AI implementation using langchain4j and springAI frameworks with Java
Workshop for Talent Arena 2025
Showcases how to integrate Spring AI's support for MCP (Model Context Protocol) within Spring Boot applications, covering both server-side and client-side implementations.
Java and Spring-Boot based AI app allowing users to create artificial profiles, add them as friends and chat with them with help of locally hosted LLM models & Spring AI. Features microservices architecture powered by Spring Cloud, OAuth2 security with Keycloak, MongoDB storage and a React-based frontend, all containerized with Docker.
This project integrates SpringAI with the Ollama Mistral model in a Java Spring Boot application to leverage generative AI capabilities. It provides an API for interacting with the Mistral model, enabling AI-powered text generation and processing.
Run a RAG-LLM locally with spring AI
Exploring interactions with LLMs : Practical insights with Spring AI
Spring AI, Ollama, model llama3.1
LLM Model Context Protocol (MCP) demo using Spring AI
Access to AI model hosted in local Ollama using Spring AI
Project to demonstrate how to use my own document to feed AI generative chat model and then ask local question related to that document for specific and effective answers. In technical term, using RAG with springboot AI. Ollama is used locally to run deepseek-r1 model.
Spring AI Functions With Local Ollama and Mistral
This project is a Spring Boot application that demonstrates an REST API using Ollama AI. It features embedding vectors, function calling, and streaming capabilities.
Basic project to use open source LLM model with Spring AI.
AGPL-3.0 Use Spring AI to create an Ollama Chatbot Client
Demo Spring Boot project with Kotlin based on Josh Long's demo scenario
Spring AI, Ollama, llama3.1, nomic-embed-text, PGVector
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