langchain-pdf-qa

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langchain-pdf-qa

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

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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 git@github.com:m-star18/langchain-pdf-qa.git
pip install -r requirements.txt
```

2. 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.

3. 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?"
```

langchain-pdf-qa

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