Introduction: Document processing is the foundation of any RAG (Retrieval-Augmented Generation) system. Before you can search and retrieve relevant information, you need to extract text from various file formats, split it into meaningful chunks, and generate embeddings for vector search. The quality of your document processing pipeline directly impacts retrieval accuracy and ultimately the quality […]
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Vector Embeddings Deep Dive: From Theory to Production Search Systems
Introduction: Vector embeddings are the foundation of modern AI applications—from semantic search to RAG systems to recommendation engines. They transform text, images, and other data into dense numerical representations that capture semantic meaning, enabling machines to understand similarity and relationships in ways that traditional keyword matching never could. This guide provides a deep dive into […]
Read more →Vector Search Algorithms: From Brute Force to HNSW and Beyond
Introduction: Vector search is the foundation of modern semantic retrieval systems, enabling applications to find similar items based on meaning rather than exact keyword matches. Understanding the algorithms behind vector search—from brute-force linear scan to sophisticated approximate nearest neighbor (ANN) methods—is essential for building efficient retrieval systems. This guide covers the core algorithms that power […]
Read more →Vector Search Optimization: The Complete Guide to Embeddings, Indexing, and Hybrid Search
Introduction: Vector search is the foundation of modern RAG systems, but naive implementations often deliver poor results. Optimizing vector search requires understanding embedding models, index types, query strategies, and reranking techniques. The difference between a basic similarity search and a well-tuned retrieval pipeline can be dramatic—both in relevance and latency. This guide covers practical vector […]
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