Java version of LangChain

A project to show howto use SpringAI with OpenAI to chat with the documents in a library. Documents are stored in a normal/vector database. The AI is used to create embeddings from documents that are stored in the vector database. The vector database is used to query for the nearest document. That document is used by the AI to generate the answer.

a rag implementation example for document-based QA, using spring-ai, ollama, and postgres-pgvector vetor db.

AI implementation using langchain4j and springAI frameworks with Java

Samples showing architectural patterns for Modular RAG using Spring AI and Ollama.

This project contains source code to demonstrate the usage of Retrival Augmented Generation (RAG) feature using Spring AI using PG Vector as the Database and Ollama as the Local LLM

🤖 Tutorial: Spring AI, OpenAI, Llama and RAG

A dynamic learning assistant designed to simplify the onboarding and training process for new hires. Users can upload documents or enter URLs for training materials. Built with Spring Boot, @langchain4j and spring-ai

Sanford utilizes LLMs, a storage bucket, and a Vector store to search for and/or summarize documents that you upload.

A Spring AI Exploring Project[Caicai].

A utility service backed by Spring AI that will help you refactor source in a Git repository. Contains a naive implementation to support refactoring Java application source.

This project uses Spring AI and PGvector to demonstrate retrieval via semantic search with vector embeddings.

agentnovax-api-rag-springboot-ollama-pgvector is a Spring Boot-based API that combines Retrieval-Augmented Generation (RAG) with the Ollama language model API and pgvector for vector search in PostgreSQL. It enables scalable, intelligent AI solutions for applications like recommendation systems, chatbots, and context-aware responses.

Simple Spring AI demo

A comprehensive HR recruitment assistant powered by GPT-4 and pgvector for intelligent candidate CV analysis and ranking.

Practicing AI with Spring

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.

SpringAI Study

a rag implementation example for document-based QA, using spring-ai, ollama, and postgres-pgvector vetor db.

A microservices based Appointment System using Spring Boot - Spring AI ..