LLMs

Combining RAG and LLMs: A Review of Retrieval-Augmented Large Language Models

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Combining RAG and LLMs: A Review of Retrieval-Augmented Large Language Models

As one of the most advanced technologies in artificial intelligence, Retrieval-Augmented Generation (RAG) technology can provide reliable and up-to-date external knowledge, offering great convenience for numerous tasks. Especially in the era of AI-Generated Content (AIGC), RAG’s powerful retrieval capabilities in providing additional knowledge enable it to assist existing generative AI in producing high-quality outputs. Recently, … Read more

Enhancement Techniques for Large Model Retrieval (RAG)

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Enhancement Techniques for Large Model Retrieval (RAG)

Click the bottom “Read Original” to browse the detailed content of “CCF Digital Focus” Issue 48 Editor’s Note Large language models (LLMs) still face many challenges when dealing with domain-specific or knowledge-intensive tasks, such as generating hallucinations, outdated knowledge, and opaque, untraceable reasoning processes. Retrieval-Augmented Generation (RAG) technology has emerged to address these issues. RAG … Read more

Overview of Agentic Retrieval-Augmented Generation

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Overview of Agentic Retrieval-Augmented Generation

Large language models (LLMs) have revolutionized the field of artificial intelligence (AI) by enabling human-like text generation and natural language understanding. However, their reliance on static training data limits their responsiveness to dynamic real-time queries, resulting in outdated or inaccurate outputs. Retrieval-Augmented Generation (RAG) serves as a solution by integrating real-time data retrieval to enhance … Read more

Creating AI Agents with Memory and Tools Using Phidata

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Creating AI Agents with Memory and Tools Using Phidata

Aitrainee | Official Account: AI Trainee 🌟Phidata adds memory, knowledge, and tools to LLMs. ⭐️ Phidata:https://git.new/phidata Phidata is a framework for building autonomous assistants (also known as agents) that have long-term memory, contextual knowledge, and can perform actions through function calls. Recommended a tutorial video from YouTuber WorldofAl: Why Choose Phidata? Problem: Large Language Models … Read more

Phidata vs Langchain: A Comparative Framework for Smart Agents

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Follow us on WeChat: “Full Stack AI Knowledge” Set as a “Star”, bringing you new insights every day When building smart agents, Phidata and Langchain are undoubtedly the focal points in the industry. Both aim to enhance the performance of large language models (LLMs), but each has its unique focus and advantages. This article provides … Read more

Interpreting the JARVIS Project: Connecting ChatGPT and HuggingFace to Solve AI Issues

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Interpreting the JARVIS Project: Connecting ChatGPT and HuggingFace to Solve AI Issues

The latest online sharing session by Machine Heart invited Song Kaitao, a researcher at Microsoft Research Asia, to share their recent open-source project JARVIS. Recently, large language models (LLMs), represented by ChatGPT, have garnered significant attention in both industry and academia. However, LLMs, which primarily handle text, still face numerous bottlenecks when addressing many complex … Read more

RAG System Privacy Leakage Attack Framework

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RAG System Privacy Leakage Attack Framework

Click Follow us by clicking the blue text above The RAG system poses privacy leakage risks, and researchers from the University of Perugia, the University of Siena, and the University of Pisa have proposed a correlation-based attack framework that utilizes open-source language models and sentence encoders to adaptively explore hidden knowledge bases, efficiently extracting private … Read more