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Assessing the Relevance of Search Results Retrieved Through TF-IDF Compared to Meaning-Based Vector Embeddings

Requirements

  • Go
  • pnpm
  • PostgreSQL
  • POSIX environment
  • 200GB of storage per million sites

How to Run

  • make to compile all binaries and get dependencies
  • make init-db to initialise the database
  • make get-embeddings to download the pre-trained GloVe embeddings
  • ./crawler/crawler to begin discovering sites
  • Once the crawler has reached the desired number of sites, you can run ./postprocessing/postprocessing to create the index
  • After this completes, run ./api/api and cd ui/artifacts/search-ui/ && pnpm run dev to run the search engine UI
  • Then, once the API has finished loading the embeddings, the search engine is ready for testing

About

Proof-of-Concept Search Engine to compare TF-IDF and Vector search for the Oxford University Lifelong Learning Data Science Course

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