ZiadSheriif/IntelliQuery
A semantic search indexing system designed to efficiently retrieve top matching results from a database of 20 million documents. Given the embedding of a search query, it quickly identifies and returns the most relevant documents
Implements multiple approximate nearest neighbor indexing strategies—Inverted File Index (IVF) with parallel region processing, Local Sensitive Hashing (LSH), Product Quantization (PQ), and hybrid PQ-LSH—enabling trade-offs between search speed and accuracy. The final IVF approach uses standard KMeans clustering with initial centroids computed from the first data chunk, optimizing both memory efficiency and query latency across the massive document corpus.
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Jupyter Notebook
License
MIT
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Last pushed
Nov 20, 2024
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