Explore how vector, keyword, document, and graph databases work together in RAG systems to balance speed, semantic recall, and structured retrieval.

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The post provides a comprehensive overview of database patterns in Retrieval-Augmented Generation (RAG) systems. It emphasizes the importance of using multiple types of databases including vector databases, search databases, document stores, and graph databases to achieve optimal speed, recall, and structure. It also discusses design patterns for integrating these databases, highlighting their individual strengths and limitations, and offers real-world implementation guidance.

RAG Database Patterns: Speed, Recall, and Structure