#Milvus
Showing 37 of 37 repositories tagged #milvus, ranked by stars
Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM, Qwen 与 Llama 等语言模型的 RAG 与 Agent 应用 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain
LangChain4j is an idiomatic, open-source Java library for building LLM-powered applications on the JVM. It offers a unified API over popular LLM providers and vector stores, and makes implementing tool calling (including MCP support), agents and RAG easy. It integrates seamlessly with enterprise Java frameworks like Quarkus and Spring Boot.
🔍大模型应用开发实战一：RAG 技术全栈指南，在线阅读地址：https://datawhalechina.github.io/all-in-rag/
Semantic cache for LLMs. Fully integrated with LangChain and llama_index.
Open Source Deep Research Alternative to Reason and Search on Private Data. Written in Python.
Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc.
A persistent, unified memory layer for all your AI agents (e.g. Claude Code, Codex), backed by Markdown and Milvus.
Knowhere extracts, parses, and outputs structured chunks ready for AI Agents and RAG.
CookHero是一个基于 LLM + RAG + Agent + 多模态的智能饮食与烹饪管理平台，支持智能菜谱查询、个性化饮食计划、AI 饮食记录、营养分析、Web 搜索增强，以及可扩展的 ReAct Agent / Subagent 工具体系，帮助厨房新手轻松成为“烹饪英雄”。
Visualize hnsw, faiss and other anns index
面向 OA、ERP、CRM、工单等企业系统快速构建智能体，让 AI 在权限、审计与治理约束下安全调用真实业务能力。Build agents quickly for enterprise systems such as OA, ERP, CRM, and ticketing, enabling AI to safely call real business capabilities under permissions, audit, and governance controls.
📚 从零开始的向量数据库原理与实践教程，在线阅读地址：https://easy-vecdb.datawhale.cc/
Comprehensive Vector Data Tooling. The universal interface for all vector database, datasets and RAG platforms. Easily export, import, backup, re-embed (using any model) or access your vector data from any vector databases or repository.
Akcio is a demonstration project for Retrieval Augmented Generation (RAG). It leverages the power of LLM to generate responses and uses vector databases to fetch relevant documents to enhance the quality and relevance of the output.
A web UI Project In order to learn the large language model. This project includes features such as chat, quantization, fine-tuning, prompt engineering templates, and multimodality.
A modern desktop application for exploring, managing, and analyzing vector databases
Graph RAG with pure vector search, achieving SOTA performance in multi-hop reasoning scenarios.
A lightweight, production-ready RAG (Retrieval Augmented Generation) library in Go.
🍽️基于图RAG技术的AI美食推荐助手 - Datawhale all-in-rag教程实战案例，集成Neo4j图数据库、Milvus向量检索与智能对话系统
Production-grade RAG API built in Rust. Hybrid search with HNSW dense vectors and BM25 sparse matching, cross-encoder reranking, layout-aware document extraction via Docling, and 94.5% accuracy on Open RAG Bench. Powered by Cerebras, Groq, Milvus, and Jina AI.
PII Masker is an open-source tool for protecting sensitive data by automatically detecting and masking PII using advanced AI, powered by DeBERTa-v3. It provides high-precision detection, scalable performance, and a simple Python API for seamless integration into workflows, ensuring privacy compliance in various industries.
Open Source, Cloud Native, RESTful Search Engine Powered by Neural Networks
面向教育场景的RAG智能问答系统，融合关键词匹配与语义检索双引擎，融合MySQL和RAG技术，先经过MySQL数据库的检索(还融合了Redis辅助储存和搜索)，若无符合条件答案，则进入RAG系统，RAG知识库中的知识储存在Milvus向量数据库中
A Web Crawler based on LLMs implemented with Ray and Huggingface. The embeddings are saved into a vector database for fast clustering and retrieval. Use it for your RAG.
A context harness for AI agents: all your scattered context — code, memory, docs, databases, SaaS — in one searchable, browsable, file-like interface.
The helm chart to deploy Milvus
以图搜图基于Towhee（resnet50 模型） + Milvus
Build-An-LLM-RAG-Chatbot-With-LangChain-Python
Explore Multiple Vector Databases and chat with documents on Multiple LLM models, private LLM models
An experimental Retrieval-Augmented Generation (RAG) system specialised in ingesting MediaWiki sites via their API and providing an OpenAI API interface to interact with them.
Based on the large model development framework of langchain, LangGraph is integrated to create a scalable workflow architecture. RAG。基于langchain的大模型开发框架，集成LangGraph创建一个可扩展的工作流架构，RAG：结合检索向量知识库（Milvus），增强模型的知识检索能力，支持ChatGLM & OpenAI大语言
Insurance AI Assistant A smart system combining PostgreSQL, Milvus, and specialized AI agents (Life/Home/Auto) to answer insurance queries accurately. Features real-time sync, semantic search via OpenAI embeddings, and a Streamlit UI. Perfect for insurance tech demos or customer service augmentation.
This repository contains hybrid-rag a LLMOPS python package
🚀 多Agent协作智能知识库系统 — 五层可编排Agent架构 + RAG全链路 + 统一ToolRegistry，Spring Boot + FastAPI + React，核心亮点是 五层可编排 Agent 架构——Interface、Orchestrator、Tool、Memory、Evaluation 五层解耦，支持意图识别→问题改写→检索→充分性判断→答案生成的完整工作流。系统内置 Router / Retrieval / Ops / Inspection 多 Agent 协作，通过统一 Tool Registry 管理 AI 能力，支持 runId/traceId 全链路追踪、四级记忆体系与 Caffeine + Redis 多级缓存。适合Agent后端开发学习与简历展示
A RAG-driven image product search that showcases MERN, Milvus for vector indexing, Transformers, and a local Ollama Gemma LLM. Explore integrated embeddings, store vectors in Milvus, and manipulate queries with advanced language understanding.
Windows + Docker + Python 基于 Milvus + MySQL + Redis + 大模型的私有化 RAG 问答系统