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RAGFlow - 开源深度文档理解RAG引擎
An open-source Retrieval-Augmented Generation (RAG) engine built upon deep document understanding, designed to power intelligent question answering and knowledge retrieval applications.
Python
Added on 2025年7月6日59,022
Stars5,865
ForksPython
LanguageProject Introduction
Summary
RAGFlow is a comprehensive open-source engine for building RAG applications. It focuses on providing robust document processing, efficient retrieval mechanisms, and flexible LLM integration to simplify the creation of intelligent AI systems that can interact with large knowledge bases.
Problem Solved
Building robust RAG systems that can effectively handle complex document structures, perform accurate retrieval, and integrate with generative models is challenging. RAGFlow addresses this by providing a ready-to-use engine with advanced document processing and optimized workflows.
Core Features
Deep Document Understanding
Intelligently parses, cleans, and structures documents from various formats (PDF, Word, TXT, etc.).
Automated Chunking and Embedding
Automatically segments documents into meaningful chunks and generates high-quality vector embeddings.
Optimized Vector Retrieval
Provides efficient retrieval from large vector databases to find relevant document snippets.
LLM Integration
Seamlessly integrates with various Large Language Models (LLMs) for answer generation.
Scalability and Deployment Flexibility
Offers flexible deployment options and scales to handle large document collections and user loads.
Tech Stack
Python
PyTorch/TensorFlow
Transformers (Hugging Face)
Vector Databases (e.g., Milvus, Qdrant)
FastAPI/Django
Docker
使用场景
RAGFlow can be applied in various scenarios where extracting accurate information from large document collections and generating human-like responses is needed:
企业内部知识库问答
Details
Deploy RAGFlow as a backend for chatbots or virtual assistants that need to answer user questions based on product manuals, internal policies, or FAQs.
User Value
Improve customer support efficiency and provide instant access to internal information.
法律/医疗/金融文档分析
Details
Utilize RAGFlow to build applications that can analyze and extract key information from complex legal, medical, or financial documents.
User Value
Accelerate research, due diligence, and information discovery in domain-specific fields.
文档智能助手
Details
Integrate RAGFlow into platforms to enable users to get answers directly from uploaded documents, reports, or research papers.
User Value
Empower users with faster access to information contained within documents, enhancing productivity.
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