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langchain-pdf-qa

Use langchain to create a model that returns answers based on online PDFs that have been read.

How to use

  1. Clone the repository and install dependencies
git clone [email protected]:m-star18/langchain-pdf-qa.git
pip install -r requirements.txt
  1. Specify the PDF link and OPEN_API_KEY to create the embedding model
# Example
python src/make_emb.py --pdf_path "https://arxiv.org/pdf/2005.14165.pdf" --OPEN_API_KEY ""

The following options can also be specified as arguments

  • chunk_size: Please specify the chunk_size for CharacterTextSplitter within a number less than or equal to 4096.
  • chunk_overlap: Please specify the chunk_overlap for CharacterTextSplitter within a number less than or equal to 4096.
  • split_mode: Please specify the split mode. (character, recursive_character, nltk, tiktoken)
  • faiss_save_path: Please specify the name of the created Faiss object.
  1. Enter a question and generate an answer from the extracted text
# Example
python src/pdf_qa.py --query "On which datasets does GPT-3 struggle?"