embedding

Java AI application development framework (supports LLM-tool,skill; RAG; MCP; Agent-ReAct,Team-Agent). Compatible with java8 ~ java25. It can also be embedded in SpringBoot, jFinal, Vert.x, Quarkus, and other frameworks.

Java 8+ agentic SDK: unified LLM access (OpenAI/Anthropic/DashScope/Doubao/DeepSeek...), Tool Calling, MCP, RAG, Agent Runtime, and a built-in Coding Agent CLI/TUI/ACP.

Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem

Mistral-java-client is a client for the Mistral.ai API. It allows you to easily interact with the Mistral AI models. Currently supports all mistral chat completion, OCR and embedding models.

Code of our paper "Method-Level Bug Severity Prediction using Source Code Metrics and LLMs" which is accepted to ISSRE 2023.

A curated collection of Spring Boot projects demonstrating AI and LLM integrations, including examples of AI-powered applications, multi-provider LLM setups, and best practices for Spring AI, modular design, and integration testing.

The watsonx.ai Java SDK is an open-source library that simplifies the integration of IBM watsonx.ai APIs into Java applications. It supports chat completions, streaming, tool calling, embeddings, text classification, text extraction, text detection, reranking, and time-series forecasting.

Incremental Fast Lightweight (y) virtual network Embedding framework

Create embeddings for RDF-graphs using the GloVe model

Image embedding in Java

Migration to Android V2 embedding (V1->V2)

It is the java implantation of paper "Translating Embeddings for Modeling Multi-relational Data".

Rest client for Qdrant vector database

A Spring Boot AI-powered chatbot that answers questions about a person's academic background, skills, professional experiences, and projects — based on uploaded documents

Demos for graalvm with springboot projects.

Spring Boot API for semantic product catalog search using LLM embeddings and cosine similarity.

In this project we study the paper: "Sentiment Classification using Document Embeddings trained with Cosine Similarity", by Thongtan et al. and try to test the model on a new dataset. The new dataset used for testing consists of amazon reviews and the results obtained confirm the need to do further testing on different datasets to prove the theo…

임베딩(vector) 스토어를 위한 캐시 매니저

Built a hands-on Spring AI project using Spring Boot and Ollama to explore Generative AI integration in backend applications. Implemented chat completion, prompt engineering, embeddings, vector search, RAG, chat memory, multimodal AI, and structured AI responses.