Many organizations struggle to effectively process and gain insights from large, unstructured datasets like text, images, and audio. At the same time, there is a growing demand for advanced capabilities like conversational search, natural language understanding, and personalized recommendations. Vector Databases & Embeddings solutions on AWS help organizations harness the power of techniques like word embeddings, neural networks, and similarity search to efficiently analyze multi-modal data and enhance their artificial intelligence and machine learning (AI/ML) workloads. With these solutions, organizations can improve the performance and availability of their applications, and deliver more connected, contextual insights to better serve their customers and drive business value.

Guidance

Prescriptive architectural diagrams, sample code, and technical content

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

    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.
  • 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.
  • 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.
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