

Ragflow
Overview :
RAGFlow is an open-source Retrieval-Augmented Generation (RAG) engine based on deep document understanding, offering a streamlined RAG workflow suitable for enterprises of all sizes. It combines Large Language Models (LLM) to provide authentic Q&A capabilities and supports referencing verifiable citations from a variety of complex data formats.
Target Users :
["Suitable for enterprise users who need to handle large volumes of documents and data","Ideal for researchers who require extracting useful information from complex data","Perfect for business processes that demand automated document processing and report generation"]
Use Cases
Automated Q&A system in enterprise document management
Automated analysis and summary generation for legal documents
Automatic organization of teaching materials and Q&A in the education field
Features
Knowledge extraction based on deep document understanding
templated text block processing
visualized text segmentation with manual intervention capability
support for multiple data sources, including Word, PPT, Excel, TXT, images, etc.
automated RAG workflow for both individual users and large enterprises
support for configurable LLM and embedded models
support for file management
support for local LLM deployment
How to Use
Step 1: Ensure the system meets the minimum requirements for CPU, RAM, disk, and Docker
Step 2: Adjust vm.max_map_count to meet Docker runtime requirements
Step 3: Clone the RAGFlow GitHub repository to your local machine
Step 4: Build the pre-built Docker image and start the server
Step 5: Check the server status to confirm that the system has started successfully
Step 6: Input the server IP address in the browser and log in to RAGFlow
Step 7: Configure the backend services and environment settings as needed
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