Local LLM Application
A production-grade local LLM platform built with Java 21 and Spring AI. Features reactive document processing, conversational AI with session management, and MongoDB-backed persistence. Uses Ollama for local inference.
Tech Stack & Infrastructure
The Problem
Java enterprise teams need LLM capabilities but existing tools are Python-only, creating a skills gap.
The Solution
A Spring Boot application that brings LLM capabilities to the Java ecosystem using Spring AI, with reactive programming for high-throughput document processing.
Architecture Overview
A Spring Boot application using WebFlux for reactive endpoints, MongoDB for session storage, and Spring AI for model orchestration.
Engineering Decisions
Adopted Spring AI to bridge the gap between Java enterprise ecosystems and modern LLM capabilities.
Key Tradeoffs
Spring AI is still evolving, requiring occasional custom implementations for advanced agentic workflows compared to Python's LangChain.
Core Challenges
Handling streaming responses reactively via WebFlux without blocking the event loop.
Results & Impact
Delivered an enterprise-ready Java application that allows teams to seamlessly integrate local LLMs.
Future Roadmap
Add comprehensive RBAC (Role-Based Access Control) and multi-tenant capabilities.
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