Building a RAG Assistant with Dify: A Practical Guide to Knowledge Bases, Workflows, and Agent Nodes

Why Dify Is a Good Entry Point for RAG and Agents

Many people building RAG assistants in Dify habitually wire knowledge bases, workflows, and Agent nodes together however they like—and end up with apps that either answer the wrong questions or can't be shipped at all. This article breaks down, from a hands-on perspective, how these three components divide responsibilities and connect, so you can build a RAG application that actually works.

Dify is a visual LLM application development tool that lets you build agents, knowledge base Q&A, and workflows by dragging and dropping. According to a course shared by a Bilibili creator, for beginners or those just getting started with LLMs, Dify presents abstract concepts like "agents," "RAG," and "knowledge bases" in a very intuitive way, lowering the barrier to understanding.

Tools similar to Dify include Coze, and both are well suited for beginners to get up to speed quickly. But the creator also cautions that if you really want to pursue LLM development and become an LLM application engineer, drag-and-drop alone isn't enough—you also need to understand frameworks like LangChain, LangGraph, and Deep Agent, as well as the concept of Harness architecture. Dify's value is in helping you first build an intuitive grasp of agents and RAG, which makes tackling code frameworks smoother afterward.

RAG (Retrieval-Augmented Generation) is a technical approach that has the LLM retrieve external materials before answering. An LLM's parameters bake in the knowledge from its training—which may be outdated and cannot cover an enterprise's private documents. RAG works by first retrieving relevant text chunks from a vector database using the user's question, then stitching those chunks into the prompt so the model answers based on the provided material. Its core value is reducing hallucination, making answers traceable, and allowing knowledge updates by simply swapping documents rather than retraining the model. Understanding this makes clear why "retrieval quality" and "how the prompt constrains the material" are the keys to a RAG application's success.

LangChain and LangGraph are the two most common Python frameworks for LLM application development. LangChain provides standardized components such as model calls, prompt templates, retrievers, and tools, organizing scattered API calls into reusable chains; LangGraph builds on top of it with a "graph" execution model, using nodes and edges to describe complex flows with branching, loops, and state passing—well suited to building agents that need multi-turn reasoning and multi-role collaboration. A useful comparison: Dify's drag-and-drop canvas is conceptually close to LangGraph's graph structure, just replacing code with visual nodes. Familiarizing yourself with the wiring logic on the canvas first makes it easier to understand the functions and state transitions behind each node when you later read framework code.

What Knowledge Bases, Workflows, and Agent Nodes Each Handle

This is the part most easily confused when building a RAG assistant. The three are not interchangeable building blocks—they serve different responsibilities.

Knowledge bases handle storage and retrieval. The documents you upload are chunked and vectorized, and relevant chunks are retrieved when a user asks a question. A knowledge base does not generate answers itself—it only provides "reference material."

Workflows handle orchestration. They define which steps a request goes through from input to output—for example, first retrieving from the knowledge base, then having the LLM generate an answer based on the retrieval results, and finally formatting the output. A workflow's strength is a deterministic path and strong controllability.

Agent nodes handle autonomous decision-making. They can decide on their own which tool to call and whether multiple rounds of retrieval are needed, offering high flexibility but relatively weaker controllability.

Common wiring mistakes include stuffing a knowledge base directly into an Agent node, or letting an Agent repeatedly call the knowledge base inside a workflow without setting boundaries. The former dilutes retrieval results with the Agent's free-form improvisation; the latter easily leads to runaway flows and slow responses.

Vectorization and similarity thresholds are two key mechanisms in the knowledge base retrieval step. After upload, documents are split into text chunks, each converted by an embedding model into a high-dimensional vector and stored in a vector database; when a user asks a question, it is likewise converted into a vector, and the system uses measures like cosine similarity to find the closest chunks. The number of retrieved chunks determines how much reference material the LLM gets—too many easily introduces noise and eats into the context window, while too few may miss key information. The similarity threshold is a filter line: chunks below it are discarded, preventing irrelevant content from being forced onto the model. In practice, you typically start with a moderate threshold and observe retrieval performance, then adjust based on specific symptoms like off-topic or incomplete answers.

The Connection Logic for a Shippable RAG Assistant

A shippable RAG assistant's core chain should be: user input → knowledge base retrieval → LLM generates based on retrieval results → output. Orchestrating this chain with a workflow is the most reliable approach, because every step is predictable and debuggable.

Specifically, in the workflow, first place a knowledge base retrieval node and set the retrieval count and similarity threshold; then pass the retrieved chunks as context into the LLM node, and explicitly require in the prompt that it "answer only based on the provided material"; finally add an output node to format the result. Agent nodes are better suited to scenarios requiring dynamic judgment—for example, when a user's question falls outside the knowledge base's scope, the Agent decides whether to hand off to a human or call other tools.

The creator also emphasizes in the course that these skills need to be followed from start to finish, because the connections among knowledge bases, workflows, and Agent nodes are continuous—skipping around makes it easy to err at the wiring stage.

From Drag-and-Drop to Code: What to Learn Next

Dify gives you an intuitive feel for agents and RAG, but it's not the destination. The creator notes that to truly enter the field of LLM development, you need to master frameworks like LangChain and LangGraph, understand Agent orchestration logic, and know what Harness architecture is. These are all necessary steps on the path from "using tools" to "doing development."

For beginners, the suggested path is: first use Dify to get thoroughly familiar with the concepts and wiring of knowledge bases, workflows, and Agent nodes, and build a small app that runs end to end; then take that intuition into learning framework code and understand what's really happening behind the drag-and-drop. This way, learning won't feel abstract, and it's easier to combine tool proficiency with development skills.

Back to the opening question: don't wire things arbitrarily when building a RAG assistant in Dify. The key is to distinguish that knowledge bases handle retrieval, workflows handle orchestration, and Agents handle decision-making—let each module do its own job, and the application will truly be ready to ship.

Harness architecture, in the LLM context, refers to a runtime framework wrapped around the model that handles engineering concerns such as context assembly, tool-call dispatching, multi-turn state maintenance, error retries, and safety constraints. The model itself only handles "generation," while a real, usable agent request also involves extensive peripheral logic like prompt assembly, feeding tool results back in, and deciding when to terminate loops—exactly what the Harness handles. It's fitting to think of it as "the agent's shell and orchestration hub": the same model, wrapped in different Harnesses, can differ greatly in stability and capability boundaries. That's also why, when moving from using visual tools to real development, understanding this layer of architecture matters more than memorizing a particular API.