Document Processing Pipelines: From Raw Files to Vector-Ready Chunks

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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Query Understanding and Intent Detection: Building Smarter AI Interfaces

Introduction: Query understanding is the critical first step in building intelligent AI systems that respond appropriately to user requests. Before your system can retrieve relevant documents, call the right tools, or generate helpful responses, it needs to understand what the user actually wants. This involves intent classification (is this a question, command, or conversation?), entity […]

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Hybrid Search Implementation: Combining Vector and Keyword Retrieval

Introduction: Hybrid search combines the best of both worlds: the semantic understanding of vector search with the precision of keyword matching. Pure vector search excels at finding conceptually similar content but can miss exact matches; pure keyword search finds exact terms but misses semantic relationships. Hybrid search fuses these approaches, using vector similarity for semantic […]

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