 **Vaessl: Project Goals**

# Primary Objective

The goal of Vaessl is to bridge the gap between unstructured physical data (images) and structured digital systems (Inventory APIs). This project serves as a technical deep-dive into the integration of Generative AI within a traditional Full-Stack Java/Spring Boot environment.

# Learning & Development Pillars

While the end product is a functional utility, the development process is specifically designed to master the following domains:

- Modern Spring Boot & Spring AI: Moving beyond basic CRUD to orchestrate complex AI workflows, utilizing the Spring AI ecosystem to handle prompts and structured outputs.

- Vector Databases & Retrieval: Gaining hands-on experience in using vector databases in combination with classical development stacks.

- System Architecture: Designing a "Bridge" architecture that is decoupled from the target application, allowing for a flexible, provider-agnostic middleware service.

- AI Gateway Implementation: Mastering LiteLLM to manage multiple LLM providers (Gemini, OpenAI, Local LLMs).

- Containerized Orchestration: Delivering a production-ready environment via Docker Compose that manages networked services (App, DB, Proxy) with a single command.

# Product Goals (Public Value)

Vaessl is intended to be a useful tool for the public, specifically those utilizing self-hosted inventory systems like Homebox.

- Intuitive Discovery: Replace rigid keyword searches with intent-based search (e.g., finding a "Soldering Iron" when the user searches for "fix electronics").

- Improve productivity: Reduce the time it takes to catalog physical items through AI-assisted batch processing.

# Short-Term Roadmap & Milestones

- Phase 1: Foundation (Current)
  * Deploy the core infrastructure (PostgreSQL + pgvector, LiteLLM, Spring Boot Skeleton, Vite frontend).
  * Establish the bridge between the Vaessl backend and a demo Homebox instance.

- Phase 2: The Processing Pipeline
  * Implement the image processing workflow: Upload $\rightarrow$ AI analysis $\rightarrow$ staging Table.
  * Develop the Vite interface for data refinement and verification.

- Phase 3: Semantic Search & Demo
  * Integrate Spring AI for embedding generation.
  * Launch a public-facing demo to showcase the full-stack solution.

# Long-Term Roadmap & Milestones

TBD