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检索增强一代的终结?新兴的体系结构标志着一种转变

Retrieval Augmented Generation (RAG) has been a cornerstone in enhancing large language models (LLMs) for complex, knowledge-driven tasks. By pulling in relevant data from a vector database, RAG has empowered LLMs with factual grounding, significantly reducing instances of fabricated information. But is this the end of the road for RAG?

Devin,新的人工智能,能取代人类软件工程师吗?

A new AI named Devin claiming the title of the world’s first AI software engineer. From coding entire projects to fixing GitHub issues, Devin seems to be the new topic. And with such sensational capabilities, the rumor mill is working overtime, sparking fears that the era of human software engineers might be coming to an end. But before you join the panic parade, let’s take a look and see why, despite these advancements, we’re not heading for the job market exit anytime soon.

如何在没有矢量数据库的情况下进行RAG

Introduction

When it comes to bestowing Large Language Models (LLMs) with long-term memory, the prevalent approach often involves a Retrieval Augmented Generation (RAG) solution, with vector databases acting as the storage mechanism for the long-term memory. This begs the question: Can we achieve the same results without vector databases?