（Spring Boot 3. X Microservices framework） 基于Spring Boot 3.X 的 Spring Cloud Alibaba / Spring Cloud Tencent + React的微服务框架。🔝 🔝 点个starrred 关注更新。Chat GPT(RAG、TTS、STT、LLM)

Local Video RAG Engine. A FastAPI microservice for video understanding: Scene Detection + Whisper ASR + Qwen3-VL. Optimized for Apple Silicon (MLX) & Windows/Linux (Llama.cpp).

The Journey of RAG: From Notebook to Microservices

RAG-Microservice: A robust, scalable question-answering service leveraging the Retriever-Answer Generator (RAG) architecture. Built with [Tech/Models used, e.g., Elasticsearch, GPT-3], this service efficiently retrieves relevant documents and generates precise answers for complex queries.

This system implements a micro-microservice architecture, and uses vuetify+langchain+local large model+local vector database to build a RAG system.

Project demonstrates the power and simplicity of NVIDIA NIM (NVIDIA Inference Model), a suite of optimized cloud-native microservices, by setting up and running a Retrieval-Augmented Generation (RAG) pipeline.

Develop a Flipkart Product Recommender Chatbot using Retrieval-Augmented Generation (RAG) to provide accurate, context-aware product recommendations based on a specific dataset. The system leverages LangChain and LangGraph within a containerized microservices architecture for scalability and observability.

Chabo is a modular, microservices-oriented RAG framework

A microservices-based RAG platform that supports multi-format document parsing, semantic search, and conversational AI, powered by Google AI and Qdrant.

A complete RAG microservice that ingests from multiple sources, stores embeddings in PGVector with hybrid search, and provides both raw chunk retrieval and LLM-powered answer generation. Built with FastAPI, Celery, Redis, and LlamaIndex. Production-ready with rate limiting, health checks, and Docker deployment.

Microservice chatbot LLM RAG

Enterprise Go microservices for syncing GitHub repositories to Pinecone for RAG

Guide and advise people with Can Tho Public Service. Using RAG and Microservices architecture.

A Telegram bot that simulates communication with Harry Potter (based on the LLM YandexGPT and the RAG system with character ENT), built on a microservice architecture using FastAPI and Docker.

Agent4GoMicroservice: A Go-based multi-agent microservice solution. Combines LLM tool-calling, Eino workflow orchestration, and Milvus-powered RAG for cross-domain automation (business tasks + knowledge queries). Supports dynamic agent registration & MCP integration.

A full-stack, microservices-based application for secure document management, enabling users to upload, parse, index, and query various file types through advanced NLP and RAG agents. Built with Docker and Kubernetes for scalability, and integrates Elasticsearch, Redis, and AWS S3 for efficient storage and search capabilities.

Microservice delivering AI-powered steering models for web minigames in RAG-2.

A production-ready backend microservice for securely storing and managing RAG-based chatbot conversations, built with Java 21, Spring Boot, PostgreSQL, Redis, and Dockerized microservice architecture.

Intelligent email RAG system with AI classification, real-time sync, and semantic search. Dockerized microservices using go-imap-sql, PostgreSQL, Qwen models, and UI to browser processed conversations

This repository contains a modular, scalable chatbot system leveraging LLMs (Large Language Models) with a microservices architecture. The chatbot is built for retrieval-augmented generation (RAG), enabling intelligent, context-aware responses.

This is a RAG Microservice Backend built using Redis (to cache conversations), Postgres/pgvector (as the vector store), the Unstructured library (to aid in table/image extraction in PDFs), LangChain, OpenAI API, and FastAPI.

Enterprise-grade microservices-based Retrieval-Augmented Generation (RAG)

production-ready Retrieval-Augmented Generation (RAG) chatbot microservice built with FastAPI, designed for enterprise workloads requiring high performance, scalability, and reliability. The service delivers real-time streaming responses, robust LLM caching, advanced vector search, and a seamless knowledge ingestion pipeline, all wrapped in a clean

This project implements a lightweight Retrieval-Augmented Generation (RAG) system using a microservices architecture. It includes embedding, file storage, main RAG, document parsing, API gateway, and frontend services. Suitable for knowledge base, intelligent Q&A, and similar scenarios.

Microservice delivering AI-powered steering models for web minigames in RAG-2.

RAGraph is a hybrid Graph-RAG microservice that combines vector search with knowledge graphs for enhanced document understanding and explainable AI responses.

A microservice RAG system using PageIndex's reasoning-based approach to achieve high retrieva accuracy without vector embeddings. Built with 9 FastAPI services and Next.js, eliminating theneed for vector databases.

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.

Production-grade event-driven AI chat platform with RAG and real-time token streaming. Built with .NET 10 microservices using Event Sourcing, CQRS, Wolverine sagas, Marten, RabbitMQ, SignalR, Keycloak, and Kong, with an Angular 21 frontend powered by NgRx SignalStore. Runs locally with Docker Compose using Ollama or OpenAI.

A RAG Microservice built with Catalyst,Open AI and Pinecone