The author details their experience building a hybrid search engine as part of the LLM Zoomcamp 2026. They explain the fundamental differences between traditional keyword search and vector search, emphasizing that vector search matches meaning rather than exact words by converting text into numerical vectors. The project involved implementing text embeddings using a lightweight ONNX model, creating a vector search from scratch with NumPy, and chunking long documents for improved retrieval accuracy. Finally, the author integrated keyword and vector search using Reciprocal Rank Fusion (RRF) to combine the strengths of both approaches, leading to more precise search results. AI
IMPACT This project demonstrates practical application of vector search and RRF, potentially improving the accuracy and efficiency of AI-powered search systems.
RANK_REASON The item describes a personal project building a search engine, detailing technical implementation and learning outcomes, which aligns with research and development in AI infrastructure. [lever_c_demoted from research: ic=1 ai=1.0]
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