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Description
Since we now support embedding models in llama.cpp
we should add a simple example to demonstrate retrieval functionality. Here is how it should work:
- load a set of text files (provided from the command line)
- split the text into chunks of user-configurable size, each chunk ending on a configurable stop string
- embed all chunks using an embedding model (BERT / SBERT)
- receive input from the command line, embed it and display the top N most relevant chunks based on cosine similarity between the input and chunk emebeddings
lin72h, ylsdamxssjxxdd, phymbert, V1ki, PredatorIWD and 3 morelin72h, ylsdamxssjxxdd and Nojus10