RAGFlow

Overview

RAGFlow is an open-source deep RAG (Retrieval-Augmented Generation) knowledge engine that provides a full-stack, production-ready pipeline for enterprise knowledge bases. The project has surged past 30,000 GitHub stars, becoming one of the most popular open-source RAG frameworks.

RAGFlow's key differentiator is its approach to document parsing. Rather than treating all documents uniformly, it applies layout-aware parsing using models like LayoutLMv3 to extract structured content from complex documents — tables, figures, multi-column layouts, and scanned documents. This "deep parsing" approach significantly improves retrieval quality compared to naive chunking.

The framework is designed for enterprise deployment: it supports multi-tenant architecture, RBAC, audit logging, and integrates with popular vector databases (Milvus, Elasticsearch, Weaviate, Chroma) and LLM providers (OpenAI, Anthropic, DeepSeek, Dify, Ollama, local models).

Features

• Deep document parsing: Layout-aware parsing with LayoutLMv3 for tables, figures, and complex layouts
• Multi-tenant architecture: Enterprise-ready with RBAC and audit logging
• Visual workflow designer: Drag-and-drop interface for building RAG pipelines
• Knowledge base management: Document upload, parsing, chunking, and indexing in one platform
• Multi-vector-database support: Milvus, Elasticsearch, Weaviate, Chroma, PgVector
• Multi-LLM provider: OpenAI, Anthropic, DeepSeek, Dify, Ollama, local models
• Template library: Pre-built templates for common document types (PDF, Word, Excel, Markdown, HTML, email)
• API-first: RESTful API for programmatic integration
• Docker Compose deployment: One-command setup for local and production environments

Installation

# Quick start with Docker Compose
git clone https://github.com/infiniflow/ragflow.git
cd ragflow
cp .env.example .env
docker compose up -d

# Access the web UI at http://localhost:9380

# Alternatively via pip for development
pip install ragflow

Quick Start

from ragflow import RAGFlow

# Initialize
ragflow = RAGFlow(
    api_key="your-api-key",
    base_url="http://localhost:9380/api/v1"
)

# Create a knowledge base
kb = ragflow.create_knowledge_base(
    name="product-docs",
    description="Product documentation for RAG",
    parser_id="naive"
)

# Upload documents
kb.upload_documents("path/to/docs/")

# Query
response = ragflow.query(
    knowledge_base_id=kb.id,
    question="How do I deploy RAGFlow in production?",
    top_k=5
)
for chunk in response.chunks:
    print(f"Source: {chunk.source} | Content: {chunk.content}")

Core Concepts

Deep Parsing

RAGFlow's deep parsing pipeline uses a multi-stage approach:

1. Document recognition: Classify document type (PDF, image, scanned)
2. Layout analysis: Detect layout elements (text blocks, tables, figures, headers) using LayoutLMv3
3. Content extraction: Extract text, tables (as structured data), and image descriptions
4. Chunking: Apply document-aware chunking strategies (semantic, fixed, or hybrid)
5. Embedding: Generate vector embeddings for each chunk

Visual Workflow Designer

The drag-and-drop interface allows building custom RAG pipelines without code:

• Data source → Parsing → Chunking → Embedding → Indexing → Query → Response Generation
• Each step is configurable and can be combined with custom logic

Knowledge Base Management

• Multi-format support: PDF, DOCX, XLSX, PPTX, Markdown, HTML, email, images
• Incremental indexing for updated documents
• Version tracking for document updates
• Permission-based access control per knowledge base

Advanced Features

Hybrid Search

Combines vector similarity search with keyword-based BM25 search for higher recall. Configurable fusion strategy (weighted sum, RRF, or learned reranking).

Multi-Round Dialogue

Maintains conversation context across turns, enabling follow-up questions and clarification without repeating context.

Citation & Traceability

Every generated answer includes exact source citations with page numbers and document links, enabling auditability and verification.

Reranking

Post-retrieval reranking with cross-encoder models (BGE-Reranker, Cohere Rerank) for precision tuning.

Pros

• ✅ Excellent document parsing quality (deep parsing)
• ✅ Enterprise-ready (multi-tenant, RBAC, audit)
• ✅ Open source under Apache 2.0
• ✅ Visual workflow designer for non-coders
• ✅ Multi-provider LLM support
• ✅ Large, active community (30K+ stars)
• ✅ One-command Docker deployment

Cons

• ❌ Resource-intensive (requires GPU for deep parsing models)
• ❌ Docker-only deployment (no native bare-metal install)
• ❌ Learning curve for advanced configurations
• ❌ Primarily Python ecosystem
• ❌ No built-in streaming API for real-time responses

When to Use

• Building enterprise knowledge bases with complex documents
• Needing high-quality document parsing without building custom pipelines
• Deploying RAG at scale with multi-tenant support
• Teams wanting visual workflow design alongside code
• Organizations standardizing on open-source RAG infrastructure

Resources

• Official Documentation
• GitHub Repository
• Online Demo
• Community Forum