Build a Next-Gen Social Media Backend: Spring Boot, Neo4j, Langchain4j, & OpenAI Integration!

Published: 01 January 1970
on channel: Code With Bisky
252
14

Build a Next-Gen Social Media Backend: Spring Boot, Neo4j, Langchain4j, & OpenAI Integration!
Welcome back to our advanced tutorial series on building a powerful social media backend application! In this video, we’re diving into some exciting new features that will supercharge your app using Spring Boot, Neo4j, Langchain4j, and OpenAI.

What you’ll learn:
Sentiment Analysis & AI-Powered Recommendations: Use Langchain4j and OpenAI to intelligently create INTEREST relationships that enhance user engagement.
Page & User Follower Management: Learn how to fetch Page Followers and User Followers effortlessly.
Post Analytics & Interaction: Get Page Posts with detailed data on likes, comments, and followers, and learn how to Delete Comments dynamically.
Page Statistics: Retrieve comprehensive page performance stats.
Neo4JClient Mastery: Unleash the power of Neo4j for complex queries and advanced relationship modeling.
Whether you're just joining us or continuing from our previous tutorials, this video will take your backend skills to the next level. Make sure to check out our earlier videos for a complete step-by-step guide on building a social media platform from scratch!

🚀 Don’t forget to Like, Subscribe, and hit the Notification bell to stay updated with our latest content. Let’s build something amazing together!

#SpringBoot
#Neo4j
#Langchain4j
#OpenAI
#AI
#BackendDevelopment
#SocialMediaApp
#SentimentAnalysis
#RecommendationSystem
#JavaDevelopment
#APIDevelopment
#FullStackDevelopment
#GraphDatabase
#MachineLearning
#AIinTech


Watch video Build a Next-Gen Social Media Backend: Spring Boot, Neo4j, Langchain4j, & OpenAI Integration! online without registration, duration hours minute second in high quality. This video was added by user Code With Bisky 01 January 1970, don't forget to share it with your friends and acquaintances, it has been viewed on our site 25 once and liked it 1 people.