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Vector Search

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How Content Chunking Works in Amazon Bedrock Knowledge Bases: How AI Really Reads Your Documents

2026-01-11T12:03:57+00:00

Modern generative AI systems often appear to “read” entire documents instantly, returning precide answers form long PDFs or dense technical manuals. In reality, large language models do not consume documents holistically. Instead, they rely on carefully prepared context that is retrieved and supplied at query time. One  of the most critical and often misunderstood mechanisms behind this process is content chunking. At its core, content chunking determines how raw documents such as PDFs, webpages, or text files are transformed into smaller, meaningful units that can be indexed, embedded, and retrieved efficiently. Understanding how chunking works and how to configure it [...]

How Content Chunking Works in Amazon Bedrock Knowledge Bases: How AI Really Reads Your Documents2026-01-11T12:03:57+00:00

Mastering Cloud-Based Semantic Search: Advanced Cloud Search Architectures Made Easy

2025-10-28T18:05:37+00:00

In today's AI-driven world, finding information based on meaning and context rather than exact keywords has become crucial. Good thing we now have vector search, enabling semantic search, personalized recommendations, image similarity, and more. However, building and managing vector search infrastructure can be complex. Fortunately, AWS offers zero-infrastructure managed services that let you implement powerful vector search capabilities without worrying about servers, scaling, or maintenance. Let's walk through creating a simple yet effective vector search demo using AWS services within the Free Tier so you can follow along without incurring costs. What is Vector Search? Vector search is a groundbreaking [...]

Mastering Cloud-Based Semantic Search: Advanced Cloud Search Architectures Made Easy2025-10-28T18:05:37+00:00

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