TL;DR: Flow (RAGFlow) is an open-source RAG-based AI search assistant that lets you build knowledge bases from your own documents and get accurate, citation-backed answers — perfect for enterprise document Q&A.
Official Website: Visit Flow | Tool Page: Flow details & alternatives
The Enterprise Knowledge Problem
Enterprises lose an estimated $3.1 million annually per 1,000 employees due to knowledge workers unable to find information. Traditional search across internal documents is keyword-based and misses context. RAG (Retrieval-Augmented Generation) solves this by combining document retrieval with AI understanding, but most RAG solutions are expensive SaaS products. RAGFlow makes enterprise-grade RAG accessible as open-source software.
What is Flow (RAGFlow)?
RAGFlow is an open-source Retrieval-Augmented Generation platform that lets you create AI-powered search over your own documents. Upload PDFs, Word files, web pages, or databases, and RAGFlow indexes them into a searchable knowledge base. Ask questions in natural language and get accurate answers with direct citations from your source documents. It supports multiple LLM backends including OpenAI, DeepSeek, and local models.
Getting Started
Step 1: Deploy RAGFlow
- Visit Flow and follow the Docker deployment guide
- Run
docker compose up -dto start the application - Access the web UI at localhost:9380
Step 2: Create a Knowledge Base
- Upload documents (PDF, DOCX, TXT, HTML, Markdown)
- RAGFlow automatically chunks, indexes, and embeds your content
- Review and adjust chunking settings for optimal retrieval
Step 3: Ask Questions
- Open the chat interface and select your knowledge base
- Ask questions in natural language
- RAGFlow retrieves relevant document chunks and generates cited answers
Core Features
RAG Pipeline
| Feature | Details |
|---|---|
| Document parsing | PDF, DOCX, TXT, HTML, Markdown, Excel |
| Smart chunking | Table-aware, layout-aware document splitting |
| Embedding models | BGE, OpenAI, custom embedding support |
| LLM backends | OpenAI, DeepSeek, Ollama, any OpenAI-compatible API |
| Citations | Every answer linked to source document locations |
| Multi-modal | Extract text from images, charts, and tables in PDFs |
Enterprise Features
- Multi-tenant support for team-based knowledge bases
- API for integrating RAG into existing applications
- Fine-grained access controls per knowledge base
- Conversation history and analytics dashboard
Real-World Use Cases
Legal Team: Contract Analysis
A legal team uploads 500+ contracts and uses RAGFlow to instantly find specific clauses, compare terms across agreements, and answer compliance questions.
Engineering Team: Technical Documentation
A software team indexes their entire documentation wiki, allowing engineers to ask "How do I configure the OAuth flow?" and get precise answers with links to the relevant doc pages.
Tips for Best Results
- Start with well-structured documents — clean PDFs with proper headings produce better chunks
- Use the table-aware parser for documents with complex tables and layouts
- Test different chunking sizes — smaller chunks (512 tokens) for precise Q&A, larger for summaries
- Use a local embedding model (BGE) if data privacy is a concern
Pricing
RAGFlow is free and open-source under the Apache 2.0 license. You only pay for compute resources (your own servers or cloud instances) and any external LLM API calls you make.
Try Flow
Ready to build AI search over your own documents? Visit Flow to get started, or explore Flow details, alternatives & reviews for ratings, alternatives, and user reviews.
FAQs
Do I need coding skills to use RAGFlow?
Basic Docker knowledge is needed for deployment, but once running, the web UI is user-friendly. Document upload, knowledge base creation, and querying all happen through the browser interface.
Can I use RAGFlow with local LLMs?
Yes. RAGFlow supports Ollama and any OpenAI-compatible API, so you can run everything locally without sending data to external servers.
How does RAGFlow handle table-heavy documents?
RAGFlow includes a table-aware parser that correctly identifies and preserves table structures during chunking, ensuring tabular data is accurately retrieved and presented in answers.
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Last updated: February 2026
