ragflow
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
What is it?
What it is
RAGFlow is an open-source RAG engine with deep document understanding. It parses complex documents (PDFs, tables, layouts) and provides grounded Q&A with citations.
Why it exists
Standard RAG pipelines struggle with complex document layouts. RAGFlow focuses on accurate parsing and retrieval for enterprise knowledge bases.
Who should use it
Teams building enterprise RAG over PDFs, manuals, reports, or internal documentation requiring citation-backed answers.
Who should avoid it
Simple chatbot use cases where basic chunking is sufficient.
How it works
A quick walkthrough in plain English
How RAGFlow answers from your documents
Step 1 of 4
You upload documents
PDFs, reports, and manuals go into the knowledge base.
Features
Advantages
- Strong document parsing accuracy
- Citations reduce hallucination risk
- Self-hosted data control
- Active development
Disadvantages
- Heavier setup than lightweight RAG tools
- Resource intensive for large document sets
- Newer project, smaller community than LangChain
Installation
docker
git clone https://github.com/infiniflow/ragflow.git cd ragflow/docker docker compose up -d
FAQ
RAGFlow vs Dify?
Both are RAG platforms. RAGFlow emphasizes document parsing depth. Dify is broader LLM app platform.
What vector DB does RAGFlow use?
Supports Elasticsearch and Infinity as vector stores.
Does it support local LLMs?
Yes. Configure Ollama, Xinference, or other local endpoints.
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