许多组织在有效处理大型非结构化数据集(如文本、图像和音频)并从中获取见解方面有些吃力。与此同时,人们对对话式搜索、自然语言理解和个性化推荐等高级功能的需求也日益增长。AWS 上的向量数据库和嵌入解决方案可帮助组织利用词嵌入、神经网络和相似性搜索等技术的强大功能,有效分析多模态数据并增强其人工智能和机器学习(AI/ML)工作负载处理能力。借助这些解决方案,组织可以提高其应用程序的性能和可用性,并提供更具关联性的情境见解,从而更好地服务客户并提升业务价值。

指导

规范性的架构图、示例代码和技术内容

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  • Similarity Search-Based Retrieval Augmented Generation (RAG) on AWS

    This Guidance shows how to build an advanced question-answering application using the latest AI tools from AWS and its partners.
  • E-Commerce Products Similarity Search on AWS

    This Guidance shows how to create a product catalog with a similarity search capability by integrating AWS and artificial intelligence (AI) services with the pgvector extension.
  • Sentiment Analysis on AWS

    This Guidance demonstrates how to use pgvector and Amazon Aurora PostgreSQL for sentiment analysis, a powerful natural language processing (NLP) task.
  • High Speed RAG Chatbots on AWS

    This Guidance demonstrates how to build a high-performance Retrieval-Augmented Generation (RAG) chatbot using Amazon Aurora PostgreSQL and the pgvector open-source extension, using AWS artificial intelligence (AI) services and open-source frameworks.
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