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RAGFlow

What it is

RAGFlow is an open-source Retrieval-Augmented Generation (RAG) engine that integrates deep document understanding with agentic capabilities. It is designed to handle complex, unstructured data and provide a high-fidelity context layer for LLMs.

What problem it solves

It solves the "garbage in, garbage out" problem in RAG systems by using advanced document parsing (DeepDoc) to extract structured knowledge from complicated PDF formats, tables, and images. It minimizes hallucinations by ensuring retrieval is grounded in well-parsed evidence.

Where it fits in the stack

Tool / Infra: It serves as a specialized RAG infrastructure and toolset for document processing and retrieval.

Typical use cases

  • Complex PDF Parsing: Extracting accurate information from financial reports, legal documents, and technical manuals that contain complex layouts.
  • Agentic Knowledge Retrieval: Serving as the knowledge backend for agents that need to perform multi-step reasoning over large document collections.
  • Enterprise Search: Building a private, self-hosted search engine across heterogeneous data sources like Notion, Google Drive, and S3.

Strengths

  • DeepDoc Parsing: Superior extraction of knowledge from unstructured data compared to simple text chunking.
  • Template-based Chunking: Provides intelligent and explainable options for segmenting data.
  • Multi-modal Capabilities: Can reason over images within documents using vision models.
  • Agent Integration: Fuses RAG with agentic workflows for more dynamic task execution.

Limitations

  • Hardware Requirements: High resource consumption (minimum 4 cores, 16GB RAM).
  • Setup Complexity: Requires multiple services (Elasticsearch/Infinity, Redis, MySQL, MinIO) making it more complex to deploy than lightweight RAG wrappers.

When to use it

  • When document layouts are too complex for standard RAG systems.
  • When production-grade grounding and citation accuracy are critical.
  • When building agents that require a deep, well-structured knowledge base.

When not to use it

  • For simple, text-only RAG tasks where lightweight solutions like a basic vector DB would suffice.
  • In resource-constrained environments (e.g., low-power edge devices).

Getting started

Installation

# Clone the repository
git clone https://github.com/infiniflow/ragflow.git
cd ragflow/docker

# Increase vm.max_map_count for Elasticsearch
sudo sysctl -w vm.max_map_count=262144

# Start the server using Docker Compose
docker compose up -d

Basic usage

Access the RAGFlow UI at http://localhost (default port 80). Log in and configure your LLM API keys in the settings.

CLI examples

# Pull the latest RAGFlow image
docker pull infiniflow/ragflow:v0.25.1

# Check backend logs
docker logs -f ragflow-server

# Enter the backend container for maintenance
docker exec -it ragflow-server bash

API examples

# RAGFlow provides a Python SDK for programmatic access
from ragflow_sdk import RAGFlow

# Initialize the client
ragflow = RAGFlow(api_key="YOUR_API_KEY", base_url="http://ragflow-server")

# Create a dataset
dataset = ragflow.create_dataset(name="My Knowledge Base")

# Upload a document
dataset.upload_document(filename="manual.pdf")

Sources / references

Contribution Metadata

  • Last reviewed: 2026-02-26
  • Confidence: medium