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AI Customer Support Agent Demo with LangChain4j and Spring AI

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AI Customer Support Agent Demo with LangChain4j and Spring AI

I've been exploring AI libraries for Java recently and built a demo to showcase how to build a AI-powered full-stack app on Spring Boot. Features: - RAG (Retrieval-Augmented Generation): uses a terms of service doc for context - Function calling: can fetch, modify, and cancel reservations as allowed by the terms of service - React UI with streaming answers and live database view - LangChain4j and Spring AI implementations I hope it can be helpful to folks who want to explore AI tools in Java but haven't had a chance yet. Let me know what you think.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, context · Missing: mac, agents, macos
80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: answers · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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