JAVA版本的检索增强生成(RAG)项目，包括知识库、搜索 | JAVA version of retrieval enhancement generation(RAG) project ,including knowledge base, search

Samples showing how to build Java applications powered by Generative AI and LLMs using Spring AI and Spring Boot.

All-in-One LLM AI Assistant

Odin Runes, a java-based GPT client, liberates you from vendor lock-in, allowing seamless interaction with your preferred GPT model right through your favorite text editor. There is more: It also facilitates prompt-engineering by extracting context from diverse sources using technologies such as OCR, enhancing overall productivity and saving costs.

This repository contains a documentation bot powered by an LLM using @langchain4j to swiftly find answers to your Spring Boot questions. It provides easy browsing of Spring documentation and leverages the RAG technique to retrieve relevant details on demand.

Retrieval Augmented Generation for (semi-)structured data using APIs

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

AI Assistant using RAG technique to give contextualized responses

A chat bot for domain specific knowledge

Simple local RAG sample using Spring-AI, Ollama LLMs and Vespa-AI

This demo demonstrates the integration of a local LLama3 model for handling JSON responses within the Spring AI framework, positioning the model as a 'business logic' executor

Retrieval Augmented Generation QnA application with Azure OpenAI and SpringAI

AEM Rockstars 2024 Project - ChatAEM AEM Integration

Retrieval Augmented Generation Demo with Quarkus

Simple spring-boot application which ingest a document to apply RAG pattern in order to enrich model knowledge about doc content.

🔎📚 This document processing system is designed to efficiently analyze user documents and provide accurate responses to user queries related to the content. Powered by advanced algorithms, it offers a seamless experience for users seeking insights or information within their documents.

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

Retrieval Augmented Generation QnA application with OpenAI and SpringAI

This repository contains the source code of a chatbot for a medical practice. It is based on a microservices architecture and uses a large language model to generate relevant and natural responses, following the RAG principle.

Large Language Model Retrieval Augmented Generation (LLM-RAG) with Spring AI.