Conversation
| - [Metrics](https://github.com/benhamner/Metrics) - Machine learning evaluation metrics. | ||
| - [MindsDB](https://github.com/mindsdb/mindsdb) - MindsDB is an open source AI layer for existing databases that allows you to effortlessly develop, train and deploy state-of-the-art machine learning models using standard queries. | ||
| - [PraisonAI](https://github.com/MervinPraison/PraisonAI) - Production-ready Multi-AI Agents framework with self-reflection, 100+ LLM support, MCP integration, and agentic workflows. | ||
| - [RAGFlow](https://github.com/infiniflow/ragflow) - An open-source RAG engine for document understanding and question answering with LLMs. |
| - [MindsDB](https://github.com/mindsdb/mindsdb) - MindsDB is an open source AI layer for existing databases that allows you to effortlessly develop, train and deploy state-of-the-art machine learning models using standard queries. | ||
| - [PraisonAI](https://github.com/MervinPraison/PraisonAI) - Production-ready Multi-AI Agents framework with self-reflection, 100+ LLM support, MCP integration, and agentic workflows. | ||
| - [RAGFlow](https://github.com/infiniflow/ragflow) - An open-source RAG engine for document understanding and question answering with LLMs. | ||
| - [scikit-learn](http://scikit-learn.org/) - The most popular Python library for Machine Learning. |
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Project
RAGFlow
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How It Differs
RAGFlow differs from typical machine learning libraries by providing an end-to-end Retrieval-Augmented Generation (RAG) system, rather than isolated components such as vector databases or model wrappers. It integrates document parsing, chunking, embedding, retrieval, and LLM orchestration into a single engine, focusing on production-ready knowledge QA workflows. This makes it distinct from general ML frameworks or embedding tools, which usually require significant custom glue code to build a complete RAG pipeline.