<ul><li>The article discusses building a system for video content search and analysis using Amazon Bedrock, Transcribe, and Aurora PostgreSQL.</li><li>It outlines the challenges in handling video content and presents a solution using Amazon services.</li><li>The solution involves creating searchable vector representations for visual and audio content.</li><li>Visual content processing includes extracting frames, generating embeddings, and selecting key frames for storage.</li><li>Audio content processing involves speech-to-text conversion, text segmentation, and generating text embeddings.</li><li>The system supports cross-modal search capabilities, enabling searches across visual and audio content.</li><li>Searches can be performed based on vector similarity using techniques like Cosine Similarity and L2 Distance.</li><li>The article also discusses implementing Retrieval-Augmented Generation (RAG) for context-based responses.</li><li>The solution allows for complex queries and responses based on context from images and text.</li><li>The article concludes with implementation notes and hints at a serverless solution for video analysis.</li></ul>

Building a RAG System for Video Content Search and Analysis

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