A LangGraph-based advanced agentic RAG with standard business guides, AI-based web search, trusted sources, and a hybrid search leveraging multiple models
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After the launch of ChatGPT and the following surge of Large Language Models (LLMs), their inherent limitations of hallucination, knowledge cutoff date, and the inability to provide organization- or person-specific information soon became evident and were seen as major drawbacks. To address these issues, Retrieval Augment Generation (RAG) methods soon gained traction which integrate external data to LLMs and guide their behavior to answer questions from a given knowledge base.
Interestingly, the first paper on RAG was published in 2020 by researchers from Facebook AI Research (now Meta AI), but it was not until the advent of ChatGPT that its potential was fully realized. Since then, there has been no stopping. More advanced and complex RAG frameworks were introduced which not only improved the accuracy of this technology but also enabled it to deal with multimodal data, expanding its potential for a wide range of applications. I wrote on this topic in detail in the following articles, specifically discussing contextual multimodal RAG, multimodal AI search for business applications, and information…