RAGFlow: Build a RAG Engine That Actually Understands Documents, Complete With Citations

Hey folks, it is me again. If you have ever played around with RAG (Retrieval Augmented Generation), you already know the frustration of watching your bot spit out nonsense just because the chunking was a mess. PDF documents full of tables, figures, and two-column layouts get sliced up carelessly by ordinary RAG tools, and the result is answers that are either wrong or straight up hallucinated. In this tutorial I want to introduce you to RAGFlow, an open-source RAG engine that is laser focused on deep document understanding.

RAGFlow is different from most RAG tools that just split text by characters or tokens. It has what is called deep document understanding, so it genuinely understands the structure of your documents: which part is a heading, which is a paragraph, which is a table, which is a figure, and it can even run OCR on scanned documents. On top of that it uses template-based intelligent chunking, so the way it cuts your documents adapts to the document type. And my favorite part, every answer it gives comes with grounded citations, so you can see exactly which page and which snippet an answer came from. That matters a lot for trust, especially if you want to use it for serious use cases like legal, medical, or financial work.

In this article I will walk you from zero: deploying RAGFlow with Docker Compose, creating a knowledge base, uploading documents, picking the right chunking method, configuring the chat assistant, and finally querying with the HTTP API and the Python SDK, complete with citations. Grab a coffee and let us take it slow.

Introduction: Why RAGFlow Is Different

Before we get into the technical stuff, I want you to understand why RAGFlow is worth learning. The most common problem in the RAG world is not the LLM itself, it is the quality of the retrieval. The "garbage in, garbage out" principle applies hard here. If the document chunk you feed into the LLM is already cut in the middle of a table, or mixes a footer with the actual content, the answer that comes out will be bad too.

RAGFlow attacks this problem at the root. A few things make it stand out:

First, deep document understanding. RAGFlow has a dedicated model for recognizing document layout. It can distinguish headers, paragraphs, lists, tables, and figures. For scanned documents or photos, it runs OCR first. So tables, which are usually a nightmare for other RAG tools, get extracted in RAGFlow with their structure intact.

Second, template-based chunking. RAGFlow provides several chunking templates tuned for different document types. There are templates for general documents, books, academic papers, tables, resumes, presentations, and more. So you are not forced to use a single chunking strategy for every document type. This is what makes the retrieval far more relevant.

Third, grounded citations. Every answer that comes out of RAGFlow always points back to the source chunk and the document page. So if someone doubts the bot's answer, they just check the source. This drastically reduces hallucination risk because the LLM is forced to answer based on the available context instead of making things up.

Fourth, it is all open-source and self-hosted. You can run it on your own server, your data never leaves your infrastructure, and you can use any LLM model, whether that is OpenAI, a local model via Ollama, or another provider. For those of you who care about data privacy, that is a huge plus.

Okay, enough theory. Let us jump straight into deployment.

Installation: Deploy RAGFlow With Docker Compose

The easiest and recommended way to run RAGFlow is with Docker Compose. RAGFlow is not actually a single application, it is a collection of several services: its own web server, Elasticsearch or Infinity for the vector store, MySQL for metadata, MinIO for object storage, and Redis. Docker Compose takes care of all of that at once.

System Preparation

Before we start, make sure your system meets the minimum requirements. RAGFlow is fairly heavy because it bundles Elasticsearch, so do not try running it on a potato machine, folks. Here is what you need:

• CPU with at least 4 cores
• At least 16 GB RAM (recommended, 8 GB works but it will be slow)
• At least 50 GB disk
• Docker version 24 or newer
• Docker Compose version 2.26 or newer

There is one important thing that often trips people up during deployment: Elasticsearch needs a high enough vm.maxmapcount setting. If it is too low, the Elasticsearch container will keep crashing. So let us set it first.