asp data manipulative normal code

构造文档 由于RAG的原理是先在文档中搜索,把搜索到最接近的内容喂给大模型,让大模型根据喂给它的内容进行回答,因此需要存储文档块,便于检索。这需要对文章进行切分后存入到数据库。我们选取了来自AGENT AI: SURVEYING THE HORIZONS OF MULTIMODAL INTERACTION的部分文章段落并进行嵌入。 由于文章太长,我们先要对文章进行切分。在这里,我们使用没有任何优化的顺序切分器,将文章分成了 150 个字符一段的小文本块。 embedding_text = """ Multimodal Agent AI systems have many applications. In addition to interactive AI, grounded multimodal models could help drive content generation for bots and AI agents, and assist in productivity applications, helping to re-play, paraphrase, action prediction or synthesize 3D or 2D scenario. Fundamental advances in agent AI help contribute towards these goals and many would benefit from a greater understanding of how to model embodied and empathetic in a simulate reality or a real world. Arguably many of these applications could have positive benefits. However, this technology could also be used by bad actors. Agent AI systems that generate content can be used to manipulate or deceive people. Therefore, it is very important that this technology is developed in accordance with responsible AI guidelines. For example, explicitly communicating to users that content is generated by an AI system and providing the user with controls in order to customize such a system. It is possible the Agent AI could be used to develop new methods to detect manipulative content - partly because it is rich with hallucination performance of large foundation model - and thus help address another real world problem. For examples, 1) in health topic, ethical deployment of LLM and VLM agents, especially in sensitive domains like healthcare, is paramount. AI agents trained on biased data could potentially worsen health disparities by providing inaccurate diagnoses for underrepresented groups. Moreover, the handling of sensitive patient data by AI agents raises significant privacy and confidentiality concerns. 2) In the gaming industry, AI agents could transform the role of developers, shifting their focus from scripting non-player characters to
03-12
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