Vector Search
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What Is Reranking in Vector Search? A Practical Guide
Learn how reranking improves vector search relevance, measure nDCG and candidate recall, and instrument a PyTorch reranker without hiding latency costs.
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Product Quantization Explained for Vector Search
Learn how product quantization compresses embeddings, how IVF-PQ changes recall, and how to evaluate a Faiss index with MLflow before deployment.
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HNSW M Parameter Tuning: Recall, Memory, and Latency
The guide explains how M affects graph connectivity and memory, maps engine-specific settings, and shows a sweep for recall@10, p99, and bytes per vector.
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Cosine Similarity vs Dot Product: How Rankings Differ
Vector norms determine when cosine similarity and dot product rank results identically and when magnitude changes retrieval order.
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HNSW ef_search Parameter: Recall and Latency Tradeoffs
The HNSW ef_search parameter sets query beam width, balancing recall against latency across vector search engines and filtered queries.