From ec3db429baf2c283314f56da52ebbfdda1a69b9a Mon Sep 17 00:00:00 2001 From: kasun Date: Wed, 18 Mar 2026 02:45:26 +0100 Subject: [PATCH] added project goals documentation --- docs/01-Planning/03-Project-goals.md | 45 ++++++++++++++++++++++++++++ 1 file changed, 45 insertions(+) create mode 100644 docs/01-Planning/03-Project-goals.md diff --git a/docs/01-Planning/03-Project-goals.md b/docs/01-Planning/03-Project-goals.md new file mode 100644 index 0000000..1513223 --- /dev/null +++ b/docs/01-Planning/03-Project-goals.md @@ -0,0 +1,45 @@ + Vassal: Project Goals + +# Primary Objective + +The goal of Vassal 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) + +Vassal 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, Next.Js frontend). + * Establish the bridge between the Vassal 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 Next.js 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 \ No newline at end of file