibility of a Framework, Modern Software Architecture

 

Instant Microservices, for Integration and App Backends

For Developers and their organizations seeking to increase business agility, API Logic Server provides

• AI Simplicity: create projects from a Natural Language business oriented prompts, or existing database, in seconds

• Microservice Automation: create executable projects with 1 command:

  1. API Automation: crud for each table, with pagination, optimistic locking, filtering and sorting, and
  2. App Automation: a multi-page, multi-table Admin App.

• Standards-Based Customization: use standard tools (your IDE, Python, Flask, SQLAlchemy, GitHub and Docker), with API Logic Server support for:

  3. Logic Automation: unique rules - 40X more concise multi-table derivations and constraints.

  4. Deployment Automation: scripts created to containerize your system, and deploy to Azure.
     
     

API Logic Server differentiates from traditional approaches:

• Unlike basic GenAI, API Logic Server creates systems from prompts at the business level, without tedious framework-level AI prompt engineering.

• Unlike frameworks, API Logic Servers preserves full flexibility and standard Dev Tools, with microservice automation to eliminate weeks-to-months of complex development.

• Unlike Low Code, API Logic Server provides logic automation, and preserves the framework flexibility.

 

Enterprise-class Architecture, Standards and Flexibility

API Logic Server creates projects with a modern, service-based architecture. Systems are:

• API-enabled by default; self-serve means most new requests will not require custom api development

• Shared logic enforced in the middle tier: unlike manually coded frameworks which may result in non-sharable logic on UI controllers, shared logic enables not only traditional forms, but also services, AI-driven bots, etc.

• Servers are stateless for scalability

• Scripts are provided to automate containterization

Developers appreciate a standards-based development approach.

• Projects are customized using standard IDEs, leveraging code completion for declaring logic.

• Projects can utilize modern tooling, such as Git for source control, testing tools, etc.

Developers require the flexibility of a framework. In fact,

• API Logic Server is based on Flask, so you can add new endpoints as required.

• You have the full power of Python and its many libaries, enabling you to address requirements such as integration (e,g, with message-based integration with systems like Kafka)

 

Speed of Low Code - including logic

Unlike Low Code systems, API Logic Server:

• Provides unique Logic Automation: for anything beyond trivial logic, missing backend logic automation means your project will simply not get moving.

• Automates modern, standard architectures

• Is IDE based, unlocking a wealth of tooling for customization

• Automates Instant Admin Apps, eliminating the need to use (and learn) a screen painter, and are executeable instantly

And, for systems requiring highly customized interfaces for Business Intelligence, API Logic Server works with Low Code.

 

Simplicity of AI

Technology only becomes useful when it's simple enough for teams to be effective. API Logic Server employs AI:

• Use Natural Language to create databases
• Use Natural Language to declare rules

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Videos, Tour

API Logic Server is an open source Python project. It is a CLI for project creation, and set of runtimes (SAFRS API, Flask, SQLAlchemy ORM, business logic engine) for project execution.

It runs as a standard pip install, or under Docker. For more on API Logic Server Architecture, see here.

Explore it below.

Video Tutorial (4 min)

 

Click the image below for a video tutorial, showing complete project creation, execution, customization and debugging. Or, see this article: click here.

Quick Screenshot Tour of using API Logic Server: Create, Run, Customize, Deploy

1. Create: Microservice Automation

Plug into your database, or database model: Microservice Automation means create projects instantly, with a single CLI command:

ApiLogicServer create --project_name=ApiLogicProject --db_url=nw

 

2. Run: API Automation and App Automation

Microservice Automation creates a project that is executable, with:

• API Automation means you have a running JSON:API
• App Automation means you have a running Admin App

The API unblocks UI Developers from waiting on lengthy API development cycles.
The Admin App can be used for instant business user collaboration.

See JSON:API and Admin App

 

You can run directly (python api_logic_server_run.py), or open it in your IDE and use the pre-created run configurations:

Unlike frameworks which require significant time and expertise, the create command builds a complete API for your database, with endpoints for each table, including swagger. The Admin App provides a link to the Swagger:

 

3. Customize: Logic Automation, Python Flexibility

Customize created projects in your IDE, with Python and standard libaries. Significantly, Microservice Automation also includes:

• Logic Automation means you customize logic using Rules and Python in your IDE

Rules are unique and confer significant business agility - 40X more concise than code,
for security and multi-table derivations and constraints.

See Logic With Rules and Python

 

Rules are 40X more concise than code, and are extensible with Python:

For more on customization, click here.

 

Customization also provides no-code ad hoc integrations,
and enables Instant Business Relationships.

See Integration: APIs and Messages

 

The automatically created JSON:API provides no-code ad hoc integrations, enabling organizations to move beyond ETL. For example, other applications might require a customer record, and their addresses. The automatically created self-serve JSON:API requires no code, and reduces future custom API development:

1. Create the JSON:API
2. Declare security, to control access and row level authorization

Integrate with B2B Partners by creating custom endpoints using Python and Flask, with under 10 lines of code. Instant business relationships. Observe that:

1. Update logic is partitioned out of each service - or UI - into shared Logic
2. Mapping between SQLAlchemy rows and requests is automated with the RowDictMapper

Integrate internal systems with Kafka, using business logic events:

For more on integration, explore running code in the Application Integration Sample Tutorial.

 

4. Deploy: Deployment Automation

The system creates scripts to containerize your project, and deploy it to Azure.

See Deployment Automation

 

The ApiLogicServer create command builds scripts for containerizing your project, testing it locally, and deploying to Azure:

 

Key Features

API Features

Feature	Notes
API Automation	Unlike Frameworks, API created automatically
Logic	Update requests automatically enforce relevant logic
Security	Role-based result filtering
Self-Serve JSON:API	UI Developers and Partners don't require API Dev
Standards-based	JSON:API
Optimistic Locking	Ensure User-1 does not overwrite changes from User-2
Multi-table	Retrieve related data (e.g. customers, with orders)
Pagination	Performance - deliver large result sets a page at a time
Filtering	Injection-safe filtering

Logic Features

Feature	Notes
Conciseness	Rules reduce the backend half your system by 40X
Automatic Ordering	Simplifies Maintenance
Automatic Optimization	Reduce SQLs by pruning and adjustment-based aggregates
Automatic Invocation	Rules called automatically to help ensure quality
Multi-Field	Formulas and contraints can access parent data, with optional cascade
Multi-table	Sum / Count Rules can aggregate child data, with optional qualification
Extensible	Formulas, Constraints and Events can invoke Python
Debugging	Use IDE Debugger, and logic log to see which rules fire

Security Features

Feature	Notes
Authentication	Control login access
Authorization	Row level access based on roles, or user properties
Authorization	Global filters (e.g, multi-tenant)
Extensible	Use sql for authentication, or your own provider

Admin App Features

Feature	Notes
App Automation	Unlike frameworks, Multi-Page App is created automatically
Multi-Table - Parents	Automatic Joins (e.g., Items show Product Name, not Product Id)
Multi-Table - Children	Parent pages provide tab sheets for related child data (e,g, Customer / Order List)
Lookups	E.g., Item Page provides pick-lists for Product
Cascade Add	E.g., Add Order defaults the Customer Id
Declarative Hiding	Hide fields based on expression, or insert/update/delete state
Intelligent Layout	Names and join fields at the start, Ids at the end
Simple Customization	Simple yaml file (not complex html, framework, JavaScript)
Images	Show image for fields containing URLs
Data Types	Define customfields for your data types

Other Features

Feature	Notes
Microservice Automation	One-command API / App Projects
Application Integration	Automation with APIs and Kafka Messages
AI-Driven Automation	Use Copilot to automate database creation
Multiple Databases	Application Integration
Deployment Automation	Automated Container Creation, Azure Deployment

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Scenarios

Application Integration

As illustrated below, API Logic Server supports transactions from User Interfaces, and 3 alternatives for Application Integration:

1. Ad Hoc Integration: the automatically created JSON:API provides no-code ad hoc integrations, enabling organizations to move beyond ETL. For example, other applications might require a customer record, and their addresses from an existing database.

   • JSON:API are a standard for self-serve APIs -- where clients can select the columns and the related data they require.

   • Analogous to GraphQL, self-serve APIs reduce the need for ongoing custom API development.

2. B2B Partners: you can use Python, Flask and SQLAlchemy to create Custom APIs, e.g. for B2B Partners. These are simplified by automatic reuse of Logic, and Integration Mapping.

3. Messages: Application Integration support also provides automation for producing and consuming Kafka messages. Here's an article: click here. To see these services in a tutorial, click here.

Unblock Client App Dev

Framework-based API development is time-consuming and complex. Since client App Dev depends on APIs, front-end dev is often blocked. This serialized dev process reduces business agility, and increases pressure on the team.

API Logic server can change that.

1. API Automation means client App Dev can start as soon as you have a database

2. Logic Automation means that

   1. Such logic - a substantial element of the system - is automatically partitioned out of each client into server-based logic. This reduces client coding, and enables the logic to be shared between user interfaces and services.
   2. Logic development can proceed in parallel with client App Dev

Here's an article, here. Or, the the Tutorial, here.

Instant Microservices with AI-Driven Schema Automation

API and Logic Automation begins with a database. But what if it's a new project, and there is no database.

You can certainly use your SQL tools. But we all know that SQL can be... tiresome.

AI provides a solution: Schema Automation. You can use Copilot to create the SQL DDL like this:

Create database definitions from Copilot

Create a sqlite database for customers, orders, items and product

Hints: use autonum keys, allow nulls, Decimal types, foreign keys, no check constraints.

Include a notes field for orders.

Create a few rows of only customer and product data.

Enforce the Check Credit requirement:

1. Customer.Balance <= CreditLimit
2. Customer.Balance = Sum(Order.AmountTotal where date shipped is null)
3. Order.AmountTotal = Sum(Items.Amount)
4. Items.Amount = Quantity * UnitPrice
5. Store the Items.UnitPrice as a copy from Product.UnitPrice

Then, employ API Logic Server API and Logic Automation, and use Python and standard frameworks to finish the job.

Here's a tutorial you can to explore this: click here,or see this article.

  For additional Use Cases, click here.

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Docs

To see the docs, click here.