Enhance language models with real-time document retrieval and dynamic knowledge integration using retrieval-augmented generation and LlamaIndex.
llm-rag
Using knowledge graphs and AI to retrieve, filter, and summarize medical journal articles
Retrieval-Augmented Generation (RAG) is a machine learning framework that combines the advantages of both retrieval-based and generation-based models. The RAG framework is highly regarded for its ability to handle large amounts of information and produce coherent, contextually accurate responses. It leverages external data sources by retrieving relevant documents or facts and then generating an answer or output based on the retrieved information and the user query. This blend of retrieval and generation leads to better-informed outputs that are more accurate and comprehensive than models that rely solely on generation. The evolution of RAG has led to various types and approaches,
While RAG will remain a staple of production applications, Gemini 1.5 Pro and similar models will help enterprise data science teams.
In the ever-evolving landscape of artificial intelligence, businesses face the perpetual challenge of harnessing vast amounts of unstructured data. Meet RAGFlow, a groundbreaking open-source AI project that promises to revolutionize how companies extract insights and answer complex queries with an unprecedented level of truthfulness and accuracy. What Sets RAGFlow Apart RAGFlow is an innovative engine that leverages Retrieval-Augmented Generation (RAG) technology to provide a powerful solution for information retrieval. Unlike traditional keyword searches, RAGFlow combines large language models (LLMs) with deep document understanding to extract relevant information from a vast amount of data. Intelligent template-based chunking and visualized text chunking
In a previous post, I demonstrated how to implement RAG using the Load-Transform-Embed-Store...