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🚀 Welcome to uxopian-ai

uxopian-ai is a complete, standalone framework designed to accelerate and simplify the integration of powerful AI features into any enterprise application.

Built on a solid foundation of Java 21 LTS and Spring 3.5, it goes far beyond a simple library by providing a full suite of tools — from backend services to frontend components — to create sophisticated, reliable, and scalable AI solutions.

✨ The uxopian-ai Advantage: More Than Just a Library

While uxopian-ai uses the excellent Langchain4j library as its core for LLM interactions, it builds a complete enterprise-ready ecosystem around it. Here’s the added value:

✅ Standalone Service, Not Just Code A pre-packaged, deployable service that saves you months of development and infrastructure setup.

✅ Ready-to-Use UI Components Instantly integrate AI with web-components (IIFE compiled, scoped CSS), plus plug-and-play integration scripts.

✅ Advanced Orchestration Engine The unique Goal system enables dynamic prompt selection based on context — no need to build this from scratch.

✅ Complete Conversation Management Persistent conversations with cost tracking, response regeneration, and user feedback support.

✅ Data-Driven Insights A comprehensive admin panel to monitor ROI, token usage, and adoption trends.

🔍 Key Features at a Glance

⚙️ Effortless & Scalable Integration

  • Standalone Service: Deployable via Docker or as a Java 21 application.
  • Multi-Tenant Architecture: Designed for internal deployments with clear logical separation and distinct tenant management.
  • Web-Component UI: Lightweight, embeddable components for any web app.
  • Rich REST API: Fully documented (Swagger) for seamless integration.

📊 Powerful Admin & Analytics

  • Granular Token Monitoring: Visualize input and output token consumption globally, by specific users, or per conversation.
  • ROI & Efficiency Tracking: specific metrics allow you to view the number of times a prompt is used and estimate the total time saved.
  • Usage Trends: Analyze activity over time (requests per week), monitor LLM model distribution, and track the adoption of advanced features like multi-modal capabilities.

🧠 Intelligent Orchestration

  • Goal System: Define context-aware workflows using filters and priorities. Example: A "comparison" goal automatically picks a legal prompt for contracts, and a generic one for others.
  • Templating Engine: Dynamic data injection, custom Java services, and conditional logic with Thymeleaf.
  • Template Helpers: Add your own Java functions to enrich prompts.

🤖 Robust LLM Interaction

  • Broad Support: Compatible with many LLM providers out-of-the-box.
  • Custom Connectors: Add private or fine-tuned models easily.
  • Advanced Features: Native support for function calling, multi-modal requests (text + image), and streaming/non-streaming responses.
  • MCP Server Client: Acts as a client for Multi-Content Platform (MCP) servers.

💬 Complete Conversation Management

  • Persistent History: Conversations and messages are stored with full context.
  • Feedback Loop: Gather specific user feedback (Good/Bad/Neutral) on responses to improve prompt quality.
  • Rich UX: Regenerate, copy, and manage conversation content easily.

👥 Who Is This For?

This documentation is tailored for integrators and developers looking to deploy, configure, and extend the uxopian-ai framework to deliver cutting-edge AI features faster.

🚀 Getting Started

Ready to dive in? Check out the Installation Guide to set up your first instance of uxopian-ai.