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implemented vector similarity algorithms to understand their inner workings, used local embeddding models

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matsjfunke/rag-from-scratch

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More on how I personally think about the algorithms here

How to run scripts, and experiment

  1. clone repo
    git clone https://github.com/matsjfunke/rag-from-scratch.git
  2. install dependences
    python3 -m venv env
    source env/bin/activate
    pip install -r requirements.txt
  3. run scripts
    • will take some time on the first run because embeddings aren’t cached (have a look at the handle_embeddings() function to understand)
    python dot-product-rag.py
  4. tinkering optiions
    • each script contains a main() function this function gives you inside on tweaks you can make
    • different data: upload other .txt files in the root dir of the repo and specify thier name in file_name variable of main()
    • different prompt: change prompt vairable in main()

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