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You are here		mccormickml.com Product Quantizers for k-NN Tutorial Part 1 · Chris McCormick
\|	\|	0x65.dev Indexing Billions of Text Vectors Optimizing memory-usage for approximate nearest neighbor search	2.3 parsecs Travel
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\|	\|	lantern.dev Estimating memory footprint of your HNSW index \| Lantern Blog This interactive visualization will help you quickly reason about the resources necssary to host your embeddings and serve nearest neighbor queries over them	3.1 parsecs Travel
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\|	\|	erikbern.com New approximate nearest neighbor benchmarks · Erik Bernhardsson Approximate nearest neighbor search is very useful when you have a large dataset of millions of datapoints and you learn some kind of vector representation of these items. I built a benchmark suite called ann-benchmarks. It pits a number of algorithms in a brutal showdown.	3.3 parsecs Travel
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\|	\|	github.com GitHub - AssemblyAI-Community/rag-langchain-audio-data: Retrieval Augmented Generation (RAG) on audio data with LangChain Retrieval Augmented Generation (RAG) on audio data with LangChain - AssemblyAI-Community/rag-langchain-audio-data	21.4 parsecs Travel
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