Vaessl/docs/01-Planning/03-Project-goals.md

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