DataChat

A modern enterprise AI chat application with RAG (Retrieval-Augmented Generation) capabilities. Chat with an AI that understands your documents and databases.

Features

Core Features

• AI-Powered Chat - Interactive chat interface with streaming responses powered by OpenAI, Azure OpenAI, or Ollama
• RAG (Retrieval-Augmented Generation) - AI responses grounded in your documents and data
• File Uploads - Upload images, PDFs, Excel files, CSV, and documents directly in chat for AI analysis
• Multi-Provider LLM Support - Choose between OpenAI, Azure OpenAI, or Ollama for chat and embeddings
• SQL Data Sources - Connect to SQL Server databases and query tables/views as knowledge sources
• File System Indexing - Index documents from folders with pattern matching
• Vector Search - SQL Server 2025 native VECTOR type for semantic search
• User Management - Role-based access control with local or Windows authentication
• Data Source Permissions - Control who can access which data sources
• Personal Documents - Users can upload private documents only they can search

Chat Features

• Message Reactions - Thumbs up/down feedback on AI responses with optional comments
• AI Memory - Persistent memory that remembers user preferences and context across conversations
• Dark Mode - Full dark theme support with automatic persistence

Admin Dashboard

• Configuration Status - Visual dashboard showing red/yellow/green status for all system components
• Analytics Dashboard - Usage metrics, daily activity charts, top users, and engagement statistics
• Audit Log Viewer - Searchable log of all system actions with filtering and export
• Cost Tracking - Monitor API token usage and costs with budget alerts
• Announcement Banner - Display system-wide announcements with scheduling and styling options
• Branding Configuration - Customize application name, colors, and logo
• User Management - Manage users, roles, and AD group mappings
• Data Source Management - Configure and monitor knowledge base sync status
• Authentication Configuration - Configure Local or Windows Authentication with domain restrictions

Screenshots

Technology Stack

Category	Technologies
Backend	.NET 8, ASP.NET Core, Entity Framework Core 8
Frontend	Blazor Server, Microsoft Fluent UI
Database	SQL Server 2025 (native VECTOR support)
AI	OpenAI, Azure OpenAI, or Ollama (GPT-4o, GPT-4, Llama, etc.)
Architecture	Clean Architecture, CQRS with MediatR
Real-time	SignalR for streaming responses

Prerequisites

Before you begin, ensure you have the following installed:

• .NET 8 SDK
• SQL Server 2025 (required for native VECTOR type)
• One of the following AI providers:
  • OpenAI API Key
  • Azure OpenAI Service
  • Ollama (for local/self-hosted models)

Quick Start

1. Clone the Repository

git clone https://github.com/Chrisrokc/DataChat.git
cd DataChat

2. Configure the Database Connection

Option A: Environment Variable (Recommended for Production)

Set the connection string via environment variable to avoid storing credentials in config files:

# Linux/macOS
export ConnectionStrings__DefaultConnection="Server=YOUR_SERVER;Database=DataChat;User Id=sa;Password=YOUR_PASSWORD;TrustServerCertificate=True;"

# Windows PowerShell
$env:ConnectionStrings__DefaultConnection="Server=YOUR_SERVER;Database=DataChat;User Id=sa;Password=YOUR_PASSWORD;TrustServerCertificate=True;"

# Windows Command Prompt
set ConnectionStrings__DefaultConnection=Server=YOUR_SERVER;Database=DataChat;User Id=sa;Password=YOUR_PASSWORD;TrustServerCertificate=True;

Option B: appsettings.json (Development Only)

For local development, you can edit src/Presentation/DataChat.Web/appsettings.json:

{
  "ConnectionStrings": {
    "DefaultConnection": "Server=YOUR_SERVER;Database=DataChat;User Id=sa;Password=YOUR_PASSWORD;TrustServerCertificate=True;"
  }
}

Security Note: Never commit credentials to source control. The default appsettings.json ships with an empty connection string to prevent accidental credential exposure.

For Windows Authentication:

{
  "ConnectionStrings": {
    "DefaultConnection": "Server=YOUR_SERVER;Database=DataChat;Integrated Security=True;TrustServerCertificate=True;"
  }
}

3. Run the Application

cd src/Presentation/DataChat.Web
dotnet run

The application will start on http://localhost:5159.

4. Initial Setup (Setup Wizard)

On first run, DataChat automatically detects that setup is needed and redirects you to the Setup Wizard. The wizard guides you through three steps:

1. Database Connection

   • Enter your SQL Server connection details (server, database, credentials)
   • Click Test Connection to verify connectivity
   • The database will be created automatically if it doesn't exist
   • Click Save & Continue

2. Apply Migrations

   • Review the pending database migrations
   • Click Apply Migrations to create the database schema
   • Progress is displayed in real-time

3. Create Admin Account

   • Enter a username, display name, and password for the administrator account
   • Click Create Admin & Finish

After setup completes, you'll be redirected to the login page.

5. Configure AI Provider

After logging in as admin:

1. Go to Admin > Configuration > AI Settings
2. Select your LLM Provider (OpenAI, Azure OpenAI, or Ollama)
3. Enter the required credentials for your provider
4. Click Test & Save Settings to verify the configuration

The Status tab will show green indicators when everything is configured correctly.

Setting Up Example Data

To test the RAG functionality with sample data, you can create an example employee directory database.

1. Create a Sample Database

Connect to your SQL Server and run:

-- Create a sample database for testing
CREATE DATABASE CompanyData;
GO

USE CompanyData;
GO

-- Create Departments table
CREATE TABLE Departments (
    DepartmentId INT PRIMARY KEY IDENTITY(1,1),
    DepartmentName NVARCHAR(100) NOT NULL,
    Location NVARCHAR(100),
    Budget DECIMAL(18,2)
);

-- Create Employees table
CREATE TABLE Employees (
    EmployeeId INT PRIMARY KEY IDENTITY(1,1),
    FirstName NVARCHAR(50) NOT NULL,
    LastName NVARCHAR(50) NOT NULL,
    Email NVARCHAR(100),
    JobTitle NVARCHAR(100),
    DepartmentId INT FOREIGN KEY REFERENCES Departments(DepartmentId),
    HireDate DATE,
    Salary DECIMAL(18,2),
    ManagerId INT NULL FOREIGN KEY REFERENCES Employees(EmployeeId)
);

-- Insert sample departments
INSERT INTO Departments (DepartmentName, Location, Budget) VALUES
('Engineering', 'Building A, Floor 3', 2500000.00),
('Marketing', 'Building B, Floor 1', 800000.00),
('Human Resources', 'Building A, Floor 1', 400000.00),
('Finance', 'Building C, Floor 2', 600000.00),
('Sales', 'Building B, Floor 2', 1200000.00),
('IT Support', 'Building A, Floor 2', 350000.00),
('Legal', 'Building C, Floor 3', 500000.00),
('Product', 'Building A, Floor 4', 900000.00);

-- Insert sample employees
INSERT INTO Employees (FirstName, LastName, Email, JobTitle, DepartmentId, HireDate, Salary, ManagerId) VALUES
-- Engineering
('Sarah', 'Chen', 'sarah.chen@company.com', 'VP of Engineering', 1, '2018-03-15', 185000.00, NULL),
('Michael', 'Johnson', 'michael.johnson@company.com', 'Senior Software Engineer', 1, '2019-06-01', 145000.00, 1),
('Emily', 'Williams', 'emily.williams@company.com', 'Software Engineer', 1, '2021-02-14', 115000.00, 2),
('David', 'Kim', 'david.kim@company.com', 'Software Engineer', 1, '2022-08-22', 105000.00, 2),
('Jessica', 'Martinez', 'jessica.martinez@company.com', 'Junior Developer', 1, '2023-11-01', 75000.00, 2),

-- Marketing
('Robert', 'Brown', 'robert.brown@company.com', 'Marketing Director', 2, '2017-09-10', 155000.00, NULL),
('Amanda', 'Davis', 'amanda.davis@company.com', 'Marketing Manager', 2, '2020-01-20', 95000.00, 6),
('Christopher', 'Wilson', 'chris.wilson@company.com', 'Content Specialist', 2, '2022-04-15', 65000.00, 7),

-- Human Resources
('Jennifer', 'Taylor', 'jennifer.taylor@company.com', 'HR Director', 3, '2016-11-08', 135000.00, NULL),
('Daniel', 'Anderson', 'daniel.anderson@company.com', 'HR Specialist', 3, '2021-07-12', 72000.00, 9),

-- Finance
('Michelle', 'Thomas', 'michelle.thomas@company.com', 'CFO', 4, '2015-05-20', 210000.00, NULL),
('Kevin', 'Garcia', 'kevin.garcia@company.com', 'Financial Analyst', 4, '2020-09-14', 85000.00, 11),

-- Sales
('Brian', 'Rodriguez', 'brian.rodriguez@company.com', 'Sales Director', 5, '2018-02-28', 160000.00, NULL),
('Stephanie', 'Lee', 'stephanie.lee@company.com', 'Account Executive', 5, '2021-05-03', 78000.00, 13),
('Jason', 'White', 'jason.white@company.com', 'Account Executive', 5, '2022-01-17', 75000.00, 13),

-- IT Support
('Nicole', 'Harris', 'nicole.harris@company.com', 'IT Manager', 6, '2019-04-22', 110000.00, NULL),
('Ryan', 'Clark', 'ryan.clark@company.com', 'IT Support Specialist', 6, '2022-06-30', 62000.00, 16),

-- Legal
('Patricia', 'Lewis', 'patricia.lewis@company.com', 'General Counsel', 7, '2017-08-14', 195000.00, NULL),

-- Product
('Andrew', 'Walker', 'andrew.walker@company.com', 'Product Manager', 8, '2020-03-09', 130000.00, NULL),
('Rachel', 'Hall', 'rachel.hall@company.com', 'UX Designer', 8, '2021-10-25', 95000.00, 19);
GO

-- Create a view for the Employee Directory (this is what DataChat will index)
CREATE VIEW vw_EmployeeDirectory AS
SELECT
    e.EmployeeId,
    e.FirstName + ' ' + e.LastName AS FullName,
    e.Email,
    e.JobTitle,
    d.DepartmentName,
    d.Location AS DepartmentLocation,
    e.HireDate,
    e.Salary,
    CASE
        WHEN m.EmployeeId IS NOT NULL
        THEN m.FirstName + ' ' + m.LastName
        ELSE 'N/A'
    END AS ManagerName
FROM Employees e
JOIN Departments d ON e.DepartmentId = d.DepartmentId
LEFT JOIN Employees m ON e.ManagerId = m.EmployeeId;
GO

2. Add Database Connection in DataChat

1. Log in as admin
2. Go to Admin > Data Sources
3. Click Manage Connections
4. Click Add Connection
5. Fill in:
   • Name: Company Database
   • Server: Your SQL Server address
   • Database: CompanyData
   • Authentication: SQL Server or Windows Auth
   • If SQL Auth, enter username/password
6. Click Test Connection to verify
7. Click Save

3. Create SQL View Data Source

1. Go to Admin > Data Sources
2. Click Add Data Source
3. Select SQL View
4. Fill in:
   • Name: Employee Directory
   • Description: Company employee information including names, titles, and departments
   • Connection: Select Company Database
   • View/Table Name: vw_EmployeeDirectory
5. Click Create
6. Click Sync Now to index the data

4. Test with Example Questions

Go to the chat and try these questions:

• "Who works in the Engineering department?"
• "What is Sarah Chen's job title?"
• "List all employees hired in 2022"
• "Who is the highest paid employee?"
• "Which department has the largest budget?"
• "Who reports to Robert Brown?"
• "How many employees are in Sales?"

Configuration Options

All configuration is managed through the Admin > Configuration panel, which provides a visual status dashboard and organized tabs for each configuration area.

Configuration Status Dashboard

The Status tab provides at-a-glance health indicators:

• 🟢 Green - Configured and working
• 🟡 Yellow - Configured with warnings
• 🔴 Red - Not configured or error

Components monitored:

• SQL Server Connection
• Chat Model
• Embedding Model
• RAG Settings
• Authentication

AI Settings Tab

Configure your LLM provider for chat and embeddings.

OpenAI

Setting	Description	Default
API Key	Your OpenAI API key	Required
Chat Model	GPT model for chat	gpt-4o
Embedding Model	Model for vector embeddings	text-embedding-ada-002
Temperature	Response creativity (0-1)	0.7
Max Tokens	Maximum response length	4096

Azure OpenAI

Setting	Description	Example
Endpoint	Azure OpenAI endpoint URL	https://myinstance.openai.azure.com
API Key	Azure OpenAI API key	Required
Chat Deployment	Deployment name for chat model	gpt-4o-deployment
Embedding Deployment	Deployment name for embeddings	text-embedding-deployment
API Version	Azure OpenAI API version	2024-02-15-preview

Ollama (Self-Hosted)

Setting	Description	Default
Endpoint	Ollama server URL	http://localhost:11434
Chat Model	Model name for chat	llama3.2
Embedding Model	Model name for embeddings	nomic-embed-text

Click Test & Save Settings to validate your configuration before saving.

SQL Server Tab

Configure a SQL Server 2025 connection for queryable views as knowledge sources.

Setting	Description
Host	SQL Server hostname or IP
Port	SQL Server port (default: 1433)
Database	Database name
Username/Password	SQL Server authentication
Use Integrated Security	Use Windows Authentication
Trust Server Certificate	Skip certificate validation

RAG Settings Tab

Configure Retrieval-Augmented Generation behavior.

Setting	Description	Default
Enable Source Preview	Allow users to see document chunks used in responses	Enabled
Enable Document Preview	Allow in-browser document viewing	Enabled
Enable Document Download	Allow document downloads	Enabled
Token Expiration	Document access token validity (minutes)	10
Min Relevance	Minimum relevance % to show sources (0 = show all)	0
Max Sources	Maximum sources to display per response	5

Authentication Tab

Configure user authentication mode.

Local Authentication (Default)

• Username/password stored in database
• Passwords encrypted with ASP.NET Core Data Protection
• Session-based with configurable expiration

Windows Authentication

Setting	Description	Default
Auto-provision users	Create user accounts on first Windows login	Enabled
Default Role	Role for auto-provisioned users	User
Allowed Domains	Restrict to specific AD domains (semicolon-separated)	All domains

Note: Windows Authentication settings require an application restart to take effect.

AD Group Permissions: Map Active Directory groups to application roles or data source permissions via the User Management page.

appsettings.json

Base configuration file (authentication mode is now managed via Admin panel):

{
  "ConnectionStrings": {
    "DefaultConnection": "Server=...;Database=DataChat;..."
  },
  "Logging": {
    "LogLevel": {
      "Default": "Information"
    }
  }
}

Project Structure

DataChat/
├── src/
│   ├── Core/
│   │   ├── DataChat.Domain/        # Entities, enums, value objects
│   │   └── DataChat.Application/   # CQRS commands/queries, interfaces
│   ├── Infrastructure/
│   │   └── DataChat.Infrastructure/ # EF Core, OpenAI, vector store
│   └── Presentation/
│       └── DataChat.Web/           # Blazor UI, API endpoints
├── docs/
│   └── images/                     # Screenshots and documentation images
├── scripts/
│   └── create_test_data.sql        # Sample data script
└── README.md

Supported File Types

Chat Attachments

• Images: PNG, JPG, GIF, WebP (sent to vision API)
• PDFs: Rendered as images for AI analysis
• Spreadsheets: Excel (XLSX, XLS), CSV (parsed and converted to text)
• Text: TXT, MD, JSON (extracted as text)

Data Source Indexing

• Documents: PDF, DOCX, DOC, TXT, MD
• Spreadsheets: Excel (XLSX, XLS), CSV
• Images: PNG, JPG, JPEG (for personal documents)
• Data: SQL Server tables and views

Authentication Modes

Authentication is configured via Admin > Configuration > Authentication.

Local Authentication (Default)

• Username/password stored in database
• Passwords encrypted with ASP.NET Core Data Protection
• Session-based with 7-day sliding expiration

Windows Authentication

To enable Windows Authentication:

1. Go to Admin > Configuration > Authentication
2. Select Windows Authentication from the dropdown
3. Configure options:
   • Auto-provision users: Automatically create accounts for Windows users on first login
   • Default Role: Role assigned to auto-provisioned users (User or Admin)
   • Allowed Domains: Restrict login to specific AD domains (e.g., CORP;PARTNERS)
4. Click Save Authentication Settings
5. Restart the application for changes to take effect

Domain Restrictions

Enter allowed domains separated by semicolons. Leave blank to allow all domains.

Example: CORP;MYDOMAIN;PARTNERS

Users from domains not in the list will see an "Access Denied" page.

AD Group Permissions

Map Active Directory groups to application roles or data source permissions:

1. Go to Admin > User Management
2. Use the AD Group mappings section to configure permissions

Deployment

Development

dotnet run --project src/Presentation/DataChat.Web

Production (Kestrel)

dotnet publish -c Release -o ./publish
cd publish
dotnet DataChat.Web.dll

Environment Variables

You can override settings with environment variables:

export ConnectionStrings__DefaultConnection="Server=...;Database=...;"

Docker

DataChat includes Docker support for containerized deployments.

Quick Start with Docker Compose

The easiest way to run DataChat with Docker is using docker-compose.yml, which sets up both the application and SQL Server:

# Start DataChat and SQL Server
docker-compose up -d

# View logs
docker-compose logs -f datachat

The application will be available at http://localhost:8080. On first run, the Setup Wizard will guide you through configuration.

Docker Compose Configuration

The default docker-compose.yml includes:

• DataChat on port 8080
• SQL Server 2022 on port 1433

To use an external SQL Server instead, set the connection string environment variable:

services:
  datachat:
    environment:
      - ConnectionStrings__DefaultConnection=Server=your-server;Database=DataChat;User Id=sa;Password=YourPassword;TrustServerCertificate=True;

Building the Docker Image

# Build the image
docker build -t datachat:latest .

# Run standalone (requires external SQL Server)
docker run -d -p 8080:8080 \
  -e ConnectionStrings__DefaultConnection="Server=host.docker.internal;Database=DataChat;User Id=sa;Password=YourPassword;TrustServerCertificate=True;" \
  datachat:latest

Environment Variables for Docker

Variable	Description	Default
ConnectionStrings__DefaultConnection	SQL Server connection string	(empty - triggers Setup Wizard)
ASPNETCORE_ENVIRONMENT	Runtime environment	Production
Setup__Enabled	Enable Setup Wizard	true

Health Check

The container includes a health check endpoint at /health:

curl http://localhost:8080/health

---

Deploying to IIS

DataChat can be hosted on IIS (Internet Information Services) for production deployments on Windows Server.

Prerequisites

1. Windows Server with IIS installed
2. .NET 8 Hosting Bundle - Download and install from Microsoft .NET Downloads
3. SQL Server 2025 accessible from the IIS server

Step 1: Install the .NET Hosting Bundle

1. Download the .NET 8.0 Hosting Bundle (not just the runtime)
2. Run the installer on your Windows Server
3. Restart IIS after installation:

   net stop was /y
   net start w3svc

Step 2: Publish the Application

On your development machine, publish the application:

dotnet publish src/Presentation/DataChat.Web/DataChat.Web.csproj -c Release -o ./publish

Copy the contents of the ./publish folder to your IIS server (e.g., C:\inetpub\wwwroot\DataChat).

Step 3: Create the IIS Site

1. Open IIS Manager
2. Right-click Sites > Add Website
3. Configure:
   • Site name: DataChat
   • Physical path: C:\inetpub\wwwroot\DataChat
   • Binding: Choose your IP, port (e.g., 80 or 443), and hostname
4. Click OK

Step 4: Configure the Application Pool

1. In IIS Manager, go to Application Pools
2. Find the pool created for DataChat (or create a new one)
3. Right-click > Basic Settings:
   • .NET CLR Version: No Managed Code
   • Managed pipeline mode: Integrated
4. Right-click > Advanced Settings:
   • Start Mode: AlwaysRunning (recommended for Blazor Server)
   • Idle Time-out (minutes): 0 (prevents app pool recycling)

Step 5: Configure web.config

The publish process creates a web.config file. Verify it looks like this:

<?xml version="1.0" encoding="utf-8"?>
<configuration>
  <location path="." inheritInChildApplications="false">
    <system.webServer>
      <handlers>
        <add name="aspNetCore" path="*" verb="*" modules="AspNetCoreModuleV2" resourceType="Unspecified" />
      </handlers>
      <aspNetCore processPath="dotnet"
                  arguments=".\DataChat.Web.dll"
                  stdoutLogEnabled="false"
                  stdoutLogFile=".\logs\stdout"
                  hostingModel="inprocess">
        <environmentVariables>
          <environmentVariable name="ASPNETCORE_ENVIRONMENT" value="Production" />
        </environmentVariables>
      </aspNetCore>
    </system.webServer>
  </location>
</configuration>

Step 6: Configure appsettings.Production.json

Create appsettings.Production.json in the publish folder:

{
  "ConnectionStrings": {
    "DefaultConnection": "Server=YOUR_SQL_SERVER;Database=DataChat;User Id=datachat_user;Password=YOUR_PASSWORD;TrustServerCertificate=True;"
  },
  "Serilog": {
    "MinimumLevel": {
      "Default": "Warning"
    }
  }
}

Step 7: Set Folder Permissions

The IIS application pool identity needs permissions:

1. Right-click the DataChat folder > Properties > Security
2. Click Edit > Add
3. Enter: IIS AppPool\DataChat (replace "DataChat" with your app pool name)
4. Grant Read & Execute permissions
5. For the logs folder (if using stdout logging), grant Write permissions

Step 8: Configure HTTPS (Recommended)

1. Obtain an SSL certificate (Let's Encrypt, commercial CA, or self-signed for testing)
2. In IIS Manager, select your site > Bindings
3. Add an HTTPS binding (port 443) and select your certificate
4. Optionally, add URL Rewrite rules to redirect HTTP to HTTPS

Step 9: Enable WebSockets

Blazor Server requires WebSockets:

1. In Server Manager > Add Roles and Features
2. Navigate to Web Server (IIS) > Web Server > Application Development
3. Check WebSocket Protocol
4. Complete the installation

Or via PowerShell:

Install-WindowsFeature Web-WebSockets

Step 10: Configure Windows Authentication (Optional)

If using Windows Authentication:

1. In IIS Manager, select your site
2. Double-click Authentication
3. Enable Windows Authentication
4. Disable Anonymous Authentication (or keep both enabled for mixed mode)
5. In the DataChat admin panel:
   • Go to Admin > Configuration > Authentication
   • Select Windows Authentication
   • Configure auto-provisioning and allowed domains
   • Save and restart the application

Troubleshooting IIS Deployment

500.19 - Configuration Error

• Ensure the .NET Hosting Bundle is installed
• Check that the web.config is valid XML

502.5 - Process Failure

• Enable stdout logging in web.config (stdoutLogEnabled="true")
• Check .\logs\stdout*.log for errors
• Verify the connection string is correct
• Ensure SQL Server is accessible from the IIS server

503 - Service Unavailable

• Check if the Application Pool is running
• Verify the app pool identity has folder permissions

Blazor SignalR Connection Issues

• Ensure WebSockets are enabled in IIS
• Check that no proxy/load balancer is blocking WebSocket connections
• Verify the app pool idle timeout is set to 0

View Logs

Enable stdout logging temporarily:

<aspNetCore ... stdoutLogEnabled="true" stdoutLogFile=".\logs\stdout">

Create the logs folder and grant write permissions to the app pool identity.

---

API Endpoints

Endpoint	Method	Description
/api/login	POST	Authenticate user
/logout	GET	Sign out
/health	GET	Health check endpoint
/setup	GET	Setup wizard (only available during initial setup)

Security Considerations

1. Complete the Setup Wizard on first run to configure your database and create an admin account
2. Use HTTPS in production
3. Secure your OpenAI API key - it's encrypted in the database
4. Regular backups - chat history and documents are stored in the database
5. Review data source permissions - control who can access sensitive data

Troubleshooting

Database Connection Issues

• Verify SQL Server 2025 is installed (required for VECTOR type)
• Check connection string in appsettings.json
• Ensure SQL Server allows TCP/IP connections
• Test with SQL Server Management Studio first

OpenAI API Issues

• Verify API key is valid and has credits
• Check model availability in your OpenAI account
• Review logs for rate limiting errors

Vector Search Not Working

• Ensure SQL Server 2025 with native VECTOR support
• Check that data sources are synced (green status)
• Verify embedding model is configured

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• OpenAI for the GPT and embedding models
• Microsoft Fluent UI for the component library
• MediatR for the CQRS implementation