Caching

[haesleinhuepf]

Assume we have a list of PDF filenames and we have a function embed(pdf_filename, slide_no). Now we would like to establish a persistent cache for the results of this function. E.g. today I call the function with parameters "test.pdf" and 0 ( for the first slide), it will return a vector of 1500 numbers. It takes a while to compute this. Hence, if I call the function again tomorrow, the computation should be avoided. Instead we retrieve the vector from a persistent storage; a file.

git-bob think about strategies to achieve this

[github-actions]

^{This message was generated by git-bob (version: 0.12.0, model: github_models:gpt-4o, log), an experimental AI-based assistant. It can make mistakes and has limitations. Check its messages carefully.}

To establish a persistent cache for the embed function, you can use Python's shelve module, which provides a simple persistent storage for key-value pairs. Here's a basic implementation strategy:

import shelve

def cache_embed(pdf_filename, slide_no):
    key = f"{pdf_filename}_{slide_no}"
    with shelve.open('embed_cache.db') as cache:
        if key in cache:
            return cache[key]
        else:
            result = embed(pdf_filename, slide_no)
            cache[key] = result
            return result

With this setup, the cache_embed function will first check if the result is already in the cache. If it is, the cached result is returned. Otherwise, it computes the result and stores it in the cache for future use. This avoids redundant computations and leverages disk storage for persistence across sessions.

[haesleinhuepf]

CC @lea-33 - I asked gitbob in a more structured way than in #13 ;-)