Dify and Zilliz Cloud Integration

Dify is an open-source platform designed to simplify building AI applications by combining Backend-as-a-Service with LLMOps. It supports mainstream LLMs, offers an intuitive prompt orchestration interface, high-quality RAG engines, and a flexible AI agent framework. With low-code workflows, easy-to-use interfaces, and APIs, Dify enables both developers and non-technical users to focus on creating innovative, real-world AI solutions without dealing with complexity.

By integrating with Zilliz Cloud (fully managed Milvus), Dify gains access to a fully managed, scalable vector database that enhances its RAG engine with efficient vector search capabilities, enabling applications to retrieve relevant context from large datasets with improved accuracy and relevance for more contextually appropriate responses.

• Simplified AI app development with powerful retrieval: Dify's low-code platform makes it easy to build AI applications, while Zilliz Cloud provides the high-performance vector storage and retrieval that powers the RAG engine behind the scenes.
• Enhanced response accuracy: Zilliz Cloud's vector similarity search retrieves the most relevant context from large knowledge bases, improving the accuracy and relevance of LLM-generated responses within Dify applications.
• Docker-based rapid deployment: The integration supports deployment via Docker Compose with Milvus as the vector store, enabling teams to get a complete AI application stack running with minimal configuration.
• Flexible LLM support: Dify supports mainstream language models while Zilliz Cloud handles the vector storage layer, giving developers flexibility to choose and switch between models without changing the retrieval infrastructure.
• Low-code accessibility: Both developers and non-technical users can build sophisticated RAG applications through Dify's intuitive interface, backed by enterprise-grade vector search from Zilliz Cloud.

Dify serves as the application platform, providing the LLMOps environment with prompt orchestration, RAG engines, AI agent framework, and low-code workflow builder. It handles user interactions, query processing, and response generation through its intuitive web interface.

Zilliz Cloud serves as the vector database backend for Dify's RAG engine, storing and indexing document embeddings for fast similarity search. During user interactions, queries are processed and relevant vectors are retrieved from Zilliz Cloud to provide enhanced context to the language models.

Together, Dify and Zilliz Cloud create a complete AI application platform: documents are ingested and embedded into Zilliz Cloud through Dify's data pipeline. When users interact with the application, Dify's RAG engine retrieves relevant context from Zilliz Cloud's vector store and passes it to the LLM, producing more accurate and contextually appropriate responses — all accessible through Dify's low-code interface.

1. Clone the Repository

Clone the Dify source code to your local machine:

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

2. Set the Environment Variables

Navigate to the Docker directory in the Dify source code:

cd dify/docker

Copy the environment configuration file:

cp .env.example .env

Change the value VECTOR_STORE in the .env file:

VECTOR_STORE=milvus

Make sure the Milvus configuration in the .env file has the following line:

MILVUS_URI=http://host.docker.internal:19530

Note that by specifying VECTOR_STORE=milvus, Dify will bring up a Milvus Standalone server in Docker. Even though you can access the server from outside of the Docker through http://localhost:19530, for other Dify containers to talk to it inside Docker environment, they need to connect to the special DNS name host.docker.internal. Thus we set http://host.docker.internal:19530 as MILVUS_URI. For production deployment you may want to customize the authentication. For more information about how to set token or username and password in Milvus, you can refer to the authenticate page.

3. Start the Docker Containers

Choose the appropriate command to start the containers based on the Docker Compose version on your system:

docker compose up -d

4. Log in to Dify

Open your browser and go to the Dify installation page, where you can set your admin account:

http://localhost/install

Then log in to the main Dify page for further usage. For further usage and guidance, please refer to the Dify documentation.

• Deploying Dify with Milvus — Official Milvus tutorial for deploying Dify with Milvus
• Build a RAG Document Assistant in 10 Minutes with Dify and Milvus — Milvus blog hands-on tutorial
• How to Build RAG Applications with Dify and Milvus — Zilliz step-by-step guide on building RAG with Dify
• Dify GitHub Repository — Dify source code and community resources
• Dify Documentation — Official Dify documentation