Cell experiments project

Intro¶

The project is there to grow, add useful features, reflect an on prem capability landscape

See https://github.com/alfrepo/cell-project

The project is there to grow, add useful features, reflect an on prem capability landscape.

The goal: - use as many modern methods as possible - vibe coding - ai agents - define common dockerization patterns

Scope and Context¶

Cell: Custom Spring Boot service¶

Spring Boot applicaiton

Feature: Spring AI¶

Demonstrating some of - Chat Completion - Embedding - Text to Image - Audio Transcription - Text to Speech - Moderation

https://spring.io/projects/spring-ai

projects-root/
├── digital.alf.customsbs/
│   ├── .gradle/
│   │   └── ...
│   ├── src/
│   │   ├── main/
│   │   │   ├── java/
│   │   │   │   └── digital/
│   │   │   │       └── alf/
│   │   │   │           └── customsbs/
│   │   │   │               ├── CustomSbsApplication.java
│   │   │   │               ├── config/
│   │   │   │               ├── controller/
│   │   │   │               ├── model/
│   │   │   │               ├── repository/
│   │   │   │               └── service/
│   │   │   └── resources/
│   │   │       ├── static/
│   │   │       └── application.yml
│   │   └── test/
│   │       └── java/
│   │           └── digital/
│   │               └── alf/
│   │                   └── customsbs/
│   ├── build.gradle
│   ├── Dockerfile
│   ├── gradlew
│   ├── gradlew.bat
│   └── settings.gradle
│
├── another-project/
│   └── ...
│
└── docker-compose.yml

Cell: Angular PrimeNG based UI & Progressive Web App¶

The UI with - desktop - mobile experience

Cell: IAM KeyCloack¶

Should provide IAM for upper service.

Outcome: understand multi-tenancy, realms

Cell Analytics¶

Should take data from upper Service

Cell: Kestra.io¶

Outcome: To get data from my service and put it into the raw data layer

analytical databases 1 : DuckDB¶

DuckDB

analytical databases 2 : Clickhouse¶

Clickhouse

SuperSet¶

AI Dashboard

Cell: local RAG¶

Deployment overview https://medium.com/@miroslavmerreider/unlocking-the-power-of-local-offline-retrieval-augmented-generation-with-ollama-and-open-webui-19cd0bc67e81

Example with RAG https://github.com/alexandrainst/alex-rag-webui

Start Ollama: The user initiates the Ollama application, which acts as the server for the local LLM.

Pull & Serve Llama 3: Ollama connects to the internet to download the Llama 3 model from Meta's repository and loads it onto the user's machine, making it ready for processing requests. 3. Open WebUI Docker: The user runs Open WebUI as a Docker container, isolating the application and its dependencies.

User Interaction: The user opens a browser, navigates to the Open WebUI interface, and enters a question.

Process UI Requests: The Open WebUI container receives the user's question. It then sends this request to the Ollama server.

Process Model Answer: Ollama receives the question, processes it using the Llama 3 model, and generates a response.

Render Answer: The generated answer is sent back from Ollama to the Open WebUI container, which then displays the final response to the user's browser.

Web Search for RAG: Perform web searches using providers like SearXNG, Google PSE, Brave Search, serpstack, serper, Serply, DuckDuckGo, TavilySearch, SearchApi and Bing and inject the results directly into your chat experience.

Flutter Mobile App.¶

Hello World to access my API.

Business Case of App¶

Use AI on Edge. Recognize hand written code (KLeiderbörse). Append to a Google Docs file, written in configured online store.

Columns

code
price
date time
complete recognized string

Constraints¶

Solution Strategy¶

Building Block View¶

Runtime View¶

Deployment View¶

Cross-cutting Concepts¶

Architectural Decisions¶

Problem	Decision	Optimize for attribute
Delivery package	Docker Cpmpose	Simplicity of deployment
Delivery inside of VM	Ansible	Simplicity of deployment
CICD	GitHub CICD