Dify from Beginner to Deployment: Building a RAG Knowledge Base and Private Agents

Dify is an open-source platform for developing large model applications, focused on building AI apps through visual drag-and-drop, enabling non-technical users to participate in defining AI applications and managing data. For developers just getting started with large models, it offers a path that bypasses complex coding and leads directly to understanding RAG, agents, and knowledge base concepts. This article lays out a practical, runnable main line from platform positioning and private deployment to subsequent knowledge base construction and web embedding.

Why Choose Dify Over Cloud-Hosted Tools

Among similar tools, Coze is often compared with Dify. The two have clearly different orientations: Coze is primarily cloud-hosted, suited for non-programmers to quickly generate mini-programs, web apps, or client applications; Dify supports private deployment, allowing data to remain 100% local.

This difference directly determines tool selection in industries with data compliance requirements, such as finance and healthcare. Handing enterprise data to third-party cloud management versus locking data on your own machine involves completely different risk models. For learners who want to get a demo running first and then migrate to real projects, locally deployed Dify is also more suitable as a long-term practice environment.

Private deployment refers to installing and running software on an organization's own servers or local machines rather than using a vendor's cloud service. Its core value lies in data sovereignty: all business data, model invocation records, and knowledge base content remain within the local network and do not pass through third-party servers. The opposite is the SaaS cloud-hosted model, where data is stored and managed by the service provider. For heavily regulated industries such as finance and healthcare, private deployment is often a necessary condition for meeting data compliance requirements; it also facilitates intranet isolation, auditing, and integration with existing IT systems. The trade-off is that you must bear the installation, operations, and upgrade costs yourself.

Trade-offs Among Three Deployment Methods

Dify's installation paths fall roughly into three categories.

Windows Local Deployment: Recommended Route

Install WSL and Docker Desktop on the current Windows machine, then run Dify through Docker. This route only requires installing a few pieces of software, has relatively light resource usage, and fully preserves the data control of private deployment, making it the explicitly recommended option by the course author. The specific steps include: checking the relevant options in Windows Features under "Programs" in Control Panel, installing WSL and verifying the version with wsl --version; if it reports an invalid version, WSL needs to be updated, during which Ubuntu is automatically installed and an account password is set, then exit and verify again. Next, install Docker Desktop, follow the prompts, and restart the machine.

WSL (Windows Subsystem for Linux) is a compatibility layer built into Windows 10/11 that allows a full Linux distribution (such as Ubuntu) to run directly on Windows without a traditional virtual machine or dual boot. It provides a near-native Linux command-line environment, can directly access the Windows file system, and is deeply integrated with Docker Desktop. Dify's official deployment scripts and container orchestration are mainly oriented toward Linux environments, so WSL becomes a key prerequisite for Windows users to get Dify running. After installation, you can verify the version with wsl --version and update if necessary with wsl --update.

Virtual Machine Deployment: Data Controllable but Heavy

Running Dify in a virtual machine also keeps data entirely on your own hard drive, but it requires opening an additional virtual machine, occupying at least 20G and in practice about 15 to 20G or more of space. It is generally heavy, and the author personally does not recommend it.

Official Cloud Service: Convenient but Data Overseas

Using the official online service directly is the least troublesome, but data is stored on servers in the eastern United States. For users who value data localization, this approach contradicts the original intent of private deployment.

Docker Startup and Dify Initialization

After Docker Desktop is installed, you can skip the welcome screen on first run and use it without registering or logging in. A running indicator in the lower left corner means the Docker engine has started; after entering the container settings, download the image according to the configuration and paste the command into the corresponding location to execute.

Dify's source code and environment variable configuration are also modified according to the documentation and then started. Seeing the Dify icon in a running state in Docker Desktop means the service has been brought up. Compared with the virtual machine approach, this method is much lighter and more memory-friendly.

When entering Dify for the first time, you need to set a username, password, and email. After logging in, you can see modules such as the home page, studio, knowledge base, integrations, and Marketplace. Data for newly created applications is stored on the local machine, not in the cloud.

Docker is a containerization platform that packages applications and their dependencies into lightweight, portable images that run in isolated environments. Docker Desktop is the desktop version for Windows/macOS, with a built-in Docker engine, command line, and graphical interface. Dify's source package usually comes with a docker-compose.yaml file, which can bring up multiple containers such as the API, web frontend, database, and vector store with a single command, greatly reducing deployment complexity. Before use, make sure the Docker engine is running; if image pulling is slow, you can configure a domestic image acceleration address.

The Main Line from Chunking and Building the Knowledge Base to Embedding in a Web Page

Installation is only the starting point. The course's main line is to use Dify's workflow and agent capabilities to complete a full RAG knowledge base assistant build: chunk documents, build the knowledge base index, configure the retrieval and generation process, and finally embed the assistant into a web page. This chain covers several core concepts that beginners need to understand—RAG addresses the boundaries of model knowledge, the knowledge base handles the storage and retrieval of external knowledge, and the agent is responsible for orchestrating calls.

A common pitfall during learning is getting bogged down in the details of every underlying component. The author's advice is to grasp the main line: first get Dify running and get the application running end to end; the principles of tools like WSL and Docker can be filled in after class. For non-technical people, this "run first, understand later" path is more effective than obsessing over installation details.

RAG (Retrieval-Augmented Generation) is a technical paradigm that lets large models retrieve from an external knowledge base before answering: the user's question is vectorized, relevant document fragments are recalled from the knowledge base, and then these are passed to the model together with the question to generate an answer. It alleviates the problems of outdated model knowledge and hallucinations, and does not require retraining the model. In Dify, the knowledge base is responsible for document upload, chunking, vectorization, and indexing; the chunking granularity directly affects retrieval accuracy. The agent is responsible for orchestrating tool calls and multi-step reasoning, connecting retrieval, generation, external APIs, and other steps into an executable workflow. Together, these three form the core capabilities of building a knowledge base assistant with Dify.

Suggested Learning Path

Dify and Coze are not substitutes but correspond to different scenarios: choose Dify when data localization and enterprise compliance requirements are needed; when you need to quickly generate lightweight applications and do not care about cloud hosting, Coze has a lower barrier to entry. Recent versions of Coze have iterated significantly and are friendlier to non-programmers, but their design approach differs noticeably from older versions, which may feel uncomfortable to users accustomed to the old version.

After getting Dify's deployment and RAG process running, looking back at the differences among these tools will make your judgment clearer.

One-line Summary

A hands-on guide to private Dify deployment: from WSL+Docker installation to the main line of building a RAG knowledge base assistant.

Paragraph Summary

This article introduces the positioning and hands-on path of Dify, an open-source large model application development platform. It first compares Dify and Coze: the former supports private deployment and fully local data, suitable for compliance scenarios in finance and healthcare; the latter is mainly cloud-hosted with a lower barrier to entry. It then outlines three deployment methods—Windows local deployment via WSL and Docker Desktop is the lightest and recommended, the virtual machine approach keeps data controllable but is heavy, and the official cloud service stores data in the eastern United States. Next, it explains the key steps for Docker startup and Dify initialization. Finally, it points out that the learning main line is to run through a complete RAG knowledge base assistant build: document chunking, knowledge base indexing, configuring retrieval and generation, and embedding in a web page, and suggests running it first before filling in the underlying principles.