Build Enterprise‑Grade RAG Apps with Milvus and Dify: A Step‑by‑Step Guide

Principle Introduction

Large language models often hallucinate due to limited knowledge. Retrieval‑augmented generation (RAG) connects external knowledge bases to mitigate this, and a powerful vector database is essential. This article focuses on Alibaba Cloud Milvus and the low‑code AI platform Dify to quickly build an enterprise‑grade RAG application.

Milvus Basics

Milvus is a distributed vector similarity search database. Its core technologies include approximate nearest neighbor (ANN) search (HNSW, IVF, PQ), multiple index types (FLAT, IVF_FLAT, IVF_PQ, HNSW), and sharding‑based distributed computation. It adopts a cloud‑native micro‑service architecture with four layers (access, coordination, execution, storage) and relies on etcd and object storage.

Use Cases

Image/video search (e‑commerce, facial recognition, video tracking)

Text semantic search (intelligent customer service, document knowledge bases, code search)

Personalized recommendation

Frontier science & security (drug screening, anomaly detection)

Autonomous driving data mining

Dify Platform Overview

Dify is an open‑source low‑code AI application development platform that integrates Backend‑as‑a‑Service and LLMOps, providing stable APIs, data management, and a visual prompt‑engineering interface. Its built‑in RAG engine connects to private knowledge bases to reduce hallucinations.

Prerequisites

Alibaba Cloud Milvus instance (see quick‑create guide)

Alibaba Cloud DashScope API‑KEY

Git, Docker, Docker‑Compose installed

Install and Configure Dify

git clone https://github.com/langgenius/dify.git

cd dify
cd docker
cp .env.example .env

VECTOR_STORE=milvus
MILVUS_URI=http://YOUR_ALIYUN_MILVUS_ENDPOINT:19530
MILVUS_USER=YOUR_ALIYUN_MILVUS_USER
MILVUS_PASSWORD=YOUR_ALIYUN_MILVUS_PASSWORD

docker compose up -d

Access http://127.0.0.1/ to set admin credentials and log in.

Set Default Model

In the user menu choose Settings → Model Provider, install the Tongyi Qwen model using the DashScope API‑KEY.

Create Knowledge Base

Import sample text (e.g., “Alibaba Cloud Milvus Introduction”) to create a collection; Milvus will build indexes automatically.

Validate Vector Retrieval

Check Dify logs or the Milvus Attu console to confirm data ingestion.

Test RAG Effect

Create a Knowledge Retrieval + Chatbot workflow, select the created knowledge base and the Qwen‑max model, then publish and run a query to see AI‑generated answers.

PAI Integration (Optional)

Deploy a model on Alibaba Cloud PAI‑EAS (e.g., Qwen3‑235B‑A22B‑Thinking‑2507) and configure Dify to call the PAI endpoint for inference.