In simple English
This tool helps you build a smart search engine over your company files, PDFs, and web pages. It reads messy documents, cuts them into smart pieces, and lets an artificial intelligence answer questions from them. You can run it on your own server using Docker, so your private data stays safe. It connects to popular language models and gives you a web interface to manage everything. It costs you server resources and API fees for the language models you choose.
What it is
RAGFlow is a retrieval-augmented generation engine that combines document parsing with agent-based reasoning. It turns unstructured files into structured knowledge for language models.
What it is good for
Solo builders who want to create an accurate internal search engine or customer support bot over large document collections without building a retrieval pipeline from scratch.
- Parses complex documents like PDFs, spreadsheets, and scanned copies accurately.
- Provides a ready-to-use web interface for managing datasets and testing queries.
- Supports agentic retrieval with multiple thinking modes for complex questions.
- Needs a powerful server with at least 16 GB of memory to run smoothly.
- Setup requires familiarity with Docker and Docker Compose.
- Ongoing maintenance includes managing database migrations and external model API keys.
Building a custom document search and retrieval pipeline with vector databases and custom scrapers.
Used together with Ollama to run local language models alongside your document store.
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git clone https://github.com/infiniflow/ragflow.git cd ragflow/docker docker compose -f docker-compose.yml up -d