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Vector Databases Explained: How They Power Modern AI Applications

Cosine similarity: Measures the angle between vectors, ignoring magnitude. Most commonly used for text embeddings where the direction of the vector matters more than its length.
Euclidean distance (L2): Measures the straight-line distance between vectors. Used when magnitude is meaningful.
Dot product: Combines direction and magnitude. Often used when vectors are normalized, in which case it is equivalent to cosine similarity.

A comprehensive guide to vector databases, covering how they store and search high-dimensional embeddings, their role in RAG and recommendation systems, and how to choose the right one.

theAIcatchup Apr 24, 2026 5 min read

⚡ Key Takeaways

{'point': 'ANN algorithms make similarity search practical at scale', 'detail': 'Algorithms like HNSW and IVF trade small accuracy reductions for massive speed improvements, enabling sub-millisecond searches across millions of vectors.'} 𝕏
{'point': 'RAG quality depends directly on retrieval quality', 'detail': "In retrieval-augmented generation pipelines, the vector database's ability to find the most relevant document chunks determines whether the LLM's response will be accurate and helpful."} 𝕏
{'point': 'Choose based on scale and operational needs', 'detail': 'pgvector works for smaller datasets within existing PostgreSQL stacks, while purpose-built databases like Pinecone, Milvus, and Weaviate are necessary for large-scale production workloads.'} 𝕏