by Joche Ojeda | May 5, 2025 | Boring systems, ERP
After returning home from an extended journey through the United States, Greece, and Turkey, I found myself contemplating a common challenge over my morning coffee. There are numerous recurring problems in system design and ORM (Object-Relational Mapping) implementation that developers face repeatedly.
To address these challenges, I’ve decided to tackle a system that most professionals are familiar with—an ERP (Enterprise Resource Planning) system—and develop a design that achieves three critical goals:
- Performance Speed: The system must be fast and responsive
- Technology Agnosticism: The architecture should be platform-independent
- Consistent Performance: The system should maintain its performance over time
Design Decisions
To achieve these goals, I’m implementing the following key design decisions:
- Utilizing the SOLID design principles to ensure maintainability and extensibility
- Building with C# and net9 to leverage its modern language features
- Creating an agnostic architecture that can be reimplemented in various technologies like DevExpress XAF or Entity Framework
Day 1: Foundational Structure
In this first article, I’ll propose an initial folder structure that may evolve as the system develops. I’ll also describe a set of base classes and interfaces that will form the foundation of our system.
You can find all the source code for this solution in the designated repository.
The Core Layer
Today we’re starting with the core layer—a set of interfaces that most entities will implement. The system design follows SOLID principles to ensure it can be easily reimplemented using different technologies.
Base Interfaces
Here’s the foundation of our interface hierarchy:
- IEntity: Core entity interface defining the Id property
- IAuditable: Interface for entities with audit information
- IArchivable: Interface for entities supporting soft delete
- IVersionable: Interface for entities with effective dating
- ITimeTrackable: Interface for entities requiring time tracking
Service Interfaces
To complement our entity interfaces, we’re also defining service interfaces:
- IAuditService: Interface for audit-related operations
- IArchiveService: Interface for archiving operations
Repo
egarim/SivarErp: Open Source ERP
Next Steps
In upcoming articles, I’ll expand on this foundation by implementing concrete classes, developing the domain layer, and demonstrating how this architecture can be applied to specific ERP modules.
The goal is to create a reference architecture that addresses the recurring challenges in system design while remaining adaptable to different technological implementations.
Stay tuned for the next installment where we’ll dive deeper into the implementation details of our core interfaces.
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by Joche Ojeda | Sep 4, 2024 | A.I, Semantic Kernel, XPO
In today’s AI-driven world, the ability to quickly and efficiently store, retrieve, and manage data is crucial for developing sophisticated applications. One tool that helps facilitate this is the Semantic Kernel, a lightweight, open-source development kit designed for integrating AI models into C#, Python, or Java applications. It enables rapid enterprise-grade solutions by serving as an effective middleware.
One of the key concepts in Semantic Kernel is memory—a collection of records, each containing a timestamp, metadata, embeddings, and a key. These memory records can be stored in various ways, depending on how you implement the interfaces. This flexibility allows you to define the storage mechanism, which means you can choose any database solution that suits your needs.
In this blog post, we’ll walk through how to use the IMemoryStore interface in Semantic Kernel and implement a custom memory store using DevExpress XPO, an ORM (Object-Relational Mapping) tool that can interact with over 14 database engines with a single codebase.
Why Use DevExpress XPO ORM?
DevExpress XPO is a powerful, free-to-use ORM created by DevExpress that abstracts the complexities of database interactions. It supports a wide range of database engines such as SQL Server, MySQL, SQLite, Oracle, and many others, allowing you to write database-independent code. This is particularly helpful when dealing with a distributed or multi-environment system where different databases might be used.
By using XPO, we can seamlessly create, update, and manage memory records in various databases, making our application more flexible and scalable.
Implementing a Custom Memory Store with DevExpress XPO
To integrate XPO with Semantic Kernel’s memory management, we’ll implement a custom memory store by defining a database entry class and a database interaction class. Then, we’ll complete the process by implementing the IMemoryStore interface.
Step 1: Define a Database Entry Class
Our first step is to create a class that represents the memory record. In this case, we’ll define an XpoDatabaseEntry
class that maps to a database table where memory records are stored.
public class XpoDatabaseEntry : XPLiteObject {
private string _oid;
private string _collection;
private string _timestamp;
private string _embeddingString;
private string _metadataString;
private string _key;
[Key(false)]
public string Oid { get; set; }
public string Key { get; set; }
public string MetadataString { get; set; }
public string EmbeddingString { get; set; }
public string Timestamp { get; set; }
public string Collection { get; set; }
protected override void OnSaving() {
if (this.Session.IsNewObject(this)) {
this.Oid = Guid.NewGuid().ToString();
}
base.OnSaving();
}
}
This class extends XPLiteObject
from the XPO library, which provides methods to manage the record lifecycle within the database.
Step 2: Create a Database Interaction Class
Next, we’ll define an XpoDatabase
class to abstract the interaction with the data store. This class provides methods for creating tables, inserting, updating, and querying records.
internal sealed class XpoDatabase {
public Task CreateTableAsync(IDataLayer conn) {
using (Session session = new(conn)) {
session.UpdateSchema(new[] { typeof(XpoDatabaseEntry).Assembly });
session.CreateObjectTypeRecords(new[] { typeof(XpoDatabaseEntry).Assembly });
}
return Task.CompletedTask;
}
// Other database operations such as CreateCollectionAsync, InsertOrIgnoreAsync, etc.
}
This class acts as a bridge between Semantic Kernel and the database, allowing us to manage memory entries without having to write complex SQL queries.
Step 3: Implement the IMemoryStore Interface
Finally, we implement the IMemoryStore
interface, which is responsible for defining how the memory store behaves. This includes methods like UpsertAsync
, GetAsync
, and DeleteCollectionAsync
.
public class XpoMemoryStore : IMemoryStore, IDisposable {
public static async Task ConnectAsync(string connectionString) {
var memoryStore = new XpoMemoryStore(connectionString);
await memoryStore._dbConnector.CreateTableAsync(memoryStore._dataLayer).ConfigureAwait(false);
return memoryStore;
}
public async Task CreateCollectionAsync(string collectionName) {
await this._dbConnector.CreateCollectionAsync(this._dataLayer, collectionName).ConfigureAwait(false);
}
// Other methods for interacting with memory records
}
The XpoMemoryStore
class takes advantage of XPO’s ORM features, making it easy to create collections, store and retrieve memory records, and perform batch operations. Since Semantic Kernel doesn’t care where memory records are stored as long as the interfaces are correctly implemented, you can now store your memory records in any of the databases supported by XPO.
Advantages of Using XPO with Semantic Kernel
- Database Independence: You can switch between multiple databases without changing your codebase.
- Scalability: XPO’s ability to manage complex relationships and large datasets makes it ideal for enterprise-grade solutions.
- ORM Abstraction: With XPO, you avoid writing SQL queries and focus on high-level operations like creating and updating objects.
Conclusion
In this blog post, we’ve demonstrated how to integrate DevExpress XPO ORM with the Semantic Kernel using the IMemoryStore
interface. This approach allows you to store AI-driven memory records in a wide variety of databases while maintaining a flexible, scalable architecture.
In future posts, we’ll explore specific use cases and how you can leverage this memory store in real-world applications. For the complete implementation, you can check out my GitHub fork.
Stay tuned for more insights and examples!