Clelia Astra Bertelli

Hi!

I generally use LangChain + PyPDF, I leave here a code snippet:

from langchain_community.document_loaders import PyPDFLoader

def preprocess(pdf: str) -> list:
    """
    Uses LangChain's PyPDFLoader to extract text.
    """
    loader = PyPDFLoader(pdf)
    documents = loader.load()
    for doc in documents:
        print(doc.page_content)    

This should give a more solid result :)

PS: Langchain is distributed under an MIT license, see their GitHub (https://github.com/langchain-ai/langchain)

Thank you so much for letting me know! This is indeed a very interesting role :)

Hi and thanks a lot for the specification!🥰

Just as a note from my side, in the article I specify that there is a difference between "open weights" and "open source" models, and I link this blog post: https://www.agora.software/en/llm-open-source-open-weight-or-proprietary/ for a deeper explanation of the difference. I never (and I would never) claimed that Llama is open source, let alone a free software (see the introduction in this article of mine on privacy and data "stealing" risks: https://huggingface.co/blog/as-cle-bert/build-an-ai-powered-search-engine-from-scratch).

And I would have gladly used also DeepSeek, if it had been available on HuggingChat! :)

I nevertheless highly appreciate your comment and I'll for sure be more cautious in using the word "open/open source" in the future. Thanks!✨

Both PdfItDown and SenTrEv only work with text for now: in future releases, support for image will be added :)
For text extraction, I use PyPDF + Langchain