Vector Search Fundamentals for RAG Design Explained

This article delves into the fundamentals of vector search, a crucial component for Retrieval-Augmented Generation (RAG) systems. It explains Approximate Nearest Neighbor (ANN) search, focusing on the Hierarchical Navigable Small World (HNSW) algorithm and its hierarchical graph structure for efficient searching. The piece also covers various similarity and distance metrics like cosine similarity, dot product, and L2 distance, alongside metadata filtering techniques. Finally, it introduces BM25, a keyword search algorithm, highlighting its role in hybrid search strategies for RAG systems. AI

IMPACT Understanding vector search is key to optimizing RAG systems for better accuracy, speed, and cost efficiency.

RANK_REASON Article explains technical concepts related to AI infrastructure. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• BM25
• cosine similarity
• Hierarchical Navigable Small World graphs
• L2 distance
• Pinecone
• qdrant
• retrieval-augmented generation
• scalar product
• Weaviate

• infra

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