Comprehensive Dify Tutorial: Transforming Company Knowledge Base into AI Customer Service and Workflow without Coding
With 138,000 GitHub stars, the open-source LLMOps platform Dify allows you to create a RAG knowledge base, AI customer service, and agent workflow on a visual canvas. This article will guide you through setting up Dify from scratch, whether on-premises or in the cloud, building a knowledge base, and publishing it as a web page and API.
Last month, a traditional industry owner from Taichung asked me, "Our product catalog is 200 pages long, and our sales team is bombarded with the same specification questions every day. Is there a way to let AI handle this?" I suggested that their young colleagues try using Dify - they uploaded the catalog PDF to the knowledge base, created a conversation flow, and deployed it on their official website. In just three afternoons, an AI customer service that could query the catalog was online. No coding was required.
This is Dify's positioning: to turn "creating an AI application that understands your company's data" from an engineering project into a drag-and-drop operation.
What is Dify?
Dify is an open-source LLMOps/agent workflow platform. As of April 2026, it has surpassed 138,000 stars on GitHub, exceeding Flowise and approaching n8n, making it the most popular open-source AI application construction platform. It integrates five functions into a single interface: visual workflow editing, RAG knowledge base pipeline, agent framework, model management, and operational monitoring (LLMOps).
There are two deployment methods: the cloud version has a free Sandbox plan, with Professional and Team plans available for $59 and $159 per month, respectively. Alternatively, you can self-deploy using Docker Compose, with the core functions available for free and data stored locally.
What can it do?
- RAG knowledge base Q&A: Upload PDFs, Word documents, or web pages, and automatically segment and vectorize them, allowing AI to "answer after querying" and significantly reducing hallucinations.
- AI customer service: Connect the knowledge base to a conversation flow and publish it as a web app, embedded chat window, or API with one click.
- Agent: Define an agent using Function Calling or ReAct, with over 50 built-in tools (such as Google search, image generation, and calculation) that can be directly used.
- Multi-step workflow: Create complex flows like "classification → search → generation → review" on a canvas, with each node using a different model.
How to use it? Four steps to get started
Step 1: Choose a deployment method. If you want to get started quickly, use the cloud-based Sandbox for free; if you prioritize data sovereignty (e.g., for legal, medical, or internal documents), self-deploy on a machine with Docker using the official compose file, and have model calls go through your own API key.
Step 2: Build a knowledge base. Create a Knowledge in the backend and drag your files into it. For newbies, use the default segmentation strategy, but here are two small tips: organize your files into a "one segment per topic" format, and convert table-like data into CSV or Markdown before uploading.
Step 3: Create a Chatflow. Choose the "knowledge base Q&A" template, hang your Knowledge on the search node, select a model (Claude, GPT, Gemini, or connect to a local Ollama model), and write clear role and answer rules in the system prompt - "if you can't find it, say you don't know, don't make it up" is a must.
Step 4: Publish. Publish with one click as a standalone web page, an embedded chat bubble on your official website, or integrate it into your own product using an API key. At this point, an AI application that understands your company's data is alive.
Advanced techniques
- Labeling feedback loop: The operational interface can see all real conversations, and incorrect Q&A can be labeled as correct answers and fed back, making the customer service more accurate over time.
- Multi-model division of labor: Use cheap and fast models for classification and rewriting, and high-end models for final answers, which can cut costs by more than half.
- Sensitive words and review nodes: For external customer service, output review is a must to prevent agents from saying things they shouldn't.
- Combining with n8n: Dify manages the "AI application itself", while n8n manages "front-end and back-end processes" (receiving forms → calling Dify API → writing back to CRM). See our n8n tutorial for combination techniques.
Precautions
- Free Sandbox has usage and feature limitations, so confirm that the message volume is sufficient before formally going online; if not, upgrade or self-deploy.
- Self-deployment is not equal to zero cost: model API token fees, hosting fees, and embedding fees are all real expenses, so grab a budget before going online.
- Data governance needs to be considered in advance: what files can be put into the knowledge base, how long conversation records are stored, and who can see the backend - these are more important than technical issues when enterprises introduce Dify.
TheAI Academy's evaluation
Evaluation: Dify is currently the open-source solution with the highest completion rate for "non-engineers to create RAG", and it has the lowest trial and error cost for small and medium-sized enterprises that want to create their own AI customer service. Start with it.
Further reading: To compare similar tools, see Flowise and Langflow; to find automatable scenarios, the customer service and knowledge base categories in the task list are suitable as the first project.
(This article's functional and pricing information was verified in July 2026, based on the latest official announcements.)
Common misconceptions / debunking myths
Many people misunderstand Dify as just a simple AI customer service tool, when in fact it is a complete LLMOps/agent workflow platform that can help enterprises establish their own knowledge base, agent framework, and model management system. Additionally, many people also misunderstand Dify as only being used for customer service, when in fact it can be used in various application scenarios, such as automated workflows and document queries.
Reasons to choose Dify
| Item | Description |
|---|---|
| Ease of use | Dify provides a visual workflow editing interface and drag-and-drop operations, making it easy for non-engineers to use |
| Autonomy | Dify supports self-deployment, allowing enterprises to fully control their data and models |
| Scalability | Dify supports multiple models and tools, allowing enterprises to expand according to their needs |
| Cost | Dify offers a free Sandbox plan and reasonable paid plans, allowing enterprises to choose according to their needs |
Practical steps
- Assess needs: Enterprises need to assess their needs, including customer service, knowledge base, and workflow requirements.
- Choose a deployment method: Enterprises need to choose a suitable deployment method, including cloud-based and self-deployment options.
- Build a knowledge base: Enterprises need to build their own knowledge base, including uploading files and establishing an agent framework.
- Configure models: Enterprises need to configure their own models, including selecting models and setting parameters.
- Test and optimize: Enterprises need to test and optimize their AI application, including labeling feedback loops and multi-model division of labor.
Future trends
Dify's future trends include:
- More models and tools: Dify will support more models and tools, including natural language processing, computer vision, and more.
- More powerful workflows: Dify will provide more powerful workflow functions, including multi-step workflows and conditional judgments.
- Better user experience: Dify will provide a better user experience, including a simpler interface and faster feedback.
Frequently Asked Questions
What are the limitations of using Dify for free?
The cloud-based Sandbox plan is free but has limitations on message volume and functionality, making it suitable for testing ideas. The self-hosted version offers a free core functionality, with data stored on your own server, but you'll need to pay for model API fees and hosting costs. For commercial operations, the Professional plan ($59/month) or self-hosting are common choices.
How does Dify differ from n8n, and which one should I choose?
Dify and n8n have complementary positions: Dify focuses on "AI application entities" (knowledge base, conversation flow, and agents), while n8n focuses on "process automation" (triggers, connections, and scheduling). Choose Dify for building AI customer service and n8n for cross-system automation. Mature teams often use both platforms together.
Will AI customer service built with Dify provide inaccurate responses?
The RAG architecture allows models to "retrieve before responding," significantly reducing hallucinations, but not eliminating them entirely. In practice, it's essential to set clear system prompts, add output review nodes, and utilize annotation features to continuously correct and retrain the model with incorrect cases.
Is it secure to store company internal files in Dify?
When using the self-hosted version, data remains on your own server, and security depends on your server management. With the cloud version, you'll need to evaluate the supplier's data policy. For sensitive industries (law, medicine, finance), it's recommended to self-host and establish governance rules, such as which files can be stored and how long conversation records are kept.