Aspire AI Chat

Aspire AI Chat is a full-stack chat sample that combines modern technologies to deliver a ChatGPT-like experience.

Architecture

graph TB
    User[User Browser]
    
    subgraph "YARP Reverse Proxy"
        YARP[chatui - YARP]
    end
    
    subgraph "Frontend"
        UI[React + TypeScript UI<br/>Vite Build]
    end
    
    subgraph "Backend API"
        API[ChatApi - ASP.NET Core<br/>SignalR Hub]
    end
    
    subgraph "Data Layer"
        PG[(PostgreSQL<br/>Conversation History)]
        Redis[(Redis<br/>Message Stream Cache)]
    end
    
    subgraph "AI Layer"
        LLM{AI Provider}
        Ollama[Ollama<br/>phi4 model<br/>Linux/Windows]
        OpenAI[OpenAI<br/>gpt-4.1<br/>macOS/Production]
    end
    
    User -->|HTTP/WS| YARP
    YARP -->|Static Files| UI
    YARP -->|/api/*| API
    
    API -->|SignalR| User
    API -->|EF Core| PG
    API -->|Pub/Sub| Redis
    API -->|IChatClient| LLM
    
    LLM -.->|Local| Ollama
    LLM -.->|Cloud| OpenAI
    
    style YARP fill:#0078d4,stroke:#004578,stroke-width:2px,color:#fff
    style API fill:#f37021,stroke:#b85419,stroke-width:2px,color:#fff
    style UI fill:#5c2d91,stroke:#3d1e61,stroke-width:2px,color:#fff
    style PG fill:#336791,stroke:#274466,stroke-width:2px,color:#fff
    style Redis fill:#dc382d,stroke:#a52a22,stroke-width:2px,color:#fff
    style LLM fill:#ffc107,stroke:#c79100,stroke-width:2px,color:#000
    style User fill:#2d333b,stroke:#1c2128,stroke-width:2px,color:#fff
    style Ollama fill:#4caf50,stroke:#388e3c,stroke-width:2px,color:#fff
    style OpenAI fill:#10a37f,stroke:#0d8267,stroke-width:2px,color:#fff

High-Level Overview

• Backend API:
  The backend is built with ASP.NET Core and interacts with an LLM using Microsoft.Extensions.AI. It leverages IChatClient to abstract the interaction between the API and the model. Chat responses are streamed back to the client using SignalR for real-time communication.

• Data & Persistence:
  Uses Entity Framework Core with PostgreSQL for reliable relational data storage. Redis is used for caching and broadcasting live message streams across multiple clients.

• AI & Chat Capabilities:

  • Uses Ollama (via OllamaSharp) for local inference on Linux/Windows.
  • On macOS, the application uses OpenAI directly for better compatibility.
  • In production, the application can be configured to use various AI providers through the abstraction layer.

• Frontend UI:
  Built with React and TypeScript using Vite for fast development and builds. The UI provides a modern chat interface with support for markdown rendering and conversation history.

• Reverse Proxy & Serving:
  Uses YARP (Yet Another Reverse Proxy) to serve the static frontend and proxy API requests, providing a unified endpoint.

Getting Started

Prerequisites

• .NET 10.0 SDK
• Docker or Podman
• Node.js (LTS version recommended)

Running the Application

Run the application:

aspire run

This project uses Aspire to orchestrate the application components in containers.

Configuration

• By default, the application uses Ollama (phi4 model) for local inference on Linux/Windows.
• On macOS, it automatically switches to OpenAI and will prompt for your OpenAI API key if not already configured.
• The PostgreSQL database and Redis cache are automatically provisioned when running with Aspire.
• Access the Aspire dashboard to monitor resources and view logs.

Deployment

To deploy the application using Docker Compose:

aspire deploy

This generates a Docker Compose configuration in the aspire-output directory and runs the application stack. The deployment includes:

• All application services (ChatApi, UI)
• PostgreSQL database with persistent volume
• Redis cache
• Configured networking between services

The application will be accessible at the configured ports, with all services orchestrated through Docker Compose.

CI/CD

The project uses Aspire's pipeline system to build and publish container images. The custom push-gh pipeline step (defined in PipelineExtensions.cs) handles:

• Building container images through Aspire's build pipeline
• Tagging images with format: <branch>-<build-number>-<git-sha>
• Pushing images to GitHub Container Registry (GHCR)

The GitHub Actions workflow invokes the pipeline with environment variables:

- name: Push to GitHub Container Registry
  run: aspire do push-gh
  env:
    GHCR_REPO: ghcr.io/${{ github.repository_owner }}
    BRANCH_NAME: ${{ github.ref_name }}
    BUILD_NUMBER: ${{ github.run_number }}
    GIT_SHA: ${{ github.sha }}

The Aspire pipeline step reads these values, sanitizes the branch name for Docker compatibility, creates a semantic tag, and pushes the images to GHCR. This approach integrates seamlessly with Aspire's orchestration, allowing the AppHost to define both local development and CI/CD workflows in one place.

Images are available at: ghcr.io/<owner>/chatapi and ghcr.io/<owner>/chatui