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Correctly Install and Run RAGFlow Locally with Llama/Ollama and Create Local Knowledge Base and Chat
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#ragflow #RAG #RAGflow #ollama #llama31 #LLM #machinelearning #aiassistance
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In this machine learning and Large Language Model (LLM) tutorial, we explain how to install RAGFlow engine and how to run it locally to create your knowledge base. The knowledge base is based on Llama 3.1 and Ollama to retrieve and formulate answers on the basis of the local documents and files. RAGFlow is an open source Retrieval-Augmented Generation (RAG) engine that is based on deep document understanding and interpretation. It can be integrated with LLMs as well as other machine learning and AI algorithms. After being integrated with LLMs, RAGFlow can provide meaningful answers to complex questions. Finally, as we show in this tutorial, you can build a powerful knowledge base by using local text and data files stored on your computer.
DISCLAIMER: The procedure for installing RAGFlow that I explained in this tutorial might involve changing some network settings and playing with the permissions to access some network ports. I do not take any legal or financial or moral responsibility or any responsibility for possible loss of data or external intrusion in your system through network channels that might stay open. In my case, the system was perfectly protected, and all the port permissions are revoked. If you decide to implement the steps I explained in this tutorial, you are doing this on your own responsibility, and the assumption is that you know what are the possible consequences of not properly securing your network. That is, always make sure that you are on the safe side, and make sure that your network is protected.
It takes a significant amount of time and energy to create these free video tutorials. You can support my efforts in this way:
- You Can also press the Thanks YouTube Dollar button
In this machine learning and Large Language Model (LLM) tutorial, we explain how to install RAGFlow engine and how to run it locally to create your knowledge base. The knowledge base is based on Llama 3.1 and Ollama to retrieve and formulate answers on the basis of the local documents and files. RAGFlow is an open source Retrieval-Augmented Generation (RAG) engine that is based on deep document understanding and interpretation. It can be integrated with LLMs as well as other machine learning and AI algorithms. After being integrated with LLMs, RAGFlow can provide meaningful answers to complex questions. Finally, as we show in this tutorial, you can build a powerful knowledge base by using local text and data files stored on your computer.
DISCLAIMER: The procedure for installing RAGFlow that I explained in this tutorial might involve changing some network settings and playing with the permissions to access some network ports. I do not take any legal or financial or moral responsibility or any responsibility for possible loss of data or external intrusion in your system through network channels that might stay open. In my case, the system was perfectly protected, and all the port permissions are revoked. If you decide to implement the steps I explained in this tutorial, you are doing this on your own responsibility, and the assumption is that you know what are the possible consequences of not properly securing your network. That is, always make sure that you are on the safe side, and make sure that your network is protected.
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